Feeding system and feeding method

Abstract

The present invention discloses a feeding system and feeding method. The feeding system includes a cross-contamination inventory turnover processing cloud computing module, a cross-contamination demand forecasting and evaluation big data module, a cross-contamination filtration separation evaluation module, and a filtration separation control module. The present invention calculates an inventory turnover evaluation coefficient and a cross-contamination processing cost efficiency evaluation coefficient through the cross-contamination inventory turnover processing cloud computing module; calculates a cross-contamination demand forecasting and evaluation big data module to obtain a cross-contamination demand forecasting and evaluation coefficient and a cross-contamination evaluation coefficient; calculates a cross-contamination filtration separation evaluation coefficient through the cross-contamination filtration separation evaluation module; and the filtration separation control module controls some parameters in filtration separation through the cross-contamination filtration separation evaluation coefficient, thereby achieving the effect of improving the applicability of filtration and separation technology to cross-contamination, and solving the problem of insufficient applicability of filtration and separation technology to cross-contamination in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of cross contamination assessment, and in particular to a feeding system and a feeding method. Background Art

[0002] As the concept of Industry 4.0 gradually becomes a reality, the feeding system, as a key component of intelligent manufacturing, is developing towards automation, informatization, and networking. This requires the feeding system to not only ensure accurate material supply but also integrate with production management systems, enterprise resource planning, and other systems to achieve synchronization of material and information flows.

[0003] The existing feeding system and feeding method are combined with the production management system and enterprise resource planning to ensure the timely supply of materials and the matching of production needs. Through data analysis, the production management system helps to identify quality problems and adjust the feeding system in time to prevent the production of substandard products, thus realizing the efficient integration of the feeding system and enterprise management.

[0004] For example, the invention patent announcement with announcement number: CN112668961B discloses an automatic feeding system, method and medium, including: an order processing module, a station binding module, an inventory analysis module and a material transportation module; the order processing module is used to obtain order information and send it to the station binding module; the station binding module is used to select a station and bind the station information of the station with the order information to obtain binding information; the inventory analysis module is used to allocate materials corresponding to the binding information to the station; the material transportation module is used to collect materials at the station and execute corresponding cyclic collection measures; the present invention can automatically collect material orders from the server, thereby improving the accuracy of materials, the safety of materials and the efficiency of material transmission.

[0005] For example, the automated material supply management method, system, and storage medium disclosed in the invention patent announcement with the announcement number CN115564317B include: material demand aggregation and priority sorting; obtaining the current available supply of materials based on whether the logistics center is enabled; branch selection based on whether the material is an alternative material, performing material supply and demand balance calculations separately, and determining whether there is a material shortage; splitting the demand for the missing materials to generate the net material demand for suppliers; calculating the actual material demand based on whether the logistics center is enabled; setting the delivery address based on the different dimensions of the material, and automatically generating supply information for suppliers. By aggregating and sorting various demands, then splitting the demand for the missing materials, and automatically configuring the delivery address, and automatically generating complete supply information for suppliers, automatic on-demand material delivery is achieved. In the face of dynamic changes in product demand, the efficiency and accuracy of calculating the material demand for suppliers are improved, and the timeliness of response is improved.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, since the feeding system uses filtration and separation technology to solve the cross contamination when dealing with cross contamination, there is a problem that the filtration and separation technology is not sufficiently applicable to the treatment of cross contamination. Summary of the Invention

[0008] The embodiments of the present application solve the problem of insufficient applicability of filtration and separation technologies for cross-contamination in the prior art by providing a feeding system and a feeding method, thereby achieving the effect of improving the applicability of filtration and separation technologies for cross-contamination.

[0009] An embodiment of the present application provides a feeding system, including: a cross-contamination inventory turnover processing cloud computing module including an inventory turnover unit and a cross-contamination processing cost efficiency unit, wherein the cross-contamination inventory turnover processing cloud computing module refers to a module that uses a continuous integration tool to optimize the inventory management and cost efficiency calculation of the feeding system;

[0010] The cross-contamination demand forecasting and evaluation big data module includes a cross-contamination demand forecasting unit and a cross-contamination evaluation unit. The cross-contamination demand forecasting and evaluation big data module refers to the module part that uses data mining and machine learning to process, analyze and predict the feeding process. The cross-contamination demand forecasting and evaluation big data module has real-time data processing and analysis capabilities;

[0011] The cross contamination filtration separation assessment module is a module used to analyze and evaluate the feed process.

[0012] An embodiment of the present application provides a feeding method, the specific steps of which are: using continuous integration tools to optimize the inventory management and cost efficiency of the feeding system, and analyzing and calculating the inventory management and cost efficiency; using data mining and machine learning to process, analyze and predict the feeding process, and the real-time data processing and analysis capabilities of the cross-contamination demand prediction and evaluation big data module to obtain cross-contamination demand prediction results and cross-contamination evaluation results; analyzing and evaluating the feeding process to obtain cross-contamination filtration separation evaluation results; and regulating some parameters in filtration separation through the cross-contamination filtration separation evaluation results.

