PLC-based multimodal calculation method for material feeding priority

Abstract

This invention provides a PLC-based multimodal calculation method for material loading priority, characterized by the following steps: Step 1: Multimodal status information acquisition: The PLC periodically acquires the status information of each machine tool, with the acquisition period adjusted according to actual needs, typically ranging from 100ms to 500ms; Step 2: Dynamic optimization of the priority calculation model: A multi-factor dynamic fusion model is adopted to calculate the material loading priority of each machine tool using formulas. This invention eliminates the need for additional host computers or dedicated computing equipment, fully utilizing the PLCs already deployed in the industrial site to complete complex material loading priority scheduling calculations without requiring additional hardware investment. Compared with traditional scheduling schemes relying on host computers, this reduces hardware costs and eliminates the integration work such as communication module adaptation and dedicated software development between the host computer, PLC, machine tools, and material loading equipment, thus reducing system integration costs.

Description

Technical Field

[0001] This invention relates to the field of industrial automatic control technology, and in particular to a PLC-based multimodal calculation method for material feeding priority. Background Technology

[0002] In intelligent manufacturing and automated production lines, the collaborative operation of multiple machine tools has become a core mode for improving production efficiency and achieving large-scale production. The rational planning of the material loading sequence is a crucial link in ensuring the efficient collaborative operation of multiple machine tools. Currently, the most widely used methods for determining the material loading sequence when multiple machine tools are operating collaboratively in industrial production fall into two main categories: sensor detection methods and fixed sequence methods. These two methods, due to their simple structure and convenient deployment, have been applied to a certain extent in small and medium-sized production lines.

[0003] The core logic of the sensor-based detection method is to install various sensors (such as photoelectric sensors and proximity switches) at key locations such as the feed inlet and hopper of each machine tool to detect single parameters in real time, such as the machine tool's material shortage status and the amount of material remaining in the hopper. Based on the signals from the sensors, the control system prioritizes feeding the machine tool with the most severe material shortage and the least remaining material in the hopper, thus avoiding production line interruptions caused by a single machine tool running out of material. This method does not require complex calculations, has a fast response speed, and is suitable for scenarios with a single material type, a small number of machine tools, and a relatively fixed production rhythm, such as a batch processing production line for a single type of part. However, this method cannot take into account various practical influencing factors in the production process, such as the machine tool's waiting time for feeding, the matching degree between the type of material fed and the machine tool's processing requirements, the machine tool's processing priority, and the movement path losses of the feeding equipment, making it less adaptable to complex production scenarios.

[0004] The fixed-sequence method uses a pre-set, fixed feeding sequence (such as according to machine tool number or workstation order) and a fixed feeding rhythm. The control system drives the feeding equipment to feed materials to each machine tool in a predetermined order, without the need for real-time monitoring of machine tool operating status or material shortages. Its advantages are simple control logic, low deployment cost, and avoidance of production disorder caused by chaotic feeding sequences. However, the drawback of this method is the lack of dynamic adjustment capability. Regardless of the real-time operating status of the machine tools, the feeding equipment executes tasks in a fixed sequence, which can easily lead to some machine tools waiting for feeding for a long time, while others experience feeding redundancy. Summary of the Invention

[0005] To address the above technical issues, this invention provides a PLC-based multimodal calculation method for material loading priority, which utilizes multiple sensors to detect material level status, automatically calculates material consumption time of machine tools, integrates multimodal data, and employs dynamic optimization algorithms to achieve more accurate, efficient, low-cost, and flexible multi-machine tool loading scheduling.

[0006] This invention provides a PLC-based multimodal calculation method for material loading priority, characterized by the following steps: Step 1: Multimodal status information acquisition: The PLC periodically acquires the status information of each machine tool. The acquisition period is adjusted according to actual needs, with a typical value of 100ms~500ms. Step 2: Dynamic optimization priority calculation model: A multi-factor dynamic fusion model is adopted to calculate the loading priority of each machine tool through formulas; Step 3: Multimodal data fusion and dynamic optimization: The system integrates the status information detection results of each machine tool, uses a weighted average algorithm to improve the accuracy of material level detection, and predicts material demand in advance based on historical material consumption time data, dynamically adjusting the data in the priority calculation model; Step 4: Priority Sorting and Update: The PLC sorts all machine tools according to the calculation results in Step 2, using the bubble sort algorithm for efficient sorting, and updates the sorting results in real time; the system dynamically adjusts and optimizes priority calculation based on historical data and production scenarios. Step 5: Loading task allocation: Loading tasks are allocated in descending order of priority. When multiple machine tools have the same priority, the first-come, first-served principle is adopted. When a machine tool malfunctions or communication is interrupted, the system automatically removes it from the priority calculation and includes it back in the calculation after the fault is restored.

