Environment perception based adaptive switching method and system for working mode of industrial control motherboard

Abstract

Embodiments of the present application disclose an environment-aware industrial control mainboard working mode adaptive switching method and system, which comprises: obtaining environment information of the industrial control mainboard during operation, performing feature analysis to obtain an environment analysis result; obtaining corresponding communication information of the industrial control mainboard during operation, performing stability analysis to obtain a communication analysis result; correlating and evaluating the communication analysis result and the environment analysis result to obtain correlation information, so as to determine the environment type in which the industrial control mainboard is currently located, and select a target working mode matching the environment type from a plurality of preset working modes; controlling the industrial control mainboard to switch from the current working mode to the target working mode, and synchronously adjusting hardware operation parameters and communication parameters corresponding to the target working mode. Through the above scheme, the problem of switching lag or unreasonable mode selection when the industrial field environment changes complexly and the communication state and environmental factors influence each other is overcome.

Description

Technical Field

[0001] This application relates to the technical field of industrial control motherboards, and more specifically, to a method and system for adaptive switching of operating modes of industrial control motherboards based on environmental awareness. Background Technology

[0002] In complex industrial environments, industrial control motherboards typically need to operate continuously for extended periods and face various uncertainties such as temperature and humidity changes, vibration interference, power fluctuations, and unstable communication links, which places higher demands on the motherboard's adaptability to different operating modes.

[0003] In existing technologies, the switching of operating modes on industrial control motherboards is typically based on manual configuration or triggered by a single operating parameter. For example, performance mode, energy-saving mode, or security mode may be switched according to load level, task type, or fixed threshold. Some solutions also independently monitor environmental parameters or communication status and execute corresponding control strategies when preset thresholds are exceeded, thereby improving system stability and security to a certain extent.

[0004] Although basic operating mode switching can be achieved by monitoring operating parameters or single status information, existing solutions cannot fully reflect the actual operating status of the motherboard when the industrial environment is complex and communication status and environmental factors interact. This can easily lead to problems such as switching lag or unreasonable mode selection. Summary of the Invention

[0005] The embodiments of this application provide an adaptive switching method and system for the working mode of an industrial control motherboard based on environmental awareness, which can overcome the problems of switching lag or unreasonable mode selection when the industrial environment is complex and communication status and environmental factors interact.

[0006] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0007] According to one aspect of the embodiments of this application, an adaptive switching method for the operating mode of an industrial control motherboard based on environment awareness is provided, comprising: acquiring environmental information of the industrial control motherboard during operation, and performing feature analysis on the environmental information to obtain environmental analysis results; acquiring communication information corresponding to the industrial control motherboard during operation, and performing stability analysis on the communication information to obtain communication analysis results; performing correlation evaluation between the communication analysis results and the environmental analysis results to obtain correlation information, and determining the current environment type of the industrial control motherboard based on the correlation information; selecting a target operating mode that matches the environment type from a preset variety of operating modes; controlling the industrial control motherboard to switch from the current operating mode to the target operating mode, and synchronously adjusting the hardware operating parameters and communication parameters corresponding to the target operating mode.

[0008] In one embodiment of this application, the step of acquiring environmental information of the industrial control motherboard during operation and performing feature analysis on the environmental information to obtain environmental analysis results includes: acquiring environmental information of the industrial control motherboard during operation through sensors deployed on the motherboard; wherein, the environmental information includes ambient temperature data, ambient humidity data, vibration intensity data, and voltage fluctuation parameters; calculating the average, maximum, minimum, and standard deviation of the ambient temperature data and ambient humidity data for each time period to obtain temperature feature vectors and humidity feature vectors; extracting features from the vibration intensity data using fast Fourier transform to obtain vibration frequency domain feature parameters; calculating the fluctuation amplitude and fluctuation frequency of the power supply voltage fluctuation data, and constructing voltage fluctuation parameters using the fluctuation amplitude and fluctuation frequency; combining the temperature feature vector, humidity feature vector, vibration frequency domain feature parameters, and voltage fluctuation parameters after normalization processing to form an environmental feature matrix; inputting the environmental feature matrix into a pre-trained environmental classification model for analysis to obtain environmental analysis results; wherein, the environmental analysis results include environmental stability level and environmental severity level.

[0009] In one embodiment of this application, the step of acquiring the communication information corresponding to the industrial control motherboard during operation and performing stability analysis on the communication information to obtain communication analysis results includes: real-time monitoring of the data transmission status of each communication channel to obtain communication information, and using the communication information as a communication stability indicator; classifying each communication stability indicator into levels, scoring reliability, and assessing interruption risk according to a preset stability threshold range to obtain a communication stability level, a communication reliability score, and a communication interruption risk coefficient; and constructing a communication analysis result based on the communication stability level, the communication reliability score, and the communication interruption risk coefficient.

[0010] In one embodiment of this application, the communication information includes transmission success rate, reception success rate, communication delay time, packet loss rate, retransmission count, and signal strength. The step of real-time monitoring of the data transmission status of each communication channel to obtain communication information and using the communication information as a communication stability indicator includes: calculating the mean and variance of the time series of the transmission success rate and reception success rate within a preset statistical period, and determining the fluctuation degree of the communication success rate by the magnitude of the mean and variance; establishing a sliding time window for the communication delay time and calculating the median and percentile distribution of the delay time within the window to identify abnormal delay peaks; constructing a communication quality attenuation curve based on the packet loss rate and the retransmission count, and determining the rate of communication quality deterioration by fitting the slope of the attenuation curve; continuously sampling the signal strength data and calculating its short-time energy and zero-crossing rate to identify the signal attenuation trend; and using the fluctuation degree, abnormal delay peaks, communication quality deterioration rate, and signal attenuation trend as communication stability indicators.

