A human-machine interface layout optimization method for laser cleaning equipment based on particle swarm-sparrow algorithm

Abstract

The present invention proposes a method for optimizing the layout of the human-machine interface of laser cleaning equipment based on the particle swarm-sparrow algorithm, which belongs to the field of ergonomic design of equipment interfaces. According to the cognitive characteristics and operational comfort of the operator, the present invention constructs a multi-objective optimization model for the layout of the human-machine interface based on visual attention, relevance, simplicity, balanced comfort and reachable domain as layout objectives. The present invention also proposes a solution algorithm based on the particle swarm-sparrow hybrid intelligent algorithm, which introduces the exploration and predation mechanism of the sparrow algorithm into the particle swarm algorithm to solve the problem that the particle swarm algorithm is prone to falling into local optimal solutions due to low local search accuracy and ability. The beneficial effects of the present invention are: it well meets the operator's requirements for high comfort and high safety in the interaction of the human-machine interface of laser cleaning equipment, and provides a very ideal design basis for the optimization design of the human-machine interface layout of laser cleaning equipment.

Description

Technical Field

[0001] The invention relates to a method for optimizing the human-machine interface layout of laser cleaning equipment based on a particle swarm-sparrow algorithm, and belongs to the field of equipment interface ergonomic design. Background Art

[0002] Laser cleaning is based on irradiating the surface of the workpiece with a high-energy laser beam, causing the object to be cleaned or the substrate to undergo physical and chemical reactions such as vibration, vaporization, and ablation to achieve the effect of removing dirt. It involves a complex process with multiple systems and multiple parameters and has been widely used in the operation and maintenance of high-end equipment in aerospace, marine engineering, rail transportation, etc. During the laser cleaning process, the operator needs to perform human-machine interaction with multiple systems such as the laser generation system, motion system, and cold water system to perform laser cleaning tasks, which presents operational characteristics such as complex interactive information, numerous operating procedures, and complex cognition, which directly affect the efficiency of human-machine interaction, the stability of cleaning quality, and operational safety. Therefore, conducting research on the optimization design of the human-machine interface layout of laser cleaning equipment is the key to effectively improving the operational safety, efficiency, and comfort of human-machine interaction during laser cleaning.

[0003] Due to the high degree of integration and complexity of structure and function, the human-machine interface layout structure of laser cleaning equipment is intricate and has a large capacity for coded information. It is difficult to meet the requirements of multi-information, multi-process visual encoding, decoding and other human-machine interaction tasks of laser cleaning, resulting in poor comfort and easily causing cognitive fatigue and human errors in operators, endangering the efficient and safe operation of the human-machine system. Therefore, the rationality of the human-machine interface layout design has become an important factor in improving the efficiency and safety of human-machine interaction of laser cleaning equipment. At present, domestic and foreign scholars have carried out a lot of research on the equipment human-machine interface layout optimization model and solution algorithm, and have achieved certain results. However, due to the high degree of integration and complexity of laser cleaning equipment in terms of functions and operational tasks, the existing human-machine interface layout optimization method is difficult to apply to the complex visual encoding, decoding and other human-machine interaction tasks of laser cleaning equipment. The following problems are mainly to be solved:

[0004] (1) Existing methods mostly use ergonomic layout principles such as importance and frequency of use to optimize the design of the human-computer interface. Although they can meet the basic cognitive requirements of operators, they ignore the impact of factors such as visual attention and the aesthetics of the interface layout on information cognition, restricting the operator's visual extraction and cognitive recognition of interface information, and easily causing cognitive fatigue.

[0005] (2) The existing methods focus on optimizing the layout of the human-machine interface with a function-oriented approach, which makes it difficult to systematically take into account the high comfort and safety operation requirements under the special process and complex operation tasks of laser cleaning. As a result, the operator cannot achieve rapid response operations on the laser cleaning equipment, which seriously affects the improvement of human-machine interaction efficiency.

[0006] (3) When solving the complex multi-dimensional layout model of laser cleaning equipment with multiple parameters and multiple objectives, the traditional single algorithm is prone to miss the optimal solution and gather at the local optimal point due to unreasonable algorithm parameter settings, resulting in deviation in solution quality, and thus it is difficult to achieve the ideal layout effect. Summary of the Invention

[0007] The present invention aims to provide a human-machine interface layout optimization method for laser cleaning equipment based on particle swarm-sparrow algorithm, which provides an effective way for the human-machine interaction optimization design of laser cleaning equipment.

[0008] To achieve the above object, the present invention is implemented by the following steps:

[0009] S1. Determine the multi-objective function for optimizing the layout of the human-computer interaction interface.

[0010] S2. Determine the constraints of the human-computer interaction interface layout,

[0011] S3. Construct a human-machine interface layout optimization model for laser cleaning equipment.

[0012] S4. The established laser cleaning equipment human-machine interface layout optimization model is calculated using a particle swarm-sparrow hybrid intelligent algorithm to determine the Pareto optimal solution set.

[0013] S5. Use fuzzy set theory to select an optimal solution from the determined Pareto solution set.

[0014] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S1 determines the multi-objective function for optimizing the human-machine interface layout, and the specific method is:

[0015] (1) Visual attention optimization goal

[0016] The goal of visual attention optimization is to place the most important and frequently used operating device functional modules in the human-computer interaction interface within the operator's optimal field of view, so that the operator can observe and extract interface information in a timely manner, reduce the operator's short-term memory burden, and thus reduce the occurrence of human errors such as information extraction errors and memory errors; based on the determination of the importance and frequency of use of each functional module, a visual attention optimization objective function is constructed.

[0017] 1) Determination of the importance of each functional module

[0018] In order to reduce the subjectivity and ambiguity of the evaluation information, the importance of each operating device functional module is calculated by rough AHP (analytic hierarchy process); let U be a non-empty real number set, Y be any element in U, and divide it into S classes according to its value, and R be the class set containing all elements in U.

[0019] R={G1 G2 G3 ... G s}, G1<G2<...<G s (1)

[0020] For any G t ∈R,1≤t≤s, define G t The lower approximation and upper approximation are Apr (G t )and The lower and upper limits are Lim (G t )and but

[0021] Apr (G t )=∪{Y∈U|R(Y)≤G t} (2)

[0022]

[0023]

[0024]

[0025] Where M L and M U are the number of elements in the lower approximation set and the upper approximation set respectively, then G t The rough number representation of

[0026]

[0027] The specific steps for determining the importance of each operating device functional module in the human-machine interface of laser cleaning equipment through rough AHP are as follows:

[0028] Step 1: Conduct an AHP survey to determine the judgment matrix; k experts compare the importance of n functional modules pairwise based on the hierarchical analysis method, and obtain the n×n dimensional AHP judgment matrix A given by the k experts. e , 1≤e≤k, and perform consistency test on it. When the consistency test index CR≤0.1, the consistency test passes.

