Intelligent production control method and system for potassium chloride granular preparation composition

Abstract

The invention discloses an intelligent production control method and system for a potassium chloride granular preparation composition, relates to the technical field of production control, and solves the technical problem that in the prior art, synchronous analysis cannot be performed through finished products produced by a preparation production line and the equipment operation state of the corresponding production line, and the operation efficiency of the preparation production line is reduced. The method specifically comprises the following steps: production characteristic analysis: carrying out production line characteristic analysis on a potassium chloride granular preparation composition, carrying out characteristic analysis on a preparation production line, carrying out time period division according to data acquisition and analysis, and carrying out targeted control according to time period types; the operation parameters are monitored in a floating manner in a complex characteristic or dynamic high influence period; control precision requirement analysis: performing processing control precision requirement analysis on each process of the preparation production line, and dividing the processes into a high-precision process and a low-precision process according to data analysis; after analysis of the preparation production line is completed, targeted control is conducted, and production quality detection is conducted on the preparation production line.

Description

Technical Field

[0001] The present invention relates to the technical field of production control, and particularly to an intelligent production control method and system for a potassium chloride granule preparation composition. Background Art

[0002] Potassium chloride granule preparation compositions usually contain potassium chloride as the main active ingredient. When preparing, it is first necessary to ensure that the quality of the potassium chloride raw material meets the pharmaceutical standards and conduct inspections on it, including the detection of indicators such as purity and impurity content; at the same time, various excipients are prepared, such as fillers, binders, lubricants, disintegrants, etc., and these excipients also need to meet the corresponding quality standards.

[0003] However, in the prior art, the preparation production line cannot perform characteristic analysis on the production line, so that the production line cannot be controlled specifically during processing at different times, and the control requirements for the process accuracy cannot be controlled, resulting in a decrease in the operating stability of each process and the inability to quickly trace back. In addition, the production quality of the preparation production line cannot be detected, and the output products of the preparation production line and the operating status of the corresponding production line equipment cannot be analyzed synchronously, reducing the operating efficiency of the preparation production line.

[0004] In view of the above technical defects, a solution is proposed now. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and propose an intelligent production control method and system for a potassium chloride granule preparation composition.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An intelligent production control system for a potassium chloride granule preparation composition includes a production control platform, wherein the production control platform is communicatively connected to a production characteristic analysis unit, a control precision requirement analysis unit, and a production quality real-time detection unit;

[0008] The production characteristic analysis unit conducts production line characteristic analysis on the potassium chloride granule preparation composition, conducts characteristic analysis on the preparation production line, divides time periods according to data collection and analysis, and sends them to the production control platform;

[0009] The control precision requirement analysis unit conducts analysis on the processing control precision requirements of each process of the preparation production line, and divides the processes into high-precision processes and low-precision processes according to data analysis;

[0010] After completing the analysis of the preparation production line, targeted control is carried out;

[0011] The production quality real-time detection unit conducts production quality detection on the preparation production line.

[0012] As a preferred embodiment of the present invention, the process of the production characteristic analysis unit is as follows:

[0013] The operation data of interrelated processes collected at different operation production speeds are used to control the corresponding numerical control deviation and the span value of the actual production speed exceeding the set operation production speed through an algorithm. The numerical control deviation and the production speed exceeding span are numerically compared to obtain the control influence ratio; the response buffer duration of the production line adaptation operation when the production line parameters change instantaneously during the operation of the preparation production line is obtained.

[0014] As a preferred embodiment of the present invention, if the control influence ratio exceeds the ratio threshold, or the response buffer duration of the production line adaptation operation exceeds the duration threshold, the operation period of the preparation production line is marked as a complex production line period or a dynamic high-influence period; if the control influence ratio does not exceed the ratio threshold and the response buffer duration of the production line adaptation operation does not exceed the duration threshold, the operation period of the preparation production line is marked as a stable production line period or a dynamic low-influence period, and is synchronously transferred to the production control platform.

[0015] As a preferred embodiment of the present invention, the process of the control precision requirement analysis unit is as follows:

[0016] The actual parameter floating unit quantity change span and the corresponding actual parameter numerical monitoring deviation value of the operation parameters of each process in the preparation production line are collected, and the ratio calculation is performed to obtain the change deviation ratio; at the same time, the span increase value of the operation environment parameters of each process in the preparation production line exceeding the control range and the duration of the production speed of the production line not reaching the set production speed value in the current period are collected, and the span duration ratio is obtained according to the ratio calculation.

