CN115206524A - State monitoring system for team cooperation - Google Patents
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Abstract
The invention provides a team cooperation-oriented state monitoring system, which comprises: the data acquisition module is provided with a data acquisition unit and a preprocessing unit, wherein the data acquisition unit acquires acquisition contents of team and individual data objects from a preset data source and then preprocesses the acquisition contents through the preprocessing unit; the basic state analysis module is provided with a basic state analysis unit and used for acquiring parameter values of a basic state of the data object by a basic state analysis method to obtain a basic state parameter set; the basic state knowledge graph module is provided with a knowledge graph construction unit and a graph database, constructs a basic state knowledge graph according to a basic state parameter set and stores the basic state knowledge graph to the graph database; the target state analysis module is provided with a target state analysis unit and obtains a target state based on the basic state knowledge graph through a target state analysis method; and the target state display module is provided with an integration unit and a man-machine interaction unit and is used for providing data integration, visual display and interactive retrieval of the target state.
Description
Technical Field
The invention relates to a state monitoring system, in particular to a state monitoring system for team cooperation.
Background
At present, the state monitoring system is widely used in road traffic, public safety, production, teaching, hotels, meetings and communities. The invention patents in the prior art, which can represent the mainstream products in the current market, include: invention A (an employee status monitoring system based on artificial intelligence, application number 201910081688.3); invention B (a method, device and system for monitoring student learning state, application number 201510124898.8); invention C (university student classroom state real-time monitoring system based on classroom multimedia equipment, application number 201910109418.9). The three invention respectively have the following aspects in the aspects of monitoring objects, scenes, monitored target states, state monitoring equipment and data acquisition methods, and state analysis models and associated analysis algorithms:
(1) Monitoring objects, scenes, monitored target states aspects: the invention A comprises the following steps: the working state and the psychological state of a single worker on a production site, wherein the monitored typical target state is whether the physiological state before the post meets the post requirement or not, or whether the physiological state can be qualified for the current work during the work; the invention B comprises the following steps: the learning state of a single student when learning a course, the monitored typical target state being the conflicting emotion to the teacher or the course; the invention C comprises the following steps: typical target states monitored by all students in a class in a classroom are attendance, sleeping, and enthusiasm. All three inventions provide only learning emotion or limited physiological state analysis, do not face the fundamental concerns and needs of the users of the state monitoring system, provide target states such as conservation, collaboration, knowledge grasping, mental handicap, and monitor only the emotion or mental state facing the learning and work themselves, rather than considering the mental state from a health perspective.
(2) The aspects of the state monitoring equipment and the data acquisition method are as follows: the invention A comprises the following steps: the multifunctional sensor is used for acquiring the audio and video information and the personal physiological data information of the field working environment of the employee, and the information processor identifies the working state and the real-time physiological health condition of the employee; the invention B comprises the following steps: detecting heart rate corresponding to a user by adopting a heart rate sensor and an acceleration sensor to obtain heart rate data; the invention C comprises the following steps: each seat in the classroom needs to be correspondingly provided with a sub-controller, a seat position recognition device and a seat position state detection device, a face camera of the seat recognition device regularly collects face images, an image motion judgment module carries out similarity comparison, and a learning state is judged according to the similarity. The three inventions lack consideration for the team learning state and the performance of the individual corresponding to the role in the team cooperation scene; and most data sources are video or information of specific websites, and correlation analysis of multi-source, heterogeneous and heterogeneous data is lacked.
(3) The aspects of the state analysis model and the correlation analysis algorithm are as follows: the invention A comprises the following steps: training through a 3-class classification prediction model of a classification prediction submodule of the staff, identifying facial expressions, eye spirit and limb action indexes of the staff in different psychological states of joy, anger, sadness, funeral and fright, and judging whether the real-time psychological health of the staff is adequate for work; the invention B comprises the following steps: judging the learning state through data comparison according to preset course information, action data, heart rate historical data and normal heart rate data of the age group corresponding to the age of the user; the invention C: the thinking is similar to that of the invention B, the conditions of attendance, attention and addiction to the electronic equipment are judged through image similarity comparison and learning state judgment rules, and classroom performance is judged according to the conditions. The three state analysis models are all single, and a unified knowledge graph is not provided to support deeper association analysis.
Disclosure of Invention
The present invention is made to solve the above problems, and an object of the present invention is to provide a team cooperation-oriented status monitoring system.
The invention provides a team cooperation-oriented state monitoring system, which is characterized by comprising the following components: the data acquisition module is provided with a data acquisition unit and a preprocessing unit, the data acquisition unit acquires corresponding acquisition contents of the monitored team and individual data objects from a preset data source, and the preprocessing unit preprocesses the acquisition contents; a basic state analysis module having a basic state analysis unit for acquiring parameter values of basic states of the data object by a predetermined basic state analysis method and forming a basic state parameter set; the knowledge map building unit builds a basic state knowledge map based on the basic state parameter set, and the built basic state knowledge map is stored in the map database; the target state analysis module is provided with a target state analysis unit and is used for obtaining a target state based on the basic state knowledge map through a preset target state analysis method, wherein the target state comprises a team learning target state, an individual learning target state and an individual mental health target state in a team cooperation scene; and the target state display module is provided with an integration unit and a man-machine interaction unit and is used for providing data integration, visual display and interactive retrieval of the target state.
