Construction disclosure arrangement management method and system

Abstract

The embodiment of the invention provides a construction disclosure arrangement management method and system, and the method comprises the steps: determining a construction task of a worker in a construction project based on a contract list in the construction project; on the basis of the corresponding relation between the construction task and the information entity, obtaining the information known by the worker in the construction project; obtaining known information of the worker; the known information comprises the information mastering degree of the worker on task items of historical learning; on the basis of the sensing information and the known information, determining the mastering degree of the sensing information of the worker; and obtaining a construction disclosure test question from a construction disclosure information base based on the known information mastering degree.

Description

Technical Field

[0001] This specification relates to the field of engineering project management, and in particular to a construction briefing arrangement management method and system. Background Art

[0002] In the current construction industry, construction briefing is an important part of ensuring construction safety and improving construction quality. Workers need to complete safety education before joining the project. At present, construction projects usually use a unified question bank and unified safety education materials as construction briefings for workers to learn. For example, before workers start construction, the team leader will conduct technical and safety briefings, but usually fixed templates are used as construction briefings, without considering the workers' actual grasp of the construction briefings, nor whether the construction briefings are in line with the current construction project. Safety education has become a formality.

[0003] Therefore, this specification provides a construction briefing arrangement management method and system to provide a more accurate, efficient and personalized construction briefing arrangement management method. Summary of the invention

[0004] One or more embodiments of the present specification provide a construction briefing arrangement management method, the method comprising: determining the construction tasks of workers in the construction project based on a contract list within the construction project; obtaining the information that the workers should know in the construction project based on the correspondence between the construction tasks and information entities; obtaining the known information of the workers; the known information includes the workers' mastery of information on task items learned historically; based on the information that should be known and the known information, determining the workers' mastery of the information that should be known; based on the mastery of the information that should be known, obtaining construction briefing test questions from a construction briefing information database.

[0005] One or more embodiments of the present specification provide a construction briefing arrangement management system, which includes: a first determination module, used to determine the construction tasks of workers in the construction project based on the contract list within the construction project; a first acquisition module, used to obtain the information that the workers should know in the construction project based on the correspondence between the construction tasks and the information entities; a second acquisition module, used to obtain the known information of the workers; the known information includes the degree of mastery of the information of the task items learned historically by the workers; the second determination module, used to determine the degree of mastery of the information that the workers should know based on the information that should be known and the known information; a third acquisition module, used to obtain construction briefing test questions from the construction briefing information database based on the degree of mastery of the information that should be known.

[0006] One or more embodiments of the present specification provide a construction briefing arrangement management device, including a processor, wherein the processor is used to execute a construction briefing arrangement management method.

[0007] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a construction briefing arrangement management method. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0009] Figure 1 is a schematic diagram of an exemplary application scenario of a construction briefing arrangement management system according to some embodiments of this specification;

[0010] Figure 2 is an exemplary flow chart of a construction briefing arrangement management method according to some embodiments of this specification;

[0011] Figure 3 is an exemplary flow chart of obtaining hidden features of a node according to some embodiments of this specification;

[0012] Figure 4 is an exemplary flow chart of establishing a known information database according to some embodiments of this specification;

[0013] Figure 5 is an exemplary flow chart of establishing a construction briefing information database according to some embodiments of this specification;

[0014] Figure 6 is an exemplary flow chart of a method for updating the mastery level of a test question according to some embodiments of this specification;

[0015] Figure 7 It is an exemplary module diagram of a construction briefing arrangement management system shown in some embodiments of this specification. DETAILED DESCRIPTION

[0016] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0017] It should be understood that the "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 words can achieve the same purpose, the words can be replaced by other expressions.

[0018] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" 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.

[0019] 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 preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0020] At present, the safety education and technical briefings conducted before workers enter many construction projects are usually unified and standardized, and cannot be personalized according to the background, experience and construction tasks of different workers. Every time a worker joins a new project, he needs to receive safety education or technical briefings again, and the content is repetitive and too general. For example, a worker may already have a certain level of safety awareness or technical level, but because his knowledge has not been evaluated, he is still forced to accept repeated briefings, which not only wastes time but also fails to effectively improve the worker's operating skills.

[0021] At the same time, due to the lack of systematic and logical arrangement of construction briefing content, many briefing materials are scattered and disordered. During the briefing process, workers are faced with a large amount of information and it is difficult to understand the relationship between various knowledge points in a short period of time. For example, workers may receive content that is irrelevant to their positions during the briefing process, or some relatively basic content that they have already mastered, which makes it difficult for workers to focus on the most critical part of the construction project, affecting the effectiveness of the briefing. And because each worker has a different level of cognition, unified briefing content often cannot effectively meet the learning needs of each worker.

[0022] The construction briefing arrangement management method disclosed in some embodiments of this specification can be applied to scenarios such as safety education for workers' progress projects, technical briefings and safety briefings for workers before construction, etc.

[0023] Figure 1It is a schematic diagram of an exemplary application scenario of a construction briefing arrangement management system shown in some embodiments of this specification.

[0024] like Figure 1 As shown, the application scenario of the construction briefing arrangement management system 100 may include a current user 110, a network 120, a terminal 130, a processor 140, and a storage device 150, etc.

[0025] The current user 110 refers to a person who needs to arrange / test the construction briefing or a client used by the person, for example, the current user 110 may include a construction worker, a team leader, a construction management personnel (such as a construction worker, an acceptance personnel, etc.), and the like.

[0026] In some embodiments, personnel of different identities often correspond to different work tasks, and even personnel of 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, a suitable construction briefing test can be arranged for the current user based on his or her identity and the degree of his or her mastery of the relevant construction briefing content. In some embodiments, the format of the briefing content includes at least one form such as animation, video, document, and picture.

[0027] The network 120 may connect various components in the construction briefing arrangement management system 100 and / or other components outside the construction briefing arrangement management system 100. In some embodiments, one or more components of the construction briefing arrangement management system 100 (e.g., the terminal 130, the processor 140, and the storage device 150, etc.) may be connected and / or communicate with each other through the network 120. For example, the terminal 130 may send the worker's answer results to the construction briefing test questions to the processor 140, etc. through the network 120.

[0028] The terminal 130 can provide functional components related to user interaction and can implement user interaction functions (for example, providing or displaying information and data to the user). The user can be the current user 110. As an example only, the terminal 130 can be one or any combination of a mobile device, a tablet computer, a laptop computer, a desktop computer, or the like, or other devices with input and / or output functions. In some embodiments, the terminal 130 can have a positioning function. For example, the terminal 130 can obtain the spatial position information of the current user 130. In some embodiments, the terminal 130 can have an image acquisition function. For example, the terminal 130 can obtain the answer sheet image of the current user 110 to the paper-based construction briefing test questions. In some embodiments, the terminal 130 can also have a display function. For example, the terminal 130 can display the electronic version of the construction briefing test questions to the current user 110. The above description of the terminal 130 is only an example, and the terminal 130 can also have other functions, such as input functions, etc., which are not limited here.

