CN111858807B - Task processing method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a task processing method, device, equipment and storage medium, and relates to the technical fields of electronic maps, big data and deep learning. The specific implementation scheme is as follows: determining the acquisition capacity of a user according to the number and time of the historical submitted pictures of the user; determining the acquisition probability of the task to be processed according to the acquisition capability of the user, the current position of the user and the position of the task to be processed; and distributing excitation elements to the task to be processed according to the acquisition probability. The method solves the problem of low task touch rate of the existing map panning, and provides a new idea for improving the task touch rate.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to electronic map, big data, and deep learning technologies, and in particular, to a task processing method, apparatus, device, and storage medium.
Background
Map panning is a crowd-sourced application of crowd-sourced POI (Point of Interest, points of interest) data, and the task mode is mainly based on regional tasks. The user previews all tasks at the panning APP end of the map, actively claiming a certain regional task for operation in combination with task distance and the like, particularly shooting, editing, submitting and the like all POIs contained in the task, and rapidly acquiring a certain reward after the task passes the audit. However, due to the limitation of task distance, some tasks are not operated by the user in the plurality of tasks put in the same batch, and the touch rate is low.
Disclosure of Invention
The embodiment of the disclosure provides a task processing method, device, equipment and storage medium, so as to improve the touch rate.
According to an aspect of the present disclosure, there is provided a task processing method, including:
determining the acquisition capacity of a user according to the number and time of the historical submitted pictures of the user;
determining the acquisition probability of the task to be processed according to the acquisition capability of the user, the current position of the user and the position of the task to be processed;
and distributing excitation elements to the task to be processed according to the acquisition probability.
According to another aspect of the present disclosure, there is provided a task processing device including:
the acquisition capacity determining module is used for determining the acquisition capacity of the user according to the number and time of the user history submitted pictures;
the acquisition probability determining module is used for determining the acquisition probability of the task to be processed according to the acquisition capacity of the user, the current position of the user and the position of the task to be processed;
and the element distribution module is used for distributing excitation elements to the task to be processed according to the acquisition probability.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the task processing methods described in any one of the embodiments of the present application.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the task processing method according to any one of the embodiments of the present application.
According to the technology, the problem of low task touch rate of the existing map panning task is solved, and a new thought is provided for improving the task touch rate.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for better understanding of the present solution and do not constitute a limitation of the present application. Wherein:
FIG. 1 is a flow chart of a task processing method provided according to an embodiment of the present application;
FIG. 2A is a flow chart of another task processing method provided in accordance with an embodiment of the present application;
FIG. 2B is a task block diagram provided in accordance with an embodiment of the present application;
FIG. 3 is a flow chart of another task processing method provided in accordance with an embodiment of the present application;
FIG. 4 is a flow chart of another task processing method provided in accordance with an embodiment of the present application;
FIG. 5 is a flow chart of another task processing method provided in accordance with an embodiment of the present application;
FIG. 6 is a schematic diagram of a task processing device according to an embodiment of the present application;
fig. 7 is a block diagram of an electronic device for implementing a task processing method of an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart of a task processing method according to an embodiment of the present application. The method and the device are suitable for the situation of how to improve the task touch rate. The method is particularly suitable for the situations of ensuring that the tasks, especially the tasks in a region far away from the resident position of the user, can be operated by the user under the scenes of a plurality of users and a plurality of tasks (a plurality of tasks put in the same batch), so as to improve the touch rate. The embodiments may be performed by task processing devices that may be implemented in software and/or hardware and may be integrated into an electronic device, such as a server, that carries task processing functions. As shown in fig. 1, the method includes:
s101, determining the acquisition capability of the user according to the number and time of the user history submitted pictures.
In this embodiment, the number of user history submitted pictures is the number of user history submitted POI face images, and the time of history submitted pictures includes the time of submitting each POI face image. The acquisition capability of the user is used for representing the efficiency of the user to acquire the POI door face map.
Optionally, in a multi-user multitasking scenario of the map panning application, for each user, the number and time of the user history submitted pictures may be weighted, so as to obtain the acquisition capability of the user according to the weighted result. The acquisition capabilities of the user may also be determined based on a pre-trained capability detection model. Specifically, the number and time of the user history submitted pictures can be input into a pre-trained capability detection model, and the acquisition capability of the user is output. In order to reduce complexity, further, a user identifier (such as a user ID) of each user may be obtained, and then the user identifier of each user is bound with the number and time of the user history submitted pictures and input to the capability detection model, so as to obtain the acquisition capability of all users together.
