CN119671219B

CN119671219B - Task list processing method and system

Info

Publication number: CN119671219B
Application number: CN202510187811.5A
Authority: CN
Inventors: 赵燚; 聂贤; 龚治宁
Original assignee: Yizhi Technology Chengdu Co ltd
Current assignee: Yizhi Technology Chengdu Co ltd
Priority date: 2025-02-20
Filing date: 2025-02-20
Publication date: 2025-06-10
Anticipated expiration: 2045-02-20
Also published as: CN119671219A

Abstract

The invention provides a task list processing method and a task list processing system, the method comprises the steps of obtaining historical access data of a historical user, determining a vector set of the historical access data, wherein each historical access data comprises an exposure task list and clicking data of each exposure task list, determining a vector in the vector set comprises personnel parameter values, task list parameter values and clicking parameter values, determining a first model based on the vector set, obtaining user access information of a target user, wherein the user access information comprises user identity information corresponding to the target user, determining a second model corresponding to the target user based on the user access information and the first model, and determining a recommended task list corresponding to the target user based on the second model. The invention can lead the presentation and/or recommendation of the task list of each user to be more accurate and personalized aiming at the user group and/or the individual preference of the user, thereby improving the working efficiency of the user.

Description

Task list processing method and system

Technical Field

The present invention relates to the field of data processing, and in particular, to a task list processing method and system.

Background

Task allocation and dispatch are often involved in engineering projects, and task related personnel (such as task senders, receiver, etc.) often need to view the task list in the task list and perform corresponding operations on the task list. However, the tasks in the engineering project are often more, the staff at each level or post is also thinner, and a large number of task sheets can be formed after the source tasks are distributed layer by layer. The task list of the task related personnel may have more task lists, and how to quickly find the target task list has important significance for task execution. Meanwhile, different people pay attention to different tasks or task lists, and the actual conditions of the tasks (such as whether emergency treatment, rectification, overdue or not are needed) are different. The efficiency is low when the user manually sorts the task sheets, and the flexibility is poor when the task sheets are sorted according to fixed task sheet information (such as task starting time, task sheet state and the like), so that the actual requirement is difficult to meet. Therefore, how to present (e.g., order) and/or recommend task sheets is a highly desirable problem.

Therefore, the task list processing method and system can flexibly sort the task list by combining the characteristics of personnel, the characteristics of the task list, the actual conditions of the task list and the like, so that the presentation and/or recommendation of the task list are more personalized and targeted.

Disclosure of Invention

The invention provides a task list processing method which comprises the steps of obtaining historical access data of a historical user, determining a vector set of the historical access data, wherein each historical access data comprises an exposure task list and clicking data of each exposure task list, determining a first model based on the vector set, obtaining user access information of a target user, wherein the user access information comprises user identity information corresponding to the target user, determining a second model corresponding to the target user based on the user access information and the first model, and determining a recommended task list corresponding to the target user based on the second model.

The invention provides a task list processing system which comprises a first acquisition module, a vector set determination module and a recommendation module, wherein the first acquisition module is configured to acquire historical access data of a historical user, each historical access data comprises an exposure task list and clicking data of each exposure task list, the vector set determination module is configured to determine a vector set of the historical access data, vectors in the vector set comprise personnel parameter values, task list parameter values and clicking parameter values, the clicking parameter values are determined based on the clicking data, the first model determination module is configured to determine a first model based on the vector set, the second acquisition module is configured to acquire user access information of a target user, the user access information comprises user identity information corresponding to the target user, the second model determination module is configured to determine a second model corresponding to the target user based on the user access information and the first model, and the recommendation module is configured to determine a recommendation task list corresponding to the target user based on the second model.

The method has the advantages that (1) a first model which accords with the preferences of most people can be obtained through a large amount of historical access data corresponding to a large amount of historical users so as to comprehensively sort the task lists of any individuals rapidly, (2) a second model is generated on the basis of the first model by combining the information of the target individuals, so that the sequence of an automatically generated task list is more personalized and accurate, (3) the first model and/or the second model are updated regularly, the updated first model and/or the second model can adapt to the access condition of the task list information and the change condition of different user information in an actual application scene, so that the sorting of the task lists can accord with actual demands better, and (4) the influence factors of the trigger users in the task lists on the task lists can be analyzed more accurately through the effective parameter value combination and the design of influence factors, so that the sorting of the first model and/or the second model on the task lists is more accurate, and the demands of the users are met.

Drawings

The invention will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic illustration of an application scenario of a task sheet processing system according to some embodiments of the invention;

FIG. 2 is a block diagram of a task sheet processing system according to some embodiments of the invention;

FIG. 3 is an exemplary flow chart of a method of task sheet processing according to some embodiments of the invention;

FIG. 4a is an exemplary flow chart of a method of determining a first model according to some embodiments of the invention;

FIG. 4b is a schematic diagram of a process for determining a first model, shown in accordance with some embodiments of the present invention;

FIG. 5a is a schematic diagram of a process for determining a second model, shown in accordance with some embodiments of the present invention;

FIG. 5b is a schematic diagram of a process for updating a second model, shown in accordance with some embodiments of the present invention;

FIG. 6 is a schematic diagram illustrating a process for determining recommended task sheets according to some embodiments of the invention.

Detailed Description

In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present invention, and it is apparent to those of ordinary skill in the art that the present invention may be applied to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

The terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly indicates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in the present invention to describe the operations performed by a system according to embodiments of the present invention. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

FIG. 1 is a schematic illustration of an application scenario of a task sheet processing system according to some embodiments of the invention.

As shown in fig. 1, an application scenario 100 of a task sheet processing system includes a processing device 110, a network 120, a terminal 130, a storage device 140, and a task information database 150.

Processing device 110 may process data and/or information obtained from terminal 130, storage device 140, and task information database 150. For example, the processing device 110 may obtain task related information of the task information database 150 and generate a task list according to the task related information. For another example, the processing device 110 may generate the first model and/or a second model corresponding to the target user according to the task related information, for ordering the task list. More about the first model, the second model can be found elsewhere in the present invention (e.g., fig. 3).

In some embodiments, the processing device 110 may be a single server or a group of servers. In some embodiments, the processing device 110 may be local or remote. The processing device 110 may be directly connected to the terminal 130, the storage device 140, and the task information database 150 to access stored or retrieved information and/or data. In some embodiments, the processing device 110 may be implemented on a cloud platform. For example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, or the like, or any combination thereof. In some embodiments, the processing device 110 may be a distributed server group, which may include a plurality of server nodes.

The network 120 may include any suitable network that facilitates the exchange of information and/or data of the application scenario 100 of the task sheet processing system. In some embodiments, one or more components of the application scenario 100 of the task sheet processing system (e.g., the terminal 130, the processing device 110, the storage device 140, or the task information database 150) may communicate information and/or data with one or more other components of the application scenario 100 of the task sheet processing system via the network 120. For example, processing device 110 may obtain task related information from storage device 140 and/or task information database 150 via network 120.

In some embodiments, network 120 may be any one or more of a wired network or a wireless network. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies.

The terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a notebook computer 130-3, etc., or any combination thereof. In some embodiments, the terminal 130 may interact with other components in the application scenario 100 of the task sheet processing system through the network 120. In some embodiments, terminal 130 may receive information and/or instructions entered by a user and send the received information and/or instructions to processing device 110 via network 120. For example, the terminal 130 may receive instructions from a user (e.g., a person-by-person), retrieve task-related information from the storage device 140 and/or the task information database 150 via the network 120, and present one or more task sheets in the form of a list.

