Mitochondrial nutrient goat milk powder and production process control method thereof

Abstract

The invention belongs to the field of production control, and discloses mitochondria nutrient goat milk powder. Comprising the following raw materials in parts by weight: 845 per thousand of whole goat milk powder, 145 per thousand of inulin, 0.05 per thousand of epigallocatechin gallate, 0.05 per thousand of lactoferrin, 0.2 per thousand of N-acetylneuraminic acid, 0.6 per thousand of taurine, 0.2 per thousand of cistanche powder, 0.2 per thousand of American ginseng powder, 0.2 per thousand of lucid ganoderma powder, 0.2 per thousand of ginseng powder, 0.2 per thousand of yeast # imgabs0 #-glucan, 6.8 per thousand of milk mineral salt and 0.2 per thousand of bifidobacterium animalis subsp. Lactis Bb-124. The production process control method comprises the following steps: establishing a simulation production model to simulate a production process; and on the basis of a preset stage rule, obtaining and comparing actual production information of each production stage and simulation production information generated by the simulation production model, obtaining a difference result, and optimizing the production process. The production efficiency of the mitochondrial nutrient goat milk powder is improved.

Description

Technical Field

[0001] The invention belongs to the field of production control, and in particular relates to a mitochondrial nutrient goat milk powder and a production process control method thereof. Background Art

[0002] As the elderly age and their physiological functions deteriorate, their nutritional needs and metabolic capacity change, requiring personalized nutritional intervention. Mitochondrial nutrients, as a type of nutrient that can repair mitochondrial damage and maintain normal cell function, have potential value in improving the health of the elderly. Goat milk has high nutritional value and is easy to digest and absorb. By adding active ingredients such as mitochondrial nutrients to goat milk, its nutritional value can be further enriched to meet the nutritional needs of the elderly. This production process control method for goat milk powder is designed to ensure the high quality and nutritional value of the product.

[0003] Existing product production control methods are mostly implemented using artificial intelligence methods. For example, the Chinese patent application with publication number CN118466426A proposes an optimization control method for the production process of fermented glutinous rice steamed buns based on artificial intelligence, which specifically relates to the field of artificial intelligence technology. The specific steps include installing sensors to collect production characteristics and environmental optimization information during the production of fermented glutinous rice steamed buns, and transmitting them to the database for storage and management. Use historical data to train machine learning models, analyze the impact of data on fermentation effects, establish mathematical models, predict fermentation effects, and prompt production personnel. According to monitoring data and prediction results, dynamically adjust the proportion of raw materials, introduce a closed-loop control system, and continuously optimize the production status. Deploy a computer vision system to detect production effects in real time, and feed back the results to the production system. Combined with model analysis, further optimize production control, it can effectively avoid waste of raw materials caused by abnormal production failures, reduce production costs, and effectively evaluate production efficiency when there is gas transfer or leakage in the fermented glutinous rice container. For another example, the Chinese patent application with publication number CN117215277A proposes a production control method and system for veterinary feed, the method comprising: constructing a crushing effect model, the dosage vector including the dosage of each raw material; constructing a unit temperature rise model; determining the target temperature range according to the active temperature range of each raw material in the veterinary feed; constructing an optimization problem based on the target temperature range, the unit temperature rise model and the crushing effect model, the optimization problem including an objective function and constraints; solving the optimization problem to obtain the target power and the target crushing time, and controlling the crushing equipment to complete the crushing process based on the target power and the target crushing time.

[0004] However, there is no production process control method for mitochondrial nutrient goat milk powder in the prior art solutions, so a production process control method for mitochondrial nutrient goat milk powder is needed to improve the production efficiency of mitochondrial nutrient goat milk powder. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a mitochondrial nutrient goat milk powder and a production process control method thereof to solve the problems in the prior art.

[0006] In order to achieve the above-mentioned purpose of the invention, the present invention provides a mitochondrial nutrient goat milk powder, comprising the following raw materials in parts by weight: Whole goat milk powder 845‰, inulin 145‰, epigallocatechin gallate 0.5‰, lactoferrin 0.5‰, N-acetylneuraminic acid 0.2‰, taurine 0.6‰, Cistanche deserticola powder 0.2‰, American ginseng powder 0.2‰, Ganoderma lucidum powder 0.2‰, ginseng powder 0.2‰, yeast - Glucan 0.2‰, milk mineral salt 6.8‰ and Bifidobacterium animalis subsp. lactis Bb-12 0.4‰; A production process control method for producing the above-mentioned mitochondrial nutrient goat milk powder comprises the following steps: S1: Establish a production control system, set the production process of the mitochondrial nutrient goat milk powder, the production process includes multiple production stages, the production stages include raw material preparation stage, mixing stage, sterilization and concentration stage, drying stage, quality inspection stage and packaging stage, establish a simulation production model of the mitochondrial nutrient goat milk powder based on the multiple production stages in the production process, and simulate the production process based on the simulation production model; S2: Mixing the prepared raw materials based on a preset mixing rule to obtain a first intermediate product, sterilizing, concentrating and drying the first intermediate product to obtain a second intermediate product, setting a collaborative processing flow, and performing quality inspection and packaging on the second intermediate product based on the collaborative processing flow to obtain a final product; S3: defining the start time and end time of each production stage, the output of the stage product produced in the production stage within a preset time interval, and the production time spent on the stage product with the preset output as stage information of the production stage, acquiring the stage information of each production stage in real time, defining it as actual production information, simulating each production stage based on the simulation production model, and obtaining corresponding simulated production information; S4: Compare the actual production information and the simulated production information to obtain difference results, analyze the causes of the differences based on the difference results, and optimize the production process of the mitochondrial nutrient goat milk powder based on the causes of the differences.

