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

Abstract

The present disclosure provides an industrial single-chip automatic control method based on timing event triggering, which comprises multi-cycle sampling and time window division of original signals of industrial field sensors or on-off input, and extraction of signal time distribution characteristics; signal stability determination and false edge elimination based on the signal time distribution characteristics, output of a stable edge event set; event confirmation delay and multi-level logic determination of the stable edge event set, generation of a preliminary timing trigger signal; collection of environmental feature data and acquisition of real-time feedback of an actuator, dynamic correction of system determination parameters; generation of a control command and verification of execution results based on the preliminary timing trigger signal and the actuator feedback, which can dynamically distinguish real signals from noise under vibration interference, while ensuring system real-time performance and trigger reliability, and solving the problems of insufficient edge detection stability and false trigger caused by mechanical vibration.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of industrial automation control, and in particular to an industrial single-chip microcomputer automatic control method based on time sequence event triggering. BACKGROUND

[0002] In large industrial equipment such as numerical control machine tools and hydraulic servo platforms, industrial single-chip microcomputers trigger control instructions by detecting the jump edges of sensor output signals, which is the core technology for realizing automatic operation of the equipment. The ideal signal edge is clear and stable, and can be reliably recognized by the single-chip microcomputer. However, strong vibrations caused by motor start-stop and mechanical impact are common in actual industrial sites, and such vibrations can interfere with sensors and their lines, resulting in a large number of short-lived level glitches mixed in the output digital signal.

[0003] The existing technology mainly relies on hardware filtering circuits or software fixed delays to deal with this problem. Hardware filtering will delay the signal response speed and reduce the control real-time performance; software fixed delay is faced with a dilemma, that is, too short delay cannot effectively filter out the interference, and too long delay will lag the response to the real action, affecting the control accuracy. Both methods use static thresholds and cannot adapt to the dynamic changes of vibration intensity and frequency.

[0004] When the scale of the control system is expanded and the number of monitoring points is increased, the real edges and vibration glitches in the mass of signals are mixed with each other, and the traditional centralized processing and fixed threshold criterion mode will cause the system delay to increase significantly, and the false triggering and instruction lag phenomenon occurs frequently. This not only restricts the production efficiency and processing accuracy, but also may cause safety risks of the equipment in high-speed and 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 triggering reliability of the system, to solve the long-standing technical problem of insufficient edge detection stability and false triggering phenomenon caused by mechanical vibration. SUMMARY

[0006] In view of this, in order to solve the problems brought by the prior art, the present application provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering.

[0007] In the first aspect, the present disclosure provides an industrial single-chip microcomputer automatic control method based on time sequence event triggering, which comprises:

[0008] S1, performing multi-cycle sampling and time window division on the original signal of the industrial field sensor or on-off input, and extracting the signal time distribution feature;

[0009] S2, performing signal stability determination and false edge elimination based on the signal time distribution feature, and outputting a stable edge event set;

[0010] S3, event validation delay and multi-stage logic determination on the stable edge event set, generating preliminary time sequence trigger signal;

[0011] S4, collecting environmental feature data and obtaining real-time feedback of actuators, dynamically correcting system determination parameters;

[0012] S5, generating control command and verifying execution result based on the preliminary time sequence trigger signal and actuator feedback.

[0013] Optionally, the step S3 comprises:

[0014] For each edge event in the stable edge event set, its validation delay parameter is independently calculated, which integrates the basic validation delay period and the device vibration period;

[0015] After the respective delay ends, the corresponding signal state is detected again, and the event validity is confirmed based on the comparison result of the initial state and the state after the delay;

[0016] The events confirmed as valid are arranged in chronological order, matched with the pre-stored multi-stage logic template, and the logic event group conforming to the predetermined control behavior mode is identified;

[0017] Based on the identified logic event group, a preliminary time sequence trigger signal set containing trigger time attribute and event type is generated.

[0018] Optionally, the matching with the pre-stored multi-stage logic template comprises:

[0019] The events confirmed as valid are arranged in chronological order to form an event time sequence vector;

[0020] The event time sequence vector is scanned in chronological order, and continuous event sequences are extracted in turn;

[0021] The extracted event sequence is compared with the pre-stored logic template. When and only when 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 by the pre-stored logic template, it is determined that the event sequence matches successfully, and is identified as a valid logic event group;

[0022] If there is no event sequence in the event time sequence vector that can successfully match any of the pre-stored logic templates, no trigger signal corresponding to the event time sequence vector is generated, and the feature data of the unmatched event sequence is recorded.

[0023] Optionally, the step S1 comprises:

[0024] Based on the mechanical operation cycle and vibration characteristics of the device, the sampling period, the number of sampling points and the time window are initialized;

[0025] Multi-cycle sampling is performed to obtain a level state matrix, and the number of signal transitions and the average duration of levels in each sampling cycle are counted based on the matrix;

[0026] According to the time window, the number of transitions and the average duration of levels in all sampling cycles within the window are aggregated and counted to obtain the average number of transitions in each window and the average duration of levels in each window;

[0027] Based on the average number of transitions and the average duration of levels, the stability level of each time window is determined, and a structured signal time distribution feature set is generated.

[0028] Optionally, the step S2 comprises:

[0029] Based on the signal time distribution feature, the window-level stability index of each time window is calculated;

[0030] The window-level stability index is compared with the stability threshold and the maximum allowed number of transitions, and preliminary screening is performed to form a preliminary set of valid edge events;

[0031] According to the real-time detected device vibration frequency, the stability threshold is dynamically corrected, and the preliminary set of valid edge events is re-confirmed and screened using the corrected threshold;

[0032] The set of stable edge events screened by the dynamic threshold is output and the rejected pseudo-edge features are recorded.

