Industrial single-chip microcomputer automatic control method based on time sequence event triggering

Abstract

The invention provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering, and the method comprises the steps: carrying out the multi-period sampling and time window division of an original signal inputted by an industrial field sensor or a switching value, and extracting the time distribution characteristics of the signal; performing signal stability judgment and pseudo edge elimination based on the signal time distribution characteristics, and outputting a stable edge event set; performing event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary time sequence trigger signal; environment characteristic data are collected, real-time feedback of an actuator is obtained, and system judgment parameters are dynamically corrected; and generating a control command and verifying an execution result based on the preliminary time sequence trigger signal and actuator feedback, the method can dynamically distinguish real signals and noise under vibration interference, simultaneously ensures the real-time performance and trigger reliability of the system, and solves the problems of insufficient edge detection stability and false trigger phenomenon caused by mechanical vibration.

Description

Technical Field

[0001] This disclosure relates to the field of industrial automation control technology, and in particular to an industrial microcontroller automatic control method based on timing event triggering. Background Technology

[0002] In large industrial equipment such as CNC machine tools and hydraulic servo platforms, industrial microcontrollers trigger control commands by detecting the transition edges of sensor output signals, which is a core technology for achieving automated equipment operation. Ideally, the signal edges should be clear and stable, allowing for reliable recognition by the microcontroller. However, in actual industrial environments, there are often strong vibrations caused by motor starting and stopping and mechanical shocks. These vibrations can interfere with sensors and their circuitry, resulting in numerous transient level glitches in the output digital signal.

[0003] Existing technologies primarily rely on hardware filtering circuits or fixed software delays to address this problem. Hardware filtering slows down signal response speed and reduces control real-time performance; fixed software delays face a dilemma: too short a delay cannot effectively filter out interference, while too long a delay will lag the response to actual actions, affecting control accuracy. Both methods use static thresholds, which cannot adapt to dynamic changes in vibration intensity and frequency.

[0004] As control systems expand in scale and the number of monitoring points increases, real edges and vibration spikes in the massive signals become intertwined. Traditional centralized processing and fixed threshold criterion modes lead to a significant increase in system latency, resulting in frequent false triggers and command lags. This not only restricts production efficiency and processing accuracy but may also pose equipment safety risks in high-speed, high-precision control scenarios.

[0005] Therefore, there is an urgent need for an adaptive control method that can dynamically distinguish between real signals and noise under vibration interference, while ensuring the real-time performance and trigger reliability of the system, in order to solve the long-standing technical problem of insufficient edge detection stability and false triggering caused by mechanical vibration. Summary of the Invention

[0006] In view of this, in order to solve the problems caused by the existing technology, this application provides an industrial microcontroller automatic control method based on timing event triggering.

[0007] In a first aspect, this disclosure provides an industrial microcontroller automatic control method based on timing event triggering, the method comprising: S1 performs multi-cycle sampling and time window division on the raw signals of industrial field sensors or switch inputs to extract the signal time distribution characteristics; S2, based on the signal time distribution characteristics, determine signal stability and eliminate false edges, and output a set of stable edge events; S3, perform event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary timing trigger signal; S4 collects environmental characteristic data and obtains real-time feedback from the actuator to dynamically correct system judgment parameters; S5. Based on the preliminary timing trigger signal and actuator feedback, generate control commands and verify the execution results.

[0008] Optionally, step S3 includes: For each edge event in the set of stable edge events, its acknowledgment delay parameter is calculated independently. The acknowledgment delay parameter combines the basic acknowledgment delay period and the equipment vibration period. After their respective delays, the corresponding signal states are checked a second time, and the validity of the event is confirmed based on the comparison between the initial state and the delayed state. The confirmed valid events are arranged in chronological order and matched with the pre-stored multi-level logic templates to identify logical event groups that conform to the predetermined control behavior pattern. Based on the identified logical event groups, a preliminary set of timing trigger signals containing trigger time attributes and event types is generated.

[0009] Optionally, the matching with the pre-stored multi-level logical template includes: The events that are confirmed to be valid are arranged in chronological order to form an event timeline vector. Scan the event time vector in chronological order to extract consecutive event sequences. The extracted event sequence is compared with the pre-stored logic template. If and only if the event type combination of the event sequence is consistent with the event type sequence defined in the pre-stored logic template, and the time interval between adjacent events in the event sequence meets the time interval requirement defined in the pre-stored logic template, the event sequence is determined to be a successful match and identified as a valid logical event group. If there is no event sequence in the event timing vector that can be successfully matched with any of the pre-stored logic templates, then no trigger signal corresponding to the event timing vector is generated, and the feature data of the unmatched event sequence is recorded.

[0010] Optionally, step S1 includes: Based on the mechanical operating cycle and vibration characteristics of the equipment, initialize the sampling period, number of sampling points and time window; Perform multi-cycle sampling to obtain a level state matrix, and based on the matrix, count the number of signal transitions and the average duration of the level in each sampling cycle; Based on the time window, the number of transitions and the average duration of the level for all sampling periods within the window are aggregated and statistically analyzed to obtain the average number of transitions and the average duration of the level for each window. Based on the average number of transitions and the average level duration, the stability level of each time window is determined, and a structured signal time distribution feature set is generated.

[0011] Optionally, step S2 includes: Based on the aforementioned signal time distribution characteristics, the window-level stability index for each time window is calculated; The window-level stability index is compared with the stability threshold and the maximum allowed number of transitions to perform preliminary screening and form a preliminary set of valid edge events. Based on the real-time detected equipment vibration frequency, the stability threshold is dynamically corrected, and the corrected threshold is used to reconfirm and filter the preliminary effective edge event set. Output the set of stable edge events filtered by the dynamic threshold and record the false edge features that were removed.

[0012] Optionally, the dynamically corrected stability threshold is expressed using the following formula: ,in, This is the corrected stability threshold. This is the initial stability threshold. For real-time equipment vibration frequency, The reference frequency is denoted by k, which is a correction factor ranging from 0.1 to 0.3.