In some embodiments, the application scenario 100 of the task sheet processing system further includes a preset client application, which may be software or an application program installed on the terminal 130, for example, a mobile phone application installed on the mobile device 130-1, a desktop application of the notebook 130-3, and the like. In some embodiments, the client application includes a visual user interface to present task related information to the user. For example, the user interface presents a task list that includes a plurality of task sheets that are ordered based on an ordering algorithm. Wherein the ranking algorithm may be preset/default or specified by the user. In some embodiments, the ranking algorithm may also be implemented based on the first model or the second model.

In some embodiments, the client application may also interact with the user (e.g., send a person, receive a person, etc.) through a user interface. For example, the client application may respond to user operations on a task sheet presented on the user interface and generate a corresponding operational event record. For example, it may respond to a user's click event on a task sheet (e.g., a mouse click/double click event, a gesture click event, etc.), and generate a click event record. The click event records include, but are not limited to, operator id, task sheet id of the clicked task sheet, click time, number of clicks, etc. In some embodiments, the client application may send the operational event record (e.g., click event record) to the storage device 140 and/or the task information database 150 for storage via the network 120. In some embodiments, the operational event record may be generated by the processing device 110.

In some embodiments, the processing device 110 may perform deduplication processing on the click event records. For example, the processing device 110 may obtain a plurality of click event records from the task information database 150, and perform deduplication processing based on the operator id, the task sheet id, the click time, and a preset deduplication time threshold in the plurality of click event records. Illustratively, in response to the operator id and the task sheet id being the same among the plurality of click event records, and the difference between the plurality of click times being within the deduplication time threshold (e.g., 2 s), the processing device 110 stores only one of the click event records (e.g., the earliest click event record).

In some embodiments, the client application may deduplicate the click event based on a deduplication time threshold (e.g., 2 seconds). For example, when the user performs the clicking operation multiple times (e.g., 3 times) within the deduplication time threshold, the client application only sends the click event record corresponding to the first click event to the storage device 140 for storage. By performing deduplication processing of click events at the client application, transmission of data may be reduced while reducing data processing pressure of the processing device 110.

The storage device 140 may store data and/or instructions. In some embodiments, the storage device 140 may store data obtained from the processing device 110, the terminal 130, and/or the task information database 150. For example, the storage device 140 may store data (e.g., task related information) or the like retrieved from the task information database 150. In some embodiments, storage device 140 may store data and/or instructions for processing device 110 to perform the exemplary methods described herein. For example, the storage device 140 may store instructions for the processing device 110 to perform the methods illustrated in the various flowcharts. In some embodiments, storage device 140 may include mass storage devices, removable storage devices, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. In some embodiments, storage device 140 may be implemented on a cloud platform. In some embodiments, the storage device 140 may be part of the processing device 110.

The task information database 150 refers to a source for providing data related to tasks. For example, it may be a database of an operator (e.g., enterprise, construction entity, etc.) and/or a third party service platform (e.g., information service provider). In some embodiments, the task information database 150 may be used to provide various types of task related information. For example, the task related information includes a value of a task sheet parameter (e.g., task ID, creation time, dispatch time, order taking time, associated subtasks, task execution status, etc.), a value of a person parameter (e.g., ID, name, post, etc. of person-dispatching, person-receiving, person-responsible, person-executing, etc.). As another example, the task related information may include historical access information for the task sheet (e.g., number of times queried or clicked, etc.). The task related information may include various forms of information such as text, graphics, sound, and video.

In some embodiments, the task information database 150 may interact with other components in the application scenario 100 of the task sheet processing system through the network 120. For example, it may send task related information (e.g., task sheet information, personnel information, etc.) to the processing device 110 over the network 120 to cause the processing device 110 to perform analysis and/or processing of the task sheet. For another example, the task information database 150 may transmit task related information to the terminal 130 through the network 120 such that the terminal 130 presents the task related information to the user. In some embodiments, the task information database 150 may be integrated or deployed in the storage device 140.

The foregoing description is for illustrative purposes only, and various changes may be made in the actual application scenario.

It should be noted that the application scenario 100 of the task sheet processing system is provided for illustrative purposes only and is not intended to limit the scope of the present invention. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. However, such changes and modifications do not depart from the scope of the present invention.

FIG. 2 is a block diagram of a task sheet processing system according to some embodiments of the invention.

As shown in FIG. 2, the task sheet processing system 200 can include a first acquisition module 210, a vector set determination module 220, a first model determination module 230, a second acquisition module 240, a second model determination module 250, and a recommendation module 260.

The first acquisition module 210 is configured to acquire historical access data of a historical user, each of the historical access data including an exposure task sheet and click data of each exposure task sheet.

The vector set determination module 220 is configured to determine a vector set of historical access data, the vectors in the vector set including personnel parameter values, task sheet parameter values, and click parameter values, wherein the click parameter values are determined based on the click data.

In some embodiments, the set of vectors includes a vector for each exposure job ticket for each historical access data. The vector set determination module 220 is further configured to determine, for each exposure job ticket of each historical access data, a job ticket sub-vector based on a job ticket parameter value of the exposure job ticket, a person sub-vector based on a person parameter value of the exposure job ticket, a click sub-vector based on a click parameter value of the exposure job ticket, and a vector corresponding to the exposure job ticket based on the job ticket sub-vector, the person sub-vector, and the click sub-vector.

The first model determination module 230 is configured to determine a first model based on the set of vectors.

The first model reflects the extent to which the task sheet related parameter values affect click events of the historical user.

In some embodiments, the first model determination module 230 is further configured to determine a plurality of valid parameters based on the vectors in the vector set, determine a plurality of valid parameter value combinations based on the plurality of valid parameters, each valid parameter value combination including at least one personnel parameter value and at least one task sheet parameter value, determine an impact factor for each valid parameter value combination based on the vectors in the vector set, and determine the first model based on each valid parameter value combination and its impact factor.

In some embodiments, the first model determination module 230 is further configured to determine a frequency of occurrence for each parameter based on the vectors in the set of vectors, and to determine a plurality of valid parameters based on the frequency of occurrence for each parameter and a preset frequency threshold.

In some embodiments, the first model determination module 230 is further configured to determine an effective vector based on the plurality of effective parameters and their corresponding activation ranges for each vector in the vector set, and determine at least one effective parameter value combination based on the effective vectors, wherein the plurality of effective parameter value combinations includes at least one effective parameter value combination for each vector.

In some embodiments, the first model determination module 230 is further configured to determine the impact factor based on the number of occurrences and/or clicks for each valid parameter value combination.

The second obtaining module 240 is configured to obtain user access information of the target user, where the user access information includes user identity information corresponding to the target user.

The second model determination module 250 is configured to determine a second model corresponding to the target user based on the user access information and the first model.

The second model reflects the extent to which the task sheet related parameter values affect the click events of the target user.

In some embodiments, the second model determination module 250 is further configured to determine a dedicated parameter value combination and an impact factor thereof corresponding to the target user based on the user identity information corresponding to the target user and the personnel parameter value in each of the valid parameter value combinations in the first model, and to determine the second model based on the dedicated parameter value combination and the impact factor thereof.

In some embodiments, the second model determination module 250 is further configured to obtain a target exposure task sheet corresponding to the target user and target click data of the target exposure task sheet by the target user, and update the second model based on the target exposure task sheet and the target click data.

In some embodiments, the second model determination module 250 is further configured to obtain identity change information of the target user, determine an update-specific parameter value combination and an impact factor thereof corresponding to the target user based on the identity change information, and update the second model corresponding to the target user based on the update-specific parameter value combination and the impact factor thereof.

