CN116991133B - Efficient feeding control method and device based on intelligent optimization of flow - Google Patents
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Abstract
The invention discloses a high-efficiency feeding control method and device based on intelligent optimization of a flow. The high-efficiency feeding control method based on intelligent optimization of the flow comprises the following steps: s1, customizing and optimizing a process flow; s2, preprocessing the data of each process link; s3, dehumidification drying efficiency evaluation; s4, evaluating the suction conveying efficiency; s5, evaluating the metering proportioning efficiency; s6, carrying out correction and evaluation on the process flow types; s7, adjusting a high-efficiency feeding method. According to the invention, through the evaluation of the dehumidification drying, the material sucking conveying, the metering proportioning and the process flow type correction links in the process flow, the details of the control flow are adjusted according to the evaluation results, and the final control method is determined, so that the effect of greatly improving the accuracy of the adjustment and optimization high-efficiency feeding control method is realized, and the problem that the optimal feeding control method cannot be determined due to the insufficient intelligent optimization evaluation accuracy in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of internet of things feeding, in particular to a high-efficiency feeding control method and device based on intelligent optimization of flow.
Background
With the development of the technology of the Internet of things, the high-efficiency feeding control method based on intelligent optimization of the flow has wide application prospect, and can play an important role in the fields of manufacturing industry, logistics and supply chain management, food processing and medical manufacturing, energy and environment fields, intelligent home, the Internet of things and the like; the high-efficiency feeding control method is mainly realized by using the technologies of sensor technology, real-time data processing, automation and robot technology, optimization algorithm and the like.
The specific implementation steps of the existing high-efficiency feeding control method based on intelligent optimization of the flow relate to the following aspects: analyzing and evaluating the feeding process comprehensively; designing and planning, namely designing and planning a scheme of a high-efficiency feeding control method according to an analysis result; selecting and purchasing, namely selecting proper feeding equipment and a related control system; installing and debugging, namely installing the feeding equipment into the existing process flow, and performing preliminary debugging and testing; integrating and controlling the feeding equipment and the control system, and establishing corresponding control logic and control accuracy; testing and optimizing, performing comprehensive testing of a feeding system, and verifying the effectiveness and stability of a feeding control method; training and operating, wherein operators are trained to know the operation and maintenance requirements of the feeding equipment; monitoring and maintaining, establishing a monitoring mechanism of the feeding system, and monitoring key parameters and indexes in the feeding process.
For example, publication No.: the invention patent application of CN110876992a discloses a material supply device, a material supply system and a material supply method, the material supply device includes: a storage device on which an information storage part is provided, the tracking information of the supply material in the storage device being recorded; the buffer device, the extraction device and the spraying device are communicated with each other; an information identifying unit that identifies the tracking information in the information storing unit; and the control device acquires the tracking information of the information storage component and compares the tracking information with the target information of the target supply material to judge whether the tracking information and the target information are matched.
For example, bulletin numbers: the feeder determination method and feeder determination apparatus of the patent publication CN113228842a include the steps of: an actual work data registration step of registering actual work data, which is obtained by associating the size or type of the component used in the completed assembly work with the identification information of the feeder to which the component is supplied, in a database; an actual operation data search step of searching whether or not candidate data obtained by combining the size or the type of the component used in the mounting operation to be performed with the identification information of the feeder as a candidate for supplying the component matches any one of actual operation data in a database; a step of determining the size or type of the component and the combination of the feeder based on the feeding operation data of the feeder when the candidate data and the actual operation data are identical; and a performance criterion determining step of determining a combination of the size or the type of the component and the feeder based on the supply performance of the feeder when the candidate data does not match the actual operation data.
However, in the process of implementing the technical scheme of the invention in the embodiment of the application, the inventor of the application finds that at least the following technical problems exist in the above technology:
in the prior art, since the intelligent optimization of the production process flow of the efficient feeding method is mainly determined according to the prior experience, the problem that the optimal feeding control method cannot be determined due to insufficient accuracy of intelligent optimization evaluation exists.
Disclosure of Invention
According to the method and the device for controlling the high-efficiency feeding based on the intelligent optimization of the process, the problem that in the prior art, the optimal feeding control method cannot be determined due to insufficient accuracy of intelligent optimization evaluation is solved, and the effect of adjusting and redetermining details of the control process according to the result of each process flow factor in the comprehensive evaluation high-efficiency feeding control method is achieved, so that the accuracy of the adjustment optimization high-efficiency feeding control method is greatly improved.
The embodiment of the application provides a high-efficiency feeding control method based on intelligent optimization of a flow, which is used for a server and comprises the following steps: s1, acquiring data of each link of process optimization; s2, preprocessing the data of each link of the process optimization to obtain a dehumidification drying sub-data set, a material sucking and conveying sub-data set, a metering proportion sub-data set and a process type correction sub-data set; s3, evaluating the processing efficiency of the flow optimization dehumidification drying link according to the dehumidification drying sub-data to obtain a dehumidification drying efficiency coefficient; s4, evaluating the processing efficiency of the flow optimization suction conveying link according to the suction conveying sub-data set to obtain a suction conveying efficiency coefficient; s5, evaluating the processing efficiency of the flow optimization metering and proportioning link according to the metering and proportioning sub-data set to obtain a metering and proportioning efficiency coefficient; s6, evaluating the process flow supplement corrections of the process flow optimization according to the process flow type correction sub-data set to obtain a material type correction coefficient and a process flow correction coefficient; and S7, comprehensively evaluating the overall effect of each link of the process optimization according to the dehumidifying and drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering and proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient to obtain a high-efficiency feeding comprehensive evaluation coefficient, and adjusting the high-efficiency feeding control method according to the high-efficiency feeding comprehensive evaluation coefficient.
Further, the specific steps of obtaining the flow optimization data of each link are as follows: s11, obtaining a process flow overall optimization technical scheme obtained by comprehensively researching field investigation results of a specific factory; s12, acquiring automatic production equipment of a modularized functional design obtained by integrally optimizing a technical scheme of a process flow; s13, acquiring the original data of all automatic production equipment.
