Industrial multi-protocol communication gateway adaptive adaptation method and system

Abstract

The invention discloses an industrial multi-protocol communication gateway adaptive adaptation method and system, and relates to the technical field of industrial communication, and the method comprises the following steps: deploying a communication performance collection engine and a physical environment collection engine on a system in parallel, capturing a key performance index KPIs in real time, capturing a physical layer signal reflecting an electromagnetic activity state in a case, and transmitting the physical layer signal to a server; generating a real-time data stream with a timestamp; according to the method, a communication performance acquisition engine and a physical environment acquisition engine are deployed in parallel, key performance indexes KPIs are acquired, physical layer signals are captured, and a multi-dimensional associated communication-electromagnetic combined baseline model is established to sense the dynamic electromagnetic environment in the baseline model; a hidden fault of inter-symbol interference (ISI) caused by internal electromagnetic coupling can be accurately identified and quantified, a communication logic error and a physical layer electromagnetic phenomenon are accurately associated on a timeline, and accurate positioning of an interference source is realized.

Description

Technical Field

[0001] This invention relates to the field of industrial communication technology, specifically to an adaptive adaptation method and system for industrial multi-protocol communication gateways. Background Technology

[0002] Today, industrial automation is rapidly developing towards intelligence and integration. Industrial multi-protocol communication gateways play a crucial role in connecting devices with different industrial communication protocols and enabling data interaction and sharing, allowing various industrial devices to work collaboratively and improving production efficiency and quality. However, with the increasing number of devices in industrial fields and the growing demand for communication gateway integration, in order to save space and cost, communication modules supporting multiple protocols are highly integrated into a single chassis. This dense deployment leads to a more complex electromagnetic environment for the gateway, and the internal electromagnetic interference problem generated by multi-protocol operation is becoming increasingly serious.

[0003] For example, in modern industrial production environments, various communication protocols exist, such as 100Mbps / 1Gbps Ethernet protocols like EtherCAT and Profinet, and serial protocols like RS-232 / 485. To achieve interconnection between devices using different protocols, industrial multi-protocol communication gateways have emerged. In dense industrial control cabinets, when a gateway runs multiple protocols simultaneously, high-frequency signals can generate electromagnetic interference (EMI) on low-frequency analog signal lines through spatial radiation or power coupling. This interference is not constant but dynamically changes with the data load of high-speed channels, causing EMI to significantly increase during large-scale data exchange. The direct technical consequence of magnetic coupling in industrial multi-protocol communication is inter-symbol interference (ISI). ISI refers to the phenomenon where the waveform of one signal symbol "tails" and affects the next symbol during data transmission, causing decoding errors. In industrial communication, accurate data transmission is crucial. However, when subjected to high-frequency noise interference, the current or voltage waveform of one symbol will be distorted and "tail" into the timing interval of the next symbol, causing errors in the sampling and decision-making process at the receiving end. This manifests as a sharp increase in the bit error rate (BER), and in severe cases, it can even cause communication interruption. Therefore, any decoding error can lead to malfunction of industrial equipment, thereby affecting the safety and stability of industrial production.

[0004] Existing technologies have the following shortcomings: Traditional industrial communication gateways often treat each protocol as an independent and unaffected channel, focusing on the correctness and efficiency of protocol conversion. When faced with complex electromagnetic environments, they cannot respond to interference in a timely and effective manner, leading to a decline in communication quality or even communication interruption. They lack the ability to perceive and coordinate the mutual interference between the physical layers of different protocols coexisting in the same chassis. Regarding electromagnetic interference in industrial communications, when low-speed channels experience communication quality degradation due to ISI, the gateway's response is often limited to a "one-size-fits-all" strategy such as retransmission mechanisms at the protocol stack level or simple link reset. This post-event remedial strategy cannot identify whether the root cause of the error comes from the external network or its own internal electromagnetic coupling, and cannot fundamentally avoid interference that affects the accurate adaptation of business needs. Instead, retransmission increases the network load.

[0005] Therefore, there is an urgent need to provide an adaptive adaptation method and system for industrial multi-protocol communication gateways, enabling industrial multi-protocol gateways to intelligently perceive their internal dynamic electromagnetic environment, accurately diagnose interference events between protocols, and on this basis, achieve adaptive collaborative adaptation across protocol channels, thereby fundamentally improving the reliability and stability of communication in complex and dense environments.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide an adaptive adaptation method and system for industrial multi-protocol communication gateways. This invention collects multi-dimensional data on communication performance and physical environment, performs time-series correlation analysis, accurately locates internal interference sources, and then generates dynamic and collaborative multi-mode avoidance strategies. It can generate execution scheduling instructions, quantify and evaluate effects, and dynamically optimize the strategy library to solve the problems mentioned in the background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an adaptive adaptation method for an industrial multi-protocol communication gateway, comprising the following steps: S1. Deploy the communication performance acquisition engine and the physical environment acquisition engine in parallel on the system to capture key performance indicators (KPIs) and physical layer signals in real time, generate real-time data streams with timestamps, and start a learning cycle in the early stage of gateway deployment without interference by using the system's automatic learning mode to establish a multi-dimensional related communication-electromagnetic joint baseline model and obtain a dynamic baseline model as a comparison benchmark. S2. Compare and analyze the collected real-time data stream with the dynamic baseline model. When interference event diagnosis is triggered, retrieve all relevant data within ±1 second before and after the precise time point of the sudden increase in bit error rate. Use the dynamic time warping (DTW) method to calculate the correlation between the bit error rate curve of the low-speed channel and the activity intensity curve of each high-speed channel. Compare with the preset interference threshold to determine the interference source and generate a structured diagnostic report. S3. The system maintains a multi-mode avoidance strategy library. Based on the structured diagnostic report and combined with the current total load status of the system, the built-in strategy decision engine adaptively selects one or combines multiple optimal strategies from the multi-mode avoidance strategy library to generate executable scheduling instructions. S4. Once the executable scheduling command is issued and the execution strategy is implemented, the acquisition engine is immediately activated to intensively monitor the communication quality indicators of the interfered ports, collect the actual effect data after the strategy is executed, compare it with the expected goal of the execution strategy and conduct quantitative evaluation of the effect, use statistical analysis to evaluate the effectiveness of different strategies in different scenarios, and dynamically optimize the priority of strategies in the multi-mode avoidance strategy library.

[0009] Optionally, the steps for generating the timestamped real-time data stream are as follows: The communication performance acquisition engine and the physical environment acquisition engine, which are deployed in parallel, start simultaneously and collect key performance indicators (KPIs) and physical layer signals concurrently. The communication performance acquisition engine is directly embedded into the driver / MAC layer of various protocol stacks such as EtherCAT, Profinet, and ModbusTCP / RTU, and captures key performance indicators (KPIs) in real time at a high frequency of 1kHz. All KPI data are then marked with high-precision timestamps. The physical environment acquisition engine is responsible for capturing physical layer signals that reflect the state of electromagnetic activity within the chassis, including chip-level status registers and board-level RF sampling ADCs; At the end of each sampling period, each acquisition engine packages all the data collected in this session into a data packet, injects a high-precision timestamp into the data packet, and stores it in the internal cache queue. The timestamped data packets were arranged and combined in the order of their collection time to form a timestamped real-time data stream.

