Group cognition improvement method and device based on multi-person collaboration

Abstract

The invention discloses a group cognition improvement method and device based on multi-person cooperation, a storage medium and electronic equipment. The method comprises the following steps: constructing a dynamic social model; wherein the network nodes in the dynamic social model are team members, and the interaction relationship among the team members is expressed as edges for connecting the network nodes; constructing a weight matrix, and updating a weight value of the dynamic social network through the weight matrix; and describing all activities of the team members through the updated dynamic social network. According to the method, the weight value of the dynamic social network is updated by constructing the weight matrix so as to describe all activities of team members, a complete mathematical model is established to describe cognitive interaction and enhancement effects among the team members, and the dynamic evolution process of the group cognitive level is accurately described; and the collective intelligence effect generated in the team cooperation process can be accurately evaluated and optimized.

Description

Technical Field

[0001] The present application relates to the technical field of cognitive ability training, and in particular to a method, device, storage medium and electronic device for improving group cognition based on multi-person collaboration. Background Art

[0002] With the rapid development of the Internet and artificial intelligence technologies, group learning and collaborative innovation are receiving more and more attention.

[0003] The current collaborative learning system records the basic information data of learners, extracts attribute features from the basic information data, and constructs a neural network to analyze the attribute features. The current collaborative learning system mainly focuses on the recording and analysis of individual learning behaviors, and fails to establish a complete mathematical model to describe the cognitive interaction and enhancement effect between team members. It is difficult to accurately describe the dynamic evolution of the group's cognitive level, which makes it difficult to accurately evaluate and optimize the collective wisdom effect generated in the team collaboration process. Summary of the invention

[0004] The embodiments of the present application provide a method, device, storage medium and electronic device for improving group cognition based on multi-person collaboration, which can accurately depict the dynamic evolution process of group cognition level.

[0005] The present application embodiment provides a method for improving group cognition based on multi-person collaboration, including: Constructing a dynamic social model; wherein the network nodes in the dynamic social model are team members, and the interactive relationships between the team members are represented as edges connecting the network nodes; Constructing a weight matrix, and updating the weight value of the dynamic social network through the weight matrix; The updated dynamic social network describes the overall activities of the team members.

[0006] Furthermore, in the above-mentioned group cognition improvement method based on multi-person collaboration, the weight matrix is: w_ij(t) = α·tanh(I_ij) + β·(C_ij)² + γ·ln(1 + P_ij) Among them, w_ij(t) represents the interaction intensity between node i and node j at time t, I_ij represents the frequency of information exchange, C_ij represents cognitive synergy, P_ij represents task dependence, α, β, γ are weight coefficients and satisfy α + β + γ =1.

[0007] Furthermore, the above-mentioned method for improving group cognition based on multi-person collaboration, wherein the method also includes: Calculating a social distance function based on the weight matrix; The team state field is obtained through the social distance function and the state vector of each team member.

[0008] Furthermore, in the above-mentioned group cognition improvement method based on multi-person collaboration, the expression of the team state field is:

[0009] in, is the state vector of team member individual i at time t, and D(i,j) is the social distance function.

[0010] Furthermore, the above-mentioned method for improving group cognition based on multi-person collaboration, wherein the method also includes: Calculate the norms of individual state fields of team members and team state fields respectively; The difference between the norm of the team state field and the sum of the norms of the individual state fields of the team members is calculated to obtain the emergence effect.

[0011] Furthermore, in the above-mentioned method for improving group cognition based on multi-person collaboration, the expression of the emergence effect is: E(t) = ||Ψ(t)|| - ∑|| || Among them, when E(t) is a positive value, it means that a positive emergence effect has occurred.

[0012] Furthermore, the above-mentioned method for improving group cognition based on multi-person collaboration, wherein the method also includes: Modeling each of the team members as an intelligent agent, and continuously optimizing the decision-making strategies of the team members through a Q-learning algorithm; The reward function of agent i after taking action a in state s is defined as: R(i,s,a) = α·P(s,a) + β·T(S) + γ·E(t) Among them, P(s,a) is the individual benefit, T(S) is the team benefit, and E(t) is the emergence effect value; Each of the agents updates the Q value table of the agent by the following formula: Q(s,a) = (1-α)Q(s,a) + α[R + γ·maxQ(s',a')] Among them, α is the learning rate, s is the current state, γ is the discount factor, s' is the next state, and a' is the learning rate of the next state.