[0013] Optionally, step S4 includes: Real-time acquisition of environmental data, including mechanical vibration frequency, ambient temperature, and actuator response delay time; Based on the collected environmental data, a comprehensive disturbance index is calculated and the environmental disturbance level is classified accordingly. Based on the environmental disturbance level, the system's level hold time threshold and acknowledgment delay period are dynamically adjusted. The corrected parameters are updated to the system operating environment, and self-calibration is performed based on the deviation between the actual actuator response and the command to optimize the corrected parameters.

[0014] Optionally, the comprehensive disturbance index is calculated using a weighted fusion method, with the specific formula as follows: ,in, For real-time equipment vibration frequency, For ambient temperature, For actuator response delay, , and These represent the reference vibration frequency, standard ambient temperature, and reference response time determined during the system calibration phase, respectively. , and These are the weighting coefficients.

[0015] Optionally, step S5 includes: The initial timing trigger signal is mapped into a control command that includes the target execution unit, action type, and planned execution time; The drive execution unit executes the control command and detects the completion status of the action through the sensor after a preset delay. The execution results are verified by comparing the expected state of the instruction with the actual feedback state, and backtracking analysis is performed on abnormal execution. The failure record is fed back to the system parameter learning module for subsequent optimization of control parameters.

[0016] Secondly, this disclosure provides a computer storage medium storing a computer program that, when executed, implements the method described in the first aspect.

[0017] The beneficial effects of this disclosure are that, compared with the prior art, this disclosure has the following advantages: 1) By using multi-cycle sampling and time window division, the temporal distribution characteristics of the signal under vibration environment are extracted, and a dynamic stability judgment threshold is constructed accordingly. This threshold can adaptively adjust with vibration frequency and intensity, thereby automatically increasing the strictness of the judgment to filter out noise under strong disturbances and decreasing the judgment to quickly respond to the real signal under weak disturbances, fundamentally overcoming the dilemma of traditional methods between filtering out interference and response lag.

[0018] 2) To address the issue of false triggering caused by vibration glitches, the system not only performs initial screening for signal stability but also introduces independent delay confirmation and secondary state detection for each edge event, effectively eliminating transient false edges. Simultaneously, by matching the confirmed event sequence with pre-stored multi-level logic templates, the system can identify timing action units conforming to predetermined control logic from isolated edge events. This elevates trigger judgment from a single level transition to the logic sequence level, significantly reducing the probability of false triggering and pseudo-instructions.

[0019] 3) Real-time acquisition of multi-source environmental data, including vibration, temperature, and actuator response delay, quantifies the intensity of environmental disturbances and dynamically adjusts the system's core judgment parameters accordingly. Furthermore, by comparing commands and execution feedback, the system can evaluate the effectiveness of parameter settings and perform self-calibration, forming a continuously optimizing closed loop. This mechanism enables the system to maintain high trigger sensitivity and reliability over long periods without manual intervention, significantly improving its adaptability and robustness in complex industrial environments. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0021] Figure 1 A flowchart of an industrial microcontroller automatic control method based on timing event triggering provided in an embodiment of this disclosure is shown.

[0022] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0023] The present disclosure will be further described below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of the present disclosure more clearly, and should not be used to limit the scope of protection of the present disclosure.

[0024] The components of the embodiments of the invention described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0025] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of the invention, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0026] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the invention pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the invention.

[0027] Figure 1This is a flowchart of an industrial microcontroller automatic control method based on timing event triggering, provided as an embodiment of this disclosure. In this embodiment, a high-speed CNC machine tool spindle is used as the target device controlled by the industrial microcontroller, and the method is described in detail below. Figure 1 As shown, the process may include the following steps: S1: Perform multi-cycle sampling and time window division on the raw signals of industrial field sensors or switch inputs to extract the signal time distribution characteristics.

[0028] Multi-cycle sampling and time characteristic analysis are performed on the raw signals of sensors or switch inputs in industrial fields. By establishing a time window model that matches the vibration characteristics of the equipment, the foundation is laid for subsequent signal stability determination.

[0029] S1.1: Sampling initialization and parameter setting.

[0030] Initialize the timing parameters required for signal sampling based on the type of target equipment and its control accuracy requirements. Core parameter: Sampling period. Through formula The calculation shows that, among which This represents the sampling frequency. Taking a high-speed CNC machine tool spindle as an example, its sampling frequency... It can be set to 20 kHz, corresponding to the sampling period. The time is 0.05 milliseconds. Simultaneously, the inherent mechanical operating cycle of the device needs to be obtained. For example, the mechanical cycle of a high-speed CNC machine tool spindle with a rotation speed of 12,000 rpm. It is 5 milliseconds. Based on this, using the formula... Calculate the number of sampling points required in one complete mechanical cycle. For example, the number of sampling points of a high-speed CNC machine tool spindle within one machine cycle. The calculation is for 100 points. To ensure an absolutely constant sampling interval, the system's sampling control module will use the calculated... A high-precision timing interrupt signal is generated to drive the entire sampling process. This step establishes a precise time scale and synchronization reference for the entire signal acquisition process.

[0031] At the same time, the system initializes the following key system parameters, which will be dynamically adjusted based on environmental feedback during operation: Level hold time threshold The initial value of the time threshold used as the baseline for signal stability assessment is determined through on-site experimental calibration, typically ranging from 1 millisecond to 5 milliseconds. This parameter will serve as the stability threshold in subsequent signal stability assessment steps. The initial value and adjustment benchmark.

[0032] Basic confirmation delay period : The base value used for calculating event confirmation delay, with an initial value range of 2 milliseconds to 10 milliseconds. This parameter serves as the benchmark for calculating individual delays in subsequent steps.

[0033] S1.2: Multi-cycle signal sampling and level status recording.

