Method, apparatus, and electronic device for determining map-making behavior

Abstract

The present disclosure provides a method, an apparatus, and an electronic device for determining a map-making behavior, relating to the field of artificial intelligence technology, and particularly to the fields of intelligent maps and intelligent transportation technology. The specific implementation solution is as follows: Obtain N behavior sequences of a target task type, where the N behavior sequences respectively include: the behavior sequences of an input device during the production processes of N production tasks, the N production tasks are based on the input device, and the N production tasks are map data production tasks belonging to the target task type; cluster the N behavior sequences to obtain M behavior sequence types; determine a target behavior sequence type among the M behavior sequence types, and use the behavior sequences of the target behavior sequence type as reference behavior sequences, where the reference behavior sequences are reference behavior sequences for producing production tasks of the target task type. The present disclosure can improve the efficiency of determining reference behavior sequences for production tasks.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, particularly to the fields of intelligent maps and intelligent transportation technology, and specifically relates to a method, apparatus, and electronic device for determining map production behaviors. Background Art

[0002] Currently, many map data production tasks are mainly completed by manually operating input devices. For example, map data production tasks are often completed by manually operating a keyboard and a mouse. In the actual production process, there are often multiple staff members performing production tasks of the same task type. Technicians closely observe the production processes of these multiple staff members and select a reference production behavior for this task type based on the observed content and the technicians' experience. Summary of the Invention

[0003] The present disclosure provides a method, apparatus, and electronic device for determining map production behaviors.

[0004] According to one aspect of the present disclosure, there is provided a method for determining map production behaviors, including:

[0005] Obtaining N behavior sequences of a target task type, where the N behavior sequences respectively include: behavior sequences of an input device during the production processes of N production tasks, the N production tasks are based on the input device, and the N production tasks are map data production tasks belonging to the target task type, and N is an integer greater than 1;

[0006] Clustering the N behavior sequences to obtain M behavior sequence types, where M is an integer less than or equal to N;

[0007] Determining a target behavior sequence type among the M behavior sequence types, and using the behavior sequence of the target behavior sequence type as a reference behavior sequence, where the reference behavior sequence is a reference behavior sequence for producing a production task of the target task type.

[0008] According to another aspect of the present disclosure, there is provided a device for determining map production behaviors, including:

[0009] A first obtaining module, configured to obtain N behavior sequences of a target task type, where the N behavior sequences respectively include: behavior sequences of an input device during the production processes of N production tasks, the N production tasks are based on the input device, and the N production tasks are map data production tasks belonging to the target task type, and N is an integer greater than 1;

[0010] A first clustering module, configured to cluster the N behavior sequences to obtain M behavior sequence types, where M is an integer less than or equal to N;

[0011] A first determination module, configured to determine a target behavior sequence type from the M behavior sequence types, and use the behavior sequence of the target behavior sequence type as a reference behavior sequence, where the reference behavior sequence is a reference behavior sequence for manufacturing a manufacturing task of the target task type.

[0012] According to another aspect of the present disclosure, there is provided an electronic device, including:

[0013] At least one processor; and

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the map manufacturing behavior determination method provided by the present disclosure.

[0016] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause the computer to execute the manufacturing behavior determination method provided by the present disclosure.

[0017] According to another aspect of the present disclosure, there is provided a computer program product, including a computer program, where the computer program, when executed by a processor, implements the map manufacturing behavior determination method provided by the present disclosure.

[0018] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. Among them:

[0020] Figure 1 is a flowchart of a map manufacturing behavior determination method provided by the present disclosure;

[0021] Figures 2a to 2c is a structural diagram of a map manufacturing behavior determination device provided by the present disclosure;

[0022] Figure 3 is a block diagram of an electronic device used to implement the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, descriptions of well-known functions and structures are omitted in the following description for clarity and conciseness.

[0024] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for determining a map-making behavior provided by the present disclosure. As Figure 1 shown, it includes the following steps:

[0025] Step S101: Obtain N behavior sequences of a target task type, where the N behavior sequences respectively include: behavior sequences of an input device during the production processes of N production tasks. The N production tasks are based on the input device, and the N production tasks are map data production tasks belonging to the target task type, and N is an integer greater than 1.

[0026] The above input device is the input device of the electronic device for producing the above production tasks. For example, when a staff member completes the above production on a computer, the above input device is the input device of the computer.