[0010] Optionally, the steps for establishing the communication-electromagnetic joint baseline model are as follows: In the initial stage of gateway deployment without interference, a learning cycle is started using the system's automatic learning mode, and real-time data streams with timestamps are collected through the communication performance acquisition engine and the physical environment acquisition engine. The two acquisition engines collect real-time data streams with timestamps and perform statistical analysis on the data. The sample values ​​of each performance indicator collected during the statistical learning period are used to calculate the statistical characteristics of the mean and standard deviation of each performance indicator within the normal range. Based on the statistical characteristics, a dynamic threshold is set to form the baseline of the corresponding performance indicator. The baseline values ​​of each calculated performance index are saved and collectively form a communication-electromagnetic joint baseline model.

[0011] Optionally, the triggering steps for the interference event diagnosis are as follows: The collected real-time data streams are compared and analyzed with the dynamic baseline model to monitor the key performance indicators (KPIs) of each protocol channel in real time. The system maintains a fixed-length sliding time window for each monitored protocol channel, performs statistics on all sample datasets within the sliding time window, and then calculates the statistical characteristics of the window mean and window standard deviation. When a new bit error rate (BER) sample value enters the sliding time window using the Z-Score detection method, the Z-Score value of the new BER sample value relative to the statistical characteristics of the current window is calculated. The formula for calculating the Z-Score value is as follows: In the formula, Represented as a standard score describing the degree to which a new data point deviates from the window mean. This represents the newly acquired, yet-to-be-tested bit error rate value. It is expressed as the arithmetic mean of all bit error rate samples within the current sliding window. It is expressed as the standard deviation of all bit error rate samples within the current sliding window; The Z-Score value is compared with a preset significance threshold. When the Z-Score value is not less than the preset significance threshold, it is determined that the bit error rate index has undergone a statistically significant change, and interference event diagnosis is immediately triggered. The current trigger time point is recorded as the starting point of the interference event.

[0012] Optionally, the steps for calculating the correlation degree using the Dynamic Time Warping (DTW) method are as follows: The system uses the trigger time point as the time center and retrieves all relevant data within ±1 second before and after the time center point of the sudden increase in bit error rate to obtain the time series of the bit error rate curve of the interfered low-speed channel and the activity intensity curve of each high-speed channel to be inspected. Calculate the distance between each pair of elements in the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel, and construct a distance matrix; Use dynamic programming to calculate the cumulative cost matrix from the distance matrix; In the cumulative cost matrix, starting from the end point and tracing back to the beginning point, the cumulative distance between the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel is obtained, and a path that minimizes the total cumulative distance is found as the optimal regularization path. The total cumulative distance of the optimal regular path is mapped to a normalized correlation score.

[0013] Optionally, the steps for generating the structured diagnostic report are as follows: The preset interference judgment threshold compares the correlation score calculated between each high-speed channel and the interfered low-speed channel with the preset interference judgment threshold to determine the interference source. When the correlation score is higher than the interference judgment threshold, the corresponding high-speed channel is marked as a suspected interference source. When multiple high-speed channels are marked as suspected interference sources, they are sorted according to their correlation scores, and the high-speed channel with the highest correlation score is identified as the main interference source. The correlation score with the highest score is used as the confidence level for the diagnosis of this interference event. The growth factor of the peak bit error rate within the sliding time window of the interference event relative to the baseline mean and baseline standard deviation of the high-speed channel bit error rate in the dynamic baseline model is used as the interference intensity level. The system collects the trigger time of the sudden increase in bit error rate of interference events, the identification of the interference source port, the throughput of each high-speed channel, the transmit power, the physical layer electromagnetic noise energy information, the confidence level, and the interference intensity level. All of the above information is then organized and packaged into a structured diagnostic report according to a certain data structure.

[0014] Optionally, the steps for generating the executable scheduling instructions are as follows: Input a structured diagnostic report and parse it to extract the severity of the interference. Combine the service level agreement of the affected business with the current total load status of the system. The severity of the interference is consistent with the quantified interference intensity value. Based on the classification of interference intensity level, it is confirmed to be mild, moderate or severe. The strategies in the multi-mode avoidance strategy library are parsed into operable parameters, including parameterized micro-slot scheduling strategy, physical layer parameter soft adjustment strategy and frequency domain avoidance strategy. Among them, the structured diagnostic report is input into the micro-slot scheduling strategy to calculate the silent window start point and silent window duration parameters and set the limiting action. The structured diagnostic report is input into the physical layer parameter soft adjustment strategy to establish a mapping table of interference intensity-Z-Score value-target resistance value and calculate the signal slope adjustment parameter. Input the structured diagnostic report into the frequency domain avoidance strategy to determine the frequency bands to be avoided; The system's built-in strategy decision engine selects or combines multiple optimal strategies from a multi-mode avoidance strategy library based on the collected interference severity, service priority, and total system load status, and outputs a strategy combination. Based on the selected strategy combination, the strategy is transformed into executable scheduling instructions at the hardware / software level. Once verified, executable scheduling instructions are sent to the protocol stack and hardware driver layer for execution.

[0015] Optionally, the steps for quantifying and evaluating the effects are as follows: At the moment the strategy begins execution, the system immediately sets an evaluation time window, which ranges from 2 to 5 minutes. Within the evaluation time window, the acquisition engine is activated to collect communication quality indicators of the interfered port at a frequency higher than that of regular monitoring, including indicators such as bit error rate, packet loss rate, retransmission rate, throughput, and signal strength. The actual effect data after the strategy is implemented is collected and recorded as the actual indicator vector. From the historical database, extract communication quality data for a period of time with the same evaluation window length and comparable business load level before the strategy is executed. Use this data to determine the expected target of the strategy, i.e. the communication quality index value expected to be achieved after the strategy is executed, and denote it as the expected index vector. Compare the corresponding elements in the actual indicator vector and the expected indicator vector respectively, and calculate the deviation of each indicator; Based on the deviation of each indicator, a quantitative value of the strategy's effectiveness is calculated to evaluate the actual effect of the strategy. Set a success threshold for the strategy and compare it with the quantified effect value to determine whether the strategy was successful in this interference event.