[0011] In one embodiment of this application, the step of correlating the communication analysis results with the environmental analysis results to obtain correlation information, and determining the current environment type of the industrial control motherboard based on the correlation information, includes: extracting the environmental stability level and environmental severity level from the environmental analysis results as environmental dimension parameters, and extracting the communication stability level and communication reliability score from the communication analysis results as communication dimension parameters; mapping the environmental dimension parameters to the first dimension of a preset two-dimensional correlation evaluation matrix, and mapping the communication dimension parameters to the second dimension of the two-dimensional correlation evaluation matrix to determine the corresponding correlation evaluation region in the two-dimensional correlation evaluation matrix; calculating the Pearson correlation coefficient between the environmental severity level and the communication interruption risk coefficient, and generating correlation information based on the correlation evaluation region and the Pearson correlation coefficient; calculating the Euclidean distance between the correlation information and each environmental type feature pattern in a preset environmental type feature library, and selecting the environmental type with the smallest Euclidean distance as the current environment type of the industrial control motherboard.

[0012] In one embodiment of this application, the step of generating association information based on the association assessment region and the Pearson correlation coefficient includes: calculating the Pearson correlation coefficient between the environmental severity level and the communication interruption risk coefficient; when the Pearson correlation coefficient is greater than a preset positive correlation threshold, it is determined that the environmental factor has a significant impact on the communication status, and the degree of impact is quantified as environmental-communication coupling degree; when the Pearson correlation coefficient is less than or equal to the preset positive correlation threshold, it is determined that the environmental factor has a weak impact on the communication status, and the environmental-communication coupling degree is set to a preset minimum coupling degree value; calculating a comprehensive association index using the location coordinates of the association assessment region and the environmental-communication coupling degree, and generating association information based on the comprehensive association index.

[0013] In one embodiment of this application, the step of selecting a target working mode that matches the environment type from a set of preset working modes includes: filtering a set of candidate working modes that match the environment type tag from the set of preset working modes according to the environment type; when there are multiple working modes in the set of candidate working modes, reading the real-time load rate, task priority queue and resource usage of the current industrial control motherboard as auxiliary judgment parameters; calculating the comprehensive adaptability of each working mode in the set of candidate working modes based on the auxiliary judgment parameters, and selecting the working mode with the highest comprehensive adaptability as the target working mode.

[0014] According to one aspect of the embodiments of this application, an environmental awareness-based adaptive switching system for industrial control motherboard operating modes is provided, comprising: an environmental data acquisition module, used to acquire environmental information of the industrial control motherboard during operation, and perform feature analysis on the environmental information to obtain environmental analysis results; a communication data acquisition module, used to acquire communication information corresponding to the industrial control motherboard during operation, and perform stability analysis on the communication information to obtain communication analysis results; a data association evaluation module, used to perform association evaluation between the communication analysis results and the environmental analysis results to obtain association information, and determine the current environmental type of the industrial control motherboard based on the association information; an operating mode matching module, used to select a target operating mode that matches the environment type from a variety of preset operating modes; and an operating mode switching module, used to control the industrial control motherboard to switch from the current operating mode to the target operating mode, and synchronously adjust the hardware operating parameters and communication parameters corresponding to the target operating mode.

[0015] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device performs the environmentally aware industrial control motherboard working mode adaptive switching method described above.

[0016] According to one aspect of the embodiments of this application, the embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor of an electronic device, causes the electronic device to perform the environmentally aware adaptive switching method for industrial control motherboard operating modes as described above.

[0017] In the technical solution provided by the embodiments of this application, by synchronously acquiring environmental and communication information during the operation of the industrial control motherboard, and performing feature analysis on the environmental information and stability analysis on the communication information respectively, and then correlating and evaluating the communication analysis results with the environmental analysis results, the influence relationship between environmental factors and communication status can be accurately characterized. Furthermore, the current environment type of the industrial control motherboard is determined by the correlation information, and a target working mode matching it is selected from a variety of preset working modes based on the environment type, so that the working mode selection process takes into account both environmental status and communication stability requirements. After determining the target working mode, the hardware operating parameters and communication parameters corresponding to the target working mode are adjusted synchronously to avoid the problem of overall operational incoordination caused by only adjusting a single parameter locally. This enables the industrial control motherboard to achieve adaptive switching of working modes in complex and ever-changing industrial environments, improving operational stability, communication reliability, and environmental adaptability, and overcoming the problems of switching lag or unreasonable mode selection when the industrial environment is complex and communication status and environmental factors influence each other.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 This is a flowchart illustrating an exemplary embodiment of the present application of an adaptive switching method for the working mode of an industrial control motherboard based on environmental awareness; Figure 2This is a schematic block diagram illustrating the structure of an environmentally aware industrial control motherboard operating mode adaptive switching system, as shown in an exemplary embodiment of this application. Figure 3 An exemplary embodiment of this application illustrates a schematic block diagram of the structure of an electronic device. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0022] The flowcharts shown in the accompanying diagrams are merely illustrative and do not necessarily include all content and operations, nor do they necessarily have to be executed in the described order. For example, some operations may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0023] It should also be noted that "multiple" as mentioned in this application refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0024] The technical solutions of the embodiments of this application will be described in detail below.

[0025] Example 1: like Figure 1 As shown in the example, this application provides a method for adaptive switching of operating modes of an industrial control motherboard based on environmental awareness. This method can extract environmental analysis results reflecting physical operating conditions through structured analysis of environmental information during the operation of the industrial control motherboard, thus providing a reliable basis for operating mode selection. This includes: Step S10: Obtain environmental information of the industrial control motherboard during operation, perform feature analysis on the environmental information, and obtain environmental analysis results.

[0026] During operation, the industrial control motherboard periodically collects environmental information corresponding to its operating environment, and performs categorization and feature analysis on the collected environmental information. This transforms the raw measurement data into analytical results that reflect the trend and stability of environmental changes, thereby reducing the impact of instantaneous fluctuations in environmental information on the judgment results.

[0027] For example, the environmental information during the operation of the industrial control motherboard can be divided into multiple consecutive time periods according to the collection time. The environmental information can be uniformly analyzed and processed within each time period to ensure that the environmental analysis results can reflect the overall characteristics of the environmental state within that time period.

[0028] In another example, step S10 may also be preferably: The environmental information of the industrial control motherboard during operation is obtained by sensors deployed on the motherboard; the environmental information includes ambient temperature data, ambient humidity data, vibration intensity data, and voltage fluctuation parameters.

[0029] Ambient temperature and humidity data are collected by temperature and humidity sensors installed in key parts of the industrial control motherboard. Vibration intensity data is collected by vibration sensors. Voltage fluctuation parameters are obtained by recording voltage sampling data from the power supply port of the industrial control motherboard. All kinds of environmental information are stored synchronously according to a unified timestamp.

