Intelligent management method and system of a tin paste warehouse

Abstract

The application discloses a kind of intelligent management method and system of tin paste warehouse, it is related to warehouse management technical field, including, analysis MES system work order data, synchronous acquisition refrigerator temperature, material tank coordinates and weight data, construct dynamic mapping three-dimensional warehouse model, adopt quantum annealing algorithm to optimize mechanical hand movement path, calculate multi-objective parameter, output optimal path instruction to drive equipment;Record tin after distribution and use state, secondary weighing verification to return warehouse material tank, according to the comparison result of weight change rate and pre-set scrap weight threshold, update the material state of three-dimensional warehouse model;Compare mechanical hand actual path with mechanical hand optimization calculation, iteratively update path weight coefficient in three-dimensional warehouse model and synchronize three-dimensional warehouse state.The application constructs three-dimensional dynamic warehouse model based on refrigerator temperature gradient distribution and material tank position weight, optimizes refrigeration area space utilization rate by coordinate mapping, and eliminates mechanical hand positioning error by combining space-time compensation algorithm.

Description

Technical Field

[0001] This invention relates to the field of warehouse management technology, and in particular to an intelligent management method and system for solder paste warehouses. Background Technology

[0002] With the significant increase in demand for automation in solder paste warehouse management in the electronics manufacturing industry, traditional solutions mainly revolve around warehouse modeling, path planning, and material status monitoring. In terms of warehouse modeling, existing technologies utilize 3D scanning and IoT sensing to achieve digital mapping of static warehouse layouts; path optimization commonly employs genetic algorithms or A* algorithms, focusing on single-objective path planning; and material status monitoring relies on the linkage between RFID tags and weighing sensors to achieve early warning functions for basic inventory levels.

[0003] However, traditional solutions have significant limitations in terms of dynamic environmental adaptability, multi-objective optimization, and material lifecycle management. Specifically, traditional path planning algorithms struggle to simultaneously optimize multiple objective parameters such as robot movement distance, energy consumption, and time in dynamic warehousing environments, and their convergence speed is limited by the complexity of the high-dimensional solution space. Material status monitoring lacks a dynamic scrapping mechanism based on weight change rate, and the secondary verification of returned storage tanks and the update of the warehousing model's status fail to form a closed loop, resulting in a high scrapping error rate. Furthermore, FIFO-based replenishment strategies cannot predict conflicts arising from multiple tank reheating, easily leading to work order execution delays. Additionally, the setting of static torque thresholds is difficult to adapt to dynamic load changes during robot operation, resulting in accumulated path execution deviations. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an intelligent management method for solder paste warehouses to solve the problem of low efficiency in dynamic multi-objective path optimization in solder paste storage management.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an intelligent management method for solder paste storage, comprising: parsing work order data from the MES system, synchronously collecting data on freezer temperature, material container coordinates, and weight, constructing a dynamically mapped three-dimensional storage model, optimizing the movement path of a robotic arm using a quantum annealing algorithm, calculating multi-objective parameters, and outputting optimal path instructions to the drive device; obtaining batch information of incoming solder paste and weighing it, generating storage coordinates based on the remaining capacity of the cold storage area and FIFO rules in the three-dimensional storage model, and driving the robotic arm to store the paste according to the optimized path; and when the inventory in the reheating zone is lower than a preset lower inventory threshold, [further details to be added]. The system reverse-engineers the time window for work order demand, simulates the probability of conflict in multiple reheating tanks, generates a replenishment queue, extracts out-of-warehouse tanks according to the optimal path instructions, dynamically adjusts the revolution speed curve based on simulation parameters in the 3D warehouse model, and automatically corrects operating parameters when torque deviation exceeds limits. It records the tin usage status after delivery, performs secondary weighing verification on returned tanks, and updates the material status of the 3D warehouse model based on the comparison between the weight change rate and the preset scrap weight threshold. It iteratively updates the path weight coefficients in the 3D warehouse model and synchronizes the 3D warehouse status by comparing the actual path of the robotic arm with its optimized calculations.

[0008] As a preferred embodiment of the intelligent management method for solder paste storage described in this invention, the method integrates the temperature of the freezer, the coordinates of the material tank, and the weight data to output a data set of equipment status.

[0009] Based on the warehouse layout, a spatial framework including freezers, tanks and robotic arms is constructed using 3D modeling tools, and a 3D model is output.

[0010] The equipment status dataset is dynamically mapped to a 3D model, the status of the material tanks and the capacity of the freezer are labeled, and a 3D warehouse model is output.

[0011] In a preferred embodiment of the intelligent management method for solder paste storage according to the present invention, the specific steps for outputting the optimal path instruction to the driving device are as follows:

[0012] The quantum annealing algorithm is used to construct an objective function that minimizes the travel distance, energy consumption, and time.

[0013] The optimal path is generated through annealing iteration and decomposed into a discrete set of optimal path instructions.

[0014] Extract a single instruction that matches the current task from the optimal path instruction set, dynamically adjust the instruction parameters, and then transmit it to the robot arm drive device for execution.

[0015] As a preferred embodiment of the intelligent management method for solder paste storage described in this invention, the method of driving the robotic arm to store along an optimized path refers to controlling the robotic arm to execute movement instructions sequentially based on the optimal path instruction set generated by the quantum annealing algorithm, thereby transporting the solder paste container to the specified cold storage area coordinates in the three-dimensional storage model.

