An automated production line for air springs and its control system

Abstract

This invention provides an automated production line and control system for air springs, relating to the field of air spring manufacturing technology. The system includes: a raw material module for automatically allocating raw materials using a dynamic programming algorithm and conveying them to a processing and forming module; a processing and forming module for cutting, shaping, and assembling the raw materials into air springs; a quality inspection module for performing quality inspections on the processed air springs, including dimensional, airtightness, and strength tests, to obtain an inspection report; and a finished product packaging module for packaging qualified air springs and conveying them to a designated location according to the final transport path. This invention, through integrated modular design and intelligent algorithm control, achieves a high degree of automation and optimized resource utilization in the production process, improving production efficiency and product quality.

Description

Technical Field

[0001] This invention relates to the field of air spring manufacturing technology, and in particular to an automated air spring production line and its control system. Background Technology

[0002] Traditional air spring production lines rely heavily on manual operations, such as material handling, assembly, and testing. These manual operations are not only time-consuming but also difficult to maintain an efficient and stable work rhythm. For example, during the assembly of air springs, workers need to manually align and fix each component, a process that is not only inefficient but also prone to assembly errors.

[0003] In addition, traditional production lines have a low level of automation, lacking advanced automated equipment and intelligent control systems, making it impossible to achieve rapid response and flexible adjustment in the production process. For example, when a production line malfunctions or needs to adjust production specifications, a considerable downtime is required for manual adjustments or repairs. In the prior art, the invention patent with patent publication number CN118664323A discloses a gas spring end component assembly and testing production line, classified as B23P. It includes a dustproof sealing component assembly unit, which comprises a first conveying unit, a dustproof cover assembly mechanism and a dustproof cover testing mechanism arranged sequentially along the rear side of the first conveying unit, and a bullet-loading mechanism, a dustproof cover pre-assembly mechanism, a bullet-unloading mechanism, and a dustproof cover assembly mechanism arranged sequentially along the front side of the first conveying unit. A bullet-loading mechanism and a bullet-unloading mechanism are provided between the bullet-loading mechanism and the bullet-unloading mechanism. The joint assembly unit includes a second conveying unit, a joint tightening station and a joint angle adjustment station arranged sequentially along the second conveying unit, a first joint assembly mechanism and a second joint assembly mechanism arranged opposite each other at the joint tightening station, and a joint angle adjustment mechanism located at the joint angle adjustment station. This system can automatically assemble the dustproof sealing components at both ends of the cylinder, automatically assemble and test the large and small joints at both ends, greatly improving production efficiency. Secondly, patent publication number CN118752198A discloses an assembly device and method for air spring production, classified as B23P. It includes a first conveying device for conveying the cylinder; a second conveying device for conveying the rod; a support frame located between the first and second conveying devices; a nut mounted on the support; a screw rotatably mounted on the support, threadedly connected to the nut, the screw having a first end and a second end. After rotation, the first end moves away from the first conveying device, and the second end moves closer to the second conveying device; a first cleaning component is rotatably mounted relative to the first end, its rotation axis coinciding with the screw axis, and is used to clean the inner wall of the cylinder; a second cleaning component is rotatably mounted relative to the second end, and is used to clean the outer wall of the rod. This technical solution solves the problem in the prior art where impurities on the rod and cylinder affect the service life of the air spring.

[0004] However, energy consumption control in traditional air spring production lines is relatively rudimentary, lacking precise energy consumption monitoring and management methods. Energy consumption during the production process is often difficult to monitor in real time, leading to a failure to promptly identify and resolve excessive energy consumption issues. For example, some aging production equipment may have low energy efficiency, but these problems are often overlooked due to the lack of effective energy consumption monitoring methods.

[0005] Meanwhile, the production processes and procedures of traditional production lines may not be optimized enough, leading to unnecessary energy waste during production. For example, the manufacturing process of air springs may require multiple heating and cooling processes. If the temperature and time control of these processes are not precise enough, it will result in increased energy consumption. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide an automated production line for air springs and its control system, which improves the efficiency of the air spring production line and reduces energy consumption.

[0007] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0008] Firstly, an automated production line for air springs includes:

[0009] The raw material module is used to automatically allocate raw materials through dynamic programming algorithms and transport them to the processing and forming module;

[0010] The processing and forming module is used to cut, shape, and assemble raw materials into air springs;

[0011] The quality inspection module is used to perform quality inspection on the processed air springs, including dimensional, airtightness and strength inspection, in order to obtain an inspection report;

[0012] The finished product packaging module is used to package qualified air springs and transport the packaged air springs to the designated location according to the final conveying path;

[0013] An automated conveyor module is used to achieve seamless material transfer between the raw material conveying module, processing and forming module, quality inspection module, and finished product packaging module through conveyor belts and positioning sensors.

[0014] Furthermore, raw materials are automatically allocated and transported to the processing and forming module using a dynamic programming algorithm, including:

[0015] Sensors are used to identify the type, specifications, quantity, and production requirements of raw materials;

[0016] Raw materials are categorized and stored in designated storage areas, and each type of raw material is assigned a unique index.

[0017] Initialize the data structure for the dynamic programming algorithm, including the state array, and determine the state transition equations for the dynamic programming algorithm;

[0018] Set the initial conditions for the dynamic programming algorithm, and gradually fill the state array according to the state transition equation and the initial conditions until the final solution is obtained;

[0019] Based on the final solution, starting from the last state in the state array, we trace back step by step to determine the types and quantities of selected raw materials and the final raw material selection and allocation plan.

[0020] Send instructions to the automated conveying equipment, select the corresponding raw materials from the storage area according to the instructions, and mix or adjust the final raw material selection and allocation plan to obtain the adjusted raw materials.

[0021] The mixed raw materials are transported to the entrance of the processing and forming module via a conveyor belt.

[0022] Furthermore, the manufactured air springs undergo quality inspection, including dimensional, airtightness, and strength testing, to obtain an inspection report, including:

[0023] A certain number of air springs are selected sequentially from the production line as test samples, and an automated dimensional measuring instrument is used to measure the outer diameter, inner diameter, and height of the test samples.

[0024] Record the size data of each sample and compare it with the set standard to determine whether the size of each sample is qualified, so as to obtain the size judgment result;

[0025] Based on the size determination results, the size-qualified samples are placed in the airtightness testing equipment, and gas at a certain pressure is introduced into the size-qualified samples and maintained for a period of time.

[0026] A pressure sensor is used to monitor the pressure change of a sized sample. Based on the pressure change, the airtightness of the sized sample is determined to obtain the airtightness determination result.

[0027] Based on the airtightness assessment results, the airtightness qualified samples are installed in the strength testing machine, and pressure is gradually applied to the airtightness qualified samples according to the set standards until the maximum design pressure is reached.