[0030] Temperature and humidity sensors collect ambient temperature and humidity data at a fixed sampling period, vibration sensors collect vibration intensity data at a higher sampling frequency than ambient temperature, and voltage sampling modules at the power supply port record voltage change data within the same time period to ensure consistency of different types of environmental information over time.

[0031] For each time period, calculate the average, maximum, minimum, and standard deviation of the ambient temperature and humidity data to obtain the temperature feature vector and humidity feature vector.

[0032] By statistically analyzing the ambient temperature and humidity data collected within the same time period, the data from multiple discrete sampling points are compressed into representative statistical features to reflect the overall level and fluctuation of ambient temperature and humidity within that time period.

[0033] For example, within a preset time period, the average value of the collected ambient temperature data is calculated to represent the overall temperature level, the maximum and minimum values ​​are calculated to represent the temperature variation range, and the standard deviation is calculated to represent the degree of temperature fluctuation, thus forming a temperature feature vector containing multiple statistics; the ambient humidity data is processed in the same way to form a humidity feature vector.

[0034] Vibration intensity data is subjected to feature extraction using Fast Fourier Transform to obtain vibration frequency domain feature parameters.

[0035] The Fast Fourier Transform algorithm is used to convert vibration intensity data in the time domain to the frequency domain to extract vibration frequency domain feature parameters that reflect the main frequency components and energy distribution characteristics of the vibration signal, thereby avoiding the instability caused by relying solely on the time domain amplitude to judge the vibration state.

[0036] For example, a fast Fourier transform is performed on vibration intensity data collected over a period of time to calculate the amplitude distribution of the vibration signal in different frequency ranges. The amplitude corresponding to the dominant frequency component and the concentration of spectral energy are selected as vibration frequency domain characteristic parameters to characterize the mechanical vibration characteristics of the environment in which the industrial control motherboard is located.

[0037] Calculate the fluctuation amplitude and frequency of the power supply voltage fluctuation data, and construct the voltage fluctuation parameters using the fluctuation amplitude and frequency.

[0038] By analyzing the power supply voltage sampling data within the same time period, the difference between the maximum and minimum values ​​of the power supply voltage within that time period is calculated as the fluctuation amplitude. At the same time, the number of times the voltage change exceeds a preset change threshold is counted to determine the fluctuation frequency, thereby forming a voltage fluctuation parameter that can reflect the stability of the power supply.

[0039] If the power supply voltage changes by a certain percentage more than the rated voltage multiple times within a preset time period, the corresponding fluctuation frequency is high. Combined with the fluctuation amplitude, the degree of influence of the power supply environment on the operation of the industrial control motherboard can be judged.

[0040] The temperature feature vector, humidity feature vector, vibration frequency domain feature parameters, and voltage fluctuation parameters are normalized and then combined into an environmental feature matrix.

[0041] Temperature feature vectors, humidity feature vectors, vibration frequency domain feature parameters, and voltage fluctuation parameters with different dimensions and numerical ranges are normalized to ensure that all features are expressed within a unified numerical range. These features are then combined according to a pre-defined feature order to form an environmental feature matrix. Minimum-maximum value normalization is performed on the statistics in the temperature and humidity feature vectors, and proportional normalization is performed on the vibration frequency domain feature parameters and voltage fluctuation parameters. Finally, the normalized features are concatenated according to time periods to form the environmental feature matrix.

[0042] The environmental feature matrix is ​​input into a pre-trained environmental classification model for analysis. The environmental classification model calculates the environmental stability score and the environmental severity score based on the numerical range of each parameter in the environmental feature matrix and the correlation between the parameters.

[0043] The environmental classification model adopts the supervised classification model in the existing technology. It is obtained by training on historical environmental feature samples. It is used to output environmental stability score and environmental severity score based on the combination of various features in the environmental feature matrix, thereby quantifying the impact of the environment on the operation of the industrial control motherboard.

[0044] The environmental classification model calculates an environmental stability score representing the degree of environmental fluctuation and an environmental severity score representing the degree of environmental adverseness to equipment operation based on multi-dimensional feature input. Both scores are represented using a preset numerical range.

[0045] The environmental analysis results are based on the environmental stability score and the environmental severity score; the environmental analysis results include the environmental stability level and the environmental severity level.

[0046] By comparing the environmental stability score and environmental severity score with their corresponding level classification thresholds, the continuous numerical score results are converted into discrete level forms for environmental stability and environmental severity. The environmental stability score is mapped to multiple preset level intervals, generating corresponding environmental stability levels; similarly, the environmental severity score is mapped to multiple preset level intervals, generating corresponding environmental severity levels, thus forming a complete environmental analysis result.

[0047] Step S20: Obtain the communication information corresponding to the industrial control motherboard during operation, perform stability analysis on the communication information, and obtain the communication analysis results.

[0048] By calling the data statistics unit configured in the communication interface driver layer of the industrial control motherboard, the real-time transmission status of each communication channel of the industrial control motherboard is continuously collected during operation, and the collected communication process data is organized into a set of communication information that can be used for stability analysis; the communication information is updated in a fixed statistical period to ensure that the analysis results can reflect the current communication operation status.

[0049] When the industrial control motherboard interacts with external control devices via an Ethernet interface, by enabling the transmission status statistics function in the communication protocol stack, the message sending results, receiving results, and transmission delay in each statistical period are recorded to form corresponding communication information.

[0050] In another example, step S20 may also be preferably: The data transmission status of each communication channel is monitored in real time to obtain communication information, which is then used as an indicator of communication stability.

[0051] Each enabled communication channel on the industrial control motherboard is treated as an independent monitoring object. The data transmission behavior of each communication channel during operation is continuously monitored, and the corresponding communication information is statistically analyzed separately for each communication channel. This avoids mutual interference between the states of different communication channels and ensures the accuracy of the communication stability analysis results.

[0052] If an industrial control motherboard simultaneously enables industrial Ethernet communication channels and serial communication channels, separate data statistics buffers should be established for each of the two communication channels, and their corresponding transmission success rate, reception success rate, and communication delay time should be recorded respectively.

