Predicting downstream planning effects of user task assignment

Abstract

Techniques for managing task assignments to workers in a work environment are disclosed. The system identifies one or more workers having eligibility that matches the recommended eligibility for performing the task in the work environment. The system applies the trained machine learning model to task execution data associated with the worker, such as a past history of executed tasks and statistics associated with execution of the tasks. The machine learning model generates predictions of downstream actions associated with assigning tasks to users. Downstream effects include delays and performance improvements in subsequent tasks performed by workers at a work center in a work environment as well as effects on tasks performed by other workers.

Description

[0001] Statement of Interest; Related Applications; Incorporation by Reference

[0002] This application claims priority to and incorporates by reference the following (4) U.S. patent applications:

[0003] U.S. application No. 18 / 446,375, filed on August 8, 2023;

[0004] U.S. application No. 63 / 416,504, filed on October 15, 2022;

[0005] U.S. application Ser. No. 18 / 359,930, filed on July 27, 2023; and

[0006] U.S. application No. 18 / 343,612 filed on June 28, 2023. Technical Field

[0007] The present disclosure relates to work center resource network integration. In particular, the present disclosure relates to operations and user interfaces for predicting the downstream effect of assigning a specific work center task to a specific user. Background Art

[0008] In a physical facility, such as a manufacturing plant, workers at many different workstations interact with equipment to perform tasks on materials, such as product components. Many different events may lead to poor performance of a manufacturing facility. For example, a worker's productivity decline may cause subsequent tasks to be delayed and products to be delivered on time. Equipment damage may cause a work center to temporarily stop functioning, resulting in delays to any tasks that depend on the work center. Tracking multiple different performance metrics across a manufacturing facility may be a complex data collection and analysis process. Identifying the source of a problem, whether it is a failure or an inability to meet a specified performance metric, may be even more challenging. Determining how to reorder tasks performed by workers to improve performance metrics based on the identified problems adds another layer of complexity. Task managers may not have a clear idea of ​​how rescheduling tasks will affect other tasks or how much effect these changes will have on improving the performance metrics that task managers care most about. For example, if a machine fails, a task manager can send materials to another work center so that another worker performs the task. However, task managers may not be able to predict how much effect the change will have on overall equipment utilization, worker utilization, or on-time delivery. Task managers may also not be able to predict the repercussions that task reordering may have on additional tasks at other work centers.

[0009] The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Embodiments are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings. It should be noted that references to "one" or "an" embodiment in the present disclosure do not necessarily refer to the same embodiment, and mean at least one. In the drawings:

[0011] Figure 1 illustrates a system according to one or more embodiments;

[0012] Figure 2A-2B illustrates a set of example operations for predicting downstream effects of assigning tasks to users in a work environment in accordance with one or more embodiments;

[0013] Figure 3 illustrates a set of example operations for training a machine learning model to predict downstream effects of task assignments in accordance with one or more embodiments;

[0014] Figure 4A-4C An example embodiment for implementing qualification-based task management is illustrated; and

[0015] Figure 5 A block diagram illustrating a computer system in accordance with one or more embodiments is shown. DETAILED DESCRIPTION

[0016] In the following description, for the purpose of explanation, many specific details are set forth to provide a thorough understanding. One or more embodiments may be practiced without these specific details. Features described in one embodiment may be combined with features described in different embodiments. In some examples, well-known structures and devices are described in reference block diagram form to avoid unnecessarily obscuring the present invention.

[0017] 1. General Overview

[0018] 2. System Architecture

[0019] 3. Generate predictions about downstream effects of task assignments to users

[0020] 4. Train the machine learning model

[0021] 5. Example Embodiments

[0022] 6. Computer Networks and Cloud Networks

[0023] 7. Miscellaneous; Extensions

[0024] 8. Hardware Overview

[0025] 1. General Overview

[0026] One or more embodiments use machine learning models to predict the downstream effects of assigning tasks to users. A work environment can have many different work centers. Each work center can be utilized by multiple different workers to perform different tasks at various times or even simultaneously. The speed and efficiency with which a worker is able to perform tasks affects downstream tasks performed by the same worker and by different workers at both the same work center and other work centers. Therefore, assigning a specific task to a specific worker affects the worker's task plan, the task plan associated with the work center where the task is performed, and the task plans of other workers who perform tasks at other work centers. The machine learning model predicts the downstream effects of assigning tasks to users on the task plans of multiple workers at multiple work centers.

[0027] One or more embodiments use a data set describing user attributes, work center attributes, and material attributes to train a machine learning model. For example, the machine learning model identifies the relationship between a user's experience in performing a task or using specific equipment and materials and the time required for the user to complete the task. As an example, the system can detect a fault in a piece of equipment. The system can identify two users with qualifications that match the user qualifications recommended for the task of repairing the equipment. Based on (a) the user's work history, (b) the user's proficiency specified in the user's profile, and (c) the planning data of additional tasks to be performed in the work environment, the work center available for performing the task, and other users available for performing the task, the system predicts the downstream effects of assigning the repair task to the first user and the second user. The predicted downstream effects take into account not only which tasks are performed at various work centers, but also which workers are assigned to the tasks at the work center. The system can predict that the first user can perform the task 10% faster than the second user at the same work center. However, the system can further predict that assigning the task to the first user results in additional delays in tasks performed by other users in the work environment. The system can determine that those additional delays result in a 10% reduction in weekly on-time delivery from the work environment compared to when the repair task is assigned to the second user. The system may further determine that assigning the task to the first user results in an increased financial cost of 1%, thereby reflecting overtime pay to the first worker who performed the task. One or more embodiments avoid assigning a task to a user based on determining that a downstream effect of the assignment results in a cost (or negative performance indicator) that exceeds a threshold. In addition, or in the alternative, the system may present two or more options for assigning a task to a user to allow the administrator to select a user to assign the task to.

[0028] One or more embodiments described in the specification and / or recited in the claims may not be included in this general summary.

[0029] 2. System Architecture

[0030] Figure 1 A system 100 is illustrated according to one or more embodiments. Figure 1 As shown in FIG. 1 , the system 100 includes a work environment management platform 110 and a data warehouse 130. The work environment management platform 110 monitors and manages operations in the work environment 120. As an example, the work environment can be a manufacturing facility. The facility includes work centers 121a-121n. Each work center includes a set of equipment 122a-122n. One work center can be a component assembly work center. At the component assembly work center, workers can assemble components from materials 123. The components assembled at one work center can be materials required to perform additional tasks at another work center. Another work center can be a component testing work center. The component testing work center can include equipment for testing components assembled at the component assembly work center to check for defects or failures in the components. Another work center can be a quality assurance work center. The quality assurance work center can include equipment for running tests on components assembled at the component assembly work center and tested at the component testing work center to ensure that the components meet the specifications. One or more workers 124 can be assigned to work at a specific work center 121a-121n. When a worker 124 logs into a terminal at a work center, the terminal identifies the tasks to be performed by the worker 124. The terminal may also grant and deny access to equipment 122a-122n at the work center according to the worker's assigned tasks and authorization level.

[0031] According to one or more embodiments, the work environment 120 includes work centers 121a-121n associated with a variety of equipment 122a-122n. The work center may include user terminals, test equipment, manufacturing equipment (e.g., saws, drills, etc.), or any other equipment used to manufacture, assemble, and test components. Different types of equipment require different qualifications for workers 124 to operate the equipment. One or more embodiments analyze worker qualifications to manage worker access to equipment associated with the work center.

[0032] According to one or more embodiments, different workers 124 may access terminals at the same work center 121a-121n. When a first worker logs into a terminal, the task management engine 112 may obtain user identification information, map the user identification information to stored user qualification information, and identify one or more tasks that the first worker is authorized and / or qualified to perform at the work center. When a second worker logs into the same terminal, the task management engine 112 may identify a different set of tasks that the second worker is authorized and / or qualified to perform at the work center.

[0033] In an embodiment, the work environment management platform 110 is implemented on one or more digital devices. The term "digital device" generally refers to any hardware device including a processor. A digital device may refer to a physical device that executes an application or a virtual machine. Examples of digital devices include computers, tablet computers, laptops, desktops, netbooks, servers, web servers, network policy servers, proxy servers, general machines, specific function hardware devices, hardware routers, hardware switches, hardware firewalls, hardware firewalls, hardware network address translators (NATs), hardware load balancers, mainframes, televisions, content receivers, set-top boxes, printers, mobile handheld terminals, smart phones, personal digital assistants ("PDAs"), wireless receivers and / or transmitters, base stations, communication management devices, routers, switches, controllers, access points, and / or client devices.

[0034] The work environment management platform 110 includes a work environment monitoring engine 111 to monitor the properties of the work environment 120. The work environment monitoring engine 111 can monitor worker status data, equipment status data, material status data, and other work environment data. Monitoring worker status data can include detecting worker login / logout at a work center terminal, detecting a worker's selection at a work center terminal to start or pause a task or indicate that a specific task has been completed, and detecting a notification requesting a specific worker at a specific work center 121a-121n in the work environment 120. Monitoring equipment status data can include monitoring whether a piece of equipment is operational or out of service (e.g., in a faulty state), monitoring the calibration status of the equipment, and monitoring whether the equipment is being used and how long the equipment has been used. Monitoring material data can include detecting the location of materials in the work environment 120 and detecting the amount of materials available in the work environment. As an example, a work center can include a box containing materials for assembling equipment. When a user removes materials from the box, a sensor can detect a change in the weight of the box. The work environment monitoring engine 111 can calculate the amount of materials in the box based on the weight of the box.