[0013] Furthermore, the cross-contamination inventory turnover processing cloud computing module includes: an inventory turnover unit and a cross-contamination processing cost efficiency unit; the cross-contamination demand forecasting and evaluation big data module includes: a cross-contamination demand forecasting unit and a cross-contamination module;

[0014] Furthermore, the specific process of the inventory turnover unit is: statistically extracting inventory turnover related data that affects cross contamination in the feeding system, including: inventory turnover rate, total inventory turnover, inventory error rate, production cycle of the feeding system and inventory accumulation rate of the feeding system.

[0015] Furthermore, the specific constraint formula of the inventory turnover evaluation unit is:

[0016]

[0017] In the formula, several inventory turnover time monitoring points are set and numbered in sequence. Indicates the number of the inventory turnover time monitoring point, Indicates the total number of numbers of inventory turnover time monitoring points, Represents the inventory turnover evaluation coefficient, which is used to evaluate the impact of inventory turnover efficiency on the feeding system. Indicates the The inventory turnover rate of each inventory turnover time monitoring point, Indicates the The total number of inventory turnovers at the inventory turnover time monitoring points, Indicates the The inventory error rate of each inventory turnover time monitoring point, Indicates the production cycle of the feeding system, Indicates the inventory accumulation rate of the feeding system.

[0018] Furthermore, the specific process of the cross-contamination treatment cost efficiency unit is: statistically extracting cross-contamination treatment cost efficiency related data that affects cross-contamination in the feeding system, including: the cost of maintaining and treating cross-contamination equipment, the cost of detection and monitoring, the cost of cross-contamination cleaning of the feeding system, the cross-contamination treatment investment cost of the feeding system and the order processing efficiency of the feeding system.

[0019] Furthermore, the specific constraint formula of the cross-contamination treatment cost efficiency evaluation unit is:

[0020]

[0021] In the formula, several cross-contamination monitoring points are set up and numbered in sequence. Indicates the number of the cross-contamination monitoring point, Indicates the total number of cross-contamination monitoring points. It represents the cross-contamination treatment cost efficiency evaluation coefficient, which is used to evaluate the impact of the cross-contamination treatment cost efficiency on the feeding system. Several feeding cycle monitoring points are set and numbered in sequence. Indicates the number of the monitoring point in the feeding cycle, Indicates the total number of monitoring points in the feeding cycle. It represents the cross-contamination treatment cost efficiency evaluation coefficient, which is used to evaluate the impact of cross-contamination treatment cost efficiency on the feeding system. Indicates the The cost of maintaining and handling cross-contamination equipment at each cross-contamination monitoring point, Indicates the The cost of monitoring cross contamination at each cross contamination monitoring point, Indicates the The cost of cross-contamination cleaning under each cross-contamination monitoring point, Indicates the The input cost of cross contamination treatment at each monitoring point in the feeding cycle, represents the weight factor of cross-contamination treatment cost, Indicates the The order processing efficiency of the feeding system under each feeding cycle monitoring point, Represents a natural constant.

[0022] Furthermore, the specific process of the cross-contamination demand prediction unit is: statistically extract relevant data in the feeding system that affects the cross-contamination demand prediction, including: historical cross-contamination values, cross-contamination differences, order values, order growth rates, cross-contamination processing efficiency and maximum defective product rate.

[0023] Furthermore, the specific constraint formula of the cross-contamination demand prediction unit is:

[0024]

[0025] In the formula, several cross-contamination time monitoring points are set and numbered in sequence. Indicates the number of the cross contamination time monitoring point, Indicates the total number of cross-contamination time monitoring points, It represents the cross-contamination demand forecast assessment coefficient, which is used to evaluate the impact of cross-contamination demand forecast on the feeding system. Indicates the The historical cross contamination values ​​of each cross contamination time monitoring point, Indicates the The cross contamination difference of each cross contamination time monitoring point, Indicates the The order value of each cross-contamination time monitoring point, Indicates the The order growth rate of each cross-contamination time monitoring point, Indicates the Cross-contamination treatment efficiency at each cross-contamination time monitoring point, Indicates the maximum defective rate of the feed.

[0026] Furthermore, the specific process of the cross-contamination assessment unit is: statistically extracting relevant data affecting cross-contamination assessment in the feeding system, including: cross-contamination cleaning efficiency, cross-contamination rate, cleaning interval time, cross-contamination threshold and system capacity.

[0027] Furthermore, the specific constraint formula of the cross-contamination assessment unit is:

[0028]

[0029] In the formula, several spatial monitoring points are set and numbered in sequence. Indicates the number of the spatial monitoring point, Indicates the total number of spatial monitoring points. Indicates the cross contamination assessment coefficient, which is used to evaluate the impact of cross contamination on the feeding system. Indicates the Cross-contamination cleaning efficiency of each space monitoring point, Shidi The cross contamination rate of each spatial monitoring point, Indicates the The cleaning interval of each space monitoring point, Indicates the cross contamination threshold of the feeding system, Indicates the system capacity of the feeding system, Represents a natural constant.

[0030] Furthermore, the specific process of the cross-contamination filtration separation evaluation module is: statistically extracting relevant data on the cross-contamination filtration separation evaluation in the feeding system, including: inventory turnover evaluation coefficient, cross-contamination treatment cost efficiency evaluation coefficient, cross-contamination demand forecast evaluation coefficient and cross-contamination evaluation coefficient.