[0053] The communication information includes transmission success rate, reception success rate, communication delay time, packet loss rate, number of retransmissions, and signal strength.

[0054] The transmission success rate is defined as the ratio of the number of successfully transmitted data frames to the total number of transmitted data frames within the statistical period; the reception success rate is defined as the ratio of the number of successfully received data frames to the expected number of received data frames within the statistical period; the communication delay time is defined as the time elapsed from the sending end to the receiving end acknowledging the reception of a data frame; the packet loss rate is defined as the ratio of the number of unacknowledged received data frames to the total number of transmitted data frames within the statistical period; the retransmission count is defined as the cumulative number of data frames that trigger the retransmission mechanism within the statistical period; and the signal strength is defined as the received signal strength indication value reported by the communication physical layer.

[0055] For example, if 1000 data frames are sent within a statistical period, and 980 data frames are successfully received, the transmission success rate is 0.98 and the packet loss rate is 0.02. At the same time, the round-trip time of each data frame is recorded as a communication delay time sample.

[0056] Furthermore, the step of monitoring the data transmission status of each communication channel in real time, obtaining communication information, and using the communication information as an indicator of communication stability can be preferably: Within a preset statistical period, the mean and variance of the time series of the transmission success rate and the reception success rate are calculated respectively, and the degree of fluctuation of the communication success rate is determined by the magnitude of the mean and variance.

[0057] The transmission success rate and reception success rate over multiple consecutive statistical periods are constructed as time series data. The mean and variance calculation algorithms in existing statistical methods are used to calculate the time series data. The mean is used to reflect the overall level of communication success rate, and the variance is used to reflect the degree of fluctuation of communication success rate over time.

[0058] For example, 10 transmission success rate values ​​are obtained within 10 consecutive statistical periods. The arithmetic mean of these 10 values ​​is used to obtain the average transmission success rate, and the variance is calculated using the standard variance formula. When the variance is higher than the preset fluctuation threshold, it is determined that there is a significant fluctuation in the communication success rate.

[0059] A sliding time window is established for the communication delay time, and the median and percentile distribution of the delay time within the window are calculated to identify abnormal delay peaks.

[0060] Using the sliding window algorithm in existing data analysis techniques, a fixed-length sliding time window is established for the communication delay time sample. Within each window, the median, 90th percentile, and 95th percentile of the delay time are calculated to characterize the central tendency and extreme value distribution of the communication delay.

[0061] If the sliding time window length is set to 50 delay time samples, when the 95th percentile within the window is significantly higher than the median within the window, it is determined that there is an abnormal delay peak within that time window.

[0062] A communication quality degradation curve is constructed based on packet loss rate and retransmission count, and the rate of communication quality deterioration is determined by fitting the slope of the degradation curve.

[0063] The packet loss rate and retransmission count within a continuous statistical period are constructed into a communication quality change sequence in chronological order. The linear regression algorithm in the existing regression analysis method is used to fit the communication quality change sequence to obtain the communication quality degradation curve. By calculating the slope value of the degradation curve, the rate of deterioration of communication quality over time is quantified.

[0064] When the slope obtained from linear regression is negative and the absolute value is greater than the preset deterioration threshold, the communication quality is determined to be in a state of rapid deterioration.

[0065] Signal strength data is continuously sampled and its short-time energy and zero-crossing rate are calculated to identify signal attenuation trends.

[0066] We employ short-time energy calculation and zero-crossing rate calculation methods from the field of signal processing to analyze signal strength data obtained from continuous sampling. Short-time energy is used to reflect the overall amplitude level of signal strength, while zero-crossing rate is used to reflect the frequency of signal strength changes.

[0067] In a continuously sampled signal strength sequence, if the short-term energy shows a continuous decreasing trend and the zero-crossing rate increases significantly, it is determined that the communication signal has a significant attenuation trend.

[0068] The degree of fluctuation, abnormal peak latency, rate of communication quality deterioration, and signal attenuation trend are used as indicators of communication stability.

[0069] The fluctuations in transmission and reception success rates, abnormal peak values ​​of communication delay time, slope values ​​of communication quality attenuation curves, and signal attenuation trend analysis results are uniformly grouped into a set of communication stability indicators, and each communication stability indicator is normalized to a uniform dimension.

[0070] For example, continuous numerical indicators such as volatility and deterioration rate can be mapped to the [0,1] interval, and abnormal peaks and decay trends can be converted into corresponding numerical labels.

[0071] Based on the preset stability threshold range, each communication stability indicator is classified into levels, reliability scores are obtained, and outage risk is assessed to obtain the communication stability level, communication reliability score, and communication outage risk coefficient.

[0072] For each communication stability indicator, a corresponding stability threshold range is pre-set, and the communication status is classified according to the position of each indicator within the threshold range. At the same time, a communication reliability score is calculated by weighted scoring, and a communication interruption risk coefficient is calculated based on the communication quality deterioration rate and signal attenuation trend.

[0073] When more than half of the communication stability indicators fall into the low stability threshold range, the communication stability level is determined to be low, and the communication interruption risk coefficient is set to a high-risk range value.

[0074] Communication analysis results are constructed based on communication stability level, communication reliability score, and communication interruption risk coefficient.

[0075] The communication stability level, communication reliability score, and communication interruption risk coefficient are combined to form a unified communication analysis result description, which is used to characterize the communication stability and communication risk level of the industrial control motherboard under the current operating state.

[0076] For example, the combined result of a medium communication stability level, a communication reliability score of 0.72, and a communication interruption risk coefficient of 0.18 can be used as the communication analysis result for the current statistical period.

[0077] Step S30: Correlate the communication analysis results with the environmental analysis results to obtain correlation information, and determine the current environment type of the industrial control motherboard based on the correlation information.

[0078] By incorporating environmental and communication analysis results into a unified correlation assessment process, and by quantitatively analyzing the correspondence between environmental and communication states, the degree of correlation between environmental factors and communication states is determined, thereby forming correlation information for environmental type determination.

[0079] For example, when the environmental stability level is low and the risk of communication interruption is high, the operating state is determined to be an associated state in which environmental factors have a significant impact on the communication state.

[0080] In another example, step S30 may also be preferably: The environmental stability level and environmental severity level from the environmental analysis results are extracted as environmental dimension parameters, and the communication stability level and communication reliability score from the communication analysis results are extracted as communication dimension parameters.

