Digital intelligent process control system for VF fruit and vegetable product production

Abstract

The invention discloses a digital intelligent process control system for VF fruit and vegetable product production, relates to the technical field of digital intelligent control, and solves the problems that the management is disjointed in the links of raw and auxiliary material use, pretreatment, fixation, quick freezing, frying, packaging and the like at present, lean production cannot be achieved, the production cost is low, and the like. In order to solve the problems that the production process has serious randomness and unpredictability in the prior art, accurate control over production of VF fruit and vegetable products is achieved through all-around monitoring and data acquisition of the production process. The link information collection module of the system covers all key links from order management to finished product quality verification, ensures comprehensive acquisition and transmission of production information, greatly improves the controllability of the production process and the stability of the product quality through a digital means, and improves the production efficiency. Technical support is provided for achieving efficient and low-cost VF fruit and vegetable product production, meanwhile, lean production is achieved, and the production process is controllable.

Description

Technical Field

[0001] The present invention relates to the technical field of digital intelligent control, and in particular to a digital intelligent process control system for the production of VF fruit and vegetable products. Background Art

[0002] Low-temperature vacuum frying (VF) is a food processing technology that maintains the natural color, aroma and taste of food and reduces the amount of oil absorbed by frying at low temperature and in a vacuum environment. Compared with traditional high-temperature frying, low-temperature vacuum frying has the following characteristics: Low-temperature frying: Traditional frying is usually carried out at a high temperature of 170-190°C, while low-temperature vacuum frying is generally carried out at a low temperature of about 80-120°C. This can reduce the loss of nutrients (such as vitamins, minerals, etc.) in food due to high temperature, and at the same time reduce the generation of certain harmful substances (such as acrylamide). Vacuum environment: The ambient pressure in a vacuum state is lower than the normal atmospheric pressure, so the boiling point of water is also reduced, which allows the water in the food to evaporate at a lower temperature, thereby avoiding the browning and nutritional damage of food caused by high temperature in the traditional frying process. Preserve food flavor and color: Because the frying temperature is low and carried out in a vacuum environment, the color, taste and aroma of the food can be better preserved. At the same time, this process can also maximize the preservation of the natural nutrients and flavor of the food, especially fruits and vegetables, which are easy to deteriorate at high temperatures. Reduce oil absorption: During the vacuum frying process, the surface of the food is less likely to form a hardened surface due to the low temperature, and it is more difficult for oil to penetrate into the food, thereby reducing the oil content of the finished product and making the food healthier.

[0003] In recent years, big data has been increasingly used in all walks of life, but it is rarely used in vacuum fried products, especially in the production process of VF fruit and vegetable products. Each link of production, such as raw material quality, pretreatment control, quick freezing, frying, etc., has a great impact on the quality of the finished product. For specific VF fruit and vegetable products, the production specifically includes the following physical processes: order placement--raw material procurement--production pretreatment--quick freezing--frying--packaging--finished product quality compliance verification--delivery--customer feedback. At present, there is a management disconnection in the use of raw and auxiliary materials, pretreatment, withering, quick freezing, frying, packaging and other links, and lean production cannot be achieved. The production process is seriously random and unpredictable. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a digital intelligent process control system for the production of VF fruit and vegetable products, which solves the problems in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A digital intelligent process control system for VF fruit and vegetable product production, comprising: a link information collection module, used to collect production information of each link, and send the production information of each link to a data storage center for storage;

[0006] The data storage center is used to classify and store the information of each link collected by the link information collection module;

[0007] The schedule making module is used to call the customer order information of the data storage center through the data calling module, and preliminarily generate the production schedule according to the detailed information of the customer order through the preset generation model, and then use the schedule as the production process comparison table after the first and second level review and approval;

[0008] The monitoring and analysis module is used to call the corresponding data of the data storage center through the data calling module, simplify the corresponding data, compare and analyze the simplified results with the production process comparison table, and determine whether to generate independent warning information or comprehensive warning information by calculating the matching degree and comprehensive matching degree of each process of each production process with the production process comparison table;

[0009] The verification module is used to verify the quantitative timing information in the product production process. When the verification fails, specific verification warning information is generated;

[0010] The early warning monitoring and prompting module is used to receive independent early warning information, comprehensive early warning information and specific verification early warning information, and remind staff to conduct corresponding inspections and verifications.

[0011] Preferably, in the link information collection module, the link information collection module includes an order management unit, a raw material procurement unit, a production pretreatment unit, a quick freezing process control unit, a vacuum frying control unit, a packaging process monitoring unit, a finished product quality verification unit and a customer feedback unit.

