Interface design optimization method, evaluation method and device for intelligent dispatching system

Abstract

The application discloses an interface design optimization method and evaluation method and equipment for an intelligent scheduling system, relates to the field of interface design of the intelligent scheduling system, and comprises the following steps: determining interface colors and graphics of an interface of the intelligent scheduling system; obtaining an initial layout of the interface of the intelligent scheduling system; optimizing the initial layout of the interface of the intelligent scheduling system by adopting a hierarchical genetic algorithm and a fitness total function, to obtain an optimal interface layout of the intelligent scheduling system; and filling the optimal interface layout of the intelligent scheduling system according to the required interface colors and graphics of the interface of the intelligent scheduling system, to obtain an optimal interface of the intelligent scheduling system. The hierarchical genetic algorithm and the fitness total function are combined to optimize the interface layout of the intelligent scheduling system, the scientificity of the interface layout of the intelligent scheduling system is improved, and then the operation efficiency and the satisfaction of a user are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent dispatching system interface design, in particular to an interface design optimization method, an evaluation method and equipment for an intelligent dispatching system. BACKGROUND

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

[0003] Currently, the interface design of the subway dispatching system faces the following problems: first, the design is often based on the subjective judgment of the designer, without fully considering the operation habits and cognitive characteristics of the dispatchers, lacking scientificity. Second, the interface layout does not fully consider the operation process and visual habits, resulting in frequent switching of the dispatcher's perspective, affecting the operation efficiency. In addition, the evaluation method for the design effect relies mainly on questionnaires and subjective evaluation, lacking objective and quantitative evaluation means, and it is difficult to accurately reflect the design effect. Therefore, there is an urgent need for an interface design optimization method, an evaluation method and equipment for an intelligent dispatching system. SUMMARY

[0004] The purpose of the present application is to provide an interface design optimization method, an evaluation method and equipment for an intelligent dispatching system, which can optimize the layout of the interface of the intelligent dispatching system of the subway, improve the scientificity of the interface of the intelligent dispatching system, and thus improve the operation efficiency and satisfaction of the user.

[0005] To achieve the above-mentioned purpose, the present application provides the following solutions:

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

[0007] determining the interface colors and graphics required by the interface of the intelligent dispatching system; the interface colors include background color, text color and graphics color.

[0008] obtaining an initial interface layout of the intelligent dispatching system; the initial interface layout of the intelligent dispatching system 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 an optimal intelligent scheduling system interface layout; the total fitness function includes a hierarchical degree sub-function, a correlation degree sub-function and a safety degree sub-function of the interface layout.

[0010] The optimal intelligent scheduling system interface layout is filled according to the required interface colors and graphics of the intelligent scheduling system interface to obtain an optimal intelligent scheduling system interface.

[0011] In the second aspect, the application provides an interface evaluation method for an intelligent scheduling system, which is applied to the interface design optimization method for an intelligent scheduling system as described in the first aspect, and includes the following steps:

[0012] The optimal intelligent scheduling system interface is ergonomically evaluated by using a Petri net.

[0013] Optionally, the ergonomically evaluating the optimal intelligent scheduling system interface by using a Petri net specifically includes:

[0014] An ergonomics index in an interface design evaluation system is obtained.

[0015] The ergonomics index is summarized to form an ergonomics index system.

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

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

[0018] According to the structural characteristics of the fault handling task performance evaluation Petri net model, a number of performance indexes are screened from the ergonomics index system to construct a performance index system of the intelligent scheduling system interface layout based on a Petri net.

[0019] The weight coefficients of each performance index in the performance index system are determined; the weight coefficients of the performance indexes are obtained according to a pre-set second discrimination table.

[0020] Based on the performance indexes and the weight coefficients of the performance indexes, an intelligent scheduling system interface layout scoring formula is constructed.

[0021] The optimal intelligent scheduling system interface is ergonomically evaluated based on the intelligent scheduling system interface layout scoring formula.

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

[0023] F scores =W1*TCT+W2*NMC+W3*MMD+W4*TCR+W5*ER;

[0024] Wherein, TCT represents task completion time, W1 is a weight coefficient representing task completion time; NMC represents the false touch rate, W2 is a weight coefficient representing the false touch rate; MMD represents the task completion rate, W3 is a weight coefficient representing the task completion rate; TCR represents the number of mouse clicks, W4 is a weight coefficient representing the number of mouse clicks; ER represents the task completion time, W5 is a weight coefficient representing the task completion time.

[0025] W1, W2, W3, W4 and W5 are determined by the preset second discrimination table.