[0029] The processor 140 is capable of processing information and / or data related to the construction briefing arrangement management system 100 to perform one or more functions described in the present invention. In some embodiments, the processor 140 can determine the construction tasks of the workers in the construction project based on the contract list within the construction project; obtain the information that the workers should know in the construction project based on the correspondence between the construction tasks and the information entities; obtain the known information of the workers; the known information includes the workers' mastery of information on the task items of historical learning; based on the information that should be known and the known information, determine the workers' mastery of the information that should be known; based on the mastery of the information that should be known, obtain construction briefing test questions from the construction briefing information library. The obtained construction briefing test questions can be sent to the terminal 130 used by each worker via the network 120. A detailed description of the relevant content can be found later (such as Figures 2 to 6 ) related description.

[0030] 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, etc., 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. As an 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, etc., or any combination thereof. In some embodiments, the processor 140 may be integrated in the terminal 130.

[0031] The storage device 150 can store data, instructions and / or any other information. In some embodiments, the storage device 150 can store data obtained from the terminal 130, the processor 140, etc., such as storing various types of information such as construction projects, construction briefings, and the degree of workers' mastery of construction briefings. 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 can be executed on a cloud platform. In some embodiments, the storage device 150 can be connected to the network 120 to communicate with one or more other components (e.g., the terminal 130, the processor 140, etc.) of the construction briefing arrangement management system 100. In some embodiments, the storage device 150 can be part of the processor 140. In some embodiments, the storage device 150 may include multiple databases related to construction briefing arrangements, such as a construction briefing information library, a logic chain library, a known information library, a test question library, etc. For detailed descriptions of various databases, please refer to the relevant parts of the flowchart below.

[0032] It is worth noting that the construction briefing arrangement management system 100 is provided for illustrative purposes only and is not intended to limit the scope of the present invention. For those of ordinary skill in the art, various changes and modifications can be made based on the description of the present invention. For example, the construction briefing arrangement management system 100 may also include an information source, etc. For example, the information source can be used to obtain information to generate construction briefings. For another example, the construction briefing arrangement management system 100 can 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.

[0033] Figure 2 200 is an exemplary flow chart of a construction briefing arrangement management method according to some embodiments of this specification. In some embodiments, process 200 may be executed by a construction briefing arrangement management system or a processor (eg, processor 140). Figure 2 As shown, process 200 includes the following steps.

[0034] Step 202, based on the contract list in the construction project, determine the construction tasks of the workers in the construction project.

[0035] A construction project is an organized and systematic construction task carried out in accordance with the planning, design and construction requirements of a building project. A construction project can include multiple stages of a project, for example, from preliminary preparation (such as planning, design, resource allocation, etc.), to the actual construction process, to the completion and commissioning stage of the project.

[0036] The contract list in a construction project refers to the list of contract documents involved in the construction project. The contract list may include specific contents such as project scope, construction content, construction period, and responsibility allocation.

[0037] Construction tasks refer to the specific work content that each worker needs to perform within the construction project according to his or her position and responsibilities. For example, construction tasks can include specific processes, technical requirements, safety specifications, etc.

[0038] In some embodiments, the processor may read the contract list of the construction project from a storage device, a database, etc., and identify the construction task to which each worker belongs from the contract list. For example, the construction task corresponding to each worker is determined based on the responsibility allocation content in the contract list of the construction project.

[0039] Step 204: Based on the correspondence between the construction tasks and the information entities, obtain the information that the workers should know in the construction project.

[0040] Information entities refer to information units related to specific task items and / or task characteristics during the construction briefing process. Information entities can include specific data, knowledge points or standards related to construction tasks, worker responsibilities and task requirements.

[0041] The correspondence relationship refers to the connection between the construction task and the information entity that the worker needs to know. For example, if a construction task involves specific safety precautions, technical requirements or quality standards, the information entity corresponding to the construction task can include specific safety precautions, technical requirements or quality standards.

[0042] Information that should be known refers to the information that workers should know (or need to know) when performing construction tasks. For example, information that should be known can refer to the construction methods, safety measures, operating procedures, emergency plans, etc. that workers need to know when performing a certain construction task.

[0043] In some embodiments, the processor may pre-establish associations between construction tasks and related information that should be known. For example, a specific construction task (such as "concrete pouring") may correspond to multiple information that should be known (such as "concrete mix requirements", "construction safety precautions", "equipment operation procedures", etc.). The established associations may be stored in a database. Afterwards, the processor may search for the information that should be known corresponding to each worker based on the worker's construction tasks and the established associations.

[0044] In some embodiments, the processor may also determine the task items and task features that the worker needs to complete in the project based on the work content and content features in the contract list of the construction project; and, based on the association relationship between the task items and the information entities, obtain the information entity associated with the worker in the project to obtain the information that the worker should know. In some embodiments, the information that the worker should know can be identified in the form of a vector. For example, the information vector of a worker's information in a project can be [worker ID, project ID, information ID to be known, information ID to be known, information ID to be known].

[0045] Step 206, obtaining known information of the worker.

[0046] Known information refers to the knowledge and skills that the worker has mastered related to the construction task. In some embodiments, the known information also includes the worker's information mastery of the task items learned in the past. The information mastery level refers to the worker's mastery of the knowledge points or skills learned. The information mastery level can be determined through a briefing test. For example, the information mastery level can be determined through a comprehensive evaluation of the worker's learning performance, test scores, or experience feedback of the task items learned in the past.

[0047] In some embodiments, the degree of information mastered can be marked as mastered, not mastered, partially mastered, etc.

[0048] In some embodiments, the information mastery level can also be identified in a quantitative manner. For example, the information mastery level is divided into 10 levels, represented by 1-10 respectively. The closer the identification number is to 1, the lower the worker's information mastery level is.

[0049] In some embodiments, each worker's level of mastery of information corresponding to the task items of historical learning can be collected through historical training, worker self-assessment or test evaluation, and the worker's level of mastery of known information can be marked based on the completion of the task items of historical learning.

[0050] In some embodiments, the obtaining of the known information of the worker includes: obtaining the known information of the worker from a known information library. For example, the processor may directly read the known information from the known information library.

[0051] For more information about known repositories, see Figure 4 Related description.

[0052] Step 208: Determine the worker's mastery of the information he should know based on the information he should know and the known information.

[0053] The degree of mastery of required information refers to the degree to which workers master the information they should know (or should master) in order to perform the current construction task.

[0054] In some embodiments, the processor may compare the information that the worker should know with the known information to determine the worker's mastery of the information that the worker should know in the construction task. For example, after comparing the information that should be known with the known information, the proportion of the worker's known information in the information that should be known can be known, and the value obtained after converting the proportion into a percentage is used as the worker's mastery of the information that should be known, such as 50%, 60%, 80%, etc.

[0055] Step 210, based on the mastery level of the required information, obtain construction briefing test questions from the construction briefing information database.

[0056] The construction briefing information database refers to a database used to store information related to construction briefings. For example, the construction briefing information database may store safety briefings, technical briefings, operation guides, test questions, etc.

