CN110457122B - Task processing method, task processing device and computer system - Google Patents
Task processing method, task processing device and computer system Download PDFInfo
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- CN110457122B CN110457122B CN201910706757.5A CN201910706757A CN110457122B CN 110457122 B CN110457122 B CN 110457122B CN 201910706757 A CN201910706757 A CN 201910706757A CN 110457122 B CN110457122 B CN 110457122B
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
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- G06F9/4806—Task transfer initiation or dispatching
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Abstract
The present disclosure provides a task processing method, including: acquiring a plurality of atomic functions of a first task; pushing a first set to a plurality of clients, wherein the first set comprises a plurality of identification information respectively corresponding to the plurality of atomic functions, so that any client in the plurality of clients distributes the plurality of identification information to one or more groups to form a first grouping result of the any client; receiving a plurality of first packet results from the plurality of clients; determining a distance matrix characterizing a correlation of the plurality of atomic functions with each other based on the plurality of first grouping results; and determining a functional hierarchy for the first task based on the distance matrix. The disclosure also provides a task processing device and a computer system.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a task processing method, a task processing apparatus, and a computer system.
Background
With the continuous development of internet technology, more and more functions can be provided by specific tasks (such as websites and various applications), and users often find specific functions and often have the problems of multiple function hierarchies, unreasonable classification, difficulty in understanding of naming and the like, and need to design a more reasonable function hierarchy structure for the specific tasks. In the related art, a plurality of users are generally organized on-line to test and evaluate the function hierarchy of a task to determine an optimized function hierarchy.
However, there are problems in that: the field participation of users, the number of the users and the range of the test function are all limited. And moreover, the data is manually input and analyzed by a tester, so that the efficiency is low and the error is easy to make.
Disclosure of Invention
One aspect of the present disclosure provides a task processing method, including: a plurality of atomic functions of a first task is obtained. And then pushing a first set to a plurality of clients, wherein the first set comprises a plurality of identification information respectively corresponding to a plurality of atomic functions, so that any client in the plurality of clients distributes the plurality of identification information to one or more groups to form a first grouping result of the any client. A plurality of first packet results from a plurality of clients is received. A distance matrix characterizing a correlation of the plurality of atomic functions with each other is then determined based on the plurality of first grouping results. A functional hierarchy is then determined for the first task based on the distance matrix.
Optionally, the determining distance matrix data for characterizing the correlations of the plurality of atomic functions with each other based on the plurality of first grouping results includes: for any first grouping result, the distance value between the atomic functions corresponding to any two pieces of identification information belonging to the same group is set to 0, and the distance value between the atomic functions corresponding to any two pieces of identification information belonging to different groups is set to 1. And accumulating the distance values between any two atomic functions in the plurality of atomic functions based on the plurality of first grouping results to obtain a comprehensive distance value between any two atomic functions. And then a distance matrix is constructed by using the comprehensive distance value between any two atomic functions in the multiple atomic functions.
Optionally, the determining a function hierarchy structure related to the first task based on the distance matrix includes: the distance matrix is converted to a similarity matrix. And then, carrying out clustering analysis on the similarity matrix by using a hierarchical clustering analysis algorithm to obtain a dendrogram. The tree representation is characterized by a plurality of levels from low to high, each level comprising one or more categories, each category comprising one or more atomic functions. A functional hierarchy for the first task is then determined based on the tree.
Optionally, the determining a functional hierarchy structure related to the first task based on the tree diagram includes: an average group number of the plurality of first grouped results is determined based on the group number of each of the plurality of first grouped results. The levels in the tree having the same number of categories as the average number of groups are taken as the target highest level. A functional hierarchy for the first task is then constructed based on the structure from the lowest level to the target highest level in the tree.
Optionally, the method further includes: and acquiring a preset function hierarchical structure of the second task, wherein the preset function hierarchical structure comprises a preset grouping result, and the preset grouping result comprises one or more preset groups and a plurality of atomic functions. And then pushing a second set to the plurality of clients, wherein the second set comprises the one or more preset groups and a plurality of identification information respectively corresponding to the plurality of atomic functions, so that any client in the plurality of clients allocates the plurality of identification information to the one or more preset groups, and the allocated one or more preset groups form a second grouping result of the any client. Then, a plurality of second grouping results from the plurality of clients are received, and the degree of difference between the preset grouping result and the plurality of second grouping results is determined. Finally, the preset function hierarchy is adjusted based on the degree of difference so as to obtain a function hierarchy related to the second task.