[0035] Monitoring the work environment includes detecting anomalies and failures in the work environment. For example, the work environment monitoring engine 111 may receive a notification from a piece of work equipment 122a at a work center 121a that the equipment is not functioning properly. In addition, the work environment monitoring engine 111 may monitor the performance statistics of the workers 124 to determine that a particular worker at a work center 121n is not performing tasks at an expected rate, resulting in delays in the work environment 120.

[0036] The task management engine 112 generates and manages the assignment of tasks to workers 124 in the work environment 120. The task management engine 112 detects that a worker logs into a work center 121a-121n. The task management engine 112 analyzes (a) worker data 133, including worker qualifications, (b) equipment data 135 corresponding to equipment available at the work center 121a to which the worker logs in, and (c) material data 134 of materials available at the work center 121a to which the worker logs in. Based on the worker data 133, the equipment data 135, and the material data 134, the task management engine 112 presents a subset of tasks to be performed by the worker at the work center 121a from a set of available tasks 131 to the worker at the work center 121a. In one or more embodiments, the task management engine 112 generates a subset of tasks taking into account dependencies among the tasks. For example, repairing a piece of faulty equipment 122a may include two tasks performed by two different experts, one task following the other. When the second expert logs into the work center 121a, the task management engine 112 determines whether the first task in the set of two consecutive tasks has been completed by another expert. If so, the task management engine 112 displays the second task to the second expert to be performed. If the first task has not been completed, the task management engine 112 can avoid displaying the second task to the second expert. Additionally, or as an alternative, the task management engine 112 can generate a notification to one or both of the first expert and the second expert indicating that the first task has not been completed.

[0037] According to one or more embodiments, a manager or administrator can access the work environment management platform 110 via interface 117 to view task pools, task assignments, and worker qualifications. In one or more embodiments, interface 117 refers to hardware and / or software configured to facilitate communication between a user and the work environment management platform 110. Interface 117 presents user interface elements and receives input via user interface elements. Examples of interfaces include graphical user interfaces (GUIs) 118, command line interfaces (CLIs), tactile interfaces, and voice command interfaces. Examples of user interface elements include check boxes, radio buttons, drop-down lists, list boxes, buttons, toggle keys, text fields, date and time selectors, command lines, sliders, pages, and forms.

[0038] In one embodiment, different components of interface 117 are specified in different languages. The behavior of user interface elements is specified in a dynamic programming language such as JavaScript. The content of user interface elements is specified in a markup language such as Hypertext Markup Language (HTML) or XML User Interface Language (XUL). The layout of user interface elements is specified in a style sheet language such as Cascading Style Sheets (CSS). Alternatively, interface 117 is specified in one or more other languages ​​such as Java, C or C++.

[0039] According to one embodiment, the task configuration display engine 113 displays a representation of the task plan in the GUI 118. For example, the task configuration display engine 113 can generate data to display a Gantt chart in the GUI 118. The Gantt chart can include a work center and (a) a task that has been executed and (b) a representation of the task that is planned to be executed at the corresponding work center. The task configuration display engine 113 can generate a Gantt chart based on worker attributes. For example, the task configuration display engine 113 can access the worker plan to predict which workers are assigned to work at different work centers. The task configuration display engine 113 can modify the visual representation of the task in the GUI 118 based on which workers are assigned or predicted to perform the task. For example, the work environment 120 can include a component assembly work center. Two different workers can be assigned to work at the component assembly work center. One of the workers may have a higher efficiency rating than the other. Therefore, the task configuration display engine 113 can display a first set of tasks completed by a first worker at the work center, and a second set of tasks completed by a second worker at the work center in the Gantt chart. The task configuration display engine 113 may display more tasks in the first set of tasks than in the second set of tasks, thereby indicating a prediction that the first worker will complete more tasks than the second worker in the same time period based on the first worker's higher efficiency rating.

[0040] According to one or more embodiments, the performance metric calculation engine 114 calculates performance metrics in the work environment 120. The performance metrics include metrics for a particular worker 124, metrics for a work center 121a-121n, and metrics for the entire work environment 120. The performance metric includes a quantitative measure of the performance of one or more of (a) a worker, (b) a work center, and / or (c) a work environment. Examples of performance metrics include utilization (or the percentage of time a piece of equipment is in use) for a particular work center and / or across the work environment, task completion time for a particular task, on-time delivery statistics, efficiency statistics, cost estimation statistics, and / or overall equipment effectiveness (OEE) statistics for a worker, work center, and / or work environment.

[0041] According to one embodiment, the task configuration display engine 113 displays one or more worker task assignment selection blocks corresponding to one or more workers that the administrator can select to perform the task and a visual representation of the task plan for the task to be performed at the work center 121a-121n in the GUI 118. The task configuration display engine 113 can display a Gantt chart diagram in one area of ​​the graphical user interface and display the worker task assignment selection blocks in another area. For example, the worker task assignment selection blocks can be located above, below, or to the side of the representation of the work environment.

[0042] According to an example embodiment, the system stores task data for each available task 131, including task parameters and candidate users who are authorized and / or qualified to perform the task. For example, for task 137, the task management engine 112 identifies (a) task parameters 138, including (b) recommended user qualifications 139. The task management engine 112 identifies a pool of candidate users 140 whose qualifications match the recommended user qualifications 139 for task 137 from the workers included in the worker data 133. The task management engine 112 stores identification information of the candidate users 140 in a data object corresponding to the task 137. For example, the task management engine 112 may store an employee identification number and / or an employee name in a field or group of fields in the data object representing the task 137.

[0043] The task management engine 112 identifies a set of candidate users 140 to present to the administrator in the GUI 118. For example, the task management engine 112 may identify performance indicators and / or qualification matches associated with the users and the tasks 137 to identify a set of candidate users 140 to present to the administrator. As an example, the task 137 may store identification information of four candidate users 140 who are authorized and qualified to perform the task 137. Based on the qualification matches and performance indicators associated with the users, the task management engine 112 may identify two users from the candidate users 140 to present to the administrator. The task configuration display engine 113 displays two worker task allocation selection blocks corresponding to the two identified workers in the GUI 118.

[0044] In one or more embodiments, the task management engine 112 applies the machine learning model 116 to a set of (a) task planning data 136 and (b) worker data 133 to predict the downstream effects of assigning a particular task to a particular worker. The task planning data 136 includes information about which tasks are predicted to be performed by different workers at different work centers within a specific time period. The worker data 133 includes: worker qualification data, such as the worker's certification, demonstrated proficiency in specific equipment, and educational background; historical task assignments, completed tasks, and task execution results; performance indicators, such as the worker's efficiency in completing tasks, the worker's success rate in completing assigned tasks, and the recidivism rate (e.g., if the task involves modifying equipment, how long it takes until the same task needs to be performed on the same equipment); worker planning data, such as the worker's availability; and worker profile data, such as the identity of the worker's manager or supervisor, the worker's position on the organizational chart, and the worker's pay rate (e.g., both the base pay rate and any additional pay data, such as whether the assignment of a task to the worker results in overtime or bonuses).

[0045] The machine learning engine 115 trains the machine learning model 116 to predict the downstream effects associated with assigning a specific task to a specific worker. In some examples, one or more elements of the machine learning engine 115 can use a machine learning algorithm to train the machine learning model 116 using historical task planning data and worker performance data. A machine learning algorithm is an algorithm that can be iterated to learn a target model f that best maps a set of input variables to output variables using a set of training data. The machine learning algorithm can include a supervised component and / or an unsupervised component. Various types of algorithms can be used, such as linear regression, logistic regression, linear discriminant analysis, classification and regression trees, naive Bayes, k-nearest neighbors, learning vector quantization, support vector machines, bagging, random forests, boosting, back propagation, and / or clustering.

[0046] In an embodiment, a set of training data includes a data set and an associated label. The data set is associated with the input variables of the target model f (e.g., worker qualifications, task parameters, history of tasks performed by workers, quality scores associated with tasks performed by workers). The associated labels are associated with the output variables of the target model f (e.g., the time required for different workers at different work centers in a work environment to perform downstream tasks). The training data can be updated based on, for example, feedback on the accuracy of the current target model f. The updated training data is fed back into the machine learning algorithm, which in turn updates the target model f.

[0047] The machine learning algorithm generates a target model f such that the target model f best fits the dataset of training data to the labels of the training data. Additionally, or alternatively, the machine learning algorithm generates the target model f such that when the target model f is applied to the dataset of training data, a maximum number of results determined by the target model f match the labels of the training data.

[0048] Based on the prediction of the ML model 116, the task configuration display engine 113 displays the predicted downstream effects of assigning the task to a specific worker 124. For example, the task configuration display engine 113 displays a Gantt chart with a specific predicted time to complete the task for the selected worker and other workers at different work centers in the work environment. In addition, the performance indicator calculation engine 114 calculates performance indicators associated with the predicted downstream effects. For example, based on the ML model prediction, the task configuration display engine can display two worker task assignment selection blocks corresponding to two workers. The first block can display a performance indicator: "Utilization: 80%; On-time delivery: 75%". The second block can display a performance indicator: "Utilization: 70%; On-time delivery: 80%". Based on detecting that the administrator selects the first block, the task configuration display engine 113 displays a Gantt chart in which the task is assigned to the first worker. The Gantt chart displays the downstream effects of the task performed at different work centers based on the ML model prediction of assigning the task to the first worker. Based on detecting that the administrator selects the second block, the task configuration display engine 113 displays a Gantt chart in which the task is assigned to the second worker. The Gantt chart diagram shows the downstream effects of the tasks performed at different work centers based on the ML model prediction of assigning the task to the second worker. The task GUI 118 may include a task assignment confirmation button to allow the administrator to confirm the assignment of the task to a specific worker. Based on the assignment, the work environment management platform 110 generates a set of instructions to assign the task to the specific worker. The instructions may also include the assignment of other tasks to other workers. For example, when assigning a specific task to a specific worker requires offloading other tasks assigned to the specific worker, the instructions may include the assignment of other tasks to other workers.

