Digital management and control system for manufacturing process of heat-resistant fabric core conveyor belt

Abstract

The present application belongs to the technical field of data acquisition control, and discloses a digital management and control system for a manufacturing process of a heat-resistant fabric core conveyor belt, comprising a process benchmark construction module, which is used for process division of the manufacturing process, generation of process identity and process benchmark parameters corresponding to each process, and determination of the basic acquisition frequency of each process; a variable frequency acquisition control module, which is used for dynamic acquisition of process state signals according to the process identity; when the change rate of the process state signals exceeds a preset change rate threshold and is in a preset process interval, it is determined that a characteristic event occurs, and the acquisition frequency is increased and full acquisition is triggered; when no characteristic event occurs, the process state signals are acquired at the basic frequency, thereby generating a non-uniform acquisition time sequence that changes with the process state; and the stability of the manufacturing process of the heat-resistant fabric core conveyor belt and the product quality consistency are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and control technology, and more specifically, to a digital control system for the manufacturing process of heat-resistant fabric core conveyor belts. Background Technology

[0002] Existing digital control systems for the manufacturing process of heat-resistant fabric core conveyor belts mainly have the following problems: Heat-resistant fabric-core conveyor belts are widely used in metallurgy, building materials, and high-temperature material conveying. Their manufacturing process typically involves multiple continuous steps, including fabric pretreatment, impregnation and lamination, compression molding, and vulcanization. During these processes, temperature, tension, and the interfacial interaction between the fabric and rubber layers directly affect the conveyor belt's heat resistance, interlayer bonding strength, and overall service life. Therefore, constructing a digital control system for the manufacturing process to monitor and regulate the operation of each step in real time has become an important means of improving product quality stability.

[0003] In existing technologies, digital control of conveyor belt manufacturing processes is often achieved by periodically collecting process status signals and combining this with threshold judgment. However, in practical applications, due to differences in the process mechanisms of different processes, the rate of change of process status signals within each process is significantly inconsistent. A fixed sampling frequency cannot simultaneously cover both processes with slow and rapid state changes, resulting in insufficient sampling density during critical change phases and redundant sampling data during stable phases.

[0004] Meanwhile, existing technologies typically determine state based on signal amplitude at a single moment or simple thresholds, lacking the ability to characterize the changing trends between adjacent sampling moments. This makes it difficult to identify the transitions between different process states, thus failing to accurately capture key dynamic changes such as temperature jumps, sudden tension changes, or accelerated interface reactions. Furthermore, most existing acquisition mechanisms are only used for data recording and fail to effectively link with process control logic. This means that the acquired key state information cannot be used in control decisions in a timely manner, reducing the system's responsiveness to abnormal operating conditions.

[0005] Traditional sampling methods based on fixed time intervals can only obtain discrete state data points, failing to reflect the non-uniform changes in process states over time, resulting in insufficient accuracy in describing the state evolution process. During rapid changes in process states, the fixed sampling interval makes it difficult to capture key change nodes in a timely manner, thus affecting the accuracy of subsequent process analysis and control; while during stable state changes, fixed-frequency sampling generates a large amount of redundant data, increasing the system's storage and computational burden.

[0006] In view of this, the present invention proposes a digital control system for the manufacturing process of heat-resistant fabric core conveyor belts to solve the above problems. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a digital control system for the manufacturing process of heat-resistant fabric core conveyor belts, comprising: The process baseline construction module is used to divide the manufacturing process into steps, generate process identification and process baseline parameters corresponding to each step, and determine the basic acquisition frequency of each step. The frequency conversion acquisition and control module is used to dynamically acquire process status signals based on process identification. When the rate of change of the process status signal exceeds the preset rate of change threshold and is within the preset process range, a characteristic event is determined to have occurred, and the acquisition frequency is increased and full acquisition is triggered. When no characteristic event occurs, acquisition is performed at the basic frequency, thereby generating a non-uniform acquisition time sequence that changes with the process status. The process state characterization module is used to construct the process state characterization results of each process based on the non-uniform acquisition time sequence and the corresponding process state signal. It assigns time weights to the process state signals according to the time interval between adjacent acquisition times to form a process state sequence with sequential correlation. The process control and correction module is used to compare and analyze the process state sequence with the process reference parameters, identify process deviation information that deviates from the process reference parameters, generate corresponding process control instructions, record relevant control results, and correct and update the process reference parameters based on the control results.

[0008] Preferably, the method for generating process identification identifiers and process baseline parameters corresponding to each process includes: Based on the preset process flow structure, the manufacturing process of the heat-resistant fabric core conveyor belt is divided into process units arranged in different sequences according to the predetermined process sequence, and a unique process identity is assigned to each process unit. For each process unit, a pre-established process parameter calibration table is called. Based on the process identification, an index match is performed in the process parameter calibration table to read the process reference parameters corresponding to that process unit and determine the process reference parameter range. The process reference parameters include the temperature, tension, and interface parameters corresponding to each process unit; the process reference parameter ranges include the temperature range, tension range, and interface parameter range corresponding to each process unit, and each process reference parameter is given in the form of upper and lower limits.

