Industrial control equipment circuit board stable operation regulation and control method and system

Abstract

The invention relates to the technical field of automatic control, and discloses an industrial control equipment circuit board stable operation regulation and control method and system, and the method comprises the steps: analyzing the characteristics of a circuit board corresponding to industrial control equipment, analyzing the operation stability factor of the circuit board, and deploying a multi-source heterogeneous sensing network of the circuit board; the method comprises the following steps: collecting multi-source heterogeneous data of a circuit board, analyzing industrial control characteristics of the circuit board, constructing a dynamic stability matrix of the circuit board, and determining an environment disturbance propagation path of the circuit board in the dynamic stability matrix; generating a stability margin index of the circuit board after calculating a parameter fluctuation variance of the circuit board in the dynamic stability matrix; and determining a dynamic regulation and control parameter of the circuit board, analyzing a twinborn stability optimization coefficient of the dynamic regulation and control parameter, and when the twinborn stability optimization coefficient is greater than a preset threshold value, executing stable operation regulation and control of the circuit board by using the dynamic regulation and control parameter. According to the invention, the timeliness and accuracy of circuit board regulation and control can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for regulating and controlling the stable operation of a circuit board of industrial control equipment, belonging to the technical field of automatic control. Background Art

[0002] PCB stability control refers to monitoring, adjusting, and controlling the performance of printed circuit boards (PCBs) under various operating conditions to ensure long-term stable and reliable operation. These control measures ensure stable and reliable operation of the PCB under various operating conditions, thereby extending its service life and improving overall system performance.

[0003] Traditional methods for maintaining stable circuit board operation rely primarily on regular manual inspections, empirical judgment, and static parameter settings. Technicians regularly visually inspect the boards, record key parameters, and adjust circuit parameters or replace suspect components based on experience. However, this approach has limited frequency and accuracy, making it difficult to capture dynamic changes and high-frequency disturbances in real time. Furthermore, it cannot adapt to changing operating environments and load conditions, resulting in hysteresis and inaccuracy in circuit board control. Summary of the Invention

[0004] The present invention provides a method and system for regulating and controlling the stable operation of a circuit board of industrial control equipment, the main purpose of which is to improve the timeliness and accuracy of circuit board regulation.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for controlling the stable operation of a circuit board of an industrial control equipment, comprising:

[0006] Analyzing circuit board characteristics of a circuit board corresponding to the industrial control equipment, analyzing an operation stability factor of the circuit board based on the circuit board characteristics, and deploying a multi-source heterogeneous sensor network for the circuit board based on the operation stability factor;

[0007] Collecting multi-source heterogeneous data of the circuit board through the multi-source heterogeneous sensing network, analyzing industrial control characteristics of the circuit board based on the multi-source heterogeneous data, and constructing a dynamic stability matrix of the circuit board, wherein the industrial control characteristics include high-frequency dynamic temperature field distribution characteristics, three-dimensional mechanical vibration spectrum characteristics, electromagnetic interference intensity gradient characteristics, and current ripple characteristics;

[0008] Based on the industrial control characteristics, determining an environmental disturbance propagation path of the circuit board in the dynamic stability matrix;

[0009] Based on the environmental disturbance propagation path, after calculating the parameter fluctuation variance of the dynamic stability matrix of the circuit board, a stability margin index of the circuit board is generated;

[0010] The stability margin index is used to determine the dynamic control parameters of the circuit board, and the twin stability optimization coefficient of the dynamic control parameters is analyzed. When the twin stability optimization coefficient is greater than a preset threshold, the dynamic control parameters are used to perform stable operation control of the circuit board.

[0011] Optionally, deploying the multi-source heterogeneous sensor network of the circuit board based on the operation stability factor includes:

[0012] Analyzing the key parameters to be collected of the circuit board according to the operation stability factor;

[0013] Determining the target sensor of the circuit board based on the key parameters to be collected;

[0014] Analyzing the heat-sensitive area of ​​the circuit board;

[0015] determining a primary sensor node and a redundant sensor node of the target sensor based on the thermally sensitive area;

[0016] Establishing a star-mesh hybrid topology of the primary sensor node and the redundant sensor nodes;

[0017] A multi-source heterogeneous sensing network of the circuit board is established through the star-mesh hybrid topology.

[0018] Optionally, analyzing the industrial control features of the circuit board based on the multi-source heterogeneous data includes:

[0019] Analyzing the temperature distribution of the circuit board according to the temperature data corresponding to the multi-source heterogeneous data;

[0020] Determining a high-frequency dynamic temperature field distribution characteristic of the circuit board according to the temperature distribution;

[0021] Analyzing a frequency spectrum of the circuit board using mechanical vibration data corresponding to the multi-source heterogeneous data;

[0022] Determining a three-dimensional mechanical vibration spectrum characteristic of the circuit board based on the spectrum diagram;

[0023] Analyzing the electromagnetic interference gradient of the circuit board according to the electromagnetic interference data corresponding to the multi-source heterogeneous data;

[0024] Analyzing the electromagnetic interference intensity gradient characteristics of the circuit board through the electromagnetic interference gradient;

[0025] Analyzing the ripple frequency and ripple amplitude of the circuit board according to the current ripple data corresponding to the multi-source heterogeneous data;

[0026] Analyzing the current ripple characteristics of the circuit board based on the ripple frequency and the ripple amplitude;

[0027] The industrial control characteristics of the circuit board are determined by combining the high-frequency dynamic temperature field distribution characteristics, the three-dimensional mechanical vibration spectrum characteristics, the electromagnetic interference intensity gradient characteristics and the current ripple characteristics.