[0007] Furthermore, the simulation production model simulates the production process and includes the following steps: A unique stage label is set for each of the production stages, and the events occurring in each of the production stages are modeled, wherein the events occurring include a first event and a second event, wherein the first event is an event triggered by an external operation of the production control system, and the second event is an event automatically occurring based on an internal rule of the simulation production model, and a plurality of state machines are set in the simulation production model, each of the state machines corresponds to a state label, and each of the state machines controls one of the production stages; Get a production start signal and stage label 1, where the stage label 1 is the stage label of the raw material preparation stage. After receiving the start signal and the stage label 1, the state machine 1 of the simulation production model sets the state label corresponding to itself to the start state. State machine 1 is the state machine corresponding to the raw material preparation stage. State machine 1 executes the production stage corresponding to the stage label until the production stage is completed, sets the state label to the end state, and sends the end state and stage label 2 to the next production stage. Repeat this step until the simulation production model executes all the production stages.

[0008] Further, mixing the prepared raw materials based on a preset mixing rule comprises the following steps: The raw material characteristics of each raw material are obtained, and the raw materials are divided into three components based on the raw material characteristics. The first component includes whole goat milk powder and inulin, the second component is mitochondrial nutrients, including epigallocatechin gallate, lactoferrin, N-acetylneuraminic acid and taurine, and the third component includes Cistanche deserticola powder, American ginseng powder, Ganoderma lucidum powder, ginseng powder, yeast -glucan, milk mineral salt and animal Bifidobacterium lactis subspecies Bb-12, each of the first component, the second component and the third component is internally mixed to obtain three mixed components, the three mixed components are mixed again to obtain a first intermediate product, the mixing parameters in four mixing processes are set respectively, and the raw materials are mixed based on the mixing parameters, and the mixing parameters include mixing temperature and mixing speed.

[0009] Furthermore, quality inspection and packaging of the second intermediate product comprises the following steps: In the quality inspection stage, the second intermediate product is divided into a plurality of product units of preset weights, and a first bacteria detection and a second bacteria detection are simultaneously performed on each of the product units, wherein the first bacteria detection method is to detect the product units using a microbial detection device, and the second bacteria detection is to perform bacteria detection on the product units based on a bacteria culture method, and the number of bacteria detected based on the first bacteria detection method is defined as a first number, and the number of bacteria detected based on the second bacteria detection method is defined as a second number; If the first quantity is less than the first threshold, the product unit is deemed to have passed the first bacteria test, and the product unit is defined as the first product. The first product enters the packaging stage, a first label is set for the packaged first product, and the second bacteria test of the first product is continued. During the second bacteria test, a culture medium is allocated to each product unit for bacterial culture, and each culture medium corresponds to a second label. The first label and the second label of the first product are associated. When the second bacteria test is completed, it is determined whether the second quantity is less than the first threshold. If so, the first product is deemed to have passed the second bacteria test, and the product unit that passes the first bacteria test and the second bacteria test is defined as a qualified product. If not, the first product that fails the second bacteria test is defined as an unqualified product, and the packaged unqualified product is recovered. Based on the second label on the package of the unqualified product, the culture medium corresponding to the first label is obtained, and based on the batch information corresponding to the raw materials in the culture medium, the unqualified reason of the unqualified product is analyzed.

[0010] Further, analyzing the cause of the difference based on the difference result includes the following steps: Generate the stage time of each production stage based on the start time and the end time, calculate the difference between the stage time of the actual production information and the simulated production information of each production stage, if the difference between the two is less than 0, optimize the simulation production model based on the stage data information of the production stage, if the difference between the two is greater than 0, it means that the actual production stage is inefficient, and adjust the configuration information in the production stage based on the configuration information of the production stage of the simulation production model; The output of the intermediate product produced by the actual production information within a preset time interval is defined as a first output, and the production time spent on producing the intermediate product of the preset output is defined as a first time. The output of the intermediate product produced by the simulated production information within a preset time interval is defined as a second output, and the production time spent on producing the intermediate product of the preset output is defined as a second time. If the first output is less than the second output or the first time is greater than the second time, it means that the efficiency of the production stage is low. The configuration information in the production stage is adjusted based on the simulation production model. Otherwise, the simulation production model is optimized based on the stage data information of the production stage.