Further, the specific steps of preprocessing the flow optimization link data are as follows: acquiring all the flow optimization raw data to obtain a flow optimization raw data set, recording the data category of the flow optimization raw data set as,optimizing the total number of data categories of the original data group for the flow, wherein the flow optimizes the data of the original data groupData of class is recorded as->Optimizing the original data set for the flow +.>The total number of class data is->Class I->The data of the individual flow optimization raw data set is recorded as +.>And according to this the +.>Individual flow optimization raw data set white noise evaluation value +.>The specific calculation formula isWherein->Indicate->Class I->Optimizing the data white noise threshold standard value of the original data set by a set flow, < ->Representing the flow optimized raw data set data noise value read error factor, Indicate->Class I->Optimizing the data white noise difference standard value of the original data set by a set flow +.>Indicate->Class I->Optimizing the white noise correction coefficient of the original data set by a predefined process; will be->Class I->Comparing the white noise evaluation value of each flow optimization original data set with the white noise evaluation value of the set flow optimization original data set, reserving the corresponding flow optimization original data set original data within the error allowable range, repeating the step for all flow optimization original data sets, and recording all reserved data as an effective flow optimization data set; the effective main system data set is classified according to the type of the feed control application to obtain four classification sub-data sets which are respectively marked as a dehumidification drying sub-data set, a material sucking conveying sub-data set, a metering proportion sub-data set and a process flow type correction sub-data set.
Further, the specific steps for obtaining the dehumidifying and drying efficiency coefficient are as follows: obtaining dehumidification efficiency from a dehumidification drying sub-data setDrying efficiency->Drying temperature->Cleaning efficiency->And obtaining the dehumidification drying efficiency coefficient +.>The specific calculation formula is thatWherein->Indicating that the influence matching factor corresponding to the dehumidification efficiency is set, < - >Indicating the influence matching factor corresponding to the set drying efficiency, < ->Indicating the influence matching factor corresponding to the set drying temperature, < ->Representing the mutual superposition influence coefficient of the predefined dehumidification efficiency, drying efficiency and drying temperature, < >>Representing a predefined cleaning efficiency criterion value.
Further, the specific steps for obtaining the suction conveying efficiency coefficient are as follows: delivery capacity derived from suction delivery sub-data setsTransport distance->Delivery efficiency->Friction loss efficacy->And according to this, the suction conveying efficiency coefficient +.>The specific calculation formula is thatWherein->An influence matching factor corresponding to the set transport capacity is indicated, < ->Indicating the influence matching factor corresponding to the set delivery efficiency, < ->Indicating the influence matching factor corresponding to the set transit distance, < ->Representing a predefined basal tubing efficacy,/->Representing the predefined pipeline actual delivery efficacy,/->Representing predefined pipe material properties and aerodynamic layout correction factors, < ->The influence correction factors representing the predefined transport capacity, transport distance, transport efficiency and frictional loss effectiveness are superimposed on one another, < >>Representing natural constants.
Further, the specific steps for obtaining the efficiency coefficient of the metering proportioning are as follows: obtaining actual integrated accuracy from metering sub-data sets Flow range->Maximum real-time calculation rate->Stable adaptation coefficient->And obtaining the metering ratio efficiency coefficient +.>The specific calculation formula is thatWherein->Andrepresents a maximum flow range and a minimum flow range, respectively, < >>Indicating the difference corresponding to the set flow range affects the matching factor, < ->A joint influence matching factor which indicates that the set maximum real-time calculation rate corresponds to the maximum flow range>Representing a predefined actual integrated accuracy standard value +.>The predefined actual integrated accuracy, flow range, maximum real-time calculation rate and stable adaptation coefficient are mutually superposed to influence the correction coefficient.
Further, the specific steps of obtaining the material type correction coefficient are as follows: acquiring a predefined material surface property coefficient from a process flow type correction sub-data setPredefining the coefficient of thermal properties of the material ∈>Predefined material machinability coefficient +.>Predefined material specific property coefficient ∈ ->And obtaining the material type correction coefficient +_ according to the calculation formula>The specific calculation formula is->Wherein->And->Respectively representing the weight factors corresponding to the predefined material surface property coefficient, the predefined thermal property coefficient and the predefined machinability property coefficient, Superposition influence coefficient representing predefined material surface property coefficient, thermal property coefficient and machinability property coefficient,/->Representing the setting of a predefined material specific property systemAnd (3) correcting factors corresponding to the numbers.
Further, the specific acquisition steps of the process flow correction coefficient are as follows: acquisition of predefined modular scalability coefficients from process flow type syndrome data setsPractical maximum production efficiency->Practical maximum energy utilization efficiency->And the actual occupied space size->And obtaining the process flow correction coefficient ++through the calculation formula>The specific calculation formula is->Wherein->And->Respectively represents the maximum production efficiency of the set standard, the maximum energy utilization efficiency of the set standard and the size of the occupied space of the set standard,and->Weight factors corresponding to the maximum production efficiency ratio, the maximum energy utilization efficiency ratio and the occupied space size ratio are respectively expressed, and the weight factors are->Influence matching factor representing predefined modular scalability coefficients, < ->Superposition effect coefficient representing predefined modular scalability coefficient, actual maximum production efficiency, actual maximum energy utilization efficiency and actual occupied space size +.>Representing a predefined customized process correction factor.
Further, the specific steps of obtaining the high-efficiency feeding comprehensive evaluation coefficient are as follows: acquiring all data evaluation coefficients from S3 to S6, and recording the dehumidifying and drying efficiency coefficient in S3 asSequentially traversing all steps from S3 to S6, the process flow correction coefficient in S6 is marked as +.> Representing 5 evaluation coefficients evaluated sequentially from steps S3 to S6, the total time of all steps data processing from S3 to S6 is +.>And accordingly obtaining the high-efficiency feeding comprehensive evaluation coefficient +.>The specific calculation formula is->Wherein the method comprises the steps ofRepresentation->When determining +_>Corresponding accuracy weighting factor, +.>And (3) representing the total processing time standard value of all the steps, comparing the high-efficiency feeding comprehensive evaluation coefficient with a predefined high-efficiency feeding comprehensive evaluation coefficient threshold value, if the high-efficiency feeding comprehensive evaluation coefficient is within a predefined error allowable range, marking the high-efficiency feeding control method corresponding to the high-efficiency feeding comprehensive evaluation coefficient as an effective high-efficiency feeding control method, otherwise, respectively calculating the dehumidification drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient in sequence, and if the calculated value exceeds the corresponding maximum predefined coefficient threshold value, sequentially traversing each factor data for efficiency evaluation in the corresponding sub-data set, and when the factor data exceeds the threshold value, re-optimizing the corresponding efficiency specific factor until the effective high-efficiency feeding control method is obtained.