[0028] Optionally, analyzing the temperature distribution of the circuit board according to the temperature data corresponding to the multi-source heterogeneous data includes:

[0029] Analyzing the initial temperature and heat source of the circuit board based on the temperature data;

[0030] Marking the real-time power of the heat source;

[0031] The temperature of the circuit board is calculated according to the heat source and the real-time power using the following formula:

[0032]

[0033] Among them, T base (x, y, t) represents the temperature value of the position coordinate (x, y) at time t, T0 represents the initial temperature, Q c (t) represents the real-time power of the cth heat source, k represents the thermal conductivity of the material, r c represents the Euclidean distance from the position coordinate (x, y) to the cth heat source, m represents the number of heat sources, α represents the thermal diffusivity, π represents the circumference, and e represents the exponential function;

[0034] Analyzing the multi-field coupling correction term of the circuit board;

[0035] The temperature distribution of the circuit board is determined according to the temperature value and the multi-field coupling correction term.

[0036] Optionally, constructing the dynamic stability matrix of the circuit board includes:

[0037] Analyzing dynamic behavior of the circuit board;

[0038] formulating differential equations for the dynamic behavior;

[0039] calculating the Jacobian matrix of the differential equation;

[0040] A dynamic stability matrix of the circuit board is constructed according to the Jacobian matrix.

[0041] Optionally, determining the environmental disturbance propagation path of the circuit board in the dynamic stability matrix based on the industrial control feature includes:

[0042] Initializing the dynamic stability matrix to obtain an initialized stability matrix;

[0043] Marking matrix parameters of the initialization stability matrix according to the industrial control characteristics;

[0044] Analyzing the cause-effect relationship of the matrix parameters;

[0045] Based on the causal relationship, constructing a directed weighted graph of the matrix parameters;

[0046] Calculating the path influence coefficient of the directed weighted graph;

[0047] The environmental disturbance propagation path of the initialized stability matrix is ​​marked according to the path influence coefficient.

[0048] Optionally, calculating the path influence coefficient of the directed weighted graph includes:

[0049] Analyzing the chaotic characteristics and local stability of the parameter nodes corresponding to the directed weighted graph;

[0050] Calculating the cross-physical quantity covariance of the parameter node;

[0051] According to the chaotic characteristics, the local stability and the cross-physical quantity covariance, the path influence coefficient of the directed weighted graph is calculated using the following formula:

[0052]

[0053] Among them, Impact i-j (t) represents the parameter node X i To parameter node X j The path influence coefficient at time t, W i-j (t) represents the parameter node X i For parameter node X j The causal strength, ρ i (t) represents the parameter node X i The chaotic characteristics at time t, SMI j (t) represents the parameter node X j The local stability at time t, Cov(X i ,X j )(t) represents the parameter node X i To parameter node X j The covariance of the physical quantity across , tanh represents the hyperbolic tangent function, and σ represents the nonlinear scaling factor.

[0054] Optionally, the calculating the parameter fluctuation variance of the circuit board in the dynamic stability matrix based on the environmental disturbance propagation path includes:

[0055] Marking the fluctuation parameter node of the circuit board in the dynamic stability matrix according to the environmental disturbance propagation path;

[0056] Calculating the node fluctuation variance of the fluctuation parameter node;

[0057] Defining a fluctuation weight of the node fluctuation variance based on a path influence coefficient corresponding to the environmental disturbance propagation path;

[0058] The parameter fluctuation variance of the circuit board in the dynamic stability matrix is ​​determined according to the node fluctuation variance and the node fluctuation variance.

[0059] Optionally, analyzing the twin stability optimization coefficient of the dynamic control parameter includes:

[0060] Establishing a digital twin model of the circuit board corresponding to the dynamic control parameters;

[0061] Analyzing the sensitivity of the dynamic control parameters;

[0062] Defining the optimization target of the dynamic control parameters;

[0063] According to the sensitivity and the optimization target, the dynamic control parameters are integrated into the digital twin model to analyze the twin stability optimization coefficient of the dynamic control parameters.

[0064] In order to solve the above problems, the present invention also provides a stable operation control system for a circuit board of an industrial control equipment, the system comprising:

[0065] a sensor network construction module configured to analyze circuit board characteristics of a circuit board corresponding to an industrial control device, analyze an operational stability factor of the circuit board based on the circuit board characteristics, and deploy a multi-source heterogeneous sensor network for the circuit board based on the operational stability factor;

[0066] an industrial control feature analysis module, configured to collect multi-source heterogeneous data of the circuit board through the multi-source heterogeneous sensor network, analyze the industrial control features of the circuit board based on the multi-source heterogeneous data, and construct a dynamic stability matrix of the circuit board, wherein the industrial control features include high-frequency dynamic temperature field distribution features, three-dimensional mechanical vibration spectrum features, electromagnetic interference intensity gradient features, and current ripple features;

[0067] a disturbance propagation path analysis module, configured to determine, based on the industrial control characteristics, an environmental disturbance propagation path of the circuit board in the dynamic stability matrix;

[0068] a stability margin index determination module, configured to generate a stability margin index of the circuit board after calculating the parameter fluctuation variance of the dynamic stability matrix of the circuit board based on the environmental disturbance propagation path;

[0069] The stable operation control module is used to use the stability margin index to determine the dynamic control parameters of the circuit board and analyze the twin stability optimization coefficient of the dynamic control parameters. When the twin stability optimization coefficient is greater than a preset threshold, the dynamic control parameters are used to perform stable operation control of the circuit board.

