Attendance data distribution method and system

Abstract

The embodiment of the invention provides an attendance data distribution method and system, and the method comprises the steps: determining the organization affiliation of an object, the organization affiliation comprising different projects and different teams to which the object belongs; based on the organization affiliation of the object, obtaining reference information of a task needing to be executed by the object in each team and group of each project; acquiring first attendance data of the object; acquiring second attendance data of the object; and based on the first attendance data, the second attendance data and the planned construction period of each task in the reference information, determining target attendance information of the corresponding task executed by the object in each project and each team.

Description

Technical Field

[0001] This specification relates to the field of construction management, and particularly to a method and system for allocating attendance data. Background Art

[0002] The production process in the construction industry is complex, and the mobility of personnel is extremely high. The phenomena of migrant work, shared employment, multi-point practice, and flexible employment are common. Workers often cross projects, teams, and regions at the construction site, resulting in inaccurate recording of workers' attendance data, ambiguous organizational affiliation and performance, which bring great challenges to subsequent work record-keeping, salary payment, construction traceability, and responsibility division. Currently, project attendance is mostly recorded by a single device for all attendance on the same day, and the attribution of attendance duration depends on the subjective judgment of managers or team leaders, making it difficult to guarantee data authenticity and unclear responsibility boundaries.

[0003] Therefore, it is desirable to provide a method and system for allocating attendance data to optimize the process of allocating attendance data. Summary of the Invention

[0004] One or more embodiments of this specification provide a method for allocating attendance data, including: determining the organizational affiliation of an object, where the organizational affiliation includes different projects and different teams to which the object belongs; based on the organizational affiliation of the object, obtaining reference information on tasks that the object needs to perform in each project and each team; obtaining first attendance data of the object, where the first attendance data includes the start time and end time when the object conducts each project; obtaining second attendance data of the object, where the second attendance data includes the entry time and exit time when the object performs each task; based on the first attendance data, the second attendance data, and the planned duration of each task in the reference information, determining the target attendance information of the object for performing corresponding tasks in each project and each team.

[0005] One embodiment of this specification provides an attendance data allocation system, including: a determination module configured to determine the organizational affiliation of an object; a first acquisition module configured to obtain reference information on tasks that the object needs to perform in each project and each team based on the organizational affiliation of the object; a second acquisition module configured to obtain first attendance data of the object, where the first attendance data includes the start time and end time when the object conducts each project; a third acquisition module configured to obtain second attendance data of the object, where the second attendance data includes the entry time and exit time when the object performs each task; a data allocation module configured to determine the target attendance information of the object for performing corresponding tasks in each project and each team based on the first attendance data, the second attendance data, and the planned duration of each task in the reference information.

[0006] One or more embodiments of this specification provide a computer-readable storage medium storing computer instructions, which, when read by a computer, cause the computer to execute the aforementioned attendance data allocation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] This specification will be further described by way of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, where:

[0008] Figure 1 FIG. 1 is a schematic diagram of an application scenario of an attendance data allocation system according to some embodiments of this specification;

[0009] Figure 2 FIG. 2 is an exemplary flowchart of an attendance data allocation method according to some embodiments of this specification;

[0010] Figure 3 FIG. 3 is a schematic diagram of determining target attendance information according to some embodiments of this specification;

[0011] Figure 4 FIG. 4 is a schematic diagram of determining the reasons for attendance data anomalies and recommended solutions according to some embodiments of this specification;

[0012] Figure 5 FIG. 5 is a schematic diagram of a root cause analysis model according to some embodiments of this specification;

[0013] Figure 6 FIG. 6 is an exemplary conditional probability table according to some embodiments of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the drawings represent the same structures or operations.

[0015] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the described words can be replaced by other expressions.

[0016] As shown in this specification and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0018] Figure 1 is a schematic diagram of the application scenario of the attendance data distribution system shown in some embodiments of this specification. As Figure 1 shown, in some embodiments, the application scenario 100 of the attendance data distribution system may include a processor 110, a storage device 120, an attendance device 130, a user terminal 140, a user 150, etc.

[0019] The processor 110 is used to process the data and / or information of at least one component or external data source in the application scenario 100 of the attendance data distribution system. For example, the processor 110 can obtain the input information of the user 150 from the attendance device 130, can obtain the original attendance data of the user 150 from the database of the attendance device 130, can call data from the storage device 120, etc. In some embodiments, the processor can be configured to execute an attendance data distribution method, including: determining the organizational affiliation of an object, where the organizational affiliation includes different projects and different teams to which the object belongs; based on the organizational affiliation of the object, obtaining reference information on the tasks that the object needs to perform in each project and each team; obtaining the first attendance data of the object, where the first attendance data includes the start time and end time when the object conducts each project; obtaining the second attendance data of the object, where the second attendance data includes the entry time and exit time when the object performs each task; based on the first attendance data, the second attendance data, and the planned duration of each task in the reference information, determining the target attendance information of the object for performing the corresponding tasks in each project and each team.

[0020] In some embodiments, the processor 110 may include one or more sub-processors. By way of example only, the processor 110 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), etc., or any combination.

[0021] The storage device 120 can be used to store data and / or instructions. The storage device 120 can include one or more storage components, and each storage component can be an independent device or a part of other devices. For example, it can be a part of the processor 110.

[0022] The attendance device 130 is a device for recording the attendance of an object and is used to obtain the original attendance data of the object. Among them, the attendance device can include a panel computer, a user terminal, a smart wearable device, or other possible devices that can be used for attendance / check-in.

[0023] Exemplarily, the attendance device can adopt a multi-technology fusion solution and operate collaboratively based on at least one of face check-in, sensor positioning, wearable devices, and databases. Among them, face check-in realizes accurate identity verification through the attendance panel computer, and the mobile electronic fence combines geolocation technology to delimit the attendance area; the sensor network consists of Bluetooth beacons, Wi-Fi access points, and UWB positioning base stations to provide positioning coverage with an accuracy of meters to sub-meters; the intelligent safety helmet integrates a Bluetooth / Wi-Fi scanning module and a UWB tag to achieve real-time tracking and data interaction of personnel; the database generates visual attendance records by cross-comparing task documents and multi-dimensional attendance data.

[0024] The user terminal 140 is one or more terminals used by the user 150. The user 150 can realize data interaction with the processor 110 through the user terminal 140.

[0025] In some embodiments, the user terminal 140 can be a mobile device, a tablet computer, a laptop computer, a desktop computer, or any combination of other devices with input and / or output functions.

[0026] In some embodiments, one user terminal can correspond to one user or multiple users.

