Intelligent scheduling system-oriented interface design optimization method, evaluation method and equipment

Abstract

The invention discloses an intelligent scheduling system-oriented interface design optimization method, evaluation method and equipment, and relates to the field of intelligent scheduling system interface design, and the method comprises the steps: determining the interface color and primitives of an intelligent scheduling system interface; acquiring an initial layout of an intelligent scheduling system interface; optimizing the initial layout of the intelligent scheduling system interface by adopting a hierarchical genetic algorithm and a fitness total function to obtain an optimal intelligent scheduling system interface layout; and filling the optimal interface layout of the intelligent scheduling system according to interface colors and primitives required by the interface of the intelligent scheduling system to obtain the optimal interface of the intelligent scheduling system. According to the method, the interface layout of the intelligent scheduling system is optimized in a manner of combining the hierarchical genetic algorithm and the fitness total function, so that the scientificity of the interface layout of the intelligent scheduling system is improved, and the operation efficiency and satisfaction of a user are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent scheduling system interface design, and in particular to an interface design optimization method, evaluation method and device for an intelligent scheduling system. Background Art

[0002] As an important part of urban public transportation, the efficiency and reliability of the subway dispatching system are crucial to ensuring the smoothness of urban transportation. The subway dispatching system interface is the main way for dispatchers to interact with the system. The design quality of the subway dispatching system interface directly affects the dispatcher's operating efficiency and the stability of the system.

[0003] At present, the interface design of subway dispatching system faces the following problems: First, the design is often based on the subjective judgment of the designer, without fully considering the dispatcher's operating habits and cognitive characteristics, and lacks scientificity. Secondly, the interface layout does not fully consider the operating process and visual habits, which leads to the dispatcher frequently switching perspectives, affecting the operating efficiency. In addition, the evaluation methods for design effects mostly rely on questionnaires and subjective evaluations, lack objective and quantitative evaluation methods, and it is difficult to accurately reflect the design effect. Therefore, there is an urgent need for an interface design optimization method, evaluation method and equipment for intelligent dispatching systems. Summary of the invention

[0004] The purpose of this application is to provide an interface design optimization method, evaluation method and equipment for an intelligent dispatching system, which can optimize the layout of the subway intelligent dispatching system interface, improve the scientific nature of the intelligent dispatching system interface, and thereby improve user operating efficiency and satisfaction.

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

[0006] In a first aspect, the present application provides an interface design optimization method for an intelligent scheduling system, comprising:

[0007] Determine the interface colors and graphic elements required for the intelligent scheduling system interface; the interface colors include background color, text color and graphic element color.

[0008] Obtain an initial intelligent scheduling system interface layout; the initial intelligent scheduling system interface layout includes a module layout interface composed of multiple modules, and the module layout interface includes an element layout interface composed of multiple elements; the module layout interface is used to display the position information of each module; the element layout interface is used to display the position information of each element corresponding to any module.

[0009] The initial layout of the intelligent scheduling system interface is optimized by using a hierarchical genetic algorithm and a total fitness function to obtain the optimal intelligent scheduling system interface layout; the total fitness function includes an interface layout hierarchy sub-function, an interface layout correlation sub-function and an interface layout safety sub-function.

[0010] According to the interface colors and graphics elements required by the intelligent scheduling system interface, the optimal intelligent scheduling system interface layout is filled to obtain the optimal intelligent scheduling system interface.

[0011] In a second aspect, the present application provides an interface evaluation method for an intelligent scheduling system, wherein the interface evaluation method for an intelligent scheduling system is applied to an interface design optimization method for an intelligent scheduling system as described in the first aspect, and the interface evaluation method for an intelligent scheduling system comprises:

[0012] The Petri net is used to conduct an ergonomic evaluation of the optimal intelligent scheduling system interface.

[0013] Optionally, the using Petri net to perform ergonomic evaluation on the optimal intelligent scheduling system interface specifically includes:

[0014] Obtain ergonomic indicators in the interface design evaluation system.

[0015] The ergonomic indicators are summarized to form an ergonomic indicator system.

[0016] Based on the task flow chart of the intelligent scheduling system and the pre-screened interface elements, the Petri net task model of the intelligent scheduling system is built using the cpntools tool.

[0017] Based on the Petri net task model of the intelligent scheduling system, a Petri net model for fault handling task performance evaluation is constructed.

[0018] According to the structural characteristics of the Petri net model for performance evaluation of the fault handling task, several performance indicators are selected from the ergonomic indicator system, and a performance indicator system for the interface layout of the intelligent scheduling system based on the Petri net is constructed.

[0019] Determine the weight coefficient of each performance indicator in the performance indicator system; the weight coefficient of the performance indicator is obtained according to a preset second discriminant table.

[0020] Based on the performance indicators and their weight coefficients, a scoring formula for the intelligent scheduling system interface layout is constructed;

[0021] Based on the intelligent scheduling system interface layout scoring formula, an ergonomic evaluation is performed on the optimal intelligent scheduling system interface.

[0022] Optionally, the scoring formula for the optimal intelligent scheduling system interface is:

[0023] F scores =W 1 *TCT+W 2 *NMC+W 3 *MMD+W 4 *TCR+W 5 *ER;

[0024] Among them, TCT represents the task completion time, W 1 is the weight coefficient representing the task completion time; NMC represents the false touch rate, W 2 is the weight coefficient representing the false touch rate; MMD represents the task completion rate, W 3 is the weight coefficient representing the task completion rate; TCR represents the number of mouse clicks, W 4 is the weight coefficient representing the number of mouse clicks; ER represents the task completion time, W 5 is the weight coefficient representing the task completion time.

