Intelligent production control method and system for medicinal powder

Abstract

The invention relates to the technical field of control of a medicine powder production line, and specifically discloses an intelligent production control method and system for medicine powder, wherein transmission signals of different medicine powder distribution point positions are collected by intelligent sensing equipment according to a collection strategy, and medicine powder conveying state instructions at different positions are obtained according to different transmission signals; key indicator data of transmission signals of the current positions of the same medicine powder conveying state instructions are collected, and the key indicator data are monitored and analyzed through a data monitoring platform; whether the current medicine powder conveying state meets the standard is judged according to the monitoring and analysis results, and the conveying operation of the medicine powder conveying state instruction at the current position is continued according to the judgment result, an optimized scheduling strategy is generated, an early warning signal is issued, and the current conveying operation is suspended; the optimized data is obtained and input into a prediction analysis model for prediction, and the prediction trend of the collection demand is obtained, thereby improving the monitoring efficiency of the medicine powder intelligent production line and improving the accuracy of the prediction of the entire medicine powder conveying state.

Description

Technical Field

[0001] The present invention relates to the technical field of production line control, and in particular to an intelligent production control method and system for medicinal powder. Background Art

[0002] With the rapid development of science and technology, intelligent technology is leading an industrial revolution on a global scale. In this revolution, the feed processing equipment industry, as an important cornerstone supporting the development of animal husbandry, is particularly critical in terms of technology upgrades and intelligent transformation. In order to increase the production output of animal husbandry, the intelligent development of drug powder processing and transportation equipment has rapidly improved the efficiency of the animal husbandry industry.

[0003] As for the intelligent production control of medicinal powder, most of the existing control technologies can achieve precise control of the medicinal powder production line through the use of computer technology. Through automatic identification technology, intelligent drive and automated control of the entire production process, it can effectively improve the production efficiency of medicinal powder and other products and realize the mechanical automation process of medicinal powder processing and production; and for the direction of the transportation production line, precise feeding ratio and mixing of medicinal powder as well as safe transportation process are crucial for the precise line of sight of medicinal powder transportation production line.

[0004] In the existing production lines for powder proportioning and conveying, whether it is automatic control of production or precise control of powder proportioning, a large amount of data processing and analysis is required. For the entire conveying production line, the technology of converting image signals into data signals through intelligent sensing equipment requires computer software to perform repeated data analysis and process comparison, which is relatively time-consuming and labor-intensive; and the conveying monitoring of each distribution point for operation inspection requires the establishment of manual monitoring stations and the arrangement of more relevant technical personnel for real-time monitoring. If necessary, each station needs to be distributed and controlled, resulting in the complex monitoring process required for the overall powder proportioning and conveying production line and the lack of intelligent prediction and control and adjustment methods. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent production control method and system for medicinal powder to solve the following technical problems:

[0006] How to improve the monitoring efficiency of intelligent powder production lines, simplify the centralized and distributed control processing procedures, and improve the accuracy of the entire powder delivery status prediction.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An intelligent production control method for medicine powder, the method comprising:

[0009] S1. Collect transmission signals of different powder distribution points through intelligent sensing equipment according to the collection strategy, and obtain powder delivery status instructions at different positions according to different transmission signals;

[0010] The types of powder conveying status instructions include: automatic loading, mixing, and finished product output;

[0011] S2. Collect key indicator data of the current transmission signal of the same powder delivery state instruction position, and monitor and analyze the key indicator data through the data monitoring platform;

[0012] Key indicator data include: feeding quantity parameters, mixing parameters, finished product quantity parameters;

[0013] S3. Determine whether the current powder delivery status meets the standard based on the monitoring and analysis results:

[0014] If yes, continue the conveying operation of the current position powder conveying state instruction;

[0015] If not, an optimized dispatching strategy is generated, an early warning signal is issued, and the current transport operation is suspended;

[0016] S4. Obtain optimization data based on the optimization scheduling strategy, input the optimization data into the prediction analysis model for prediction, and obtain the collection demand prediction trend based on the prediction results.

