Bending bolt installation method, device, equipment and medium based on ergonomic theory to optimize assembly sequence

Abstract

The present application discloses a bending bolt installation method, device, equipment and medium for optimizing the assembly sequence based on the human-machine efficiency theory, and relates to mechanical assembly technology. The present application constructs an objective function based on the human-machine efficiency theory, and uses a simulated annealing algorithm to optimize the assembly sequence and torque combination, thereby realizing dynamic optimization of assembly parameters, significantly improving the assembly accuracy of the bending bolt and reducing the assembly defect rate while ensuring the assembly efficiency.

Description

Technical Field

[0001] The present application relates to the field of mechanical assembly technology, and in particular to a method, device, equipment and medium for installing bent bolts based on the human-machine efficiency theory to optimize the assembly sequence. Background Art

[0002] Due to its special structural shape and stress characteristics, bent bolts are widely used in the connection of key components in wind turbines, engineering machinery, aerospace and other fields. However, due to the bending of the axis of the bent bolt, the force is complex during the assembly process and the installation is difficult. Traditional bent bolt installation methods mostly use empirical assembly sequences, lack scientific guidance, and are prone to problems such as uneven bolt force and offset deformation, resulting in low assembly accuracy and low efficiency.

[0003] At present, there are some improvement plans, such as using numerical simulation to optimize the bolt arrangement plan, or realizing assembly compensation through elastic elements. In this process, the bolt arrangement plan is usually set based on manual experience, and then numerical simulation is performed. This process has the defects of being time-consuming and low-precision, and is dependent on the experience of the operator. Summary of the invention

[0004] The purpose of this application is to provide a bending bolt installation method, device, equipment and medium that optimizes the assembly sequence based on the ergonomic theory, which can automatically optimize the assembly sequence and torque, reduce the difficulty of assembly, and improve assembly accuracy and efficiency.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In a first aspect, the present application provides a method for installing a bent bolt by optimizing the assembly sequence based on the human-machine efficiency theory, comprising:

[0007] Constructing an objective function based on the human-machine efficacy theory; the objective function is error sensitivity;

[0008] A simulated annealing algorithm is used to solve the installation parameters that make the objective function optimal, as target installation parameters; the installation parameters include: the assembly sequence of multiple bending bolts and the torque of each bending bolt;

[0009] Each bent bolt is installed based on the target installation parameters.

[0010] Optionally, an objective function is constructed based on the human-machine efficacy theory, specifically including:

[0011] The calculation formula for operator fatigue during the assembly process based on the ergonomics theory is:

[0012] F = α × S_f + β × T_f;

[0013] Among them, F is fatigue degree, α and β are weight coefficients, satisfying α+β=1; S_f is the influence factor of the assembly sequence change amplitude on fatigue degree; T_f is the influence factor of the torque change amplitude on fatigue degree;

[0014] Based on the calculation formula of fatigue strength, the objective function is constructed as follows:

[0015]

[0016] Where E is the error sensitivity, ΔS is the change in assembly sequence, ΔM is the change in torque, and k s and k m are the coefficients of the influence of assembly sequence and torque on position offset respectively.

[0017] Optionally, a simulated annealing algorithm is used to solve the installation parameters that optimize the objective function as target installation parameters, specifically including:

[0018] Initializing parameters and installation parameters of the simulated annealing algorithm, wherein the parameters of the simulated annealing algorithm include: initial temperature, termination temperature, temperature reduction coefficient, and number of iterations at each temperature;

[0019] Let the value of k be 1;

[0020] Generate new installation parameters;

[0021] According to the value of the objective function of the new installation parameter and the value of the objective function of the current installation parameter, the current installation parameter is updated according to the Metropolis criterion;

[0022] Determine whether the value of k is less than L, and obtain a first determination result; wherein L is the number of iterations at each temperature;

[0023] If the first judgment result is yes, the value of k is increased by 1, and the process returns to the step of "generating new installation parameters";

[0024] If the first judgment result is no, then judging whether the iteration end condition is met to obtain a second judgment result; the iteration end condition is that the current temperature is not greater than the termination temperature, or the current iteration number is greater than a preset iteration number threshold;

[0025] If the second judgment result is yes, the temperature is lowered according to the temperature reduction coefficient, the current temperature is updated, and the process returns to the step of "setting the value of k to 1";

[0026] If the second judgment result is no, the current installation parameters are output as target installation parameters.