[0016] As a preferred embodiment of the intelligent management method for solder paste warehouse described in this invention, the preset lower inventory threshold refers to the minimum inventory threshold set based on the cold storage capacity in the three-dimensional warehouse model, the reverse calculation result of the work order demand time window, and the real-time status of the reheating zone.

[0017] The specific steps for automatically correcting operating parameters when torque deviation exceeds the limit are as follows.

[0018] Collect real-time torque, speed, and position data of the robotic arm, and output operational status monitoring data;

[0019] Based on the simulation parameters in the 3D warehouse model and the historical operating status monitoring data, the deviation range of the current torque is dynamically calculated and the dynamic deviation range value is output.

[0020] When the real-time torque exceeds the dynamic deviation range, the correction logic is automatically triggered, and the robot's revolution speed is adjusted in real time based on the simulation parameters, and the corrected operating parameters are output.

[0021] As a preferred embodiment of the intelligent management method for solder paste storage in this invention, the preset scrap weight threshold refers to the minimum remaining weight critical value for determining whether a solder paste can be deemed to be ineffective, based on the secondary weighing results of returned solder paste cans and by calculating the weight change rate.

[0022] The specific steps for updating the material status of the 3D warehouse model are as follows:

[0023] Record the status of tin usage after delivery and generate a usage log;

[0024] Perform a second weighing verification on the returned material tank and output the returned weight data;

[0025] Calculate the weight change rate and compare it with the preset scrap weight threshold. If the weight change rate is greater than the preset scrap weight threshold, output the material scrap status indicator; otherwise, output the material usable status indicator.

[0026] Update the material status of the 3D warehouse model based on the material status identifier.

[0027] As a preferred embodiment of the intelligent management method for solder paste storage according to the present invention, the specific steps for iteratively updating the path weight coefficients in the three-dimensional storage model and synchronizing the three-dimensional storage status are as follows:

[0028] Real-time acquisition of the robot arm's actual movement trajectory, time, and energy consumption data, and output of actual path dataset;

[0029] By combining the actual path dataset with the optimal path instruction set, distance deviation, time error and energy consumption difference are calculated through spatiotemporal alignment, and the path deviation dataset is output.

[0030] Based on the path deviation dataset, the path weight coefficients in the 3D warehouse model are dynamically adjusted, the updated path weight parameters are output, mapped to the 3D warehouse model, the dynamic path planning logic in the 3D warehouse model is recalculated, and the synchronously completed 3D warehouse status is output.

[0031] Secondly, this invention provides an intelligent management system for a solder paste warehouse, including a data parsing module, a path optimization module, a warehouse management module, a status update module, and a model iteration module. The data parsing module is used to parse work order data from the MES system, synchronously collect refrigeration cabinet temperature, material tank coordinates, and weight data, construct a dynamically mapped three-dimensional warehouse model, optimize the robot arm's movement path using a quantum annealing algorithm, calculate multi-objective parameters, and output the optimal path command to the drive device. The path optimization module is used to obtain batch information of incoming solder paste and weigh it, generate storage coordinates based on the remaining capacity of the refrigeration area and FIFO rules in the three-dimensional warehouse model, and drive the robot arm to store the paste according to the optimized path. The management module is used to reverse-calculate the work order demand time window when the inventory in the recovery zone is lower than the preset inventory lower limit threshold, generate a replenishment queue after simulating the probability of multiple tank recovery conflicts, extract the outbound tanks according to the optimal path instructions, dynamically adjust the revolution speed curve according to the simulation parameters in the 3D warehouse model, and automatically correct when the torque deviation exceeds the limit; the status update module is used to record the tin usage status after delivery, perform secondary weighing verification on the returned tanks, and update the material status of the 3D warehouse model according to the comparison results of the weight change rate and the preset scrap weight threshold; the model iteration module is used to compare the actual path of the robot with the robot's optimized calculation, iteratively update the path weight coefficients in the 3D warehouse model, and synchronize the 3D warehouse status.

[0032] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the intelligent management method for solder paste storage as described in the first aspect of the present invention.

[0033] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent management method for solder paste library as described in the first aspect of the present invention.

[0034] The beneficial effects of this invention are as follows: This invention reconstructs the path planning of a robotic arm using a quantum annealing algorithm, integrates movement distance, energy consumption, and time parameters to establish a multi-objective optimization function, and utilizes the characteristics of quantum computing to overcome the search bottleneck of traditional algorithms in high-dimensional solution spaces, achieving real-time path generation and correction in dynamic warehousing environments. A three-dimensional dynamic warehousing model is constructed based on the temperature gradient distribution of the refrigerated display case and the position weights of the storage tanks. Coordinate mapping optimizes the space utilization of the refrigerated area, and a spatiotemporal compensation algorithm is combined to eliminate robotic arm positioning errors. For material management, a work order time-series reverse analysis mechanism is designed to predict replenishment conflicts and generate priority queues, while synchronously integrating dynamic torque compensation logic to cope with sudden load fluctuations. In quality control, dynamic scrapping rules are established by comparing the secondary weighing of returned storage tanks with historical data trends. Simultaneously, iterative feedback of path execution data is used to optimize the adaptability of the warehousing model, ultimately forming a closed-loop control system of environmental perception, intelligent decision-making, and autonomous correction. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of the intelligent management method for the solder paste warehouse in Example 1.