[0017] Preferably, the collection strategy in step S1 is:

[0018] Mark each distribution point with a transmission feature and determine different position numbers according to different transmission feature marks;

[0019] Obtaining a preset number of position numbers of each transmission feature mark to determine a feature information table;

[0020] Determine the primary collection quantity, primary collection frequency and primary collection interval duration during the transmission process according to the characteristic information table;

[0021] The number of primary collections, the frequency of primary collections and the duration of the primary collection interval are adjusted based on the forecast results.

[0022] Preferably, the method of transmitting the signal to obtain the powder delivery status instruction in step S1 is:

[0023] The start and pause instructions for the current position powder delivery status are given based on the transmission characteristics in the transmission signal and the monitoring and analysis results of key indicator data.

[0024] Preferably, the monitoring and analysis in step S2 includes:

[0025] Collect the previous data under the same powder delivery status instruction The location information of each powder distribution point can be used to obtain the key indicator data of the transmission signal at all locations:

[0026] By formula Calculate the control coefficient under the current powder delivery state ;

[0027] in, For the The location influence coefficient of each powder distribution point, For the Scheduling application coefficient of key indicator data of each powder distribution point;

[0028] The calculation formula is:

[0029]

[0030] in, Apply influence functions to presets; It is the powder delivery status instruction type. The command types included are: "automatic loading" command, "mixing and stirring" command, and "finished product output" command; It is the preset weight coefficient under the influence of different powder delivery state instructions; The first command obtained for the current powder delivery status Key indicator data of the transmission signal of each powder distribution point; It is the standard key indicator data of the current powder delivery status instruction.

[0031] Preferably, in step S3, the method for judging whether the current medicine powder delivery state meets the standard is:

[0032] The control coefficient of the current powder delivery state and the preset control coefficient threshold range To compare:

[0033] like , then the current conveying state is judged to meet the control standard and the conveying operation continues;

[0034] like < , then the current transportation status is judged to be lower than the control standard, and an optimized scheduling strategy is generated;

[0035] like > , it is judged that the current conveying status is higher than the control standard, an early warning signal is issued and the current conveying operation is suspended.

[0036] Preferably, the optimization scheduling strategy in step S3 includes:

[0037] Get the control coefficient under the current powder delivery state The corresponding scheduling application coefficient ;

[0038] Get the historical production of the current batch , Historical Inventory and delivery path signal ;

[0039] By formula Calculate the dispatch coefficient ;

[0040] in, is the standard output; is the standard inventory quantity; It is the standard delivery path signal; is the preset output deviation value; is the preset inventory deviation value; is the preset delivery path signal deviation value; Apply influence functions for production scheduling; Apply influence functions for inventory scheduling; Apply influence functions for delivery route signal scheduling.

[0041] Preferably, it also includes:

[0042] The scheduling coefficient The preset standard threshold range For comparison:

[0043] like , then it is judged that the current batch scheduling meets the requirements;

[0044] like , then the current batch scheduling is judged to be not in line with the requirements, and the output of the current batch is adjusted , Inventory and delivery route signals ; and based on the adjusted production , Inventory and delivery route signals Get the optimized scheduling application coefficient .

[0045] Preferably, the optimized scheduling application coefficient Substitute it into the calculation formula to obtain the current powder delivery status instruction Optimization data of key indicator data of transmission signal of powder distribution point location ; will optimize data Input the forecast analysis model to make forecasts, and obtain the forecast trend of the collection demand based on the forecast results:

[0046] Collect the standard time variation curve of key indicator data under the powder delivery status in the historical time period ;

[0047] Collect the time variation curve of key indicator data under the optimized powder delivery state within the same time period ;

[0048] Compare curves within the same time period in the curve analysis software With curve The number of intersections , get the area difference between every two adjacent intersection points ;

[0049] Calculate and analyze the sum of the area differences between all adjacent intersection points ,in ;

[0050] Will Deviation threshold from preset standard area To compare:

[0051] like > , then the predicted collection demand is high, increase the primary collection quantity value, increase the primary collection frequency and extend the primary collection interval duration.