[0025] W 1 , W 2 , W 3 , W 4 and W 5 It is determined by the preset second discrimination table.

[0026] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the interface design optimization method for an intelligent scheduling system described in any one of the first aspects, or the processor executes the computer program to implement the interface evaluation method for an intelligent scheduling system described in any one of the second aspects.

[0027] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0028] This application first determines the interface color and graphics of the intelligent scheduling system interface; then, obtains the initial layout of the intelligent scheduling system interface, and optimizes the initial layout of the intelligent scheduling system interface using a hierarchical genetic algorithm and a total fitness function to obtain the optimal intelligent scheduling system interface layout; fills the optimal intelligent scheduling system interface layout according to the interface color and graphics required by the intelligent scheduling system interface to obtain the optimal intelligent scheduling system interface. This application optimizes the intelligent scheduling system interface layout by combining a hierarchical genetic algorithm with a total fitness function, thereby improving the scientific nature of the intelligent scheduling system interface layout and thereby improving the user's operating efficiency and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 This is an application environment diagram of an interface design optimization method for an intelligent scheduling system in one embodiment of the present application;

[0031] Figure 2 A flowchart of an interface design optimization method for an intelligent scheduling system provided in one embodiment of the present application;

[0032] FIG3 is a schematic diagram of the overall color matching of the intelligent scheduling system interface provided by an embodiment of the present application;

[0033] Figure 4 A schematic diagram of the graphic element design of the intelligent scheduling system interface provided in one embodiment of the present application;

[0034] Figure 5 A schematic diagram of interface elements of an intelligent scheduling system interface provided in an embodiment of the present application;

[0035] Figure 6a A schematic diagram of the interface layout of the intelligent scheduling system provided in one embodiment of the present application;

[0036] Figure 6b A schematic diagram of another intelligent scheduling system interface layout provided by an embodiment of the present application;

[0037] Figure 7 A task flow chart of an intelligent scheduling system provided in one embodiment of the present application;

[0038] Figure 8 A schematic diagram of a method for optimizing the interface layout of an intelligent scheduling system provided in one embodiment of the present application;

[0039] Fig. 9 A hierarchical genetic algorithm flow chart provided in one embodiment of the present application;

[0040] Fig.10a The optimal intelligent scheduling system interface for the comprehensive monitoring interface provided in one embodiment of the present application;

[0041] Fig.10b The optimal intelligent scheduling system interface for the fault handling interface provided in one embodiment of the present application;

[0042] Fig.11A flow chart of a method for performing ergonomic evaluation of an optimal intelligent scheduling system interface using Petri nets provided in an embodiment of the present application;

[0043] Fig.12a A Petri net task model of an intelligent scheduling system provided in an embodiment of the present application;

[0044] Figure 12b A flowchart of a Petri net task model of an intelligent scheduling system provided in one embodiment of the present application;

[0045] Fig.13a A Petri net model for fault handling task performance evaluation provided in an embodiment of the present application;

[0046] Fig.13b A flowchart of a Petri net model for performance evaluation of a fault handling task provided in an embodiment of the present application;

[0047] Fig.14 A schematic diagram of a performance indicator system provided for an embodiment of the present application;

[0048] Fig.15 A Petri net performance evaluation diagram before layout optimization provided in an embodiment of the present application;

[0049] Fig.16 A Petri net performance evaluation diagram of the optimal intelligent scheduling system interface provided in one embodiment of the present application.

[0050] Fig.17 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0053] An interface design optimization method for an intelligent scheduling system provided in an embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the initial intelligent scheduling system interface layout, interface color and primitives to the server 104. After the server 104 receives the initial intelligent scheduling system interface layout, interface color and primitives, the server 10 uses a hierarchical genetic algorithm and a total fitness function to optimize the initial layout of the intelligent scheduling system interface to obtain the optimal intelligent scheduling system interface layout; according to the interface color and primitives required by the intelligent scheduling system interface, the optimal intelligent scheduling system interface layout is filled to obtain the optimal intelligent scheduling system interface. The server 104 can feedback the obtained optimal intelligent scheduling system interface to the terminal 102. In addition, in some embodiments, the interface design optimization method for the intelligent scheduling system can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the initial intelligent scheduling system interface layout, interface color and graphics, or the server 104 can obtain the initial intelligent scheduling system interface layout, interface color and graphics from the data storage system and process the initial intelligent scheduling system interface layout, interface color and graphics.

[0054] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0055] In an exemplary embodiment, Figure 2 As shown, a method for optimizing interface design for an intelligent scheduling system is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 Taking the server 104 in the example as an example, the following steps 201 to 204 are included:

[0056] Step 201, determine the interface colors and graphics elements required by the intelligent scheduling system interface, wherein the interface colors include background colors, text colors, and graphics element colors.

[0057] In step 201, the interface color required by the intelligent scheduling system interface is determined based on the semantic difference method. The semantic characteristics of the interface color are determined by clustering and semantic analysis of the commonly used colors of the scheduling system interface. Then, according to the characteristics of the scheduling system interactive tasks, appropriate background colors, text colors and primitive colors are selected to match the elements of the scheduling system interface, thereby improving the readability and aesthetics of the intelligent scheduling system interface.

[0058] As an optional implementation, the interface color required for the intelligent scheduling system interface is determined, and the specific steps are as follows:

[0059] Based on the SD method (semantic differential method), a questionnaire survey was conducted, and 9 color samples were combined with the selected color image semantics. The SD method was used to make a five-level evaluation questionnaire to represent the psychological changes in different dimensions, and the color intention evaluation score statistics were obtained, see Table 1. Among them, the color image evaluation value is set between 0 and 1, 0 represents very dynamic, 1 represents very quiet, and 0.5 represents neutral.