[0017] As a preferred embodiment of the present invention, if the change deviation ratio does not exceed the span deviation ratio threshold, or the span duration ratio does not exceed the span duration ratio threshold, it is inferred that the control precision requirement analysis of the current process of the preparation production line is abnormal, and the current process is marked as a high-precision requirement process; if the change deviation ratio exceeds the span deviation ratio threshold and the span duration ratio exceeds the span duration ratio threshold, it is inferred that the control precision requirement analysis of the current process of the preparation production line is normal, and the current process is marked as a low-precision requirement process.

[0018] As a preferred embodiment of the present invention, the process of the production quality real-time detection unit is as follows:

[0019] The operation period of the preparation production line is divided into several operation moments, and the numerical ratio of the actual operation data and the rated operation data of the equipment constituting the preparation production line corresponding to each operation moment is collected; if the numerical ratio of the operation data exceeds the numerical ratio threshold, the current operation moment is marked as a high-intensity operation moment; otherwise, if the numerical ratio of the operation data does not exceed the numerical ratio threshold, the current operation moment is marked as a low-intensity operation moment.

[0020] As a preferred embodiment of the present invention, when the actually set operating parameters are within the rated operating data range, the reduction span of the ratio of the number of device operating satisfaction times to dissatisfaction times of the currently collected operating parameters of the preparation production line is collected, and the reduction span of the ratio is analyzed. If the reduction span of the ratio exceeds the reduction span threshold, the current operating time is marked as the device stable time; conversely, if the reduction span of the ratio does not exceed the reduction span threshold, the current operating time is marked as the device abnormal time.

[0021] As a preferred embodiment of the present invention, the finished product performance parameters of the preparation production line corresponding to each operating time are collected; the finished product performance parameters are statistically analyzed and a curve is constructed according to the corresponding operating time; the quality of the produced finished product is inferred according to the floating trend of the curve, that is, when the curve is lower than the red line value and shows a downward trend or a slow upward trend, the period constructed according to the corresponding operating time is marked as the low-quality period; when the curve is higher than the red line value and shows a downward trend and continues to decline without an upward trend, the period constructed according to the corresponding operating period is marked as the quality decline period; the remaining periods are marked as normal periods, that is, normal operation without treatment.

[0022] As a preferred embodiment of the present invention, the low-quality period and the quality decline period are analyzed, and the overlapping frequency of the high-intensity operating time and the device abnormal time within the corresponding period is collected, and the rising span of the number of overlaps between the low-intensity operating time and the device abnormal time:

[0023] If the overlapping frequency of the high-intensity operating time and the device abnormal time within the corresponding period exceeds the overlapping frequency threshold, a load abnormal signal is generated; if the rising span of the number of overlaps between the low-intensity operating time and the device abnormal time exceeds the number rising span threshold, a device state abnormal signal is generated; if the overlapping frequency of the high-intensity operating time and the device abnormal time within the corresponding period does not exceed the overlapping frequency threshold, and the rising span of the number of overlaps between the low-intensity operating time and the device abnormal time does not exceed the number rising span threshold, a normal production quality detection signal is generated and the normal production quality detection signal is sent to the production control platform.

[0024] An intelligent production control method for a potassium chloride granule preparation composition, the production control method is as follows:

[0025] Production characteristic analysis, analyze the production line characteristics of the potassium chloride granule preparation composition, analyze the characteristics of the preparation production line, divide the time period according to data collection and analysis, and perform targeted control according to the time period type; that is, monitor the floating of the operating parameters in the complex characteristics or dynamic high-impact time periods;

[0026] Analysis of control accuracy requirements, analyze the control accuracy requirements of each process of the preparation production line, and divide the processes into high-precision processes and low-precision processes according to data analysis;

[0027] After completing the analysis of the preparation production line, targeted control is carried out, and production quality inspection is carried out on the preparation production line.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] 1. In the present invention, through the analysis of the characteristics of the production line, adaptive adjustment can be made according to real-time characteristics, ensuring that the production line can have a fast response ability during the rapid change process, and can also match the appropriate production line speed when controlling the output speed, ensuring the feasibility of the production line execution, improving the control efficiency of each link of the production line, and guaranteeing the production capacity of the production line at each stage.

