Tobacco primary processing control evaluation method, device, equipment and storage medium

Abstract

The embodiment of the invention discloses a tobacco primary processing control evaluation method, device and equipment and a storage medium, and the method comprises the steps: obtaining dynamic process parameter data of target primary processing control equipment, and splitting the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data; determining an adjustment deviation parameter based on the adjustment stage parameter data, and determining an operation deviation parameter based on the operation stage parameter data; and determining a control steady-state evaluation score based on the adjustment deviation parameter and the operation deviation parameter, and taking the control steady-state evaluation score as a processing capability evaluation result of the target cut tobacco making control equipment. According to the technical scheme of the embodiment of the invention, the problems that the existing index steady-state evaluation technology only performs control steady-state evaluation for the stable operation stage and the evaluation system is incomplete are solved, fusion analysis of the adjustment stage and the operation stage can be performed on the process parameter data, the cut tobacco production control evaluation system is further improved, and the quality of cut tobacco production is improved. And the accuracy of cut tobacco making control evaluation is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of tobacco processing detection technology, and in particular to a method, apparatus, equipment and storage medium for controlling and evaluating tobacco processing. Background Technology

[0002] Currently, PID feedback control is widely used in tobacco processing to achieve precise control of multiple process parameters. Optimal configurations of key parameters such as settling time, overshoot, and peak time have been achieved during the system design and optimization phase. To further improve process stability and predictability, real-time acquisition of actual operating data during production is used, and dynamic comparison and analysis are performed with preset second-order system response curves. Combined with online monitoring of actuator parameters such as valve opening and motor speed, system anomalies (such as parameter deviations from thresholds or actuator failures) can be quickly identified and located. Simultaneously, a multi-dimensional evaluation model is constructed based on the changing trends of process parameters. The model's output data is used to predict the system performance degradation cycle, providing scientific data support for the early development of targeted optimization strategies.

[0003] Currently, the evaluation system for various process indicators only focuses on steady-state data (such as target values ​​and standard deviations of indicators under stable operating conditions), while lacking clear evaluation standards and quantitative analysis results for the performance during the system's adjustment and transition phase. However, parameter anomalies during the adjustment phase are crucial to the control accuracy and stability of the system after it enters steady state, therefore, the evaluation system for process indicators needs further improvement. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and storage medium for tobacco processing control and evaluation, which can further improve the tobacco processing control and evaluation system and enhance the accuracy of tobacco processing control and evaluation.

[0005] In a first aspect, embodiments of the present invention provide a method for controlling and evaluating tobacco processing, the method comprising:

[0006] Acquire dynamic process parameter data of the target yarn-making control equipment, and break down the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data; determine adjustment deviation parameters based on the adjustment stage parameter data, and determine operation deviation parameters based on the operation stage parameter data; determine a control steady-state evaluation score based on the adjustment deviation parameters and the operation deviation parameters, and use the control steady-state evaluation score as the processing capacity evaluation result of the target yarn-making control equipment.

[0007] Secondly, embodiments of the present invention provide a tobacco processing control and evaluation device, the device comprising:

[0008] The data acquisition module is used to acquire dynamic process parameter data of the target yarn-making control equipment and split the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data; the deviation parameter determination module is used to determine the adjustment deviation parameter based on the adjustment stage parameter data and the operation deviation parameter based on the operation stage parameter data; the control steady-state evaluation module is used to determine the control steady-state evaluation score based on the adjustment deviation parameter and the operation deviation parameter, and use the control steady-state evaluation score as the processing capacity evaluation result of the target yarn-making control equipment.

[0009] Thirdly, embodiments of the present invention provide a computer device, the computer device comprising:

[0010] One or more processors;

[0011] Memory, used to store one or more programs;

[0012] When the one or more programs are executed by the one or more processors, the one or more processors implement the tobacco processing control and evaluation method described in any embodiment.

[0013] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the tobacco processing control and evaluation method described in any embodiment.