[0037] Figure 2 This is a flowchart of the construction of the three-dimensional warehouse model in Example 1.

[0038] Figure 3 This is a flowchart of the quantum annealing algorithm path optimization in Example 1.

[0039] Figure 4 This is a flowchart of the robot path optimization and correction in Example 1. Detailed Implementation

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0043] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides an intelligent management method for a solder paste warehouse, including the following steps:

[0044] S1. Parse the work order data of the MES system, synchronously collect the temperature of the freezer, the coordinates and weight of the tank, construct a dynamically mapped three-dimensional warehouse model, use the quantum annealing algorithm to optimize the movement path of the robot arm, calculate multi-objective parameters, and output the optimal path instruction to the drive equipment.

[0045] Specifically, it includes the following steps:

[0046] By accessing work order data through the MES system API, the work order number, material type, batch number, and priority are extracted, and a standardized work order information table is output.

[0047] Connect the freezer temperature sensor, the RFID tag of the container and the weighing equipment to collect temperature, coordinate and weight data in real time and output the equipment status dataset.

[0048] Integrate the temperature, tank coordinates, and weight data of the freezer to output a data set of equipment status.

[0049] Specifically, temperature, coordinate, and weight data are aligned by timestamps to generate a unified time-series dataset. A unique identifier (such as ID) is generated for each tank and associated with the temperature, coordinate, and weight data.

[0050] Based on the warehouse layout, a spatial framework including freezers, tanks, and robotic arms is constructed using 3D modeling tools, and a 3D model is output.

[0051] It should be noted that warehouse layout refers to the planning and arrangement of the physical location and spatial relationship of equipment, materials and functional areas within a warehouse.

[0052] Specifically, import the warehouse layout file (such as CAD drawings or JSON format) into a 3D modeling tool (such as Blender or Unity 3D). Define the physical dimensions (length, width, height) and coordinate system of the warehouse in the tool. Initialize the 3D model templates for the freezers, storage tanks, and robotic arms.

[0053] Based on the layout file, place the freezer model in three-dimensional space and define its internal storage units and capacity.

[0054] Place the tank model, generate a unique identifier (such as ID) for each tank, and associate it with the initial coordinates and weight.

[0055] Place the robotic arm model and define its range of motion and operating logic (such as gripping and placing material containers).

[0056] The output three-dimensional spatial framework model is in a standard format (such as FBX or GLTF).

[0057] The equipment status dataset is dynamically mapped to a 3D model, the status of the material tanks and the capacity of the freezer are labeled, and a 3D warehouse model is output.

[0058] Specifically, read the equipment status dataset (including timestamp, tank ID, temperature, coordinates, and weight).

[0059] The coordinates and weight of the material tank in the 3D model are updated frame by frame according to the timestamp, and the transition is smoothed using interpolation, as shown below:

[0060]

[0061] In the formula, P a and P b These are the coordinates of the previous frame and the current frame, respectively, where t is the current time. a t represents the timestamp of the previous frame. b Indicates the timestamp of the current frame;

[0062] Update the freezer temperature data and dynamically adjust the color coding of the freezer model (e.g., green for normal, red for abnormal).

[0063] Furthermore, the rate of change of the material tank's weight is calculated to determine the tank's condition, expressed as:

[0064]

[0065] In the formula, W a For the previous weight, W b Current weight;

[0066] The normal and abnormal states of the material tank are marked according to the weight change rate: if the weight change rate is within the range of [-5%, +5%], it is judged as a normal state; if the weight change rate exceeds +5% or is lower than -5%, it is judged as an abnormal state.

[0067] The positive threshold is +5% (the maximum allowable weight increase percentage). The negative threshold is -5% (the maximum allowable weight decrease percentage). The positive and negative thresholds are buffer zones determined based on the solder paste volatility characteristics, the accuracy of the weighing equipment (e.g., ±0.5%), and the tolerance of the production process to prevent misjudgment.

[0068] The normal and overcapacity of the freezer are marked according to the freezer temperature: if the freezer temperature is within the closed range of [2°C, 8°C], it is judged as normal capacity; if the temperature is higher than 8°C or lower than 2°C, it is judged as overcapacity.

[0069] The upper and lower thresholds are defined as follows: 8°C (the upper limit of solder paste storage temperature; exceeding this temperature will reduce flux activity). 2°C (the lower limit of solder paste storage temperature; below this temperature may induce crystallization). These upper and lower thresholds are determined with reference to the solder paste storage temperature requirements in the J-STD-004 standard and verified through accelerated aging experiments.

[0070] Export the dynamically mapped 3D model into an interactive format (such as WebGL or VR format) as a 3D warehouse model to support real-time monitoring and operation.

[0071] The quantum annealing algorithm is used to construct an objective function that minimizes the travel distance, energy consumption, and time.

[0072] Specifically, the path optimization problem of the robotic arm is modeled as a multi-objective optimization problem. By adjusting the path planning variable x, the goal is to minimize the weighted sum of path distance, energy consumption, and time. The objective function Z(x) is expressed as:

[0073] Z(x)=w1·D(x)+w2·E(x)+w3·Q(x);

[0074] In the formula, D(x) is the total path distance, E(x) is the energy consumption, Q(x) is the total time, and w1, w2 and w3 are weighting coefficients that are dynamically adjusted according to actual needs;

[0075] It should be noted that the weight coefficients w1, w2 and w3 are used to balance the priorities of path distance, energy consumption and time, and their values ​​range from [0,1] and satisfy the normalization condition: W1+W2+W3=1;

[0076] The path planning variable x is defined as follows: Control point sequence: The robot's motion path consists of a series of spatial coordinate points (e.g., (x, y, z)). Joint angle sequence: The robot's motion path consists of a sequence of angle changes for each joint.