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

[0027] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0028] The present application first determines the interface color and graphic element of the intelligent scheduling system interface; then, the initial layout of the intelligent scheduling system interface is obtained, and the initial layout of the intelligent scheduling system interface is optimized by using the hierarchical genetic algorithm and the fitness total function, to obtain the optimal intelligent scheduling system interface layout; the optimal intelligent scheduling system interface layout is filled according to the required interface color and graphic element of the intelligent scheduling system interface, to obtain the optimal intelligent scheduling system interface. The present application uses the combination of hierarchical genetic algorithm and fitness total function to optimize the intelligent scheduling system interface layout, which improves the scientificity of the intelligent scheduling system interface layout, and further improves the operation efficiency and satisfaction of the user. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0030] Figure 1An application environment diagram of a method for interface design optimization of an intelligent dispatching system according to an embodiment of the present application;

[0031] Figure 2 A flow diagram of a method for interface design optimization of an intelligent dispatching system according to an embodiment of the present application;

[0032] FIG. 3 is a diagram of an overall color scheme of an intelligent dispatching system interface according to an embodiment of the present application;

[0033] Figure 4 A diagram of icon design of an intelligent dispatching system interface according to an embodiment of the present application;

[0034] Figure 5 A diagram of interface elements of an intelligent dispatching system interface according to an embodiment of the present application;

[0035] Figure 6a A diagram of interface layout of an intelligent dispatching system interface according to an embodiment of the present application;

[0036] Figure 6b A diagram of another interface layout of an intelligent dispatching system interface according to an embodiment of the present application;

[0037] Figure 7 A task flow diagram of an intelligent dispatching system according to an embodiment of the present application;

[0038] Figure 8 A diagram of an interface layout optimization method of an intelligent dispatching system according to an embodiment of the present application;

[0039] Figure 9 A flow diagram of a hierarchical genetic algorithm according to an embodiment of the present application;

[0040] Figure 10a An optimal intelligent dispatching system interface of a comprehensive monitoring interface according to an embodiment of the present application;

[0041] Figure 10b An optimal intelligent dispatching system interface of a fault handling interface according to an embodiment of the present application;

[0042] Figure 11 A flow diagram of a method for ergonomics evaluation of an optimal intelligent dispatching system interface using Petri nets according to an embodiment of the present application;

[0043] Figure 12a A Petri net task model of an intelligent dispatching system according to an embodiment of the present application;

[0044] Figure 12b A flow diagram of a Petri net task model of an intelligent dispatching system according to an embodiment of the present application;

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

[0046] Figure 13b A flow chart of the fault handling task performance evaluation Petri net model is provided for an embodiment of the present application.

[0047] Figure 14 A performance index system diagram is provided for an embodiment of the present application.

[0048] Figure 15 A Petri net performance evaluation diagram before layout optimization is provided for an embodiment of the present application.

[0049] Figure 16 A Petri net performance evaluation diagram of an optimal intelligent scheduling system interface is provided for an embodiment of the present application.

[0050] Figure 17 A structural diagram of a computer device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0052] In order to make the above objectives, characteristics and advantages of the present application more apparent, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0053] The interface design optimization method for the intelligent scheduling system provided by an embodiment of the present application can be applied to, for example, Figure 1The application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data required by the server 104 to process. The data storage system can be set up separately, or 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 figure to the server 104, and after the server 104 receives the initial intelligent scheduling system interface layout, interface color and figure, the server 10 optimizes the initial layout of the intelligent scheduling system interface by using hierarchical genetic algorithm and fitness total function, and obtains the optimal intelligent scheduling system interface layout; according to the interface color and figure required by the intelligent scheduling system interface, the optimal intelligent scheduling system interface layout is filled, and the optimal intelligent scheduling system interface is obtained. The server 104 can feed back the optimal intelligent scheduling system interface obtained to the terminal 102. In addition, in some embodiments, the interface design optimization method for intelligent scheduling system can also be realized by the server 104 or the terminal 102 alone, such as the terminal 102 can directly process the initial intelligent scheduling system interface layout, interface color and figure, or the server 104 can obtain the initial intelligent scheduling system interface layout, interface color and figure from the data storage system and process the initial intelligent scheduling system interface layout, interface color and figure.

[0054] Among them, the terminal 102 can be but not limited to various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart televisions, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be realized by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0055] In an exemplary embodiment, as Figure 2 shown, an interface design optimization method for intelligent scheduling system is provided, which is executed by a computer device, specifically by a terminal or a server, etc. Computer device alone, or by a terminal and a server together, in the embodiment of the application, take the server 104 in the Figure 1 as an example to illustrate, including the following steps 201 to step 204:

[0056] Step 201, determine the interface color and figure required by the intelligent scheduling system interface. Among them, the interface color includes background color, text color and figure color.