The recommendation module 260 is configured to determine a recommended task sheet corresponding to the target user based on the second model.

In some embodiments, the recommendation module 260 is further configured to determine a target task sheet parameter value corresponding to each target task sheet based on target task sheet information corresponding to a target task sheet associated with the target user, determine a plurality of target parameter value combinations corresponding to each target task sheet based on the target task sheet parameter value and the target person parameter value of the target user, determine a recommendation weight for each target task sheet based on the plurality of target parameter value combinations and the second model, and determine a recommendation task sheet based on the recommendation weight.

In some embodiments, the first model determination module 230 is further configured to group the vector sets to obtain a plurality of vector groups, determine a first model based on each vector group to obtain a plurality of first models, and the second model determination module 250 is further configured to select a target first model corresponding to the target user from the plurality of first models, and determine a second model based on the target first model and the user access information.

It should be noted that the above description of the task sheet processing system 200 and its modules is for convenience of description only and is not intended to limit the invention to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. For example, the first obtaining module 210, the vector set determining module 220, the first model determining module 230, the second obtaining module 240, the second model determining module 250, and the recommending module 260 may be different modules in the system, or may be one module to implement the functions of two or more modules. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the invention.

FIG. 3 is an exemplary flow chart of a method of task sheet processing according to some embodiments of the invention.

In some embodiments, the process 300 may be performed by a task sheet processing system. As shown in fig. 3, the process 300 includes the following steps.

In step S310, history access data of the history user is obtained, and each history access data includes an exposure task sheet and click data of each exposure task sheet.

Historical users refer to users who have accessed the task sheet data through the client application. For example, a history user may input a query or search instruction through a client application, which may present a corresponding list of task sheets for access by the history user. A user refers to a person associated with a task, which may be any person in an operator (e.g., business, construction unit). The user may include a person-dispatching, a person-receiving, a supervisor, a responsible person, a executor, etc. of the task. Where a task refers to an item that requires a user to perform or process, it may be represented in the form of a task document (i.e., a task sheet). The task sheet may include a combination of one or more of various types of task related information. See fig. 1 and the description thereof for further details regarding task related information.

Historical access data refers to a record of access events of a historical user to the task sheet data over a period of time, and contains various types of information related to the access events. For example, the past period of time may be within the past month, three months, half year, etc. The information related to the access event includes access time, access content, historical user interactions (e.g., clicks) with the access content, and the like.

The exposure task sheet refers to a task sheet that has been presented to a historical user for browsing. For example, it may be one or more task sheets presented in the historical user's view on the interface of the client application. For example, when a history user enters a query or search instruction via a client application, a corresponding list of task sheets may be presented for access by the history user. When more task sheets are in the task sheet list, the task sheets need to be displayed in a paging mode. For a plurality of task sheets displayed in a paging mode, the current page and the plurality of task sheets in the browsed page are exposure task sheets, and the task sheets which are not browsed by the page turning of the historical user belong to unexposed task sheets.

In some embodiments, processing device 110 may mark the exposure status of the task sheet (e.g., 1 for exposed, 0 for unexposed) by the client application to determine a corresponding set of exposure task sheets for each historical user.

Click data for an exposure task sheet relates to historical user click events for the exposure task sheet, including click parameter values for click parameters. Click parameters include click time, number of clicks, id of historic personnel performing click events, id of exposure task sheet clicked, etc. In some embodiments, the click data of the exposure job ticket includes a post-deduplication click event record. For example, if the historic person id and the exposure task list id in the plurality of click event records are the same, and the difference value of the plurality of click times is within the de-duplication time threshold (e.g. 2 s), the plurality of click event records only keep one click event record, and correspondingly, the corresponding multi-click event is recorded as a one-click event.

In some embodiments, the historical access data also includes other relevant data for the exposure task sheet, such as personnel data (e.g., personnel parameter values) and task sheet data (e.g., task sheet parameters). For more on the personnel parameters and the task sheet parameters, see the description of step S320.

In step S320, a vector set of the history access data is determined, the vectors in the vector set including a person parameter value, a task sheet parameter value, and a click parameter value.

A vector set of historical access data refers to a set of multiple vector constructs generated from the historical access data that may reflect the data characteristics of the historical access data. Each vector in the vector set includes a type or parameter (e.g., name, department of ownership) of the data feature and a corresponding feature value or parameter value (e.g., "dawn", "administrative") of the data feature.

In some embodiments, the task sheet processing system may generate a vector corresponding to each historical access data (e.g., database record) based on the historical access data. Wherein the vector comprises values of a plurality of elements, each element representing a parameter, the values of the elements representing specific parameter values. The Vector is (101, dawn, administrative, length of the part), where the parameters of the Vector are the personnel number, name, department, job, respectively, and the parameter values are 101, dawn, administrative, length of the part, respectively.

It will be appreciated that different access data may include different information, and that the corresponding vectors may be different, and that the task sheet processing system may generate a set of vectors based on a plurality of vectors corresponding to a plurality of historical access data.

In some embodiments, the set of vectors includes a vector for each exposure job ticket for each historical access data. For each exposure task list of each historical access data, the task list processing system can determine a task list sub-vector based on a task list parameter value of the exposure task list, determine a personnel sub-vector based on a personnel parameter value of the exposure task list, determine a clicking sub-vector based on a clicking parameter value of the exposure task list, and determine a vector corresponding to the exposure task list based on the task list sub-vector, the personnel sub-vector and the clicking sub-vector. Hereinafter, the vector corresponding to the exposure task sheet is simply referred to as an exposure task sheet vector.

The job ticket parameter value refers to the value of the job ticket parameter of the exposure job ticket. Exemplary task sheet parameters include task ID, creation time, dispatch time, order time, associated subtasks, task execution status, audit status, hierarchy to which the task sheet pertains, and the like. The task list processing system can determine task list vectors according to the task list parameters and parameter values corresponding to each exposure task list. In some embodiments, the task sheet parameter value is determined based on the task sheet data in the acquired history access data of step S310. In some embodiments, the task sheet processing system obtains the task sheet parameter values of the exposure task sheet by querying the task information database 150.

The personnel parameter value refers to a value of a personnel parameter of the exposure job ticket. Exemplary personnel parameters include id, name, age, department, post, etc. of the person associated with the task, e.g., person to whom the person is assigned, person to whom the person is responsible, person to whom the person is being subjected, etc. The task list processing system can determine personnel sub-vectors according to personnel parameters and parameter values corresponding to each exposure task list. In some embodiments, the task sheet parameter value is determined based on personnel data in the acquired historical access data of step S310. In some embodiments, the job ticket processing system obtains personnel parameter values for the exposure job ticket by querying the job information database 150.

The click parameter value refers to the value of the click parameter of the exposure job ticket. The task sheet processing system may determine the click sub-vectors based on the click parameters and parameter values for each exposure task sheet. The click parameter value may be determined based on click data in the acquired history access data of step S310.

It should be noted that, the vector corresponding to the exposure task list may also include other parameter information, for example, parameter values of access parameters (such as query time, number of queries, etc.) of the related personnel to the exposure task list.

In some embodiments, the task sheet processing system may splice the task sheet vector, the person sub-vector, and the click sub-vector to obtain an exposure task sheet vector. Illustratively, the exposure task sheet vector V is (F _task、F_member、F_tap), where F _task、F_member、F_tap represents a task sheet vector, a person sub-vector, and a click sub-vector, respectively.

