A method, system and device for rapid function recommendation for the construction industry

Abstract

The present invention provides a method, system and device for quickly recommending functions for the construction industry. The method comprises: obtaining multiple candidate tasks from system construction tasks based on the spatial position information of the current user; obtaining visual information of the current user, and determining key elements of the task based on the visual information; determining a target task from the multiple candidate tasks based on the key elements of the task and the identity information of the current user; determining the user intention of the current user based on the target task; and determining functions recommended to the current user based on the user intention. The method, system and device provided by the present invention have a high level of intelligence and automation, and can improve operational convenience and construction project management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of construction, and in particular to a method, system and device for quickly recommending functions for the construction industry. Background Art

[0002] In the construction industry, work scenarios are complex and diverse, involving numerous people with distinct responsibilities. The tools and functions required vary depending on the specific task and scenario. Traditional construction-related software or systems often have cumbersome function selection, requiring users to navigate numerous menus to find the required function. This is inefficient and prone to errors, seriously affecting work progress and quality.

[0003] In view of this, the present invention provides a method, system and device for quickly recommending functions for the construction industry to solve the above problems. Summary of the Invention

[0004] One or more embodiments of the present invention provide a method for quickly recommending functions for the construction industry, the method comprising: obtaining multiple candidate tasks from system construction tasks based on spatial location information of a current user; obtaining visual information of the current user, and determining key elements of the task based on the visual information; determining a target task from the multiple candidate tasks based on the key elements of the task and identity information of the current user; determining the user intent of the current user based on the target task; and determining functions recommended to the current user based on the user intent.

[0005] One or more embodiments of the present invention also provide a rapid function recommendation system for the construction industry, the system comprising: an acquisition module, configured to acquire multiple candidate tasks from system construction tasks based on the spatial location information of the current user; a first determination module, configured to acquire visual information of the current user and determine task key elements based on the visual information; a second determination module, configured to determine a target task from the multiple candidate tasks based on the task key elements and the identity information of the current user; a third determination module, configured to determine the user intention of the current user based on the target task; and a fourth determination module, configured to determine the function recommended to the current user based on the user intention.

[0006] One or more embodiments of the present invention also provide a device for quickly recommending functions for the construction industry, the device comprising: at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to implement the method for quickly recommending functions for the construction industry as described in the above embodiments.

[0007] The method, system and device for rapid function recommendation for the construction industry provided in the present invention combine the user's visual information with the spatial position, system tasks and user identity to achieve rapid and accurate judgment of the current construction progress and user location. At the same time, it combines the task management system with the construction tasks to achieve accurate inference of the operational intentions of various personnel. It has a high level of intelligence and automation, and can improve operational convenience and construction project management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0009] Figure 1 2. This is a schematic diagram of an application scenario of a rapid function recommendation system for the construction industry according to some embodiments of the present invention;

[0010] Figure 2 is an exemplary flow chart of a method for rapid function recommendation for the construction industry according to some embodiments of the present invention;

[0011] Figure 3 is an exemplary flow chart of determining a target task according to some embodiments of the present invention;

[0012] Figure 4 is an exemplary flow chart of determining user intent according to other embodiments of the present invention;

[0013] Figure 5 FIG. 1 is a schematic diagram of internal modules of a processor according to some embodiments of the present invention. DETAILED DESCRIPTION

[0014] To more clearly illustrate the technical solutions of the present invention, the following briefly describes the drawings used in the description of the embodiments. Obviously, the drawings described below are merely examples or embodiments of the present invention. Those skilled in the art will be able to apply the present invention to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0015] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0016] As used herein and in the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0017] Flowcharts are used in this disclosure to illustrate the operations performed by systems according to embodiments of the present invention. It should be understood that the preceding and following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0018] Figure 1 Schematic diagram of an application scenario of a function rapid recommendation system for the construction industry according to some embodiments of the present invention. Figure 1 As shown, the function rapid recommendation system 100 for the construction industry includes a current user 110, a network 120, a terminal 130, a processor 140, and a storage device 150.

[0019] The current user 110 refers to the person who needs function recommendation and / or the client used by him / her. For example, the current user 110 may include a construction worker, a team leader, a construction management person (such as a construction worker, an acceptance person, etc.) or the client used by him / her.

[0020] In some embodiments, personnel with different identities often correspond to different work tasks. Even personnel with the same identity may have different tasks to undertake in different areas of a construction project. For example, the work tasks corresponding to construction personnel include construction guidance, task drawing viewing, and tool query; the work tasks corresponding to team leaders include task splitting, task self-inspection, initiation of acceptance and other management-related tasks; the work tasks corresponding to construction management personnel include project acceptance, construction inspection and other tasks. For another example, construction personnel in the electrical installation area correspond to work tasks related to electrical installation, and in the water supply and drainage installation area correspond to work related to pipeline installation and regulation. In some embodiments, function recommendations can be made based on the current user's location information, visual information and identity information. For more information on related content, please refer to the relevant parts below, such as Figures 2 to 4 .