[0028] The displacement sensor monitors the deformation of the airtight sample under pressure in real time, and determines whether the strength of the airtight sample is qualified based on the deformation, so as to obtain the strength judgment result.

[0029] The results of size determination, airtightness determination, and strength determination are integrated to generate a test report, which includes the test data, determination results, and classification labels for each sample.

[0030] Furthermore, the qualified air springs are packaged, and the packaged air springs are transported to the designated location according to the final conveying path, including:

[0031] Based on the size, shape, weight of the air spring and the characteristics of available packaging materials, a genetic algorithm is used to calculate the final packaging scheme, which includes the type, specifications, and packaging method of the packaging materials.

[0032] Based on the final packaging plan, control the packaging equipment to determine the appropriate packaging materials and packaging methods, and package the qualified air springs.

[0033] After packaging is completed, the final conveying path is calculated using a path planning algorithm based on the layout of the production line, the status of the conveying equipment, the target storage location, and the transportation time. Based on the final conveying path, the packaged air springs are then conveyed to the designated storage location.

[0034] Furthermore, based on the size, shape, weight of the air spring and the characteristics of available packaging materials, a genetic algorithm is used to calculate the final packaging scheme. The packaging scheme includes the type, specifications, and packaging method of the packaging materials, including:

[0035] Sensors on the production line automatically detect and acquire the physical parameters of the current air spring's size, shape, and weight, and collect data on packaging material characteristics, including type, thickness, strength, weight, cost, and environmental index.

[0036] The physical parameters of air springs and the characteristics of packaging materials are integrated to form a dataset, and the basic parameters of the genetic algorithm are set, including population size, number of iterations, crossover rate, and mutation rate.

[0037] Define the optimization objective, and randomly generate a set of initial packaging schemes based on the characteristics of the packaging materials and the physical parameters of the air springs. Each scheme includes the type, specifications and packaging method of the packaging materials.

[0038] Each individual, i.e. the initial packaging scheme, is represented in the population as a chromosome, and the genes on the chromosome represent the various parameters of the packaging scheme;

[0039] Fitness assessment is performed on each individual, which involves calculating the objective function value for each individual and determining candidate individuals based on the objective function value of each individual;

[0040] Candidate individuals are crossovered to generate new individuals by exchanging some genes, and the new individuals are then mutated to update the individual population.

[0041] Repeatedly perform crossover and mutation operations until the preset number of iterations is reached. The final solution, i.e. the final packaging scheme, is determined from the final population based on the objective function value of each individual.

[0042] Furthermore, the formula for calculating the objective function value for each individual is as follows:

[0043]

[0044] Where f(x) represents the objective function value for each individual; α and β represent weighting coefficients; n represents the number of types of packaging materials; i represents the index of the type of packaging material; p i q represents the unit price of the i-th packaging material; i (x) represents the amount of the i-th packaging material used in packaging scheme x; γ i δ represents the loss coefficient of the i-th packaging material; i V represents the discount factor for the i-th packaging material; s V represents the volume of the air spring; ∈ represents the packaging efficiency coefficient; V p θ represents the total volume of the packaging material; λ represents the compression coefficient of the packaging material; c represents the weighting coefficient of transportation costs; and d(x,e) represents the actual transportation distance of the goods from the origin to the destination under the given packaging scheme x and transportation route e.

[0045] Furthermore, after packaging is completed, based on the production line layout, the status of the conveying equipment, the target storage location, and the transportation time, a path planning algorithm is used to calculate the final conveying path. Following this final conveying path, the packaged air springs are then conveyed to the designated storage location, including:

[0046] Obtain the layout diagram of the production line, including equipment locations, connection relationships, and aisle widths, and treat the production line layout as a graph structure, where nodes represent storage locations and edges represent conveying paths;

[0047] Assign a weight to each edge and initialize an open list and a closed list;

[0048] Add the starting position to the open list, and repeatedly execute the steps of retrieving the node with the lowest current cost, checking the storage location, traversing adjacent nodes and updating the list until the open list is empty, thus obtaining the final path consisting of a series of nodes from the starting position to the storage location;

[0049] Based on the final path, the control conveyor will sequentially transport the packaged air springs to each node on the path.

[0050] Secondly, a control system for an automated air spring production line achieves the following functions:

[0051] Data is collected from various sensors on the production line, including air compressor pressure, variable pressure reducing valve opening, solenoid valve status, air spring deformation height, displacement sensor signal, and pressure sensor signal.

[0052] Based on the predefined air spring production process and quality control requirements, a control strategy is formulated, which includes air spring inflation and deflation control, displacement control, and pressure control.

[0053] According to the control strategy, the control unit sends control commands to the actuators on the production line, monitors the operating status of the production line in real time, and receives feedback signals.

[0054] Real-time data and operating status are transmitted to remote terminals to monitor the production line's operating data and status in real time, and to dynamically adjust control strategies based on feedback signals.

[0055] When an abnormality or malfunction occurs on the production line, an alarm signal is automatically issued and the alarm information is transmitted to a remote terminal.

[0056] The above-described solution of the present invention has at least the following beneficial effects:

[0057] The raw material module automatically allocates raw materials using a dynamic programming algorithm, reducing manual intervention and accelerating raw material preparation and conveying. The processing and forming module automates the cutting, shaping, and assembly of raw materials, improving production line efficiency and shortening the production cycle. The automated conveying module achieves seamless material transfer between modules through conveyor belts and positioning sensors, reducing material waiting time and further improving overall production efficiency. The quality inspection module performs comprehensive quality inspections on the processed air springs, including dimensions, airtightness, and strength, ensuring product quality and reliability. The automated production line reduces errors caused by manual operation, improving product consistency and stability.

[0058] Automated production lines reduce unnecessary energy waste by optimizing production processes and procedures. The application of dynamic programming algorithms in raw material allocation makes raw material use more rational, avoiding energy consumption caused by over-purchasing and inventory backlog. The entire production line adopts advanced automation technologies and equipment, such as conveyor belts and positioning sensors, achieving automation and intelligence in the production process. Increased automation not only improves production efficiency but also reduces manual labor intensity and improves the working environment. The design of automated production lines is highly flexible and scalable, allowing for rapid adjustment and optimization based on changes in market demand and production scale. Attached Figure Description

[0059] Figure 1This is a schematic diagram of an automated air spring production line provided by an embodiment of the present invention.