[0027] Optionally, according to the value of the objective function of the new installation parameters and the value of the objective function of the current installation parameters, update the current installation parameters according to the Metropolis criterion, specifically including:

[0028] Judge whether the value of the objective function of the new installation parameters is better than the value of the objective function of the current installation parameters, and obtain the third judgment result;

[0029] If the third judgment result is yes, accept the new installation parameters as the current installation parameters;

[0030] If the third judgment result is no, accept the new installation parameters as the current installation parameters with a probability P; where, ΔT is the absolute value of the difference between the value of the objective function of the new installation parameters and the value of the objective function of the current installation parameters, and T is the initial temperature.

[0031] Optionally, the process of generating new installation parameters is:

[0032] Within the neighborhood of the current installation parameters, generate new installation parameters by swapping the assembly order and adjusting the torque.

[0033] Optionally, install each bending bolt based on the target installation parameters, specifically including:

[0034] Tighten each bending bolt in sequence according to the assembly order and torque in the target installation parameters;

[0035] Measure the installation position deviation of each bending bolt:

[0036] Determine the bending bolts with installation position deviation exceeding the preset range as the target bending bolts;

[0037] According to the direction and magnitude of the installation position deviation of the target bending bolts, adjust the torque of the target bending bolts according to an adjustment ratio of 5%, and adjust the torque of the adjacent bending bolts of the target bending bolts according to an adjustment ratio of 2%, and return to "Measure the installation position deviation of each bending bolt" until the installation position deviation of each bending bolt is within the preset range; where, the torque corresponding to the 5% adjustment ratio is 5% of the torque of the target bending bolt in the target installation parameters, and the torque corresponding to the 2% adjustment ratio is 2% of the torque of the target bending bolt in the target installation parameters.

[0038] Optionally, the correspondence between torque and pre-tightening force during the tightening process is:

[0039] 25 N·m corresponds to a pre-tightening force of 4.6 kN;

[0040] 50 N·m corresponds to a pre-tightening force of 11.2 kN;

[0041] 75N·m corresponds to a preload of 18.4kN;

[0042] 100N·m corresponds to a preload of 25.6kN;

[0043] 125N·m corresponds to a preload of 33.4kN;

[0044] 150N·m corresponds to a preload of 40.4kN;

[0045] 175N·m corresponds to a preload of 46.2kN;

[0046] 200N·m corresponds to a preload of 51.8kN.

[0047] In a second aspect, the present application provides a bending bolt installation device based on the human-machine efficiency theory to optimize the assembly sequence, the bending bolt installation device based on the human-machine efficiency theory to optimize the assembly sequence applies the above-mentioned bending bolt installation method based on the human-machine efficiency theory to optimize the assembly sequence, the bending bolt installation device based on the human-machine efficiency theory to optimize the assembly sequence includes:

[0048] An objective function construction module is used to construct an objective function based on the human-machine efficacy theory; the objective function is error sensitivity;

[0049] An optimization solution module is used to use a simulated annealing algorithm to solve the installation parameters that make the objective function optimal as target installation parameters; the installation parameters include: an assembly sequence of multiple bending bolts and a torque of each bending bolt;

[0050] An installation module is used to install each bent bolt based on the target installation parameters.

[0051] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned bent bolt installation method that optimizes the assembly sequence based on the ergonomic theory.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned bent bolt installation method based on the ergonomic theory to optimize the assembly sequence.

[0053] According to the specific embodiments provided in this application, this application has the following technical effects.