[0077] The optimal path is generated through annealing iteration and decomposed into a discrete set of movement instructions.

[0078] The robot path optimization problem is mapped to the Hamiltonian of the quantum annealing algorithm. The initial temperature T0 and annealing rate α are set to initialize the quantum state.

[0079] The annealing rate α, which has a value range of 0 < α < 1, is used to control the rate at which the temperature decreases.

[0080] In each iteration, the temperature T is gradually decreased, as shown in:

[0081] T k+1 =α·T k ;

[0082] In the formula, T k+1 T represents the temperature value for the next iteration. k This indicates the temperature value for the current cycle;

[0083] The quantum state is updated based on the quantum tunneling effect, candidate paths are generated, and the objective function value f(x) is calculated.

[0084] The Metropolis criterion is used to determine whether to accept a new path, expressed as follows:

[0085]

[0086] Δf=f(x new )-f(x current );

[0087] In the formula, P(Δf) represents the probability of accepting the new path, used to determine whether to accept the current candidate path, and Δf represents the change in the objective function value of the path, i.e., the objective function value f(x) of the new path. new ) and the current path objective function value f(x) current The difference between )

[0088] It should be noted that the Metropolis criterion is an acceptance probability criterion used in the Simulated Annealing algorithm to determine whether to accept a new solution that is worse than the current one. Its core idea is to accept a worse solution with a certain probability to avoid the algorithm getting trapped in local optima, thereby increasing the likelihood of finding the global optimum.

[0089] In contrast, by using the Metropolis criterion, the optimization algorithm can significantly improve the success rate of finding the global optimum while ensuring search efficiency, providing a powerful solution for complex problems such as path planning and combinatorial optimization.

[0090] Record the current optimal path and its objective function value. When the temperature drops to the termination temperature T... end When the optimal path is reached, output the best path.

[0091] The optimal path is decomposed into discrete movement commands, including movement direction, speed and target position. A dynamic programming algorithm is used to optimize the command set to ensure command consistency.

[0092] Extract a single instruction that matches the current task from the optimal path instruction set, dynamically adjust the instruction parameters, and then transmit it to the robot arm drive device for execution.

[0093] Extract the single optimal path instruction required for the current task, transmit it to the robot arm drive device, the robot arm executes the instruction, and the execution status is fed back to the system in real time;

[0094] By comparing the actual path taken by the robotic arm with the optimized path, the deviation value is calculated and expressed as follows:

[0095] ∈=||D1-D2||

[0096] In the formula, ∈ represents the path deviation value, D1 represents the total distance of the actual movement path of the robot, and D2 represents the total distance of the optimal path calculated by the quantum annealing algorithm;

[0097] If the deviation value exceeds the path deviation threshold, iterative update of path optimization based on quantum annealing algorithm is triggered;

[0098] It should be noted that the path deviation threshold is 5% of the total path distance, and it is calculated as follows:

[0099] Path deviation threshold = total distance of optimized path × 5%;

[0100] When ||Actual total path distance - Optimized total path distance|| > the path deviation threshold, it is determined that the path deviation exceeds the limit.

[0101] The path deviation threshold is defined based on the following: the repeatability accuracy of the robot (typically ±0.1mm) and the size of the warehouse layout (assuming a 10m level); the 5% threshold can cover the robot's transmission error (approximately 0.5%), sensor error (approximately 0.2%), and dynamic disturbance (approximately 4.3%); through Monte Carlo simulation verification, the 5% threshold can control the replanning trigger frequency within a reasonable range of 3-5 times / day.

[0102] The path weight coefficients in the 3D warehouse model are updated based on the actual path data. A single optimal path instruction is extracted from the optimal path instruction set and transmitted to the robot drive device.

[0103] S2. Obtain batch information and weigh the solder paste entering the warehouse. Generate storage coordinates based on the remaining capacity of the cold storage area and FIFO rules in the three-dimensional warehouse model, and drive the robot arm to store it according to the optimized path.

[0104] Specifically, it includes the following steps:

[0105] By accessing the batch information of solder paste entering the warehouse through the MES system API, the batch number, material type and priority are extracted, and the weight of the material can is obtained by combining the weighing equipment, and a standardized warehouse data table is output.

[0106] Based on a 3D warehouse model, the system queries the remaining capacity of the cold storage area in real time, verifies whether the currently stored material tanks meet the storage conditions, and outputs the capacity status of the cold storage area.

[0107] Based on the FIFO rule (First In First Out) and the remaining capacity of the cold storage area, combined with the priority of the storage tanks, the optimal storage coordinates are calculated, and the storage coordinate instructions are output.

[0108] Based on the optimal path instruction set generated by the quantum annealing algorithm, the robot arm is controlled to execute the movement instructions in sequence, accurately transport the solder paste container to the designated cold storage coordinates, and output the storage completion status.

[0109] The storage coordinates, weight, and status of the incoming material tanks are dynamically updated to the 3D storage model, the material tanks are marked as "stored", and the updated 3D storage model is output.