[0057] In step 201, the interface color required by the intelligent dispatching system interface is determined based on the semantic difference method. By clustering and semantic analysis of the commonly used color of the dispatching system interface, the semantic characteristics of the interface color are determined. Then, according to the characteristics of the dispatching system interaction task, the appropriate background color, text color and figure color are selected to perform element color matching on the dispatching system interface, thereby improving the readability and aesthetics of the intelligent dispatching system interface.

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

[0059] Based on the SD method (semantic difference method), a questionnaire survey is conducted, 9 color samples are combined with selected color image semantics, and a five-level evaluation questionnaire is made by applying the SD method to represent the psychological change amount of different dimensions, to obtain a color intention evaluation score statistics table, refer to Table 1. Among them, the color image evaluation value is set between 0 and 1, 0 represents very dynamic, 1 represents very calm, and 0.5 represents neutral.

[0060] Table 1. Color intention evaluation score statistics table

[0061]

[0062] In the subway dispatching and monitoring system, the interfaces that need to set colors are mainly divided into three categories: background color, text color and figure color. As can be seen from Table 1, among the 9 colors, the intention score of dark blue is the highest, indicating that dark blue is a color with a calm semantic bias. When processing the background color, since the interface of the dispatching and monitoring system needs to be monitored by the monitoring personnel for a long time, the dark blue color with a semantic bias towards calm semantics is selected as the background color of the entire interface, which helps to relieve the visual fatigue of the monitor and enables them to think more calmly.

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

[0064] Table 2. Visibility of different color combinations

[0065] Background color Target color priority order Black White > Yellow > Orange-Yellow > Orange > Red > Green > Blue White Black > Red > Purple > Purple-Red > Blue > Green > Yellow Blue White > Yellow > Orange-Yellow > Orange > Red > Black > Green Yellow Black > Red > Blue > Blue-Violet > Yellow-Green > Green > White Green White > Yellow > Red > Black > Orange-Yellow > Blue > Purple Purple White > Yellow > Orange-Yellow > Orange > Green > Blue > Black > Red Gray Yellow > Yellow-Green > Orange > Purple > Blue > Black

[0066] As can be seen from Table 2, when the background color is blue, the priority order of the text color is white > yellow > orange > orange > red > black > green, therefore, white is selected as the text color of the interface. As for the color of the graphic element, green and blue generally represent the normal operation state of the system, but dark blue has been determined as the background color, in order to ensure that the graphic element can be distinguished obviously, green is selected as the normal working condition of the monitoring system. In addition, transition color and gradient color are added in the color selection to increase the visual level. The overall color matching of the intelligent dispatching system interface is as shown in Figure 3a and Figure 3b .

[0067] The design of the graphic element selects the standard in the TIAS system graphic element design guideline document, and the graphic element design schematic diagram is shown in Figure 4 .

[0068] In step 202, an initial intelligent dispatching system interface layout is obtained.

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

[0070] As an optional implementation, the determination process of the initial intelligent dispatching system interface layout is as follows: the interface elements in the intelligent dispatching system interface are sorted to obtain the interface elements required when the intelligent dispatching system interface layout is obtained, and the initial positions of the interface elements are laid out according to the fault handling operation process shown in the intelligent dispatching system task flow chart to obtain the initial intelligent dispatching system interface layout.

[0071] The interface elements in the intelligent dispatching system interface elements can be referred to Figure 5 In this embodiment, eight elements (including fault station and fault train; wherein the fault station includes: fault station, fault content, reporting vehicle and fault type. The fault train includes: fault train, fault content, departure location and fault type) of the event handling interface event area card module of Figure 5 are selected as the elements considered when designing the intelligent dispatching system interface layout. The importance, frequency of use, correlation coefficient and safety risk coefficient of the eight elements are shown in Tables 3, 4 and 5:

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

[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 coefficient of eight elements of event area card module in event handling interface

[0075] 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. Safety risk coefficient of eight elements of event area card module in 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, correlation r and safety risk coefficient R are obtained by expert scoring, and the frequency f is obtained by checking the log of the subway intelligent dispatching system.

[0079] The schematic diagram of the initial intelligent dispatching system interface layout can be referred to Figure 6a and Figure 6b . The intelligent dispatching system interface 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, and the comprehensive monitoring area A contains four sub-modules A1, A2, A3 and A4, which are the comprehensive monitoring sub-module A1, the passenger flow and operation index overview A2, the current line passenger flow information change A3 and the 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 order D. The fault handling B includes four sub-modules B1, B2, B3 and B4, which are fault notification B1, fault confirmation pop-up window B2, fault handling B3 and fault recovery pop-up window B4. The fault handling pop-up window B3 further includes three sub-modules B3_1, B3_2 and B3_3, which are processing flowchart B3_1, auxiliary solution B3_2 and information interaction B3_3. It should be noted that the B area, the B3 area and the B3_1 area all belong to the module layout interface. When optimizing the module layout interface, it is divided into three rounds of optimization process. Specifically, the first round of optimization: the layout optimization between B, C and D areas is started. Since the comprehensive monitoring interface only has A area, the first round does not need to be optimized; the second round of optimization: A1, A2, A3 and A4 in the first type of interface are optimized, and B1, B2, B3 and B4 in the second type of interface are optimized; the third round of optimization: the positions of B3_1, B3_2 and B3_3 sub-modules in B1 are optimized.