[0049] In one or more embodiments, system 100 may include Figure 1 More or fewer components than those shown in the illustrations. Figure 1 The components illustrated in may be local or remote from each other. Figure 1 The components illustrated in the figure may be implemented in software and / or hardware. Each component may be distributed across multiple applications and / or machines. Multiple components may be combined into one application and / or machine. The operations described with respect to one component may be performed by another component instead.

[0050] Additional embodiments and / or examples related to computer networks are described below in Section 6 entitled “Computer Networks and Cloud Networks”.

[0051] In one or more embodiments, the data warehouse 130 is any type of storage unit and / or device for storing data (e.g., a file system, a database, a collection of tables, or any other storage mechanism). Further, the data warehouse 130 may include multiple different storage units and / or devices. Multiple different storage units and / or devices may or may not be of the same type or located at the same physical site. In addition, the data warehouse 130 may be implemented or executed on the same computing system as the work environment management platform 110. Alternatively, or in addition, the data warehouse 130 may be implemented or executed on a computing system separate from the work environment management platform 110. The data warehouse 130 may be coupled to the work environment management platform 110 via a direct connection or via network communication.

[0052] Information describing a set of tasks available for execution in work environment 120, task plan metrics 132, worker data 133, material data 134, equipment data 135, and historical task plan data 136 may be implemented across any component within system 100. However, for purposes of clarity and explanation, this information is illustrated within data warehouse 130.

[0053] In one or more embodiments, the work environment management platform 110 refers to hardware and / or software configured to perform the operations described herein for recommending and implementing a task plan for a work environment. An example of the operations for recommending and implementing a task plan for a work environment is described below with reference to FIG.

[0054] 3. Generate predictions about downstream effects of task assignments to users

[0055] Figure 2A and Figure 2B An example set of operations for reordering work center tasks is illustrated in accordance with one or more embodiments. Figure 2A and Figure 2B One or more of the operations illustrated in the figure may be modified, rearranged or omitted altogether. Figure 2A and Figure 2B The particular sequence of operations illustrated in the drawings should not be construed as limiting the scope of one or more embodiments.

[0056] The system identifies a set of task parameters associated with a task to be performed by one or more users at one or more work centers in a work environment (operation 202). The task parameters include: equipment required to perform the task, equipment that may need to be operated on to perform the task (such as equipment in a faulty state), materials required to repair the fault, and user qualifications required to repair the fault.

[0057] For example, a task may include assembling an assembly. Task parameters may include: (a) the subassemblies required for assembly, (b) the machines required to weld the subassemblies together, and (c) user qualifications indicating proficiency in operating the machines.

[0058] According to another example, the work environment monitoring platform can identify a failure of a piece of equipment in the work environment. The system can generate a task to repair the failure. Task parameters may include: (a) the equipment in the failed state, (b) the tools or equipment required to repair the failure, and (c) user qualifications indicating proficiency with the equipment in the failed state and the tools required to repair the failure.

[0059] According to one embodiment, the system identifies the task parameters of the task in response to a triggering event. For example, the task may have been assigned to a user or a pool of potential users who subsequently become unavailable to perform the task. According to an alternative example, the task may be a newly created task. For example, after detecting a failure in the working environment, the system may generate a new task and determine the task parameters of the new task. According to yet another example, the task may be an existing task in a task management system, and a manager or administrator may interact with a user interface to change the assignment of the task from one user or user pool to a different user.

[0060] The system determines whether the task parameters match the user's qualifications (operation 204). According to one embodiment, the system accesses a database to retrieve the user's qualifications. Qualifications can be defined as a function of one or more of the following: certifications, work history, training, education, and any other expertise recorded in the database.

[0061] If the task parameters do not match the user qualifications of any user identified in the database, the system may (a) avoid assigning the task to the user, or (b) identify one or more alternative task parameters and / or user qualifications for assigning the task (operation 206). According to an example embodiment, if the system avoids assigning the task to the user, the system may generate a notification to the administrator, thereby marking the task for the administrator's attention. The administrator may then determine whether to assign the task to the user or modify the task parameters. The system may provide the administrator with a visual representation of available workers and / or a visual representation of recommended task requirements. The system may display fields in the GUI for the administrator to select from among the available workers to assign the task to.

[0062] According to one or more embodiments, identifying alternative task parameters for a task may include applying the task parameters to a machine learning model to identify one or more alternative task parameters for the task. For example, when the system identifies a task for which the system does not detect a match between the recommended qualifications and the user qualifications, the system applies a set of task parameters to the machine learning model to identify a user or a pool of candidate users to whom the task is to be assigned. The machine learning model can learn the relationship between the task description and the terms in the user profile, the historical tasks performed by the user, the educational information associated with the user, or the training of the user. The system can determine that the machine "M100" needs maintenance. However, no user may have the qualifications that include the "M100" model. For example, searching the database for user experience, training, and work history may not return results for "M100". The machine learning model can learn during training that users who are qualified to perform maintenance on the "P300" model have a high success rate for servicing the "M100" model. Therefore, the machine learning model can generate a recommended qualification that includes the term "P300". When the system searches for "P300" in the database, the system identifies three workers who may be qualified to perform maintenance on the M100 model. Therefore, the system adds a new task to the task management platform to perform maintenance on the M100 machine, including the user qualification "P300 Maintenance". As another example, the machine learning model can learn that a particular manager relies on a particular user to perform maintenance on every machine in the manager's organization with a high success rate. Therefore, the machine learning model can generate a recommended user qualification for a particular task, including the user's name or other identifying information, such as "Department B, Employee Type: Service Technician".

[0063] Based on determining that the qualifications of one or more users match the recommended user qualifications for performing the task, the system matches the task with the user (operation 208). For example, the system can store one or more tables representing the tasks in a database. The system can store user identification information in a table associated with the task that matches the qualifications of the corresponding user.

[0064] The system selects a candidate user from a group of candidate users associated with a particular task (operation 210). According to one embodiment, the system selects a candidate user in response to a trigger event (such as the trigger event described above in connection with operation 202). For example, a task may have been assigned to a user or a pool of potential users that subsequently becomes unavailable to perform the task. According to an alternative example, a manager or administrator may interact with a user interface to change the assignment of a task from one user or user pool to a different user. In an embodiment where the system stores a data object representing a task in a database, the system may obtain identification information for the candidate from a specific field in the data object.

[0065] The system applies the trained machine learning model to the task plan data and the user data to predict the downstream effects of assigning the task to the candidate user to perform (operation 212). The downstream effects include the costs associated with assigning the task to the candidate user and the benefits obtained from assigning the task to the candidate user. Examples of downstream effects include: the use of equipment at the work center based on the user (a) performing the task at the work center, or the use of equipment at the work center based on the user (b) being moved to another work center to perform a task on equipment at other work centers; the use of materials based on (a) the user using materials to perform the task, or based on (b) the user performing a task that does not require materials, which the user might have used if the task was not assigned to the user; the delay of a set of tasks that the user would have performed if the user had not been assigned the task; the delay (or improvement in execution time) of a set of tasks that depend on the task assigned to the user; the modification of the availability of the user to perform other tasks; the modification of the availability of equipment based on the user being assigned the task; and the modification of the availability of materials based on the user being assigned the task. The machine learning model receives as input not only the identity of the user but also user attributes (including attributes such as expertise with specific equipment, historical success rate of performing tasks, degree of similarity between task parameters and user qualifications, and equipment attributes such as the location and type of equipment that are affected by the user's task assignment).

[0066] Some downstream effects can be measured by performance indicators. Performance indicators include quantitative measures of the performance of one or more of (a) workers, (b) work centers, and / or (c) work environments. Examples of performance indicators include utilization (or the percentage of time a piece of equipment is in use) for a particular work center and / or across the entire work environment, task completion time for a particular task, on-time delivery statistics, efficiency statistics, cost estimation statistics, and / or overall equipment effectiveness (OEE) statistics for workers, work centers, and / or work environments. Thus, the system can calculate one or more performance indicators based on assigning tasks to users.

[0067] According to one example, a machine learning model learns that, for a particular worker, a particular piece of equipment has a correlation with a particular piece of equipment. Based at least in part on the correlation, the machine learning model can predict a set of downstream effects from assigning tasks corresponding to the equipment to the worker, including (a) an increase in productivity from the work center in which the equipment is located, and (b) a decrease in productivity from the worker's regular work center. The machine learning model can also predict that, for a different worker, productivity from the work center in which the equipment is located will also increase, but the increase will be smaller than if the earlier worker were present, based at least in part on the later worker's reduced historical success rate.

[0068] According to another example, a machine learning model predicts that assigning a task to a particular worker results in: (a) a delay in the time for the worker to move from one work center to another to perform the task, (b) a delay in product output for a set of components that the worker will assemble in a set of corresponding component assembly tasks, and (c) an improvement in product output of components output from the work center to which the worker was moved to perform the task based on the worker reconfiguring a piece of equipment. The system can predict a reduced efficiency performance indicator for the worker, corresponding to the time the worker is predicted to reconfigure the equipment at the new work center, during which the worker will not assemble components at the worker's original work center. The system can predict an improved efficiency performance indicator for the work center at which the worker reconfigures the equipment. The system can also predict an on-time delivery performance indicator that includes an overall improvement in the work environments of both work centers based on the worker's reconfiguration of the equipment.

