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CN-122018470-A - Intelligent management and control system and method for food processing production line

CN122018470ACN 122018470 ACN122018470 ACN 122018470ACN-122018470-A

Abstract

The application provides an intelligent management and control system and method for a food processing production line, wherein the intelligent management and control system comprises the steps of activating a device control algorithm to perform parameter adjustment on a delivery device if a difference characteristic vector exceeds a preset threshold range, determining adjusted delivery rate and volume parameters, optimizing the parameters based on device performance and material flow characteristics, updating a formula proportion model of various raw materials through a feedback mechanism according to the precision coincidence mark, determining updated formula proportion coefficients, adjusting the proportion relation among the raw materials to maintain formula balance, generating a delivery execution instruction sequence according to a stable fusion data set, wherein the instruction sequence comprises an action sequence and parameter configuration, and outputting the instruction sequence to an executor to realize raw material delivery control with consistent formula.

Inventors

  • PING FAN
  • ZHU YAJING

Assignees

  • 湖北湖源食品有限公司

Dates

Publication Date
20260512
Application Date
20260320

Claims (8)

  1. 1. An intelligent control system and method for a food processing production line, wherein the method comprises the following steps: Acquiring physical characteristic data of solid materials and liquid materials in a production line in real time through a sensor array, wherein the physical characteristic data comprise key indexes such as density, viscosity, particle size distribution and the like, and obtaining material characteristic parameters for describing basic properties of the materials; according to the material characteristic parameters, adopting a data analysis model to carry out quantization treatment on the difference characteristics of the solid material and the liquid material to obtain a difference characteristic vector, wherein the difference characteristic vector represents deviation information of the two materials on physical properties; If the difference characteristic vector exceeds a preset threshold range, an equipment control algorithm is activated to carry out parameter adjustment on the throwing equipment, and the adjusted throwing rate and volume parameters are determined, wherein the parameters are optimized based on equipment performance and material flow characteristics; Extracting metering demand data of trace additives from the regulated delivery rate and volume parameters, judging whether the metering demand data meets a preset high-precision standard or not, and obtaining a precision meeting mark, wherein the mark reflects whether the metering precision meets the process requirement or not; Updating the formula proportion model of various raw materials by a feedback mechanism aiming at the precision coincidence mark, and determining updated formula proportion coefficients, wherein the coefficients are used for adjusting the proportion relation among the raw materials to maintain the balance of the formula; Acquiring the updated formula proportionality coefficient, adopting an environment data integration algorithm to fuse dynamic environment data of a production line, wherein the environment data comprises influencing factors such as temperature, humidity and the like, judging whether the fused data reach a stable state or not, and obtaining a stable fusion data set; and generating a throwing execution instruction sequence according to the stable fusion data set, wherein the instruction sequence comprises an action sequence and parameter configuration, and outputting the action sequence and the parameter configuration to an executor to realize the raw material throwing control with consistent formulas.
  2. 2. The intelligent control system and method for a food processing production line according to claim 1, wherein physical characteristic data of solid materials and liquid materials in the production line are collected in real time through a sensor array, the physical characteristic data comprise key indexes such as density, viscosity and particle size distribution, and the like, and material characteristic parameters for describing basic properties of the materials are obtained, and the intelligent control system comprises: real-time data acquisition is carried out on solid materials and liquid materials in a production line through a sensor array, and physical data such as density indexes, viscosity indexes, particle distribution and the like are covered to obtain preliminary material characteristic parameters; according to the collected characteristic parameters of the materials, distinguishing solid materials from liquid materials by adopting a pre-established classification model, and determining the attribution category of the respective physical data; If the classified physical data attribution category is inconsistent with the preset material attribute standard, performing deviation adjustment on the acquired density index and viscosity index through a data correction module to acquire corrected material characteristic values; Aiming at the corrected material characteristic values, carrying out mode analysis on the particle distribution data by adopting a support vector machine algorithm, and judging whether the particle distribution accords with a preset uniformity threshold; if the particle distribution does not reach the preset uniformity threshold, optimizing and adjusting the particle distribution data through data smoothing processing to obtain smoothed distribution parameters; Generating comprehensive material attribute description through a data integration module according to the smoothed distribution parameters and combining correction values of the density index and the viscosity index, and determining a final material basic characterization result; and continuously monitoring the comprehensive material attribute description, and updating data parameters in the production line in real time to obtain the dynamically adjusted material characteristic state.