[0011] Furthermore, optimizing the simulation production model based on the stage data information of the production stage includes the following steps: A machine learning module is set in the simulation production model, and the stage data information of the production stage is input into the machine learning module, the stage data information includes sensor information inside the production stage and batch information of raw materials used, and feature extraction is performed on the stage data information to obtain the corresponding stage characteristics of the production stage, and the neural network model of the machine learning module is trained based on the stage characteristics until the evaluation indicators of the neural network model reach preset standards, and the evaluation indicators include the stage time, stage temperature and stage output of the stage product of the production stage.

[0012] Compared with the prior art, the beneficial effects of the present invention are at least as follows: The present invention applies mitochondrial nutrients to goat milk powder, sets the production process of mitochondrial nutrient goat milk powder as a raw material preparation stage, a mixing stage, a sterilization and concentration stage, a drying stage, a quality inspection stage and a packaging stage, and establishes a simulation production model based on the production process, so that the production process can be simulated and tested without interfering with actual production; the stability and activity of each raw material component in the mixing process are ensured by setting mixing rules, and nutrient loss due to improper handling is avoided; a collaborative processing process of quality inspection and packaging is set, and two types of bacterial detection are performed in parallel to ensure that the microbial content of the product unit is lower than a preset threshold, thereby improving the safety of the product and the health protection of consumers, and also improving the production efficiency of the product; the information of each production stage is obtained in real time, the production progress and efficiency are monitored, and by comparing the actual production information with the simulated production information, the differences and deviations in the production process can be identified, the causes of the differences can be analyzed, and the process can be optimized in a targeted manner to reduce costs, improve product quality, and further improve the production efficiency of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flowchart of the steps of a mitochondrial nutrient goat milk powder and a production process control method thereof.

[0014] Figure 2 The present invention is a production flow chart of mitochondrial nutrient goat milk. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0017] A mitochondrial nutrient goat milk powder comprises the following raw materials in parts by weight: Whole goat milk powder 845‰, inulin 145‰, epigallocatechin gallate 0.5‰, lactoferrin 0.5‰, N-acetylneuraminic acid 0.2‰, taurine 0.6‰, Cistanche deserticola powder 0.2‰, American ginseng powder 0.2‰, Ganoderma lucidum powder 0.2‰, ginseng powder 0.2‰, yeast -Glucan 0.2‰, milk mineral salt 6.8‰ and Bifidobacterium animalis subsp. lactis Bb-12 0.4‰.

[0018] like Figure 1 As shown, a production process control method comprises: S1: Establish a production control system and set the production process of mitochondrial nutrient goat milk powder. The production process includes multiple production stages, including raw material preparation stage, mixing stage, sterilization and concentration stage, drying stage, quality inspection stage and packaging stage. Based on the multiple production stages in the production process, a simulation production model of mitochondrial nutrient goat milk powder is established, and the production process is simulated based on the simulation production model.

[0019] Specifically, a production control system is established to control the production process of mitochondrial nutrient goat milk powder. The production process includes raw material preparation stage, mixing stage, sterilization and concentration stage, drying stage, quality inspection stage and packaging stage. Sensors and data acquisition equipment are deployed in each production stage. Real-time data is captured from the factory workshop based on these devices. The collected data is processed and integrated to establish a simulation production model, simulate the operation of each stage, and use simulation software such as Plant Simulation to simulate and optimize the process.

[0020] like Figure 2 The above is a production flow chart of mitochondrial nutrient goat milk. The raw material preparation stage includes preparing various raw materials including whole goat milk powder, inulin, epigallocatechin gallate, lactoferrin, N-acetylneuraminic acid, taurine, Cistanche powder, American ginseng powder, Ganoderma lucidum powder, ginseng powder, yeast-glucan, milk mineral salts and animal Bifidobacterium lactis subspecies Bb-12.

[0021] In the mixing stage, the materials in the mixing bin and the batching tank are sucked into the mixing tank by the vacuum mixer under computer control, mixed and fixed in a preset mixing order, and the batching liquid is homogenized and pumped into the middle tank for cold storage. The dry process is to add the purchased large package of whey powder into the mixing tank, put in various solid added nutrients, mix them thoroughly, and then directly package them out. The dry process makes it easy to add nutrients to heat-sensitive materials.

[0022] The sterilization stage needs to comprehensively consider the heat resistance and sensitivity of various ingredients. Pasteurization technology or microwave sterilization technology can be used to sterilize the first intermediate product to ensure the safety and nutritional value of the product.