[0027] In some embodiments, the user 150 can include management users and objects. Among them, the management user is a user who performs management functions during the construction management process. For example, the management user can be the general contractor person in charge, the subcontractor person in charge, the team leader, etc. Among them, the general contractor refers to the general responsible unit of the construction project; the subcontractor refers to the subordinate unit or contracting unit responsible for one or more sub-projects; the team is a grass-roots work collective affiliated to the subordinate unit or contracting unit and is responsible for completing the production tasks in the sub-projects. The team can include multiple workers. The general contractor person in charge, the subcontractor person in charge, and the team leader are the persons in charge of the general contractor, the subcontractor, and the team respectively. The management user can manage matters during the construction process, for example, including but not limited to issuing task sheets, sorting out or confirming attendance data, etc. The object can be a worker in the team.

[0028] An object may need to perform multiple tasks over a period of time. During the execution of multiple tasks, the object may belong to multiple work teams in multiple different projects. Therefore, during the process of construction management by the management user, it is necessary to accurately judge the organizational affiliation of the workers and determine the performance of the workers in performing different tasks in different projects, so as to provide reference for subsequent work record keeping and salary payment, and construction traceability.

[0029] In some embodiments, the components of the application scenario 100 of the attendance data distribution system can be connected through a network (not shown in the figure). In some embodiments, the network can be any one or more of a wired network or a wireless network. For example, the network can include a cable network, a fiber optic network cable connection, etc. or any combination thereof. The network connection between the parts can be in one of the above ways or in multiple ways.

[0030] It should be noted that the above description of the application scenario 100 of the construction document processing system is only for illustrative purposes and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various modifications and changes can be made according to this specification. However, these changes and modifications do not depart from the scope of this specification.

[0031] In some embodiments, the attendance data distribution system and its various modules can be configured in the processor 110.

[0032] In some embodiments, the attendance data distribution system can include a determination module, a first acquisition module, a second acquisition module, a third acquisition module, and a data distribution module.

[0033] In some embodiments, the determination module can be configured to determine the organizational affiliation of the object.

[0034] In some embodiments, the first acquisition module can be configured to obtain reference information on the tasks that the object needs to perform in each work team of each project based on the organizational affiliation of the object.

[0035] In some embodiments, the second acquisition module can be configured to obtain the first attendance data of the object.

[0036] In some embodiments, the third acquisition module can be configured to obtain the second attendance data of the object.

[0037] In some embodiments, the data distribution module can be configured to determine the target attendance information of the object in each project and each work team for performing the corresponding tasks based on the planned construction period of each task in the first attendance data, the second attendance data, and the reference information.

[0038] For a detailed description of the functions implemented by the attendance data distribution system and its modules, reference can be made to this specification Figures 2 - 5 and its related descriptions.

[0039] It should be noted that the above description of the attendance data distribution system and its modules is only for convenience of description and does not limit this specification within the scope of the examples given. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, make any combination of each module, or form a subsystem and connect it with other modules.

[0040] Figure 2 is an exemplary flowchart of the attendance data distribution method shown in some embodiments of this specification. As Figure 2 shown, process 200 includes the following steps. In some embodiments, process 200 can be executed by a processor or an attendance data distribution system.

[0041] Step 210, determine the organizational affiliation of the object.

[0042] The object refers to an individual for whom the attendance data needs to be determined. For example, workers corresponding to a construction project, etc.

[0043] Among them, the attendance data is information recording the individual's attendance situation. For example, the attendance data may include the personal information of workers corresponding to a construction project, the attendance time (e.g., the start time and end time of performing tasks), leave records, overtime records, etc.

[0044] The organizational affiliation of the object refers to the organization to which the object belongs.

[0045] The organization refers to a group responsible for specific work or tasks in a specific construction project. In some embodiments, the organization can be the work team to which the object belongs. Among them, the work team is the basic unit for implementing the project.

[0046] The project is different engineering parts in a construction project. For example, a construction project can be divided into multiple sub-projects or sub-items, and these sub-projects or sub-items can be called projects (or "construction projects"). Further for example, the general contractor will subcontract specific construction tasks or services. Subcontracting means outsourcing certain specific construction tasks or services to professional contractors. Subcontractors usually focus on a specific field, such as electrical, plumbing, air conditioning, civil engineering, etc. Each subcontract will form one or more project departments responsible for the corresponding projects, and each project department is further divided into multiple work teams.

[0047] In some embodiments, determining the organizational affiliation of the object may include determining at least one work team in at least one project to which the object belongs.

[0048] Exemplarily, a certain construction project is divided into three projects (Project A, Project B, Project C), and there are 4 teams (Team a, Team b, Team c, Team d). There can be a total of 3 * 4 = 12 organizational affiliations under this construction project. Worker Q can belong to Team a, Team b, and Team c simultaneously to participate in the construction. Moreover, Team a and Team b participate in the construction of Project A, and Team c participates in the construction of Project B. Then, it can be considered that the organizational affiliations of the worker are Team a and Team b of Project A, and Team c of Project B.

[0049] In some embodiments, the organizational affiliation of an object can be represented in the form of a vector or other possible forms. Taking the aforementioned Worker Q as an example, the organizational affiliation of this worker can be represented as {(Project A, Team a), (Project A, Team b), (Project B, Team c)}. An element in the vector represents a team in a project where the object is located.

[0050] The processor can determine the organizational affiliation of an object in various ways.

[0051] In some embodiments, the processor can call multiple work orders associated with the object in the database, and based on the multiple work orders associated with the object, determine the organizational affiliation of the object.

[0052] A work order is a document that records and circulates multiple construction tasks in a construction project. Construction tasks (also known as "tasks") can be recorded in the work order in the form of task items, and each work order can include one or more task items. Various information related to the task items is also recorded in the work order, including but not limited to the project to which it belongs, the team to which it belongs, the construction area, the performer, the planned construction period, etc. The construction area refers to the physical area where the task item should be constructed. The performer refers to who will complete the task item, and the performer is the object to which the work order is issued. The planned construction period refers to the planned execution time of the task item, which can include the planned start time, the planned completion time, etc.

[0053] Work orders can be divided into different levels according to their creators or the objects to which they are issued. For example, those created by the general contractor are general contractor work orders, those created by the subcontractor are subcontractor work orders, those created by the team are team work orders, and those created by the group are group work orders. Among them, team work orders and / or group work orders are directly issued to workers and can also be called worker work orders.

[0054] It can be understood that work orders are created by different creators based on the actual construction needs of the project. Therefore, the work order establishes an association relationship with the construction project and different levels of organizations when it is generated, and the worker work order establishes an association relationship with the object when it is issued. Therefore, the processor can determine the organization to which the object belongs and the corresponding project based on the multiple work orders associated with the object through the aforementioned association relationships.