[0014] The technical solution provided by this invention acquires dynamic process parameter data of the target yarn-making control equipment and breaks down this data into adjustment stage parameter data and operation stage parameter data. Based on the adjustment stage parameter data, an adjustment deviation parameter is determined, and based on the operation stage parameter data, an operation deviation parameter is determined. Based on the adjustment deviation parameter and the operation deviation parameter, a control steady-state evaluation score is determined, and this score is used as the evaluation result of the processing capacity of the target yarn-making control equipment. This invention solves the problem that existing index steady-state evaluation techniques only evaluate the control steady-state during the stable operation stage, resulting in an incomplete evaluation system. It allows for the integrated analysis of process parameter data during the adjustment and operation stages, further improving the yarn-making control evaluation system and enhancing its accuracy. Attached Figure Description

[0015] Figure 1 This is a flowchart of a tobacco processing control and evaluation method provided in an embodiment of the present invention;

[0016] Figure 2 This is a flowchart of another tobacco processing control and evaluation method provided in an embodiment of the present invention;

[0017] Figure 3This is a schematic diagram of dynamic process parameter data provided in an embodiment of the present invention;

[0018] Figure 4 This is a schematic diagram of the structure of a tobacco processing control and evaluation device provided in an embodiment of the present invention;

[0019] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. 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. The acquisition, storage, use, and processing of data in the technical solutions of the embodiments of the present invention all comply with the relevant provisions of national laws and regulations.

[0021] Figure 1 This is a flowchart of a tobacco processing control evaluation method provided by an embodiment of the present invention. The embodiment of the present invention can be applied to scenarios where the steady-state control capability of tobacco processing equipment is evaluated. The method can be executed by a tobacco processing control evaluation device, which can be implemented by software and / or hardware.

[0022] like Figure 1 As shown, the tobacco processing control and evaluation method includes the following steps:

[0023] S110. Obtain the dynamic process parameter data of the target yarn-making control equipment, and break down the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data.

[0024] The target tobacco processing control equipment can be tobacco processing equipment that requires steady-state control evaluation. Dynamic process parameter data can be process parameters used to measure the control state of the target tobacco processing control equipment. Specifically, the dynamic process parameter data can include process parameters during the adjustment and operation phases. The technical solution of this embodiment can fuse and analyze the process parameter data during the adjustment and operation phases to improve the tobacco processing control evaluation system and enhance the completeness and accuracy of the tobacco processing control evaluation. Furthermore, the process parameters of the target tobacco processing control equipment can be collected once at a preset time period, and the data set composed of the time-series collected process parameters can be used as dynamic process parameter data. The specific process parameters can be set by the user, such as temperature, humidity, flow rate, and actuator operating data. The technical solution of this embodiment can analyze each process parameter individually, and analyze the stable state of the target tobacco processing control equipment by analyzing the dynamic changes of a single process parameter.

[0025] Furthermore, the adjustment phase parameter data can be the process parameter data of the target yarn-making control equipment during the adjustment phase. Similarly, the operation phase parameter data can be the process parameter data of the target yarn-making control equipment during the operation phase. Specifically, the critical point between the adjustment phase and the operation phase (e.g., reaching a set threshold) can be determined based on a preset judgment method, and then the dynamic process parameter data can be split into adjustment phase parameter data and operation phase parameter data based on this critical point.

[0026] S120. Determine the adjustment deviation parameter based on the adjustment phase parameter data, and determine the operation deviation parameter based on the operation phase parameter data.

[0027] The adjustment deviation parameter can be the deviation parameter generated by the target yarn-making control equipment during the adjustment phase. Specifically, it can be determined by performing anomaly analysis based on the actual values ​​of process parameters in the adjustment phase parameter data and the corresponding fluctuation standard values ​​of the adjustment phase. The operating deviation parameter can be the deviation parameter generated by the target yarn-making control equipment during the operating phase. Specifically, it can be determined by performing anomaly analysis based on the actual values ​​of process parameters in the operating phase parameter data and the corresponding fluctuation standard values ​​of the operating phase.

[0028] S130. Determine the steady-state evaluation score of the control based on the adjustment deviation parameter and the operation deviation parameter, and use the steady-state evaluation score of the control as the evaluation result of the processing capacity of the target yarn-making control equipment.

[0029] The steady-state control evaluation score can be an evaluation score representing the stable control capability of the target yarn-making control equipment. Specifically, the control steady-state evaluation score is obtained by multiplying the adjustment deviation parameter and the operating deviation parameter by their respective weighting coefficients, summing the results, and then subtracting this sum from the full evaluation score. Furthermore, the processing capability evaluation result can be an evaluation result of the stable operating capability of the target yarn-making control equipment. Specifically, the processing capability of the target yarn-making control equipment is directly proportional to the steady-state control evaluation score. By using the steady-state control evaluation score as the processing capability evaluation result of the target yarn-making control equipment, the processing stability of the target yarn-making control equipment can be quantified.