Optionally, the adjusting the preset function hierarchy based on the difference between the preset grouping result and the second grouping results includes: determining, based on the second plurality of grouping results, a position coordinate of each of the plurality of atomic functions in an N-dimensional space, where N is equal to the number of the preset groups. And then determining a preset position coordinate of each atomic function in the plurality of atomic functions in the N-dimensional space based on the preset grouping result. Then, based on the position coordinates and the preset position coordinates of each of the plurality of atomic functions, a classification difference vector for characterizing the degree of difference is determined. On this basis, the adjusting the preset function hierarchy based on the difference degree comprises: and adjusting the preset function hierarchy by utilizing the classification difference vector.
Optionally, the method further includes: and acquiring a preset function hierarchical structure of the second task. Pushing the preset function hierarchy to the plurality of clients so that any client executes an operation of searching any atomic function in the plurality of atomic functions based on the preset function hierarchy. Then obtaining operation behavior data from the plurality of clients, the operation behavior data including at least one of: an operation start time, an operation completion time, an operation behavior path, a trigger time for the any one atomic function, and a number of triggers for the any one atomic function. Then, based on the operation behavior data, determining evaluation information about any one of the atomic functions, wherein the evaluation information comprises at least one of the following items: operation success rate, operation failure rate, operation duration, operation path length, straight line completion rate, and backoff number. And finally, adjusting the preset function hierarchy based on the evaluation information of each of the plurality of atomic functions so as to obtain the function hierarchy related to the second task.
Optionally, the method further includes: based on the function hierarchy for the first task, presenting a human-machine interface having a navigation structure corresponding to the function hierarchy.
Another aspect of the present disclosure provides a task processing apparatus including: the device comprises a first acquisition module, a first pushing module, a first receiving module, a distance determination module and a structure determination module. The first obtaining module is used for obtaining a plurality of atomic functions of the first task. The first pushing module is used for pushing a first set to a plurality of clients, wherein the first set comprises a plurality of identification information respectively corresponding to the atomic functions, so that any client in the clients can distribute the identification information to one or more groups to form a first grouping result of the client. The first receiving module is used for receiving a plurality of first grouping results from the plurality of clients. The distance determination module is used for determining a distance matrix for characterizing the relevance of the atomic functions to each other based on the first grouping results. A structure determination module is to determine a functional hierarchy structure for the first task based on the distance matrix.
Another aspect of the present disclosure provides a computer system comprising: memory, a processor and a computer program stored on the memory and executable on the processor for implementing the method as described above when the processor executes the computer program.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the disclosure, when the function hierarchy structure of a task needs to be designed, the function hierarchy structure of the task is obtained by means of understanding of the relationship among a plurality of atomic functions of the task by users of a plurality of clients. Specifically, a first set of identification information of a plurality of atomic functions including the task is pushed to a plurality of clients respectively, so as to obtain a plurality of first grouping results returned by the plurality of clients. And determining a distance matrix which accords with the correlation among the characteristic atomic functions expected by a plurality of users based on the first grouping result, and determining a corresponding function level result based on the distance matrix. The process can complete the collection of a large number of user opinions (such as a first grouping result) on line through the interaction with a plurality of clients, and further determines the optimized function hierarchical structure of the task based on the large number of user opinions, and is efficient, accurate and reasonable.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
FIG. 1 schematically illustrates an exemplary system architecture of an application task processing method and apparatus according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow chart of a task processing method according to an embodiment of the present disclosure;
FIG. 3 schematically shows a flow chart of a task processing method according to another embodiment of the present disclosure;
FIG. 4 schematically shows a flow chart of a task processing method according to another embodiment of the present disclosure;
FIG. 5 schematically shows a block diagram of a task processing device according to an embodiment of the present disclosure; and
FIG. 6 schematically shows a block diagram of a computer system suitable for implementing a method of task processing according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides a task processing method and device and a computer system. The method includes a first acquisition process, a first push process, a first receive process, a distance determination process, and a structure determination process. In a first obtaining process, a plurality of atomic functions of a first task are obtained. Then, a first pushing process is carried out, and a first set containing a plurality of identification information corresponding to the plurality of atomic functions respectively is pushed to a plurality of clients, so that any client distributes the plurality of identification information in the first set to one or more groups to form a first grouping result of any client. Then, a first receiving process is performed to receive a plurality of first grouping results from a plurality of clients. Based on the received first packet results, a distance determination process may be performed, i.e. a distance matrix is determined which characterizes the relevance of the atomic functions to each other. Finally, a result determination process is carried out on the basis of the distance matrix, i.e. a functional hierarchy for the first task is determined
Fig. 1 schematically illustrates an exemplary system architecture 100 to which the task processing method and apparatus may be applied, according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The terminal devices 101, 102, 103 may have various client applications installed thereon, such as a bank-like application, a shopping-like application, a search-like application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only). The terminal devices 101, 102, 103 may interact with the server 105 through the above various client applications to send various requests to the server 105 or to receive results returned by the server 105.