[0033] Optionally, the dynamic correction of the stability threshold uses the following formula:

[0034] wherein, is the corrected stability threshold, is the initial stability threshold, is the real-time device vibration frequency, is the reference frequency, and k is a correction coefficient with a value range of 0.1 to 0.3.

[0035] Optionally, the step S4 comprises:

[0036] Real-time acquisition of environmental data including mechanical vibration frequency, ambient temperature and actuator response delay time;

[0037] Based on the collected environmental data, a comprehensive disturbance index is calculated and the environmental disturbance level is divided accordingly;

[0038] According to the environmental disturbance level, dynamically correct the level holding time threshold value and the confirmation delay period of the system;

[0039] The corrected parameters are updated to the system operating environment, and self-calibration is performed based on the deviation of the actual response of the actuator from the instruction to optimize the correction parameters.

[0040] Optionally, the calculation of the comprehensive disturbance index adopts a weighted fusion method, and the specific formula is:

[0041] , wherein, is the real-time device vibration frequency, is the environmental temperature, is the actuator response delay, , and respectively represent the reference vibration frequency, the standard environmental temperature and the reference response time determined by the system in the calibration stage, , and are weight coefficients.

[0042] Optionally, the step S5 comprises:

[0043] The preliminary timing trigger signal is mapped into a control command containing a target execution unit, an action type and a planned execution time;

[0044] The execution unit is driven to execute the control command, and after a preset delay, the action completion state is detected through sensor feedback;

[0045] By comparing the expected state of the instruction with the actual feedback state, the execution result is verified and abnormal execution is analyzed;

[0046] The execution failure record is fed back to the system parameter learning module for optimization of subsequent control parameters.

[0047] In a second aspect, the disclosure provides a computer storage medium storing a computer program, which is executed to implement the method of the first aspect.

[0048] The disclosure has the following advantages compared with the prior art:

[0049] 1) By multi-cycle sampling and time window division, the time distribution characteristics of the signal in the vibration environment are extracted, and a dynamic stability judgment threshold is constructed accordingly. This threshold can adaptively adjust with the vibration frequency and intensity, so as to automatically improve the criterion strictness to filter out noise in strong disturbance, and reduce the criterion to quickly respond to the real signal in weak disturbance, thus fundamentally overcoming the dilemma between filtering interference and response lag in traditional methods.

[0050] 2) For the problem of false triggering caused by vibration glitches, not only the initial screening of signal stability is carried out, but also an independent delay confirmation and secondary state detection are introduced for each edge event, effectively eliminating the transient false edges. At the same time, by matching the confirmed event sequence with the pre-stored multi-level logic template, the system can identify the timing action unit that meets the predetermined control logic from the isolated edge event, thereby improving the trigger judgment from a single level jump to a logic sequence level, significantly reducing the probability of false triggering and false instruction generation.

[0051] 3) Real-time collection of vibration, temperature and actuator response delay and other multi-source environmental data, quantification of environmental disturbance intensity, and dynamic correction of system core decision parameters. In addition, by comparing the instructions and execution feedback, the system can evaluate the effectiveness of parameter setting and perform self-calibration, forming a continuous optimization closed loop. This mechanism enables the system to maintain high trigger sensitivity and reliability without human intervention for a long time, significantly improving the adaptability and robustness in complex industrial environments. BRIEF DESCRIPTION OF DRAWINGS

[0052] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, further serve to explain the principles of the present disclosure.

[0053] Figure 1 A flow chart of the industrial single-chip microcomputer automatic control method based on timing event triggering provided by the embodiments of the present disclosure is shown.

[0054] Through the above drawings, the specific embodiments of the present disclosure have been shown, and more detailed descriptions will be given hereinafter. These drawings and written descriptions are not intended to limit the scope of the concept of the present disclosure by any means, but to illustrate the concept of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0055] The present disclosure will be further described below in conjunction with the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present disclosure, and cannot limit the protection scope of the present disclosure.

[0056] The components of the embodiments of the present disclosure generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the claimed present disclosure, but only represents selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present disclosure.

[0057] Hereinafter, the terms "include", "have", and their conjugates, which are used in various embodiments of the present application, merely indicate the presence of the features, numbers, steps, operations, elements, components, or combinations thereof, and do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

[0058] Unless defined otherwise, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present application belong. Terms such as those defined in a generally used dictionary will be interpreted to have the same meaning as those in the context of the relevant art and will not be interpreted to have idealized or overly formal meanings, unless clearly defined in various embodiments of the present application.

[0059] Figure 1 The flowchart of the industrial single-chip automatic control method based on timing event triggering provided in the embodiment of the present disclosure will be described in detail in the embodiment, taking the spindle of a high-speed numerical control machine tool as the target device controlled by the industrial single-chip. Figure 1 As shown in the figure, the method can include the following steps:

[0060] S1: Multi-cycle sampling and time window division are performed on the original signals of the industrial field sensors or switch quantity inputs, and signal time distribution characteristics are extracted.

[0061] Multi-cycle sampling and time characteristic analysis are performed on the original signals of the industrial field sensors or switch quantity inputs, and a time window model matched with the vibration characteristics of the device is established, thereby laying a foundation for subsequent signal stability determination.