In the team cooperation-oriented state monitoring system provided by the invention, the system also has the following characteristics: wherein the data objects are classified according to data properties, the data properties including tasks, environments, videos, voices, body parameters, and public sentiments.
In the team cooperation-oriented state monitoring system provided by the invention, the system can also have the following characteristics: the data source comprises manual input, a related information system, video equipment, voice equipment, wearable equipment and a network, the data structure of a data object comprises unstructured data, semi-structured data, structured data and streaming data, a data acquisition unit acquires the acquisition content of the data object by adopting a corresponding acquisition method aiming at the data objects with different properties, the acquisition method comprises the steps of manual input, related information system calling, equipment interface calling and network data crawling, the network data crawling method comprises the steps of calling a network search engine based on team and personal keywords and extracting the related data object from the search result; based on a predefined website address, acquiring webpage content according to a team and a personal keyword, identifying associated content through character matching, and downloading the associated content; based on an APP interface, based on team and personal keywords, obtaining APP content, identifying associated content through character matching, downloading the content, preprocessing the acquired content by a preprocessing unit through a corresponding preprocessing method aiming at data objects with different properties, wherein the preprocessing method comprises data noise reduction, label processing, deletion processing, feature selection, text analysis, video analysis and audio analysis, the data acquisition module is also provided with a data object storage unit, the data object storage unit stores the preprocessed acquired content through a data object storage method, and the data object storage method comprises database storage, file storage and block link storage.
In the team cooperation-oriented state monitoring system provided by the invention, the system can also have the following characteristics: the data object storage method comprises the steps that data objects with the data property of tasks are acquired, wherein the acquired contents comprise task names, types, task targets, task contents, team and member individual roles and role tasks, data sources comprise manual input and associated information systems, a data structure is structured data, corresponding acquisition methods comprise manual input and calling of the associated information systems, the associated information systems are mechanism information systems used for extracting current task information, corresponding preprocessing methods comprise content extraction, label processing and text analysis, and the data object storage method comprises database storage and block link storage; aiming at a data object with data property as an environment, the collected content comprises a site name, a site purpose, an equipment name, an equipment purpose and a member coordinate, the data source comprises a manual input and associated information system, the data structure is structured data, the corresponding collection method comprises the manual input and the calling of the associated information system, the associated information system is an organization information system used for extracting the information of the environment, the corresponding preprocessing method comprises tag processing, content extraction and text analysis, and the data object storage method comprises database storage and block link storage; aiming at a data object with video data, the collected content comprises a video name, a video type, video properties and video parameters, the data source comprises video equipment, the data structure is streaming data, the corresponding collecting method comprises a calling equipment interface and a calling associated information system, the associated information system is an information system for extracting team and personal video data, the corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and the data object storage method comprises database storage, file storage and block link storage; aiming at a data object with voice data property, acquiring content comprises a voice name, a voice type, the voice property and voice parameters, a data source comprises voice equipment, a data structure is streaming data, a corresponding acquisition method comprises calling an equipment interface and calling a related information system, the related information system is an information system for extracting team and personal voice data, a corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and a data object storage method comprises database storage, file storage and block chaining storage; aiming at a data object with data property of wearable equipment, the acquisition content comprises brain waves, heart rate, eye movement, body temperature, blood pressure and movement, the data source comprises brain waves, heart rate, eye movement, body temperature, blood pressure and movement detection instruments, the data structure comprises streaming data and structured data, the corresponding acquisition method comprises calling an equipment interface and calling a related information system, the related information system is an information system for extracting team and personal health parameters and comprises a medical institution information system, the corresponding preprocessing method comprises denoising, label processing, missing processing and feature selection, and the data object storage method comprises database storage, file storage and block chaining evidence; aiming at a data object with public sentiment data nature, the collected content comprises published words and associated information, the published words comprise posts of news, online texts, blog texts, microblogs, weChats and forums, the associated information comprises associated words, associated comments, associated conversations and associated content, the data source comprises a network source and an associated information system, the network source comprises websites, blogs, microblogs, weChas and forums, the associated information system is an information system for extracting team and personal published words and associated information and comprises a social institution information system and an institution information system, the data structure comprises unstructured data, semi-structured data, structured data and streaming data, the corresponding collection method comprises crawling and calling of the associated information system for the network data, the corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and the data object storage method comprises database storage, file storage and block link storage.
In the team cooperation-oriented state monitoring system provided by the invention, the system can also have the following characteristics: the basic state analysis module is also provided with a basic state storage unit which stores a basic state parameter set by a basic state storage method, and the basic state storage method comprises database storage, file storage and block chaining storage.