[0034] After parameter initialization is complete, the multi-cycle signal sampling phase begins. This process is based on the sampling period determined in the preceding steps. and number of sampling points The system continuously and periodically captures the state of the input signal. At the arrival of each sampling period, the signal from the sensor input channel is read, and its level state is recorded in binary form, where 0 represents low level and 1 represents high level. All these recorded state data are organized into a two-dimensional level state matrix. In this matrix, index i represents the sampling point number within one mechanical cycle, and its value ranges from 1 to... Index j represents the period number of continuous sampling, and its total number of periods. The value is typically set between 10 and 50, depending on the system's requirements for vibration statistical characteristics. Each element in the matrix... The signal state at the i-th sampling moment within the j-th sampling period was clearly recorded. This was achieved through continuous acquisition. In one complete mechanical cycle, the system can acquire enough samples to fully reveal the statistical characteristics of the signal under mechanical vibration interference.

[0035] S1.3: Statistics on level duration and transition frequency.

[0036] After obtaining the multi-cycle level-state matrix, the signal characteristics within each independent cycle are then quantized and analyzed. This process involves traversing the matrix. This is achieved by counting each column, i.e., each sampling period. For a given period j, the number of times the signal level changes within that period is precisely counted and recorded as the transition count. Next, using the formula Calculate the average duration of the signal level within this period. (when When =0, = Average duration This is a key indicator, directly reflecting the signal's ability to remain stable within a unit transition interval. After traversing all cycles, two important characteristic sequences are obtained: one is the transition count sequence. The other is a duration sequence. A higher number of jumps generally indicates more severe vibration interference to the signal within that cycle; conversely, a shorter average duration indicates a more unstable signal, making it more prone to glitches. These two sequences provide the core data foundation for the next step of dividing the time window based on the equipment's vibration rhythm.

[0037] S1.4: Time window division based on vibration characteristics.

[0038] To ensure that signal analysis is synchronized with the physical vibration patterns of the equipment, the time axis needs to be divided into a series of time windows related to the vibration period. First, based on the inherent vibration frequency of the equipment... Determine the base window period Its calculation formula is For example, the typical vibration frequency of a high-speed CNC machine tool spindle. If it is 500 Hz, then its basic window period is... The time interval is 2 milliseconds. After dividing the time window, the previously obtained feature sequences will be aggregated and statistically analyzed within each time window. Specifically, the average level duration of all cycles contained within each window will be calculated. and average number of jumps Average level duration Through formula The calculation involves calculating the average duration of all sampling periods falling within that time window. Summing and then dividing by the number of cycles contained in the window. Similarly, the average number of jumps Through formula The calculation involves determining the number of transitions across all sampling periods within the window. Summing and dividing by .in, This indicates the number of complete sampling periods contained within a specific time window. These average values ​​reflect the overall stability of the signal within each time window, calculated by averaging across all time windows. and By performing multi-window comparisons, a macroscopic distribution curve of signal stability in the time dimension can be formed, thereby revealing the overall impact of vibration on the signal at different stages.

[0039] S1.5: Extraction and output of signal time distribution features.

[0040] The stability parameters for each time window are comprehensively extracted and formatted for output. The average duration is calculated for each window. Average number of jumps A comprehensive evaluation is conducted, and a stability level is assigned to it based on preset rules. This level is usually based on and The ratio or numerical range in which a value lies is divided into three levels: high stability, medium stability, and low stability, thus transforming continuous characteristic quantities into discrete, easily discriminable state indicators. For example, when... High value and A lower value indicates a high stability level; conversely, a higher value indicates a lower stability level. Low value and Higher values ​​are classified as low stability. Ultimately, all these features are organized into a structured set of signal temporal distribution features. This feature set comprehensively characterizes the behavior patterns of the input signal under different vibration phases. The system stores this feature set in a specific data buffer and passes it as crucial input data to the subsequent signal stability determination and pseudo-edge removal modules, thereby completing the entire transformation process from the original signal to feature information.

[0041] In the technical solution of this disclosure, a time window model matching the vibration characteristics of equipment is established by performing multi-cycle sampling and time feature analysis on the original signals from the industrial site. This process can fully capture the statistical characteristics of the signal under mechanical vibration interference, laying a solid foundation for subsequent processing. By extracting the time distribution characteristics of the signal under different vibration phases, an effective transformation from continuous time domain to discrete feature information is achieved, enabling the stability of the signal to be synchronously correlated with the physical vibration law of the equipment, thereby providing a reliable data basis for accurately distinguishing real signals from vibration noise.

[0042] S2: Based on the signal time distribution characteristics, determine the signal stability and eliminate false edges, and output a set of stable edge events.

[0043] The signal time distribution characteristics extracted in step S1 are analyzed in depth. Based on the stability of the signal within the time window, false edges caused by mechanical vibration are identified and eliminated, and finally a cleaned set of valid edge events is output.

[0044] S2.1: Stability index calculation.

[0045] Receive the signal time distribution feature set from step S1.5 This set contains the average duration of each time window. Average number of jumps and stability level To transform multidimensional features into a comprehensive scalar that facilitates unified threshold determination, the system calculates a window-level stability index for each time window. This indicator is expressed by the formula. The calculation yields the result. In this formula, the molecule... This represents the average ability of a signal to remain stable within a window; a larger value generally indicates better stability. The denominator... This introduces the influence of the number of jumps, where the increment operation is to avoid the mathematical undefined case where the denominator is zero when the number of jumps is zero. Number of jumps The larger the value, the larger the denominator, which in turn leads to a decrease in the stability index. The decrease accurately reflects the negative impact of frequent jumps on signal stability. After calculation, the stability indices for all time windows are... Arranged in chronological order, forming a stability index sequence. , where n is the total number of time windows. This sequence provides a quantitative and unified basis for the next step of pseudo-edge identification.

[0046] S2.2: False edge identification and preliminary removal.