[0110] S3. When the inventory in the recovery zone is lower than the preset lower limit of inventory, the work order demand time window is calculated in reverse. After simulating the probability of conflict between multiple tanks in the recovery zone, a replenishment queue is generated. The material tanks are extracted according to the optimal path instruction. The revolution speed curve is dynamically adjusted according to the simulation parameters in the three-dimensional storage model. The operating parameters are automatically corrected when the torque deviation exceeds the limit.

[0111] Specifically, it includes the following steps:

[0112] The preset lower limit of inventory refers to the minimum critical value of inventory set based on the cold storage capacity in the three-dimensional warehouse model, the reverse calculation results of the work order demand time window, and the real-time status of the warming zone.

[0113] Specifically, the total capacity of the cold storage area (such as the number or weight of storable tanks) is obtained from the three-dimensional storage model;

[0114] The remaining capacity of the cold storage area is calculated in real time by subtracting the current inventory from the total capacity.

[0115] Extract work order data from the MES system, including work order requirement time, required quantity (such as solder paste usage), and priority;

[0116] Based on the work order demand time, the consumption time window of the material tank in the reheating zone is calculated by subtracting the reheating time from the work order demand time.

[0117] The total demand within the estimated time window is calculated, and the inventory in the recovery zone (such as the number or weight of material tanks) is obtained in real time through sensors or system interfaces.

[0118] The consumption rate of inventory in the recovery zone is calculated based on historical data, using the following formula:

[0119] Consumption rate = historical consumption amount / historical time interval;

[0120] Furthermore, the formula for calculating the preset inventory lower limit threshold is as follows:

[0121] Preset inventory lower limit threshold = total demand × safety factor;

[0122] When work order demand, cold storage capacity, or warming zone status changes, the preset inventory lower limit threshold is dynamically updated.

[0123] The robot arm collects real-time torque, speed, and position data through sensors, and outputs operational status monitoring data.

[0124] Specifically, torque data, speed data, and position data are collected in real time through sensors (such as torque sensors, encoders, and position sensors) installed on the robotic arm.

[0125] Furthermore, the collected raw data is filtered to remove noise and outliers, and the data is converted into standard units (such as torque in N·m, speed in m / s, and position in mm).

[0126] The processed data is packaged into runtime status monitoring data and stored in the database.

[0127] Based on the simulation parameters in the 3D warehouse model and the historical operating status monitoring data, the deviation range of the current torque is dynamically calculated and the dynamic deviation range value is output.

[0128] Specifically, torque, speed and position data of the robot are collected in real time through torque sensors, encoders and position sensors. After filtering and unit standardization, the operating status monitoring data in JSON format is output and stored in the database.

[0129] The robot's load, path, and performance parameters are extracted from the 3D warehouse model. Combined with historical operating data (torque, speed, and position), a reasonable deviation range is dynamically calculated based on the historical average torque and standard deviation. The dynamic deviation range value is then output and synchronized to the database.

[0130] When the real-time torque exceeds the dynamic deviation range, the correction logic is automatically triggered, and the robot's revolution speed is adjusted in real time based on the simulation parameters, and the corrected operating parameters are output.

[0131] Specifically, the current torque value is obtained from the operating status monitoring data and compared in real time with the dynamic deviation range value ([μ-2σ,μ+2σ]).

[0132] Extract simulation parameters for the current task from the 3D warehouse model;

[0133] Adjust the revolution speed V according to the direction of torque deviation (too high or too low). new The formula is

[0134]

[0135] In the formula, ΔP is the torque deviation, which is the difference between the current torque and the target torque.max P represents the maximum permissible value of torque. min V represents the minimum permissible value of torque. b Indicates the current orbital speed;

[0136] If the corrected orbital speed V new If the speed exceeds the robot's maximum permissible speed, it will be forcibly set to the maximum speed.

[0137] If the speed is too low, which may cause the task to time out, then path replanning will be triggered (such as shortening the travel distance).

[0138] The generated revised operating parameters include the new orbital speed, the optimized path (if path replanning is triggered), and the expected torque value (the theoretical torque calculated based on the revised speed).

[0139] The parameters are encapsulated into instructions and sent to the robot drive device in real time, and the robot status in the 3D warehouse model is updated synchronously.

[0140] S4. Record the status of tin usage after delivery, perform secondary weighing verification on the returned material container, and update the material status of the three-dimensional warehouse model based on the comparison results of the weight change rate and the preset scrap weight threshold.

[0141] Specifically, it includes the following steps:

[0142] The preset scrap weight threshold refers to the minimum remaining weight threshold for determining whether a material tank is in failure, which is based on the secondary weighing results of the returned material tank and calculated by the weight change rate.

[0143] Specifically, collect secondary weighing data of historical returned material tanks, including initial weight and returned weight, and perform data cleaning and classification.

[0144] The rate of change of weight is calculated and expressed as:

[0145]

[0146] Based on production needs and safety margins, a preset scrap weight threshold is set, expressed as:

[0147] Preset scrap weight threshold = initial weight × (failure threshold percentage + safety margin percentage);

[0148] Verify the rationality of the preset scrap weight threshold by simulating historical data and conducting actual tests to ensure its accuracy and reliability;

[0149] The preset scrap weight threshold is dynamically adjusted, and the preset scrap weight threshold is regularly evaluated and optimized according to changes in the production environment.