[0030] 2. In the present invention, through the analysis of the control precision requirements, the processes are classified so as to be able to control the processing precision of the production line targeted, ensuring that the output finished products of the preparation production line can meet the actual processing requirements. At the same time, when the quality of the output finished products of the preparation production line cannot meet the requirements, traceability control can also be carried out according to the process type to reduce the abnormal processing time of the preparation production line and reduce the impact brought by the production of defective products.

[0031] 3. In the present invention, production quality inspection is carried out on the preparation production line. By synchronously analyzing the output finished products of the preparation production line and the operation status of the corresponding production line equipment, the limitation of evaluating production quality by the quality of the output finished products is reduced, so as to improve the pertinence of control when the production quality of the preparation production line is abnormal, such as adjusting and optimizing the production line process or optimizing and maintaining the performance of the production line equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0033] Figure 1 is the system principle block diagram of the present invention;

[0034] Figure 2 is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In order to enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] Reference to "embodiments" in this specification means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0037] Please refer to Figure 1 As shown, an intelligent production control system for a potassium chloride granule preparation composition includes a production control platform, wherein the production control platform is communicatively connected to a production characteristic analysis unit, a control precision requirement analysis unit, and a production quality real-time detection unit;

[0038] The production control platform generates a production characteristic analysis signal and sends the production characteristic analysis signal to the production characteristic analysis unit;

[0039] The production characteristic analysis unit is used to receive the production characteristic analysis signal and analyze the production line characteristics of the potassium chloride granule preparation composition, hereinafter collectively referred to as the preparation production line. Through the production line characteristic analysis, it can be adaptively adjusted according to real-time characteristics to ensure that the production line can have a fast response ability during the rapid change process, and can also match the appropriate production line speed when controlling the output speed, ensuring the execution feasibility of the production line, improving the control efficiency of each link of the production line, and ensuring the production capacity of the production line at each stage;

[0040] Analyze the characteristics of the preparation production line. There are multiple interrelated processes set in the preparation production line, such as raw material mixing, granulation, drying, packaging, etc., and the parameters of each link affect each other; the operation data of the interrelated processes collected at different operating production speeds are used to control the corresponding numerical control deviation and the span value of the actual production speed exceeding the set operating production speed through an algorithm. The numerical control deviation and the span of the production speed exceeding are numerically compared to obtain a control influence ratio, where the numerical comparison only compares the numerical values of the two parameters, and the production speed is expressed as the output quantity speed of the production line;

[0041] Obtain the response buffer duration of the production line for adapting operation when the production line parameters change instantaneously during the operation of the preparation production line, where the production line parameters are expressed as parameters such as the temperature in the drying stage and the material flow rate, and the adapting operation means that after the production line parameters change, the production line still ensures the current production speed according to the changed production line;

[0042] Compare the control influence ratio and the response buffer duration of the production line for adapting operation with a ratio threshold and a duration threshold respectively:

[0043] If the control influence ratio exceeds the ratio threshold, or the response buffer duration for the production line to adapt and operate exceeds the duration threshold, it is inferred that the characteristic analysis of the current pharmaceutical production line is abnormal, and the operation period of the pharmaceutical production line is marked as a complex production line period or a dynamic high-influence period; and the various types of periods of the pharmaceutical production line are sent to the production control platform. After receiving the various types of periods, the production control platform performs targeted parameter control according to the real-time operation process, and at the same time conducts source control according to the fluctuation of the operation parameters during the operation period to reduce the fluctuation frequency of the production line operation parameters;

[0044] If the control influence ratio does not exceed the ratio threshold, and the response buffer duration for the production line to adapt and operate does not exceed the duration threshold, it is inferred that the characteristic analysis of the current pharmaceutical production line is normal, and the operation period of the pharmaceutical production line is marked as a stable production line period or a dynamic low-influence period, and is synchronously transferred to the production control platform;

[0045] At the same time, a control precision requirement analysis signal is generated and sent to the control precision requirement analysis unit;