[0013] The embodiment of the present application also provides a group cognition improvement device based on multi-person collaboration, including: A construction module, used to construct a dynamic social model; wherein the network nodes in the dynamic social model are team members, and the interactive relationships between the team members are represented as edges connecting the network nodes; A weight calculation module, used to construct a weight matrix, and update the weight value of the dynamic social network through the weight matrix; An updating module is used to describe the overall activities of the team members through the updated dynamic social network.

[0014] An embodiment of the present application also provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor to execute any of the above-mentioned group cognition improvement methods based on multi-person collaboration.

[0015] An embodiment of the present application also provides an electronic device, including a processor and a memory, wherein the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used for the steps in any of the above-mentioned methods for improving group cognition based on multi-person collaboration.

[0016] The present application provides a method, device, storage medium and electronic device for improving group cognition based on multi-person collaboration. The present application updates the weight value of a dynamic social network by constructing a weight matrix to describe the overall activities of team members, and establishes a complete mathematical model to describe the cognitive interaction and enhancement effect between team members, accurately depicting the dynamic evolution process of the group's cognitive level, so that the collective wisdom effect generated in the team collaboration process can be accurately evaluated and optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0018] Figure 1 A flowchart of a method for improving group cognition based on multi-person collaboration provided in an embodiment of the present application.

[0019] Figure 2 Another flow chart of the group cognition improvement method based on multi-person collaboration provided in an embodiment of the present application.

[0020] Figure 3 A schematic diagram of the structure of a group cognition enhancement device based on multi-person collaboration provided in an embodiment of the present application.

[0021] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0022] Figure 5 Another schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0024] Existing technologies have the following main problems in supporting team collaboration and the emergence of collective intelligence: 1. Lack of systematic modeling of team dynamics The current collaborative learning system mainly focuses on the recording and analysis of individual learning behaviors, and fails to establish a complete mathematical model to describe the cognitive interaction and enhancement effect between team members. The existing platform lacks in-depth modeling of the interactive behavior between learners, and it is difficult to accurately describe the dynamic evolution of the group's cognitive level. This makes it difficult to accurately evaluate and optimize the collective wisdom effect generated in the team collaboration process.

[0025] 2. Insufficient application of social network analysis Although existing technologies are aware of the importance of social interaction, the utilization of the dynamic characteristics of social networks is still in its infancy. Using social data as an auxiliary feature can fully utilize the catalytic role of social networks in knowledge dissemination and capacity improvement. This results in the inability to fully stimulate the synergy between team members.

[0026] 3. It is difficult to quantify the emergence effect Traditional evaluation methods often use simple accumulation or average to measure team performance, ignoring the nonlinear emergent effects that may be generated by group collaboration. There is a lack of scientific quantitative indicators to evaluate whether the overall creativity of the team has truly surpassed the simple addition of individual capabilities, which makes the improvement of team effectiveness lack of effective guidance.

[0027] 4. The optimization mechanism is too simple Existing team collaboration optimization methods mostly use simple rule matching or linear programming, and fail to fully utilize advanced algorithms such as deep learning and reinforcement learning to achieve dynamic optimization. This limits the system's ability to adapt to complex collaboration scenarios and makes it difficult to provide teams with more intelligent decision support.

[0028] 5. Lack of systematic approach guided by theory Most of the current solutions are aimed at improving the technology of a specific link, lacking a systematic method that organically integrates multidisciplinary theories such as physical field theory, social network analysis, and machine learning. This makes it impossible to fundamentally improve the team's collaborative innovation capabilities.

[0029] To solve the above problems, the embodiments of the present application provide a method, device, storage medium and electronic device for improving group cognition based on multi-person collaboration. The embodiment of the present application provides a group cognition improvement device based on multi-person collaboration, which can be integrated in an electronic device, which can be a terminal, a server and other devices, wherein the terminal can include a tablet computer, a laptop computer, a personal computer (PC), a micro processing box, or other devices.

