Centralized feeding system for central feeding system

Abstract

The invention relates to the technical field of centralized feeding systems, and discloses a centralized feeding system for a central feeding system.The centralized feeding system comprises a feeding module, a heating module and an injection molding module, the feeding module comprises multiple sets of independent feeding branches, and each set of branch is provided with a distribution station, an intelligent material selecting valve, a material stopping valve and a material recognition sensor; and the material identification sensor comprises a near-infrared spectrometer and a density detector, is used for identifying the types and physical properties of the raw materials, is in signal connection with an injection molding equipment control system, and automatically sets injection molding parameters according to the identification result of the types of the raw materials. Through multi-module collaboration, intelligent control and innovative design, the problems of an existing central feeding system in the aspects of material switching, excess material processing, feeding control, expansibility, multi-machine collaboration management and the like are solved, accurate feeding, efficient production, intelligent operation and maintenance and data safety are achieved, the production efficiency, the product quality and the resource utilization rate are improved, and the production cost is reduced. The cost and the fault risk are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of centralized feeding system, more particularly, the present application relates to a centralized feeding system for central feeding system. BACKGROUND

[0002] The patent document with the publication number CN118024500A discloses an injection molding central centralized feeding system. The device can use the multi-stage filter screen and cleaning stirring pool of the impurity removal unit to place the raw materials that need to be fed on the multi-stage filter screen by manual operation, and use the multi-stage filter screen to screen and clean the large particle impurities in the raw materials, so that the uniformly sized raw materials can be used for plastic processing. However, the device has the following technical problems when in use: 1. The material switching and residual material processing are not convenient, the material replacement flexibility is low, and the operation complexity problem exists, and the product performance is easily affected by the residual old material; 2. There is a risk of cross contamination of raw materials, and it is not convenient to accurately control the feeding amount and realize the recovery of residual materials; 3. It is difficult to adapt to frequent material replacement requirements. The centralized system has slow response speed when facing frequent material replacement requirements, and it is not convenient to realize intelligent parameter setting and operation of injection molding equipment, intelligent identification of injection molding raw materials; 4. The system has insufficient scalability and flexibility, poor adaptability to special materials, large fault influence range, and is not convenient for multi-machine collaborative management; Therefore, the present application provides a centralized feeding system for central feeding system to solve the technical problems in the background art. SUMMARY

[0003] In order to overcome the shortcomings of the prior art, the present application provides a centralized feeding system for central feeding system. The present application solves the problems of material switching, residual material processing, feeding control, scalability and multi-machine collaborative management of the existing central feeding system through multi-module cooperation, intelligent control and innovative design, realizes precise feeding, efficient production, intelligent operation and maintenance and data security, improves production efficiency, product quality and resource utilization, and reduces cost and fault risk.

[0004] To achieve the above purpose, the present application provides the following technical scheme: a centralized feeding system for central feeding system, comprising a feeding module, a heating module and an injection molding module; The feeding module comprises a plurality of independent feeding branches, each branch is provided with a distribution station, an intelligent material selection valve, a material cutting valve and a material identification sensor; The material identification sensor includes a near-infrared spectrometer and a density detector, is used for identifying the material type and physical properties, is connected with a signal of an injection molding equipment control system, automatically sets injection molding parameters according to the material type identification result, and further includes: A control module integrates an AI algorithm, dynamically optimizes the feeding amount, temperature and pressure parameters, avoids overfeeding through real-time weighing sensor feedback data, and supports directional removal of residual materials in specific pipelines or specific modules when the system is in material replacement, failure or process adjustment; A surplus material recycling module includes a negative pressure adsorption device, a melting reprocessing unit and a residual amount detection sensor, is used for directional removal of residual materials in specific pipelines or modules, melts the recycled old materials and returns them to the material bin, and updates the material traceability label; A multi-machine cooperative management module includes a state identification unit, a life prediction unit and a dynamic scheduling control unit, and realizes task allocation, peak-shaving maintenance and health state monitoring of multiple injection molding equipment; The memory is integrated with a blockchain data recording unit; The system adopts a distributed architecture and modular extension.

[0005] As a preferred technical solution of the present application, each group of branches in the feeding module is further provided with: A impurity removal unit including a multi-stage filter screen and a cleaning stirring tank, is used for screening and density sorting of the raw materials; A drying unit including a dryer and a humidity sensor, is used for drying the raw materials after impurity removal; A weighing unit is used for weighing the dried raw materials and adjusting the feeding amount in linkage with the AI optimization unit.