[0080] It should be noted that the three rounds of optimization process mentioned in the previous paragraph belong to the optimization of the module layout interface, which is irrelevant to the optimization of the element layout interface. That is to say, Figure 6a and Figure 6b the optimization process is equivalent to further refining the left half of the process in Figure 9 , which is irrelevant to the right half of the content in Figure 9 . Or, "B1, B2, B3, B4" and "B3_1, B3_2, B3_3" all belong to "modules", not "elements".

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

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

[0083] In step 203, 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 a hierarchy degree sub-function of the interface layout, a correlation degree sub-function of the interface layout, and a safety degree sub-function of the interface layout. The total fitness function is a first fitness function or a second fitness function.

[0084] To make the technical solutions of the present application more intuitive and easy to understand, Figure 8 a schematic diagram of the intelligent scheduling system interface layout method is provided. In combination with Figure 8 and Figure 9 It can be seen that the layout optimization of the intelligent scheduling system interface uses a hierarchical genetic algorithm, which divides the layout optimization process into two steps: first, the modules in the interface are laid out, and then the elements in the modules are laid out.

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

[0086] S1: According to the first fitness function, the first fitness function value of each individual in the first population corresponding to the current iteration number is calculated; wherein when the current iteration number is the first time, the individual in the first population corresponding to the current iteration number is obtained after the initialization operation on the module layout interface.

[0087] S2: Determine whether the current iteration number reaches the first preset iteration number.

[0088] If not, according to the first selection operator and the first fitness function value of each individual in the population corresponding to the current iteration number, the initial population corresponding to the next iteration number is determined, and the individuals in the initial population corresponding to the next iteration number are operated by using the first crossover rate and the first mutation rate to obtain the final population corresponding to the next iteration number. The first population corresponding to the current iteration number in step S1 is updated to the final population corresponding to the next iteration number, the current iteration number is increased by 1, and the step S1 is returned.

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

[0090] S3: performing 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 the current iteration number is the first time, and the individual in the second population corresponding to the current iteration number is obtained after the initialization operation is performed on the element layout interface.

[0092] S4: determining whether the current iteration number reaches the second preset iteration number.

[0093] If not, 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, the initial population corresponding to the next iteration number is determined, and the individual in the initial population corresponding to the next iteration number is operated by using the second crossover rate and the second mutation rate to obtain the final population corresponding to the next iteration number, the second population corresponding to the current iteration number in step S3 is updated to the final population corresponding to the next iteration number, the current iteration number is increased by 1, and the step S3 is returned.

[0094] If yes, 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 and the optimal element layout to obtain the 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 comprises:

[0097] Setting the genetic operation parameters of the hierarchical genetic algorithm; the genetic operation parameters include the first population size, the second population size, the first preset iteration number, the second preset iteration number, the first selection operator, the second selection operator, the first crossover rate, the second crossover rate, the first mutation rate and the second mutation rate.

[0098] As an optional implementation, the genetic operation parameters of the hierarchical genetic algorithm are set as: the first population size is 100, the second population size is 50, the first preset iteration number is 200, the second preset iteration number is 500, the first crossover rate is 0.8, the second crossover rate is 0.6, the first mutation rate is 0.02 and the second mutation rate is 0.1. The selection operator adopts the tournament selection method, the first selection operator is N1=2, and the second selection operator is N2=2.

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

[0100] wherein, F a is an interface layout hierarchy degree sub-function, W a is a weight coefficient of F a .

[0101] F r is an interface layout relevance degree sub-function, W r is a weight coefficient of F r .

[0102] F s is an interface layout security degree sub-function, W s is a weight coefficient of F s .

[0103] wherein, W a , W r and W s are 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 weight coefficients of interface layout hierarchy degree, interface layout relevance degree and interface layout security degree, respectively. According to Table 3, the total function of the interface layout adaptability of the intelligent dispatching system is F(x, y) = 0.249F a + 0.332F r + 0.415F s . Meanwhile, it is obvious that the total function of the adaptability of the optimal interface layout of the intelligent dispatching system is maxF(x, y).

[0108] Further, the expressions of F a , F r and F s are as follows:

[0109]

[0110]

[0111] F a is an interface layout hierarchy degree sub-function; k = 1, 2, 3, 4 represent left upper, right upper, left lower and right lower areas of the interface, respectively; λ k is an advantage degree weight of the k area.