[0070] Compared with the problems described in the background technology, first, by analyzing the circuit board characteristics of the industrial control equipment corresponding to the circuit board, and analyzing the operation stability factor of the circuit board based on these characteristics, a basis is provided for the subsequent deployment of the sensor network. The deployment of the multi-source heterogeneous sensor network can comprehensively collect multi-source heterogeneous data of the circuit board, including high-frequency dynamic temperature field distribution, three-dimensional mechanical vibration spectrum, electromagnetic interference intensity gradient and current ripple and other key industrial control feature data. The collection of these data lays the foundation for in-depth analysis of the industrial control characteristics of the circuit board and construction of the dynamic stability matrix. The dynamic stability matrix constructed based on the industrial control characteristics can accurately reflect the stability state of the circuit board under different working conditions. By analyzing the propagation path of the environmental disturbance, the key factors and propagation mechanisms affecting the stability of the circuit board can be identified. Based on this, the parameter fluctuation variance of the circuit board in the dynamic stability matrix is ​​calculated, which can quantify the degree of influence of the environmental disturbance on the circuit board parameters. The evaluation of the stability of the circuit board provides a quantitative basis, and the generation of the stability margin index of the circuit board further integrates the stability information of the circuit board, providing guidance for determining the dynamic control parameters. By analyzing the twin stability optimization coefficient of the dynamic control parameters, the optimization effect of the control parameters on the stability of the circuit board can be evaluated. When the twin stability optimization coefficient is greater than the preset threshold, it indicates that the dynamic control parameters can effectively improve the stability of the circuit board. At this time, using these parameters to perform stable operation control of the circuit board can ensure that the circuit board maintains stable operation under various complex working conditions. In addition, the scheme also realizes the simulation and optimization of the dynamic control parameters of the circuit board through digital twin model technology, avoiding the risks and costs of conducting a large number of experiments on actual hardware. Through real-time monitoring and dynamic control, abnormal conditions of the circuit board can be discovered and corrected in a timely manner, preventing the occurrence of potential faults, and improving the reliability and safety of industrial control equipment. Therefore, the present invention can improve the timeliness and accuracy of circuit board control. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic flow chart of a method for regulating and controlling the stable operation of a circuit board of an industrial control device provided by one embodiment of the present invention;

[0072] Figure 2 A schematic diagram of a module for implementing a method for controlling the stable operation of a circuit board of industrial control equipment provided by an embodiment of the present invention.

[0073] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0074] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] The present embodiment provides a method for regulating and controlling the stable operation of a circuit board in industrial control equipment. The method can be executed by at least one of electronic devices, such as a server or a terminal, that can be configured to execute the method provided in the embodiments of the present application. In other words, the method can be executed by software or hardware installed on a terminal or server. The server can include, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0076] Example 1:

[0077] Reference Figure 1 FIG. 1 is a flow chart of a method for regulating and controlling the stable operation of a circuit board of an industrial control device according to an embodiment of the present invention. In this embodiment, the method for regulating and controlling the stable operation of a circuit board of an industrial control device includes:

[0078] S1. Analyze circuit board characteristics of a circuit board corresponding to an industrial control device, analyze an operation stability factor of the circuit board based on the circuit board characteristics, and deploy a multi-source heterogeneous sensor network for the circuit board based on the operation stability factor.

[0079] It should be explained that the industrial control equipment referred to herein refers to equipment used for industrial automation and control. These devices are widely used in manufacturing, process control, power systems, robotics, and other fields, and include programmable logic controllers (PLCs), inverters, servo drives, industrial computers, data acquisition systems, and the like. The circuit boards referred to herein are printed circuit boards (PCBs) used within the control equipment. These boards carry electronic components, circuit connections, and signal paths, and are the core components that enable the functions of industrial control equipment. The circuit board characteristics referred to herein are the various physical, electrical, and mechanical properties that affect the performance and reliability of the circuit board, such as electrical, thermal, and mechanical characteristics.

[0080] The present invention analyzes the operational stability factor of the circuit board to comprehensively assess the operational stability factor of the circuit board, identify potential risk points, and take corresponding measures to improve and optimize, thereby improving the stability and reliability of the circuit board. The operational stability factor refers to factors that affect the stable operation of the circuit board, such as temperature, electromagnetic field, and current.

[0081] Based on the operation stability factor, the present invention deploys a multi-source heterogeneous sensing network of the circuit board to build an efficient, reliable and intelligent multi-source heterogeneous sensing network, thereby realizing comprehensive, real-time and intelligent monitoring and control of the operation status of the circuit board, thereby improving the stability and reliability of the circuit board.

[0082] In detail, the multi-source heterogeneous sensor network of the circuit board is deployed based on the operation stability factor, including:

[0083] Analyzing the key parameters to be collected of the circuit board according to the operation stability factor;

[0084] Determining the target sensor of the circuit board based on the key parameters to be collected;

[0085] Analyzing the heat-sensitive area of ​​the circuit board;

[0086] determining a primary sensor node and a redundant sensor node of the target sensor based on the thermally sensitive area;

[0087] Establishing a star-mesh hybrid topology of the primary sensor node and the redundant sensor nodes;

[0088] A multi-source heterogeneous sensing network of the circuit board is established through the star-mesh hybrid topology.

[0089] The key parameters to be collected refer to physical quantities that need to be monitored during the operation of the circuit board. These parameters can reflect the operating status and stability of the circuit board. For example, temperature, voltage, current, and vibration amplitude. The target sensors are sensors used to monitor the key parameters to be collected, including quantum dot infrared thermal imagers, nanograting MEMS accelerometers, metamaterial broadband electric field sensors, and magnetoresistive contactless current sensors. The thermally sensitive areas are areas on the circuit board that are sensitive to temperature changes. The primary sensor nodes are sensor nodes responsible for primary data collection and transmission in a multi-source heterogeneous sensor network. The redundant sensor nodes are sensor nodes that serve as backups and can take over data collection and transmission tasks in the event of a fault or failure of the primary sensor node. The star-mesh hybrid topology is a sensor network topology that combines the characteristics of a star topology and a mesh topology. The multi-source heterogeneous sensor network is a sensor network composed of multiple sensors of different types and functions.