[0057] The construction briefing test questions are a test tool used to evaluate the workers' understanding of the construction briefing content. In some embodiments, the construction briefing test questions can be in the form of a test paper. For example, the construction briefing test questions can include multiple-choice questions, judgment questions, and operation questions. The construction briefing test questions can be used to test the workers' ability to remember, understand, and apply the construction briefing information.

[0058] In some embodiments, the processor can automatically select appropriate test questions from the briefing information library based on the worker's level of mastery of the information that should be known. For example, if the worker's level of mastery of the information that should be known is low, the processor can select basic questions; if the worker's level of mastery of the information that should be known is high, more challenging briefing content can be selected to further verify the worker's actual mastery.

[0059] In practice, construction briefing test questions should include some content that needs to be mastered for construction projects, such as test questions related to construction safety and necessary basic knowledge of construction.

[0060] In this embodiment, the process of completing the construction briefing for workers is automated based on the contract list, construction tasks and worker information of the construction project. The personal situation of the workers and the situation of the construction project are comprehensively considered in the process of construction briefing, so that the arrangement of the briefing has higher accuracy and effectiveness, which can improve construction safety and work efficiency.

[0061] In some embodiments, obtaining construction briefing test questions from a construction briefing information database based on the mastery level of the information to be known may include the following operations.

[0062] Based on the mastery level of the information to be known, determine multiple unknown information to be known; determine a logical chain corresponding to the multiple unknown information to be known; sort the multiple unknown information to be known based on the logical chain; and based on the sorted unknown information to be known, obtain the construction briefing test questions from the construction briefing information library.

[0063] Unknown information refers to information that workers have not yet mastered but should master during the construction task.

[0064] In some embodiments, the processor can compare whether the worker's mastery of the information that should be known exceeds a preset threshold, and then filter out the information that should be known that has not been mastered, and obtain the unknown information that should be known. For example, if the worker's mastery of a certain information that should be known is lower than a preset threshold, then the information can be considered as the unknown information that should be known. In practice, in addition to the information that should be known with a mastery level lower than the preset threshold, the unknown information that should be known can also randomly include some information that should be known with a mastery level higher than the preset threshold, so as to enhance the worker's mastery of the information that should be known.

[0065] In some embodiments, the first information that should be known can be taken based on the worker's known information mastery vector and the worker's information that should be known in the project. The first information that should be known can be searched in the known information vector. If found, the mastery level is stored in the worker's information that should be known mastery level vector for a certain project [worker ID, project ID, information that should be known ID: mastery level, information that should be known ID: mastery level, information that should be known ID: mastery level]. If not found, the mastery level is 0, and it is also stored in the corresponding position of the information that should be known mastery level vector. Repeating this process can obtain a complete information that should be known mastery level vector. Afterwards, based on the information that should be known mastery level vector, the information that should be known whose mastery level is higher than a threshold can be eliminated, and finally the worker's unknown information that should be known in the project can be obtained [worker ID, project ID, unknown information that should be known ID: mastery level, unknown information that should be known ID: mastery level, unknown information that should be known ID: mastery level].

[0066] The logic chain is used to represent the dependency relationship between multiple information entities. For example, the logic chain can be used to indicate which information entities need to be mastered first in a construction task in order to understand or perform subsequent tasks.

[0067] In some embodiments, the processor may construct a logical chain based on the dependencies between information entities. For example, a construction task may require understanding of safety regulations, and the information entity of the safety regulations needs to be explained before the operation steps or construction technical requirements can be explained. By analyzing the relevance and sequence of each information entity, the logical chain can be determined.

[0068] Sorting refers to arranging known and unknown information according to the priority of information mastery or the difficulty of learning. Through sorting, the dependency relationship between various information entities can be reflected to a certain extent through the order, which can help workers learn and master the information required to perform construction tasks in the correct order.

[0069] In some embodiments, after determining the logical chain of multiple unknown information that should be known, the unknown information that should be known can be sorted according to the dependency relationship in the logical chain and the learning progress of the workers. The sorting criteria can be based on any one of a variety of factors. For example, it can be based on logical dependency, giving priority to the knowledge points of the projects that are executed first in the construction task, such as learning safety information first, then learning operating steps; for another example, it can be based on learning priority, giving priority to workers to learn knowledge points that have not been mastered or have a low degree of mastery, and then learning knowledge points that have been mastered and have a high degree of mastery.

[0070] Sorting can be achieved through a permutation algorithm (such as depth-first search or greedy algorithm, etc.). In the process of sorting, duplicate information entities may be encountered. For duplicate information entities, the first entity that appears (i.e., the one with the highest sorting position) can be taken, and subsequent entities can be discarded to avoid repeated permutations.

[0071] After obtaining the ranking of the unknown information to be known, the processor can extract the corresponding test questions from the construction briefing information database according to the ranking results. For example, the processor can search for the relevant construction briefing content from the information database according to the ranking results and the ranking order for each unknown information to be known, and convert the corresponding briefing information into test questions.

[0072] In some embodiments, the learning order of unknown information can be obtained in the following manner: Obtain the unknown information vector that the worker should know in the project, and take the first unknown information that should be known; retrieve all the IDs of the unknown information that should be known in the logical chain. For example, assuming that the retrieved unknown information that should be known is on three logical chains, that is, it can be divided into three categories for independent learning. In the three chains, average the mastery level respectively, and sort the three chains from high to low according to the average mastery level. Finally, according to the sorting between chains and the sorting within the chain, readjust the order of the elements in the vector of the unknown information that the worker should know in the project.

[0073] Furthermore, the order of briefing learning for workers can be determined. First, obtain the unknown information vector of the worker after the construction project is adjusted, take the first unknown information, query the briefing corresponding to the unknown information, and put it into the briefing learning vector of the worker in the project [worker ID, project ID, briefing ID, briefing ID, briefing ID]. Repeat this process to get a complete briefing learning vector. In the above briefing learning vector, the briefing ID may be repeated, and the duplicates are removed according to the principle of retaining the first briefing ID.

[0074] In some embodiments, the processor may obtain construction briefing test questions from the construction briefing information library in the following manner.

[0075] Based on the information that the worker should know and the information that the worker should know but does not know, determine the known information that the worker should know; and based on the unknown information that the worker should know and the known information that the worker should know, obtain the construction briefing test questions from the construction briefing information database.

[0076] The known information that should be known refers to the information that the worker has mastered and should master. In some embodiments, the processor can compare the known information of the worker with the unknown information that should be known, and other information in the known information except the unknown information that should be known can be determined as the known information that should be known.

[0077] Furthermore, the processor can retrieve corresponding test questions from the construction briefing information library based on the unknown information that should be known and the known information that should be known. For example, for each unknown information that should be known, it is necessary to query the construction briefing information library to obtain relevant information and test questions to ensure that it can be verified whether the worker has mastered the relevant information. For the known information that should be known, some information can be randomly selected and the relevant information and test questions can be queried from the construction briefing information to enhance the worker's memory of the information entities that have been mastered.