It can be understood that the task mode of the map panning application is mainly based on regional tasks, and is further limited by factors such as task mode and acquisition tool, so that the acquisition capacity of a user cannot be infinitely large; meanwhile, in order to reduce the subsequent calculation complexity, the embodiment can adopt a sigmoid function, and combine the number and time of the user history submitted pictures to describe the acquisition capacity of the user, and the specific acquisition capacity of the user can be expressed as follows:wherein, user_his is the weighted result of the number and time of the user history submitted pictures; c represents a constant, preferably 5.2 in panning scenarios.
S102, determining the acquisition probability of the task to be processed according to the acquisition capability of the user, the current position of the user and the position of the task to be processed.
In this embodiment, the acquisition probability of each task to be processed represents the possibility that the task to be processed is acquired; in a multi-user multitasking scenario of a map panning application, the collection probability of each task to be processed may specifically be the sum of the collection probabilities of the task to be processed collected by all users.
Specifically, for each task to be processed, for each user, a first task distance may be determined according to the current position of the user and the position of the task to be processed; according to the determined first task distance and the acquisition capacity of the user, the acquisition probability of the task to be processed acquired by the user can be determined; and then adding the collection probability of the task to be processed collected by each user to be used as the collection probability of the task to be processed.
Optionally, the acquisition probability of each task to be processed may also be determined based on a pre-trained probability determination model. Specifically, the user identifier of each user is bound with the acquisition capability of the user and the current position of the user, and is input into a probability determination model, and meanwhile, the position and the identifier of each task to be processed are also input into the probability determination model, so that the acquisition probability of all the tasks to be processed can be obtained. Each task identifier to be processed is used for uniquely identifying the task to be processed, for example, the number of the task to be processed may be the number of the task to be processed.
S103, according to the acquisition probability, exciting elements are distributed to the task to be processed.
In this embodiment, the motivational element refers to a policy for motivating the user to actively work, and may be payroll, points, coupons, or others.
Optionally, the larger the collection probability of the task to be processed, the greater the possibility of the task to be processed being operated by a user; in the multi-user multitasking scenario of the map panning application, the acquisition probability of the task to be processed is positively correlated with the assignable excitation elements thereof, i.e. the lower the acquisition probability of the task to be processed is, the higher the assignable excitation elements thereof are relatively. Further, if the acquisition probabilities of two or more tasks to be processed are the same, the more interest points the tasks to be processed need to acquire, the higher the assignable excitation elements thereof are relatively.
It should be noted that, in the multi-user multitasking scenario of the map panning application, the embodiment can dynamically allocate excitation elements to each task based on the acquisition probability, so that not only is a high touch rate ensured for a plurality of tasks put in the same batch, but also the satisfaction degree of users on the map panning application can be improved. In addition, the job progress of each task to be processed can be estimated based on the acquisition probability, so that the time and the number of the next batch of tasks can be determined reasonably.
According to the technical scheme, under the multi-user multi-task scene of the map panning application, the acquisition probability of the task to be processed is determined by combining the number and time of pictures submitted by the user history, the current position of the user, the position of the task to be processed and other multi-dimensional data, and excitation elements are reasonably distributed for each task based on the determined acquisition probability, so that the task in any area, especially the task in the area far from the resident position of the user, can be ensured, the task can be operated by the user, the touch rate is further improved, the problem that the touch rate of the current map panning task is low is solved, and a new thought is provided for improving the task touch rate.
Fig. 2A is a flowchart of another task processing method provided according to an embodiment of the present application. The embodiment provides a scheme for determining the acquisition probability of the task to be processed on the basis of the embodiment. As shown in fig. 2A, the method specifically includes:
s201, determining the acquisition capability of the user according to the number and time of the user history submitted pictures.
S202, determining task blocks included in the task to be processed from the electronic map according to the area of the task to be processed.
It should be noted that, in the map panning practical application scenario, the electronic map may be segmented according to the road network information, so as to obtain a plurality of region blocks (for example, the electronic map may be segmented into 1km by 1km region blocks), where each region block represents a task. In order to accurately determine the possibility (i.e., the acquisition probability) that each of a plurality of tasks launched in the same batch is acquired, further, the electronic map may be finely divided, for example, the electronic map may be divided by hexagons with a side length of 100m, where each hexagon represents a task block.