The embodiment of the application provides a high-efficient feed control device based on flow intelligent optimization, which comprises a process flow customization optimizing module, each process link data preprocessing module, a dehumidification drying efficiency evaluation module, a material sucking and conveying efficiency evaluation module, a metering proportioning efficiency evaluation module, a process flow type correction evaluation module and an adjustment high-efficient feed method module: the process flow customization optimizing module is used for acquiring data of each link of flow optimization; the process link data preprocessing module is used for preprocessing the process optimization link data to obtain a dehumidification drying sub-data set, a material sucking and conveying sub-data set, a metering proportion sub-data set and a process flow type correction sub-data set; the dehumidification drying efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization dehumidification drying link according to the dehumidification drying sub-data to obtain a dehumidification drying efficiency coefficient; the suction conveying efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization suction conveying link according to the suction conveying sub-data set to obtain a suction conveying efficiency coefficient; the metering and proportioning efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization metering and proportioning link according to the metering and proportioning sub-data set to obtain a metering and proportioning efficiency coefficient; the process flow type correction evaluation module is used for evaluating different process flow correction of the process flow optimization according to the process flow type correction sub-data set to obtain a material type correction coefficient and a process flow correction coefficient; the high-efficiency feeding adjustment method module is used for comprehensively evaluating the overall effect of each link of the process optimization according to the dehumidifying and drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering and proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient to obtain a high-efficiency feeding comprehensive evaluation coefficient, and adjusting the high-efficiency feeding control method according to the high-efficiency feeding comprehensive evaluation coefficient.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. the intelligent optimized control method for the process flow is obtained through customized optimization of the process flow, all process flow links of the control method are evaluated comprehensively to obtain evaluation coefficients of all process flows, dehumidification drying efficiency evaluation, material sucking and conveying efficiency evaluation, metering proportioning efficiency evaluation and process flow type correction evaluation are sequentially carried out, so that details of the control flow are adjusted according to all evaluation results, the final control method is determined, further, the effect of greatly improving accuracy of the efficient feeding control method for adjustment and optimization is achieved, and the problem that in the prior art, the optimal feeding control method cannot be determined due to the fact that the intelligent optimized evaluation accuracy is insufficient is effectively solved.
2. And (3) carrying out evaluation on the process flow compensation correction of different process flow optimization according to the process flow type correction sub-data set to obtain material type correction coefficients and process flow correction coefficients, wherein for a specific production process, the properties of different types of materials and the requirements of customization of different process flows have specific influence on the intelligent optimization direction of the process flows, so that the efficiency evaluation of each production flow is influenced, and therefore, the correction evaluation is required, the scientificity of the whole process flow optimization is improved, and the overall efficiency of a feeding control method is further improved.
3. The overall effect of each link is comprehensively evaluated through optimizing the flow, the overall efficient feeding process flow is firstly comprehensively evaluated, if the efficient feeding control method is not adopted, the dehumidification drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient are sequentially calculated respectively, if the corresponding threshold value is exceeded, the corresponding efficiency concrete factors are re-optimized, so that an efficient feeding control method is established for the concrete production process flow, the comprehensiveness of each factor of each production process flow is guaranteed to be evaluated, and the feasibility of the efficient feeding control method is further realized.
Drawings
Fig. 1 is a schematic flow diagram of a flow intelligent optimization-based efficient feeding control method according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of comprehensive evaluation of overall effects of various links in flow optimization provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a high-efficiency feeding control device based on flow intelligent optimization according to an embodiment of the present application.
Detailed Description
According to the method and the device for controlling the high-efficiency feeding based on the intelligent optimization of the flow, the problem that in the prior art, the optimal feeding control method cannot be determined due to insufficient accuracy of intelligent optimization evaluation is solved, and the effect of adjusting and redetermining details of the control method according to the result of comprehensively evaluating all process flow factors in the high-efficiency feeding control method is achieved by comprehensively evaluating all process flow links of the control method, so that accuracy of the adjusting and optimizing the high-efficiency feeding control method is greatly improved.
The technical scheme in this application embodiment is for solving the above-mentioned, and real-time supervision feedback is incomplete accurate makes the problem that feed method can not in time adjust and control, and the overall thinking is as follows:
the intelligent optimized control method of the process is obtained through customized optimization of the process flow, all process flow links of the control method are evaluated comprehensively to obtain evaluation coefficients of all process flows, dehumidification drying efficiency evaluation, material sucking and conveying efficiency evaluation, metering proportioning efficiency evaluation and process flow type correction evaluation are sequentially carried out, so that details of the control flow are adjusted according to all evaluation results, the final control method is obtained through determination, and further the effect of greatly improving accuracy of the efficient feeding control method of adjustment optimization is achieved.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
As shown in fig. 1, a flow diagram of a flow intelligent optimization-based efficient feed control method provided in an embodiment of the present application is provided, where the method is applied to a flow intelligent optimization-based efficient feed control device, and the method is used by a server, and includes the following steps: s1, customizing and optimizing a process flow: acquiring data of each link of process optimization; s2, preprocessing the data of each process link: preprocessing the data of each link of the process optimization to obtain a dehumidification drying sub-data set, a material sucking and conveying sub-data set, a metering proportion sub-data set and a process type correction sub-data set; s3, dehumidification drying efficiency evaluation: evaluating the processing efficiency of the flow optimization dehumidification drying link according to the dehumidification drying sub-data to obtain a dehumidification drying efficiency coefficient; s4, evaluating the suction conveying efficiency: evaluating the processing efficiency of the flow optimization suction conveying link according to the suction conveying sub-data set to obtain a suction conveying efficiency coefficient; s5, evaluating the metering proportioning efficiency: evaluating the processing efficiency of the flow optimization metering and proportioning link according to the metering and proportioning sub-data set to obtain a metering and proportioning efficiency coefficient; s6, carrying out correction and evaluation on the process flow types: evaluating the process flow supplement corrections of different process flow optimization according to the process flow type supplement sub-data set to obtain a material type correction coefficient and a process flow correction coefficient; s7, adjusting a high-efficiency feeding method: and comprehensively evaluating the overall effect of each link of the flow optimization according to the dehumidifying and drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering and proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient to obtain a high-efficiency feeding comprehensive evaluation coefficient, and adjusting the high-efficiency feeding control method according to the high-efficiency feeding comprehensive evaluation coefficient.