[0055] In some embodiments, the processor may retrieve the organizational affiliation of an object from a database based on the basic information of the object. Exemplarily, when a work order is created, the processor may obtain the work team to which a preset worker belongs and the project corresponding to the work team, and upload the basic information of the worker, the work team to which the worker belongs, and the project corresponding to the work team to the database. When it is necessary to determine at least one work team in at least one project to which the object belongs, the processor may match the basic information of the object with the basic information of the worker to determine the work team to which the object belongs and the project corresponding to the work team.

[0056] In some embodiments, the processor may also determine the organizational affiliation of the object in other possible ways. For example, it may be determined based on the input of a management user, etc.

[0057] Step 220, based on the organizational affiliation of the object, obtain the reference information of the tasks that the object needs to perform in each project and each work team.

[0058] The reference information is information characterizing task-related data. For example, the reference information may include, but is not limited to, the relevant information corresponding to each task that the object needs to perform in different organizational affiliations, such as the construction area, planned construction period, etc. The reference information may be recorded separately or embodied in the form of a work order or other means.

[0059] In some embodiments, the construction area is the area where the object completes the corresponding construction task in an organizational affiliation. It can be understood that a construction project may correspond to a total project scope, and one or more construction projects under the construction project have their respective corresponding project areas. One or more construction tasks under each construction project have their respective corresponding construction areas, that is, the construction area is a part of the project scope and the project area.

[0060] In some embodiments, the construction area may be represented in the form of coordinates. For example, a three-dimensional rectangular coordinate system representing the project scope and / or project area may be pre-constructed and stored in the processor. The origin and direction of the coordinate axes may be set according to engineering practices and / or actual needs. Taking the construction of a three-dimensional rectangular coordinate system corresponding to a project area as an example, the processor may determine the center point of the project area as the origin of the coordinate axes, the X-axis represents the direction from west to east, the Y-axis represents the direction from south to north, and the Z-axis is perpendicular to the ground and points to the sky. The processor obtains the scope of the construction area through the user terminal, such as obtaining the scope circled by the user on the user terminal, the coordinate data input by the user, etc. When the user circles the scope on the user terminal, the processor may determine the three-dimensional coordinates of the boundary corner points, important structure points, etc. of the construction area according to the scope circled by the user, and calculate the three-dimensional boundary of the construction area through these coordinates.

[0061] In some embodiments, the construction area can be represented in ways such as a coordinate list, a system of inequalities, etc. For example, the coordinate list can directly list the coordinates of the key points of the construction area, and the system of inequalities can describe the extension range of the construction area in each coordinate axis direction through mathematical expressions, such as X ∈ [Xmin, Xmax], Y ∈ [Ymin, Ymax], Z ∈ [Zmin, Zmax]. The construction area can also be represented by various other possible ways, such as a 3D model, spatial inequalities, etc.

[0062] In some embodiments, the construction area and the planned construction period can be determined when creating a work order and saved to a database and / or storage device together with the work order.

[0063] In some embodiments, the processor can obtain reference information on the tasks that an object needs to perform in each team of each project based on the organizational affiliation of the object. For example, the processor can, based on the organizational affiliation of the object, retrieve the work order associated with the organization where the object is located and issued to the object, and extract data such as the construction area and the planned construction period from the work order, and then determine the reference information on the tasks that the object needs to perform in each team of each project.

[0064] Step 230, obtain the first attendance data of the object.

[0065] The first attendance data, which can also be referred to as project attendance data, refers to the attendance data of an object in a certain construction project and can represent the project attribution of the attendance data. The first attendance data can include the start time and end time when the object performs each project.

[0066] In some embodiments, the processor can obtain the first attendance data in various ways.

[0067] In some embodiments, the processor can obtain the original attendance data of the object through an attendance device, determine the project attribution of the original attendance data based on the reference information, and determine the first attendance data based on the time information and project attribution of the original attendance data.

[0068] The original attendance data is the original record of workers' clock-in, which can include the start time and end time when workers perform tasks, as well as the reviewed workers' make-up card records.

[0069] In some embodiments, the original attendance data can be obtained through an attendance device.

[0070] The project attribution of the original attendance data refers to the project corresponding to the original attendance data.

[0071] In some embodiments, the processor can determine the project attribution of the original attendance data in various ways based on the reference information.

[0072] In some embodiments, the processor may obtain the actual construction area where the object is located through an electronic fence, and determine the project attribution of the original attendance data of the object in the actual construction area by comparing the actual construction area with the construction area in the reference information. For example, if the overlapping ratio between the actual construction area where the object is located obtained through the electronic fence and the construction area R in the reference information corresponding to (Project B, Team c) is greater than the threshold, it is considered that the project attribution of the original attendance data of the object is Project B.

[0073] In some embodiments, the processor may determine the project attribution of the original attendance data based on the planned construction period in the reference information. For example, the processor may compare the time period corresponding to the original attendance data with the planned construction periods of different tasks in each project to determine whether the time period corresponding to the original attendance data belongs to the planned construction period of a certain task or whether the planned construction period of a certain task is within the time period corresponding to the original attendance data. If so, it is considered that the original attendance data belongs to the project corresponding to that task. Among them, the time period corresponding to the original attendance data belonging to the planned construction period of a certain task may include that the time period corresponding to the original attendance data is within the planned construction period range, or that the time period corresponding to the original attendance data has a time exceeding the duration threshold within the planned construction period range. For example, if the time period corresponding to the original attendance data is within the planned construction period (t3, t4) corresponding to (Project A, Team b), it is considered that the project attribution of the original attendance data is Project A.

[0074] In some embodiments, the processor may also determine the project attribution of the original attendance data obtained through the attendance device based on the project bound to the attendance device. For example, if the attendance device is bound to Project A, the project attribution of the original attendance data obtained through the attendance device is Project A.

[0075] In some embodiments, the processor may determine the start time, end time of the task executed in the foregoing original attendance data, and the project attribution of the original attendance data as the first attendance information.

[0076] In some embodiments, the processor may compare whether the identity information of the object in the original attendance data is consistent with the preset identity information of the object. If the identity information of the object in the original attendance data is consistent with the preset identity information of the object, the first attendance data is determined based on the original attendance data.

[0077] The identity information is the basic information of the worker for attendance, which may include a facial image, a worker ID number, or other information that can be used to identify the worker. In some embodiments, the identity information of the object in the original attendance data may be obtained through the attendance device.

[0078] The preset identity information is the basic information of the task performer. For a detailed description of the performer, please refer to the relevant description above. In some embodiments, the preset identity information may be determined based on the task sheet corresponding to the task in the project.