[0081] The environmental analysis results and communication analysis results are structurally decomposed, with environmental stability level and environmental severity level used as environmental dimension parameters reflecting the external environmental state, and communication stability level and communication reliability score used as communication dimension parameters reflecting the communication operation status.

[0082] For example, the environmental stability level is represented as a three-level discrete value, the environmental severity level is represented as a corresponding level number, and the communication stability level and communication reliability score are represented as level value and normalized score value, respectively.

[0083] The environmental dimension parameters are mapped to the first dimension of the preset two-dimensional correlation evaluation matrix, and the communication dimension parameters are mapped to the second dimension of the two-dimensional correlation evaluation matrix to determine the corresponding correlation evaluation area in the two-dimensional correlation evaluation matrix.

[0084] A two-dimensional correlation evaluation matrix is ​​constructed with environmental and communication parameters as coordinate axes. The first dimension represents the combined state of environmental stability and environmental severity, and the second dimension represents the combined state of communication stability level and communication reliability score. The location of the current operating state in the two-dimensional correlation evaluation matrix is ​​determined by parameter mapping rules.

[0085] For example, when the environmental severity level is high and the communication stability level is low, it falls into the preset high-risk association assessment area in the two-dimensional association assessment matrix.

[0086] The Pearson correlation coefficient between the environmental severity level and the communication interruption risk coefficient is calculated, and correlation information including environmental stability, communication stability and coupling strength is generated based on the correlation assessment area and the Pearson correlation coefficient.

[0087] By selecting environmental severity level sequences and communication interruption risk coefficient sequences from multiple consecutive statistical periods, and using the Pearson correlation coefficient calculation algorithm in existing statistical analysis methods, the correlation between the two sequences is calculated to quantify the linear correlation between changes in environmental status and changes in communication risk; and by combining the location regions in the two-dimensional correlation assessment matrix, correlation information is generated.

[0088] For example, when the Pearson correlation coefficient is positive and large, and the current location is in a high-risk association assessment area, the environmental stability is marked as low stability, the communication stability is marked as low stability, and a higher coupling strength is marked in the association information.

[0089] Furthermore, the step of generating association information including environmental stability identifiers, communication stability identifiers, and coupling strength identifiers based on the association assessment region and the Pearson correlation coefficient can preferably be: The Pearson correlation coefficient between the severity of the environment and the risk coefficient of communication interruption is calculated. When the Pearson correlation coefficient is greater than the preset positive correlation threshold, it is determined that the environmental factors have a significant impact on the communication status, and the degree of impact is quantified as the environmental-communication coupling degree.

[0090] A positive correlation threshold is set for the Pearson correlation coefficient. When the correlation coefficient is greater than the threshold, the association between environmental factors and communication status is determined to be significantly correlated. The environmental-communication coupling degree is quantified based on the magnitude of the Pearson correlation coefficient. The environmental-communication coupling degree is used to reflect the intensity of the impact of environmental changes on the communication status.

[0091] When the Pearson correlation coefficient is 0.65 and the positive correlation threshold is 0.5, the environmental communication coupling degree is set to the high coupling degree interval value corresponding to 0.65.

[0092] When the Pearson correlation coefficient is less than or equal to the preset positive correlation threshold, it is determined that the environmental factors have a weak impact on the communication status, and the environmental communication coupling degree is set to the preset minimum coupling degree value.

[0093] When the correlation calculation results do not meet the criteria for significant positive correlation, the influence relationship between the environment and communication is determined to be weakly correlated, and the coupling degree between the environment and communication is fixed to the preset minimum coupling degree value.

[0094] When the Pearson correlation coefficient is 0.18 and the positive correlation threshold is 0.5, the environmental communication coupling degree is set to the minimum coupling degree value of 0.1.

[0095] A comprehensive correlation index is calculated by associating the location coordinates of the assessment area with the environmental communication coupling degree, and correlation information including environmental stability identifier, communication stability identifier, and coupling strength identifier is generated based on the comprehensive correlation index.

[0096] The coordinates in the two-dimensional correlation evaluation matrix are weighted and calculated with the environmental-communication coupling degree to obtain a comprehensive correlation index that characterizes the overall relationship between environmental and communication states. Correlation information containing environmental stability, communication stability, and coupling strength indicators is generated based on the numerical range of the comprehensive correlation index.

[0097] When the comprehensive correlation index falls into the high correlation range, the environmental stability indicator is marked as low stability, the communication stability indicator is marked as low stability, and the high coupling strength indicator is marked in the correlation information.

[0098] The system calculates the Euclidean distance between the associated information and the feature patterns of each environment type in the preset environment type feature library, and selects the environment type with the smallest Euclidean distance as the current environment type of the industrial control motherboard. The working environment type feature library stores various working environment types and their corresponding environment-related state feature patterns.

[0099] The currently generated association information is represented as a feature vector and matched with the environment association state feature patterns corresponding to each environment type in the environment type feature library. By using the Euclidean distance calculation algorithm in the existing pattern recognition method, the Euclidean distance between the current association information and each environment type feature pattern is calculated, and the environment type with the smallest Euclidean distance is selected as the environment type of the current industrial control motherboard.

[0100] If the Euclidean distance between the current associated information and the characteristic pattern corresponding to the "high vibration-high communication risk environment" is the smallest, then the environment type is determined as the current environment type of the industrial control motherboard.

[0101] Step S40: Select the target working mode that matches the environment type from a variety of preset working modes.

[0102] Given the current environment type of the industrial control motherboard, a matching analysis is performed on various predefined operating modes to select the target operating mode that is compatible with the current environment type in terms of stability requirements, anti-interference capabilities, and performance constraints.

[0103] When the determined environment type is a high degree of environmental severity and high degree of environmental communication coupling, the working mode with higher communication reliability and stronger anti-interference capability should be selected first among multiple working modes.

[0104] In another example, step S40 may also be preferably: Multiple working modes are pre-configured in the storage module of the industrial control motherboard. Each working mode corresponds to a set of mode parameters including processor working frequency, memory access speed, communication protocol type, data verification strength and power consumption control strategy. At the same time, an applicable working environment type label and an environment adaptability score are set for each working mode. Based on the environment type, a set of candidate working modes that match the environment type label are selected from the multiple preset working modes.

