Production and processing control method of connecting block assembly for hoisting

Abstract

The invention discloses a production and processing control method of a connecting block assembly for hoisting, and relates to the field of machining and manufacturing. The method comprises the following steps: according to a bearing demand of a target hoisting connection assembly in a target processing order; generating K production processing parameter spaces; a plurality of production processing parameter combinations are obtained through multiple times of random extraction; performing quality prediction on the K parts to obtain a plurality of part quality prediction value sets; the machining precision and the assembly precision are recognized, fitness analysis is conducted on production and machining parameter combinations, and multiple fitness degrees are obtained; performing combination variation optimization to obtain a target production processing parameter combination; and carrying out production processing control on the target connecting assembly for hoisting. The technical problems that in the prior art, a production method cannot flexibly adjust machining parameters according to requirements and lacks an optimization mechanism, so that a connecting assembly cannot meet the quality requirement, and the production efficiency is low are solved, and the technical effects of customized production and production and machining parameter optimization are achieved.

Description

Technical Field

[0001] This application relates to the field of mechanical processing and manufacturing technology, specifically to a method for controlling the production and processing of connecting block assemblies for hoisting. Background Technology

[0002] In the manufacturing process of lifting connection modules, traditional production control methods often rely on experience or fixed combinations of production parameters, making it impossible to flexibly adjust to specific order requirements (such as load-bearing capacity). This approach may not only result in produced connection components that fail to meet load-bearing and other quality requirements, but also lead to resource waste and low production efficiency. Furthermore, existing production control methods lack optimization mechanisms for combinations of production parameters, failing to select the optimal solution from multiple possible combinations, thus limiting the improvement of product quality and production efficiency.

[0003] In summary, existing technologies suffer from technical problems such as production methods that cannot flexibly adjust processing parameters according to demand and lack optimization mechanisms, resulting in connecting components failing to meet quality requirements and leading to low production efficiency. Summary of the Invention

[0004] Based on this, it is necessary to provide a production and processing control method for hoisting connecting block assemblies to address the above-mentioned technical problems. This method can solve the technical problems in the existing technology where the production method cannot flexibly adjust the processing parameters according to the needs and lacks an optimization mechanism, resulting in the connecting components failing to meet quality requirements and causing low production efficiency. It achieves the technical effect of customized production and optimized production and processing parameters.

[0005] Based on this, a production and processing control method for hoisting connecting block assemblies is provided. The method includes: determining, based on the load-bearing requirements of the target hoisting connecting assembly in the target processing order, a set of K component quality standard values, K processing precisions, and K assembly precisions for K components of the target hoisting connecting assembly, where K is an integer greater than or equal to 1; retrieving from the production parameter library of the target workshop based on the K component quality standard values ​​and the K processing precisions to generate K production and processing parameter spaces; randomly selecting one production and processing parameter from the K production and processing parameter spaces each time to generate a production and processing parameter combination; obtaining multiple production and processing parameter combinations through multiple random selections; and configuring the multiple production and processing parameter combinations... The production and processing parameters are transmitted to the processing prediction unit to predict the quality of K components, resulting in multiple sets of component quality prediction values. Each set of component quality prediction values ​​includes K component quality prediction values. Based on the multiple sets of component quality prediction values ​​and the K component quality standard values, processing accuracy and assembly accuracy are identified. Combining the K sets of processing accuracy and the K sets of assembly accuracy, a fitness analysis of the production and processing parameter combinations is performed to obtain multiple fitness values. Based on the multiple fitness values, combination mutation optimization is performed on the multiple production and processing parameter combinations to obtain a target production and processing parameter combination. Based on the target production and processing parameter combination, the production and processing control of the target hoisting connection assembly is performed.

[0006] The above-mentioned production and processing control method for hoisting connecting block components solves the technical problems in the existing technology where the production method cannot flexibly adjust the processing parameters according to the needs and lacks an optimization mechanism, resulting in the connecting components failing to meet quality requirements and causing low production efficiency. It achieves the technical effect of customized production and optimized production and processing parameters.