[0054] The present application provides a method, device, equipment and medium for installing bent bolts based on the ergonomic theory to optimize the assembly sequence. The present application constructs an objective function based on the ergonomic theory, uses a simulated annealing algorithm to optimize the assembly sequence and torque combination, and realizes dynamic optimization of assembly parameters. While ensuring assembly efficiency, it significantly improves the assembly accuracy of bent bolts and reduces the assembly defect rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A flowchart of a method for installing a bent bolt that optimizes the assembly sequence based on ergonomic theory is provided in accordance with an embodiment of the present application.

[0057] Figure 2 A flowchart of the assembly of a bent bolt provided in one embodiment of the present application.

[0058] Figure 3 A schematic structural diagram of a bent bolt provided in one embodiment of the present application.

[0059] Figure 4 A schematic diagram of the arrangement of bent bolts provided in one embodiment of the present application.

[0060] Figure 5 A schematic diagram of error sensitivity calculation provided by an embodiment of the present application.

[0061] Figure 6 This is a curve diagram of the optimization process of the simulated annealing algorithm provided in one embodiment of the present application.

[0062] Figure 7 A distribution diagram of final bolt torque and position deviation provided for an embodiment of the present application.

[0063] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0065] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0066] After research, it is considered that human-computer interaction factors have a certain impact on the assembly process, and the assembly accuracy is not only affected by the assembly sequence and torque separately, but also by the combined influence of the two.

[0067] Therefore, there is an urgent need for a new bending bolt installation method that can comprehensively consider human-machine efficiency factors, optimize the assembly sequence and torque, reduce the assembly difficulty, and improve the assembly accuracy and efficiency.

[0068] In an exemplary embodiment, Figure 1 and Figure 2 As shown, the present application provides a method for installing bent bolts based on the ergonomic theory to optimize the assembly sequence, including the following steps 101 to 103.

[0069] Step 101, constructing an objective function based on the ergonomics theory; the objective function is error sensitivity.

[0070] Step 102, using a simulated annealing algorithm to solve the installation parameters that make the objective function optimal, as target installation parameters; the installation parameters include: an assembly sequence of multiple bending bolts and a torque of each bending bolt.

[0071] Step 103: installing each bending screw based on the target installation parameters.

[0072] The above-mentioned steps 101 to 103 can significantly improve the assembly accuracy of the bent bolts and reduce the assembly defect rate while ensuring the assembly efficiency.

[0073] In another exemplary embodiment of the present application, the above step 101 can be implemented using the following steps 201 to 203.

[0074] Step 201, bolt arrangement:

[0075] A plurality of bent bolts are symmetrically arranged on the surface to be installed according to a predetermined plan; the thread specification of the bent bolts is M20×1.5, and the bending angle is 45°-60°; special nuts are used in conjunction, and the flatness tolerance of the nut end face is not greater than 0.2mm.

[0076] Figure 3 The structural example diagram of M30 arc stud bolt is given. Figure 3 (a) is the plan view of the bent bolt. Figure 3 (b) is the assembly structure diagram of the bent bolt. Figure 3 (c) is a schematic diagram of the connection of the three rows of bending bolts on the left and right sides of the pipe segment.

[0077] For example, for the assembly of a wind turbine tower, multiple rows of bent bolts need to be installed to connect the blade root flange and the hub. The tower consists of three segments: bottom, middle and top. The parameters are shown in Table 1.

[0078] Table 1 Tower parameters

[0079] parameter bottom Central top Segment outer diameter 9.5m 7.0m 4.6m Segment thickness 330mm 300mm 270mm Segment height 3.6m 3.0m 2.7m Number of bolt rows 4 rows 3 rows 4 rows Taper 1.29° PVC pipe inner diameter 48mm PVC pipe outer diameter 54mm

[0080] The arrangement of the bending bolts of the tower is as follows:

[0081] On the joint surface of each segment of the tower, the bending bolts are symmetrically arranged, with 72 at the bottom, 54 in the middle, and 48 at the top, for a total of 174 bending bolts; the thread specification of the bending bolts is M20×1.5, the bending angle is 60°, and they are used in conjunction with special nuts, and the flatness tolerance of the nut end face is not greater than 0.2mm; the bolt arrangement diagram is shown in the figure. Figure 4 As shown, Figure 4 The colors in the figure represent the different positions of the segments in each section of the tower and the corresponding number of bending screw inspections, as follows:

[0082] Red part: represents the joint surface of the bottom segment, where 72 bending bolts are arranged.