[0009] Preferably, the method for determining the basic acquisition frequency for each process includes: Read the preset rate of change level identifier corresponding to the process identification in the process parameter calibration table, and select the corresponding basic acquisition frequency from the preset basic acquisition frequency according to the mapping relationship between the preset rate of change level identifier and the preset basic acquisition frequency, so that different process units correspond to different basic acquisition frequencies.

[0010] Preferably, the method for determining the occurrence of a characteristic event includes: The corresponding basic acquisition frequency is read according to the current process identification, and the process status signal is periodically acquired according to the basic acquisition frequency. During the acquisition process, a feature event monitoring function is constructed to calculate the rate of change between adjacent sampling points of the process status signal, obtain the rate of change of the process status signal at the current moment, and compare the rate of change with the preset rate of change threshold. At the same time, it is determined whether the process status signal crosses the preset process interval threshold corresponding to the current process identification. When the rate of change is greater than the preset rate of change threshold and the process status signal crosses the preset process interval threshold, a characteristic event is determined to have occurred. A sampling frequency function is constructed to dynamically adjust the sampling frequency. At the moment when the characteristic event is determined to have occurred, the sampling frequency is adjusted to a target sampling frequency higher than the basic sampling frequency, and at the same time, the full sampling of the process reference parameters is triggered.

[0011] Preferably, the method for generating a non-uniform acquisition time sequence that varies with process state includes: When the change rate does not simultaneously meet the preset change rate threshold and the process status signal crosses the preset process interval threshold, it is determined that no characteristic event has occurred. The process status signal is collected according to the basic collection frequency corresponding to the current process identity. The time interval between adjacent sampling times is determined according to the current collection frequency, and a non-uniform collection time sequence that changes with the process status is generated sequentially according to the time interval.

[0012] Preferably, the method for constructing the process state characterization results of each process includes: For the process unit corresponding to the current process identity, obtain the non-uniform sampling time sequence and the process status signal corresponding to each sampling time for that process unit; arrange the process status signals according to the sampling time order to construct the corresponding process status time sequence; combine and characterize the operating status of the current process unit based on the process status time sequence to obtain the corresponding process status characterization result.

[0013] Preferably, the method for forming a sequence of process states with sequential correlation includes: The process state signals at corresponding sampling times are weighted using time intervals as weighting coefficients. The magnitude of the weighting coefficients is determined based on the size of the sampling time interval. A time-correlated process state sequence is constructed based on the weighted process state signals, so that the correlation between each sampling point is adjusted by the weighting coefficients.

[0014] Preferably, the method for identifying process deviation information that deviates from the process reference parameters includes: For the current process identification, the process reference parameter range corresponding to the process is read from the process parameter calibration table. The process state signal corresponding to each sampling time in the process state sequence is compared with the corresponding process reference parameter range to determine whether the process state signal at each sampling time is within the corresponding parameter value range. When the process status signal is within the process reference parameter range, it is determined to be in a normal state. When the process status signal exceeds the upper or lower limit of the process reference parameter range, it is determined to be in a deviation state. The sampling time of the deviation state is marked, and the deviation duration is determined according to the time interval between adjacent deviation state sampling times. The degree of deviation is determined according to the numerical difference of the process status signal exceeding the boundary of the process reference parameter range, thereby generating the corresponding process deviation information.

[0015] Preferably, the method for generating the corresponding process control instructions includes: For the process unit corresponding to the current process identity, the corresponding deviation type is determined based on the process deviation information. When the process status signal is higher than the upper limit of the process reference parameter range, it is determined to be an upper deviation state. When the process status signal is lower than the lower limit of the process reference parameter range, it is determined to be a lower deviation state. The control direction is determined based on the deviation type, where the upper deviation state corresponds to the control direction of decreasing the process reference parameter, and the lower deviation state corresponds to the control direction of increasing the process reference parameter; the control amplitude is determined based on the degree of deviation in the process deviation information, and the control duration is determined based on the deviation duration, thereby generating a process control instruction that includes the control direction, control amplitude, and control duration. The process control command is sent to the execution unit of the corresponding process and implemented. During the control execution process, the control execution time, the content of the control command, and the changes in the corresponding process status signal after the control execution are recorded to form control result data.

[0016] Preferably, the method for correcting and updating the process baseline parameters based on the control results includes: For the process unit corresponding to the current process identification, obtain the control result data, which includes the process status signals before and after the control execution and the time series data after the control execution; determine whether the process status signal after the control execution falls within the corresponding process reference parameter range, and determine that the control is effective when the process status signal is continuously within the process reference parameter range within a preset continuous time range. If the control is deemed effective, the maximum and minimum values ​​of the process status signal after the control is executed within a preset continuous time range are extracted as the corresponding value ranges. Based on the value ranges, the upper and lower limits of the original process reference parameter range are adjusted to form the updated process reference parameter range. When there are m valid control records, the corresponding value ranges after each control are executed are statistically processed. The statistical processing includes averaging the upper and lower limits of each value range, and iteratively correcting and updating the process reference parameter range based on the statistical results, so that the updated process reference parameter range matches the actual process operation status.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention links the acquisition frequency with process identification and status change characteristics, enabling the acquisition process to automatically adjust for different processes, thus improving acquisition efficiency and targeting. By introducing a joint judgment mechanism of change rate and interval crossing, it achieves accurate identification of the process state transitioning from one operating interval to another, effectively avoiding misjudgments or omissions caused by relying solely on amplitude judgments. When characteristic events occur, the acquisition density is increased, triggering full parameter acquisition, thereby completely recording the status information of key change stages and improving the precision of process state characterization. By directly applying the characteristic event judgment results to the acquisition frequency adjustment and parameter acquisition behavior, the acquisition process participates in the process control link, improving the response speed and accuracy of subsequent process regulation. Maintaining a basic acquisition frequency in non-critical stages and dynamically increasing the acquisition density in critical stages reduces redundant acquisition while ensuring data validity, thus improving system operating efficiency.