Therefore, for each task to be processed, according to the area to which the task to be processed belongs, it can be determined which area block in the electronic map the task to be processed corresponds to, and then the task block located in the area block is the task block included in the task to be processed.
S203, determining the acquisition index of the task block according to the current position of the user, the position of the task block and the acquisition capability of the user.
In this embodiment, the acquisition index of the task block is used to characterize the radiation situation of the acquisition capability of the user on the task block, that is, the probability that the user acquires the task block with the acquisition capability of the user. In a multi-user multitasking scenario for a map panning application, the acquisition index for each task block may be the sum of the probabilities that the task block was acquired by all users with their acquisition capabilities.
Specifically, for each user, each task block may determine, according to the current location of the user, the location of the task block, and the acquisition capability of the user, a probability that the task block is acquired by the user with its acquisition capability; and then adding the probabilities that the task blocks are acquired by the users according to the acquisition capacity of the users as the acquisition index of the task blocks.
Illustratively, determining the acquisition index of the task block may be based on the current location of the user, the location of the task block, and the acquisition capability of the user:
A. determining the acquisition probability of the task block according to the current position of the user and the position of the task block;
specifically, for each user, each task block may determine a second task distance according to the current location of the user and the location of the task block; thereafter, claiming a task block at the current location based on the probability of the user at the current location and a secondThe task distance may determine a probability that the user claimed the task block at the current location. For example, as shown in FIG. 2B, each hexagonal block represents a task block, and assuming that a user resides in S blocks, the probability of claiming S blocks at the current location is α, the probability of claiming other task blocks is 1- α. If the nearest task block continues to be claimed with a probability of alpha, the user' S probability of claimed any of blocks A through F at S block isSimilarly, the probability that the user may be determined to claim any other task block at S block may be expressed as: />Where n represents the number of attenuation layers from the position of the task block to the current position of the user, e.g. the number of layers from task block a to the current position of the user is 1.
After determining the probability that each user claimed the task block at its current location, each user claimed the probability matrix of the task block at its current location as the acquisition probability of the task block. Assuming that three users a, b, and c are provided, and the probabilities of the three users claiming the task block a at their current locations are P1, P2, and P3, respectively, the acquisition probability of the task block can be expressed as:
B. and determining the acquisition index of the task block according to the acquisition probability of the task block and the acquisition capability of the user.
Specifically, for each task block, the product of the acquisition capability of the user and the acquisition probability of the task block may be used as the acquisition index of the task block. For example, if the three users a, b, and C have acquisition capacities of C1, C2, and C3, respectively, the acquisition index of the task block a may be expressed as:
wherein Ci Pi represents the task block A used by user iThe probability of acquisition of the acquisition capability Ci is 1, 2 and 3.
Further, when the acquisition index of the task block is determined, the acquisition intention of the user (the acquisition intention is the intention of whether the user has a task recently) can be combined, so that the accuracy of the acquisition probability of the task to be processed is improved. Optionally, different weights may be set according to different intensities of the acquisition intent of the user. Assuming that the acquisition willingness of the three users a, b, and c is X1, X2, and X3, respectively, the acquisition index of the task block a may be expressed as:
it should be noted that, in this embodiment, unit task blocks with finer granularity than that of the area are introduced, and the acquisition probability of the task blocks is taken as an intermediate variable, and the acquisition index of the task blocks is determined by combining multidimensional data such as the current position of the user, the position of the task blocks, the acquisition capability of the user and the like, so that the accuracy of the determined acquisition index of the task blocks is ensured, an optional mode is provided for determining the acquisition index of the task blocks, and a foundation is laid for accurately determining the acquisition probability of the task to be processed.
S204, determining the acquisition probability of the task to be processed according to the acquisition index of the task block.
Specifically, after the acquisition index of each task block is determined, for each task to be processed, the sum of the acquisition indexes of the task blocks included in the task to be processed may be used as the acquisition probability of the task to be processed.
S205, according to the acquisition probability, exciting elements are allocated to the task to be processed.
