Engineering machinery operation panel control system and method thereof

Abstract

The invention relates to the technical field of control panels, and discloses an engineering machinery operating panel control system and a method thereof. In a first preset time period, the number of interactions of an operator with each button of the control panel is collected; according to the number of interactions of the buttons, each button is divided into an interaction level to obtain an interaction grade of the buttons; in a second preset time period, according to the interaction grade of the buttons, the panel is partitioned to obtain a plurality of panel sub-areas, each panel sub-area including the same number of buttons of the interaction grade; the functions of the buttons in each panel sub-area are circulated according to preset rules; the invention balances the button load of the panel, optimizes the service life of the panel, and does not affect the operator's accurate use of the panel.

Description

Technical Field

[0001] The present invention relates to the field of control panels, and more particularly to an engineering machinery operating panel control system and a method thereof. Background Art

[0002] Control panels for construction machinery are widely used in complex industrial and construction scenarios, and their working environment is usually subject to harsh conditions such as high temperature, high humidity, dust, vibration, etc. The overall life of the panel usually depends on the durability of these vulnerable buttons. Once some buttons fail, the control panel will not work properly.

[0003] Existing control panel technology mainly extends the service life by configuring high-durability mechanical components for high-frequency keys. However, although this method can improve the durability of the keys, it increases the manufacturing cost. In addition, the static design of this key fails to solve the problem of uneven key load. Summary of the invention

[0004] The present invention provides an engineering machinery operation panel control system and a method thereof, which solve the technical problems raised in the background technology.

[0005] In a first aspect, the present invention provides an engineering machinery operation panel control system, comprising:

[0006] A data collection module, used to collect the number of interactions of the operator with each button of the control panel within a first preset time period;

[0007] A key classification module is used to classify the interaction level of each key according to the number of key interactions to obtain the key interaction classification;

[0008] A panel partitioning module, used to partition the panel according to the interaction classification of the buttons within a second preset time period to obtain a plurality of panel sub-areas, each of which includes the same number of buttons of the interaction classification;

[0009] The function flow module is used to circulate the functions of the buttons in each panel sub-area according to preset rules.

[0010] Furthermore, according to the number of key interactions, each key is divided into an interaction level to obtain a key interaction grade, including:

[0011] Step 1, randomly select three buttons from the interaction times of all buttons, and initialize the interaction times of the corresponding buttons to three centroids;

[0012] Step 2, calculate the Euclidean distance between the number of interactions of the ith button and any of the three centroids, obtain the centroid with the smallest Euclidean distance to the number of interactions of the ith button, and assign the ith button to the corresponding centroid;

[0013] Step 3, repeat step 2, assign all keys to three centroids, and obtain three clusters;

[0014] Step 4: Calculate the average number of interactions of each cluster based on the number of interactions of all the keys in each cluster, and use the average number of interactions as the cluster center;

[0015] Step 5, calculate the Euclidean distance between the number of interactions of all keys and the cluster center, redistribute all keys to the three cluster centers, obtain three updated clusters, and calculate the average number of interactions of the keys in each cluster;

[0016] Step 6, repeat steps 4 and 5 for a preset number of times to obtain three final clusters, and sort the average value of the number of interactions of each final cluster from large to small to obtain a first interaction ranking, a second interaction ranking, and a third interaction ranking.

[0017] Furthermore, the panel is partitioned according to the interaction level of the buttons to obtain several panel sub-areas, including:

[0018] Step 7, partitioning all the buttons in the panel according to the number of buttons in the first interaction classification to obtain the same number of panel sub-areas as the number of buttons in the first interaction classification;

[0019] Step 8: Allocate a combination scheme to each panel sub-area, each combination scheme includes a second interaction graded key, the same number of second interaction graded keys and the same number of third interaction graded keys.