[0030] See also Figure 1 and Figure 2 , Figure 1 A flowchart of a method for improving group cognition based on multi-person collaboration provided in an embodiment of the present application. Figure 2 Another flow chart of a method for improving group cognition based on multi-person collaboration provided in an embodiment of the present application, which is applied to an electronic device, and the method for improving group cognition based on multi-person collaboration includes the following steps: S1, construct a dynamic social model; the network nodes in the dynamic social model are team members, and the interactive relationships between team members are represented by edges connecting the network nodes.

[0031] S2, constructs a weight matrix and updates the weight value of the dynamic social network through the weight matrix.

[0032] The time-varying characteristics of the network are described by the dynamic weight matrix W(t). Each element w_ij(t) in the weight matrix represents the interaction strength between node i and node j at time t, and its calculation formula is: w_ij(t) = α·tanh(I_ij) + β·(C_ij)² + γ·ln(1 + P_ij) Among them, I_ij represents the frequency of information exchange, C_ij represents cognitive synergy, P_ij represents task dependence, α, β, and γ are weight coefficients and satisfy α + β + γ = 1.

[0033] The formula innovatively uses three different nonlinear mapping functions: the hyperbolic tangent function is used to normalize the intensity of information exchange, the square function is used to strengthen cognitive synergy, and the logarithmic function is used to smooth task dependencies. This nonlinear combination can more accurately describe the complex interaction patterns between team members.

[0034] S3, describes the overall activities of team members through the updated dynamic social network.

[0035] Furthermore, the group cognition improvement method based on multi-person collaboration also includes: S41, calculating the social distance function based on the weight matrix.

[0036] S42, the team state field is obtained through the social distance function and the state vector of each team member.

[0037] Specifically, the embodiment of the present application introduces the field concept in quantum field theory and proposes a team state field model. The model represents the state of each team member as a field quantity and calculates the state field of the entire team through the superposition principle. The calculation formula of the team state field Ψ(t) is:

[0038] in, is the state vector of individual i at time t, and D(i,j) is the social distance function. The state vector contains multiple dimensions such as the individual's cognitive level, emotional state, and behavioral characteristics. The social distance function D(i,j) is calculated by the Floyd-Warshall algorithm based on the weight matrix W(t), reflecting the shortest path length between any two points in the network.

[0039] In one embodiment, the group cognition improvement method based on multi-person collaboration also includes: S51, respectively calculating the norms of the individual state fields of team members and the team state field; S52, calculating the difference between the norm of the team state field and the sum of the norms of the individual state fields of the team members, and obtaining the emergence effect.

[0040] This embodiment specifically proposes a quantitative calculation method for the emergence effect. The emergence effect E(t) is defined as the difference between the norm of the team state field and the sum of the individual state norms: E(t) = ||Ψ(t)|| - ∑|| || When E(t) is positive, it indicates a positive emergence effect, that is, the overall creativity of the team exceeds the simple addition of individual creativity. This provides a quantitative indicator for evaluating team synergy effects.

[0041] In one embodiment, in order to optimize the team decision-making process, the embodiment of the present application constructs a game equilibrium model based on multi-agent reinforcement learning. Each team member is modeled as an agent, and its decision-making strategy is continuously optimized through the Q learning algorithm. The profit function of agent i after taking action a in state s is defined as: R(i,s,a) = α·P(s,a) + β·T(S) + γ·E(t) Among them, P(s,a) is the individual benefit, T(S) is the team benefit, and E(t) is the emergence effect value. Each agent updates its Q value table through the following formula: Q(s,a) = (1-α)Q(s,a) + α[R + γ·maxQ(s',a')] Among them, α is the learning rate, s is the current state, γ is the discount factor, s' is the next state, and a' is the learning rate of the next state.

[0042] This optimization mechanism based on reinforcement learning enables team members to adaptively adjust their decisions to promote the overall effectiveness of the team while pursuing their personal goals.