[0029]

[0030] Step 2: Construct a comprehensive judgment matrix; integrate the n-dimensional judgment matrices of k experts to construct a comprehensive judgment matrix Its formula is

[0031]

[0032] Step 3: Construct a rough judgment matrix; Convert it into a rough set form and normalize it to get the rough judgment matrix M, which is:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] Step 4: Calculate the importance of each functional module a i ,but:

[0039]

[0040]

[0041]

[0042] a i =a' i / ∑a' i (17)

[0043] 2) Determination of the usage frequency of each functional module

[0044] When determining the usage frequency of each functional module, the influence of different cleaning conditions in the operation of the laser cleaning equipment is fully considered; assuming that there are m working conditions in the laser cleaning process, the usage frequency score matrix P of n functional modules in different operating conditions can be expressed as:

[0045]

[0046] Considering that the occurrence probability of each operating condition is different during the laser cleaning process, the probability of occurrence of m operating conditions can be expressed as

[0047] O=[o1 o2 ... o m ] T (19)

[0048] The overall usage frequency of functional module i after considering the probability of occurrence of each working condition can be expressed as:

[0049] C=PO=[C1 C2 … C n ] (20)

[0050] At the same time, the overall usage frequency is normalized, and the usage frequency of the functional modules of each operating device can be finally obtained as follows:

[0051]

[0052] On the basis of determining the importance and usage frequency of each operating device functional module of the laser cleaning equipment, and taking into full consideration the position and area of ​​each functional module in each visual field area, the attention optimization goal can represent the maximum visual attention intensity of the interface, that is,

[0053]

[0054] Where, F e is the visual attention intensity of the entire interface, v = 1, 2, 3, 4 represent the upper left, upper right, lower left and lower right visual field areas of the laser cleaning equipment human-machine interface, and the attention level scores λ of the four visual field areas are v , respectively 4, 3, 2, 1 points. iv represents the area of ​​functional module i in the visual field v; g i is the chain value of the importance and usage frequency of functional module i, that is,

[0055]

[0056] (2) Relevance optimization goal

[0057] The goal of correlation optimization is to arrange the functional modules of various operating devices with high correlation in terms of function and operation logic sequence in close locations in the human-computer interaction interface to reduce the distance the operator's hands move. While improving operational comfort, it can also reduce the user's visual search time and reduce the operator's memory burden, making it easier to find the target functional module and reducing the operator's task burden.

[0058] The higher the correlation between functional modules, the smaller the distance between their center points. The correlation optimization target of the human-machine interface layout of laser cleaning equipment can be expressed as:

[0059]

[0060] Where, F r is the overall correlation of the interface layout, then the correlation matrix between each functional module is

[0061]

[0062] Where r ij Indicates the correlation between functional modules i and j; x i and y iThey represent the x-axis and y-axis coordinates of the center point of functional module i respectively; A and B represent the length and width of the overall layout interface respectively.

[0063] (3) Simplicity optimization goal

[0064] The goal of simplicity optimization is to align the functional modules of various operating devices as much as possible in the human-machine interface layout to improve the simplicity and aesthetics of the interface, effectively reduce visual interference, improve the operator's ability to observe and understand interface information, and thus effectively improve the efficiency of human-machine interaction. The simplicity optimization goal of the human-machine interface layout of laser cleaning equipment can be expressed as:

[0065]

[0066] Where, F s Indicates the overall simplicity of the interface layout; n x and n y Respectively represent the number of functional modules with equal x-axis and y-axis coordinates of the center points of each functional module;

[0067] (4) Balancing comfort optimization goals

[0068] The goal of balance comfort optimization is to minimize the difference in the overall weight of the functional modules on both sides of the horizontal and vertical symmetry axes of the operation interface in the human-machine interface layout, so as to improve the balance and coordination of the entire interface layout and reduce the operator's visual confusion.

[0069] Therefore, the human-machine interface layout of laser cleaning equipment should maintain the maximum overall balance and comfort of the interface, and the balance and comfort optimization goal can be expressed as:

[0070]

[0071] Where, F b The overall balance and comfort of the interface layout; L and R represent the left and right areas of the vertical symmetry axis of the interface, respectively; U and D represent the upper and lower areas of the horizontal symmetry axis of the interface, respectively;

[0072] At the same time, considering the influence of the distance between each functional module and the symmetry axis on the balance and coordination of the interface, we can get:

[0073]

[0074] Where w = L, R, U, D; s iw represents the area of ​​the operating device functional module i in the w region, d i is the distance between the center point of the operating device functional module i and the vertical symmetry axis of the interface or the symmetry axis;

[0075] (5) Reachable domain optimization goal

[0076] The goal of reachable domain optimization is to place the most important and frequently used functional modules in the human-machine interface within the comfortable operation area of ​​both hands. This can improve the operator's operating comfort while reducing the operating load, ultimately achieving rapid operation and response of the laser cleaning equipment.

[0077] Jack ergonomics simulation software was used to determine the optimal operating posture for the operator when operating laser cleaning equipment. Points a and b were used to represent the projections of the left and right palms of the Jack virtual human on the equipment operation interface when the virtual human was in the optimal operating posture, respectively, to determine the position of the hands. Furthermore, point c was the farthest location from point a on the laser cleaning equipment operation interface, with a distance of d1. Point d was the farthest location from point b, with a distance of d2.

[0078] The position coefficient of the reachable domain target is constructed by calculating the distance between the functional module i of each waiting-to-be-deployed operating device and points a and b in the human-computer interaction interface, and taking into full consideration the relationship with d1 and d2.

[0079] Let the coordinates of points a and b be (x a ,y a ) and (x b ,y b ), function module to be deployed i(x i ,y i ) to point a and point b are d ia and d ib but:

[0080]

[0081]

[0082] Let θ i is the target position coefficient of the reachable domain of the functional module i to be deployed, then:

[0083]

[0084] Where β1 and β2 represent the importance of the operator’s left and right hands respectively, θ i The larger the value is, the more comfortable the position of the function module i is for the operator to operate.

[0085] In the human-machine interface layout of laser cleaning equipment, the operating device function modules with higher importance and frequency of use should be placed in the area with larger location coefficient. Then the reachable domain optimization objective of the function module i to be placed can be expressed as:

[0086]

[0087] Where, F his the accessible domain position advantage of the layout interface, g i It is the chain value of importance and frequency of use, that is, the human-machine interface layout of the laser cleaning equipment should meet the maximum position advantage of the interface reachable area.

[0088] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S2 determines the constraints of the human-machine interface layout, and the specific method is:

[0089] Considering the actual situation of the human-machine interface layout of laser cleaning equipment, the human-machine interface layout constraints mainly include spacing constraints and boundary constraints. Spacing constraints require that a certain distance be maintained between each operating function module, and boundary constraints require that each function module must be laid out within the operating interface.

[0090] (1) Spacing constraint: To avoid overlapping and misoperation of functional modules, a certain distance should be maintained between each functional module, which can be expressed as:

[0091]

[0092] Where, d x min and d y min Indicates the minimum distance between functional modules i and j in the x and y directions,

[0093] (2) Boundary constraints: While ensuring that each functional module has a certain distance from the boundary, each functional module must be laid out within the layout space and not exceed the scope of the layout space. That is, the following conditions must be met:

[0094]

[0095] Where, d a min and d b min Respectively represent the minimum distance between each functional module of the operation interface and the layout boundary in the x direction and y direction.

[0096] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S3 constructs a human-machine interface layout optimization model for the laser cleaning equipment, and the specific method is:

[0097] (1) Problem description: In order to achieve the optimal layout efficiency of the human-machine interface of the laser cleaning device, the influence of factors such as operating comfort, visual attention cognition and simplicity of the interface on the operation interaction should be fully considered, and the important and frequently used functional modules should be arranged as much as possible in a position that is conducive to human-machine interaction. Therefore, the optimization model of the human-machine interface layout should systematically take into account various layout goals, fully consider the influence of various layout factors on the operation interaction, and finally achieve the overall optimal layout of the laser cleaning device human-machine interface;

[0098] (2) Conditional assumptions: This paper makes the following assumptions regarding the optimization of the equipment human-machine interface layout:

[0099] 1) Transform the human-machine interface layout design of laser cleaning equipment into a layout design problem within the space of a two-dimensional operation panel;

[0100] 2) Simplify the layout space of the laser cleaning equipment human-machine interface and the functional modules of the operating devices to be arranged into regular rectangular geometric bodies;

[0101] 3)L i and W i Respectively represent the length and width of each functional module i in the operation interface, (x i ,y i ) represents the coordinate value of the center point of the functional module i on the operation panel;

[0102] (3) Optimization model: Based on the aforementioned specific method of determining the multi-objective function of the human-machine interface layout optimization and the specific method of determining the constraints of the human-machine interface layout, and combined with the problem description and conditional assumptions, the human-machine interface layout optimization model of the laser cleaning equipment can be expressed as:

[0103] F(x i ,y i )=(max{F e},{F r},{F s},{F b},{F h})

[0104]

[0105] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S4 uses the particle swarm-sparrow hybrid intelligent algorithm to calculate the established laser cleaning equipment human-machine interface layout optimization model to determine the Pareto optimal solution set, and the specific steps are:

[0106] Step 1: Randomly initialize the population size, set parameters such as the maximum number of iterations and inertia factor, and determine the proportion of explorer particles in the population based on the proportional coefficient α;

[0107]

[0108] X best represents the number of pn explorer particles with better fitness values ​​in the population and the ability of explorer sparrows, X worst Represents n-pn follower particles with poor fitness values ​​in the population;

[0109] Step 2: Bring the initialized particle position into the objective function and calculate the particle fitness value;

[0110] Step 3: According to the dominance relationship, select the individual optimal solution pbest from the non-inferior solutions, store all non-inferior solutions in an external archive, and select the global optimal solution gbest from the external archive;

[0111] Step 4: In the large-scale global optimization process, the warning value R2 is set to be always smaller than the safety value ST. At this time, the explorer particle updates its position according to formula (37) and performs a large-scale rapid search in the problem solution space.