[0105] Each working mode is stored in the form of a structured parameter table, where the mode parameter set describes the operating strategy of the industrial control motherboard under this working mode, the environment type label is used to limit the environmental range to which the working mode is applicable, and the environment adaptability score is used to quantify the adaptability of the working mode to the corresponding environment type; the candidate working mode set is selected by the environment type label matching method.

[0106] Operating modes suitable for high-temperature and high-vibration environments are marked as high-reliability modes, and their environmental adaptability scores are set to a fixed score value higher than that of ordinary modes. When the current environment type matches this label, the operating mode is included in the candidate operating mode set.

[0107] When there are multiple working modes in the candidate working mode set, the real-time load rate, task priority queue and resource usage of the current industrial control motherboard are read as auxiliary judgment parameters.

[0108] By monitoring the current operating status of the industrial control motherboard, the real-time load rate of the processor, the priority distribution of the tasks being executed, and the usage ratio of memory and communication resources are obtained. These parameters are used as auxiliary judgment parameters to further distinguish the degree of compatibility between multiple candidate working modes.

[0109] When the real-time load is high and there are many high-priority tasks, avoid selecting a working mode that excessively reduces the processor's operating frequency.

[0110] Based on auxiliary judgment parameters, the comprehensive adaptability of each working mode in the candidate working mode set is calculated. The comprehensive adaptability is calculated by weighting and summing the environmental adaptability score of the working mode with the load matching degree calculated based on the real-time load rate. The weight coefficient of the environmental adaptability score is dynamically adjusted according to the environmental communication coupling degree. When the environmental communication coupling degree is high, the weight coefficient of the environmental adaptability score is increased. The working mode with the highest comprehensive adaptability is selected as the target working mode. At the same time, it is determined whether the target working mode and the current working mode are the same mode. If they are the same mode, the current working state is maintained and no switching is performed. If they are different modes, it is confirmed that a mode switching operation needs to be performed.

[0111] The load matching degree adopts the linear normalization calculation method in existing engineering practice, which maps the real-time load rate to the load matching degree score; the comprehensive adaptability is calculated by weighted summation, and the weight coefficient is adjusted according to the environmental communication coupling degree to enhance the consideration of environmental adaptability when the environmental impact is strong; finally, the target working mode is determined by the maximum value determination method.

[0112] When the environmental communication coupling is in the high coupling range, the weight coefficient of the environmental adaptability score is set higher than that of the load matching degree, so as to give priority to the working mode with stronger resistance to environmental interference.

[0113] Step S50: Control the industrial control motherboard to switch from the current working mode to the target working mode, and simultaneously adjust the hardware operating parameters and communication parameters corresponding to the target working mode.

[0114] Based on the determined target operating mode, the current operating mode of the industrial control motherboard is compared, and if it is confirmed that a switch is needed, the hardware operating parameters and communication parameters are adjusted in a consistent manner according to the mode parameter set corresponding to the target operating mode to complete the operating mode switch.

[0115] For example, when the target operating mode is a high-reliability communication mode, the communication protocol type is switched to the corresponding anti-interference communication protocol, and the data verification strength is increased simultaneously.

[0116] The hardware operating parameters adjustment includes configuring the processor operating frequency, memory access speed, and power consumption control strategy, while the communication parameters adjustment includes configuring the communication protocol type, data verification strength, and retransmission strategy. All parameter adjustments are performed based on the mode parameter set of the target operating mode to avoid operational anomalies caused by inconsistent parameter configurations.

[0117] For example, in the operating mode corresponding to a high-temperature environment, the processor's operating frequency is adjusted to a preset downclocking value, while the corresponding power consumption control strategy is enabled, and the communication protocol parameters remain unchanged.

[0118] Furthermore, to ensure the stability of the working mode switching process, the operating status of the industrial control motherboard is confirmed after the parameter adjustment is completed to ensure that the hardware operating parameters and communication parameters have been switched to the set values ​​corresponding to the target working mode, thereby completing the adaptive switching process of the working mode.

[0119] For example, after completing the parameter switching, the current processor frequency and communication parameter configuration values ​​are read and compared with the set values ​​in the target working mode parameter table to confirm that the switching result meets expectations.

[0120] In this embodiment, environmental information of the industrial control motherboard during operation is acquired, and feature analysis is performed on the environmental information to obtain environmental analysis results that reflect the current physical operating conditions. Simultaneously, communication information corresponding to the industrial control motherboard during operation is acquired, and stability analysis is performed on the communication information to obtain communication analysis results reflecting the communication operating status. Subsequently, the communication analysis results and the environmental analysis results are correlated and evaluated to obtain correlation information characterizing the influence relationship between environmental factors and communication status. Based on the correlation information, the current environment type of the industrial control motherboard is determined. After determining the environment type, a target operating mode matching the environment type is selected from a set of preset operating modes. Finally, the industrial control motherboard is controlled to switch from the current operating mode to the target operating mode, and the hardware operating parameters and communication parameters corresponding to the target operating mode are adjusted synchronously, thereby achieving adaptive response of the industrial control motherboard to changes in the operating environment.

[0121] By simultaneously introducing environmental and communication information, analyzing them separately, and then performing a correlation assessment, this method avoids the biased judgment caused by relying solely on a single environmental parameter or communication indicator for working mode switching. This allows the industrial control motherboard to more accurately identify its actual operating environment type. Based on the environment type, a target working mode is selected, and hardware and communication parameters are adjusted synchronously. This ensures effective linkage between the working mode switching process and the motherboard's operating status, thus balancing operational stability and system performance in complex industrial environments. It overcomes the problems of switching lag or unreasonable mode selection when the industrial environment is complex and communication status and environmental factors interact. Furthermore, this method does not rely on manually configured fixed thresholds and can dynamically adjust the working mode according to environmental changes, which helps improve the adaptability of the industrial control motherboard under various operating conditions and its long-term operational reliability.

[0122] Example 2: like Figure 2 As shown in the example of this application, an adaptive switching system 10 for the working mode of an industrial control motherboard based on environmental awareness is also provided. The adaptive switching system 10 for the working mode of an industrial control motherboard based on environmental awareness mainly includes: an environmental data acquisition module 11, a communication data acquisition module 12, a data association evaluation module 13, a working mode matching module 14, and a working mode switching module 15.