[0020] Furthermore, a combination scheme is assigned to each panel sub-area, including:

[0021] Step 81, randomly initialize and generate P individuals that meet the following constraints. Each individual is encoded as a matrix with R rows, and each row of the matrix contains the following components:

[0022] Grading keys from first interaction: Select a button in the

[0023] Keys from the second interactive grade: Select a button in;

[0024] Key points from the third interactive grade: Select b buttons;

[0025] Constraints: The keys of any two rows of the matrix are not repeated, and the keys of each row of the matrix are not repeated;

[0026] Objective function:

[0027] ;

[0028] in, , represents the number of rows of the matrix, and are the indices of the rows of the matrix, Represents the matrix The total number of interactions for the row encoding, Represents the matrix The total number of interactions for the row encoding, represents the row with the largest total number of interactions in the matrix, Represents the row in the matrix with the smallest total number of interactions;

[0029] Step 82, outputting the objective function value of each of the P individuals based on the objective function;

[0030] Step 83, sorting the P individuals from small to large according to the objective function value to obtain a first feature sorting; retaining a preset number of individuals from the front to the back of the feature sorting, and updating the remaining individuals;

[0031] Step 84, repeat steps 82 and 83 for a preset number of iterations to obtain the final P individuals, and select the individual with the smallest objective function value among the final P individuals, and use each row of the matrix of the corresponding individual as a combination scheme.

[0032] Furthermore, the remaining individuals are updated, including the high-altitude soaring stage, the low-altitude soaring stage, the circling trapping stage, and the diving pounce stage:

[0033] The formula for the high altitude phase is as follows:

[0034] ;

[0035] in, represents the updated individual matrix; Represents the individual matrix with the smallest objective function value among P individuals; Represents the convergence weight coefficient, which controls the amplitude of global exploration. ; represents the initial individual matrix; Indicates the preset number of iterations; Indicates the current iteration number; represents a random variable that follows a Levy distribution, represents the scaling factor of Levy flight, ; Represents a random variable, used to generate random samples; It means to generate a dimension A random vector following a standard normal distribution; Represents the mean matrix of P individual matrices;

[0036] The formula for the low-altitude soaring phase is as follows:

[0037] ;

[0038] in, represents the initial radius of the spiral trajectory; Represents the angle gain coefficient, which is used to control the rotation speed of the spiral trajectory. represents the fluctuation of the spiral trajectory in the horizontal direction, represents the fluctuation of the spiral trajectory in the vertical direction, represents the natural exponential function, Represents the scaling speed parameter.

[0039] The formula for the hovering trap phase is as follows:

[0040] ;

[0041] in, represents the convergence speed coefficient, ; represents the sinusoidal modulation term of the spiral trajectory, represents the sinusoidal frequency; Indicated in The random number generated within is the limiting parameter for the disturbance amplitude, ;

[0042] The formula for the dive phase is as follows:

[0043] ;

[0044] in, Represents a random number between 0 and 1.

[0045] Furthermore, the key allocation schemes of the R panel sub-areas are matched to the R combination schemes respectively, including:

[0046] Step 9, sorting the R panel sub-areas from large to small according to the total number of interactions within the first preset time period to obtain a second feature sorting;

[0047] Step 10, sorting the total number of interactions of each combination scheme of the R combination schemes from small to large to obtain a third feature sorting;

[0048] Step 11, fit the second feature sorting and the third feature sorting in sequence to obtain a fitting result, and match the corresponding panel sub-areas and the corresponding combination schemes one by one according to the fitting result.

[0049] Furthermore, the functions of the buttons in each panel sub-area are cyclically circulated according to preset rules, including:

[0050] Step 12, for the sth panel sub-region, adjusting the function of the key in the sth panel sub-region at a fixed time interval, specifically includes:

[0051] Step 13, counting the number of interactions for each button in the s-th panel sub-area within a second preset time period;

[0052] Step 14, sorting each button in the sth panel sub-area according to the number of interactions within the second preset time period to obtain a fourth characteristic sorting;

[0053] Step 15, aligning the buttons in the fourth feature sorting with the central symmetric flow of the fourth feature sorting, specifically includes: exchanging the functions of the buttons that are centrally symmetric about the fourth feature sorting, and changing the corresponding button names through the button display.