Further, the specific steps of acquiring the data of each link of the process optimization are as follows: s11, obtaining a process flow overall optimization technical scheme obtained by comprehensively researching field investigation results of a specific factory; s12, acquiring automatic production equipment of a modularized functional design obtained by integrally optimizing a technical scheme of a process flow; s13, acquiring the original data of all automatic production equipment.
In the embodiment, all refined total flows are demand communication, condition investigation, field investigation, scheme discussion, scheme determination, quotation signing, construction scheme, engineering scheduling, equipment establishment, production planning, construction arrangement, preparation for implementation, tissue acceptance, training delivery, after-sale tracking and service response; the simple flow of the overall service is briefly described above, and the following is an evaluation of the optimization effect of the specific flow; in detail, the method mainly aims at the production requirement of a specific process, comprehensively researches the field investigation result of a specific factory, and provides the technical scheme for integrally optimizing the process flow; aiming at the technical scheme of overall optimization of specific process flows, specific modularized functional design is carried out on each production process link, and corresponding modularized automatic production equipment is provided; original data are collected aiming at specific modularized functions of each production process link, and feasibility of a trial production operation evaluation scheme is evaluated.
Further, the specific steps of preprocessing the flow optimization data of each link are as follows: acquiring all flow optimization raw data to obtain a flow optimization raw data set, and recording the data category of the flow optimization raw data set asOptimizing the total number of data categories of the original data group for the flow, wherein the flow optimizes the data of the original data groupData of class is recorded as->Optimizing the original data set for the flow +.>The total number of class data is->Class I->The data of the individual flow optimization raw data set is recorded as +.>And according to this the +.>Individual flow optimization raw data set white noise evaluation value +.>The specific calculation formula isWherein->Indicate->Class I->Optimizing the data white noise threshold standard value of the original data set by a set flow, < ->Representing the flow optimized raw data set data noise value read error factor,indicate->Class I->Optimizing the data white noise difference standard value of the original data set by a set flow +.>Indicate->Class I->Optimizing the white noise correction coefficient of the original data set by a predefined process; will be->Class I->Comparing the white noise evaluation value of each flow optimization original data set with the white noise evaluation value of the set flow optimization original data set, reserving the corresponding flow optimization original data set original data within the error allowable range, repeating the step for all flow optimization original data sets, and recording all reserved data as an effective flow optimization data set; the effective main system data set is classified according to the type of the feed control application to obtain four classification sub-data sets which are respectively marked as a dehumidification drying sub-data set, a material sucking conveying sub-data set, a metering proportion sub-data set and a process flow type correction sub-data set.
In this embodiment, each process flow link generates data noise, which sometimes affects the data processing effect, and the data noise must be filtered out from the beginning, and the data noise filtering thresholds corresponding to different data acquisition modes are different, so that specific correction needs to be performed on specific situations; the high-efficiency feeding process flow is divided into three parts, namely dehumidification drying, material sucking conveying and metering proportioning, the three parts are only used for carrying out example evaluation on a general flow, and specific flows which are increased or reduced specifically are generated for specific production flows, so that the change of the flows cannot limit the invention; the invention has higher automation degree, so accurate data can be acquired, and a feeding system with lower automation degree generally adopts a laboratory or a theoretical value as a data source.
Further, the specific steps for obtaining the dehumidifying and drying efficiency coefficient are as follows: obtaining dehumidification efficiency from a dehumidification drying sub-data setDrying efficiency->Drying temperature->Cleaning efficiency->And obtaining the dehumidification drying efficiency coefficient +.>The specific calculation formula is thatWherein->Indicating that the influence matching factor corresponding to the dehumidification efficiency is set, < ->Indicating the influence matching factor corresponding to the set drying efficiency, < - >Indicating the influence matching factor corresponding to the set drying temperature, < ->Representing the mutual superposition influence coefficient of the predefined dehumidification efficiency, drying efficiency and drying temperature, < >>Representing a predefined cleaning efficiency criterion value.
In this embodiment, the dehumidification efficiency is an important index for measuring the performance of the dehumidification apparatus. It represents the ratio of the amount of moisture removed from the air per unit time to the moisture content in the air. Typically expressed as a percentage reduction in the mass of water removed or relative humidity per unit time; the drying speed refers to the ability of the dehumidifying apparatus to remove moisture from air per unit time. The high drying speed can quickly reduce the humidity in the air, and the drying temperature for improving the drying efficiency refers to the temperature of a heat source provided by the dehumidification equipment in the dehumidification process. The proper drying temperature can ensure the efficient execution of the dehumidification process and avoid the adverse effect on the quality of the dried objects; the cleaning efficiency refers to the difficulty of daily maintenance and cleaning of the dehumidifying apparatus. The equipment easy to maintain and clean can reduce the maintenance cost and time and prolong the service life of the equipment; the more efficient the automation equipment is, the higher the descending efficiency is, so that the prolonging of the maintenance period is also important to the improvement of the whole efficiency; the automatic equipment has obvious advantages in dehumidification drying, most of the equipment is semi-automatic at present, the efficiency level cannot be accurately estimated in the dehumidification drying process flow links, and the overall efficiency level cannot be estimated further.
Further, the specific steps for obtaining the material sucking and conveying efficiency coefficient are as follows: delivery capacity derived from suction delivery sub-data setsTransport distance->Delivery efficiency->Friction loss efficacy->And according to this, the suction conveying efficiency coefficient +.>The specific calculation formula is thatWherein->An influence matching factor corresponding to the set transport capacity is indicated, < ->Indicating a set transportAn effect matching factor corresponding to the efficiency, +.>Indicating the influence matching factor corresponding to the set transit distance, < ->Representing a predefined basal tubing efficacy,/->Representing the predefined pipeline actual delivery efficacy,/->Representing predefined pipe material properties and aerodynamic layout correction factors, < ->The influence correction factors representing the predefined transport capacity, transport distance, transport efficiency and frictional loss effectiveness are superimposed on one another, < >>Representing natural constants.