[0062] S1.1: Sampling initialization and parameter setting.

[0063] According to the type of the target device and the control accuracy requirement thereof, the timing parameters required for signal sampling are initialized. The core parameters include sampling period which is calculated by the formula wherein represents the sampling frequency. Taking the spindle of a high-speed numerical control machine tool as an example, the sampling frequency can be set to 20 kHz, and the corresponding sampling period is 0.05 ms. At the same time, the inherent mechanical running period of the device needs to be obtained, such as the mechanical running period of the spindle of a high-speed numerical control machine tool with a speed of 12000 rpm, which is 5 ms. Based on this, the formula The number of sampling points required in a complete mechanical cycle is calculated The number of sampling points of the high-speed CNC machine tool spindle in a mechanical cycle The calculation is 100 points. To ensure the absolute constancy of the sampling interval, the system's sampling control module will be based on the calculated Generate high-precision timing interrupt signals to drive the entire sampling process. This step establishes an accurate time scale and synchronization reference for the entire signal acquisition process.

[0064] At the same time, the system initializes the following key system parameters, which will be dynamically adjusted according to environmental feedback during operation:

[0065] Level holding time threshold : As the reference time threshold for signal stability determination, its initial value is determined through field experiments and is usually in the range of 1 to 5 milliseconds. This parameter will be used as the initial value and adjustment reference for the stability threshold in the subsequent signal stability determination link.

[0066] Basic confirmation delay period : The basic value for event confirmation delay calculation, the initial value range is 2 to 10 milliseconds. This parameter is used as a reference for calculating individual delays in subsequent steps.

[0067] S1.2: Multi-cycle signal sampling and level state recording.

[0068] After completing the parameter initialization, enter the multi-cycle signal sampling phase. This process is based on the sampling period and the number of sampling points determined in the previous steps to continuously and periodically capture the state of the input signal. At the arrival of each sampling period, the signal of the sensor input channel is read and its level state is recorded in binary form, where 0 represents low and 1 represents high. All these recorded state data are organized into a two-dimensional level state matrix . In this matrix, index i represents the sampling point number in a mechanical cycle, with a value range from 1 to ; index j represents the cycle number of continuous sampling, with a total cycle number usually set between 10 and 50 according to the system's demand for vibration statistical characteristics. Each element in the matrix clearly records the signal state at the i-th sampling time in the j-th sampling cycle. By continuously collecting a complete mechanical cycle, the system can obtain enough samples, so that the statistical characteristics of the signal under mechanical vibration interference can be fully revealed.

[0069] S1.3: Level duration and transition frequency statistics.

[0070] After obtaining the multi-period level state matrix, the quantification analysis of the signal characteristics in each independent period is started. This process is achieved by traversing each column of the matrix, i.e., each sampling period. For a specified period j, the number of signal level changes in this period is accurately counted, denoted as the transition number . Then, the average duration of the signal level in this period is calculated using the formula = 0, ). The average duration is a key indicator that intuitively reflects the ability of the signal to remain stable within a unit transition interval. After traversing all periods, two important characteristic sequences are obtained: one is the transition number sequence , and the other is the duration sequence . The more transitions, the more intense the vibration interference the signal in this period is usually subjected to; while the shorter the average duration, the more unstable the signal is, and the more likely it is to produce glitches. These two sequences provide the core data basis for the next step of time window division based on the device vibration rhythm.

[0071] S1.4: Time window division based on vibration characteristics.

[0072] In order to synchronize the signal analysis with the physical vibration law of the device, it is necessary to divide the time axis into a series of time windows associated with the vibration period. First, the base window period is determined according to the inherent vibration frequency of the device, and its calculation formula is . For example, the typical vibration frequency of the spindle of a high-speed numerical control machine tool is 500 Hz, and its base window period is 2 milliseconds. After dividing the window, the previously obtained characteristic sequences are aggregated and counted in each time window. Specifically, the average level duration and the average transition number of all periods contained in each window are calculated. The average level duration is calculated by the formula , i.e., the average duration of all sampling periods falling into this time window is summed up and then divided by the number of periods contained in the window . Similarly, the average transition number is calculated by the formula , i.e., the transition number of all sampling periods in this window is summed up.​​​​ summed and divided by wherein, denotes the number of complete sampling periods contained within this particular time window. These average values reflect the overall stability characteristics of the signal within each time window, and the are calculated for all time windows. and multi-window comparisons are made, a macroscopic distribution curve of signal stability in the time dimension can be formed, revealing the overall impact of vibration on the signal at different stages.

[0073] S1.5: Signal time distribution feature extraction and output.

[0074] The stability parameters of each time window are comprehensively refined and formatted for output. The average duration , average jump count for each window are calculated and evaluated, and a stability level is assigned to it according to the pre-set rules . This level is usually divided into high, medium and low stability according to the ratio of and or the numerical interval it is in, so that continuous features are converted into discrete, easy-to-distinguish state identifiers. For example, when is high and is low, it is determined to be high stability level; conversely, if is low and is high, it is determined to be low stability level. Finally, all these features are organized into a structured signal time distribution feature set , which fully characterizes the behavior of the input signal at different vibration phases. The system stores this feature set in a specific data buffer and passes it as vital input data to the subsequent signal stability determination and pseudo-edge elimination module, thus completing the entire transformation process from raw signal to feature information.