[0007] The status information in step one includes multimodal material level detection information, material shortage time information, required material feeding type and ready status information, machine tool feeding lock status information, and material consumption time information; Specifically, the multimodal material level detection information includes: Weighing sensor detection: The weight of the silo is detected in real time by the weighing sensor, and the material level percentage Wt is automatically calculated in combination with the preset silo capacity; Material level sensor detection: Multi-level detection is achieved through proximity switches or photoelectric switches. Detection points are set at three positions: light, medium, and heavy, corresponding to different material shortage levels Ls. When the light position is not triggered, Ls=0; when the light position is triggered, Ls=1; when the medium position is triggered, Ls=2; and when the heavy position is triggered, Ls=3. The system integrates the results of the two detection methods mentioned above and calculates the overall material shortage level using the following formula, where the overall material shortage level ranges from 0 to 5, with level 5 representing the most severe material shortage: Material shortage level = round(W1×(1 - Wt / 100)×5+W2×Ls×5 / 3) In the formula, W1 is the weighting coefficient of the weighing sensor, with a default value of 0.6; W2 is the weighting coefficient of the level sensor, with a default value of 0.4; Wt is the weight percentage of the silo, with a value range of 0-100%; Ls is the detection level of the level sensor, with a value range of 0-3; round() is the rounding function. The material shortage time information refers to the waiting time for each machine tool to be loaded, which is collected and sorted. The material consumption time information is the time when the machine tool consumes each type of material automatically recorded by the system, a material consumption time database is established, and material demand is predicted based on historical data. The required loading type and ready status information is used to detect whether the loading system is ready with the corresponding type of material.

[0008] The formula for calculating the material loading priority in step two is as follows: Material feeding priority = W1 × Material shortage level + W2 × Waiting time coefficient + W3 × Material feeding type matching coefficient + W4 × Locked status coefficient + W5 × Material consumption time coefficient; In the formula, W1-W5 are configurable weight coefficients, and satisfy W1+W2+W3+W4+W5=1; waiting time coefficient = waiting time / baseline waiting time; The material feeding type matching coefficient is 1 when the feeding system has prepared the corresponding type of material, and 0 otherwise. The locking state coefficient is 1 when the machine tool is not in the locked state and 0 when the machine tool is in the locked state; Material consumption time coefficient = historical average consumption time / current remaining production time.

[0009] The baseline waiting time is determined based on the preset production cycle of the production line and is dynamically modified as production tasks are adjusted.

[0010] In step four, the system dynamically adjusts the weight coefficients. The dynamic adjustment of weight coefficients W1-W5 is based on historical material scheduling data and the real-time status of the current production scenario, and is automatically optimized through the PLC's built-in algorithm.

[0011] The data refers to the material consumption time coefficient.

[0012] The beneficial effects of this invention are as follows: 1. This invention abandons the traditional approach of relying on a host computer or independent computing module, embedding the priority algorithm into the PLC program, and using the real-time processing capability of the PLC to complete complex scheduling calculations, thereby reducing hardware costs and system complexity.

[0013] 2. This invention adopts a dual-modal fusion detection method using a weighing sensor and a level sensor. By fusing the two detection results through a weighted average algorithm, the accuracy of level detection is improved. The level sensor achieves multi-level detection through a proximity switch / photoelectric switch, with detection points set at three locations: light, medium, and heavy. The weighted average algorithm is used to improve the accuracy of level detection and automatically converts it to a material shortage level, which is more accurate than the single sensor detection method.

[0014] 3. The system can automatically record and accurately calculate the time for each material consumed by the computer tool in real time, establish a complete material consumption time database, accurately predict material demand based on historical consumption data, and realize forward-looking scheduling and replenishment of materials; the system can dynamically adjust the weight coefficients in the priority calculation model according to historical material scheduling data and real-time production scenario changes, continuously optimize the priority calculation logic, and improve scheduling accuracy.