[0150] Compare the weight change rate with the preset scrap weight threshold, and update the can labels, inventory parameters and location coordinates in the 3D warehouse model.

[0151] Record the status of tin usage after delivery and generate a usage log;

[0152] Perform a second weighing verification on the returned material tank and output the returned weight data;

[0153] Specifically, the returned material tanks are identified by barcode or RFID technology to obtain their unique identification information, and the weight of the returned material tanks is measured by weighing equipment and the returned weight data is recorded.

[0154] The returned weight data is associated with the tank identification information, the weight change rate is calculated, and it is compared with the preset scrap weight threshold. When the weight change rate is greater than the preset scrap weight threshold, the material scrap status is output; otherwise, the material usable status is output.

[0155] Update the material status of the 3D warehouse model based on the material status identifier.

[0156] The material status in the 3D warehouse model includes can labels, inventory parameters, and location coordinates.

[0157] Specifically, based on the initial weight and the returned weight, the weight change rate is calculated, and the weight change rate is compared with the preset scrap weight threshold to determine whether the material tank has failed.

[0158] Based on the comparison results, a material status determination result (such as "normal" or "scrap") is generated.

[0159] S5. Compare the actual path of the robotic arm with the optimized calculation of the robotic arm, iteratively update the path weight coefficients in the 3D warehouse model and synchronize the 3D warehouse status.

[0160] Specifically, it includes the following steps:

[0161] Through the sensor and drive device interface, the actual movement trajectory, time and energy consumption data of the robot are collected in real time, and the actual path dataset is output.

[0162] Specifically, the system communicates with the robot drive device interface via the EtherCAT bus protocol to synchronously collect the following trajectory data and energy consumption data, and uses the IEEE 1588 Precision Time Protocol (PTP) to synchronize the sensor clock.

[0163] By combining the actual path dataset with the optimal path instruction set, distance deviation, time error and energy consumption difference are calculated through spatiotemporal alignment, and the path deviation dataset is output.

[0164] Specifically, the actual path timestamp t is established. i With the optimized path instruction timestamp t'j The correspondence is expressed as:

[0165] t′ j =t i +Δt latency ;

[0166] In the formula, Δt latency The delay of the robot arm drive (measured calibration value), t i This is the actual path timestamp, representing the point in time when the robot arm is in a certain state within the actual motion path;

[0167] For discrete coordinate points (x') in the optimized path command j ,y' j ,z′ j Perform cubic spline interpolation to generate a continuous trajectory function f. opt (t);

[0168] Distance deviation, expressed as:

[0169]

[0170] In the formula, f act (t i ) represents the actual trajectory function, f opt (t i | represents the optimized trajectory function, N represents the total number of sampling points, i represents the index variable of the sampling points, and ||||2 represents the Euclidean distance calculation, in millimeters;

[0171] It should be noted that the actual trajectory function f act (t i This can be constructed by interpolating sensor data collected by the robotic arm during actual operation. The specific steps are as follows: Data Acquisition: Using sensors such as encoders, vision systems, or laser trackers, the actual position coordinates (x, y, z) of the robotic arm at different time points are recorded in real time. Data Preprocessing: The acquired data is denoised and filtered to remove outliers and ensure data accuracy and smoothness. Interpolation Fitting: Using cubic spline interpolation or linear interpolation methods, the discrete sensor data is fitted to a continuous actual trajectory function f. act (t i The interpolation function can accurately describe the actual position of the robot at any given time point. Verification and optimization: By comparing the interpolation results with the measured data, the accuracy of the function is verified, and optimizations are made as needed.

[0172] Optimize trajectory function f opt (t iThe path can be generated using a path planning algorithm. The specific steps are as follows: Path planning: Using path planning methods such as A* algorithm, RRT (Rapid Extended Random Tree) algorithm, or Dijkstra's algorithm, the optimal path from the starting point to the target point is generated within the robot's workspace. Path smoothing: The planned path is smoothed (e.g., by Bézier curve fitting or spline interpolation) to ensure the path's continuity and executability. Time parameterization: The smoothed path is correlated with time to generate a time-parameterized optimized trajectory function f. opt (t i This describes the theoretical position of the robotic arm at any given time point. Verification and optimization: Verify the feasibility of the optimized trajectory through simulation or experiments, and adjust it according to actual needs.

[0173] Time deviation, expressed as:

[0174]

[0175] In the formula, δ t The mean time deviation represents the average difference between the actual execution time of the robot and the theoretical time of the optimized path, t. act,i Let t represent the actual execution time of the robotic arm, and let t represent the actual timestamp corresponding to the i-th sampling point. opt,i To optimize the theoretical time of the path, let |t| represent the theoretical timestamp corresponding to the i-th sampling point. act,i -t opt,i | represents the absolute deviation between the actual time and the theoretical time at the i-th sampling point;

[0176] Energy consumption deviation is expressed as:

[0177]

[0178] In the formula, δ e E represents the percentage energy consumption deviation, indicating the relative difference between the actual energy consumption of the robotic arm and the theoretical energy consumption of the optimized path. act E represents the actual energy consumption of the robotic arm, indicating the total energy consumed by the robotic arm during task execution. opt The theoretical energy consumption for optimizing the path, E, represents the energy that the robotic arm should consume to perform the task under ideal conditions. act -E opt It represents the absolute difference between actual energy consumption and theoretical energy consumption; This is a relative value of the energy consumption deviation, representing the proportion of the difference between actual and theoretical energy consumption to the theoretical energy consumption.