It should be noted that, information extraction may be performed on the exposure task list according to a preset rule to determine an exposure task list vector. In some embodiments, the task list processing system may determine a task list vector, a personnel sub-vector, and a click sub-vector according to the parameter weights of each parameter in the task list parameter, the personnel parameter, and the click parameter, respectively, so as to determine an exposure task list vector.

Taking the personnel sub-vector as an example, the parameter weights of personnel parameters such as user id, job number, name and the like can be set larger (for example, larger than a threshold value), the parameter weights of personnel parameters such as age, address and the like can be set smaller (for example, smaller than the threshold value), and the task list processing system can generate the personnel sub-vector according to the personnel parameters with the parameter weights larger than the threshold value.

In some embodiments of the present invention, by setting the parameter weights of the parameters in the task list parameter, the personnel parameter and the click parameter to determine the exposure task list vector, the parameters irrelevant to the part can be removed according to the actual needs, thereby reducing the subsequent data processing amount.

Step S330, determining a first model based on the vector set.

The first model refers to a generic task sheet ordering model generated based on historical access data (or vector sets) of a plurality of historical users.

The first model may reflect a first degree of influence of task sheet related parameter values (e.g., personnel parameters, task sheet parameters) on historical user click events, and thus may be used for task sheet sequencing. For example, the first degree of influence may be represented by a value in the [0,1] interval, with a larger value indicating that the more likely the task sheet-related parameter value is to trigger a historical user click on an exposed task sheet. In an actual scenario (for example, the task list is accessed through an application program), if the first influence degree corresponding to the value of the task list parameter of a certain task list is larger, the task list can be placed at a position in front of the task list, so that a user can check or click conveniently.

In some embodiments, the task sheet processing system may analyze historical access data of a plurality of historical users to generate a first model. For example, for a plurality of exposure task sheets of historical access data, the task sheet processing system may count the frequency of occurrence of different task sheet parameter values or combinations, as well as the number of clicks of the corresponding exposure task sheet for the task sheet parameter values or combinations. The more clicks, the greater the first degree of influence corresponding to the task sheet parameter value or combination. Further, the first model may order the plurality of exposure task sheets according to different task sheet parameter values or a combination of corresponding first influence degrees and task sheet parameters in the plurality of exposure task sheets.

In some embodiments, the task sheet processing system can process the set of vectors to determine the first model. See fig. 4a and its description for more content on the first model.

In some embodiments, the plurality of historical users may be divided into a plurality of different user groups, and the task sheet processing system may generate a plurality of different first models for the plurality of different user groups. The user group may be determined according to actual requirements, for example, the user group may include, but is not limited to, a plurality of different user groups divided by departments, sexes, posts, job types, and the like. As an example, the first model may include a first model for constructors, a first model for administrative staff, and the like.

In some embodiments, the task sheet processing system may group the sets of vectors to obtain a plurality of vector groups, and determine a first model based on each vector group to obtain a plurality of first models. See fig. 4a and its description for more relevant content.

Step S340, obtaining user access information of the target user, wherein the user access information comprises user identity information corresponding to the target user.

The target user refers to an individual user individual who has a need to access the task sheet data. For example, the target user may be a user of an administrative department. User identity information corresponding to the target user includes, but is not limited to, job number, name, post, etc. In some embodiments, the user identity information also includes login information (e.g., user id, account number, etc.) for the user.

Step S350, determining a second model corresponding to the target user based on the user access information and the first model.

The second model is a task sheet ordering model for the target user that is referred to as a specialized ordering model. The second model may reflect a second degree of influence of the task sheet related parameter values (e.g., personnel parameters, task sheet parameters) on the click event of the target user. For example, the second degree of influence may be a numerical representation of the [0,1] interval, with a larger value indicating that the more easily the task sheet-related parameter value triggers the target user to click on the exposure task sheet. In an actual scenario, if the second influence degree corresponding to the value of the task list parameter of a certain task list is greater, the task list may be placed in a position in front of the task list so as to be convenient for the target user to view or click.

In some embodiments, the task sheet processing system may determine a second model corresponding to the target user according to the identity information of the target user and the first model.

In some embodiments, the task sheet processing system may select a target first model corresponding to a target user from a plurality of first models, and determine the second model based on the target first model and the user access information. See fig. 5a and its description for more details regarding the second model.

Step S360, determining a recommended task list corresponding to the target user based on the second model.

Recommended task sheets refer to task sheets that may be of interest or interest to a target user, which may trigger a click event for the target user.

In some embodiments, the task sheet processing system may determine recommended weights that are deemed by the plurality of target tasks related to the target user based on the second model and determine a recommended task sheet based on the recommended weights. See fig. 6 and the description thereof for further content regarding recommended workflows.

In some embodiments, the task list processing system may present a recommended task list corresponding to the target user through a user interface of the client application, and may also alert the target user through text, voice, highlighting, and the like.

In some embodiments, the task sheet processing system may update the first model according to a preset update period. The preset update period may be monthly, quarterly, etc. In some embodiments, the task sheet processing system may obtain extended history access data for the history user over a preset update period, determine an extended vector set based on the extended history access data, and further determine to update the first model based on the current vector set and the extended vector set.

The extended history access data refers to history access data of a history user newly added in a preset update period. For example, at the beginning of each month, the task sheet processing system may acquire history access data of the history user of the previous month as extended history access data based on step S310. The extended vector set refers to a vector set generated from extended history access data, and for example, the extended vector set may be generated for extended history access data of the previous month based on step S320. Further, the task sheet processing system may generate a new vector set from the current vector set and the extended vector set, and generate an updated first model based on the manner of step S330.

In some embodiments of the present invention, a task list ordering model (i.e., a first model) with relatively strong versatility can be obtained by combining a large number of historical access data (such as clicks) of historical users, and the model can be used for recommending task lists for various users. Meanwhile, a task list ordering model (namely a second model) with stronger pertinence can be obtained according to the user access information of the target user, so that personalized task list recommendation is realized, and the requirements of the target user are met. In addition, through the first model and/or the second model, the focused task list can be accurately screened and presented for the user under the condition that the task number is large, and the working efficiency is improved.

Fig. 4a is an exemplary flow chart of a method of determining a first model according to some embodiments of the invention.

In some embodiments, the process 400 may be performed by a task sheet processing system. As shown in fig. 4a, the process 400 includes the following steps.

In step S410, a plurality of effective parameters are determined based on the vectors in the vector set.

The effective parameters refer to parameters that a task sheet will typically contain. For example, the effective parameters include job ticket parameters and/or personnel parameters that occur more frequently (e.g., greater than a threshold) in the exposure job ticket.

In some embodiments, the task sheet processing system may determine a frequency of occurrence for each parameter based on the vectors in the vector set and determine a plurality of valid parameters based on the frequency of occurrence for each parameter and a preset frequency threshold.

In some embodiments, the task sheet processing system may determine the frequency of occurrence of each parameter based on the number of occurrences of each parameter in the vector set and the total number of parameters in the vector set. For example, the task sheet processing system may count the total number of parameters contained in the vectors in the vector set and the number of occurrences of each parameter, and take as the frequency of occurrence of such parameters the ratio of the number of occurrences of each parameter (i.e., the number of vectors containing the parameter) to the total number of parameters. If the frequency of occurrence of a parameter is greater, it is stated that a task sheet will typically contain such a parameter (i.e., most of the task sheets contain such a parameter), whereas it is stated that a task sheet will typically not contain such a parameter (i.e., only a small number of task sheets will contain such a parameter). By way of example only, vector A contains n1 parameters, vector B contains n2 parameters, and vector C contains n3 parameters, and the frequency of occurrence of a parameter is the ratio of the number of occurrences of the parameter in vector A-C to (n1+n2+n3).