[0007] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating the manufacturing and processing control method for a hoisting connecting block assembly in one embodiment; Figure 2 This is a flowchart illustrating the random number determination process of the production and processing control method for the hoisting connecting block assembly in one embodiment. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0010] like Figure 1 As shown, this application provides a method for controlling the production and processing of a hoisting connecting block assembly, the method comprising: Based on the load-bearing requirements of the target hoisting connection assembly in the target processing order, determine the set of K component quality standard values, K processing precisions, and K assembly precisions of the K components of the target hoisting connection assembly, where K is an integer greater than or equal to 1.

[0011] Lifting connector assemblies are critical components used in lifting operations, primarily connecting lifting tools (such as slings and hooks) to the object or structure to be lifted. The role of these connector assemblies is paramount in lifting operations. They must be able to withstand the weight of the object being lifted and the various forces and moments generated during the lifting process, while also ensuring the reliability and stability of the connection. Therefore, the selection and use of lifting connector assemblies must be comprehensively considered based on specific lifting requirements and environmental conditions to ensure the safety and reliability of the connector assemblies. This application provides a production and processing control method for lifting connector assemblies. Through strict production and processing control, it can be ensured that all aspects of the connector assembly, including material selection, structural design, and manufacturing process, meet predetermined standards and requirements, optimizing the production process, reducing waste and delays, and thereby improving production efficiency.

[0012] The load-bearing requirements of the target lifting connection assembly are clearly defined, derived from engineering drawings, technical specifications, or customer requirements. For the target lifting connection assembly, the function and stress conditions of its K components need to be analyzed. Different components may bear different forces during lifting, therefore their quality standards, machining accuracy, and assembly accuracy requirements will also differ. Based on the function and stress conditions of the components, appropriate quality standard values ​​are set for each component. These standard values ​​may include the chemical composition of the material, physical properties (such as strength, hardness, toughness, etc.), dimensional tolerances, etc. Referencing industry standards, ensure that the set quality standard values ​​meet relevant requirements. Based on the function and stress conditions of the components, as well as the accuracy requirements of the lifting operation, appropriate machining accuracy is set for each component. Machining accuracy may include dimensional accuracy, shape accuracy, positional accuracy, etc. Considering factors such as the accuracy of the machining equipment, the feasibility of the machining process, and machining costs, the machining accuracy is set reasonably. Assembly accuracy refers to the accuracy of the mutual fit between components during assembly. For lifting connection assemblies, assembly accuracy directly affects their load-bearing capacity and safety. Based on the accuracy requirements of the lifting operation, as well as the function and stress conditions of the components, appropriate assembly accuracy is set for each component. Assembly accuracy may include the clearance between parts, perpendicularity, parallelism, etc. The assembly accuracy should be reasonably set by comprehensively considering factors such as assembly process, assembly equipment, and assembly cost. The quality standard values, machining accuracy, and assembly accuracy of the K parts determined above are respectively formed into sets, where K is a positive integer, for subsequent production processing and quality control.

[0013] Based on the K component quality standard values ​​and the K processing precision values, a search is performed in the production parameter library of the target workshop to generate a K production and processing parameter space.

[0014] After determining the quality standard values ​​and machining accuracy of K components of the target hoisting connection assembly, a search can be conducted in the target workshop's production parameter library to generate the corresponding production and machining parameter space. Ensure the target workshop has a complete production parameter library containing various component production and machining parameters, such as material type, cutting speed, feed rate, depth of cut, and heat treatment parameters. Define the search criteria, including component quality standard values ​​(based on the previously determined quality standard values ​​of the K components, specifying the material type and physical performance requirements for each component) and machining accuracy (based on the machining accuracy requirements for each component, specifying the required machining equipment, tools, fixtures, etc.). Based on the component quality standard values ​​and machining accuracy requirements, initially filter out the range of production and machining parameters that meet the requirements. Based on this initial filtering, further refine the search criteria, such as equipment model, tool type, and heat treatment method, to find the most suitable production and machining parameters. For each component, generate a production and machining parameter space based on the search results. This space contains all possible combinations of production and machining parameters that meet the component's quality standard values ​​and machining accuracy requirements. The production and machining parameter space is presented in the form of tables, charts, or a database for easy reference and retrieval in subsequent production and machining processes. The generation of production and processing parameter space needs to take into account the influence of various factors, such as equipment performance, tool wear, and raw material quality. Therefore, in actual operation, adjustments and optimizations are required based on the actual situation.