[0006] As a preferred technical solution of the present application, the feeding module in the present application includes: A distribution station is used for distributing the raw materials of the main feeding pipeline to each feeding branch as needed, supports parallel switching of multiple raw materials, dynamically adjusts the raw material distribution path in combination with the feedback of the material identification sensor, and optimizes the raw material distribution priority according to the load state of the injection molding equipment in linkage with the dynamic scheduling control unit, and the data feedback of the material identification sensor ensures that the raw material type matches the target injection molding equipment parameters; A cut-off valve is arranged at the end of the feeding branch, controls the raw material on-off and prevents overfeeding, adopts double sealing washers, integrates a pressure sensor, and monitors the valve opening and closing state in real time; A high-pressure gas jet cleaning device is configured in the distribution station, the cut-off valve and the feeding branch, and automatically cleans the residues in the station, the valve and the feeding branch after material replacement; An optical detection device detects the cleaning effect of the high-pressure gas jet cleaning device through an optical sensor and triggers a secondary cleaning program when the cleaning standard is not met. The distribution station and the intercepting valve work together with the intelligent material selection valve to achieve rapid switching of raw materials from multiple branches. The intelligent material selection valve optimizes the distribution logic through the AI ​​algorithm of the control module. Combined with the residual material recovery module, it simultaneously triggers the closing and cleaning procedures of the intercepting valve when switching raw materials, reducing downtime. The feeding module employs adaptive adjustment technology for multiple independent feeding branches, including telescopic pipes and intelligent positioning devices, which support module expansion or replacement within a set time through standardized interfaces.

[0007] As a preferred embodiment of the present invention, the heating module includes a hot-melting unit and a continuous heating unit: The hot-melting unit includes a hot-melting box, which is used to heat and melt the weighed raw materials. The continuous heating unit includes a pipe heating jacket and a temperature sensor. The pipe heating jacket adjusts the heating power through a PLC controller and dynamically maintains the temperature of the liquid raw material based on the detection results of the temperature sensor. The inner wall of the pipe heating jacket is coated with an anti-stick coating, and the pipe diameter is adjustable to accommodate the conveying of high-viscosity or easily caking raw materials.

[0008] As a preferred embodiment of the present invention, the injection molding module includes: The injection molding unit, the input end of which is connected to the output end of the liquid raw material conveying pipeline, is used to make plastic products from liquid raw materials; The discharge unit includes a conveyor for transporting the finished product to the collection area; The emergency material discharge unit automatically directs excess raw materials into a temporary storage bin when it detects excessive material feeding, thus avoiding machine shutdown and emptying.

[0009] As a preferred technical solution of the present invention, the system adopts a distributed configuration of key equipment and is equipped with backup equipment and intelligent fault diagnosis algorithm; The intelligent fault diagnosis algorithm automatically switches to backup equipment and quickly locates the cause of the fault when a fault occurs. The system adopts a modular design, with each functional module connected through a standardized interface, supporting rapid expansion or replacement without the need to reconstruct the pipeline layout. The system equipment hibernation mechanism automatically switches to standby mode and starts the full system self-diagnosis program when the injection molding equipment reaches the preset usage threshold, while triggering the blockchain data recording unit to store the health status of the equipment. The system is configured with a fault isolation mechanism. When a local fault is detected, the faulty module is automatically disconnected and a backup link is activated.

[0010] As a preferred embodiment of the present invention, in the multi-machine collaborative management module: The status recognition unit also includes a pressure sensor and a displacement sensor. The status recognition unit collects the operating parameters of the injection molding equipment in real time through multi-sensor fusion technology, including mold closing pressure and screw displacement parameters. The multi-sensor fusion technology in the state recognition unit performs in-depth analysis and cross-validation of the data collected by each sensor, providing data support for the lifetime prediction unit and the dynamic scheduling control unit. The lifespan prediction unit is built based on a deep learning algorithm. The input data covers multiple sources of information, including equipment operating parameters, environmental factors, and historical maintenance records. Through continuous learning and training, it can accurately predict the remaining lifespan of key equipment components and formulate personalized maintenance plans. The life prediction unit generates detailed maintenance recommendations based on the life prediction results of key components of different equipment, including maintenance time, maintenance content, and information on the parts that need to be replaced. The dynamic scheduling control unit introduces a real-time order priority algorithm to determine the priority of production tasks based on factors such as order delivery time, product complexity, and customer importance. Combined with the real-time status of the equipment, it uses an intelligent scheduling algorithm to achieve the optimal allocation of production tasks among multiple injection molding machines, thereby improving equipment utilization and production efficiency. When allocating production tasks, the dynamic scheduling control unit considers the real-time failure risk of the equipment and prioritizes assigning tasks to equipment with low failure risk to ensure the stability and continuity of the production process. The dynamic scheduling control unit generates a health index based on the operating data of the injection molding equipment, and prioritizes the allocation of production tasks to healthy equipment. The real-time order priority algorithm adjusts order priorities in real time based on market dynamics and changes in customer demand, enabling the dynamic scheduling control unit to respond promptly and optimize production task allocation.

[0011] As a preferred embodiment of the present invention, the control module includes: The AI ​​optimization unit dynamically optimizes the material feeding rate, temperature, and pressure parameters, adjusting the feeding strategy based on real-time weighing sensor feedback data to avoid overfeeding. The AI ​​optimization unit integrates a fault prediction and self-healing module. This module analyzes the operating parameters and environmental data of a specified injection molding equipment to predict potential faults and trigger at least one of the following operations: Automatically switch to backup device; Adjust the material feeding parameters to reduce the risk of failure; Generate maintenance suggestions and push them to the user interface; The targeted cleaning unit controls the residual material removal process in specific pipelines or modules during material change, malfunction, or process adjustment. The material removal process works in conjunction with the high-pressure gas jet cleaning device and the negative pressure adsorption device. The material removal process is linked with the residual material recovery module to remelt the cleaned old material and return it to the raw material silo. The multi-module collaborative unit is connected to the waste material recycling module, the dynamic scheduling control unit and the life prediction unit to realize data sharing and command coordination. The multi-module collaborative interface is linked with the dynamic scheduling control unit to dynamically adjust the material supply path and injection molding equipment task allocation according to the real-time status of the equipment and the priority of production tasks. The scheduling decision log and equipment health status are stored through the blockchain data recording unit. The real-time data acquisition unit acquires the operating parameters of the injection molding equipment, the status of raw materials, and the health information of the equipment through a sensor network, and transmits them to the AI ​​optimization unit for dynamic optimization. The user interaction unit provides a visual interface to display real-time feeding parameters, equipment status, and fault warning information, and supports users to manually adjust optimization strategies or input production priority commands.