[0112] n represents the total number of modules in the module layout interface; s ik represents the area of module i in k region in the module layout interface; represents the importance and the frequency of use of module i in the module layout interface; the importance matrix is m i =[m1m2…m n ] T ; wherein m i is the importance of module i; the frequency matrix is f i =[f1f2…f n ] T , wherein f i is the frequency of use of module i. Wherein the importance is obtained by expert scoring, and the frequency of use is obtained by log data in the subway intelligent scheduling system.

[0113] n represents the total number of elements in the element layout interface; s ik represents the area of element i in k region in the element layout interface; represents the importance and the frequency of use of element i in the element layout interface; the importance matrix is m i =[m1m2…m n ] T ; wherein m i is the importance of element i; the frequency matrix is f i =[f1f2…f n ] T , wherein f i is the frequency of use of element i. Wherein the importance is obtained by expert scoring, and the frequency of use is obtained by log data in the subway intelligent scheduling system.

[0114] F r is the interface layout correlation sub-function; the correlation matrix is

[0115] r ij represents the correlation between module i and module j; x i and y i represent the coordinates of the x-axis and y-axis of the center point of module i respectively; A and B represent the length and width of the module layout interface respectively.

[0116] r ij represents the correlation between element i and element j; x i and y irespectively represent the coordinates of the x-axis and y-axis of the center point of the element i; A and B respectively represent the length and width of the element layout interface.

[0117] F S is a security degree sub-function of the interface layout.

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

[0119] When the fitness total function is the second fitness function, n represents the total number of elements in the element layout interface; w i and w j respectively represent the importance weight of the i-th and j-th element; R ij represents the security risk coefficient between the i-th element and the j-th element.

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

[0121] Step 204, filling the optimal intelligent scheduling system interface layout according to the interface color and graphic element required by the intelligent scheduling system interface to obtain an optimal intelligent scheduling system interface.

[0122] As an exemplary embodiment, Figure 10a and Figure 10b two optimal intelligent scheduling system interfaces are given. Among them, Figure 10a is an optimal intelligent scheduling system interface of a comprehensive monitoring interface, Figure 10b is an optimal intelligent scheduling system interface of a fault handling interface.

[0123] By implementing the above steps 201 to 204, the application adopts a combination of hierarchical genetic algorithm and fitness total function to optimize the intelligent scheduling system interface layout, improves the scientificity of the intelligent scheduling system interface layout, and further improves the operation efficiency and satisfaction of the user.

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

[0125] performing an ergonomics evaluation on the optimal intelligent scheduling system interface by using a Petri net.

[0126] As an optional implementation manner, as shown in Figure 11 The ergonomics evaluation of the optimal intelligent scheduling system interface includes the following steps:

[0127] In step 301, ergonomics indexes in the interface design evaluation system are obtained.

[0128] In step 302, the ergonomics indexes are summarized to form an ergonomics index system.

[0129] In the process of selecting ergonomics indexes, the applicant investigates the ergonomics indexes in the ATS system interface design and the interface design evaluation system in other fields, summarizes many ergonomics indexes, and forms the ergonomics index system of the application. Through summarizing and combing the investigation content, and combining the structural characteristics of the Petri net, the task completion time, the mouse click times, the task completion rate, and the mis-touch rate are selected as the performance indexes for ergonomics evaluation.

[0130] In step 303, based on the task flowchart of the intelligent scheduling system and the pre-selected interface elements, the Petri net task model of the intelligent scheduling system is built by using the cpntools tool.

[0131] As an optional implementation manner, the Petri net task model of the intelligent scheduling system is as shown in Figure 12a The nodes in the model, i.e., the places, represent the interactive interface elements, and the edges between the nodes, i.e., the transitions, represent the interface element transition conditions. The whole model is a formal representation of the business process of the scheduling system. As can be seen from Figure 12b When the intelligent scheduling system fails, a fault pop-up window will be generated. After the fault pop-up window appears, it is judged whether the car needs to be stopped. If the car needs to be stopped, the car is stopped. At the same time, it is judged whether the fault needs to be disposed. If the fault needs to be disposed, the fault disposal interface is entered. In the fault disposal interface, the fault information and the disposal process are obtained according to the visual information in the interface. The fault information includes the shield door fault information, the fault occurring train, the fault occurring position, and the fault content. The disposal process includes the running graph adjustment, the system automatic car stopping, and the car stopping completion. After the car stopping completion, the intelligent scheduling system interface running graph is adjusted, and the corresponding scheme is selected. After the scheme is selected, the operation of viewing details or determining the scheme can be performed.