[0054] Furthermore, a lighting strategy is executed for the button display, and the lighting strategy includes:

[0055] Step 16, configuring display lights of different colors for buttons of different interaction levels;

[0056] Step 17, configuring display lights of different light intensities for buttons of the same interaction level.

[0057] In a second aspect, a control method for an engineering machinery operation panel is applied to the system, comprising:

[0058] In a first preset time period, the number of interactions of the operator with each button on the control panel is collected;

[0059] According to the number of key interactions, each key is divided into an interaction level to obtain the key interaction classification;

[0060] In a second preset time period, the panel is partitioned according to the interaction levels of the buttons to obtain a plurality of panel sub-areas, each of which includes the same number of buttons of the interaction level;

[0061] The functions of the buttons in each panel sub-area are cyclically circulated according to preset rules.

[0062] According to a third aspect, a control panel device is provided, wherein the control panel device is provided with a storage medium, wherein a program is stored on the storage medium, and the system is implemented when the program is executed.

[0063] The beneficial effects of the present invention are as follows: through key classification, dynamic partitioning, optimization algorithm and function flow mechanism, the usage load of keys is effectively balanced, the life of the panel is significantly extended, and at the same time, combined with light prompts, the operator's misoperation is avoided and the production cost of the control panel is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a module diagram of the engineering machinery operation panel control system of the present invention;

[0065] Figure 2 It is a flow chart of the engineering machinery operation panel control method of the present invention. DETAILED DESCRIPTION

[0066] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Various examples may omit, replace, or add various processes or components as needed. In addition, the features described in some examples may also be combined in other examples.

[0067] like Figure 1-2 As shown, the engineering machinery operation panel control system includes:

[0068] A data collection module, used to collect the number of interactions of the operator with each button of the control panel within a first preset time period;

[0069] A key classification module is used to classify the interaction level of each key according to the number of key interactions to obtain the key interaction classification;

[0070] A panel partitioning module, used to partition the panel according to the interaction classification of the buttons within a second preset time period to obtain a plurality of panel sub-areas, each of which includes the same number of buttons of the interaction classification;

[0071] The function flow module is used to circulate the functions of the buttons in each panel sub-area according to preset rules.

[0072] In one embodiment of the present invention, according to the number of key interactions, each key is divided into an interaction level to obtain the key interaction classification, including:

[0073] Step 1, randomly select three buttons from the interaction times of all buttons, and initialize the interaction times of the corresponding buttons to three centroids;

[0074] Step 2, calculate the Euclidean distance between the number of interactions of the ith button and any of the three centroids, obtain the centroid with the smallest Euclidean distance to the number of interactions of the ith button, and assign the ith button to the corresponding centroid;

[0075] Step 3, repeat step 2, assign all keys to three centroids, and obtain three clusters;

[0076] Step 4: Calculate the average number of interactions of each cluster based on the number of interactions of all the keys in each cluster, and use the average number of interactions as the cluster center;

[0077] Step 5, calculate the Euclidean distance between the number of interactions of all keys and the cluster center, redistribute all keys to the three cluster centers, obtain three updated clusters, and calculate the average number of interactions of the keys in each cluster;

[0078] Step 6, repeat steps 4 and 5 for a preset number of times to obtain three final clusters, and sort the average value of the number of interactions of each final cluster from large to small to obtain a first interaction ranking, a second interaction ranking, and a third interaction ranking.