[0052] An intelligent production control system for medicinal powder is used to implement an intelligent production control method for medicinal powder. The system includes:

[0053] The acquisition module is used to collect transmission signals of different powder distribution points through intelligent sensing equipment according to the acquisition strategy, and obtain powder delivery status instructions at different positions according to different transmission signals;

[0054] The data monitoring module is used to collect the key indicator data of the current transmission signal of the same powder delivery status instruction position, and monitor and analyze the key indicator data through the data monitoring platform;

[0055] The analysis module is used to determine whether the current powder delivery state meets the standard according to the monitoring and analysis results: if so, continue the delivery operation of the powder delivery state instruction at the current position; if not, generate an optimized scheduling strategy, issue an early warning signal and suspend the current delivery operation;

[0056] The prediction module is used to obtain optimization data based on the optimization scheduling strategy, input the optimization data into the prediction analysis model for prediction, and obtain the collection demand prediction trend based on the prediction results.

[0057] The beneficial effects of the present invention are as follows: the present invention collects transmission signals of powder distribution point locations through the collection strategy of the collection module to reduce the computer computing power cost of production line monitoring, improve the confirmation rate of the powder conveying status, and accurately obtain the transmission signals of the distribution location; collects key indicator data of the transmission signal of the current same powder conveying status instruction location through the data monitoring module, and ensures the accurate digital presentation of the monitoring conveying status information through the monitoring of key indicator data; judges the current powder conveying status according to the monitoring and analysis results through the analysis module, and realizes the conveying adjustment of the powder of the entire production line and timely feedback of the powder conveying problems by generating an optimized scheduling strategy; obtains optimization data based on the optimized scheduling strategy through the prediction module, and inputs the optimization data into the prediction analysis model for prediction, obtains the collection demand prediction trend according to the prediction result, and realizes the adjustment of the collection strategy and the intelligent and refined production control process of the powder, especially the veterinary powder conveying production line through the analysis and judgment of the demand prediction trend.

[0058] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0060] Figure 1 This is a flow chart of the steps of an intelligent production control method for medicinal powder of the present invention;

[0061] Figure 2 This is a module diagram of an intelligent production control system for medicinal powder according to the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In the existing production lines for powder proportioning and conveying, whether it is automatic control of production or precise control of powder proportioning, a large amount of data processing and analysis is required. For the entire conveying production line, the technology of converting image signals into data signals through intelligent intelligent sensing equipment requires computer software to perform repeated data analysis and process comparison, which is relatively time-consuming and labor-intensive; and the conveying monitoring of each distribution point for operation inspection requires the establishment of manual monitoring stations and the arrangement of more relevant technical personnel for real-time monitoring. If necessary, each station needs to be distributed and controlled, resulting in the complex processes required for monitoring the overall powder proportioning and conveying production line and the lack of intelligent prediction and control and adjustment methods.

[0064] See also Figure 1 As shown, the present invention is an intelligent production control method for medicinal powder, the method comprising:

[0065] S1. Collect transmission signals of different powder distribution points through intelligent sensing equipment according to the collection strategy, and obtain powder delivery status instructions at different positions according to different transmission signals;

[0066] The types of powder conveying status instructions include: automatic loading, mixing, and finished product output;

[0067] S2. Collect key indicator data of the current transmission signal of the same powder delivery state instruction position, and monitor and analyze the key indicator data through the data monitoring platform;

[0068] Key indicator data include: feeding quantity parameters, mixing parameters, finished product quantity parameters;

[0069] S3. Determine whether the current powder delivery status meets the standard based on the monitoring and analysis results:

[0070] If yes, continue the conveying operation of the current position powder conveying state instruction;

[0071] If not, an optimized dispatching strategy is generated, an early warning signal is issued, and the current transport operation is suspended;

[0072] S4. Obtain optimization data based on the optimization scheduling strategy, input the optimization data into the prediction analysis model for prediction, and obtain the collection demand prediction trend based on the prediction results.

