Intelligent pet food production management and control system and traceability code spraying device thereof

Abstract

The invention discloses a pet food intelligent production management and control system and a traceability code spraying device thereof, and relates to the technical field of pet food production, and the pet food intelligent production management and control system comprises a production planning unit, a raw material management unit, a production control unit, an intelligent mistake proofing unit and a quality traceability unit. The production planning unit optimizes a production plan and accurately allocates resources, the raw material management unit performs supplier qualification evaluation, raw material multi-level quality inspection and intelligent inventory regulation and control, quality is controlled from the source, the supply chain risk is reduced, the production control unit depends on the equipment real-time monitoring and fault early warning technology, the stability of production parameters is guaranteed, and the production efficiency is improved. The intelligent error-proofing unit integrates OCR verification, threshold detection and prediction model construction, rejects error detection data, prevents data missing, realizes real-time early warning and closed-loop processing of quality risks, and eliminates quality problems in the bud stage, and the quality tracing unit performs real-time early warning and closed-loop processing on the quality risks through full-link data acquisition and a unique tracing code. And accurate positioning and rapid recall of problem products are supported.

Description

Technical Field

[0001] This invention relates to the field of pet food production technology, specifically to a pet food intelligent production control system and its traceability coding device. Background Technology

[0002] As people's living standards improve, pets are gaining an increasingly important place in families. As an important part of pet food, pet food is also in high demand. To meet the needs of large-scale production, intelligent control systems have emerged.

[0003] For example, the "Feed Production Quality Traceability System" with the publication number CN101593323A includes a barcode printer and a barcode scanner, as well as a computer software and hardware system. The computer software and hardware system, combined with barcode technology, realizes the following functions to trace the quality of feed production: (1) raw material identification and warehouse entry and exit management; (2) formula number management and maintenance restrictions; (3) production control and production information recording; (4) small ingredient batching control; (5) feeding control; (6) finished product batch number control.

[0004] In existing technologies, intelligent control systems can automatically record key information during the production process, such as raw material batches, production time, production equipment, and operators. They also utilize information technology to store and manage this data. When product quality issues arise, it's crucial to quickly trace the source of the problem. However, due to issues like non-standard barcode scanning or data entry errors during production, the system may obtain incorrect quality inspection data, leading to inaccurate judgments about product quality. This failure to promptly detect and resolve potential quality problems in pet food production can result in problematic products entering the market, increasing quality risks. Furthermore, missing or incorrect data can affect traceability efficiency, making it difficult to accurately track the production process and related information of problematic products. It's impossible to determine key details such as the specific production time and raw material batches used, hindering the search for the root cause of the problem. Summary of the Invention

[0005] The purpose of this invention is to provide a smart production control system for pet food and its traceability coding device, in order to solve the problems mentioned in the background art, such as the system obtaining incorrect quality inspection data due to non-standard coding or data entry errors during the production process, which leads to incorrect judgments on product quality. At the same time, data loss and errors affect traceability efficiency.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a pet food intelligent production control system, comprising a production planning unit, a raw material management unit, a production control unit, an intelligent error prevention unit, and a quality traceability unit;

[0007] Based on market demand, inventory status, and sales forecasts, the production planning unit formulates a reasonable production plan, clearly defining the pet food products, quantities, specifications, and delivery times to be produced, and allocates production tasks to various production stages and equipment.

[0008] The raw material management unit is used to manage supplier information, conduct incoming inspection of raw materials, and monitor inventory.

[0009] The production control unit is used to manage the equipment and product packaging used in the production process;

[0010] The intelligent error prevention unit is used to detect and analyze product-related data in real time, build a quality anomaly prediction model, and trigger early warnings.

[0011] The intelligent error prevention unit includes a quality verification module, a data detection module, and an anomaly prediction module;

[0012] The quality verification module is used to identify the integrity of product label information, remove products with incomplete label information, capture real-time images of key workstations on the production line, and analyze the material status.

[0013] The data detection module is used to perform real-time verification of product-related data. By setting threshold rules, it checks the rationality of the data. Product-related data includes raw material information, production process parameter data, and quality inspection data.

[0014] The anomaly prediction module constructs a quality anomaly prediction model based on a neural network algorithm. The quality anomaly prediction model is used to calculate the probability of product quality risk and to predict quality anomalies by analyzing historical data.

[0015] The quality traceability unit is used to query the production process and quality information of products, accurately locate information on problematic products, comprehensively track flow information, and formulate recall strategies.

[0016] Preferably, the raw material management unit includes a supplier management module, a raw material receiving module, and an inventory management module;

[0017] The supplier management module is used to evaluate, select and manage pet food ingredient suppliers, establish supplier profiles, and record supplier qualifications, product quality, prices, and delivery time information.

[0018] The raw material warehousing module is used to conduct strict quality inspections on purchased raw materials. After the inspection is passed, the warehousing procedures are completed, detailed information of the raw materials is recorded, and the information entered into the system is subject to multi-level review.