[0016] An industrial multi-protocol communication gateway adaptive adaptation system includes a multi-dimensional joint baseline modeling and real-time data perception module: a communication performance acquisition engine and a physical environment acquisition engine are deployed in parallel to acquire performance index data and physical layer signals in real time, respectively. A communication-electromagnetic joint baseline model is established through statistical analysis, and a real-time data stream with timestamps and a dynamic baseline model as a comparison benchmark are generated and transmitted to the interference event diagnosis and quantification module. Interference event diagnosis and quantification module: During continuous system operation, the collected performance index data will be compared with the dynamic baseline in real time, and the interference event diagnosis trigger conditions will be set. All relevant data within ±1 second before and after the sudden increase in bit error rate will be retrieved. The throughput, transmit power and physical layer electromagnetic noise energy information of all high-speed protocol ports will be analyzed. The correlation between the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel will be calculated using the dynamic time warping (DTW) method. The correlation will be compared with the preset interference threshold to determine the interference source and generate a structured diagnostic report, which will be transmitted to the scheduling module that adaptively generates avoidance strategies. The adaptive avoidance strategy generation scheduling module: It learns and extends to generate a multi-mode avoidance strategy library, with a built-in strategy decision engine. Based on the structured diagnostic report and combined with the current total load status of the system, it adaptively selects one or more optimal strategies from the multi-mode avoidance strategy library, combines them, and transforms the strategies into executable scheduling instructions, which are then sent to the protocol stack and hardware driver layer for execution. The evasion effect verification and strategy optimization module: After the strategy is executed, the perception capability of the multi-dimensional joint baseline modeling and real-time data perception module is immediately activated. It intensively monitors the communication quality indicators of the interfered ports, collects the actual effect data after the strategy is executed, and compares and quantifies the effect with the expected target of the strategy selected in the scheduling module of adaptive generation of evasion strategies. Each complete diagnosis of interference event, strategy execution and actual effect verification is taken as a case. The effectiveness of different strategies in different scenarios is evaluated by statistical analysis. The case is regularly mined and analyzed to dynamically optimize the priority of strategies in the multi-mode evasion strategy library.

[0017] Optionally, the multi-mode avoidance strategy library includes micro-slot scheduling strategies, physical layer parameter soft adjustment strategies, and frequency domain avoidance strategies.

[0018] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention employs a parallel deployment of a communication performance acquisition engine and a physical environment acquisition engine to collect key performance indicators (KPIs) and capture physical layer signals, respectively. It establishes a multi-dimensional, interconnected communication-electromagnetic joint baseline model to perceive the dynamic internal electromagnetic environment. This allows for precise identification and quantification of latent inter-symbol interference (ISI) caused by internal electromagnetic coupling. It accurately correlates communication logic errors with physical layer electromagnetic phenomena over time, enabling precise location of interference sources. Furthermore, through multi-mode avoidance strategies including micro-slot scheduling, soft adjustment of physical layer parameters, and frequency domain avoidance, it can quickly and accurately respond to various interferences, reducing their impact on industrial multi-protocol communication gateways and ensuring communication link stability. After executing the strategy according to the issued executable scheduling instructions, the actual effect data after strategy execution is compared with the expected target, and the effect is quantified and evaluated. This allows for timely detection of problems during strategy execution and dynamic adjustment of the strategy. The real-time feedback and adjustment mechanism further enhances communication stability, ensuring the gateway maintains good communication in complex and ever-changing industrial environments. This avoids the traditional "one-size-fits-all" approach, enabling the system to better adapt to different interference scenarios and business needs. The system considers the service level agreements (SLPs) of affected services and the current total system load, and allocates resources and selects strategies based on the priority of different services. This fully leverages the advantages of each strategy while improving the overall resource utilization efficiency of the system. Furthermore, the system records and analyzes each complete interference event diagnosis, strategy execution, and effect verification as a case study. Through the accumulation and statistical analysis of numerous cases, the system gains a deep understanding of the effectiveness of different strategies in different scenarios, thereby allowing for targeted adjustments to strategy priorities. This data-driven optimization approach enables the strategy library to continuously adapt to new interference scenarios and business needs, improving the system's adaptability and intelligence, and ensuring the stable and reliable operation of the industrial multi-protocol communication gateway in complex electromagnetic environments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a flowchart of the adaptive adaptation method for industrial multi-protocol communication gateways according to the present invention.

[0021] Figure 2 This is a block diagram of the industrial multi-protocol communication gateway adaptive adaptation system of the present invention. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] Example 1 This invention provides, for example Figure 1 The adaptive adaptation method for an industrial multi-protocol communication gateway, as shown, includes the following steps: S1. Deploy the communication performance acquisition engine and the physical environment acquisition engine in parallel on the system to capture key performance indicators (KPIs) and physical layer signals reflecting the electromagnetic activity state inside the chassis in real time, generating real-time data streams with timestamps. In the initial stage of gateway deployment without interference, start a learning cycle using the system's automatic learning mode to perform statistical analysis on the real-time data streams collected by the acquisition engines, calculate the mean, standard deviation, and dynamic threshold of each indicator within the normal range, establish a multi-dimensional correlated communication-electromagnetic joint baseline model, and obtain a dynamic baseline model as a comparison benchmark. In subsequent operation, the communication-electromagnetic joint baseline model is updated using a sliding window and exponentially weighted moving average algorithm to adapt to the slow aging of the equipment. Specifically, the communication performance acquisition engine is directly embedded into the driver / MAC layer of various protocol stacks such as EtherCAT, Profinet, and Modbus TCP / RTU, capturing key performance indicators (KPIs) in real time at a high frequency of 1kHz. These KPIs include, but are not limited to: instantaneous data throughput values ​​of each port, cyclic redundancy check error counter increments, link layer retransmission request frequency, and symbol sampling error events specific to asynchronous serial communication. All KPI data are then marked with high-precision timestamps. The physical environment acquisition engine is responsible for capturing physical layer signals that reflect the state of electromagnetic activity within the chassis, including chip-level status registers and board-level RF sampling ADCs. Among them, the chip-level status register reads the internal registers of the high-speed Ethernet PHY chip to obtain the power level of the transmitting amplifier and the short-term fluctuation variance of the received signal strength index (RSSI) value, which can reflect the electromagnetic energy intensity radiated by the Ethernet port. Board-level RF sampling ADC: A wideband, low-cost analog-to-digital converter is integrated on the gateway's printed circuit board (PCB). A simple onboard antenna or induction coil is connected to the input to periodically scan and collect the spectrum and energy value of the ambient electromagnetic noise floor in a specific frequency band within the chassis. The specific frequency band is selected from the fundamental and harmonic frequencies of a common RS-485 baud rate, typically ranging from 300bps to 10Mbps. A common RS-485 baud rate is 9600bps, which corresponds to a fundamental frequency of 9600Hz, and the corresponding harmonic frequencies are integer multiples of the fundamental frequency.