[0027] The behavior sequence of the above input device can be the behavior sequence under the control of the input device, or the behavior sequence of the input device during the process of the staff member controlling the input device to complete the production task.

[0028] In the present disclosure, the behavior sequence of the input device can also be referred to as the input behavior sequence or operation behavior sequence of the input device. For example, the behavior sequence includes behaviors such as moving, double-clicking, editing, selecting, deleting, copying, adjusting the interface, roaming, verifying, producing data, and changing data attributes.

[0029] The above N behavior sequences can be all or part of the behavior sequences of the above target task type, where each behavior sequence corresponds to a production task. For example, if the above target task type is adding electronic eyes, the above N behavior sequences respectively include: the behavior sequence A of the input device controlled by staff member A when producing the adding electronic eye task A, the behavior sequence B of the input device controlled by staff member B when producing the adding electronic eye task B, and the behavior sequence C of the input device controlled by staff member C when producing the adding electronic eye task C.

[0030] The above N production tasks being based on the input device can be that the above N production tasks are produced by the staff based on the input device.

[0031] In the present disclosure, the above-mentioned N production tasks may be production tasks of the same type made by different staff members. For example, different staff members perform the same task on the same data materials, or different staff members perform different tasks on different data materials. For example, for the task type of modifying electronic eyes, multiple staff members modify electronic eyes at different positions to complete multiple production tasks, or multiple staff members modify electronic eyes at the same position to complete multiple production tasks.

[0032] In the present disclosure, the above-mentioned target task type may be a pre-configured task type, such as task types of modifying electronic eyes, adding electronic eyes, adding new roads, etc. Or, the above-mentioned target task type may be a complex task type selected according to time or difficulty.

[0033] The production tasks in the present disclosure are map data production tasks, specifically, they may be production tasks of map data, such as production tasks of high-precision map data. The production tasks may be production of data such as updating, adding, modifying, and deleting map data.

[0034] The above-mentioned N behavior sequences for obtaining the target task type may be obtaining the N behavior sequences of the above-mentioned target task type from a pre-obtained behavior sequence set or behavior sequence library, and these sequences are pre-recorded and stored. Or, the above-mentioned N behavior sequences for obtaining the target task type may be behavior sequences recorded in real time when the staff member performs the above-mentioned production task.

[0035] It should be noted that the behavior sequences of the production tasks in the present disclosure may be the behavior sequences recorded by the staff member when performing the above-mentioned production task based on the above-mentioned input device. And it may be the behavior sequences recorded during the process from the start to the completion of the production by the staff member, such as all the behavior sequences during the process from selecting the materials to the completion of the production.

[0036] Step S102: Cluster the N behavior sequences to obtain M behavior sequence types, where M is an integer less than or equal to N.

[0037] The above-mentioned clustering of the N behavior sequences may be taking the same behavior sequences in the N behavior sequences as the same class to obtain the above-mentioned M behavior sequence types, that is, the behavior sequences in each sequence type are the same. Or, the above-mentioned clustering of the N behavior sequences may be taking the behavior sequences with a similarity higher than a preset threshold in the N behavior sequences as the same class to obtain the above-mentioned M behavior sequence types, that is, the behavior sequences in each sequence type are the same or approximate, where the above-mentioned preset threshold may be preset according to actual requirements.

[0038] Step S103: Determine a target behavior sequence type among the M behavior sequence types, and use the behavior sequence of the target behavior sequence type as a reference behavior sequence, where the reference behavior sequence is a reference behavior sequence for manufacturing a manufacturing task of the target task type.

[0039] Determining the target behavior sequence type among the M behavior sequence types as described above can be to select the type with the most behavior sequences among the M behavior sequence types as the above-mentioned target behavior sequence. And the most behavior sequences mean the behavior sequence with the largest number of recognized people among a large number of staff. In this way, it can be considered as the optimal behavior sequence selected by a large number of operators through voting, thereby making the reference value of the selected target behavior sequence type higher.