[0023] The sterilized first intermediate product is concentrated based on freeze concentration or other concentration methods, and is dried using microwave drying to obtain a second intermediate product.

[0024] Based on the collaborative processing flow, the second intermediate product is quality tested and packaged to obtain the final product.

[0025] S2: Mix the prepared raw materials based on preset mixing rules to obtain a first intermediate product, sterilize, concentrate and dry the first intermediate product to obtain a second intermediate product, set up a collaborative processing flow, and perform quality inspection and packaging on the second intermediate product based on the collaborative processing flow to obtain a final product.

[0026] Specifically, the various raw material components of the mitochondrial nutrient goat milk powder in the present invention have different raw material properties such as heat resistance temperature, so different raw material components need to be treated separately during the mixing process to ensure the stability and activity of each raw material component during the mixing process, and avoid nutritional loss due to improper treatment. The sterilization treatment needs to comprehensively consider the heat resistance and sensitivity of various components. The first intermediate product can be sterilized by pasteurization technology or microwave sterilization technology to ensure the safety and nutritional value of the product.

[0027] The first intermediate product that has completed sterilization is concentrated based on freeze concentration or other concentration methods, and microwave drying is used to obtain the second intermediate product. In order to ensure that the mitochondrial goat milk powder meets the predetermined quality standards (first bacteria test and second bacteria test) before leaving the factory, a collaborative processing process is set up to closely coordinate the quality inspection link with the packaging link to ensure that the product can quickly enter the packaging process after the quality inspection is completed, thereby reducing the waiting time in the intermediate links, while also ensuring the quality of the product and reducing costs.

[0028] S3: The start time and end time of each production stage, the output of the stage product produced in the production stage within the preset time interval, and the production time spent on the stage product with the preset output are defined as the stage information of the production stage, and the stage information of each production stage is obtained in real time, which is defined as the actual production information. Each production stage is simulated based on the simulation production model to obtain the corresponding simulated production information.

[0029] Specifically, the production control system can obtain the start and end time of each production stage and the output of the stage products produced within the preset time interval in real time. Similarly, the start time, end time, output and other information of each stage are collected in the simulation production model. According to the actual production data, the parameters of the simulation model are set, such as equipment performance, material characteristics, operating conditions, etc. These parameters need to reflect the actual production situation as much as possible to ensure the accuracy of the simulation results.

[0030] S4: Compare the actual production information and the simulated production information to obtain the difference results, analyze the reasons for the differences based on the difference results, and optimize the production process of mitochondrial nutrient goat milk powder based on the reasons for the differences.

[0031] Specifically, by comparing actual production information with simulated production information, we obtain difference results. Based on the difference results, we analyze which production stage and which link has problems in the actual production process, such as equipment failure, operating error or raw material fluctuation. According to the analysis results, we optimize the production process, including adjusting equipment parameters, improving operating procedures, improving raw material quality, increasing resource allocation, etc. After implementing the optimization measures, we continuously monitor the production process to ensure the sustainability of the optimization effect.

[0032] The present invention applies mitochondrial nutrients to goat milk powder, sets the production process of mitochondrial nutrient goat milk powder as a raw material preparation stage, a mixing stage, a sterilization and concentration stage, a drying stage, a quality inspection stage and a packaging stage, and establishes a simulation production model based on the production process, so that the production process can be simulated and tested without interfering with actual production; the stability and activity of each raw material component in the mixing process are ensured by setting mixing rules, and nutrient loss due to improper handling is avoided; a collaborative processing process of quality inspection and packaging is set, and two types of bacterial detection are performed in parallel to ensure that the microbial content of the product unit is lower than a preset threshold, thereby improving the safety of the product and the health protection of consumers, and also improving the production efficiency of the product; the information of each production stage is obtained in real time, the production progress and efficiency are monitored, and by comparing the actual production information with the simulated production information, the differences and deviations in the production process can be identified, the causes of the differences can be analyzed, and the process can be optimized in a targeted manner to reduce costs, improve product quality, and further improve the production efficiency of the product.

[0033] As a preferred technical solution of the present invention, the simulation production model simulates the production process including the following steps: A unique stage label is set for each production stage, and the events occurring in each production stage are modeled. The occurring events include a first event and a second event. The first event is an event triggered by an external operation of the production control system, and the second event is an event that occurs automatically based on internal rules of the simulation production model. Multiple state machines are set in the simulation production model, each state machine corresponds to a state label, and each state machine controls a production stage.