In some embodiments of the present disclosure, considering that the data structure (such as parameters included in the data structure) of a task sheet (such as an exposure task sheet) is usually preset, the data structure of different task sheets (such as task sheets of different departments and work types) may be different, and in addition, the generation rule of the vector corresponding to the task sheet may be different, for example, when a user does not set a certain task sheet parameter, the vector corresponding to the task sheet may not include the parameter, or may include the parameter in the vector by replacing the parameter value with a default value. The occurrence frequency of each parameter is determined through the occurrence times of each parameter in the vector set and the total number of the parameters, so that the overall distribution condition (such as the used condition) of the parameters in the vector set can be accurately reflected, and different application scenes (such as vector generation rules) can be adapted.

In some embodiments, the task sheet processing system may determine the frequency of occurrence of each parameter based on the number of occurrences of each parameter in the vector set and the total number of vectors in the vector set. For example, for each parameter, the task sheet processing system may count the total number of vectors N _vector (e.g., 100) in the vector set, and the number of occurrences of the parameter N _para in the plurality of vectors in the vector set, and based on the ratio of N _para to N _vector as the frequency of occurrence of the parameter. For example, the total number of vectors N _vector in the vector set is 100, and the number of occurrences N _para of a certain parameter P _i in the vector set is 80, which means that the parameter P _i appears in 80 vectors in the vector set, and the frequency of occurrence of the parameter Pi is 80/100=0.8.

In some embodiments of the present disclosure, the occurrence frequency of each parameter is determined by the occurrence number of each parameter in the vector set and the total number of vectors in the vector set, so that the actual distribution situation of each parameter in the vector (task list) can be reflected, and the calculation amount is reduced.

For each parameter, the task sheet processing system determines the parameter as a valid parameter in response to the frequency of occurrence of the parameter being greater than or equal to the frequency threshold. In response to the frequency of occurrence of the parameter being below a frequency threshold, it is determined to be an invalid parameter.

In some embodiments of the present invention, the effective parameters are determined by the occurrence frequency of the parameters, and only the common parameters in the task list need to be analyzed in the subsequent analysis, so that the analysis of the ineffective parameters which are not frequently generated is not needed, thereby reducing the calculation amount.

In some embodiments, the task sheet processing system may also determine valid parameters based on the frequency of occurrence of each parameter and its corresponding parameter weight. The parameter weight can be preset according to different work types, posts and the like. For example, for a job site construction job, parameters related to a job injury accident (such as whether an accident occurs, the number of wounded persons, etc.) may be set with a larger parameter weight.

In some embodiments of the present invention, it is considered that the frequency of occurrence of a part of the parameters is low, but the importance thereof or the degree of attention is high. By setting the parameter weight, although partial parameters are sporadic, parameters which are important in practical application are reserved, so that the effective parameters are more complete and accord with practical conditions.

In some embodiments, the task sheet processing system can be partitioned into multiple vector groups based on a set of vectors, where each vector group can be partitioned for a certain user population (e.g., a constructor population, an administrative population, etc.). For example, according to personnel parameters (such as the affiliated department ids of task executives) of each vector in the vector set, the vectors with the same affiliated department ids can be classified into a vector group, so that a plurality of vector groups corresponding to different department user groups can be obtained. The task sheet processing system may process each vector group as a vector set in step S410 to determine effective parameters corresponding to each vector group, and obtain a first model corresponding to a plurality of different user groups based on the following steps S420 to S440.

Step S420, determining a plurality of valid parameter value combinations based on the plurality of valid parameters, each valid parameter value combination including at least one personnel parameter value and at least one task sheet parameter value.

The valid parameter value combination is a combination of parameter values corresponding to valid parameters in the valid parameter combination. For example, for the valid parameter combination [ department, execution state ], the corresponding valid parameter value combination may be [ administrative department, not started ], [ financial department, completed ], [ Shi Gongbu, in progress ], etc. The administrative part, the financial part and the construction part are parameter values corresponding to the department parameters, and the parameter values corresponding to the execution state parameters are not started, completed and in progress.

Fig. 4b is a schematic diagram illustrating determining a first model according to some embodiments of the invention.

As shown in fig. 4b, the validity parameters 401 include a plurality of validity parameters { P ₁,P₂,P₃,……,P_n }. The plurality of effective parameters may be combined in 2 (e.g., [ P ₁,P₂ ]), in 3 (e.g., [ P ₁,P₂,P₃ ]), in n (e.g., [ P ₁,P₂,P₃,……,P_n ]). In some embodiments, each valid parameter combination includes at least one personnel parameter and at least one job ticket parameter. For example only, for the valid parameter combination [ P ₁,P₂,P₃ ], parameter P ₁ is a person parameter (e.g., person id), and parameter P ₂,P₃ is a task sheet parameter (e.g., task name, expected completion time).

In some embodiments, for each vector in the set of vectors, the task sheet processing system can determine whether each parameter in the vector is a valid parameter, respectively. And in response to a certain parameter being an inactive parameter, eliminating the parameter and the parameter value thereof in the vector, and generating an active vector based on the remaining parameters and the parameter values thereof in the vector. Based on this approach, a plurality of valid vectors corresponding to the vector set are determined. This mode may be referred to as a culling mode.

For each vector in the vector set, the task sheet processing system can determine a valid vector 412 based on the valid parameter 401, as well as the vector set. As shown in fig. 4b, the valid vector includes （V₁₁,V₂₁,V₃₁）、（V₁₁,V₂₂,NULL）,（V₁₂,V₂₂,V₃₁）,（V₁₁,NULL,V₃₁）.. It should be noted that NULL is used to represent the parameters and the parameter values thereof that are eliminated for each vector in the original vector set, and the obtained corresponding valid vector is （V₁₁,V₂₁,V₃₁）、（V₁₁,V₂₂）,（V₁₂,V₂₂,V₃₁）,（V₁₁,V₃₁）.V_ij to represent the j-th value of the parameter P _i.

In some embodiments, the task sheet processing system may screen a plurality of parameters corresponding to a plurality of effective parameters from each vector in the vector set, and generate an effective vector based on the screened parameters and parameter values corresponding to the plurality of parameters in the vector. And then determining a plurality of effective vectors according to a plurality of vectors in the vector set. This approach may be referred to as a screening approach.

For example, for each vector in the vector set, the task sheet processing system may determine a plurality of valid parameters included in the vector according to a set of valid parameters (such as valid parameter 401), where the plurality of valid parameters may be represented in a form of a valid parameter combination, for example ,[P₁,P₂]、[P₁,P₂,P₃]、[P₁,P₂,P₃,……,P_n]. the task sheet processing system may obtain a parameter value corresponding to each valid parameter of the valid parameter combination from the vector, to obtain a valid vector （V₁₁,V₂₁）、（V₁₁,V₂₁,V₃₁）、（V₁₁,V₂₁,V₃₁,……,V_n1）., where V ₁₁、V₂₁、V₃₁、……、V_n1 represents a parameter value corresponding to a single valid parameter P ₁、P₂、P₃、……、P_n, respectively. For example, V ₁₁ represents a parameter value (e.g., administrative part) corresponding to the parameter P ₁ (e.g., department), V ₂₁ represents a parameter value (e.g., not beginning) corresponding to the parameter P ₂ (e.g., execution state), and the other similar matters. Taking the first vector of the vector set as an example, when the vector only includes P ₁、P₂ and P ₃ in the valid parameters 401, the task list processing system obtains the parameter values V ₁₁、V₂₁、V₃₁ corresponding to P ₁、P₂ and P ₃ to generate a vector (V ₁₁,V₂₁,V₃₁) as a valid vector. The other vectors in the vector set are processed in a similar manner to yield a plurality of valid vectors 412 corresponding to the vector set.