[0132] In the process of building the Petri net task model of the intelligent scheduling system by using the cpntools tool, the mapping relationship between the basic constituting elements of the Petri net, i.e., the places, the transitions, the directed arcs, and the tokens, and the human-computer interaction tasks is established, as shown in Table 7:

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

[0134]

[0135] Step 304, based on the Petri net task model of the intelligent scheduling system, a fault handling task performance evaluation Petri net model is constructed.

[0136] As an optional implementation, the fault handling task performance evaluation Petri net model is as shown in the figure, which includes modeling of the mis-touch behavior, addition of the guard function of the mis-touch probability, and aims to obtain the evaluation performance. Figure 13a Figure 13b The Chinese flow chart is given as follows: Figure 13a It can be seen that the mis-touch 1 is set in the initial interface of the intelligent scheduling system, and the mis-touch 2 is set in the fault pop-up interface and the entering handling interface. The mis-touch detection is set in different interfaces, so that the mis-touch rate of the intelligent scheduling system is detected.

[0137] In the fault handling task performance evaluation Petri net model, a moniter monitor is added to monitor the triggering times of the mis-touch transition and other transitions, and to monitor the task completion time. According to the data monitored by the monitor, the task completion time (TCT), the mis-touch rate (NMC), the task completion rate (MMD), the mouse click times (TCR), and the mouse moving distance (ER) are calculated according to the linear relationship between the operation time and the mouse moving distance.

[0138] Step 305, according to the structural characteristics of the fault handling task performance evaluation Petri net model, a number of performance indicators are selected from the ergonomics index system to construct a performance index system of the intelligent scheduling system interface layout based on Petri net.

[0139] The performance index system is shown in the figure Figure 14 . As can be seen from the figure, the performance indicators in the performance index system include task completion time, mis-touch rate, task completion rate, mouse click times, and mouse moving distance. Figure 14

[0140] Among them, the task completion time: TCT=T end -T start , wherein T start is the time when the task starts, which is determined by the time suffix of the initial token, and T end is the time when the task ends, which is determined by the time suffix of the token after the task simulation ends. The mouse click times: Among them, c i is the i th mouse click times, n is the total click times, and the value of i is determined by the number of transitions. The mouse moving distance: Among them, D i ​​is the distance of the i-th mouse movement, n is the total number of movements, D i The value of i is determined by the number of transitions. The task completion rate: where N success is the number of successful task completion, N total is the total number of attempts, which are counted by the monitor. The error touch rate: where N errors is the number of error touches (wrong operations), N total is the total number of mouse operations, which are counted by the monitor of the Petri net. After obtaining the five performance indicators, the five performance indicators are processed using the maximum-minimum normalization to scale the data to the range of 0 to 1.

[0141] Step 306, determining 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 discrimination table. The weight coefficient aims to measure the relative importance between the five operation indicators.

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

[0143] F scores = W1*TCT + W2*NMC + W3*MMD + W4*TCR + W5*ER.

[0144] where TCT represents the task completion time, W1 represents the weight coefficient of the task completion time; NMC represents the error touch rate, W2 represents the weight coefficient of the error touch rate; MMD represents the task completion rate, W3 represents the weight coefficient of the task completion rate; TCR represents the number of mouse clicks, W4 represents the weight coefficient of the number of mouse clicks; ER represents the task completion time, W5 represents the weight coefficient of the task completion time. W1, W2, W3, W4 and W5 are determined by the preset second discrimination table.

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

[0146] Table 7. Second discrimination table

[0147]

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

[0149] Step 308, based on the intelligent scheduling system interface layout score formula, ergonomics evaluation is performed on the optimal intelligent scheduling system interface.

[0150] In one exemplary embodiment, Figure 15 The Petri net performance evaluation graph before layout optimization is given, Figure 16 The Petri net performance evaluation graph of the optimal intelligent scheduling system interface is given. According to Figure 15 It can be seen that the total score of the Petri performance evaluation before layout optimization is 0.78. According to Figure 16 It can be seen that the task completion time, the false touch rate, the task completion rate, the mouse click times and the mouse movement distance score 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. The final score is 0.88. It can be seen that the performance evaluation of the intelligent scheduling system interface optimized based on the hierarchical genetic algorithm is high, the performance score is improved by 12.82%, the operation efficiency of the user is significantly improved, and the layout of the intelligent scheduling system interface is more scientific and reasonable.

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

[0152] In an exemplary embodiment, a computer device, which can be a server or a terminal, is provided. The internal structure diagram of the computer device can be as shown in Figure 17As shown in the figure. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the 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 capability. 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 graphic element. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with the terminal outside through network connection. The computer program is executed by the processor to realize an interface design optimization method for an intelligent scheduling system.

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

[0154] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in each of the method embodiments described above.

[0155] In one exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to realize the steps in each of the method embodiments described above.

[0156] In one exemplary embodiment, a computer program product is provided, including a computer program, which is executed by a processor to realize the steps in each of the method embodiments described above.