[0043] In one embodiment, the group cognition improvement method based on multi-person collaboration also includes: Construct a creative evaluation subsystem, which quantitatively evaluates the creative ideas generated by the team by calculating the semantic similarity and novelty between the creative ideas. The calculation formula for the novelty of the creative idea N(i) is: N(i) = 1 - (1 / n)∑S(i,j) Among them, S(i,j) is the similarity between idea i and existing idea j, and n is the number of existing ideas. The system uses the DBSCAN algorithm to cluster ideas and identify high-value idea clusters.

[0044] The above steps are integrated into each technical module and uniformly scheduled and managed by the system integrator. The system integrator uses an asynchronous event loop mechanism to perform network updates, emergent effect calculations, strategy optimization, and creative evaluation in each time period. The modules communicate with each other through standardized data interfaces, ensuring the scalability and maintainability of the system.

[0045] The embodiment of the present application provides a method, device, storage medium and electronic device for improving group cognition based on multi-person collaboration. The present application combines the ideas of physical field theory with social network analysis to establish a mathematical model of the team collaboration process to accurately describe the dynamic evolution law of the emergence of team wisdom. The present application also constructs a scientific quantitative evaluation system for the emergence effect. By designing a team state field model and an emergence effect calculation method, the quantitative measurement of the degree of transcendence of the team's overall creativity relative to the simple superposition of individual capabilities is achieved. The present application has developed an intelligent team decision-making optimization mechanism, which enables team members to adaptively adjust decisions to promote the improvement of the overall benefits of the team while pursuing personal goals by integrating multi-agent reinforcement learning and game theory methods. The present application establishes a creative evaluation subsystem, which quantitatively evaluates the creativity generated by the team by calculating the semantic similarity and novelty between the creativity, identifies high-value creative clusters, and provides scientific guidance for team innovation. The present application realizes the system integration of various technical modules, and through a unified scheduling and management mechanism, ensures the organic integration of various technologies such as social network analysis, cognitive modeling, and machine learning to create a complete technical solution. The present invention can effectively improve the team's cognitive level, innovative thinking and collaborative ability, and is particularly suitable for scenarios such as innovative practice, team learning and multi-person collaboration among college students. This will provide powerful technical support and practical tools for cultivating innovative talents.

[0046] The following is a specific example of applying the group cognitive improvement method based on multi-person collaboration to the cognitive rehabilitation training system: This embodiment first constructs a dynamic social network for cognitive training. When N rehabilitation training subjects (N≥3) join the training space, the system assigns a unique identifier id_i and an initial cognitive state vector to each user. The state vector calculation formula is: = [attention_i, memory_i, reasoning_i] Among them, attention_i represents the attention level (0-100), memory_i represents the memory ability (-1 to 1), and reasoning_i represents the reasoning ability (0-100). These initial values ​​are obtained through standardized cognitive assessments.

[0047] After the system starts the dynamic training network module, it records the interaction between training objects through real-time data transmission. When interaction occurs (such as collaborative task completion, mutual guidance, etc.), the system updates the interaction intensity matrix. The calculation formula for the interaction intensity between any two training objects a and b is: w_ab(t) = α·tanh(cooperation_frequency) + β·(cognitive_match)² +γ·ln(1 + assist_effect) Among them, cooperation_frequency represents the frequency of cooperation, reflecting the intensity of interaction; cognitive_match represents the matching degree of cognitive level, optimizing the training combination; assist_effect represents the mutual assistance effect, quantifying the training gain; α, β, and γ are weight coefficients, and α + β + γ = 1. This nonlinear combination can more accurately evaluate the interaction effect between training objects. For example, when two training objects with complementary abilities frequently cooperate, the system will give a higher weight through the cognitive_match item; when a training object makes significant progress with the help of others, the assist_effect item will increase accordingly.

[0048] To achieve real-time evaluation of cognitive status, the system maintains a status manager. The manager updates the team cognitive field every time interval Δt. The cognitive field calculation formula is: Ψ(t + Δt) = ∑ ·

[0049] in, is the cognitive state vector of training object i at time t + Δt, D(i,j) is the cognitive difference between training objects i and j, calculated through the state vector, is a regulating factor used to optimize training pairing. The calculation of cognitive field takes into account the ability difference effect, that is, it is easier for training subjects with similar cognitive levels to interact effectively. The introduction of this field theory enables the system to better organize collaborative training activities.