[0030] The technical solution provided by this invention acquires dynamic process parameter data of the target yarn-making control equipment and breaks down this data into adjustment stage parameter data and operation stage parameter data. Based on the adjustment stage parameter data, an adjustment deviation parameter is determined, and based on the operation stage parameter data, an operation deviation parameter is determined. Based on the adjustment deviation parameter and the operation deviation parameter, a control steady-state evaluation score is determined, and this score is used as the evaluation result of the processing capacity of the target yarn-making control equipment. This invention solves the problem that existing index steady-state evaluation techniques only evaluate the control steady-state during the stable operation stage, resulting in an incomplete evaluation system. It allows for the integrated analysis of process parameter data during the adjustment and operation stages, further improving the yarn-making control evaluation system and enhancing its accuracy.

[0031] Figure 2 This is a flowchart of another tobacco processing control evaluation method provided by an embodiment of the present invention. This embodiment of the present invention can be applied to scenarios where the control steady-state capability of tobacco processing equipment is evaluated. Based on the above embodiments, this embodiment further explains how to split dynamic process parameter data into adjustment stage parameter data and operation stage parameter data; how to determine adjustment deviation parameters based on adjustment stage parameter data and operation deviation parameters based on operation stage parameter data; and how to determine the control steady-state evaluation score based on adjustment deviation parameters and operation deviation parameters. This device can be implemented by software and / or hardware and integrated into a computer device with application development capabilities.

[0032] like Figure 2 As shown, the tobacco processing control and evaluation method includes the following steps:

[0033] S210. Obtain the dynamic process parameter data of the target yarn-making control equipment, and take the time point corresponding to the peak point of the process parameter in the dynamic process parameter data that exceeds the target process threshold as the adjustment end time point.

[0034] The target tobacco processing control equipment can be tobacco processing equipment that requires steady-state control evaluation. Dynamic process parameter data can be process parameters used to measure the control state of the target tobacco processing control equipment. Specifically, the dynamic process parameter data can include process parameters during the adjustment and operation phases. The technical solution of this embodiment can fuse and analyze the process parameter data during the adjustment and operation phases to improve the tobacco processing control evaluation system and enhance the completeness and accuracy of the tobacco processing control evaluation. Furthermore, the process parameters of the target tobacco processing control equipment can be collected once at a preset time period, and the data set composed of the time-series collected process parameters can be used as dynamic process parameter data. The specific process parameters can be set by the user, such as temperature, humidity, flow rate, and actuator operating data. The technical solution of this embodiment can analyze each process parameter individually, and analyze the stable state of the target tobacco processing control equipment by analyzing the dynamic changes of a single process parameter.

[0035] Furthermore, the target process threshold can be a reference threshold for the process parameters when the target yarn-making control equipment is operating normally. That is, it is the ideal value that the process parameters of the target yarn-making control equipment need to achieve. If the process parameters of the target yarn-making control equipment fluctuate within a small error range from the target process threshold, it indicates that the target yarn-making control equipment is in a stable state. The adjustment end time point can be the time point at which the adjustment phase of the target yarn-making control equipment ends. Specifically, during the adjustment phase of the target yarn-making control equipment, the process parameters will continuously change to around the target process threshold (e.g., continuously rise to around the target process threshold). Therefore, the time point corresponding to the peak point where the process parameters in the dynamic process parameter data exceed the target process threshold can be used as the adjustment end time point.

[0036] S220. The data from the start time to the end time of adjustment in the dynamic process parameter data shall be used as the adjustment stage parameter data, and the data in other time periods shall be used as the operation stage parameter data.

[0037] The adjustment start time point can be the time point at which the adjustment phase of the target yarn-making control equipment begins. Specifically, the adjustment start time point can be calculated from when the process parameters begin to change, or it can be set manually; there is no limitation here. The adjustment phase parameter data can be the process parameter data of the target yarn-making control equipment during the adjustment phase. Correspondingly, the operation phase parameter data can be the process parameter data of the target yarn-making control equipment during the operation phase.

[0038] S230. Obtain the reference parameter dynamic data corresponding to the adjustment phase parameter data, and determine the cumulative adjustment deviation value based on the reference parameter dynamic data and the adjustment phase parameter data.

[0039] The reference parameter dynamic data serves as a reference for evaluating whether changes in process parameters during the adjustment phase are normal. This reference parameter dynamic data can be manually set or derived based on the adjustment method during the adjustment phase. It may include dynamic change data corresponding to the standard for the process parameters. Based on this data, it can be determined whether there are significant deviations in the changes of process parameters during the adjustment phase. The cumulative adjustment deviation value is the accumulated deviation value generated during the process parameter changes during the adjustment phase. Specifically, the reference parameter dynamic data and the adjustment phase parameter data can be compared to determine the deviation values ​​at multiple time points, and then the deviation values ​​at all time points can be summed to obtain the cumulative adjustment deviation value.