[0047] Based on the generated stability index sequence The system then begins the first round of pseudo-edge filtering. This process relies on a preset stability threshold. The initial value of this threshold is taken from the system parameters. This defines the minimum duration threshold required for a signal edge to be considered stable, typically ranging from 1 to 5 milliseconds, with the specific value determined through field vibration amplitude experiments. The stability index for each time window in the sequence is then defined. With this threshold The comparison will also include the average number of jumps per window. With a preset maximum number of allowed transitions Comparison, The value range is typically 5 to 20 times per window period. The specific judgment rule is: for any time window, if its stability index... Less than the threshold or its average number of jumps Greater than the maximum allowed value If either of these two conditions is met, the signal change occurring within the window is determined to be an unreliable false edge. These signal events marked as false edges are then removed from the original signal sequence. After this round of filtering, all the edge events that were not removed and are preliminarily considered stable are collected to form a preliminary set of valid edge events. This step enables rapid and preliminary filtering of unstable signals, effectively shielding most of the high-frequency jitter caused by vibration.

[0048] S2.3: Dynamic threshold correction and reconfirmation.

[0049] To address the potential dynamic changes in vibration frequency during mechanical system operation, a dynamic threshold adjustment mechanism is introduced to enhance its adaptability to time-varying environments. This step involves acquiring the aforementioned preliminary set of effective edge events. And some environmental reference parameters from step S1, mainly the currently detected equipment vibration frequency. With time window period The stability threshold is adaptively corrected based on the real-time vibration frequency. The correction formula is as follows: In this formula, It is the new threshold after dynamic correction; It is the initial fixed threshold; It is the currently detected real-time vibration frequency of the equipment; This is the reference frequency determined by the system during the calibration phase; k is a correction coefficient, ranging from 0.1 to 0.3, typically determined by the system's self-learning module based on historical data. For example, the initial stability threshold of the system... The reference frequency is 2.0. The frequency is 500 Hz, and the correction factor k is 0.2. If the current real-time detected equipment vibration frequency... When the frequency is increased to 600 Hz, the threshold, corrected according to the formula, becomes 2.8. Based on this, the system raises the stability threshold from 2.0 to 2.08, thus employing a more stringent criterion to filter noise when vibration intensifies. The physical meaning of this formula is: when the real-time device vibration frequency... Higher than the reference frequency When this occurs, it indicates that environmental disturbances are intensifying, and the system will automatically raise the stability threshold. This makes the judgment criteria more stringent; conversely, when the vibration weakens, the threshold will be lowered accordingly to avoid being overly conservative and filtering out true effective edges. The corrected threshold is then used. For the initial effective edge event set Each event in the process is re-evaluated. Only when the stability index corresponding to the event is... Greater than or equal to the corrected threshold And its number of jumps Less than or equal to Only then is the event finally confirmed and retained. All events that pass this reconfirmation are combined into a revised set of valid edge events. Subsequently, within the current operating cycle, the system will use the revised threshold. As a stability threshold The value of .

[0050] S2.4: Pseudo-edge timing mark and recording.

[0051] To form a complete processing loop and provide data support for system optimization, it is necessary not only to output valid events but also to record all discarded false edges. The final set of valid edge events is then obtained. And the index of deleted false edge events. A detailed record entry is created for each event determined to be a false edge. These entries are organized into a timestamp matrix. Each row in the matrix corresponds to a pseudo-edge event, containing three key pieces of information: the precise time when the pseudo-edge occurred. The unit is milliseconds; the duration of this pseudo-edge. The time stamp matrix includes a flag to distinguish event types; for false edges, this flag is set to 1, and for valid edges, it is set to 0. This time stamp matrix fully records the time points and characteristics of all excluded signals, providing crucial raw data for subsequent backtracking analysis of possible misjudgments, system performance evaluation, and iterative optimization of control parameters.

[0052] S2.5: Signal filtering and reconstruction output.

[0053] Based on effective edge event set and time stamp matrix The provided information is used to reorganize and optimize the event sequence. A key optimization operation is handling consecutive pseudo-edges that are very close in time. A minimum time interval is set, typically between 0.5 and 2 milliseconds. If multiple flagged pseudo-edge events are found with a time difference less than this set interval, these events are merged and treated as a single burst of noise. In the final output, this is treated as a single, filtered unit, which helps simplify the event sequence and avoid fragmentation. After all these filtering, confirmation, and reorganization operations, a final set of stable edge events is generated. Each element in the set Each event represents a marginal event that has been verified through multiple layers and is considered genuine and reliable, while m represents the total number of stable events that are ultimately retained. This pure and ordered set of events will be passed to subsequent steps for event confirmation delays and multi-level logical judgments.

[0054] In the technical solution of this disclosure, based on in-depth analysis of signal time distribution characteristics, a window-level stability index is calculated and multiple rounds of screening are performed to effectively identify and eliminate false edges caused by mechanical vibration. This method not only relies on a preset fixed threshold for rapid initial filtering but also introduces a dynamic threshold correction mechanism, enabling the judgment criteria to adapt to dynamic changes in vibration frequency, thus enhancing the system's robustness in time-varying environments. Finally, a purified set of valid edge events is output, and the feature information of the eliminated false edges is fully recorded, significantly improving the reliability of signal edge detection and providing a clean and reliable trigger source for the generation of subsequent control commands.

[0055] S3: Perform event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary timing trigger signal.

[0056] The stable edge events after screening are subjected to secondary verification and logical correlation analysis. By introducing a delay confirmation mechanism and multi-level logic template matching, the reliability of the trigger signal is further ensured, and isolated signal edges are transformed into timing action units with clear physical meaning.

[0057] S3.1: Confirm the delay parameter settings.