[0012] As a preferred technical solution of the present invention, it also includes a memory, which stores the conveying parameters of different raw materials, including heating temperature, flow rate and cleaning program settings, for use by the AI ​​algorithm. The blockchain data recording unit stores the health status of the equipment, fault switching logs and scheduling decision records to ensure that the data cannot be tampered with.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention enables rapid switching of raw materials from multiple branches by setting up intelligent material selection valves, material shut-off valves, high-pressure gas jet cleaning devices, and residual material recovery modules. During material switching, the intelligent material selection valves can optimize the allocation logic through AI algorithms, simultaneously triggering the shut-off valves to close and the cleaning process, reducing downtime. The residual material recovery module uses a negative pressure adsorption device, a melting and reprocessing unit, and a residual amount detection sensor to directionally remove residual raw materials in specific pipes or modules. The recovered old materials are melted and returned to the raw material silo, and the material traceability label is updated. This effectively solves the problems of material switching and residual material handling, greatly improves the flexibility of material switching, reduces operational complexity, and ensures that product performance is not affected by residual old materials.

[0014] 2. In this invention, the material cutting valve of the feeding module is located at the end of the feeding branch, and adopts double sealing gaskets and integrated pressure sensors to accurately control the flow of raw materials and prevent overfeeding. The weighing unit weighs the dried raw materials and adjusts the feeding amount in conjunction with the AI ​​optimization unit to achieve accurate feeding. At the same time, the residual material recovery module effectively handles the residual raw materials, avoiding cross-contamination of different raw materials and solving the shortcomings of the existing technology in terms of feeding amount control and cross-contamination of raw materials.

[0015] 3. The material identification sensor of this invention includes a near-infrared spectrometer and a density detector, which can identify the type and physical properties of raw materials and connect to the injection molding equipment control system. Based on the identification results, the injection molding parameters are automatically set. The control module integrates AI algorithms, which can dynamically optimize the material supply, temperature and pressure parameters and quickly respond to material change requirements. In addition, the various modules of the system work together. For example, the distribution station dynamically adjusts the raw material distribution path based on the feedback from the material identification sensor, so that the entire system can operate efficiently in frequent material change scenarios and achieve intelligent operation.

[0016] 4. This invention adopts a distributed architecture and modular expansion. Each functional module is connected through a standardized interface, supporting rapid expansion or replacement without reconstructing the pipeline layout. It is convenient to add or replace functional modules to adapt to the processing requirements of special materials. Key equipment is distributed and equipped with backup equipment and intelligent fault diagnosis algorithms. In case of failure, it automatically switches to backup equipment to reduce the scope of failure impact. The multi-machine collaborative management module realizes task allocation, off-peak maintenance and health status monitoring of multiple injection molding machines through status recognition unit, life prediction unit and dynamic scheduling control unit, improving equipment utilization and production efficiency, and optimizing multi-machine collaborative management. Attached Figure Description

[0017] Fig. 1 This is a schematic diagram of the centralized feeding system of the present invention used in a central feeding system; Fig. 2 This is a schematic diagram of the heating module of the present invention; Fig. 3 This is a schematic diagram of the control module of the present invention; Fig. 4 This is a schematic diagram of the feeding module of the present invention. Detailed Implementation

[0018] 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.

[0019] like Figs. 1 to 4 As shown, the centralized feeding system for the central feeding system includes a feeding module, a heating module, and an injection molding module; The feeding module includes multiple independent feeding branches, each branch equipped with a distribution station, intelligent material selection valve, material cut-off valve and material identification sensor; The material identification sensor includes a near-infrared spectrometer and a density detector, which are used to identify the type and physical properties of raw materials and are connected to the injection molding equipment control system to automatically set the injection molding parameters based on the raw material identification results. Each branch in the feeding module is also equipped with: Impurity removal unit: includes multi-stage filter screens and a washing and mixing tank, used for screening and density sorting of raw materials; Drying unit: includes a dryer and a humidity sensor, used to dry the raw materials after impurity removal; Weighing unit: Used to weigh the dried raw materials and adjust the feeding amount in conjunction with the AI ​​optimization unit.