In some embodiments, a task sheet processing system may determine a valid vector based on a plurality of valid parameters and their corresponding activation ranges and determine at least one valid parameter value combination based on the valid vector, wherein the plurality of valid parameter value combinations includes at least one valid parameter value combination corresponding to each vector.

The activation range is used to represent the valid range of a parameter value, which is valid when the parameter value is within the activation range. The activation range may be used to reflect whether the parameter value has an effect on the user's click event. For example, when a parameter value corresponding to a certain parameter is out of the activation range, a task sheet (e.g., an exposure task sheet) containing the parameter value is not typically clicked by the user.

In some embodiments, the activation range may be preset according to the type of parameter (e.g., an effective parameter). For example, the activation range corresponding to the task parameter "completion time" may be set to be from the last half year to the current time, and if the completion time of a certain task sheet is half a year ago, it indicates that the task sheet is not in the activation range. The task sheet is either very little or not viewed or clicked by the user.

In some embodiments, for each vector in the set of vectors, the task sheet processing system determines an initial valid vector for the vector based on a plurality of valid parameters. For example, the task sheet processing system processes the vector based on the culling or filtering method described above to obtain an initial valid vector (i.e., the non-valid parameter and the corresponding parameter value will be culled) composed of the valid parameters and their parameter values in the vector. Further, the task sheet processing system may further determine the valid vector based on an activation range corresponding to each valid parameter in the initial valid vector.

In some embodiments, if the parameter value of a certain effective parameter in the initial effective vector is not in the corresponding activation range, the effective parameter and the parameter value thereof are removed from the initial effective vector, and the rest effective parameters and the parameter value thereof generate effective vectors. Based on the principle, the task sheet processing system can obtain a plurality of effective vectors based on a plurality of initial effective vectors and the activation range corresponding to each effective parameter.

In other embodiments, for each of the initial active vectors, the task sheet processing system can also determine whether a parameter value corresponding to each active parameter in the initial active vector is within an activation range. In response to a value of a valid parameter not being within the activation range, the task sheet processing system can directly cull the initial valid vector. The plurality of initial significant vectors are processed based on this principle to obtain a plurality of significant vectors (i.e., the remaining initial significant vectors will be the significant vectors).

In some embodiments, the task sheet processing system may further determine the valid parameter value combinations 413 based on the parameter values corresponding to each valid parameter of each of the valid vectors 412. As shown in fig. 4b, the valid parameter value combination 413 includes [ V ₁₁,V₂₁]、[V₁₁,V₂₂]、……、[V₁₁,V₂₂,……,V_nk ], k referring to the number of parameter values of the valid parameter P _n. For example, the task sheet processing system first determines the valid parameter value combinations corresponding to each valid vector 412, and then de-duplicates the valid parameter value combinations to obtain the final valid parameter value combination 413. With further reference to fig. 4b, the first significant vector corresponds to a significant parameter value combination comprising [V₁₁,V₂₁]、[V₁₁,V₃₁]、[V₂₁,V₃₁]、[V₁₁,V₂₁,V₃₁], the second significant vector corresponds to a significant parameter value combination comprising [ V ₁₁,V₂₂ ], which significant parameter combinations do not overlap, all of which will be part of the significant parameter value combination 413.

Step S430, determining an impact factor for each valid parameter value combination based on the vectors in the vector set.

The impact factor is used to reflect the extent to which the combination of valid parameter values affects multiple user click lists. For example, the impact factor may be in the form of a numerical value, the smaller the value of which indicates the smaller the impact, otherwise the greater the impact.

In some embodiments, the task sheet processing system may determine the impact factor based on the number of occurrences and/or clicks for each valid parameter value combination. The number of occurrences of the valid parameter value combination refers to the number of occurrences of the valid parameter value combination in the valid vector 412.

For a certain valid parameter value combination, the more the occurrence number is, the more the number of times that the user accesses the task list containing the valid parameter value combination is, the larger the corresponding value of the influence factor is. For other users, the greater the probability that the task sheet containing the valid parameter value combination is accessed, the priority should be presented to the user.

For a certain effective parameter value combination, the more the number of clicks corresponding to a task sheet containing the effective parameter value combination is, the larger the corresponding influence factor is. For other users, the greater the probability that a task sheet containing the valid parameter value combination is clicked, the more preferentially presented to the user.

In some embodiments, the task sheet processing system may determine the impact factor for each valid parameter value combination based on the first reference weight corresponding to the number of occurrences and the second reference weight corresponding to the number of clicks. Wherein the second reference weight is greater than the first reference weight. For example, for two valid parameter value combinations that occur the same number of times, the larger the number of clicks, the larger the corresponding impact factor.

As shown in fig. 4b, the task sheet processing system can derive the impact factors 414 from each of the valid parameter combinations based on the valid parameter value combinations 413, respectively. The influence factor 414 includes influence factors F ₁, F ₂, and F _m, which correspond to the m valid parameter value combinations, respectively.

In some embodiments of the present invention, through a plurality of effective parameter value combinations and corresponding influence factors thereof, access and/or click conditions of a large number of users to a task list can be comprehensively evaluated from a large number of historical access data, so as to reflect preference conditions or attention degrees of a plurality of users or user groups to the task list, and further provide a good basis for priority presentation (such as sequencing) of the task list.

In some embodiments, for each of the plurality of valid parameter value combinations, the task sheet processing system may determine the number of times T _i of its occurrences in the vector set corresponding to the historical access data (e.g., the exposure task sheet) and the click result (e.g., the number of clicks D _i) of the exposure task sheet including the valid parameter value combination, and use the ratio of D _i to T _i as the impact factor corresponding to the valid parameter value combination, thereby obtaining the impact factor 414 corresponding to the plurality of valid parameter value combinations 413.

Step S440, determining a first model based on each valid parameter value combination and its influencing factors.

The first model may be in various forms such as a mathematical model. In some embodiments, the task sheet processing system may construct a ranking factor pair based on each valid parameter value combination and its corresponding impact factor, and generate the first model based on a set of ranking factor pairs derived from the plurality of ranking factors. For example, the first model may be represented as { G ₁:F₁,……,G_m:F_m }, where m is a positive integer greater than or equal to 1. In connection with fig. 4b, G ₁ represents the effective parameter value combination [ V ₁₁,V₂₁],F₁ represents the influence factor corresponding to G ₁, G _m represents the effective parameter value combination [ V ₁₁,V₂₁,……,V_nk],F_m represents the influence factor corresponding to G _m. The task sheet processing system can generate a first model 405 based on the valid parameter value combinations 413 and the impact factors 414.

In some embodiments, the task list processing system may determine a ranking policy of a plurality of task lists of any user according to the first model, and obtain a ranked task list based on the ranking policy, where a task list with a front ranking task list indicates a task list that may be more interesting or has a higher attention of the user. For example, the user may desire to obtain, from the server, a plurality of original task sheets currently requiring processing by the application program, and the task sheet processing system may perform multiple rounds of processing on the original task sheets based on the first model to determine the ordered list of task sheets.

As an example, the ranking factor pair F _max with the largest influence factor in the first model may be obtained first, and the valid parameter value combination G _max corresponding to the ranking factor pair is obtained, then, a plurality of candidate task lists including the valid parameter value combination G _max are matched from a plurality of original task lists, and the priority of the plurality of candidate task lists is set to be larger, where the priority ranks higher. And processing the plurality of original task lists and/or candidate task lists based on the process based on the plurality of sorting factors, so as to determine a sorted task list.