In the team cooperation-oriented state monitoring system provided by the invention, the system can also have the following characteristics: aiming at a data object with data properties of tasks and environments, the basic state is the same as the acquisition content, the corresponding basic state analysis method is the same as the acquisition method, and the basic state storage method comprises database storage and block chain storage; aiming at a data object with video data properties, basic states comprise team basic states and individual basic states and correspondingly comprise facial expressions, facial feature states, gestures and body postures of a team and an individual, the team basic states are an integral geometric structure formed by overlapping the individual basic states, corresponding basic state analysis methods comprise an open source analysis model and a custom analysis model, the custom analysis model comprises a video data classification training model facing the basic states, parameter values of the basic states are distributed according to time moments, and the basic state storage method comprises database storage and block chain storage; aiming at a data object with voice data property, the basic state comprises the voiceprint, tone and volume of a team and an individual, the corresponding basic state analysis method comprises an open source analysis model and a custom analysis model, the custom analysis model comprises a voice data classification training model facing the basic state, the parameter values of the basic state are distributed according to time moments, and the basic state storage method comprises database storage and block chain storage; aiming at a data object with data property as wearable equipment, the basic state comprises a team basic state and an individual basic state, the team basic state correspondingly comprises brain wave bands, heart rate states, eye movement states, body temperature states, blood pressure states and exercise intensity of the team and the individual, the team basic state is an overall distribution state formed by overlapping the individual basic states, the corresponding basic state analysis method comprises calling an equipment interface to obtain, opening a source analysis model and a custom analysis model, the custom analysis model comprises medical health common knowledge facing the basic state, parameter values of the basic state are distributed according to time, and the basic state storage method comprises database storage and block chaining evidence; for a data object with data property of public sentiment, the basic state comprises positive energy, negative energy, praise, criticism, identity, homopathy, derogation, anger, homopathy, neutrality, jealousy and conjunction of the individual, the corresponding basic state analysis method comprises an open source analysis model and a custom analysis model, the custom analysis model comprises an emotion classification training model facing the basic state, and the basic state storage method comprises database storage and block chaining evidence.
In the team cooperation-oriented state monitoring system provided by the invention, the system can also have the following characteristics: when the knowledge graph construction unit constructs a basic state knowledge graph based on a basic state parameter set, a team is used as a root node, the individuals and the team belong to or comprise relationships, the basic states of the team and the team are in a one-way state relationship, the individuals and the basic states of the individuals are in a one-way state relationship, the basic state knowledge graph is used as a data mode, knowledge information among the individuals, groups, the basic states and target states is stored, corresponding categories and attributes are set for the team and the individuals, the category attributes comprise target states, the target states have corresponding basic states as sub-attributes, relationships among parameters of the basic states analyzed by the same data object are clear, relationships among different data objects are associated through the relationships among the individuals and the team and are mapped into corresponding knowledge, network graph data layer information is enriched through domain knowledge extraction, and the network graph data layer information is stored in a graph database supporting semantic expression in an entity-attribute-relationship mode.
In the team cooperation-oriented state monitoring system provided by the invention, the system can also have the following characteristics: the target state analysis method comprises association analysis, cross authentication, clustering algorithm and classification algorithm, the association analysis method is calculated based on a basic state knowledge graph, node data of the basic state knowledge graph represents individuals, groups, basic states and target states, edge data is used for representing relations among nodes, the target states are identified and obtained based on deep association features, association element structures and similarity methods, the target state analysis module is further provided with a target state storage unit, the target state storage unit stores the target states through a target state storage method, and the target state storage method comprises database storage and block link storage.
In the team cooperation-oriented state monitoring system provided by the invention, the system also has the following characteristics: the team learning target states comprise violation degree, cooperation tacit degree and communication degree, the individual learning target states comprise understanding degree, attention concentration degree and violation degree, and the individual mental health target states comprise stress, depression, anxiety and paranoia.
In the team cooperation-oriented state monitoring system provided by the invention, the system can also have the following characteristics: the human-computer interaction unit comprises a user interface and a human-computer interaction program and is used for providing visual display and interactive retrieval of the target state, the user interface comprises a team learning target state diagram, an individual learning target state diagram and an individual mental health target state diagram, each diagram comprises a parameter diagram of the target state, a team and individual associated basic state knowledge diagram and an associated data object, and the human-computer interaction program supports retrieval of the basic state and the associated data object from the individual.
Action and Effect of the invention
According to the team cooperation-oriented state monitoring system, the collected data objects comprise team and individual data objects, and are classified into tasks, environments, videos, voices, wearable equipment and public opinions according to the data properties, so that the monitored data not only comprise individuals but also comprise teams, the data sources are not limited to the field, and the monitored data also comprise heterogeneous data with different properties such as network public opinions, associated application systems, environments and the like, the relationship network of the teams, the individuals and the associated data is enhanced, the new state mining capability and the state recognition accuracy are improved, more states are provided, and the depth and the accuracy of indexes are improved; the basic state knowledge graph is constructed based on the basic state parameter set, and the target state is judged and obtained through a target state analysis method according to the basic state knowledge graph, so that the multi-dimensional relation among heterogeneous, heterogeneous and cross-boundary basic state parameters can be expressed through the basic state knowledge graph, the penetration analysis and the cross authentication are realized, and the new state identification mining capability and the state identification accuracy are improved; the target states monitored by the method comprise a team learning target state, an individual learning target state and an individual mental health target state under a team cooperation scene, and potential mental disorders and health problems of individuals can be found in time by monitoring the team and individual cooperation degree, the individual learning state and the individual mental health state.
Drawings
FIG. 1 is a block diagram of a team collaboration oriented status monitoring system in an embodiment of the invention;
FIG. 2 is a schematic workflow diagram of a team collaboration oriented status monitoring system in an embodiment of the invention;
fig. 3 is a schematic view of a workflow of a team cooperation-oriented state monitoring system for performing state monitoring on an operation group of a simulated training sand table in an embodiment of the present invention.
Detailed Description
In order to make the technical means and functions of the present invention easy to understand, the present invention is specifically described below with reference to the embodiments and the accompanying drawings.