[0012] Preferably, in the schedule making module, the production process comparison table includes the following content information: production time, raw material quality, raw material quantity or weight, raw material location, cutting size, blanching time, pretreatment temperature, quick freezing time, quick freezing temperature, quick freezing wind speed, frying vacuum degree, oil temperature, frying time, packaging image, finished product appearance, finished product weight, expected customer praise, and expected production end time.

[0013] Preferably, in the monitoring and analysis module, when calculating the matching degree between each process of each production process and the production process comparison table, the following specific information data are simplified: cutting size, pretreatment temperature, quick freezing temperature, quick freezing wind speed, frying vacuum degree, oil temperature, finished product weight; and the simplification method is:

[0014] Within a preset time t1, an average value P of the simplified specific information data within the period is obtained, wherein t1 is a preset value.

[0015] Preferably, the simplified result is compared and analyzed with the production process comparison table, the matching degree is calculated, and the independent warning information or the comprehensive warning information is generated as follows:

[0016] AS1: Calculate the difference between the average value P of the specific information data and the corresponding data in the production process comparison table, and take the absolute value, that is: D = |P-Ps|, where Ps is the corresponding data of the average value P of the specific information data in the production process comparison table;

[0017] AS2: Then calculate the matching degree of its specific information data using the following formula:

[0018]

[0019] Among them, Mj is the matching degree between its specific information data and the corresponding data in the production process comparison table, and Tolerance is a pre-set tolerance range;

[0020] AS3: Compare Mj with the preset value Qj. If Mj>Qj, it means that the specific information is qualified for production within the preset time t1. If Mj≤Qj, it means that the specific information is not qualified for production within the preset time t1, and an independent warning information is generated;

[0021] AS4: When all specific information data are qualified for production, calculate the comprehensive matching degree between each specific information data and the corresponding data in the production process comparison table, using the following formula:

[0022]

[0023] Among them, Z is the comprehensive matching degree between each specific information data and the production process comparison table, s is the total number of parameters, that is, all the specific information data, Wj is the weight of the jth parameter, and the calculation result Z is compared with the preset value Qs. If Z>Qs, it means that each specific information data is qualified for production within the preset time t1. If Z≤Qs, it means that each specific information data has certain defects in production within the preset time t1, and a comprehensive early warning information is generated.

[0024] Preferably, in the verification module, the quantitative timing information includes: production time, raw material quality, raw material quantity or weight, blanching time, quick freezing time, frying time, packaging image, finished product appearance, customer praise, and production end time.

[0025] Preferably, the content of verification is:

[0026] Verify whether the production time of the product is the same as the time on the production process comparison table, verify whether the quality, quantity or weight of the raw materials are the same as the quality, quantity or weight of the raw materials on the production process comparison table, verify whether the blanching time, quick freezing time and frying time are the same as the blanching time, quick freezing time and frying time on the production process comparison table, collect the packaging images and the appearance of the finished product during the production process, and compare them with the packaging images and the appearance of the finished product on the production process comparison table to form an anti-counterfeiting code and a positioning traceability code, and calculate the customer feedback praise and compare it with the expected customer praise on the production process comparison table to verify whether it is within the expected customer praise range.

[0027] Preferably, specific quantitative timing information of the verification failure is determined, and corresponding verification warning information is generated and sent to the warning monitoring prompt module.

[0028] The present invention provides a digital intelligent process control system for the production of VF fruit and vegetable products. Compared with the prior art, it has the following beneficial effects:

[0029] According to the document content, the following are the beneficial effects of each embodiment of the digital intelligent process control system for VF fruit and vegetable product production:

[0030] (1) The present invention realizes precise control over the production of VF fruit and vegetable products through all-round monitoring and data collection of the production process. The link information collection module of the system covers all key links from order management to finished product quality verification, ensuring the comprehensive acquisition and transmission of production information. This information is uniformly stored in the data storage center and can be called at any time, forming a closed-loop control system. The present invention greatly improves the controllability of the production process and the stability of product quality through digital means, and provides technical support for the realization of efficient and low-cost production of VF fruit and vegetable products. At the same time, the production achieves lean production, the production process is controllable, the product material usage and quality are accurately unified, the data is collected online in real time, and the product is controllable and traceable throughout the process, so that the production technology level reaches intelligent and digital;

[0031] (2) The present invention achieves more rigorous production process monitoring and verification by adding a verification module and an early warning monitoring and prompting module. The introduction of the verification module enables the system to perform quantitative and regular verification of key parameters in the production process, such as production time, raw material quality, weight, etc. By comparing the actual production data with the preset data on the production process comparison table, the system can instantly detect deviations in the production process and generate specific verification early warning information. This verification mechanism not only improves the accuracy of product production, but also can identify problems at an early stage, avoiding potential quality problems from spreading to the entire production batch. In addition, the early warning monitoring and prompting module can centrally process various types of early warning information, including independent warnings, comprehensive warnings, and verification warnings, and promptly remind staff to check and verify. This design makes the entire production system more intelligent and automated, greatly reducing the necessity of human intervention, while also improving production efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described below in conjunction with the accompanying drawings.