[0073] In the above technical scheme, the present invention realizes the intelligent monitoring process of the powder proportioning and conveying production line through the above steps, improves the monitoring efficiency of the intelligent powder production line, simplifies the centralized and distributed control processing procedures, and improves the accuracy of the prediction of the entire powder conveying state. In particular, in the production line control process of veterinary drug powder production and processing, the use of this intelligent production control method can further improve the production efficiency of the entire veterinary drug powder and medicine and realize the accurate monitoring process of the veterinary drug production line.

[0074] First, in step S1, the transmission signals of different powder distribution point positions are collected by intelligent sensing equipment according to the collection strategy, and the powder conveying status instructions at different positions are obtained according to different transmission signals; the types of powder conveying status instructions include: automatic loading, mixing, and finished product output; in this embodiment, a collection strategy is designed and a sampling monitoring process of the distribution point position signal is realized in this way. Through sampling monitoring, it is ensured that the computer computing power cost of production line monitoring is reduced, the confirmation rate of the powder conveying status is improved, and the transmission signal of the distribution position is accurately obtained.

[0075] Specifically, as an implementation of the present invention, the collection strategy in step S1 is:

[0076] Mark each distribution point with a transmission feature and determine different position numbers according to different transmission feature marks;

[0077] Obtaining a preset number of position numbers of each transmission feature mark to determine a feature information table;

[0078] Determine the primary collection quantity, primary collection frequency and primary collection interval duration during the transmission process according to the characteristic information table;

[0079] The number of primary collections, the frequency of primary collections and the duration of the primary collection interval are adjusted based on the forecast results.

[0080] In the above technical scheme, the collection strategy in this implementation includes: first, marking the transmission feature of each distribution point and determining different position numbers according to different transmission feature marks. Each distribution point has a corresponding signal intelligent sensing device. The next transmission operation of the transmission distribution point is obtained in real time through video surveillance images, and the characteristic signal data of the transmission operation is collected and marked. Each transmission feature mark has different position information, and these position information are numbered to facilitate the subsequent collection selection and distinction; then, the preset number of position numbers of each transmission feature mark is obtained to determine the feature information table, and the feature information table is obtained according to the numbers of different transmission feature marks to realize the collection and set processing of feature information, which is convenient for extracting and selecting the collection method; then, the collection method in the transmission process is determined according to the feature information table, including the determination of the primary collection quantity, the primary collection frequency and the primary collection interval duration; finally, the primary collection quantity, the primary collection frequency and the primary collection interval duration are adjusted according to the prediction results.

[0081] As an embodiment of the present invention, the method of transmitting a signal to obtain a powder delivery status instruction in step S1 is:

[0082] The start and pause instructions for the current position powder delivery status are given based on the transmission characteristics in the transmission signal and the monitoring and analysis results of key indicator data.

[0083] In the above technical scheme, the method for obtaining the powder conveying status instruction in this embodiment is determined based on the transmission characteristics of the transmission signal and its key indicator data. The transmission operation at the current position is started or paused by monitoring and analyzing whether the transmission status is normal. This transmission operation is determined by the powder conveying status instruction. Therefore, when the powder conveying status of the current position obtained by monitoring is determined, the powder conveying status instruction of the corresponding position is obtained, which is the main feedback operation instruction.

[0084] Secondly, in step S2, key indicator data of the current transmission signal of the same powder conveying status instruction position are collected, and the key indicator data are monitored and analyzed through the data monitoring platform; the key indicator data include: feeding quantity parameters, mixing parameters, and finished product quantity parameters; the unified distributed control processing procedure is simplified through the monitoring and analysis of the key indicator data, and the accurate digital presentation of the conveying status information is ensured by monitoring the key indicator data.