[0069] The system determines whether the calculated performance metric satisfies a threshold (operation 216). For example, the system can apply a set of rules that specify that a task should be associated with a user only if the result pair will correspond to an improvement in an OEE performance metric or an improvement in on-time delivery across a work environment. As another example, the system can apply a set of rules that specify that a task should be associated with a user only if the result pair corresponds to the same or improved equipment utilization performance metric when compared to the equipment utilization performance metric before the task was assigned to the user.

[0070] If the system determines that the predicted performance indicator does not meet the threshold, the system may not store the candidate / task pair (operation 218). On the other hand, if the system determines that the predicted performance indicator meets the threshold, the system stores the candidate / task pair (operation 220).

[0071] The system determines whether there are additional candidates among the candidate user group (operation 222). If there are no additional candidates, the system displays one or more candidates and corresponding performance indicators in a graphical user interface (GUI) for user selection (operation 224).

[0072] The system detects whether a selection has been made in association with the task (operation 226). For example, the administrator may access a user interface that gives the administrator the ability to match users to tasks. The system may detect that the administrator selected a user for a task. For example, the administrator may interact with buttons and / or fields in the GUI to select or drag and drop an element representing a user to assign the user to an element representing a task.

[0073] According to an alternative embodiment, the system detects the selection without any action from the administrator. For example, a worker logs into a terminal at a work center to obtain a task group that the worker can perform at the work center. Login can include manually typing a user identification, swiping a user identification card, or detecting the user's identification via a facial recognition application. The worker can be in a candidate user pool with qualifications that match the recommended user qualifications corresponding to the task. The system can analyze the task group that can be used for the worker to perform at the work center. Based on determining that the worker is among the candidate user group that can perform the task, the system can select a user from the candidate user group to pair with the task without further user intervention. If another worker from the user pool logs into their work center after the system has assigned the task to the worker, the system can avoid selecting a subsequent worker to perform the task.

[0074] Based on the selection of the candidate user to perform the task, the system assigns the selected candidate to the task (operation 228). For example, if the system detects the administrator's selection of a specific user to perform a specific task, the system assigns the task to the user in the task management system. When the user logs into the terminal at the work center, the task appears on the display screen for the user to perform. When other candidate users log into the work terminal, the task does not appear as an option for other candidate users to perform.

[0075] In an example embodiment in which the system assigns a task to a pool of candidate users with qualifications that match the recommended user qualifications for the task, the system may assign the task to the first candidate user who logs into the work center terminal and / or selects the task from a set of available tasks to perform. For example, if the first candidate first logs into the work center terminal, the system may assign the task to the first candidate user. If the second candidate logs into the work center terminal after the first candidate, the system may not display the task as available for the second candidate to perform. On the other hand, if the second candidate registers to the terminal before the first candidate, the system may assign the task to the second candidate. Then, when the first candidate logs into the work center terminal, the system may avoid displaying the task as available for the first candidate.

[0076] The system updates the display of the task plan representing the tasks to be performed in the work environment to reflect the downstream effects of assigning the tasks to the candidates (operation 230). For example, the system can display a Gantt chart for the administrator, which illustrates the work centers in the work environment and the tasks to be performed at the work centers. The representation of the tasks performed at the work centers takes into account which workers are assigned to work at each work center. Based on the detection of the assignment of a specific task to a specific worker, the system updates the display representing the task plan to reflect the assignment. For example, if the task assignment includes moving workers between two work centers, the system modifies the display based on the change of the worker assigned to complete the task to illustrate the time change for completing the task at the two work centers. If the task assignment includes inserting the task into the task queue of the worker at the work center, the system modifies the display to illustrate the time change for completing the task at the work center. The system can also recommend offloading one or more tasks to other workers at other work centers.

[0077] According to one embodiment, a system displays a graphical user interface that includes one or more worker task assignment selection blocks corresponding to one or more workers that a user can select to perform a task. The system can display a representation of a work environment in one area of ​​the graphical user interface and display the worker task assignment selection blocks in another area. For example, the worker task assignment selection blocks can be located above, below, or to the side of the representation of the work environment. The system can apply a trained machine learning model to determine which worker task assignment selection blocks to display. Input features of the machine learning model include historical task manager selections associated with different configurations of tasks performed by workers at a work center.

[0078] The user may interact with a user interface element in the GUI to indicate a selection of a worker task assignment selection block. In response to detecting the selection, the system modifies the GUI to display the predicted downstream effects of assigning a particular task to a particular worker. The system may display a Gantt chart diagram in which rows represent work centers and line segments or rectangles along the rows represent tasks to be completed at the work center. The system may display a set of tasks in a source work center using a first set of display characteristics to distinguish between (a) tasks that will not be changed in both the source task configuration and the target task configuration (i.e., performed simultaneously at the same work center) and (b) tasks that will be modified in the target task configuration (i.e., performed at different times, at different work centers, or both).

[0079] For example, the system may show a set of tasks that will be modified between a source configuration (e.g., before the tasks are assigned to a particular worker) and a target configuration (e.g., after the tasks are assigned to a particular worker) as (a) a gray box in the source work center row at the source time, and (b) a highlighted box at the target work center row at the target time. The system may change the appearance of the tasks in the representation of the target configuration to indicate the change in the characteristics of the tasks. For example, moving a worker from one work center to another work center to perform a particular task may cause a set of tasks to take longer to perform in the target work center. The system may lengthen the visual representation of the tasks in the target work center to indicate an estimate of the difference in time required to perform the tasks between the source work center / time and the target work center / time. Additionally, or in an alternative, moving a worker from one work center to another work center may cause a set of tasks that are performed after the assigned tasks to take less time to complete at the target work center than they would have taken if the tasks at the target work center had not been assigned to the worker. The system may shorten the visual representation of the tasks in the target work center to indicate an estimate of the difference in time required to perform the tasks between the source work center / time and the target work center / time. The visual depiction of the difference in time required to complete the task in the alternative task plans may include a visual representation of a graphical element without corresponding text. Alternatively, the visual representation may include text indicating the change in time required to complete the task.

[0080] Assigning a task to a specific worker may require lead time to reconfigure the equipment at the worker's work center. Therefore, the system can display an additional task corresponding to the time required to calibrate the equipment at the target work center to perform the assigned task in a portion of the GUI representing the target work center. Similarly, assigning a task to a worker may require redirecting materials from another location to the target work center. Redirecting materials may cause congestion within the work environment. Therefore, the system can modify the start time of the task in the target work center to account for delays caused by predicted congestion in the work environment. In addition, the system can display a depiction of the location of the predicted congestion in the visual representation of the work environment in the GUI.

[0081] The system can modify the display of tasks performed in the work center in response to receiving a selection of a different worker task assignment selection block. For example, selecting a worker task assignment selection block can produce a preview of the downstream effects of the task assignment on the tasks performed in the work environment. The administrator can then select a different worker task assignment selection block to view a different preview of a different set of downstream effects corresponding to the selected worker.

[0082] Based on the selection of receiving the assignment of confirmation tasks to specific users, the system generates and transmits instructions to the work center to implement the corresponding alternative task plan. The instructions for generating the alternative task plan include modifying the task group assigned to the worker at the workstation. For example, when a task is assigned to a worker, the system can reallocate one of the worker's tasks to another worker. If the task requires the worker to move to a different work center, the system can reallocate different workers to move to the work center of the previous worker. The system can remove a group of tasks from the task queue to be performed by a worker (assigned tasks) at the first work center, and can add the group of tasks to the task queue to be performed by one or more workers at another group of work centers. If the (one or more) workers at the latter group of work centers already have other tasks previously assigned to them, the other tasks can be replanned to different times of the (one or more) same workers, or transferred to another worker at another work center.

[0083] In some embodiments, when a user registers with a work center terminal, the system provides the user with a set of available tasks for the user to perform. The system initiates a particular task based on the user selecting the task in the user interface or based on detecting that the user has begun to perform the task. In such an embodiment, the system can (a) detect that the user has initiated the task, such as by detecting user motion in a video stream corresponding to an operation in the task, (b) assign the task to the user (and make the task unavailable to other users), and (c) modify the display to show the operations to be performed to complete the task and / or modify the work center equipment to facilitate the performance of the task.

[0084] In some alternative embodiments, when a user registers at a work center terminal, the system analyzes a set of tasks available to the user based on the user's qualifications, and displays to the user the tasks with the highest ranking among the available tasks. For example, one task may be to assemble a component at a work center. Another task may be to reset the configuration of a piece of equipment at a work center. The system may identify that the latter task takes precedence over the former task based on the latter task having a greater impact on the on-time delivery performance indicator of a particular product. Therefore, the system may display to the user the latter task to be performed. After the maintenance is completed, the system may again display to the user the component assembly task to be performed.

[0085] In some embodiments, the system modifies the work center equipment based on detecting that the user has selected a task to be performed. The system can record the task start time and the task end time. The system can turn on the equipment, unlock the equipment and / or materials, and modify the display of the user terminal to provide information for performing the task. The information may include step-by-step instructions, charts, deadline information, or any other information associated with the task. In one or more embodiments, when a task is initiated, the system generates a notification to another user. For example, the system can identify a dependent task that cannot be performed before its parent task. When the user selects a parent task on the user terminal, the system can generate a notification to the user or user group associated with the dependent task that the parent task has been initiated.