In this embodiment, conveying capacity refers to the mass or volume of material conveyed per unit time, depending on the design and operating parameters of the conveying system; the transfer distance refers to the actual conveying distance from the starting point to the end point of the material, and the length of the distance can influence the design and energy consumption of a conveying system; the conveying speed refers to the flow speed of the materials in the material sucking and conveying pipeline, and can influence the conveying efficiency and the material abrasion condition; the efficiency of the system is reduced due to friction loss effectiveness in the conveying process caused by pipeline friction and material friction, so that reduction is considered in design and operation, the conveying efficiency is effectively improved by using wear-resistant smooth pipeline material properties and reasonable spiral pneumatic layout, the friction loss effectiveness is reduced, and the modularized automatic material sucking and conveying equipment can keep relatively fixed speed, continuously and effectively perform all-weather material sucking and conveying and collect various data relatively accurately.
Further, the specific steps for obtaining the efficiency coefficient of the metering proportion are as follows: from sub-data sets of metering ratiosObtaining the actual comprehensive precisionFlow range->Maximum real-time calculation rate->Stable adaptation coefficient->And obtaining the metering ratio efficiency coefficient +.>The specific calculation formula is thatWhereinRepresents a maximum flow range and a minimum flow range, respectively, < >>Indicating the difference corresponding to the set flow range affects the matching factor, < ->Indicating that the set maximum real-time calculation rate corresponds to the combined influence matching factor of the maximum flow range, +.>Representing a predefined actual integrated accuracy standard value +.>The predefined actual integrated accuracy, flow range, maximum real-time calculation rate and stable adaptation coefficient are mutually superposed to influence the correction coefficient.
In this embodiment, the actual integrated accuracy is a key indicator of the metering ratio, and refers to the degree of deviation between the actual mixing or formulation result and the target ratio. Typically expressed as a percentage or part percentage. The high-precision metering proportion can ensure the accuracy and quality stability of the mixed materials; the flow range, the metering proportioning system should have the capability of adapting to different flow demands, representing the minimum and maximum flow limits that the system can meet; the real-time performance is that the metering and proportioning system can respond in real time and adjust the conveying rate of materials so as to maintain the required proportioning stability and accuracy, and the maximum real-time calculating rate is that the metering and proportioning system has the capability of quick response and can adjust the material flow in a short time so as to adapt to the change of production requirements; the metering and proportioning system has stability, can keep consistent proportioning accuracy and stability under different working conditions, and simultaneously adapt to the working under different environmental conditions, such as temperature, humidity, pressure and the like, and the capacity above is quantized to be a stable adaptation coefficient; the metering proportion is an important link of the process production flow, real-time detection of the output efficiency of the material is required, the requirements on real-time performance and quick response speed are high, the customized service requirement of the link is high, and correction evaluation is required for specific process requirements.
Further, the specific steps of obtaining the material type correction coefficient are as follows: acquiring a predefined material surface property coefficient from a process flow type correction sub-data setPredefining the coefficient of thermal properties of the material ∈>Predefining workable property coefficients of a partPredefined material specific property coefficient ∈ ->And obtaining the material type correction coefficient +_ according to the calculation formula>The specific calculation formula is->Wherein->Weight factors corresponding to the predefined material surface property coefficient, the predefined thermal property coefficient and the predefined machinability property coefficient are respectively represented by +.>Superposition influence coefficient representing predefined material surface property coefficient, thermal property coefficient and machinability property coefficient,/->Indicating setting of correction factors corresponding to the special property coefficients of the predefined material.
In this embodiment, for a specific part, the specific properties of each aspect of the part can be easily obtained through laboratory or previous production data, so that the predefined part properties can be directly obtained; the surface properties of the material particles comprise the size, the surface area, the friction coefficient, the shape, the surface coating and the like of the material particles, and the surface properties have influence on the lubricity, the corrosion resistance, the coating adhesion and the like of the material and need to be considered and treated in the process flow; thermal characteristics, including heat resistance, thermal conductivity, coefficient of thermal expansion, etc., which have an effect on the control and selection of heat treatment, heating and cooling processes, can affect the dimensional stability and physical properties of the product; workability, including machinability, formability, weldability, etc., of materials has an effect on the choice of machining process and equipment, the design of tools and dies, and the speed and efficiency of machining; the predefined material special properties can also comprise physical properties, material properties, electrical properties and the like, and the specific material has outstanding special requirements on the process, and the specific material is evaluated, so that the general process flow can not be additionally considered; the evaluation and correction of the production material parts enable the evaluation result to be more accurate for the materials used in the specific production process, the evaluation accuracy is improved, and the accuracy of determining the feeding method is further improved.
Further, the specific process flow correction coefficientThe acquisition steps are as follows: acquisition of predefined modular scalability coefficients from process flow type syndrome data setsPractical maximum production efficiency->Practical maximum energy utilization efficiency->And the actual occupied space size->And obtaining the process flow correction coefficient according to the calculation formula>The specific calculation formula is thatWherein->Respectively represents the maximum production efficiency of the set standard, the maximum energy utilization efficiency of the set standard and the size of the occupied space of the set standard,weight factors corresponding to the maximum production efficiency ratio, the maximum energy utilization efficiency ratio and the occupied space size ratio are respectively expressed, and the weight factors are->Influence matching factor representing predefined modular scalability coefficients, < ->Superposition effect coefficient representing predefined modular scalability coefficient, actual maximum production efficiency, actual maximum energy utilization efficiency and actual occupied space size +.>Representing a reservationDefining a custom process correction factor.
In this embodiment, the predefined modularized expandability coefficient represents the difficulty level of quantifying the whole process flow optimization if a new process link is to be expanded; the improvement of the production efficiency is mainly determined by the reduction of the production time and the improvement of the productivity, the optimization of the process flow can shorten the production period, increase the productivity of a production line, realize higher yield, and the actual maximum production efficiency represents the maximum production efficiency in one production period; the utilization rate of the total energy of the whole production can be improved through process optimization, the production cost is reduced, the labor cost, the equipment maintenance cost and the like are included, and the ratio of the total cost including manpower and material resource energy and the like to the benefit is represented through the actual maximum energy utilization efficiency; the actual occupied space size indicates that for a specific production environment, a certain production space is always available, and on the premise of ensuring the process and safety, the used space always has a standard value; the predefined customized process correction factors represent the process design direction with customized preferential emphasis for specific process flow optimization, and different emphasis is given to different process links, so that correction needs to be evaluated; the ability to make specific accurate evaluations of different customized process flows is greatly improved for process flow correction evaluations.