[0031] Furthermore, the specific constraint formula of the cross contamination on the filtration separation evaluation module is:

[0032]

[0033] Where, represents the inventory turnover evaluation coefficient, represents the cross-contamination treatment cost efficiency evaluation coefficient, represents the cross-contamination demand forecast assessment coefficient, represents the cross contamination assessment coefficient, It represents the evaluation coefficient of cross contamination on filtration separation, Represents a natural constant.

[0034] Furthermore, the specific control process of the filtration and separation control module is as follows:

[0035] Collecting filtering, separation and cleaning data of the feeding system, extracting features from the filtering, separation and cleaning data of the feeding system, and obtaining filtering, separation and cleaning feature data;

[0036] A decision tree model is selected to train the filtration separation and cleaning feature data, and the filtration separation effect is predicted by the decision tree model to obtain the prediction results of the decision tree model. The prediction results of the decision tree model are used to regulate some parameters in the filtration separation.

[0037] Furthermore, the specific steps of the feeding method are:

[0038] Use continuous integration tools to optimize the inventory management and cost efficiency of the feeding system, analyze and calculate the inventory management and cost efficiency, and obtain analysis results of inventory management efficiency and cost efficiency; use data mining and machine learning to process, analyze and predict the feeding process, and use the real-time data processing and analysis capabilities of the cross-contamination demand prediction and evaluation big data module to obtain cross-contamination demand prediction results and cross-contamination evaluation results; analyze and evaluate the feeding process to obtain cross-contamination evaluation results on filtration separation; and adjust some parameters in filtration separation through the cross-contamination evaluation results on filtration separation.

[0039] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0040] 1. Cloud services can provide high-speed data processing capabilities, allowing inventory information and cost efficiency analysis to be updated in real time, thereby obtaining inventory turnover assessment coefficients and cross-contamination treatment cost efficiency assessment coefficients, thereby improving the real-time cross-contamination processing capabilities of filtration and separation technologies, and effectively solving the problem of insufficient real-time cross-contamination processing capabilities of filtration and separation technologies in existing technologies.

[0041] 2. Through the comprehensive evaluation of the evaluation coefficients under the cross-contamination inventory turnover processing cloud computing module and the cross-contamination demand forecast evaluation big data module, the cross-contamination evaluation coefficient of filtration separation is obtained, thereby obtaining the detailed method for regulating some parameters in filtration separation, thereby achieving the effect of improving the applicability of filtration and separation technology for cross-contamination, and effectively solving the problem of insufficient applicability of filtration and separation technology for cross-contamination in existing technologies.

[0042] 3. Data technology can collect and integrate multi-source data from all links of the supply chain, including production, transportation, storage and sales, to provide data support for a comprehensive assessment of the impact of cross-contamination on filtration and separation, thereby obtaining the cross-contamination filtration and separation assessment coefficient, thereby achieving the effect of improving the comprehensiveness of filtration and separation technology for cross-contamination, and effectively solving the problem of insufficient comprehensiveness of filtration and separation technology for cross-contamination in existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the structure of the feeding system provided in an embodiment of the present application;

[0044] Figure 2 This is a schematic diagram of the structure of a cloud computing module for cross-contamination inventory turnover processing in a feeding system provided in an embodiment of the present application;

[0045] Figure 3 This is a schematic diagram of the structure of the cross-contamination demand prediction and evaluation big data module in the feeding system provided in an embodiment of the present application;

[0046] Figure 4 A schematic flow chart of the feeding method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The embodiments of the present application solve the problem of insufficient applicability of filtration and separation technologies for cross-contamination in the prior art by providing a feeding system and a feeding method. The cross-contamination filtration and separation evaluation coefficient is obtained by comprehensively evaluating the evaluation coefficients under the cross-contamination inventory turnover processing cloud computing module and the cross-contamination demand forecasting and evaluation big data module, thereby achieving the effect of improving the applicability of filtration and separation technologies for cross-contamination.

[0048] The technical solution in the embodiments of the present application is to solve the problem that the above-mentioned filtration and separation technologies are not sufficiently applicable to cross contamination. The overall idea is as follows:

[0049] The inventory turnover evaluation coefficient and the cross-contamination treatment cost efficiency evaluation coefficient are obtained through the inventory turnover unit and the cross-contamination treatment cost efficiency unit under the cross-contamination inventory turnover treatment cloud computing module;

[0050] The cross-contamination demand prediction evaluation coefficient and the cross-contamination evaluation coefficient are obtained through the cross-contamination demand prediction unit and the cross-contamination evaluation unit under the cross-contamination demand prediction evaluation big data module;

[0051] The inventory turnover assessment coefficient, cross-contamination treatment cost efficiency assessment coefficient, cross-contamination demand forecast assessment coefficient and cross-contamination assessment coefficient are comprehensively assessed through the cross-contamination filtration separation assessment module to obtain the cross-contamination filtration separation assessment coefficient;

[0052] Collecting filtering, separation and cleaning data of the feeding system, extracting features from the filtering, separation and cleaning data of the feeding system, and obtaining filtering, separation and cleaning feature data;

[0053] A decision tree model is selected to train the filtration separation and cleaning feature data, and the filtration separation effect is predicted by the decision tree model to obtain the decision tree model prediction results. The decision tree model prediction results are used to control some parameters in the filtration separation. In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] like Figure 1 As shown, it is a structural schematic diagram of the feeding system and feeding method provided in the embodiment of the present application. The feeding system provided in the embodiment of the present application includes: the cross-contamination inventory turnover processing cloud computing module refers to a module that uses continuous integration tools to optimize the inventory management and cost efficiency calculation of the feeding system; the cross-contamination demand forecasting and evaluation big data module refers to a module part that uses data mining and machine learning to process, analyze and predict the feeding process, and the cross-contamination demand forecasting and evaluation big data module has real-time data processing and analysis capabilities; the cross-contamination filtration separation evaluation module refers to a module for analyzing and evaluating the feeding process.