[0019] The inventory management module monitors raw material inventory levels in real time and replenishes and allocates raw materials promptly based on production plans and consumption, ensuring an adequate supply of raw materials while avoiding inventory backlog.

[0020] Preferably, the production control unit includes an equipment monitoring module, a fault early warning module, and a packaging management module;

[0021] The equipment monitoring module is used to monitor and operate production equipment in real time, realize automated operation, parameter adjustment and fault diagnosis of equipment, establish detailed files of production equipment, and formulate regular maintenance plans, including equipment maintenance, repair and parts replacement.

[0022] The fault early warning module can promptly detect potential faults in equipment by real-time monitoring and analysis of equipment operation data, and issue early warning information.

[0023] The packaging management module is used to manage the procurement, inventory, and use of packaging materials, and to generate a unique product identifier for each bag of pet food. The identifier includes the product name, specifications, batch number, production date, shelf life, raw material information, and production process.

[0024] Preferably, when the intelligent error prevention unit constructs a quality anomaly prediction model and triggers an early warning, it specifically includes the following steps:

[0025] S1. Data collection and processing: Collect detailed information on each batch of raw materials, and collect production process parameter data and quality inspection data;

[0026] S2. Data Analysis and Model Building: Extract representative features from the large amount of collected data, quantify and standardize the features, build a quality anomaly prediction model, and predict the quality risks of products under different conditions.

[0027] S3. Probability Real-time Monitoring and Threshold Setting: Input the raw material quality data and production process parameter data collected in real time into the trained quality anomaly prediction model to calculate the quality risk probability of the current production batch in real time and set the corresponding risk threshold.

[0028] S4. Early Warning Notification and Response Formulation: After detecting a potential quality risk exceeding the set risk threshold, the system immediately issues an early warning message, analyzes the causes of the potential quality risk, and formulates targeted improvement measures.

[0029] Preferably, in step S4, the production process parameter data includes parameters collected in real time at each stage of the production line, including ingredient ratio, mixing time, stirring speed, processing temperature, cooking time, and cooling speed; the quality inspection data includes the results of quality inspection of finished and semi-finished products, covering physical indicators, chemical indicators, microbiological indicators, and packaging inspection data. Physical indicators include particle size, hardness, and shape; chemical indicators include nutrient composition, moisture content, and pH; microbiological indicators include total bacterial count, total mold count, Salmonella, and Escherichia coli; and packaging inspection data includes packaging integrity and sealing.

[0030] Preferably, in step S4, after implementing the formulated improvement measures, the production data and quality inspection results after the measures are taken are tracked and evaluated, and the improvement measures are adjusted.

[0031] Preferably, the quality traceability unit includes a data acquisition module, a traceability code generation module, and an information query module;

[0032] The data acquisition module is used to collect data in the production process and store the data in the database. The database records the production information of each batch of pet food in detail, including the batch usage of raw materials and the specific timestamps of each stage of the production line.

[0033] The traceability code generation module generates a unique traceability code for each product based on the product identifier and associates the traceability code with the product's unique product identifier. The traceability code can be read by a scanning device.

[0034] The information query module allows users to query the entire production process and quality information of a product by looking up the traceability code, enabling full traceability from raw materials to finished products.

[0035] A traceability coding device for intelligent production control of pet food includes a conveyor belt and a coding component. The bottom end of the coding component is fixedly connected to the side of the conveyor belt. A steering mechanism is installed on one side of the conveyor belt, and a sorting mechanism is installed at the discharge end of the conveyor belt. The sorting mechanism includes a barcode scanner and a sorting frame. One end of the barcode scanner is fixedly connected to one side of the conveyor belt, and one end of the sorting frame is fixedly connected to one end of the conveyor belt. A sorting trough is opened on one side of the sorting frame. The sorting trough includes one feeding channel and two discharging channels. An adjusting baffle is provided at the connection between the feeding channel and the discharging channel of the sorting trough. A rotating rod is fixedly connected to one end of the adjusting baffle, and one end of the rotating rod passes through one side of the sorting frame.

[0036] Preferably, a mounting bracket is rotatably connected to the outside of the rotating rod. One end of the mounting bracket is fixedly connected to the bottom side of the sorting frame. A fixing plate is fixedly connected to one side of the mounting bracket. Positioning blocks are installed at both ends of the fixing plate. A steering plate is fixedly connected to the outside of the rotating rod. One end of the steering plate abuts against one end of one of the positioning blocks. A push plate is fixedly connected to the other end of the rotating rod. A push block is rotatably connected to one end of the push plate. A second electric push rod is fixedly connected to one end of the push block. A connecting plate is rotatably connected to one end of the second electric push rod. The other end of the connecting plate is fixedly connected to one side of the mounting bracket.