Further, the specific steps for obtaining the high-efficiency feeding comprehensive evaluation coefficient are as follows: acquiring all data evaluation coefficients from S3 to S6, and recording the dehumidifying and drying efficiency coefficient in S3 asSequentially traversing all steps from S3 to S6, the process flow correction coefficient in S6 is marked as +.>Representing 5 evaluation coefficients evaluated sequentially from steps S3 to S6, the total time of all steps data processing from S3 to S6 is +.>And accordingly obtaining the high-efficiency feeding comprehensive evaluation coefficient +.>The specific calculation formula is->Wherein->Representation ofWhen determining +_>Corresponding accuracy weighting factor, +.>And (3) representing the total processing time standard value of all the steps, comparing the high-efficiency feeding comprehensive evaluation coefficient with a predefined high-efficiency feeding comprehensive evaluation coefficient threshold value, if the high-efficiency feeding comprehensive evaluation coefficient is within a predefined error allowable range, marking the high-efficiency feeding control method corresponding to the high-efficiency feeding comprehensive evaluation coefficient as an effective high-efficiency feeding control method, otherwise, respectively calculating the dehumidification drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient in sequence, and if the calculated value exceeds the corresponding maximum predefined coefficient threshold value, sequentially traversing each factor data for efficiency evaluation in the corresponding sub-data set, and when the factor data exceeds the threshold value, re-optimizing the corresponding efficiency specific factor until the effective high-efficiency feeding control method is obtained. / >
In this embodiment, fig. 2 is a schematic structural diagram of comprehensive evaluation of overall effects of each link in process optimization provided in this embodiment, the evaluation coefficients of the efficient feed control method are processed, the evaluation accuracy of all feed control methods is preferably guaranteed, but if the time of data processing is too long, the evaluation accuracy of the control system is still too strong and is not inverted, predefined comparison evaluation needs to be performed on the total time of data processing in each data processing step, so that the actual effectiveness of data processing is guaranteed, the control efficiency of the system is practically improved, all the evaluation coefficients of the previous steps can be comprehensively evaluated, if the expected effect is not achieved, specific adjustment can be performed on factors corresponding to the coefficients obtained in specific data processing steps, for example, if the coefficient of dehumidification and drying efficiency does not meet the evaluation requirement, the process flow can reach the efficient control requirement by adjusting dehumidification efficiency, drying temperature and cleaning efficiency, specific requirements can be met for the specific process flow links, and the evaluation factors can be increased or decreased according to actual conditions in actual production evaluation, and the matching factors and weighting factors are also determined according to specific conditions; meanwhile, the traditional data can be continuously and deeply learned by a learning algorithm or a nerve convolution algorithm and the like, so that the high efficiency of data processing and the coordination of each technological process are maintained.
As shown in fig. 3, a schematic structural diagram of a flow intelligent optimization-based efficient feeding control device provided in an embodiment of the present application, where the flow intelligent optimization-based efficient feeding control device provided in the embodiment of the present application includes: the system comprises a process flow customization optimizing module, a process link data preprocessing module, a dehumidification drying efficiency evaluation module, a material sucking and conveying efficiency evaluation module, a metering and proportioning efficiency evaluation module, a process flow type correction evaluation module and a high-efficiency feeding adjustment method module: the process flow customization optimizing module is used for acquiring data of each link of flow optimization; the process link data preprocessing module is used for preprocessing the process optimization link data to obtain a dehumidification drying sub-data set, a material sucking and conveying sub-data set, a metering proportion sub-data set and a process flow type correction sub-data set; the dehumidification drying efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization dehumidification drying link according to the dehumidification drying sub-data to obtain a dehumidification drying efficiency coefficient; the suction conveying efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization suction conveying link according to the suction conveying sub-data set to obtain a suction conveying efficiency coefficient; the metering and proportioning efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization metering and proportioning link according to the metering and proportioning sub-data set to obtain a metering and proportioning efficiency coefficient; the process flow type correction evaluation module is used for evaluating the process flow correction of the process flow optimization difference according to the process flow type correction sub-data set to obtain a material type correction coefficient and a process flow correction coefficient; and the high-efficiency feeding method adjusting module is used for comprehensively evaluating the overall effect of each link of the flow optimization according to the dehumidifying and drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering and proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient to obtain a high-efficiency feeding comprehensive evaluation coefficient, and adjusting the high-efficiency feeding control method according to the high-efficiency feeding comprehensive evaluation coefficient.