[0046] The control precision requirement analysis unit is used to analyze the control precision requirements for each process of the pharmaceutical production line. Through the control precision requirement analysis, the processes are classified so as to be able to perform targeted production line processing precision control, ensure that the output products of the pharmaceutical production line can meet the actual processing requirements, and at the same time, when the quality of the output products of the pharmaceutical production line cannot be met, it can also perform traceability control according to the process type to reduce the abnormal processing duration of the pharmaceutical production line and reduce the impact of producing defective products;

[0047] Collect the actual parameter floating unit quantity change span and the corresponding actual parameter value monitoring deviation value of each process operation parameter in the pharmaceutical production line, and calculate the ratio to obtain the change deviation ratio. Among them, the operation parameter is expressed as the operation parameter of the pharmaceutical production line process, such as the uniformity of the finished product content, the particle size, the environmental temperature, and the humidity; the unit quantity is expressed as the floating scale value of the parameter; for example, the uniformity of one minute changes from 1 to 0.1;

[0048] At the same time, collect the span increase value of the operation environment parameters of each process in the pharmaceutical production line exceeding the control range and the duration of the production line production speed not reaching the set production speed value in the current period, and calculate the ratio to obtain the span duration ratio;

[0049] Compare the change deviation ratio and the span duration ratio with the span deviation ratio threshold and the span duration ratio threshold respectively:

[0050] If the change deviation ratio does not exceed the span deviation ratio threshold, or the span duration ratio does not exceed the span duration ratio threshold, it is inferred that the control precision requirement analysis of the current process of the pharmaceutical production line is abnormal, and the current process is marked as a high-precision requirement process;

[0051] If the variation deviation ratio exceeds the span deviation ratio threshold and the span duration ratio exceeds the span duration ratio threshold, it is inferred that the analysis of the control precision requirements for the current process of the preparation production line is normal, and the current process is marked as a low-precision requirement process;

[0052] Send the high-precision processes and low-precision processes to the production control platform together, and the production control platform receives and conducts targeted control according to the process type;

[0053] After completing the analysis of the preparation production line, the production control platform generates a real-time production quality detection signal and sends the real-time production quality detection signal to the real-time production quality detection unit;

[0054] The real-time production quality detection unit is used to conduct production quality detection on the preparation production line after receiving the real-time production quality detection signal. By synchronously analyzing the output finished products of the preparation production line and the operating status of the corresponding production line equipment, it reduces the limitations of evaluating production quality based on the quality of the output finished products, so as to improve the pertinence of control when the production quality of the preparation production line is abnormal, such as adjusting and optimizing the production line process or optimizing and maintaining the performance of the production line equipment;

[0055] Divide the operation period of the preparation production line into several operation moments, and collect the numerical ratio of the actual operation data and the rated operation data of the equipment constituting the preparation production line corresponding to each operation moment. The operation data is expressed as the operation parameters of the equipment, such as the stirring speed or stirring volume of the stirring equipment, etc. The purpose of collecting this data is to analyze the working intensity of the operating equipment; if the numerical ratio of the operation data exceeds the numerical ratio threshold, the current operation moment is marked as a high-intensity operation moment; otherwise, if the numerical ratio of the operation data does not exceed the numerical ratio threshold, the current operation moment is marked as a low-intensity operation moment;

[0056] At the same time, when the actual set operation parameters are within the range of the rated operation data, collect the reduction span of the quantity ratio of the equipment operation satisfaction moments and dissatisfaction moments of the current operation parameters of the preparation production line, and analyze the reduction span of the quantity ratio. If the reduction span of the quantity ratio exceeds the reduction span threshold, the current operation moment is marked as a device stable moment; otherwise, if the reduction span of the quantity ratio does not exceed the reduction span threshold, the current operation moment is marked as a device abnormal moment;

[0057] Collect the finished product performance parameters corresponding to each operation moment of the preparation production line. The performance parameters are expressed as parameters reflecting product performance such as particle size distribution, content uniformity, moisture content, etc.; and statistically analyze the finished product performance parameters and construct a curve according to the corresponding operation moment; infer the quality of the output finished product according to the floating trend of the curve, that is, when the curve is below the red line value and shows a downward trend or a slow upward trend, the period constructed by the corresponding operation moment is marked as a low-quality period; the slow upward trend is expressed as an upward speed lower than the set value;