[0037] Preferably, the steering mechanism includes a fixed frame, a fixed frame, and a belt assembly. One end of the fixed frame is fixedly connected to the side of the conveyor belt. A drive motor is fixedly connected to the top side of the fixed frame. The output end of the drive motor passes through one side of the fixed frame. A connecting rod is rotatably connected to the inner side of the fixed frame. The other end of the connecting rod is fixedly connected to the top side of the fixed frame. A belt assembly is connected between the outer side of the connecting rod and the drive motor. Movable plates are slidably connected to both sides of the bottom of the fixed frame. Clamping blocks are fixedly connected to both ends of the movable plates. A first electric push rod is fixedly connected to the middle of the movable plates. One end of the first electric push rod is fixedly connected to the middle of the fixed frame.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] 1. This invention optimizes production plans through a production planning unit, accurately allocates resources, reduces redundancy, and ensures delivery cycles. A raw material management unit conducts supplier qualification assessments, multi-level raw material quality inspections, and intelligent inventory control, controlling quality from the source and reducing supply chain risks. A production control unit relies on real-time equipment monitoring and fault warning technology to ensure production parameter stability and extend equipment lifespan. An intelligent error prevention unit integrates OCR verification, threshold detection, and predictive model construction to achieve real-time early warning and closed-loop processing of quality risks, preventing data loss and eliminating erroneous detection data to nip quality problems in the bud. A quality traceability unit, through end-to-end data collection and unique traceability codes, supports precise location and rapid recall of problematic products. This allows for quality control at every stage of pet food production, reducing quality issues and enhancing consumer trust.

[0040] 2. This invention uses a barcode scanner to scan the package after inkjet printing to identify whether the inkjet printing information is correct. Based on the scanning result, the second electric push rod is controlled to drive the push block to move, which in turn moves one end of the rotating plate, causing the rotating rod to rotate and change the angle of the adjusting baffle. This blocks the connection between the feed channel and different discharge channels of the sorting tank, thereby sorting products that are qualified and unqualified by inkjet printing, and rejecting products with incomplete label information to prevent affecting the scanning results of quality traceability. Attached Figure Description

[0041] Figure 1 This is a system block diagram of an intelligent pet food production control system according to the present invention;

[0042] Figure 2 This is a production flow diagram of an intelligent pet food production control system according to the present invention;

[0043] Figure 3 This is a first three-dimensional structural schematic diagram of a traceability inkjet printing device for intelligent production control of pet food according to the present invention.

[0044] Figure 4This is a second three-dimensional structural diagram of a traceability inkjet printing device for intelligent production control of pet food according to the present invention.

[0045] Figure 5 This is a schematic diagram of the sorting mechanism connection structure of a traceability inkjet printing device for intelligent production control of pet food according to the present invention;

[0046] Figure 6 This is a schematic diagram of the disassembly structure of the sorting mechanism of the traceability inkjet printing device for intelligent production control of pet food according to the present invention.

[0047] Figure 7 This is a schematic diagram of the disassembly structure of the sorting mechanism of a traceability inkjet printing device for intelligent production control of pet food according to the present invention.

[0048] Figure 8 This is a schematic diagram of the orientation mechanism connection structure of a traceability inkjet printing device for intelligent production control of pet food according to the present invention.

[0049] In the picture:

[0050] 1. Production Planning Unit; 2. Raw Material Management Unit; 21. Supplier Management Module; 22. Raw Material Warehousing Module; 23. Inventory Management Module; 3. Production Control Unit; 31. Equipment Monitoring Module; 32. Fault Early Warning Module; 33. Packaging Management Module; 4. Intelligent Error Prevention Unit; 41. Quality Verification Module; 42. Data Detection Module; 43. Anomaly Prediction Module; 5. Quality Traceability Unit; 51. Data Acquisition Module; 52. Traceability Code Generation Module; 53. Information Query Module; 6. Conveyor Belt; 7. Direction Adjustment Mechanism; 71. Drive motor; 72. Fixing frame; 73. Clamping block; 74. Moving plate; 75. Fixing frame; 76. First electric push rod; 77. Connecting rod; 78. Belt assembly; 8. Inkjet assembly; 9. Sorting mechanism; 91. Code scanner; 92. Adjusting baffle; 93. Sorting frame; 94. Sorting trough; 95. Second electric push rod; 96. Mounting frame; 97. Connecting plate; 98. Fixing plate; 99. Rotating rod; 910. Turning plate; 911. Positioning block; 912. Pushing rotating plate; 913. Pushing block. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: Refer to Figure 1The system depicts an intelligent production control system for pet food, comprising a production planning unit 1, a raw material management unit 2, a production control unit 3, an intelligent error prevention unit 4, and a quality traceability unit 5. The production planning unit 1 formulates a reasonable production plan based on market demand, inventory status, and sales forecasts, specifying the pet food products, quantities, specifications, and delivery times to be produced. It allocates production tasks to various production stages and equipment, rationally arranging the production process and time to ensure efficient and orderly production. The raw material management unit 2 manages supplier information, conducts raw material inspection upon receipt, and monitors inventory. The raw material management unit 2 includes a supplier management module 21, a raw material receiving module 22, and an inventory management module 23.