< example >
Fig. 1 is a block diagram of a team cooperation-oriented status monitoring system according to an embodiment of the present invention.
As shown in fig. 1, a team cooperation oriented state monitoring system 100 of this embodiment includes a data collection module 10, a basic state analysis module 20, a basic state knowledge graph module 30, a target state analysis module 40, and a target state display module 50. The data acquisition module 10 has a data acquisition unit and a preprocessing unit, and the data acquisition unit acquires corresponding acquisition contents of the monitored team and individual data objects from a predetermined data source, and then preprocesses the acquisition contents by the preprocessing unit. Data objects are classified according to data properties including tasks, environment, video, voice, physical parameters, and public opinion.
The data source comprises manual input, a related information system, video equipment, voice equipment, wearable equipment and a network, the data structure of a data object comprises unstructured data, semi-structured data, structured data and streaming data, the data acquisition unit acquires the acquisition content of the data object by adopting a corresponding acquisition method aiming at the data objects with different properties, the acquisition method comprises the steps of manual input, the related information system calling, the equipment calling interface and network data crawling, and the network data crawling method comprises the steps of calling a network search engine based on team and personal keywords and extracting the related data object from the search result; based on a predefined website address, acquiring webpage content according to a team and a personal keyword, identifying associated content through character matching, and downloading the associated content; based on the APP interface, based on team and personal keywords, obtaining APP content, identifying associated content through character matching, downloading the content, preprocessing the acquired content by a preprocessing unit through a corresponding preprocessing method aiming at data objects with different properties, wherein the preprocessing method comprises data noise reduction, label processing, deletion processing, feature selection, text analysis, video analysis and audio analysis, the data acquisition module 10 further comprises a data object storage unit, the data object storage unit stores the preprocessed acquired content through a data object storage method, and the data object storage method comprises database storage, file storage and block chain storage.
Aiming at a data object with a task property, the collected content comprises a task name, a type, a task target, task content, team and member individual roles and role tasks, the data source comprises a manual input and associated information system, the data structure is structured data, the collection method comprises the steps of manual input and calling of the associated information system, the associated information system is an organization information system used for extracting current task information, the corresponding preprocessing method comprises the steps of content extraction, label processing and text analysis, and the data object storage method comprises database storage and block link evidence storage;
aiming at a data object with data property as an environment, the collected content comprises a site name, a site purpose, an equipment name, an equipment purpose and a member coordinate, the data source comprises a manual input and associated information system, the data structure is structured data, the corresponding collection method comprises the manual input and the calling of the associated information system, the associated information system is an organization information system used for extracting the information of the environment, the corresponding preprocessing method comprises tag processing, content extraction and text analysis, and the data object storage method comprises database storage and block link storage;
aiming at a data object with video data property, acquiring content comprises a video name, a video type, video property and video parameters, a data source comprises video equipment, a data structure is streaming data, a corresponding acquiring method comprises a calling equipment interface and a calling associated information system, the associated information system is an information system for extracting team and personal video data, a corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and a data object storage method comprises database storage, file storage and block link storage; aiming at a data object with voice data property, acquiring content comprises a voice name, a voice type, the voice property and voice parameters, a data source comprises voice equipment, a data structure is streaming data, a corresponding acquisition method comprises calling an equipment interface and calling a related information system, the related information system is an information system for extracting team and personal voice data, a corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and a data object storage method comprises database storage, file storage and block chaining storage;
aiming at a data object with data property of wearable equipment, the acquisition content comprises brain waves, heart rate, eye movement, body temperature, blood pressure and movement, the data source comprises brain waves, heart rate, eye movement, body temperature, blood pressure and movement detection instruments, the data structure comprises streaming data and structured data, the corresponding acquisition method comprises calling an equipment interface and calling a related information system, the related information system is an information system for extracting team and personal health parameters and comprises a medical institution information system, the corresponding preprocessing method comprises denoising, label processing, missing processing and feature selection, and the data object storage method comprises database storage, file storage and block chaining evidence; aiming at a data object with public sentiment data nature, the collected content comprises published words and associated information, the published words comprise posts of news, online texts, blog texts, microblogs, weChats and forums, the associated information comprises associated words, associated comments, associated conversations and associated content, the data source comprises a network source and an associated information system, the network source comprises websites, blogs, microblogs, weChas and forums, the associated information system is an information system for extracting team and personal published words and associated information and comprises a social institution information system and an institution information system, the data structure comprises unstructured data, semi-structured data, structured data and streaming data, the corresponding collection method comprises crawling and calling of the associated information system for the network data, the corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and the data object storage method comprises database storage, file storage and block link storage.
The basic state analysis module 20 has a basic state analysis unit for acquiring parameter values of the basic state of the data object by a predetermined basic state analysis method and forming a basic state parameter set. The basic state analysis unit 20 obtains parameter values of the basic state by using a corresponding basic state analysis method for data objects with different properties, where the basic state analysis method includes direct obtaining, calling an equipment interface obtaining, using an open source analysis model, using a custom analysis model, and calling an associated information system. In this embodiment, both the open source analysis model and the custom analysis model train basic state classification through depth/machine learning. The basic state analysis module 20 further has a basic state storage unit, which stores the basic state parameter set by a basic state storage method, where the basic state storage method includes database storage, file storage, and block chain storage.