[0021] The network 120 can connect various components of the rapid function recommendation system 100 for the construction industry and / or connect other components outside the rapid function recommendation system 100 for the construction industry. In some embodiments, one or more components of the rapid function recommendation system 100 for the construction industry (e.g., the terminal 130, the processor 140, and the storage device 150, etc.) can be connected and / or communicate with each other via the network 120. For example, the terminal 130 can send the user's visual information to the processor 140, etc. via the network 120.

[0022] Terminal 130 can provide functional components related to user interaction and can implement user interaction functions (e.g., providing or displaying information and data to the user). User can refer to current user 110. By way of example only, terminal 130 can be one or any combination of a mobile device, a tablet computer, a laptop computer, a desktop computer, or other device with input and / or output functions.

[0023] In some embodiments, the terminal 130 may have a positioning function. For example, the terminal 130 may obtain the spatial location information of the current user 110.

[0024] In some embodiments, the terminal 130 may have an image acquisition function. For example, the terminal 130 may acquire visual information of the current user 110 .

[0025] In some embodiments, the terminal 130 may also have a display function. For example, the terminal 130 may display recommended functions to the current user 110. The above description of the terminal 130 is merely an example, and the terminal 130 may also have other functions, such as an input function, which are not limited here.

[0026] The processor 140 is capable of processing information and / or data related to the rapid function recommendation system 100 for the construction industry to perform one or more functions described in the present invention. In some embodiments, the processor 140 can obtain multiple candidate tasks from the system construction tasks based on the spatial location information of the current user; obtain the visual information of the current user, and determine the key elements of the task based on the visual information; determine the target task from the multiple candidate tasks based on the key elements of the task and the identity information of the current user; determine the user intention of the current user based on the target task; and determine the function recommended to the current user based on the user intention. Detailed description of the relevant content can be found later (such as Figures 2 to 4 etc.) related descriptions.

[0027] In some embodiments, the processor 140 may include a central processing unit (CPU), a digital signal processor (DSP), a microcontroller unit (MCU), a computer, a user console, or the like, or any combination thereof. In some embodiments, the processor 140 may include a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 140 may be local or remote. In some embodiments, the processor 140 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, or the like, or any combination thereof.

[0028] In some embodiments, the processor 140 may be integrated within the terminal 130 .

[0029] The storage device 150 is capable of storing data, instructions, and / or any other information. In some embodiments, the storage device 150 may store data obtained from the terminal 130, the processor 140, etc., such as the spatial location information of the current user 110, etc. In some embodiments, the storage device 150 may include a large-capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), etc., or any combination thereof. In some embodiments, the storage device 150 may be executed on a cloud platform. In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more other components of the rapid function recommendation system 100 for the construction industry (e.g., the terminal 130, the processor 140, etc.). In some embodiments, the storage device 150 may be part of the processor 140.

[0030] It is worth noting that the function rapid recommendation system 100 for the construction industry is provided for illustrative purposes only and is not intended to limit the scope of the present invention. For those skilled in the art, various changes and modifications can be made based on the description of the present invention. For example, the function rapid recommendation system 100 for the construction industry may also include a database, an information source, and the like. For another example, the function rapid recommendation system 100 for the construction industry may be implemented on other devices to achieve similar or different functions. However, these changes and modifications will not deviate from the scope of the present invention.

[0031] Figure 2 FIG2 is an exemplary flow chart of a method for quickly recommending functions for the construction industry according to some embodiments of the present invention. In some embodiments, process 200 may be executed by a processing device (eg, processor 140). Figure 2 As shown, the process 200 includes the following steps.

[0032] Step 210 : Based on the spatial location information of the current user, a plurality of candidate tasks are obtained from the system building tasks.

[0033] Spatial location information refers to data / information related to the spatial location of the current user. For example, spatial location information may include three-dimensional positioning information of the current user's location. For more information about the current user, see Figure 1 and related instructions.

[0034] In some embodiments, spatial location information can be obtained based on the positioning module of the terminal used by the user when the user logs in to the client. For example, when the current user logs in, the processor 140 can control the positioning module to obtain the current user's spatial location information. In some embodiments, the positioning module can include the Beidou positioning system, the GPS positioning system, the Bluetooth positioning system, etc. In some embodiments, the positioning module can be integrated into the terminal 130.

[0035] In some embodiments, the spatial position information may also be obtained while obtaining the user's visual information. For example, when obtaining the current user's visual information, the positioning module simultaneously obtains the spatial position information.

[0036] System construction tasks refer to pre-configured construction project-related tasks within the rapid feature recommendation system for the construction industry. These tasks encompass multiple phases of a construction project, including planning, design, construction, and management. Exemplary system construction tasks include project site selection, architectural design (e.g., structural design and electrical system design), construction (e.g., foundation preparation and concrete pouring), and safety and quality management. In some embodiments, system construction tasks can be pre-configured based on data from historical construction projects.

[0037] Candidate tasks refer to system building tasks that may be selected for recommendation to the current user.

[0038] In some embodiments, candidate tasks are correlated with the current user's spatial location information. That is, different candidate tasks correspond to different locations of the current user. Processor 140 can retrieve candidate tasks from the system's construction tasks based on the current user's spatial location information. For example, if the spatial location information indicates that the current user is located at a foundation, processor 140 can select tasks related to the foundation area from the system's construction tasks as candidate tasks (e.g., tasks such as foundation treatment, foundation excavation, and concrete pouring).