[0075] In the technical solution of the embodiments of the present disclosure, a time window model matched with the vibration characteristics of the device is established by multi-cycle sampling and time feature analysis of the original signal in the industrial field. 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 features of the signal at different vibration phases, an effective transformation from continuous time domain to discrete feature information is realized, enabling the stability performance of the signal to be synchronized with the physical vibration law of the device, thereby providing a reliable data basis for accurately distinguishing between real signals and vibration noise.

[0076] S2: Signal stability determination and pseudo-edge elimination based on the signal time distribution features, outputting a stable edge event set.

[0077] Further analysis is performed on the signal time distribution features extracted in step S1. Based on the stability performance of the signal within the time window, false edges caused by mechanical vibration are identified and removed. Finally, a set of purified valid edge events is output.

[0078] S2.1: Stability index calculation.

[0079] The signal time distribution feature set from step S1.5 is received , which contains the average duration , average number of jumps , and stability level of each time window. In order to convert the multi-dimensional features into a comprehensive scalar that is convenient for unified threshold judgment, the system calculates a window-level stability index for each time window. This index is calculated by the formula . In this formula, the numerator represents the average ability of the signal to remain stable within the window, and a larger value generally means better stability; the denominator introduces the influence of the number of jumps, where the addition of 1 is to avoid the mathematical undefined case of a denominator of zero when the number of jumps is zero. The larger the number of jumps , the larger the denominator value, resulting in a decrease in the stability index , which accurately reflects the negative impact of frequent jumps on signal stability. After calculation, the stability indices of all time windows are arranged in chronological order to form 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 false edge identification.

[0080] S2.2: False edge identification and preliminary removal.

[0081] Based on the generated stability index sequence , the system begins the first round of false edge filtering. This process relies on a pre-set stability threshold , which is initially taken from the system parameter , defining the minimum duration reference required for a signal edge to be considered stable, with a value range of 1 to 5 milliseconds, and the specific value determined by field vibration amplitude experiments. The stability index of each time window in the sequence is compared with the threshold . At the same time, the average number of jumps of each window is compared with a pre-set maximum allowed number of jumps Comparison is made, The range of the value is usually 5 to 20 times per window period. The specific determination rule is: for any time window, if its stability index is less than the threshold , or its average jump number is greater than the maximum allowed value , as long as any one of the two conditions is met, it is determined that the signal change occurring in the window is unreliable false edge. Delete these signal events marked as false edges from the original signal sequence. After this round of screening, all the un-deleted, initially considered stable edge events are collected to form a set of preliminary valid edge events This step realizes the rapid and preliminary filtering of unstable signals, effectively shielding most high-frequency jitter caused by vibration.

[0082] S2.3: Dynamic threshold correction and reconfirmation.

[0083] In order to deal with the situation that the vibration frequency of the mechanical system may change dynamically during operation, the system introduces a dynamic threshold adjustment mechanism to enhance its adaptability to time-varying environment. This step obtains the aforementioned set of preliminary valid edge events , and some environmental reference parameters from step S1, mainly the current detected device vibration frequency and the time window period . According to the real-time vibration frequency, the stability threshold is adaptively corrected. The correction formula is In this formula, is the new threshold after dynamic correction; is the initial fixed threshold; is the current real-time detected device vibration frequency; is the reference frequency determined by the system in the calibration stage; k is a correction coefficient, whose value range is between 0.1 and 0.3, usually determined by the system's self-learning module according to historical data. For example, the initial stability threshold of the system is 2.0, the calibration reference frequency is 500 Hz, and the correction coefficient k is 0.2. If the current real-time detected device vibration frequency rises to 600 Hz, then the threshold after correction according to the correction formula is 2.8. According to this, the system raises the stability threshold from 2.0 to 2.08, so that when the vibration intensifies, it uses stricter criteria to filter out noise. The physical meaning of this formula is that when the real-time device vibration frequency is higher than the reference frequency , it indicates that the environmental disturbance is intensified, and the system will automatically raise the stability threshold so that the criterion becomes more stringent; on the contrary, when the vibration is weakened, the threshold will be reduced accordingly to avoid being too conservative to filter out the real effective edges. With the revised threshold , each event in the preliminary effective edge event set is re-judged. Only when the stability index of the event is greater than or equal to the revised threshold , and the number of jumps is less than or equal to , the event is finally confirmed to be retained. All events that pass this re-confirmation are combined into the revised effective edge event set . Thereafter, in the current running cycle, the system will use the revised threshold as the value of the stability threshold .

[0084] S2.4: Pseudo-edge time point marking and recording.

[0085] In order to form a complete processing loop and provide data support for system optimization, not only the effective events need to be output, but also all the rejected pseudo-edges need to be recorded. The final effective edge event set and the deleted pseudo-edge event index are obtained. A detailed record entry is created for each event that is judged as a pseudo-edge. These entries are organized into a time marking matrix . Each row in the matrix corresponds to a pseudo-edge event, containing three key information: the exact time of the occurrence of the pseudo-edge, in milliseconds; the duration of the pseudo-edge ; and a flag bit flag for distinguishing event types, which is set to 1 for pseudo-edges and 0 for effective edges. This time marking matrix records all the time points and characteristics of the excluded signals, providing essential raw data for subsequent possible misjudgment backtracking analysis, system performance evaluation, and iterative optimization of control parameters.

[0086] S2.5: Signal screening and recombination output.