[0020] In the feeding module: The distribution station is used to distribute the raw materials from the main feeding pipeline to each feeding branch as needed. It supports parallel switching of multiple raw materials. The distribution station combines the feedback from the material identification sensor to dynamically adjust the raw material distribution path and links with the dynamic scheduling control unit to optimize the raw material distribution priority according to the load status of the injection molding equipment. The data from the material identification sensor is fed back to the distribution station to ensure that the raw material type matches the parameters of the target injection molding equipment. The material shut-off valve is located at the end of the feeding branch to control the flow of raw materials and prevent overfeeding. It uses double sealing gaskets and integrates a pressure sensor to monitor the valve's opening and closing status in real time. High-pressure gas jet cleaning device is installed in the distribution station, the material shut-off valve and the material feeding branch. It automatically cleans the residue in the station, valve and material feeding branch after material change. An optical detection device uses optical sensors to detect the cleaning effect of the high-pressure gas jet cleaning device and triggers a secondary cleaning procedure if the cleaning standard is not met. The distribution station and the intercepting valve work together with the intelligent material selection valve to achieve rapid switching of raw materials from multiple branches. The intelligent material selection valve optimizes the distribution logic through the AI ​​algorithm of the control module. Combined with the residual material recovery module, it simultaneously triggers the closing and cleaning procedures of the intercepting valve when switching raw materials, reducing downtime. The feeding module employs adaptive adjustment technology for multiple independent feeding branches, including telescopic pipes and intelligent positioning devices, which support module expansion or replacement within a set time through standardized interfaces.

[0021] During the feeding process, the raw materials first enter the impurity removal unit, where multi-stage filter screens remove impurities of different particle sizes. The cleaning and mixing tank further removes impurities with abnormal density through density sorting to ensure the purity of the raw materials. Then, the raw materials enter the drying unit, where the dryer uses humidity sensors to precisely control the degree of drying and prevent the raw materials from being affected by moisture in subsequent processing. The weighing unit weighs the dried raw materials and works in conjunction with the AI ​​optimization unit to adjust the feeding amount based on real-time weighing data to achieve precise feeding. This solves the problems of excessive raw material impurities, poor humidity control, and inaccurate material supply in existing technologies; Compared with existing technologies, it improves the quality of raw materials, reduces product defects caused by impurities and moisture, and enhances product quality and production stability. The system provides high-quality, quantitatively accurate raw materials for subsequent heating and injection molding processes, which is the foundation for ensuring the smooth operation of the entire production process. The distribution station receives raw materials from the main feeding pipeline and, combined with the raw material information fed back by the material identification sensor and the load status of the injection molding equipment, dynamically adjusts the distribution path to allocate suitable raw materials to the corresponding feeding branches, thereby improving the efficiency and accuracy of raw material distribution. The material shut-off valve is located at the end of the branch line. It uses double sealing gaskets to prevent raw material leakage and a pressure sensor to monitor the opening and closing status in real time, so as to accurately control the flow of raw materials and prevent overfeeding. The high-pressure gas jet cleaning device automatically cleans up residues after material replacement, and the optical detection device detects the cleaning effect. If the cleaning standard is not met, a secondary cleaning is triggered to ensure the cleanliness of pipelines and equipment. The intelligent material selection valve optimizes the allocation logic through AI algorithms, and when switching raw materials, it links the shut-off valve to close and the cleaning procedure to reduce downtime. This solution addresses the problems of unreasonable raw material distribution, incomplete pipeline cleaning, and long downtime when equipment switches raw materials in existing technologies. Compared with existing technologies, it improves production efficiency, reduces raw material waste and equipment failure rate. The system enables efficient switching and precise supply of raw materials from multiple branches, ensuring the continuity of production. The heating module includes a heat-melting unit and a continuous heating unit: The hot melt unit includes a hot melt box, which is used to heat and melt the weighed raw materials; The continuous heating unit includes a pipe heating jacket and a temperature sensor. The pipe heating jacket adjusts the heating power through a PLC controller and dynamically maintains the temperature of the liquid raw material based on the detection results of the temperature sensor. The inner wall of the pipe heating jacket is coated with an anti-stick coating, and the pipe diameter is adjustable, making it suitable for conveying high-viscosity or easily caking raw materials. The non-stick coating is made of polytetrafluoroethylene; After being weighed, the raw materials enter the hot melt box and are heated and melted in the hot melt unit, turning into liquid raw materials. The heating jacket of the continuous heating unit is monitored by a temperature sensor and the heating power is adjusted by a PLC controller to dynamically maintain the temperature of the liquid raw material and ensure that it maintains appropriate fluidity during transportation. The anti-stick coating on the inner wall of the pipe heating jacket prevents raw materials from adhering, and the adjustable pipe diameter can be adapted to high viscosity or easily caking raw materials. This solves the problems of uneven heating of raw materials, difficulty in stable temperature control, and difficulty in conveying high-viscosity raw materials in existing technologies; Compared with existing technologies, this technology ensures the quality stability of raw materials during heating and transportation, and reduces production failures caused by temperature fluctuations and raw material adhesion and agglomeration. In the system, providing a stable liquid raw material to the injection molding module is a key link connecting the feeding module and the injection molding module. The injection molding module includes: The injection molding unit has its input end connected to the output end of the liquid raw material conveying pipeline, and is used to make plastic products from liquid raw materials. The discharge unit includes a conveyor for transporting the finished product to the collection area; The emergency material discharge unit automatically directs excess raw materials into a temporary storage bin when it detects excessive material feeding, thus avoiding machine shutdown and emptying. After receiving liquid raw materials, the injection molding unit uses a specific process to mold them into plastic products, thus achieving product molding. The conveyor in the discharge unit promptly transports the finished product to the collection area, ensuring the continuity of the production process; When the emergency material discharge unit detects excessive material feeding, it quickly directs the excess material into a temporary storage bin to avoid shutdown and emptying operations caused by excessive material feeding. This solution addresses the problems of unsmooth injection molding processes, untimely collection of finished products, and downtime caused by excessive material feeding in existing technologies. Compared to existing technologies, it improves production efficiency, reduces equipment wear and tear and production interruptions. In the system, it is the core module for completing product production and is directly related to product quality and output. Also includes: The control module integrates AI algorithms to dynamically optimize the feeding amount, temperature and pressure parameters, and avoids overfeeding by feeding data through real-time weighing sensors. When the system changes materials, malfunctions or adjusts the process, it supports targeted removal of residual raw materials in specific pipes or modules. The control module includes: The AI ​​optimization unit dynamically optimizes the material feeding rate, temperature, and pressure parameters. Based on real-time feedback data from the weighing sensor, it adjusts the material feeding strategy to avoid overfeeding. The AI ​​optimization unit integrates a fault prediction and self-healing module. This module analyzes the operating parameters and environmental data of a specified injection molding equipment to predict potential faults and trigger at least one of the following operations: Automatically switch to backup device; Adjust the material feeding parameters to reduce the risk of failure; Generate maintenance suggestions and push them to the user interface; The AI ​​optimization unit solves the problems of difficulty in accurately adjusting feeding parameters and the inability to prevent and handle equipment failures in advance in existing technologies. Compared to existing technologies, it improves production reliability and equipment lifespan, reduces production costs, and is the core of achieving intelligent manufacturing within the system. The targeted cleaning unit controls the residual material removal process in specific pipelines or modules during material change, malfunction, or process adjustment. The material removal process works in conjunction with the high-pressure gas jet cleaning device and the negative pressure adsorption device. The material removal process is linked with the residual material recovery module to remelt the cleaned old material and return it to the raw material silo. The targeted removal unit solves the problem of improper handling of residual raw materials in the prior art, which can easily lead to raw material contamination and waste. Compared with the prior art, it reduces raw material waste, improves resource utilization, and ensures the stability of product quality. In the system, it maintains the cleanliness of the production system and ensures that different batches of products are not affected by residual raw materials. The multi-module collaborative unit connects with the waste material recycling module, dynamic scheduling control unit, and life prediction unit to achieve data sharing and command coordination. The multi-module collaborative interface is linked with the dynamic scheduling control unit to dynamically adjust the material supply path and injection molding equipment task allocation according to the real-time status of the equipment and the priority of production tasks. The scheduling decision log and equipment health status are stored through the blockchain data recording unit. The multi-module collaborative unit solves the problems of poor coordination between modules and unreasonable production scheduling in the existing technology. Compared with the existing technology, it improves the overall operating efficiency and traceability of the production system. In the system, resource allocation is optimized to ensure efficient production. The real-time data acquisition unit acquires the operating parameters of the injection molding equipment, the status of raw materials, and the health information of the equipment through a sensor network, and transmits them to the AI ​​optimization unit for dynamic optimization. The real-time data acquisition unit solves the problems of opaque system operation status and inconvenient user operation in existing technologies. Compared with existing technologies, it improves the user experience, makes it easier for users to monitor and intervene in the production process, and establishes an interactive bridge between users and the system, making the production process more in line with actual needs. The user interaction unit provides a visual interface to display real-time feeding parameters, equipment status, and fault warning information, and supports users to manually adjust optimization strategies or input production priority commands.