[0090] Optionally, establishing a star-mesh hybrid topology of the primary sensor node and the redundant sensor nodes includes: in the star topology, the sensor nodes are all connected to a central node; and in the mesh topology, the sensor nodes are interconnected to form multiple data transmission paths. The star-mesh hybrid topology combines the advantages of both, improving data transmission efficiency and enhancing network robustness.

[0091] S2. Collect multi-source heterogeneous data of the circuit board through the multi-source heterogeneous sensing network, analyze the industrial control characteristics of the circuit board based on the multi-source heterogeneous data, and construct a dynamic stability matrix of the circuit board, wherein the industrial control characteristics include high-frequency dynamic temperature field distribution characteristics, three-dimensional mechanical vibration spectrum characteristics, electromagnetic interference intensity gradient characteristics and current ripple characteristics.

[0092] It should be explained that the multi-source heterogeneous data refers to a variety of data about the operating status of the circuit board collected in different formats and at different rates from sensors of different types and functions, including temperature data, mechanical vibration data, electromagnetic interference data and current ripple data.

[0093] The present invention analyzes the industrial control characteristics of the circuit board based on the multi-source heterogeneous data, which can deeply analyze the industrial control characteristics of the circuit board and provide a basis for improving the performance and stability of industrial control equipment.

[0094] In detail, analyzing the industrial control features of the circuit board based on the multi-source heterogeneous data includes:

[0095] Analyzing the temperature distribution of the circuit board according to the temperature data corresponding to the multi-source heterogeneous data;

[0096] Determining a high-frequency dynamic temperature field distribution characteristic of the circuit board according to the temperature distribution;

[0097] Analyzing a frequency spectrum of the circuit board using mechanical vibration data corresponding to the multi-source heterogeneous data;

[0098] Determining a three-dimensional mechanical vibration spectrum characteristic of the circuit board based on the spectrum diagram;

[0099] Analyzing the electromagnetic interference gradient of the circuit board according to the electromagnetic interference data corresponding to the multi-source heterogeneous data;

[0100] Analyzing the electromagnetic interference intensity gradient characteristics of the circuit board through the electromagnetic interference gradient;

[0101] Analyzing the ripple frequency and ripple amplitude of the circuit board according to the current ripple data corresponding to the multi-source heterogeneous data;

[0102] Analyzing the current ripple characteristics of the circuit board based on the ripple frequency and the ripple amplitude;

[0103] The industrial control characteristics of the circuit board are determined by combining the high-frequency dynamic temperature field distribution characteristics, the three-dimensional mechanical vibration spectrum characteristics, the electromagnetic interference intensity gradient characteristics and the current ripple characteristics.

[0104] Among them, the temperature distribution refers to the spatial distribution of temperature values ​​at various points on the circuit board, the high-frequency dynamic temperature field distribution characteristics refer to the high-frequency components of the temperature distribution that change with time, the spectrum diagram refers to a spectrum that displays the frequency components and amplitudes of the vibration signal, the three-dimensional mechanical vibration spectrum characteristics refer to the vibration spectrum characteristics of the circuit board in three spatial dimensions, the electromagnetic interference gradient refers to the rate of change of the electromagnetic interference intensity in space, the electromagnetic interference intensity gradient characteristics refer to the characteristic parameters of the electromagnetic interference gradient, such as the direction, magnitude and change law of the gradient, the ripple frequency refers to the frequency of the current or voltage ripple signal, the ripple amplitude refers to the amplitude of the current or voltage ripple signal, the current ripple characteristics refer to the parameters used to evaluate the impact of current ripple on the performance of the circuit board, and the industrial control characteristics refer to the key performance characteristics of the circuit board in industrial control applications, including high-frequency dynamic temperature field distribution characteristics, three-dimensional mechanical vibration spectrum characteristics, electromagnetic interference intensity gradient characteristics and current ripple characteristics.

[0105] Optionally, the frequency spectrum of the circuit board analyzed using the mechanical vibration data corresponding to the multi-source heterogeneous data can be obtained by performing a fast Fourier transform (FFT) on the mechanical vibration data.

[0106] Furthermore, analyzing the temperature distribution of the circuit board according to the temperature data corresponding to the multi-source heterogeneous data includes:

[0107] Analyzing the initial temperature and heat source of the circuit board based on the temperature data;

[0108] Marking the real-time power of the heat source;

[0109] The temperature of the circuit board is calculated according to the heat source and the real-time power using the following formula:

[0110]

[0111] Among them, T base (x, y, t) represents the temperature value of the position coordinate (x, y) at time t, T0 represents the initial temperature, Q c (t) represents the real-time power of the cth heat source, k represents the thermal conductivity of the material, r c represents the Euclidean distance from the position coordinate (x, y) to the cth heat source, m represents the number of heat sources, α represents the thermal diffusivity, π represents the circumference, and e represents the exponential function;

[0112] Analyzing the multi-field coupling correction term of the circuit board;

[0113] The temperature distribution of the circuit board is determined according to the temperature value and the multi-field coupling correction term.

[0114] Among them, the initial temperature refers to the reference temperature of the circuit board before it starts working or at a specific moment. The heat source refers to the component or area on the circuit board that generates heat, such as power devices and resistors. The real-time power refers to the actual power consumption of the heat source at a specific moment. The material thermal conductivity refers to the ability of the material to transfer heat. The Euclidean distance refers to the straight-line distance between the position coordinates (x, y) and the heat source in Euclidean space. The thermal diffusivity refers to the ability of the material to diffuse heat under the action of a temperature gradient. The multi-field coupling correction term refers to the correction term that considers the mutual coupling effects of multiple physical fields (such as electric fields, magnetic fields, mechanical fields, etc.) when calculating the temperature distribution of the circuit board.