Aiming at a data object with data property of tasks and environment, the basic state is the same as the acquisition content, the corresponding basic state analysis method is the same as the acquisition method, and the basic state storage method comprises database storage and block chain storage;
aiming at a data object with video data properties, basic states comprise team basic states and individual basic states and correspondingly comprise facial expressions, facial features states, gestures and body postures of a team and an individual, the team basic states are integral geometric structures formed by overlapping the individual basic states, corresponding basic state analysis methods comprise an open source analysis model and a custom analysis model, the custom analysis model comprises a video data classification training model facing the basic states, parameter values of the basic states are distributed according to moments, and the basic state storage method comprises database storage and block chain storage;
aiming at a data object with voice data property, the basic state comprises the voiceprint, tone and volume of a team and an individual, the corresponding basic state analysis method comprises an open source analysis model and a custom analysis model, the custom analysis model comprises a voice data classification training model facing the basic state, the parameter values of the basic state are distributed according to time moments, and the basic state storage method comprises database storage and block chain storage;
aiming at a data object with data property as wearable equipment, the basic state comprises a team basic state and an individual basic state, the team basic state correspondingly comprises brain wave bands, heart rate states, eye movement states, body temperature states, blood pressure states and exercise intensity of the team and the individual, the team basic state is an overall distribution state formed by overlapping the individual basic states, the corresponding basic state analysis method comprises calling an equipment interface to obtain, opening a source analysis model and a custom analysis model, the custom analysis model comprises medical health common knowledge facing the basic state, parameter values of the basic state are distributed according to time, and the basic state storage method comprises database storage and block chaining evidence;
aiming at a data object with data nature of public sentiment, the basic state comprises positive energy, negative energy, praise, criticism, identity, sympathy, depreciation, anger, sympathy, neutrality, jealousy and conjunction of an individual, the corresponding basic state analysis method comprises an open source analysis model and a custom analysis model, the custom analysis model comprises an emotion classification training model facing the basic state, and the basic state storage method comprises database storage and block chaining evidence.
The basic state knowledge graph module 30 has a knowledge graph construction unit and a graph database, the knowledge graph construction unit constructs a basic state knowledge graph based on the basic state parameter set, and the constructed basic state knowledge graph is stored in the graph database. When the knowledge graph construction unit constructs a basic state knowledge graph based on a basic state parameter set, a team is used as a root node, the individuals and the team belong to or contain relationships, the basic states of the team and the team are in a one-way state relationship, the individuals and the basic states of the individuals are in a one-way state relationship, the basic state knowledge graph is used as a data mode, knowledge information among the individuals, groups, the basic states and target states is stored, corresponding categories and attributes are set for the team and the individuals, the category attributes comprise target states, the target states have corresponding basic states as sub-attributes, relationships among parameters of the basic states analyzed by the same data object are clear, relationships among different data objects are related through the relationships among the individuals and the teams and are mapped into corresponding knowledge, network graph data layer information is enriched through domain knowledge extraction, and the network graph data layer information is stored in a graph database supporting semantic expression in an entity-attribute-relationship mode. In the embodiment, the graph database stores structured data and unstructured data by adopting Neo4j, provides addition, deletion, modification and check operations for the stored graph structure data by using Cypher language, and supports visual graph browser operation.
The target state analysis module 40 has a target state analysis unit for obtaining target states including a team learning target state, an individual learning target state, and an individual mental health target state in a team cooperation scene based on the basic state knowledge map by a predetermined target state analysis method. The target state analysis method comprises association analysis, cross authentication, clustering algorithm and classification algorithm, the association analysis method is calculated based on a basic state knowledge graph, node data of the basic state knowledge graph represents individuals, groups, basic states and target states, edge data is used for representing relations among nodes, the target states are identified and obtained based on deep association features, association element structures and similarity methods, and the target state identification process is as follows: the method comprises the steps of adopting a similarity calculation model for target state identification, carrying out similarity calculation based on a meta-structure, representing the total number of paths of a source object and a target object in a network under a meta-structure example by similarity, representing the composite relation between two node objects in a knowledge graph network by the meta-structure, adopting an XGboost machine learning model for determining the meta-structure, and identifying and analyzing the meta-structure corresponding to a target state based on the incidence relation between the target state and a knowledge graph concept in a historical case.
The target state analysis module 40 further has a target state storage unit that stores a target state by a target state storage method including database storage and block chain storage. The team learning target states comprise violation degree, cooperative tacit degree and communication degree, the individual learning target states comprise understanding degree, attention concentration degree and violation degree, and the individual mental health target states comprise stress, depression, anxiety and paranoia.
The target state display module 50 has an integration unit and a human-computer interaction unit, and is used for providing data integration, visual display and interactive retrieval of the target state.
The integration unit is used for integrating data of a target state, a basic state knowledge graph module 30 and a target state analysis module 40 which need to be displayed are obtained by calling the basic state analysis module 10, the data object acquisition module 20, the basic state knowledge graph module 30 and the target state analysis module 40, the human-computer interaction unit comprises a user interface and a human-computer interaction program and is used for providing visual display and interactive retrieval of the target state, the user interface comprises a team learning target state graph, an individual learning target state graph and an individual mental health target state graph, each graph comprises a parameter graph of the target state, the team and individual related basic state knowledge graph and related data objects, and the human-computer interaction program supports retrieval of the basic state and related data objects from individuals.