[0060] Figure 2 This is a flowchart illustrating a control system for an automated air spring production line according to an embodiment of the present invention. Detailed Implementation

[0061] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0062] like Figure 1 As shown, an embodiment of the present invention provides an automated production line for air springs, comprising:

[0063] The raw material module 11 is used to automatically allocate raw materials through a dynamic programming algorithm and transport them to the processing and forming module;

[0064] The processing and forming module 12 is used to cut, shape, and assemble the raw materials into air springs;

[0065] The quality inspection module 13 is used to perform quality inspection on the processed air springs, including dimensional, airtightness and strength inspection, in order to obtain an inspection report;

[0066] The finished product packaging module 14 is used to package qualified air springs and transport the packaged air springs to the designated location according to the final conveying path.

[0067] The automated conveying module 15 is used to achieve seamless material transfer between the raw material conveying module, the processing and forming module, the quality inspection module, and the finished product packaging module through a conveyor belt and positioning sensors.

[0068] In this embodiment of the invention, raw materials are automatically allocated using a dynamic programming algorithm, achieving efficient and precise management of raw materials and reducing errors and delays caused by manual allocation. Automated allocation and delivery of raw materials to the processing and forming module accelerates the production process and improves the overall efficiency of the production line.

[0069] The process of cutting, shaping, and assembling raw materials into air springs automates the production process, improving efficiency and product quality. Automated processing reduces manual labor, lowers labor intensity, and enhances production safety.

[0070] Comprehensive quality testing is conducted on the manufactured air springs, including dimensions, airtightness, and strength, ensuring product pass rate and reliability. The test reports provide a basis for subsequent product traceability and quality control, helping to improve the company's quality management level.

[0071] Packaging qualified air springs protects the product from damage during transportation and storage. The packaged air springs are then transported to designated locations according to their final delivery path, enabling automated management and distribution of finished products and improving logistics efficiency.

[0072] Seamless material transfer between modules is achieved through conveyor belts and positioning sensors, reducing material waiting time and manual handling costs. Automated conveying improves the continuity and smoothness of the production line, making the entire production process more efficient and orderly.

[0073] In a preferred embodiment of the present invention, automatically allocating raw materials using a dynamic programming algorithm and conveying them to the processing and forming module may include:

[0074] Sensors are used to identify the type, specifications, quantity, and production requirements of raw materials;

[0075] Raw materials are categorized and stored in designated storage areas, and each type of raw material is assigned a unique index.

[0076] Initialize the data structure for the dynamic programming algorithm, including the state array, and determine the state transition equations for the dynamic programming algorithm;

[0077] Set the initial conditions for the dynamic programming algorithm, and gradually fill the state array according to the state transition equation and the initial conditions until the final solution is obtained;

[0078] Based on the final solution, starting from the last state in the state array, we trace back step by step to determine the types and quantities of selected raw materials and the final raw material selection and allocation plan.

[0079] Send instructions to the automated conveying equipment, select the corresponding raw materials from the storage area according to the instructions, and mix or adjust the final raw material selection and allocation plan to obtain the adjusted raw materials.

[0080] The mixed raw materials are transported to the entrance of the processing and forming module via a conveyor belt.

[0081] In this embodiment of the invention, various types of sensors (such as RFID sensors, weight sensors, and visual recognition sensors) are installed at the raw material inlet. The sensors read tag information (such as RFID tags) on the raw materials or identify the type and specifications of the raw materials using visual recognition technology, while the weight sensors measure the quantity of the raw materials. The identified information (type, specifications, quantity) and production requirements (such as order information) are transmitted to the central control system. Based on the type and specifications of the raw materials, different storage areas are designed and divided. Each raw material is assigned a unique index (such as a number or string) to uniquely identify it in the system. Once a raw material is identified, it is stored in the corresponding storage area according to the index. The inventory information in the storage area is updated, recording the quantity and location of each raw material.

[0082] Based on production needs and the types and specifications of raw materials, define the state variables and state array for the dynamic programming algorithm. State variables include the types and quantities of raw materials currently used, production progress, etc. Based on the constraints of the production process (such as the order and proportion of raw material usage), determine the state transition equation: dp[m′][p′]=max / min(dp[m][p]+value). a dded(m,p,m′,p′)); where dp[m][p] is the state array; dp[m′][p′] is the benefit given the quantity of raw materials m′ and the production progress p′; value a dded(m,p,m′,p′) represents the increase in benefit when transitioning from state (m,p) to state (m′,p′); max / min(dp[m][p]+value) a `dded(m,p,m′,p′))` means starting from the current state [m][p], considering all possible new states (m′,p′), calculating the total value after each possible transition, and selecting the final transition path that maximizes the total value (or minimizes the total cost); `max` means selecting the path with the maximum total value among all possible transition paths; `min` means selecting the path with the minimum total cost among all possible transition paths.

[0083] Suppose we have a knapsack with capacity p and n items, each with weight and value. The goal is to select items to maximize the total value.

[0084] m represents the item number currently being considered (from the 1st to the nth item), and p represents the remaining capacity of the knapsack.

[0085] The state transition formula is determined based on whether the current item m is selected:

[0086] If the m-th item is not selected, the remaining capacity and total value of the knapsack remain unchanged, i.e., dp[m][p] = dp[m-1][p].

[0087] If the m-th item is selected (provided that the remaining capacity p of the knapsack is greater than or equal to the weight t[m] of the item), then the remaining capacity of the knapsack will decrease by weight t[m], and the total value will increase by value[m], i.e., dp[m][p] = dp[m-1][p-weight[m]] + value[m].

[0088] Combining the above two cases, we can obtain the comprehensive formula for state transition: dp[m][p] = max(dp[m-1][p], dp[m-1][p-weight[m]] + value[m]).

[0089] In this formula, value a `dded(m,p,m',p')` corresponds to `value[m]`, which is the additional value added when transitioning from state (m-1,p-weight[m]) to (m,p). By comparing the total value of choosing and not choosing the current item, we can determine which item should be chosen in each state, thus obtaining the final solution to the knapsack problem.

[0090] Initialize the state array, setting the initial state (e.g., no raw materials used, production progress is 0), and define the initial conditions for the dynamic programming algorithm, such as the initial state and the target state. Based on the state transition equation, starting from the initial state, progressively calculate and fill the state array. During the filling process, record the final solution for each state (e.g., type and quantity of raw materials, production progress, etc.). When the target state is reached, stop filling, obtaining the final solution.

[0091] Starting from the last state in the state array (i.e., the target state), the process traces backward step by step. Based on the state transition equation, the previous state for each state is determined, along with the type and quantity of raw materials used from the previous state to the current state. The traced raw material types and quantities are recorded to form the final raw material selection and allocation plan. Based on this plan, instructions are generated for the automated conveying equipment, including which storage area to select which raw material from and the quantity to select. These instructions are sent to the automated conveying equipment to control its selection and conveying operations. During conveying, the raw materials need to be mixed or blended (e.g., using mixers, agitators, etc.) to obtain blended raw materials. The blended raw materials are placed on a conveyor belt, which transports them to the entrance of the processing and forming module. During conveying, the speed and direction of the conveyor belt need to be controlled to ensure that the raw materials enter the processing and forming module accurately and stably.