[0060] Table 1. Color intention evaluation score statistics

[0061]

[0062] In the subway dispatching monitoring system, the interfaces that need to set colors are mainly divided into three categories: background color, text color, and primitive color. As shown in Table 1, among the 9 colors, dark blue has the highest intention score, indicating that dark blue is a color with a calm semantics. When processing the background color, since the interface of the dispatching monitoring system requires monitoring personnel to monitor for a long time, dark blue, whose color semantics tend to be calm, is selected as the background color of the entire interface. This helps to relieve the visual fatigue of the monitors and allows them to think more calmly.

[0063] After the background color is determined, the text color is further determined according to the color combination priority table, which is shown in Table 2:

[0064] Table 2. Visibility of different color combinations

[0065] Background color Target color priority order black White>Yellow>Orange>Orange>Red>Green>Blue white Black>Red>Purple>Purple-red>Blue>Green>Yellow blue White>Yellow>Orange>Orange>Red>Black>Green yellow Black>Red>Blue>Blue-purple>Yellow-green>Green>White green White>Yellow>Red>Black>Orange>Blue>Purple purple White>Yellow>Orange>Orange>Green>Blue>Black>Red Ash Yellow>Yellow-green>Orange>Purple>Blue>Black

[0066] As shown in Table 2, when the background color is blue, the text color preference order is white>yellow>orange>orange>red>black>green, so white is selected as the text color of the interface. Regarding the color of the graphics, green and blue usually represent the normal operation of the system, but dark blue has been determined as the background color. To ensure that the graphics can be clearly distinguished, green is selected as the normal operation of the monitoring system. In addition, transition colors and gradient colors are added to the color selection to increase the visual level. The overall color matching of the intelligent scheduling system interface is as follows: Figure 3a and Figure 3b shown.

[0067] The design of the graphic element should follow the standards in the TIAS system graphic element design criteria file. For the graphic element design schematic diagram, refer to Figure 4 .

[0068] Step 202, obtaining the initial intelligent scheduling system interface layout.

[0069] Among them, the initial intelligent scheduling system interface layout includes a module layout interface composed of multiple modules, and the module layout interface includes an element layout interface composed of multiple elements; the module layout interface is used to display the position information of each module; the element layout interface is used to display the position information of each element corresponding to any module.

[0070] As an optional implementation, the process of determining the initial intelligent scheduling system interface layout is as follows: sort out the interface elements in the intelligent scheduling system interface to obtain the interface elements required for the intelligent scheduling system interface layout, and layout the initial positions of the interface elements according to the fault handling operation process shown in the intelligent scheduling system task flow chart to obtain the initial intelligent scheduling system interface layout.

[0071] Among them, the interface elements in the intelligent scheduling system interface elements can refer to Figure 5 In this embodiment, select Figure 5 The eight elements of the event area card module of the event handling interface (including faulty stations and faulty train numbers; among them, faulty stations include: faulty stations, fault content, reported vehicles and fault types. Faulty train numbers include: faulty train numbers, fault content, province of origin and fault type) are taken into consideration when designing the interface layout of the intelligent dispatching system. The importance, frequency of use, correlation coefficient and safety risk coefficient of these eight elements are shown in Tables 3, 4 and 5:

[0072] Table 3. Importance and usage frequency of eight elements in the event area card module in the event handling interface

[0073] 1 2 3 4 5 6 7 8 m 0.125 0.175 0.132 0.158 0.135 0.142 0.143 0.047 f 0.125 0.235 0.145 0.260 0.150 0.085 0.015 0.015

[0074] Table 4. Correlation coefficients of eight elements in the event area card module in the event handling interface

[0075] <![CDATA[r ij ]]> 1 2 3 4 5 6 7 8 1 1 0.7 0.5 0.9 0 0 0 0 2 0.7 1 0.9 0.7 0 0 0 0 3 0.5 0.9 1 0.5 0 0 0 0 4 0.9 0.7 0.5 1 0 0 0 0 5 0 0 0 0 1 0.9 0.5 0.7 6 0 0 0 0 0.9 1 0.7 0.9 7 0 0 0 0 0.5 0.7 1 0.9 8 0 0 0 0 0.7 0.9 0.9 1

[0076] Table 5. Security risk coefficients of eight elements in the event area card module in the event handling interface

[0077] 1 2 3 4 5 6 7 8 R 0.374 0.891 0.123 0.567 0.234 0.748 0.045 0.091

[0078] Among them, the element importance m, relevance r and safety risk coefficient R are obtained through expert scoring, and the usage frequency f is obtained by consulting the logs of the subway intelligent dispatching system.

[0079] The schematic diagram of the initial intelligent scheduling system interface layout can be referenced Figure 6a and Figure 6b . The interface of the intelligent dispatching system can be divided into two types of interfaces. The first type of interface is the comprehensive monitoring interface, which includes the comprehensive monitoring area A. The comprehensive monitoring area A contains four sub-modules: comprehensive monitoring sub-module A1, passenger flow and operation index overview A2, current line passenger flow information change A3 and current line passenger flow heat map A4; the second type of interface is the fault handling interface, which includes fault handling B, historical event management C and common dispatch orders D. Fault handling B includes fault notification B1, fault confirmation pop-up window B2, fault handling B3 and fault recovery pop-up window B4. The four sub-modules, fault handling pop-up window B3, include processing flow chart B3_1, auxiliary solution B3_2 and information interaction B3_3. It should be noted that area B, area B3 and area B3_1 are all modules in the module layout interface. When optimizing the layout of the module layout interface, it is divided into three rounds of optimization process. Specifically, the first round of optimization: start with the layout optimization between areas B, C, and D. Since the comprehensive monitoring interface only has area A, no optimization is required in the first round; the second round of optimization: optimize A1, A2, A3, and A4 in the first type of interface, and optimize B1, B2, B3, and B4 in the second type of interface; the third round of optimization: optimize the positions of sub-modules B3_1, B3_2, and B3_3 in B1.