[0112]

[0113] Where t is the current number of iterations, iter max is the maximum number of iterations, represents the position information of the i-th sparrow at time t in the j-th dimension,

[0114] Step 5: Generate the influence factor θ according to the change of the explorer particle's ability, as shown in formula (38), and introduce it into the particle swarm algorithm to realize the dynamic adjustment of the inertia factor, changing the influence of the particle's past position and speed on the present, that is, the follower particle will update its speed and position according to formula (39);

[0115]

[0116]

[0117] Where, v i represents the velocity of the particle, x i Indicates the current position of the particle; w is the inertia factor used to adjust the local and global search capabilities of the algorithm; c1 and c2 are learning factors, usually c1=c2=2; r1 and r2 are random numbers between 0 and 1;

[0118] Step 6: Calculate the fitness value of the particle after the speed and position are updated;

[0119] Step 7: Update individual extreme value pbest and global extreme value gbest as well as external files;

[0120] Step 8: Determine whether the termination condition is met (reaching the maximum number of iterations). If so, output the Pareto optimal solution set; otherwise, jump to Step 3.

[0121] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S5 adopts fuzzy set theory to select an optimal solution from the determined Pareto solution set, and the specific steps are:

[0122] The Pareto solution set of the particle swarm-sparrow hybrid intelligent algorithm contains a series of non-dominated solutions. The multi-objective decision-making of the laser cleaning equipment layout is to select a definite optimal solution in the Pareto solution set. Based on this, based on fuzzy set theory, a compromise method is adopted to comprehensively select the optimal solution for the human-machine interface layout to improve the effect of the layout scheme. The membership function β is defined i j for

[0123]

[0124] Where, f i min and f i max are the minimum and maximum values ​​of the i-th optimization objective function in the Pareto solution set, respectively, i j and β i j They represent the current value and membership value of the i-th objective function of the j-th solution, respectively.

[0125] Secondly, for each non-dominated solution k in the Pareto set of the result obtained by the particle swarm-sparrow hybrid intelligent algorithm, its dominance function is defined as

[0126]

[0127] Where l is the number of non-inferior solutions in the Pareto solution of the optimization result; n is the target number of the layout optimization model. The dominant value of each non-inferior solution in the Pareto solution set can be obtained from formula (41): The larger the value, the better the overall performance of the solution. Therefore, the solution with the largest dominance value is selected from the Pareto solution set of the human-machine interface layout results of the laser cleaning equipment, which is the optimal solution.

[0128] The beneficial effects of the present invention are: it satisfies the operator's requirements for high comfort and high safety in the human-machine interface interaction of laser cleaning equipment, and provides an ideal design basis for the optimization design of the human-machine interface layout of laser cleaning equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 This is a schematic diagram of the functional module division of the interface of an embodiment of the present invention.

[0130] Figure 2 This is a diagram dividing the interface visual field area of ​​the present invention.

[0131] Figure 3 This is the best operating posture for the operator of the present invention.

[0132] Figure 4 It is the reachable domain optimization target analysis diagram of the present invention.

[0133] Figure 5 It is a schematic diagram of the interface layout of the present invention.

[0134] Figure 6 This is a flow chart of the hybrid intelligent algorithm of the present invention.

[0135] Figure 7 3. This is a comparison diagram of the Pareto frontier points of the hybrid algorithm according to an embodiment of the present invention.

[0136] Figure 8 This is a comparison chart of the hypervolume indicators of the hybrid algorithm according to an embodiment of the present invention.

[0137] Figure 9 This is a fuzzy compromise solution in the embodiment of the present invention.

[0138] Figure 10 It is a rendering of the optimized design scheme of an embodiment of the present invention.

[0139] Figure 11 This is a comparison chart of the first fixation time before and after interface optimization according to an embodiment of the present invention.

[0140] Figure 12 This is a comparison chart of the average gaze time before and after interface optimization according to an embodiment of the present invention.

[0141] Figure 13(1) and 13(2) It is a comparison diagram of eye movement hot spots of the optimization effect of the verification experiment of the embodiment of the present invention, wherein FIG13(1) is the hot spot diagram of the original solution, and FIG13(2) is the hot spot diagram of the optimized solution. DETAILED DESCRIPTION

[0142] The present invention will be further described in detail below with reference to the accompanying drawings and examples. However, it should be understood that the examples are intended to explain the present invention but not to limit the present invention.

[0143] This embodiment provides a method for optimizing the human-machine interface layout of laser cleaning equipment based on a particle swarm optimization algorithm, comprising the following steps:

[0144] S1. Determine the multi-objective function for optimizing the layout of the human-computer interaction interface.

[0145] S2. Determine the constraints of the human-computer interaction interface layout,

[0146] S3. Construct a human-machine interface layout optimization model for laser cleaning equipment.

[0147] S4. The established laser cleaning equipment human-machine interface layout optimization model is calculated using a particle swarm-sparrow hybrid intelligent algorithm to determine the Pareto optimal solution set.

[0148] S5. Use fuzzy set theory to select an optimal solution from the determined Pareto solution set.

[0149] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S1 determines the multi-objective function for optimizing the human-machine interface layout, and the specific method is:

[0150] (1) Visual attention optimization goal

[0151] The goal of visual attention optimization is to place the most important and frequently used operating device functional modules in the human-computer interaction interface within the operator's optimal field of view, so that the operator can observe and extract interface information in a timely manner, reduce the operator's short-term memory burden, and thus reduce the occurrence of human errors such as information extraction errors and memory errors; based on the determination of the importance and frequency of use of each functional module, a visual attention optimization objective function is constructed.

[0152] 1) Determination of the importance of each functional module

[0153] In order to reduce the subjectivity and ambiguity of the evaluation information, the importance of each operating device functional module is calculated by rough AHP (analytic hierarchy process); let U be a non-empty real number set, Y be any element in U, and divide it into S classes according to its value, and R be the class set containing all elements in U.

[0154] R={G1 G2 G3 ... G s}, G1<G2<...<G s (1)

[0155] For any G t ∈R,1≤t≤s, define G t The lower approximation and upper approximation are Apr (G t )and The lower and upper limits are Lim (G t )and but

[0156] Apr (G t )=∪{Y∈U|R(Y)≤G t} (2)

[0157]

[0158]

[0159]

[0160] Where M L and M U are the number of elements in the lower approximation set and the upper approximation set respectively, then G t The rough number representation of

[0161]

[0162] The specific steps for determining the importance of each operating device functional module in the human-machine interface of laser cleaning equipment through rough AHP are as follows:

[0163] Step 1: Conduct an AHP survey to determine the judgment matrix; k experts compare the importance of n functional modules pairwise based on the hierarchical analysis method, and obtain the n×n dimensional AHP judgment matrix Ae given by k experts, 1≤e≤k, and perform a consistency test on it. When the consistency test index CR≤0.1, the consistency test passes.