[0058] When the curve is above the red line value and shows a downward trend and continues to decline without an upward trend, the time period constructed for the corresponding operation period is marked as a quality decline period; the remaining time periods are marked as normal periods, that is, normal operation without processing;

[0059] Analyze the low-quality periods and quality decline periods, collect the overlapping frequency of high-intensity operation moments and device abnormal moments within the corresponding periods, and the rising span of the number of overlapping times between low-intensity operation moments and device abnormal moments. Then compare the overlapping frequency of high-intensity operation moments and device abnormal moments, and the rising span of the number of overlapping times between low-intensity operation moments and device abnormal moments within the corresponding periods with the overlapping frequency threshold and the rising span threshold respectively:

[0060] If the overlapping frequency of high-intensity operation moments and device abnormal moments within the corresponding period exceeds the overlapping frequency threshold, it is inferred that the equipment operation load in the low-quality period or quality decline period is high and the performance of the equipment itself has declined, generating a load abnormal signal and sending the load abnormal signal to the production control platform; the production control platform obtains the abnormal cause of the low-quality period or quality decline period based on the quantity comparison between high-intensity operation moments and device abnormal moments, that is, the impact caused by equipment load or the impact caused by abnormal equipment performance;

[0061] If the rising span of the number of overlapping times between low-intensity operation moments and device abnormal moments exceeds the rising span threshold, it is inferred that the equipment operation state is abnormal in the low-quality period or quality decline period, generating an equipment state abnormal signal and sending the equipment state abnormal signal to the production control platform. After receiving it, the production control platform performs operation and maintenance on the potassium chloride granule preparation production line;

[0062] If the overlapping frequency of high-intensity operation moments and device abnormal moments within the corresponding period does not exceed the overlapping frequency threshold, and the rising span of the number of overlapping times between low-intensity operation moments and device abnormal moments does not exceed the rising span threshold, a normal production quality detection signal is generated and sent to the production control platform. After receiving the normal production quality detection signal, the production control platform makes the potassium chloride granule preparation production line operate according to the current setting.

[0063] Please refer to Figure 2 As shown, an intelligent production control method for potassium chloride granule preparation composition, the production control method is as follows:

[0064] Production characteristic analysis, analyze the production line characteristics of potassium chloride granule preparation composition, analyze the characteristics of the preparation production line, divide time periods according to data collection and analysis, and perform targeted control according to the time period type; that is, floating monitoring of operation parameters in complex characteristics or dynamic high-impact time periods;

[0065] Analysis of control precision requirements, analyze the control precision requirements for each process of the preparation production line, and divide the processes into high-precision processes and low-precision processes according to the data analysis;

[0066] After completing the analysis of the preparation production line, carry out targeted control and conduct production quality inspection on the preparation production line.

[0067] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent production control system for a potassium chloride granule preparation composition, characterized in that, It includes a production control platform, which is communicatively connected to a production characteristic analysis unit, a control precision requirement analysis unit, and a production quality real-time detection unit; The production characteristic analysis unit analyzes the production line characteristics of the potassium chloride granule preparation composition, analyzes the characteristics of the preparation production line, divides the time period according to data collection and analysis, and sends it to the production control platform; The control precision requirement analysis unit analyzes the processing control precision requirements of each process on the preparation production line, and divides the processes into high-precision processes and low-precision processes according to data analysis; After completing the analysis of the preparation production line, targeted control is carried out; The production quality real-time detection unit detects the production quality of the preparation production line.

2. The intelligent production control system of a potassium chloride granule preparation composition according to claim 1, characterized in that, The process of the production characteristic analysis unit is as follows: Collect the operation data of interrelated processes at different operating production speeds, control the corresponding numerical control deviation through an algorithm, and the span value of the actual production speed exceeding the set operating production speed. Compare the numerical control deviation with the production speed exceeding span to obtain the control influence ratio; Obtain the response buffer duration of the production line adaptation operation when the production line parameters change instantaneously during the operation of the preparation production line.

3. An intelligent production control system for a potassium chloride granule preparation composition according to claim 2, characterized in that, If the control influence ratio exceeds the ratio threshold, or the response buffer duration of the production line adaptation operation exceeds the duration threshold, mark the operation period of the preparation production line as a complex production line period or a dynamic high-influence period; If the control influence ratio does not exceed the ratio threshold, and the response buffer duration of the production line adaptation operation does not exceed the duration threshold, mark the operation period of the preparation production line as a stable production line period or a dynamic low-influence period, and synchronously transfer it to the production control platform.