[0055] like Figure 2 As shown, this is a structural diagram of the cloud computing module for cross-contamination inventory turnover processing in the feeding system provided in an embodiment of the present application. The cloud computing module for cross-contamination inventory turnover processing in the feeding system provided in an embodiment of the present application includes an inventory turnover unit and a cross-contamination processing cost efficiency unit.

[0056] Furthermore, the specific process of the inventory turnover unit is as follows: statistically extracting inventory turnover related data that affects cross contamination in the feeding system, including: inventory turnover rate, total inventory turnover, inventory error rate, production cycle of the feeding system and inventory accumulation rate of the feeding system, and obtaining the inventory turnover evaluation coefficient through the inventory turnover unit constraint formula.

[0057] In this embodiment, the inventory turnover rate and the total inventory turnover rate are determined by utilizing data from an inventory management system or an enterprise resource planning system to collect outbound records within a certain period and the average inventory at the end of the period; the inventory error rate is determined by comparing the actual inventory with the inventory recorded by the system through regular inventory checks; the system response time is determined by simulating user requests and measuring the system response time using performance testing tools such as LoadRunner or JMeter; and the inventory backlog rate is determined by maintaining a fault record log to record the number and time of system faults.

[0058] The specific constraint formula of the inventory turnover assessment unit obtained from this analysis is:

[0059]

[0060] In the formula, several inventory turnover time monitoring points are set and numbered in sequence. Indicates the number of the inventory turnover time monitoring point, Indicates the total number of numbers of inventory turnover time monitoring points, Represents the inventory turnover evaluation coefficient, which is used to evaluate the impact of inventory turnover efficiency on the feeding system. Indicates the The inventory turnover rate of each inventory turnover time monitoring point, Indicates the The total number of inventory turnovers at the inventory turnover time monitoring points, Indicates the The inventory error rate of each inventory turnover time monitoring point, Indicates the production cycle of the feeding system, Indicates the inventory accumulation rate of the feeding system.

[0061] Inventory turnover rate: Inventory turnover rate refers to the number of times inventory goods are turned over within a certain period of time. It is an important indicator to measure the level of inventory management.

[0062] Total inventory turnover: Total inventory turnover refers to the inventory volume of the feeding system at a certain point in time, which is usually related to the feeding speed and feeding cycle.

[0063] Inventory error rate: The inventory error rate refers to the ratio of the difference between the actual inventory and the book inventory during inventory counting to the book inventory. It reflects the accuracy of inventory management. The lower the inventory error rate, the more accurate the inventory management.

[0064] Production cycle: The production cycle refers to the time required to complete the entire production process from raw materials to final products. The length of the production cycle directly affects the product supply speed and inventory turnover rate.

[0065] Inventory backlog rate: Inventory backlog rate refers to the degree of inventory backlog in a certain link or time period in the entire supply chain. The level of inventory backlog rate reflects the efficiency of inventory management and the smoothness of the supply chain.

[0066] Furthermore, the specific process of the cross-contamination treatment cost efficiency unit is: statistically extracting cross-contamination treatment cost efficiency related data that affect cross-contamination in the feeding system, including: the cost of maintaining and treating cross-contamination equipment, the detection and monitoring cost, the cost of cross-contamination cleaning of the feeding system, the cross-contamination treatment input cost of the feeding system and the order processing efficiency of the feeding system, and obtaining the cross-contamination treatment cost efficiency evaluation coefficient through the cross-contamination treatment cost efficiency unit constraint formula.

[0067] In this embodiment, the cost of maintaining and handling cross-contamination equipment is determined by reviewing maintenance logs and repair invoices to determine the cost of equipment maintenance and handling cross-contamination; the detection and monitoring costs are determined by analyzing the costs of equipment and software used to detect cross-contamination; the cross-contamination handling investment costs are determined by calculating the total cost invested in preventing and managing cross-contamination, including equipment, training, cleaning, detection and maintenance-related costs; and the order processing efficiency of the feeding system is determined by using time tracking tools or manually recording the time required for an order to be received and completed.