[0164]

[0165] Step 2: Construct a comprehensive judgment matrix; integrate the n-dimensional judgment matrices of k experts to construct a comprehensive judgment matrix Its formula is

[0166]

[0167] Step 3: Construct a rough judgment matrix; Convert it into a rough set form and normalize it to get the rough judgment matrix M, which is:

[0168]

[0169]

[0170]

[0171]

[0172]

[0173] Step 4: Calculate the importance of each functional module a i ,but:

[0174]

[0175]

[0176]

[0177] a i =a' i / ∑a' i (17)

[0178] 3) Determination of the usage frequency of each functional module

[0179] When determining the usage frequency of each functional module, the influence of different cleaning conditions in the operation of the laser cleaning equipment is fully considered; assuming that there are m working conditions in the laser cleaning process, the usage frequency score matrix P of n functional modules in different operating conditions can be expressed as:

[0180]

[0181] Considering that the occurrence probability of each operating condition is different during the laser cleaning process, the probability of occurrence of m operating conditions can be expressed as

[0182] O=[o1 o2 ... o m ] T (19)

[0183] The overall usage frequency of functional module i after considering the probability of occurrence of each working condition can be expressed as:

[0184] C=PO=[C1 C2 … C n ] (20)

[0185] At the same time, the overall usage frequency is normalized, and the usage frequency of the functional modules of each operating device can be finally obtained as follows:

[0186]

[0187] On the basis of determining the importance and usage frequency of each operating device functional module of the laser cleaning equipment, and taking into full consideration the position and area of ​​each functional module in each visual field area, the attention optimization goal can represent the maximum visual attention intensity of the interface, that is,

[0188]

[0189] Where, F e is the visual attention intensity of the entire interface, v = 1, 2, 3, 4 represent the upper left, upper right, lower left and lower right visual field areas of the laser cleaning equipment human-machine interface, and the attention level scores λ of the four visual field areas are v , respectively 4, 3, 2, 1 points. iv represents the area of ​​functional module i in the visual field v; g iis the chain value of the importance and usage frequency of functional module i, that is,

[0190]

[0191] (2) Relevance optimization goal

[0192] The goal of correlation optimization is to arrange the functional modules of various operating devices with high correlation in terms of function and operation logic sequence in close locations in the human-computer interaction interface to reduce the distance the operator's hands move. While improving operational comfort, it can also reduce the user's visual search time and reduce the operator's memory burden, making it easier to find the target functional module and reducing the operator's task burden.

[0193] The higher the correlation between functional modules, the smaller the distance between their center points. The correlation optimization target of the human-machine interface layout of laser cleaning equipment can be expressed as:

[0194]

[0195] Where, F r is the overall correlation of the interface layout, then the correlation matrix between each functional module is

[0196]

[0197] Where r ij Indicates the correlation between functional modules i and j; x i and y i They represent the x-axis and y-axis coordinates of the center point of functional module i respectively; A and B represent the length and width of the overall layout interface respectively.

[0198] (3) Simplicity optimization goal

[0199] The goal of simplicity optimization is to align the functional modules of various operating devices as much as possible in the human-machine interface layout to improve the simplicity and aesthetics of the interface, effectively reduce visual interference, improve the operator's ability to observe and understand interface information, and thus effectively improve the efficiency of human-machine interaction. The simplicity optimization goal of the human-machine interface layout of laser cleaning equipment can be expressed as:

[0200]

[0201] Where, F s Indicates the overall simplicity of the interface layout; n x and n y Respectively represent the number of functional modules with equal x-axis and y-axis coordinates of the center points of each functional module;

[0202] (4) Balancing comfort optimization goals

[0203] The goal of balance comfort optimization is to minimize the difference in the overall weight of the functional modules on both sides of the horizontal and vertical symmetry axes of the operation interface in the human-machine interface layout, so as to improve the balance and coordination of the entire interface layout and reduce the operator's visual confusion.

[0204] Therefore, the human-machine interface layout of laser cleaning equipment should maintain the maximum overall balance and comfort of the interface, and the balance and comfort optimization goal can be expressed as:

[0205]

[0206] Where, F b The overall balance and comfort of the interface layout; L and R represent the left and right areas of the vertical symmetry axis of the interface, respectively; U and D represent the upper and lower areas of the horizontal symmetry axis of the interface, respectively;

[0207] At the same time, considering the influence of the distance between each functional module and the symmetry axis on the balance and coordination of the interface, we can get:

[0208]

[0209] Where w = L, R, U, D; s iw represents the area of ​​the operating device functional module i in the w region, d i is the distance between the center point of the operating device functional module i and the vertical symmetry axis of the interface or the symmetry axis;

[0210] (5) Reachable domain optimization goal

[0211] The goal of reachable domain optimization is to place the most important and frequently used functional modules in the human-machine interface within the comfortable operation area of ​​both hands. This can improve the operator's operating comfort while reducing the operating load, ultimately achieving rapid operation and response of the laser cleaning equipment.

[0212] Jack ergonomics simulation software was used to determine the optimal operating posture for the operator when operating laser cleaning equipment. Points a and b were used to represent the projections of the left and right palms of the Jack virtual human on the equipment operation interface when the virtual human was in the optimal operating posture, respectively, to determine the position of the hands. Furthermore, point c was the farthest location from point a on the laser cleaning equipment operation interface, with a distance of d1. Point d was the farthest location from point b, with a distance of d2.

[0213] The position coefficient of the reachable domain target is constructed by calculating the distance between the functional module i of each waiting-to-be-deployed operating device and points a and b in the human-computer interaction interface, and taking into full consideration the relationship with d1 and d2.

[0214] Let the coordinates of points a and b be (x a ,y a ) and (xb ,y b ), function module to be deployed i(x i ,y i ) to point a and point b are d ia and d ib but:

[0215]

[0216]

[0217] Let θ i is the target position coefficient of the reachable domain of the function module i to be deployed, then:

[0218]

[0219] Where β1 and β2 represent the importance of the operator’s left and right hands respectively, θ i The larger the value is, the more comfortable the position of the function module i is for the operator to operate.

[0220] In the human-machine interface layout of laser cleaning equipment, the operating device function modules with higher importance and frequency of use should be placed in the area with larger location coefficient. Then the reachable domain optimization objective of the function module i to be placed can be expressed as:

[0221]

[0222] Where, F h is the accessible domain position advantage of the layout interface, g i It is the chain value of importance and frequency of use, that is, the human-machine interface layout of the laser cleaning equipment should meet the maximum position advantage of the interface reachable area.

[0223] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S2 determines the constraints of the human-machine interface layout, and the specific method is:

[0224] Considering the actual situation of the human-machine interface layout of laser cleaning equipment, the human-machine interface layout constraints mainly include spacing constraints and boundary constraints. Spacing constraints require that a certain distance be maintained between each operating function module, and boundary constraints require that each function module must be laid out within the operating interface.

[0225] (1) Spacing constraint: To avoid overlapping and misoperation of functional modules, a certain distance should be maintained between each functional module, which can be expressed as:

[0226]

[0227] Where, d x min and d y minIndicates the minimum distance between functional modules i and j in the x and y directions,

[0228] (2) Boundary constraints: While ensuring that each functional module has a certain distance from the boundary, each functional module must be laid out within the layout space and not exceed the scope of the layout space. That is, the following conditions must be met:

[0229]

[0230] Where, d a min and d b min Respectively represent the minimum distance between each functional module of the operation interface and the layout boundary in the x direction and y direction.