[0086] According to yet another example, the system can skip operation 224 and assign the task to a particular candidate user from the candidate user pool without presenting the candidate and / or performance indicators for consideration by the user or administrator. For example, the system can apply a set of rules to prioritize the assignment of tasks to users based on criteria such as user availability, user efficiency, user success rate, percentage of matches between user qualifications and task parameters, user experience level, user training qualifications or certifications, and user experience level with a particular administrator or a particular piece of equipment.

[0087] According to one embodiment, the system tracks the time a user spends operating equipment. The system can store the tracked time in an employee database. The tracked time can be used for completion of qualifications for a particular equipment or for demonstrating a particular level of experience a user has with the equipment.

[0088] 4. Train the machine learning model

[0089] Figure 3 A set of example operations for training a machine learning model to predict the downstream effects of assigning tasks to users is illustrated in accordance with one or more embodiments. For example, when the system identifies a task to be performed, the system can generate a prediction of how assigning the task to different users affects additional tasks performed by the same user and other users at the same work center and other work centers within the work environment. The system obtains historical work environment performance data (operation 302). The historical work environment performance data includes: which users have performed the specified task, the equipment used to perform the task, the work center in the work environment where the task was performed, the materials used to perform the task, the user qualifications of the user who performed the task, the user's supervisor, the success rate (e.g., whether the task was successfully completed), and the recidivism rate (e.g., whether the task resolution actually closed the task, or whether the task must be re-performed within a specified time period).

[0090] After identifying the various data (or a subset thereof) in operation 302, the system generates a set of training data (operation 304). The training data may include (a) a set of tasks assigned to a corresponding set of users and / or work centers in the work environment, and (b) for each set of tasks, at least one label. Examples of labels include: user qualifications of users performing tasks, equipment used to perform tasks, work centers in the work environment performing tasks, materials used to perform tasks, information identifying supervisors, managers, or other employees associated with the tasks, and performance indicators, such as the time taken to perform tasks.

[0091] According to one embodiment, the system obtains historical data and training data sets from a data warehouse storing labeled data sets. The training data set can be generated and updated by the work environment management platform. Alternatively, the training data set can be generated and maintained by a third party.

[0092] In some embodiments, generating a training data set includes generating a set of feature vectors for labeled examples. The feature vector of the example can be n-dimensional, where n represents the number of features in the vector. The number of features selected may vary depending on the specific implementation. Features can be curated in a supervised method or automatically selected from extracted attributes during model training and / or tuning. Example features include performance indicators of the task, the identity of the worker performing the task, worker qualifications, worker plans, equipment data, and material data of the material used to perform the task. In some embodiments, the features within the feature vector are represented in numerical form by one or more bits. The system can convert categorical attributes into numerical representations using encoding schemes such as one-hot encoding, label encoding, and binary encoding. One-hot encoding creates unique binary features for each possible category in the original feature. In one-hot encoding, when one feature has a value of 1, the remaining features have a value of 0. For example, if there are ten different categories for a task attribute, the system can generate ten different features for the input data set. When there is a category (e.g., value "1"), the value "0" is assigned to the remaining features. According to another example, the system can perform label encoding by assigning a unique numerical value to each category. According to yet another example, the system can perform binary encoding by converting the numerical values ​​into binary digits and creating a new feature for each digit.

[0093] The system applies a machine learning algorithm to the training data set (operation 306). The machine learning algorithm analyzes the training data set to identify data and patterns indicating relationships between input features, including user attributes of the user performing the task and the downstream effects of the performance of the task on additional tasks in the work environment. Types of machine learning models include, but are not limited to, linear regression, logistic regression, linear discriminant analysis, classification and regression trees, naive Bayes, k-nearest neighbors, learning vector quantization, support vector machines, bagging and random forests, boosting, back propagation, and / or clustering.

[0094] In some embodiments, the system iteratively applies a machine learning algorithm to a set of input data to generate a set of output labels, compares the generated labels to pre-generated labels associated with the input data, adjusts weights and biases of the algorithm based on errors, and applies the algorithm to another set of input data.

[0095] In some embodiments, the system compares the labels estimated by one or more iterations of the machine learning model algorithm with the observed labels to determine the estimation error (operation 308). The system can perform this comparison for a test set of examples, which can be a subset of examples in the training data set that were not used to generate and fit the candidate model. The total estimation error for a particular iteration of the machine learning algorithm can be calculated as a function of the number of examples of labels that were incorrectly predicted and / or the size of the difference. In some embodiments, the system determines whether to adjust weights and / or other model parameters based on the estimation error (operation 310). Adjustments can be made until a candidate model that minimizes the estimation error or otherwise reaches a threshold level of estimation error is identified.

[0096] In some embodiments, the system selects machine learning model parameters based on the estimated error that meets the threshold accuracy level (operation 312). For example, the system can select a set of parameter values ​​for the machine learning model based on determining that the trained model has an accuracy level of at least 98% in predicting that the worker is assigned a task.

[0097] In some embodiments, the system uses back propagation to train the neural network. Back propagation is the process of updating the state of the unit in the neural network based on the gradient determined as a function of the estimated error. Through back propagation, the node is assigned a fraction of the estimated error based on the contribution to the output and adjusted based on the fraction. In the recurrent neural network, time is also considered as a factor in the back propagation process. As previously described, a given set of training data includes tasks performed by the user historically and corresponding attributes (such as user qualifications, equipment used or operated on it, and the time required to complete the task). Each task assigned to the worker can be processed as a separate discrete time instance. For example, a data set may include tasks c1, c2, and c3 that are executed corresponding to time t, t+1, and t+2, respectively. Back propagation through time can be performed by gradient descent starting at time t+2 and moving backward in time to t+1 and then to t. In addition, the back propagation process can adjust the memory parameter of the unit so that the unit remembers the contribution from the previous cost in the cost sequence. For example, a unit that calculates the contribution of e3 can have a memory of the contribution of e2, and the contribution of e2 has a memory of e1. Memories can be used as feedback connections so that the output of a unit at one time (e.g., t) is used as input at the next time in the sequence (e.g., t+1). Gradient descent techniques can take these feedback connections into account so that the contribution of a task group to the output of a unit can influence the contribution of that task group to the output of that unit. Thus, the contribution of c1 can influence the contribution of c2, and so on. Thus, the model can learn relationships between sequences of tasks performed by the user.

[0098] Additionally, or alternatively, the system can train other types of machine learning models. For example, the system can adjust the boundaries of a hyperplane in a support vector machine or the weights of nodes in a decision tree model to minimize estimation errors. Once trained, the machine learning model can be used to recommend users or groups of users to perform tasks.

[0099] In the example of a supervised ML algorithm, the system can obtain feedback on whether a particular set of downstream effects should be attributed to a particular task assignment to a user (operation 314). The feedback can confirm a particular prediction of the downstream effects. In other examples, the feedback can indicate that a particular downstream effect should not be associated with a particular task assignment to a user. Based on the feedback, the machine learning training set can be updated to improve its analysis accuracy (operation 316). Once updated, the system can further train the machine learning model by optionally applying the model to additional training data sets.

[0100] 5. Example Embodiments

[0101] For the purpose of clarity, detailed examples are described below. The components and / or operations described below should be understood as a specific example that may not be applicable to certain embodiments. Therefore, the components and / or operations described below should not be interpreted as limiting the scope of any one of the claims.

[0102] Figure 4A-4C A graphical user interface display for selecting users to be assigned to a task and presenting a forecast of the downstream effects of the forecast on the task plan is illustrated according to an example embodiment.

[0103] like Figure 4A , GUI 410 includes a Gantt chart 411 that includes tasks 412 that are scheduled to be completed at work centers 413 within a manufacturing facility. Chart 411 includes visual representations of tasks that are on time, delayed, affected by equipment failure, and rescheduled. For example, if the execution of a task is affected by an equipment failure, the system can display the task in a different color than tasks that are not affected by the equipment failure. Chart 411 illustrates a list of tasks 431-436 that have either been executed, are in the process of being executed, or are scheduled or forecasted to be executed at work centers 421-426, respectively.

[0104] exist Figure 4A-4C In the exemplary embodiment illustrated in FIG. 4 , work center 421 is an assembly work center for assembling materials into components. Row 431 illustrates tasks performed by workers (or users or employees) identified in row 441, represented by rectangular shapes. For example, rows 431 and 441 illustrate (a) three tasks 431a-431c performed by a worker with employee ID number 224, task 431d currently being performed by an employee with EID 413, another task 431e scheduled to be performed by an employee with EID 413, and another three tasks 431f-431h scheduled to be performed by an employee with EID 224. Based on the prediction that it will take longer for an employee with EID 413 to perform these tasks than it will take for an employee with EID 224 to perform these tasks, chart 411 illustrates tasks 431d and 431e as having a longer length dimension than tasks 431a-431c. In other words, all of tasks 431a-431h are the same tasks of assembling components. The system analyzes user profile data including efficiency data and productivity data to predict the time required to complete a task. Based on determining that an employee with EID 413 will take longer to perform a task than an employee with EID 224, the system modifies the visual attributes of an interface element (e.g., a rectangle) representing the task to reflect the difference in the predicted time for different users to complete the task.