[0022] The waste material recovery module includes a negative pressure adsorption device, a melting and reprocessing unit, and a residual amount detection sensor. It is used to selectively remove residual raw materials in specific pipes or modules. The recovered waste material is melted and returned to the raw material silo, and the material traceability label is updated. The workflow of the residual amount detection sensor begins with its deployment at key locations, including the outlet of the distribution station, the end of the feeding branch, the raw material inlet of the injection molding equipment, and the negative pressure adsorption port of the residual material recovery module, to ensure full coverage of the easily residual areas in the raw material flow path. The sensor employs a multi-mode fusion technology combining weight sensors, optical sensors, and ultrasonic sensors. It monitors the weight of raw materials, reflected light intensity, or material height within the pipeline in real time, and calculates the residual amount by combining this with the pipeline's cross-sectional area. In complex scenarios such as those involving high-viscosity raw materials, it enhances reliability through dual-mode weight and optical detection. Data is acquired at a frequency of once per second and transmitted to the control module via an industrial bus, formatted into a standard data packet containing the sensor ID, timestamp, and residual amount value. Simultaneously, an edge computing preprocessing algorithm is used to filter vibration noise. The dynamic threshold setting is based on the preset value of raw material type retrieved from the material matching database, and is dynamically optimized by combining AI algorithm with historical data. When the residual amount is detected to exceed the standard, the system immediately triggers the targeted cleaning procedure and records the fault code. After cleaning, a second test is conducted to check the residual amount. If it meets the standard, the material traceability label is updated and new material supply is enabled. If it exceeds the standard three times in a row, the material supply path is locked and an alarm is pushed to the user interface.