[0034] Get the start production signal and stage label 1. Stage label 1 is the stage label of the raw material preparation stage. After receiving the start signal and stage label 1, the state machine 1 of the simulation production model sets its corresponding state label to the start state. State machine 1 is the state machine corresponding to the raw material preparation stage. State machine 1 executes the production stage corresponding to the stage label until the production stage is completed, sets the state label to the end state, and sends the end state and stage label 2 to the next production stage. Repeat this step until the simulation production model executes all production stages.

[0035] Specifically, a unique stage label is assigned to each production stage. For example, the stage labels of the raw material preparation stage, mixing stage, sterilization and concentration stage, drying stage, quality inspection stage and packaging stage are F1, F2, F3, F4, F5 and F6 respectively. The events occurring in each production stage are modeled. The events occurring include the first event and the second event. The first event is such as the operator manually starting or stopping a machine in the production process, such as a mixer or evaporator. The second event is such as in the sterilization and concentration stage, when the preset temperature and time conditions are reached, the simulation production model automatically stops heating. This is an event that occurs automatically based on internal rules.

[0036] In the simulation production model, a state machine is set for each stage. Each state machine has a corresponding state label. The state machine controls the start and end of the production stage according to the events received. The next production stage can only be executed when the state of the previous production stage ends. Executing each production stage in sequence can ensure the rational use of resources. At the same time, it is convenient to monitor the progress and quality of each stage, making the production process easier to control and manage.

[0037] Mixing the prepared raw materials based on the preset mixing rules includes the following steps: The raw material characteristics of each raw material are obtained, and the raw materials are divided into three components based on the raw material characteristics. The first component includes whole goat milk powder and inulin, the second component is mitochondrial nutrients, including epigallocatechin gallate, lactoferrin, N-acetylneuraminic acid and taurine, and the third component includes Cistanche deserticola powder, American ginseng powder, Ganoderma lucidum powder, ginseng powder, yeast -glucan, milk mineral salt and animal Bifidobacterium lactis subspecies Bb-12, each of the raw materials in the first component, the second component and the third component are internally mixed to obtain three mixed components, the three mixed components are mixed again to obtain a first intermediate product, the mixing parameters in the four mixing processes are set respectively, and the raw materials are mixed based on the mixing parameters, and the mixing parameters include mixing temperature and mixing speed.

[0038] Specifically, the raw material properties include the heat resistance temperature, chemical stability, etc. of the raw materials, because the heat resistance temperature and stability of each raw material are different. It is found that since whole milk powder is in powder form and inulin is also easy to mix as a prebiotic, the two can be mixed at room temperature, usually controlled between 20°C and 25°C. This temperature range avoids destroying the nutrients in the goat milk powder, while ensuring the solubility and mixing uniformity of inulin. The mixing speed should be controlled within the low to medium speed range, for example, it can be set to 100-200 rpm. This speed is sufficient to ensure that the powders are mixed evenly without causing excessive shear force to the powders.

[0039] The second ingredient: epigallocatechin gallate, lactoferrin, N-acetylneuraminic acid and taurine, these raw materials constitute mitochondrial nutrients, these ingredients are heat sensitive, especially epigallocatechin gallate, so it is recommended to mix at low temperature, generally around 3℃ to 6℃, considering the heat sensitivity of these ingredients, it is recommended to use low speed stirring to avoid excessive shearing and heat generation. The speed can be controlled at 50-100 rpm, and the third ingredient is a trace ingredient, accounting for a small proportion. These ingredients are usually stable at room temperature and can be mixed at room temperature. The room temperature is generally controlled between 18℃ and 25℃. For trace ingredients, in order to avoid excessive shearing and excessive heat generation, it is recommended to use medium and low speed stirring, and the stirring speed can be controlled at 200-300 rpm to ensure that the ingredients are evenly dispersed without generating too much friction heat.

[0040] After the above three mixing processes, three mixed components are obtained, and the three mixed components are mixed again to obtain a first intermediate product. The mixing temperature of this process can be controlled between 20°C and 25°C, and the stirring speed can be controlled at 100-200 rpm.

[0041] The quality inspection and packaging of the second intermediate product includes the following steps: During the quality inspection stage, the second intermediate product is divided into multiple product units of preset weights, and a first bacteria test and a second bacteria test are performed simultaneously on each product unit. The first bacteria test method is to use microbial detection equipment to test the product unit, and the second bacteria test is to perform bacteria test on the product unit based on a bacterial culture method. The number of bacteria detected based on the first bacteria test method is defined as a first number, and the number of bacteria detected based on the second bacteria test method is defined as a second number.