[0087] Based on the effective edge event set and the time marking matrix The provided information optimizes the reorganization of the event sequence. An important optimization operation is to handle those consecutive pseudo edges that are very close in time. A minimum time interval is set, which is usually between 0.5 milliseconds and 2 milliseconds. If multiple pseudo edge events marked are found, the time difference between them is less than the set interval, these events are combined as a burst of a single noise event, and in the final output it is considered as a single unit filtered out, which helps to simplify the event sequence and avoid fragmentation. After completing all these screening, confirmation and reorganization operations, the final stable edge event set is generated Each element in the set represents a verified and reliable edge event, and m is the total number of final stable events retained. This pure and ordered event set will be passed to the subsequent step for event confirmation delay and multi-level logic decision.

[0088] In the technical solution of the embodiments of the present disclosure, based on the in-depth analysis of the signal time distribution characteristics, the pseudo edges caused by mechanical vibration are effectively identified and removed by calculating the window level stability index and performing multiple rounds of screening. This method not only relies on the pre-set fixed threshold for rapid preliminary filtering, but also introduces a dynamic threshold correction mechanism, so that the decision standard can adapt to the dynamic changes of the vibration frequency, and the robustness of the system in the time-varying environment is enhanced. Finally, a purified effective edge event set is output, and the characteristic information of the removed pseudo edges is recorded, which significantly improves the reliability of the signal edge detection and provides a pure and reliable trigger source for the generation of subsequent control instructions.

[0089] S3: Event confirmation delay and multi-level logic decision are performed on the stable edge event set to generate a preliminary timing trigger signal.

[0090] The stable edge events after screening are verified and logically associated for a second time, and through the introduction of delay confirmation mechanism and multi-level logic template matching, the reliability of the trigger signal is further ensured, and the isolated signal edge is converted into a timing action unit with clear physical meaning.

[0091] S3.1: Confirmation delay parameter setting.

[0092] To ensure that each edge event is fully confirmed in the mechanical vibration environment, the system independently calculates a confirmation delay parameter for each edge event in the stable edge event set . The calculation of this delay parameter considers the basic system response and the real-time vibration environment. Specifically, it is calculated by the formula . Wherein, The representative base confirmation delay period, usually ranging from 2 ms to 10 ms, is set according to the response speed of the controlled object. For example, the value may be smaller for a fast electromagnetic valve, and larger for a motor with large inertia. The device vibration period measured by the vibration sensor in real time, with a unit of milliseconds, directly introduces the environmental disturbance intensity into the calculation. k is a delay coefficient, with a value ranging from 0.1 to 0.5, used to control the amplification ratio of the delay with the vibration intensity. If the device has multiple operating modes such as starting, steady-state running and stopping, the value of the base delay is automatically adjusted according to the received mode switching signal to adapt to the reliability requirements in different operating modes. Finally, a corresponding individual delay parameter set is generated for all edge events , which will guide the subsequent secondary detection operation.

[0093] In addition, as an optional embodiment, a historical confirmation success rate is maintained for each input channel , which statistics the proportion of edge events on this channel that are finally confirmed valid in the near future. The calculation formula of the confirmation delay parameter can be further optimized as , where is the historical confirmation success rate of the channel, and k is a gain coefficient, with a value ranging from 0.1 to 0.5. When a certain channel is long-term disturbed by vibration due to specific mechanical layout, and the confirmation success rate is low, this formula will automatically assign a longer confirmation delay for the events on this channel for more stringent screening; on the contrary, for channels with a higher historical confirmation success rate , a shorter delay close to the base value is used to optimize the system response speed. This differentiated delay mechanism based on the feedback of the channel's own historical performance realizes the precise allocation of system delay resources, thereby improving the average response efficiency of the system while ensuring the overall trigger reliability.

[0094] S3.2: Secondary detection of signal state.

[0095] After obtaining the confirmation delay parameter of each edge event, the secondary detection process of the signal state is started. This process is based on the stable edge event set and the individual delay parameter set . For each edge event in the set, record the exact time of its occurrence and the initial level state corresponding to this time. Then, after waiting for the corresponding delay time to arrive, that is, at This moment, the signal level of the same input channel is sampled again, obtaining the delayed level state . The initial state and the delayed state are associated as two key states, and a detection state pair is constructed for each event . All these state pairs are systematically organized into an edge event state matrix , which clearly shows whether the signal state of each event remains consistent after experiencing a certain delay, providing a direct basis for the next validity confirmation.

[0096] S3.3: Event validity confirmation and false trigger elimination.

[0097] Based on the edge event state matrix , the final validity decision is made. The core logic of this decision is to check whether the signal level of each edge event remains the same as the initial state after experiencing a confirmation delay. Traverse each event state pair in the state matrix. For the i-th event, if its initial state is exactly the same as the delayed state , it is determined that the edge event remains stable in the disturbance environment and is a real and valid trigger signal, and it is assigned a validity flag . Otherwise, if the signal state after the delay is reversed and different from the initial state, it indicates that the edge is likely to be a transient jitter or disturbance, and it is determined to be a false trigger and assigned a validity flag . After the decision is made, the elimination operation is performed, and only events with a validity flag of 1 are retained. These confirmed events are reorganized into a new, higher-purity confirmed event set . This sub-step effectively eliminates those transient disturbances that pass the stability indicator screening but cannot maintain the state within a certain duration through the delay and re-confirmation mechanism, further improving the reliability of the event set.

[0098] S3.4: Logic sequence matching and template comparison.