[0079] In some embodiments, if the identity information of the object in the original attendance data is consistent with the preset identity information, it indicates that the performer required in the task sheet has executed the corresponding task. At this time, the project attribution of the original attendance data can be determined based on the project to which the task sheet belongs, and the first attendance data can be determined based on the original attendance data and the project attribution.

[0080] Step 240, obtain the second attendance data of the object.

[0081] The second attendance data can also be referred to as regional attendance data, which refers to the attendance data of the object in a certain construction area and can represent the regional attribution of the attendance data. The second attendance data may include the entry time and departure time when the object executes each task. Among them, the entry time refers to the time when the object enters the construction area corresponding to the task, and the departure time refers to the time when the object leaves the construction area corresponding to the task.

[0082] In some embodiments, the processor can obtain the second attendance data in various ways. For example, the processor can determine the duration of the object in the construction area through a monitoring device and use it as the second attendance data.

[0083] In some embodiments, the processor can obtain the positioning information of the object, compare the positioning information with the construction areas in the reference information to determine the actual construction area where the object is located, determine the time range of the object in the actual construction area based on the time stamp in the positioning information, and determine the second attendance data based on the actual construction area where the object is located and the time range.

[0084] The positioning information is the information indicating the location of the object.

[0085] In some embodiments, the processor can obtain the positioning information of the object through the intelligent device carried by the object. For example, when the object carries / wears an intelligent device to execute tasks in the project, the intelligent device can periodically scan Bluetooth beacons and Wi-Fi AP signals and perform signal interaction with the positioning base station to obtain the integrated positioning result. The result of the integrated positioning can share a set of three-dimensional rectangular coordinate systems with the representation of the construction area and be represented in the form of coordinates. The specific form is similar to the representation method of the construction area. Please refer to the relevant description above.

[0086] During the execution of a task by an object, an intelligent device can obtain multiple locations of the object, such as obtaining a location every minute or other time intervals. When the location information is within the boundary of a construction area (e.g., the location of the object is within 1 meter of the boundary of the construction area), the location cycle can be shortened. For example, it can be adjusted to obtain a location every 1 second.

[0087] An intelligent device is a device that obtains the location information of an object, and can include but is not limited to intelligent safety helmets, intelligent bracelets, identity cards, user terminals (such as mobile phones, etc.).

[0088] The actual construction area is the area where the object is located during the execution of tasks in a project. The area where the object is actually executing tasks should match the scope of the construction area or be part of the construction area. When the location of the object is not within the scope of the construction area, it can be considered that the object is not in a state of performing tasks.

[0089] In some embodiments, the processor can compare the location information with the construction areas of multiple tasks in the reference information, and determine the overlapping part of the two as the actual construction area where the object is located, and determine the task corresponding to the actual construction area where the object is located based on the comparison result, that is, determine which task's construction area the actual construction area belongs to from the construction areas corresponding to multiple tasks.

[0090] The time range refers to the time interval during which the location information of the object is within the construction area.

[0091] In some embodiments, the processor can determine the time range of the object in the actual construction area based on the timestamp in the location information. For example, the processor can determine the time range based on the starting time when the object enters the actual construction area and the time when it leaves the actual construction area closest to the starting time.

[0092] In some embodiments, the processor can determine the second attendance data based on the actual construction area to which the object belongs and the time range. For example, the processor can compare the location information of the object with the construction areas in the task information to determine the actual construction area where the object is located, and record the time node when the object first enters the actual construction area within a preset time period and the time node when it first leaves the actual construction area, and designate the time node when the object first enters the actual construction area and the time node when it first leaves the actual construction area as the area attendance data. If the object repeatedly enters and exits the construction area, the time between multiple entries and exits is used as the area attendance data.

[0093] Step 250: Based on the first attendance data, the second attendance data, and the planned construction period of each task in the reference information, determine the target attendance information for the object to perform the corresponding tasks in each project and each team.

[0094] The target attendance information is the actual duration and / or time range of an object performing corresponding tasks in different teams and different projects.

[0095] In some embodiments, the processor may obtain first attendance data and second attendance data generated by the object when performing each task, that is, each task has its own corresponding first attendance data and second attendance data. Based on this, the processor may determine the execution situation of each task, and further determine the attendance information of the object in each project and the corresponding team.

[0096] In some embodiments, the processor may determine the target attendance information of the object performing the corresponding task in each project and each team through various methods based on the planned duration of each task in the first attendance data, the second attendance data, and the reference information. For example, if the time ranges corresponding to the first attendance data, the second attendance data, and the planned duration are less than a preset duration when pairwise compared, any one of the first attendance data, the second attendance data, and the planned duration may be determined as the target attendance information based on user input.

[0097] In some embodiments, the processor may determine the overlapping time period among the first attendance data, the second attendance data, and the planned duration corresponding to each task as the target attendance duration of the object performing the corresponding task in the team of the project corresponding to the task. For more detailed descriptions, reference may be made to this specification Figure 3 and its related descriptions.

[0098] In some embodiments of this specification, by comparing the first attendance data, the second attendance data, and the planned duration to determine the attendance allocation, it is possible to automatically determine the construction tasks and corresponding construction durations of workers in the case of multiple tasks, multiple projects, and multiple teams for construction workers, improving the accuracy, authenticity, and management efficiency of the attendance allocation data.

[0099] In some embodiments, the processor may also determine whether at least one of the first attendance data, the second attendance data, and the planned duration is abnormal, and recommend corresponding solutions. For more detailed descriptions, reference may be made to this specification Figure 4 and its related descriptions.

[0100] It should be noted that the above description of process 200 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0101] Figure 3 is a schematic diagram of determining the target attendance information according to some embodiments of this specification. As Figure 3As shown, determining the target attendance information may include the following. In some embodiments, determining the target attendance information may be performed by a processor.

[0102] In some embodiments, the processor may determine the overlapping time periods among the first attendance data, the second attendance data, and the planned construction period as the target attendance duration corresponding to the tasks performed by the object in different teams under different projects.

[0103] In some embodiments, the processor may specify the overlapping time periods among the first attendance data corresponding to each task, the second attendance data corresponding to each task, and the planned construction period corresponding to each task as the target attendance duration of the object in each team under each project. Herein, a task refers to the task that the object should perform in the team under the corresponding project and can be determined based on the task order issued by the team.

[0104] In some embodiments, after the object completes a full project attendance, the processor may retrieve three types of attendance records in the database and / or storage device for comparison. Among them, a full project attendance may be that on a construction day, the attendance device obtains the start time and end time of the object performing a specific project, that is, obtains the first attendance data.