[0015] Obtain multiple sample first component quality standard values, multiple sample first processing precisions, and multiple sample first production processing parameter combinations; construct a two-dimensional space, where the x-axis represents the quality standard value and the y-axis represents the processing precision; input the multiple sample first component quality standard values ​​and the multiple sample first processing precisions into the two-dimensional space to generate multiple sample points, and use the multiple sample first production processing parameter combinations to identify the multiple sample points; based on the identified multiple sample points and the two-dimensional space, generate a first component production parameter library; determine K component production parameter libraries, and summarize the K component production parameter libraries to generate the production parameter library.

[0016] Acquire the quality standard values, machining accuracy, and corresponding production and processing parameter combinations for multiple sample first-component parts. This sample data can come from previous production records, test data, or expert advice. Create a two-dimensional space, where the x-axis represents the quality standard value and the y-axis represents the machining accuracy. This two-dimensional space will be used to visualize the sample data. Input the collected quality standard values ​​and machining accuracy of the multiple sample first-component parts into the two-dimensional space, with each sample point representing a specific quality standard value and machining accuracy for a component. Identify the sample points using the corresponding production and processing parameter combinations for subsequent analysis and use. Based on the identified sample points and the two-dimensional space, the relationship or trend between the quality standard values, machining accuracy, and production and processing parameters can be observed. Based on these relationships or trends, combined with production experience and actual needs, determine one or more recommended production and processing parameter combinations for each sample point. Associate these recommended production and processing parameter combinations with the corresponding sample points (i.e., quality standard values ​​and machining accuracy) to form a production parameter library for the first-component parts. Repeat the above steps to collect and process sample data for the other K-1 components, i.e., quality standard values, machining accuracy, and corresponding production and processing parameter combinations. Construct a corresponding two-dimensional space for each component and generate a corresponding production parameter library. The production parameter databases for K components are compiled to form a complete production parameter database. This database contains the quality standard values, processing accuracy, and recommended combinations of production parameters for all K components. In practical applications, appropriate combinations of production parameters can be selected from this database based on the specific requirements of the target lifting connection assembly (such as load-bearing capacity, dimensions, etc.). Through these steps, a comprehensive production parameter database containing multiple component production parameter databases can be constructed based on historical sample data and actual needs, providing effective reference and guidance for the production and processing of the target lifting connection assembly.

[0017] Extract the first component quality standard value and first processing precision corresponding to the first component production parameter library; input the first component quality standard value and the first processing precision into the first component production parameter library to obtain the first target point; obtain the m sample points in the first component production parameter library that are closest to the first target point, and calculate the mean of the m sample production processing parameter combinations of the m sample points to determine the first target production processing parameter combination, where m is a positive integer greater than or equal to 3; search the production parameter library according to the K component quality standard values ​​and the K processing precisions to obtain the K production processing parameter space.

[0018] The quality standard value and machining precision of the first target component are clearly defined. These values ​​will serve as input conditions for the retrieval. The extracted quality standard value and machining precision of the first component are input into the first component production parameter library. In the production parameter library, these values ​​will correspond to a specific point, called the first target point. In the first component production parameter library, the m nearest sample points to the first target point are searched. Here, m is a positive integer greater than or equal to 3, used to ensure sufficient sample points to calculate the mean. These m sample points should represent the combination of production and processing parameters used under similar quality standard values ​​and machining precision conditions. The mean of the production and processing parameter combinations of these m sample points is calculated. The mean can be an arithmetic mean, a weighted average, etc., depending on the actual situation and needs. By calculating the mean, a set of production and processing parameter combinations is obtained. This set of parameter combinations is considered to be the most likely applicable under given quality standard values ​​and machining precision conditions. This is called the first target production and processing parameter combination. For each of the K components, there is a corresponding set of quality standard values ​​and machining precision. These quality standard values ​​and machining precision are input into the production parameter library for retrieval. For each component, based on the search results, one or more potentially applicable combinations of production and processing parameters can be obtained. These combinations constitute a production and processing parameter space. Through the above steps, not only can production and processing parameter combinations matching the given quality standard value and processing accuracy be determined from the first component's production parameter library, but also potentially applicable production and processing parameter spaces can be found for the other K components. These parameters and parameter spaces will provide valuable reference and guidance for subsequent production and processing.