[0079] By using a clustering method based on the number of key interactions, the keys on the control panel are divided into three interaction levels (first interaction level, second interaction level, and third interaction level), thus providing a scientific basis for the optimization and partitioning of the control panel. Specifically: Randomly select the initial centroid, randomly select three keys from the number of interactions of all keys, and use their numbers of interactions as the initial centroids. By randomly selecting the initial centroids, human intervention is avoided and the diversity of initial conditions is ensured. The centroid represents the center point of a cluster, and its value is the average number of interactions of all keys in the current cluster. Assign each key to the centroid target closest to its number of interactions. Calculate the Euclidean distance between the number of interactions of each key and each centroid, and assign each key to the cluster corresponding to the centroid with the smallest distance. Key assignment is dynamic, ensuring that each key is assigned to the closest cluster according to its number of interactions, reflecting the similarity between keys. According to the assignment rule in step 2, calculate the distance from each key to the centroid one by one and complete the assignment, forming three initial clusters, each of which contains keys with similar numbers of interactions. Recalculate the center (centroid) of the cluster according to the number of interactions of the keys in the current cluster. Take the average of the number of key interactions in each cluster and calculate a new centroid. The new centroid value is used to replace the previous centroid as the basis for the next iteration. By recalculating the centroid, the centroid more accurately reflects the average interaction characteristics of the keys in the current cluster. Based on the updated centroid, readjust the assignment of each key. Repeat step 2, calculate the Euclidean distance between the number of key interactions and the updated centroid, and assign the key to the cluster with the closest distance. The updated cluster more accurately reflects the number of key interactions. After multiple iterations, the final stable clustering result is achieved. According to steps 4 and 5, the process of recalculating the centroid and key assignment is repeated until one of the following conditions is met: the preset number of iterations is reached, or the cluster assignment no longer changes (clustering results converge). Three final clusters are obtained, and the difference in the number of key interactions in each cluster is the smallest. According to the average value of the number of interactions in the three clusters, they are sorted from large to small and defined as follows: First interaction level: keys with the highest number of interactions. Second interaction level: keys with medium number of interactions. Third interaction level: keys with the lowest number of interactions. The buttons are scientifically divided into three categories: high interaction, medium interaction, and low interaction, providing a basis for button optimization and partitioning of the control panel.

[0080] In one embodiment of the present invention, the panel is partitioned according to the interaction classification of the buttons to obtain several panel sub-areas, including:

[0081] Step 7, partitioning all the buttons in the panel according to the number of buttons in the first interaction classification to obtain the same number of panel sub-areas as the number of buttons in the first interaction classification;

[0082] Step 8: Allocate a combination scheme to each panel sub-area, each combination scheme includes a second interaction graded key, the same number of second interaction graded keys and the same number of third interaction graded keys.

[0083] In one embodiment of the present invention, a combination scheme is allocated to each panel sub-area, specifically including:

[0084] Step 81, randomly initialize and generate P individuals that meet the following constraints. Each individual is encoded as a matrix with R rows, and each row of the matrix contains the following components:

[0085] Grading keys from first interaction: Select a button in the

[0086] Keys from the second interactive grade: Select a button in;

[0087] Key points from the third interactive grade: Select b buttons;

[0088] Constraints: The keys of any two rows of the matrix are not repeated, and the keys of each row of the matrix are not repeated;

[0089] Objective function:

[0090] ;

[0091] in, , represents the number of rows of the matrix, and are the indices of the rows of the matrix, Represents the matrix The total number of interactions for the row encoding, Represents the matrix The total number of interactions for the row encoding, represents the row with the largest total number of interactions in the matrix, Represents the row in the matrix with the smallest total number of interactions;

[0092] Step 82, outputting the objective function value of each of the P individuals based on the objective function;

[0093] Step 83, sorting the P individuals from small to large according to the objective function value to obtain a first feature sorting; retaining a preset number of individuals from the front to the back of the feature sorting, and updating the remaining individuals;

[0094] Step 84, repeat steps 82 and 83 for a preset number of iterations to obtain the final P individuals, and select the individual with the smallest objective function value among the final P individuals, and use each row of the matrix of the corresponding individual as a combination scheme.

[0095] Specifically, define the individual structure: each individual is represented by an R-row matrix (R is the number of panel sub-areas). Each row of the matrix represents a combination scheme, including: 1 first interaction level button, a second interaction level button, b third interaction level button. In order to extend the service life of the control panel, it is necessary to balance the number of interactions in each partition of the control panel to ensure that the number of interactions in each partition is similar. Therefore, set the objective function .

[0096] The objective function is used to obtain the objective function values ​​of P individuals. The smaller the objective function value, the better the quality of the individual's solution. Therefore, the P individuals are sorted according to the objective function value, and some individuals with the highest order are retained. The last individual in the sorting is updated as the parent. When this process is repeated for the preset number of iterations, the update is stopped and the individual with the lowest objective function value is output.