[0115] The present invention constructs a dynamic stability matrix of the circuit board, which can deeply analyze the dynamic behavior and stability of the circuit board.

[0116] In detail, the construction of the dynamic stability matrix of the circuit board includes:

[0117] Analyzing dynamic behavior of the circuit board;

[0118] formulating differential equations for the dynamic behavior;

[0119] calculating the Jacobian matrix of the differential equation;

[0120] A dynamic stability matrix of the circuit board is constructed according to the Jacobian matrix.

[0121] The dynamic behavior refers to the process by which the state of a circuit board changes over time when subjected to internal or external stimuli. This includes changes in physical quantities such as temperature, voltage, current, mechanical stress, and electromagnetic fields on the circuit board. The differential equation is a mathematical equation that describes the law of change in the system state. It expresses the relationship between the rate of change of the system state variables (such as temperature, voltage, displacement, etc.) with time and other state variables or the system itself. The Jacobian matrix is ​​the first-order partial derivative matrix of a multivariate function, which describes the relationship between the rate of change of the system state variables and the system parameters or state variables. The dynamic stability matrix is ​​a matrix used to analyze the dynamic stability of the system.

[0122] Optionally, the Jacobian matrix of the differential equation is calculated by using a numerical method (such as finite difference method, finite element method, etc.).

[0123] Optionally, constructing the dynamic stability matrix of the circuit board according to the Jacobian matrix can be performed by calculating the eigenvalues ​​and eigenvectors of the Jacobian matrix.

[0124] S3. Based on the industrial control characteristics, determine the environmental disturbance propagation path of the circuit board in the dynamic stability matrix.

[0125] Based on the industrial control characteristics, the present invention determines the environmental disturbance propagation path of the circuit board in the dynamic stability matrix to provide a basis for subsequent circuit board disturbance optimization.

[0126] In detail, determining the environmental disturbance propagation path of the circuit board in the dynamic stability matrix based on the industrial control characteristics includes:

[0127] Initializing the dynamic stability matrix to obtain an initialized stability matrix;

[0128] Marking matrix parameters of the initialization stability matrix according to the industrial control characteristics;

[0129] Analyzing the cause-effect relationship of the matrix parameters;

[0130] Based on the causal relationship, constructing a directed weighted graph of the matrix parameters;

[0131] Calculating the path influence coefficient of the directed weighted graph;

[0132] The environmental disturbance propagation path of the initialized stability matrix is ​​marked according to the path influence coefficient.

[0133] Among them, the initialized stability matrix refers to assigning initial values ​​to the dynamic stability matrix based on the initial conditions or assumptions of the system; the matrix parameters refer to specific elements in the initialized stability matrix, representing the characteristic values ​​of a physical quantity at a specific time and space, including high-frequency dynamic temperature field distribution characteristics, three-dimensional mechanical vibration spectrum characteristics, electromagnetic interference intensity gradient characteristics and current ripple characteristics; the causal relationship refers to the time-series driving relationship between industrial control characteristic parameters, that is, the historical changes of a certain parameter have statistically significant analytical capabilities for the current state of another parameter; the directed weighted graph refers to a graph structure composed of nodes and weighted edges, which is used to intuitively represent the causal relationship and influence intensity between industrial control characteristic parameters; the path influence coefficient refers to the quantification of the contribution of each edge in the directed weighted graph to the stability of the system; the environmental disturbance propagation path refers to the trajectory of external interference (such as electromagnetic pulses, mechanical vibrations) spreading in the system through a specific causal chain.

[0134] Furthermore, the calculating of the path influence coefficient of the directed weighted graph includes:

[0135] Analyzing the chaotic characteristics and local stability of the parameter nodes corresponding to the directed weighted graph;

[0136] Calculating the cross-physical quantity covariance of the parameter node;

[0137] According to the chaotic characteristics, the local stability and the cross-physical quantity covariance, the path influence coefficient of the directed weighted graph is calculated using the following formula:

[0138]

[0139] Among them, Impact i-j (t) represents the parameter node X i To parameter node X j The path influence coefficient at time t, W i-j (t) represents the parameter node X i For parameter node X j The causal strength, ρ i (t) represents the parameter node X i The chaotic characteristics at time t, SMI j (t) represents the parameter node X j The local stability at time t, Cov(X i ,X j )(t) represents the parameter node X i To parameter node X j The covariance of the physical quantity across , tanh represents the hyperbolic tangent function, and σ represents the nonlinear scaling factor.

[0140] Among them, the parameter node refers to a variable or state representing a circuit board, the chaotic characteristic refers to the unpredictability of the change of the parameter node, the local stability refers to the stability near the parameter node, the cross-physical quantity covariance refers to the covariance between different physical quantities (such as temperature, pressure, flow, etc.), the causal strength refers to the strength of the causal relationship from one parameter node to another parameter node in a directed weighted graph, the hyperbolic tangent function refers to a nonlinear function used to compress the input value to the interval of (-1,1), and the nonlinear scaling factor refers to a parameter used to adjust the degree of nonlinearity in the formula.

[0141] Optionally, the analysis of the chaotic characteristics of the parameter nodes corresponding to the directed weighted graph can be determined by calculating the Lyapunov exponent and fractal dimension of the parameter nodes.

[0142] S4. Based on the environmental disturbance propagation path, after calculating the parameter fluctuation variance of the dynamic stability matrix of the circuit board, generate a stability margin index of the circuit board.

[0143] The present invention calculates the parameter fluctuation variance of the circuit board in the dynamic stability matrix based on the environmental disturbance propagation path to provide a basis for evaluating the stability of the circuit board.