[0015] 4. By setting a material shortage time sorting mechanism, the system records and sorts the waiting time of each machine tool in real time, ensuring that machine tools with longer waiting times can receive material feeding services first. This breaks the unfairness of the traditional fixed order method, while making the material feeding scheduling logic more transparent, facilitating staff monitoring and management, and improving the rationality and traceability of production scheduling.

[0016] 5. The system can automatically detect the locking status of machine tools during loading, exclude locked machine tools from the priority calculation range, avoid invalid calculation operations, reduce the PLC's computing load, ensure the efficiency of priority calculation and task allocation, and further guarantee the smooth operation of the production line. Attached Figure Description

[0017] Figure 1 This is a diagram showing the overall architecture of the PLC control system of this invention. Figure 2 This is a flowchart of the priority calculation process for this invention. Detailed Implementation

[0018] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. Furthermore, the technical features involved in the different embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. Example 1

[0021] This embodiment is applicable to a small-to-medium-scale production line consisting of 4 machine tools, mainly used for single-product, batch production scenarios. The core objective is to achieve precise material feeding scheduling and low-cost operation, and to verify the basic functions and core advantages of the present invention.

[0022] 1. Hardware Configuration Details The hardware configuration prioritizes cost-effectiveness and ease of deployment, selecting commonly used equipment in industrial settings without requiring additional customization. Details are as follows: - PLC controller: Siemens S7-1200 series PLC (model: CPU 1214C DC / DC / DC) is selected. This controller integrates 14 digital inputs and 10 digital outputs, supports high-speed counters and pulse outputs, has strong real-time processing capabilities, can meet the status acquisition, priority calculation and task allocation requirements of 4 machine tools, and has strong compatibility with commonly used equipment in industrial sites and low deployment costs.

[0023] - Sensor Components: ① Proximity switch (model: E3F-DS10C4), used for light and medium level detection, installed at the corresponding height on the side wall of the silo, with a trigger accuracy of ±1mm; ② Photoelectric sensor (model: E3Z-LS61), used for heavy level detection, installed at the bottom of the silo to avoid misjudgment caused by material residue; ③ Weighing sensor (model: MT1241), with a range of 0-500kg and an accuracy of ±0.1%, installed at the bottom of the silo to detect the total weight of the silo in real time. It calculates the percentage of material level Wt based on the preset silo capacity (500kg) and integrates the results with the level sensor detection to improve detection accuracy.

[0024] - Actuators: ① Servo motor (model: 1FL6034-2AF21-1AA1), in conjunction with a ball screw, drives the loading robot to move, with a positioning accuracy of ±0.05mm, ensuring accurate loading position; ② Cylinder (model: SC63×50), used for hopper discharge control, with a response time of <10ms, to achieve rapid discharge and replenishment.

[0025] - Auxiliary equipment: HMI (Model: KTP700 Basic) for parameter setting, status monitoring and fault alarm, which can display information such as priority, material shortage level and waiting time of each machine tool in real time, making it convenient for staff to operate and manage; Signal isolator (Model: RS-485) for isolated transmission of sensor signals to avoid signal distortion caused by electromagnetic interference in the industrial field.

[0026] 2. Software Configuration Details The software configuration is based on an industry-standard programming environment and features a modular design, facilitating future maintenance and functional expansion, as detailed below: - Programming software: Siemens TIA Portal V16 programming software is used, which supports three programming methods: ladder diagram (LD), function block diagram (FBD), and structured text (ST). Function block diagram (FBD) programming is used in this case to facilitate logic sorting and later modification.

[0027] - Core program modules: ① Priority calculation program block (implemented by FB function block), encapsulates a multi-factor dynamic fusion calculation model, which can be directly called and supports online modification of weight coefficients; ② Multimodal data processing module, which specifically processes the detection data of weighing sensors and level sensors, realizes data filtering, outlier removal (such as invalid data when sensors malfunction), and fuses the two detection results through a weighted average algorithm to output an accurate comprehensive material shortage level; ③ Material consumption time database management module, which uses the PLC's built-in data storage area to automatically record the time for each machine to consume each type of material, update the historical average consumption time in real time, and supports data query and export; ④ Dynamic weight adjustment module, which automatically adjusts each weight coefficient based on historical material scheduling data (such as scheduling accuracy and machine waiting time) and optimizes the priority calculation logic; ⑤ HMI human-machine interface program, which realizes parameter setting (such as weight coefficients and acquisition cycle), real-time status display, fault alarm (such as sensor failure and machine lock), etc., and the operation is simple and intuitive.