[0097] In one embodiment of the present invention, the remaining individuals are updated, including the high-altitude soaring stage, the low-altitude soaring stage, the hovering trapping stage and the diving pounce stage:

[0098] The formula for the high altitude phase is as follows:

[0099] ;

[0100] in, represents the updated individual matrix; Represents the individual matrix with the smallest objective function value among P individuals; Represents the convergence weight coefficient, which controls the amplitude of global exploration. ; represents the initial individual matrix; Indicates the preset number of iterations; Indicates the current iteration number; represents a random variable that follows a Levy distribution, represents the scaling factor of Levy flight, ; Represents a random variable, used to generate random samples; It means to generate a dimension A random vector following a standard normal distribution; Represents the mean matrix of P individual matrices;

[0101] The formula for the low-altitude soaring phase is as follows:

[0102] ;

[0103] in, represents the initial radius of the spiral trajectory; Represents the angle gain coefficient, which is used to control the rotation speed of the spiral trajectory. represents the fluctuation of the spiral trajectory in the horizontal direction, represents the fluctuation of the spiral trajectory in the vertical direction, represents the natural exponential function, Represents the scaling speed parameter.

[0104] The formula for the hovering trap phase is as follows:

[0105] ;

[0106] in, represents the convergence speed coefficient, ; represents the sinusoidal modulation term of the spiral trajectory, represents the sinusoidal frequency; Indicated in The random number generated within is the limiting parameter for the disturbance amplitude, ;

[0107] The formula for the dive phase is as follows:

[0108] ;

[0109] in, Represents a random number between 0 and 1.

[0110] Specifically, the high-altitude soaring stage means that individual red-tailed hawks search for prey by flying long distances at high altitudes, with the goal of quickly locking onto areas where prey may exist. This stage emphasizes global exploration capabilities to avoid falling into local optimal solutions. High-altitude flight: Red-tailed hawks observe vast areas on the ground from high altitudes, covering as much range as possible. Through the Levy flight strategy, the red-tailed hawk's flight path contains a mixture of long-distance jumps and short-distance jumps, which enhances global exploration capabilities. The search is guided by the current optimal solution and the population mean matrix. The update of the current solution comprehensively considers the global optimal position, the overall distribution of the population, and the disturbance of random flight trajectories to ensure the diversity of exploration. Among them, It is used to decrease with the number of iterations, with stronger exploration ability in the early stage and gradual convergence in the later stage.

[0111] It should be noted that generating a dimension A random vector from a standard normal distribution, where Represents a preset dimension, ≥1.

[0112] It should be noted that ; Represents the mean matrix The Line List, represents the index of P individual matrices, Indicates The individual matrix Line List.

[0113] The low-altitude soaring stage means that the red-tailed hawk lowers its flight altitude and gradually focuses on areas where prey may exist, entering the local exploration stage. This stage emphasizes further search for potential targets. Lowering the flight altitude: By reducing the spiral radius, the red-tailed hawk gradually narrows the search range and focuses on a smaller area. The position update contains the sinusoidal components of and to simulate spiral flight. The spiral trajectory enhances the search accuracy of the local area by dynamically adjusting the radius and angle. The step size depends on the distance between the population mean and the optimal solution, and is gradually reduced to improve the search accuracy. Used to control the search range, which is larger at the beginning and converges to the vicinity of the target later. Gradually lock the local area of ​​the search space. Combine the spiral path and dynamic step size adjustment to improve the coverage and capture capabilities of the target area.

[0114] The hovering trapping stage means that after locking the target area, the red-tailed hawk further observes the dynamics of the prey through hovering flight and adjusts its own position in order to prepare for the final capture. This stage emphasizes local fine-tuning and perturbation. The red-tailed hawk flies in a spiral trajectory with the target area as the center. Through the sine modulation term, the flight trajectory is dynamically adjusted to simulate the hovering behavior of the hawk. A certain degree of randomness is introduced to avoid falling into the local optimum. The hovering stage comprehensively considers the current position, target position and random perturbation to ensure the balance between adjustment flexibility and accuracy. Simulate the observation and trial behavior of the red-tailed hawk before capture to enhance the adaptability of the algorithm. Optimize the individual position before capture to ensure the effectiveness of the final dive behavior.