[0078] After obtaining the test questions, the processor can send the construction briefing test questions to the workers for testing, and update the known information library according to the test results. Exemplarily, the processor can implement the test of the workers and the update of the known information library through the following operations.

[0079] In some embodiments, the method of dynamically assembling test questions can be adopted to verify the mastery of the unknown information that should be known in combination with the briefing learning situation; verify the mastery of the known information that should be known according to the strategy; re-evaluate the mastery according to the test results, and arrange new briefing learning; mark the learning focus in the briefing according to the mastery, and screen the learning situation to be learned. According to the correlation between the answer to the test question and the mastery of the information that should be known. If there is a certain proportion of questions corresponding to the known information with a high mastery, if the answer is poor, the questions corresponding to the known information that should be known but is judged to have been mastered will be dynamically tested. If it fails, the mastery will be readjusted and re-learning will be arranged.

[0080] Exemplarily, the test questions can be assembled in the following way. Obtain the information vector of a worker that should know in a project, and the adjusted unknown information vector of the worker that should know in the project; subtract the known information vector from the unknown information vector that should be known to obtain the known known vector; extract the information entities to be verified from the unknown information vector that should be known and the known known information vector according to a certain ratio. For example, the unknown information that should be known can be extracted at 60%, and the known information that should be known can be extracted at 30%; query the associated test questions based on the extracted information entity ID, and assemble the initial test questions. Afterwards, the construction briefing test questions can be sent to the worker for testing, and the test results can be obtained; based on the test results, the latest mastery of the information that should be known can be obtained; and, based on the latest mastery of the information that should be known, the unknown information that should be known can be reordered and the known information library can be updated.

[0081] The test result is the score or evaluation result obtained by the worker after completing the construction briefing test questions. The test result can reflect the worker's mastery of the knowledge related to the test questions.

[0082] In some embodiments, the processor can send the obtained construction briefing test questions to relevant workers through a digital platform (such as a mobile application, website), or distribute them through on-site paper test papers. After the workers complete the test questions, the invigilator can collect the test papers and upload them to the system through electronic scanning or other methods. For digital platforms, the processor can obtain the test results after the workers complete and submit the answers. The test results will be recorded in the system as a basis for evaluating the workers' knowledge mastery.

[0083] The mastery degree of the information that should be known refers to the worker's proficiency in the information that should be known, which can be expressed as a score or a grade. In some embodiments, the mastery degree of the information that should be known can be obtained in the following way: in the above-mentioned initial test questions, the test questions involving the known that should be known are pushed to the workers first. If the workers answer all the questions correctly, the mastery degree vector of the known information that should be known is updated, and the mastery degree is updated to 1. If the worker answers the questions incorrectly, the mastery degree of the information entity is updated to 0, and the test questions corresponding to the information entities that have not been extracted are pushed again until the unknown is answered correctly. When there are consecutive wrong answers, multiple pushes can be made and the mastery degree of the information entity corresponding to the wrong questions can be updated to 0 until all the test questions of the information entities are verified. In addition, after the test of the known information that should be known is completed, the unknown information that should be known can continue to be tested according to the above method, and the verified mastery degree vector of the information that should be known is obtained based on the answer results.

[0084] In some embodiments, the processor can analyze the test results to obtain the degree to which each worker has mastered the required information corresponding to each test question. For example, if a knowledge point corresponding to a test question has a high score, it means that the worker has a good grasp of the knowledge point corresponding to the test question.

[0085] The known information base refers to a database used to store the information that workers already know and the extent of their knowledge.

[0086] In some embodiments, after obtaining the worker's latest mastery of the information that should be known, the records in the known information library can be updated. For example, the records in the known information library are updated to add the worker's newly mastered information and adjust the mastery of the existing information. For example, a certain knowledge point is updated from "partial mastery" to "full mastery".

[0087] In some embodiments, when sorting the multiple should-be-known unknown information based on the logical chain, the processor can determine the average mastery level of the should-be-known unknown information corresponding to each logical chain based on the distribution of the multiple should-be-known unknown information on the logical chain; sort the logical chains based on the average mastery level, and sort the corresponding should-be-known unknown information based on the internal information logic of the logical chains.

[0088] The average mastery level is the average mastery level of the unknown information contained in each logical chain. The mastery level can be expressed as a percentage value between 0 (not mastered) and 1 (fully mastered).

[0089] In some embodiments, the processor can first confirm what should-be-known unknown information each logical chain contains based on the distribution of the should-be-known unknown information in the logical chain, and then, for each should-be-known unknown information, query its current mastery level identifier, and then calculate the average mastery level of all should-be-known unknown information contained in each logical chain.

[0090] After obtaining the average mastery of each logic chain, the processor can sort all logic chains according to the average mastery of each logic chain, for example, sorting the logic chains from high to low according to the average mastery of the logic chains. Through sorting, it can be more intuitively reflected which logic chains have better mastery of the known unknown information and which logic chains need more attention or learning.

[0091] It can be understood that when sorting, in addition to sorting the logical chain based on the average mastery level of the known unknown information in the logical chain, the corresponding known unknown information can also be sorted based on the internal information logic of the logical chain, that is, the known unknown information within the logical chain can be sorted.

[0092] Internal information logic refers to the dependencies and learning order between the known and unknown information within the logic chain. For example, some information needs to be learned before other information.

[0093] In some embodiments, the processor may sort the known unknown information according to the dependency relationship of the known unknown information in the logic chain. For example, the sorting within the logic chain may follow the principle of "prerequisites first, subsequent dependencies", that is, the knowledge points that come first (for example, the tasks that perform construction first) may be prioritized. For example, if information A depends on information B, information B may be ranked before A. In the sorting within the logic chain, the processor may arrange the known unknown information according to the dependency relationship by utilizing a topological sorting algorithm or a rule-based sorting algorithm.

[0094] In this embodiment, by sorting the logical chain and the unknown information that should be known within the logical chain, it can be ensured that workers follow a reasonable learning sequence during the learning process, so as to ensure the effective transmission of information and the gradual improvement of workers' knowledge.

[0095] In some embodiments, the logical chain corresponding to multiple pieces of known unknown information can be determined in the following manner.

[0096] The processor may determine key unknown information based on the unknown information that should be known; obtain a logical chain including the key unknown information from a logical chain library based on the key unknown information; and use the obtained logical chain as a logical chain corresponding to the unknown information that should be known.

[0097] Key unknown information refers to the information that plays a key role in the unknown information that should be known. Key unknown information can be information that has an important impact on the successful completion of the construction task, the work efficiency and safety of the workers, etc.

[0098] In some embodiments, the processor can determine the key unknown information based on the unknown information that should be known through a data analysis method (such as task-based demand analysis). For example, for each construction task, it is possible to comprehensively determine which unknown information that should be known is the key unknown information based on the characteristics of the task, the worker's historical learning records, and the task requirements. Exemplarily, construction methods or safety specifications are usually what every worker needs to master, so the unknown information that should be known related to construction methods and safety specifications can be determined as key unknown information. The specific method for determining the key unknown information can be automated rules or machine learning model recognition, etc., which is not limited in this embodiment.