FIG. 2 is a schematic workflow diagram of a team collaboration oriented status monitoring system according to an embodiment of the present invention.
As shown in fig. 2, a work flow of a team cooperation-oriented state monitoring system 100 in an embodiment of the present invention is as follows: the data acquisition module 10 acquires corresponding acquisition contents of monitored team and individual data objects from data sources, the basic state analysis module 20 acquires parameter values of basic states of the data objects by a preset basic state analysis method and forms a basic state parameter set, the basic state knowledge map module 30 constructs a basic state knowledge map based on the basic state parameter set, the target state analysis module 40 acquires target states comprising team learning target states, individual learning target states and individual mental health target states in a team cooperation scene by a target state analysis method based on the basic state knowledge map, and finally, the target state display module 50 performs data integration, visual display and interactive retrieval of the target states.
In this embodiment, a state monitoring system 100 for team cooperation is further used to monitor the state of an operation group of the virtual simulation training sand table, perform state analysis in a multi-role cooperation scene, and implement the overall process supervision of the training teaching. The virtual simulation practical training sand table in the embodiment is oriented to hotel operation management, and is cooperatively operated by practical training groups, each group has six operation roles which are respectively responsible for the operation of a front-office guest room department, a marketing department, an engineering operation and maintenance department, a human resource department, a financial management department and a catering and entertainment department, and the construction, operation and management of a hotel are jointly completed. In addition, the team cooperation-oriented state monitoring system 100 of the embodiment adopts a system architecture of a Web browser/a client/a wechat applet/a Web server/a database server, the Web browser and the wechat applet provide a target state display interface for a teacher, and the virtual simulation training sand table serves as a desktop client to provide early warning information of the target state display interface for a training group.
Fig. 3 is a schematic diagram of a workflow of a state monitoring system for team cooperation to monitor a state of an operation group of a simulated practical training sand table in an embodiment of the present invention.
As shown in fig. 3, in the present embodiment, when the team cooperation-oriented state monitoring system 100 monitors the state of an operation team, the data acquisition module 10 acquires the corresponding acquisition content of the monitored team and the individual data object from the data sources, where the data sources are as follows: manual input: tasks, environments, teams and individuals are input by teachers; voluntary mental health questionnaire. The related information system comprises: the virtual simulation-oriented practical training sand table comprises tasks, environments, teams and personal information, a school information management system and other teaching and educational affair-related information systems integrated with a state monitoring system. The video device: camera system, cell-phone. Voice equipment: recording pen, meeting system and mobile phone. A wearable device: brain wave instrument, cardiotachometer, thermometer, bracelet, sphygmomanometer, weighing instrument. Network: websites, blogs, microblogs, weChat, forums.
The acquisition content of the data object is obtained as follows: task: each task comprises simulation training of an appointed turn, all processes from hotel establishment to operation management are completed once in each turn, and team members collaboratively complete the operation management of the hotel and evaluate according to quantitative operation achievement. Environment: the practical training room is a virtual simulation practical training sand table for hotel operation management, and the operating space of each sand table is about 5X7 square meters. Video: and operating the multi-path camera in a training way. And (3) voice: the group voices. Physical parameters: brain waves, heart rate, temperature, blood pressure. Public opinion: and customizing news and microblogs.
The basic state analysis module 20 obtains parameter values of the basic state of the data object by a basic state analysis method, and forms a basic state parameter set, where the obtained basic state is as follows: whether the physical health state of the wearable equipment such as brain waves, blood pressure and heart rate is normal or not is judged; whether the video is involved or not and healthy, and whether the emotion is boring, puzzled, frustrated, joyful and involved or not are determined; a sound-based physical health status; homonymy, energy correcting, rationality, harmony and depreciation based on the speech; based on external evaluations such as paranoia, disputes, irritability.
The basic state knowledge graph module 30 constructs a basic state knowledge graph based on the basic state parameter set, and the target state analysis module 40 obtains the following target states based on the basic state knowledge graph through a target state analysis method: team learning objective states such as engagement, interaction, individual learning objective states such as comprehension, attention, discipline, individual mental health objective states such as stress, paranoia, anxiety, depression. And finally, performing data integration through a target state display module 50, visually displaying a parameter chart of the target state, a team and individual associated basic state knowledge graph and an associated data object through a human-computer interaction unit, and providing interactive retrieval.
Effects and effects of the embodiments
According to the team cooperation-oriented state monitoring system, the collected data objects comprise team and individual data objects, and the data objects are classified into tasks, environments, videos, voices, wearable devices and public opinions according to the data properties, so that the monitored data not only comprise individuals but also comprise teams, the data sources are not limited to the field, and the monitored data also comprise heterogeneous data with different properties such as network public opinions, associated application systems, environments and the like, the system is beneficial to enhancing the relation network of the teams, the individuals and the associated data, improving the new state mining capability and the state recognition accuracy, providing more states and improving the depth and accuracy of indexes; the basic state knowledge graph is constructed based on the basic state parameter set, the target state is judged and obtained through a target state analysis method according to the basic state knowledge graph, the multi-dimensional relation among heterogeneous, heterogeneous and cross-boundary basic state parameters can be expressed through the basic state knowledge graph, penetrating analysis and cross authentication are achieved, and the new state identification mining capability and the state identification accuracy are improved; the target states monitored by the embodiment comprise a team learning target state, an individual learning target state and an individual mental health target state in a team cooperation scene, and potential mental disorders and health problems of individuals can be found in time by monitoring the team and individual cooperation degree, the individual learning state and the individual mental health state.