[0058] To ensure that each edge event is fully confirmed under mechanical vibration conditions, the system provides a stable set of edge events. Each edge event in Calculate a confirmation delay parameter independently The calculation of this delay parameter comprehensively considers both the basic system response and the real-time vibration environment. Specifically, it is achieved through the formula... Perform the calculations. Among them, This represents the basic confirmation delay period, which typically ranges from 2 milliseconds to 10 milliseconds. The specific value is set according to the response speed of the controlled object. For example, for a fast solenoid valve, this value may be smaller, while for a motor with greater inertia, this value may be larger. This represents the equipment vibration period measured in real time by vibration sensors, in milliseconds. It directly incorporates the intensity of environmental disturbance into the calculation. `k` is a delay coefficient, ranging from 0.1 to 0.5, used to control the amplification ratio of the delay to the vibration intensity. If the equipment has multiple operating modes, such as start-up, steady-state operation, and shutdown, the base delay is automatically adjusted based on the received mode switching signal. The values ​​are selected to adapt to reliability requirements under different operating conditions. Finally, a corresponding set of individual delay parameters is generated for all edge events. This set will guide subsequent secondary detection operations.

[0059] In addition, as an optional implementation, a historical acknowledgment success rate is maintained for each input channel. This success rate statistic represents the proportion of recent edge events occurring on this channel that have been ultimately confirmed as valid. The confirmation delay parameter... The calculation formula can be further optimized to ,in, This refers to the historical confirmation success rate of this channel. This is a gain coefficient, ranging from 0.1 to 0.5. When a channel is subjected to long-term vibration interference due to a specific mechanical layout, the success rate is confirmed. When the latency is low, the formula automatically assigns a longer confirmation delay to events on that channel for more stringent screening; conversely, for historical confirmation success rates... Higher-performance channels use shorter delays close to the baseline value to optimize system response speed. This differentiated delay mechanism, based on feedback from the channel's historical performance, enables precise allocation of system delay resources, thereby improving the average response efficiency of the system while ensuring overall trigger reliability.

[0060] S3.2: Secondary detection of signal status.

[0061] After obtaining the acknowledgment delay parameter for each edge event, a secondary detection process for the signal state is initiated. This process is based on a stable set of edge events. and individual delay parameter set For each edge event in the set Record the precise moment it occurs. and the initial level state corresponding to that moment. Then, wait for the corresponding delay time. Upon arrival, immediately At this moment, the signal level of the same input channel is sampled again to obtain the delayed level state. By associating the initial state with the delayed state as two key states, a detection state pair is constructed for each event. All these state pairs are systematically organized into an edge event state matrix. The matrix clearly shows whether the signal state of each event remains consistent after a specific delay, providing a direct basis for the next step of validity confirmation.

[0062] S3.3: Event validity verification and spurious trigger removal.

[0063] Based on edge event state matrix Perform the final validity determination. The core logic of this determination is to check whether the signal level of each edge event remains in its initial state after experiencing the acknowledgment delay. Iterate through each event state pair in the state matrix. For the i-th event, if its initial state... With delayed state If they are completely identical, then the edge event is determined to have remained stable under the disturbance environment, is a genuine and valid trigger signal, and is assigned a validity flag. Conversely, if the signal state after the delay is reversed and differs from the initial state, it indicates that the edge is likely a brief jitter or interference, and is therefore judged as a false trigger, and a validity flag is assigned. After the judgment is completed, a removal operation is performed, retaining only all validity markers. Events with a value of 1. These confirmed events are reorganized into a new set of confirmed events with higher purity. This sub-step, through a delayed reconfirmation mechanism, effectively eliminates transient disturbances that, although passing stability index screening, cannot maintain their state over the duration, further improving the reliability of the event set.

[0064] S3.4: Logical sequence matching and template comparison.

[0065] After confirming the validity of the events, the system's focus shifts from verifying the authenticity of individual events to analyzing the logical relationships between multiple events. It then receives a set of confirmed events. First, the events are sorted in ascending order according to their timestamps to form an event timeline vector. Next, the timing vector is compared with internally pre-stored multi-level logic templates. These logic templates define valid control behavior patterns and are stored in non-volatile memory as lookup tables. For example, a logic template for identifying spindle startup is defined as follows: the template contains an event sequence pattern [rising edge, falling edge], and requires that the time interval Δt between adjacent events satisfies 10 milliseconds < Δt < 100 milliseconds. The timing vector is then scanned sequentially. Calculate the time interval between adjacent events in the sequence. The system determines whether the event type combinations and their corresponding time intervals in an event sequence meet the matching conditions of any preset logical template. The calling and matching rules are as follows: The system scans the time sequence vector in chronological order, attempting to match consecutive events with all pre-stored templates. During matching, both the event type sequence and the time interval must meet the template requirements to be considered a valid logical event group. If, after scanning, no event sequence matches a pre-stored template, the system will not generate a trigger signal and will record the characteristics of the unmatched event sequence. This record can be used for subsequent system diagnostics or template library expansion and optimization. If a consecutive event subsequence is found to completely match the definition of a template, these independent events are determined to constitute a valid logical combination and identified as a logical event group. Finally, all identified logical event groups are output as logical event sequences. This step is a crucial one in realizing the transformation from low-level signal changes to high-level control behaviors.

[0066] S3.5: Initial timing trigger signal generation.

[0067] logical event sequence This is transformed into specific trigger signals that can be used by subsequent steps. A corresponding initial timing trigger signal is generated for each identified logical event group. Each trigger signal contains three key attributes: trigger start time, trigger end time, and event type. The trigger start time is typically taken from the timestamp of the first event in that logical event group. The trigger end time is taken from the timestamp of the last event in this logical event group. The event type directly records the matched template number. The generation rule for the trigger signal can be expressed as follows: Where k is the index of the logical event group. After generation, these preliminary timing trigger signals are stored in a dedicated trigger buffer, with each signal accompanied by a validity flag for dynamic correction or execution in subsequent steps. This batch of trigger signal sets This signifies that not only stable edges have been extracted from the original signal, but also meaningful sequences of control actions have been identified, preparing for the final execution control.

[0068] In the technical solution of this disclosure embodiment, the screened stable edge events undergo secondary verification and logical correlation analysis. By independently calculating the confirmation delay for each event and performing secondary signal state detection, transient interferences that pass the initial stability screening but cannot maintain their state over the duration are effectively eliminated. Furthermore, by comparing the confirmed event sequence with a pre-stored multi-level logic template, the system realizes the transformation from isolated signal edges to timing action units with clear physical meaning. This process ensures that the trigger signal is not only reliable at the time point but also conforms to an effective control behavior pattern in the logical sequence, thereby generating a preliminary timing trigger signal with high confidence.