[0039] In some embodiments, the candidate tasks can be represented by a set. For example, the candidate task set R can be represented as R = {r1, r2, r3, ..., r m}. Among them, r1, r2, r3, ..., r m Represent each candidate task respectively, and m is the number of candidate tasks.

[0040] In some embodiments of the present invention, multiple candidate tasks are determined by the spatial location information of the current user, and tasks related to the user's area can be screened out from a large number of system building tasks, thereby narrowing the search range of the target task and improving processing efficiency.

[0041] Step 220 : Obtain visual information of the current user, and determine the key elements of the task based on the visual information.

[0042] Visual information refers to information / data obtained by analyzing images and / or video data captured by image acquisition devices (such as cameras, webcams, etc.). For example, visual information can include objects (such as construction materials and tools), people, and scenes (such as kitchens and bathrooms).

[0043] In some embodiments, the image acquisition device can automatically or manually acquire image information of the space in which the current user resides. For example, the current user can manually control the image acquisition device to acquire image information of the space in which the current user resides. For another example, after the current user logs in, the processor 140 can automatically control the image acquisition device to regularly or periodically acquire image information of the space in which the current user resides. The image acquisition device can be integrated into the terminal 130.

[0044] In some embodiments, the processor 140 may further process the acquired image information to obtain visual information. Exemplary image processing methods may include image recognition technology, machine vision technology, and the like.

[0045] Key task elements refer to the critical data and / or information relevant to determining the target task. For example, these elements may include construction equipment and raw materials. They can also reflect information such as the type of task and the execution area. The target task refers to the task that the current user is most likely to perform.

[0046] In some embodiments, processor 140 may select data and / or information relevant to the target task from the visual information as task-critical elements. For example, processor 140 may select building components (e.g., columns, beams, walls, floor slabs, windows, doors, etc.), construction equipment (e.g., scaffolding, cranes), and construction materials (e.g., bricks, rebar, concrete) from the visual information as task-critical elements. In some embodiments, a task-critical element may be a collection of multiple different elements. For example, different building components, construction equipment, and construction materials may be used as multiple different elements to form a task-critical element.

[0047] Step 230 : determining a target task from a plurality of candidate tasks based on the key elements of the task and the identity information of the current user.

[0048] User identity information refers to data related to the identity of the current user. The current user's identity information includes name, age, role (such as construction worker, team leader, quality inspector, etc.), contact information, etc.

[0049] In some embodiments, the processor 140 can determine whether the key elements of the task are related to the task information of the candidate task, and determine the target task based on the matching degree between the candidate task and the identity information of the current user. Figure 3 and its related parts.

[0050] Step 240 : Determine the user intention of the current user based on the target task.

[0051] User intent refers to the current user's goals or needs when using construction-related software / systems. For example, user intent may include obtaining construction instructions, viewing drawings, and team management.

[0052] In some embodiments, the processor 140 can determine one or more task states corresponding to the target task and their corresponding task events; determine the state vector corresponding to each task state based on the task state and its corresponding task event; determine the task vector based on the state vector and the target task; determine the event vector corresponding to each task event; determine the task event matrix based on the event vector corresponding to each task event and the task vector; and determine the user intention of the current user based on the task event matrix.

[0053] The task status refers to the execution status of the target task. In some embodiments, the task status includes not started, in progress, pending acceptance, completed, etc.

[0054] Task events refer to the operations that need to be performed when executing the target task.

[0055] In some embodiments, different task states may correspond to different task events. For example, Z1 indicates that the task state is not started, and the corresponding task events include: planning S1, task allocation S2, and material preparation S3; Z2 indicates that the task state is in progress, and the corresponding task events include: progress tracking S4, task self-inspection S5, and quality acceptance S6; Z3 indicates that the task state is in progress, and the corresponding task events include: acceptance application S7, problem feedback S8, and rectification initiation S9; Z4 indicates that the task state is completed, and the corresponding task events include: construction summary S 10 , Achievements Display 11 The above correspondence between task status and task events is only illustrative and does not constitute a limitation. Task status may also correspond to other task events (for example, the task event corresponding to the task status "in progress" may also include a specific construction process, etc.).

[0056] The state vector refers to a vectorized representation of a task state. In some embodiments, the state vector may represent task events corresponding to different task states.

[0057] In some embodiments, the processor 140 may combine the task state and the task event to obtain a state vector. For example, the processor 140 may combine the task state Z1 (indicating that the task state is not started) with the corresponding task events S1, S2, and S3 to obtain a state vector Z1=[S1, S2, S3] indicating that the task state is not started. Similarly, the processor 140 may also obtain state vectors Z2=[S4, S5, S6], Z3=[S7, S8, S9], and Z4=[S1, S2, S3] corresponding to the task states in progress, pending acceptance, and completed, respectively. 10 , S 11 ]wait.