[0105] It can be understood that the first attendance data may correspond to multiple tasks. Only as an example, if a certain object participates in the construction of Project A and Project B at the same time, in Project A, the object performs Task 1 and Task 2 based on task orders p1 and p2 in teams a and b respectively, and in Project B, the object performs Task 3 based on task order p3 in team c.

[0106] Such as Figure 3As shown, after the object completes the full project attendance for Project A, the processor can call the first attendance data (tA1, tA2) corresponding to Project A, and respectively determine the planned durations (t1, t2), (t3, t4) corresponding to Task 1 and Task 2 based on work order p1 and work order p2; determine the second attendance data (tα1, tα2), (tγ1, tγ2) corresponding to Task 1 and Task 2 respectively; obtain the overlapping time periods (tx1, tx2) based on the first attendance data (tA1, tA2), the planned duration (t1, t2) of Task 1, and the second attendance data (tα1, tα2) of Task 1. The overlapping time periods (tx1, tx2) belong to work order p1, team a, and Project A, that is, the overlapping time periods (tx1, tx2) are the target attendance durations corresponding to the object's execution of Task 1 in team a of Project A; obtain the overlapping time periods (tx3, tx4) based on the first attendance data (tA1, tA2), the planned duration (t3, t4) of Task 2, and the second attendance data (tγ1, tγ2) of Task 2. The overlapping time periods (tx3, tx4) belong to work order p2, team b, and Project A, that is, the overlapping time periods (tx3, tx4) are the target attendance durations corresponding to the object's execution of Task 2 in team b of Project A.

[0107] Similarly, after the object completes the attendance for Project B, the processor can call the first attendance data (tB1, tB2) corresponding to Project B, determine the planned duration (t5, t6) corresponding to Task 3 based on work order p3, determine the second attendance data (tβ1, tβ2) corresponding to Task 3, obtain the overlapping time periods (tx5, tx6), and determine the overlapping time periods (tx5, tx6) as the target attendance durations corresponding to the object's execution of Task 3 in team c of Project B.

[0108] In some embodiments of the present specification, by combining the attendance system and regional positioning, the actual attendance duration of workers is determined according to three time periods of the first attendance data, the second attendance data, and the planned duration, and the actual time used by workers to handle different tasks in different projects and different teams is determined, which can achieve intelligent allocation of attendance duration, improve the efficiency of attendance data allocation, and avoid the interference of human factors, making the determined attendance duration more authentic and reliable.

[0109] Figure 4 It is a schematic diagram showing the determination of the reasons for abnormal attendance data and recommended solutions according to some embodiments of the present specification. As Figure 4 shown, determining the reasons for abnormal attendance data and recommended solutions may include the following content. In some embodiments, determining the reasons for abnormal attendance data and recommended solutions may be executed by the processor.

[0110] In some embodiments, the processor may obtain time difference data 440 between first attendance data 410, second attendance data 420, and the planned duration 430 of a task; in response to the time difference data 440 not meeting a preset condition 450, the processor may determine that there is an abnormality in the attendance data of the task.

[0111] The time difference data refers to the differences between the first attendance data, the second attendance data, and the planned duration of the task pairwise. In some embodiments, the time difference data may include at least one of a first time difference, a second time difference, a third time difference, and missing attendance data.

[0112] The first time difference refers to the difference between the first attendance data and the planned duration.

[0113] The second time difference refers to the difference between the first attendance data and the second attendance data.

[0114] The third time difference value is the difference between the second attendance data and the planned duration.

[0115] Missing attendance data means that at least one of the first attendance data, the second attendance data, and the planned duration is missing. The missing first attendance data may be because the object failed to enter the project through identity recognition and could not prove that the construction object was the person himself, and it was determined as invalid attendance; the missing second attendance data may be because the object did not enter the construction area corresponding to the planned duration to participate in construction, and it was determined as invalid attendance; the missing planned duration may be because the object did not enter the construction area for construction within the planned duration, and it was determined as invalid attendance.

[0116] It should be noted that missing attendance data does not necessarily mean that a certain attendance data cannot be obtained. It may also be that the attendance data exists, but there is no overlapping period with other attendance data. For example, if the object did not enter the construction area for construction within the planned duration, there is no overlapping time period between the first attendance data and the second attendance data generated and the planned duration, representing the missing planned duration. In some embodiments, the processor calculates the first time difference, the second time difference, and the third time difference based on the first attendance data, the second attendance data, and the planned duration of the task respectively. Taking the calculation of the first time difference on a certain construction day as an example, the processor may determine the actual construction duration of the task in the project according to the start time and end time of the first attendance data, determine the planned construction duration of the task in the project according to the planned duration, and determine the difference between the actual construction duration and the planned construction duration as the first time difference. The calculation of the second time difference and the third time difference can be carried out in a similar manner.

[0117] The preset condition is the condition for judging whether there is an abnormality in the attendance data.

[0118] In some embodiments, the preset condition may include that the attendance data is missing. When the attendance data is missing, the processor may directly determine that the attendance data is abnormal.

[0119] In some embodiments, when the attendance data is abnormal due to missing, the processor may determine that the attendance data is invalid and does not count towards the working hours of the object.

[0120] In some embodiments, if there is no overlapping period between the first attendance data and the second attendance data of the object obtained when the object performs a certain task and the planned construction period corresponding to the task, it indicates that the planned construction period is missing, and the periods corresponding to the first attendance data and the second attendance data are not within the planned construction period of the task. It may be because the object did not perform the task within the planned construction period. The processor may determine that the attendance data is abnormal and does not count the working hours. At this time, the processor may prompt the management user to confirm. If it is confirmed that the record is true, a new task order within this period is reissued. In response to receiving the feedback that the object has received the new task order, the processor calculates the corresponding working hours.

[0121] In some embodiments, if there is an overlapping period between the first attendance data of the object obtained when the object performs a certain task and the planned construction period, and there is no overlapping period between the first attendance data and the planned construction period and the second attendance data, it indicates that the second attendance data is missing. It may be because the object did not enter the construction area corresponding to the task during the planned construction period of the task. The processor may determine that the attendance data is abnormal and does not count the working hours.

[0122] In some embodiments, if there is an overlapping period between the second attendance data of the object obtained when the object performs a certain task and the planned construction period, and there is no overlapping period between the second attendance data and the planned construction period and the first attendance data, it indicates that the first attendance data is missing. It may be because the object did not enter the construction area corresponding to the task through identity recognition and cannot prove that the object himself entered the construction area. The processor may determine that the attendance data is abnormal and does not count the working hours. At this time, the processor may prompt the worker to initiate a project card replacement application and prompt the management user to confirm the worker's project card replacement application. In response to the project card replacement application being approved, the processor calculates the corresponding working hours.