[0179] Based on the path deviation dataset, the path weight coefficients (such as distance weight, energy consumption weight, and priority weight) in the 3D warehouse model are dynamically adjusted, and the updated path weight parameters are output and mapped to the 3D warehouse model.

[0180] Specifically, the weight update rule updates the weight coefficients w based on the gradient of the objective function. new , is represented as:

[0181]

[0182]

[0183] In the formula, w old This represents the weight values ​​before the update, and η is the learning rate (η = 0.01). This represents the partial derivative (gradient) of the loss function L with respect to the weights w;

[0184] The updated weight parameters are injected into the quantum annealing algorithm to reconstruct the path optimization objective.

[0185] Recalculate the dynamic path planning logic in the 3D warehouse model and output the synchronized 3D warehouse status.

[0186] Specifically, a Hamiltonian is generated based on the new weights, the optimal path instruction set is re-solved, and the updated warehouse model parameters are broadcast to all subsystems via the OPC UA protocol;

[0187] Status parameters such as freezer capacity and tank coordinates are updated in real time with a delay of <50ms.

[0188] This embodiment also provides an intelligent management system for a solder paste warehouse, including: a data parsing module, a path optimization module, a warehouse management module, a status update module, and a model iteration module; the data parsing module is used to parse work order data from the MES system, synchronously collect refrigeration cabinet temperature, material tank coordinates, and weight data, construct a dynamically mapped three-dimensional warehouse model, optimize the robot arm's movement path using a quantum annealing algorithm, calculate multi-objective parameters, and output the optimal path command to the drive device; the path optimization module is used to obtain batch information of incoming solder paste and weigh it, generate storage coordinates based on the remaining capacity of the refrigeration area and FIFO rules in the three-dimensional warehouse model, and drive the robot arm to store the solder paste according to the optimized path; the warehouse management module... The system comprises four modules: a module for calculating the work order demand time window when the inventory in the recovery zone falls below a preset lower inventory threshold, generating a replenishment queue after simulating the probability of conflicts between multiple recovery tanks, extracting the outbound tanks according to the optimal path instructions, dynamically adjusting the revolution speed curve based on simulation parameters in the 3D warehouse model, and automatically correcting when the torque deviation exceeds the limit; a status update module for recording the tin usage status after delivery, performing secondary weighing verification on the returned tanks, and updating the material status of the 3D warehouse model based on the comparison between the weight change rate and the preset scrap weight threshold; and a model iteration module for comparing the actual path of the robotic arm with the optimized calculation of the robotic arm, iteratively updating the path weight coefficients in the 3D warehouse model, and synchronizing the 3D warehouse status.

[0189] This embodiment also provides a computer device applicable to the intelligent management method of solder paste storage, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the intelligent management method of solder paste storage as proposed in the above embodiment.

[0190] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0191] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the intelligent management method for solder paste storage as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0192] In summary, this invention reconstructs the robot's path planning using a quantum annealing algorithm, integrates travel distance, energy consumption, and time parameters to establish a multi-objective optimization function, and leverages the characteristics of quantum computing to overcome the search bottleneck of traditional algorithms in high-dimensional solution spaces, achieving real-time path generation and correction in dynamic warehousing environments. A three-dimensional dynamic warehousing model is constructed based on the temperature gradient distribution of the refrigerated display case and the position weights of the storage tanks. Coordinate mapping optimizes the space utilization of the refrigerated area, and a spatiotemporal compensation algorithm is used to eliminate robot positioning errors. For material management, a work order time-series reverse analysis mechanism is designed to predict replenishment conflicts and generate priority queues, while synchronously integrating dynamic torque compensation logic to cope with sudden load fluctuations. In quality control, dynamic scrapping rules are established by comparing the secondary weighing of returned storage tanks with historical data trends. Iterative feedback from path execution data is used to optimize the adaptability of the warehousing model, ultimately forming a closed-loop control system of environmental perception, intelligent decision-making, and autonomous correction.