[0080] It should be noted that the three rounds of optimization mentioned in the previous paragraph are all optimizations of the module layout interface and have nothing to do with the optimization of the element layout interface. Figure 6a and Figure 6b The optimization process is equivalent to Fig. 9 Further refinement of the left half of the process, and Fig. 9 The content in the right half is irrelevant. In other words, "B1, B2, B3, B4" and "B3_1, B3_2, B3_3" are all "modules" instead of "elements".

[0081] The task flow chart of the intelligent scheduling system can be found in Figure 7 .

[0082] It should be noted that the interface elements mentioned above can refer to both modules in the module layout interface and elements in the element layout interface. In other words, when performing module layout, "module" is an interface element, and when performing element layout, "element" is an interface element.

[0083] Step 203, using a hierarchical genetic algorithm and a total fitness function to optimize the initial layout of the intelligent scheduling system interface to obtain an optimal intelligent scheduling system interface layout. The total fitness function includes an interface layout hierarchy degree sub-function, an interface layout relevance degree sub-function, and an interface layout safety degree sub-function. The total fitness function is the first fitness function or the second fitness function.

[0084] In order to make the technical solution of this application more intuitive and easy to understand, Figure 8 Provides a schematic diagram of the intelligent scheduling system interface layout method. Figure 8 and Fig. 9 It can be seen that the layout optimization of the intelligent scheduling system interface adopts a hierarchical genetic algorithm, which divides the layout optimization process into two steps: first, the modules in the interface are laid out, and when the module layout is completed, the elements in the module are then laid out.

[0085] Furthermore, in an exemplary embodiment, step 203 specifically includes:

[0086] S1: Calculate the first fitness function value of each individual in the first population corresponding to the current iteration number according to the first fitness function; wherein, when the current iteration number is the first time, the individuals in the first population corresponding to the current iteration number are obtained after the module layout interface is initialized.

[0087] S2: Determine whether the current number of iterations reaches a first preset number of iterations.

[0088] If not, determine the initial population corresponding to the next iteration number according to the first selection operator and the first fitness function value of each individual in the population corresponding to the current iteration number, and use the first crossover rate and the first mutation rate to operate on the individuals in the initial population corresponding to the next iteration number to obtain the final population corresponding to the next iteration number, update the first population corresponding to the current iteration number in step S1 to the final population corresponding to the next iteration number, add 1 to the current iteration number, and return to step S1

[0089] If so, the individual corresponding to the maximum first fitness function value in the current iteration number is determined as the optimal module layout.

[0090] S3: Perform the following operations on the element layout interface in the optimal module layout:

[0091] According to the second fitness function, the second fitness function value of each individual in the second population corresponding to the current iteration number is calculated; wherein, when the current iteration number is the first time, the individuals in the second population corresponding to the current iteration number are obtained after the element layout interface is initialized.

[0092] S4: Determine whether the current number of iterations reaches a second preset number of iterations.

[0093] If not, then determine the initial population corresponding to the next iteration number according to the second selection operator and the second fitness function value of each individual in the second population corresponding to the current iteration number, and use the second crossover rate and the second mutation rate to operate on the individuals in the initial population corresponding to the next iteration number to obtain the final population corresponding to the next iteration number, update the second population corresponding to the current iteration number in step S3 to the final population corresponding to the next iteration number, add 1 to the current iteration number, and return to step S3.

[0094] If so, the individual corresponding to the maximum second fitness function value in the current iteration number is determined as the optimal element layout.

[0095] S5: Combining the optimal module layout with the optimal element layout to obtain an optimal intelligent scheduling system interface layout.

[0096] In an exemplary embodiment, in order to improve the optimization effect of the hierarchical genetic algorithm on the intelligent scheduling system interface, the step 203 further includes:

[0097] The genetic operation parameters of the hierarchical genetic algorithm are set; the genetic operation parameters include a first population size, a second population size, a first preset number of iterations, a second preset number of iterations, a first selection operator, a second selection operator, a first crossover rate, a second crossover rate, a first mutation rate, and a second mutation rate.

[0098] As an optional implementation, the genetic operation parameters of the hierarchical genetic algorithm are set as follows: first population size 100, second population size 50, first preset number of iterations 200, second preset number of iterations 500, first crossover rate 0.8, second crossover rate 0.6, first mutation rate 0.02, and second mutation rate 0.1. The selection operator adopts the tournament selection method, and the first selection operator is N 1 =2, the second selection operator N 2 =2.

[0099] In an exemplary embodiment, the total fitness function is F(x, y)=W a F a +Wr F r +W s F s ;

[0100] Among them, F a is the interface layout hierarchy function, W a F a The weight coefficient of .

[0101] F r is the interface layout related subfunction, W r F r The weight coefficient of .

[0102] F s is the interface layout safety function, W s F s The weight coefficient of .

[0103] Among them, W a , W r and W s It is determined by a preset first discrimination table.