[0123] In some embodiments, the preset condition may include that the time difference data is not greater than the time threshold. Wherein, the time threshold is the maximum allowable value of the time difference data and can be determined based on prior experience. The time difference data not being greater than the time threshold means that the difference between the actual construction duration and the planned construction duration is not greater than the time threshold.

[0124] The attendance data being without missing means that there is an overlapping period between the first attendance data, the second attendance data and the planned construction period.

[0125] In some embodiments, the time threshold may include at least one of a first threshold, a second threshold, and a third threshold.

[0126] In some embodiments, if at least one of the following conditions is met: the first time difference is greater than the first threshold, the second time difference is greater than the second threshold, and the third time difference is greater than the third threshold, it can be determined that there is an abnormality in the attendance data.

[0127] In some embodiments, if the first time difference is greater than the first threshold, it indicates that the time when the object punches in for the project is too different from the planned construction period; if the second time difference is greater than the second threshold, that is, the difference between the first attendance data and the second attendance data is too large, it indicates that the object fails to enter the construction area in a timely manner after punching in for the project; if the third time difference is greater than the third threshold, that is, the difference between the second attendance data and the planned construction period is too large, it indicates that the construction efficiency of the object is lower than expected.

[0128] In some embodiments, if there is an abnormality in the attendance data, the processor can further analyze the cause of the abnormality and determine a recommended solution.

[0129] In some embodiments of this specification, by analyzing the differences among the three time periods of the first attendance data, the second attendance data, and the planned construction period, the abnormal cause leading to the abnormality in the attendance data can be accurately determined, and a targeted solution can be automatically matched based on the abnormal cause, providing decision-making support for construction project management, which is beneficial to reducing the task delay rate and management cost.

[0130] In some embodiments, the implementation comparison of the first attendance data, the second attendance data, and the planned construction period of the worker task sheet can be displayed through the user terminal. Data analysis is performed when the first attendance data or the second attendance data has not changed for 1 hour to detect whether there is an abnormality in the attendance data, and the warning content and the corresponding recommended solution are pushed. The warning content may include the abnormal situation of the attendance data, the abnormal cause, and the recommended solution. For example, the warning content may be "The regional attendance time exceeds the plan by 15%. Possible reasons: 1. Equipment failure (probability 65%); 2. Insufficient personnel skills (probability 30%). Recommended solution: Check the equipment status (predicted optimization 20%), arrange skill training (predicted optimization 10%)".

[0131] Figure 5 is a schematic diagram of the root cause analysis model shown in some embodiments of this specification. As Figure 5 shown, the root cause analysis model and its execution may include the following content.

[0132] In some embodiments, the processor can determine the abnormal cause 550 that causes the abnormality in the attendance data through the root cause analysis model 540, and determine the recommended solution based on the abnormal cause and the preset solution.

[0133] The root cause analysis model is a model that analyzes the root cause of the time difference based on input features.

[0134] In some embodiments, the root cause analysis model may include, but is not limited to, one or more of the 5 Whys analysis method, fishbone diagram (cause-and-effect diagram or Ishikawa diagram), fault tree analysis (FTA), Pareto analysis, and causal loop diagram.

[0135] In some embodiments, the root cause analysis model may also be a Bayesian network.

[0136] The input features of the root cause analysis model 540 may include time difference data 440, device status 510, object attribute information 520, and environmental data 530.

[0137] Device status information is data that describes the operating conditions of intelligent devices at the construction site. For example, the device status information may include indicators such as the online / offline status (such as network connection stability), signal strength (such as wireless transmission power value), and faulty device code (such as hardware error code type) of devices such as panel machines, Bluetooth beacons, and UWB base stations.

[0138] Object attribute information is multi-dimensional data that describes worker attributes and job characteristics. For example, the object attribute information may include, but is not limited to, one or more of worker skill level, historical attendance rate, work efficiency, and training records.

[0139] Environmental data is data that describes the environmental characteristics of the construction site. For example, the environmental data may include, but is not limited to, one or more external factors that affect construction, such as the weather at the construction site (such as temperature, humidity, rain, snow, etc.), lighting conditions, and noise level.

[0140] In some embodiments, the processor may determine the device status information by communicating with the construction site devices and / or user input, determine the object attribute information by calling the data pre-stored in the database and / or obtaining the worker information input by the user, and obtain the environmental data by networking and / or sensors set at the construction site.

[0141] For a detailed description of the time difference data, refer to the previous description.

[0142] The output of the root cause analysis model 540 may include the abnormal cause 550. The abnormal cause is the root cause of the abnormal time difference. In some embodiments, the abnormal cause may include one or more of device failure, worker skill level not meeting requirements, and weather impact, and may also include other possible causes of abnormal time difference.

[0143] In some embodiments, the root cause analysis model may include a multi-layer variable network composed of multiple nodes and edges connecting the nodes. For example, the processor may obtain input features as nodes, divide the input features into root cause variables and observed variables, connect the nodes based on the dependence relationships between the variables, determine multiple directed edges, and thereby construct a multi-layer variable network.

[0144] Nodes are used to represent variables. For example, the root cause analysis model may include nodes representing variables such as device status (D), weather (W), personnel skills (S), attendance time difference (A), attendance data anomaly (R), etc.

[0145] In some embodiments, variables may include root cause variables and observed variables. Among them, root cause variables refer to variables that can cause changes in other variables, and their values or states do not depend on other variables; observed variables are variables that can be observed or measured to describe the status of attendance data, and their values or states are affected by other variables (such as root cause variables).

[0146] In some embodiments, root cause variables may include one or more of device status (D), weather (W), and personnel skills (S); observed variables may include attendance time difference (A), attendance data anomaly (R), etc. Among them, attendance data anomaly includes missing attendance data and / or time difference data not meeting the preset conditions.

[0147] In some embodiments, the nodes corresponding to root cause variables may be parent nodes, and the nodes corresponding to observed variables may be child nodes. The parent nodes and child nodes are connected by directed edges.

[0148] Edges are used to represent the causal relationships between variables. The direction of the edge can be from the parent node to the child node, indicating the dependence of the child node on the parent node, that is, a change in the parent node can cause the state of the child node to change accordingly. The nodes follow the principle of direct dependence, and the state of each child node is determined only by its direct parent node, forming a directed dependence chain. Non-directly associated variables are independent of each other, and their state changes do not interfere with each other.