[0105] exist Figure 4A-4CIn the GUI 410 illustrated in FIG. 4 , the work center 413 includes assembly work centers 421-423 where workers assemble components, testing work centers 424 and 425 where workers test assembled components, and a quality assurance work center 426 where workers perform quality assurance checks on tested components. In addition, Figure 4A-4C The GUI 410 illustrated in illustrates rows 441-446 that depict which workers have performed tasks / are performing tasks / are scheduled or predicted to perform tasks at work centers 421-426. Multiple different workers may work at the same work center. Figure 4A-4C The work center is illustrated as one worker working at a time. However, embodiments encompass multiple workers being able to work at the work center at the same time. For example, some tasks may require two or more workers to complete. Other work centers may have space for two workers to work concurrently at different pieces of equipment. In addition, one or more embodiments track which workers are planned or predicted to work at the work center without displaying a specific worker in GUI 410. When the system detects that different users are associated with the work center, the system analyzes user attributes such as user performance indicators (e.g., how productive the worker is, and what the worker's experience level is with the equipment at the work center) to determine (a) which tasks will be performed at the work center, and (b) how long it will take for the worker to perform these tasks. For example, the system can predict or plan that a worker performs a series of tasks A, B, and C at the work center. The system can predict or plan that another worker repeats the same tasks A, A, A at the work center. The system can predict that a user will take 15 minutes to perform task A. The system can predict that another user will take 30 minutes to perform task A.

[0106] exist Figure 4A-4C In the example illustrated in , GUI 410 includes a task 437 representing the repair and recalibration of a piece of equipment at test work center 424. For example, the system can detect a fault in the equipment, generate a task to repair the equipment, and generate a representation of task 437 in GUI 410.

[0107] The system generates a recommendation to assign task 437 to a worker. The system further applies a machine learning model to predict the downstream effects of assigning tasks to workers. The system displays two blocks 414 and 415 corresponding to two options for assigning task 437 to workers. According to option 1 (block 414), the system assigns the task to a worker with EID 413. According to option 2 (block 415), the system assigns the task to a worker with EID 213. The user can select a block associated with an option to see how the option will affect the scheduled tasks among all work centers. In addition, the user can further customize the re-planning of tasks by selecting and moving individual tasks, task groups, and workers in GUI 410.

[0108] The system displays the predicted performance indicators 418 and 419 associated with the corresponding options in blocks 414 and 415. For example, the system predicts that option 1 (block 414) will result in 70% utilization, 70% OEE, no cost increase, and 75% on-time delivery. The system predicts that option 2 (block 415) will result in 75% utilization, 80% OEE, no cost increase, and 87% on-time delivery. Although Figure 4A An example is illustrated where blocks 414 and 415 include the same set of performance indicators, but in one or more embodiments, the system displays blocks with different performance indicators and / or with costs associated with assigning tasks to different users.

[0109] like Figure 4B As illustrated in , when the user selects block 414 associated with assigning task 437a to a worker with EID 413, the system modifies GUI 410 to provide the user with a preview of the modifications. Figure 4BBar 447a is shown indicating that the worker with EID 413 has been selected to be assigned to perform task 437a. The system displays the downstream effects of assigning task 437a to the worker with EID 413, including: tasks 431d and 431e will not be performed at work center 421 (as a result of the worker with EID 413 being unavailable to perform the tasks), tasks 432a and 432b will not be performed at work center 422 (as a result of tasks 431d and 431e not being performed to assemble the components required for tasks 432a and 432b), tasks 433a-433c cannot be performed (as a result of the worker with EID 413 being unavailable), task 434a is being performed (as a result of completion of task 437a), tasks 435a and 435b cannot be performed (as a result of the inability to perform tasks 433a-433c), and task 436a cannot be performed (as a result of work center 424 being shut down during maintenance and recalibration of task 437a). The system displays a confirmation window 451 in the GUI 410 , including a task assignment confirmation button 452 , to allow the user to accept the displayed task assignment to the corresponding user (eg, the worker with the EID 413 ).

[0110] like Figure 4C As illustrated in , when the user selects block 415 associated with assigning task 437a to the worker with EID 213, the system modifies GUI 410 to provide the user with a preview of the modifications. Figure 4C Bar 447b is shown indicating that the worker with EID 213 is selected to be assigned to perform task 437a. The system displays the downstream effects of assigning task 437a to the worker with EID 213, including: tasks 434b-434d are being performed (as a result of the completion of task 437a). Figure 4C , GUI 410 displays relative to Figure 4A and Figure 4B The shortened task 437a represents a system prediction, based on user attributes indicating that the user has a high proficiency with the MLRR machine and that the user has a high productivity rating, that the user will complete the task ahead of the average completion time. The system further displays a representation of task 436a in the GUI 410 indicating that the task cannot be performed (as a result of the downtime of work center 424 during the repair and recalibration task 437a). The system displays a confirmation window 451 in the GUI 410, including a task assignment confirmation button 452, to allow the user to accept the displayed task assignment to the corresponding user (e.g., the worker with EID 413).

[0111] Based on receiving user input selecting button 454, the system assigns the task to the worker with EID 213. The system may send a notification (such as a voice message or text message) to the worker to indicate the assignment. Alternatively, when the worker logs into any of work centers 421-426, the system may notify the worker of the assignment of the task at work center 424.

[0112] Although Figure 4A-4C An example embodiment is illustrated in which a user or administrator assigns a task to a user using a GUI, but one or more embodiments assign tasks to users without using a GUI. For example, the system may apply a set of predefined rules to identify a group of workers who are (a) qualified to perform a task and (b) authorized to perform a task. The system may apply a machine learning model to worker attribute data and plan data to separately predict the downstream effects of assigning tasks to each of the workers. Based on the predicted downstream effects, the system calculates corresponding predicted performance indicators for the corresponding assignments to the corresponding workers. The system compares the predicted performance indicators of the workers with defined thresholds to generate a subset of candidate workers to whom the system can assign tasks. If the system detects that any of the candidate workers is logged into a work center terminal, the system can assign tasks to the candidate workers without intervening user or administrator input.

[0113] 6. Computer Networks and Cloud Networks

[0114] In one or more embodiments, a computer network provides connectivity between a set of nodes. The nodes can be local to each other and / or remote from each other. The nodes are connected by a set of links. Examples of links include coaxial cables, unshielded twisted pair cables, copper cables, optical fibers, and virtual links.

[0115] A subset of nodes implements a computer network. Examples of such nodes include switches, routers, firewalls, and network address translators (NATs). Another subset of nodes uses a computer network. Such nodes (also referred to as "hosts") can execute client processes and / or server processes. Client processes request computing services (such as the execution of a specific application and / or the storage of a specific amount of data). Server processes respond by executing the requested service and / or returning corresponding data.

[0116] A computer network may be a physical network, comprising physical nodes connected by physical links. A physical node is any digital device. A physical node may be a hardware device of a specific function, such as a hardware switch, a hardware router, a hardware firewall, and a hardware NAT. Additionally or alternatively, a physical node may be a general-purpose machine configured to execute various virtual machines and / or applications that perform corresponding functions. A physical link is a physical medium that connects two or more physical nodes. Examples of links include coaxial cables, unshielded twisted pair cables, copper cables, and optical fibers.

[0117] A computer network may be an overlay network. An overlay network is a logical network implemented on top of another network (such as a physical network). Each node in the overlay network corresponds to a corresponding node in the underlying network. Therefore, each node in the overlay network is associated with an overlay address (to address the overlay node) and an underlying address (to address the underlying node that implements the overlay node). Overlay nodes may be digital devices and / or software processes (such as virtual machines, application instances, or threads). The links connecting overlay nodes are implemented as tunnels through the underlying network. The overlay nodes at either end of the tunnel treat the underlying multi-hop path between them as a single logical link. Tunnels are performed by encapsulation and decapsulation.

[0118] In an embodiment, the client can be local to the computer network and / or remote from the computer network. The client can access the computer network through other computer networks (such as a private network or the Internet). The client can use a communication protocol such as the Hypertext Transfer Protocol (HTTP) to pass the request to the computer network. The request is passed through an interface such as a client interface (such as a web browser), a program interface, or an application programming interface (API).

[0119] In an embodiment, a computer network provides connectivity between clients and network resources. Network resources include hardware and / or software configured to execute server processes. Examples of network resources include processors, data storage, virtual machines, containers, and / or software applications. Network resources are shared between multiple clients. Clients request computing services from a computer network independently of each other. Network resources are dynamically allocated to requests and / or clients on an on-demand basis. Network resources allocated to each request and / or client can be scaled up or down based on, for example, (a) computing services requested by a specific client, (b) aggregated computing services requested by a specific tenant, and / or (c) aggregated computing services requested by a computer network. Such a computer network may be referred to as a "cloud network".

[0120] In an embodiment, a service provider provides a cloud network to one or more end users. Various service models can be implemented through a cloud network, including but not limited to software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). In SaaS, a service provider provides end users with the ability to use the service provider's applications executed on network resources. In PaaS, a service provider provides end users with the ability to deploy customized applications on network resources. Customized applications can be created using programming languages, libraries, services, and tools supported by the service provider. In IaaS, a service provider provides end users with the ability to supply processing, storage, network, and other basic computing resources provided by network resources. Any arbitrary application, including an operating system, can be deployed on network resources.

[0121] In an embodiment, a computer network can implement various deployment models, including but not limited to private cloud, public cloud and hybrid cloud. In a private cloud, network resources are supplied for exclusive use by a specific group of one or more entities (the term "entity" used herein refers to a company, organization, individual or other entity). Network resources can be local to a specific group of entities and / or away from a specific group of entities. In a public cloud, cloud resources are supplied for multiple entities (also referred to as "tenants" or "customers") that are independent of each other. A computer network and its network resources are accessed by clients corresponding to different tenants. Such a computer network can be referred to as a "multi-tenant computer network". Several tenants can use the same specific network resource at different times and / or at the same time. Network resources can be local to a tenant's place and / or away from a tenant's place. In a hybrid cloud, a computer network includes a private cloud and a public cloud. The interface between a private cloud and a public cloud allows data and application portability. Data stored at a private cloud and data stored at a public cloud can be exchanged through an interface. Applications implemented at a private cloud and applications implemented at a public cloud can have dependencies on each other. Calls from an application at the private cloud to an application at the public cloud (and vice versa) may be performed through the interface.