[0018] By incorporating the acquisition frequency into the sampling time generation mechanism, the sampling time interval can be dynamically adjusted according to changes in process status, thereby avoiding the mismatch problem caused by fixed sampling. The non-uniform sampling time sequence can reflect the time density differences of different stages, making the expression of the state change process more refined in the time dimension. In the stage with a high acquisition frequency, the sampling interval is reduced, thereby acquiring more state information points, which is beneficial for identifying key nodes in the process of process status change. In the stage with a low acquisition frequency, the sampling interval is increased, reducing unnecessary data acquisition and reducing storage and computing burden. The generation of sampling time directly depends on the acquisition frequency, which is jointly determined by process identification and characteristic events, thereby linking the sampling behavior with the process operation status and improving the responsiveness and control effectiveness of the overall system. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the digital control system for the manufacturing process of the heat-resistant fabric core conveyor belt of the present invention; Figure 2 This is a schematic diagram of the digital control system for the manufacturing process of the heat-resistant fabric core conveyor belt of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0021] Please see Figure 1 As shown, this embodiment provides a digital control system for the manufacturing process of heat-resistant fabric core conveyor belts, specifically including the following steps: The process baseline construction module is used to divide the manufacturing process into steps, generate process identification and process baseline parameters corresponding to each step, and determine the basic acquisition frequency of each step. The frequency conversion acquisition and control module is used to dynamically acquire process status signals based on process identification. When the rate of change of the process status signal exceeds the preset rate of change threshold and is within the preset process range, a characteristic event is determined to have occurred, and the acquisition frequency is increased and full acquisition is triggered. When no characteristic event occurs, acquisition is performed at the basic frequency, thereby generating a non-uniform acquisition time sequence that changes with the process status. The process state characterization module is used to construct the process state characterization results of each process based on the non-uniform acquisition time sequence and the corresponding process state signal. It assigns time weights to the process state signals according to the time interval between adjacent acquisition times to form a process state sequence with sequential correlation. The process control and correction module is used to compare and analyze the process state sequence with the process reference parameters, identify process deviation information that deviates from the process reference parameters, generate corresponding process control instructions, record relevant control results, and correct and update the process reference parameters based on the control results.

[0022] Methods for generating process identification identifiers and process baseline parameters corresponding to each process include: Based on the preset process flow structure, the manufacturing process of the heat-resistant fabric core conveyor belt is divided into process units arranged in different sequences according to the predetermined process sequence, and a unique process identity is assigned to each process unit. It should be noted that in this embodiment, the manufacturing process of the heat-resistant fabric core conveyor belt is first divided into steps based on a preset process flow structure. The process flow structure is a predefined standard manufacturing process used to describe the sequence and connection relationships of each process step. According to this process flow structure, the complete manufacturing process is divided into multiple process units in a predetermined order. Each process unit corresponds to an independent process stage, and a unique process identification identifier is assigned to each process unit in the system. The process units are arranged sequentially according to the order in the process flow structure, thereby forming a process sequence with a defined sequential relationship.

[0023] For each process unit, a pre-established process parameter calibration table is called. Based on the process identification, an index match is performed in the process parameter calibration table to read the process reference parameters corresponding to that process unit and determine the process reference parameter range. The process reference parameters include temperature, tension, and interface parameters corresponding to each process unit; the interface parameters are parameters used to characterize the interfacial bonding state between the fabric layer and the rubber layer, and the interface parameters include at least one of impregnation amount, interface temperature, pressing pressure, and interfacial bonding strength; the process reference parameter ranges include the temperature value range, tension value range, and interface parameter value range corresponding to each process unit, and each process reference parameter is given in the form of upper and lower limit ranges.

[0024] The process parameter calibration table uses process identification as an index to configure a corresponding set of process baseline parameters for each process unit. Specifically, for each process unit, based on established process specifications or stable production conditions, ranges are set for the key process parameters involved in the operation of that process. These ranges include temperature ranges, tension ranges, and interface effect parameter ranges, and each process baseline parameter is stored in the calibration table with clearly defined upper and lower limits. Simultaneously, the process parameter calibration table pre-sets a corresponding rate of change level for each process unit to characterize the speed of change in the process state under normal operating conditions.

[0025] Methods for determining the basic acquisition frequency for each process include: Read the preset rate of change level identifier corresponding to the process identification in the process parameter calibration table, and select the corresponding basic acquisition frequency from the preset basic acquisition frequency according to the mapping relationship between the preset rate of change level identifier and the preset basic acquisition frequency, so that different process units correspond to different basic acquisition frequencies.