[0123] The environmental data acquisition module 11 is used to acquire environmental information of the industrial control motherboard during operation, perform feature analysis on the environmental information, and obtain environmental analysis results.

[0124] The communication data acquisition module 12 is used to acquire the corresponding communication information of the industrial control motherboard during operation, and to perform stability analysis on the communication information to obtain the communication analysis results.

[0125] The data association evaluation module 13 is used to associate and evaluate the communication analysis results with the environmental analysis results to obtain association information, and determine the current environment type of the industrial control motherboard based on the association information.

[0126] The working mode matching module 14 is used to select a target working mode that matches the environment type from a variety of preset working modes.

[0127] The working mode switching module 15 is used to control the industrial control motherboard to switch from the current working mode to the target working mode, and to simultaneously adjust the hardware operating parameters and communication parameters corresponding to the target working mode.

[0128] In this embodiment, the environmental data acquisition module 11 acquires environmental information of the industrial control motherboard during operation and performs temperature, humidity, vibration, and power supply voltage fluctuation characteristic analysis to obtain environmental analysis results. The communication data acquisition module 12 collects the transmission success rate, reception success rate, communication delay time, packet loss rate, retransmission count, and signal strength of each communication channel in real time and performs fluctuation amplitude, abnormal delay peak, communication quality attenuation rate, and signal attenuation trend analysis on each indicator to obtain communication analysis results. The data association evaluation module 13 maps the environmental analysis results and communication analysis results to a two-dimensional association evaluation matrix, calculates the Pearson correlation coefficient between the environmental severity level and the communication interruption risk coefficient, and generates association information for environmental stability, communication stability, and environmental-communication coupling degree. The system determines the current environment type of the industrial control motherboard. The working mode matching module 14 filters a set of candidate working modes matching the environment type label from a variety of preset working modes. It then calculates the overall adaptability of each candidate mode based on real-time load rate, task priority queue, and resource usage, selecting the working mode with the highest overall adaptability as the target working mode. The working mode switching module 15 controls the industrial control motherboard to switch from the current working mode to the target working mode, simultaneously adjusting the processor 22's operating frequency, memory access speed, communication protocol type, data verification strength, and power consumption control strategy. This enables the industrial control motherboard to dynamically and adaptively switch working modes under different operating environments, ensuring a high degree of matching between hardware operating parameters and communication parameters and the target working mode, thereby improving the operational stability and communication reliability of the industrial control motherboard.

[0129] Example 3: like Figure 3 As shown in the example of this application, an electronic device 20 is also provided, including: One or more processors 22; A storage device for storing one or more programs, which, when executed by one or more processors 22, cause the electronic device 20 to execute the environment-aware industrial control motherboard operating mode adaptive switching method of Embodiment 1.

[0130] In this embodiment, the processor 22 in the electronic device 20 executes a program stored in the storage device to obtain environmental and communication information of the industrial control motherboard during operation. Feature analysis and stability analysis are then performed on the environmental and communication information to obtain environmental and communication analysis results. Furthermore, the coupling relationship between the environmental and communication analysis results is calculated through correlation evaluation to determine the current environment type of the industrial control motherboard. Based on the environment type, a target operating mode matching the current operating mode is selected, and a comprehensive suitability is calculated by combining real-time load rate, task priority queue, and resource usage to select the target operating mode. Finally, the industrial control motherboard is controlled to switch from the current operating mode to the target operating mode, and the processor 22's operating frequency, memory access speed, communication protocol type, data verification strength, and power consumption control strategy corresponding to the target operating mode are adjusted synchronously. This enables the electronic device 20 to dynamically and adaptively switch the operating mode of the industrial control motherboard, improving environmental adaptability and communication stability.

[0131] Example 4: In this application example, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by the processor of an electronic device, causes the electronic device to execute the environmentally aware industrial control motherboard working mode adaptive switching method of Embodiment 1.

[0132] In this embodiment, a program on a computer-readable storage medium is executed on the processor of an electronic device, enabling the electronic device to acquire environmental and communication information of the industrial control motherboard during operation. Feature analysis and stability analysis are performed to obtain environmental and communication analysis results. Furthermore, the coupling degree between the environmental and communication analysis results is evaluated through data correlation, generating environmental stability, communication stability, and coupling strength indicators, and determining the current environment type of the industrial control motherboard. Based on the environment type, a target working mode is selected from a range of preset working modes, and a comprehensive suitability is calculated by combining real-time load rate, task priority, and resource usage to select the target working mode. The industrial control motherboard is controlled to switch from the current working mode to the target working mode, and the processor operating frequency, memory access speed, communication protocol type, data verification strength, and power consumption control strategy are adjusted synchronously. This achieves environment-aware adaptive mode switching of the industrial control motherboard, thereby improving its operational stability, task execution efficiency, and communication reliability under different environmental conditions.

[0133] It should be noted that although several modules or units of the system for executing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0134] Other embodiments of this application will readily conceive of by considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0135] The above content is merely a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.

Claims

1. A method for adaptive switching of operating modes of an industrial control motherboard based on environment perception, characterized in that, include: The environmental information of the industrial control motherboard during operation is acquired, and the environmental information is subjected to feature analysis to obtain the environmental analysis results. The communication information of the industrial control motherboard during operation is obtained, and the stability of the communication information is analyzed to obtain the communication analysis results. The communication analysis results and the environmental analysis results are correlated and evaluated to obtain correlation information, and the current environment type of the industrial control motherboard is determined based on the correlation information. Based on the environment type, select the target working mode that matches it from a variety of preset working modes; The control industrial control motherboard switches from the current working mode to the target working mode, and simultaneously adjusts the hardware operating parameters and communication parameters corresponding to the target working mode.