[0040] Optionally, the cumulative deviation value of regulation is determined based on the reference parameter dynamic data and the regulation phase parameter data, including: for each regulation phase data value in the regulation phase parameter data, determining the reference data value corresponding to the regulation phase data value from the regulation phase parameter data, and taking the difference between the regulation phase data value and the reference data value as the regulation data point difference; squaring the regulation data point differences corresponding to all regulation phase data values, summing them, and then taking the square root of the ratio of the sum to the number of regulation data points to obtain the cumulative deviation value of regulation.

[0041] The adjustment phase data value can be the actual process parameter value corresponding to a single time point in the adjustment phase. Specifically, the process parameter value obtained at each acquisition time point in the reference parameter dynamic data can be used as the adjustment phase data value. The reference data value can be the standard process parameter value corresponding to a single time point in the adjustment phase. Specifically, for the adjustment phase data value corresponding to a single time point, the data value corresponding to that time point can be matched from the adjustment phase parameter data, and the matched data value can be used as the reference data value. The adjustment data point difference can be used to represent the degree of deviation between the actual process parameter and the standard process parameter in the adjustment phase. Specifically, the degree of deviation between the actual process parameter and the standard process parameter is proportional to the adjustment data point difference, and the absolute value of the difference between the actual process parameter and the standard process parameter can be used as the adjustment data point difference. Furthermore, the number of adjustment data points can be the number of adjustment phase data values ​​collected in the adjustment phase. The cumulative adjustment deviation value can be obtained by squaring the adjustment data point differences corresponding to all adjustment phase data values, summing them, and then taking the square root of the ratio of the sum to the number of adjustment data points. For example, the formula for determining the cumulative adjustment deviation value θ1 is as follows:

[0042]

[0043] Where n represents the number of adjustment data points, This represents the data value in the i-th adjustment phase. This represents the reference data value corresponding to the data value in the i-th adjustment stage.

[0044] S240. Determine the actual adjustment time based on the adjustment stage parameter data, determine the adjustment time deviation value based on the actual adjustment time and the preset adjustment time, and use the cumulative adjustment deviation value and the adjustment time deviation value as the adjustment deviation parameter.

[0045] The actual adjustment time can be the actual time taken by the target yarn-making control equipment during the adjustment phase. The preset adjustment time can be a reference time used to judge whether the time taken during the adjustment phase is normal. Specifically, the preset adjustment time can be set manually or determined according to the type of target yarn-making control equipment and process parameters. The adjustment time deviation value can be the deviation value corresponding to the time spent by the target yarn-making control equipment during the adjustment phase. Specifically, the absolute value of the difference between the actual adjustment time and the preset adjustment time can be used as the adjustment time deviation value. The adjustment deviation parameter can be the deviation parameter generated by the target yarn-making control equipment during the adjustment phase. Specifically, the cumulative adjustment deviation value and the adjustment time deviation value can be used together as the adjustment deviation parameter.

[0046] S250. Determine the average historical process parameters based on the parameter data of the operation phase, and use the difference between the average historical process parameters and the preset average threshold as the deviation value of the operation average.

[0047] The historical average process parameter can be the average data of the process parameters after the target yarn-making control equipment enters the operation phase. For example, an operating parameter sequence can be constructed based on the process parameters after the target yarn-making control equipment enters the operation phase. Each time new data is added to the sequence, the average data in the sequence is recalculated, and the recalculated average is used as the updated historical average process parameter. The preset average threshold can be a reference threshold for evaluating the average process parameters during the operation phase. Specifically, the preset average threshold can be set manually, determined based on the type of target yarn-making control equipment and process parameters, or the aforementioned target process threshold can be used as the preset average threshold. The operating average deviation value can represent the degree of deviation between the operating average process parameter and the standard average. Specifically, the absolute value of the difference between the historical average process parameter and the preset average threshold can be used as the operating average deviation value.

[0048] S260. Determine the actual operating deviation value based on the operating phase parameter data and the target process threshold, and determine the dynamic operating deviation value based on the actual operating deviation value and the preset deviation threshold. Use the average operating deviation value and the dynamic operating deviation value as operating deviation parameters.