[0026] The mapping relationship follows a predefined correspondence rule, with different rate of change level identifiers corresponding to different base acquisition frequencies. This ensures that process units with faster changes in process state correspond to higher base acquisition frequencies, while those with slower changes correspond to lower base acquisition frequencies, thus determining the base acquisition frequency for each process unit. For example, the set of rate of change level identifiers is pre-defined as three levels: Level 1, Level 2, and Level 3. The corresponding rate of change threshold ranges are as follows: Level 1 has a rate of change less than or equal to 10, Level 2 has a rate of change greater than 10 but less than or equal to 30, and Level 3 has a rate of change greater than 30. Simultaneously, the set of base acquisition frequencies is pre-defined as three levels: 1Hz, 5Hz, and 10Hz, and the following correspondence is established: Level 1 rate of change level identifiers correspond to a base acquisition frequency of 1Hz, Level 2 rate of change level identifiers correspond to a base acquisition frequency of 5Hz, and Level 3 rate of change level identifiers correspond to a base acquisition frequency of 10Hz.

[0027] When a process unit's rate of change level is identified as Level 2 in the process parameter calibration table, a base acquisition frequency of 5Hz is selected based on the above mapping relationship; when the rate of change level is Level 3, a base acquisition frequency of 10Hz is selected. This method enables different process units to automatically match their corresponding base acquisition frequencies based on their preset rate of change level.

[0028] Methods for determining the occurrence of characteristic events include: The corresponding basic acquisition frequency is read according to the current process identification, and the process status signal is periodically acquired according to the basic acquisition frequency. During the acquisition process, a feature event monitoring function is constructed to calculate the rate of change between adjacent sampling points of the process status signal, obtain the rate of change of the process status signal at the current moment, and compare the rate of change with the preset rate of change threshold. At the same time, it is determined whether the process status signal crosses the preset process interval threshold corresponding to the current process identification. Feature event monitoring function: Where E(t) represents the characteristic event determination result at time t, and its value is 1 or 0, where 1 indicates that the characteristic event has occurred and 0 indicates that the characteristic event has not occurred; t represents the time variable, corresponding to the sampling time of the process status signal; σ(t) represents the process status signal collected at time t, which is used to characterize the current process operation status; λ represents the rate of change of the process status signal relative to time. S This represents a preset rate of change threshold, used to define the boundary for determining the rate of change of the process status signal, and this rate of change threshold is associated with the process identification identifier S; θ S This represents a preset process interval threshold, used to divide key intervals of the process state, and this threshold is also associated with the process identification S; Λ represents logical AND, indicating that two conditions must be met simultaneously, only when both are satisfied... And σ(t) spans θ S The value of the feature event monitoring function is 1 only when the value of V is 1, indicating that a feature event has occurred; V represents logical OR, indicating that either of the two conditions is not met, and the value of the feature event monitoring function is 0, indicating that no feature event has occurred. When the rate of change is greater than the preset rate of change threshold and the process status signal crosses the preset process interval threshold, a characteristic event is determined to have occurred. A sampling frequency function is constructed to dynamically adjust the sampling frequency. At the moment when the characteristic event is determined to have occurred, the sampling frequency is adjusted to a target sampling frequency higher than the basic sampling frequency, and at the same time, the full sampling of the process reference parameters is triggered.

[0029] It should be noted that, in this embodiment, the process state signal crossing a preset process interval threshold refers to the process in which the positional relationship of the process state signal relative to the preset process interval threshold changes between two adjacent sampling times. Specifically, when the process state signal is located on one side of the preset process interval threshold at the previous sampling time and on the other side at the next sampling time, it is determined that the process state signal has crossed the preset process interval threshold; crossing includes two cases: changing from below the threshold to above the threshold, or changing from above the threshold to below the threshold. Through this method, the change process of the process state signal is transformed from a single-moment numerical judgment to a judgment of state changes between adjacent moments, thereby identifying dynamic change events in the process state.

[0030] Acquisition frequency function: f c (t)=f base (S)·[1+β S E(t)]+f v E(t); where f c (t) represents the actual sampling frequency at time t; f base (S) represents the basic acquisition frequency corresponding to the current process identification S; S represents the process identification of the current process; β S This represents the frequency adjustment coefficient, used to proportionally amplify the base acquisition frequency when a characteristic event occurs, and this coefficient is related to the process identification identifier S; f v This represents the additional sampling frequency component added when an event is triggered, used to further increase the sampling density when a characteristic event occurs.

[0031] This solution addresses the following technical problems of existing technologies: The rate of state change varies significantly across different processes, and a fixed sampling frequency cannot simultaneously cover both slowly changing and rapidly changing processes, resulting in insufficient sampling during critical change phases and redundant sampling during stable phases; Existing technologies often rely on signal amplitude at a single moment or simple threshold judgments, failing to identify the changing trends of process states between adjacent moments, especially struggling to capture state transitions across intervals; Traditional acquisition methods are only used for data recording and do not participate in process control logic, resulting in key process change information failing to effectively drive control strategy adjustments; During rapid temperature changes, sudden tension changes, or accelerated interface reactions, insufficient sampling density cannot fully reflect the state evolution process, affecting the accuracy of subsequent process analysis.