  3. 3. The intelligent control system and method for a food processing production line according to claim 1, wherein the quantization processing is performed on the difference characteristics of the solid material and the liquid material by using a data analysis model according to the material characteristic parameters to obtain a difference characteristic vector, and the difference characteristic vector represents deviation information of the two materials on physical properties, and the method comprises the following steps: acquiring physical attribute data of solid materials and liquid materials, and acquiring data aiming at key attributes such as density, viscosity, granularity and the like in material characteristics to obtain an initial attribute data set; Processing the initial attribute data set by adopting a pre-established data analysis model, calculating the difference value of the solid material and the liquid material on the key attribute, and determining an attribute difference matrix; According to the attribute difference matrix, main deviation information between the solid material and the liquid material is extracted, a difference characteristic vector is generated, and a comparison result of the two materials on physical attributes is reflected; Judging the deviation weight of each key attribute through component analysis of the difference characteristic vector, and marking the deviation weight of a certain attribute as a significant difference attribute if the deviation weight of the certain attribute exceeds a preset threshold value to obtain a significant difference attribute set; aiming at the significant difference attribute set, acquiring corresponding physical attribute data distribution, classifying the significant difference attribute by adopting a support vector machine model, and determining classification boundary information; And according to the classification boundary information, generating a comparison map of the solid material and the liquid material on the obvious difference attribute, and obtaining a final material comparison result for subsequent analysis and treatment.
  4. 4. The intelligent control system and method for a food processing production line according to claim 1, wherein if the difference characteristic vector exceeds a preset threshold range, an equipment control algorithm is activated to perform parameter adjustment on the dispensing equipment, and the adjusted dispensing rate and volume parameters are determined, wherein the parameters are optimized based on equipment performance and material flow characteristics, and the method comprises: firstly, acquiring real-time operation data of a delivery device, comparing and analyzing the difference characteristics with a vector range, and if the difference characteristics are detected to exceed a preset threshold value, recording the current data state to obtain an abnormal trigger signal; step two, activating a device control algorithm according to the abnormal trigger signal, performing comprehensive calculation aiming at the device performance and the material flow data, and determining an adjusted delivery rate parameter; thirdly, carrying out secondary calibration on the volume parameter by combining the material flow characteristic through the throwing rate parameter to obtain an optimized volume parameter value; step four, adopting the optimized volume parameter value and the throwing rate parameter to issue an adjusting instruction to the equipment control module, and judging whether the equipment completes parameter updating; step five, if the equipment completes parameter updating, collecting updated operation data, and monitoring the material flow state in real time to obtain a flow stability index; Step six, analyzing the matching degree of the equipment performance and the throwing rate according to the flow stability index, and judging whether the preset operation standard is met or not; And step seven, if the matching degree meets the operation standard, storing the current parameter configuration, and continuously monitoring the subsequent difference characteristic data to determine the operation stability of the equipment.
  5. 5. The intelligent control system and method for a food processing production line according to claim 1, wherein the extracting the metering demand data of the trace additive from the adjusted delivery rate and volume parameters, judging whether the metering demand data meets a preset high precision standard, and obtaining a precision meeting mark, wherein the mark reflects whether the metering precision meets the process requirement, comprises: Acquiring original metering data of trace additives from records of the throwing rate and the volume parameters, and removing abnormal values through data cleaning to obtain a metering data set of preliminary arrangement; aiming at the metering data set of the preliminary arrangement, adopting a preset standard to carry out comparison and analysis, and marking as unqualified data if the metering value in the data set deviates from the range of the preset standard to obtain a classified data set; extracting the data part marked as unqualified according to the classified data set, determining the deviation degree through statistical analysis, and obtaining a deviation analysis result; aiming at the deviation analysis result, combining with the high-precision requirement of a preset standard, if the deviation degree exceeds a preset threshold, generating a mark with non-conforming precision, and obtaining a precision judgment mark; According to the precision judgment mark, correlating the requirements of the process specification, determining whether the specification condition is met or not through logic comparison analysis, and obtaining a process compliance result; aiming at the process compliance result, adopting a record storage mode, and carrying out association storage on the result and the original data of the release rate and the volume parameter to obtain a final analysis record; and generating a comparison chart of metering precision and process consistency through a data visualization tool according to the final analysis record, and determining the execution state of the whole business process.