[0024] Specifically, the steps for generating a timestamped real-time data stream are as follows: The communication performance acquisition engine and the physical environment acquisition engine, which are deployed in parallel, are started simultaneously to collect key performance indicators (KPIs) and physical layer signals concurrently. At the end of each sampling period, each acquisition engine packages all the data collected in that period into a data packet, injects a high-precision timestamp into the data packet, and stores it in an internal buffer queue. The high-precision timestamp is taken from the high-resolution clock of the Linux kernel in the gateway operating system to ensure a unified time base throughout the system. The timestamp marks the sampling time point of the data packet in each sampling period; the expression for the sampling time point is: In the formula, The time point of the nth sampling is represented as the timestamp of the nth data packet. This represents the baseline time for the two acquisition engines to start concurrently. Represented as the sampling number sequence number, This represents the time interval of the sampling period; The timestamped data packets were arranged and combined in chronological order of collection to form a timestamped real-time data stream. The expression for the timestamped real-time data stream is as follows: In the formula, Represented as a real-time data stream with timestamps. This represents the first data packet, arranged chronologically and timestamped. Represented as the first The timestamp added for each sample. This indicates that the communication performance acquisition engine collected the first data packet. Key performance indicators data, This indicates that the physical environment acquisition engine collected the first data packet. A physical layer signal, This indicates that the communication performance acquisition engine has collected the first... In the data packet Key performance indicators data, This indicates that the physical environment acquisition engine has collected the first... In the data packet Physical layer signals.

[0025] Specifically, the steps for establishing the joint communication-electromagnetic baseline model are as follows: In the initial stage of gateway deployment without interference, a learning cycle is started using the system's automatic learning mode. Real-time data streams with timestamps are collected through the communication performance acquisition engine and the physical environment acquisition engine. The learning cycle is a time sliding window, which is customized to 24 hours. The system collects real-time data streams with timestamps from two acquisition engines and performs statistical analysis. Considering the different sampling frequencies of different acquisition engines, the system will use the lower frequency sampling points as the benchmark to aggregate the high frequency data, unify all parameter indicators on the same time axis, and remove obvious illegal values. For each performance indicator, sample values ​​are collected during the statistical learning period. The statistical characteristics of the mean and standard deviation within the normal range for each performance indicator are calculated. Based on these statistical characteristics, a dynamic threshold is set to form a baseline for the corresponding performance indicator. The formula for calculating the mean is as follows: ,and In the formula, This indicates that the communication performance acquisition engine has collected data. The average value of each key performance indicator during the learning period. This represents the total number of samples during the learning period. This indicates that the communication performance acquisition engine has collected data. The first key performance indicator data corresponds to the first Each sample value This indicates that the physical environment acquisition engine has collected the data. The average value of each physical layer signal during the learning cycle. This indicates that the physical environment acquisition engine has collected the data. The corresponding physical layer signal of the first Each sample value; The formula for calculating standard deviation is: ,and In the formula, This indicates that the communication performance acquisition engine has collected data. The standard deviation of each key performance indicator data within the learning period. This indicates that the physical environment acquisition engine has collected the data. The standard deviation of each physical layer signal during the learning cycle; The expression for setting the dynamic threshold is: ,and In the formula, This indicates that the communication performance acquisition engine has collected data. The dynamic thresholds of key performance indicators within the learning cycle. This indicates that the physical environment acquisition engine has collected the data. The dynamic threshold of each physical layer signal within the learning cycle; The calculated baseline values ​​for each performance index are saved, collectively forming a communication-electromagnetic joint baseline model. The expression for the communication-electromagnetic joint baseline model is as follows: In the formula, It is represented as a multidimensional baseline vector in the communication-electromagnetic joint baseline model.

[0026] Specifically, the update steps for the joint communication-electromagnetic baseline model are as follows: During the subsequent operation of the system, new real-time data streams with timestamps are continuously acquired; Using a sliding window method, statistical analysis is performed on the most recent data from the current time period. The expression for the new data is: ,and In the formula, This represents a new data point collected at the current time. Indicates the current time. This is represented by the size of the sliding window. This is represented as the communication performance acquisition engine. Real-time data collection Key performance indicators data, This refers to the physical environment acquisition engine. Real-time data collection One physical layer signal; The exponentially weighted moving average (EWMA) algorithm is used to fuse the new data and the old baseline model, updating the mean and standard deviation. The expression for updating the model using the EWMA algorithm is as follows: ,and In the formula, This is represented as the updated mean of the new data. Represented as a smoothing factor, This represents the mean of the data before the update. This is expressed as the updated standard deviation of the new data. This is expressed as the standard deviation of the data before the update; The dynamic threshold is updated based on the updated mean and standard deviation, where the expression for updating the dynamic threshold is: In the formula, This is represented as the dynamic threshold for updating new data; The updated mean, standard deviation, and dynamic threshold are combined to update the communication-electromagnetic joint baseline model, thereby tracking the long-term drift of the system. The expression for updating the communication-electromagnetic joint baseline model is: In the formula, This is represented as the multidimensional baseline vector for updating the communication-electromagnetic joint baseline model.

[0027] S2. Compare and analyze the collected real-time data stream with the dynamic baseline model. When a statistically significant change in the bit error rate index of any low-rate, weak anti-interference protocol channel is detected within a short time window, interference event diagnosis is immediately triggered. By retrieving all relevant data within ±1 second before and after the precise time point of the bit error rate surge, the throughput, transmit power, and physical layer electromagnetic noise energy information of all high-speed protocol ports are analyzed. The correlation between the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel is calculated using the dynamic time warping (DTW) method. The correlation is compared with the preset interference threshold to determine the interference source and generate a structured diagnostic report. Specifically, the triggering steps for interference event diagnosis are as follows: The collected real-time data streams are compared and analyzed with the dynamic baseline model to monitor the key performance indicators (KPIs) of each protocol channel in real time, with particular attention paid to the bit error rate or symbol error rate sequence of low-rate protocol channels with weak anti-interference capabilities. The system maintains a fixed-length sliding time window for each monitored protocol channel, performs statistics on all sample datasets within the sliding time window, and calculates the statistical characteristics of the window mean and window standard deviation to check whether there are statistically significant abrupt changes in the bit error rate index of low-rate, low-interference-resistance protocol channels. When a new bit error rate (BER) sample value enters the sliding time window using the Z-Score detection method, the Z-Score value of the new BER sample value relative to the statistical characteristics of the current window is calculated. The formula for calculating the Z-Score value is as follows: In the formula, Represented as a standard score describing the degree to which a new data point deviates from the window mean. This represents the newly acquired, yet-to-be-tested bit error rate value. It is expressed as the arithmetic mean of all bit error rate samples within the current sliding window. It is expressed as the standard deviation of all bit error rate samples within the current sliding window; The Z-Score value is compared with a preset significance threshold. When the Z-Score value is not less than the preset significance threshold, it is determined that the bit error rate index has undergone a statistically significant change, and interference event diagnosis is immediately triggered. The current trigger time point is recorded as the starting point of the interference event.