The technical scheme in the embodiment of the application at least has the following technical effects or advantages: relative to publication No.: according to the material supply device, the material supply system and the material supply method disclosed in the patent application of CN110876992A, the embodiment of the application comprehensively evaluates the overall effect of each link of process optimization, so that an effective and efficient material supply control method is established for a specific production process flow, the comprehensiveness of each factor of each production process flow is ensured to be evaluated, and the feasibility of the efficient material supply control method is further realized; compared with the feeder decision method and feeder decision device of the invention patent publication with the publication number of CN113228842A, the feeder decision method and feeder decision device in the embodiment of the application can improve the scientificity of the whole process flow optimization by evaluating the supplementary corrections of different process flows of the flow optimization, thereby further improving the whole efficiency of the feeding control method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, 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 embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. The high-efficiency feeding control method based on intelligent optimization of the flow is used for a server and is characterized by comprising the following steps of:
S1, acquiring data of each link of process optimization;
s2, preprocessing the data of each link of the process optimization to obtain a dehumidification drying sub-data set, a material sucking and conveying sub-data set, a metering proportion sub-data set and a process type correction sub-data set;
s3, evaluating the processing efficiency of the flow optimization dehumidification drying link according to the dehumidification drying sub-data to obtain a dehumidification drying efficiency coefficient;
s4, evaluating the processing efficiency of the flow optimization suction conveying link according to the suction conveying sub-data set to obtain a suction conveying efficiency coefficient;
s5, evaluating the processing efficiency of the flow optimization metering and proportioning link according to the metering and proportioning sub-data set to obtain a metering and proportioning efficiency coefficient;
s6, evaluating the process flow supplement corrections of the process flow optimization according to the process flow type correction sub-data set to obtain a material type correction coefficient and a process flow correction coefficient;
s7, comprehensively evaluating the overall effect of each link of the process optimization according to the dehumidification drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient to obtain a high-efficiency feeding comprehensive evaluation coefficient, and adjusting a high-efficiency feeding control method according to the high-efficiency feeding comprehensive evaluation coefficient;
The specific steps of the process for obtaining the data of each link are as follows:
s11, obtaining a process flow overall optimization technical scheme obtained by comprehensively researching field investigation results of a specific factory;
s12, acquiring automatic production equipment of a modularized functional design obtained by integrally optimizing a technical scheme of a process flow;
s13, acquiring the original data of all automatic production equipment;
the specific steps of preprocessing the flow optimization data of each link are as follows:
acquiring all flow optimization raw data to obtain a flow optimization raw data set, wherein the data category of the flow optimization raw data set is marked as a 0 ，a 0 =1, 2..a, a is the total number of flow optimized raw data set data categories, flow optimized raw data set data a 0 The data of the class is denoted as c 0 ，c 0 =1, 2,..c, c, c is the flow optimized raw data set a 0 The total number of class data is a 0 Class c 0 The data of the individual flow optimization original data set is recorded asAnd according to the above, the a-th is obtained by a calculation formula 0 Class flow optimizing original data set white noise evaluation value +.>The specific calculation formula is thatWherein->Represents the a 0 Class c 0 A predefined flow optimization raw data set data white noise threshold standard value, alpha represents a flow optimization raw data set data noise value reading error factor, Represents the a 0 Class c 0 A predefined process optimizes the standard value of the white noise difference value of the original data set data, b represents a-th 0 Class c 0 Optimizing the white noise correction coefficient of the original data set by a predefined process;
will be a 0 Class c 0 White noise evaluation value pair of individual flow optimization raw data sets and predefined flow optimization raw data setsThe corresponding original data of the flow optimization original data set is reserved within the error allowable range, the step is repeated for all the data of the flow optimization original data set, and all the reserved data are recorded as an effective flow optimization data set;
the effective flow optimization data set is classified according to the type of the feed control application to obtain four classification sub-data sets, which are respectively marked as a dehumidification drying sub-data set, a material sucking conveying sub-data set, a metering proportion sub-data set and a process flow type correction sub-data set.
2. The efficient feeding control method based on intelligent optimization of a process as set forth in claim 1, wherein the specific steps of obtaining the dehumidifying and drying efficiency coefficient are as follows:
obtaining dehumidification efficiency D from a dehumidification drying sub-data set Wet state Drying efficiency D Drying Drying temperature E Drying Cleaning efficiency F Efficacy of And obtaining the dehumidifying and drying efficiency coefficient beta according to a specific calculation formula, wherein the specific calculation formula is that Wherein χ represents an influence matching factor corresponding to the set dehumidification efficiency, γ represents an influence matching factor corresponding to the set drying efficiency, ε represents an influence matching factor corresponding to the set drying temperature, φ represents a mutually superimposed influence coefficient of the predefined dehumidification efficiency, drying efficiency and drying temperature, F Pre-preparation Representing a predefined cleaning efficiency criterion value.
3. The efficient feeding control method based on intelligent optimization of flow according to claim 2, wherein the specific steps of obtaining the suction conveying efficiency coefficient are as follows:
delivery capacity G from suction delivery sub-data set Feeding the articles Distance of transport H Distance from each other Delivery efficiency D Feeding the articles Friction loss efficacy I Damage to And obtaining the suction conveying efficiency coefficient according to the specific calculation formulaThe specific calculation formula isWherein η represents an influence matching factor corresponding to the set conveying capacity, iota represents an influence matching factor corresponding to the set conveying efficiency, κ represents an influence matching factor corresponding to the set transfer distance, I Base group Representing a predefined basal tubing delivery efficacy, I Pre-preparation Representing the actual delivery effectiveness of a predefined pipeline, J Pre-preparation Representing a predefined pipe material property and aerodynamic layout correction factor, lambda representing a predefined conveying capacity, conveying distance, conveying efficiency and friction loss effectiveness, superimposed on each other, influencing a correction factor, e representing a natural constant.
4. The efficient feeding control method based on intelligent optimization of flow as claimed in claim 3, wherein the specific steps of obtaining the efficiency coefficient of the metering proportioning are as follows:
obtaining actual comprehensive precision K, flow range L, maximum real-time calculation rate M and stable adaptation coefficient N from the metering proportion sub-data set, and obtaining metering proportion efficiency coefficient mu according to the actual comprehensive precision K, the flow range L, the maximum real-time calculation rate M and the stable adaptation coefficient N through a specific calculation formula, wherein the specific calculation formula is thatWherein maxL and minL respectively represent a maximum flow range and a minimum flow range, v represents a difference influence matching factor corresponding to the set flow range, omicron represents a joint influence matching factor corresponding to the set maximum real-time calculation rate and the maximum flow range, K Is provided with Representing a predefined actual integrated accuracy standard value, < >>The predefined actual integrated accuracy, flow range, maximum real-time calculation rate and stable adaptation coefficient are mutually superposed to influence the correction coefficient.
5. The efficient feeding control method based on intelligent optimization of flow as claimed in claim 4, wherein the specific steps of obtaining the correction coefficient of the material type are as follows:
the surface property coefficient d, the thermal property coefficient f, the machinability property coefficient g and the special property coefficient h of the predefined material are obtained from the process flow type correction sub-data set, and the material type correction coefficient theta is obtained according to a calculation formula, wherein the specific calculation formula is as follows Wherein O, P and Q represent the predefined workpiece surface property coefficients, the predefined thermal property coefficients, and the weight factors corresponding to the predefined machinability property coefficients, respectively, θ represents the superposition influence coefficients of the predefined workpiece surface property coefficients, the thermal property coefficients, and the machinability property coefficients, and ρ represents setting the correction factors corresponding to the predefined workpiece specific property coefficients.