[0083] Blue part: represents the joint surface of the middle segment, which is arranged with 54 bending bolts.

[0084] Green part: represents the joint surface of the top segment, where 48 bending bolts are arranged.

[0085] These colors visually demonstrate the symmetrical arrangement of the bending bolts at different positions on the tower column, making it easier to understand and install.

[0086] Step 202, establishing a human-machine efficacy model:

[0087] Considering the operator's fatigue during the assembly process, the calculation formula of fatigue F is defined as:

[0088] F=α×S_f+β×T_f

[0089] Among them, F is fatigue degree, α and β are weight coefficients, satisfying α+β=1; S_f is the influence factor of the change amplitude of assembly sequence on fatigue degree; T_f is the influence factor of the torque change amplitude on fatigue degree.

[0090] For example, taking the tower as an example, the weight coefficients α=0.5, β=0.5 are set to satisfy α+β=1;

[0091] The calculation formula of fatigue degree F is:

[0092] F=0.5×S_f+0.5×T_f

[0093] Step 203, calculate the error sensitivity as the objective function:

[0094] Based on the change of assembly sequence and torque, the error sensitivity E is calculated using the finite difference method:

[0095]

[0096] Where E is the error sensitivity, ΔS is the change in assembly sequence, ΔM is the change in torque, and k s and k m They are the coefficients of the influence of assembly sequence and torque on position offset, and the influence of changes in assembly sequence and torque on error sensitivity, such as Figure 5 shown.

[0097] For example, taking the tower as an example, the assembly sequence influence coefficient k is set as s =0.1, torque influence coefficient k m =0.05; and the finite difference method is used to calculate the assembly sequence change ΔS and torque change ΔM.

[0098] In another exemplary embodiment, in the above step 102, a simulated annealing algorithm is used to optimize the installation parameters, set the initial temperature, cooling coefficient, number of iterations and other parameters, optimize the assembly sequence and the initial torque combination, minimize the error sensitivity E as the goal, and perform the process of initial solution generation, neighborhood solution search, current solution update using acceptance criteria, and cooling based on cooling strategy. Wherein:

[0099] Initial solution generation: Randomly generate initial assembly sequence and torque combination.

[0100] Neighborhood solution search: Generate new solutions within the neighborhood of the current solution by exchanging assembly order and adjusting torque.

[0101] Acceptance criteria: Based on the Metropolis criteria, decide whether to accept the new solution.

[0102] Cooling strategy: gradually reduce the temperature according to the set cooling coefficient until the termination condition is reached.

[0103] Specifically, the above step 102 can be implemented by the following steps 301 to 309.

[0104] Step 301, initializing parameters and installation parameters of the simulated annealing algorithm, wherein the parameters of the simulated annealing algorithm include: initial temperature, termination temperature, temperature reduction coefficient, and the number of iterations at each temperature.

[0105] Step 302, set the value of k to 1.

[0106] Step 303: Generate new installation parameters.

[0107] Step 304: Update the current installation parameters according to the Metropolis criterion based on the value of the objective function of the new installation parameters and the value of the objective function of the current installation parameters.

[0108] Step 305, determine whether the value of k is less than L, and obtain a first determination result; wherein L is the number of iterations at each temperature.

[0109] Step 306: If the first judgment result is yes, the value of k is increased by 1, and the process returns to the step of "generating new installation parameters".

[0110] Step 307, if the first judgment result is no, determine whether the iteration end condition is met to obtain a second judgment result; the iteration end condition is that the current temperature is not greater than the termination temperature, or the current iteration number is greater than a preset iteration number threshold.

[0111] Step 308: If the second judgment result is yes, the temperature is lowered according to the temperature reduction coefficient, the current temperature is updated, and the process returns to the step of "setting the value of k to 1".

[0112] Step 309: If the second judgment result is no, output the current installation parameters as target installation parameters.