[0028] Specifically, the steps for calculating the correlation degree using the Dynamic Time Warping (DTW) method are as follows: The system uses the trigger time point as the time center and retrieves all relevant data within ±1 second before and after the time center point of the sudden increase in bit error rate. This yields the time series of the bit error rate curve for the interfered low-speed channel and the activity intensity curves for each high-speed channel to be inspected. The time series expression for the bit error rate curve of the low-speed channel is as follows: In the formula, This is represented as a time series of the low-speed channel bit error rate curve. Represented as the bit error rate curve of the low-speed channel. One element, This represents the number of elements in the low-speed channel bit error rate curve. The time series expressions for the activity intensity curves of each high-speed channel are as follows: In the formula, Represented as Time series of high-speed channel activity intensity curves This represents the number of high-speed lanes. Represented as the first The first high-speed channel activity intensity curve One element, This is represented by the number of elements in the high-speed channel activity intensity curve; Calculate the distance between each pair of elements in the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel, and construct a distance matrix. The formula for calculating the distance matrix is ​​as follows: ,and In the formula, Represented as the bit error rate curve of the low-speed channel. The element and the first The first high-speed channel activity intensity curve Local distance between elements Represented as a distance matrix; Using a dynamic programming algorithm, the cumulative cost matrix is ​​calculated from the distance matrix, where the formula for calculating the cumulative cost matrix is: In the formula, Indicated as from arrive The minimum cumulative distance, This indicates getting to the current point. The path with the minimum cumulative cost. Indicated as from arrive The minimum cumulative distance, Indicated as from arrive The minimum cumulative distance, Indicated as from arrive The minimum cumulative distance; In the cumulative cost matrix, starting from the end point and tracing back to the beginning point, the cumulative distance between the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel is obtained, and a path that minimizes the total cumulative distance is found as the optimal regularization path. The total cumulative distance of the optimal regularized path is mapped to a normalized correlation score, where the correlation score is calculated using the following formula: In the formula, Represented as the low-speed channel bit error rate curve and Normalized correlation scores between the activity intensity curves of each high-speed channel.

[0029] Specifically, the steps for generating a structured diagnostic report are as follows: The preset interference judgment threshold compares the correlation score calculated between each high-speed channel and the interfered low-speed channel with the preset interference judgment threshold to determine the interference source. When the correlation score is higher than the interference judgment threshold, the corresponding high-speed channel is marked as a suspected interference source. When multiple high-speed channels are marked as suspected interference sources, they are sorted according to their correlation scores, and the high-speed channel with the highest correlation score is identified as the main interference source. The correlation score with the highest score is used as the confidence level for the diagnosis of this interference event. The peak bit error rate (BER) within the sliding time window of the interference event is calculated as the growth factor of the high-speed channel BER relative to the baseline mean and baseline standard deviation in the dynamic baseline model. This growth factor is used as the interference intensity level, where the expression for the interference intensity level is: In the formula, Represented as a quantized interference intensity value, It is expressed as the peak value of the bit error rate within the sliding time window of the interference event. This represents the baseline mean of the high-speed channel bit error rate in the dynamic baseline model. Expressed as the baseline standard deviation of the high-speed channel bit error rate in the dynamic baseline model; For the classification of interference intensity levels, the interference intensity value calculated by bit error rate quantification is compared with the baseline standard deviation of the high-speed channel bit error rate in the dynamic baseline model. When the interference intensity level is classified as mild, When the interference intensity level is classified as moderate, At that time, the interference intensity level was classified as severe; The system collects the trigger time of the sudden increase in bit error rate of interference events, the identification of the interference source port, the throughput of each high-speed channel, the transmit power, the physical layer electromagnetic noise energy information, the confidence level, and the interference intensity level. All of the above information is then organized and packaged into a structured diagnostic report according to a certain data structure.

[0030] S3. The system maintains a multi-mode avoidance strategy library that includes micro-slot scheduling strategies, physical layer parameter soft adjustment strategies, and frequency domain avoidance strategies. Based on the severity of interference and the service level agreement of the affected services in the structured diagnostic report, and combined with the current total load status of the system, the built-in strategy decision engine adaptively selects one or combines multiple optimal strategies from the multi-mode avoidance strategy library and generates executable scheduling instructions based on the selected strategy. Specifically, the multi-mode avoidance strategy library includes micro-slot scheduling strategy, physical layer parameter soft adjustment strategy, and frequency domain avoidance strategy. Among them, the micro-slot scheduling strategy: for non-real-time Modbus RTU query commands, within the millisecond time of critical data frame transmission of the interfered channel, dynamically delays the transmission queue priority of the associated high-speed port, or instantaneous bandwidth limitation, to create an electromagnetic silence window and reduce the electromagnetic interference of the high-speed port to the interfered channel. Physical layer parameter soft adjustment strategy: Dynamically enable stronger forward error correction (FEC) coding for the interfered RS-485 protocol channel to improve the reliability of data transmission, or dynamically adjust the signal rising edge slope control resistor on the driver of the interfered serial port channel through a digital potentiometer to slow down the edge speed and reduce high-frequency radiation and anti-inter-symbol interference (ISI) characteristics. Frequency domain avoidance strategy: By slightly adjusting the clock frequency of the high-speed port using hardware, the harmonic components inside the chassis are deviated from the sensitive frequency band of the interfered channel, thus avoiding the generation of electromagnetic interference.

[0031] Specifically, the steps for generating executable scheduling instructions are as follows: Input a structured diagnostic report and parse it to extract the severity of the interference. Combine the service level agreement of the affected business with the current total load status of the system. The severity of the interference is consistent with the quantified interference intensity value. Based on the classification of interference intensity level, it is confirmed to be mild, moderate or severe. The strategies in the multi-mode avoidance strategy library are parsed into operable parameters, including parameterized micro-slot scheduling strategy, physical layer parameter soft adjustment strategy, and frequency domain avoidance strategy. The structured diagnostic report is input into the micro-slot scheduling strategy to calculate the silent window start point and silent window duration parameters and set the limiting action. The silent window start point is the instant when the executable scheduling instruction needs to be triggered when the transmit enable signal TX_EN on the interfered port changes from low to high level, i.e. when a frame of data is transmitted. The formula for calculating the duration of the silent window is: In the formula, This represents the duration of the silent window. This indicates that the startup time of the affected port is the start point of the silent window. This represents the maximum number of bytes in a single data frame of the port communication protocol. This represents the transmission time per bit. This is represented as a safety margin. The limiting action dynamically applies bandwidth limits to the transmission queue of the interference source port based on the interference intensity value, including limiting the maximum transmission bandwidth to 70% of the original capacity for mild interference, 50% for moderate interference, and 30% for severe interference. The structured diagnostic report is input into the physical layer parameter soft adjustment strategy to establish a mapping table of interference intensity, Z-score value, and target resistance value. The signal slope adjustment parameter is then calculated using the following formula: In the formula, This represents the target resistance value that needs to be set for the digital potentiometer. This represents the minimum allowable resistance value. Represented as a quantized interference intensity value, This represents the minimum value of the preset interference intensity. This represents the maximum allowable resistance value. This represents the maximum value of the preset interference intensity. Input the structured diagnostic report into the frequency domain avoidance strategy to determine the frequency bands to be avoided; The system's built-in strategy decision engine selects or combines multiple optimal strategies from a multi-mode avoidance strategy library based on the collected interference severity, service priority, and total system load status, and outputs a strategy combination. Based on the selected strategy combination, the strategy is transformed into hardware-level / software-level executable scheduling instructions. The hardware-level executable scheduling instructions include generating an instruction recognizable by an interrupt service routine (ISR) for micro-slot scheduling, or generating an I²C or SPI bus write command for physical layer parameter adjustment. For software-level executable scheduling instructions, the protocol stack needs to cooperate in enabling stronger FEC actions to generate a configuration function call; Before issuing executable scheduling instructions, resource conflict checks and instruction sequence packaging are performed to verify the executability of the scheduling instructions; Once verified, executable scheduling instructions are sent to the protocol stack and hardware driver layer for execution.