[0028] - Communication configuration: The Profinet communication protocol is adopted to realize real-time communication between PLC and HMI, sensors and actuators. The communication rate is 100Mbps, ensuring the timeliness and stability of data transmission, and the communication delay is <5ms.

[0029] 3. Parameter settings and basis The parameter settings are based on the production cycle time of the four machine tools (each machine tool has a processing cycle of approximately 200 seconds), taking into account both scheduling accuracy and system load. The specific parameters and settings are based on the following: - Weighting coefficient settings: W1 (material shortage level weight) = 0.35, W2 (waiting time weight) = 0.25, W3 (material loading type matching weight) = 0.15, W4 (locked state weight) = 0.1, W5 (material consumption time weight) = 0.15, satisfying W1+W2+W3+W4+W5=1; This setting is based on the fact that in conventional batch production, material shortage status and waiting time are the core factors affecting scheduling efficiency, so they are given higher weights. Material loading type matching, locked state, and material consumption time are used as auxiliary factors and given reasonable weights.

[0030] - Acquisition period: 100ms. This is based on the fact that the status changes of the four machine tools are relatively infrequent. The 100ms acquisition period can ensure real-time capture of machine tool status changes while avoiding excessive PLC computational load.

[0031] - Baseline waiting time: 300s. This is based on the fact that the machine tool processing cycle is about 200s. Setting 300s as the baseline waiting time can prevent the machine tool from stopping due to excessive waiting time for loading, and at the same time prevent frequent switching of loading due to excessively short waiting time.

[0032] - Weighing sensor calibration cycle: once a week. This is because weighing sensors in industrial settings are susceptible to vibration and temperature effects. Weekly calibration ensures detection accuracy and avoids errors in calculating material shortage levels due to sensor drift.

[0033] - Other parameters: The height of the detection points for mild, moderate and severe material shortage levels are set to 70%, 40% and 10% of the total height of the silo, respectively, corresponding to Ls=1, Ls=2 and Ls=3, to adapt to the uniformity of material consumption in batch production; the material consumption time database update cycle is consistent with the collection cycle (100ms) to ensure data real-time performance.

[0034] 4. Operational testing and result analysis A continuous 72-hour operation test was conducted on a production line consisting of four machine tools. The test included priority calculation efficiency, system response speed, material feeding scheduling efficiency, and material level detection accuracy. The test results and analysis are as follows: - Priority calculation time: <10ms, far below the real-time processing limit of PLC (50ms), indicating that the calculation model of the present invention is simple and efficient, does not increase the PLC's computational load, and ensures stable system operation.

[0035] - System response time: <40ms, meeting the real-time requirements of industrial production (usually ≤50ms), and can respond to changes in machine tool status in real time (such as material shortage, lockout), and adjust priorities and feeding tasks in a timely manner.

[0036] - Material loading scheduling efficiency: 18% higher than the traditional fixed sequence method. During the test, no machine tools stopped due to waiting for material loading, and the production line cycle time was balanced, which verified the rationality of the scheduling logic of this invention.

[0037] - Material level detection accuracy: ±2%, significantly improving accuracy and eliminating feeding errors caused by material level misjudgment, ensuring the accuracy of priority calculation.

[0038] - Material consumption time calculation accuracy: >98%. The material consumption time database can accurately record and update data, providing reliable support for material demand forecasting and priority calculation.

[0039] - Dynamic weight adjustment effect: scheduling accuracy >99.5%. During the test, the system automatically fine-tunes the weight coefficients based on historical scheduling data (e.g., if a machine tool waits for too long, the weight of W2 is temporarily increased) to further optimize the scheduling effect.

[0040] Test results show that the configuration of this embodiment can stably realize the core function of the present invention, complete the multi-machine tool loading and scheduling at low cost and high precision, and is suitable for small and medium-scale batch production scenarios. Example 2

[0041] This embodiment is applicable to a medium-to-large-scale production line consisting of 10 machine tools, covering three different material feeding types (material A, material B, and material C). The production scenarios include three dynamic scenarios: peak production period, changeover period, and material shortage period. The core objective is to verify the dynamic adaptability and flexibility of the present invention and meet the scheduling requirements of complex production scenarios.