[0068] The specific constraint formula of the cross-contamination treatment cost efficiency evaluation unit obtained from this analysis is:

[0069]

[0070] In the formula, several cross-contamination monitoring points are set up and numbered in sequence. Indicates the number of the cross-contamination monitoring point, Indicates the total number of cross-contamination monitoring points. It represents the cross-contamination treatment cost efficiency evaluation coefficient, which is used to evaluate the impact of the cross-contamination treatment cost efficiency on the feeding system. Several feeding cycle monitoring points are set and numbered in sequence. Indicates the number of the monitoring point in the feeding cycle, Indicates the total number of monitoring points in the feeding cycle. It represents the cross-contamination treatment cost efficiency evaluation coefficient, which is used to evaluate the impact of cross-contamination treatment cost efficiency on the feeding system. Indicates the The cost of maintaining and handling cross-contamination equipment at each cross-contamination monitoring point, Indicates the The cost of monitoring cross contamination at each cross contamination monitoring point, Indicates the The cost of cross-contamination cleaning under each cross-contamination monitoring point, Indicates the The input cost of cross contamination treatment at each monitoring point in the feeding cycle, represents the weight factor of cross-contamination treatment cost, Indicates the The order processing efficiency of the feeding system under each feeding cycle monitoring point, Represents a natural constant.

[0071] The costs of maintaining and handling cross-contamination equipment include: initial design costs. In order to reduce the risk of cross-contamination, more advanced filtration and separation are required during the design phase of the feeding system; equipment purchase costs. In order to prevent cross-contamination, higher quality equipment needs to be purchased; and component replacement costs. In the feeding system, certain components that are susceptible to cross-contamination need to be replaced frequently, which also constitutes the cost of maintaining and handling cross-contamination equipment.

[0072] Detection and monitoring costs refer to the costs of installing online monitoring systems, particle counters, pH monitors, and regular sampling and analysis in order to promptly detect and address cross-contamination problems.

[0073] The cost of cross-contamination cleaning of the feeding system refers to the expenses incurred by a series of measures and means taken in the industrial production process to prevent cross-contamination between different materials.

[0074] The cross-contamination treatment investment cost of the feeding system refers to the total amount of various expenses required to prevent and deal with cross-contamination problems that occur during the feeding process of materials during industrial production.

[0075] The order processing efficiency of the feeder system is an important indicator of how effectively a company responds to market demand. Fast order processing improves customer satisfaction, reduces inventory costs, and thus improves cost efficiency.

[0076] like Figure 3 As shown, it is a structural diagram of the cross-contamination demand prediction and evaluation big data module in the feeding system provided in an embodiment of the present application. The cross-contamination demand prediction and evaluation big data module includes a cross-contamination demand prediction unit and a cross-contamination evaluation unit.

[0077] Furthermore, the specific process of the cross-contamination demand forecasting unit is as follows: statistically extract relevant data in the feeding system that affect the cross-contamination demand forecast, including: historical cross-contamination values, cross-contamination differences, order values, order growth rates, cross-contamination processing efficiency and maximum defective product rate, and obtain the cross-contamination demand forecasting evaluation coefficient through the cross-contamination demand forecasting unit constraint formula.

[0078] In this embodiment, the historical cross-contamination value is obtained by extracting data on historical cross-contamination events from the quality management system or database by checking past quality control records, audit reports and any relevant violation history; the cross-contamination difference is collected by utilizing market research reports, industry analysis, competitor intelligence and market trend forecasts, using market analysis tools such as Gartner, IDC or Forrester research reports; the order value and order growth rate are obtained by collecting order data from the order management system or ERP system; the cross-contamination processing efficiency is evaluated by analyzing the frequency of cross-contamination events, processing time and resource consumption; the maximum defective rate is collected by collecting quality defect data through quality control inspections and product reviews.

[0079] From this analysis, the specific constraint formula of the cross-contamination demand forecast unit is obtained as follows:

[0080]

[0081] In the formula, several cross-contamination time monitoring points are set and numbered in sequence. Indicates the number of the cross contamination time monitoring point, Indicates the total number of cross-contamination time monitoring points, It represents the cross-contamination demand forecast assessment coefficient, which is used to evaluate the impact of cross-contamination demand forecast on the feeding system. Indicates the The historical cross contamination values ​​of each cross contamination time monitoring point, Indicates the The cross contamination difference of each cross contamination time monitoring point, Indicates the The order value of each cross-contamination time monitoring point, Indicates the The order growth rate of each cross-contamination time monitoring point, Indicates the Cross-contamination treatment efficiency at each cross-contamination time monitoring point, Indicates the maximum defective rate of the feed.

[0082] Historical cross-contamination values ​​refer to the monitoring records and data of cross-contaminants generated during the operation of the feeding system over a period of time in the past. These data include particulate matter concentration, gas emissions and water quality parameters.

[0083] The cross-contamination difference refers to the difference in the degree of cross-contamination that may occur during the production or processing process. The cross-contamination difference of the feeding system can be used to measure the degree of cross-contamination risk between different products.

[0084] Order value refers to the price required for a customer order. This indicator helps companies understand the average sales volume of their products and thus predict future cross-contamination needs, such as raw material consumption and cross-contamination emissions during the production process.

[0085] Order growth rate refers to the rate of increase in order volume over a given period. This metric measures market demand growth trends by comparing changes in order volume over different time periods. It helps companies predict future sales trends and cross-contamination demand, enabling them to adjust production and cross-contamination control strategies accordingly.

[0086] Cross-contamination efficiency refers to the efficiency of equipment or systems used to reduce or eliminate cross-contamination emissions during the feeding process. This involves treating cross-contamination generated, such as particulate matter, gases, and wastewater, to meet relevant environmental regulations and standards.