[0053] The supplier management module 21 is used to evaluate, select, and manage pet food raw material suppliers, establish supplier files, and record supplier qualifications, product quality, price, and delivery time information to ensure the stability and quality of raw material supply. The raw material warehousing module 22 is used to conduct strict quality inspections on purchased raw materials, including component analysis, microbial testing, and harmful substance testing. After passing the inspection, warehousing procedures are completed, and detailed information about the raw materials is recorded, including batch, quantity, and warehousing time. A data review mechanism is established to conduct multi-level review of the information entered into the system. For example, after the raw material procurement data is entered, it is reviewed by the head of the procurement department and the quality control personnel to ensure the accuracy and completeness of the data. The inventory management module 23 monitors the raw material inventory level in real time and replenishes and allocates raw materials in a timely manner according to the production plan and consumption, ensuring an adequate supply of raw materials while avoiding inventory backlog.

[0054] The production control unit 3 is used to manage the equipment and product packaging used in the production process. The production control unit 3 includes an equipment monitoring module 31, a fault early warning module 32, and a packaging management module 33.

[0055] The equipment monitoring module 31 is used to monitor and operate the production equipment in real time, realize the automated operation, parameter adjustment and fault diagnosis of the equipment, establish detailed files of the production equipment, record the equipment model, specifications, purchase date, installation and commissioning status and maintenance records, provide a basis for equipment management and maintenance, and formulate regular maintenance plans based on the equipment usage and maintenance requirements, including equipment maintenance, repair and parts replacement, to ensure the normal operation of the equipment and extend the service life of the equipment. For example, it controls the accurate metering of the batching system, the stirring speed and time of the mixing equipment, the temperature and pressure of the extrusion equipment, etc., to ensure the stability and consistency of the production process.

[0056] The fault early warning module 32 can detect potential faults in the equipment in a timely manner by monitoring and analyzing the equipment operation data in real time, and issue early warning information. When the equipment fails, the system can quickly locate the cause of the failure and provide corresponding solutions, notify maintenance personnel to carry out maintenance, reduce equipment downtime, and improve production efficiency.

[0057] The packaging management module 33 is used to manage the procurement, inventory, and use of packaging materials, ensuring that the quality of packaging materials meets the requirements and can meet production needs. It generates a unique product identifier for each bag of pet food, which includes the product name, specifications, batch number, production date, shelf life, raw material information, and production process to facilitate product quality traceability.

[0058] The intelligent error prevention unit 4 is used to detect and analyze product-related data in real time, build a quality anomaly prediction model, and trigger early warnings. The intelligent error prevention unit 4 includes a quality verification module 41, a data detection module 42, and an anomaly prediction module 43.

[0059] The quality verification module 41 is used to identify the integrity of product label information. It reads the text on the product label using OCR technology, compares it with the standard template in the database, and identifies whether the label printing is clear and whether the content is complete. It also checks the integrity of the contents of the packaging bag, including whether the packaging bag is sealed properly, whether there is any damage, and whether there are any foreign objects mixed in. It identifies whether the material is mixed evenly, takes real-time pictures of key workstations on the production line, analyzes the material status, and judges whether the raw materials are mixed evenly through color recognition. When there are abnormalities, it automatically adjusts the mixing time and removes products with incomplete label information. OCR technology is an abbreviation for Optical Character Recognition. It is a computer input technology that converts the text of various tickets, newspapers, books, manuscripts and other printed materials into image information through optical input methods such as scanning, and then uses text recognition technology to convert the image information into usable text.

[0060] The data detection module 42 is used to perform real-time verification of product-related data. By setting threshold rules, it checks the rationality of the data. When the data exceeds the set threshold, the system will automatically issue an alarm and refuse to receive the data to prevent erroneous data from entering the system.

[0061] The anomaly prediction module 43 constructs a quality anomaly prediction model based on a neural network algorithm. The quality anomaly prediction model is used to calculate the probability of product quality risk and to predict quality anomalies by analyzing historical data.

[0062] When the intelligent error prevention unit 4 constructs a quality anomaly prediction model and triggers an early warning, it specifically includes the following steps:

[0063] S1. Data Collection and Organization: Collect detailed information on each batch of raw materials, such as the content of nutrients such as protein, fat, vitamins, and minerals, as well as the source, supplier, batch number, and test report of the raw materials. Monitor environmental data of the raw materials during storage, such as temperature, humidity, and ventilation. At the same time, collect production process parameter data and quality test data.