[0033] Figure 1 This is a structural framework diagram of a digital intelligent process control system for VF fruit and vegetable product production according to the present invention;

[0034] Figure 2 This is a diagram of internal units of a link information collection module of a digital intelligent process control system for VF fruit and vegetable product production according to the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Embodiment 1

[0037] See also Figure 1-Figure 2 , the present invention provides a digital intelligent process control system for VF fruit and vegetable product production, including;

[0038] The link information collection module is used to collect the production information of each link and send the production information of each link to the data storage center for storage;

[0039] The link information collection module includes order management unit, raw material procurement unit, production pretreatment unit, quick freezing process control unit, vacuum frying control unit, packaging process monitoring unit, finished product quality verification unit and customer feedback unit;

[0040] An order management unit, used to receive customer orders and transmit customer orders to a subsequent data storage center;

[0041] The raw material procurement unit is used to monitor the raw material procurement information in real time, including procurement progress, raw material quality, raw material location, etc., and send the raw material procurement information to the subsequent data storage center;

[0042] Production pre-processing unit, used to monitor and collect processing information during the pre-processing process, including: cutting size, blanching time, pre-processing temperature, etc.; and send the processing information to the subsequent data storage center;

[0043] The quick freezing process control unit is used to monitor and collect quick freezing information in real time during the quick freezing process, including quick freezing temperature, quick freezing time, quick freezing wind speed, and send the quick freezing information to the subsequent data storage center;

[0044] Vacuum frying control unit, used to monitor and collect frying information in real time, including vacuum degree, oil temperature, frying time, etc., and send the frying information to the subsequent data storage center;

[0045] The packaging process monitoring unit is used to monitor and collect packaging images in real time and send the packaging images to the subsequent data storage center;

[0046] Finished product quality verification unit, which is used to automatically detect finished product quality information, including finished product appearance, weight and packaging integrity, and send the quality information to the data storage center;

[0047] A customer feedback unit, used to collect customer feedback information and send the feedback information to a subsequent data storage center;

[0048] The data storage center is used to classify and store the information of each link collected by the link information collection module;

[0049] The data calling module is used to receive the control instructions of the monitoring and analysis module and retrieve the corresponding information from the data storage center;

[0050] The schedule making module is used to call the customer order information of the data storage center through the data calling module, and preliminarily generate the production schedule according to the detailed information of the customer order through the preset generation model, and then use the schedule as the production process comparison table after the first and second level review and approval;

[0051] It should be noted that the preset generation model is set by experienced professionals based on the historical data of order information and detailed information on the order. For example, based on the VF fruit product and product quantity information on the order, the raw material information of the production process comparison table can be determined based on the past historical data and detailed information on the order, and the quantity information of the raw materials can be inferred;

[0052] Among them, the production process comparison table includes the following content information:

[0053] Production time, raw material quality, raw material quantity or weight, raw material location, cutting size, blanching time, pretreatment temperature, quick freezing time, quick freezing temperature, quick freezing wind speed, frying vacuum degree, oil temperature, frying time, packaging image, finished product appearance, finished product weight, expected customer satisfaction, expected production end time;

[0054] After the preset generation model is generated based on the above content information, it will be submitted to the first and second level reviewers for inspection and approval. After approval, it will be used to determine the information in the production process;

[0055] The monitoring and analysis module is used to call the corresponding data of the data storage center through the data calling module, simplify the corresponding data, compare and analyze the simplified results with the production process comparison table, and determine the defects of the specific process corresponding to the corresponding data;

[0056] Calculate the matching degree between each process of each production process and the production process comparison table, and monitor whether there are any problems with the product during the production process;

[0057] Among them, the following specific information data are simplified: cutting size, pretreatment temperature, quick freezing temperature, quick freezing wind speed, frying vacuum degree, oil temperature, finished product weight, etc.;

[0058] The simplified way is:

[0059] Within the preset time t1, obtain the average value P of the simplified specific information data during the period, where t1 is a preset value and the specific parameters are set by the staff;

[0060] For example, the specific information data is cut n times within the preset time t1, and the size of each cutting is Hi, and then the average value of the n cutting sizes is calculated, where 1≤i≤n. Similarly, the same average value calculation is performed on other specific information data;

[0061] Compare and analyze the simplified results with the production process comparison table and calculate their matching degree:

[0062] AS1: Calculate the difference between the average value P of the specific information data and the corresponding data in the production process comparison table, and take the absolute value, that is: D = |P-Ps|, where Ps is the corresponding data of the average value P of the specific information data in the production process comparison table;

[0063] AS2: Then calculate the matching degree of its specific information data using the following formula:

[0064]

[0065] Among them, Mj is the matching degree between its specific information data and the corresponding data in the production process comparison table, and Tolerance is a pre-set tolerance range, which indicates the allowable error range in the production process and is usually set by professionals based on experience and product quality requirements;

[0066] AS3: Compare Mj with the preset value Qj. If Mj>Qj, it means that the specific information is qualified for production within the preset time t1. If Mj≤Qj, it means that the specific information is not qualified for production within the preset time t1, and an independent warning information is generated;

[0067] AS4: When all specific information data are qualified for production, calculate the comprehensive matching degree between each specific information data and the corresponding data in the production process comparison table, using the following formula:

[0068]

[0069] Among them, Z is the comprehensive matching degree of each specific information data and the production process comparison table, s is the total number of parameters, that is, all specific information data, Wj is the weight of the jth parameter, and the calculation result Z is compared with the preset value Qs. If Z>Qs, it means that each specific information data is qualified for production within the preset time t1. If Z≤Qs, it means that each specific information data has certain defects in production within the preset time t1, and a comprehensive warning information is generated;

[0070] Embodiment 2

[0071] In the specific implementation process, this embodiment is based on the first embodiment and is different from the first embodiment in that this embodiment further includes:

[0072] The verification module is used to verify the quantitative timing information during the product production process. When the verification fails, specific verification warning information is generated. The quantitative timing information includes: production time, raw material quality, raw material quantity or weight, blanching time, quick freezing time, frying time, packaging image, finished product appearance, customer praise, production end time, etc.

[0073] Specifically, verify whether the production time of the product is the same as the time on the production process comparison table, verify whether the quality, quantity or weight of the raw materials are the same as the quality, quantity or weight of the raw materials on the production process comparison table, verify whether the blanching time, quick freezing time and frying time are the same as the blanching time, quick freezing time and frying time on the production process comparison table, collect the packaging images and the appearance of the finished product during the production process, and compare them with the packaging images and the appearance of the finished product on the production process comparison table to form an anti-counterfeiting code and a positioning traceability code, calculate the customer feedback praise and compare it with the expected customer praise on the production process comparison table to verify whether it is within the expected customer praise range. The above-mentioned verification process and specific methods are all existing technologies and will not be elaborated here.

[0074] The early warning monitoring prompt module is used to receive independent early warning information, comprehensive early warning information and specific verification early warning information, and remind the staff to conduct corresponding inspections and verifications to ensure accurate production;

[0075] By adding verification modules and early warning monitoring and prompting modules, more rigorous production process monitoring and verification are achieved. The introduction of the verification module enables the system to perform quantitative and regular verification of key parameters in the production process, such as production time, raw material quality, weight, etc. By comparing the actual production data with the preset data on the production process comparison table, the system can instantly detect deviations in the production process and generate specific verification early warning information. This verification mechanism not only improves the accuracy of product production, but also can identify problems at an early stage, avoiding the spread of potential quality problems to the entire production batch. In addition, the early warning monitoring and prompting module can centrally process various types of early warning information, including independent early warning, comprehensive early warning and verification early warning, and promptly remind staff to check and verify. This design makes the entire production system more intelligent and automated, greatly reducing the necessity of human intervention, while also improving production efficiency and accuracy.