[0032] Compared to existing technologies, the advantages are as follows: By associating the acquisition frequency with process identification and status change characteristics, the acquisition process can be automatically adjusted for different processes, improving acquisition efficiency and targeting; by introducing a joint judgment mechanism of change rate and interval crossing, the process of transitioning from one operating interval to another can be accurately identified, effectively avoiding misjudgments or omissions caused by judging solely based on amplitude; when characteristic events occur, the acquisition density is increased and full parameter acquisition is triggered, thereby completely recording the status information of key change stages and improving the precision of process status representation; by directly applying the judgment results of characteristic events to the acquisition frequency adjustment and parameter acquisition behavior, the acquisition process participates in the process control link, improving the response speed and accuracy of subsequent process control; by maintaining the basic acquisition frequency in non-critical stages and dynamically increasing the acquisition density in critical stages, redundant acquisition is reduced while ensuring data validity, thereby improving system operating efficiency.

[0033] Methods for generating non-uniform acquisition time sequences that vary with process status include: When the change rate does not simultaneously meet the preset change rate threshold and the process status signal crosses the preset process interval threshold, it is determined that no characteristic event has occurred. The process status signal is collected according to the basic collection frequency corresponding to the current process identity. The time interval between adjacent sampling times is determined according to the current collection frequency, and a non-uniform collection time sequence that changes with the process status is generated sequentially according to the time interval.

[0034] Non-uniform acquisition time series are generated through the following recursive relationship: ; where t k+1 t represents the (k+1)th sampling time; k Indicates the time of the kth sampling; f represents the time interval between two consecutive samples; c (t k ) represents the sampling frequency corresponding to the kth sampling time; k represents the sampling sequence number; it should be noted that since different process identifications correspond to different basic sampling frequencies, and the sampling frequencies differ depending on whether the feature event is triggered, the time interval of the sampling time sequence is non-constant, thus forming a non-uniform sampling time sequence that changes with the process status.

[0035] This invention addresses the following technical problems of existing technologies: fixed-interval sampling cannot reflect the differences in the rate of change of process state, resulting in insufficient sampling during rapid state changes and redundant sampling during stable state changes; equal-interval sampling only records discrete point data, failing to reflect the non-uniformity of state changes in the time dimension, leading to a lack of fine structural information in the description of the change process; during temperature jumps, tension abrupt changes, or accelerated interface reactions, the fixed sampling interval makes it difficult to capture key nodes in the change process, affecting the accuracy of subsequent analysis and control; traditional sampling time generation methods are independent of process state changes and cannot dynamically adjust the sampling rhythm according to the actual operating state.

[0036] Compared to existing technologies, the advantages are as follows: By incorporating the acquisition frequency into the sampling time generation mechanism, the sampling time interval can be dynamically adjusted according to changes in process status, thereby avoiding the mismatch problem caused by fixed sampling; the non-uniform sampling time sequence can reflect the time density differences of different stages, making the expression of the state change process more refined in the time dimension; in the stage with a high acquisition frequency, the sampling interval is reduced, thereby acquiring more state information points, which is beneficial for identifying key nodes in the process of process status change; in the stage with a low acquisition frequency, the sampling interval is increased, reducing unnecessary data acquisition and reducing storage and computing burden; the generation of sampling time directly depends on the acquisition frequency, which is jointly determined by the process identification and characteristic events, thereby forming a linkage between sampling behavior and process operation status, improving the responsiveness and control effectiveness of the overall system.

[0037] Methods for constructing the process state characterization results for each process include: For the process unit corresponding to the current process identity, obtain the non-uniform sampling time sequence and the process status signal corresponding to each sampling time for that process unit; arrange the process status signals according to the sampling time order to construct the corresponding process status time sequence; combine and characterize the operating status of the current process unit based on the process status time sequence to obtain the corresponding process status characterization result.

[0038] After obtaining the process state time series arranged in the order of sampling time, the time series is segmented and statistically analyzed, and its features are combined to form a comprehensive representation of the current process unit's operating state. Specifically, based on the process state time series, multiple representational quantities reflecting the process operation characteristics are extracted. These representational quantities include the time-weighted average, maximum value, minimum value, and statistical values ​​of the rate of change between adjacent sampling points. The time-weighted average is calculated using the time interval between adjacent sampling times as a weighting coefficient to reflect the contribution of the state signal in different time intervals. Simultaneously, the rate of change of each sampling point in the process state time series is calculated, and the extreme values ​​or average values ​​of the rate of change are extracted to represent the intensity of the process state change. On this basis, the above representational quantities are fused according to a preset combination method to form a multi-dimensional state representation vector containing amplitude features and change features. This multi-dimensional state representation vector is used as the process state representation result of the current process unit. Through the above method, the obtained process state representation result not only reflects the numerical level of the process state but also reflects its change characteristics in the time dimension, thereby providing a basis for subsequent process deviation analysis and control.

[0039] Methods for forming a sequence of process states with sequential relationships include: The process state signals at corresponding sampling times are weighted using time intervals as weighting coefficients. The magnitude of the weighting coefficients is determined based on the size of the sampling time interval. A time-correlated process state sequence is constructed based on the weighted process state signals, so that the correlation between each sampling point is adjusted by the weighting coefficients.