[0099] The logic chain library refers to a database used to store logic chains. The logic chains in the logic chain library record the dependency relationship between information entities, learning order and other information.

[0100] In some embodiments, the processor may search from the logic chain library according to the key unknown information to obtain a logic chain including the key unknown information. The retrieved logic chain may be one or more.

[0101] The logical chain corresponding to the unknown information to be known refers to the logical chain including the unknown information to be known. After obtaining the logical chain including the key unknown information, the processor can match the obtained logical chain with each unknown information to be known. If the logical chain contains the unknown information to be known, the logical chain can be used as the logical chain corresponding to the unknown information to be known.

[0102] In some embodiments, the logic chain library can be pre-built. For example, the logic chain library can be built in the manner shown in the following embodiments.

[0103] The processor can perform feature extraction based on the information entity to obtain feature information of the information entity; process the feature information of the information entity through a dependency mining model to obtain dependency relationships between multiple information entities; determine one or more logical chains based on the dependency relationships; and determine the logical chain library based on the one or more logical chains.

[0104] Feature extraction refers to extracting attributes or dimensions that describe the characteristics of an information entity from an information entity. For example, the type, importance, and relationship of an information entity with other information. Feature information refers to various types of data extracted from an information entity that can describe its characteristics and functions. In some embodiments, feature information may include the category, content, source, and correlation with other information of an information entity.

[0105] In some embodiments, the processor may extract key information from each information entity using natural language processing (NLP) technology or machine learning models (such as TF-IDF, BERT, etc.), and extract feature information of the information entity through text analysis, semantic analysis, etc. For example, feature information such as "construction requirements" and "safety standards" may be extracted from the "construction specification" information entity. The extracted feature information may include text features (such as keywords, category labels, etc.) and numerical features (such as frequency of occurrence, information relevance, etc.).

[0106] Dependency mining models refer to models based on data analysis or machine learning. Dependency mining models can be used to analyze the dependencies between information entities. For example, some information entities may be the prerequisites of other information entities, or there may be causal relationships between information entities. Dependency relationships can describe the interdependence between two or more information entities. For example, the mastery of information A requires the learning of information B, or the appearance of information A may lead to changes in information B.

[0107] In some embodiments, the dependency mining model can be trained based on a large amount of training data. For example, multiple information entities are used as training samples, and the dependency relationships between the information entities are used as labels for model training. The labels can be obtained by manual annotation. The specific training method includes but is not limited to the gradient descent method.

[0108] In some embodiments, in addition to the dependency mining model, the processor may also use graph analysis technology or sequence models (such as dependency analysis, association rule mining, etc.) to identify and establish dependency relationships between information entities. For example, mining may be based on knowledge graph technology, where each information entity may be a node in the knowledge graph and the dependency relationship is the edge between the nodes.

[0109] In some embodiments, the input of the dependency mining model also includes node hidden features of the information entity. Node hidden features refer to features that are related to the information entity but are not reflected in the literal meaning of the information entity. For example, in engineering construction, the subsequent projects are often carried out on the basis that the previous project has been completed, and the content of some projects themselves does not reflect the feature of "requiring the previous project to be completed". It is also possible that the implementation of a project requires the completion of multiple other projects, and node hidden features can potentially represent this relationship. For more information about the dependency mining model, see Figure 3 Related description.

[0110] After determining the dependency, the processor can sort the information entities according to the dependency based on the dependency. Each logical chain obtained after sorting can include all information entities from the initial information entity to the final target, for example, the initial information entity of a construction task to the final information entity at the completion of the construction task.

[0111] For complex construction tasks, multiple logical chains may be formed, each chain representing different task completion steps. For example, task A may depend on the knowledge points in chain 1, while task B depends on the knowledge points in chain 2. For another example, a construction task may include multiple subtasks, each of which may correspond to a logical chain, and the logical chains of different subtasks may be associated with each other. For example, subtask A may depend on the completion of a node in subtask B.

[0112] After one or more logic chains are determined, the determined logic chains can be stored in a structured manner in a logic chain library for easy query.

[0113] In some embodiments of this specification, by intelligently arranging construction briefings and generating test questions, for example, arranging the test and learning of construction briefings according to the information that workers should know for the tasks to be completed in the project and the workers' mastery of the information, the pertinence of the construction briefing arrangement can be improved. In the process of arranging construction briefings, the workers' mastery of the information is comprehensively considered, and the briefings are arranged according to the dependency logic between clear information entities, which improves the logic of the briefings. At the same time, the workers' mastery of the information is verified by means of construction briefing test questions, which further improves the effectiveness of construction briefings for workers.

[0114] Figure 3 is an exemplary flow chart of obtaining node hidden features according to some embodiments of this specification. In some embodiments, process 300 may be executed by a construction briefing arrangement management system or a processor (eg, processor 140). Figure 3 As shown, process 300 includes the following steps.

[0115] Step 302: Obtain a task plan for the construction project.

[0116] The task plan of a construction project refers to a detailed plan developed to complete the construction project. The task plan may include the subtasks of each stage of the construction project, the start and end time of the subtasks, the required resources, the construction personnel, and the relevant responsible persons.

[0117] In some embodiments, the processor can obtain the task plan of the construction project through a project management system or database. The task plan can be in a table or document form, recording the main tasks, construction schedules and relevant persons in charge of the entire project. The task plan of the construction project can be pre-formulated and stored by the person in charge of the construction project, and the processor can directly read it by reading data, calling a data interface, etc.

[0118] Step 304: extracting task items and their corresponding task dates based on the task plan.

[0119] Task items refer to the independent work units that make up a construction project, such as "foundation construction", "wall construction", etc.

[0120] The task date refers to the start and end time of each task item. The task date reflects the specific construction time range of the task item.

[0121] In some embodiments, the processor may extract each task item and its corresponding task date from the task plan. For example, for the task of "foundation construction", the task item is "foundation construction" and the task date is "January 1, 2024 to January 10, 2024".

[0122] Step 306: based on the task date and the task type of the task item, the task item is divided to obtain a plurality of sub-task items and corresponding task sub-dates.

[0123] Task type refers to the nature or category of a task item, for example, construction task, inspection task, verification task, etc.

[0124] Subtask items are smaller independent task units obtained by subdividing a larger task item. For example, the "foundation construction" task can be divided into "foundation earth excavation", "foundation base construction" and other subtask items.

[0125] Task sub-date refers to the specific time schedule for each sub-task.

[0126] In some embodiments, the processor may further divide each task item according to the task type. Assuming the task type is "construction task", the task can be subdivided into multiple subtask items, such as "earth excavation", "foundation treatment", etc. And assign a specific task date to each subtask item. For example, if the time of the "foundation construction" task is from January 1, 2024 to January 10, 2024, then "earth excavation" may be from January 1, 2024 to January 3, 2024, and "foundation treatment" may be from January 4, 2024 to January 6, 2024. Specifically, the date division can be estimated based on the completion time of the subtasks related to the task plan or history.