[0042] If the first quantity is less than the first threshold, the product unit is determined to have passed the first bacteria test and the product unit is defined as the first product. The first product enters the packaging stage, a first label is set for the packaged first product, and the second bacteria test of the first product is continued. During the second bacteria test, a culture medium is assigned to each product unit for bacterial culture. Each culture medium corresponds to a second label, and the first label and the second label of the first product are associated. When the second bacteria test is completed, it is determined whether the second quantity is less than the first threshold. If so, the first product is determined to have passed the second bacteria test and the product units that pass the first bacteria test and the second bacteria test are defined as qualified products. If not, the first product that fails the second bacteria test is defined as an unqualified product, and the packaged unqualified products are recalled. Based on the second label on the packaging of the unqualified product, the culture medium corresponding to the first label is obtained, and based on the batch information corresponding to the raw materials in the culture medium, the reasons for the unqualified products are analyzed.

[0043] Specifically, the present invention uses two bacterial detection methods to detect product quality. The first bacterial detection uses microbial detection equipment for rapid detection, which takes a short time and can quickly exclude most safe products. However, this rapid detection may miss some products that actually have a high amount of bacteria. Therefore, a second bacterial detection is required. The second bacterial detection can identify specific bacterial species and quantities through bacterial culture, providing more accurate quality assurance and ensuring product safety. Although it takes a long time and high cost, it helps to deeply identify product quality problems.

[0044] In actual operation, in order to reduce unnecessary packaging and recycling work, pre-packaging can be carried out after the first bacterial test is passed, using packaging materials that are easy to dismantle and reprocess, so that if it fails the second bacterial test later, the packaging can be reused.

[0045] During the packaging process, a first label is set for each product unit. This label can be a QR code for tracking. During the second bacterial detection process, a culture medium is assigned to each product unit for bacterial culture. Each culture medium corresponds to a second label. This second label is A1, for example, and is associated with the first label and stored in the database. By associating the first label and the second label, the specific culture medium and raw material batch can be tracked, which helps to analyze the cause of unqualified products and take preventive measures. When the second bacterial detection is completed, if the number of bacteria (the second number) is less than the first threshold, the range of the first threshold can be set according to the national food safety standard, then the first product is deemed to have passed the second bacterial detection and become a qualified product. If the second bacterial detection fails, the product is defined as an unqualified product and is recalled. The first bacterial detection and the second bacterial detection are performed in parallel. Through double detection, it is ensured that the microbial content of the product unit is lower than the preset threshold, which improves the safety of the product and the health protection of consumers, and also improves the production efficiency of the product.

[0046] Analyzing the causes of the differences based on the difference results includes the following steps: The stage time of each production stage is generated based on the start time and the end time, and the difference in stage time between the actual production information and the simulated production information of each production stage is calculated. If the difference between the two is less than 0, the simulation production model is optimized based on the stage data information of the production stage. If the difference between the two is greater than 0, it means that the actual production stage is inefficient. Based on the configuration information of the production stage of the simulation production model, the configuration information in the production stage is adjusted.

[0047] The output of the intermediate product produced by the actual production information within the preset time interval is defined as the first output, the production time spent on producing the intermediate product of the preset output is defined as the first time, the output of the intermediate product produced by the simulated production information within the preset time interval is defined as the second output, and the production time spent on producing the intermediate product of the preset output is defined as the second time. If the first output is less than the second output or the first time is greater than the second time, it means that the efficiency of the production stage is low, and the configuration information in the production stage is adjusted based on the simulation production model, otherwise the simulation production model is optimized based on the stage information of the production stage.

[0048] Specifically, the actual stage time is calculated by recording the start time and end time of each production stage, and the stage time in the actual production information is compared with the stage time in the simulated production information to calculate the difference. If the difference between the two is less than 0, that is, the actual time is less than the simulation time, it means that the actual production efficiency may be higher than the expectations of the simulation model, and the simulation model may be too conservative or need to be updated. By using the actual production information to optimize the simulated production model, the accuracy of the model can be improved, making it closer to the actual production situation, and further enhancing the prediction ability of the simulation model. The optimization method will be explained later.

[0049] If the difference between the two is greater than 0, that is, the actual time exceeds the simulation time, this indicates that the actual production efficiency is low. Based on the configuration information of the simulated production model, the configuration of the production stage is adjusted, such as changing the workflow, increasing resource input, or optimizing equipment parameters. This step can timely discover production bottlenecks and provide a basis for subsequent improvement measures.

[0050] The output of actual production and simulated production within a preset time interval is defined as the first output and the second output respectively. According to the comparison results of the output, the configuration of the production stage is adjusted again, and the production process is continuously improved to adapt to the ever-changing production needs and market environment.

[0051] Optimizing the simulation production model based on the stage data information of the production stage includes the following steps: A machine learning module is set up in the simulation production model, and the stage data information of the production stage is input into the machine learning module. The stage data information includes the sensor information inside the production stage and the batch information of the raw materials used. The stage data information is subjected to feature extraction to obtain the stage characteristics of the corresponding production stage. The neural network model of the machine learning module is trained based on the stage characteristics until the evaluation indicators of the neural network model reach the preset standards. The evaluation indicators include the stage time, stage temperature and stage output of the stage product of the production stage.