[0049] The actual operating deviation value can be the deviation between the real-time process parameters and the standard values ​​during the operating phase. Specifically, the difference between the current process parameter value and the target process threshold in the operating phase parameter data can be used as the actual operating deviation value. The preset deviation threshold can be a reference threshold used to evaluate the actual deviation value during the operating phase. For example, the preset deviation threshold can be set manually or determined according to the type of target yarn-making control equipment and process parameters. The dynamic operating deviation value can be used to represent the degree of difference between the actual deviation value and the reference deviation value of the process parameters during the operating phase. Specifically, the absolute value of the difference between the actual operating deviation value and the preset deviation threshold can be used as the dynamic operating deviation value.

[0050] Operating deviation parameters can be the deviation parameters generated by the target yarn-making control equipment during operation. Specifically, the average operating deviation value and the dynamic operating deviation value are used together as operating deviation parameters.

[0051] S270. The cumulative deviation value, adjustment time deviation value, average running deviation value, and dynamic running deviation value are multiplied by their respective weighting coefficients and summed. The difference between the preset evaluation benchmark value and the sum is used to obtain the control steady-state evaluation score. The control steady-state evaluation score is used as the evaluation result of the processing capacity of the target yarn-making control equipment.

[0052] The preset evaluation benchmark value can be an evaluation reference value corresponding to the stable control of the target yarn-making control equipment. The control steady-state evaluation score can be an evaluation score used to represent the stable control capability of the target yarn-making control equipment. Specifically, the cumulative adjustment deviation value, adjustment time deviation value, operating average deviation value, and dynamic operating deviation value can be multiplied by their respective corresponding weight coefficients (which can be set manually), summed, and then the preset evaluation benchmark value is subtracted from the summed value to obtain the control steady-state evaluation score. For example, the formula for determining the control steady-state evaluation score is: α = 100 - (k1θ1 + k2θ1 + k3θ1 + k4θ1).

[0053] Wherein, θ1 represents the cumulative adjustment deviation value, and k1 represents the weight coefficient corresponding to the cumulative adjustment deviation value; θ2 represents the adjustment time deviation value, and k2 represents the weight coefficient corresponding to the adjustment time deviation value; θ3 represents the running average deviation value, and k3 represents the weight coefficient corresponding to the running average deviation value; θ4 represents the dynamic running deviation value, and k4 represents the weight coefficient corresponding to the dynamic running deviation value.

[0054] Furthermore, the processing capability evaluation result can be used to evaluate the stable operating capability of the target yarn-making control equipment. Specifically, the processing capability of the target yarn-making control equipment is directly proportional to the steady-state control evaluation score. By using the steady-state control evaluation score as the processing capability evaluation result of the target yarn-making control equipment, the processing stability of the target yarn-making control equipment can be quantified.

[0055] Optionally, if the steady-state evaluation score is lower than the preset evaluation threshold, a control anomaly warning message corresponding to the target silk-making control equipment can be generated and sent to the equipment management terminal corresponding to the target silk-making control equipment.

[0056] The preset evaluation threshold can be a pre-defined reference threshold used to judge the steady-state evaluation score of the control system. Specifically, the preset evaluation threshold can be set manually or matched according to the type of process parameters; this is not limited here. The control anomaly warning information can be warning information about abnormal control status of the target yarn-making control equipment. Specifically, if the steady-state evaluation score is lower than the preset evaluation threshold, it indicates that the process parameters of the target yarn-making control equipment are fluctuating drastically and the control status is unstable. In this case, a control anomaly warning information corresponding to the target yarn-making control equipment can be generated and sent to the equipment management terminal corresponding to the target yarn-making control equipment, so that the maintenance personnel at the equipment management terminal can promptly receive the anomaly information of the target yarn-making control equipment and carry out subsequent maintenance as soon as possible.

[0057] For example, in order to better understand the technical solution provided by the present invention, specific embodiments are described below: Figure 3 This is a schematic diagram of dynamic process parameter data provided in an embodiment of the present invention. For example... Figure 3 As shown, dynamic process parameter data can be divided into adjustment and operation phases. The solid line represents the dynamic change curve fitted based on the collected process parameter values, the horizontal dashed line represents the target process threshold, and the vertical curve represents the boundary point between the adjustment and operation phases, corresponding to the adjustment end time point. The workflow for evaluating tobacco processing control includes the following steps:

[0058] System model fitting: The transfer function is directly obtained or derived from system settings such as PID coefficients and target values, generating theoretical control feedback values ​​for the system control timing. This can be achieved using tools such as Python.

[0059] Timing determination: Determine the timing of the adjustment phase and the operation phase based on the above content;

[0060] Data Acquisition: A mature data acquisition system covering the entire production process has been established, which can acquire key process parameters such as temperature, humidity, and flow rate, as well as actuator operating data in real time;

[0061] System capability evaluation is divided into two stages: adjustment and operation. The adjustment stage is based on the cumulative deviation of the actual timing process parameters and the adjustment time deviation between the actual adjustment time and the set adjustment time. The operation stage uses the deviation between the average of the actual operation data and the standard average. The dynamic operation deviation is calculated as (actual standard deviation - system set standard deviation).