[0122] In an embodiment, the tenants of a multi-tenant computer network are independent of each other. For example, the business or operation of one tenant can be separated from the business or operation of another tenant. Different tenants can require different network requirements for the computer network. Examples of network requirements include processing speed, data storage capacity, security requirements, performance requirements, throughput requirements, latency requirements, elasticity requirements, quality of service (QoS) requirements, tenant isolation and / or consistency. The same computer network may need to implement different network requirements required by different tenants.

[0123] In one or more embodiments, in a multi-tenant computer network, tenant isolation is implemented to ensure that applications and / or data of different tenants are not shared with each other. Various tenant isolation methods can be used.

[0124] In an embodiment, each tenant is associated with a tenant ID. Each network resource of the multi-tenant computer network is marked with a tenant ID. A tenant is allowed to access a specific network resource only if the tenant and the specific network resource are associated with the same tenant ID.

[0125] In an embodiment, each tenant is associated with a tenant ID. Each application implemented by the computer network is tagged with the tenant ID. Additionally or alternatively, each data structure and / or data set stored by the computer network is tagged with the tenant ID. A tenant is allowed to access a particular application, data structure, and / or data set only if the tenant and the particular application, data structure, and / or data set are associated with the same tenant ID.

[0126] As an example, each database implemented by a multi-tenant computer network can be marked with a tenant ID. Only the tenant associated with the corresponding tenant ID can access the data of a particular database. As another example, each entry in a database implemented by a multi-tenant computer network can be marked with a tenant ID. Only the tenant associated with the corresponding tenant ID can access the data of a particular entry. However, a database can be shared by multiple tenants.

[0127] In an embodiment, the subscription list indicates which tenants have authorized access to which applications. For each application, a list of tenant IDs of tenants authorized to access the application is stored. A tenant is allowed to access a specific application only if the tenant ID of the tenant is included in the subscription list corresponding to the specific application.

[0128] In an embodiment, network resources corresponding to different tenants (such as digital devices, virtual machines, application instances, and threads) are isolated from tenant-specific overlay networks maintained by a multi-tenant computer network. As an example, packets from any source device in a tenant overlay network can be transmitted only to other devices within the same tenant overlay network. Encapsulation tunnels are used to prohibit any transmission from a source device on a tenant overlay network to devices in other tenant overlay networks. Specifically, a packet received from a source device is encapsulated in an outer packet. The outer packet is transmitted from a first encapsulation tunnel endpoint (communicating with a source device in the tenant overlay network) to a second encapsulation tunnel endpoint (communicating with a destination device in the tenant overlay network). The second encapsulation tunnel endpoint decapsulates the outer packet to obtain the original packet transmitted by the source device. The original packet is transmitted from the second encapsulation tunnel endpoint to a destination device in the same specific overlay network.

[0129] 7. Miscellaneous; Extensions

[0130] Embodiments are directed to a system having one or more devices including a hardware processor and configured to perform any of the operations described herein and / or recited in any of the following claims.

[0131] In an embodiment, a non-transitory computer-readable storage medium includes instructions that, when executed by one or more hardware processors, cause performance of any of the operations described herein and / or recited in any of the claims.

[0132] Any combination of the features and functions described herein may be used according to one or more embodiments. In the foregoing description, embodiments have been described with reference to many specific details that may vary from implementation to implementation. Therefore, the description and drawings are to be considered in an illustrative rather than a restrictive sense. The sole and exclusive indication of the scope of the invention, and what the applicant intends to be the scope of the invention, is the literal and equivalent range of the set of claims claimed for protection in this application, in the specific form in which such claims are claimed, including any subsequent corrections.

[0133] 8. Hardware Overview

[0134] According to one embodiment, the technology described herein is implemented by one or more special-purpose computing devices. The special-purpose computing device can be hard-wired to perform the technology, or can include a digital electronic device (such as one or more application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or network processing units (NPUs)) that is permanently programmed to perform the technology, or can include one or more general-purpose hardware processors that are programmed to perform the technology according to program instructions in firmware, memory, other storage devices, or a combination thereof. Such a special-purpose computing device can also combine customized hard-wired logic, ASICs, FPGAs, or NPUs with customized programming to complete the technology. The special-purpose computing device can be a desktop computer system, a portable computer system, a handheld device, a networking device, or any other device that incorporates hard-wired and / or program logic to implement the technology.

[0135] For example, Figure 5 is a block diagram illustrating a computer system 500 upon which embodiments of the present invention may be implemented. Computer system 500 includes a bus 502 or other communication mechanism for communicating information, and a hardware processor 504 coupled to bus 502 for processing information. Hardware processor 504 may be, for example, a general purpose microprocessor.

[0136] The computer system 500 also includes a main memory 506, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 502 to store information and instructions to be executed by the processor 504. The main memory 506 may also be used to store temporary variables or other intermediate information during execution of instructions to be executed by the processor 504. When such instructions are stored in a non-transitory storage medium accessible to the processor 504, the computer system 500 is presented as a special-purpose machine customized to perform the operations specified in the instructions.

[0137] Computer system 500 also includes a read only memory (ROM) 508 or other static storage device coupled to bus 502 for storing static information and instructions for processor 504. A storage device 510, such as a magnetic or optical disk, is provided and coupled to bus 502 for storing information and instructions.

[0138] The computer system 500 may be coupled to a display 512, such as a cathode ray tube (CRT), via the bus 502 to display information to a computer user. An input device 514, including alphanumeric and other keys, is coupled to the bus 502 to communicate information and command selections to the processor 504. Another type of user input device is a cursor control 516, such as a mouse, trackball, or cursor direction keys, for communicating directional information and command selections to the processor 504 and for controlling cursor movement on the display 512. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), which allows the device to specify positioning in a plane.

[0139] The computer system 500 may implement the techniques described herein using customized hardwired logic, one or more ASICs or FPGAs, firmware, and / or program logic that is combined with the computer system to make the computer system 500 a special purpose machine or programmed to be a special purpose machine. According to one embodiment, the techniques herein are performed by the computer system 500 in response to the processor 504 executing one or more sequences of one or more instructions contained in the main memory 506. Such instructions may be read into the main memory 506 from another storage medium, such as the storage device 510. Execution of the sequences of instructions contained in the main memory 506 causes the processor 504 to perform the process steps described herein. In alternative embodiments, hardwired circuitry may be used in place of or in combination with software instructions.

[0140] The term "storage medium" as used herein refers to any non-temporary medium that stores data and / or instructions that cause a machine to operate in a particular manner. Such storage media may include non-volatile media and / or volatile media. Non-volatile media include, for example, optical or magnetic disks, such as storage device 510. Volatile media include dynamic memory, such as main memory 506. Common forms of storage media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, tapes or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with hole patterns, RAM, PROM and EPROM, FLASH-EPROM, NVRAM, any other memory chip or tape cartridge, content addressable memory (CAM) and ternary content addressable memory (TCAM).

[0141] Storage media are distinct from but can be used in conjunction with transmission media. Transmission media participate in the transfer of information between storage media. For example, transmission media include coaxial cables, copper wire, and optical fiber, including the wires that comprise bus 502. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

[0142] Various forms of media may be involved in carrying one or more sequences of one or more instructions to the processor 504 for execution. For example, the instructions may initially be carried on a disk or solid-state drive of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the computer system 500 may receive the data over the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector may receive the data carried in the infrared signal and appropriate circuitry may place the data on the bus 502. The bus 502 carries the data to the main memory 506, from which the processor 504 retrieves and executes the instructions. The instructions received by the main memory 506 may optionally be stored on the storage device 510 before or after execution by the processor 504.

[0143] Computer system 500 also includes a communication interface 518 coupled to bus 502. Communication interface 518 provides bidirectional data communication coupled to a network link 520 connected to a local network 522. For example, communication interface 518 can be an integrated services digital network (ISDN) card, a cable modem, a satellite modem, or a modem that provides a data communication connection to a corresponding type of telephone line. As another example, communication interface 518 can be a local area network (LAN) card that provides a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, communication interface 518 sends and receives electrical signals, electromagnetic signals, or optical signals that carry digital data streams representing various types of information.

[0144] The network link 520 typically provides data communication to other data devices through one or more networks. For example, the network link 520 can provide a connection to a host 524 or to data equipment operated by an Internet Service Provider (ISP) 526 through a local network 522. The ISP 526 in turn provides data communication services through the global packet data communication network now commonly referred to as the "Internet" 528. Both the local network 522 and the Internet 528 use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 520 and through the communication interface 518 (which carry the digital data to and from the computer system 500) are example forms of transmission media.

[0145] Computer system 500 can send messages and receive data, including program code, through the network(s), network link 520, and communication interface 518. In the Internet example, server 530 can transmit the requested code for an application program through Internet 528, ISP 526, local network 522, and communication interface 518.

[0146] The received code may be executed by processor 504 as it is received, and / or stored in storage device 510 or other non-volatile storage for later execution.