[0069] S4: Collect environmental characteristic data and obtain real-time feedback from the actuator to dynamically correct system judgment parameters.

[0070] Establish a closed-loop control system that can adapt to environmental changes. By collecting operating environment data in real time and dynamically adjusting key decision parameters, the system can maintain optimal trigger sensitivity and reliability over a long period of time, effectively coping with the challenges brought about by equipment aging, changes in operating conditions and environmental fluctuations.

[0071] S4.1: Environmental feature acquisition and real-time feedback.

[0072] The system continuously monitors environmental parameters closely related to signal stability during operation. It receives the preliminary timing trigger signal set obtained in the preceding steps. Whenever a trigger signal is generated, the system initiates an environmental data acquisition cycle to ensure that environmental perception and system actions are synchronized. The acquired data mainly includes three types: First, the mechanical vibration frequency is acquired in real time through accelerometers installed on key parts of the equipment. This frequency directly reflects the intensity of the main interference source, with a typical value range between 5 Hz and 200 Hz; secondly, the ambient temperature of the control system location is obtained through a temperature sensor. Temperature variations can affect the response characteristics of electronic components, and their monitoring range typically covers -20 degrees Celsius to 80 degrees Celsius. Finally, by comparing the control commands sent to the actuator with the actual status feedback signals returned by the actuator, the actuator's response delay time is accurately calculated. This parameter reflects the actual time elapsed from the issuance of the command to the commencement of the action, measured in milliseconds, typically ranging from 0.5 to 10 milliseconds. The system integrates these three types of real-time data into a single environmental feature dataset. This dataset provides the most basic data support for subsequent assessment of the intensity of environmental disturbances.

[0073] S4.2: Quantification and Level Determination of Environmental Disturbance Intensity.

[0074] After obtaining the raw environmental feature data, it is necessary to fuse and calculate this multi-source information to derive a comprehensive disturbance evaluation index. This is based on the environmental feature dataset. The calculation is performed. It uses a comprehensive disturbance index. This index is used to quantify the severity of the current environment. The formula for calculating this index is: ,in, , and These represent the reference vibration frequency, standard ambient temperature, and reference response time determined during the calibration phase of the system, respectively, which define the average operating conditions of the system during normal operation. , and These are weighting coefficients, used to measure the contribution of vibration, temperature, and response delay to the overall disturbance assessment. These coefficients range from 0 to 1 and satisfy the following conditions: The specific values ​​for this condition are set based on the equipment type and control priority. The calculated comprehensive disturbance index... It is a dimensionless numerical value. Based on the magnitude of this index, the system classifies the intensity of environmental disturbances into three distinct levels. .when When it is less than 0.3, it is judged as a low disturbance level; when When the value is between 0.3 and 0.7, it is judged as a medium disturbance level; when... A value greater than or equal to 0.7 is considered a high disturbance level. This level parameter will serve as the direct basis for subsequent dynamic parameter adjustments.

[0075] S4.3: Dynamic correction calculation of stability determination parameters.

[0076] Based on the quantified disturbance level, key decision parameters within the system are adjusted to match the system's behavior to the current environment. Two core parameters are modified: the level hold time threshold. and confirmation delay period The correction calculation follows the formula: as well as .in, and These are the corrected level hold time threshold and the acknowledgment delay period, respectively. It is a disturbance level with a value of 1, 2 or 3. and These are dynamic correction coefficients, which determine the sensitivity of parameters to changes in disturbance level. Their values ​​typically range from 0.05 to 0.2, with the specific value preset based on the equipment type and desired response characteristics. When the next control cycle begins, the corrected level holds for a certain time. It will be used as a threshold for determining signal stability. The new initial values ​​are then used. When the system detects an increase in disturbance level, such as a change from low disturbance to high disturbance, it automatically increases the judgment threshold and delay period. This makes the signal stability criteria more stringent, thereby enhancing the system's robustness against interference in harsh environments. All corrected parameters are then aggregated into a set of corrected parameters. It is waiting to be updated to the running system.

[0077] S4.4: Real-time parameter writing and runtime cycle updates.

[0078] For dynamic corrections to take effect, the new parameters must be safely updated to the system's operating environment. At the end of the current trigger control cycle, the system performs a parameter write operation. It holds the corrected level for the specified time threshold. Confirm delay period and the current system timestamp Together they form a new system operating parameter table. To ensure the system can quickly recover to its most recent valid state after an unexpected power outage, this parameter table is written to non-volatile memory, such as FLASH or EEPROM, in real time. When the next control cycle starts, the system no longer uses the factory default or previous static parameters, but automatically reads this latest table from memory. The table, and its contents and This is used as a new initial decision criterion in the signal processing flow. This enables continuous iteration and optimization of the control parameters across different operating cycles.

[0079] S4.5: Self-calibration feedback and error correction evaluation.

[0080] To further improve long-term adaptation accuracy, the system introduces a calibration step based on execution feedback. This step is initiated after the parameters are updated and the system has been running for a period of time. The system's operating parameter table is then retrieved again. And some execution feedback data from step S3, especially the actual execution time of the trigger signal and the action response delay. Calculate the currently used delay parameters. Actual response delay time of the actuator The deviation between them. To quantify this deviation, the delay error proportion E is calculated using the following formula: This error ratio reflects the gap between the theoretical settings and the actual system performance. An error tolerance is set, for example, 0.2. If the calculated error ratio E is greater than this tolerance, it is determined that the previous parameter correction was insufficient and further adjustments are needed. In this case, the dynamic correction coefficient is automatically recalculated, for example, using... and The coefficients are updated to allow for a larger adjustment step size in the next correction. Using the updated coefficients, a secondary calculation is performed to obtain the final self-calibrated parameter set. Then, it is written back to the storage area. This final step establishes a sophisticated, feedback-based closed-loop self-calibration mechanism, enabling the system to automatically compensate for performance degradation caused by environmental drift or equipment aging during long-term operation, ensuring the long-term stability and accuracy of the trigger decision logic.