[0040] In this embodiment, to construct a process state sequence with sequential correlation, the time interval between adjacent sampling times in the non-uniform acquisition time sequence is first obtained, and the time interval is used as the basis for weighting coefficients to perform weighted processing on the process state signals corresponding to each sampling time. Specifically, a weighting coefficient is assigned to the process state signal at the k-th sampling time. The weighting coefficient is determined according to the corresponding time interval, and the weighting coefficient is inversely proportional to the time interval, so that sampling points with smaller time intervals correspond to larger weighting coefficients, and sampling points with larger time intervals correspond to smaller weighting coefficients, in order to enhance the ability to express state changes within the high-density sampling interval. After normalizing all time intervals, weight coefficients are determined so that the sum of all weight coefficients satisfies preset constraints. Based on this, the weighted signals are arranged according to the sampling time sequence to construct a process state sequence. In this way, the correlation between adjacent sampling points is adjusted by the corresponding weight coefficients. In the interval with a smaller sampling interval, the correlation between adjacent sampling points is enhanced, while in the interval with a larger sampling interval, the correlation is relatively weakened. This allows the constructed process state sequence to reflect the non-uniform change characteristics of the process state in the time dimension.

[0041] Methods for identifying process deviations from process baseline parameters include: For the current process identification, the process reference parameter range corresponding to the process is read from the process parameter calibration table. The process state signal corresponding to each sampling time in the process state sequence is compared with the corresponding process reference parameter range to determine whether the process state signal at each sampling time is within the corresponding parameter value range. When the process status signal is within the process reference parameter range, it is determined to be in a normal state. When the process status signal exceeds the upper or lower limit of the process reference parameter range, it is determined to be in a deviation state. The sampling time of the deviation state is marked, and the deviation duration is determined according to the time interval between adjacent deviation state sampling times. The degree of deviation is determined according to the numerical difference of the process status signal exceeding the boundary of the process reference parameter range, thereby generating the corresponding process deviation information.

[0042] It should be noted that, in this embodiment, after determining and marking the deviation states at each sampling time, the sampling times of consecutive or adjacent deviation states are divided into segments. Sampling points that are temporally adjacent and all are in a deviation state are grouped into the same deviation segment. For any deviation segment, its deviation duration is obtained by accumulating the time intervals between adjacent deviation state sampling times within the segment, that is, the time span between the start and end sampling times of the deviation segment is used as the deviation duration of the segment. At the same time, for each deviation state sampling time, the deviation of the corresponding process state signal relative to the boundary of the process reference parameter interval is calculated. When the process state signal is higher than the upper limit, the difference between the sampling value and the upper limit value is used as the positive deviation; when it is lower than the lower limit, the absolute value of the difference between the sampling value and the lower limit value is used as the negative deviation. The deviations of each sampling time within the deviation segment are summarized to obtain a numerical index characterizing the degree of deviation of the segment. Based on the combination of deviation duration and deviation degree, the corresponding process deviation information is generated.

[0043] Methods for generating corresponding process control instructions include: For the process unit corresponding to the current process identity, the corresponding deviation type is determined based on the process deviation information. When the process status signal is higher than the upper limit of the process reference parameter range, it is determined to be an upper deviation state. When the process status signal is lower than the lower limit of the process reference parameter range, it is determined to be a lower deviation state. The control direction is determined based on the deviation type, where the upper deviation state corresponds to the control direction of decreasing the process reference parameter, and the lower deviation state corresponds to the control direction of increasing the process reference parameter; the control amplitude is determined based on the degree of deviation in the process deviation information, and the control duration is determined based on the deviation duration, thereby generating a process control instruction that includes the control direction, control amplitude, and control duration. The process control command is sent to the execution unit of the corresponding process and implemented. During the control execution process, the control execution time, the content of the control command, and the changes in the corresponding process status signal after the control execution are recorded to form control result data.

[0044] Methods for correcting and updating process baseline parameters based on control results include: For the process unit corresponding to the current process identification, obtain the control result data, which includes the process status signals before and after the control execution and the time series data after the control execution; determine whether the process status signal after the control execution falls within the corresponding process reference parameter range, and determine that the control is effective when the process status signal is continuously within the process reference parameter range within a preset continuous time range. If the control is deemed effective, the maximum and minimum values ​​of the process status signal after the control is executed within a preset continuous time range are extracted as the corresponding value ranges. Based on the value ranges, the upper and lower limits of the original process reference parameter range are adjusted to form the updated process reference parameter range. When there are m valid control records, the corresponding value ranges after each control are executed are statistically processed. The statistical processing includes averaging the upper and lower limits of each value range, and iteratively correcting and updating the process reference parameter range based on the statistical results, so that the updated process reference parameter range matches the actual process operation status.

[0045] The preset rate of change threshold is set by staff based on historical data analysis results. This historical analysis process includes the system collecting multiple rates of change and calculating their average value as a reference to obtain the preset rate of change threshold. Similarly, the preset process range threshold, the upper limit of the process reference parameter range, and the lower limit of the process reference parameter range are also set by staff based on the system's historical operating data and the specific application scenario requirements. These can be adjusted by staff during system operation according to the actual situation.