4. The intelligent production control system for a potassium chloride granule preparation composition according to claim 1, wherein The process of the control precision requirement analysis unit is as follows: Collect the actual parameter floating unit quantity change span and the corresponding actual parameter numerical monitoring deviation value of each process operation parameter in the preparation production line, and calculate the ratio to obtain the change deviation ratio; At the same time, collect the span increase value of the operating environment parameters of each process in the preparation production line exceeding the control range and the duration of the production speed of the production line not reaching the set production speed value in the current period, and calculate the ratio to obtain the span duration ratio.

5. The intelligent production control system for a potassium chloride granule preparation composition according to claim 4, wherein If the change deviation ratio does not exceed the span deviation ratio threshold, or the span duration ratio does not exceed the span duration ratio threshold, it is inferred that the control precision requirement analysis of the current process of the preparation production line is abnormal, and mark the current process as a high-precision requirement process; If the change deviation ratio exceeds the span deviation ratio threshold, and the span duration ratio exceeds the span duration ratio threshold, it is inferred that the control precision requirement analysis of the current process of the preparation production line is normal, and mark the current process as a low-precision requirement process.

6. The intelligent production control system of a potassium chloride granule preparation composition according to claim 1, wherein The process of the production quality real-time detection unit is as follows: Divide the operation period of the preparation production line into several operation moments, and collect the numerical ratio of the actual operation data and the rated operation data of the equipment constituting the preparation production line corresponding to each operation moment; If the numerical ratio of the operation data exceeds the numerical ratio threshold, mark the current operation moment as a high-intensity operation moment; On the contrary, if the numerical ratio of the operation data does not exceed the numerical ratio threshold, mark the current operation moment as a low-intensity operation moment.

7. An intelligent production control system for a potassium chloride granule preparation composition according to claim 6, characterized in that, When the actually set operating parameters are within the range of the rated operating data, collect the reduction span of the ratio of the number of device operating satisfaction moments to the number of dissatisfaction moments of the current operating parameters of the preparation production line, and analyze the reduction span of the ratio. If the reduction span of the ratio exceeds the reduction span threshold, mark the current operating moment as the device stable moment; otherwise, if the reduction span of the ratio does not exceed the reduction span threshold, mark the current operating moment as the device abnormal moment.

8. An intelligent production control system for a potassium chloride granule preparation composition according to claim 7, characterized in that, Collect the finished product performance parameters of the preparation production line corresponding to each operating moment; count the finished product performance parameters and construct a curve according to the corresponding operating moment; infer the quality of the produced finished product based on the floating trend of the curve, that is, when the curve is below the red line value and shows a downward trend or a slow upward trend, mark the time period constructed according to the corresponding operating moment as the low-quality time period; When the curve is above the red line value and shows a downward trend and continues to decline without an upward trend, mark the time period constructed according to the corresponding operating time period as the quality decline time period; Mark the remaining time periods as normal time periods, that is, normal operation without treatment.

9. The intelligent production control system of a potassium chloride granule preparation composition according to claim 8, characterized in that, Analyze the low-quality time period and the quality decline time period, and collect the overlap frequency of the high-intensity operating moments and the device abnormal moments, and the upward span of the overlap times of the low-intensity operating moments and the device abnormal moments within the corresponding time period: If the overlap frequency of the high-intensity operating moments and the device abnormal moments within the corresponding time period exceeds the overlap frequency threshold, generate a load abnormal signal; if the upward span of the overlap times of the low-intensity operating moments and the device abnormal moments exceeds the times upward span threshold, generate a device state abnormal signal; if the overlap frequency of the high-intensity operating moments and the device abnormal moments within the corresponding time period does not exceed the overlap frequency threshold, and the upward span of the overlap times of the low-intensity operating moments and the device abnormal moments does not exceed the times upward span threshold, generate a normal production quality detection signal and send the normal production quality detection signal to the production control platform.

10. A method for intelligent production control of a potassium chloride granule preparation composition, characterized in that, Applied to an intelligent production control system for a potassium chloride granule preparation composition as described in any one of claims 1-9 above.

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