[0149] Based on the dependencies between nodes, the connection relationships between different variables can be determined. For example, weather changes affect the device status, and an abnormal device status leads to abnormal attendance data. Then, the connection relationships between several nodes, namely device status (D), weather (W), and abnormal attendance data (R), can be determined as: Weather (W) → Device Status (D) → Attendance Time Difference (A). Among them, weather (W) is the parent node of device status (D), device status (D) is the parent node of attendance time difference (A), and device status (D) and attendance time difference (A) are the child nodes of weather (W) and device status (D) respectively. Another example is that if the personnel skills do not meet the requirements, it may cause abnormal attendance data, and abnormal attendance data may lead to attendance time differences. Then, the connection relationship can be determined as: Personnel Skills (S) → Attendance Time Difference (A) → Abnormal Attendance Data (R). Another example is that the device status also affects the attendance time difference. Then, the connection relationship can be determined as: Device Status (D) → Attendance Time Difference (A). Based on the foregoing multiple different connection relationships, the processor can determine a multi-layer variable network.

[0150] In some embodiments, the root cause variable triggers a cascading effect through the state transfer path. For example, a change in the parent node state triggers an update of the child node state, and the child node can be further transformed into the parent node of the next level, continuing to affect deeper child nodes. In the multi-layer variable network, there is a unique root node, representing the root cause of the anomaly. By tracing the dependency chain, the initial trigger variable can be located to achieve anomaly attribution.

[0151] In some embodiments, the root cause analysis model further includes a conditional probability table.

[0152] The conditional probability table is data that quantifies the strength of the causal association between variables and can describe the likelihood of different states of the observed variable occurring under specific values of the root cause variable through a probability distribution.

[0153] In some embodiments, the conditional probability table corresponds to the connection relationships in the multi-layer variable network. For example, the conditional probability table includes multiple combinations of parent node states and the conditional probabilities of different child node states under different combinations of parent node states. Such as Figure 6As shown, in the relationship chain of "Weather (W) → Equipment Status (D) → Attendance Time Difference (A)", if the weather includes two states of heavy rain and sunny, the conditional probability table including the prior probability of weather (W), the conditional probability of equipment status (D) affected by weather (W), and the conditional probability of attendance time difference (A) affected by equipment status (D) can be determined. Among them, the conditional probability of equipment status (D) affected by weather (W) can quantify the probability of equipment failure / non-failure under different weather conditions, and the conditional probability of attendance time difference (A) affected by equipment status (D) can describe the probability distribution of attendance anomalies / non-anomalies under different failure states of the equipment. Through the conditional probability table, the Bayesian network can be supported to reverse-infer the probability distribution of the root cause variable based on the state of the child node.

[0154] In some embodiments, the conditional probability table can be obtained based on historical data and / or expert knowledge. For example, the processor can mine the analysis results of historical data, determine the variable association patterns therein, refine the initial probability distribution law, and introduce domain expert knowledge to correct the initial probability to form a conditional probability table.

[0155] In some embodiments, the conditional probability table can be stored as a prior rule in a database and / or storage device, and the root cause analysis model can call the conditional probability table to analyze the abnormal cause and its probability. In some embodiments, the conditional probability table can also be directly stored in the root cause analysis model.

[0156] In some embodiments, in response to the existence of anomalies in attendance data, the processor can input the attendance data anomaly as an evidence node into the root cause analysis model, and reverse-collect the states of the corresponding nodes of each observed variable along the connection relationships in the multi-layer variable network; calculate the prior probability distribution of each node according to the conditional probability table, and then update the posterior probability of the corresponding node of the root cause variable based on the states of each node to achieve reverse correction of the probability, and determine the root cause variable with the highest probability as the abnormal cause. If there are multiple high-probability root causes, the root cause analysis model can generate a combination of abnormal causes and sort them according to the probability size.

[0157] Exemplarily, if the attendance time difference and attendance data anomaly are observed, the probability of inferring potential causes through the posterior probability by the processor can be expressed by the following formula: P(D│A,R)=(P(A,R│D)·P(D)) / (P(A,R)) (Formula 1) P(S│A,R)=(P(A,R│S)·P(S)) / (P(A,R)) (Formula 2)

[0158] Wherein, A represents the observed difference in attendance time, R represents the observed abnormality in attendance data, D represents equipment failure, S represents insufficient personnel skills. Formula (1) represents the probability of attendance time difference and attendance data abnormality caused by equipment failure, and formula (2) represents the probability of attendance time difference and attendance data abnormality caused by insufficient personnel skills.

[0159] In some embodiments, the processor can determine a recommended solution based on the cause of the abnormality and the set solution.

[0160] The preset solution is a countermeasure pre-set to solve the abnormality in attendance data. In some embodiments, the abnormalities in attendance data caused by different reasons can correspond to different preset solutions.

[0161] In some embodiments, the preset solution can be obtained based on historical experience and stored in a database and / or storage device.

[0162] The recommended solution is a countermeasure that can be adopted to solve the abnormality in attendance data.

[0163] In some embodiments, the processor can compare the probabilities of multiple causes of abnormality, query in the database and / or storage device according to the cause of abnormality with the highest probability, call the preset solution corresponding to the cause of abnormality, and recommend it to the user as the recommended solution.

[0164] In some embodiments, the root cause analysis model can be built-in with historical root cause data 541. At this time, the processor can obtain at least one of the device status information 510, object attribute information 520, and environmental data 530. Through the root cause analysis model 540, based on the time difference data 440, device status 510, object attribute information 520, environmental data 530, and historical root cause data 541, judge the cause of abnormality 550, the probability of the cause of abnormality 560, and output the corresponding recommended solution 570.

[0165] The historical root cause data is a record of the root cause analysis results and the effectiveness of the solutions for the abnormality in attendance data in historical data. Exemplarily, one of the historical root cause data can include "when the cause of the abnormality is a faulty device, replacing the device is used as a solution, and the corresponding success rate is 85%".

[0166] In some embodiments, the historical root cause data can be built-in in the root cause analysis model and does not need to be input each time. When the processor analyzes the cause of the abnormality in attendance data through the root cause analysis model, the root cause analysis model can match one or more solutions with the highest success rate corresponding to the cause of abnormality in the historical root cause data according to the cause of abnormality with the highest probability, as the recommended solution, and push it to the user through the user terminal.

[0167] In some embodiments, the historical root cause data can be determined based on historical data. For example, the processor can count the historical abnormal causes and corresponding historical solutions when the historical attendance data is abnormal in the historical data, and count the success rate of the historical solution. The historical abnormal cause, the corresponding historical solution and its success rate are used as the historical root cause data.

[0168] Among them, the success rate refers to the proportion of the solution that can successfully solve the abnormal cause. If the attendance data anomaly no longer occurs after applying the solution, it can be considered that the solution has successfully eliminated the abnormal cause. The processor can count the proportion of the number of times that a certain solution is successfully used to eliminate the same abnormal cause in the historical data to the total number of times that the abnormal cause appears, and use this proportion as the success rate of the solution.