[0104] In an exemplary embodiment, the first discrimination table is shown in Table 6:

[0105] Table 6. First discrimination table

[0106]

[0107] In Table 6, A, B and C represent the weight coefficients of interface layout hierarchy, interface layout relevance and interface layout safety respectively. According to Table 3, the total function of the interface layout fitness of the intelligent scheduling system is F(x, y) = 0.249F a +0.332F r +0.415F s At the same time, it is obvious that the total fitness function of the optimal intelligent scheduling system interface layout is maxF(x, y).

[0108] Furthermore, F a 、F r and F s The expressions are:

[0109]

[0110]

[0111] F a is the interface layout hierarchy function; k = 1, 2, 3, 4 represent the upper left, upper right, lower left and lower right areas of the interface respectively; λ k is the dominance weight of region k.

[0112] When the total fitness function is the first fitness function, n represents the total number of modules in the module layout interface; s ik is the area of ​​module i in region k in the module layout interface; The chain value representing the importance and usage frequency of module i in the module layout interface; the importance matrix is ​​m i =[m 1 m 2 …m n ] T ; Among them, m i is the importance of module i; the frequency matrix used is f i =[f 1 f 2 …f n ] T , where f i is the usage frequency of module i. The importance is obtained through expert scoring, and the usage frequency is obtained through log data in the subway intelligent dispatching system.

[0113] When the total fitness function is the second fitness function, n represents the total number of elements in the element layout interface; s ik is the area of ​​element i in region k in the element layout interface; The chain value representing the importance and usage frequency of element i in the element layout interface; the importance matrix is ​​m i =[m 1 m 2 …m n ] T ; Among them, m i is the importance of element i; the frequency matrix used is f i =[f 1 f 2 …f n ] T , where f i is the usage frequency of element i. The importance is obtained through expert scoring, and the usage frequency is obtained through log data in the subway intelligent dispatching system.

[0114] F r is the interface layout relevance subfunction; the relevance matrix is

[0115] When the total fitness function is the first fitness function, r ij Indicates the correlation between module i and module j; x i and i They represent the x-axis and y-axis coordinates of the center point of module i respectively; A and B represent the length and width of the module layout interface respectively.

[0116] When the total fitness function is the second fitness function, r ij Indicates the correlation between element i and element j; x i and i They represent the x-axis and y-axis coordinates of the center point of element i respectively; A and B represent the length and width of the element layout interface respectively.

[0117] F S It is the interface layout security function.

[0118] When the total fitness function is the first fitness function, n represents the total number of modules in the module layout interface; w i and w j Represent the importance weights of the i-th and j-th modules respectively; R ij Represents the safety risk coefficient between the i-th module and the j-th module.

[0119] When the total fitness function is the second fitness function, n represents the total number of elements in the element layout interface; w i and w j Represent the importance weights of the i-th and j-th elements respectively; R ij Represents the safety risk factor between the i-th element and the j-th element.

[0120] f risk (R ij ) is a weight function based on the risk factor; f distance (d ij ,R ij ) is a piecewise function based on distance and risk factor.

[0121] Step 204, filling the optimal intelligent scheduling system interface layout according to the interface colors and graphics elements required by the intelligent scheduling system interface to obtain the optimal intelligent scheduling system interface.

[0122] As an exemplary embodiment, Fig.10a and Fig.10b Two optimal intelligent scheduling system interfaces are given. Fig.10a It is the best intelligent dispatching system interface for comprehensive monitoring interface. Fig.10b It is the optimal intelligent scheduling system interface for fault handling interface.

[0123] By implementing the above steps 201 to 204, the present application optimizes the interface layout of the intelligent scheduling system by combining a hierarchical genetic algorithm with a total fitness function, thereby improving the scientific nature of the interface layout of the intelligent scheduling system and thereby improving the operating efficiency and satisfaction of the user.

[0124] An embodiment of the present application further provides an interface evaluation method for an intelligent scheduling system, the interface evaluation method for an intelligent scheduling system comprising:

[0125] The Petri net is used to conduct an ergonomic evaluation of the optimal intelligent scheduling system interface.

[0126] As an optional implementation, Fig.11 As shown, the ergonomic evaluation of the optimal intelligent scheduling system interface using Petri nets specifically includes:

[0127] Step 301, obtaining ergonomic indicators in the interface design evaluation system.

[0128] Step 302: Summarize the ergonomic indicators to form an ergonomic indicator system.

[0129] In the process of selecting ergonomic indicators, the applicant investigated the ergonomic indicators in the interface design of the ATS system and the interface design evaluation system in other fields, summarized and summarized many ergonomic indicators, and formed the ergonomic indicator system of this application. By summarizing and sorting out the research content and combining the structural characteristics of the Petri net, the task completion time, number of mouse clicks, task completion rate, and false touch rate were selected as performance indicators for ergonomic evaluation.

[0130] Step 303: Based on the task flow chart of the intelligent scheduling system and the pre-screened interface elements, a Petri net task model of the intelligent scheduling system is constructed by using the cpntools tool.

[0131] As an optional implementation, the Petri net task model of the intelligent scheduling system is as follows: Fig.12a As shown in the figure, the nodes in the model, i.e., the libraries, represent interactive interface elements, and the edges between the nodes, i.e., transitions, represent the transfer conditions of interface elements. The entire model is a formalized representation of the business process of the scheduling system. Figure 12b It can be seen that when a fault occurs in the intelligent dispatching system, a fault pop-up window will be generated. After the fault pop-up window appears, it will be determined whether the vehicle should be detained. If necessary, the vehicle will be detained. At the same time, it is determined whether the fault needs to be handled. If it needs to be handled, the fault handling interface will be entered; in the fault handling interface, the fault information and handling process are obtained according to the visual information in the interface. Among them, the fault information includes the fault information of the shield door, the number of the train where the fault occurred, the location of the fault, and the content of the fault. The handling process includes the adjustment of the operation diagram, the automatic exit of the system, and the completion of the detention of the vehicle. After the detention of the vehicle is completed, the operation diagram of the intelligent dispatching system interface is adjusted, and the corresponding plan is selected. After selecting the plan, you can view the details or confirm the plan.