[0062] Fitting system capability evaluation model: Evaluation on a percentage scale α = 100 - (k1θ1 + k2θ1 + k3θ1 + k4θ1).

[0063] Data recording and analysis: Record evaluation scores

[0064] Data analysis: Analyze the scoring trend, determine the decline in process execution capability, identify the control loops where the execution capability drops to the threshold, accurately identify system faults, and ensure the overall system operation capability.

[0065] The technical solutions provided by this invention can monitor the operating status of key actuators such as valves and fans in real time. Combined with the dynamic changes in related process parameters such as medium pressure and flow rate, it can promptly identify and warn of abnormalities such as actuator jamming, mechanical wear, or medium leakage, ensuring the continuous and stable operation of the process. Furthermore, based on the dynamic change patterns of various process parameters and historical data models, it can accurately predict the process stabilization time of core subsystems such as loosening and rehydration, and blade drying, formulating equipment start-up and shutdown optimization strategies adapted to the production cycle, effectively reducing equipment idling energy consumption and helping enterprises achieve their core goals of cost reduction and efficiency improvement. Moreover, by deeply analyzing system performance degradation characteristics such as decreased control precision and slower response speed, it can construct a multi-dimensional performance degradation prediction model, proactively plan equipment maintenance cycles and process optimization schemes, extend system lifespan, and reduce the risk of unplanned downtime.

[0066] The technical solution provided in this invention acquires dynamic process parameter data of the target yarn-making control equipment, and uses the time point corresponding to the peak point where the process parameter exceeds the target process threshold in the dynamic process parameter data as the adjustment end time point; uses the data from the adjustment start time point to the adjustment end time point in the dynamic process parameter data as adjustment stage parameter data, and uses the data in other time periods as operation stage parameter data; acquires reference parameter dynamic data corresponding to the adjustment stage parameter data, and determines the cumulative adjustment deviation value based on the reference parameter dynamic data and the adjustment stage parameter data; determines the actual adjustment time based on the adjustment stage parameter data, and determines the adjustment time based on the actual adjustment time and the preset adjustment time. The deviation value is calculated by using the cumulative adjustment deviation value and the adjustment time deviation value as adjustment deviation parameters; the historical process parameter mean is determined based on the parameter data of the operation phase, and the difference between the historical process parameter mean value and the preset mean threshold value is used as the operation mean deviation value; the actual operation deviation value is determined based on the parameter data of the operation phase and the target process threshold value, and the dynamic operation deviation value is determined based on the actual operation deviation value and the preset deviation threshold value, and the operation mean deviation value and the dynamic operation deviation value are used as operation deviation parameters; the cumulative adjustment deviation value, the adjustment time deviation value, the operation mean deviation value, and the dynamic operation deviation value are multiplied by their corresponding weight coefficients and summed, and the difference between the preset evaluation benchmark value and the sum value is used to obtain the control steady-state evaluation score.

[0067] The technical solution of this invention solves the problem that existing steady-state evaluation technologies only evaluate the steady-state control during the stable operation phase, resulting in an incomplete evaluation system. It can integrate the analysis of process parameter data during the adjustment and operation phases, further improve the yarn production control evaluation system, and enhance the accuracy of yarn production control evaluation.

[0068] Figure 4 This is a schematic diagram of a tobacco processing control and evaluation device provided in an embodiment of the present invention. The embodiment of the present invention can be applied to scenarios where the steady-state control capability of tobacco processing equipment is evaluated. The device can be implemented by software and / or hardware and integrated into a computer device with application development capabilities.

[0069] like Figure 4 As shown, the tobacco processing control and evaluation device includes: a data acquisition module 310, a control parameter determination module 320, and a control parameter determination module 330.

[0070] The data acquisition module 310 is used to acquire dynamic process parameter data of the target yarn-making control equipment and split the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data; the deviation parameter determination module 320 is used to determine the adjustment deviation parameter based on the adjustment stage parameter data and the operation deviation parameter based on the operation stage parameter data; the control steady-state evaluation module 330 is used to determine the control steady-state evaluation score based on the adjustment deviation parameter and the operation deviation parameter, and use the control steady-state evaluation score as the processing capacity evaluation result of the target yarn-making control equipment.