[0087] The maximum defective rate is the ratio of the maximum quality problems or defects that occur during the production process. This indicator reflects the limit of quality control of the feeding system during the production process, that is, the maximum quality problem of the product or raw material.

[0088] Furthermore, the specific process of the cross-contamination assessment unit is: statistically extract relevant data affecting the cross-contamination assessment in the feeding system, including: cross-contamination cleaning efficiency, cross-contamination rate, cleaning interval time, cross-contamination threshold and system capacity, and obtain the cross-contamination assessment coefficient through the cross-contamination assessment unit constraint formula.

[0089] In this embodiment, the cross-contamination cleaning efficiency is evaluated by recording key indicators in the cleaning process, such as cleaning time, cleaning materials used, and cleaning effect; the cross-contamination rate is measured by laboratory testing or online monitoring equipment; the cleaning interval is determined by recording the frequency and time interval of cleaning activities, analyzing the data, and determining the average cleaning interval; the cross-contamination threshold is determined by product quality standards and regulatory requirements to determine the acceptable cross-contamination threshold; the system capacity is determined by system design documents, technical specifications, or actual operation data.

[0090] The specific constraint formula of the cross-contamination assessment unit obtained from this analysis is:

[0091]

[0092] In the formula, several spatial monitoring points are set and numbered in sequence. Indicates the number of the spatial monitoring point, Indicates the total number of spatial monitoring points. Indicates the cross contamination assessment coefficient, which is used to evaluate the impact of cross contamination on the feeding system. Indicates the Cross-contamination cleaning efficiency of each space monitoring point, Indicates the The cross contamination rate of each spatial monitoring point, Indicates the The cleaning interval of each space monitoring point, Indicates the cross contamination threshold of the feeding system, Indicates the system capacity of the feeding system.

[0093] Cross-contamination cleaning efficiency refers to the ability of cleaning measures to remove existing cross-contamination during the production process. Specifically, it reflects the effectiveness of the cleaning process, that is, the ratio of the amount of cross-contamination remaining in the feed system or on the components after the cleaning operation to the original cross-contamination.

[0094] Cross-contamination rate refers to the degree of cross-contamination caused by one material to another material during the transmission or processing process. It is used to quantify the amount or proportion of cross-contaminants released from one material to another material.

[0095] The cleaning interval is the length of time required to clean and disinfect the feed system between production runs. The length of the cleaning interval directly affects the risk of cross-contamination, as it determines how long cross-contamination substances remain in the system.

[0096] The cross-contamination threshold is the maximum acceptable level of cross-contamination, above which there is a risk of cross-contamination. The cross-contamination threshold is measured in concentration or quantity.

[0097] System capacity refers to the maximum amount of material that the feeding system can handle or the maximum working capacity of the system. This capacity is determined based on the design parameters of the system, such as the volume of the hopper, the width of the conveyor belt, the size of the pipeline and the number of tons processed per hour.

[0098] Furthermore, the specific process of the cross-contamination on filtration separation evaluation module is as follows: statistically extract relevant data on the cross-contamination on filtration separation evaluation in the feeding system, including: inventory turnover evaluation coefficient, cross-contamination treatment cost efficiency evaluation coefficient, cross-contamination demand forecast evaluation coefficient and cross-contamination evaluation coefficient, and obtain the cross-contamination on filtration separation evaluation coefficient through the constraint formula of the cross-contamination on filtration separation evaluation module.

[0099] In this embodiment, the inventory turnover evaluation coefficient, the cross-contamination treatment cost efficiency evaluation coefficient, the cross-contamination demand forecast evaluation coefficient and the cross-contamination evaluation coefficient are calculated by specific constraint formulas in the inventory turnover unit, the cross-contamination treatment cost efficiency unit, the cross-contamination demand forecast unit and the cross-contamination evaluation unit.

[0100] From this analysis, the specific constraint formula for cross contamination on the filtration separation evaluation module is obtained as follows:

[0101]

[0102] Where, represents the inventory turnover evaluation coefficient, represents the cross-contamination treatment cost efficiency evaluation coefficient, represents the cross-contamination demand forecast assessment coefficient, represents the cross contamination assessment coefficient, It represents the evaluation coefficient of cross contamination on filtration separation, Represents a natural constant.

[0103] Furthermore, the specific control process of the filtration and separation control module is as follows:

[0104] Collecting filtering, separation and cleaning data of the feeding system, extracting features from the filtering, separation and cleaning data of the feeding system, and obtaining filtering, separation and cleaning feature data;

[0105] A decision tree model is selected to train the filtration separation and cleaning feature data, and the filtration separation effect is predicted by the decision tree model to obtain the prediction results of the decision tree model. The prediction results of the decision tree model are used to regulate some parameters in the filtration separation.

[0106] In this embodiment, the filtration separation of the feeding system is regulated by the cross-contamination filtration separation evaluation coefficient obtained by the cross-contamination filtration separation evaluation module, and a suitable filter medium is selected. For different cross-contamination sources, a filter medium with higher filtration efficiency and anti-pollution ability is selected to improve its adsorption capacity for cross pollutants, thereby reducing the risk of cross contamination; adjusting some parameters of filtration separation, such as filtration speed, pressure, temperature, etc., can make the filtration separation process more stable and reduce the possibility of cross contamination.