[0076] Embodiment 3

[0077] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0078] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0079] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A digital intelligent process control system for VF fruit and vegetable product production, characterized in that: include: The link information collection module is used to collect the production information of each link and send the production information of each link to the data storage center for storage; The data storage center is used to classify and store the information of each link collected by the link information collection module; The schedule making module is used to call the customer order information of the data storage center through the data calling module, and preliminarily generate the production schedule according to the detailed information of the customer order through the preset generation model, and then use the schedule as the production process comparison table after the first and second level review and approval; The monitoring and analysis module is used to call the corresponding data of the data storage center through the data calling module, simplify the corresponding data, compare and analyze the simplified results with the production process comparison table, and determine whether to generate independent warning information or comprehensive warning information by calculating the matching degree and comprehensive matching degree of each process of each production process with the production process comparison table; The verification module is used to verify the quantitative timing information in the product production process. When the verification fails, specific verification warning information is generated; The early warning monitoring and prompting module is used to receive independent early warning information, comprehensive early warning information and specific verification early warning information, and remind staff to conduct corresponding inspections and verifications.

2. A digital intelligent process control system for VF fruit and vegetable product production according to claim 1, characterized in that: In the link information collection module, the link information collection module includes an order management unit, a raw material procurement unit, a production pretreatment unit, a quick freezing process control unit, a vacuum frying control unit, a packaging process monitoring unit, a finished product quality verification unit and a customer feedback unit.

3. A digital intelligent process control system for VF fruit and vegetable product production according to claim 1, characterized in that: In the schedule making module, the production process comparison table includes the following content information: production time, raw material quality, raw material quantity or weight, raw material location, cutting size, blanching time, pretreatment temperature, quick freezing time, quick freezing temperature, quick freezing wind speed, frying vacuum degree, oil temperature, frying time, packaging image, finished product appearance, finished product weight, expected customer praise, and expected production end time.

4. A digital intelligent process control system for VF fruit and vegetable product production according to claim 1, characterized in that: In the monitoring and analysis module, when calculating the matching degree between each process of each production process and the production process comparison table, the following specific information data are simplified: cutting size, pretreatment temperature, quick freezing temperature, quick freezing wind speed, frying vacuum degree, oil temperature, and finished product weight; and the simplification method is: Within a preset time t1, an average value P of the simplified specific information data within the period is obtained, wherein t1 is a preset value.

5. A digital intelligent process control system for VF fruit and vegetable product production according to claim 4, characterized in that: Compare and analyze the simplified results with the production process comparison table, calculate their matching degree, and generate independent warning information or comprehensive warning information in the following way: AS1: Calculate the difference between the average value P of the specific information data and the corresponding data in the production process comparison table, and take the absolute value, that is: D = |P-Ps|, where Ps is the corresponding data of the average value P of the specific information data in the production process comparison table; AS2: Then calculate the matching degree of its specific information data using the following formula: Among them, Mj is the matching degree between its specific information data and the corresponding data in the production process comparison table, and Tolerance is a pre-set tolerance range; AS3: Compare Mj with the preset value Qj. If Mj>Qj, it means that the specific information is qualified for production within the preset time t1. If Mj≤Qj, it means that the specific information is not qualified for production within the preset time t1, and an independent warning information is generated; AS4: When all specific information data are qualified for production, calculate the comprehensive matching degree between each specific information data and the corresponding data in the production process comparison table, using the following formula: Among them, Z is the comprehensive matching degree between each specific information data and the production process comparison table, s is the total number of parameters, that is, all the specific information data, Wj is the weight of the jth parameter, and the calculation result Z is compared with the preset value Qs. If Z>Qs, it means that each specific information data is qualified for production within the preset time t1. If Z≤Qs, it means that each specific information data has certain defects in production within the preset time t1, and a comprehensive early warning information is generated.

6. A digital intelligent process control system for VF fruit and vegetable product production according to claim 1, characterized in that: In the verification module, quantitative timing information includes: production time, raw material quality, raw material quantity or weight, blanching time, quick freezing time, frying time, packaging image, finished product appearance, customer praise, and production end time.

7. A digital intelligent process control system for VF fruit and vegetable product production according to claim 6, characterized in that: The content of verification is: Verify whether the production time of the product is the same as the time on the production process comparison table, verify whether the quality, quantity or weight of the raw materials are the same as the quality, quantity or weight of the raw materials on the production process comparison table, verify whether the blanching time, quick freezing time and frying time are the same as the blanching time, quick freezing time and frying time on the production process comparison table, collect the packaging images and the appearance of the finished product during the production process, and compare them with the packaging images and the appearance of the finished product on the production process comparison table to form an anti-counterfeiting code and a positioning traceability code, and calculate the customer feedback praise and compare it with the expected customer praise on the production process comparison table to verify whether it is within the expected customer praise range.

8. A digital intelligent process control system for VF fruit and vegetable product production according to claim 7, characterized in that: Determine the specific quantitative timing information of the verification failure, and generate corresponding verification warning information and send it to the warning monitoring prompt module.

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