  6. 6. The intelligent control system and method according to claim 1, wherein updating the recipe proportion model of the plurality of raw materials by a feedback mechanism for the precision coincidence flag, determining updated recipe proportion coefficients for adjusting the proportional relationship between the raw materials to maintain the recipe balance, comprising: analyzing the state of the precision mark by collecting historical data and real-time feedback information to obtain a preliminary deviation evaluation result; According to the deviation evaluation result, calculating the adjustment direction of each raw material proportion by adopting a pre-established formula proportion model, and determining a preliminary update coefficient range; If the primary updating coefficient range exceeds a preset threshold value, carrying out secondary calibration on the raw material proportion relation through an information processing link to obtain calibrated coefficient data; aiming at the calibrated coefficient data, the balance state of each raw material formula is judged by combining the real-time data flow of a feedback mechanism, and the correction requirement of formula balance is obtained; Performing iterative updating on the proportion model by adopting a logistic regression model through the correction requirement, and determining a final formula proportion coefficient; according to the final formula proportion coefficient, adjusting the proportion relation among the raw materials to obtain an updated formula balance scheme; If the updated formula balance scheme deviates from the requirement of the precision mark, the data of the feedback mechanism is subjected to deep analysis through an information processing link, so that a new adjustment basis is obtained.
  7. 7. The intelligent control system and method for a food processing production line according to claim 1, wherein the obtaining the updated formula proportionality coefficient, and fusing dynamic environmental data of the production line by using an environmental data integration algorithm, wherein the environmental data includes influencing factors such as temperature and humidity, and judging whether the fused data reaches a stable state, and obtaining a stable fusion data set includes: acquiring dynamic environment data through a real-time acquisition module of a production line, wherein the data comprises original records of temperature factors and humidity factors, and storing the original records into a preset data warehouse to obtain a preliminary environment data set; according to the preliminary environmental data set, carrying out standardized processing on temperature factors and humidity factors by adopting an environmental data integration algorithm to generate a processed environmental characteristic data set; Aiming at the processed environment characteristic data set, a preset judging standard is called, the fluctuation range of the data is analyzed, if the fluctuation range exceeds a preset threshold value, the abnormal points are subjected to smoothing processing, and the smoothed environment characteristic data set is obtained; Extracting key environment variables from the smoothed environment characteristic data set, calculating an adjustment coefficient of the environment variables to the formula proportion by combining the initial values of the formula proportion, and determining an adjusted formula proportion data set; Acquiring an adjusted formula proportion data set, analyzing the matching degree of the formula proportion data set and the stable state, and performing iterative optimization on an adjustment coefficient if the matching degree is lower than a preset threshold value to obtain an optimized formula proportion data set; Generating a final fusion data set through the optimized formula proportion data set, judging whether the final fusion data set meets the requirement of a stable state, and outputting a stable fusion data set meeting the standard; and aiming at the stable fusion data set, storing the stable fusion data set into a preset database, and transmitting the stable fusion data set to a production line control module through a data interface to finish data closed-loop processing.
  8. 8. The intelligent control system and method for a food processing production line according to claim 1, wherein the generating a put-in execution instruction sequence according to the stable fusion data set, the instruction sequence including an action sequence and a parameter configuration, and outputting the instruction sequence to an actuator to realize a raw material put-in control with a consistent formula, includes: The method comprises the steps of constructing an initial data processing frame by extracting key information from a stable data set and a fusion data set to obtain an original data set for generating a subsequent instruction; According to the original data set, analyzing the action sequence of raw material throwing, adopting a preset rule base to match, and determining the time node and the execution priority corresponding to each action; if the time node and the execution priority meet a preset threshold range, generating a corresponding execution instruction, and acquiring instruction sequence content related to the action sequence; aiming at the generated instruction sequence, checking by adopting a logic checking tool in combination with constraint conditions of parameter configuration, and judging whether the parameter configuration meets the requirement of consistent formula; if the parameter configuration meets the requirement of consistent formula, integrating the instruction sequence and the parameter configuration into a complete release control scheme, and transmitting the complete release control scheme to an executor end; Receiving a release control scheme through an executor, and analyzing an execution instruction and parameter configuration in the release control scheme to obtain a final control implementation path; and according to the control realization path, monitoring the state change of the raw material throwing in real time, recording key data in the execution process, and determining a complete closed loop of throwing control.