[0085] Specifically, as an implementation of the present invention, the monitoring and analysis in step S2 includes:

[0086] Collect the previous data under the same powder delivery status instruction The location information of each powder distribution point can be used to obtain the key indicator data of the transmission signal at all locations:

[0087] By formula Calculate the control coefficient under the current powder delivery state ;

[0088] in, For the The location influence coefficient of each powder distribution point, For the Scheduling application coefficient of key indicator data of each powder distribution point;

[0089] The calculation formula is:

[0090]

[0091] in, Apply influence functions to presets; It is the powder delivery status instruction type. The command types included are: "automatic loading" command, "mixing and stirring" command, and "finished product output" command; It is the preset weight coefficient under the influence of different powder delivery state instructions; The first command obtained for the current powder delivery status Key indicator data of the transmission signal of each powder distribution point; It is the standard key indicator data of the current powder delivery status instruction.

[0092] In the above technical solution, in this embodiment, the formula Calculate the control coefficient under the current powder delivery state ; According to the size of the control coefficient under the current powder conveying state, the monitoring status of the conveying state is determined, and then the next step of conveying optimization is carried out. According to the scheduling application coefficients of the key indicator data of different powder distribution points, the influence of the conveying state instructions at the position characteristics and the response position on the overall powder distribution state is determined; the scheduling application coefficient of the key indicator data of the powder distribution point is calculated by the formula ; Considering the influence of the key indicator data of the transmission signal obtained by the conveying state instruction, generate its scheduling application coefficient; and obtain the control coefficient of the current powder conveying state according to the position influence characteristic analysis of the scheduling application coefficient .

[0093] It should be noted that the position influence coefficient It is pre-determined based on the impact of different powder distribution point locations on transportation, historical transportation speed and transportation operations, etc.; preset weight coefficient It is obtained under the influence of different powder conveying status instructions, where the conveying status instructions include automatic loading, mixing and stirring, and finished product output. This is also obtained by monitoring the production line during the entire powder conveying process and confirmed in combination with the production line image. Therefore, the size of the preset weight coefficient can be set in advance based on the historical existing powder production line. The specific details are not described here.

[0094] It should also be noted that the preset application affects the function It is an adjustment function set according to historical data to ensure that the control coefficient under the powder delivery state is within a specific reasonable range; standard key indicator data It is obtained by pre-fitting calculation based on historical experience data and will not be elaborated here.

[0095] Third, in step S3, it is determined whether the current powder conveying state meets the standard based on the monitoring and analysis results. If it does, the conveying operation of the powder conveying state instruction at the current position is continued; if not, an optimized scheduling strategy is generated, an early warning signal is issued and the current conveying operation is suspended. The powder conveying of the entire production line is adjusted through the optimized scheduling strategy, thereby improving the accuracy of the prediction of the entire powder conveying state.

[0096] As an implementation mode of the present invention, the method for judging whether the current medicine powder delivery state meets the standard in step S3 is:

[0097] The control coefficient of the current powder delivery state and preset control coefficient threshold range To compare:

[0098] like , then the current conveying state is judged to meet the control standard and the conveying operation continues;

[0099] like < , then the current transportation status is judged to be lower than the control standard, and an optimized scheduling strategy is generated;

[0100] like > , it is judged that the current conveying status is higher than the control standard, an early warning signal is issued and the current conveying operation is suspended.

[0101] In the above technical solution, in this embodiment, according to the monitoring and analysis results, an optimization scheduling strategy is generated or an early warning signal is issued and the current conveying operation is suspended. Specifically, the control coefficient of the current powder conveying state obtained by the calculation in step 2 is determined. The size of the control coefficient threshold interval Comparison is carried out to achieve specific control, and the delivery adjustment of the powder in the entire production line is achieved by generating an optimized scheduling strategy.

[0102] In addition, as an implementation of the present invention, the optimization scheduling strategy in step S3 includes:

[0103] Get the control coefficient under the current powder delivery state The corresponding scheduling application coefficient ;

[0104] Get the historical production of the current batch , Historical Inventory and delivery path signal ;

[0105] By formula Calculate the dispatch coefficient ;

[0106] in, is the standard output; is the standard inventory quantity; It is the standard delivery path signal; is the preset output deviation value; is the preset inventory deviation value; is the preset delivery path signal deviation value; Apply influence functions for production scheduling; Apply influence functions for inventory scheduling; Apply influence functions for delivery route signal scheduling.