[0113] According to the value of the objective function of the new installation parameter and the value of the objective function of the current installation parameter, the current installation parameter is updated according to the Metropolis criterion, specifically including: judging whether the value of the objective function of the new installation parameter is better than the value of the objective function of the current installation parameter, and obtaining a third judgment result; if the third judgment result is yes, accepting the new installation parameter as the current installation parameter; if the third judgment result is no, accepting the new installation parameter as the current installation parameter with probability P; wherein, ΔT is the absolute value of the difference between the value of the objective function of the new installation parameters and the value of the objective function of the current installation parameters, and T is the initial temperature.

[0114] Take the tower mentioned above as an example:

[0115] Initial parameter setting: initial temperature T initial =1000; termination temperature T min =0.001; cooling coefficient α = 0.95; number of iterations at each temperature N = 100;

[0116] Optimization process: Randomly generate initial assembly order and torque combination; in each iteration, generate new solutions by exchanging assembly order and adjusting torque; calculate the error sensitivity E of the new solution; decide whether to accept the new solution according to the Metropolis criterion; reduce the temperature T = α × T until T ≤ T min or the maximum number of iterations is reached.

[0117] Optimization results: The optimal assembly sequence and torque combination are obtained to minimize the error sensitivity E.

[0118] The optimization process curve is as follows: Figure 6 shown.

[0119] In another exemplary embodiment, in the above step 103, each bent bolt is tightened in sequence according to the optimized assembly sequence and torque, that is, the assembly sequence and torque in the target installation parameters, and the installation position deviation of each bent bolt is measured in real time during the tightening process.

[0120] If the position deviation exceeds the preset range (±0.2mm), the torque is dynamically adjusted at a 5% adjustment ratio based on the direction and amplitude of the deviation; considering the influence of adjacent bolts, the position of the tightened bolts is fine-tuned with an adjustment amplitude of 2%.

[0121] During the tightening process, the corresponding relationship between torque and preload force is: 25N·m corresponds to a preload force of 4.6kN; 50N·m corresponds to a preload force of 11.2kN; 75N·m corresponds to a preload force of 18.4kN; 100N·m corresponds to a preload force of 25.6kN; 125N·m corresponds to a preload force of 33.4kN; 150N·m corresponds to a preload force of 40.4kN; 175N·m corresponds to a preload force of 46.2kN; 200N·m corresponds to a preload force of 51.8kN.

[0122] Furthermore, the embodiment of the present application also uses a measuring device to detect the position of the bent bolts after assembly to ensure that the installation position deviation of all bent bolts is within the allowable range.

[0123] For example, take the tower tube mentioned above as an example:

[0124] First, perform bolt tightening and dynamic adjustment:

[0125] According to the optimized assembly sequence and torque, tighten each bent bolt in turn. The torque distribution after tightening is as follows: Figure 7 Use precision measuring equipment to monitor the installation position deviation of each bolt in real time, such as Figure 7As shown in (b) in the figure. If the position deviation exceeds ±0.2mm, adjust the torque according to the following principles: if the deviation is positive (the bolt position is higher than the standard position), reduce the torque by 5%; if the deviation is negative (the bolt position is lower than the standard position), increase the torque by 5%. Considering the influence of the two adjacent bolts, if the average deviation exceeds ±0.1mm, fine-tune the torque of the current bolt by 2%.

[0126] Then, visualize the results:

[0127] The final bolt torque and position deviation distribution diagram is as follows: Figure 7 shown. Figure 7 (a) in the figure reflects the final torque distribution of each bending bolt, as shown in Figure 7 (b) in the figure reflects the distribution of the final installation position deviation of each bent bolt. The torque distribution shows the torque value that should be applied to each bolt after optimization, which may have slight differences to meet the overall assembly accuracy requirements. Although the position deviation distribution shows the position deviation of the bolt after installation, it is within the allowable range (+02mm). Figure 7 It shows that the assembly quality has met the design requirements. The figure intuitively demonstrates the effectiveness of the optimization method in improving assembly accuracy and balancing bolt force.