[0231] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S3 constructs a human-machine interface layout optimization model for the laser cleaning equipment, and the specific method is:

[0232] (1) Problem description: In order to achieve the optimal layout efficiency of the human-machine interface of the laser cleaning device, the influence of factors such as operating comfort, visual attention cognition and simplicity of the interface on the operation interaction should be fully considered, and the important and frequently used functional modules should be arranged as much as possible in a position that is conducive to human-machine interaction. Therefore, the optimization model of the human-machine interface layout should systematically take into account various layout goals, fully consider the influence of various layout factors on the operation interaction, and finally achieve the overall optimal layout of the laser cleaning device human-machine interface;

[0233] (2) Conditional assumptions: This paper makes the following assumptions regarding the optimization of the equipment human-machine interface layout:

[0234] 1) Transform the human-machine interface layout design of laser cleaning equipment into a layout design problem within the space of a two-dimensional operation panel;

[0235] 2) Simplify the layout space of the laser cleaning equipment human-machine interface and the functional modules of the operating devices to be arranged into regular rectangular geometric bodies;

[0236] 3)L i and W i Respectively represent the length and width of each functional module i in the operation interface, (x i ,y i ) represents the coordinate value of the center point of the functional module i on the operation panel;

[0237] (3) Optimization model: Based on the aforementioned specific method of determining the multi-objective function of the human-machine interface layout optimization and the specific method of determining the constraints of the human-machine interface layout, and combined with the problem description and conditional assumptions, the human-machine interface layout optimization model of the laser cleaning equipment can be expressed as:

[0238] F(xi ,y i )=(max{F e},{F r},{F s},{F b},{F h})

[0239]

[0240] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S4 uses the particle swarm-sparrow hybrid intelligent algorithm to calculate the established laser cleaning equipment human-machine interface layout optimization model to determine the Pareto optimal solution set, and the specific steps are:

[0241] Step 1: Randomly initialize the population size, set parameters such as the maximum number of iterations and inertia factor, and determine the proportion of explorer particles in the population based on the proportional coefficient α;

[0242]

[0243] X best represents the number of pn explorer particles with better fitness values ​​in the population and the ability of explorer sparrows, X worst Represents n-pn follower particles with poor fitness values ​​in the population;

[0244] Step 2: Bring the initialized particle position into the objective function and calculate the particle fitness value;

[0245] Step 3: According to the dominance relationship, select the individual optimal solution pbest from the non-inferior solutions, store all non-inferior solutions in an external archive, and select the global optimal solution gbest from the external archive;

[0246] Step 4: In the large-scale global optimization process, the warning value R2 is set to be always smaller than the safety value ST. At this time, the explorer particle updates its position according to formula (37) and performs a large-scale rapid search in the problem solution space.

[0247]

[0248] Where t is the current number of iterations, iter max is the maximum number of iterations, represents the position information of the i-th sparrow at time t in the j-th dimension,

[0249] Step 5: Generate the influence factor θ according to the change of the explorer particle's ability, as shown in formula (38), and introduce it into the particle swarm algorithm to realize the dynamic adjustment of the inertia factor, changing the influence of the particle's past position and speed on the present, that is, the follower particle will update its speed and position according to formula (39);

[0250]

[0251]

[0252] Where, v i represents the velocity of the particle, x i Indicates the current position of the particle; w is the inertia factor used to adjust the local and global search capabilities of the algorithm; c1 and c2 are learning factors, usually c1=c2=2; r1 and r2 are random numbers between 0 and 1;

[0253] Step 6: Calculate the fitness value of the particle after the speed and position are updated;

[0254] Step 7: Update individual extreme value pbest and global extreme value gbest as well as external files;

[0255] Step 8: Determine whether the termination condition is met (reaching the maximum number of iterations). If so, output the Pareto optimal solution set; otherwise, jump to Step 3.

[0256] Furthermore, in the laser cleaning equipment human-machine interface layout optimization method based on the particle swarm-sparrow algorithm, the step S5 adopts fuzzy set theory to select an optimal solution from the determined Pareto solution set, and the specific steps are:

[0257] The Pareto solution set of the particle swarm-sparrow hybrid intelligent algorithm contains a series of non-dominated solutions. The multi-objective decision-making of the laser cleaning equipment layout is to select a definite optimal solution in the Pareto solution set. Based on this, based on fuzzy set theory, a compromise method is adopted to comprehensively select the optimal solution for the human-machine interface layout to improve the effect of the layout scheme. The membership function β is defined i j for

[0258]

[0259] Where, f i min and f i max are the minimum and maximum values ​​of the i-th optimization objective function in the Pareto solution set, respectively, i j and β i jThey represent the current value and membership value of the i-th objective function of the j-th solution, respectively.

[0260] Secondly, for each non-dominated solution k in the Pareto set of the result obtained by the particle swarm-sparrow hybrid intelligent algorithm, its dominance function is defined as

[0261]

[0262] Where l is the number of non-inferior solutions in the Pareto solution of the optimization result; n is the target number of the layout optimization model. The dominant value of each non-inferior solution in the Pareto solution set can be obtained from formula (41): The larger the value, the better the overall performance of the solution. Therefore, the solution with the largest dominance value is selected from the Pareto solution set of the human-machine interface layout results of the laser cleaning equipment, which is the optimal solution.

[0263] See also Figure 1 , which shows the result of the interface functional module classification in this embodiment of the present invention. According to step S1, this embodiment invited six expert users of laser cleaning equipment to evaluate the importance of each functional module in the laser cleaning equipment human-machine interface using the rough AHP method. Finally, the importance of each functional module was determined using the rough AHP method, as shown in Table 1.

[0264] Table 1 Importance of each functional module

[0265]

[0266] According to step S1, the operational tasks in this embodiment primarily include equipment preparation, instruction, formal cleaning, and final maintenance. To determine the frequency of use of different functional modules under each operating condition, this embodiment invited laser cleaning equipment experts to assign precise numerical scores on a scale of 1-5. This yielded a frequency of use score matrix P for each functional module under different operating conditions, as shown in Table 2.

[0267] Table 2 Frequency of use rating matrix of each functional module in different working conditions

[0268]

[0269]

[0270] According to step S1, in order to determine the occurrence probability of each operating condition, this embodiment invites six laser cleaning equipment expert users to score and judge it according to the steps of rough AHP. The final probability rough judgment matrix M1 is as follows:

[0271]

[0272] According to step S1, based on formulas (14) to (17), the occurrence probabilities of the four major working conditions (equipment preparation, teaching, formal cleaning, and end of maintenance) are 0.1002, 0.4426, 0.3705, and 0.0867, respectively. Based on this, the usage frequency of each functional module is finally obtained according to formulas (20) and (21), as shown in Table 3.

[0273] Table 3 Frequency of use of each functional module

[0274]

[0275] According to step S1, the chain value of the importance and usage frequency of each functional module can be obtained according to formula (23), as shown in Table 4.

[0276] Table 4 Chain value of importance and usage frequency of each functional module

[0277]

[0278] See also Figure 2 , is the result of the interface visual field area division of the present invention. According to step S1, this embodiment divides the entire operation interface into four areas: upper left, upper right, lower left, and lower right. The attention level scores of the four areas in the visual attention optimization objective function are 4, 3, 2, and 1 points respectively.

[0279] See also Figure 3 、 Figure 4 According to step S1, this embodiment combines the reachability domain optimization objective with Jack ergonomics simulation to determine the coordinates of the projections of the operator's left and right palms on the operating interface when the operator is in the optimal operating posture: points a and b: (91.86, 156.48) and (374.14, 156.48), respectively. Actual measurement shows that the distance from point c to point a is equal to the distance from point d to point b: i.e., d1 = d2 = 433.79 mm. Laser cleaning equipment experts were also invited to evaluate the importance of the left and right hands using the rough AHP method, ultimately determining the importance of the left and right hands, β1 and β2, to be 0.36 and 0.64, respectively.

[0280] According to step S1, this embodiment fully considers the operating tasks of the laser cleaning equipment and the operating functions of each functional module, and uses 1, 3, 5, 7, and 9 to represent [AEIOU], which indicates the correlation between each functional module. The higher the correlation, the greater the relationship value. The correlation of the functional module itself is represented by 10. The specific correlation between the functional modules is shown in Table 5.