According to the technical scheme, under the multi-user multi-task scene of map panning application, unit task blocks with finer granularity than that of areas are introduced, the acquisition indexes of the task blocks are taken as bridges, and the acquisition probability of the task to be processed is determined by combining the number and time of pictures submitted by the user historic, the current position of the user, the position of the task to be processed and other multi-dimensional data, so that the accuracy of the acquisition probability is greatly improved. In addition, the excitation elements are reasonably distributed to each task based on the determined acquisition probability, so that the task of any region, especially the task of the region far away from the resident position of the user, can be ensured to be operated by the user, and the touch rate is further improved.
Fig. 3 is a flowchart of another task processing method provided according to an embodiment of the present application. The embodiment provides a scheme for distributing excitation elements to the tasks to be processed based on the sequencing results of the tasks to be processed on the basis of the embodiment. As shown in fig. 3, the method specifically includes:
s301, determining the acquisition capability of the user according to the number and time of the user history submitted pictures.
S302, determining the acquisition probability of at least two tasks to be processed according to the acquisition capability of the user, the current position of the user and the positions of the at least two tasks to be processed.
S303, sequencing at least two tasks to be processed according to the acquisition probability.
In this embodiment, a preset sorting manner may be adopted to sort the collection probabilities, so as to implement sorting of the multiple tasks to be processed. For example, the collection probabilities may be ordered in a descending order to achieve ordering of the plurality of tasks to be processed. Further, if the collection probabilities of two or more tasks to be processed are the same, the number of interest points required to be collected by the tasks to be processed can be ranked at the rear.
S304, according to the sequencing result, exciting elements are distributed to at least two tasks to be processed.
Alternatively, the higher the order of the subsequent tasks to be processed, the higher the assignable motivation elements thereof. Specifically, the distribution ratio can be determined according to the sorting result; and then, according to the allocation proportion, allocating excitation elements for a plurality of tasks to be processed. The element base number of each task to be processed is the same, and the distribution ratio is different. For example, there are 4 tasks to be processed, a corresponding first allocation ratio arranged at 1, a corresponding second allocation ratio arranged at 2, a corresponding third allocation ratio arranged at 3, and a corresponding fourth allocation ratio arranged at 4, wherein the first allocation ratio, the second allocation ratio, the third allocation, and the fourth allocation ratio are sequentially increased.
It should be noted that, in the multi-user multi-task scenario of the map panning application, based on the sorting results of the plurality of tasks to be processed, not only excitation elements can be intuitively allocated to the tasks to be processed, but also the job progress of each task to be processed can be intuitively estimated.
According to the technical scheme, under the multi-user multi-task scene of the map panning application, the collection probability of the tasks to be processed is determined by combining the number and time of pictures submitted by the user history, the current position of the user, the positions of the tasks to be processed and other multi-dimensional data, then the tasks to be processed are sequenced according to the collection probability, and excitation elements can be intuitively and reasonably distributed for each task based on the sequencing result, so that the tasks in any area, especially the tasks in the area far from the resident position of the user, can be ensured, and the user can also operate the tasks, so that the touch rate is improved.
Fig. 4 is a flowchart of another task processing method provided according to an embodiment of the present application. The embodiment further provides a scheme for distributing the excitation elements for the task to be processed on the basis of the embodiment. As shown in fig. 4, the method specifically includes:
s401, determining the acquisition capability of the user according to the number and time of the user history submitted pictures.
S402, determining the acquisition probability of the task to be processed according to the acquisition capability of the user, the current position of the user and the position of the task to be processed.
S403, determining the number of interest points included in the area to which the task to be processed belongs.
In this embodiment, the number of interest points included in the region to which the task to be processed belongs is the number of interest points required to be acquired by the task to be processed.
For example, for each task to be processed, according to the region to which the task to be processed belongs, which region block in the electronic map the task to be processed corresponds to can be determined, and then the number of all interest points included in the region block can be directly searched from the electronic map, namely the number of interest points included in the region to which the task to be processed belongs.
S404, according to the acquisition probability and the number of the interest points, exciting elements are distributed for the task to be processed.
Alternatively, if the acquisition probabilities are the same, the more points of interest, the higher the assignable excitation elements are relative. If the number of points of interest is the same, the lower the acquisition probability, the higher the assignable excitation element is relatively.
In addition, under the multi-user multi-task scene of the map panning application, a preset ordering mode can be adopted, and a plurality of tasks to be processed are ordered based on the two-dimensional data of the acquisition probability and the number of the points of interest; and then distributing excitation elements for a plurality of tasks to be processed according to the sequencing result.