[0042] 1. Scenario Description This production line is used for multi-variety, medium-batch production. The 10 machine tools are divided into 3 groups, each processing different types of parts, corresponding to 3 material feeding types (Material A is compatible with machine tools 1-3, Material B with machine tools 4-7, and Material C with machine tools 8-10). Three dynamic scenarios exist during production: ① Peak production period: All machine tools operate at full capacity, prioritizing material supply to machines with material shortages to avoid downtime; ② Changeover period: Some machine tools switch processing types, prioritizing machine tools with matching material feeding types to shorten changeover time; ③ Material shortage period: Inventory of one or more materials is insufficient, prioritizing machine tools with rapid material consumption and high urgency to reduce material waste.

[0043] 2. Configuration Adjustment (Based on the basic configuration of Example 1) Based on Example 1, the hardware configuration adds 2 weighing sensors and 4 proximity switches to meet the material level detection requirements of 10 machine tools; the PLC controller is replaced with a Siemens S7-1200 series CPU 1215C, increasing the number of input / output points to meet the requirements of multi-machine tool status acquisition and actuator control; the HMI is replaced with a KTP1200, adding a display interface and operating permissions to facilitate multi-machine tool status monitoring.

[0044] The software configuration adds a feeding type recognition module, which is used to identify the feeding type required by each machine tool, match it with the material readiness status of the feeding system, and output the feeding type matching coefficient; the dynamic weight adjustment module is optimized to support preset weight adjustment rules according to different production scenarios, so as to realize automatic weight adjustment when switching scenarios.

[0045] Parameter settings are dynamically adjusted according to different production scenarios. The specific adjustment scheme is as follows: - Peak production period: The core objective is to avoid machine tool downtime due to material shortages. Adjust the weighting coefficients to W1=0.5, W2=0.2, W3=0.15, W4=0.1, W5=0.15; increase the weight of material shortage level (W1) to ensure that machines with severe material shortages are given priority for material feeding, reducing downtime; reduce the weight of waiting time (W2) to avoid affecting the overall peak production efficiency due to long waiting times of individual machines.

[0046] - Changeover Period: The core objective is to shorten changeover time. Adjust the weighting coefficients to W1=0.3, W2=0.2, W3=0.3, W4=0.1, W5=0.2; increase the weighting of material feeding type matching (W3) to ensure that machine tools that have completed changeover and have the required materials ready are fed first, thus speeding up the changeover process; increase the weighting of material consumption time (W5) to prioritize machine tools with fast material consumption after changeover and avoid secondary material shortages.

[0047] - During periods of material shortage: The core objective is to rationally allocate scarce materials, adjusting the weighting coefficients to W1=0.3, W2=0.2, W3=0.15, W4=0.1, and W5=0.25; significantly increasing the weighting of material consumption time (W5) to ensure that machine tools with fast material consumption and high urgency receive priority material supply, reducing material waste and ensuring the production of core products.

[0048] Other parameter adjustments: The acquisition cycle was adjusted to 200ms to balance the status acquisition needs of 10 machine tools and the PLC's computing load; the baseline waiting time was adjusted to 400s to adapt to the processing cycle differences of various products; the weighing sensor calibration cycle was adjusted to once every 3 days to ensure the consistency of multi-sensor detection.

[0049] 3. Operational Results and Analysis A continuous 120-hour dynamic scenario test was conducted on this production line, covering three scenarios: peak production period, changeover period, and material shortage period. Each scenario was run for 40 hours, and the results are as follows: - System response time: <50ms. Even when 10 machine tools are running simultaneously and their status changes frequently, the system can still respond quickly, adjust priorities and loading tasks in a timely manner, and prevent production interruptions caused by response delays.

[0050] - Priority calculation accuracy: >99%. In three scenarios, the dynamic adjustment of weight coefficients can accurately adapt to the scenario requirements. The priority sorting is reasonable, with no mismatch or omission.

[0051] - Improved production efficiency: 15-20% higher than traditional host computer scheduling schemes, verifying the scheduling advantages of this invention in complex scenarios.

[0052] - Reduced changeover time: During the changeover period, the average machine tool changeover time was reduced by 25%, mainly due to the increased weighting of material type matching, which ensures that the machine tool can quickly obtain the required materials after the changeover and reduces changeover waiting time.