[0107] In the above technical solution, the optimization scheduling strategy in this embodiment is based on obtaining the control coefficient under the current powder delivery state. The corresponding scheduling application coefficient And get the historical output of the current batch , Historical Inventory and delivery path signal Obtained through calculation and analysis; through the scheduling coefficient It can confirm whether the scheduling information of the current batch is qualified, and the confirmed historical production of powder under the conveying state of the current scheduling application coefficient , Historical Inventory and delivery path signal The calculation results are analyzed by the formula Calculate the dispatch coefficient , ensuring the optimization of the scheduling process through scheduling coefficient size analysis.

[0108] It should be explained that the standard value , , All of them are obtained by pre-fitting calculation based on historical experience data, which will not be elaborated here; preset output deviation value , preset inventory deviation value , preset delivery path signal deviation value All are pre-set based on empirical data and will not be described in detail here; the influence function , , They are all set according to the impact of the scheduling application on the production, inventory and distribution path signals in the historical database. They can reflect the control conditions of the production, inventory and distribution path that need to be scheduled, and ensure the accurate impact on the scheduling coefficient.

[0109] As an embodiment of the present invention, it also includes:

[0110] The scheduling coefficient The preset standard threshold range For comparison:

[0111] like , then it is judged that the current batch scheduling meets the requirements;

[0112] like , then the current batch scheduling is judged to be not in line with the requirements, and the output of the current batch is adjusted , Inventory and delivery route signals ; and based on the adjusted production , Inventory and delivery route signals Get the optimized scheduling application coefficient .

[0113] In the above technical solution, in this embodiment, the scheduling coefficient The size reflects the scheduling status of the current batch by adding the scheduling coefficient The preset standard threshold range Compare and determine whether it is within the standard range. If so, continue the currently set scheduling mode. If not, adjust the scheduling mode by adjusting the output, inventory and distribution path signals of the current batch; and further adjust the output according to the adjusted output. , Inventory and delivery route signals Get the optimized scheduling application coefficient ; By obtaining the optimized scheduling application coefficient Predict current scheduling trends.

[0114] Fourthly, in step S4, optimization data is obtained based on the optimization scheduling strategy, and the optimization data is input into the prediction analysis model for prediction. The collection demand forecast trend is obtained according to the prediction result. The collection strategy is adjusted and the intelligent and refined production control process of the powder delivery production line is realized by analyzing and judging the demand forecast trend.

[0115] Specifically, as an implementation of the present invention, first, the optimized scheduling application coefficient Substitute it into the calculation formula to obtain the current powder delivery status instruction Optimization data of key indicator data of transmission signal of powder distribution point location ; Then, the optimized data Input the prediction analysis model to make predictions; finally, obtain the collection demand forecast trend based on the prediction results:

[0116] Collect the standard time variation curve of key indicator data under the powder delivery status in the historical time period ;

[0117] Collect the time variation curve of key indicator data under the optimized powder delivery state within the same time period ;

[0118] Compare curves within the same time period in the curve analysis software With curve The number of intersections , get the area difference between every two adjacent intersection points ;

[0119] Calculate and analyze the sum of the area differences between all adjacent intersection points ,in ;

[0120] Will Deviation threshold from preset standard area To compare:

[0121] like > , then the predicted collection demand is high, increase the primary collection quantity value, increase the primary collection frequency and extend the primary collection interval duration.

[0122] In the above technical solution, in this embodiment, the optimized scheduling application coefficient Into its basic formula: ; Get the current powder delivery status command to obtain the first Optimization data of key indicator data of transmission signal of powder distribution point location ; will optimize data Input the machine calculation for prediction in the prediction analysis model, and judge the trend change of the collection demand according to the prediction results: First, collect the standard time change curve of the key indicator data of the powder delivery state in the historical time period ; Then, collect the time variation curve of key indicator data under the optimized powder delivery state in the same time period ; Next, compare the curves within the same time period in the curve analysis software With curve The number of intersections , get the area difference between every two adjacent intersection points ; Finally, calculate and analyze the sum of the area differences between all adjacent intersection points ,in ;Will Deviation threshold from preset standard area To compare:

[0123] like > , then the predicted collection demand is high, increase the primary collection quantity value, increase the primary collection frequency and extend the primary collection interval duration.