The above embodiments are preferred examples of the present invention, and are not intended to limit the scope of the present invention.
Claims (10)
1. A team collaboration-oriented condition monitoring system, comprising:
the system comprises a data acquisition module and a data preprocessing unit, wherein the data acquisition module is provided with a data acquisition unit and a preprocessing unit, the data acquisition unit acquires corresponding acquisition contents of monitored team and individual data objects from a preset data source, and then the preprocessing unit preprocesses the acquisition contents;
a basic state analysis module having a basic state analysis unit for acquiring parameter values of basic states of the data objects by a predetermined basic state analysis method and forming a basic state parameter set;
the knowledge graph building unit builds a basic state knowledge graph based on the basic state parameter set, and the built basic state knowledge graph is stored in the graph database;
the target state analysis module is provided with a target state analysis unit and is used for obtaining a target state based on the basic state knowledge map through a preset target state analysis method, and the target state comprises a team learning target state, an individual learning target state and an individual mental health target state in a team cooperation scene; and
and the target state display module is provided with an integration unit and a man-machine interaction unit and is used for providing data integration, visual display and interactive retrieval of the target state.
2. The team collaboration oriented condition monitoring system of claim 1, wherein:
wherein the data objects are classified according to data properties, the data properties including tasks, environments, videos, voices, physical parameters, and public opinions.
3. The team collaboration oriented condition monitoring system of claim 1, wherein:
wherein the data sources include human input, associated information systems, video devices, voice devices, wearable devices, and networks,
the data structure of the data object includes unstructured data, semi-structured data, and stream data,
the data acquisition unit adopts corresponding acquisition methods to acquire the acquisition contents of the data objects aiming at the data objects with different properties, the acquisition methods comprise manual input, associated information system calling, equipment interface calling and network data crawling,
the network data crawling method comprises the steps of calling a network search engine based on team and personal keywords, and extracting relevant data objects from search results; based on a predefined website address, acquiring webpage content according to a team and a personal keyword, identifying associated content through character matching, and downloading the associated content; and based on the APP interface, based on team and personal keywords, obtaining APP content, identifying associated content through character matching, downloading content,
the preprocessing unit preprocesses the acquired content by corresponding preprocessing methods aiming at the data objects with different properties, wherein the preprocessing methods comprise data noise reduction, label processing, deletion processing, feature selection, text analysis, video analysis and audio analysis,
the data acquisition module is also provided with a data object storage unit, the data object storage unit stores the acquired content after the preprocessing by a data object storage method, and the data object storage method comprises database storage, file storage and block link storage.
4. The team collaboration oriented status monitoring system of claim 3, wherein:
the data source comprises a manual input and associated information system, the data structure is structured data, the corresponding acquisition method comprises manual input and call of the associated information system, the associated information system is an organization information system used for extracting current task information, the corresponding preprocessing method comprises content extraction, label processing and text analysis, and the data object storage method comprises database storage and block link storage;
aiming at the data object with the data property of the environment, the collected content comprises a site name, a site purpose, an equipment name, an equipment purpose and a member coordinate, the data source comprises a manual input and associated information system, the data structure is structured data, the corresponding collection method comprises a manual input and call associated information system, the associated information system is an organization information system used for extracting the information of the environment, the corresponding preprocessing method comprises label processing, content extraction and text analysis, and the data object storage method comprises database storage and block chain storage;
aiming at the data object with the data property of the video, the acquired content comprises a video name, a video type, the video property and a video parameter, the data source comprises video equipment, the data structure is streaming data, the corresponding acquisition method comprises a calling equipment interface and a calling associated information system, the associated information system is an information system for extracting team and personal video data, the corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and the data object storage method comprises database storage, file storage and block chaining evidence;
aiming at a data object with the data property of the voice, the collected content comprises a voice name, a voice type, the voice property and a voice parameter, the data source comprises voice equipment, the data structure is streaming data, the corresponding collecting method comprises a calling equipment interface and a calling associated information system, the associated information system is an information system for extracting team and personal voice data, the corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and the data object storage method comprises database storage, file storage and block chaining evidence;
aiming at the data object with the data property of the wearable device, the acquired content comprises brain waves, heart rate, eye movement, body temperature, blood pressure and movement, the data source comprises brain waves, heart rate, eye movement, body temperature, blood pressure and movement detection instruments, the data structure comprises streaming data and structured data, the corresponding acquisition method comprises calling an equipment interface and calling a related information system, the related information system comprises an information system for extracting team and personal health parameters, the related information system comprises a medical institution information system, the corresponding preprocessing method comprises denoising, label processing, deletion processing and feature selection, and the data object storage method comprises database storage, file storage and block chaining evidence;
the data object with the data property being the public sentiment comprises published words and associated information, the published words comprise news, online texts, blog texts, microblogs, weChats and forum posts, the associated information comprises associated words, associated comments, associated conversations and associated contents, the data source comprises a network source and an associated information system, the network source comprises websites, blogs, microblogs, weChats and forums, the associated information system is an information system for extracting team and personal published words and associated information and comprises a social institution information system and a local institution information system, the data structure comprises unstructured data, semi-structured data, structured data and streaming data, the corresponding acquisition method is a network data crawling and associated information system, the corresponding preprocessing method comprises denoising, tag processing, deletion processing and feature selection, and the data object storage method comprises database storage, file storage and block chaining evidence.