[0040] Alternatively, determining the target behavior sequence type among the M behavior sequence types as described above can be to sort the above-mentioned M behavior sequence types in descending order according to the number of behavior sequences, and select the first J behavior sequence types in this sorting as the above-mentioned target behavior sequence, or select one from the first J behavior sequence types as the above-mentioned target behavior sequence, where J is an integer greater than or equal to 1. For example: Select one from the above-mentioned first J behavior sequence types as the above-mentioned target behavior sequence based on the user's input, such as letting the process personnel select one from the above-mentioned first J behavior sequence types as the above-mentioned target behavior sequence. In this embodiment, since the above-mentioned target behavior sequence is determined among the first J behavior sequence types, the finally determined reference behavior sequence can be generally recognized by the workers. Because the first J behavior sequence types are the J behavior sequence types with the largest number of behavior sequences, making the J behavior sequence types the J behavior sequence types most recognized by a large number of staff.

[0041] Using the behavior sequence of the target behavior sequence type as a reference behavior sequence can be that, when the behavior sequences in the target behavior sequence type are the same behavior sequences, any behavior sequence in the target behavior sequence type is used as the reference behavior sequence; or, when the target behavior sequence type includes different behavior sequences, the target behavior sequence in the target behavior sequence type is used as the reference behavior sequence, where the target behavior sequence is the behavior sequence with the largest number in the target behavior sequence, or the target behavior sequence is the behavior sequence of a staff member with a preset label operating an input device, and the preset label is used to represent staff members with preset conditions, such as staff members with the most work evaluations or the highest work levels, etc.

[0042] The above-mentioned reference behavior sequence for manufacturing a manufacturing task of the target task type can be understood as that when subsequently manufacturing a map data manufacturing task of the above-mentioned target task type, the staff can refer to the above-mentioned reference behavior sequence for manufacturing, or the electronic device directly uses the behaviors in the reference behavior sequence for manufacturing without personnel participation.

[0043] It should be noted that the task type in the present disclosure can also be referred to as a task scenario, such as task scenarios of adding an electronic eye, modifying an electronic eye, adding a road, modifying a road, etc. In addition, the reference behavior sequence in the present disclosure can also be referred to as the production model or production standard behavior of the above-mentioned target task type.

[0044] In the present disclosure, through the above steps, it is possible to cluster N behavior sequences of the target task type and select the behavior of the target behavior sequence type as the reference behavior sequence for the production task of the target task type, thereby improving the efficiency of determining the reference behavior sequence of the production task.

[0045] In addition, since the reference behavior sequence is automatically determined without manual determination, the labor cost can also be reduced.

[0046] It should be noted that the above method can be applied to an electronic device, that is, all steps in the above steps should be performed by the electronic device, and the electronic device includes but is not limited to: electronic devices such as computers, servers, and mobile phones.

[0047] In one embodiment, Figure 1 The input device in the shown embodiment includes at least one of the following:

[0048] A keyboard or a mouse.

[0049] In this embodiment, the behavior sequence is the behavior sequence of the staff operating the keyboard and mouse. For example: the behavior sequence includes all keyboard behaviors and all mouse behaviors of the staff during the production task. For example: after the staff starts the production task through a tool, all keyboard and mouse behaviors of the staff are collected, including operation behaviors such as adjusting the interface, roaming, verifying, producing data, and changing material properties.

[0050] It should be noted that in some embodiments, the behavior sequence includes some keyboard behaviors and some mouse behaviors of the staff during the production task. For example: for the production task, it can be the keyboard behaviors and mouse behaviors that record complex events in the task.

[0051] In this embodiment, since the input device includes at least one of a keyboard or a mouse, it is possible to determine the reference behavior sequence of at least one of the keyboard or the mouse, so that the subsequent staff can operate at least one of the keyboard or the mouse with reference to the reference behavior sequence to quickly and accurately complete the task production.

[0052] It should be noted that the present disclosure does not limit that the input device includes at least one of a keyboard or a mouse. For example: in some embodiments, the input device can be a touch screen, that is, the behavior sequence includes touch behaviors.

[0053] In one embodiment, the above method further includes:

[0054] Obtaining behavior record information of multiple production tasks, where the behavior record information includes the behavior sequence and behavior duration, and the multiple production tasks are map data production tasks;

[0055] Clustering the multiple production tasks to obtain T task types, where T is an integer greater than or equal to 1;

[0056] Calculating the target duration of each task type, where the target duration includes the average duration or the median duration of the behavior duration;

[0057] Determining the target task type among the T task types, where the target duration of the target task type satisfies a preset condition, and the preset condition includes at least one of the following:

[0058] The target duration is greater than or equal to a first preset duration;

[0059] The target duration is among the top H in the sorting, where the sorting is the sorting of the target durations of the T task types from high to low, and H is an integer greater than or equal to 1.