[0092] Suppose we need to produce a composite material composed of three raw materials (A, B, and C). Each raw material has different specifications and quantity requirements, and the production requirement is 1000 pieces of the composite material per day.

[0093] The sensor detected quantities of raw materials A (specification 1), B (specification 2), and C (specification 3) of 500 kg, 300 kg, and 200 kg respectively, and the production requirement is to produce 1000 pieces of composite materials per day. Raw material A is stored in storage area 1 with the index "A1"; raw material B is stored in storage area 2 with the index "B2"; and raw material C is stored in storage area 3 with the index "C3".

[0094] Define the state variables as (quantity of A used, quantity of B used, quantity of C used, production progress); initialize the state array and set the initial state to (0, 0, 0, 0);

[0095] Determine the state transition equation, such as: if the current state is (x, y, z, p), and the selected quantities are a of raw material A, b of raw material B, and c of raw material C, then the next state is (x+a, y+b, z+c, p+min(a / A_per_piece, b / B_per_piece, c / C_per_piece)), where A_per_piece, B_per_piece, and C_per_piece are the quantities of A, B, and C required for each composite material.

[0096] Based on the state transition equation and initial conditions, the state array is filled step by step. During the filling process, the final solution of each state is recorded. Starting from the target state (e.g., (400, 300, 200, 1000)), the process is traced backward step by step to determine the previous state of each state, as well as the types and quantities of raw materials used from the previous state to the current state, thus obtaining the final raw material selection and allocation scheme, such as using 400 kg of A, 300 kg of B, and 200 kg of C to produce 1000 pieces of composite materials.

[0097] The automated conveyor system sends instructions to select 400 kg of material A from storage area 1, 300 kg of material B from storage area 2, and 200 kg of material C from storage area 3. The selected raw materials are then mixed or blended to obtain blended raw materials. These blended raw materials are placed on a conveyor belt, which transports them to the entrance of the processing and forming module.

[0098] By automatically allocating raw materials using dynamic programming algorithms, the order and proportion of raw material usage can be optimized, reducing waiting time and waste in the production process, thereby improving production efficiency. Dynamic programming algorithms can automatically select the optimal raw material selection and allocation scheme based on production needs and raw material inventory, thus reducing production costs. Precise raw material selection and allocation ensures the quality stability and consistency of each composite material, improving product quality. When production needs or raw material inventory change, dynamic programming algorithms can quickly adjust the raw material selection and allocation scheme, enhancing the flexibility of the production system. By combining sensors, automated conveying equipment, and dynamic programming algorithms, automatic identification, classification, storage, allocation, and transportation of raw materials can be achieved, thus realizing automated production.

[0099] In a preferred embodiment of the present invention, the quality inspection of the processed air spring includes dimensional, airtightness, and strength testing to obtain an inspection report, which may include:

[0100] A certain number of air springs are selected sequentially from the production line as test samples, and an automated dimensional measuring instrument is used to measure the outer diameter, inner diameter, and height of the test samples.

[0101] Record the size data of each sample and compare it with the set standard to determine whether the size of each sample is qualified, so as to obtain the size judgment result;

[0102] Based on the size determination results, the size-qualified samples are placed in the airtightness testing equipment, and gas at a certain pressure is introduced into the size-qualified samples and maintained for a period of time.

[0103] A pressure sensor is used to monitor the pressure change of a sized sample. Based on the pressure change, the airtightness of the sized sample is determined to obtain the airtightness determination result.

[0104] Based on the airtightness assessment results, the airtightness qualified samples are installed in the strength testing machine, and pressure is gradually applied to the airtightness qualified samples according to the set standards until the maximum design pressure is reached.

[0105] The displacement sensor monitors the deformation of the airtight sample under pressure in real time, and determines whether the strength of the airtight sample is qualified based on the deformation, so as to obtain the strength judgment result.

[0106] The results of size determination, airtightness determination, and strength determination are integrated to generate a test report, which includes the test data, determination results, and classification labels for each sample.

[0107] In this embodiment of the invention, sensors (such as RFID or barcode scanners) on the production line are used to identify each air spring. According to the production plan, a certain number of air springs are selected as test samples, and a conveyor belt transports the selected samples to the testing area.

[0108] The sample is placed on an automated dimension measuring instrument, which uses technologies such as laser rangefinders and optical sensors to automatically measure the outer diameter, inner diameter, and height, and transmits the measurement data to the data processing system in real time.

[0109] The data processing system compares the measured data with the preset standard dimensions, and the judgment algorithm determines whether the dimensions of each sample are qualified based on the deviation range, and records the size judgment results.

[0110] A conveyor belt moves sized samples to an airtightness testing device. The device automatically fills the sample with gas at a set pressure and maintains this pressure for a set time to ensure stability. A high-precision pressure sensor monitors pressure changes within the sample in real time. Based on the range of pressure changes, the device determines whether the airtightness is acceptable and records the result.

[0111] Samples that pass the airtightness test are installed in a strength testing machine, which gradually applies pressure according to set standards until the maximum design pressure is reached. Displacement sensors monitor the deformation of the samples under pressure in real time. Based on the degree of deformation, the strength is determined and the result is recorded.

[0112] The data processing system integrates the dimensional judgment results, airtightness judgment results, and strength judgment results, and automatically generates a test report, including the test data, judgment results, and classification marks (such as qualified or unqualified) for each sample. The report can be saved in PDF, Excel, and other formats and transmitted to the production management system or quality control system.

[0113] Suppose there are 100 air springs on the production line, and the system randomly selects 10 as the test samples, identifying and selecting the air springs numbered 1, 5, 10, 15, 20, 25, 30, 35, 40, 45. The measuring instrument measures the outer diameter of sample 1 as 150.0 mm, the inner diameter as 100.0 mm, and the height as 200.0 mm. Repeat this process to measure the dimensions of all selected samples. The dimensions of sample 1 are compared with the standard (outer diameter 150.0 ± 0.5 mm, inner diameter 100.0 ± 0.5 mm, height 200.0 ± 1.0 mm) and determined to be qualified. Repeat this process to determine the dimensions of all samples. Sample 1 is moved to the airtightness test equipment, filled with 1.5 MPa of gas, and maintained for 1 minute. Repeat this process to conduct airtightness tests on all samples with qualified dimensions. The pressure change of sample 1 is within the allowable range and is determined to be qualified. Repeat this process to determine the airtightness of all samples. Sample 1 is installed on the strength testing machine, and pressure is gradually applied to 2.0 MPa. The displacement sensor monitors the deformation situation and is determined to be qualified. Repeat this process to conduct strength tests on all samples with qualified airtightness. Integrate all the determination results to generate an inspection report, showing the detailed data and determination results of each sample.