Or, a weight value can be preset for the collection probability and the number of the points of interest, so that for each task to be processed, the collection probability and the number of the points of interest of the task to be processed can be multiplied by the corresponding weight values respectively, and the sum of the products of the collection probability and the number of the points of interest is taken as the total probability of the task to be processed; and sequencing the plurality of tasks to be processed according to the total probability of each task to be processed, and distributing excitation elements for the plurality of tasks to be processed according to the sequencing result.
According to the technical scheme, under the multi-user multi-task scene of the map panning application, the acquisition probability of the task to be processed is determined by combining the multi-dimensional data such as the number and time of pictures submitted by the user history, the current position of the user and the position of the task to be processed, and then the excitation elements are dynamically allocated to each task by combining the multi-dimensional data such as the acquisition probability and the number of points of interest, so that the rationality and the flexibility of the allocation of the excitation elements are further ensured, meanwhile, the task in any area, especially the task in the area far from the resident position of the user, can be ensured, and the touch rate is further improved.
Fig. 5 is a flowchart of another task processing method provided according to an embodiment of the present application. The embodiment further provides a scheme for distributing the excitation elements for the task to be processed on the basis of the embodiment. As shown in fig. 5, the method specifically includes:
s501, determining the acquisition capability of a user according to the number and time of the user history submitted pictures.
S502, determining the acquisition probability of the task to be processed according to the acquisition capability of the user, the current position of the user and the position of the task to be processed.
S503, determining the acquisition difficulty of the task to be processed according to the regional environment of the task to be processed.
In this embodiment, the regional environment may include, but is not limited to: road (road grade, road clear condition, etc.), weather (temperature, humidity, wind power grade, haze grade, depth of rain or snow, seasons, etc.), and network. Alternatively, a weight value may be set in advance for each parameter in the regional environment. Further, under the same parameters, the parameter values are different, and the weight values are different, for example, for seasons, the weight values in summer are larger than those in spring.
Furthermore, for each task to be processed, the sum of the weight values of the parameters in the regional environment to which the task to be processed belongs can be used as the acquisition difficulty of the task to be processed.
Optionally, each parameter in the area to which the task to be processed belongs may be input into a pre-trained difficulty detection model, so as to obtain the acquisition difficulty of the task to be processed.
S504, according to the acquisition probability and the acquisition difficulty, exciting elements are distributed to the task to be processed.
Alternatively, if the acquisition probabilities are the same, the greater the acquisition difficulty, the higher the relative assignable excitation elements. If the acquisition difficulty is the same, the lower the acquisition probability, the higher the assignable excitation element is relatively. Further, if the acquisition probability and the acquisition difficulty are the same, the more the number of interest points is, the higher the assignable excitation elements are relatively.
In addition, under the multi-user multi-task scene of the map panning application, a preset ordering mode can be adopted, and a plurality of tasks to be processed are ordered based on the acquisition probability and the acquisition difficulty two-dimensional data; and then distributing excitation elements for a plurality of tasks to be processed according to the sequencing result.
According to the technical scheme, under the multi-user multi-task scene of the map panning application, the acquisition probability of the task to be processed is determined by combining the number and time of pictures submitted by the user history, the current position of the user, the position of the task to be processed and other multi-dimensional data, and then excitation elements are dynamically allocated to each task by combining the acquisition probability, the acquisition difficulty and other multi-dimensional data, so that the rationality and the flexibility of allocation of the excitation elements are further ensured, meanwhile, the task in any area, especially the task in the area far from the resident position of the user, can be ensured, and the touch rate is further improved.
Fig. 6 is a schematic structural diagram of a task processing device according to an embodiment of the present application. The method and the device are suitable for the situation of how to improve the task touch rate. The method is particularly suitable for the situations of ensuring that the tasks, especially the tasks in a region far away from the resident position of the user, can be operated by the user under the scenes of a plurality of users and a plurality of tasks (a plurality of tasks put in the same batch), so as to improve the touch rate. The device can realize the task processing method according to any embodiment of the application. The apparatus may be integrated in an electronic device, such as a server, that carries task processing functions. The apparatus 600 specifically includes:
the acquisition capacity determining module 601 is configured to determine the acquisition capacity of the user according to the number and time of the user history submitted pictures;
the acquisition probability determining module 602 is configured to determine an acquisition probability of a task to be processed according to an acquisition capability of a user, a current position of the user, and a position of the task to be processed;
the element allocation module 603 is configured to allocate excitation elements for the task to be processed according to the collection probability.