[0032] S4. Once the executable scheduling command is issued and the execution strategy is implemented, the acquisition engine is immediately activated to intensively monitor the communication quality indicators of the interfered ports, collect the actual effect data after the strategy is executed, compare it with the expected goal of the execution strategy, and conduct quantitative evaluation of the effect. The system treats each complete interference event diagnosis, strategy execution, and actual effect verification as a case, uses statistical analysis to evaluate the effectiveness of different strategies in different scenarios, and regularly mines and analyzes the cases to dynamically optimize the priority of strategies in the multi-mode avoidance strategy library.

[0033] Specifically, the steps for quantitative evaluation of effectiveness are as follows: At the moment the strategy begins execution, the system immediately sets an evaluation time window, which is 2-5 minutes long. This window should be long enough to capture the stable effect of the strategy, but not too long to avoid the impact of instantaneous fluctuations. Within the evaluation time window, the acquisition engine is activated to collect communication quality indicators (MMI) of the interfered port at a frequency higher than regular monitoring. These MMI indicators include bit error rate, packet loss rate, retransmission rate, throughput, and signal strength. The actual effect data after the strategy execution is collected and denoted as the actual MMI vector. The expression for the actual MMI vector is: In the formula, Expressed as the actual bit error rate, This is expressed as the actual packet loss rate. This is expressed as the actual retransmission rate. Represented as actual throughput, Represented as actual signal strength; From the historical database, communication quality data for a period of time, the same length as the evaluation window and with a comparable level of business load, is extracted before the strategy is implemented. This data serves as the expected target for determining the strategy, i.e., the communication quality metric value expected to be achieved after the strategy is implemented. This target is denoted as the expected metric vector, where the expression for the expected metric vector is: In the formula, This is expressed as the expected bit error rate. This is expressed as the expected packet loss rate. This is expressed as the expected retransmission rate. This is expressed as the expected throughput. This is expressed as the expected signal strength; The corresponding elements in the actual indicator vector and the expected indicator vector are compared separately, and the deviation of each indicator is calculated. The formula for calculating the indicator deviation is as follows: , , , , In the formula, This represents the difference in bit error rate before and after strategy execution. This is expressed as the deviation in packet loss rate before and after strategy execution. This represents the difference in retransmission rate before and after the strategy is implemented. This is represented as the throughput deviation before and after strategy execution. This is expressed as the signal strength deviation before and after strategy execution; Based on the deviation of each indicator, a quantitative value for the effectiveness of the strategy execution is calculated to evaluate the actual effect of the strategy. The formula for calculating the quantitative value of the effectiveness is as follows: ,and In the formula, This is represented as a quantitative value of the effect, with a range of values. Furthermore, the closer the quantified effect value is to 1, the better the strategy execution effect. This is represented as the weighting coefficient corresponding to the bit error rate deviation. This is represented by the weighting coefficient corresponding to the packet loss rate deviation. This is represented as the weighting coefficient corresponding to the retransmission rate deviation. This is represented by the weighting coefficient for the corresponding throughput deviation. This is represented as the weighting coefficient corresponding to the signal strength deviation; Set a strategy success threshold and compare it with the quantified effect value to determine whether the adopted strategy was successful in this interference event, including when... When the strategy adopted is deemed successful in this interference event, At that time, it was determined that the adopted strategy failed in this interference event, among which, This is represented as the success threshold of the strategy.

[0034] Specifically, the dynamic optimization steps of the multi-mode evasion strategy library are as follows: After each interference event is handled, the system will generate a complete case and store it in a dedicated historical experience database. The case includes interference event diagnosis, policy implementation, and actual effect verification. Interference event diagnosis also includes interference type, type of interfered port, type of interference source port, severity of interference, service level agreement of affected services, and total system load status. Policy implementation includes policy combination and parameterization. Actual effect verification includes quantified effect value and whether the policy was successful. The system periodically performs data mining and analysis on stored cases, with the goal of identifying patterns between strategies and results. For each selected strategy, the similar interference scene features are grouped, and the success rate and average performance score of each strategy are calculated to obtain the statistical results. Based on statistical results, the priority weight of each strategy in the multi-mode avoidance strategy library is dynamically adjusted in different scenarios. For example, in a specific scenario, the higher the average performance score of a strategy, the higher the priority weight of the corresponding strategy is increased. Conversely, for strategies with lower average performance scores, the priority is reduced or the corresponding strategy is marked as not recommended. For the adjustable parameters in the strategy, the gradient descent optimization algorithm is used to fine-tune the parameters based on the performance of historical cases in order to seek better parameter settings. The updated strategy priority weights and adjusted parameters are updated to the multi-mode avoidance strategy library. When the strategy decision engine encounters the same / similar interference scenario again, it will prioritize the more effective strategy in the history.