The terminal devices 101, 102, 103 may be various electronic devices including, but not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a background management server (for example only) that provides various service support. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the task processing method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the task processing device provided by the embodiment of the present disclosure may be generally disposed in the server 105. The task processing method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the task processing device provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired.
Fig. 2 schematically shows a flow chart of a task processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the method may include the following operations S201 to S205.
In operation S201, a plurality of atomic functions of a first task are acquired.
Before the first task is brought online, a function hierarchy structure related to the first task needs to be designed, so as to set a navigation structure of the first task on a human-computer interface according to the function hierarchy structure, thereby assisting a user to use the Application or the website more conveniently. Atomic functionality refers to the smallest granularity of functionality that cannot be subdivided.
In operation S202, a first set is pushed to a plurality of clients.
The first set comprises a plurality of identification information respectively corresponding to the atomic functions, so that any one of the clients distributes the identification information to one or more groups to form a first grouping result of the client. Each client can obtain a respective first grouping result based on the first set, and the first grouping result of one client indicates that the client divides the plurality of identification information into several groups, and each group includes which identification information and other information, which indicates understanding of a user of the client on a relationship between the plurality of atomic functions.
In operation S203, a plurality of first packet results from a plurality of clients are received.
In operation S204, a distance matrix for characterizing a correlation of the plurality of atomic functions with each other is determined based on the plurality of first grouping results.
Based on a plurality of first grouping results returned by the plurality of clients, namely based on understanding of relationships among a plurality of atomic functions by different users returned by the plurality of clients, a distance matrix representing the relevance among the plurality of atomic functions of the first task can be determined.
In operation S205, a function hierarchy structure with respect to the first task is determined based on the distance matrix.
Wherein the function hierarchy for the first task reflects the integrated expectations of the plurality of users for the function hierarchy design of the first task, as the function hierarchy for the first task is determined based on a distance matrix, which in turn is determined by the understanding of the relationship of the plurality of atomic functions of the first task by the different users returned by the plurality of clients to each other.
It can be understood by those skilled in the art that, when a task needs to be designed with a function hierarchy structure, the method shown in fig. 2 obtains the function hierarchy structure of the task by means of understanding of relationships among a plurality of atomic functions of the task by users of a plurality of clients. Specifically, a first set of identification information of a plurality of atomic functions including the task is pushed to a plurality of clients respectively, so as to obtain a plurality of first grouping results returned by the plurality of clients. And determining a distance matrix which accords with the correlation among the characteristic atomic functions expected by a plurality of users based on the first grouping result, and determining a corresponding function level result based on the distance matrix. The process can be used for completing the collection of a large number of user opinions (such as a first grouping result) on line through remote interaction with a plurality of clients, and further determining the optimized function hierarchical structure of the task based on the large number of user opinions, so that the process is efficient, accurate and reasonable.
For example, the above process of determining distance matrix data for characterizing the correlations between the plurality of atomic functions may be performed based on the plurality of first grouping results as follows: first, for any first grouping result, the distance value between the atomic functions corresponding to any two pieces of identification information belonging to the same group is set to 0, and the distance value between the atomic functions corresponding to any two pieces of identification information belonging to different groups is set to 1. Then, based on the first grouping results, the distance values between any two atomic functions in the atomic functions are accumulated to obtain a comprehensive distance value between any two atomic functions. And then, constructing a distance matrix by using the comprehensive distance value between any two atomic functions in the multiple atomic functions.
For example, the first task includes 5 atomic functions. A first set {1, 2, 3, 4, 5} of identification information containing 5 atomic functions is pushed to client 1, client 2, and client 3, respectively. The first grouping result of client 1 is: {{1,2},{3,4},{5}}. The first grouping result of client 2 is: {{1,5},{2,3,4}}. The first grouping result of the client 3 is: {{1,2},{3,4,5}}.