[0019] Each time, a production and processing parameter is randomly selected from the K production and processing parameter spaces to generate a production and processing parameter combination. After multiple random selections, multiple production and processing parameter combinations are obtained.

[0020] For each component, a production processing parameter is randomly selected from its corresponding production processing parameter space. This process is repeated K times to ensure that each component has a parameter extracted from its corresponding parameter space. The K production processing parameters extracted from the K production processing parameter spaces are then combined to form a complete production processing parameter combination. This combination represents a specific set of production processing parameters for the K components. The above random selection and combination process is repeated multiple times to generate multiple different production processing parameter combinations. The specific number of selections can be determined as needed, for example, to generate enough combinations for subsequent simulations, experiments, or analyses. Through the above method, a diverse set of production processing parameter combinations can be obtained, which can provide multiple possible solutions for subsequent production processing.

[0021] The multiple production and processing parameters are combined and transmitted to the processing prediction unit to predict the quality of K parts, thereby obtaining multiple sets of predicted quality values ​​for parts, wherein each set of predicted quality values ​​for parts includes K predicted quality values ​​for parts.

[0022] Multiple combinations of randomly generated production and processing parameters are organized to ensure that each combination contains production and processing parameters corresponding to K parts. These combinations are then transmitted to the processing prediction unit. Upon receiving the combinations, the processing prediction unit uses its internal prediction model or algorithm to predict the quality of each combination. This model or algorithm may be based on machine learning, deep learning, physical simulation, or other prediction techniques, capable of predicting the quality of the corresponding parts based on the given production and processing parameters. For each combination of production and processing parameters, the processing prediction unit outputs a set of predicted part quality values. This set contains the predicted quality values ​​for K parts, each value corresponding to the predicted quality result of a part under the given parameter combination. This quality prediction process is repeated until all combinations of production and processing parameters have been predicted. The set of predicted part quality values ​​for each combination is then organized and stored. Ultimately, multiple sets of predicted part quality values ​​are obtained, each containing the predicted quality values ​​for K parts. These sets of predicted quality values ​​can also be used for subsequent comparison and guidance to improve quality control and efficiency in the production process, thereby enhancing product quality and production efficiency.

[0023] Based on the set of multiple component quality prediction values ​​and the K component quality standard values, the machining accuracy and assembly accuracy are identified. Then, the fitness analysis of the combination of production and processing parameters is performed by combining the K sets of machining accuracy and the K sets of assembly accuracy to obtain multiple fitness values.

[0024] For each set of predicted quality values ​​for a component, the predicted quality values ​​of K corresponding components are compared with the corresponding standard quality values ​​of K components. Based on the comparison results, the processing accuracy of each component under a given combination of production processing parameters can be evaluated. Processing accuracy can be measured by the deviation or degree of conformity between the predicted quality value and the target standard quality value. Simultaneously, by combining the processing accuracy of the K components, the assembly accuracy of the entire product can be further evaluated. Assembly accuracy can be judged based on the matching degree and coordination of the processing accuracy of each component. The fitness function is a quantitative indicator used to measure the degree of matching between the combination of production processing parameters and the quality standard. The design of the fitness function should be formulated according to actual production conditions and needs, considering factors such as processing accuracy, assembly accuracy, cost, and production efficiency. For each combination of production processing parameters, a fitness value is calculated based on its performance in processing accuracy and assembly accuracy identification, and the definition of the fitness function. This value reflects the ability and effectiveness of the parameter combination in meeting the quality standard. For multiple combinations of production processing parameters, the fitness value calculation is repeated until the fitness values ​​for all combinations have been calculated. The fitness values ​​of all production and processing parameter combinations are compared to analyze the differences in their strengths and weaknesses. Based on the fitness values, superior combinations are identified. These combinations have higher fitness values, indicating better performance and effectiveness in meeting quality standards. These superior combinations are then applied to actual production to improve product quality and production efficiency. Furthermore, the fitness function and evaluation method can be further optimized and improved based on actual production conditions. Through these steps, processing accuracy and assembly accuracy can be identified based on multiple sets of predicted component quality values ​​and K component quality standard values. Fitness analysis is then used to evaluate the performance and effectiveness of different combinations of production and processing parameters. This helps in selecting the optimal or near-optimal solution from numerous possible parameter combinations, guiding parameter setting and optimization in the actual production process.