[0081] The technical solution of this disclosure establishes a closed-loop control system capable of adapting to environmental changes. It continuously collects multi-source environmental data such as vibration, temperature, and actuator response delay, and comprehensively quantifies and classifies the disturbance intensity. Based on this, core judgment parameters such as the level hold time threshold and acknowledgment delay period are dynamically corrected, and safe iteration and updating of parameters are achieved between different operating cycles. Furthermore, the introduced self-calibration mechanism based on execution feedback can automatically compensate for performance degradation caused by environmental drift or equipment aging, enabling the system to autonomously maintain optimal trigger sensitivity and reliability over long periods, effectively addressing various long-term operational challenges.

[0082] S5: Based on the preliminary timing trigger signal and actuator feedback, generate control commands and verify the execution results.

[0083] The trigger signals, which have undergone rigorous screening and logical verification through the aforementioned steps, are reliably transformed into actual control actions. The accuracy and reliability of the system output are ensured through closed-loop verification of the execution results, thereby forming a complete control closed loop.

[0084] S5.1: Control command generation and action mapping.

[0085] The system is based on the final threshold parameter set generated after self-calibration. and the generated timing event trigger signal set Then, it begins generating specific control commands. Among them, Includes the level hold time threshold after environmental adaptive adjustment. and confirmation delay period These parameters ensure the robustness of the command generation process. The system analyzes each trigger signal. This signal contains the trigger start time. Trigger end time and logical event types Based on the event type, the system maps it to a predefined control action. For example, a rising edge triggered event (type 1) might be mapped to starting a motor, while a falling edge triggered event (type 2) might be mapped to closing a valve. Each control command is formatted as a structure. .in, It is a unique number for the target execution unit, used to identify a specific motor, valve, or relay; It specifies the type of control action, such as turning on, turning off, pulse width modulation, or direction control; This is the scheduled execution timestamp of the command, usually taken directly from the trigger signal. During command generation, parameters must be strictly followed. The specified minimum action duration is managed through a timer mechanism to prevent frequent start-stop of the execution unit due to signal jitter or short-term repeated triggering, thereby protecting the equipment and improving control quality. All generated control commands are aggregated to form a control command set. And pass it on to the next stage of execution.

[0086] S5.2: Execution unit drive and action completion detection.

[0087] According to the control command set The corresponding execution units are driven one by one through the underlying driver circuit. When the command is scheduled to execute... Upon arrival, the system's output module generates a corresponding level signal or pulse signal, which acts on the designated execution unit, such as sending an enable signal to the motor driver or energizing the relay coil. To confirm whether the action has been executed correctly, the system waits for a final delay parameter after the drive command is issued. During the stable control period, sufficient time is ensured for mechanical or electrical actions to complete and reach a steady state. Afterward, the actual state of the actuator is acquired through a sensor feedback loop, such as reading valve limit switch signals or detecting whether a motor is actually running using a current sensor. The system compares the acquired actual state with the desired command state to generate the execution feedback result. In this structure, It is the state that the command expects to achieve. This refers to the actual state reported by the sensor. To ensure the reliability of the detection results, multiple samples are taken within a short detection cycle. If the actual state detected three times consecutively remains consistent and matches the expected state, the detection of this drive and action is considered successful.

[0088] S5.3: Execution result verification and exception backtracking.

[0089] Execution feedback result set Final verification is the last line of defense to ensure system reliability. The system compares each feedback result with the expected state. Compared with the actual state For each execution unit, verify the conclusions. The determination rule is as follows: if and only if and If all verification results are completely consistent, the execution is considered successful and marked as PASS; otherwise, it is considered a failure and marked as FAIL. All verification results are compiled into a verification result set. Once a FAIL result is reached, the system will immediately trigger the exception backtracking mechanism. This mechanism will automatically record detailed fault information, including the execution unit number. The system records the fault timestamp and the deviation between the expected and actual states. Subsequently, the system calls the timing determination module in step S3 to re-analyze the original event logic associated with the failed command, checking for any misjudgments in the entire chain from signal triggering to command generation. Simultaneously, this failure record is sent to the system's parameter learning module as a basis for future dynamic adjustment of threshold parameters. and This provides a reference for the system, enabling continuous self-optimization. For successfully verified operations, the system confirms their completion and uses the final triggering and execution results as the successful output of this control loop, while also providing a reference benchmark for the next round of system operation.

[0090] In the technical solution of this disclosure embodiment, trigger signals that have undergone rigorous screening and logical verification are reliably mapped to specific control commands and drive the execution unit's actions. By comparing the expected state of the command with the actual state fed back by the sensors, the system completes the final verification of the execution result and anomaly backtracking. This closed-loop verification mechanism constitutes the last line of defense for system reliability, ensuring the accuracy and authenticity of the control action output. Simultaneously, any execution failure is recorded and fed back to the parameter learning module, thus forming a complete, continuously self-optimizing control closed loop, ultimately ensuring the overall accuracy and reliability of the automated operation of industrial equipment.

[0091] In summary, this invention extracts the temporal distribution characteristics of signals under vibration environments through multi-cycle sampling and time window division, and constructs a dynamic stability judgment threshold accordingly. This threshold can adaptively adjust with vibration frequency and intensity, thereby automatically increasing the strictness of the judgment criteria to filter out noise under strong disturbances and decreasing the criteria to quickly respond to the real signal under weak disturbances, fundamentally overcoming the dilemma of traditional methods between filtering interference and response lag. Addressing the problem of false triggering caused by vibration spikes, the invention not only performs initial screening of signal stability but also introduces independent delay confirmation and secondary state detection for each edge event, effectively eliminating transient false edges. Simultaneously, by matching the confirmed event sequence with pre-stored multi-level logic templates, the system can identify timing action units conforming to predetermined control logic from isolated edge events, thereby elevating trigger judgment from a single level transition to the logic sequence level, significantly reducing the probability of false triggering and pseudo-instructions. Real-time acquisition of multi-source environmental data such as vibration, temperature, and actuator response delay quantifies the intensity of environmental disturbances and dynamically corrects the system's core judgment parameters accordingly. Furthermore, by comparing commands and execution feedback, the system can evaluate the effectiveness of parameter settings and perform self-calibration, forming a closed loop of continuous optimization. This mechanism enables the system to maintain high trigger sensitivity and reliability over long periods without manual intervention, significantly improving its adaptability and robustness in complex industrial environments.