[0114] The automated inspection process reduces manual intervention and significantly improves the inspection speed and efficiency. Using high-precision sensors and equipment ensures the accuracy and reliability of the inspection data. Strict inspection standards and comprehensive inspection items help to detect and eliminate unqualified products and improve product quality. The detailed inspection report records the inspection data of each sample, facilitating subsequent quality traceability and analysis. Real-time feedback of inspection results helps to promptly adjust production parameters, optimize the production process, and the automated inspection reduces the labor demand and lowers the labor cost.

[0115] In a preferred embodiment of the present invention, packaging the qualified air springs and transporting the packaged air springs to a designated location according to the final transportation path may include:

[0116] According to the size, shape, weight of the air spring and the characteristics of the available packaging materials, use the genetic algorithm to calculate the final packaging scheme. The packaging scheme includes the type, specification and packaging method of the packaging materials;

[0117] According to the final packaging scheme, control the packaging equipment to determine the corresponding packaging materials and packaging methods, and package the qualified air springs;

[0118] After packaging, according to the layout of the production line, the status of the transportation equipment, the target storage location and the transportation time, use the path planning algorithm to calculate the final transportation path, and according to the final transportation path, transport the packaged air springs to the designated storage location.

[0119] In this embodiment of the invention, data such as the dimensions (outer diameter, inner diameter, height), shape, and weight of the air spring are collected, as well as the types, specifications, strength, and cost characteristics of available packaging materials. A set of initial packaging schemes is randomly generated, each scheme including the type, specifications, and packaging method of the packaging material. A fitness function for the genetic algorithm is set, which evaluates the schemes based on their feasibility (e.g., whether they meet requirements for product protection, cost-effectiveness, etc.). A selection operation is performed, selecting the better packaging scheme for propagation according to the fitness function. A crossover operation is performed, combining the genes (i.e., the type, specifications, and packaging method of the packaging material) of two or more packaging schemes to generate new packaging schemes. A mutation operation is performed, randomly changing some genes of the new packaging scheme to increase the diversity of the schemes. The selection, crossover, and mutation operations are repeated until a preset number of iterations is reached. The packaging scheme with the highest fitness is selected as the final scheme, including the type, specifications, and packaging method of the packaging material.

[0120] Based on the final packaging plan, select appropriate packaging materials and methods, and adjust the parameters of the packaging equipment, such as packaging speed and pressure, to meet the requirements of the packaging plan. Place the qualified air springs on the packaging equipment, and the packaging equipment will package the air springs according to the preset program and parameters. During the packaging process, monitor the working status of the packaging equipment to ensure packaging quality and safety.

[0121] The system collects production line layout information, including equipment locations and aisle widths, and conveyor status information, such as conveyor belt speed and direction, to determine the target storage location and transportation time requirements. Based on the production line layout and conveyor equipment status, an initial set of conveyor paths is generated. A fitness function for the path planning algorithm is defined, evaluating paths based on feasibility (e.g., collision avoidance, meeting transportation time requirements). A selection operation is performed, choosing the optimal conveyor path based on the fitness function for propagation. A crossover operation combines the genes (nodes and order) of two or more conveyor paths to generate new paths. A mutation operation randomly changes some genes of the new conveyor path to increase path diversity. The selection, crossover, and mutation operations are repeated until a preset number of iterations is reached. The conveyor path with the highest fitness is selected as the final path, including its nodes and order. Based on the final conveyor path, the conveyor equipment is controlled to transport the packaged air springs to the designated storage location. During transport, the operating status of the conveyor equipment is monitored to ensure safe and timely delivery.

[0122] Suppose there are 10 qualified air springs that need to be packaged and transported to a designated storage location. Collect data on the air springs' dimensions, shape, weight, and the characteristics of available packaging materials. Use a genetic algorithm to calculate the final packaging scheme, such as using foam plastic as the packaging material, with dimensions of 200mm × 200mm × 300mm, and packaging the air springs in a foam plastic box and securing them with tape. Based on the final packaging scheme, select the foam plastic box and tape as the packaging materials, and adjust the packaging equipment parameters, such as packaging speed and pressure. Place the qualified air springs on the packaging equipment, which packages them according to a preset program and parameters. Collect production line layout information, conveyor equipment status information, and target storage location and transportation time requirements. Use a path planning algorithm to calculate the final transport path, such as starting from the packaging equipment, passing through conveyor belt A, turning machine B, conveyor belt C, and finally reaching storage location D. Control the conveyor equipment to transport the packaged air springs to the designated storage location according to the final transport path.

[0123] Automated packaging and conveying processes reduce manual intervention, significantly improving packaging and conveying speed and efficiency. Genetic algorithms are used to calculate packaging solutions, ensuring the rationality of packaging material selection and methods, thus improving packaging quality. Path planning algorithms are used to calculate conveying paths, avoiding collisions and congestion, optimizing the conveying process, and improving conveying efficiency. Automated packaging and conveying reduce the need for manual labor, lowering labor costs. During automated packaging and conveying, the operating status of equipment is monitored, and abnormal situations are detected and handled promptly, improving production safety. Efficient packaging and conveying processes ensure on-time and undamaged product delivery, increasing customer satisfaction and trust.

[0124] In another preferred embodiment of the invention, based on the size, shape, weight of the air spring and the characteristics of available packaging materials, a genetic algorithm is used to calculate the final packaging scheme. The packaging scheme includes the type, specifications, and packaging method of the packaging materials, and may include:

[0125] Sensors on the production line automatically detect and acquire the physical parameters of the current air spring's size, shape, and weight, and collect data on packaging material characteristics, including type, thickness, strength, weight, cost, and environmental index.

[0126] The physical parameters of air springs and the characteristics of packaging materials are integrated to form a dataset, and the basic parameters of the genetic algorithm are set, including population size, number of iterations, crossover rate, and mutation rate.

[0127] Define the optimization objective, and randomly generate a set of initial packaging schemes based on the characteristics of the packaging materials and the physical parameters of the air springs. Each scheme includes the type, specifications and packaging method of the packaging materials.

[0128] Each individual, i.e. the initial packaging scheme, is represented in the population as a chromosome, and the genes on the chromosome represent the various parameters of the packaging scheme;

[0129] Fitness assessment is performed on each individual, which involves calculating the objective function value for each individual and determining candidate individuals based on the individual's objective function value;

[0130] Candidate individuals are crossovered to generate new individuals by exchanging some genes, and the new individuals are then mutated to update the individual population.