[0025] Construct a fitness function, and input the multiple sets of predicted component quality values ​​and the K component quality standard values ​​into the fitness function to obtain the multiple fitness values.

[0026] To construct a fitness function and calculate the fitness of multiple combinations of production and processing parameters, the definition of the fitness function needs to be clearly defined. A fitness function is typically used to evaluate the merits of a solution (in this case, a combination of production and processing parameters) based on a given objective or standard. The fitness function is then input into the multiple sets of predicted component quality values ​​and the K component quality standard values ​​to obtain the multiple fitness values.

[0027] The fitness function is: ; in, For fitness, These are the weighting coefficients used in fitness calculations based on deviations in machining accuracy. These are the weighting coefficients used in fitness calculations based on deviations in assembly accuracy. Let be the component quality standard value for the i-th component. Let be the predicted quality value of the i-th component. Let represent the machining accuracy of the i-th component, and k be the number of K components, where k is a positive integer greater than or equal to 1. It is the predicted quality value of the j-th mating component that mates with the i-th component. This represents the number of mating parts that mate with the i-th part. It is a positive integer less than or equal to k. Let be the assembly accuracy of the i-th component and the j-th mating component.

[0028] Define a name The fitness function is used to evaluate the quality of a combination of production and processing parameters. Specifically, this function combines the machining accuracy, assembly accuracy, and deviation from standard values ​​of the parts, deriving a comprehensive fitness value through a weighted average. By comprehensively considering machining accuracy and assembly accuracy, a quantitative index is provided to measure the quality of the combination of production and processing parameters. A higher value indicates that the processing and assembly of the parts under this parameter combination is better and closer to the expected quality standard; while a lower value indicates better performance. A lower value indicates poor performance, requiring adjustment of parameter combinations to improve production quality.

[0029] Based on the multiple fitness values, the multiple combinations of production and processing parameters are combined and mutated to obtain the target combination of production and processing parameters.

[0030] To optimize multiple combinations of production and processing parameters based on multiple fitness levels to obtain a target combination of production and processing parameters, a genetic algorithm (GA) or other evolutionary algorithms can be used. The target combination of production and processing parameters refers to the desired combination of production and processing parameters.

[0031] The production and processing parameters corresponding to the maximum value among the multiple fitness values ​​are used as the head particle, and the remaining multiple production and processing parameters are used as multiple tail particles. Using the head particle as the mutation direction, the multiple tail particles are mutated and adjusted according to a preset adjustment scheme to obtain multiple mutated tail particles. The preset adjustment scheme involves increasing or decreasing the production and processing parameters in the multiple tail particles according to a preset adjustment step size. The multiple mutated tail particles are transmitted to the processing prediction unit for component quality prediction, and the prediction results are analyzed using the fitness function to obtain the fitness of multiple mutated tail particles. It is determined whether the maximum fitness of the multiple mutated tail particles is greater than the fitness corresponding to the head particle. If not, a random number generator is invoked. When the output of the random number generator is 1, the head particle is iteratively updated using the mutated tail particle corresponding to the maximum fitness of the multiple mutated tail particles to obtain an updated head particle and multiple updated mutated tail particles. Mutation optimization is performed based on the updated head particle and multiple updated mutated tail particles until a preset number of iterations is met. The updated head particle corresponding to the maximum fitness value during the iteration process is used as the target particle, and the production and processing parameter combination corresponding to the target particle is used as the target production and processing parameter combination.