[0127] Step 308: construct a knowledge graph based on the multiple subtask items and the corresponding task subdates.

[0128] A knowledge graph is a graph structure that uses nodes and edges to represent entities and relationships between entities. A node is used to represent an entity in a knowledge graph, and an edge is used to represent the relationship between nodes.

[0129] In this embodiment, the nodes of the knowledge graph are corresponding subtask items, and the node attributes include the task content of the subtask item and the corresponding task subdate; the edges are directed edges, and the direction of the edges represents the execution order of each subtask item.

[0130] The knowledge graph can use subtask items as nodes. For example, the attributes of each subtask item node may include the specific subtask content (such as "earth excavation") and the task subdate (such as "January 1, 2024 to January 3, 2024"). Establish an edge between every two related subtask items. For example, if "earth excavation" needs to be performed before "foundation treatment", add a directed edge between the corresponding nodes of the two subtasks in the graph, and the direction of the edge points from the "earth excavation" node to the "foundation treatment" node.

[0131] Step 310: Perform graph reasoning based on the knowledge graph to obtain the hidden features of the node.

[0132] Graph reasoning is the process of using graph structures and relationships in graphs to perform logical deductions. For example, through graph reasoning, we can obtain the dependencies between subtask items and the order of task execution.

[0133] Hidden node features can be extracted through deep learning or graph learning methods. In some embodiments, the constructed knowledge graph can be inferred through a graph neural network (GNN) or other graph learning algorithms, and the node hidden features can be extracted by inferring the node and edge relationships in the learning graph. Specifically, the extraction of node hidden features can be achieved by performing convolution operations on the knowledge graph or a deep learning model based on the graph structure. For example, the features of the node can be updated through the node's neighbor node information to obtain the node hidden features of the node. The obtained node hidden features may include information such as the execution priority of the task item and the task relevance. The node hidden features can be used for scheduling, risk prediction, etc. of construction projects.

[0134] In some embodiments, graph reasoning can be performed based on the start time and end time in the subdate corresponding to the subtask item. For example, for task items A and B, there are three subtasks A1, A2 and A3, B1, B2 and B3 under task item A and task item B respectively. Assuming that the end time of subtask item A1 is January 1, the start time of subtask item A2 is January 2, and the start time of subtask item B1 is also January 2, then there may be a certain dependency between subtask B1 and subtask A1 or A2. For example, the progress of subtask item B1 may need to depend on the completion of subtask item A1.

[0135] In this embodiment, by dividing the task items into multiple subtasks, the date corresponding to the task is smaller in time span, the division of task nodes also makes the task unit smaller, and the correspondence between the task and the time node is clearer. For example, for task item A and task item B, if they are not divided, it is difficult to know at which stage of task item A the completion of project A1 is, and it is also impossible to know whether the start time of subtask item B1 is related to task item A. Therefore, by dividing the task items into multiple subtask items and performing graph reasoning to obtain node hidden features, more hidden dependencies between task items of the construction project can be mined, thereby providing more accurate data support for the arrangement of construction briefing.

[0136] Figure 4 is an exemplary flow chart of establishing a known information library according to some embodiments of this specification. In some embodiments, process 400 may be executed by a construction briefing arrangement management system or a processor (eg, processor 140). Figure 4 As shown, process 400 includes the following steps.

[0137] Step 402, obtaining the registered worker list; the registered worker list includes registered workers and their corresponding IDs.

[0138] Registered workers refer to workers who have registered in the construction system or platform and registered their personal information, work experience, task records, etc. The registered worker list is a record containing registered workers and their unique identifiers (IDs).

[0139] In some embodiments, the list of registered workers can be obtained based on the worker's registration information. For example, the list of registered workers can be obtained by collecting worker information online or offline, including name, ID number, etc., and assigning a unique ID to each worker. The worker ID can be his ID number or work number, etc.

[0140] Step 404, obtaining the historical tasks of the registered worker.

[0141] Historical tasks refer to tasks that workers have participated in in the past. Historical tasks can include task description, time, and location, etc.

[0142] In some embodiments, the processor may query the registered worker's historical tasks in the corresponding information management system through the registered worker's ID.

[0143] Step 406: Acquire the information entity corresponding to the historical task.

[0144] In some embodiments, the processor may query the corresponding information storage space (eg, a database) according to the historical tasks to obtain the information entity corresponding to the historical tasks.

[0145] In some embodiments, the processor may also extract information entities such as technical specifications, operating procedures, and safety requirements required for each historical task based on the description of the task.

[0146] Step 408, establishing a mapping relationship between the registered workers and the information entities of their corresponding historical tasks, and obtaining the known information of each of the registered workers.

[0147] The mapping relationship may be an association relationship between a registered worker and the information entity of the corresponding historical task. One registered worker may correspond to multiple information entities, for example, registered worker A may correspond to information entity 1, information entity 2, information entity 3, and so on.

[0148] The information entity that has established a mapping relationship with the registered worker can be used as the known information of the registered worker, reflecting that the worker has already understood the corresponding information entity when performing historical tasks.

[0149] Step 410, based on the task items, known information and rectification orders of the registered worker's historical tasks, determine the registered worker's mastery of known information.

[0150] A rectification sheet refers to a solution proposed through adjustment, modification or supplementation to deficiencies or problems in historical tasks.

[0151] In some embodiments, the processor can evaluate the worker's mastery of information entities by analyzing the task items involved in the worker's historical tasks and the information entities known to the worker. Specifically, the worker's mastery of relevant knowledge can be evaluated based on the quality of the worker's completion of historical tasks, the complexity of the task items, and the execution of the rectification order, and used as the worker's mastery of known information.

[0152] In some embodiments, the processor can determine the degree of mastery of known information in the following manner: for any worker, query the task items, known information, and rectification orders corresponding to the worker's historical task orders; for one of the task items, the degree of mastery of the known information associated with the task item = (1-the number of associated rectification orders) / the number of occurrences of the task item; for one of the known information, the degree of mastery of the known information = the sum of the mastery of the known information corresponding to each associated task item / the number of associated task items; in this way, a worker's degree of mastery of known information vector [worker ID, known information ID: degree of mastery, known information ID: degree of mastery] can be obtained.

[0153] Step 412, determining the known information database based on the registered worker's mastery of known information.

[0154] In some embodiments, the processor may establish a known information library including known information of multiple workers according to each worker's mastery of the task items and information entities.

[0155] For example, the processor establishes a database based on the mastery levels of known information of multiple registered workers, thereby obtaining the known information database.

[0156] After establishing the known information database, the processor may also perform regular maintenance on the known information database, for example, updating the workers' mastery of known information recorded in the known information database according to the workers' results in subsequent construction briefing tests.

[0157] In this embodiment, by establishing a known information database to record the workers' mastery of briefing knowledge and arranging briefing tasks in a customized manner, not only can the pertinence and effectiveness of the briefing be improved, but also the shortcomings of information indoctrination and formalism in traditional methods can be avoided.