[0144] In detail, the calculation of the parameter fluctuation variance of the circuit board in the dynamic stability matrix based on the environmental disturbance propagation path includes:

[0145] Marking the fluctuation parameter node of the circuit board in the dynamic stability matrix according to the environmental disturbance propagation path;

[0146] Calculating the node fluctuation variance of the fluctuation parameter node;

[0147] Defining a fluctuation weight of the node fluctuation variance based on a path influence coefficient corresponding to the environmental disturbance propagation path;

[0148] The parameter fluctuation variance of the circuit board in the dynamic stability matrix is ​​determined according to the node fluctuation variance and the node fluctuation variance.

[0149] Among them, the fluctuation parameter node refers to a node on the environmental disturbance propagation path whose parameter value is affected by the disturbance and fluctuates; the node fluctuation variance refers to the degree of discreteness or fluctuation amplitude of a single fluctuation parameter node during the fluctuation of its parameter value; the fluctuation weight refers to the contribution degree of each fluctuation parameter node to the overall parameter fluctuation variance defined based on the path influence coefficient corresponding to the environmental disturbance propagation path; the parameter fluctuation variance refers to the overall parameter fluctuation variance of the circuit board in the dynamic stability matrix determined after comprehensively considering the node fluctuation variance of all fluctuation parameter nodes and their corresponding fluctuation weights.

[0150] Optionally, the calculation of the node fluctuation variance of the fluctuation parameter node may be performed by analyzing changes in parameter values ​​of the node over a period of time.

[0151] The present invention generates a stability margin index for the circuit board, which can generate a stability margin index that quantifies the stability of the circuit board, providing a basis for optimizing the stability of the circuit board. The stability margin index is used to assess the stability of a system or device in the face of internal parameter fluctuations and external environmental disturbances. Specifically, the stability margin index can be analyzed using parameter fluctuation variance and preset stability margin indicators (phase margin, gain margin, damping ratio, etc.).

[0152] S5. Use the stability margin index to determine the dynamic control parameters of the circuit board, and analyze the twin stability optimization coefficient of the dynamic control parameters. When the twin stability optimization coefficient is greater than a preset threshold, use the dynamic control parameters to perform stable operation control of the circuit board.

[0153] It should be explained that the dynamic control parameters are those that can be adjusted in real time to affect the performance of the circuit board, including amplifier gain, filter cutoff frequency, power supply voltage, vibration control parameters, etc.

[0154] The present invention analyzes the twin stability optimization coefficient of the dynamic control parameters to systematically analyze and optimize the twin stability optimization coefficient of the dynamic control parameters, thereby improving the stability and performance of the circuit board.

[0155] In detail, the analysis of the twin stability optimization coefficient of the dynamic control parameter includes:

[0156] Establishing a digital twin model of the circuit board corresponding to the dynamic control parameters;

[0157] Analyzing the sensitivity of the dynamic control parameters;

[0158] Defining the optimization target of the dynamic control parameters;

[0159] According to the sensitivity and the optimization target, the dynamic control parameters are integrated into the digital twin model to analyze the twin stability optimization coefficient of the dynamic control parameters.

[0160] Among them, the digital twin model refers to an accurate digital copy that can simulate the behavior of the circuit board under various conditions, including normal operating conditions and abnormal conditions. The sensitivity refers to the degree of influence of the dynamic control parameters on the performance or stability of the circuit board. The optimization target refers to the specific goal achieved by adjusting the dynamic control parameters, which can be to maximize stability. The twin stability optimization coefficient refers to the coefficient used to quantify the optimization effect of the dynamic control parameter adjustment on the stability of the circuit board.

[0161] Optionally, the digital twin model of the circuit board corresponding to the dynamic control parameters is established by combining computer-aided design (CAD) software with system simulation software (such as MATLAB / Simulink) to establish the digital twin model of the circuit board.

[0162] Optionally, the analysis of the sensitivity of the dynamic control parameters can be determined by analyzing the changes of the dynamic control parameters in the entire definition domain through global sensitivity analysis (Sobol method, extended Fourier Amelie basis (eFAST)), and evaluating the overall impact of the dynamic control parameters on the system output.

[0163] Finally, the present invention uses dynamic control parameters to perform stable operation control of the circuit board when the twin stability optimization coefficient is greater than a preset threshold value, which can achieve efficient and stable operation control of the circuit board. Among them, the preset threshold value refers to the threshold value used to judge whether the twin stability optimization coefficient meets the optimization requirements. Compared with the problem described in the background technology, first, by analyzing the circuit board characteristics of the industrial control equipment corresponding to the circuit board, and analyzing the operation stability factor of the circuit board based on these characteristics, it provides a basis for the subsequent deployment of the sensor network. The deployment of the multi-source heterogeneous sensor network can comprehensively collect multi-source heterogeneous data of the circuit board, including high-frequency dynamic temperature field distribution, three-dimensional mechanical vibration spectrum, electromagnetic interference intensity gradient and current ripple and other key industrial control feature data. The collection of these data lays the foundation for in-depth analysis of the industrial control characteristics of the circuit board and construction of the dynamic stability matrix. The dynamic stability matrix constructed based on the industrial control characteristics can accurately reflect the stability state of the circuit board under different working conditions. By analyzing the propagation path of the environmental disturbance, the key factors and propagation mechanisms that affect the stability of the circuit board can be identified. Based on this, the parameter fluctuation variance of the circuit board in the dynamic stability matrix is ​​calculated, which can be quantified. The degree of influence of environmental disturbances on circuit board parameters provides a quantitative basis for evaluating the stability of the circuit board. Generating a stability margin index for the circuit board further integrates the stability information of the circuit board and provides guidance for determining dynamic control parameters. By analyzing the twin stability optimization coefficient of the dynamic control parameters, the optimization effect of the control parameters on the stability of the circuit board can be evaluated. When the twin stability optimization coefficient is greater than the preset threshold, it indicates that the dynamic control parameters can effectively improve the stability of the circuit board. At this time, using these parameters to perform stable operation control of the circuit board can ensure that the circuit board maintains stable operation under various complex working conditions. In addition, the solution also uses digital twin model technology to simulate and optimize the dynamic control parameters of the circuit board, avoiding the risks and costs of conducting a large number of experiments on actual hardware. Through real-time monitoring and dynamic control, abnormal conditions of the circuit board can be discovered and corrected in a timely manner, preventing the occurrence of potential faults, and improving the reliability and safety of industrial control equipment. Therefore, the present invention can improve the timeliness and accuracy of circuit board control.