[0032] Multiple combinations of production and processing parameters, i.e., a particle swarm, are obtained through some method (such as random generation or experience-based methods). For each particle (combination of production and processing parameters) in the particle swarm, its fitness value is calculated using a fitness function. The particle with the highest fitness value is designated as the head particle (optimal solution), and the remaining particles are designated as tail particles. Using the head particle as the mutation direction, the production and processing parameters in the tail particles are adjusted according to a preset adjustment scheme, either by increasing or decreasing them. This "increasing or decreasing" is usually based on a heuristic or stochastic strategy; for example, the direction and step size of the adjustment can be determined based on the difference in parameters between the head and tail particles. Multiple mutated tail particles are obtained after mutation. These mutated tail particles are transmitted to the processing prediction unit, where part quality prediction is performed on each mutated tail particle. The prediction results are analyzed using a fitness function to obtain the fitness values ​​of the multiple mutated tail particles. The algorithm determines whether the maximum fitness value of the mutated tail particles is greater than the fitness value of the head particle. If so, iteratively updates the head particle using the mutated tail particle corresponding to the maximum fitness value of the multiple mutated tail particles, obtaining an updated head particle and multiple updated mutated tail particles. If not, it calls a random number generator. When the output of the random number generator is 1, it updates the head particle using the mutated tail particle with the maximum fitness value and updates the corresponding tail particle set. Based on the updated head and tail particles, the above steps of mutation adjustment, quality prediction, fitness analysis, and iterative update are repeated. The iterative process continues until a preset number of iterations is reached or other termination conditions are met (such as the fitness value reaching a preset threshold). After the iteration ends, the head particle corresponding to the maximum fitness value during the iteration is taken as the target particle. The production and processing parameter combination corresponding to the target particle is taken as the target production and processing parameter combination and output. This optimization process combines the "following the optimal solution" idea in particle swarm optimization algorithm and adds a custom mutation adjustment mechanism to improve the algorithm's exploration ability and optimization effect.

[0033] like Figure 2 As shown, the random number judge is constructed, wherein the random number judge includes a random number generation unit and a random number judgment unit; the random number generation unit is constructed based on the rand function, and a first random number and a second random number are generated according to the random number generation unit; the random number judgment unit compares the size of the first random number and the second random number, and when the first random number is greater than or equal to the second random number, the output result is 0; when the first random number is less than the second random number, the output result is 1.

[0034] The random number generation unit will generate random numbers based on the `rand` function. In most programming languages, the `rand` function is a standard library function used to generate pseudo-random numbers. However, random numbers generated directly using the `rand` function are usually integers within a fixed range. To generate two random numbers (the first and second random numbers), the `rand` function can be called twice consecutively. After generating the first and second random numbers, the random number judging unit will be responsible for comparing their sizes and outputting 0 or 1 based on the comparison result. A `RandomNumberJudge` class can be created, which has the functions of initializing the random number seed, generating random numbers, and comparing the sizes of two random numbers. The `judge` method is responsible for executing the entire process and returning the comparison result. In the `main` function, a `RandomNumberJudge` object is created and its `judge` method is called to test the functionality of the random number judge.

[0035] When the output of the random number detector is 0, the first adjustment method of the multiple mutated tail particles is added to the disabled list, and the multiple tail particles are mutated again with the head particle as the mutation direction according to the preset adjustment scheme to obtain multiple updated mutated tail particles. The disabled list has a disabled number identifier. Based on the head particle and the multiple updated mutated tail particles, mutation optimization is performed until the preset number of iterations is met. The updated head particle corresponding to the maximum fitness value during the iteration process is taken as the target particle, and the production and processing parameter combination corresponding to the target particle is taken as the target production and processing parameter combination.

[0036] The DisabledList stores mutation methods that have proven to be less effective. The DisableCount flag tracks the number of times each mutation method has been disabled for potential re-enabling. When the random number generator outputs 0, the currently used mutation method (first adjustment method) is recorded. This mutation method is added to the DisabledList, and its DisableCount flag is incremented. Mutations in the DisabledList are ignored. Using the head particle as the mutation direction again, multiple tail particles are mutated according to a preset adjustment scheme (excluding disabled mutation methods) to obtain multiple updated mutated tail particles. Based on the updated head particles and multiple updated mutated tail particles, quality prediction, fitness analysis, and iterative updates are repeated. During iteration, the mutation methods in the DisabledList are continuously monitored, and the DisableCount flag is updated as needed. The mutation methods in the DisabledList are checked periodically. If the DisableCount flag of a mutation method is lower than a preset threshold, re-enabling that mutation method can be considered to increase the algorithm's exploration capability. Iteration stops when the preset number of iterations or other termination conditions are met. The updated head particle corresponding to the maximum fitness value during iteration is taken as the target particle. The combination of production and processing parameters corresponding to the target particle is used as the target production and processing parameter combination and output.