[0046] In this embodiment, by associating the acquisition frequency with process identification and status change characteristics, the acquisition process can be automatically adjusted for different processes, improving acquisition efficiency and targeting. By introducing a joint judgment mechanism of change rate and interval crossing, the system can accurately identify the process state transitioning from one operating interval to another, effectively avoiding misjudgments or omissions caused by judging solely based on amplitude. When a characteristic event occurs, the acquisition density is increased and full parameter acquisition is triggered, thereby completely recording the status information of key change stages and improving the precision of process state representation. By directly applying the characteristic event judgment results to the acquisition frequency adjustment and parameter acquisition behavior, the acquisition process participates in the process control link, improving the response speed and accuracy of subsequent process regulation. The system maintains a basic acquisition frequency in non-critical stages and dynamically increases the acquisition density in critical stages, thereby reducing redundant acquisition while ensuring data validity and improving system operating efficiency.

[0047] By incorporating the acquisition frequency into the sampling time generation mechanism, the sampling time interval can be dynamically adjusted according to changes in process status, thereby avoiding the mismatch problem caused by fixed sampling. The non-uniform sampling time sequence can reflect the time density differences of different stages, making the expression of the state change process more refined in the time dimension. In the stage with a high acquisition frequency, the sampling interval is reduced, thereby acquiring more state information points, which is beneficial for identifying key nodes in the process of process status change. In the stage with a low acquisition frequency, the sampling interval is increased, reducing unnecessary data acquisition and reducing storage and computing burden. The generation of sampling time directly depends on the acquisition frequency, which is jointly determined by process identification and characteristic events, thereby linking the sampling behavior with the process operation status and improving the responsiveness and control effectiveness of the overall system.

[0048] Example 2 Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A digital control method for the manufacturing process of heat-resistant fabric core conveyor belts is provided, including: S1. Divide the manufacturing process into steps, generate step identification and process baseline parameters corresponding to each step, and determine the basic acquisition frequency for each step; S2. Based on the process identification, dynamically collect process status signals; when the rate of change of the process status signal exceeds the preset rate of change threshold and is within the preset process range, determine that a characteristic event has occurred, increase the collection frequency and trigger full collection; when no characteristic event has occurred, collect at the basic frequency to generate a non-uniform collection time sequence that changes with the process status. S3. Construct the process state characterization results of each process based on the non-uniform acquisition time sequence and the corresponding process state signal. Assign time weights to the process state signals according to the time interval between adjacent acquisition times to form a process state sequence with sequential correlation. S4. Compare and analyze the process state sequence with the process reference parameters, identify process deviation information that deviates from the process reference parameters, generate corresponding process control instructions, record the relevant control results, and correct and update the process reference parameters based on the control results.

[0049] Example 3 This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the operation mode of the digital control system for the manufacturing process of the heat-resistant fabric core conveyor belt described above.

[0050] Since the electronic device described in this embodiment is the electronic device used in implementing the digital control system for the manufacturing process of heat-resistant fabric core conveyor belts in this application embodiment, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the digital control system for the manufacturing process of heat-resistant fabric core conveyor belts described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any electronic device used by those skilled in the art in implementing the digital control system for the manufacturing process of heat-resistant fabric core conveyor belts in this application embodiment falls within the scope of protection of this application.

[0051] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0052] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A digital control system for the manufacturing process of heat-resistant fabric core conveyor belts, characterized in that, include: The process baseline construction module is used to divide the manufacturing process into steps, generate process identification and process baseline parameters corresponding to each step, and determine the basic acquisition frequency of each step. The frequency conversion acquisition and control module is used to dynamically acquire process status signals based on process identification. When the rate of change of the process status signal exceeds the preset rate of change threshold and is within the preset process range, a characteristic event is determined to have occurred, and the acquisition frequency is increased and full acquisition is triggered; when no characteristic event has occurred, acquisition is performed at the basic frequency, thereby generating a non-uniform acquisition time sequence that varies with the process status. The process state characterization module is used to construct the process state characterization results of each process based on the non-uniform acquisition time sequence and the corresponding process state signal. It assigns time weights to the process state signals according to the time interval between adjacent acquisition times to form a process state sequence with sequential correlation. The process control and correction module is used to compare and analyze the process state sequence with the process reference parameters, identify process deviation information that deviates from the process reference parameters, generate corresponding process control instructions, record relevant control results, and correct and update the process reference parameters based on the control results.

2. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 1, characterized in that, The method for generating process identification identifiers and process reference parameters corresponding to each process includes: Based on the preset process flow structure, the manufacturing process of the heat-resistant fabric core conveyor belt is divided into process units arranged in different sequences according to the predetermined process sequence, and a unique process identity is assigned to each process unit. For each process unit, a pre-established process parameter calibration table is called. Based on the process identification, an index match is performed in the process parameter calibration table to read the process reference parameters corresponding to that process unit and determine the process reference parameter range. The process reference parameters include the temperature, tension, and interface parameters corresponding to each process unit; the process reference parameter ranges include the temperature range, tension range, and interface parameter range corresponding to each process unit, and each process reference parameter is given in the form of upper and lower limits.

3. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 2, characterized in that, The method for determining the basic acquisition frequency for each process includes: Read the preset rate of change level identifier corresponding to the process identification in the process parameter calibration table, and select the corresponding basic acquisition frequency from the preset basic acquisition frequency according to the mapping relationship between the preset rate of change level identifier and the preset basic acquisition frequency, so that different process units correspond to different basic acquisition frequencies.

4. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 3, characterized in that, The method for determining the occurrence of a characteristic event includes: The corresponding basic acquisition frequency is read according to the current process identification, and the process status signal is periodically acquired according to the basic acquisition frequency. During the acquisition process, a feature event monitoring function is constructed to calculate the rate of change between adjacent sampling points of the process status signal, obtain the rate of change of the process status signal at the current moment, and compare the rate of change with the preset rate of change threshold. At the same time, it is determined whether the process status signal crosses the preset process interval threshold corresponding to the current process identification. When the rate of change is greater than the preset rate of change threshold and the process status signal crosses the preset process interval threshold, a characteristic event is determined to have occurred. A sampling frequency function is constructed to dynamically adjust the sampling frequency. At the moment when the characteristic event is determined to have occurred, the sampling frequency is adjusted to a target sampling frequency higher than the basic sampling frequency, and at the same time, the full sampling of the process reference parameters is triggered.

5. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 4, characterized in that, The method for generating a non-uniform acquisition time sequence that varies with process state includes: When the change rate does not simultaneously meet the preset change rate threshold and the process status signal crosses the preset process interval threshold, it is determined that no characteristic event has occurred. The process status signal is collected according to the basic collection frequency corresponding to the current process identity. The time interval between adjacent sampling times is determined according to the current collection frequency, and a non-uniform collection time sequence that changes with the process status is generated sequentially according to the time interval.

6. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 5, characterized in that, The method for constructing the process state characterization results for each process includes: For the process unit corresponding to the current process identity, obtain the non-uniform sampling time sequence and the process status signal corresponding to each sampling time for that process unit; arrange the process status signals according to the sampling time order to construct the corresponding process status time sequence; combine and characterize the operating status of the current process unit based on the process status time sequence to obtain the corresponding process status characterization result.

7. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 6, characterized in that, The method for forming a sequence of process states with sequential correlation includes: The process state signals at corresponding sampling times are weighted using time intervals as weighting coefficients. The magnitude of the weighting coefficients is determined based on the size of the sampling time interval. A time-correlated process state sequence is constructed based on the weighted process state signals, so that the correlation between each sampling point is adjusted by the weighting coefficients.

8. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 7, characterized in that, The method for identifying process deviation information that deviates from the process reference parameters includes: For the current process identification, the process reference parameter range corresponding to the process is read from the process parameter calibration table. The process state signal corresponding to each sampling time in the process state sequence is compared with the corresponding process reference parameter range to determine whether the process state signal at each sampling time is within the corresponding parameter value range. When the process status signal is within the range of process reference parameters, it is determined to be in a normal state. When the process status signal exceeds the upper or lower limit of the process reference parameter range, it is determined to be in a deviation state. The sampling time of the deviation state is marked, and the deviation duration is determined according to the time interval between adjacent deviation state sampling times. The degree of deviation is determined according to the numerical difference of the process status signal exceeding the boundary of the process reference parameter range, thereby generating the corresponding process deviation information.

9. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 8, characterized in that, The method for generating the corresponding process control instructions includes: For the process unit corresponding to the current process identity, the corresponding deviation type is determined based on the process deviation information. When the process status signal is higher than the upper limit of the process reference parameter range, it is determined to be an upper deviation state. When the process status signal is lower than the lower limit of the process reference parameter range, it is determined to be a lower deviation state. The control direction is determined based on the deviation type, where the upper deviation state corresponds to the control direction of decreasing the process reference parameter, and the lower deviation state corresponds to the control direction of increasing the process reference parameter; the control amplitude is determined based on the degree of deviation in the process deviation information, and the control duration is determined based on the deviation duration, thereby generating a process control instruction that includes the control direction, control amplitude, and control duration. The process control command is sent to the execution unit of the corresponding process and implemented. During the control execution process, the control execution time, the content of the control command, and the changes in the corresponding process status signal after the control execution are recorded to form control result data.

10. The digital control system for the manufacturing process of heat-resistant fabric core conveyor belts according to claim 9, characterized in that, The method for correcting and updating process baseline parameters based on the control results includes: For the process unit corresponding to the current process identification, obtain the control result data, which includes the process status signals before and after the control execution and the time series data after the control execution; determine whether the process status signal after the control execution falls within the corresponding process reference parameter range, and determine that the control is effective when the process status signal is continuously within the process reference parameter range within a preset continuous time range. If the control is deemed effective, the maximum and minimum values ​​of the process status signal after the control is executed within a preset continuous time range are extracted as the corresponding value ranges. Based on the value ranges, the upper and lower limits of the original process reference parameter range are adjusted to form the updated process reference parameter range. When there are m valid control records, the corresponding value ranges after each control are executed are statistically processed. The statistical processing includes averaging the upper and lower limits of each value range, and iteratively correcting and updating the process reference parameter range based on the statistical results, so that the updated process reference parameter range matches the actual process operation status.

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