According to the technical scheme, under the multi-user multi-task scene of the map panning application, the acquisition probability of the task to be processed is determined by combining the number and time of pictures submitted by the user history, the current position of the user, the position of the task to be processed and other multi-dimensional data, and excitation elements are reasonably distributed for each task based on the determined acquisition probability, so that the task in any area, especially the task in the area far from the resident position of the user, can be ensured, the task can be operated by the user, the touch rate is further improved, the problem that the touch rate of the current map panning task is low is solved, and a new thought is provided for improving the task touch rate.
Illustratively, the acquisition probability determination module 602 includes:
the task block determining unit is used for determining task blocks included in the task to be processed from the electronic map according to the area to which the task to be processed belongs;
the acquisition index determining unit is used for determining the acquisition index of the task block according to the current position of the user, the position of the task block and the acquisition capacity of the user;
and the acquisition probability determining unit is used for determining the acquisition probability of the task to be processed according to the acquisition index of the task block.
The acquisition index determining unit is specifically configured to:
determining the acquisition probability of the task block according to the current position of the user and the position of the task block;
and determining the acquisition index of the task block according to the acquisition probability of the task block and the acquisition capability of the user.
Illustratively, the number of tasks to be processed is at least two; correspondingly, the element allocation module 603 is specifically configured to:
sequencing at least two tasks to be processed according to the acquisition probability;
and allocating excitation elements for at least two tasks to be processed according to the sorting result.
Illustratively, the element allocation module 603 is further specifically configured to:
determining the number of interest points included in the area to which the task to be processed belongs;
and distributing excitation elements to the task to be processed according to the acquisition probability and the number of the interest points.
Illustratively, the element allocation module 603 is further specifically configured to:
determining the acquisition difficulty of the task to be processed according to the regional environment of the task to be processed;
and distributing excitation elements to the task to be processed according to the acquisition probability and the acquisition difficulty.
According to embodiments of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 7, a block diagram of an electronic device according to a task processing method according to an embodiment of the present application is shown. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.
As shown in fig. 7, the electronic device includes: one or more processors 701, memory 702, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 701 is illustrated in fig. 7.
Memory 702 is a non-transitory computer-readable storage medium provided herein. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the task processing methods provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to execute the task processing method provided by the present application.
The memory 702 is used as a non-transitory computer readable storage medium, and may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules (e.g., the acquisition capability determining module 601, the acquisition probability determining module 602, and the element allocation module 603 shown in fig. 6) corresponding to the task processing method in the embodiments of the present application. The processor 701 executes various functional applications of the server and data processing, i.e., implements the task processing method in the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory 702.
Memory 702 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the electronic device of the task processing method, and the like. In addition, the memory 702 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 702 optionally includes memory remotely located relative to processor 701, which may be connected to the electronic device of the task processing method via a network. Examples of such networks include, but are not limited to, the internet, intranets, blockchain networks, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the task processing method may further include: an input device 703 and an output device 704. The processor 701, the memory 702, the input device 703 and the output device 704 may be connected by a bus or otherwise, in fig. 7 by way of example.