The task sheet processing system in turn presents the list of task sheets to the interface of the application program, thereby enabling the user to more quickly process (e.g., view, click operation) the task sheet.

According to the method and the device for processing the task list, the ordered task list of any user can be generated quickly through the first model, so that the user can locate, view and/or process the task list which is possibly focused more quickly, and the working efficiency is improved.

FIG. 5a is a schematic diagram illustrating the determination of a second model according to some embodiments of the invention.

In some embodiments, the task sheet processing system may determine a dedicated parameter value combination corresponding to the target user and a corresponding impact factor thereof based on the user identity information corresponding to the target user and the personnel parameter value in each of the valid parameter value combinations in the first model, and determine the second model based on the dedicated parameter value combination and the impact factor thereof.

In some embodiments, the first model is a first model that may be for all users. In other embodiments, the first model may be a target first model corresponding to a target user selected from a plurality of first models. The plurality of first models may be first models corresponding to a plurality of different user groups. The task sheet processing system may further determine a dedicated parameter value combination corresponding to the target user and a corresponding impact factor thereof based on the user identity information corresponding to the target user and the personnel parameter value in each of the valid parameter value combinations in the target first model, and determine the second model based on the dedicated parameter value combination and the impact factor thereof. More related content about the first model can be found elsewhere in the present invention (e.g., fig. 3 and 4 a).

The dedicated parameter value combination refers to a valid parameter value combination associated with the target user. For example, at least one of the personal parameter values in the dedicated parameter value combination is the same as the personal parameter value of the target user.

As shown in fig. 5a, the task sheet processing system can determine a dedicated parameter value combination 503 based on the valid parameter value combination 413 and user identity information 501 of the target user. The user identity information 501 of the target user may include, but is not limited to, a combination of one or more of the user id (e.g., 10), job number (e.g., 9001), name (e.g., dawn), department (e.g., administrative department), etc. of the target user.

In some embodiments, the task sheet processing system may match one or more personnel parameter values in the user identity information 501 of the target user with personnel parameter values in the valid parameter value combinations 413, thereby screening out h dedicated parameter value combinations 503 from the valid parameter value combinations 413, where h is less than or equal to m, as will be appreciated. The dedicated parameter value combinations 503 are a subset of the valid parameter value combinations 413. The task sheet processing system can further determine an impact factor 504 of the screened dedicated parameter value combination 503. For example, the dedicated parameter value combination g _h represents the dedicated parameter value combination [ V ₁₁,V₂₁,……,V_nh ], and the influence factor f _h represents the influence factor corresponding to the dedicated parameter value combination g _h. Wherein, in each of the screened dedicated parameter value combinations 503, there is at least one personnel parameter value identical to the personnel parameter value of the target user. For example, the value of the "department" parameter in each dedicated parameter value combination 503 is the same as the value of the "department" parameter of the target user. Further, the task sheet processing system can determine the second model 505 based on the dedicated parameter value combination 503 and the impact factor 504. The second model 505 is represented in a manner similar to the first model 405 and will not be described in detail herein. For example, the second model may be represented as { g ₁:f₁,……,g_h:f_m }.

The influence factor of the dedicated parameter value combination is used to reflect the extent to which the dedicated valid parameter value combination affects the target user's click on the task sheet. Because the special parameter value combination is screened from the effective parameter value combination of the first model, the influence factor corresponding to the special parameter combination in the first model can be directly used as the influence factor of the second model. That is, the influence factors of the dedicated parameter value combination in the first model and the second model are the same.

In some embodiments, the task sheet processing system may further obtain historical access data corresponding to the target user (e.g., an exposure task sheet corresponding to the target user), and determine, based on the historical access data corresponding to the target user, an impact factor corresponding to the dedicated parameter value combination by way of determining the impact factor of the first model. At this time, the influence factor of the dedicated parameter value combination in the first model and the influence factor in the second model are different.

In some embodiments of the present invention, the second model is matched with the identity of the target user by matching the identity information of the target user from the first model to the dedicated parameter value combination corresponding to the identity information of the target user. In addition, the historical access data corresponding to the target user is considered, so that the second model is more in accordance with the preference of the target user for the task list, and the second model is more accurate and more targeted.

In some embodiments, the task sheet processing system may update the second model corresponding to the target user based on the identity change information of the target user. For example, the task sheet processing system may obtain identity change information of the target user, determine an update-specific parameter value combination corresponding to the target user and an impact factor corresponding to the update-specific parameter value combination based on the identity change information, and update a second model corresponding to the target user based on the update-specific parameter value combination and the impact factor.

The identity change information comprises personnel parameters changed by the target user and updated personnel parameter values. For example, if the department of the target user changes, the identity change information may include the updated department of the target user. In some embodiments, the task sheet processing system can match the updated personnel parameter values with personnel parameter values in the valid parameter value combinations to screen out updated special parameter value combinations from the valid parameter value combinations. The manner of determining the updated dedicated parameter value combinations is similar to the manner of determining the dedicated parameter value combinations and is not described in detail herein.

In some embodiments, as shown in fig. 5b, the task list processing system may further obtain a target exposure task list 506 corresponding to the target user and target click data 507 of the target user on the target exposure task list, and update the second model 505 based on the target exposure task list 506 and the target click data 507, to obtain an updated second model 508.

The target exposure task sheet refers to a task sheet presented to a target user for browsing. The target click data is related to a target click event of the target user on the target exposure task sheet. The target click data includes click parameter values for the target click event. The target click data and the target click event are similar to the click data and the click event described in step S310, and will not be described again.

In some embodiments, the task sheet processing system may determine a target effective vector corresponding to the vector of the clicked target exposure task sheet based on the target click data, and determine a target effective parameter value combination based on the target effective vector. The combination of the target significant vector and the target significant parameter value is determined in a manner similar to the determination of the combination of the significant vector and the significant parameter value described above. Further, if the target valid parameter value combination is included in the dedicated parameter value combination of the second model 505, the task sheet processing system may increase the influence factor of the dedicated parameter value combination, thereby obtaining the corresponding updated influence factor. If the target valid parameter value combination is not included in the dedicated parameter value combination of the second model 505, the task sheet processing system can supplement the target valid parameter value combination as a new dedicated parameter value combination and determine its impact shadow based on its number of occurrences and/or number of clicks. In some embodiments, if the target valid parameter value combination is not included in the dedicated parameter value combination of the second model 505, the task sheet processing system may supplement the target valid parameter value combination as a new dedicated parameter value combination only after the number of target click events corresponding to the target valid parameter value combination exceeds a threshold.

In some embodiments, the job ticket processing system may set an update period to update the second model based on the target exposure job ticket and the target click data. The update period may be the same day, one week, etc. In some embodiments of the present invention, the second model may be updated periodically by setting an update period, while reducing the data processing load.

FIG. 6 is a schematic diagram of determining a recommended task sheet corresponding to a target user, according to some embodiments of the invention.

As shown in fig. 6, the task sheet processing system may determine a target task sheet parameter value 602 corresponding to each target task sheet based on target task sheet information corresponding to a target task sheet 601 related to a target user.

The target task sheet 601 may be one or more task sheets that are queried or retrieved by the target user. Or the target task sheet 601 may be a task sheet to be processed by the target user. The target task sheet information includes various types of information related to the target task sheet 601. The target task sheet parameter value 602 refers to the value of the task sheet parameter of the target task sheet 601. The target person parameter value refers to the value of the person parameter of the target user. For more details on the task sheet parameters, personnel parameters, see fig. 3 and its description.