[0071] The technical solution provided by this invention acquires dynamic process parameter data of the target yarn-making control equipment and breaks down this data into adjustment stage parameter data and operation stage parameter data. Based on the adjustment stage parameter data, an adjustment deviation parameter is determined, and based on the operation stage parameter data, an operation deviation parameter is determined. Based on the adjustment deviation parameter and the operation deviation parameter, a control steady-state evaluation score is determined, and this score is used as the evaluation result of the processing capacity of the target yarn-making control equipment. This invention solves the problem that existing index steady-state evaluation techniques only evaluate the control steady-state during the stable operation stage, resulting in an incomplete evaluation system. It allows for the integrated analysis of process parameter data during the adjustment and operation stages, further improving the yarn-making control evaluation system and enhancing its accuracy.

[0072] In one optional implementation, the road information acquisition module 310 is specifically used to: take the time point corresponding to the peak point where the process parameter in the dynamic process parameter data exceeds the target process threshold as the adjustment end time point; take the data from the adjustment start time point to the adjustment end time point in the dynamic process parameter data as the adjustment stage parameter data, and take the data in other time periods as the operation stage parameter data.

[0073] In one optional implementation, the deviation parameter determination module 320 includes: a adjustment phase deviation analysis unit, configured to: acquire reference parameter dynamic data corresponding to the adjustment phase parameter data; determine a cumulative adjustment deviation value based on the reference parameter dynamic data and the adjustment phase parameter data; determine an actual adjustment time based on the adjustment phase parameter data; determine an adjustment time deviation value based on the actual adjustment time and a preset adjustment time; and use the cumulative adjustment deviation value and the adjustment time deviation value as the adjustment deviation parameter.

[0074] In an optional implementation, the deviation parameter determination module 320 includes: an adjustment cumulative deviation analysis unit, configured to: for each adjustment stage data value in the adjustment stage parameter data, determine a reference data value corresponding to the adjustment stage data value from the adjustment stage parameter data, and use the difference between the adjustment stage data value and the reference data value as the adjustment data point difference; square the adjustment data point differences corresponding to all adjustment stage data values, sum them, and then take the square root of the ratio of the sum to the number of adjustment data points to obtain the adjustment cumulative deviation value.

[0075] In an optional implementation, the deviation parameter determination module 320 further includes: an operation deviation analysis unit, configured to: determine the historical average process parameter based on the operation stage parameter data, and use the difference between the historical average process parameter and a preset average threshold as the operation average deviation value; determine the actual operation deviation value based on the operation stage parameter data and a target process threshold, and determine the dynamic operation deviation value based on the actual operation deviation value and the preset deviation threshold; and use the operation average deviation value and the dynamic operation deviation value as the operation deviation parameter.

[0076] In one optional implementation, the control steady-state evaluation module 330 is specifically used to: multiply the cumulative adjustment deviation value, the adjustment time deviation value, the operating mean deviation value, and the dynamic operating deviation value by their respective weighting coefficients and then sum them up, and subtract the preset evaluation benchmark value from the sum value to obtain the control steady-state evaluation score.

[0077] In one optional embodiment, the tobacco processing control evaluation device further includes a control anomaly early warning module, used to generate control anomaly early warning information corresponding to the target tobacco processing control device when the control steady-state evaluation score is lower than a preset evaluation threshold, and send the control anomaly early warning information to the device management terminal corresponding to the target tobacco processing control device.

[0078] The tobacco processing control and evaluation device provided in the embodiments of the present invention can execute the tobacco processing control and evaluation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0079] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Figure 5 A block diagram of an exemplary computer device 12 suitable for implementing embodiments of the present invention is shown. Figure 5 The computer device 12 shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. The computer device 12 can be any terminal device with computing capabilities and can be configured in a tobacco processing control and evaluation device.

[0080] like Figure 5 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0081] Bus 18 can be one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0082] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0083] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0084] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0085] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 5 As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although... Figure 5 As not shown, it can be used in conjunction with computer device 12 with other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0086] Processing unit 16 executes various functional applications and data processing by running programs stored in system memory 28, such as implementing the tobacco processing control and evaluation method provided in this embodiment of the invention, which includes:

[0087] Acquire dynamic process parameter data of the target yarn-making control equipment, and break down the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data; determine adjustment deviation parameters based on the adjustment stage parameter data, and determine operation deviation parameters based on the operation stage parameter data; determine a control steady-state evaluation score based on the adjustment deviation parameters and the operation deviation parameters, and use the control steady-state evaluation score as the processing capacity evaluation result of the target yarn-making control equipment.