[0193] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An intelligent management method for solder paste storage, characterized in that: include, The system analyzes MES system work order data, synchronously collects refrigeration cabinet temperature, tank coordinates and weight data, constructs a dynamically mapped 3D warehouse model, uses quantum annealing algorithm to optimize the robot arm movement path, calculates multi-objective parameters, and outputs the optimal path command to the drive equipment. Obtain batch information and weigh incoming solder paste; generate storage coordinates based on the remaining capacity of the cold storage area and FIFO rules in the 3D warehouse model; and drive the robotic arm to store the solder paste along the optimal path. When the inventory in the recovery zone is lower than the preset lower limit of inventory, the work order demand time window is calculated in reverse, and a replenishment queue is generated after simulating the probability of conflict between multiple tanks during recovery. The outbound tanks are extracted according to the optimal path instruction, and the revolution speed curve is dynamically adjusted according to the simulation parameters in the three-dimensional warehouse model. The operating parameters are automatically corrected when the torque deviation exceeds the limit. Record the status of tin usage after delivery, verify the secondary weighing of the returned material cans, and update the material status of the three-dimensional warehouse model based on the comparison results of the weight change rate and the preset scrap weight threshold. By comparing the actual path of the robotic arm with the optimal path, the distance weight coefficient, energy consumption weight coefficient, and priority weight coefficient in the 3D warehouse model are iteratively updated, and the 3D warehouse status is synchronized. The specific steps are as follows: Real-time acquisition of the robot arm's actual movement trajectory, time, and energy consumption data, and output of actual path dataset; By combining the actual path dataset with the optimal path instruction set, distance deviation, time error and energy consumption difference are calculated through spatiotemporal alignment, and the path deviation dataset is output. Based on the path deviation dataset, the distance weight coefficient, energy consumption weight coefficient, and priority weight coefficient in the 3D warehouse model are dynamically adjusted, the updated path weight coefficients are output, mapped to the 3D warehouse model, the dynamic path planning logic in the 3D warehouse model is recalculated, and the synchronously completed 3D warehouse status is output. The process of optimizing the robot's movement path using the quantum annealing algorithm involves calculating multi-objective parameters and outputting the optimal path command to the drive device. The specific steps are as follows: The quantum annealing algorithm is used to construct an objective function that minimizes the travel distance, energy consumption, and time. The optimal path is generated through annealing iteration and decomposed into a discrete set of optimal path instructions. Extract a single instruction that matches the current task from the optimal path instruction set, dynamically adjust the instruction parameters, and then transmit it to the robot drive device for execution; The specific steps for automatically correcting operating parameters when torque deviation exceeds the limit are as follows. Collect real-time torque, speed, and position data of the robotic arm, and output operational status monitoring data; Based on the simulation parameters in the 3D warehouse model and the historical operating status monitoring data, the deviation range of the current torque is dynamically calculated and the dynamic deviation range value is output. When the real-time torque exceeds the dynamic deviation range, the correction logic is automatically triggered, and the robot's revolution speed is adjusted in real time based on the simulation parameters, and the corrected operating parameters are output.

2. The intelligent management method for solder paste storage as described in claim 1, characterized in that: Integrate the temperature, tank coordinates, and weight data of the freezer to output a data set of equipment status. Based on the warehouse layout, a spatial framework including freezers, tanks and robotic arms is constructed using 3D modeling tools, and a 3D model is output. The equipment status dataset is dynamically mapped to a 3D model, the status of the material tanks and the capacity of the freezer are labeled, and a 3D warehouse model is output.

3. The intelligent management method for solder paste storage as described in claim 1, characterized in that: The "driving the robotic arm to store along the optimal path" refers to controlling the robotic arm to execute movement instructions sequentially based on the optimal path instruction set generated by the quantum annealing algorithm, transporting the solder paste container to the specified cold storage area coordinates in the three-dimensional storage model.

4. The intelligent management method for solder paste storage as described in claim 3, characterized in that: The preset lower inventory threshold refers to the minimum inventory level set based on the cold storage capacity in the three-dimensional warehousing model, the reverse calculation results of the work order demand time window, and the real-time status of the warming zone.

5. The intelligent management method for solder paste storage as described in claim 1, characterized in that: The preset scrap weight threshold refers to the minimum remaining weight threshold for determining whether a material tank is in failure, which is determined by calculating the weight change rate based on the secondary weighing results of the returned material tank. The specific steps for updating the material status of the 3D warehouse model are as follows: Record the status of tin usage after delivery and generate a usage log; Perform a second weighing verification on the returned material tank and output the returned weight data; Calculate the weight change rate and compare it with the preset scrap weight threshold. If the weight change rate is greater than the preset scrap weight threshold, output the material scrap status indicator; otherwise, output the material usable status indicator. Update the material status of the 3D warehouse model based on the material status identifier.

6. An intelligent management system for a solder paste storage facility, based on the intelligent management method for a solder paste storage facility according to any one of claims 1 to 5, characterized in that: It includes a data parsing module, a path optimization module, a warehouse management module, a status update module, and a model iteration module; The data parsing module is used to parse work order data from the MES system, synchronously collect data on the temperature of the freezer, the coordinates and weight of the storage tank, construct a dynamically mapped three-dimensional storage model, use the quantum annealing algorithm to optimize the movement path of the robotic arm, calculate multi-objective parameters, and output the optimal path command to the drive device. The path optimization module is used to obtain batch information of incoming solder paste and weigh it, generate storage coordinates based on the remaining capacity of the cold storage area and FIFO rules in the three-dimensional warehouse model, and drive the robot arm to store it according to the optimal path. The warehouse management module is used to reverse calculate the work order demand time window when the inventory in the reheating zone is lower than the preset inventory lower limit threshold, generate a replenishment queue after simulating the probability of multiple tank reheating conflicts, extract the out-of-warehouse tanks according to the optimal path instruction, dynamically adjust the revolution speed curve according to the simulation parameters in the three-dimensional warehouse model, and automatically correct when the torque deviation exceeds the limit. The status update module is used to record the status of tin usage after delivery, perform secondary weighing verification on the returned material cans, and update the material status of the three-dimensional warehouse model based on the comparison result of the weight change rate and the preset scrap weight threshold. The model iteration module is used to compare the actual path of the robotic arm with the optimal path instruction set, iteratively update the distance weight coefficient, energy consumption weight coefficient and priority weight coefficient in the three-dimensional warehouse model, and synchronize the three-dimensional warehouse status.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent management method for solder paste library according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent management method for the solder paste library as described in any one of claims 1 to 5.

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