[0023] Sensor data is further deeply integrated with AI algorithms to train the life prediction unit to predict pipeline blockage risks and dynamically adjust the detection interval and feeding strategy in frequent material change scenarios, prioritizing the clearing of paths corresponding to high-value orders. In addition, the system performs zero-point calibration of the sensors daily. When the deviation exceeds 5%, a fault is marked and the system switches to a backup sensor. Meanwhile, the optical sensors are equipped with dust covers, the ultrasonic sensors have built-in temperature compensation modules, and high-temperature areas are encapsulated with high-temperature resistant ceramics to ensure environmental adaptability. The waste material recycling module solves the problems of environmentally unfriendly waste material handling and chaotic material management in existing technologies. Compared with existing technologies, it reduces production costs, improves the environmental benefits and material management level of enterprises, and forms a closed-loop management of materials in the system, reducing resource waste. The multi-machine collaborative management module includes a status identification unit, a life prediction unit, and a dynamic scheduling control unit, which realizes task allocation, off-peak maintenance, and health status monitoring of multiple injection molding machines; In the multi-machine collaborative management module: The status recognition unit also includes pressure sensors and displacement sensors. The status recognition unit collects the operating parameters of the injection molding equipment in real time through multi-sensor fusion technology, including mold closing pressure and screw displacement parameters. The multi-sensor fusion technology in the state recognition unit performs in-depth analysis and cross-validation of the data collected by each sensor, providing data support for the life prediction unit and the dynamic scheduling control unit. The life prediction unit is built based on deep learning algorithms. The input data covers multiple sources of information such as equipment operating parameters, environmental factors and historical maintenance records. Through continuous learning and training, it can accurately predict the remaining service life of key equipment components and formulate personalized maintenance plans. The life prediction unit generates detailed maintenance recommendations based on the life prediction results of key components of different equipment, including maintenance time, maintenance content, and information on the parts that need to be replaced. The dynamic scheduling control unit introduces a real-time order priority algorithm to determine the priority of production tasks based on factors such as order delivery time, product complexity, and customer importance. Combined with the real-time status of the equipment, the intelligent scheduling algorithm is used to achieve the optimal allocation of production tasks among multiple injection molding machines, thereby improving equipment utilization and production efficiency. When allocating production tasks, the dynamic scheduling control unit considers the real-time failure risk of equipment and prioritizes assigning tasks to equipment with low failure risk to ensure the stability and continuity of the production process. The dynamic scheduling control unit generates a health index based on the operating data of the injection molding equipment, and prioritizes the allocation of production tasks to healthy equipment. The real-time order priority algorithm adjusts order priorities in real time based on market dynamics and changes in customer demand, enabling the dynamic scheduling control unit to respond promptly and optimize production task allocation. The status recognition unit solves the problems of incomplete and inaccurate monitoring of the operating status of injection molding equipment in the existing technology. Compared with the existing technology, it improves the accuracy and reliability of equipment status monitoring and provides important data for equipment maintenance and production scheduling in the system. The life prediction unit solves the problem of insufficient scientific basis for equipment maintenance in existing technologies, which easily leads to over-maintenance or untimely maintenance. Compared with existing technologies, it improves the scientific nature and pertinence of equipment maintenance, extends equipment life, reduces maintenance costs, ensures stable equipment operation, and reduces production interruptions caused by equipment failure. The dynamic scheduling control unit solves the problems of unreasonable production scheduling and low equipment utilization in existing technologies. Compared with existing technologies, it improves equipment utilization and production efficiency, enhances the market responsiveness of enterprises, and realizes the optimal allocation of production resources in the system. The system adopts a distributed architecture and modular expansion.

[0024] The system adopts a distributed configuration of key equipment and is equipped with backup equipment and intelligent fault diagnosis algorithms; Intelligent fault diagnosis algorithm, automatically switches to backup equipment and quickly locates the cause of the fault when a fault occurs; The system adopts a modular design, with each functional module connected through standardized interfaces, supporting rapid expansion or replacement without the need to restructure the pipeline layout; The system equipment hibernation mechanism automatically switches to standby mode and starts the full system self-diagnosis program when the injection molding equipment reaches the preset usage threshold, while triggering the blockchain data recording unit to store the health status of the equipment. The system is configured with a fault isolation mechanism. When a local fault is detected, the faulty module is automatically disconnected and a backup link is activated.