According to the first grouping result of the client 1, the atomic function 1 and the atomic function 2 are grouped into the same group, and the distance value L between the two groups112＝L1210. Atomic function 1 and atomic function 3 are divided intoDifferent groups, distance value L between them113＝L1311. The atomic function 3 and the atomic function 5 are grouped into different groups, the distance value L between the two135＝L1531. The same holds true for the distance between any two of the 5 atomic functions.
According to the first grouping result of the client 2, the atomic function 1 and the atomic function 2 are grouped into different groups, and the distance value L between the two groups212＝L2211. The atomic function 1 and the atomic function 3 are grouped into different groups, with a distance value L between them213＝L2311. The atomic function 1 and the atomic function 5 are grouped into the same group with a distance value L between them215＝L2510. The same holds true for the distance between any two of the 5 atomic functions.
According to the first grouping result of the client 3, the atomic function 1 and the atomic function 2 are grouped into the same group, and the distance value L between the two groups312＝L3210. The atomic function 1 and the atomic function 3 are grouped into different groups, with a distance value L between them313＝L3311. The atomic function 3 and the atomic function 5 are grouped into the same group with a distance value L between them335＝L3530. Similarly, the distance between any two of the 5 atomic functions can be determined.
Thus, in this example, the value of the combined distance L between atomic function 1 and atomic function 212＝L21＝L112+L212+L3121. Value of the integrated distance L between atomic function 1 and atomic function 213＝L31＝L113+L213+L3133. Similarly, a composite distance value between any two of the 5 atomic functions may be obtained by accumulating the corresponding distance values. A distance matrix, for example, as shown in equation (1) can then be constructed based on these composite distance values:
in this example, due toA task has 5 atomic functions, Lii(i is an integer of 1 to 5 inclusive) is a distance of the atomic function itself, and may be set to 0 in general. L isij＝Lji(i is an integer of 1 to 5 inclusive, j is an integer of 1 to 5 inclusive, and i is not equal to j). This example is merely an illustration, and does not impose any limitations on the number of atomic functions, the number of clients, and the distance values between atomic functions.
In one embodiment of the present disclosure, the above process of determining the function hierarchy structure related to the first task based on the distance matrix may be performed as follows: the distance matrix is converted to a similarity matrix. Then, the similarity matrix is subjected to Clustering analysis by using a Hierarchical Clustering analysis algorithm to obtain a dendogram (Dendrogram). The tree characterizes a plurality of levels from low to high, each level comprising one or more classes (clusters), each class comprising one or more atomic functions. A functional hierarchy for the first task is then determined based on the tree.
Illustratively, the determining the functional hierarchy based on the tree graph may include: an average group number of the plurality of first grouped results is first determined based on a group number of each of the plurality of first grouped results. Then, the hierarchy having the same number of categories as the above average number of groups in the tree is set as the target highest hierarchy. And constructing a function hierarchy structure related to the first task based on a structure from a lowest hierarchy to a target highest hierarchy in the tree diagram.
Following the example above, for the distance matrix shown in equation (1), due to Lij＝LjiAnd L isiiOnly the lower left or upper right part of the distance matrix may be retained to get the similarity matrix. Then, a hierarchical clustering analysis algorithm is utilized to perform clustering analysis on the similarity matrix, and the principle is as follows: on the basis that the integrated distance value between any two of the 5 atomic functions has been measured, the 5 atomic functions are taken as 5 nodes, and the 5 nodes are located at the lowest level of the tree diagram and can be referred to as the root level. At the lowest layerAnd level, merging the nodes with the closest comprehensive distance into a new node by adopting a minimum distance method, wherein the new node and the rest nodes are positioned in the first level of the tree graph. And at the first level, combining the nodes with the shortest comprehensive distance into a new node by adopting a minimum distance method, wherein the new node and the rest nodes are positioned at the second level of the tree-shaped graph. And by analogy, a plurality of hierarchies of the tree diagram are sequentially constructed from bottom to top, each hierarchy comprises one or more nodes, each node represents one category, and each category comprises one or more atomic functions. And merging the nodes into one node, namely only one category, till the top layer of the tree graph.
In the above example, the first grouping result of client 1 is: {{1,2},{3,4},{5}}. The first grouping result of client 2 is: {{1,5},{2,3,4}}. The first grouping result of the client 3 is: {{1,2},{3,4,5}}. Therefore, the number of groups of the first grouping result of the client 1 is 3, the number of groups of the first grouping result of the client 2 is 2, and the number of groups of the first grouping result of the client 3 is 2. The average number of groups is: (3+2+2)/3 is equal to about 2. Therefore, if a hierarchy containing 2 categories is searched in the obtained tree diagram, for example, the second hierarchy is used as the target highest hierarchy, and a functional hierarchy structure related to the first task is constructed based on the structure from the lowest hierarchy to the second hierarchy in the tree diagram.