[0099] After confirming the validity of the events, the system's focus shifts from the authenticity verification of individual events to the logical associativity analysis between multiple events. The confirmed event set is received and first sorted in ascending order according to the event occurrence timestamp to form an event time sequence vector The time sequence is then compared with pre-stored multi-level logic templates. These logic templates define valid control behavior patterns and are stored in a look-up table in non-volatile memory. For example, a logic template for identifying a spindle start-up is defined as follows: the template contains a pattern of event sequences [rising edge, falling edge] and requires that the time interval Δt between adjacent events satisfy 10 ms < Δt < 100 ms. The time sequence is scanned sequentially , and the time interval Δt between adjacent events in the sequence is calculated . The combination of event types and their corresponding time intervals in the event sequence are judged to see if they match any of the pre-set logic templates. The matching rule is as follows: the system scans the time sequence in chronological order and attempts to match consecutive events with all pre-stored templates sequentially. When a match is found, the event type sequence must be consistent and the time interval must meet the template requirements, so as to determine that a valid logic event group has been formed. If no event sequence matches any of the pre-stored templates after scanning is complete, the system will not generate a trigger signal and will record the characteristics of the unmatched event sequence, which can be used for subsequent system diagnosis or optimization of the template library. If a continuous event sub-sequence is found to fully match a template, it is determined that the independent events collectively constitute a valid logic combination and are identified as a logic event group. Finally, all identified logic event groups are output as a logic event sequence . This step is a key link in the conversion from low-level signal changes to high-level control behavior.

[0100] S3.5: Generation of preliminary time sequence trigger signals.

[0101] The logic event sequence is converted into specific trigger signals that can be used by subsequent steps. A corresponding preliminary time sequence trigger signal is generated for each identified logic event group. Each trigger signal contains three key attributes: trigger start time, trigger end time, and event type. The trigger start time is usually taken from the timestamp of the first event in the logic event group . The trigger end time is taken from the timestamp of the last event in the logic event group . The event type is directly recorded as the template number that matches. The generation rule of the trigger signal can be expressed as , where k is the index of the logic event group. After generation, these preliminary time sequence trigger signals are stored in a dedicated trigger buffer area, and each signal is accompanied by a validity flag for dynamic correction or execution in subsequent steps. This set of trigger signals indicates that not only stable edges have been extracted from the original signals, but also meaningful control action sequences have been identified, preparing for the final execution control.

[0102] In the technical solutions of the embodiments of the present disclosure, the screened stable edge events are subjected to secondary verification and logical correlation analysis, the confirmation delay is calculated independently for each event, and the secondary detection of the signal state is performed, so that the transient interference which passes the preliminary stability screening but cannot maintain the state within the duration is effectively eliminated. Further, by comparing the confirmed event sequence with the pre-stored multi-level logical template, the system realizes the conversion from the isolated signal edge to the time sequence action unit with clear physical meaning. The process ensures that the trigger signal is not only reliable in time point, but also conforms to the effective control behavior pattern in logical sequence, thereby generating the preliminary time sequence trigger signal with high confidence.

[0103] S4: Collecting environmental feature data and obtaining real-time feedback of the actuator, dynamically correcting system judgment parameters.

[0104] A closed-loop control system capable of adapting to environmental changes is established, and by collecting real-time running environment data and dynamically adjusting key judgment parameters, the system can maintain the best trigger sensitivity and reliability for a long time, effectively dealing with the challenges brought by equipment aging, working condition changes and environmental fluctuations.

[0105] S4.1: Environmental feature collection and real-time feedback acquisition.

[0106] The system continuously monitors the environmental parameters closely related to signal stability during operation. The preliminary time sequence trigger signal set obtained in the foregoing steps is received . Whenever a trigger signal is generated, the system starts an environmental data collection cycle to ensure that environmental perception and system action are synchronized. The collected data mainly includes three categories: first, the mechanical vibration frequency is obtained in real time through the acceleration sensor installed at the key part of the device, which directly reflects the intensity of the main interference source, and the typical value range is between 5 Hz and 200 Hz; second, the environmental temperature of the control system is obtained through the temperature sensor, and the temperature change may affect the response characteristics of electronic components, and the monitoring range usually covers minus 20 degrees Celsius to 80 degrees Celsius; finally, the response delay time of the actuator is accurately calculated by comparing the control command sent to the actuator with the actual state feedback signal returned by the actuator, which reflects the actual time consumption from the instruction issuance to the action start, and the unit is millisecond, and the common range is between 0.5 milliseconds and 10 milliseconds. The system integrates the three types of real-time data into an environmental feature data set , which provides the most original data support for subsequent evaluation of environmental disturbance intensity.

[0107] S4.2: Environmental disturbance intensity quantification and grade determination.

[0108] After obtaining the raw environmental characteristic data, it is necessary to perform fusion calculation on these multi-source information to obtain a comprehensive disturbance evaluation index. Based on the environmental characteristic data set , calculation is performed. It adopts a comprehensive disturbance index to quantify the severity of the current environment. The calculation formula of the index is , wherein , and represent the reference vibration frequency, the standard environmental temperature and the reference response time determined by the system in the calibration stage, which define the average working condition of the normal operation of the system. , and are weight coefficients, respectively used to measure the contribution proportion of vibration, temperature and response delay in the overall disturbance evaluation, the values of these coefficients range from 0 to 1, and satisfy the condition , the specific values of which are set according to the device type and the control priority. The comprehensive disturbance index calculated is a dimensionless value. According to the size of the index, the system divides the environmental disturbance intensity into three clear levels . When is less than 0.3, it is determined as low disturbance level; when is between 0.3 and 0.7, it is determined as medium disturbance level; when is greater than or equal to 0.7, it is determined as high disturbance level. This level parameter will be used as the direct basis for subsequent dynamic correction of parameters.