[0281] Table 5 Correlations between functional modules

[0282]

[0283] According to step S1, the total length A=466 mm and the width B=376 mm of the human-machine interface of the existing laser cleaning equipment machine tool motion system are measured. The dimensions of each functional module are shown in Table 6.

[0284] Table 6 Dimensions of each functional module

[0285]

[0286]

[0287] According to step S1, the above obtained data is substituted into the relevant objective function of the human-machine interface layout of the laser cleaning equipment.

[0288] See also Figure 5 According to step S2, in the spacing constraint condition, according to the ergonomic requirements, the minimum spacing of each functional module in the x direction and the y direction is set to d x min =d y min =6.5mm, and the minimum distance between each module and the layout space boundary in the x and y directions is specified as d in the boundary constraint. a min =d b min =10mm.

[0289] According to step S3, the relevant layout data obtained from steps S1 and S2 are substituted into the human-machine interface layout optimization model of the laser cleaning equipment.

[0290] See also Figure 6 According to step S4, the present invention provides a method for solving the interface layout of laser cleaning equipment based on a particle swarm-sparrow hybrid intelligent algorithm. The particle swarm algorithm is prone to falling into a local optimal solution due to its low local search accuracy and ability. To achieve the above purpose, the present invention is implemented through the following steps:

[0291] Step 1: Randomly initialize the population size, set parameters such as the maximum number of iterations and inertia factor, and determine the proportion of explorer particles in the population based on the proportional coefficient α;

[0292]

[0293] X best represents the number of pn explorer particles with better fitness values ​​in the population and the ability of explorer sparrows, X worst Represents n-pn follower particles with poor fitness values ​​in the population;

[0294] Step 2: Bring the initialized particle position into the objective function and calculate the particle fitness value;

[0295] Step 3: According to the dominance relationship, select the individual optimal solution pbest from the non-inferior solutions, store all non-inferior solutions in an external archive, and select the global optimal solution gbest from the external archive;

[0296] Step 4: In the large-scale global optimization process, the warning value R2 is set to be always smaller than the safety value ST. At this time, the explorer particle updates its position according to formula (37) and performs a large-scale rapid search in the problem solution space.

[0297]

[0298] Where t is the current number of iterations, iter max is the maximum number of iterations, X i t ,j represents the position information of the i-th sparrow at time t in the j-th dimension,

[0299] Step 5: Generate the influence factor θ according to the change of the explorer particle's ability, as shown in formula (38), and introduce it into the particle swarm algorithm to realize the dynamic adjustment of the inertia factor, changing the influence of the particle's past position and speed on the present, that is, the follower particle will update its speed and position according to formula (39);

[0300]

[0301]

[0302] Where, v i represents the velocity of the particle, x i Indicates the current position of the particle; w is the inertia factor used to adjust the local and global search capabilities of the algorithm; c1 and c2 are learning factors, usually c1=c2=2; r1 and r2 are random numbers between 0 and 1;

[0303] Step 6: Calculate the fitness value of the particle after the speed and position are updated;

[0304] Step 7: Update individual extreme value pbest and global extreme value gbest as well as external files;

[0305] Step 8: Determine whether the termination condition is met (reaching the maximum number of iterations). If so, output the Pareto optimal solution set; otherwise, jump to Step 3.

[0306] According to step S5, fuzzy set theory is used to select an optimal solution from the Pareto solution set determined by the particle swarm-sparrow hybrid intelligent algorithm.

[0307] The algorithm was compiled in Matlab based on the particle swarm-sparrow hybrid intelligent algorithm process and fuzzy set theory. The optimization model was solved with a total number of iterations of 300, a swarm size of 100, c1 = c2 = 2, and an inertia factor of w = 0.9.

[0308] See also Figure 7 、 Figure 8 , the same model was solved using both NSGAII and a multi-objective particle swarm algorithm, and the results were compared with the particle swarm-sparrow algorithm for solution quality. The particle swarm-sparrow algorithm outperformed the multi-objective particle swarm algorithm and the NSGAII algorithm in terms of the Pareto frontier points of the first three objective functions. The Pareto solution set was more evenly distributed and did not fall into local extremes. This, to a certain extent, reflects the particle swarm-sparrow algorithm's superior global search capabilities in large-scale optimization scenarios for multi-objective optimization models of the human-machine interface layout of laser cleaning equipment with complex parameters. At the same time, the particle swarm-sparrow search algorithm had the largest value for the Hyper Volume (HV) quality index, followed by the multi-objective particle swarm algorithm and the NSGAII algorithm, further demonstrating that the particle swarm-sparrow search algorithm achieved superior solution results.

[0309] See also Figure 9 , which represents the optimal Pareto solution obtained by the particle swarm-sparrow algorithm based on fuzzy set theory in this embodiment, namely the fuzzy compromise layout solution. Based on this, the positions of the functional modules are fine-tuned to improve the layout aesthetics. The coordinates of the functional modules are shown in Table 7.

[0310] Table 7 Coordinates of each functional module after interface optimization

[0311]

[0312] See also Figure 10 , forming an optimization scheme design rendering. In the optimization scheme for the human-machine interface layout of laser cleaning equipment, functional modules such as the program editing management area, menu selection area, and information processing confirmation area, which are highly important and highly relevant in terms of function and operation logic sequence, are arranged in adjacent positions in the upper right area of ​​the interface, so as to facilitate operation by the operator's right hand. The CNC display selection area is arranged in the middle area of ​​the interface, which is more in line with the operator's visual browsing habits and helps the operator concentrate on extracting relevant operation interaction information. The mode selection control and machine tool motion control functional modules that undertake important operation tasks and have a high correlation are arranged in adjacent positions and are located in the upper left area of ​​the human-machine interface, which facilitates the operator to extract interface information and respond in a timely manner. At the same time, the aesthetics and coordination of the optimized interface are further improved, which can reduce the operator's visual confusion, help the operator to accurately identify the interface information, and improve the safety of operation.

[0313] To demonstrate the effectiveness of the optimization scheme in this embodiment of the present invention in improving operator comfort and identifying and extracting human-machine interaction information, this embodiment employs an eye movement experiment for practical verification. The experiment utilizes the ErgoLAB human-machine environment synchronization testing platform to collect eye movement data while operators perform laser cleaning tasks based on the original and optimized human-machine interface. The platform consists of a Tobii X2-60 eye movement data recorder, a laptop computer, and ErgoLAB V2.0 human-machine environment testing and analysis software. The eight subjects participating in this experiment had an average age of 26. Four of them were experienced users and four were novice users. All had normal or corrected vision and volunteered to participate. The specific experimental process involved subjects completing corresponding tasks using both the original and optimized human-machine interface schemes, according to the operating procedures of a large-scale component laser cleaning system. The interaction data was recorded in real time using the ErgoLAB human-machine environment synchronization testing platform. Each experiment took approximately 1.5 minutes to complete.

[0314] See also Figure 11 , which is the comparison result of the first fixation time before and after the optimization of the human-machine interface layout of the laser cleaning equipment in this embodiment. The specific results are also shown in Table 8.

[0315] Table 8 First fixation time before and after interface optimization

[0316]

[0317] Comparisons of first fixation times show that the average first fixation time for experienced users decreased by 0.20 seconds, while that for novice users decreased by 0.48 seconds. This indicates that the optimized interface layout facilitates users' retrieval and recognition of interface information. It also demonstrates that the interface's learnability has been improved, helping novices quickly master the operational process. A two-way ANOVA was conducted on the first fixation times before and after optimization, with the area of ​​interest and design scheme as the independent variables and first fixation time as the dependent variable. The results are shown in Table 9.