Fig. 3 schematically shows a flowchart of a task processing method according to another embodiment of the present disclosure.
As shown in fig. 3, the method may include the following operations S301 to S305.
In operation S301, a preset function hierarchy of the second task is acquired.
The second task may be, for example, an application or a website, for which the function hierarchy has been preset. The preset function hierarchy structure comprises a preset grouping result, the preset grouping result comprises one or more preset groups and a plurality of atomic functions, and the preset grouping result can represent the distribution condition of the preset atomic functions in the preset groups.
In operation S302, the second set is pushed to a plurality of clients.
The second set includes the one or more preset groups and a plurality of identification information respectively corresponding to the atomic functions, so that any one of the plurality of clients allocates the plurality of identification information to the one or more preset groups, and the allocated one or more preset groups form a second grouping result of the any one client. For example, the preset grouping result includes: { preset group 1{1, 2}, preset group 2{3, 4, 5} }. The second set is { preset group 1, preset group 2, {1, 2, 3, 4, 5} }. That is, when the second set is pushed to the multiple clients, the multiple clients are only informed of the preset groups and which atomic functions are shared, but the distribution relationship between the preset groups and the atomic functions of the multiple clients is not informed, and the respective users of the multiple clients distribute the multiple atomic functions to the preset groups according to their own experiences, so as to check whether the preset grouping result meets the expectations of the multiple users, i.e., to test the availability of the preset function hierarchy of the second task. The second grouping result of a client indicates how the client assigns the atomic functions to the preset groups, and indicates understanding of the relationship between the atomic functions and the preset groups by a user of the client.
In operation S303, a plurality of second grouping results from a plurality of clients are received.
In operation S304, a degree of difference between a preset grouping result and the plurality of second grouping results is determined.
In operation S305, a preset function hierarchy is adjusted based on the difference degree so as to obtain a function hierarchy for the second task.
Those skilled in the art can understand that, when the usability of the preset function hierarchy of a task needs to be evaluated, by means of understanding of relationships between a plurality of atomic functions of the task and relationships between the plurality of atomic functions and preset groups by users of a plurality of clients, the method shown in fig. 3 obtains a difference degree between the preset function hierarchy of the task and an expected function hierarchy of the task by the plurality of users, and then the preset function hierarchy can be adjusted to be close to the expected function hierarchy of the plurality of users based on the difference degree. The process can complete the collection of a large amount of user opinions (such as a second grouping result) on line through remote interaction with a plurality of clients, and further determines the adjustment direction of the optimization function hierarchical structure of the task based on the large amount of user opinions, so that the process is efficient, accurate and reasonable.
For example, the above process of determining the degree of difference between the preset grouping result and the plurality of second grouping results may be performed as follows: based on the second plurality of grouping results, position coordinates of each of the plurality of atomic functions in the N-dimensional space are determined, where N is equal to the number of preset groups. Then, based on the preset grouping result, a preset position coordinate of each of the plurality of atomic functions in the N-dimensional space is determined. Determining a classification difference vector for characterizing the degree of difference based on the position coordinates and the preset position coordinates of each of the plurality of atomic functions. Thus, the adjusting the preset function hierarchy based on the degree of difference may include: the predetermined function hierarchy is adjusted using the categorical disparity vector to approximate the categorical disparity vector to 0.
For example, there are 5 preset groups, and for a second grouping result, if the atomic function 1 falls into the preset group 2 in the second grouping result, the corresponding position coordinate of the atomic function 1 is: (0,1,0,0,0). For a second grouping result, if the atomic function 1 falls into the preset group 3 in the second grouping result, the corresponding position coordinate of the atomic function 1 is: (0,0,3,0,0). By analogy, the coordinate position of any one of the atomic functions in the N-dimensional space can be obtained. And similarly, the preset coordinate position of any atomic function in the N-dimensional space can be obtained. Based on the coordinate position of each atomic function in the N-dimensional space and the preset coordinate position, a variation vector of the atomic function in the N-dimensional space can be obtained. Based on the variation vectors of the plurality of atomic functions in the N-dimensional space, a classification difference vector representing the difference between the preset function hierarchy and the optimized function hierarchy expected by the plurality of users can be obtained.