5. The team collaboration oriented condition monitoring system of claim 1, wherein:
wherein, the basic state analysis unit adopts the corresponding basic state analysis method to obtain the parameter value of the basic state aiming at the data objects with different properties, the basic state analysis method comprises the steps of directly obtaining, calling an equipment interface to obtain, adopting an open source analysis model, adopting a custom analysis model and calling an associated information system,
the basic state analysis module is also provided with a basic state storage unit, and the basic state storage unit stores the basic state parameter set by a basic state storage method, wherein the basic state storage method comprises database storage, file storage and block chain storage.
6. A team collaboration-oriented status monitoring system as claimed in claim 5, wherein:
for the data object with the data property of the task and the environment, the basic state is the same as the acquisition content, the corresponding basic state analysis method is the same as the acquisition method, and the basic state storage method comprises database storage and block chain storage;
aiming at the data object with the data property of the video, the basic states comprise team basic states and individual basic states, the corresponding team basic states comprise facial expressions, facial features, gestures and body postures of the team and the individual, the team basic states are overall geometrical structures formed by superposition of the basic states of the individual, the corresponding basic state analysis methods comprise an open source analysis model and a custom analysis model, the custom analysis model comprises a video data classification training model facing the basic states, parameter values of the basic states are distributed according to time moments, and the basic state storage method comprises database storage and block chaining evidence;
aiming at the data object with the data property of the voice, the basic state comprises the voiceprint, tone and volume of teams and individuals, the corresponding basic state analysis method comprises an open source analysis model and a custom analysis model, the custom analysis model comprises a voice data classification training model facing the basic state, the parameter values of the basic state are distributed according to time, and the basic state storage method comprises database storage and block chain storage;
aiming at the data object with the data property of the wearable device, the basic state comprises a team basic state and an individual basic state, the team basic state correspondingly comprises brain wave bands, heart rate states, eye movement states, body temperature states, blood pressure states and exercise intensity of teams and individuals, the team basic state is an overall distribution state formed by overlapping the individual basic states, the corresponding basic state analysis method comprises calling device interface acquisition, an open source analysis model and a custom analysis model, the custom analysis model comprises medical health general knowledge facing the basic state, parameter values of the basic state are distributed at any moment, and the basic state storage method comprises database storage and block chaining evidence;
for the data object of which the data property is the public sentiment, the basic states comprise positive energy, negative energy, praise, criticism, endorsement, synopsis, deprecation, anger, synopsis, neutrality, jealousy and contingency of an individual, and the corresponding basic state analysis methods are open source analysis models and custom analysis models, the custom analysis models comprise sentiment classification training models facing the basic states, and the basic state storage method comprises database storage and block chaining.
7. The team collaboration oriented condition monitoring system of claim 1, wherein:
wherein, when the knowledge graph constructing unit constructs the basic state knowledge graph based on the basic state parameter set, the team is taken as a root node, the individual and the team belong to or have a containing relationship, the team and the team have a one-way state relationship, and the individual have a one-way state relationship,
the basic state knowledge graph is used as a data mode to store knowledge information among individuals, groups, basic states and target states, corresponding categories and attributes are set for teams and individuals, the category attributes comprise the target states, the target states have the corresponding basic states as sub-attributes, the relationship among all parameters of the basic states analyzed by the same data objects is clear, the relationship among different data objects is associated through the relationship among the individuals and the teams and is mapped into corresponding knowledge, network graph data layer information is enriched through domain knowledge extraction, and the network graph data base supporting semantic expression is stored in an entity-attribute-relationship mode.
8. The team collaboration oriented condition monitoring system of claim 1, wherein:
wherein the target state analysis method comprises correlation analysis, cross authentication, clustering algorithm and classification algorithm,
the method for association analysis is calculated based on the basic state knowledge graph, the node data of the basic state knowledge graph represents individuals, groups, basic states and target states, the edge data is used for representing the relation among nodes, the target states are identified and acquired based on deep association features, association element structures and similarity methods,
the target state analysis module is also provided with a target state storage unit, and the target state storage unit stores the target state through a target state storage method, wherein the target state storage method comprises database storage and block chain storage.
9. The team collaboration oriented condition monitoring system of claim 1, wherein:
wherein the team learning objective state comprises violation degree, cooperation tacit degree and communication degree,
the individual learning objective state includes a degree of understanding, a concentration degree and whether there is a violation of a discipline,
the individual mental health target states include stress, depression, anxiety, and paranoia.
10. The team collaboration oriented condition monitoring system of claim 1, wherein:
wherein, the integration unit is used for data integration of the target state, and acquires the target state, the basic state, the team and the individual associated basic state knowledge graph and the associated data object to be displayed by calling the basic state analysis module, the data object acquisition module, the basic state knowledge graph module and the target state analysis module,
the human-computer interaction unit comprises a user interface and a human-computer interaction program, is used for providing visual display and interactive retrieval of the target state,
the user interface includes a diagram of the team learning goal state, a diagram of an individual learning goal state, and a diagram of the individual mental health goal state, each diagram including a chart of parameters of the goal state, team and individual associated base state knowledge maps, and associated data objects,
the human-computer interaction program supports the retrieval of the basic state and its associated data objects from an individual.
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