[0164] Example 2:

[0165] like Figure 2 The figure shows a functional module diagram of a stable operation control system for a circuit board of industrial control equipment according to the present invention.

[0166] The industrial control equipment circuit board stable operation control system 200 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the industrial control equipment circuit board stable operation control system may include a sensor network construction module 201, an industrial control feature analysis module 202, a disturbance propagation path analysis module 203, a stability margin index determination module 204, and a stable operation control module 205. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These modules are stored in the electronic device's memory.

[0167] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0168] The sensor network construction module 201 is configured to analyze circuit board characteristics of a circuit board corresponding to an industrial control device, analyze an operation stability factor of the circuit board based on the circuit board characteristics, and deploy a multi-source heterogeneous sensor network for the circuit board based on the operation stability factor;

[0169] The industrial control feature analysis module 202 is configured to collect multi-source heterogeneous data of the circuit board through the multi-source heterogeneous sensor network, analyze the industrial control features of the circuit board based on the multi-source heterogeneous data, and construct a dynamic stability matrix of the circuit board, wherein the industrial control features include high-frequency dynamic temperature field distribution features, three-dimensional mechanical vibration spectrum features, electromagnetic interference intensity gradient features, and current ripple features;

[0170] The disturbance propagation path analysis module 203 is configured to determine the environmental disturbance propagation path of the circuit board in the dynamic stability matrix based on the industrial control characteristics;

[0171] The stability margin index determination module 204 is configured to generate a stability margin index of the circuit board after calculating the parameter fluctuation variance of the dynamic stability matrix of the circuit board based on the environmental disturbance propagation path;

[0172] The stable operation control module 205 is used to use the stability margin index to determine the dynamic control parameters of the circuit board and analyze the twin stability optimization coefficient of the dynamic control parameters. When the twin stability optimization coefficient is greater than a preset threshold, the dynamic control parameters are used to perform stable operation control of the circuit board.

[0173] In detail, the modules in the industrial control equipment circuit board stable operation control system 200 in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means are used as the method for controlling the stable operation of the industrial control equipment circuit board described in the previous section and can produce the same technical effects, so they will not be repeated here.

[0174] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling the stable operation of a circuit board of an industrial control equipment, characterized in that: The method comprises: Analyzing circuit board characteristics of a circuit board corresponding to the industrial control equipment, analyzing an operation stability factor of the circuit board based on the circuit board characteristics, and deploying a multi-source heterogeneous sensor network for the circuit board based on the operation stability factor; Collecting multi-source heterogeneous data of the circuit board through the multi-source heterogeneous sensing network, analyzing industrial control characteristics of the circuit board based on the multi-source heterogeneous data, and constructing a dynamic stability matrix of the circuit board, wherein the industrial control characteristics include high-frequency dynamic temperature field distribution characteristics, three-dimensional mechanical vibration spectrum characteristics, electromagnetic interference intensity gradient characteristics, and current ripple characteristics; Based on the industrial control characteristics, determining an environmental disturbance propagation path of the circuit board in the dynamic stability matrix; Based on the environmental disturbance propagation path, after calculating the parameter fluctuation variance of the dynamic stability matrix of the circuit board, a stability margin index of the circuit board is generated; The stability margin index is used to determine the dynamic control parameters of the circuit board, and the twin stability optimization coefficient of the dynamic control parameters is analyzed. When the twin stability optimization coefficient is greater than a preset threshold, the dynamic control parameters are used to perform stable operation control of the circuit board.

2. The method for controlling the stable operation of a circuit board of industrial control equipment according to claim 1, wherein: The deploying of the multi-source heterogeneous sensor network of the circuit board based on the operation stability factor includes: Analyzing the key parameters to be collected of the circuit board according to the operation stability factor; Determining the target sensor of the circuit board based on the key parameters to be collected; Analyzing the heat-sensitive area of ​​the circuit board; determining a primary sensor node and a redundant sensor node of the target sensor based on the thermally sensitive area; Establishing a star-mesh hybrid topology of the primary sensor node and the redundant sensor nodes; A multi-source heterogeneous sensing network of the circuit board is established through the star-mesh hybrid topology.

3. The method for controlling the stable operation of a circuit board of industrial control equipment according to claim 2, wherein: Analyzing the industrial control characteristics of the circuit board based on the multi-source heterogeneous data includes: Analyzing the temperature distribution of the circuit board according to the temperature data corresponding to the multi-source heterogeneous data; Determining a high-frequency dynamic temperature field distribution characteristic of the circuit board according to the temperature distribution; Analyzing a frequency spectrum of the circuit board using mechanical vibration data corresponding to the multi-source heterogeneous data; Determining a three-dimensional mechanical vibration spectrum characteristic of the circuit board based on the spectrum diagram; Analyzing the electromagnetic interference gradient of the circuit board according to the electromagnetic interference data corresponding to the multi-source heterogeneous data; Analyzing the electromagnetic interference intensity gradient characteristics of the circuit board through the electromagnetic interference gradient; Analyzing the ripple frequency and ripple amplitude of the circuit board according to the current ripple data corresponding to the multi-source heterogeneous data; Analyzing the current ripple characteristics of the circuit board based on the ripple frequency and the ripple amplitude; The industrial control characteristics of the circuit board are determined by combining the high-frequency dynamic temperature field distribution characteristics, the three-dimensional mechanical vibration spectrum characteristics, the electromagnetic interference intensity gradient characteristics and the current ripple characteristics.