[0060] The above multiple production tasks may be multiple map data production tasks that have been completed, involving the behavior sequences of a large number of staff operating input devices. In some embodiments, the production tasks of the present disclosure may be the behavior sequences of excellent staff, so that the finally determined reference behavior sequence is the optimal behavior sequence voted by a large number of excellent staff, making the reference of the reference behavior sequence higher.

[0061] The above obtaining of the behavior record information of multiple production tasks may be obtaining the behavior record information of multiple production tasks from a behavior record set or a behavior record library, where the behavior record set or the behavior record library records the behavior record information of multiple production tasks. For example: recording the behavior of staff during task production and uploading the collected information to the database of the server at an appropriate time (such as regularly or after the task ends or when the production tool is closed).

[0062] For example: The behavior sequence of a certain production task includes: the behavior record set RAB between extracting all selected materials of the production task from the database (this behavior is called behavior A) and modifying the status of the operation materials to completed (this behavior is called behavior B) (including AB). The behavior record information of this production task may include a record of an AB behavior segment obtained by converting the behavior sequence between AB and the time interval.

[0063] The above-mentioned behavior duration can be the duration from the start to the end of the production task. For example, when a worker conducts operations guided by operation materials, the worker first selects the operation materials on the work order panel, verifies the correctness of the materials, modifies the content of the base map data, and finally modifies the status of the operation materials to completed. The above-mentioned behavior duration is the duration from selecting the operation materials to modifying the status of the operation materials to completed.

[0064] The above-mentioned clustering of the multiple production tasks can be to cluster the data of the production tasks by using the partitioning method, such as clustering according to specific types of data materials of production resources, such as clustering according to adding new electronic eyes, modifying electronic eyes, adding roads, modifying roads, etc.

[0065] The above-mentioned calculation of the target duration for each task type can be to calculate the average value or median value of the time for each category after clustering.

[0066] The above-mentioned determination of the target task type among the T task types can be to select the top H task types with the highest average value or median value of time in the type as the target task types, or the task types whose target duration is greater than or equal to the first preset duration as the target task types.

[0067] The above-mentioned first preset duration can be a duration preset according to actual requirements.

[0068] Through the above-mentioned clustering and preset conditions, it is possible to preferably select complex task types with longer durations to determine the reference behavior sequence of complex task types. In this way, when performing complex production tasks in complex task types during subsequent production, the above-mentioned reference behavior sequence can be referred to improve the production efficiency of complex production tasks.

[0069] In one embodiment, the clustering of the multiple production tasks to obtain T task types includes:

[0070] Performing data preprocessing on the multiple production tasks, and clustering the production tasks after data preprocessing to obtain T task types;

[0071] Among them, the data preprocessing includes at least one of the following:

[0072] Deleting production tasks with a behavior duration less than or equal to the second preset duration

[0073] Deleting production tasks with a behavior duration greater than or equal to the third preset duration;

[0074] The first preset duration is greater than the second preset duration, and the first preset duration is less than the third preset duration.

[0075] The above-mentioned second preset duration and third preset duration can be preset according to actual requirements. For example, the second preset duration is 10 seconds, 20 seconds or 1 minute, and the third preset duration is 30 minutes, 35 minutes, etc.

[0076] In this way, through the above data preprocessing, the production tasks can be cleaned to filter out production tasks with short or long durations, saving computing overhead. Because, short-duration tasks may be simple production tasks, which have little significance for determining the reference behavior sequence in this scenario, while long-duration production tasks may have messages or exceptions in the middle, which have little significance for determining the reference behavior sequence in this scenario.

[0077] In one embodiment, determining the target behavior sequence type among the M behavior sequence types and using the behavior sequence of the target behavior sequence type as the reference behavior sequence includes:

[0078] Determining the target behavior sequence type among the M behavior sequence types and outputting the behavior sequence of the target behavior sequence type;

[0079] Receiving a confirmation message for the behavior sequence of the target behavior sequence type;

[0080] Based on the confirmation message, using the behavior sequence of the target behavior sequence type as the reference behavior sequence.

[0081] The above output of the behavior sequence of the target behavior sequence type can be displayed for the process personnel to view, so that the process personnel can perform manual evaluation to determine the reference behavior sequence, or send the behavior sequence of the target behavior sequence type to the equipment corresponding to the process personnel, so that the process personnel can perform manual evaluation to determine the reference behavior sequence.