[0035] Example 2 This invention provides, for example Figure 2 The industrial multi-protocol communication gateway adaptive adaptation system shown includes a multi-dimensional joint baseline modeling and real-time data perception module: a communication performance acquisition engine and a physical environment acquisition engine are deployed in parallel to collect performance index data and physical layer signals in real time, respectively. A communication-electromagnetic joint baseline model is established through statistical analysis, and a real-time data stream with timestamps and a dynamic baseline model as a comparison benchmark are generated and transmitted to the interference event diagnosis and quantification module. Interference event diagnosis and quantification module: During continuous system operation, the collected performance index data will be compared with the dynamic baseline in real time. The interference event diagnosis trigger condition is set to detect a statistically significant change in the bit error rate index of any low-rate, weak anti-interference protocol channel within a short time window. All relevant data within ±1 second before and after the bit error rate surge are retrieved. The throughput, transmit power and physical layer electromagnetic noise energy information of all high-speed protocol ports are analyzed. The correlation degree between the bit error rate curve of the low-speed channel and the activity intensity curve of each high-speed channel is calculated using the dynamic time warping (DTW) method. The correlation degree is compared with the preset interference threshold to determine the interference source and generate a structured diagnostic report. The structured diagnostic report includes the main interference source port, the interfered port, the start time of the interference event, the correlation degree confidence level, and the interference intensity level. It is then transmitted to the scheduling module that adaptively generates avoidance strategies. The adaptive scheduling module for generating avoidance strategies: It learns and extends to generate a multi-mode avoidance strategy library, including micro-slot scheduling strategies, physical layer parameter soft adjustment strategies, and frequency domain avoidance strategies. It has a built-in strategy decision engine that, based on the severity of interference in the structured diagnostic report, the service level protocol of the affected services, and the current total load status of the system, adaptively selects one or more optimal strategies from the multi-mode avoidance strategy library, combines them, and converts the strategies into executable scheduling instructions, which are then sent to the protocol stack and hardware driver layer for execution. The evasion effect verification and strategy optimization module: After the strategy is executed, the perception capability of the multi-dimensional joint baseline modeling and real-time data perception module is immediately activated. It intensively monitors the communication quality indicators of the interfered ports, collects the actual effect data after the strategy is executed, and compares and quantifies the effect with the expected target of the strategy selected in the scheduling module of adaptive generation of evasion strategies. Each complete diagnosis of interference event, strategy execution and actual effect verification is taken as a case. The effectiveness of different strategies in different scenarios is evaluated by statistical analysis. The case is regularly mined and analyzed to dynamically optimize the priority of strategies in the multi-mode evasion strategy library.

[0036] The present invention provides an industrial multi-protocol communication gateway adaptive adaptation system, which is implemented by the above-mentioned industrial multi-protocol communication gateway adaptive adaptation method. For details of the specific method and process of the industrial multi-protocol communication gateway adaptive adaptation system, please refer to the above-mentioned embodiment of the industrial multi-protocol communication gateway adaptive adaptation method, which will not be repeated here.

[0037] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0038] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0039] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0040] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0041] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An adaptive adaptation method for an industrial multi-protocol communication gateway, characterized in that, Includes the following steps: S1. Deploy the communication performance acquisition engine and the physical environment acquisition engine in parallel on the system to capture key performance indicators (KPIs) and physical layer signals in real time, generate real-time data streams with timestamps, and start a learning cycle in the early stage of gateway deployment without interference by using the system's automatic learning mode to establish a multi-dimensional related communication-electromagnetic joint baseline model and obtain a dynamic baseline model as a comparison benchmark. S2. Compare and analyze the collected real-time data stream with the dynamic baseline model. When interference event diagnosis is triggered, retrieve all relevant data within ±1 second before and after the precise time point of the sudden increase in bit error rate. Use the dynamic time warping (DTW) method to calculate the correlation between the bit error rate curve of the low-speed channel and the activity intensity curve of each high-speed channel. Compare with the preset interference threshold to determine the interference source and generate a structured diagnostic report. S3. The system maintains a multi-mode avoidance strategy library. Based on the structured diagnostic report and combined with the current total load status of the system, the built-in strategy decision engine adaptively selects one or combines multiple optimal strategies from the multi-mode avoidance strategy library to generate executable scheduling instructions. S4. Once the executable scheduling command is issued and the execution strategy is implemented, the acquisition engine is immediately activated to intensively monitor the communication quality indicators of the interfered ports, collect the actual effect data after the strategy is executed, compare it with the expected goal of the execution strategy and conduct quantitative evaluation of the effect, use statistical analysis to evaluate the effectiveness of different strategies in different scenarios, and dynamically optimize the priority of strategies in the multi-mode avoidance strategy library.

2. The adaptive adaptation method for an industrial multi-protocol communication gateway according to claim 1, characterized in that, The steps for generating the timestamped real-time data stream are as follows: The parallel-deployed communication performance acquisition engine and physical environment acquisition engine start simultaneously, concurrently acquiring key performance indicators (KPIs) and physical layer signals. The communication performance acquisition engine is directly embedded into the driver / MAC layer of each protocol stack, capturing key performance indicators (KPIs) in real time at a high frequency of 1kHz, and then marking all KPIs data with high-precision timestamps. The physical environment acquisition engine is responsible for capturing physical layer signals that reflect the state of electromagnetic activity within the chassis, including chip-level status registers and board-level RF sampling ADCs; At the end of each sampling period, each acquisition engine packages all the data collected in this session into a data packet, injects a high-precision timestamp into the data packet, and stores it in the internal cache queue. The timestamped data packets were arranged and combined in the order of their collection time to form a timestamped real-time data stream.

3. The adaptive adaptation method for an industrial multi-protocol communication gateway according to claim 2, characterized in that, The steps for establishing the communication-electromagnetic joint baseline model are as follows: In the initial stage of gateway deployment without interference, a learning cycle is started using the system's automatic learning mode, and real-time data streams with timestamps are collected through the communication performance acquisition engine and the physical environment acquisition engine. The two acquisition engines collect real-time data streams with timestamps and perform statistical analysis on the data. The sample values ​​of each performance indicator collected during the statistical learning period are used to calculate the statistical characteristics of the mean and standard deviation of each performance indicator within the normal range. Based on the statistical characteristics, a dynamic threshold is set to form the baseline of the corresponding performance indicator. The baseline values ​​of each calculated performance index are saved and collectively form a communication-electromagnetic joint baseline model.

4. The adaptive adaptation method for an industrial multi-protocol communication gateway according to claim 3, characterized in that, The triggering steps for the interference event diagnosis are as follows: The collected real-time data streams are compared and analyzed with the dynamic baseline model to monitor the key performance indicators (KPIs) of each protocol channel in real time. The system maintains a fixed-length sliding time window for each monitored protocol channel, performs statistics on all sample datasets within the sliding time window, and then calculates the statistical characteristics of the window mean and window standard deviation. When a new bit error rate (BER) sample value enters the sliding time window using the Z-Score detection method, the Z-Score value of the new BER sample value relative to the statistical characteristics of the current window is calculated. The formula for calculating the Z-Score value is as follows: In the formula, Represented as a standard score describing the degree to which a new data point deviates from the window mean. This represents the newly acquired, yet-to-be-tested bit error rate value. It is expressed as the arithmetic mean of all bit error rate samples within the current sliding window. It is expressed as the standard deviation of all bit error rate samples within the current sliding window; The Z-Score value is compared with a preset significance threshold. When the Z-Score value is not less than the preset significance threshold, it is determined that the bit error rate index has undergone a statistically significant change, and interference event diagnosis is immediately triggered. The current trigger time point is recorded as the starting point of the interference event.