[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 for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0158] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to 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 storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0159] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0160] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0161] The principles and implementation modes of the present application are described by applying specific examples herein. The above description of the embodiments is only used to help understand the method and its core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range can be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. An interface design optimization method for an intelligent dispatching system, characterized in that, The interface design optimization method for the intelligent scheduling system comprises the following steps: determining interface colors and graphics required by the interface of the intelligent scheduling system; the interface colors comprise background colors, text colors and graphic colors; obtaining an initial interface layout of the intelligent scheduling system; the initial interface layout of the intelligent scheduling system comprises a module layout interface composed of multiple modules, and the module layout interface comprises an element layout interface composed of multiple elements; the module layout interface is used to display position information of each module; and the element layout interface is used to display position information of each element corresponding to any module; the initial interface layout of the intelligent scheduling system comprises a module layout interface composed of multiple modules and an element layout interface composed of multiple elements; the module layout interface comprises multiple modules and position information of each module; the element layout interface comprises multiple elements and position information of each element; when any module in the module layout interface is selected, the element layout interface corresponding to the module is displayed; the initial interface layout of the intelligent scheduling system comprises a module layout sub-interface and an element layout sub-interface; the module layout sub-interface is used to display multiple modules and display position information of each module; and the element layout sub-interface is used to display multiple elements corresponding to any module and display position information of each element; the initial layout of the interface of the intelligent scheduling system is optimized by using a hierarchical genetic algorithm and a fitness total function, and the layout optimization process is divided into two steps: first, the modules in the interface are laid out, and then the elements in the modules are laid out to obtain an optimal interface layout of the intelligent scheduling system; the fitness total function comprises a hierarchy degree sub-function of the interface layout, a correlation degree sub-function of the interface layout and a safety degree sub-function of the interface layout; the fitness total function is a first fitness function or a second fitness function; the expression of the safety degree sub-function of the interface layout is: wherein F S is an interface layout security sub-function; n represents the total number of modules in the module layout interface; w i and w j respectively represent the importance weight of the i-th and j-th modules; R ij represents the security risk coefficient between the i-th module and the j-th module; d ij represents the distance between the i-th module and the j-th module; n represents the total number of elements in the element layout interface; w i and w j respectively represent the importance weight of the i-th and j-th elements; R ij represents the security risk coefficient between the i-th element and the j-th element; d ij represents the distance 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 the distance and the risk factor; the optimal interface of the intelligent scheduling system is obtained by filling the optimal interface layout of the intelligent scheduling system according to the interface colors and graphics required by the interface of the intelligent scheduling system.

2. The interface design optimization method for intelligent dispatching system according to claim 1, characterized in that, the initial layout of the interface of the intelligent scheduling system is optimized by using a hierarchical genetic algorithm and a fitness total function to obtain an optimal interface layout of the intelligent scheduling system, and specifically comprises the following steps: S1: according to the first fitness function, the first fitness function value of each individual in the first population corresponding to the current iteration number is calculated; wherein, when the current iteration number is the first time, the individual in the first population corresponding to the current iteration number is obtained after the initialization operation is performed on the module layout interface; S2: determining whether the current iteration number reaches a first preset iteration number; if not, the initial population corresponding to the next iteration number is determined according to the first selection operator and the first fitness function value of each individual in the population corresponding to the current iteration number, the individuals in the initial population corresponding to the next iteration number are operated by using the first crossover rate and the first mutation rate to obtain the final population corresponding to the next iteration number, the first population corresponding to the current iteration number in step S1 is updated to the final population corresponding to the next iteration number, the current iteration number is increased by 1, and the step S1 is returned; If yes, the individual corresponding to the maximum first fitness function value in the current iteration number is determined as the optimal module layout; S3: The following operations are performed 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 individual in the second population corresponding to the current iteration number is obtained after the initialization operation is performed on the element layout interface; S4: Determine whether the current iteration number reaches the second preset iteration number; If no, the initial population corresponding to the next iteration number is determined 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 the individuals in the initial population corresponding to the next iteration number are operated using the second crossover rate and the second mutation rate to obtain the final population corresponding to the next iteration number, the second population corresponding to the current iteration number in step S3 is updated to the final population corresponding to the next iteration number, the current iteration number is increased by 1, and the step S3 is returned; If yes, the individual corresponding to the maximum second fitness function value in the current iteration number is determined as the optimal element layout; S5: The optimal intelligent scheduling system interface layout is obtained by combining the optimal module layout and the optimal element layout.

3. The interface design optimization method for intelligent dispatching system of claim 2, wherein, The hierarchical genetic algorithm and the total fitness function are used to optimize the initial layout of the intelligent scheduling system interface to obtain the optimal intelligent scheduling system interface layout, and the method further comprises: The genetic operation parameters of the hierarchical genetic algorithm are set; the genetic operation parameters include the first population size, the second population size, the first preset iteration number, the second preset iteration number, the first selection operator, the second selection operator, the first crossover rate, the second crossover rate, the first mutation rate, and the second mutation rate.