[0052] Specifically, a machine learning module is integrated into the simulation production model to process and analyze production data. This module will serve as the core of optimization and improve the model's predictive ability by learning patterns and trends in actual production data. Stage data such as sensor information and raw material batch information in the production stage are input into the machine learning module. These data will serve as the basis for training the neural network model. Feature extraction is performed on the input data to obtain the characteristics of the production stage. For example, feature extraction is performed on the actual production information of the mixing stage to obtain the mixing time, mixing temperature, mixing speed, including the source of raw materials, production date, shelf life, and vibration, noise, current and voltage of the mixer, as well as operating parameters of the mixing equipment, such as energy consumption, maintenance cycle, failure rate, etc. These extracted features are used to train the neural network model. During the training process, the neural network learns patterns in the data by adjusting weights and biases to minimize prediction errors. The performance of the neural network model is evaluated using a preset test set in the database. The evaluation indicators may include key parameters such as time, temperature and output in the production stage. These indicators help to measure the accuracy and reliability of the model. Through continuous training and optimization of the machine learning model, the accuracy of prediction of the production process can be improved.

[0053] Test Example 1 Experimental Materials Test sample 1: milk powder prepared in an embodiment of the present invention, prepared with boiling water to form a 0.2 g / mL solution; Test sample 2: Commercially available ordinary goat milk powder sample, prepared with boiling water to a 0.2 g / mL solution; Test sample 3: boiled water.

[0054] Experimental subjects: Wistar rats, female, weighing 180-200 g; Experimental method: The animals were randomly divided into several groups, and each group was given different treatments (mitochondrial nutrient goat milk powder, ordinary goat milk powder and boiled water). The animals were allowed to ingest the milk powder with different treatments through gavage or dietary addition. After a certain period of time, mitochondrial health indicators such as mitochondrial function, oxidative stress level, cellular energy metabolism, etc. were evaluated through anatomical or biochemical analysis.

[0055] Table 1 shows the effects of various test samples on rat mitochondrial health indicators Recipe Type ATP generation efficiency (pmol ATP / min) Antioxidant capacity (ABTS elimination rate%) Cellular energy metabolism (difference between maximum respiration and basal respiration, pmol O2 / min) Mitochondrial nutrients goat milk powder 120 80 30 Ordinary goat milk powder 100 70 25 Boiled water 90 60 20 According to Table 1, it can be concluded that the mitochondrial nutrient goat milk powder in this embodiment has a good effect in protecting mitochondrial health, the ordinary goat milk powder group also has a certain effect, and the boiled water group has a less obvious effect. Therefore, the present invention can protect mitochondrial function to a certain extent.

[0056] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0057] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0058] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0059] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0060] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A mitochondrial nutrient goat milk powder, characterized in that: The invention comprises the following raw materials in parts by weight: Whole goat milk powder 845‰, inulin 145‰, epigallocatechin gallate 0.5‰, lactoferrin 0.5‰, N-acetylneuraminic acid 0.2‰, taurine 0.6‰, Cistanche deserticola powder 0.2‰, American ginseng powder 0.2‰, Ganoderma lucidum powder 0.2‰, ginseng powder 0.2‰, yeast -Glucan 0.2‰, milk mineral salt 6.8‰ and Bifidobacterium animalis subsp. lactis Bb-12 0.4‰.

2. A production process control method, the method is used to realize the mitochondrial nutrient goat milk powder in claim 1, characterized in that: The method comprises the following steps: S1: Establish a production control system, set the production process of the mitochondrial nutrient goat milk powder, the production process includes multiple production stages, the production stages include raw material preparation stage, mixing stage, sterilization and concentration stage, drying stage, quality inspection stage and packaging stage, establish a simulation production model of the mitochondrial nutrient goat milk powder based on the multiple production stages in the production process, and simulate the production process based on the simulation production model; S2: Mixing the prepared raw materials based on a preset mixing rule to obtain a first intermediate product, sterilizing, concentrating and drying the first intermediate product to obtain a second intermediate product, setting a collaborative processing flow, and performing quality inspection and packaging on the second intermediate product based on the collaborative processing flow to obtain a final product; S3: defining the start time and end time of each production stage, the output of the stage product produced in the production stage within a preset time interval, and the production time spent on the stage product with the preset output as stage information of the production stage, acquiring the stage information of each production stage in real time, defining it as actual production information, simulating each production stage based on the simulation production model, and obtaining corresponding simulated production information; S4: Compare the actual production information and the simulated production information to obtain difference results, analyze the causes of the differences based on the difference results, and optimize the production process of the mitochondrial nutrient goat milk powder based on the causes of the differences.