[0092] According to embodiments of this disclosure, an electronic device is also provided, which may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other via the communication bus. The processor can invoke logical instructions stored in the memory to execute the methods described above.

[0093] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] On the other hand, this disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the methods provided by the above methods.

[0095] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0096] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0097] It should be understood that the above embodiments are only used to illustrate the technical solutions of this disclosure, and not to limit them; although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure.

Claims

1. An industrial microcontroller automatic control method based on timing event triggering, characterized in that, The method includes: S1 performs multi-cycle sampling and time window division on the raw signals of industrial field sensors or switch inputs to extract the signal time distribution characteristics; S2, based on the signal time distribution characteristics, determine signal stability and eliminate false edges, and output a set of stable edge events; S3, perform event confirmation delay and multi-level logic judgment on the stable edge event set to generate a preliminary timing trigger signal; S4 collects environmental characteristic data and obtains real-time feedback from the actuator to dynamically correct system judgment parameters; S5. Based on the preliminary timing trigger signal and actuator feedback, generate control commands and verify the execution results.

2. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 1, characterized in that, Step S3 includes: For each edge event in the set of stable edge events, its acknowledgment delay parameter is calculated independently. The acknowledgment delay parameter combines the basic acknowledgment delay period and the equipment vibration period. After their respective delays, the corresponding signal states are checked a second time, and the validity of the event is confirmed based on the comparison between the initial state and the delayed state. The confirmed valid events are arranged in chronological order and matched with the pre-stored multi-level logic templates to identify logical event groups that conform to the predetermined control behavior pattern. Based on the identified logical event groups, a preliminary set of timing trigger signals containing trigger time attributes and event types is generated.

3. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 2, characterized in that, The matching with the pre-stored multi-level logic template includes: The events that are confirmed to be valid are arranged in chronological order to form an event timeline vector. Scan the event time vector in chronological order to extract consecutive event sequences. The extracted event sequence is compared with the pre-stored logic template. If and only if the event type combination of the event sequence is consistent with the event type sequence defined in the pre-stored logic template, and the time interval between adjacent events in the event sequence meets the time interval requirement defined in the pre-stored logic template, the event sequence is determined to be a successful match and identified as a valid logical event group. If there is no event sequence in the event timing vector that can be successfully matched with any of the pre-stored logic templates, then no trigger signal corresponding to the event timing vector is generated, and the feature data of the unmatched event sequence is recorded.

4. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 1, characterized in that, Step S1 includes: Based on the mechanical operating cycle and vibration characteristics of the equipment, initialize the sampling period, number of sampling points and time window; Perform multi-cycle sampling to obtain a level state matrix, and based on the matrix, count the number of signal transitions and the average duration of the level in each sampling cycle; Based on the time window, the number of transitions and the average duration of the level for all sampling periods within the window are aggregated and statistically analyzed to obtain the average number of transitions and the average duration of the level for each window. Based on the average number of transitions and the average level duration, the stability level of each time window is determined, and a structured signal time distribution feature set is generated.

5. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 1, characterized in that, Step S2 includes: Based on the aforementioned signal time distribution characteristics, the window-level stability index for each time window is calculated; The window-level stability index is compared with the stability threshold and the maximum allowed number of transitions to perform preliminary screening and form a preliminary set of valid edge events. Based on the real-time detected equipment vibration frequency, the stability threshold is dynamically corrected, and the corrected threshold is used to reconfirm and filter the preliminary effective edge event set. Output the set of stable edge events filtered by the dynamic threshold and record the false edge features that were removed.

6. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 5, characterized in that, The dynamic correction stability threshold is adopted using the following formula: ,in, This is the corrected stability threshold. This is the initial stability threshold. For real-time equipment vibration frequency, The reference frequency is denoted by k, which is a correction factor ranging from 0.1 to 0.

3.

7. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 1, characterized in that, Step S4 includes: Real-time acquisition of environmental data, including mechanical vibration frequency, ambient temperature, and actuator response delay time; Based on the collected environmental data, a comprehensive disturbance index is calculated and the environmental disturbance level is classified accordingly. Based on the environmental disturbance level, the system's level hold time threshold and acknowledgment delay period are dynamically adjusted. The corrected parameters are updated to the system operating environment, and self-calibration is performed based on the deviation between the actual actuator response and the command to optimize the corrected parameters.

8. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 7, characterized in that, The comprehensive disturbance index is calculated using a weighted fusion method, and the specific formula is as follows: ,in, For real-time equipment vibration frequency, For ambient temperature, For actuator response delay, , and These represent the reference vibration frequency, standard ambient temperature, and reference response time determined during the system calibration phase, respectively. , and These are the weighting coefficients.

9. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 1, characterized in that, Step S5 includes: The initial timing trigger signal is mapped into a control command that includes the target execution unit, action type, and planned execution time; The drive execution unit executes the control command and detects the completion status of the action through the sensor after a preset delay. The execution results are verified by comparing the expected state of the instruction with the actual feedback state, and backtracking analysis is performed on abnormal execution. The failure record is fed back to the system parameter learning module for subsequent optimization of control parameters.

10. A computer storage medium, characterized in that, It stores a computer program, which, when executed, implements the industrial single-chip microcomputer automatic control method based on timing event triggering according to any one of claims 1-9.

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