[0131] Repeatedly perform crossover and mutation operations until the preset number of iterations is reached. The final solution, i.e. the final packaging scheme, is determined from the final population based on the objective function value of each individual.

[0132] In this embodiment of the invention, a pre-set sensor network (such as laser rangefinders, visual recognition systems, weighing sensors, etc.) is used to monitor the air springs on the production line in real time. The sensors transmit the collected data (size, shape, weight) to a data processing unit. Simultaneously, the characteristic data of the packaging materials (type, thickness, strength, weight, cost, environmental index) are acquired through a database or external input devices (such as RFID scanners) and integrated into the same data processing unit. The data processing unit matches and integrates the physical parameters of the air springs and the characteristic data of the packaging materials to form a dataset containing all necessary information. Based on the complexity of the problem and computational resources, basic parameters of the genetic algorithm are set, such as population size (representing the number of packaging schemes considered simultaneously), number of iterations (the number of loops in which the algorithm runs), crossover rate (the probability of gene exchange), and mutation rate (the probability of random gene changes).

[0133] The optimization objective is defined based on actual needs (such as cost minimization, environmental protection maximization, and protection performance optimization). A predefined set of rules is used to randomly generate a set of initial packaging schemes based on the characteristics of the packaging materials and the physical parameters of the air springs. Each initial packaging scheme is encoded as a chromosome, with genes (such as binary bits, real numbers, etc.) representing various parameters of the packaging scheme (such as packaging material type, specifications, and packaging method). The fitness of each individual is evaluated using a predefined objective function. Individuals are ranked according to their fitness values ​​(i.e., objective function values), and individuals with high fitness are selected as candidate individuals.

[0134] Two candidate individuals are selected and crossover is performed by exchanging parts of their genes (e.g., single-point crossover, multi-point crossover, etc.) to generate new individuals. The newly generated individuals are then mutated (e.g., the value of a gene is randomly changed) to increase population diversity. This crossover and mutation process is repeated until a predetermined number of iterations is reached. From the final population, the individual with the highest fitness is selected as the final solution, i.e., the final packaging scheme.

[0135] Suppose we are dealing with the packaging problem of an air spring. The air spring is 50cm long, 30cm wide, and 20cm high, weighs 5kg, and is rectangular in shape. There are three available packaging materials: foam plastic (5cm thick, medium strength, 0.2kg weight, cost 1 yuan / kg, environmental index 3), corrugated cardboard (2cm thick, high strength, 0.5kg weight, cost 0.5 yuan / kg, environmental index 5), and bubble wrap (0.5cm thick, low strength, 0.1kg weight, cost 2 yuan / kg, environmental index 2).

[0136] By integrating the physical parameters of the air springs and the characteristic data of the packaging materials into a single dataset, the following three initial packaging schemes were randomly generated:

[0137] Option 1: Use foam plastic, with dimensions of 55cm in length, 35cm in width, and 25cm in height, and wrap it completely.

[0138] Option 2: Use corrugated cardboard with dimensions of 52cm in length, 32cm in width, and 22cm in height. Wrap the bottom and all four sides of the cardboard.

[0139] Option 3: Use bubble wrap with dimensions of 51cm in length, 31cm in width, and 21cm in height. Wrap the top and sides of the bubble wrap.

[0140] Each solution is encoded as a chromosome; for example, the chromosome for solution 1 might be [1, 55, 35, 25, 1] ​​(where 1 represents foam plastic, the last four numbers represent the specifications, and the last 1 represents full coverage). The fitness value of each solution is calculated. Considering factors such as cost, environmental impact, and protective performance, solutions with higher fitness are subjected to crossover and mutation operations to generate new solutions. The crossover and mutation operations are repeated until a preset number of iterations is reached. The solution with the highest fitness is selected from the final population as the final solution; for example, this might be a solution using corrugated cardboard for the bottom and sides.

[0141] Automated optimization of packaging solutions using genetic algorithms can significantly reduce the time spent on manual trial and error, thereby improving packaging efficiency. Genetic algorithms can comprehensively consider multiple factors such as cost, environmental impact, and protective performance to find the lowest-cost packaging solution. By optimizing the use of packaging materials with high environmental performance, genetic algorithms help reduce the environmental impact of packaging waste. Based on the physical parameters of air springs and the characteristics of packaging materials, genetic algorithms can find the optimal packaging method to improve product protection. The integration of genetic algorithms with sensors, data processing units, and other equipment enables the automation and intelligence of packaging solutions, providing strong support for intelligent manufacturing.

[0142] In another preferred embodiment of the present invention, the formula for calculating the objective function value for each individual is as follows:

[0143]

[0144] Where f(x) represents the objective function value for each individual; α and β represent weighting coefficients; n represents the number of types of packaging materials; i represents the index of the type of packaging material; p i q represents the unit price of the i-th packaging material; i (x) represents the amount of the i-th packaging material used in packaging scheme x; γ i δ represents the loss coefficient of the i-th packaging material; i V represents the discount factor for the i-th packaging material; s V represents the volume of the air spring; ∈ represents the packaging efficiency coefficient; V p θ represents the total volume of the packaging material; λ represents the compression coefficient of the packaging material; c represents the weighting coefficient of transportation costs; and d(x,e) represents the actual transportation distance of the goods from the origin to the destination under the given packaging scheme x and transportation route e.

[0145] In this embodiment of the invention, all relevant parameters are collected, including the weight of packaging material cost α = 0.6, the weight of packaging efficiency adjustment term β = 0.3, and the weight of transportation cost λ = 0.1; the packaging material parameters include the types and quantities of packaging materials n, and the unit price p of the i-th type of packaging material. i The amount q of the i-th packaging material in packaging scheme x i (x), loss coefficient γ of the i-th type of packaging material i =1.1, Discount coefficient δ for the i-th type of packaging material i =0.95; Volume V of the air spring s Packaging efficiency coefficient ∈ = 0.85, total volume of packaging materials, compression coefficient of packaging materials θ = 0.75; unit price of transportation c, and actual transportation distance d(x,e) of goods from the origin to the destination under the given packaging scheme x and transportation route e.

[0146] Calculating packaging costs For each packaging material i, multiply it by its unit price, usage, loss coefficient, and discount coefficient, then sum the results. Multiply the result by a weighting coefficient α to adjust the proportion of packaging cost in the overall objective function.