[0132] In the process of using cpn tools to build the Petri net task model of the intelligent scheduling system, a mapping relationship is established between the basic elements of the Petri net, namely, place, transition, directed arc, token and human-computer interaction tasks, as shown in Table 7:

[0133] Table 7. Mapping relationship between the basic elements of Petri net and the components of human-computer interaction tasks

[0134]

[0135] Step 304: construct a Petri net model for fault handling task performance evaluation based on the Petri net task model of the intelligent scheduling system.

[0136] As an optional implementation, the fault handling task performance evaluation Petri net model is as follows: Fig.13a As shown, the model includes the modeling of false touch behavior and the addition of a guard function for the false touch probability, aiming to obtain evaluation performance. Fig.13b It gives Fig.13a In the Chinese flowchart, it can be seen that false touch 1 is set in the initial interface of the intelligent dispatching system, and false touch 2 is set in the fault pop-up window interface and the entry disposal interface. This application detects the false touch rate of the intelligent dispatching system by setting false touch detection in different interfaces.

[0137] Add a monitor to the fault handling task performance evaluation Petri net model to monitor the triggering times of false touch transitions and other transitions, and monitor the task completion time. According to the data monitored by the monitor, calculate the task completion time (TCT), false touch rate (NMC), task completion rate (MMD), number of mouse clicks (TCR), and obtain the mouse movement distance (ER) based on the linear relationship between operation time and mouse movement distance.

[0138] Step 305 , based on the structural characteristics of the Petri net model for performance evaluation of the fault handling task, select a number of performance indicators from the ergonomic indicator system, and construct a performance indicator system for the interface layout of the intelligent scheduling system based on the Petri net.

[0139] See the schematic diagram of the performance indicator system for Fig.14 .from Fig.14 It can be seen that the performance indicators in the performance indicator system include task completion time, false touch rate, task completion rate, number of mouse clicks and mouse movement distance.

[0140] Among them, task completion time: TCT = T end -T start , where T startis the time when the task starts, which is determined by the time suffix of the initial token. end The time when the task ends, which is determined by the time suffix of the token after the task simulation ends. Number of mouse clicks: where c i is the number of mouse clicks for the i-th time, n is the total number of clicks, and the value of i is determined by the number of transitions. Mouse movement distance: Where D i is the distance of the mouse movement for the ith time, n is the total number of moves, D i The value of is determined by the time suffix of each transition, because time and distance are proportional, and the value of i is determined by the number of transitions. Task completion rate: Where N success is the number of successful task completions, N total It is the total number of attempts, which is counted by the monitor. Where N errors is the number of false touches (wrong operations), N total is the total number of mouse operations. These parameters are all counted by the Petri net monitor. After obtaining the five performance indicators, the five performance indicators are normalized using the maximum-minimum normalization to scale the data to the range of 0 to 1.

[0141] Step 306, determine the weight coefficient of each performance indicator in the performance indicator system; the weight coefficient of the performance indicator is obtained according to the preset second discriminant table. The weight coefficient is intended to measure the relative importance of the five operation indicators.

[0142] Step 307: construct a scoring formula for the intelligent scheduling system interface layout based on the performance indicators and the weight coefficients of the performance indicators. The optimal scoring formula for the intelligent scheduling system interface layout is:

[0143] F scores =W 1 *TCT+W 2 *NMC+W 3 *MMD+W 4 *TCR+W 5 *ER.

[0144] Among them, TCT represents the task completion time, W 1 is the weight coefficient representing the task completion time; NMC represents the false touch rate, W 2 is the weight coefficient representing the false touch rate; MMD represents the task completion rate, W 3 is the weight coefficient representing the task completion rate; TCR represents the number of mouse clicks, W 4 is the weight coefficient representing the number of mouse clicks; ER represents the task completion time, W 5is the weight coefficient representing the task completion time. 1 , W 2 , W 3 , W 4 and W 5 It is determined by the preset second discrimination table.

[0145] As an optional implementation manner, the second discrimination table is shown in Table 7:

[0146] Table 7. Second discrimination table

[0147]

[0148] From Table 7, we can see that F scores =0.200TCT+0.075NMC+0.075MMD+0.325TCR+0.325ER.

[0149] Step 308: Based on the intelligent scheduling system interface layout scoring formula, an ergonomic evaluation is performed on the optimal intelligent scheduling system interface.

[0150] In an exemplary embodiment, Fig.15 The performance evaluation diagram of the Petri net before layout optimization is given. Fig.16 The optimal Petri net performance evaluation diagram of the intelligent scheduling system interface is given. Fig.15 It can be seen that the total score of Petri performance evaluation before layout optimization is 0.78. Fig.16 It can be seen that the scores of task completion time, false touch rate, task completion rate, mouse click times and mouse moving distance of the optimal intelligent scheduling system interface are 0.92, 0.86, 0.77, 0.90, 0.87, respectively, and the weight coefficients are 0.2, 0.075, 0.075, 0.325, 0.325, respectively, and the final score is 0.88. It can be seen that the performance evaluation of the intelligent scheduling system interface after optimization based on the hierarchical genetic algorithm is relatively high, and the performance score has increased by 12.82%, which significantly improves the user's operating efficiency, and the layout of the intelligent scheduling system interface is more scientific and reasonable.