[0115] The swooping phase means that the red-tailed hawk quickly rushes towards the prey and accurately captures the target. This phase emphasizes fast convergence and enhanced optimization of the solution. Near the target area, the algorithm focuses on the optimal solution, converges quickly and completes the final solution optimization. The algorithm convergence is accelerated in the final stage of the search. Ensure that the solution converges to a high-precision solution near the global optimum and completes the final optimization.

[0116] In one embodiment of the present invention, a final individual matrix is ​​obtained; each element of the matrix represents a different number of interactions; each element of the individual matrix is ​​matched to a specific key; for example, the matrix ; For each element in this matrix, match all the keys according to the number of key interactions. For example, the number of key interactions is as follows:

[0117]

[0118] For the matrix element " " matches the table "104" (matrix element " " is the element with the largest matrix element value, and table "104" is the largest parameter in the table), then the key corresponding to table "104" is the forward key, then the matrix element " " is replaced by the forward key; repeat this step, match all elements of the matrix to the number of interactions of all keys respectively, and then replace to obtain the key matrix.

[0119] In one embodiment of the present invention, the key allocation schemes of the R panel sub-areas are matched to the R combination schemes respectively, including:

[0120] Step 9, sorting the R panel sub-areas from large to small according to the total number of interactions within the first preset time period to obtain a second feature sorting;

[0121] Step 10, sorting the total number of interactions of each combination scheme of the R combination schemes from small to large to obtain a third feature sorting;

[0122] Step 11, fit the second feature sorting and the third feature sorting in sequence to obtain a fitting result, and match the corresponding panel sub-areas and the corresponding combination schemes one by one according to the fitting result.

[0123] Specifically, the number of interactions of each panel sub-area of ​​the control panel in the first preset time period is different, so at the beginning of the second preset time period, the number of interactions of each panel sub-area of ​​the control panel is unbalanced. In addition, in the obtained matrix key scheme, there is still a small difference in the number of interactions of each row. Therefore, by matching the smallest number of interactions of a row in the matrix key scheme with the largest panel sub-area among the panel sub-areas, the number of interactions of each panel sub-area of ​​the control panel in the first preset time period is corrected, which is conducive to extending the service life of the control panel.

[0124] In one embodiment of the present invention, the functions of the buttons in each panel sub-area are cyclically circulated according to a preset rule, including:

[0125] Step 12, for the sth panel sub-region, adjusting the function of the key in the sth panel sub-region at a fixed time interval, specifically includes:

[0126] Step 13, counting the number of interactions for each button in the s-th panel sub-area within a second preset time period;

[0127] Step 14, sorting each button in the sth panel sub-area according to the number of interactions within the second preset time period to obtain a fourth characteristic sorting;

[0128] Step 15, aligning the buttons in the fourth feature sorting with the central symmetric flow of the fourth feature sorting, specifically includes: exchanging the functions of the buttons that are centrally symmetric about the fourth feature sorting, and changing the corresponding button names through the button display.

[0129] For a button in a panel sub-area, after being operated within the second preset time period, the number of interactions for a button of the first interaction level will be significantly greater than that for other buttons, for example:

[0130] Button 1 (100 times), Button 2 (50 times), Button 3 (10 times), then Button 1 and Button 3 are symmetrically flowed (exchanged) in function, and the button display of Button 1 and the button display of Button 3 are exchanged at the same time.

[0131] In one embodiment of the present invention, a lighting strategy is executed for key display, and the lighting strategy includes:

[0132] Step 16, configuring display lights of different colors for buttons of different interaction levels;

[0133] Step 17, configuring display lights of different light intensities for buttons of the same interaction level.

[0134] For example, in one panel sub-area, key 1, key 2, and key 3 are included.

[0135] The color of button 1 is red, and the light intensity is 100; the color of button 2 is green, and the light intensity is 80; the color of button 3 is purple, and the light intensity is 60.