[0169] In some embodiments, the processor can obtain the effect data 580 of the recommended solution 570, and update the historical root cause data 541 based on the abnormal cause 550, the recommended solution 570 and the corresponding effect data 580, so as to optimize the root cause analysis model 540.

[0170] The effect data is the data that quantitatively evaluates the actual effectiveness of the recommended solution in reducing the attendance data anomaly. The effect data can be represented by a numerical value. The larger the numerical value, the better the actual effectiveness of the recommended solution in reducing the attendance data anomaly solution.

[0171] In some embodiments, the effect data can be determined by comparing the time difference ratio before and after the execution of the recommended solution. For example, the processor can collect the time ratio of the regional attendance time of the object exceeding the planned construction period before the execution of the recommended solution and record it as the reference ratio; after the execution of the recommended solution, collect the time ratio of the regional attendance time of the object in the same area exceeding the planned construction period again; compare the time ratio after the execution of the solution with the reference ratio, and use the percentage of the reduction value of the time ratio to the reference ratio as the effect data of the recommended solution.

[0172] In some embodiments, the processor can determine the effectiveness of the solution corresponding to each piece of data in the historical root cause data in a similar way and use it as the label of this piece of data.

[0173] In some embodiments, the processor may update the historical root cause data in the root cause analysis model based on the cause of the exception, the recommended solution, and the corresponding effectiveness data, thereby optimizing the root cause analysis model. For example, if the cause of the exception is a new cause not present in the historical root cause data, or the recommended solution is a solution for resolving a certain historical cause of the exception that is not present in the historical root cause data, then the cause of the exception and its corresponding recommended solution are recorded as new historical root cause data, and the corresponding effectiveness data is used as a label; if the cause of the exception and its corresponding recommended solution are data that have appeared in the historical root cause data, then the cause of the exception and its corresponding recommended solution are included in the statistics, and the success rate of this solution is updated.

[0174] In some embodiments of this specification, by feeding back the effectiveness data to the root cause analysis model, the root cause analysis model can be dynamically optimized, enabling it to more accurately identify the root cause of the attendance exception and optimizing the effectiveness of the recommended solutions of the root cause analysis model, thereby enhancing the intelligent level of attendance management.

[0175] In some embodiments of this specification, by inputting time difference data, device status, object attribute information, and environmental data, factors related to the abnormality of attendance data can be fully considered, which is conducive to the root cause analysis model outputting more accurate results and improving the accuracy of abnormal cause analysis.

[0176] Some embodiments of this specification also provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the aforementioned attendance data allocation method.

[0177] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is merely an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0178] At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0179] In addition, unless otherwise specified in the claims, the order of the processing elements and sequences, the use of numerical and alphabetical characters, or the use of other names described in this specification are not used to limit the order of the processes and methods in this specification. Although various examples have been discussed in the above disclosure for some currently useful embodiments of the invention, it should be understood that such details are for illustrative purposes only. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0180] Similarly, it should be noted that, in order to simplify the presentation of the disclosure in this specification and thus assist in the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, various features are sometimes grouped together in one embodiment, drawing, or description thereof. However, this method of disclosure does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0181] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. This excludes application history documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0182] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered to be in accordance with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. An attendance data allocation method, comprising: Determine the organizational affiliation of the object, where the organizational affiliation includes different projects and different teams to which the object belongs; Based on the organizational affiliation of the object, obtain reference information on the tasks that the object needs to perform in each project and each team; Obtain the first attendance data of the object, where the first attendance data includes the start time and end time when the object conducts each project; Obtain the second attendance data of the object, where the second attendance data includes the entry time and exit time when the object performs each task; Based on the first attendance data, the second attendance data, and the planned duration of each task in the reference information, determine the target attendance information for the object to perform the corresponding tasks in each project and each team.

2. The method according to claim 1, where obtaining the first attendance data of the object includes: Obtain the original attendance data of the object through an attendance device; Determine the project affiliation of the original attendance data based on the reference information; Based on the time information of the original attendance data and the project affiliation, determine the first attendance data.

3. The method according to claim 1, wherein The obtaining the second attendance data of the object includes: Obtain the positioning information of the object; Compare the positioning information with the construction areas in the reference information to determine the actual construction area where the object is located; Based on the timestamp in the positioning information, determine the time range of the object in the actual construction area; Based on the actual construction area where the object is located and the time range, determine the second attendance data.

4. The method according to claim 1, wherein The determining the target attendance information for the object to perform the corresponding tasks in different teams and different projects based on the first attendance data, the second attendance data, and the planned duration in the reference information includes: Determine the overlapping period among the first attendance data, the second attendance data, and the planned duration of the corresponding task in the task corresponding reference information as the target attendance duration for the object to perform the corresponding task in the team under the project.

5. The method according to claim 1, wherein Further includes: Obtain the time difference data among the first attendance data, the second attendance data, and the planned duration of the task; In response to the time difference data not meeting the preset conditions, determine that there is an abnormality in the attendance data, where the attendance data includes at least one of the first attendance data, the second attendance data, and the planned duration.

6. The method according to claim 5, wherein Further includes: Determine the abnormal cause leading to the abnormality in the attendance data through a root cause analysis model; And Determine a recommended solution according to the abnormal cause and the preset solution.

7. The method according to claim 5, wherein Further includes: Obtain at least one of device status information, object attribute information, and environmental data; Through a root cause analysis model, based on the time difference data, the device status, the object attribute information, the environmental data, and historical root cause data, judge the abnormal cause, the probability of the abnormal cause, and the corresponding recommended solution.

8. The method according to claim 7, wherein Further includes: Obtain the effect data of the recommended solution; Update the historical root cause data and optimize the root cause analysis model based on the abnormal cause, the recommended solution, and the corresponding effect data.

9. An attendance data distribution system, comprising: A determination module configured to determine the organizational affiliation of an object; A first acquisition module configured to acquire reference information on tasks that the object needs to perform in each project and each workgroup based on the organizational affiliation of the object; A second acquisition module configured to acquire first attendance data of the object, where the first attendance data includes the start time and end time when the object performs each project; A third acquisition module configured to acquire second attendance data of the object, where the second attendance data includes the entry time and exit time when the object performs each task; A data distribution module configured to determine target attendance information for the object to perform corresponding tasks in each project and each workgroup based on the first attendance data, the second attendance data, and the planned duration of each task in the reference information.

10. A computer-readable storage medium storing computer instructions, where when a computer reads the computer instructions in the storage medium, the computer executes the attendance data distribution method according to any one of claims 1 to 8.

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