[0109] S4.3: Dynamic correction calculation of stability determination parameters.

[0110] According to the quantified disturbance level, the key determination parameters in the system are adjusted to make the behavior of the system match the current environment. Two core parameters are modified: the level holding time threshold and the confirmation delay period . The modification calculation follows the following formula: and . Among them, and are the modified level holding time threshold and confirmation delay period, respectively. is the disturbance level, which takes the value of 1, 2 or 3. and are dynamic correction coefficients, which determine the sensitivity of the parameters to the disturbance level, and their values usually range from 0.05 to 0.2, the specific values of which are set according to the device type and the expected response characteristics in advance. When the next control cycle starts, the modified level holding time threshold will be used as the signal stability determination threshold When the system detects an increase in disturbance level, for example from low to high, it automatically increases the decision threshold and the delay period, which makes the signal stability criterion more stringent, thus enhancing the system's robustness against interference in harsh environments. All the revised parameters are collected into a revised parameter set , waiting to be updated to the running system.

[0111] S4.4: Real-time parameter writing and running period update.

[0112] In order for the dynamic revision to take effect, the new parameters must be safely updated to the running environment of the system. At the end of the current trigger control period, the system will perform a parameter writing operation. It will combine the revised level holding time threshold , the confirmation delay period and the current system timestamp into a new system running parameter table . To ensure that the system can quickly recover to the most recent valid state after an unexpected power failure, this parameter table is written to non-volatile memory, such as FLASH or EEPROM, in real time. When the next control period starts, the system no longer uses the factory default or the last static parameters, but automatically reads this latest table from memory and applies and in it as new initial decision conditions to the signal processing flow. This way, the control parameters are continuously iterated and optimized between different running periods.

[0113] S4.5: Self-calibration feedback and error correction evaluation.

[0114] To further improve long-term adaptation accuracy, the system introduces a calibration link based on execution feedback. This step is started after the parameters are updated and run for a period of time. The system running parameter table and part of the execution feedback data from step S3, especially the actual execution time of the trigger signal and the action response delay, are obtained again. Calculate the deviation between the current delay parameter and the actual response delay time of the actuator . To quantify this deviation, calculate the delay error ratio E, the formula is . This error ratio reflects the gap between the theoretical setting and the actual system performance. Set an error tolerance, for example 0.2. If the calculated error ratio E is greater than this tolerance, it is determined that the last parameter revision is insufficient and needs to be further adjusted. At this time, the dynamic revision 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.

[0115] 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.

[0116] S5: Based on the preliminary timing trigger signal and actuator feedback, generate control commands and verify the execution results.

[0117] 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.

[0118] S5.1: Control command generation and action mapping.

[0119] 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.

[0120] S5.2: Execution unit drive and action completion detection.

[0121] 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.

[0122] S5.3: Execution result verification and exception backtracking.

[0123] 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 the comparison is completely consistent, the execution is determined to be successful and marked as PASS; otherwise, it is determined to be failed and marked as FAIL. All verification conclusions are collected into a verification conclusion set Once a FAIL conclusion occurs, the system immediately triggers an abnormal backtracking mechanism. The mechanism automatically records detailed information of the failure, including the execution unit number , the failure timestamp, and the deviation value between the expected and actual states. Subsequently, the system calls the timing judgment module of step S3 to reanalyze the original event logic associated with the failed command and check whether there is a misjudgment in the entire chain from signal triggering to command generation. At the same time, the failure record is sent to the parameter learning module of the system as a reference basis for future dynamic adjustment of threshold parameters and , thereby realizing continuous self-optimization of the system. For a verification successful operation, the system confirms its completion and takes the final triggering and execution result as the successful output of the current control cycle, while providing a reference benchmark for the next round of system operation.

[0124] In the technical solution of the embodiments of the present disclosure, the triggering signal that has undergone strict screening and logic confirmation is reliably mapped to a specific control command and drives the execution unit to act. By comparing the expected state of the instruction with the actual state of the sensor feedback, the system completes the final verification and abnormal backtracking of the execution result. This closed-loop verification mechanism constitutes the last link of system reliability, ensuring the accuracy and authenticity of the control action output. At the same time, any execution failure will be recorded and fed back to the parameter learning module, thereby forming a complete control closed loop that can continuously self-optimize, ultimately ensuring the overall precision and reliability of the automatic operation of industrial equipment.

[0125] In conclusion, the present application extracts the time distribution characteristics of the signal in the vibration environment through multi-cycle sampling and time window division, and constructs a dynamic stability judgment threshold accordingly. The threshold can adaptively adjust with the vibration frequency and intensity, thereby automatically improving the criterion strictness to filter out noise in strong disturbance, and reducing the criterion to quickly respond to the real signal in weak disturbance, fundamentally overcoming the dilemma between filtering interference and response lag in the traditional method. In view of the problem of false triggering caused by vibration burr, not only the initial screening of signal stability is carried out, but also an independent delay confirmation and secondary state detection are introduced for each edge event, effectively eliminating the instantaneous false edge. At the same time, by matching the confirmed event sequence with the pre-stored multi-level logic template, the system can identify the timing action unit conforming to the predetermined control logic from the isolated edge event, thereby improving the trigger judgment from a single level jump to a logic sequence level, significantly reducing the probability of false triggering and false instruction. Real-time collection of vibration, temperature and actuator response delay and other multi-source environmental data, quantification of environmental disturbance intensity, and dynamic correction of the system core judgment parameters. In addition, by comparing the instruction and execution feedback, the system can evaluate the effectiveness of the parameter setting and perform self-calibration, forming a continuous optimization closed loop. The mechanism enables the system to maintain high trigger sensitivity and reliability without human intervention for a long time, significantly improving the adaptability and robustness in complex industrial environments.