[0064] Production process parameter data includes real-time collection of parameters at each stage of the production line, including ingredient ratios, mixing time, stirring speed, processing temperature, cooking time, and cooling rate. Changes in these parameters directly affect the quality of pet food and may lead to product quality problems. Quality inspection data includes the results of quality inspections on finished and semi-finished products, covering physical indicators, chemical indicators, microbiological indicators, and packaging inspection data. Physical indicators include particle size, hardness, and shape; chemical indicators include nutrient composition, moisture content, and pH; microbiological indicators include total bacterial count, total mold count, Salmonella, and E. coli; and packaging inspection data includes packaging integrity and sealing.

[0065] S2. Data Analysis and Model Building: Representative features are extracted from the large amount of collected data. By analyzing historical data, raw material component indicators and key production process parameters that have a significant impact on product quality are identified. The representative features are quantified and standardized to facilitate processing and analysis by machine learning algorithms. Based on the features in the historical data and the corresponding product quality results, a quality anomaly prediction model is established. The input of the model is the processed raw material quality data, production process parameter data, and other features. The output is the category of quality problems that may occur in the product. The model can predict whether the product will have problems such as substandard nutritional components, excessive microorganisms, or poor taste, and give the corresponding probabilities, predicting the quality risk of the product under different conditions.

[0066] S3. Real-time Probability Monitoring and Threshold Setting: Input the raw material quality data and production process parameter data collected in real time into the trained quality anomaly prediction model to calculate the quality risk probability of the current production batch in real time. Based on the company's quality standards and risk tolerance, set corresponding risk thresholds for different quality problems. For example, when the model predicts that the risk probability of the product exceeding the microbial standard exceeds 10%, it is considered to have exceeded the threshold and triggers an early warning.

[0067] S4. Early Warning Notification and Response Formulation: Upon detecting a potential quality risk exceeding the threshold, the system immediately issues an early warning message. This message can be sent to relevant personnel through various means, such as SMS, email, and pop-up windows in the production management system. The notification includes the type of potential quality problem, risk level, potentially affected product batches, and recommended measures. This reminds the production department to take timely adjustments and handle the situation to prevent quality incidents. After an early warning occurs, professional personnel conduct an in-depth analysis of the causes of the potential quality risk. By comparing normal production data with data from when the risk occurred, and considering the actual production situation, possible causes such as raw material quality fluctuations and abnormal production process parameters are identified. Based on the results of the cause analysis, targeted improvement measures are formulated, such as adjusting raw material suppliers, optimizing production process parameters, strengthening equipment maintenance, and improving operator skills. These improvement measures are then implemented promptly.

[0068] In step S4, after implementing the formulated improvement measures, the production data and quality inspection results after the measures are taken are tracked and evaluated to observe whether the quality risks have been effectively reduced or eliminated. If the measures are not effective, the reasons are further analyzed, the improvement measures are adjusted, and a closed-loop management of quality risk early warning and handling is formed to continuously optimize the production process and improve the stability and reliability of product quality.

[0069] The quality traceability unit 5 is used to query the production process and quality information of products, accurately locate information on problematic products, comprehensively track flow information and formulate recall strategies. The quality traceability unit 5 includes a data acquisition module 51, a traceability code generation module 52 and an information query module 53.

[0070] The data acquisition module 51 is used to collect data from the production process and store the data in the database. The database records the production information of each batch of pet food in detail, including the usage of raw material batches and the specific timestamps of each stage on the production line. When a product quality problem is found, by inputting the relevant characteristics of the problematic product, the system can quickly filter out the corresponding production batch and the accurate production time period. For example, if a batch of pet food is found to have excessive levels of a certain microorganism, the system can quickly locate the production time range of the batch of products based on the production batch number associated with the quality test data, accurate to the hour or even the minute, and determine that it was produced on a certain production line and in a certain shift. During the data acquisition process, it is necessary to maintain the accuracy, completeness and timeliness of the data.

[0071] The traceability code generation module 52 generates a unique traceability code for each product based on the product identifier and associates the traceability code with the relevant product information. The traceability code can be read by a scanning device, making it convenient for consumers and internal personnel of the enterprise to query the detailed information of the product.

[0072] The information query module 53 allows users to query the entire production process and quality information of a product by searching the traceability code, enabling full traceability from raw materials to finished products. At the same time, it can promptly relay information to relevant departments for processing when consumers have questions about product quality and file complaints.