4. The method for controlling the stable operation of a circuit board of industrial control equipment according to claim 3, wherein: The analyzing the temperature distribution of the circuit board according to the temperature data corresponding to the multi-source heterogeneous data includes: Analyzing the initial temperature and heat source of the circuit board based on the temperature data; Marking the real-time power of the heat source; The temperature of the circuit board is calculated according to the heat source and the real-time power using the following formula: Among them, T base (x, y, t) represents the temperature value of the position coordinate (x, y) at time t, T0 represents the initial temperature, Q c (t) represents the real-time power of the cth heat source, k represents the thermal conductivity of the material, r c represents the Euclidean distance from the position coordinate (x, y) to the cth heat source, m represents the number of heat sources, α represents the thermal diffusivity, π represents the circumference, and e represents the exponential function; Analyzing the multi-field coupling correction term of the circuit board; The temperature distribution of the circuit board is determined according to the temperature value and the multi-field coupling correction term.

5. The method for controlling the stable operation of a circuit board of industrial control equipment according to claim 4, wherein: The constructing of the dynamic stability matrix of the circuit board includes: Analyzing dynamic behavior of the circuit board; formulating differential equations for the dynamic behavior; calculating the Jacobian matrix of the differential equation; A dynamic stability matrix of the circuit board is constructed according to the Jacobian matrix.

6. The method for controlling the stable operation of a circuit board of industrial control equipment according to claim 5, wherein: The determining, based on the industrial control characteristics, an environmental disturbance propagation path of the circuit board in the dynamic stability matrix includes: Initializing the dynamic stability matrix to obtain an initialized stability matrix; Marking matrix parameters of the initialization stability matrix according to the industrial control characteristics; Analyzing the cause-effect relationship of the matrix parameters; Based on the causal relationship, constructing a directed weighted graph of the matrix parameters; Calculating the path influence coefficient of the directed weighted graph; The environmental disturbance propagation path of the initialized stability matrix is ​​marked according to the path influence coefficient.

7. The method for controlling the stable operation of a circuit board of industrial control equipment according to claim 6, wherein: The calculating of the path influence coefficient of the directed weighted graph includes: Analyzing the chaotic characteristics and local stability of the parameter nodes corresponding to the directed weighted graph; Calculating the cross-physical quantity covariance of the parameter node; According to the chaotic characteristics, the local stability and the cross-physical quantity covariance, the path influence coefficient of the directed weighted graph is calculated using the following formula: Among them, Impact i-j (t) represents the parameter node X i To parameter node X j The path influence coefficient at time t, W i-j (t) represents the parameter node X i For parameter node X j The causal strength, ρ i (t) represents the parameter node X i The chaotic characteristics at time t, SMI j (t) represents the parameter node X j The local stability at time t, Cov(X i ,X j )(t) represents the parameter node X i To parameter node X j The covariance of the physical quantity across , tanh represents the hyperbolic tangent function, and σ represents the nonlinear scaling factor.

8. The method for controlling the stable operation of a circuit board of industrial control equipment according to claim 7, wherein: The calculating, based on the environmental disturbance propagation path, the parameter fluctuation variance of the circuit board in the dynamic stability matrix includes: Marking the fluctuation parameter node of the circuit board in the dynamic stability matrix according to the environmental disturbance propagation path; Calculating the node fluctuation variance of the fluctuation parameter node; Defining a fluctuation weight of the node fluctuation variance based on a path influence coefficient corresponding to the environmental disturbance propagation path; The parameter fluctuation variance of the circuit board in the dynamic stability matrix is ​​determined according to the node fluctuation variance and the node fluctuation variance.

9. The method for controlling the stable operation of a circuit board of industrial control equipment according to claim 8, wherein: The analysis of the twin stability optimization coefficient of the dynamic control parameter includes: Establishing a digital twin model of the circuit board corresponding to the dynamic control parameters; Analyzing the sensitivity of the dynamic control parameters; Defining the optimization target of the dynamic control parameters; According to the sensitivity and the optimization target, the dynamic control parameters are integrated into the digital twin model to analyze the twin stability optimization coefficient of the dynamic control parameters.

10. A stable operation control system for industrial control equipment circuit boards, characterized in that: The system comprises: a sensor network construction module configured to analyze circuit board characteristics of a circuit board corresponding to an industrial control device, analyze an operational stability factor of the circuit board based on the circuit board characteristics, and deploy a multi-source heterogeneous sensor network for the circuit board based on the operational stability factor; an industrial control feature analysis module, configured to collect multi-source heterogeneous data of the circuit board through the multi-source heterogeneous sensor network, analyze the industrial control features of the circuit board based on the multi-source heterogeneous data, and construct a dynamic stability matrix of the circuit board, wherein the industrial control features include high-frequency dynamic temperature field distribution features, three-dimensional mechanical vibration spectrum features, electromagnetic interference intensity gradient features, and current ripple features; a disturbance propagation path analysis module, configured to determine, based on the industrial control characteristics, an environmental disturbance propagation path of the circuit board in the dynamic stability matrix; a stability margin index determination module, configured to generate a stability margin index of the circuit board after calculating the parameter fluctuation variance of the dynamic stability matrix of the circuit board based on the environmental disturbance propagation path; The stable operation control module is used to use the stability margin index to determine the dynamic control parameters of the circuit board and analyze the twin stability optimization coefficient of the dynamic control parameters. When the twin stability optimization coefficient is greater than a preset threshold, the dynamic control parameters are used to perform stable operation control of the circuit board.

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