[0025] The distributed architecture enables the functions of each part of the system to work relatively independently yet collaboratively, improving the system's reliability and flexibility. Modular expansion allows for the convenient addition or replacement of functional modules according to production needs without refactoring the pipeline layout. This solves the problems of poor system scalability and difficulty in adapting to changes in production scale in existing technologies. Compared with existing technologies, it reduces the cost of system upgrades and transformations, improves the enterprise's ability to respond to market changes, and provides convenience for the enterprise's future development and production adjustments within the system. The distributed configuration of key equipment reduces the risk of the entire system being paralyzed due to a single point of failure. The intelligent fault diagnosis algorithm monitors the equipment status in real time. Once a fault is detected, it automatically switches to backup equipment to ensure uninterrupted production and quickly locates the cause of the fault. This solves the problems of equipment failure affecting production continuity and the difficulty of fault diagnosis in existing technologies. Compared with existing technologies, it improves the continuity and stability of production, reduces losses caused by equipment failure, and ensures the smooth operation of production in the system. Modular design and standardized interfaces solve the problems of complex and time-consuming system maintenance in existing technologies; Compared to existing technologies, this system improves maintainability, reduces maintenance costs and downtime, and ensures efficient production operations. The fault isolation mechanism solves the problem of local faults affecting the operation of the entire system in existing technologies. Compared with existing technologies, it improves the fault tolerance and stability of the system and ensures the continuity of production.

[0026] If a fault occurs in a branch of the feeding module, the branch should be cut off in time and the backup branch should be activated so as not to affect other branches and overall production. It also includes a memory that stores the conveying parameters of different raw materials, including heating temperature, flow rate and cleaning program settings, for use by AI algorithms. The memory integrates a blockchain data recording unit that stores the equipment health status, fault switching logs and scheduling decision records to ensure that the data is tamper-proof.

[0027] The storage and blockchain data recording functions solve the problems of chaotic raw material parameter management and easy data tampering in existing technologies. Compared with existing technologies, it improves the accuracy of production control and the security of data, and provides a guarantee for the optimization of production process and data management in the system.

[0028] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0029] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A centralized feeding system for a central feeding system, comprising a feeding module, a heating module, and an injection molding module, characterized in that: The feeding module includes multiple independent feeding branches, each branch being equipped with a distribution station, intelligent material selection valve, material cutting valve, and material identification sensor; The material identification sensor includes a near-infrared spectrometer and a density detector, used to identify the type and physical properties of raw materials, and is connected to the injection molding equipment control system to automatically set injection molding parameters based on the raw material identification results. It also includes: The control module integrates AI algorithms to dynamically optimize the feeding amount, temperature and pressure parameters, and avoids overfeeding by feeding data through real-time weighing sensors. When the system changes materials, malfunctions or adjusts the process, it supports targeted removal of residual raw materials in specific pipes or modules. The waste material recovery module includes a negative pressure adsorption device, a melting and reprocessing unit, and a residual amount detection sensor. It is used to selectively remove residual raw materials in specific pipes or modules. The recovered waste material is melted and returned to the raw material silo, and the material traceability label is updated. The multi-machine collaborative management module includes a status identification unit, a life prediction unit, and a dynamic scheduling control unit, which realizes task allocation, off-peak maintenance, and health status monitoring of multiple injection molding machines; The system adopts a distributed architecture and modular expansion; The memory integrates a blockchain data recording unit.

2. The centralized feeding system for a central feeding system according to claim 1, characterized in that: Each branch in the feeding module is also equipped with: Impurity removal unit: includes multi-stage filter screens and a washing and mixing tank, used for screening and density sorting of raw materials; Drying unit: includes a dryer and a humidity sensor, used to dry the raw materials after impurity removal; Weighing unit: Used to weigh the dried raw materials and adjust the feeding amount in conjunction with the AI ​​optimization unit.

3. The centralized feeding system for a central feeding system according to claim 1, characterized in that: In the feeding module: The distribution station is used to distribute the raw materials from the main feeding pipeline to each feeding branch as needed, and supports parallel switching of multiple raw materials. The distribution station combines the feedback from the material identification sensor to dynamically adjust the raw material distribution path and links with the dynamic scheduling control unit to optimize the raw material distribution priority according to the load status of the injection molding equipment. The data from the material identification sensor is fed back to the distribution station to ensure that the raw material type matches the target injection molding equipment parameters. The material shut-off valve is located at the end of the feeding branch to control the flow of raw materials and prevent overfeeding. It uses double sealing gaskets and integrates a pressure sensor to monitor the valve's opening and closing status in real time. High-pressure gas jet cleaning device is installed in the distribution station, the material shut-off valve and the material feeding branch. It automatically cleans the residue in the station, valve and material feeding branch after material change. An optical detection device uses optical sensors to detect the cleaning effect of the high-pressure gas jet cleaning device and triggers a secondary cleaning procedure if the cleaning standard is not met. The distribution station and the intercepting valve work together with the intelligent material selection valve to achieve rapid switching of raw materials from multiple branches. The intelligent material selection valve optimizes the distribution logic through the AI ​​algorithm of the control module. Combined with the residual material recovery module, it simultaneously triggers the closing and cleaning procedures of the intercepting valve when switching raw materials, reducing downtime. The feeding module employs adaptive adjustment technology for multiple independent feeding branches, including telescopic pipes and intelligent positioning devices, which support module expansion or replacement within a set time through standardized interfaces.

4. The centralized feeding system for a central feeding system according to claim 1, characterized in that: The heating module includes a hot-melting unit and a continuous heating unit: The hot-melting unit includes a hot-melting box, which is used to heat and melt the weighed raw materials. The continuous heating unit includes a pipe heating jacket and a temperature sensor. The pipe heating jacket adjusts the heating power through a PLC controller and dynamically maintains the temperature of the liquid raw material based on the detection results of the temperature sensor. The inner wall of the pipe heating jacket is coated with an anti-stick coating, and the pipe diameter is adjustable to accommodate the conveying of high-viscosity or easily caking raw materials.