Description

Intelligent management and control system and method for food processing production line Technical Field The invention relates to the technical field of information, in particular to an intelligent management and control system and method for a food processing production line. Background In the field of food processing, intelligent management of production lines has become an important support for improving product quality and production efficiency. With the continuous improvement of the requirements of consumers on food safety and quality, how to ensure accurate operation and stable output in the processing process becomes a key direction of industry development. The application of intelligent management and control systems is considered as an important path for solving the production complexity and diversified requirements, and not only relates to the production efficiency, but also directly influences the mouthfeel and safety standards of products. However, many current ways of management of food processing lines often face dual challenges of adaptability and accuracy in dealing with complex formulations and diverse raw materials. The existing method is difficult to flexibly cope with the changes of different raw material characteristics and processing conditions in a dynamic production environment, and particularly when the types of products are required to be quickly switched or the formula proportion is required to be regulated, the operation deviation is easy to occur, so that the quality of the products is unstable. This limitation makes the control accuracy and response speed of the production process unsatisfactory for the high standard requirements of the modern food industry. Focusing on the technical level, the core difficulty of intelligent control of food processing production lines is how to realize accurate control of raw material delivery. Particularly for different forms of materials, such as solid materials and liquid materials, the difference of physical characteristics requires extremely high adaptability of the control system, otherwise metering deviation can be caused, and the consistency of the formula of the final product is affected. The problem of the further layer is that for the addition of trace additives, the traditional metering mode often cannot meet the milligram-grade precision requirement due to the extremely small dosage but the key effect. Such an insufficient precision condition may be directly manifested in the actual production as a disorder of the proportion of certain key components, for example, when a certain nutritional beverage is processed, the addition of micronutrients is insufficient or excessive, which may cause the functional index of the product to be not up to standard and even affect the experience of consumers. Therefore, how to ensure high-precision control of various material delivery in a dynamic production environment and realize extremely accurate metering aiming at trace components becomes a key problem to be solved in the research. Disclosure of Invention The invention provides an intelligent management and control system and method for a food processing production line, which mainly comprise the following steps: Acquiring physical characteristic data of solid materials and liquid materials in a production line in real time through a sensor array, wherein the physical characteristic data comprise key indexes such as density, viscosity, particle size distribution and the like, and obtaining material characteristic parameters for describing basic properties of the materials; according to the material characteristic parameters, adopting a data analysis model to carry out quantization treatment on the difference characteristics of the solid material and the liquid material to obtain a difference characteristic vector, wherein the difference characteristic vector represents deviation information of the two materials on physical properties; If the difference characteristic vector exceeds a preset threshold range, an equipment control algorithm is activated to carry out parameter adjustment on the throwing equipment, and the adjusted throwing rate and volume parameters are determined, wherein the parameters are optimized based on equipment performance and material flow characteristics; Extracting metering demand data of trace additives from the regulated delivery rate and volume parameters, judging whether the metering demand data meets a preset high-precision standard or not, and obtaining a precision meeting mark, wherein the mark reflects whether the metering precision meets the process requirement or not; Updating the formula proportion model of various raw materials by a feedback mechanism aiming at the precision coincidence mark, and determining updated formula proportion coefficients, wherein the coefficients are used for adjusting the proportion relation among the raw materials to maintain the balance of the formula; Acquiring the updated for