[0053] The results show that the present invention can flexibly adjust the configuration parameters and weight coefficients according to the dynamic changes of complex production scenarios, adapt to the scheduling needs of multi-variety, medium and large-scale production lines, and take into account both production efficiency and material utilization.

[0054] Example 3: Extended Application Example (Cross-Domain Adaptation Application) The core technical solution of this invention has strong versatility and can break through the application scenario of multi-machine tool collaborative feeding, and be extended to multiple industrial automation fields such as robot automatic loading and unloading, intelligent warehousing and logistics, and flexible manufacturing. The implementation methods of each extended scenario are described in detail below.

[0055] 1. Extended Application 1: Intelligent Warehousing and Logistics System Application scenario: In a smart warehouse, multiple AGVs (Automated Guided Vehicles) are responsible for the handling and replenishment of materials, involving 8 AGVs and 20 storage locations. The AGV tasks need to be dynamically scheduled according to the urgency of the material demand at the storage locations, the distance between AGVs, and the load status of the AGVs to improve the efficiency of warehousing and logistics.

[0056] Implementation configuration: The PLC controller is selected from the Mitsubishi FX5U series, which is adapted to the rapid response requirements of warehousing and logistics scenarios; the sensor component adds an RFID reader (model: HF-RFID) to identify the AGV location and material information; the actuator consists of 8 AGVs (model: MiR 100), which communicate with the PLC via Wi-Fi to receive handling task instructions; a new AGV path planning module is added, which works in conjunction with the priority calculation module to optimize the AGV handling path.

[0057] Core adjustments: Replace "machine tool" with "warehouse location", and "material loading task" with "AGV handling task"; adapt the parameters in the priority calculation model to the warehousing scenario, replace the material shortage level with the urgency of the material demand at the location (level 0-5), replace the waiting time with the waiting time for the location to be replenished, replace the material loading type matching coefficient with the AGV and material type matching coefficient, and add an AGV distance coefficient (W6). The distance coefficient = the distance between the current position of the AGV and the location / the maximum distance of the warehouse. The closer the distance, the smaller the coefficient and the higher the priority.

[0058] Operational results: AGV handling efficiency increased by 25%, waiting time for resupply at storage locations was reduced by 35%, and warehouse material turnover efficiency increased by 20%, achieving precise scheduling of intelligent warehousing and logistics.

[0059] 2. Extended Application Two: Flexible Manufacturing Production Line Application scenario: Flexible manufacturing production line, which includes 12 machine tools that can switch between processing types, supports multi-variety, small-batch production, dynamically adjusts production tasks, and needs to achieve rapid adaptation of material feeding priorities to avoid production bottlenecks.

[0060] Implementation configuration: The PLC controller uses Siemens S7-1200 series CPU 1217C, which supports multi-task parallel processing; the software configuration adds a production task identification module, which receives production plan instructions in real time and identifies the processing tasks and material requirements of each machine tool; the material consumption time database is optimized to support the recording and prediction of consumption time for multiple types of materials, and a material shortage early warning function is added.

[0061] Core adjustments: The dynamic weight adjustment module is linked with the production plan, automatically adjusting the priority weight of each machine tool according to the priority of production tasks (such as urgent orders and regular orders); the priority calculation model adds a production task priority coefficient (W6), the higher the task priority, the larger the coefficient, ensuring that the machine tools corresponding to urgent orders are given priority in material feeding.

[0062] Operational results: Production line changeover efficiency increased by 30%, emergency order delivery cycle was shortened by 28%, and the scheduling flexibility of multi-variety production was significantly improved, fully adapting to the dynamic needs of flexible manufacturing.

[0063] The core advantage of this invention lies in the fact that it eliminates the need for additional host computer equipment. Relying solely on a PLC, it can achieve multi-modal status acquisition, dynamic priority calculation, and task allocation. Furthermore, through parameter adjustment and module optimization, it can quickly adapt to the scheduling needs of different fields, exhibiting strong versatility and scalability. Whether it's robotic loading and unloading, intelligent warehousing and logistics, or flexible manufacturing, efficient and precise scheduling can be achieved through simple configuration adjustments, reducing system costs and improving production efficiency.