5. The adaptive adaptation method for an industrial multi-protocol communication gateway according to claim 4, characterized in that, The steps for calculating the correlation degree using the Dynamic Time Warping (DTW) method are as follows: The system uses the trigger time point as the time center and retrieves all relevant data within ±1 second before and after the time center point of the sudden increase in bit error rate to obtain the time series of the bit error rate curve of the interfered low-speed channel and the activity intensity curve of each high-speed channel to be inspected. Calculate the distance between each pair of elements in the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel, and construct a distance matrix; Use dynamic programming to calculate the cumulative cost matrix from the distance matrix; In the cumulative cost matrix, starting from the end point and tracing back to the beginning point, the cumulative distance between the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel is obtained, and a path that minimizes the total cumulative distance is found as the optimal regularization path. The total cumulative distance of the optimal regular path is mapped to a normalized correlation score.

6. The adaptive adaptation method for an industrial multi-protocol communication gateway according to claim 5, characterized in that, The steps for generating the structured diagnostic report are as follows: The preset interference judgment threshold compares the correlation score calculated between each high-speed channel and the interfered low-speed channel with the preset interference judgment threshold to determine the interference source. When the correlation score is higher than the interference judgment threshold, the corresponding high-speed channel is marked as a suspected interference source. When multiple high-speed channels are marked as suspected interference sources, they are sorted according to their correlation scores, and the high-speed channel with the highest correlation score is identified as the main interference source. The correlation score with the highest score is used as the confidence level for the diagnosis of this interference event. The growth factor of the peak bit error rate within the sliding time window of the interference event relative to the baseline mean and baseline standard deviation of the high-speed channel bit error rate in the dynamic baseline model is used as the interference intensity level. The system collects the trigger time of the sudden increase in bit error rate of interference events, the identification of the interference source port, the throughput of each high-speed channel, the transmit power, the physical layer electromagnetic noise energy information, the confidence level, and the interference intensity level. All of the above information is then organized and packaged into a structured diagnostic report according to a certain data structure.

7. The adaptive adaptation method for an industrial multi-protocol communication gateway according to claim 6, characterized in that, The steps for generating the executable scheduling instructions are as follows: Input a structured diagnostic report and parse it to extract the severity of the interference. Combine the service level agreement of the affected business with the current total load status of the system. The severity of the interference is consistent with the quantified interference intensity value. Based on the classification of interference intensity level, it is confirmed to be mild, moderate or severe. The strategies in the multi-mode avoidance strategy library are parsed into operable parameters, including parameterized micro-slot scheduling strategy, physical layer parameter soft adjustment strategy and frequency domain avoidance strategy. Among them, the structured diagnostic report is input into the micro-slot scheduling strategy to calculate the silent window start point and silent window duration parameters and set the limiting action. The structured diagnostic report is input into the physical layer parameter soft adjustment strategy to establish a mapping table of interference intensity-Z-Score value-target resistance value and calculate the signal slope adjustment parameter. Input the structured diagnostic report into the frequency domain avoidance strategy to determine the frequency bands to be avoided; The system's built-in strategy decision engine selects or combines multiple optimal strategies from a multi-mode avoidance strategy library based on the collected interference severity, service priority, and total system load status, and outputs a strategy combination. Based on the selected strategy combination, the strategy is transformed into executable scheduling instructions at the hardware / software level. Once verified, executable scheduling instructions are sent to the protocol stack and hardware driver layer for execution.

8. The adaptive adaptation method for an industrial multi-protocol communication gateway according to claim 7, characterized in that, The steps for quantitative evaluation of the results are as follows: At the moment the strategy begins execution, the system immediately sets an evaluation time window, which ranges from 2 to 5 minutes. Within the evaluation time window, the acquisition engine is activated to collect communication quality indicators of the interfered port at a frequency higher than that of regular monitoring, including indicators such as bit error rate, packet loss rate, retransmission rate, throughput, and signal strength. The actual effect data after the strategy is implemented is collected and recorded as the actual indicator vector. From the historical database, extract communication quality data for a period of time with the same evaluation window length and comparable business load level before the strategy is executed. Use this data to determine the expected target of the strategy, i.e. the communication quality index value expected to be achieved after the strategy is executed, and denote it as the expected index vector. Compare the corresponding elements in the actual indicator vector and the expected indicator vector respectively, and calculate the deviation of each indicator; Based on the deviation of each indicator, a quantitative value of the strategy's effectiveness is calculated to evaluate the actual effect of the strategy. Set a success threshold for the strategy and compare it with the quantified effect value to determine whether the strategy was successful in this interference event.

9. An industrial multi-protocol communication gateway adaptive adaptation system, implemented by the industrial multi-protocol communication gateway adaptive adaptation method according to any one of claims 1-8, characterized in that, It includes a multi-dimensional joint baseline modeling and real-time data perception module: a communication performance acquisition engine and a physical environment acquisition engine are deployed in parallel to collect performance index data and physical layer signals in real time, respectively. A communication-electromagnetic joint baseline model is established through statistical analysis, and a real-time data stream with timestamps and a dynamic baseline model as a comparison benchmark are generated and transmitted to the interference event diagnosis and quantification module. Interference event diagnosis and quantification module: During continuous system operation, the collected performance index data will be compared with the dynamic baseline in real time, and the interference event diagnosis trigger conditions will be set. All relevant data within ±1 second before and after the sudden increase in bit error rate will be retrieved. The throughput, transmit power and physical layer electromagnetic noise energy information of all high-speed protocol ports will be analyzed. The correlation between the low-speed channel bit error rate curve and the activity intensity curve of each high-speed channel will be calculated using the dynamic time warping (DTW) method. The correlation will be compared with the preset interference threshold to determine the interference source and generate a structured diagnostic report, which will be transmitted to the scheduling module that adaptively generates avoidance strategies. The adaptive avoidance strategy generation scheduling module: It learns and extends to generate a multi-mode avoidance strategy library, with a built-in strategy decision engine. Based on the structured diagnostic report and combined with the current total load status of the system, it adaptively selects one or more optimal strategies from the multi-mode avoidance strategy library, combines them, and transforms the strategies into executable scheduling instructions, which are then sent to the protocol stack and hardware driver layer for execution. The evasion effect verification and strategy optimization module: After the strategy is executed, the perception capability of the multi-dimensional joint baseline modeling and real-time data perception module is immediately activated. It intensively monitors the communication quality indicators of the interfered ports, collects the actual effect data after the strategy is executed, and compares and quantifies the effect with the expected target of the strategy selected in the scheduling module of adaptive generation of evasion strategies. Each complete diagnosis of interference event, strategy execution and actual effect verification is taken as a case. The effectiveness of different strategies in different scenarios is evaluated by statistical analysis. The case is regularly mined and analyzed to dynamically optimize the priority of strategies in the multi-mode evasion strategy library.

10. An industrial multi-protocol communication gateway adaptive adaptation system according to claim 9, characterized in that, The multi-mode avoidance strategy library includes micro-slot scheduling strategies, physical layer parameter soft adjustment strategies, and frequency domain avoidance strategies.

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