[0058] The task vector is a vectorized representation of the task state of the target task. In some embodiments, the task vector can represent different task states of the target task. For example, the task vector R = [Z1, Z2, Z3, Z4]. In some embodiments, when there are multiple target tasks, the multiple target tasks can also be represented by the task vector. For example, R1 = [Z 1-1 , Z 1-2 ,0,Z 1-4 ],R2=[Z 2-1 ,0,0,Z 2-4 ], R3=[Z 3-1 , Z 3-2 , Z 3-3 , Z 3-4 ], etc. Among them, the first number in the vector value represents the target task number, the second number represents the task status number, and the Z 2-1 Taking as an example, the number 2 represents the sequence number of the target task, 1 represents the first state of the target task (corresponding to state Z1); the vector value 0 represents that the target task does not exist in this state.

[0059] The event vector is a vectorized representation of the task event. For example, the event vector can be represented as [S 1-1 , S 1-2 , S 1-3 , S 1-4 , S 1-5 ], etc. Among them, the first number in the vector value represents the target task number, the second number represents the task event number, and the S 1-2 For example, the number 1 represents the sequence number of the target task, and 2 represents the sequence number of the task event.

[0060] The task event matrix is ​​a matrix representing all task events that need to be executed to complete the target task. In some embodiments, the processor 140 can obtain the task event matrix by replacing the state vector in the task vector with the event vector. In some embodiments, for a task vector R = [Z1, Z2, Z3, Z4] of a target task, the processor 140 can replace the state vectors Z1, Z2, Z3, Z4 with the corresponding event vectors respectively to obtain the task event matrix M = [S1, S2, S3, S4, S5, S6, 0, 0, 0, S 10 , S 11 ]. Among them, the vector value 0 indicates that the task state has no task event that needs to be executed.

[0061] In some embodiments, for multiple task vectors (eg, R1 = [Z 1-1 , Z 1-2 ,0,Z 1-4 ],R2=[Z 2-1 ,0,0,Z 2-4 ], R3=[Z 3-1 , Z 3-2 , Z 3-3 , Z 3-4 ], etc.), the processor 140 can replace the state vector with the event vector of the task event corresponding to each task state, obtain multiple sub-matrices, and combine the multiple sub-matrices to obtain a task event matrix. Exemplarily, formula (1) is an exemplary representation of the task event matrix M' obtained by combining multiple sub-matrices corresponding to multiple target tasks.

[0062]

[0063] In some embodiments, the processor 140 can assign a value to each element in the task event matrix based on the event completion status corresponding to each task event to obtain a task event status matrix; based on a preset task screening method, filter out the target event from the task event status matrix; and based on the target event, determine the user intention of the current user.

[0064] The event completion status includes completed, uncompleted, etc. In some embodiments, the processor 140 may assign a value of 1 to a task event whose event completion status is completed in the task event matrix, and assign a value of 0 to a task event whose event completion status is uncompleted. 1-1 If the completion status is completed, the task event S 1-1 Assigned a value of 1 if not completed, and 0 if not completed.

[0065] The task event status matrix is ​​a matrix representation of the completion status of the task event. In some embodiments, after the processor 140 completes the assignment of values ​​to the elements in the task event matrix, the matrix obtained is the task event status matrix.

[0066] The target event is the task event that requires the highest priority within the target task. Understandably, task events within a target task are often sequential. This means that subsequent tasks cannot be completed without completing the predecessor task. Therefore, prioritizing the task as the target event ensures smooth execution of the target task, ensuring that subsequent recommended features are more tailored to the user's needs.

[0067] In some embodiments, the processor 140 may filter out target events from the task event status matrix based on a preset task screening method. An exemplary preset task screening method includes that the processor 140 filters out task events assigned a value of 0 from the task event status matrix and selects the task event with the smallest sequence number as the target event. For example, for the task event status matrix P = [S1 = 1, S2 = 1, S3 = 1, S4 = 0, S5 = 0, S6 = 0, 0, 0, 0, S 10 =0, S 11 =0], indicating that the task events S1, S2, and S3 in the target task have been completed, and the processor 140 selects the unfinished task events S4, S5, S6, and S7 with a value of 0. 10 , S 11 , and select the task event S4 with the smallest task event number as the target event that needs to be completed most.

[0068] In some embodiments, the preset task screening method also includes various other methods. For example, in the task event matrix, the priority of the task events can be arranged from left to right in descending order of priority. After the processor 140 screens out the uncompleted events assigned a value of 0, it selects the leftmost uncompleted event as the target event. For another example, it can also be arranged in descending order of priority and the rightmost uncompleted event is selected as the target event, which is not limited here.

[0069] In some embodiments, the processor 140 may use the relevant operations corresponding to the target event as the user intention of the current user. For example, if the target event is concrete pouring, the user intention of the current user may include construction personnel allocation, construction guidance, construction drawing viewing, etc.

[0070] In some embodiments, the processor 140 may also determine the user intention of the current user through a machine learning model. For more information on related content, please refer to Figure 4 and related instructions.

[0071] In some embodiments, the processor 140 may further verify the user's intent based on the current user's identity information. For example, if the current user's identity information is a construction worker and the determined user intent is project acceptance, the verification fails, the user's identity information does not match the user's intent, and the user's intent needs to be re-determined.

[0072] In some embodiments of the present invention, verifying the user intention based on the identity information of the current user can further improve the accuracy of the user intention and obtain better function recommendation effects.