[0158] Figure 5500 is an exemplary flow chart of establishing a construction briefing information database according to some embodiments of this specification. In some embodiments, process 500 may be executed by a construction briefing arrangement management system or a processor (eg, processor 140). Figure 5 As shown, process 500 includes the following steps.

[0159] Step 502, obtaining a construction briefing list.

[0160] The construction briefing list refers to a list containing relevant construction briefing tasks. In some embodiments, the briefing content in the construction briefing list includes at least one of technical briefing and safety briefing. Technical briefing refers to briefing content related to engineering technology, process, operating methods, etc. Safety briefing refers to briefing content related to construction safety.

[0161] In some embodiments, the processor may obtain the construction briefing list from a construction management platform or database.

[0162] Step 504: extract information entities and entity feature attributes contained in the disclosure content.

[0163] Entity characteristic attributes refer to the specific parameters or descriptive characteristics of information entities.

[0164] In some embodiments, the processor can extract information entities related to the briefing content from each construction briefing record, and extract characteristic attributes of each information entity. Specifically, the characteristic attributes can be automatically extracted through natural language processing technology or a large language model. For example, the characteristic attributes of "tall template erection" may include "template height", "template span", "template erection", etc.

[0165] Step 506: Based on the information entity and entity characteristic attributes, a mapping relationship between the information entity and the task items of the construction project is established.

[0166] The mapping relationship refers to the corresponding relationship between information entities and task items. For example, if a task item requires the technical support of "tall template erection", then "tall template erection" is the knowledge or skills required for the task item, and there is a corresponding relationship between "tall template erection" and the task item.

[0167] In some embodiments, the processor can establish a mapping relationship between the "information entity" and the "task item" extracted from the briefing content. For example, for each task item, the information entity involved is determined, and the information entity involved and the task item are connected through a mapping relationship. For example, the task item of "tall template erection" can establish a mapping relationship with information entities such as "template height" and "span".

[0168] Step 508, adding an identifier to the information entity to obtain the construction briefing information database.

[0169] The identification may be a unique identifier assigned to each information entity, and may include a disclosure number, a disclosure category, an information entity number, etc.

[0170] In some embodiments, the processor may identify all extracted "information entities" to ensure that each information entity has a unique identifier in the construction briefing information library.

[0171] In some embodiments, an identifier of the information entity can be generated according to the briefing code (such as "project category-briefing category-briefing number"). By storing all identifiers and corresponding information entities, feature attributes, task items, etc. together in the information library, a construction briefing information library can be obtained.

[0172] In this embodiment, by establishing a construction briefing information database, each construction briefing is associated with a corresponding information entity; the information entity is associated with task items and task features; the information entity has a logical relationship; the information entity is associated with test questions, and the test results are associated with the degree of information mastery.

[0173] In some embodiments, the construction briefing information library includes a logical chain library, which can be obtained in the following ways: determining the dependency relationship between the information entities; determining the logical chain of the information entities based on the dependency relationship; determining the logical chain library based on the logical chain of the information entities.

[0174] The logical chain library refers to a logical chain collection that contains all construction briefing information entities.

[0175] In some embodiments, the processor can determine which entities are prerequisites and which are dependent entities by analyzing the logical relationships between various information entities. For example, some technical requirements in "safety disclosure" may need to be completed before construction, while other operations depend on the completion of previous information entities. The processor can extract and record the dependencies through a rule engine or expert system to form a logical relationship between information entities. In some embodiments, manual analysis can also be used to extract the previous and next dependency information of each task.

[0176] In some embodiments, a topological sorting algorithm or similar dependency analysis tool can be used to generate an ordered logical chain based on the dependency relationship between information entities. For example, if "template erection" depends on "construction site inspection", "construction site inspection" should be executed before "template erection".

[0177] In some embodiments, after all logical chains are stored in a database, a logical chain library containing multiple logical chains can be obtained.

[0178] In some embodiments, the construction briefing information database further includes a test question database and an entity mastery database. The test question database includes the association relationship between the information entity and the test question; wherein the test question associated with the information entity is used to test the worker's mastery of the relevant content of the information entity. The entity mastery database includes the worker's mastery of the test question in the test question database.

[0179] In some embodiments, the association between the test question and the information entity can be established in the following manner: extracting the knowledge point features of the test question and the entity features of the information entity; calculating the similarity between the knowledge point features and the entity features; in response to the similarity being greater than a preset threshold, establishing an association between the corresponding test question and the information entity.

[0180] In some embodiments, the features of each information entity may be extracted by manual analysis, expert review, or automated tool extraction to establish a feature vector for the entity.

[0181] Similarity is an indicator used to measure the similarity between the knowledge point features of the test questions and the entity features of the information entity. In some embodiments, common similarity calculation methods, such as cosine similarity, Jaccard similarity, etc., can be used to calculate the similarity between the knowledge point features of each test question and the entity features of the information entity. For test questions and information entities with similarities greater than a preset threshold, an association is established between them, and the association relationship is stored in the database for subsequent query and update.

[0182] In some embodiments, the entity features of the information entity include implicit features. The implicit features are node hidden features obtained by graph reasoning based on the knowledge graph. For example, the implicit features can be as described in this specification. Figure 3 The node hidden features are obtained by the method of the embodiment.

[0183] In this embodiment, by establishing a construction briefing information database, it is possible to more finely manage information entities in the construction briefing process, workers' learning progress, the relationship between test questions and information entities, etc., thereby achieving accurate briefing arrangements and effective assessment of workers' mastery level.

[0184] Figure 6 is an exemplary flow chart of a method for updating the mastery level of a test question according to some embodiments of this specification. In some embodiments, process 600 may be executed by a construction briefing arrangement management system or a processor (eg, processor 140). Figure 6 As shown, process 600 includes the following steps.

[0185] Step 602, obtaining the worker's historical answers to the test questions in the construction briefing information database.

[0186] Historical answers refer to the answers given by workers to test questions during past learning or assessment processes.

[0187] In some embodiments, the processor may extract the worker's historical answer records for each test question from a database to obtain historical answers.

[0188] Step 604: Determine the initial mastery level of the test question based on the historical answers.

[0189] The initial mastery level refers to the worker's initial mastery level of the test questions. For example, if the worker answers a test question completely correctly, the initial mastery level is 100% (complete mastery); if the worker answers incorrectly, it is 0% (no mastery), or it can be a certain percentage (such as 50%).

[0190] In some embodiments, a scoring algorithm can be used to calculate the worker's mastery level based on the worker's historical responses to test questions. For example, the worker's initial mastery level can be determined by direct scoring, weighted scoring, or proportional calculation. Direct scoring refers to directly assigning scores based on whether the answer is correct or not (such as 1 point for correct answers and 0 points for errors). Weighted scoring means that if some questions have different levels of difficulty, different scores can be assigned based on the weight of the questions. Proportional calculation refers to calculating the percentage of a worker's mastery level based on the ratio of the number of correct answers to the total number of questions (for example, if 8 questions are answered correctly and there are a total of 10 questions, the mastery level is 80%), and the calculation result can be used as the initial mastery level.