Furthermore, the usability of the preset function hierarchy can be evaluated according to the operation behavior data of a plurality of users in the preset function hierarchy. Exemplarily, the task processing method according to the embodiment of the present disclosure may further include: and acquiring a preset function hierarchical structure of the second task. And pushing a preset function hierarchy to a plurality of clients so that any client can execute the operation of searching any atomic function in the atomic functions based on the preset function hierarchy. Then, operation behavior data from a plurality of clients is obtained, wherein the operation behavior data comprises at least one of the following items: an operation start time, an operation completion time, an operation behavior path, a trigger time for the any one atomic function, and a number of triggers for the any one atomic function. Determining evaluation information about any one of the atomic functions based on the operation behavior data, the evaluation information including at least one of: operation success rate, operation failure rate, operation duration, operation path length, straight line completion rate, and backoff number. Then, the preset function hierarchy is adjusted based on the evaluation information of each of the plurality of atomic functions to obtain a function hierarchy for the second task.
In an embodiment of the disclosure, after obtaining the function hierarchy of the first task, or after adjusting the preset function hierarchy of the second task, an optimized function hierarchy of the first task or the second task may be obtained. The task processing method according to the embodiment of the present disclosure may further include: based on a function hierarchy for the first task, a human-machine interface having a navigation structure corresponding to the function hierarchy is presented.
Fig. 4 schematically shows a flowchart of a task processing method according to another embodiment of the present disclosure.
As shown in fig. 4, the method may include the following operations S401 to S407.
In operation S401, a test task type to be employed is determined according to a test target and a scenario.
The test task types comprise an open test task and a closed test task. The open test task may be the first task described in the method shown in fig. 2, and its function hierarchy is not yet set, that is, the number of function packets and the name of the packet are not determined, and a function architecture field meeting the user's expectations needs to be obtained. The closed type test task may be a second task described in the method shown in fig. 2, which has a preset function hierarchy structure, that is, a scenario where the number of the function groups and the group names are determined, and the matching degree of the user to the specific function group and the determined function group is evaluated, or a scenario where the user searches for convenience or evaluation of the specific function in the existing function architecture. The open type test task and the closed type test task can be used singly or in a mixed mode according to the specific scene requirements.
In operation S402, a function to be tested is introduced into the test task, and a virtual card is generated.
In this example, the virtual cards are used as identification information of the atomic functions, and the virtual cards correspond to the atomic functions one to one. The open type test task generates an ungrouped virtual card list, and the card names correspond to the names of the functions to be tested one by one; the closed test task generates a grouped virtual card list, and the card names and the grouping conditions correspond to the existing navigation structure or function to be evaluated one by one.
In operation S403, a test task specification is set for guiding the reference user to execute the test task with reference to the specification.
In operation S404, the plurality of clients respectively perform a test task.
Wherein, the reference users execute the test tasks through respective clients. For example, in an open test task, a user can complete function grouping by dragging a virtual card at a corresponding client. In the closed test task, a user finishes specific function grouping by dragging a virtual card at a corresponding client, or searches for a specific function by clicking an interactive virtual card function tree.
In operation S405, test data of a plurality of users returned by a plurality of clients is recorded.
Wherein the test data may be the first packet result and the second packet result as described above. For example, in an open test task, if a user places two virtual cards in the same group, the distance between the two is recorded as 0. If the user places two virtual cards in different groups, the distance between the two is recorded as 1. In the closed test task, recording the frequency of placing the virtual cards in different groups by the user to evaluate the matching degree of the user on a specific function group and the existing function architecture, or recording behavior data such as user starting task time T1, exiting task time T2, time Ti of clicking the virtual card i (i is an integer which is more than or equal to 1 and less than or equal to the total number of atomic functions), the frequency Ci of clicking the virtual card i, an operation path and the like.
In operation S406, the user test data is analyzed.