[0073] Example 2: Refer to Figure 2 - Figure 7 As shown: A traceability coding device for intelligent production control of pet food includes a conveyor belt 6 and a coding component 8. The bottom end of the coding component 8 is fixedly connected to the side of the conveyor belt 6. A steering mechanism 7 is installed on one side of the conveyor belt 6. A sorting mechanism 9 is installed at the discharge end of the conveyor belt 6. The sorting mechanism 9 includes a barcode scanner 91 and a sorting frame 93. One end of the barcode scanner 91 is fixedly connected to one side of the conveyor belt 6, and one end of the sorting frame 93 is fixedly connected to one end of the conveyor belt 6. A sorting trough 94 is opened on one side of the sorting frame 93. The sorting trough 94 includes one feeding channel and two discharging channels. An adjusting baffle 92 is provided at the connection between the feeding channel and the discharging channel of the sorting trough 94. A rotating rod 99 is fixedly connected to one end of the adjusting baffle 92. One end of the rotating rod 99 passes through... On one side of the sorting rack 93, a mounting bracket 96 is rotatably connected to the outside of the rotating rod 99. One end of the mounting bracket 96 is fixedly connected to the bottom side of the sorting rack 93. A fixing plate 98 is fixedly connected to one side of the mounting bracket 96. Positioning blocks 911 are installed at both ends of the fixing plate 98. A steering plate 910 is fixedly connected to the outside of the rotating rod 99. One end of the steering plate 910 abuts against one end of one of the positioning blocks 911. A push plate 912 is fixedly connected to the other end of the rotating rod 99. A push block 913 is rotatably connected to one end of the push plate 912. A second electric push rod 95 is fixedly connected to one end of the push block 913. A connecting plate 97 is rotatably connected to one end of the second electric push rod 95. The other end of the connecting plate 97 is fixedly connected to one side of the mounting bracket 96.

[0074] The steering mechanism 7 includes a fixed frame 72, a fixed frame 75, and a belt assembly 78. One end of the fixed frame 72 is fixedly connected to the side of the conveyor belt 6. A drive motor 71 is fixedly connected to the top side of the fixed frame 72. The output end of the drive motor 71 passes through one side of the fixed frame 72. A connecting rod 77 is rotatably connected to the inner side of the fixed frame 72. The other end of the connecting rod 77 is fixedly connected to the top side of the fixed frame 75. A belt assembly 78 is connected between the outer side of the connecting rod 77 and the drive motor 71. Movable plates 74 are slidably connected to both sides of the bottom of the fixed frame 75. Clamping blocks 73 are fixedly connected to both ends of the movable plates 74. A first electric push rod 76 is fixedly connected to the middle of the movable plates 74. One end of the first electric push rod 76 is fixedly connected to the middle of the fixed frame 75.

[0075] When the generated traceability code is sprayed onto the product packaging, the conveyor belt 6 is used to transport the product packaging with the inkjet code. First, the inkjet code position on the product packaging is identified by the camera. If the inkjet code direction is incorrect, the two first electric push rods 76 work to drive the connected moving plate 74 to move. The moving plate 74 slides on the bottom side of the fixed frame 75, fixing the two moving plates 74 to make linear motion. The clamping block 73 at the bottom of the moving plate 74 scoops up and clamps the product packaging. The belt assembly 78 consists of two pulleys and a conveyor belt. The drive motor 71 can drive the connecting rod 77 to rotate. The fixed frame 75 rotates accordingly, causing the orientation of the product packaging to change, thereby adjusting the orientation of the inkjet code position on the product packaging.

[0076] The coding assembly 8 consists of a horizontal moving assembly, a vertical moving assembly, and a coding machine. It can move the position of the coding machine to code the product packaging. Then, the scanner 91 scans the coded packaging to identify whether the coding information is correct. The packaging bag enters the feeding channel of the sorting tank 94 from the discharge end of the conveyor belt 6. According to the scanning result of the scanner 91, the second electric push rod 95 drives the connected push block 913 to move, which in turn drives the rotating plate 912 to move, causing the rotating rod 99 to rotate and change the angle of the adjusting baffle 92. This blocks the connection between the feeding channel and different discharge channels of the sorting tank 94, thereby sorting the coded products into qualified and unqualified ones. While the rotating rod 99 rotates, the steering plate 910 moves accordingly. The steering plate 910 abuts against the positioning blocks 911 on both sides, thereby controlling the rotation angle of the adjusting baffle 92. The scanner 91 is one of an RFID reader / writer, a barcode scanner, and a QR code scanner.

[0077] This invention utilizes a production planning unit 1 to formulate detailed production plans, rationally allocate tasks to various production stages and equipment, and plan processes and timelines to ensure efficient production. The raw material management unit 2 can evaluate, select, and manage suppliers, establishing files to record their qualifications and other information. Purchased raw materials undergo rigorous testing for composition, microorganisms, and harmful substances; only qualified materials are stored and subject to multi-level audits to ensure data accuracy and completeness. Simultaneously, inventory is monitored in real-time, and replenishment and allocation are made according to the production plan to ensure supply and avoid stockpiling. During production, the production control unit 3 performs real-time monitoring and operation of equipment, achieving automated operation, parameter adjustment, and fault diagnosis. Equipment files are established, and maintenance plans are formulated. By analyzing equipment operating data, proactive measures are taken. The intelligent error prevention unit 4 uses OCR technology to identify the integrity of labels, verify product information data, build a quality anomaly prediction model, calculate the probability of quality risks by inputting real-time data, issue warnings when thresholds are exceeded, notify relevant personnel, analyze the causes, formulate improvement measures, and conduct follow-up evaluations to form a closed-loop management system. The quality traceability unit 5 collects production process data and stores it in a database, recording detailed production information. It can quickly locate batches and production times based on the characteristics of quality problems, generate a unique traceability code for each product, and associate relevant information. Consumers and company personnel can scan the code to query the entire production and quality information of the product, realizing accurate traceability and recall management of problematic products.