[0124] See also Figure 2 As shown, the present invention uses an intelligent production control system for medicinal powder to implement an intelligent production control method for medicinal powder, and the system includes:

[0125] The acquisition module is used to collect transmission signals of different powder distribution points through intelligent sensing equipment according to the acquisition strategy, and obtain powder delivery status instructions at different positions according to different transmission signals;

[0126] The data monitoring module is used to collect the key indicator data of the current transmission signal of the same powder delivery status instruction position, and monitor and analyze the key indicator data through the data monitoring platform;

[0127] The analysis module is used to determine whether the current powder delivery state meets the standard according to the monitoring and analysis results: if so, continue the delivery operation of the powder delivery state instruction at the current position; if not, generate an optimized scheduling strategy, issue an early warning signal and suspend the current delivery operation;

[0128] The prediction module is used to obtain optimization data based on the optimization scheduling strategy, input the optimization data into the prediction analysis model for prediction, and obtain the collection demand prediction trend based on the prediction results.

[0129] In the above technical scheme, the present invention realizes the intelligent production control method of medicine powder by designing four modules, namely, an acquisition module, a data monitoring module, an analysis module, and a prediction module, so as to ensure that the transmission signal of the medicine powder distribution point position is collected through the acquisition strategy of the acquisition module to reduce the computer computing power cost of production line monitoring, improve the confirmation rate of the medicine powder conveying state, and accurately obtain the transmission signal of the distribution position; collect the key indicator data of the transmission signal of the current same medicine powder conveying state instruction position through the data monitoring module, and ensure the accurate data presentation of the monitoring conveying state information through the monitoring of the key indicator data; judge the current medicine powder conveying state according to the monitoring and analysis results through the analysis module, and realize the conveying adjustment of the medicine powder of the entire production line and timely feedback of the medicine powder conveying problems by generating an optimized scheduling strategy; obtain the optimization data based on the optimized scheduling strategy through the prediction module, and input the optimization data into the prediction analysis model for prediction, obtain the collection demand prediction trend according to the prediction result, and realize the adjustment of the collection strategy and the intelligent and refined production control process of the medicine powder conveying production line by analyzing and judging the demand prediction trend.