[0088] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the tobacco processing control and evaluation method as provided in any embodiment of the present invention, including:

[0089] Acquire dynamic process parameter data of the target yarn-making control equipment, and break down the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data; determine adjustment deviation parameters based on the adjustment stage parameter data, and determine operation deviation parameters based on the operation stage parameter data; determine a control steady-state evaluation score based on the adjustment deviation parameters and the operation deviation parameters, and use the control steady-state evaluation score as the processing capacity evaluation result of the target yarn-making control equipment.

[0090] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0091] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0092] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0093] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as C, Java, Smalltalk, C++, C#, and Python, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0094] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0095] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for controlling and evaluating tobacco processing, characterized in that, include: Acquire dynamic process parameter data of the target yarn-making control equipment, and break down the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data; The adjustment deviation parameter is determined based on the adjustment phase parameter data, and the operation deviation parameter is determined based on the operation phase parameter data; The control steady-state evaluation score is determined based on the adjustment deviation parameter and the operation deviation parameter, and the control steady-state evaluation score is used as the processing capability evaluation result of the target yarn-making control equipment.

2. The method according to claim 1, characterized in that, The step of splitting the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data includes: The time point corresponding to the peak point where the process parameter in the dynamic process parameter data exceeds the target process threshold is taken as the adjustment end time point. The data from the start time to the end time of adjustment in the dynamic process parameter data is used as the adjustment stage parameter data, and the data in other time periods are used as the operation stage parameter data.

3. The method according to claim 1, characterized in that, The determination of the adjustment deviation parameter based on the adjustment phase parameter data includes: Obtain the reference parameter dynamic data corresponding to the adjustment phase parameter data, and determine the cumulative adjustment deviation value based on the reference parameter dynamic data and the adjustment phase parameter data; The actual adjustment time is determined based on the adjustment stage parameter data, and the adjustment time deviation value is determined based on the actual adjustment time and the preset adjustment time. The cumulative adjustment deviation value and the adjustment time deviation value are used as the adjustment deviation parameters.

4. The method according to claim 3, characterized in that, The step of determining the cumulative adjustment deviation value based on the reference parameter dynamic data and the adjustment phase parameter data includes: For each adjustment stage data value in the adjustment stage parameter data, a reference data value corresponding to the adjustment stage data value is determined from the adjustment stage parameter data, and the difference between the adjustment stage data value and the reference data value is used as the adjustment data point difference. The cumulative adjustment deviation value is obtained by squaring the differences between the adjustment data points corresponding to all adjustment stage data values, summing them, and then taking the square root of the ratio of the sum to the number of adjustment data points.

5. The method according to claim 3, characterized in that, The determination of operational deviation parameters based on the operational phase parameter data includes: The historical average process parameter value is determined based on the operational phase parameter data, and the difference between the historical average process parameter value and the preset average threshold value is used as the operational average deviation value. The actual operating deviation value is determined based on the operating phase parameter data and the target process threshold, and the dynamic operating deviation value is determined based on the actual operating deviation value and the preset deviation threshold. The mean deviation value and the dynamic deviation value are used as the operating deviation parameters.

6. The method according to claim 5, characterized in that, The determination of the control steady-state evaluation score based on the adjustment deviation parameter and the operating deviation parameter includes: The cumulative adjustment deviation value, the adjustment time deviation value, the average operating deviation value, and the dynamic operating deviation value are multiplied by their respective weighting coefficients and then summed. The difference between the preset evaluation benchmark value and the sum is used to obtain the control steady-state evaluation score.

7. The method according to claim 1, characterized in that, The method further includes: If the control steady-state evaluation score is lower than the preset evaluation threshold, a control anomaly warning message corresponding to the target silk-making control device is generated, and the control anomaly warning message is sent to the device management terminal corresponding to the target silk-making control device.

8. A tobacco processing control and evaluation device, characterized in that, The device includes: The data acquisition module is used to acquire dynamic process parameter data of the target yarn-making control equipment and to break down the dynamic process parameter data into adjustment stage parameter data and operation stage parameter data. The deviation parameter determination module is used to determine the adjustment deviation parameter based on the adjustment phase parameter data, and to determine the operation deviation parameter based on the operation phase parameter data; The control steady-state evaluation module is used to determine the control steady-state evaluation score based on the adjustment deviation parameter and the operation deviation parameter, and to use the control steady-state evaluation score as the processing capability evaluation result of the target yarn-making control equipment.

9. A computer device, characterized in that, The computer device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the tobacco processing control and evaluation method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the tobacco processing control and evaluation method as described in any one of claims 1-7.

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