[0318] Table 9 Results of two-way ANOVA of first fixation time

[0319]

[0320] Note: R-squared = .968 (adjusted R-squared = .944)

[0321] Table 9 shows that the significance value of the region of interest (p = 0.006 > 0.05), and the F statistic of the optimized solution is 39.986, with p = 0.002 < 0.05. This indicates that the optimized solution has a significant impact on the first fixation time of the laser cleaning equipment human-machine interface, while the region of interest has a smaller impact. This further verifies that the optimized layout scheme can improve the efficiency of the laser cleaning equipment operator in searching for target information on the interface, allowing for quick operational responses, improving human-machine interaction efficiency, and laser cleaning safety.

[0322] See also Figure 12 , which shows the comparison results of the average gaze time before and after the optimization of the human-machine interface layout of the laser cleaning equipment in this embodiment. The specific results are also shown in Table 10.

[0323] Table 10 Average fixation time before and after interface optimization

[0324]

[0325] Comparing the average fixation times of experienced and novice users reveals that their average fixation times decreased by 0.20s and 0.42s, respectively. This indicates that the optimized interface improved the speed with which users searched for specific information, indicating that the optimized interface layout better aligns with the operator's visual cognitive characteristics. A two-way ANOVA was conducted on the average fixation times before and after the interface layout optimization, with the area of ​​interest and design scheme as the independent variables and the average fixation time as the dependent variable. The results are shown in Table 11.

[0326] Table 11 Results of two-way ANOVA on average fixation time

[0327]

[0328] Note: R-squared = .626 (adjusted R-squared = .485)

[0329] The results in Table 11 show that the significance p-values ​​of the ROI and optimization scheme are 0.004 and 0.005, respectively, and both are less than 0.05, indicating that both the ROI and the optimization scheme have a significant impact on the average gaze time of the subjects. This further verifies that the optimized layout scheme is more in line with the operator's cognitive characteristics and operating habits, can effectively reduce the operator's cognitive fatigue, and promote the effective improvement of human-computer interaction efficiency and safety.

[0330] See also Figure 13(1) and 13(2)The hotspot maps before and after optimization show that the optimized interface has a relatively concentrated distribution of hotspots. Darker red indicates a hotter hotspot, indicating more focused vision. This further demonstrates that participants paid more attention to the functional modules of the optimized interface, with fewer repetitive saccades when searching for relevant information in different focus areas, helping operators focus their attention and quickly search and identify interface information.

[0331] In summary, the optimized human-machine interface has improved operational comfort, safety, and layout rationality. While reducing operator cognitive fatigue and professionalism and improving interface inclusiveness, the optimized interface does not sacrifice human-machine interaction efficiency, but rather reduces operation time to a certain extent.

[0332] The above description is a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art may modify the above technical solution or replace some of the technical features with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the human-machine interface layout of laser cleaning equipment based on particle swarm-sparrow algorithm, characterized in that: The steps include: S1. Determine the multi-objective function for optimizing the layout of the human-computer interaction interface. S2. Determine the constraints of the human-computer interaction interface layout, S3. Construct a human-machine interface layout optimization model for laser cleaning equipment. S4. The established laser cleaning equipment human-machine interface layout optimization model is calculated using a particle swarm-sparrow hybrid intelligent algorithm to determine the Pareto optimal solution set. S5. Use fuzzy set theory to select an optimal solution from the determined Pareto solution set. The step S3 constructs a human-machine interface layout optimization model for laser cleaning equipment, and the specific method is: (1) Problem description: In order to achieve the optimal layout efficiency of the human-machine interface of laser cleaning equipment, the influence of operation comfort, visual attention cognition and simplicity of the interface on operation interaction should be fully considered, and the important and frequently used functional modules should be arranged as much as possible in the position that is conducive to human-machine interaction. Therefore, the optimization model of the human-machine interface layout should systematically take into account various layout goals and fully consider the influence of various layout factors on operation interaction, so as to finally achieve the overall optimal layout of the human-machine interface of laser cleaning equipment; (2) Conditional assumptions: This paper makes the following assumptions regarding the optimization of the equipment human-machine interface layout: 1) Transform the human-machine interface layout design of laser cleaning equipment into a layout design problem within the space of a two-dimensional operation panel; 2) Simplify the layout space of the laser cleaning equipment human-machine interface and the functional modules of the operating devices to be arranged into regular rectangular geometric bodies; 3)L i and W i Respectively represent the length and width of each functional module i in the operation interface, (x i ,y i ) represents the coordinate value of the center point of the functional module i on the operation panel; (3) Optimization model: Based on the multi-objective function and constraints, and combined with the problem description and conditional assumptions, the human-machine interface layout optimization model of laser cleaning equipment can be expressed as: Among them, F e is the visual attention intensity of the entire interface, F r is the overall relevance of the interface layout, F s Indicates the overall simplicity of the interface layout, F b For the overall balance and comfort of the interface layout, F h is the accessible domain position advantage of the layout interface, d xmin and d ymin Indicates the minimum distance between functional modules i and j in the x and y directions, d amin and d bmin Respectively represent the minimum distance between each functional module of the operation interface and the layout boundary in the x direction and y direction.