[0073] Step 250: Determine the functions recommended to the current user based on the user's intention.

[0074] The functions recommended to the current user refer to the functions in the construction-related software / system that the current user is most likely to want to use.

[0075] In some embodiments, the processor 140 may recommend functions corresponding to the user's intent to the current user. For example, when the user's intent includes construction guidance, construction drawing viewing, etc., the processor 140 may recommend the functions of construction guidance and construction drawing viewing to the user.

[0076] In some embodiments of the present invention, by combining the user's visual information with multimodal information such as spatial location, system tasks, and user identity, it is possible to comprehensively analyze and quickly select the most relevant and applicable function options for the user, eliminating the tedious and complex function search process in the construction software / system, reducing the risk of function selection errors, and improving operational convenience and project management efficiency.

[0077] Figure 3 FIG3 is an exemplary flow chart of determining a target task according to some embodiments of the present invention. In some embodiments, process 300 may be executed by a processing device (eg, processor 140). Figure 3 As shown, the process 300 includes the following steps.

[0078] Step 310 : For each of the plurality of candidate tasks, determine whether the candidate task is related to the task key element based on its corresponding task information.

[0079] Task information refers to the information / data required to execute the candidate task. For example, task information includes construction task items, construction areas, construction schedules, task collaborators, construction phases corresponding to the construction tasks, construction drawings of the construction areas, etc. For more information on key elements of tasks, please refer to Figure 2 and its related parts.

[0080] In some embodiments, for each of the multiple candidate tasks, the processor 140 may determine whether the corresponding task information and the task key element include the same content. If so, the processor 140 may determine that the candidate task is related to the task key element. For example, if the task key element includes specific construction equipment and / or construction materials, and the task information of a candidate task includes content related to the use or maintenance of the equipment / materials, the processor 140 may determine that the candidate task is related to the task key element.

[0081] In some embodiments, task information and task key elements may also be represented by vectors. Processor 140 may determine whether a candidate task is related to the task key element based on the vector distance between the task information and the vector corresponding to the task key element. For example, if the vector distance between the task information corresponding to the candidate task and the task key element is less than a preset threshold, processor 140 may determine that the candidate task is related to the task key element. The vector distance may include one or more of cosine distance, Euclidean distance, Manhattan distance, and Chebyshev distance.

[0082] Step 320 : In response to the candidate task being associated with the key task element, determining a degree of match between the candidate task and the identity information of the current user.

[0083] Matching is a parameter that evaluates the relevance of a candidate task to the current user. Different users have different relevance to different tasks. A higher matching degree indicates a higher relevance to the candidate task and a higher likelihood that the current user will want to perform that task.

[0084] In some embodiments, the processor 140 can determine the task focus of the current user based on the identity information of the current user; determine the weight of each element in the task key elements based on the task focus; and perform weighted calculation on the correlation between the candidate tasks and each element in the task key elements based on the weight to determine the degree of matching.

[0085] Task focus refers to the aspects of a candidate task that the current user is concerned about, values, or prioritizes. Different users may have different focus points for the same candidate task. For example, for a construction operation-related task (such as concrete pouring), a team leader's focus might include task splitting and acceptance, while a construction manager's focus might include progress monitoring, quality control, and task acceptance.

[0086] In some embodiments, there is a preset correspondence between the task focus and the user's identity information. For example, the storage device 150 may pre-store a preset comparison table of user identity information and task focus in different system building tasks. The processor 140 may obtain the current user's task focus by reading the preset comparison table from the storage device 150 based on the user's identity information.

[0087] The weight is a parameter that measures the current user's attention to each element in the mission-critical elements. The larger the weight, the more attention the current user pays to the element in the mission-critical elements. In some embodiments, the weights of each element in the mission-critical elements corresponding to different user identities can be preset.

[0088] The correlation between a candidate task and each element in the task-critical elements refers to the degree of correlation between the candidate task and the task-critical elements. In some embodiments, processor 140 may determine the correlation between the candidate task and each element in the task-critical elements based on the vector distance between the task information of the candidate task and the vector corresponding to each element in the task-critical elements. For example, the smaller the vector distance, the greater the correlation between the candidate task and the element in the task-critical elements. Furthermore, processor 140 may perform data processing such as normalization and standardization on the vector distance to represent the correlation between the candidate task and each element in the task-critical elements.

[0089] In some embodiments, the processor may perform a weighted calculation on the relevance between the candidate task and each element in the task key element based on the weight to determine the matching degree. For example, the matching degree may be determined based on the following formula (2):

[0090] S(r i )=p1×ω1+p2×ω2+…+p n ×ω n (2)

[0091] Among them, S(r i ) represents the matching degree between the candidate task and the current user’s identity information; p1, p2, …, p n Respectively represent the correlation between the candidate task and each element in the task key element; ω1, ω2, ..., ω n Respectively represent the weight of each element in the mission-critical elements.

[0092] Step 330: Determine the target task based on the matching degree.

[0093] In some embodiments, the processor 140 may determine the candidate task with the highest matching degree as the target task. In some embodiments, the processor 140 may also determine one or more candidate tasks with a matching degree greater than a matching degree threshold as the target task.