[0107] like Figure 4 FIG. 1 is a flow chart of a feeding method according to an embodiment of the present application. The specific steps of the feeding method are as follows:

[0108] Use continuous integration tools to optimize the inventory management and cost efficiency of the feeding system, analyze and calculate inventory management and cost efficiency, and obtain analysis results of inventory management efficiency and cost efficiency;

[0109] Use data mining and machine learning to process, analyze and predict the feeding process. The cross-contamination demand prediction and assessment big data module has real-time data processing and analysis capabilities to obtain cross-contamination demand prediction results and cross-contamination assessment results.

[0110] Analyze and evaluate the feeding process to obtain the evaluation results of cross contamination on filtration separation;

[0111] The results of filtration separation evaluation based on cross contamination are used to regulate some parameters in filtration separation.

[0112] The technical solution in the above-mentioned embodiment of the present application has at least the following technical effects or advantages: relative to an automatic feeding system, method and medium disclosed in the invention patent publication No. CN112668961B, comprising: an order processing module, a station binding module, an inventory analysis module and a material transportation module; the order processing module is used to obtain order information and send it to the station binding module; the station binding module is used to select a station and bind the station information of the station with the order information to obtain binding information; the inventory analysis module is used to allocate materials corresponding to the binding information to the station; the material transportation module is used to collect materials at the station and perform corresponding cyclic collection measures; the present invention can automatically collect material orders from the server, thereby improving the accuracy, safety and efficiency of materials. In the embodiment of the present application, the cloud service can provide high-speed data processing capabilities, so that inventory information and cost efficiency analysis can be updated in real time, thereby obtaining an inventory turnover assessment coefficient and a cross-contamination treatment cost efficiency assessment coefficient, thereby achieving the effect of improving the real-time cross-contamination processing capability of filtration and separation technology, effectively solving the problem of insufficient real-time cross-contamination processing capability of filtration and separation technology in the prior art.

[0113] The automated material supply management method, system and storage medium relative to the invention patent announcement with announcement number: CN115564317B include: material demand aggregation and priority sorting; obtaining the current available supply of materials based on whether the logistics center is enabled; branch selection based on whether the material is an alternative material, and performing material supply and demand balance calculations separately to determine whether there is a shortage of materials; splitting the demand for the missing materials to generate the net material demand for suppliers; calculating the actual material demand based on whether the logistics center is enabled; setting the delivery address according to the different dimensions of the material, and automatically generating supply information for the supplier. By summarizing and sorting various demands, then splitting the demands for missing materials, automatically configuring the delivery address, and automatically generating the supplier's complete supply information, the automatic on-demand delivery of materials is realized. In the face of dynamic changes in product demand, the calculation efficiency and accuracy of the material demand for suppliers are improved, and the timeliness of response is improved. The data technology in the embodiment of the present application can collect and integrate multi-source data from various links of the supply chain, including production, transportation, storage and sales, etc., to provide data support for a comprehensive assessment of the impact of cross-contamination on filtration and separation, thereby obtaining a cross-contamination filtration and separation evaluation coefficient, and then achieving the effect of improving the comprehensiveness of filtration and separation technology for cross-contamination, effectively solving the problem of insufficient comprehensiveness of filtration and separation technology for cross-contamination in the existing technology.

[0114] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0118] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0119] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A feeding system, characterized in that: It includes a cross-contamination inventory turnover processing cloud computing module, a cross-contamination demand forecast and evaluation big data module, a cross-contamination filtration and separation evaluation module, and a filtration and separation control module: The cross-contamination inventory turnover processing cloud computing module is used to analyze inventory management efficiency using a continuous integration tool, calculate cost efficiency using a continuous integration tool, and obtain analysis results of inventory management efficiency and cost efficiency. The cross-contamination inventory turnover processing cloud computing module includes an inventory turnover unit and a cross-contamination processing cost efficiency unit; The cross-contamination demand prediction and evaluation big data module is used to process, analyze and predict the feeding process by using data mining and machine learning to obtain cross-contamination demand prediction results and cross-contamination evaluation results. The cross-contamination demand prediction and evaluation big data module includes a cross-contamination demand prediction unit and a cross-contamination evaluation unit; The cross contamination filtration separation assessment module is used to analyze and evaluate the feeding process to obtain a cross contamination filtration separation assessment result; The specific evaluation process of the cross contamination filtration separation evaluation module is as follows: Statistically extract relevant data on the effect of cross contamination on filtration and separation in the feeding system, and obtain a cross contamination on filtration and separation evaluation coefficient based on the relevant data using a cross contamination on filtration and separation evaluation constraint formula; The filtration separation control module is used to control some parameters in the filtration separation based on the filtration separation evaluation results through cross contamination; The specific constraint formula of the cross contamination on the filtration separation evaluation module is: Where, represents the inventory turnover evaluation coefficient, represents the cross-contamination treatment cost efficiency evaluation coefficient, represents the cross-contamination demand forecast assessment coefficient, represents the cross contamination assessment coefficient, It represents the evaluation coefficient of cross contamination on filtration separation, represents a natural constant; The specific control process of the filtration and separation control module is as follows: Collecting filtering, separation and cleaning data of the feeding system, extracting features from the filtering, separation and cleaning data of the feeding system, and obtaining filtering, separation and cleaning feature data; A decision tree model is selected to train the filtration separation and cleaning feature data, and the filtration separation effect is predicted by the decision tree model to obtain the prediction results of the decision tree model. The prediction results of the decision tree model are used to regulate some parameters in the filtration separation.