The input device 703 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device of the task processing method, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer stick, one or more mouse buttons, a track ball, a joystick, etc. The output device 704 may include a display apparatus, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end 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: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
According to the technical scheme of the embodiment of the application, under the multi-user multi-task scene of the map panning application, the acquisition probability of the task to be processed is determined by combining the multi-dimensional data such as the number and time of pictures submitted by the user history, the current position of the user, the position of the task to be processed and the like, and excitation elements are reasonably distributed for each task based on the determined acquisition probability, so that the task in any area, especially the task in the area far from the resident position of the user, can be ensured, the task can be operated by the user, the touch rate is further improved, the problem that the touch rate of the current map panning task is low is solved, and a new thought is provided for improving the task touch rate.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions disclosed in the present application can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (10)
1. A task processing method, comprising:
determining the acquisition capacity of a user according to the number and time of the historical submitted pictures of the user;
determining the acquisition probability of the task to be processed according to the acquisition capability of the user, the current position of the user and the position of the task to be processed, wherein the method comprises the following steps: determining task blocks included in a task to be processed from an electronic map according to an area to which the task to be processed belongs;
determining the acquisition probability of the task block according to the current position of the user and the position of the task block;
determining an acquisition index of the task block according to the acquisition probability of the task block and the acquisition capability of the user;
determining the acquisition probability of the task to be processed according to the acquisition index of the task block;
or determining the acquisition probability of the task to be processed according to the acquisition capability of the user, the current position of the user and the position of the task to be processed, including: for each task to be processed, determining a first task distance according to the current position of the user and the position of the task to be processed for each user; determining the acquisition probability of the task to be processed acquired by the user according to the first task distance and the acquisition capability of the user; adding the collection probability of the task to be processed collected by each user to be used as the collection probability of the task to be processed;
or determining the acquisition probability of the task to be processed according to the acquisition capability of the user, the current position of the user and the position of the task to be processed, including: binding the user identification of each user with the acquisition capacity of the user and the current position of the user, and inputting the bound user identification of each user with the acquisition capacity of the user and the current position of the user, and the positions of each task to be processed and the task identification to be processed into a probability determination model to obtain the acquisition probability of all the tasks to be processed;
and distributing excitation elements to the task to be processed according to the acquisition probability.
2. The method of claim 1, wherein the number of tasks to be processed is at least two; correspondingly, according to the acquisition probability, allocating excitation elements to the task to be processed, including:
sequencing at least two tasks to be processed according to the acquisition probability;
and distributing excitation elements to the at least two tasks to be processed according to the sequencing result.
3. The method of claim 1, wherein assigning excitation elements to the task to be processed in accordance with the acquisition probability comprises:
determining the number of interest points included in the area to which the task to be processed belongs;
and distributing excitation elements to the task to be processed according to the acquisition probability and the number of the interest points.
4. The method of claim 1, wherein assigning excitation elements to the task to be processed in accordance with the acquisition probability comprises:
determining the acquisition difficulty of the task to be processed according to the regional environment of the task to be processed;
and distributing excitation elements to the task to be processed according to the acquisition probability and the acquisition difficulty.
5. A task processing device comprising:
the acquisition capacity determining module is used for determining the acquisition capacity of the user according to the number and time of the user history submitted pictures;
the acquisition probability determining module comprises:
the task block determining unit is used for determining task blocks included in the task to be processed from the electronic map according to the area to which the task to be processed belongs;
the acquisition index determining unit is used for determining the acquisition probability of the task block according to the current position of the user and the position of the task block; determining an acquisition index of the task block according to the acquisition probability of the task block and the acquisition capability of the user;
the acquisition probability determining unit is used for determining the acquisition probability of the task to be processed according to the acquisition index of the task block;
or, the acquisition probability determining module is further configured to: for each task to be processed, determining a first task distance according to the current position of the user and the position of the task to be processed for each user; determining the acquisition probability of the task to be processed acquired by the user according to the first task distance and the acquisition capability of the user; adding the collection probability of the task to be processed collected by each user to be used as the collection probability of the task to be processed;
or, the acquisition probability determining module is further configured to: binding the user identification of each user with the acquisition capacity of the user and the current position of the user, and inputting the bound user identification of each user with the acquisition capacity of the user and the current position of the user, and the positions of each task to be processed and the task identification to be processed into a probability determination model to obtain the acquisition probability of all the tasks to be processed;
and the element distribution module is used for distributing excitation elements to the task to be processed according to the acquisition probability.
6. The apparatus of claim 5, wherein the number of tasks to be processed is at least two; correspondingly, the element allocation module is specifically configured to:
sequencing at least two tasks to be processed according to the acquisition probability;
and distributing excitation elements to the at least two tasks to be processed according to the sequencing result.
7. The apparatus of claim 5, wherein the element allocation module is further specifically configured to:
determining the number of interest points included in the area to which the task to be processed belongs;
and distributing excitation elements to the task to be processed according to the acquisition probability and the number of the interest points.
8. The apparatus of claim 5, wherein the element allocation module is further specifically configured to:
determining the acquisition difficulty of the task to be processed according to the regional environment of the task to be processed;
and distributing excitation elements to the task to be processed according to the acquisition probability and the acquisition difficulty.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the task processing method of any one of claims 1-4.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the task processing method of any one of claims 1-4.
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