In some embodiments, the task sheet processing system may determine a plurality of target parameter value combinations 603 corresponding to each target task sheet based on the target task sheet parameter values 602 and target person parameter values of the target user. The target parameter value combination 603 may be a combination of at least one of the target task sheet parameter values 602 and at least one of the target personnel parameter values, e.g., a combination of 2 parameter values, a combination of 3 parameter values, etc.

In some embodiments, the task sheet processing system can determine a recommended weight for each target task sheet based on the target parameter value combination 603 and the second model 505.

The recommendation weight may reflect the priority of the target task sheet, with a greater recommendation weight indicating that the target task sheet is preferentially presented to the target user. In some embodiments, the task sheet processing system may determine the recommended weight for each target task sheet based on the target parameter value combinations 603 by each dedicated valid parameter value combination in the second model 505 and its corresponding impact factor. For example, for each target task sheet, the task sheet processing system may match its corresponding target parameter value combinations with the dedicated valid parameter value combinations in the second model, determine which dedicated valid parameter value combinations are the same as the target parameter value combinations, and then determine the recommendation weights based on the impact factors of the dedicated valid parameter value combinations. The larger the impact factor corresponding to the combination of the special effective parameter values, the larger the recommended weight.

As shown in FIG. 6, the task sheet processing system can determine recommendation weights 604 based on the second model 505 and the target parameter value combinations 603. For example, the number of the cells to be processed, the recommendation weights 604 include recommendation weight 1 for target task sheet 1, recommendation weight 2 for target task sheet 2, recommendation weight n for target task sheet n. Further, the recommendation weights for the plurality of target task sheets may be ranked to obtain a plurality of recommendation task sheets 605. Recommended task sheet 1, recommended task sheet m as shown in fig. 6. In some embodiments, the plurality of recommended task sheets 605 may be target task sheets with recommended weights greater than a weight threshold. In some embodiments, the plurality of recommended task sheets 605 are target task sheets for m before the recommendation weight.

According to the method, the device and the system, the recommended task list corresponding to the target user can be determined quickly through the second model, so that the processing efficiency of the target user on the task list in actual work is improved.

It should be noted that the above description of the flow is only for the purpose of illustration and description, and does not limit the application scope of the present invention. Various modifications and changes to the flow may be made by those skilled in the art under the guidance of the present invention. However, such modifications and variations are still within the scope of the present invention.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the invention may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.

Meanwhile, the present invention uses specific words to describe embodiments of the present invention. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the invention. Thus, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the invention may be combined as suitable.

Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the invention is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of example, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the invention. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject invention requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations in some embodiments for use in determining the breadth of the range, in particular embodiments, the numerical values set forth herein are as precisely as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited herein is hereby incorporated by reference in its entirety. Except for the application history file that is inconsistent or conflicting with this disclosure, the file (currently or later attached to this disclosure) that limits the broadest scope of the claims of this disclosure is also excluded. It is noted that the description, definition, and/or use of the term in the appended claims controls the description, definition, and/or use of the term in this invention if there is a discrepancy or conflict between the description, definition, and/or use of the term in the appended claims.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present invention. Other variations are also possible within the scope of the invention. Thus, by way of example, and not limitation, alternative configurations of embodiments of the invention may be considered in keeping with the teachings of the invention. Accordingly, the embodiments of the present invention are not limited to the embodiments explicitly described and depicted herein.

Claims

1. A method of processing a task sheet, comprising:

Acquiring historical access data of a historical user, wherein each historical access data comprises an exposure task list and click data of each exposure task list;

Determining a vector set of the historical access data, wherein the vector in the vector set comprises personnel parameter values, task sheet parameter values and click parameter values, and the click parameter values are determined based on the click data;

determining a first model based on the set of vectors, the determining the first model based on the set of vectors comprising:

Determining a plurality of significant parameters based on the vectors in the set of vectors;

determining a plurality of valid parameter value combinations based on the plurality of valid parameters, each of the valid parameter value combinations including at least one of the personnel parameter values and at least one of the task sheet parameter values;

Determining an impact factor for each of the valid parameter value combinations based on the vectors in the vector set;

determining the first model based on each of the valid parameter value combinations and its impact factors;

Acquiring user access information of a target user, wherein the user access information comprises user identity information corresponding to the target user;

Determining a second model corresponding to the target user based on the user access information and the first model, wherein the determining the second model corresponding to the target user based on the user access information and the first model comprises:

Determining a special parameter value combination corresponding to the target user and an influence factor thereof based on the user identity information corresponding to the target user and the personnel parameter value in each effective parameter value combination in the first model, wherein the special parameter value combination refers to an effective parameter value combination associated with the target user, and at least one personnel parameter value in the special parameter value combination is the same as the personnel parameter value of the target user;

Determining the second model based on the dedicated parameter value combination and its influencing factors;

and determining a recommended task list corresponding to the target user based on the second model.

2. The method of claim 1, wherein the determining a plurality of validity parameters based on the vectors in the set of vectors comprises;

determining the occurrence frequency of each parameter based on the vectors in the vector set;

the plurality of valid parameters are determined based on the frequency of occurrence of each of the parameters and a preset frequency threshold.

3. The method of claim 1, wherein the determining a plurality of valid parameter value combinations based on the plurality of valid parameters comprises:

For each vector in the set of vectors,

Determining an effective vector based on the plurality of effective parameters and the corresponding activation ranges thereof;

And determining at least one effective parameter value combination based on the effective vectors, wherein the plurality of effective parameter value combinations comprise at least one effective parameter value combination corresponding to each vector.

4. The method of claim 1, wherein said determining an impact factor for each said valid parameter value combination based on a vector in said set of vectors comprises:

the influence factor is determined based on the number of occurrences and/or the number of clicks of each of the valid parameter value combinations.

5. The method according to claim 1, wherein the method further comprises:

acquiring a target exposure task list corresponding to the target user and target click data of the target user on the target exposure task list;

and updating the second model based on the target exposure task sheet and the target click data.

6. The method according to claim 1, wherein the method further comprises:

Acquiring identity change information of the target user;

Based on the identity change information, determining an updated special parameter value combination corresponding to the target user and an influence factor thereof;

And updating the second model corresponding to the target user based on the combination of the updated special parameter values and the influence factors thereof.

7. The method of claim 2, wherein the determining, based on the second model, the recommended task sheet corresponding to the target user comprises:

determining a target task list parameter value corresponding to each target task list based on target task list information corresponding to a target task list related to the target user;

Determining a plurality of target parameter value combinations corresponding to each target task sheet based on the target task sheet parameter values and target personnel parameter values of the target users;

determining a recommendation weight for each of the target task sheets based on the plurality of target parameter value combinations and the second model;

and determining the recommended task list based on the recommended weight.

8. A task sheet processing system, comprising:

The first acquisition module is configured to acquire historical access data of a historical user, wherein each historical access data comprises an exposure task sheet and click data of each exposure task sheet;

A vector set determination module configured to determine a vector set of the historical access data, the vectors in the vector set comprising personnel parameter values, task sheet parameter values, and click parameter values, wherein the click parameter values are determined based on the click data;

The system comprises a vector set, a first model determining module configured to determine a first model based on the vector set, the first model determining module further configured to:

The second acquisition module is configured to acquire user access information of a target user, wherein the user access information comprises user identity information corresponding to the target user;

the system comprises a target user, a second model determining module configured to determine a second model corresponding to the target user based on the user access information and the first model, and a second model determining module further configured to:

And the recommending module is configured to determine a recommending task list corresponding to the target user based on the second model.