[0128] Then, verify the assembly results:

[0129] A three-coordinate measuring machine was used to detect the installation position of all bolts; the results showed that the installation position deviation of all bolts was within ±0.2mm, meeting the design requirements; the assembly qualification rate reached 99%, and the assembly efficiency was improved by 15% compared with the traditional method.

[0130] The present invention has the following beneficial effects:

[0131] Improve assembly accuracy: By comprehensively considering the two key factors of assembly sequence and torque, and optimizing assembly parameters based on the ergonomics theory, the assembly difficulty of bent bolts can be significantly reduced and assembly accuracy can be improved.

[0132] Reduce the assembly defect rate: Use the error sensitivity model to dynamically optimize the assembly parameters, effectively balance the force of the bent bolts, avoid bolt offset and deformation, and reduce the assembly defect rate.

[0133] Improve assembly efficiency: The simulated annealing algorithm is used to find the optimal solution in a complex solution space, thereby improving the applicability and efficiency of the assembly method.

[0134] Enhanced practicality: The method of dynamically adjusting the bolt loading force takes into account the influence of adjacent bolts, is closer to the actual assembly situation, and has good industrial application value.

[0135] Based on the same inventive concept, the embodiment of the present application also provides a device for installing bent bolts based on the human-machine efficiency theory to optimize the assembly sequence for implementing the above-mentioned method for installing bent bolts based on the human-machine efficiency theory to optimize the assembly sequence. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for installing bent bolts based on the human-machine efficiency theory to optimize the assembly sequence provided below can refer to the limitations of the method for installing bent bolts based on the human-machine efficiency theory to optimize the assembly sequence, and will not be repeated here.

[0136] In another exemplary embodiment, a bending bolt installation device is provided for optimizing the assembly sequence based on the ergonomic theory, comprising:

[0137] The objective function construction module is used to construct an objective function based on the human-machine efficacy theory; the objective function is error sensitivity.

[0138] The optimization solution module is used to use a simulated annealing algorithm to solve the installation parameters that make the objective function optimal as target installation parameters; the installation parameters include: the assembly sequence of multiple bending bolts and the torque of each bending bolt.

[0139] An installation module is used to install each bent bolt based on the target installation parameters.

[0140] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above-mentioned method embodiments are implemented.

[0141] Those skilled in the art will understand that Figure 8The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0142] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0144] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0145] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0146] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for installing bent bolts based on the ergonomic theory to optimize the assembly sequence, characterized in that: include: Construct objective functions based on human-machine efficacy theory; The objective function is error sensitivity; A simulated annealing algorithm is used to solve the installation parameters that make the objective function optimal, as target installation parameters; the installation parameters include: the assembly sequence of multiple bending bolts and the torque of each bending bolt; installing each bent bolt based on the target installation parameters; The objective function is constructed based on the human-machine efficacy theory, including: The calculation formula for operator fatigue during the assembly process based on the ergonomics theory is: F = α × S_f + β × T_F; Among them, F is fatigue degree, α and β are weight coefficients, satisfying α+β=1; S_f is the influence factor of the assembly sequence change amplitude on fatigue degree; T_f is the influence factor of the torque change amplitude on fatigue degree; Based on the calculation formula of fatigue, the objective function is constructed as follows: Where E is the error sensitivity, ΔS is the change in assembly sequence, ΔM is the change in torque, and k s and k m are the coefficients of the influence of assembly sequence and torque on position offset respectively; The simulated annealing algorithm is used to solve the installation parameters that make the objective function optimal, as the target installation parameters, which specifically include: Initializing parameters and installation parameters of the simulated annealing algorithm, wherein the parameters of the simulated annealing algorithm include: initial temperature, termination temperature, temperature reduction coefficient, and number of iterations at each temperature; Let the value of k be 1; Generate new installation parameters; According to the value of the objective function of the new installation parameter and the value of the objective function of the current installation parameter, the current installation parameter is updated according to the Metropolis criterion; Determine whether the value of k is less than L, and obtain a first determination result; wherein L is the number of iterations at each temperature; If the first judgment result is yes, the value of k is increased by 1, and the process returns to the step of "generating new installation parameters"; If the first judgment result is no, then judging whether the iteration end condition is met to obtain a second judgment result; the iteration end condition is that the current temperature is not greater than the termination temperature, or the current iteration number is greater than a preset iteration number threshold; If the second judgment result is yes, the temperature is lowered according to the temperature reduction coefficient, the current temperature is updated, and the process returns to the step of "setting the value of k to 1"; If the second judgment result is no, the current installation parameters are output as target installation parameters.