5. The centralized feeding system for a central feeding system according to claim 1, characterized in that: The injection molding module includes: The injection molding unit, the input end of which is connected to the output end of the liquid raw material conveying pipeline, is used to make plastic products from liquid raw materials; The discharge unit includes a conveyor for transporting the finished product to the collection area; The emergency material discharge unit automatically directs excess raw materials into a temporary storage bin when it detects excessive material feeding, thus avoiding machine shutdown and emptying.

6. The centralized feeding system for a central feeding system according to claim 1, characterized in that: The system adopts a distributed configuration of key equipment and is equipped with backup equipment and intelligent fault diagnosis algorithms. The intelligent fault diagnosis algorithm automatically switches to backup equipment and quickly locates the cause of the fault when a fault occurs. The system adopts a modular design, with each functional module connected through a standardized interface, supporting rapid expansion or replacement without the need to reconstruct the pipeline layout. The system equipment hibernation mechanism automatically switches to standby mode and starts the full system self-diagnosis program when the injection molding equipment reaches the preset usage threshold, while triggering the blockchain data recording unit to store the health status of the equipment. The system is configured with a fault isolation mechanism. When a local fault is detected, the faulty module is automatically disconnected and a backup link is activated.

7. The centralized feeding system for a central feeding system according to claim 1, characterized in that: In the multi-machine collaborative management module: The status recognition unit also includes a pressure sensor and a displacement sensor. The status recognition unit collects the operating parameters of the injection molding equipment in real time through multi-sensor fusion technology, including mold closing pressure and screw displacement parameters. The multi-sensor fusion technology in the state recognition unit performs in-depth analysis and cross-validation of the data collected by each sensor, providing data support for the lifetime prediction unit and the dynamic scheduling control unit. The lifespan prediction unit is built based on a deep learning algorithm. The input data covers multiple sources of information, including equipment operating parameters, environmental factors, and historical maintenance records. Through continuous learning and training, it can accurately predict the remaining lifespan of key equipment components and formulate personalized maintenance plans. The life prediction unit generates detailed maintenance recommendations based on the life prediction results of key components of different equipment, including maintenance time, maintenance content, and information on the parts that need to be replaced. The dynamic scheduling control unit introduces a real-time order priority algorithm to determine the priority of production tasks based on factors such as order delivery time, product complexity, and customer importance. Combined with the real-time status of the equipment, it uses an intelligent scheduling algorithm to achieve the optimal allocation of production tasks among multiple injection molding machines, thereby improving equipment utilization and production efficiency. When allocating production tasks, the dynamic scheduling control unit considers the real-time failure risk of the equipment and prioritizes assigning tasks to equipment with low failure risk to ensure the stability and continuity of the production process. The dynamic scheduling control unit generates a health index based on the operating data of the injection molding equipment, and prioritizes the allocation of production tasks to healthy equipment. The real-time order priority algorithm adjusts order priorities in real time based on market dynamics and changes in customer demand, enabling the dynamic scheduling control unit to respond promptly and optimize production task allocation.

8. The centralized feeding system for a central feeding system according to claim 1, characterized in that: The control module includes: The AI ​​optimization unit dynamically optimizes the material feeding rate, temperature, and pressure parameters, adjusting the feeding strategy based on real-time weighing sensor feedback data to avoid overfeeding. The AI ​​optimization unit integrates a fault prediction and self-healing module. This module analyzes the operating parameters and environmental data of a specified injection molding equipment to predict potential faults and trigger at least one of the following operations: Automatically switch to backup device; Adjust the material feeding parameters to reduce the risk of failure; Generate maintenance suggestions and push them to the user interface; The targeted cleaning unit controls the residual material removal process in specific pipelines or modules during material change, malfunction, or process adjustment. The material removal process works in conjunction with the high-pressure gas jet cleaning device and the negative pressure adsorption device. The material removal process is linked with the residual material recovery module to remelt the cleaned old material and return it to the raw material silo. The multi-module collaborative unit is connected to the waste material recycling module, the dynamic scheduling control unit and the life prediction unit to realize data sharing and command coordination. The multi-module collaborative interface is linked with the dynamic scheduling control unit to dynamically adjust the material supply path and injection molding equipment task allocation according to the real-time status of the equipment and the priority of production tasks. The scheduling decision log and equipment health status are stored through the blockchain data recording unit. The real-time data acquisition unit acquires the operating parameters of the injection molding equipment, the status of raw materials, and the health information of the equipment through a sensor network, and transmits them to the AI ​​optimization unit for dynamic optimization. The user interaction unit provides a visual interface to display real-time feeding parameters, equipment status, and fault warning information, and supports users to manually adjust optimization strategies or input production priority commands.

9. The centralized feeding system for a central feeding system according to claim 1, characterized in that: It also includes a memory that stores the conveying parameters of different raw materials, including heating temperature, flow rate and cleaning program settings, for use by the AI ​​algorithm. The blockchain data recording unit stores the health status of the equipment, fault switching logs and scheduling decision records to ensure that the data is tamper-proof.

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