[0082] The above confirmation message can be a confirmation message for determining the final reference behavior sequence. In addition, if the target behavior sequence type includes multiple different behavior sequences, this confirmation message can determine some or all of these multiple different behavior sequences as the reference behavior sequence.

[0083] In this embodiment, since the reference behavior sequence is determined based on the confirmation message, the accuracy of the reference behavior sequence can be improved.

[0084] It should be noted that in the above embodiment of determining the target task type by clustering, the target task type can also be output in this embodiment, and then based on the confirmation message of the target task type, the target task type is determined.

[0085] In one embodiment, the reference behavior sequence includes: the behavior sequence of the input device during the process from the start of producing the target production task to the completion of the target production task, where the target production task is the production task corresponding to the reference behavior sequence.

[0086] The above reference behavior sequence may include: all the behavior sequences of the input device during the process from the start of producing the target production task to the completion of the target production task.

[0087] The understanding that the above target production task is the production task corresponding to the reference behavior sequence means that among the above N production tasks, it is the production task completed based on the reference behavior sequence.

[0088] In this embodiment, since it includes the behavior sequence from the start to the end of the task, it can enable the production tasks in the subsequent above target behavior sequence types to be produced with reference to the above reference behavior sequence from the start to the end, so as to further improve the referability of the reference behavior sequence.

[0089] In one embodiment, the above method further includes:

[0090] In the case where there is a production task to be produced of the target task type, perform the behaviors included in the reference behavior sequence on the task data of the production task to be produced, so as to produce the production task to be produced.

[0091] The above production task to be produced is a production task that has not been completed and is waiting to be produced in the above target task type. For example: the above N production tasks are adding production tasks for N electronic eyes at different positions, and the production task to be produced is adding an electronic eye at a position other than the above N different positions. Since they are all of the task type of adding electronic eyes, for the above production task to be produced, the above reference behavior sequence can be used for production, that is, perform the behaviors included in the reference behavior sequence on the task data of the production task to be produced to complete the production of the production task to be produced.

[0092] The above performing the behaviors included in the reference behavior sequence on the task data of the production task to be produced may be to apply the behaviors included in the reference behavior sequence to the task data of the production task to be produced to complete the production of the production task to be produced. For example: the behavior sequence of adding an electronic eye at position A is the reference behavior sequence. When adding an electronic eye at position B, this reference behavior sequence can be applied to the task data of adding an electronic eye at position B to complete the task of adding an electronic eye at position B.

[0093] In this embodiment, it can be realized to perform the production of the production task to be produced according to the reference behavior sequence, that is, change manual production to program production, thereby improving the efficiency of task production and saving labor costs.

[0094] In the present disclosure, it is possible to cluster N behavior sequences of a target task type, and select a behavior of the target behavior sequence type as a reference behavior sequence for creating a creation task of the target task type, thereby improving the efficiency of determining the reference behavior sequence for the creation task. Moreover, since the reference behavior sequence is automatically determined without manual determination, the labor cost can also be reduced.

[0095] Please refer to Figure 2a , Figure 2a which is a device for determining map creation behaviors provided by the present disclosure. As Figure 2a shown in

[0096] The first acquisition module 201 is configured to acquire N behavior sequences of a target task type, where the N behavior sequences respectively include: the behavior sequences of the input device during the creation processes of N creation tasks, the N creation tasks are created based on the input device, and the N creation tasks are map data creation tasks belonging to the target task type, and N is an integer greater than 1;

[0097] The first clustering module 202 is configured to cluster the N behavior sequences to obtain M behavior sequence types, where M is an integer less than or equal to N;

[0098] The first determination module 203 is configured to determine a target behavior sequence type among the M behavior sequence types, and use the behavior sequence of the target behavior sequence type as a reference behavior sequence, where the reference behavior sequence is a reference behavior sequence for creating a creation task of the target task type.

[0099] Optionally, the input device includes at least one of the following:

[0100] Keyboard or mouse.