For example, in an open test task, the distances between every two virtual cards are accumulated and counted to form card distance matrix data, the card distance matrix data are converted into a similarity matrix, clustering analysis is completed through a hierarchical clustering analysis algorithm, and a tree diagram is formed to obtain the function grouping expected by a user. In the closed test task, the difference degree between the user expected grouping and the existing function is obtained by calculating the statistical distance between the user grouping and the established grouping on the multidimensional space. For example, each card may be regarded as 1 point in n given category dimensions (n-dimensional space), the data of the card in n categories in the existing classification manner may be regarded as 1 point in the n-dimensional space, the statistical distance between the two points in the n-dimensional space is calculated, and then the classification difference vector of all cards is obtained, and the length of the vector is regarded as the difference degree of the two grouping manners. And the analysis of the success rate of the user task, the failure rate of the task, the task completion time, the user click path, the linear completion rate, the rollback times and the like can be analyzed through behavior data such as the user starting task time T1, the task exiting time T2, the time Ti of clicking the virtual card i, the times Ci of clicking the virtual card i and the like. The task completion rate refers to the number of people who successfully click the target virtual card, the task failure rate refers to the number of people who do not click the target virtual card by the time of exiting the task, the task completion duration refers to the time difference between the user clicking the target virtual card and starting the task, the straight line completion rate refers to the number of times that any one virtual card is not clicked repeatedly, the target card is directly found according to the expected test path, and the rollback times refer to the number of times that the same virtual card is clicked repeatedly and are used for displaying the positions where function naming or classification may have problems.
In operation S407, the test result is displayed.
The function groups expected by the user in the open type test task can be displayed through a matrix diagram and a function tree. The task execution condition of the user in the closed type test can be displayed in a visual mode through a pie chart, a bar chart, a path analysis chart and the like. The tester is then able to learn the user's desired functional hierarchy for a given task in order to make the corresponding design or adjustment.
Fig. 5 schematically shows a block diagram of a task processing device according to an embodiment of the present disclosure.
As shown in fig. 5, the task processing device 500 includes: a first acquisition module 510, a first pushing module 520, a first receiving module 530, a distance determining module 540, and a structure determining module 550.
The first obtaining module 510 is configured to obtain a plurality of atomic functions of a first task.
The first pushing module 520 is configured to push a first set to a plurality of clients, where the first set includes a plurality of identification information respectively corresponding to the plurality of atomic functions, so that any client in the plurality of clients allocates the plurality of identification information to one or more groups to form a first grouping result of the any client.
The first receiving module 530 is configured to receive a plurality of first packet results from the plurality of clients.
The distance determining module 540 is configured to determine a distance matrix for characterizing the relevance of the plurality of atomic functions to each other based on the plurality of first grouping results.
The structure determination module 550 is configured to determine a functional hierarchy structure for the first task based on the distance matrix.
It should be noted that the implementation, solved technical problems, implemented functions, and achieved technical effects of each module/unit/subunit and the like in the apparatus part embodiment are respectively the same as or similar to the implementation, solved technical problems, implemented functions, and achieved technical effects of each corresponding step in the method part embodiment, and are not described herein again.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, the first obtaining module 510, the first pushing module 520, the first receiving module 530, the distance determining module 540, and the structure determining module 550. Any of which may be combined into a single module or any of which may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, the first obtaining module 510, the first pushing module 520, the first receiving module 530, the distance determining module 540, and the structure determining module 550. At least one of which may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging circuits, or in any one of or a suitable combination of software, hardware, and firmware. Alternatively, the first obtaining module 510, the first pushing module 520, the first receiving module 530, the distance determining module 540, and the structure determining module 550. May be at least partially implemented as computer program modules which, when executed, may perform corresponding functions.
Fig. 6 schematically shows a block diagram of a computer system suitable for implementing the above described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 6 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 6, a computer system 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the disclosure.
In the RAM 603, various programs and data necessary for the operation of the system 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or RAM 603. It is to be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, system 600 may also include an input/output (I/O) interface 605, input/output (I/O) interface 605 also connected to bus 604. The system 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be understood by those skilled in the art that while the present disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (8)
1. A method of task processing, comprising:
acquiring a plurality of atomic functions of a first task;
pushing a first set to a plurality of clients, wherein the first set comprises a plurality of identification information respectively corresponding to the plurality of atomic functions, so that any client in the plurality of clients distributes the plurality of identification information to one or more groups to form a first grouping result of the any client;
receiving a plurality of first packet results from the plurality of clients;
determining a distance matrix characterizing a correlation of the plurality of atomic functions with each other based on the plurality of first grouping results; and
determining a functional hierarchy for the first task based on the distance matrix;
wherein said determining a functional hierarchy for the first task based on the distance matrix comprises:
converting the distance matrix into a similarity matrix;
performing clustering analysis on the similarity matrix by using a hierarchical clustering analysis algorithm to obtain a dendrogram, wherein the dendrogram represents a plurality of hierarchies from low to high, each hierarchy comprises one or more categories, and each category comprises one or more atomic functions;
determining an average number of groups of the plurality of first grouped results based on the number of groups of each of the plurality of first grouped results;
taking a level in the tree having the same number of categories as the average number of groups as a target highest level; and
and constructing the function hierarchy based on a structure from a lowest level to a target highest level in the tree diagram.