[0130] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0131] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent production control method for medicinal powder, characterized in that: The method comprises: S1. Collect transmission signals of different powder distribution points through intelligent sensing equipment according to the collection strategy, and obtain powder delivery status instructions at different positions according to different transmission signals; The powder delivery status instruction types include: automatic loading, mixing and stirring, and finished product output; S2. Collect key indicator data of the current transmission signal of the same powder delivery state instruction position, and monitor and analyze the key indicator data through the data monitoring platform; The key indicator data include: feeding quantity parameters, mixing parameters, and finished product quantity parameters; The monitoring and analysis in step S2 includes: Collect the previous data under the same powder delivery status instruction The location information of each powder distribution point can be used to obtain the key indicator data of the transmission signal at all locations: By formula Calculate the control coefficient under the current powder delivery state ; in, For the The location influence coefficient of each powder distribution point, For the Scheduling application coefficient of key indicator data of each powder distribution point; The calculation formula is: ， in, Apply influence functions to presets; It is the powder delivery status instruction type. The command types included are: "automatic loading" command, "mixing" command, and "finished product output" command; It is the preset weight coefficient under the influence of different powder delivery state instructions; The first command obtained for the current powder delivery status Key indicator data of the transmission signal of each powder distribution point; Standard key indicator data for current powder delivery status instructions; S3. Determine whether the current powder delivery status meets the standard based on the monitoring and analysis results: If yes, continue the conveying operation of the current position powder conveying state instruction; If not, an optimized dispatching strategy is generated, an early warning signal is issued, and the current transport operation is suspended; S4. Obtain optimization data based on the optimization scheduling strategy, input the optimization data into the prediction analysis model for prediction, and obtain the collection demand prediction trend according to the prediction results; The optimization scheduling strategy in step S3 includes: Get the control coefficient under the current powder delivery state The corresponding scheduling application coefficient ; Get the historical production of the current batch , Historical Inventory and delivery path signal ; By formula Calculate the dispatch coefficient ; in, is the standard output; is the standard inventory quantity; It is the standard delivery path signal; is the preset output deviation value; is the preset inventory deviation value; is the preset delivery path signal deviation value; Apply influence functions for production scheduling; Apply influence functions for inventory scheduling; Apply influence functions for delivery route signal scheduling; Also includes: The scheduling coefficient The preset standard threshold range For comparison: like , then it is judged that the current batch scheduling meets the requirements; like , then the current batch scheduling is judged to be not in line with the requirements, and the output of the current batch is adjusted , Inventory and delivery route signals ; and based on the adjusted production , Inventory and delivery route signals Get the optimized scheduling application coefficient ; The optimized scheduling application coefficient Substitute it into the calculation formula to obtain the current powder delivery status instruction Optimization data of key indicator data of transmission signal of powder distribution point location ; will optimize data Input the forecast analysis model to make forecasts, and obtain the forecast trend of the collection demand based on the forecast results: Collect the standard time variation curve of key indicator data under the powder delivery status in the historical time period ; Collect the time variation curve of key indicator data under the optimized powder delivery state within the same time period ; Compare curves within the same time period in the curve analysis software With curve The number of intersections , get the area difference between every two adjacent intersection points ; Calculate and analyze the sum of the area differences between all adjacent intersection points ,in ; Will Deviation threshold from preset standard area To compare: like > , then the predicted collection demand is high, increase the primary collection quantity value, increase the primary collection frequency and extend the primary collection interval duration.

2. The intelligent production control method of medicine powder according to claim 1 is characterized in that: The acquisition strategy in step S1 is: Mark each distribution point with a transmission feature and determine different position numbers according to different transmission feature marks; Obtaining a preset number of position numbers of each transmission feature mark to determine a feature information table; Determine the number of primary collections, the primary collection frequency, and the duration of the primary collection interval during the transmission process according to the characteristic information table; The number of primary collections, the frequency of primary collections and the duration of the primary collection interval are adjusted based on the forecast results.

3. The intelligent production control method of medicine powder according to claim 2 is characterized in that: The method for transmitting a signal to obtain a powder delivery status instruction in step S1 is: The start and pause instructions for the current position powder delivery status are given based on the transmission characteristics in the transmission signal and the monitoring and analysis results of key indicator data.

4. The intelligent production control method of medicine powder according to claim 1, characterized in that: The method for judging whether the current powder delivery state meets the standard in step S3 is: The control coefficient of the current powder delivery state and preset control coefficient threshold range To compare: like , then the current conveying state is judged to meet the control standard and the conveying operation continues; like < , then the current transportation status is judged to be lower than the control standard, and an optimized scheduling strategy is generated; like > , it is judged that the current conveying state is higher than the control standard, an early warning signal is issued and the current conveying operation is suspended.

5. An intelligent production control system for medicinal powder, characterized in that: A method for intelligent production control of a medicine powder according to any one of claims 1 to 4, the system comprising: The acquisition module is used to collect transmission signals of different powder distribution points through intelligent sensing equipment according to the acquisition strategy, and obtain powder delivery status instructions at different positions according to different transmission signals; The data monitoring module is used to collect the key indicator data of the current same powder delivery status instruction position transmission signal, and monitor and analyze the key indicator data through the data monitoring platform; The analysis module is used to determine whether the current powder delivery state meets the standard according to the monitoring and analysis results: if so, continue the delivery operation of the powder delivery state instruction at the current position; if not, generate an optimized scheduling strategy, issue an early warning signal and suspend the current delivery operation; The prediction module is used to obtain optimization data based on the optimization scheduling strategy, input the optimization data into the prediction analysis model for prediction, and obtain the collection demand prediction trend based on the prediction results.

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