[0037] The production and processing control of the target hoisting connection component is based on the target production and processing parameter combination.

[0038] The target production and processing parameter combination is verified to ensure it meets the requirements of the production equipment and processes. Corresponding materials are prepared based on the target production and processing combination. The target production and processing parameter combination is then input into the production equipment's control system. The production equipment is started, initiating the production and processing of the target hoisting connection components. The production process is monitored in real time to ensure stable operation of the equipment according to the set parameters. During production, key process steps are closely monitored to ensure product quality and process stability. This process ensures that the production and processing control of the target hoisting connection components is carried out according to the target production and processing parameter combination, thereby improving production efficiency and product quality. Furthermore, through data recording and analysis, production parameters can be continuously optimized, achieving continuous improvement.

[0039] In summary, the beneficial effects of this application include: 1. Based on the load-bearing requirements of the target hoisting connection components in the target processing order, the quality standard values, processing accuracy, and assembly accuracy set of the parts are determined, thereby realizing customized production and ensuring that the produced connection components can meet specific quality requirements.

[0040] 2. By searching a production parameter database, multiple production and processing parameter spaces are generated. Then, through random sampling and combined mutation optimization, the target combination of production and processing parameters is obtained. This method can select the optimal solution from multiple possible combinations of production and processing parameters, improving product quality and production efficiency.

[0041] 3. The method of the present invention is based on data-driven approach. By collecting and analyzing sample data, a production parameter library is constructed, and a processing prediction unit is used to predict quality, providing data support for production and processing control and helping to optimize and improve the production process.

[0042] 4. The method of the present invention can be flexibly applied to the production and processing control of different types of hoisting connecting block assemblies, and can be expanded and optimized according to actual needs to adapt to different production environments and requirements.

[0043] For specific embodiments regarding the production and processing control method of the hoisting connecting block assembly, please refer to the above text. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0044] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A method for controlling production and processing of a hoisting connection block assembly, characterized by The method comprises: According to the load bearing requirement of the target lifting connecting assembly in the target processing order, determine the K component quality standard value, K processing precision and K assembly precision set of K components of the target lifting connecting assembly, wherein K is an integer greater than or equal to 1; Based on the K component quality standard value and the K processing precision, search in the production parameter library of the target workshop to generate K production processing parameter spaces; Randomly extract one production processing parameter from each of the K production processing parameter spaces to generate one production processing parameter combination each time, and obtain multiple production processing parameter combinations through multiple random extractions; Transmit the multiple production processing parameter combinations to the processing prediction unit for quality prediction of the K components to obtain multiple component quality prediction value sets, wherein each component quality prediction value set includes K component quality prediction values; Based on the multiple component quality prediction value sets and the K component quality standard values, identify the processing precision and the assembly precision, and analyze the fitness of the production processing parameter combination in combination with the K processing precision and the K assembly precision set to obtain multiple fitnesses; Based on the multiple fitnesses, optimize the multiple production processing parameter combinations to obtain a target production processing parameter combination; Based on the target production processing parameter combination, control the production and processing of the target lifting connecting assembly.

2. The production process control method of the hoisting connecting block assembly as claimed in claim 1, wherein It comprises: Obtain multiple sample first component quality standard values, multiple sample first processing precisions and multiple sample first production processing parameter combinations; Construct a two-dimensional space, wherein the x-axis of the two-dimensional space is the quality standard value and the y-axis is the processing precision; Input the multiple sample first component quality standard values and the multiple sample first processing precisions into the two-dimensional space to generate multiple sample points, and identify the multiple sample points using the multiple sample first production processing parameter combinations; Based on the identified multiple sample points and the two-dimensional space, generate a first component production parameter library; Determine K component production parameter libraries, and aggregate the K component production parameter libraries to generate the production parameter library.

3. The method of claim 2, wherein the method further comprises: determining the number of the lifting blocks to be used for the lifting of the object; and determining the number of the lifting block assemblies to be used for the lifting of the object. It comprises: Extract the first component quality standard value and the first processing precision corresponding to the first component production parameter library; Input the first component quality standard value and the first processing precision into the first component production parameter library to obtain a first target point; Obtain the m sample points closest to the first target point in the first component production parameter library, and perform mean value calculation on the m sample production processing parameter combinations of the m sample points to determine a first target production processing parameter combination, wherein m is a positive integer greater than or equal to 3; According to the K component quality standard value and the K processing precision, search in the production parameter library to obtain the K production processing parameter spaces.