[0136] Through the above method, the operator avoids the situation that the operator is unclear about the position of the key due to the disordered flow of the key (balancing the number of interactions of the key) during the second preset time period. Through the partitioning of the panel sub-area, the operator can easily find the panel sub-area where any key is located, and can find the correct key in the corresponding panel sub-area again according to the color and intensity of the light. The present invention balances the number of interactions and service life of each key to a certain extent, and avoids the problem that the operator is unclear about the position of the key due to the replacement of the key.

[0137] The control method of the engineering machinery operation panel is applied to the system, comprising:

[0138] In a first preset time period, the number of interactions of the operator with each button on the control panel is collected;

[0139] According to the number of key interactions, each key is divided into an interaction level to obtain the key interaction classification;

[0140] In a second preset time period, the panel is partitioned according to the interaction levels of the buttons to obtain a plurality of panel sub-areas, each of which includes the same number of buttons of the interaction level;

[0141] The functions of the buttons in each panel sub-area are cyclically circulated according to preset rules.

[0142] A control panel device is provided with a storage medium, a program is stored on the storage medium, and the system is implemented when the program is executed.

[0143] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. Engineering machinery operation panel control system, characterized in that: include: A data collection module, used to collect the number of interactions of the operator with each button of the control panel within a first preset time period; The key classification module is used to classify the interaction level of each key according to the number of key interactions, and obtain the key interaction classification, which specifically includes: Step 1, randomly select three buttons from the interaction times of all buttons, and initialize the interaction times of the corresponding buttons to three centroids; Step 2, calculate the Euclidean distance between the number of interactions of the ith button and any of the three centroids, obtain the centroid with the smallest Euclidean distance to the number of interactions of the ith button, and assign the ith button to the corresponding centroid; Step 3, repeat step 2, assign all keys to three centroids, and obtain three clusters; Step 4: Calculate the average number of interactions of each cluster based on the number of interactions of all the keys in each cluster, and use the average number of interactions as the cluster center; Step 5, calculate the Euclidean distance between the number of interactions of all keys and the cluster center, redistribute all keys to the three cluster centers, obtain three updated clusters, and calculate the average number of interactions of the keys in each cluster; Step 6, repeating steps 4 and 5 for a preset number of times to obtain three final clusters, and sorting the average value of the number of interactions of each final cluster from large to small to obtain a first interaction ranking, a second interaction ranking, and a third interaction ranking; The panel partitioning module is used to partition the panel according to the interaction classification of the buttons within the second preset time period to obtain a plurality of panel sub-areas, each of which includes the same number of buttons with the same interaction classification; partitioning the panel specifically includes: Step 7, partitioning all the buttons in the panel according to the number of buttons in the first interaction classification to obtain the same number of panel sub-areas as the number of buttons in the first interaction classification; Step 8: Allocate a combination scheme to each panel sub-area, each combination scheme includes a second interactive hierarchical key, the same number of second interactive hierarchical keys and the same number of third interactive hierarchical keys; wherein, allocating a combination scheme to each panel sub-area specifically includes: Step 81, randomly initialize and generate P individuals that meet the following constraints. Each individual is encoded as a matrix with R rows, and each row of the matrix contains the following components: Grading keys from first interaction: Select a button in the Select from the buttons of the second interaction level buttons; Select from the third interactive level buttons buttons; Constraints: The keys of any two rows of the matrix are not repeated, and the keys of each row of the matrix are not repeated; Objective function: ； in, , represents the number of rows of the matrix, and are the indices of the rows of the matrix, Represents the matrix The total number of interactions for the row encoding, Represents the matrix The total number of interactions for the row encoding, represents the row with the largest total number of interactions in the matrix, Represents the row in the matrix with the smallest total number of interactions; Step 82, outputting the objective function value of each of the P individuals based on the objective function; Step 83, sorting the P individuals from small to large according to the objective function value to obtain a first feature sorting; retaining a preset number of individuals from the front to the back of the feature sorting, and updating the remaining individuals; Step 84, repeating steps 82 and 83 for a preset number of iterations to obtain the final P individuals, and selecting the individual with the smallest objective function value among the final P individuals, and taking each row of the matrix of the corresponding individual as a combination scheme; The function flow module is used to circulate the functions of the buttons in each panel sub-area according to preset rules.