[0126] According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, which can include a processor, a communications interface, a memory and a communications bus, wherein the processor, the communications interface and the memory complete mutual communication through the communications bus. The processor can invoke the logic instructions in the memory to execute the above-mentioned methods.

[0127] In addition, the logic instructions in the memory described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present disclosure essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present disclosure. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0128] In another aspect, the present disclosure also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the method provided by the above method.

[0129] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0130] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some part of the embodiments.

[0131] It should be understood that the above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than limit them; although the present 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 recorded in the foregoing embodiments, or make equivalent replacements to some technical features therein; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A time sequence event trigger-based industrial single-chip microcomputer automatic control method, characterized in that, The method comprises: S1, multi-cycle sampling and time window division are performed on the original signal of the industrial field sensor or switch input, and a signal time distribution feature is extracted; S2, signal stability determination and false edge elimination are performed based on the signal time distribution feature, and a stable edge event set is output; S3, event confirmation delay and multi-level logic determination are performed on the stable edge event set, and a preliminary time sequence trigger signal is generated; S4, environmental feature data is collected and real-time feedback of the actuator is obtained, and system determination parameters are dynamically corrected; S5, based on the preliminary time sequence trigger signal and the actuator feedback, a control command is generated and the execution result is verified; The step S2 comprises: Based on the signal time distribution feature, a window-level stability index of each time window is calculated; The window-level stability index is compared with a stability threshold and a maximum allowed number of jumps to perform preliminary screening to form a preliminary valid edge event set; According to the real-time detected device vibration frequency, the stability threshold is dynamically corrected, and the preliminary valid edge event set is re-confirmed and screened using the corrected threshold; The stable edge event set filtered by the dynamic threshold is output, and the eliminated false edge features are recorded; The dynamic correction of the stability threshold uses the following formula: wherein, is the modified stability threshold, is the initial stability threshold, is the real-time device vibration frequency, is the reference frequency, k is a correction factor with a value ranging between 0.1 and 0.

3.

2. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 1, characterized in that, The step S3 comprises: For each edge event in the stable edge event set, its confirmation delay parameter is independently calculated, which integrates the basic confirmation delay period and the device vibration period; After the respective delay ends, the corresponding signal state is detected again, and the event validity is confirmed based on the comparison result of the initial state and the state after the delay; The events confirmed as valid are arranged in time sequence, matched with a pre-stored multi-level logic template, and the logic event group meeting the predetermined control behavior mode is identified; Based on the identified logic event group, a preliminary time sequence trigger signal set 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 comprises: The events confirmed as valid are arranged in time sequence to form an event time sequence vector; The event time sequence vector is scanned in time sequence, and continuous event sequences are extracted in turn; The extracted event sequences are compared with the pre-stored logic template. When and only when 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, it is determined that the event sequence matches successfully, and is identified as a valid logic event group; If there is no event sequence in the event time sequence vector that can successfully match any of the pre-stored logic templates, no trigger signal corresponding to the event time sequence 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, The step S1 comprises: Based on the mechanical operation cycle and vibration characteristics of the device, the sampling period, the number of sampling points, and the time window are initialized; Multi-cycle sampling is performed to obtain a level state matrix, and the signal jump number and the average level duration of each sampling cycle are counted based on the matrix; According to the time window, the number of jumps and the average duration of each window are aggregated to obtain the average number of jumps and the average duration of each window. Based on the average number of jumps and the average 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, The 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, the comprehensive disturbance index is calculated and the environmental disturbance level is divided accordingly; According to the environmental disturbance level, the level holding time threshold and the confirmation delay period of the system are dynamically corrected; The corrected parameters are updated to the system running environment, and self-calibration is performed based on the deviation of the actual response of the actuator and the instruction to optimize the corrected parameters.

6. The industrial single-chip microcomputer automatic control method based on a timing event trigger according to claim 5, characterized in that, The calculation of the comprehensive disturbance index adopts a weighted fusion method, and the specific formula is: wherein, is the real-time device vibration frequency, is the ambient temperature, is the actuator response delay, , and respectively represent the reference vibration frequency, the standard ambient temperature and the reference response time determined by the system during the calibration phase, , and are weight coefficients.

7. The industrial single-chip microcomputer automatic control method based on timing event triggering according to claim 1, characterized in that, The step S5 includes: Mapping the preliminary time sequence trigger signal to a control command containing the target execution unit, action type and planned execution time; Driving the execution unit to execute the control command, and detecting the action completion state through sensor feedback after a preset delay; By comparing the expected state of the instruction with the actual feedback state, the execution result is verified and abnormal execution is analyzed; The execution failure record is fed back to the system parameter learning module for optimization of subsequent control parameters.

8. A computer storage medium, characterized in that It stores a computer program, which is executed to implement the time sequence event triggered industrial single-chip microcomputer automatic control method according to any one of claims 1-7.

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