[0191] Step 606: updating the mastery level of the test question based on a preset practice mechanism and / or a preset decay mechanism.

[0192] The preset practice mechanism refers to the method designed to update the worker's mastery of the test questions according to the worker's actual operation or practice activities. For example, if a worker repeatedly applies a skill in actual construction, the score of the skill can be increased to reflect the improvement of his mastery.

[0193] During the construction process, the workers' mastery level is tested with practical results. In the event of acceptance failure, rectification, or punishment, the task items and information entities associated with the acceptance form, rectification form, and punishment form are queried to reduce the corresponding mastery level. When the reduction is below the threshold, the workers need to be re-introduced until it returns to above the threshold.

[0194] The preset decay mechanism means that as time goes by or the knowledge becomes outdated, the worker's mastery of certain skills will gradually decay, and the mastery level can be maintained through repeated learning or testing. For example, if a worker has not operated a certain skill for a long time, the processor can automatically reduce its mastery level.

[0195] In some embodiments, the processor may track the worker's operational performance based on the construction projects the worker actually participates in. If the worker successfully applies a skill in an actual task, the worker's mastery level may be updated by adding points or improving the score.

[0196] The preset decay mechanism can be through setting a time threshold (such as 6 months, 1 year, etc.), if the worker does not perform related operations or tests within this time, the mastery of the relevant skills will automatically decay. The decay can be set according to the time delay, for example, the longer the skill is not used, the greater the decline in mastery, until the mastery is restored when re-evaluated or tested.

[0197] In some embodiments, the processor may periodically update the mastery level of the test questions according to a preset practice mechanism and / or a preset decay mechanism, and store the updated data in the level library.

[0198] It should be noted that the description of each process above is only for example 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 each process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, a storage step is added between each step, etc.

[0199] Figure 7 is an exemplary module diagram of a construction briefing arrangement management system according to some embodiments of this specification. Figure 7 As shown, the system 700 may include: a first determination module 710 , a first acquisition module 720 , a second acquisition module 730 , a second determination module 740 and a third acquisition module 750 .

[0200] A first determination module 710 is used to determine the construction tasks of the workers in the construction project based on the contract list in the construction project;

[0201] A first acquisition module 720, configured to acquire information that the worker should know in the construction project based on the correspondence between the construction task and the information entity;

[0202] The second acquisition module 730 is used to acquire the known information of the worker; the known information includes the worker's mastery of information on the task items of historical learning;

[0203] A second determination module 740 is used to determine the worker's mastery of the information that should be known based on the information that should be known and the known information;

[0204] The third acquisition module 750 is used to acquire construction briefing test questions from the construction briefing information database based on the mastery level of the required information.

[0205] For detailed description of each module of the system 700, please refer to the description of the relevant process, for example, Figures 2 to 6 The relevant instructions will not be repeated here.

[0206] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of 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 belong to the spirit and scope of the exemplary embodiments of this specification.

[0207] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in 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.

[0208] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with 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 by software solutions, such as installing the described system on an existing server or mobile device.

[0209] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0210] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0211] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0212] 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, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent 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. A construction briefing arrangement management method, the method comprising: Determining the construction tasks of workers in the construction project based on the contract list within the construction project; Based on the correspondence between the construction tasks and the information entities, obtaining the information that the workers should know in the construction project; Acquire the known information of the worker; the known information includes the worker's mastery of information on the task items of history learning; Based on the information that should be known and the known information, determining the worker's mastery of the information that should be known; Based on the mastery level of the required information, construction briefing test questions are obtained from a construction briefing information database.

2. The method according to claim 1, wherein obtaining construction briefing test questions from a construction briefing information database based on the mastery degree of the information to be known comprises: Based on the mastery degree of the information that should be known, determining a plurality of unknown information that should be known; Determine a logical chain corresponding to the plurality of unknown information that should be known; Sorting the plurality of known unknown information based on the logic chain; Based on the sorted unknown information, the construction briefing test questions are obtained from the construction briefing information database.

3. The method according to claim 2, sorting the plurality of known unknown information based on the logic chain, comprising: Based on the distribution of the plurality of unknown information to be known on the logic chain, determining the average mastery degree of the unknown information to be known corresponding to each logic chain; The logic chains are sorted based on the average mastery level, and the corresponding known unknown information is sorted based on the internal information logic of the logic chains.

4. The method according to claim 2, wherein obtaining the known information of the worker comprises: Acquire the known information of the worker from a known information database; wherein the known information database is established in the following manner: Obtain the registered worker list; the registered worker list includes registered workers and their corresponding IDs; Get the historical tasks of the registered worker; Acquire the information entity corresponding to the historical task; Establishing a mapping relationship between the registered workers and the information entities of their corresponding historical figures, and obtaining known information of each of the registered workers; Determine the registered worker's mastery of known information based on the task items, known information, and rectification orders of the registered worker's historical tasks; The known information database is determined based on the registered worker's mastery of known information.

5. According to the method of claim 4, the step of obtaining the construction briefing test questions from the construction briefing information database based on the sorted known unknown information comprises: Determining the known information that should be known based on the worker's known information and unknown information that should be known; Based on the unknown information that should be known and the known information that should be known, the construction briefing test questions are obtained from the construction briefing information database.

6. The method according to claim 5, further comprising: Sending the construction briefing test questions to the workers for testing, and obtaining the test results; Based on the test results, the latest level of knowledge of required information is obtained; Based on the mastery level of the latest information to be known, the unknown information to be known is reordered and the known information library is updated.

7. According to the method of claim 1, the construction disclosure information database is obtained by: Obtaining a construction briefing list; the briefing contents in the construction briefing list include at least one of technical briefing and safety briefing; Extracting information entities and entity feature attributes contained in the disclosure content; Based on the information entity and the entity characteristic attributes, a mapping relationship between the information entity and the task items of the construction project is established; An identification is added to the information entity to obtain the construction briefing information database.

8. According to the method of claim 7, the construction disclosure information library includes a logic chain library, and the logic chain library is obtained by: Determining dependencies between the information entities; Based on the dependency relationship, determining the logical chain of the information entity; Based on the logical chain of the information entity, the logical chain library is determined.

9. The method according to claim 1, further comprising: Obtaining the worker's historical answers to the test questions in the construction briefing information database; Based on the historical answers, determining the initial mastery level of the test questions; Based on a preset practice mechanism and / or a preset decay mechanism, the mastery level of the test questions is updated.

10. A construction briefing arrangement management system, the system comprising: A first determination module is used to determine the construction tasks of the workers in the construction project based on the contract list in the construction project; A first acquisition module, used for acquiring information that the worker should know in the construction project based on the correspondence between the construction task and the information entity; A second acquisition module is used to acquire the known information of the worker; the known information includes the worker's mastery of information on the task items of history learning; A second determination module is used to determine the worker's mastery of the information that should be known based on the information that should be known and the known information; The third acquisition module is used to acquire construction briefing test questions from the construction briefing information database based on the mastery degree of the required information.

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