2. The method of claim 1, wherein said determining distance matrix data characterizing a correlation of said plurality of atomic functions with each other based on said first plurality of packet results comprises:
for any first grouping result, setting a distance value between atomic functions corresponding to any two identification information belonging to the same group to 0, and setting a distance value between atomic functions corresponding to any two identification information belonging to different groups to 1;
accumulating distance values between any two atomic functions in the atomic functions based on the first grouping results to obtain a comprehensive distance value between any two atomic functions; and
and constructing the distance matrix by using the comprehensive distance value between any two atomic functions in the plurality of atomic functions.
3. The method of claim 1, further comprising:
acquiring a preset function hierarchical structure of a second task, wherein the preset function hierarchical structure comprises a preset grouping result, and the preset grouping result comprises one or more preset groups and a plurality of atomic functions;
pushing a second set to the plurality of clients, wherein the second set comprises the one or more preset groups and a plurality of identification information respectively corresponding to the plurality of atomic functions, so that any client allocates the plurality of identification information to the one or more preset groups, and the allocated one or more preset groups form a second grouping result of the any client;
receiving a plurality of second grouping results from the plurality of clients;
determining a degree of difference between the preset grouping result and the plurality of second grouping results; and
adjusting the preset function hierarchy based on the degree of difference to obtain a function hierarchy for the second task.
4. The method of claim 3, wherein the determining a degree of difference between the preset grouping result and the second plurality of grouping results comprises:
determining, based on the second plurality of grouping results, a position coordinate of each of the plurality of atomic functions in an N-dimensional space, where N is equal to the number of the preset groups;
determining a preset position coordinate of each atomic function in the plurality of atomic functions in the N-dimensional space based on the preset grouping result; and
determining a classification difference vector for characterizing the degree of difference based on the position coordinates and the preset position coordinates of each of the plurality of atomic functions;
the adjusting the preset functional hierarchy based on the degree of difference comprises: and adjusting the preset function hierarchy by utilizing the classification difference vector.
5. The method of claim 1, further comprising:
acquiring a preset function hierarchical structure of a second task;
pushing the preset function hierarchy to the plurality of clients so that any client executes an operation of searching any atomic function in the plurality of atomic functions based on the preset function hierarchy;
obtaining operational behavior data from the plurality of clients, the operational behavior data including at least one of: an operation start time, an operation completion time, an operation behavior path, a trigger time for the any one atomic function, and a number of triggers for the any one atomic function;
determining evaluation information about the any one atomic function based on the operational behavior data, the evaluation information including at least one of: the operation success rate, the operation failure rate, the operation duration, the operation path length, the straight line completion rate and the rollback times; and
adjusting the preset function hierarchy based on the evaluation information of each of the plurality of atomic functions so as to obtain a function hierarchy for the second task.
6. The method of claim 1, further comprising:
based on the function hierarchy for the first task, presenting a human-machine interface having a navigation structure corresponding to the function hierarchy.
7. A task processing device comprising:
the first acquisition module is used for acquiring a plurality of atomic functions of a first task;
a first pushing module, configured to push a first set to multiple clients, where the first set includes multiple pieces of identification information corresponding to the multiple atomic functions, so that any client in the multiple clients distributes the multiple pieces of identification information to one or multiple groups to form a first grouping result of the any client;
a first receiving module, configured to receive a plurality of first grouping results from the plurality of clients;
a distance determination module for determining a distance matrix characterizing a correlation of the plurality of atomic functions with each other based on the plurality of first grouping results; and
a structure determination module to determine a functional hierarchy structure for the first task based on the distance matrix;
wherein said determining a functional hierarchy for the first task based on the distance matrix comprises:
converting the distance matrix into a similarity matrix;
performing clustering analysis on the similarity matrix by using a hierarchical clustering analysis algorithm to obtain a dendrogram, wherein the dendrogram represents a plurality of hierarchies from low to high, each hierarchy comprises one or more categories, and each category comprises one or more atomic functions;
determining an average number of groups of the plurality of first grouped results based on the number of groups of each of the plurality of first grouped results;
taking a level in the tree having the same number of categories as the average number of groups as a target highest level; and
and constructing the function hierarchy based on a structure from a lowest level to a target highest level in the tree diagram.
8. A computer system, comprising:
a memory storing computer readable instructions;
a processor for executing the computer readable instructions to implement the task processing method of any one of claims 1 to 6.
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