2. The engineering machinery operation panel control system according to claim 1, characterized in that: Update the remaining individuals, including the high-altitude soaring stage, low-altitude soaring stage, hovering trapping stage, and swooping stage: The formula for the high altitude phase is as follows: ； in, represents the updated individual matrix; Represents the individual matrix with the smallest objective function value among P individuals; Represents the convergence weight coefficient, which controls the amplitude of global exploration. ; represents the initial individual matrix; Indicates the preset number of iterations; Indicates the current iteration number; represents a random variable that follows a Levy distribution, represents the scaling factor of Levy flight, ; Represents a random variable, used to generate random samples; It means to generate a dimension A random vector following a standard normal distribution; Represents the mean matrix of P individual matrices; The formula for the low-altitude soaring phase is as follows: ； in, represents the initial radius of the spiral trajectory; Represents the angle gain coefficient, which is used to control the rotation speed of the spiral trajectory. represents the fluctuation of the spiral trajectory in the horizontal direction, represents the fluctuation of the spiral trajectory in the vertical direction, represents the natural exponential function, Represents the scaling speed parameter; The formula for the hovering trap phase is as follows: ； in, represents the convergence speed coefficient, ; represents the sinusoidal modulation term of the spiral trajectory, represents the sinusoidal frequency; Indicated in The random number generated within is the limiting parameter for the disturbance amplitude, ; The formula for the dive phase is as follows: ； in, Represents a random number between 0 and 1.

3. The engineering machinery operation panel control system according to claim 1, characterized in that: The key allocation schemes of the R panel sub-areas are matched to the R combination schemes respectively, including: Step 9, sorting the R panel sub-areas from large to small according to the total number of interactions within the first preset time period to obtain a second feature sorting; Step 10, sorting the total number of interactions of each combination scheme of the R combination schemes from small to large to obtain a third feature sorting; Step 11, fit the second feature sorting and the third feature sorting in sequence to obtain a fitting result, and match the corresponding panel sub-areas and the corresponding combination schemes one by one according to the fitting result.

4. The engineering machinery operation panel control system according to claim 3, characterized in that: The functions of the buttons in each panel sub-area are cyclically circulated according to preset rules, including: Step 12, for the sth panel sub-region, adjusting the function of the key in the sth panel sub-region at a fixed time interval, specifically includes: Step 13, counting the number of interactions for each button in the s-th panel sub-area within a second preset time period; Step 14, sorting each button in the s-th panel sub-area according to the number of interactions within the second preset time period to obtain a fourth characteristic sorting; Step 15, aligning the buttons in the fourth feature sorting with the central symmetric flow of the fourth feature sorting, specifically includes: exchanging the functions of the buttons that are centrally symmetric about the fourth feature sorting, and changing the corresponding button names through the button display.

5. The engineering machinery operation panel control system according to claim 4, characterized in that: Execute the lighting strategy for the button display. The lighting strategy includes: Step 16, configuring display lights of different colors for buttons of different interaction levels; Step 17, configuring display lights of different light intensities for buttons of the same interaction level.

6. A control method for an engineering machinery operation panel, characterized in that: The control system for the operation panel of an engineering machinery applied to any one of claims 1 to 5 comprises: In a first preset time period, the number of interactions of the operator with each button on the control panel is collected; According to the number of key interactions, each key is divided into an interaction level to obtain the key interaction classification; In a second preset time period, the panel is partitioned according to the interaction levels of the buttons to obtain a plurality of panel sub-areas, each of which includes the same number of buttons of the interaction level; The functions of the buttons in each panel sub-area are cyclically circulated according to preset rules.

7. A control panel device, characterized in that: The control panel device is provided with a storage medium, on which a program is stored, and when the program is executed, the engineering machinery operation panel control system according to any one of claims 1 to 5 is implemented.

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