2. The method for optimizing the human-machine interface layout of laser cleaning equipment based on the particle swarm optimization algorithm according to claim 1, characterized in that: The step S1 determines the multi-objective function for optimizing the layout of the human-computer interaction interface, and the specific layout objectives are: (1) Visual attention optimization goal The goal of visual attention optimization is to place the most important and frequently used operating device functional modules in the human-computer interaction interface within the operator's optimal field of view, so that the operator can observe and extract interface information in a timely manner, reduce the operator's short-term memory burden, and thus reduce the occurrence of human errors such as information extraction errors and memory errors; based on the determination of the importance and frequency of use of each functional module, a visual attention optimization objective function is constructed. 1) Determination of the importance of each functional module In order to reduce the subjectivity and ambiguity of evaluation information, the importance of each operating device functional module is calculated by rough AHP. Let U be a non-empty real number set, Y be any element in U, and divide it into S classes according to its value. R is the class set containing all elements in U. R={G1 G2 G3 ... G s },G1 <G2<...<G s (1) For any G t ∈R,1≤t≤s, define G t The lower approximation and upper approximation are Apr (G t )and The lower and upper limits are Lim (G t )and but Apr (G t )=∪{Y∈U|R(Y)≤G t } (2) Where M L and M U are the number of elements in the lower approximation set and the upper approximation set respectively, then G t The rough number representation of The specific steps for determining the importance of each operating device functional module in the human-machine interface of laser cleaning equipment through rough AHP are as follows: Step 1: Conduct an AHP survey to determine the judgment matrix; k experts compare the importance of n functional modules pairwise based on the hierarchical analysis method, and obtain the n×n dimensional AHP judgment matrix A given by the k experts. e , 1≤e≤k, and perform consistency test on it. When the consistency test index CR≤0.1, the consistency test passes. Step 2: Construct a comprehensive judgment matrix; integrate the n-dimensional judgment matrices of k experts to construct a comprehensive judgment matrix Its formula is Step 3: Construct a rough judgment matrix; Convert it into a rough set form and normalize it to get the rough judgment matrix M, which is: Step 4: Calculate the importance of each functional module a i ,but: a i =a i / ∑a′ i (17) 2) Determination of the usage frequency of each functional module When determining the usage frequency of each functional module, the influence of different cleaning conditions in the operation of the laser cleaning equipment is fully considered; assuming that there are m working conditions in the laser cleaning process, the usage frequency score matrix P of n functional modules in different operating conditions can be expressed as: Considering that the occurrence probability of each operating condition is different during the laser cleaning process, the probability of occurrence of m operating conditions can be expressed as O=[o1 o2 ... o m ] T (19) The overall usage frequency of functional module i after considering the probability of occurrence of each working condition can be expressed as: C=PO=[C1 C2…C n ] (20) At the same time, the overall usage frequency is normalized, and the usage frequency of the functional modules of each operating device can be finally obtained as follows: On the basis of determining the importance and usage frequency of each operating device functional module of the laser cleaning equipment, and taking into full consideration the position and area of ​​each functional module in each visual field area, the attention optimization goal can represent the maximum visual attention intensity of the interface, that is, Where, F e is the visual attention intensity of the entire interface, v = 1, 2, 3, 4 represent the upper left, upper right, lower left and lower right visual field areas of the laser cleaning equipment human-machine interface, and the attention level scores λ of the four visual field areas are v , respectively 4, 3, 2, 1 points, u iv represents the area of ​​functional module i in the visual field v; g i is the chain value of the importance and usage frequency of functional module i, that is, (2) Relevance optimization goal The goal of correlation optimization is to arrange the functional modules of various operating devices with high correlation in terms of function and operation logic sequence in close locations in the human-computer interaction interface to reduce the distance the operator's hands move. While improving operational comfort, it can also reduce the user's visual search time and reduce the operator's memory burden, making it easier to find the target functional module and reducing the operator's task burden. The higher the correlation between functional modules, the smaller the distance between their center points. The correlation optimization target of the human-machine interface layout of laser cleaning equipment can be expressed as: Where, F r is the overall correlation of the interface layout, then the correlation matrix between each functional module is Where r ij Indicates the correlation between functional modules i and j; x i and y i They represent the x-axis and y-axis coordinates of the center point of functional module i respectively; A and B represent the length and width of the overall layout interface respectively. (3) Simplicity optimization goal The goal of simplicity optimization is to align the functional modules of various operating devices as much as possible in the human-machine interface layout to improve the simplicity and aesthetics of the interface, effectively reduce visual interference, improve the operator's ability to observe and understand interface information, and thus effectively improve the efficiency of human-machine interaction. The simplicity optimization goal of the human-machine interface layout of laser cleaning equipment can be expressed as: Where, F s Indicates the overall simplicity of the interface layout; n x and n y Respectively represent the number of functional modules with equal x-axis and y-axis coordinates of the center points of each functional module; (4) Balancing comfort optimization goals The goal of balance comfort optimization is to minimize the difference in the overall weight of the functional modules on both sides of the horizontal and vertical symmetry axes of the operation interface in the human-machine interface layout, so as to improve the balance and coordination of the entire interface layout and reduce the operator's visual confusion. Therefore, the human-machine interface layout of laser cleaning equipment should maintain the maximum overall balance and comfort of the interface, and the balance and comfort optimization goal can be expressed as: Where, F b The overall balance and comfort of the interface layout; L and R represent the left and right areas of the vertical symmetry axis of the interface, respectively; U and D represent the upper and lower areas of the horizontal symmetry axis of the interface, respectively; At the same time, considering the influence of the distance between each functional module and the symmetry axis on the balance and coordination of the interface, we can get: Where w = L, R, U, D; s iw represents the area of ​​the operating device functional module i in the w region, d i is the distance between the center point of the operating device functional module i and the vertical symmetry axis of the interface or the symmetry axis; (5) Reachable domain optimization goal The goal of reachable domain optimization is to place the most important and frequently used functional modules in the human-machine interface within the comfortable operation area of ​​both hands. This can improve the operator's operating comfort while reducing the operating load, ultimately achieving rapid operation and response of the laser cleaning equipment. Jack ergonomics simulation software was used to determine the optimal operating posture for the operator when operating laser cleaning equipment. Points a and b were used to represent the projections of the left and right palms of the Jack virtual human on the equipment operation interface when the virtual human was in the optimal operating posture, respectively, to determine the position of the hands. Furthermore, point c was the farthest location from point a on the laser cleaning equipment operation interface, with a distance of d1. Point d was the farthest location from point b, with a distance of d2. The position coefficient of the reachable domain target is constructed by calculating the distance between the functional module i of each waiting-to-be-deployed operating device and points a and b in the human-computer interaction interface, and taking into full consideration the relationship with d1 and d2. Let the coordinates of points a and b be (x a ,y a ) and (x b ,y b ), function module to be deployed i(x i ,y i ) to point a and point b are d ia and d ib but: Let θ i is the target position coefficient of the reachable domain of the function module i to be deployed, then: Where β1 and β2 represent the importance of the operator’s left and right hands respectively, θ i The larger the value is, the more comfortable the position of the function module i is for the operator to operate. In the human-machine interface layout of laser cleaning equipment, the operating device function modules with higher importance and frequency of use should be placed in the area with larger location coefficient. Then the reachable domain optimization objective of the function module i to be placed can be expressed as: Where, F h is the accessible domain position advantage of the layout interface, g i It is the chain value of importance and frequency of use, that is, the human-machine interface layout of the laser cleaning equipment should meet the maximum position advantage of the interface reachable area.

3. The method for optimizing the human-machine interface layout of laser cleaning equipment based on the particle swarm-sparrow algorithm according to claim 1, characterized in that: The step S4 uses a particle swarm-sparrow hybrid intelligent algorithm to calculate the established laser cleaning equipment human-machine interface layout optimization model to determine the Pareto optimal solution set. The specific steps are as follows: Step 1: Randomly initialize the population size, set the maximum number of iterations and inertia factor, and determine the proportion of explorer particles in the population according to the proportional coefficient α; X best represents the number of pn explorer particles with better fitness values ​​in the population and the ability of explorer sparrows, X worst Represents n-pn follower particles with poor fitness values ​​in the population; Step 2: Bring the initialized particle position into the objective function and calculate the particle fitness value; Step 3: According to the dominance relationship, select the individual optimal solution pbest from the non-inferior solutions, store all non-inferior solutions in an external archive, and select the global optimal solution gbest from the external archive; Step 4: In the large-scale global optimization process, the warning value R2 is set to be always smaller than the safety value ST. At this time, the explorer particle updates its position according to formula (37) and performs a large-scale rapid search in the problem solution space. Where t is the current number of iterations, iter max is the maximum number of iterations, represents the position information of the i-th sparrow at time t in the j-th dimension, Step 5: Generate the influence factor θ according to the change of the explorer particle's ability, as shown in formula (38), and introduce it into the particle swarm algorithm to realize the dynamic adjustment of the inertia factor, changing the influence of the particle's past position and speed on the present, that is, the follower particle will update its speed and position according to formula (39); Where, v i represents the velocity of the particle, x i Indicates the current position of the particle; w is the inertia factor used to adjust the local and global search capabilities of the algorithm; c1 and c2 are learning factors, usually c1=c2=2; r1 and r2 are random numbers between 0 and 1; Step 6: Calculate the fitness value of the particle after the speed and position are updated; Step 7: Update individual extreme value pbest and global extreme value gbest as well as external files; Step 8: Determine whether the termination condition is met. If so, output the Pareto optimal solution set; otherwise, jump to Step 3.

4. The method for optimizing the human-machine interface layout of laser cleaning equipment based on the particle swarm-sparrow algorithm according to claim 1, characterized in that: The step S5 adopts fuzzy set theory to select an optimal solution from the determined Pareto solution set. The specific method is as follows: The Pareto solution set of the particle swarm-sparrow hybrid intelligent algorithm contains a series of non-dominated solutions. The multi-objective decision-making of the laser cleaning equipment layout is to select a definite optimal solution in the Pareto solution set. Based on this, based on fuzzy set theory, a compromise method is adopted to comprehensively select the optimal solution for the human-machine interface layout to improve the effect of the layout plan and define the membership function for Where, and are the minimum and maximum values ​​of the i-th optimization objective function in the Pareto solution set, respectively. and They represent the current value and membership value of the i-th objective function of the j-th solution, respectively. Secondly, for each non-dominated solution k in the Pareto set of the result obtained by the particle swarm-sparrow hybrid intelligent algorithm, its dominance function is defined as Where l is the number of non-inferior solutions in the Pareto solution of the optimization result; n is the target number of the layout optimization model. The dominance value of each non-inferior solution in the Pareto solution set can be obtained by formula (41): The larger the value, the better the overall performance of the solution. Therefore, the solution with the largest dominance value is selected from the Pareto solution set of the human-machine interface layout results of the laser cleaning equipment, which is the optimal solution.

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