[0101] Optionally, as Figure 2b shown in

[0102] The second acquisition module 204 is configured to acquire behavior record information of multiple creation tasks, where the behavior record information includes the behavior sequences and behavior durations, and the multiple creation tasks are map data creation tasks;

[0103] The second clustering module 205 is configured to cluster the multiple creation tasks to obtain T task types, where T is an integer greater than or equal to 1;

[0104] The calculation module 206 is configured to calculate the target duration of each task type, where the target duration includes the average duration or the median duration of the behavior durations;

[0105] A second determination module 207, configured to determine the target task type among the T task types, where the target duration of the target task type meets a preset condition, and the preset condition includes at least one of the following:

[0106] The target duration is greater than or equal to a first preset duration;

[0107] The target duration is among the top H in the sorting, where the sorting is the sorting of the target durations of the T task types from high to low, and H is an integer greater than or equal to 1.

[0108] Optionally, the second clustering module 205 is configured to perform data preprocessing on the multiple production tasks, and cluster the production tasks after data preprocessing to obtain T task types;

[0109] Wherein, the data preprocessing includes at least one of the following:

[0110] Delete the production tasks whose behavior duration is less than or equal to a second preset duration

[0111] Delete the production tasks whose behavior duration is greater than or equal to a third preset duration;

[0112] The first preset duration is greater than the second preset duration, and the first preset duration is less than the third preset duration.

[0113] Optionally, the first determination module 203 is configured to:

[0114] Determine a target behavior sequence type among the M behavior sequence types, and output the behavior sequence of the target behavior sequence type;

[0115] Receive a confirmation message for the behavior sequence of the target behavior sequence type;

[0116] Based on the confirmation message, use the behavior sequence of the target behavior sequence type as the reference behavior sequence.

[0117] Optionally, the reference behavior sequence includes: the behavior sequence of the input device during the process from starting to produce the target production task to the completion of the production of the target production task, where the target production task is the production task corresponding to the reference behavior sequence.

[0118] Optionally, as shown in Figure 2C, it further includes:

[0119] A production module 208, configured to, in the case that there is a production task of the target task type, perform the behaviors included in the reference behavior sequence on the task data of the production task, so as to produce the production task.

[0120] The production behavior determination device provided by the present disclosure can implement each process implemented by the map production behavior determination method provided by the present disclosure and achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0121] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0122] The above-mentioned electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the map production behavior determination method provided by the present disclosure.

[0123] The above-mentioned readable storage medium stores computer instructions, wherein the computer instructions are used to cause the computer to execute the map production behavior determination method provided by the present disclosure.

[0124] The above-mentioned computer program product includes a computer program, and the computer program implements the map production behavior determination method provided by the present disclosure when executed by a processor.

[0125] In the technical solution of the present disclosure, the acquisition, storage, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0126] Figure 3 FIG. shows a schematic block diagram of an exemplary electronic device 300 that can be used to implement the embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0127] As Figure 3As shown, device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0128] Multiple components in device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disc, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0129] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 executes the various methods and processes described above, such as the map-making behavior determination method. For example, in some embodiments, the map-making behavior determination method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the map-making behavior determination method described above can be executed. Alternatively, in other embodiments, the computing unit 301 can be configured to execute the map-making behavior determination method by any other appropriate means (e.g., by means of firmware).

[0130] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0134] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0135] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, can also be a server of a distributed system, or a server incorporating a blockchain.

[0136] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0137] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for determining map-making behaviors, including: Obtaining N behavior sequences of a target task type, where the N behavior sequences respectively include: the behavior sequences of an input device during the production processes of N production tasks, the N production tasks are based on the input device for production, and the N production tasks are map data production tasks belonging to the target task type, and N is an integer greater than 1; Clustering the N behavior sequences to obtain M behavior sequence types, where M is an integer less than or equal to N; Determining a target behavior sequence type among the M behavior sequence types, and using the behavior sequences of the target behavior sequence type as reference behavior sequences, where the reference behavior sequences are the reference behavior sequences for producing production tasks of the target task type; further including: Obtaining the behavior record information of multiple production tasks, where the behavior record information includes the behavior sequences and behavior durations, and the multiple production tasks are map data production tasks; Clustering the multiple production tasks to obtain T task types, where T is an integer greater than or equal to 1; Calculating the target duration of each task type, where the target duration includes the average duration or the median duration of the behavior durations; Determining the target task type among the T task types, where the target duration of the target task type meets a preset condition, and the preset condition includes at least one of the following: The target duration is greater than or equal to a first preset duration; The target duration is among the top H in the sorting, where the sorting is the sorting of the target durations of the T task types from high to low, and H is an integer greater than or equal to 1.