[0094] In some embodiments of the present invention, the relevance of the task to the user's identity and the user's visual information (i.e., the key elements of the task) is comprehensively considered, and the task that the user is most likely to perform is determined based on the degree of matching. This can ensure that the task recommended to the user best suits his or her current situation and needs, and improve the accuracy of subsequent recommended functions for the user.

[0095] Figure 4 FIG4 is an exemplary flow chart of determining user intent according to other embodiments of the present invention. In some embodiments, process 400 may be executed by a processing device (eg, processor 140). Figure 4 As shown, process 400 includes the following steps.

[0096] Step 410: Obtain the task content corresponding to the target task.

[0097] Task content refers to the specific operations and work requirements required to complete the target task. Different target tasks may have different task content. For example, foundation construction tasks may include earthwork excavation and surveying, foundation reinforcement, and concrete pouring; while safety management tasks may include developing safety training plans, regular safety inspections, and accident reporting.

[0098] In some embodiments, the processor 140 may obtain the task content corresponding to the target task in a variety of ways. For example, the task content may be obtained through manual input; or, for another example, the processor 140 may process the target task through methods such as data mining and text analysis, or machine learning models to obtain the corresponding task content.

[0099] Step 420: Perform feature extraction based on the task content to obtain a first feature vector.

[0100] The first feature vector is a vector that represents the features of the task content.

[0101] In some embodiments, the first eigenvector can be obtained based on a variety of methods. For example, the first eigenvector can be extracted by processing the task content based on statistical methods, transformation methods (such as Fourier transform), dimensionality reduction methods (such as principal component analysis (PCA), and pre-trained machine learning models.

[0102] Step 430: Perform feature extraction based on the identity information of the current user to obtain a second feature vector.

[0103] The second feature vector is a vector representing features of the identity information of the current user.

[0104] In some embodiments, the second feature vector can be obtained based on a variety of methods. For example, the second feature vector can be extracted by processing the current user's identity information based on statistical methods, transformation methods (such as Fourier transform), dimensionality reduction methods (such as principal component analysis (PCA), and pre-trained machine learning models.

[0105] Step 440: Fuse the first eigenvector and the second eigenvector to obtain a fused eigenvector.

[0106] The fused feature vector is the result obtained by fusing multiple feature vectors.

[0107] In some embodiments, the processor 140 may fuse the first eigenvector and the second eigenvector using various methods. For example, the processor 140 may fuse the first eigenvector and the second eigenvector using linear fusion (e.g., vector concatenation), weighted fusion, nonlinear fusion (e.g., deep learning), and other methods to obtain a fused eigenvector.

[0108] In some embodiments, the processor 140 can also obtain the historical user intentions of the current user; determine the intention tendency vector based on the identity information of the current user and the historical user intentions; obtain a fused feature vector based on the intention tendency vector, the first feature vector and the second feature vector through a feature fusion model; the feature fusion model is a machine learning model.

[0109] Historical user intent refers to the goals or needs of current users when using construction-related software / systems in historical data.

[0110] In some embodiments, the historical user intent can be obtained based on historical data. For example, the storage device 150 may store a large amount of historical data including historical user intent, and the processor 140 can read the historical user intent of the current user from the storage device 150.

[0111] The intention tendency vector is a vector that represents the target user's tendency towards different task events in the target task. In some embodiments, the intention tendency vector can be represented by letters + numbers. For example, the intention tendency vector Q can be expressed as Q = [ABC + numbers], where the letters represent the identity information of the current user (such as quality inspectors, managers, construction workers, etc.), and the numbers represent the current user's tendency towards the task content, which can be represented by 0 or 1, 1 represents intention tendency, and 0 represents no intention tendency.

[0112] The feature fusion model is a machine learning model. For example, the feature fusion model can be one or more of a Transformer model, a neural network model, a deep neural network model, and the like. In some embodiments, the processor 140 can input the intent tendency vector, the first feature vector, and the second feature vector into the feature fusion model, which then outputs a fused feature vector.

[0113] In some embodiments, the feature fusion model can be acquired based on training. In some embodiments, the first training sample of the feature fusion model may include a large number of historical intention tendency vectors, historical first feature vectors, and historical second feature vectors. The first label may include a feature fusion vector obtained by manual annotation. In some embodiments, when annotating to obtain the first label, the intention tendency vector can be used as a weighted weight of the first feature vector and the second feature vector. For example, if the tendency of a certain task is 1, it is more inclined to retain the vector value corresponding to the task content (corresponding to the vector value of the first feature vector) during fusion. If the tendency of a certain task is 0, it is more inclined not to retain the vector value corresponding to the task during fusion, but to retain the vector value of the user identity (corresponding to retaining the vector value of the second feature vector). In some embodiments, when annotating to obtain the first label, the tendency weight can also be set according to the value of the intention tendency vector, and the values ​​of the first feature vector and the second feature vector can be weightedly fused.

[0114] In some embodiments of the present invention, by fusion of features and inputting more feature vectors (such as intent tendency vectors, etc.), various types of information input into the user intent determination model can be increased, thereby improving the accuracy of model prediction.

[0115] Step 450 : Determine the user intention of the current user through a user intention determination model based on the fused feature vector.