4. The manufacturing and processing control method for the hoisting connecting block assembly as described in claim 1, characterized in that, It comprises: Construct a fitness function and input the multiple component quality prediction value sets and the K component quality standard values into the fitness function to obtain the multiple fitnesses.

5. The method of claim 4, wherein the method further comprises the steps of: determining the number of the lifting blocks to be used for the lifting of the object; and determining the number of the lifting block assemblies to be used for the lifting of the object. The fitness function is: ； wherein, is an adaptability, is a weight coefficient when the adaptability is calculated according to the deviation of the machining precision, is a weight coefficient when the adaptability is calculated according to the deviation of the assembly precision, is a part quality standard value of the i-th part, is a part quality prediction value of the i-th part, is a machining precision of the i-th part, k is the number of K parts, and k is a positive integer greater than or equal to 1, is a part quality prediction value of the j-th mating part mating with the i-th part, is the number of mating parts mating with the i-th part, is a positive integer less than or equal to k, is an assembly precision of the i-th part and the j-th mating part mating therewith.

6. The method of claim 5, wherein the method further comprises the steps of: determining the number of the lifting blocks to be used for the lifting of the object; and determining the number of the lifting block assemblies to be used for the lifting of the object. It comprises: The production and processing parameter combination corresponding to the maximum value of the plurality of fitness is taken as a head particle, and the remaining plurality of production and processing parameter combinations are taken as tail particles; The head particle is taken as a mutation direction, and the plurality of tail particles are mutated and adjusted according to a preset adjustment scheme to obtain a plurality of mutated tail particles, wherein the preset adjustment scheme is to increase or decrease the production and processing parameters in the plurality of tail particles according to a preset adjustment step; The plurality of mutated tail particles are transmitted to a processing prediction unit for part quality prediction, and the prediction results are analyzed by using the fitness function to obtain a plurality of mutated tail particle fitness; It is judged whether the maximum value of the plurality of mutated tail particle fitness is greater than the fitness corresponding to the head particle, and if not, a random number judge is called, and when the output result of the random number judge is 1, the head particle is iteratively updated by using the mutated tail particle corresponding to the maximum value of the plurality of mutated tail particle fitness to obtain an updated head particle and a plurality of updated mutated tail particles; Based on the updated head particle and the plurality of updated mutated tail particles, mutation optimization is performed until a preset iteration number is satisfied, the updated head particle corresponding to the maximum value of the fitness in the iteration process is taken as a target particle, and the production and processing parameter combination corresponding to the target particle is taken as a target production and processing parameter combination.

7. The method of claim 6, wherein the method further comprises the steps of: determining the number of the lifting blocks to be used for the lifting of the object; and determining the number of the lifting block assemblies to be used for the lifting of the object. It comprises: The random number judge is constructed, wherein the random number judge comprises a random number generation unit and a random number judgment unit; The random number generation unit is constructed based on a rand function, and a first random number and a second random number are generated according to the random number generation unit; The first random number and the second random number are compared in size by using the random number judgment unit, and when the first random number is greater than or equal to the second random number, the output result is 0; When the first random number is less than the second random number, the output result is 1.

8. The method of claim 6, wherein the method further comprises the steps of: determining the number of the lifting blocks to be used for the lifting of the object; and determining the number of the lifting block assemblies to be used for the lifting of the object. It comprises: When the output result of the random number judge is 0, a first adjustment mode for obtaining the plurality of mutated tail particles is added to a disabled list, and the head particle is taken as a mutation direction again, and the plurality of tail particles are mutated and adjusted according to a preset adjustment scheme to obtain a plurality of updated mutated tail particles, wherein the disabled list has a disabled number identifier; Based on the head particle and the plurality of updated mutated tail particles, mutation optimization is performed until a preset iteration number is satisfied, the updated head particle corresponding to the maximum value of the fitness in the iteration process is taken as a target particle, and the production and processing parameter combination corresponding to the target particle is taken as a target production and processing parameter combination.

Images