2. The method according to claim 1, wherein, The input device includes at least one of the following: Keyboard or mouse.

3. The method according to claim 1, wherein, The clustering the multiple production tasks to obtain T task types includes: Performing data preprocessing on the multiple production tasks, and clustering the production tasks after data preprocessing to obtain T task types; wherein the data preprocessing includes at least one of the following: Deleting production tasks with behavior durations less than or equal to a second preset duration or deleting production tasks with behavior durations greater than or equal to a third preset duration; The first preset duration is greater than the second preset duration, and the first preset duration is less than the third preset duration.

4. The method according to any one of claims 1 to 3, the determining a target behavior sequence type among the M behavior sequence types, and using the behavior sequences of the target behavior sequence type as reference behavior sequences, including: Determining a target behavior sequence type among the M behavior sequence types, and outputting the behavior sequences of the target behavior sequence type; Receiving a confirmation message for the behavior sequences of the target behavior sequence type; Based on the confirmation message, using the behavior sequences of the target behavior sequence type as the reference behavior sequences.

5. The method according to any one of claims 1 to 3, wherein, The reference behavior sequence includes: the behavior sequence of the input device during the process from the start of making a target production task to the completion of the target production task, where the target production task is the production task corresponding to the reference behavior sequence.

6. The method according to claim 5, further including: When there is a production task of the target task type to be produced, performing the behaviors included in the reference behavior sequence on the task data of the to-be-produced task to produce the to-be-produced task.

7. A device for determining map production behaviors, including: A first acquisition module, configured to acquire N behavior sequences of a target task type, where the N behavior sequences respectively include: the behavior sequences of the input device during the production processes of N production tasks, the N production tasks are based on the input device for production, and the N production tasks are map data production tasks belonging to the target task type, and N is an integer greater than 1; A first clustering module, configured to cluster the N behavior sequences to obtain M behavior sequence types, where M is an integer less than or equal to N; A first determination module, configured to determine a target behavior sequence type among the M behavior sequence types, and use the behavior sequence of the target behavior sequence type as a reference behavior sequence, where the reference behavior sequence is a reference behavior sequence for producing a production task of the target task type; further including: A second acquisition module, configured to acquire behavior record information of multiple production tasks, where the behavior record information includes the behavior sequence and the behavior duration, and the multiple production tasks are map data production tasks; A second clustering module, configured to cluster the multiple production tasks to obtain T task types, where T is an integer greater than or equal to 1; A calculation module, configured to calculate the target duration of each task type, where the target duration includes the average duration or the median duration of the behavior duration; A second determination module, configured to determine the target task type among the T task types, where the target duration of the target task type satisfies a preset condition, and the preset condition includes at least one of the following: The target duration is greater than or equal to a first preset duration; The target duration is among the top H in the sorting, where the sorting is the sorting of the target durations of the T task types from high to low, and H is an integer greater than or equal to 1.

8. The device according to claim 7, wherein, The input device includes at least one of the following: Keyboard or mouse.

9. The device according to claim 7, wherein, The second clustering module is configured to perform data preprocessing on the multiple production tasks, and cluster the production tasks after data preprocessing to obtain T task types; wherein, the data preprocessing includes at least one of the following: Deleting the production tasks with a behavior duration less than or equal to a second preset duration or deleting the production tasks with a behavior duration greater than or equal to a third preset duration; The first preset duration is greater than the second preset duration, and the first preset duration is less than the third preset duration.

10. The apparatus according to any one of claims 7 to 9, wherein the first determination module is configured to: determine a target behavior sequence type among the M behavior sequence types, and output a behavior sequence of the target behavior sequence type; receive a confirmation message for the behavior sequence of the target behavior sequence type; based on the confirmation message, use the behavior sequence of the target behavior sequence type as the reference behavior sequence.

11. The apparatus according to any one of claims 7 to 9, wherein the reference behavior sequence includes: the behavior sequence of the input device during the process from the start of manufacturing a target manufacturing task to the completion of the manufacturing of the target manufacturing task, and the target manufacturing task is the manufacturing task corresponding to the reference behavior sequence.

12. The apparatus according to claim 11, further comprises: a manufacturing module, configured to, when there is a to-be-manufactured task of the target task type, execute the behaviors included in the reference behavior sequence for the task data of the to-be-manufactured task, so as to manufacture the to-be-manufactured task.

13. An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

15. A computer program product comprising a computer program, which when executed by a processor, implements the method according to any one of claims 1 to 6.

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