[0151] The present application also provides an application scenario, which applies the above-mentioned interface design optimization method for intelligent dispatching system and interface evaluation method for intelligent dispatching system. Specifically: This embodiment can be applied in the evaluation scenario of subway dispatching system. The evaluation of subway dispatching system includes the evaluation link of dispatching function integrity, the evaluation link of dispatching safety and reliability, the evaluation link of dispatching intelligence level and the evaluation link of human-computer interaction experience. The interface design optimization method for intelligent dispatching system and the interface evaluation method for intelligent dispatching system provided in this embodiment both belong to the human-computer interaction experience evaluation link. On the one hand, the present application improves the evaluation score of the human-computer interaction experience evaluation link (improves user operation efficiency and satisfaction) by optimizing the interface layout of the intelligent dispatching system; on the other hand, the present application provides a feasible solution for interface layout evaluation based on Petri nets for the human-computer interaction experience evaluation link in the evaluation of subway dispatching system.

[0152] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.17 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the initial intelligent scheduling system interface layout, interface color and primitives. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an interface design optimization method for an intelligent scheduling system is implemented.

[0153] Those skilled in the art will understand that Fig.17 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0154] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0155] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0156] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0157] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0158] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0159] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0160] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0161] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An interface design optimization method for an intelligent scheduling system, characterized in that: The interface design optimization method for the intelligent scheduling system includes: Determine the interface colors and graphics required by the intelligent scheduling system interface; the interface colors include background color, text color and graphics color; Acquire an initial intelligent scheduling system interface layout; the initial intelligent scheduling system interface layout includes a module layout interface composed of multiple modules, and the module layout interface includes an element layout interface composed of multiple elements; the module layout interface is used to display the position information of each module; the element layout interface is used to display the position information of each element corresponding to any module; Obtaining an initial intelligent scheduling system interface layout; the initial intelligent scheduling system interface layout includes a module layout interface composed of multiple modules and an element layout interface composed of multiple elements; the module layout interface includes multiple modules and location information of each module; the element layout interface includes multiple elements and location information of each element; when any module in the module layout interface is selected, the element layout interface corresponding to the module will be displayed; The initial intelligent scheduling system interface layout includes a module layout sub-interface and an element layout sub-interface, wherein the module layout sub-interface is used to display multiple modules and the location information of each module, and the element layout sub-interface is used to display multiple elements corresponding to any module and the location information of each element; The initial layout of the intelligent scheduling system interface is optimized by using a hierarchical genetic algorithm and a total fitness function to obtain an optimal intelligent scheduling system interface layout; the total fitness function includes an interface layout hierarchical degree sub-function, an interface layout related degree sub-function and an interface layout safety degree sub-function; According to the interface colors and graphics elements required by the intelligent scheduling system interface, the optimal intelligent scheduling system interface layout is filled to obtain the optimal intelligent scheduling system interface.

2. The interface design optimization method for an intelligent scheduling system according to claim 1, characterized in that: The total fitness function is the first fitness function or the second fitness function; The use of a hierarchical genetic algorithm and a total fitness function to optimize the initial layout of the intelligent scheduling system interface to obtain the optimal intelligent scheduling system interface layout specifically includes: S1: Calculate the first fitness function value of each individual in the first population corresponding to the current iteration number according to the first fitness function; wherein, when the current iteration number is the first time, the individuals in the first population corresponding to the current iteration number are obtained after the module layout interface is initialized; S2: Determine whether the current number of iterations reaches a first preset number of iterations; If not, then determine the initial population corresponding to the next iteration number according to the first selection operator and the first fitness function value of each individual in the population corresponding to the current iteration number, and use the first crossover rate and the first mutation rate to operate on the individuals in the initial population corresponding to the next iteration number to obtain the final population corresponding to the next iteration number, update the first population corresponding to the current iteration number in step S1 to the final population corresponding to the next iteration number, add 1 to the current iteration number, and return to step S1; If so, the individual corresponding to the maximum first fitness function value in the current iteration number is determined as the optimal module layout; S3: Perform the following operations on the element layout interface in the optimal module layout: According to the second fitness function, the second fitness function value of each individual in the second population corresponding to the current iteration number is calculated; wherein, when the current iteration number is the first time, the individuals in the second population corresponding to the current iteration number are obtained after the element layout interface is initialized; S4: Determine whether the current number of iterations reaches a second preset number of iterations; If not, then determine the initial population corresponding to the next iteration number according to the second selection operator and the second fitness function value of each individual in the second population corresponding to the current iteration number, and use the second crossover rate and the second mutation rate to operate on the individuals in the initial population corresponding to the next iteration number to obtain the final population corresponding to the next iteration number, update the second population corresponding to the current iteration number in step S3 to the final population corresponding to the next iteration number, add 1 to the current iteration number, and return to step S3; If so, the individual corresponding to the maximum second fitness function value in the current number of iterations is determined as the optimal element layout; S5: Combining the optimal module layout with the optimal element layout to obtain an optimal intelligent scheduling system interface layout.

3. The interface design optimization method for an intelligent scheduling system according to claim 2, characterized in that: The initial layout of the intelligent scheduling system interface is optimized by using a hierarchical genetic algorithm and a fitness total function to obtain an optimal intelligent scheduling system interface layout, and further comprising: The genetic operation parameters of the hierarchical genetic algorithm are set; the genetic operation parameters include a first population size, a second population size, a first preset number of iterations, a second preset number of iterations, a first selection operator, a second selection operator, a first crossover rate, a second crossover rate, a first mutation rate, and a second mutation rate.