2. The method for installing bent bolts based on the ergonomic theory to optimize the assembly sequence according to claim 1 is characterized in that: According to the value of the objective function of the new installation parameters and the value of the objective function of the current installation parameters, the current installation parameters are updated according to the Metropolis criterion, including: Determine whether the value of the objective function of the new installation parameter is better than the value of the objective function of the current installation parameter to obtain a third determination result; If the third judgment result is yes, accepting the new installation parameters as the current installation parameters; If the third judgment result is no, then the new installation parameters are accepted as the current installation parameters with probability P; wherein, ΔT is the absolute value of the difference between the value of the objective function of the new installation parameters and the value of the objective function of the current installation parameters, and T is the initial temperature.

3. The method for installing bent bolts based on the ergonomic theory to optimize the assembly sequence according to claim 1 is characterized in that: The process of generating new installation parameters is: In the neighborhood of the current installation parameters, new installation parameters are generated by exchanging the assembly sequence and adjusting the torque.

4. The method for installing bent bolts based on the ergonomic theory to optimize the assembly sequence according to claim 1 is characterized in that: Installing each bent bolt based on the target installation parameters specifically includes: Tighten each bent bolt in sequence according to the assembly sequence and torque in the target installation parameters; Measure the installation position deviation of each bent bolt: Determine the bent bolts whose installation position deviation exceeds a preset range as target bent bolts; According to the direction and amplitude of the installation position deviation of the target bent bolt, the torque of the target bent bolt is adjusted at an adjustment ratio of 5%, and the torque of the adjacent bent bolts of the target bent bolt is adjusted at an adjustment ratio of 2%, and the result is returned to "measure the installation position deviation of each bent bolt" until the installation position deviation of each bent bolt is within a preset range; wherein the torque corresponding to the 5% adjustment ratio is 5% of the torque of the target bent bolt in the target installation parameters, and the torque corresponding to the 2% adjustment ratio is 2% of the torque of the target bent bolt in the target installation parameters.

5. The method for installing bent bolts based on the ergonomic theory to optimize the assembly sequence according to claim 4 is characterized in that: The corresponding relationship between torque and preload during tightening is: 25N·m corresponds to a preload of 4.6kN; 50N·m corresponds to a preload of 11.2kN; 75N·m corresponds to a preload of 18.4kN; 100N·m corresponds to a preload of 25.6kN; 125N·m corresponds to a preload of 33.4kN; 150N·m corresponds to a preload of 40.4kN; 175N·m corresponds to a preload of 46.2kN; 200N·m corresponds to a preload of 51.8kN.

6. A bending bolt installation device that optimizes the assembly sequence based on the human-machine efficiency theory, characterized in that: The bending bolt installation device based on the ergonomic theory to optimize the assembly sequence applies the bending bolt installation method based on the ergonomic theory to optimize the assembly sequence according to any one of claims 1 to 5, and the bending bolt installation device based on the ergonomic theory to optimize the assembly sequence includes: An objective function construction module is used to construct an objective function based on the human-machine efficacy theory; the objective function is error sensitivity; An optimization solution module is used to use a simulated annealing algorithm to solve the installation parameters that make the objective function optimal as target installation parameters; the installation parameters include: an assembly sequence of multiple bending bolts and a torque of each bending bolt; An installation module is used to install each bent bolt based on the target installation parameters.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the bent bolt installation method according to any one of claims 1 to 5 that optimizes the assembly sequence based on the ergonomic theory.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the bent bolt installation method according to any one of claims 1 to 5 is implemented, which optimizes the assembly sequence based on the ergonomic theory.

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