[0147] In the foregoing specification, embodiments of the present invention have been described with reference to numerous specific details that may vary from implementation to implementation. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what the applicant intends to be the scope of the invention, is the literal and equivalent range of the set of claims claimed in this application, in the specific form in which such claims are claimed, including any subsequent corrections.

Claims

1. A non-transitory computer-readable medium comprising instructions that, when executed by one or more hardware processors, cause operations including: A first task corresponding to a first set of task parameters is identified, the first set of task parameters comprising at least: a piece of equipment associated with performance of a first task and a first set of user qualifications recommended for performing the first task; comparing the first set of user qualifications to a plurality of sets of user qualifications corresponding to a plurality of users; In response to comparing the first set of user qualifications with the plurality of sets of user qualifications corresponding to the plurality of users: identifying a first user having user qualifications that match the first set of user qualifications recommended for the first task; Retrieving first task execution information of a first user, the first task execution information including at least one of (a) plan information of the first user and (b) historical task completion information of the first user; Based on the first task execution information: generating a first candidate task plan for the first user, the first candidate task plan including allocation of the first task to the first user; and applying the machine learning model to the first candidate task plan to predict a first downstream effect of assigning the first task to the first user, wherein the first downstream effect comprises an effect on the execution of one or more tasks in the second task plan, The first downstream action includes a first modification to the second mission plan.

2. The non-transitory computer-readable medium of claim 1, wherein the first task execution information comprises at least one of the following: Planning information for the first user includes one or more tasks assigned to the first user, the time available for the first user to perform the tasks, the success rate of the first user based on previously completed tasks, and the productivity of the first user corresponding to the amount of time it takes the first user to complete the tasks.

3. The non-transitory computer-readable medium of claim 1 , wherein applying the machine learning model to the first candidate mission plan further comprises: Applying the machine learning model to a set of input data including a first candidate mission plan and at least one of: (a) equipment characteristics of equipment required to perform the first task, and (b) equipment characteristics of one or more groups of equipment corresponding to one or more work centers corresponding to the performance of one or more additional tasks in the second task plan; as well as (a) material properties of materials required to perform the first task, and (b) one or more sets of material properties corresponding to the performance of the one or more additional tasks in the second mission plan.

4. The non-transitory computer readable medium of claim 1, wherein the operations further comprise: generating a first digital representation of a second mission plan in a graphical user interface (GUI); as well as modifying the first digital representation of the second mission plan to include an interface element representing the first mission to generate a second digital representation of a third mission plan including the first mission, The second digital representation depicts a first task associated with the first user.

5. The non-transitory computer readable medium of claim 1 , wherein the second task plan comprises a first configuration of a set of tasks to be performed at a plurality of work centers in the work environment, Wherein predicting a first downstream effect of assigning the first task to the first user includes predicting a delay in executing one or more tasks in the second task plan resulting from including the first task in the second task plan.

6. The non-transitory computer readable medium of claim 1, wherein the operations further comprise: In response to comparing the first set of user qualifications with the plurality of sets of user qualifications corresponding to the plurality of users: identifying a filtered group of users having user qualifications that match the first set of user qualifications recommended for the first task, the filtered group of users including the first user, Wherein applying the machine learning model to a first candidate task plan for a first user is performed in response to detecting a selection of the first user from among the filtered group of users.

7. The non-transitory computer readable medium of claim 6, wherein the operations further comprise: displaying in a graphical user interface (GUI) a first task assignment selection block corresponding to the first user and a second task assignment selection block corresponding to the second user among the filtered user group, Wherein detecting the selection of the first user from among the filtered group of users comprises detecting a user interaction with a first task assignment selection block.

8. The non-transitory computer-readable medium of claim 6, wherein detecting the selection of the first user from among the filtered group of users comprises detecting a first login of the first user to a first terminal running the task management application before detecting any login of any other user from the filtered group of users to any terminal running the task management application.

9. The non-transitory computer readable medium of claim 1, wherein the operations further comprise: In response to comparing the first set of user qualifications with the plurality of sets of user qualifications corresponding to the plurality of users: identifying a second user having user qualifications that match the first set of user qualifications recommended for the first task; Retrieving second task execution information of a second user; Based on the second task execution information: generating a second candidate task plan for the second user, the second candidate task plan including allocation of the first task to the second user; as well as applying the machine learning model to a second candidate task plan to predict a second downstream effect of assigning the first task to a second user, wherein the second downstream effect comprises an effect on the performance of one or more tasks in the second task plan, The second downstream action includes a second modification to the second mission plan.

10. The non-transitory computer readable medium of claim 1, wherein the operations further comprise: The machine learning model is trained to predict the downstream effect of assigning tasks to users, the training comprising: Get the training data set, each training data set includes: Historical task planning data describing the tasks performed by users in the work center; Historical user data of users who perform the task at the work center, the historical user data including: historical user qualifications, historical work history data, historical task completion success rate, and historical time for completing the task; and The machine learning model is trained based on the training data set.

11. The non-transitory computer readable medium of claim 1 , wherein the operations further comprise: Detect faults in the working environment; as well as In response to detecting the failure: generating a task parameter set for a new task, wherein the task parameter set includes a first set of user qualifications for performing the new task, Wherein comparing the first set of user qualifications with a plurality of sets of user qualifications corresponding to a plurality of users is performed in response to generating the set of task parameters for the new task.

12. A method comprising: identifying a first task corresponding to a first set of task parameters, the first set of task parameters comprising at least: a piece of equipment associated with performance of the first task and a first set of user qualifications recommended for performing the first task; comparing the first set of user qualifications to a plurality of sets of user qualifications corresponding to a plurality of users; In response to comparing the first set of user qualifications with the plurality of sets of user qualifications corresponding to the plurality of users: identifying a first user having user qualifications that match the first set of user qualifications recommended for the first task; Retrieving first task execution information of a first user, the first task execution information including at least one of (a) plan information of the first user and (b) historical task completion information of the first user; Based on the first task execution information: generating a first candidate task plan for the first user, the first candidate task plan including allocation of the first task to the first user; and applying the machine learning model to the first candidate task plan to predict a first downstream effect of assigning the first task to the first user, wherein the first downstream effect comprises an effect on the execution of one or more tasks in the second task plan, The first downstream action includes a first modification to the second mission plan.

13. The method of claim 12, wherein the first task execution information comprises at least one of the following: Planning information for the first user includes one or more tasks assigned to the first user, the time available for the first user to perform the tasks, the success rate of the first user based on previously completed tasks, and the productivity of the first user corresponding to the amount of time it takes the first user to complete the tasks.

14. The method of claim 12, wherein applying the machine learning model to the first candidate mission plan further comprises: Applying the machine learning model to a set of input data including a first candidate mission plan and at least one of: (a) equipment characteristics of equipment required to perform the first task, and (b) equipment characteristics of one or more groups of equipment corresponding to one or more work centers corresponding to the performance of one or more additional tasks in the second task plan; as well as (a) material properties of materials required to perform the first task, and (b) one or more sets of material properties corresponding to the performance of the one or more additional tasks in the second mission plan.

15. The method of claim 12, further comprising: generating a first digital representation of a second mission plan in a graphical user interface (GUI); as well as modifying the first digital representation of the second mission plan to include an interface element representing the first mission to generate a second digital representation of a third mission plan including the first mission, The second digital representation depicts a first task associated with the first user.

16. The method of claim 12, wherein the second task plan includes a first configuration of a set of tasks to be performed at a plurality of work centers in the work environment, Wherein predicting a first downstream effect of assigning the first task to the first user includes predicting a delay in executing one or more tasks in the second task plan resulting from including the first task in the second task plan.

17. The method of claim 12, further comprising: In response to comparing the first set of user qualifications with the plurality of sets of user qualifications corresponding to the plurality of users: identifying a filtered group of users having user qualifications that match the first set of user qualifications recommended for the first task, the filtered group of users including the first user, Wherein applying the machine learning model to a first candidate task plan for a first user is performed in response to detecting a selection of the first user from among the filtered group of users.

18. The method of claim 17, wherein detecting the selection of the first user from among the filtered group of users comprises detecting a first login of the first user to a first terminal running the task management application before detecting any login of any other user from the filtered group of users to any terminal running the task management application.

19. The method of claim 12, further comprising: The machine learning model is trained to predict the downstream effect of assigning tasks to users, the training comprising: Get the training data set, each training data set includes: Historical task planning data describing the tasks performed by users in the work center; Historical user data of users who perform the task at the work center, the historical user data including: historical user qualifications, historical work history data, historical task completion success rate, and historical time for completing the task; and The machine learning model is trained based on the training data set.

20. A system comprising: one or more processors; as well as A memory storing instructions, which, when executed by the one or more processors, cause the system to perform operations including: identifying a first task corresponding to a first set of task parameters, the first set of task parameters comprising at least: a piece of equipment associated with performance of the first task and a first set of user qualifications recommended for performing the first task; comparing the first set of user qualifications to a plurality of sets of user qualifications corresponding to a plurality of users; In response to comparing the first set of user qualifications with the plurality of sets of user qualifications corresponding to the plurality of users: identifying a first user having user qualifications that match the first set of user qualifications recommended for the first task; Retrieving first task execution information of a first user, the first task execution information including at least one of (a) plan information of the first user and (b) historical task completion information of the first user; Based on the first task execution information: generating a first candidate task plan for the first user, the first candidate task plan including allocation of the first task to the first user; and applying the machine learning model to the first candidate task plan to predict a first downstream effect of assigning the first task to the first user, wherein the first downstream effect comprises an effect on the execution of one or more tasks in the second task plan, The first downstream action includes a first modification to the second mission plan.

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