6. The method for controlling efficient feeding based on intelligent optimization of process as set forth in claim 5, wherein the specific obtaining steps of the process correction coefficient are as follows:
the predefined modularized expandability coefficient j, the actual maximum production efficiency k, the actual maximum energy utilization efficiency l and the actual occupied space size m are obtained from the process flow type correction sub-data set, and the process flow correction coefficient sigma is obtained through a calculation formula according to the predefined modularized expandability coefficient j, the actual maximum production efficiency k, the actual maximum energy utilization efficiency l and the actual occupied space size m, wherein the specific calculation formula is thatWherein k is Is provided with 、l Is provided with And m Is provided with The method comprises the steps of respectively representing the maximum production efficiency of a set standard, the maximum energy utilization efficiency of the set standard and the size of the occupied space of the set standard, U, V and W respectively representing the weight factors corresponding to the maximum production efficiency ratio, the maximum energy utilization efficiency ratio and the size ratio of the occupied space, and R representing the influence matching factors of the predefined modularized expandability coefficient >And tau represents a predefined customized process correction factor.
7. The method for controlling efficient feeding based on intelligent optimization of flow as set forth in claim 6, wherein the specific steps of obtaining the comprehensive evaluation coefficient of efficient feeding are as follows:
obtaining a dehumidifying and drying efficiency coefficient, a material sucking and conveying efficiency coefficient, a metering proportion efficiency coefficient, a material type correction coefficient and a process flow correction coefficient in S3 to S6, and recording the dehumidifying and drying efficiency coefficient in S3 as t 1 Sequentially traversing all steps from S3 to S6, the process flow correction coefficient in S6 is marked as t 5 Let s= {1,2,3,4,5}, ts denote the dehumidification drying efficiency coefficient, the suction conveying efficiency coefficient, the metering proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient which are sequentially evaluated in the steps from S3 to S6, the total data processing time of all the steps from S3 to S6 is T, and the high-efficiency feeding comprehensive evaluation coefficient ψ is obtained according to the calculation formula, wherein the specific calculation formula is thatWherein X is s Representation t when s is determined s The corresponding accuracy weight factor, Z represents the standard value of the total processing time of all steps, the high-efficiency feeding comprehensive evaluation coefficient is compared with the predefined high-efficiency feeding comprehensive evaluation coefficient threshold value, if the high-efficiency feeding comprehensive evaluation coefficient is within the predefined error allowable range, the high-efficiency feeding control method corresponding to the high-efficiency feeding comprehensive evaluation coefficient is marked as the effective high-efficiency feeding control method, otherwise, the dehumidification drying efficiency coefficient, the material sucking conveying efficiency coefficient, the metering proportioning efficiency coefficient, the material part type correction coefficient and the process flow correction coefficient are respectively calculated in sequence, if the calculated value exceeds the corresponding maximum predefined coefficient threshold value, the factor data for efficiency evaluation in the corresponding sub-data set is traversed in sequence, and when the factor data exceeds the threshold value, the factor data is re-optimized And (3) changing specific factors corresponding to the efficiency until an effective and efficient feeding control method is obtained.
8. The high-efficiency feeding control device based on intelligent optimization of the process is characterized by comprising a process flow customization optimizing module, a process link data preprocessing module, a dehumidification drying efficiency evaluation module, a material sucking and conveying efficiency evaluation module, a metering proportioning efficiency evaluation module, a process flow type correction evaluation module and a high-efficiency feeding adjustment method module:
the process flow customization optimizing module is used for acquiring data of each link of flow optimization;
the process link data preprocessing module is used for preprocessing the process optimization link data to obtain a dehumidification drying sub-data set, a material sucking and conveying sub-data set, a metering proportion sub-data set and a process flow type correction sub-data set;
the dehumidification drying efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization dehumidification drying link according to the dehumidification drying sub-data to obtain a dehumidification drying efficiency coefficient;
the suction conveying efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization suction conveying link according to the suction conveying sub-data set to obtain a suction conveying efficiency coefficient;
The metering and proportioning efficiency evaluation module is used for evaluating the processing efficiency of the flow optimization metering and proportioning link according to the metering and proportioning sub-data set to obtain a metering and proportioning efficiency coefficient;
the process flow type correction evaluation module is used for evaluating different process flow correction of the process flow optimization according to the process flow type correction sub-data set to obtain a material type correction coefficient and a process flow correction coefficient;
the high-efficiency feeding adjustment method module is used for comprehensively evaluating the overall effect of each link of the flow optimization according to the dehumidification drying efficiency coefficient, the material sucking and conveying efficiency coefficient, the metering proportioning efficiency coefficient, the material type correction coefficient and the process flow correction coefficient to obtain a high-efficiency feeding comprehensive evaluation coefficient, and adjusting the high-efficiency feeding control method according to the high-efficiency feeding comprehensive evaluation coefficient;
the specific steps of the process for obtaining the data of each link are as follows:
s11, obtaining a process flow overall optimization technical scheme obtained by comprehensively researching field investigation results of a specific factory;
s12, acquiring automatic production equipment of a modularized functional design obtained by integrally optimizing a technical scheme of a process flow;
S13, acquiring the original data of all automatic production equipment;
the specific steps of preprocessing the flow optimization data of each link are as follows:
acquiring all flow optimization raw data to obtain a flow optimization raw data set, wherein the data category of the flow optimization raw data set is marked as a 0 ，a 0 =1, 2..a, a is the total number of flow optimized raw data set data categories, flow optimized raw data set data a 0 The data of the class is denoted as c 0 ，c 0 =1, 2,..c, c, c is the flow optimized raw data set a 0 The total number of class data is a 0 Class c 0 The data of the individual flow optimization original data set is recorded asAnd according to the above, the a-th is obtained by a calculation formula 0 Individual flow optimization raw data set white noise evaluation value +.>The specific calculation formula is thatWherein->Represents the a 0 Class c 0 The data white noise threshold standard value of each predefined flow optimization original data set, and alpha represents the flow optimization original data setThe data noise value reads the error factor,represents the a 0 Class c 0 A predefined process optimizes the standard value of the white noise difference value of the original data set data, b represents a-th 0 Class c 0 Optimizing the white noise correction coefficient of the original data set by a predefined process;
will be a 0 Class c 0 Comparing the white noise evaluation value of each flow optimization original data set with the white noise evaluation value of the predefined flow optimization original data set, reserving the corresponding flow optimization original data set original data within the error allowable range, repeating the step for all flow optimization original data set data, and recording all reserved data as an effective flow optimization data set;
The effective flow optimization data set is classified according to the type of the feed control application to obtain four classification sub-data sets, which are respectively marked as a dehumidification drying sub-data set, a material sucking conveying sub-data set, a metering proportion sub-data set and a process flow type correction sub-data set.
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