[0147] Calculating Packaging Efficiency and Material Utilization First, calculate the ratio of the air spring volume to the total volume of the packaging material, and multiply it by the reciprocal of the packaging efficiency coefficient and the compression coefficient. Then, multiply the result by the weighting coefficient -β to adjust the proportion of packaging efficiency and material utilization in the overall objective function, and make it negative so as to minimize this part during the optimization process.

[0148] To calculate the transportation cost component λ×c×d(x,e), first multiply the unit transportation price by the actual transportation distance, then multiply by the weighting coefficient λ. This component represents the contribution of transportation cost to the overall objective function. Adding the packaging cost component, the packaging efficiency and material utilization component, and the transportation cost component together yields the objective function value f(x) for each individual component.

[0149] By comprehensively considering packaging material costs, packaging efficiency, and transportation costs, this function helps companies find the lowest-cost option among different packaging and transportation solutions, improving overall economic efficiency. Loss and discount factors make the calculations more realistic, helping companies optimize material procurement and usage, and reduce waste. By calculating transportation distance and costs, companies can choose more economical transportation routes and methods, reducing logistics costs. This objective function provides companies with a quantitative evaluation tool, helping decision-makers compare different options and make more scientific and rational decisions.

[0150] In another preferred embodiment of the present invention, after packaging is completed, the final conveying path is calculated using a path planning algorithm based on the layout of the production line, the status of the conveying equipment, the target storage location, and the transportation time. Then, according to the final conveying path, the packaged air spring is conveyed to the designated storage location, which may include:

[0151] Obtain the layout diagram of the production line, including equipment locations, connection relationships, and aisle widths, and treat the production line layout as a graph structure, where nodes represent storage locations and edges represent conveying paths;

[0152] Assign a weight to each edge and initialize an open list and a closed list;

[0153] Add the starting position to the open list, and repeatedly execute the steps of retrieving the node with the lowest current cost, checking the storage location, traversing adjacent nodes and updating the list until the open list is empty, thus obtaining the final path consisting of a series of nodes from the starting position to the storage location;

[0154] Based on the final path, the control conveyor will sequentially transport the packaged air springs to each node on the path.

[0155] In this embodiment of the invention, the layout information of the production line is obtained through sensors or a preset data interface.

[0156] Key information such as equipment location, connection relationships, and channel width are extracted to construct a graph structure. Based on the obtained layout information, a graph structure is built where nodes represent possible storage locations on the production line, and edges represent transport paths from one storage location to another. Each edge is assigned a weight based on factors such as path length, channel width, and equipment status; the weight can represent transport cost and time consumption. An open list is created to store nodes awaiting processing, and a closed list is created to store nodes that have already been processed.

[0157] Add the starting position (i.e., the initial position of the air spring) after packaging to the open list. Take the node with the lowest current cost from the open list and check if it is located at the target storage location; if so, path planning is complete; otherwise, traverse all adjacent nodes of that node. For each adjacent node, calculate the total cost from the starting position to that node and update its weight and parent node information. If an adjacent node is not in the open list, add it. If an adjacent node is already in the open list and its new total cost is lower, update its weight and parent node information. Add the current node to the closed list to prevent duplicate processing. Repeat the above steps until the open list is empty or the target storage location is found. Based on the parent node information, backtrack to obtain the final path consisting of a series of nodes from the starting position to the storage location.

[0158] The final path is sent to the control system of the conveying equipment. Based on the path information, the control system controls the conveying equipment to sequentially deliver the air springs to each node on the path.

[0159] Assume the production line layout is as follows:

[0160] Node A: Starting position (position of the air spring after packaging is complete);

[0161] Node B: Intermediate storage location 1;

[0162] Node C: Intermediate storage location 2;

[0163] Node D: Target storage location.

[0164] The edges and weights are as follows:

[0165] A to B: Weight 2 (representing transportation cost or time);

[0166] A to C: Weight 4;

[0167] From B to C: Weight 1;

[0168] B to D: Weight 3;

[0169] From C to D: Weight 2.

[0170] The steps for executing the path planning algorithm are as follows:

[0171] Initialize the open list (containing node A) and the closed list (empty). Remove node A from the open list (currently lowest cost), and check if it is not the target storage location. Iterate through node A's neighboring nodes B and C, calculate the total cost, and update the weights and parent node information. Add nodes B and C to the open list. Remove node B from the open list (currently lowest cost), and check if it is not the target storage location. Iterate through node B's neighboring nodes C and D, calculate the total cost, and update the weights and parent node information. Add node D (if it was not previously in the open list) to the open list or update its weight, and add node B to the closed list. Repeat the above steps until the target storage location D is found or the open list is empty. Backtracking yields the final path: A->B->D. Based on the final path, control the conveyor to sequentially deliver the air springs to nodes B and D.

[0172] Path planning algorithms can find the shortest or lowest-cost path from the starting point to the target storage location, helping to reduce transport time and improve the overall efficiency of the production line. Optimizing the path reduces energy consumption, equipment wear and tear, and labor costs during transport, thus lowering operating costs and improving economic efficiency. Path planning algorithms can adapt to dynamic changes in the production line, such as equipment failures and channel blockages, enhancing the flexibility and adaptability of the production line and improving its ability to handle unforeseen circumstances. Automated path planning and transport control reduce manual intervention and increase the automation level of the production line, helping to lower labor costs and improve the stability and consistency of the production line. Rational planning of transport paths fully utilizes the resources and space of the production line, reducing resource waste and improving resource utilization efficiency.

[0173] like Figure 2 As shown, embodiments of the present invention also provide a control system for an automated air spring production line, comprising:

[0174] Data is collected from various sensors on the production line, including air compressor pressure, variable pressure reducing valve opening, solenoid valve status, air spring deformation height, displacement sensor signal, and pressure sensor signal.

[0175] Based on the predefined air spring production process and quality control requirements, a control strategy is formulated, which includes air spring inflation and deflation control, displacement control, and pressure control.

[0176] According to the control strategy, the control unit sends control commands to the actuators on the production line, monitors the operating status of the production line in real time, and receives feedback signals.

[0177] Real-time data and operating status are transmitted to remote terminals to monitor the production line's operating data and status in real time, and to dynamically adjust control strategies based on feedback signals.

[0178] When an abnormality or malfunction occurs on the production line, an alarm signal is automatically issued and the alarm information is transmitted to a remote terminal.

[0179] It should be noted that this method corresponds to the aforementioned automated air spring production line. All implementation methods in the above-mentioned automated air spring production line embodiment are applicable to this embodiment and can achieve the same technical effect.