2. The adaptive switching method for industrial control motherboard operating modes based on environment awareness according to claim 1, characterized in that, The steps of acquiring environmental information of the industrial control motherboard during operation and performing feature analysis on the environmental information to obtain environmental analysis results include: The environmental information of the industrial control motherboard during operation is acquired by sensors deployed on the motherboard; the environmental information includes ambient temperature data, ambient humidity data, vibration intensity data, and voltage fluctuation parameters. For the ambient temperature data and ambient humidity data within each time period, calculate their respective average, maximum, minimum, and standard deviation to obtain temperature feature vectors and humidity feature vectors; The vibration intensity data is subjected to fast Fourier transform to extract features, thereby obtaining vibration frequency domain feature parameters; Calculate the fluctuation amplitude and fluctuation frequency of the power supply voltage fluctuation data, and construct voltage fluctuation parameters using the fluctuation amplitude and the fluctuation frequency; The temperature feature vector, humidity feature vector, vibration frequency domain feature parameters, and voltage fluctuation parameters are normalized and then combined into an environmental feature matrix. The environmental feature matrix is ​​input into a pre-trained environmental classification model for analysis to obtain environmental analysis results; wherein, the environmental analysis results include environmental stability level and environmental severity level.

3. The adaptive switching method for industrial control motherboard operating modes based on environment awareness according to claim 2, characterized in that, The steps of acquiring communication information corresponding to the industrial control motherboard during operation, performing stability analysis on the communication information, and obtaining communication analysis results include: Real-time monitoring of the data transmission status of each communication channel is used to obtain communication information, which is then used as an indicator of communication stability. Based on the preset stability threshold range, each communication stability indicator is classified into levels, reliability scores are obtained, and outage risk is assessed to obtain the communication stability level, communication reliability score, and communication outage risk coefficient. Communication analysis results are constructed based on the communication stability level, the communication reliability score, and the communication interruption risk coefficient.

4. The adaptive switching method for industrial control motherboard operating modes based on environment awareness according to claim 3, characterized in that, The communication information includes transmission success rate, reception success rate, communication delay time, packet loss rate, number of retransmissions, and signal strength; The step of real-time monitoring of the data transmission status of each communication channel, obtaining communication information, and using the communication information as a communication stability indicator includes: Within a preset statistical period, the mean and variance of the time series of the sending success rate and receiving success rate are calculated respectively, and the degree of fluctuation of the communication success rate is determined by the magnitude of the mean and the variance. A sliding time window is established for the communication delay time, and the median and percentile distribution of the delay time within the window are calculated to identify abnormal delay peaks; A communication quality degradation curve is constructed based on the packet loss rate and the number of retransmissions, and the rate of communication quality deterioration is determined by fitting the slope of the degradation curve. The signal strength data is continuously sampled and its short-time energy and zero-crossing rate are calculated to identify the signal attenuation trend; The fluctuation level, abnormal peak delay, communication quality deterioration rate, and signal attenuation trend are used as communication stability indicators.

5. The adaptive switching method for industrial control motherboard operating modes based on environment awareness according to claim 3, characterized in that, The step of correlating and evaluating the communication analysis results with the environmental analysis results to obtain correlation information, and determining the current environment type of the industrial control motherboard based on the correlation information, includes: The environmental stability level and environmental severity level from the environmental analysis results are extracted as environmental dimension parameters, and the communication stability level and communication reliability score from the communication analysis results are extracted as communication dimension parameters. The environmental dimension parameters are mapped to the first dimension of a preset two-dimensional correlation evaluation matrix, and the communication dimension parameters are mapped to the second dimension of the two-dimensional correlation evaluation matrix, so as to determine the corresponding correlation evaluation region in the two-dimensional correlation evaluation matrix; Calculate the Pearson correlation coefficient between the environmental severity level and the communication interruption risk coefficient, and generate correlation information based on the correlation assessment area and the Pearson correlation coefficient; Calculate the Euclidean distance between the associated information and each environmental type feature pattern in the preset environmental type feature library, and select the environmental type with the smallest Euclidean distance as the current environmental type of the industrial control motherboard.

6. The adaptive switching method for industrial control motherboard operating modes based on environment awareness according to claim 5, characterized in that, The step of generating association information based on the association assessment region and the Pearson correlation coefficient includes: Calculate the Pearson correlation coefficient between the environmental severity level and the communication interruption risk coefficient. When the Pearson correlation coefficient is greater than a preset positive correlation threshold, it is determined that the environmental factors have a significant impact on the communication status, and the degree of impact is quantified as the environmental-communication coupling degree. When the Pearson correlation coefficient is less than or equal to the preset positive correlation threshold, it is determined that the environmental factors have a weak impact on the communication status, and the environmental communication coupling degree is set to the preset minimum coupling degree value. A comprehensive correlation index is calculated using the location coordinates of the correlation evaluation area and the environmental communication coupling degree, and correlation information is generated based on the comprehensive correlation index.

7. The adaptive switching method for industrial control motherboard operating modes based on environment awareness according to claim 1, characterized in that, The step of selecting a target working mode that matches the environment type from a variety of preset working modes includes: Based on the environment type, a set of candidate working modes matching the environment type label is selected from a variety of preset working modes; When there are multiple working modes in the candidate working mode set, the real-time load rate, task priority queue and resource usage of the current industrial control motherboard are read as auxiliary judgment parameters. Based on the auxiliary judgment parameters, the comprehensive fit degree of each working mode in the candidate working mode set is calculated, and the working mode with the highest comprehensive fit degree is selected as the target working mode.

8. An adaptive switching system for the operating mode of an industrial control motherboard based on environmental perception, characterized in that, include: The environmental data acquisition module is used to acquire environmental information of the industrial control motherboard during operation, and to perform feature analysis on the environmental information to obtain environmental analysis results. The communication data acquisition module is used to acquire the communication information corresponding to the industrial control motherboard during operation, and to perform stability analysis on the communication information to obtain the communication analysis results. The data association evaluation module is used to associate and evaluate the communication analysis results with the environmental analysis results to obtain association information, and determine the current environment type of the industrial control motherboard based on the association information. The working mode matching module is used to select a target working mode that matches the environment type from a variety of preset working modes. The working mode switching module is used to control the industrial control motherboard to switch from the current working mode to the target working mode, and to simultaneously adjust the hardware operating parameters and communication parameters corresponding to the target working mode.

9. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to perform the environmentally aware adaptive switching method for industrial control motherboard operating modes according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by the processor of the electronic device, causes the electronic device to perform the environmentally aware adaptive switching method for the working mode of the industrial control motherboard according to any one of claims 1 to 7.

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