2. The feeding system according to claim 1, characterized in that: The cross-contamination inventory turnover processing cloud computing module includes an inventory turnover unit and a cross-contamination processing cost efficiency unit; The specific analysis process for analyzing inventory management efficiency through the inventory turnover unit using continuous integration tools is as follows: the continuous integration tool is used to statistically extract inventory turnover-related data that affects cross contamination in the feeding system, and the inventory turnover constraint formula is used to evaluate the inventory turnover evaluation coefficient based on the inventory turnover-related data. The inventory turnover evaluation coefficient is used to analyze inventory management efficiency; The specific analysis process for calculating cost efficiency through the cross-contamination treatment cost efficiency unit using the continuous integration tool is as follows: statistically extract data related to the cross-contamination treatment cost efficiency in the feeding system through the continuous integration tool, and evaluate the cross-contamination treatment cost efficiency constraint formula based on the cross-contamination treatment cost efficiency data to obtain a cross-contamination treatment cost efficiency evaluation coefficient, which is used to analyze the cross-contamination treatment cost efficiency.

3. The feeding system according to claim 1, characterized in that: The cross-contamination demand prediction and evaluation big data module includes a cross-contamination demand prediction unit and a cross-contamination evaluation unit; Specific analysis process of predicting the feeding process by using data mining and machine learning through the cross-contamination demand prediction unit: extracting relevant data affecting the cross-contamination demand prediction in the feeding system through data mining and machine learning, and obtaining a cross-contamination demand prediction evaluation coefficient based on the relevant data of the cross-contamination demand prediction through the cross-contamination demand prediction constraint formula. The cross-contamination demand prediction evaluation coefficient is used to predict the feeding process; The specific analysis process of analyzing the feeding process by using data mining and machine learning through the cross-contamination assessment unit: the relevant data affecting the cross-contamination assessment in the feeding system are statistically extracted through data mining and machine learning, and the cross-contamination assessment coefficient is obtained according to the relevant data of the cross-contamination assessment through the cross-contamination assessment constraint formula. The cross-contamination assessment coefficient is used to analyze the feeding process.

4. The feeding system according to claim 2, characterized in that: The inventory turnover constraint formula is: In the formula, several inventory turnover time monitoring points are set and numbered in sequence. Indicates the number of the inventory turnover time monitoring point, Indicates the total number of numbers of inventory turnover time monitoring points, Represents the inventory turnover evaluation coefficient, which is used to evaluate the impact of inventory turnover efficiency on the feeding system. Indicates the The inventory turnover rate of each inventory turnover time monitoring point, Indicates the The total number of inventory turnovers at the inventory turnover time monitoring points, Indicates the The inventory error rate of each inventory turnover time monitoring point, Indicates the production cycle of the feeding system, Indicates the inventory accumulation rate of the feeding system.

5. The feeding system according to claim 3, characterized in that: The cross contamination demand prediction constraint formula is: In the formula, several cross-contamination time monitoring points are set and numbered in sequence. Indicates the number of the cross contamination time monitoring point, Indicates the total number of cross-contamination time monitoring points, It represents the cross-contamination demand forecast assessment coefficient, which is used to evaluate the impact of cross-contamination demand forecast on the feeding system. Indicates the The historical cross contamination values ​​of each cross contamination time monitoring point, Indicates the The cross contamination difference of each cross contamination time monitoring point, Indicates the The order value of each cross-contamination time monitoring point, Indicates the The order growth rate of each cross-contamination time monitoring point, Indicates the Cross-contamination treatment efficiency at each cross-contamination time monitoring point, Indicates the maximum defective rate of the feed.

6. The feeding system according to claim 3, characterized in that: The specific constraint formula of the cross contamination assessment unit is: In the formula, several spatial monitoring points are set and numbered in sequence. Indicates the number of the spatial monitoring point, Indicates the total number of spatial monitoring points. Indicates the cross contamination assessment coefficient, which is used to evaluate the impact of cross contamination on the feeding system. Indicates the Cross-contamination cleaning efficiency of each space monitoring point, Indicates the The cross contamination rate of each spatial monitoring point, Indicates the The cleaning interval of each space monitoring point, Indicates the cross contamination threshold of the feeding system, Indicates the system capacity of the feeding system, Represents a natural constant.

7. A feeding method, applied to the feeding system according to any one of claims 1 to 6, characterized in that: The specific steps of the feeding method are: Use continuous integration tools to optimize the inventory management and cost efficiency of the feeding system, analyze and calculate inventory management and cost efficiency, and obtain analysis results of inventory management efficiency and cost efficiency; Use data mining and machine learning to process, analyze and predict the feeding process. The cross-contamination demand prediction and assessment big data module has real-time data processing and analysis capabilities to obtain cross-contamination demand prediction results and cross-contamination assessment results. Analyze and evaluate the feeding process to obtain the evaluation results of cross contamination on filtration separation; The results of filtration separation evaluation based on cross contamination are used to regulate some parameters in filtration separation.

Images