[0180] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An automated production line for air springs, characterized in that, The application relates to an air spring production line, which comprises a raw material module, a processing and forming module, a quality detection module and a finished product packaging module. The raw material module is used for automatically allocating raw materials through a dynamic programming algorithm and conveying the raw materials to the processing and forming module, and comprises the following steps: identifying the type, specification, quantity and production demand of the raw materials through a sensor; storing the raw materials in a designated storage area in a classified manner, and allocating a unique index to each type of raw material; initializing the data structure of the dynamic programming algorithm, including a state array, and determining the state transition equation of the dynamic programming algorithm; setting the initial conditions of the dynamic programming algorithm, and gradually filling the state array according to the state transition equation and the initial conditions until the final solution is obtained; according to the final solution, starting from the last state of the state array, the selected raw material type and quantity are traced back step by step to determine the final raw material selection and allocation scheme; sending an instruction to an automatic conveying device to select the corresponding raw materials from the storage area according to the instruction, and mixing or allocating the final raw material selection and allocation scheme to obtain the allocated raw materials; and conveying the allocated raw materials to the entrance of the processing and forming module through a conveying belt. The processing and forming module is used for cutting, shaping and assembling the raw materials into air springs. The quality detection module is used for detecting the quality of the processed and formed air springs, including size, air tightness and strength detection, to obtain a detection report, and comprises the following steps: determining a certain number of air springs in sequence from the production line as detection samples, and measuring the outer diameter, inner diameter and height of the detection samples by using an automatic size measuring instrument; recording the size data of each sample, and comparing the size data with the set standard to determine whether the size of each sample is qualified to obtain a size determination result; according to the size determination result, placing the samples with qualified sizes in an air tightness testing device, and filling a certain pressure of gas into the samples with qualified sizes for a period of time; monitoring the pressure change of the samples with qualified sizes by using a pressure sensor, and determining whether the air tightness of the samples with qualified sizes is qualified according to the pressure change to obtain an air tightness determination result; according to the air tightness determination result, installing the samples with qualified air tightness to a strength testing machine, and gradually applying pressure to the samples with qualified air tightness according to the set standard until the maximum design pressure is reached; a displacement sensor monitors the deformation of the samples with qualified air tightness under the action of pressure in real time, and determines whether the strength of the samples with qualified air tightness is qualified according to the deformation to obtain a strength determination result; and integrating the size determination result, the air tightness determination result and the strength determination result to generate the detection report, including the detection data, the determination result and the classification mark of each sample. The finished product packaging module is used for packaging the qualified air springs, and conveying the packaged air springs to a designated position according to a final conveying path, and comprises the following steps: calculating a final packaging scheme by using a genetic algorithm according to the size, shape, weight and available packaging material characteristics of the air springs, the packaging scheme including the type, specification and packaging method of the packaging material, and the final packaging scheme is used for packaging the air springs. ​ The sensor on the production line automatically detects and obtains the size, shape and weight physical parameters of the current air spring, and collects the packaging material characteristic data, including type, thickness, strength, weight, cost, environmental protection index; the air spring physical parameters and the packaging material characteristic data are integrated to form a data set, and the basic parameters of the genetic algorithm are set, including population size, iteration number, crossover rate and mutation rate; the optimization target is defined, and a group of initial packaging schemes are randomly generated according to the characteristics of the packaging material and the physical parameters of the air spring, each scheme including the type, specification and packaging method of the packaging material; each individual, that is, the initial packaging scheme, is represented as a chromosome in the population, and the genes on the chromosome represent the parameters of the packaging scheme; the fitness of each individual is evaluated, that is, the objective function value corresponding to each individual is calculated, and the candidate individuals are determined according to the objective function value of the individual; the crossover operation is performed on the candidate individuals, new individuals are generated by exchanging part of the genes, and the new individuals are subjected to mutation operation to update the individual population; the crossover and mutation operations are repeated until the preset iteration number is reached, and the final solution, that is, the final packaging scheme, is determined from the final population according to the objective function value of the individual; According to the final packaging scheme, the packaging equipment is controlled to determine the corresponding packaging material and packaging method, and the qualified air spring is packaged; after packaging, the final conveying path is calculated by using a path planning algorithm according to the layout of the production line, the state of the conveying equipment, the target storage location and the transportation time, and the packaged air spring is conveyed to the specified storage location according to the final conveying path; The automatic conveying module is used to realize seamless material transmission between the raw material conveying module, the processing and forming module, the quality detection module and the finished product packaging module through the transmission belt and the positioning sensor.

2. The air spring automated production line of claim 1, wherein, The calculation formula of the objective function value corresponding to each individual is: ； in, This represents the objective function value for each individual. , Indicates the weighting coefficient; Indicates the types and quantities of packaging materials; An index indicating the types of packaging materials; Indicates the first The unit price of the packaging materials; Indicates the first Packaging materials in packaging solutions Dosage in; Indicates the first Loss coefficient of various packaging materials; Indicates the first Discount factor for various packaging materials; Indicates the volume of the air spring; Indicates the packaging efficiency coefficient; Indicates the total volume of the packaging materials; Indicates the compression coefficient of the packaging material; The weighting coefficient representing transportation costs; Indicates the unit price of transportation; Indicates a given packaging scheme and transportation routes Under certain conditions, the actual transportation distance of goods from the origin to the destination.

3. The air spring automated production line of claim 2, wherein, After packaging, the final conveying path is calculated by using a path planning algorithm according to the layout of the production line, the state of the conveying equipment, the target storage location and the transportation time, and the packaged air spring is conveyed to the specified storage location according to the final conveying path, including: Obtain the layout diagram of the production line, including the device position, connection relationship, channel width, and take the production line layout as a graph structure, wherein the node represents the storage location and the edge represents the conveying path; Assign a weight to each edge, and initialize an open list and a closed list; Add the starting position to the open list, and cyclically execute the steps of taking out the node with the lowest cost, checking the storage location, traversing the adjacent nodes and updating the list until the open list is empty, to obtain the final path composed of a series of nodes from the starting position to the storage location; According to the final path, the conveying equipment is controlled to convey the packaged air spring to each node on the path in turn.

4. A control system for an air spring automated production line as claimed in any one of claims 1 to 3, characterized in that, The control system realizes the following functions: Collect data from various sensors on the production line, including air compressor pressure, variable pressure reducing valve opening, solenoid valve state, air spring deformation height, displacement sensor signal, pressure sensor signal; According to the pre-defined air spring production process and quality control requirements, a control strategy is formulated, which includes air spring inflation and deflation control, displacement control and pressure control; According to the control strategy, the control unit sends control instructions to the execution device on the production line, and monitors the running state of the production line in real time, and receives feedback signals; Real-time data and running state are transmitted to remote terminal to monitor the running data and state of the production line in real time, and the control strategy is dynamically adjusted according to the feedback signals; When the production line appears abnormal or failure, the alarm signal is automatically sent, and the alarm information is transmitted to the remote terminal.

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