4. The interface design optimization method for an intelligent scheduling system according to claim 2, characterized in that: The total fitness function is: F(x, y) = W a F a +W r F r +W s F s ; Among them, F a is the interface layout hierarchy function, W a F a The weight coefficient of F r is the interface layout related subfunction, W r F r The weight coefficient of F s is the interface layout safety function, W s F s The weight coefficient of Among them, W a , W r and W s It is determined by a preset first discrimination table.

5. The interface design optimization method for an intelligent scheduling system according to claim 4, characterized in that: F a 、F r and F s The expressions are: F a is the interface layout hierarchy function; k = 1, 2, 3, 4 represent the upper left, upper right, lower left and lower right areas of the interface respectively; λ k is the dominance weight of region k; When the total fitness function is the first fitness function, n represents the total number of modules in the module layout interface; s ik is the area of ​​module i in region k in the module layout interface; The chain value representing the importance and usage frequency of module i in the module layout interface; the importance matrix is ​​m i =[m1m2…m n ] T ; Among them, m i is the importance of module i; the frequency matrix used is f i =[f1f2…f n ] T , where f i is the usage frequency of module i; When the total fitness function is the second fitness function, n represents the total number of elements in the element layout interface; s ik is the area of ​​element i in region k in the element layout interface; The chain value representing the importance and usage frequency of element i in the element layout interface; the importance matrix is ​​m i =[m1m2…m n ] T ; Among them, m i is the importance of element i; the frequency matrix used is f i =[f1f2…f n ] T , where f i is the usage frequency of element i; F r is the interface layout relevance subfunction; the relevance matrix is When the total fitness function is the first fitness function, r ij Indicates the correlation between module i and module j; x i and i Respectively represent the x-axis and y-axis coordinates of the center point of module i; A and B represent the length and width of the module layout interface respectively; When the total fitness function is the second fitness function, r ij Indicates the correlation between element i and element j; x i and i Respectively represent the x-axis and y-axis coordinates of the center point of element i; A and B represent the length and width of the element layout interface respectively; F S It is the interface layout security function; When the total fitness function is the first fitness function, n represents the total number of modules in the module layout interface; w i and w j Represent the importance weights of the i-th and j-th modules respectively; R ij Represents the safety risk coefficient between the i-th module and the j-th module; When the total fitness function is the second fitness function, n represents the total number of elements in the element layout interface; w i and w j Represent the importance weights of the i-th and j-th elements respectively; R ij Represents the safety risk coefficient between the i-th element and the j-th element; f risk (R ij ) is a weight function based on the risk factor; f distance (d ij ,R ij ) is a piecewise function based on distance and risk factor.

6. The interface design optimization method for an intelligent scheduling system according to claim 1, characterized in that: The first preset condition is that the number of iterations is greater than 200; the second preset condition is that the number of iterations is greater than 500.

7. An interface evaluation method for an intelligent scheduling system, characterized in that: The interface evaluation method for the intelligent scheduling system is applied to an interface design optimization method for the intelligent scheduling system as described in any one of claims 1 to 6; The interface evaluation method for the intelligent scheduling system includes: The Petri net is used to conduct an ergonomic evaluation of the optimal intelligent scheduling system interface.

8. The interface evaluation method for an intelligent scheduling system according to claim 7, characterized in that: The ergonomic evaluation of the optimal intelligent scheduling system interface using Petri nets specifically includes: Obtain ergonomic indicators in the interface design evaluation system; Summarizing the ergonomic indicators to form an ergonomic indicator system; Based on the task flow chart of the intelligent scheduling system and the pre-screened interface elements, the Petri net task model of the intelligent scheduling system is built through the cpntools tool; Based on the Petri net task model of the intelligent scheduling system, a Petri net model for fault handling task performance evaluation is constructed; According to the structural characteristics of the Petri net model for performance evaluation of the fault handling task, several performance indicators are selected from the ergonomic indicator system to construct a performance indicator system for the interface layout of the intelligent scheduling system based on the Petri net; Determine the weight coefficient of each performance indicator in the performance indicator system; the weight coefficient of the performance indicator is obtained according to a preset second discriminant table; Based on the performance indicators and their weight coefficients, a scoring formula for the intelligent scheduling system interface layout is constructed; Based on the intelligent scheduling system interface layout scoring formula, an ergonomic evaluation is performed on the optimal intelligent scheduling system interface.

9. The interface evaluation method for an intelligent scheduling system according to claim 8, characterized in that: The performance indicators include: task completion time, false touch rate, task completion rate, number of mouse clicks and mouse movement distance.

10. The interface evaluation method for an intelligent scheduling system according to claim 9, characterized in that: The scoring formula for the optimal intelligent scheduling system interface is: F scores =W1*TCT+W2*NMC+W3*MMD+W4*TCR+W5*ER; Among them, TCT represents the task completion time, W1 is the weight coefficient representing the task completion time; NMC represents the false touch rate, W2 is the weight coefficient representing the false touch rate; MMD represents the task completion rate, W3 is the weight coefficient representing the task completion rate; TCR represents the number of mouse clicks, W4 is the weight coefficient representing the number of mouse clicks; ER represents the task completion time, W5 is the weight coefficient representing the task completion time; Among them, W1, W2, W3, W4 and W5 are determined by the preset second discrimination table.

11. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement an interface design optimization method for an intelligent scheduling system as described in any one of claims 1 to 6; or the processor executes the computer program to implement an interface evaluation method for an intelligent scheduling system as described in any one of claims 7 to 10.

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