3. The method according to claim 2, characterized in that The simulation production model simulates the production process and includes the following steps: A unique stage label is set for each of the production stages, and the events occurring in each of the production stages are modeled, wherein the events occurring include a first event and a second event, wherein the first event is an event triggered by an external operation of the production control system, and the second event is an event automatically occurring based on an internal rule of the simulation production model, and a plurality of state machines are set in the simulation production model, each of the state machines corresponds to a state label, and each of the state machines controls one of the production stages; Get a production start signal and stage label 1, where the stage label 1 is the stage label of the raw material preparation stage. After receiving the start signal and the stage label 1, the state machine 1 of the simulation production model sets the state label corresponding to itself to the start state. State machine 1 is the state machine corresponding to the raw material preparation stage. State machine 1 executes the production stage corresponding to the stage label until the production stage is completed, sets the state label to the end state, and sends the end state and stage label 2 to the next production stage. Repeat this step until the simulation production model executes all the production stages.

4. The method according to claim 2, characterized in that: Mixing the prepared raw materials based on a preset mixing rule comprises the following steps: The raw material characteristics of each raw material are obtained, and the raw materials are divided into three components based on the raw material characteristics. The first component includes whole goat milk powder and inulin, the second component is mitochondrial nutrients, including epigallocatechin gallate, lactoferrin, N-acetylneuraminic acid and taurine, and the third component includes Cistanche deserticola powder, American ginseng powder, Ganoderma lucidum powder, ginseng powder, yeast -glucan, milk mineral salt and animal Bifidobacterium lactis subspecies Bb-12, each of the first component, the second component and the third component is internally mixed to obtain three mixed components, the three mixed components are mixed again to obtain a first intermediate product, the mixing parameters in four mixing processes are set respectively, and the raw materials are mixed based on the mixing parameters, and the mixing parameters include mixing temperature and mixing speed.

5. The method according to claim 4, characterized in that The quality inspection and packaging of the second intermediate product comprises the following steps: In the quality inspection stage, the second intermediate product is divided into a plurality of product units of preset weights, and a first bacteria detection and a second bacteria detection are simultaneously performed on each of the product units, wherein the first bacteria detection method is to detect the product units using a microbial detection device, and the second bacteria detection is to perform bacteria detection on the product units based on a bacteria culture method, and the number of bacteria detected based on the first bacteria detection method is defined as a first number, and the number of bacteria detected based on the second bacteria detection method is defined as a second number; If the first quantity is less than the first threshold, the product unit is determined to have passed the first bacteria test, and the product unit is defined as the first product. The first product enters the packaging stage, a first label is set for the packaged first product, and the second bacteria test of the first product is continued. During the second bacteria test, a culture medium is allocated to each product unit for bacterial culture, and each culture medium corresponds to a second label. The first label and the second label of the first product are associated. When the second bacteria test is completed, it is determined whether the second quantity is less than the first threshold. If so, the first product is determined to have passed the second bacteria test, and the product unit that passes the first bacteria test and the second bacteria test is defined as a qualified product. If not, the first product that fails the second bacteria test is defined as an unqualified product, and the packaged unqualified product is recovered. Based on the second label on the package of the unqualified product, the culture medium corresponding to the first label is obtained, and based on the batch information corresponding to the raw materials in the culture medium, the unqualified reason of the unqualified product is analyzed.

6. The method according to claim 2, characterized in that Analyzing the cause of the difference based on the difference result includes the following steps: Generate the stage time of each production stage based on the start time and the end time, calculate the difference between the stage time of the actual production information and the simulated production information of each production stage, if the difference between the two is less than 0, optimize the simulation production model based on the stage data information of the production stage, if the difference between the two is greater than 0, it means that the actual production stage is inefficient, and adjust the configuration information in the production stage based on the configuration information of the production stage of the simulation production model; The output of the intermediate product produced by the actual production information within a preset time interval is defined as a first output, the production time spent on producing the intermediate product with a preset output is defined as a first time, the output of the intermediate product produced by the simulated production information within a preset time interval is defined as a second output, and the production time spent on producing the intermediate product with a preset output is defined as a second time. If the first output is less than the second output or the first time is greater than the second time, it means that the efficiency of the production stage is low, and the configuration information in the production stage is adjusted based on the simulation production model; otherwise, the simulation production model is optimized based on the stage information of the production stage.

7. The method according to claim 6, characterized in that Optimizing the simulation production model based on the stage information of the production stage comprises the following steps: A machine learning module is set in the simulation production model, and the stage data information of the production stage is input into the machine learning module, the stage data information includes sensor information inside the production stage and batch information of raw materials used, and feature extraction is performed on the stage data information to obtain the corresponding stage characteristics of the production stage, and the neural network model of the machine learning module is trained based on the stage characteristics until the evaluation indicators of the neural network model reach preset standards, and the evaluation indicators include the stage time, stage temperature and stage output of the stage product of the production stage.

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