[0064] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. The various components mentioned in this invention are common technologies in the existing field. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A PLC-based multimodal calculation method for material feeding priority, characterized in that... Includes the following steps: Step 1: Multimodal status information acquisition: The PLC periodically acquires the status information of each machine tool. The acquisition period is adjusted according to actual needs, with a typical value of 100ms~500ms. Step 2: Dynamic optimization priority calculation model: A multi-factor dynamic fusion model is adopted to calculate the loading priority of each machine tool through formulas; Step 3: Multimodal data fusion and dynamic optimization: The system integrates the status information detection results of each machine tool, uses a weighted average algorithm to improve the accuracy of material level detection, and predicts material demand in advance based on historical material consumption time data, dynamically adjusting the data in the priority calculation model; Step 4: Priority Sorting and Update: The PLC sorts all machine tools according to the calculation results in Step 2, using the bubble sort algorithm for efficient sorting, and updates the sorting results in real time; the system dynamically adjusts and optimizes priority calculation based on historical data and production scenarios. Step 5: Loading task allocation: Loading tasks are allocated in descending order of priority. When multiple machine tools have the same priority, the first-come, first-served principle is adopted. When a machine tool malfunctions or communication is interrupted, the system automatically removes it from the priority calculation and includes it back in the calculation after the fault is restored.

2. The PLC-based multimodal calculation method for material feeding priority according to claim 1, characterized in that... The status information in step one includes multimodal material level detection information, material shortage time information, required material feeding type and ready status information, machine tool feeding lock status information, and material consumption time information; Specifically, the multimodal material level detection information includes: Weighing sensor detection: The weight of the silo is detected in real time by the weighing sensor, and the material level percentage Wt is automatically calculated in combination with the preset silo capacity; Material level sensor detection: Multi-level detection is achieved through proximity switches or photoelectric switches. Detection points are set at three positions: light, medium, and heavy, corresponding to different material shortage levels Ls. When the light position is not triggered, Ls=0; when the light position is triggered, Ls=1; when the medium position is triggered, Ls=2; and when the heavy position is triggered, Ls=3. The system integrates the results of the two detection methods mentioned above and calculates the overall material shortage level using the following formula, where the overall material shortage level ranges from 0 to 5, with level 5 representing the most severe material shortage: Material shortage level = round(W1×(1 - Wt / 100)×5+W2×Ls×5 / 3) In the formula, W1 is the weighting coefficient of the weighing sensor, with a default value of 0.6; W2 is the weighting coefficient of the level sensor, with a default value of 0.4; Wt is the weight percentage of the silo, with a value range of 0-100%; Ls is the detection level of the level sensor, with a value range of 0-3; round() is the rounding function. The material shortage time information refers to the waiting time for each machine tool to be loaded, which the system records and sorts in real time; the material consumption time information refers to the time when each machine tool consumes each type of material, which the system automatically records and calculates, establishes a material consumption time database, and predicts material demand based on historical data; the required loading type and readiness status information refers to whether the loading system is ready with the corresponding type of material.

3. The PLC-based multimodal calculation method for material feeding priority according to claim 2, characterized in that... The formula for calculating the material loading priority in step two is as follows: Material feeding priority = W1 × Material shortage level + W2 × Waiting time coefficient + W3 × Material feeding type matching coefficient + W4 × Locked status coefficient + W5 × Material consumption time coefficient; In the formula, W1-W5 are configurable weight coefficients, and satisfy W1+W2+W3+W4+W5=1; waiting time coefficient = waiting time / baseline waiting time; The material feeding type matching coefficient is 1 when the feeding system has prepared the corresponding type of material, and 0 otherwise. The locking state coefficient is 1 when the machine tool is not in the locked state and 0 when the machine tool is in the locked state; Material consumption time coefficient = historical average consumption time / current remaining production time.

4. The PLC-based multimodal calculation method for material feeding priority according to claim 3, characterized in that... The baseline waiting time is determined based on the preset production cycle of the production line and is dynamically modified as production tasks are adjusted.

5. The PLC-based multimodal calculation method for material feeding priority according to claim 4, characterized in that... In step four, the system dynamically adjusts the weight coefficients. The dynamic adjustment of weight coefficients W1-W5 is based on historical material scheduling data and the real-time status of the current production scenario, and is automatically optimized through the PLC's built-in algorithm.

6. The PLC-based multimodal calculation method for material feeding priority according to claim 5, characterized in that... The data refers to the material consumption time coefficient.

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