[0078] Conveyor belt 6 is used to transport product packaging with inkjet printing. The photographic equipment identifies the product packaging delivered to conveyor belt 6. Two first electric push rods 76 drive the moving plates 74 to move towards the center, causing multiple clamping blocks 73 to cooperate in lifting and clamping the product packaging. The drive motor 71 drives the connecting rod 77 to rotate, causing the fixed frame 75 to rotate and change the orientation of the product packaging. Subsequently, the first electric push rods 76 release the clamped product packaging, and the adjusted product packaging continues to be transported along conveyor belt 6. The inkjet printing group... Item 8 prints a code on the product packaging, and then the barcode scanner 91 scans the printed packaging to identify the integrity of the code information. The packaging bag enters the feeding channel of the sorting tank 94 from the discharge end of the conveyor belt 6. According to the scanning result of the barcode scanner 91, the second electric push rod 95 drives the push block 913 to move, which drives the rotating rod 99 to rotate and change the angle of the adjusting baffle 92, blocking the connection between the feeding channel and different discharge channels of the sorting tank 94, thereby sorting the products that have passed the code printing and rejecting the product packaging that has failed the scan.

[0079] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart production control system for pet food, characterized in that: It includes a production planning unit (1), a raw material management unit (2), a production control unit (3), an intelligent error prevention unit (4), and a quality traceability unit (5); The production planning unit (1) formulates a reasonable production plan based on market demand, inventory status and sales forecast results, clarifies the pet food products, quantities, specifications and delivery time information to be produced, and allocates production tasks to each production link and equipment. The raw material management unit (2) is used to manage supplier information, conduct incoming inspection of raw materials, and monitor inventory. The production control unit (3) is used to manage the equipment and product packaging used in the production process; The intelligent error prevention unit (4) is used to detect and analyze product-related data in real time, build a quality anomaly prediction model, and trigger early warning. It includes a quality verification module (41), a data detection module (42), and an anomaly prediction module (43). The quality verification module (41) is used to identify the integrity of product label information, remove products with incomplete label information, take real-time photos of key workstations on the production line, and analyze the material status. The data detection module (42) is used to perform real-time verification of product-related data. By setting threshold rules, it checks the rationality of the data. Product-related data includes raw material information, production process parameter data and quality inspection data. The anomaly prediction module (43) constructs a quality anomaly prediction model based on a neural network algorithm. The quality anomaly prediction model is used to calculate the probability of product quality risk and to predict quality anomalies by analyzing historical data. The quality traceability unit (5) is used to query the production process and quality information of products, locate information on problematic products, track flow information, and formulate recall strategies.

2. The intelligent production control system for pet food according to claim 1, characterized in that: The raw material management unit (2) includes a supplier management module (21), a raw material receiving module (22), and an inventory management module (23); The supplier management module (21) is used to evaluate, select and manage pet food raw material suppliers, establish supplier files, and record supplier qualifications, product quality, price and delivery time information; The raw material warehousing module (22) is used to conduct strict quality inspections on the purchased raw materials, and after the inspection is qualified, the warehousing procedures are completed, the detailed information of the raw materials is recorded, and the information entered into the system is subject to multi-level review. The inventory management module (23) monitors the raw material inventory level in real time and replenishes and allocates raw materials in a timely manner according to the production plan and consumption.

3. The intelligent production control system for pet food according to claim 2, characterized in that: The production control unit (3) includes an equipment monitoring module (31), a fault early warning module (32), and a packaging management module (33); The equipment monitoring module (31) is used to monitor and operate the production equipment in real time, realize the automated operation, parameter adjustment and fault diagnosis of the equipment, establish detailed files of the production equipment, and formulate regular maintenance plans, including equipment maintenance, repair and parts replacement. The fault warning module (32) can promptly detect potential fault hazards in the equipment by real-time monitoring and analysis of the equipment operation data and issue warning information; The packaging management module (33) is used to manage the procurement, inventory and use of packaging materials, and to generate a unique product identifier for each bag of pet food, which includes the product name, specifications, batch, production date, shelf life, raw material information and production process.

4. The intelligent production control system for pet food according to claim 3, characterized in that: The intelligent error prevention unit (4) constructs a quality anomaly prediction model and triggers early warnings in advance, specifically including the following steps: S1. Data collection and processing: Collect detailed information on each batch of raw materials, and collect production process parameter data and quality inspection data; S2. Data Analysis and Model Building: Extract representative features from the large amount of collected data. These features include raw material composition, production process parameters, environmental factors, and product attributes. Quantify and standardize these representative features, and build a quality anomaly prediction model based on neural network algorithms to predict product quality risks under different conditions. S3. Probability Real-time Monitoring and Threshold Setting: Input the raw material quality data and production process parameter data collected in real time into the trained quality anomaly prediction model to calculate the quality risk probability of the current production batch in real time and set the corresponding risk threshold. S4. Early Warning Notification and Response Formulation: After detecting a potential quality risk exceeding the set risk threshold, the system immediately issues an early warning message, analyzes the causes of the potential quality risk, and formulates targeted improvement measures.