[0116] The user intent determination model is a machine learning model. For example, the user intent determination model can be one or more of a neural network model, a deep neural network model, and the like.

[0117] In some embodiments, the processor 140 may input the fused feature vector into a user intention determination model, and the user intention determination model outputs the user intention of the current user. For more information about user intention, see Figure 2 and its related parts.

[0118] In some embodiments, the input of the user intent determination model further includes a third eigenvector and / or a fourth eigenvector.

[0119] The third eigenvector is a vector representing the eigenvalues ​​of the task event matrix. In some embodiments, the third eigenvector can be extracted by processing the task event matrix based on statistical methods, transformation methods (such as Fourier transform), dimensionality reduction methods (such as principal component analysis PCA), and pre-trained machine learning models. For more information about the task event matrix, please refer to Figure 2 and its related parts.

[0120] The fourth eigenvector is a vector representing the eigenvalues ​​of the task event state matrix. In some embodiments, the fourth eigenvector can be extracted by processing the task event state matrix based on statistical methods, transformation methods (such as Fourier transform), dimensionality reduction methods (such as principal component analysis PCA), and pre-trained machine learning models. For more information about the task event state matrix, please refer to Figure 2 and its related parts.

[0121] In some embodiments of the present invention, the input of the user intention determination model includes the third eigenvector and / or the fourth eigenvector, which can more accurately determine the user's true intention based on the specific task event and the corresponding status, thereby improving the accuracy of the subsequent recommended functions for the user.

[0122] In some embodiments, the user intent determination model can be acquired through training. In some embodiments, the second training sample of the user intent determination model can include a large amount of historical data (such as historical first eigenvectors and historical second eigenvectors). The second label can include the user intent obtained by manual annotation or the user intent inferred based on the historical functions selected by the current user.

[0123] In some embodiments of the present invention, determining the user intention of the current user through a machine learning model method can reduce the influence of subjective factors and make the identification of user intention more accurate.

[0124] It should be noted that the above descriptions of processes 200, 300, and 400 are for illustration and purpose only and do not limit the scope of this specification. Those skilled in the art may, under the guidance of this specification, make various modifications and alterations to processes 200, 300, and 400. However, such modifications and alterations remain within the scope of this specification.

[0125] Figure 5 1 is a schematic diagram of the internal modules of a processor according to some embodiments of the present invention. Figure 5 As shown, the processor 140 includes an acquisition module 510 , a first determination module 520 , a second determination module 530 , a third determination module 540 and a fourth determination module 550 .

[0126] The acquisition module 510 is configured to acquire a plurality of candidate tasks from the system building tasks based on the spatial location information of the current user.

[0127] The first determination module 520 is configured to obtain visual information of the current user and determine the key elements of the task based on the visual information.

[0128] The second determining module 530 is configured to determine a target task from a plurality of candidate tasks based on the key elements of the task and the identity information of the current user.

[0129] In some embodiments, the second determination module 530 is further configured to determine, for each of a plurality of candidate tasks, whether the candidate task is related to the task key element based on its corresponding task information; in response to the candidate task being related to the task key element, determine the degree of match between the candidate task and the identity information of the current user; and determine the target task based on the degree of match.

[0130] In some embodiments, the second determination module 530 is further configured to determine the task focus of the current user based on the identity information of the current user; determine the weight of each element in the task key elements based on the task focus; and perform weighted calculation on the correlation between the candidate task and each element in the task key elements based on the weight to determine the degree of matching.

[0131] The third determination module 540 is configured to determine the user intention of the current user based on the target task.

[0132] In some embodiments, the third determination module 540 is further configured to determine one or more task states corresponding to the target task and their corresponding task events; determine the state vector corresponding to each task state based on the task state and its corresponding task event; determine the task vector based on the state vector and the target task; determine the event vector corresponding to each task event; determine the task event matrix based on the event vector corresponding to each task event and the task vector; and determine the user intention of the current user based on the task event matrix.

[0133] In some embodiments, the third determination module 540 is further configured to assign a value to each element in the task event matrix based on the event completion status corresponding to each task event to obtain a task event status matrix; based on a preset task screening method, filter out the target event from the task event status matrix; and determine the user intention of the current user based on the target event.

[0134] In some embodiments, the third determination module 540 is further configured to obtain the task content corresponding to the target task; perform feature extraction based on the task content to obtain a first feature vector; perform feature extraction based on the identity information of the current user to obtain a second feature vector; fuse the first feature vector and the second feature vector to obtain a fused feature vector; based on the fused feature vector, determine the user intent of the current user through a user intent determination model; the user intent determination model is a machine learning model.

[0135] In some embodiments, the third determination module 540 is further configured to obtain the historical user intention of the current user; determine the intention tendency vector based on the identity information of the current user and the historical user intention; obtain a fused feature vector based on the intention tendency vector, the first feature vector and the second feature vector through a feature fusion model; the feature fusion model is a machine learning model.

[0136] The fourth determining module 550 is configured to determine a function to be recommended to the current user based on the user intention.