4. The interface design optimization method for intelligent dispatching system of claim 2, wherein, The fitness total function is: F(x,y) = W a F a +W r F r +W s F s ; Wherein, F a is the interface layout hierarchy degree sub-function, W a is the weight coefficient of F a . F r is an interface layout relevance sub-function, W r is a weight coefficient for F r ​ F s W is the interface layout security sub-function s F s is the weight coefficient; wherein W a , W r and W s are determined by a preset first discrimination table.

5. The interface design optimization method for intelligent dispatching system according to claim 4, characterized in that, F a , F r The expressions of the above are respectively: F a is the interface layout hierarchy degree sub-function; k = 1, 2, 3, 4 respectively represent the left upper, right upper, left lower and right lower regions of the interface; λ k is the dominance degree weight of the k region; n represents the total number of modules in the module layout interface; s ik represents the area of module i in the k region in the module layout interface; represents the chain value of the importance and the frequency of use of module i in the module layout interface; the importance matrix is m i =[m1m2…m n ] T ; wherein m i is the importance of module i; the frequency matrix is f i =[f1f2…f n ] T , wherein f i is the frequency of use of module i; n represents the total number of elements in the element layout interface; s ik is the area of element i in the k region in the element layout interface; is the chain value representing the importance and frequency of use of element i in the element layout interface; the importance matrix is m i = [m1m2…m n ] T ; wherein m i is the importance of element i; the frequency of use matrix is f i = [f1f2…f n ] T , wherein f i is the frequency of use of element i; F r is the interface layout correlation degree sub-function; the correlation matrix is r ij represents the correlation degree between module i and module j; x i and y i respectively represent the coordinates of the x-axis and y-axis of the center point of module i; A and B respectively represent the length and width of the module layout interface; r when the fitness total function is a second fitness function ij represents the degree of correlation between element i and element j; x i and y i respectively represent the coordinates of the x-axis and y-axis of the center point of element i; A and B respectively represent the length and width of the element layout interface.

6. An interface evaluation method for a smart dispatching system, characterized in that, The interface evaluation method for the intelligent scheduling system is applied to the interface design optimization method for the intelligent scheduling system according to any one of claims 1-5; The interface evaluation method for the intelligent scheduling system comprises: The optimal intelligent scheduling system interface is ergonomically evaluated using a Petri net.

7. The interface evaluation method for the intelligent dispatching system according to claim 6, wherein, The ergonomics index in the interface design evaluation system is obtained; The ergonomics indexes are summarized to form an ergonomics index system; Based on the task flowchart of the intelligent scheduling system and the pre-screened interface elements, a Petri net task model of the intelligent scheduling system is built by using the cpntools tool; Based on the Petri net task model of the intelligent scheduling system, a fault handling task performance evaluation Petri net model is constructed; According to the structural characteristics of the fault handling task performance evaluation Petri net model, a number of performance indicators are selected from the ergonomics index system to construct a performance indicator system for the intelligent scheduling system interface layout based on the Petri net; The weight coefficient of each performance indicator in the performance indicator system is determined; the weight coefficient of the performance indicator is obtained according to a pre-set second discrimination table; ​ Based on the performance indicators and the weight coefficients of the performance indicators, a score formula of the interface layout of the intelligent scheduling system is constructed; Based on the score formula of the interface layout of the intelligent scheduling system, ergonomics evaluation is performed on the optimal interface of the intelligent scheduling system.

8. The interface evaluation method for the intelligent dispatching system according to claim 7, wherein, The performance indicators include task completion time, mis-touch rate, task completion rate, mouse click times and mouse movement distance.

9. The interface evaluation method for the intelligent dispatching system according to claim 8, wherein, The score formula of the optimal interface of the intelligent scheduling system is: F scores = W1*TCT + W2*NMC + W3*MMD + W4*TCR + W5*ER; Wherein, TCT represents task completion time, W1 is the weight coefficient of task completion time; NMC represents mis-touch rate, W2 is the weight coefficient of mis-touch rate; MMD represents task completion rate, W3 is the weight coefficient of task completion rate; TCR represents mouse click times, W4 is the weight coefficient of mouse click times; ER represents task completion time, W5 is the weight coefficient of task completion time; Wherein, W1, W2, W3, W4 and W5 are determined by the second preset discrimination table.

10. A computer device comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the interface design optimization method for the intelligent scheduling system according to any one of claims 1-5; or the processor executes the computer program to implement the interface evaluation method for the intelligent scheduling system according to any one of claims 6-9.

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