5. The intelligent production control system for pet food according to claim 4, characterized in that: In step S1, the production process parameter data includes parameters collected in real time at each stage of the production line, including ingredient ratio, mixing time, stirring speed, processing temperature, cooking time, and cooling rate; the quality inspection data includes the results of quality inspection of finished and semi-finished products, covering physical indicators, chemical indicators, microbiological indicators, and packaging inspection data. Physical indicators include particle size, hardness, and shape; chemical indicators include nutrient composition, moisture content, and pH; microbiological indicators include total bacterial count, total mold count, Salmonella, and Escherichia coli; and packaging inspection data includes packaging integrity and sealing.

6. The intelligent production control system for pet food according to claim 5, characterized in that: In step S4, after implementing the formulated improvement measures, the production data and quality inspection results after the measures are taken are tracked and evaluated, and the improvement measures are adjusted accordingly.

7. The intelligent production control system for pet food according to claim 6, characterized in that: The quality traceability unit (5) includes a data acquisition module (51), a traceability code generation module (52), and an information query module (53); The data acquisition module (51) is used to collect data in the production process and store the data in the database. The database records the production information of each batch of pet food in detail, including the usage of raw material batches and the specific timestamps of each link in the production line. The traceability code generation module (52) generates a unique traceability code for each product based on the product identifier and associates the traceability code with the unique product identifier of the product. The traceability code can be read by a scanning device. The information query module (53) allows users to query the entire production process and quality information of a product by querying the traceability code, enabling full traceability from raw materials to finished products.

8. A traceability inkjet printing device for intelligent production control of pet food, using the intelligent production control system for pet food as described in any one of claims 1-7, characterized in that: The system includes a conveyor belt (6) and a coding assembly (8). The bottom end of the coding assembly (8) is fixedly connected to the side of the conveyor belt (6). A steering mechanism (7) is installed on one side of the conveyor belt (6). A sorting mechanism (9) is installed at the discharge end of the conveyor belt (6). The sorting mechanism (9) includes a barcode scanner (91) and a sorting frame (93). One end of the barcode scanner (91) is fixedly connected to one side of the conveyor belt (6). One end of the sorting frame (93) is fixedly connected to one end of the conveyor belt (6). A sorting trough (94) is provided on one side of the sorting frame (93). The sorting trough (94) includes a feeding channel and two discharge channels. An adjusting baffle (92) is provided at the connection between the feeding channel and the discharge channel of the sorting trough (94). A rotating rod (99) is fixedly connected to one end of the adjusting baffle (92). One end of the rotating rod (99) passes through one side of the sorting frame (93).

9. A traceability coding device for intelligent production control of pet food according to claim 8, characterized in that: A mounting bracket (96) is rotatably connected to the outside of the rotating rod (99). One end of the mounting bracket (96) is fixedly connected to the bottom side of the sorting rack (93). A fixing plate (98) is fixedly connected to one side of the mounting bracket (96). Positioning blocks (911) are installed at both ends of the fixing plate (98). A steering plate (910) is fixedly connected to the outside of the rotating rod (99). One end of the steering plate (910) abuts against one end of one of the positioning blocks (911). A push plate (912) is fixedly connected to the other end of the rotating rod (99). A push block (913) is rotatably connected to one end of the push plate (912). A second electric push rod (95) is fixedly connected to one end of the push block (913). A connecting plate (97) is rotatably connected to one end of the second electric push rod (95). The other end of the connecting plate (97) is fixedly connected to one side of the mounting bracket (96).

10. A traceability coding device for intelligent production control of pet food according to claim 8, characterized in that: The steering mechanism (7) includes a fixed frame (72), a fixed frame (75), and a belt assembly (78). One end of the fixed frame (72) is fixedly connected to the side of the conveyor belt (6). A drive motor (71) is fixedly connected to the top side of the fixed frame (72). The output end of the drive motor (71) passes through one side of the fixed frame (72). A connecting rod (77) is rotatably connected to the inner side of the fixed frame (72). The other end of the connecting rod (77) is fixedly connected to the top side of the fixed frame (75). A belt assembly (78) is connected between the outside of the connecting rod (77) and the drive motor (71). A movable plate (74) is slidably connected to both sides of the bottom of the fixed frame (75). A clamping block (73) is fixedly connected to both ends of the movable plate (74). A first electric push rod (76) is fixedly connected to the middle of the movable plate (74). One end of the first electric push rod (76) is fixedly connected to the middle of the fixed frame (75).

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