[0137] It should be noted that the above description of the processor 140 and its modules is for convenience only and does not limit the present invention to the scope of the embodiments. Figure 5 The acquisition module 510, first determination module 520, second determination module 530, third determination module 540, and fourth determination module 550 of the processor 140 disclosed in the disclosure may be different modules in a system, or a single module may implement the functions of two or more of the aforementioned modules. For example, the modules may share a storage module, or each module may have its own storage module.

[0138] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit the present invention. Although not explicitly described herein, those skilled in the art may make various modifications, improvements, and revisions to the present invention. Such modifications, improvements, and revisions are suggested in the present invention and remain within the spirit and scope of the exemplary embodiments of the present invention.

[0139] It should be noted that, in order to simplify the presentation of the present disclosure and facilitate understanding of one or more embodiments, the foregoing description of the present invention sometimes combines multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of the present invention requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

Claims

1. A method for rapid function recommendation for the construction industry, characterized in that: The method comprises: Based on the spatial location information of the current user, multiple candidate tasks are obtained from the system building tasks; Acquiring visual information of the current user and determining a task key element based on the visual information; the task key element is used to reflect the type and execution area of ​​the task; Determining a target task from the plurality of candidate tasks based on the task key elements and the identity information of the current user; Determining the user intention of the current user based on the target task; Based on the user intention, determining a function to recommend to the current user; Wherein, determining the key elements of the task based on the visual information includes: selecting data and / or information related to determining the target task from the visual information as the key elements of the task; the visual information includes building components, construction equipment and construction raw materials.

2. The method according to claim 1, wherein The determining of a target task from the plurality of candidate tasks based on the key task elements and the identity information of the current user includes: For each of the plurality of candidate tasks, determining whether the candidate task is related to the task key element based on the corresponding task information; In response to the candidate task being associated with the task key element, determining a degree of match between the candidate task and the identity information of the current user; The target task is determined based on the matching degree.

3. The method according to claim 2, wherein Determining the degree of matching between the candidate task and the identity information of the current user includes: Determining a task focus of the current user based on the identity information of the current user; Determining the weight of each of the key elements of the task based on the task focus; The relevance between the candidate task and each element in the task key elements is weightedly calculated based on the weight to determine the matching degree.

4. The method according to claim 1, wherein The determining the user intention of the current user based on the target task includes: Determine one or more task states and their corresponding task events corresponding to the target task; Determining a state vector corresponding to each task state based on the task state and the corresponding task event; Determining a task vector based on the state vector and the target task; Determining an event vector corresponding to each of the task events; Determining a task event matrix based on the event vector and the task vector corresponding to each task event; Based on the task event matrix, a user intention of the current user is determined.

5. The method according to claim 4, wherein The determining the user intention of the current user based on the task event matrix includes: Assigning a value to each element in the task event matrix based on the event completion status corresponding to each task event to obtain a task event status matrix; Based on a preset task screening method, screening target events from the task event state matrix; Based on the target event, a user intention of the current user is determined.

6. The method according to claim 1, wherein The determining the user intention of the current user based on the target task includes: Obtaining the task content corresponding to the target task; Perform feature extraction based on the task content to obtain a first feature vector; Perform feature extraction based on the identity information of the current user to obtain a second feature vector; fusing the first eigenvector and the second eigenvector to obtain a fused eigenvector; Based on the fused feature vector, the user intention of the current user is determined through a user intention determination model; the user intention determination model is a machine learning model.

7. The method according to claim 6, wherein The fusing the first eigenvector and the second eigenvector to obtain a fused eigenvector includes: Obtaining historical user intentions of the current user; Determining an intention tendency vector based on the identity information of the current user and the historical user intentions; Based on the intention tendency vector, the first feature vector and the second feature vector, the fused feature vector is obtained through a feature fusion model; the feature fusion model is a machine learning model.

8. A function rapid recommendation system for the construction industry, characterized by: The system comprises: an acquisition module, configured to acquire a plurality of candidate tasks from system building tasks based on spatial location information of a current user; A first determination module is configured to obtain visual information of the current user and determine task-critical elements based on the visual information; the task-critical elements are used to reflect the type and execution area of ​​the task; wherein determining the task-critical elements based on the visual information includes: selecting data and / or information related to the determined target task from the visual information as the task-critical elements; the visual information includes building components, construction equipment, and construction materials; A second determining module is configured to determine a target task from the plurality of candidate tasks based on the key elements of the task and the identity information of the current user; a third determining module, configured to determine the user intention of the current user based on the target task; The fourth determining module is configured to determine a function to be recommended to the current user based on the user intention.

9. The system according to claim 8, wherein The third determining module is further configured to: Determine one or more task states and their corresponding task events corresponding to the target task; Determining a state vector corresponding to each task state based on the task state and the corresponding task event; Determining a task vector based on the state vector and the target task; Determining an event vector corresponding to each of the task events; Determining a task event matrix based on the event vector and the task vector corresponding to each task event; Based on the task event matrix, a user intention of the current user is determined.

10. A function rapid recommendation device for the construction industry, characterized in that: The apparatus comprises: at least one processor and at least one memory; The at least one memory is for storing computer instructions; The at least one processor is configured to execute at least part of the computer instructions to implement the method for rapid function recommendation for the construction industry as described in any one of claims 1 to 7.

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