[0050] In terms of training strategy optimization, the system equips each training object with an intelligent auxiliary agent. Based on the reinforcement learning algorithm, the agent optimizes the training plan by continuously observing the training effect and feedback. Specifically, when the system needs to make a training pairing decision, it uses the following probability calculation formula: P(match) = σ(w1·level_match + w2·progress_trend + w3·fatigue_state) Among them, level_match represents the cognitive level match, progress_trend represents the recent progress trend, fatigue_state represents the fatigue state assessment, w1, w2, w3 are weight coefficients, and σ is the Sigmoid function, which maps the output to the [0,1] interval. The intelligent agent continuously adjusts the weight coefficient through reinforcement learning, so that the training arrangement can promote ability improvement without causing excessive fatigue. For example, when it is detected that the memory ability of a training object has significantly improved, the system will adjust the training intensity and pairing strategy accordingly.

[0051] In order to quantitatively evaluate the training effect, the system defines cognitive improvement indicators. The calculation formula is: E(t) = ||Ψ(t)|| - ∑|| || This indicator measures whether collaborative training has a positive promotion effect by comparing the strength of the team's cognitive field with the sum of individual cognitive levels. When E(t) is positive, it indicates that the effect of team training is better than individual training.

[0052] The actual deployment of the system adopts a standard Web architecture, including cognitive training interface, data analysis panel, collaboration space and evaluation report function modules. The cognitive training interface displays the current task, progress and feedback; the data analysis panel displays the training effect and trend; the collaboration space supports communication and interaction between training subjects; and the evaluation report regularly generates personalized training reports.

[0053] The training tasks are designed to include three categories: attention training, memory training, and reasoning ability training. Attention training uses multi-person collaborative visual search tasks, memory training uses team memory matching games, and reasoning ability training is achieved through multi-person logical reasoning challenges.

[0054] According to the method described in the above embodiment, this embodiment will be further described from the perspective of a group cognition improvement device based on multi-person collaboration. The group cognition improvement device based on multi-person collaboration can be implemented as an independent entity or integrated in an electronic device. The electronic device can be a terminal, a server, and the like. The terminal can include a tablet computer, a laptop computer, a personal computer (PC), a micro processing box, or other devices.

[0055] See also Figure 3 , Figure 3 The group cognition improvement device based on multi-person collaboration provided by the embodiment of the present application is specifically described, and is applied to an electronic device. The group cognition improvement device based on multi-person collaboration may include: A construction module, used to construct a dynamic social model; wherein the network nodes in the dynamic social model are team members, and the interactive relationships between the team members are represented as edges connecting the network nodes; A weight calculation module, used to construct a weight matrix, and update the weight value of the dynamic social network through the weight matrix; An updating module is used to describe the overall activities of the team members through the updated dynamic social network.

[0056] During specific implementation, the above modules and / or units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above modules and / or units can refer to the previous method embodiments. The specific beneficial effects that can be achieved can also refer to the beneficial effects in the previous method embodiments, which will not be repeated here.

[0057] In addition, an embodiment of the present application also provides an electronic device, which may be a computer, a tablet computer, or other device. Figure 4 As shown, the electronic device 400 includes a processor 401 and a memory 402. The processor 401 is electrically connected to the memory 402.

[0058] The processor 401 is the control center of the electronic device 400. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or loading applications stored in the memory 402 and calling data stored in the memory 402, thereby monitoring the electronic device as a whole.

[0059] In this embodiment, the processor 401 in the electronic device 400 loads instructions corresponding to the processes of one or more application programs into the memory 402 according to the following steps, and the processor 401 runs the application programs stored in the memory 402 to implement various functions: Constructing a dynamic social model; wherein the network nodes in the dynamic social model are team members, and the interactive relationships between the team members are represented as edges connecting the network nodes; Constructing a weight matrix, and updating the weight value of the dynamic social network through the weight matrix; The updated dynamic social network describes the overall activities of the team members.

[0060] The electronic device can implement the steps in any embodiment of the group cognition improvement method based on multi-person collaboration provided in the embodiments of the present application. Therefore, it can achieve the beneficial effects that can be achieved by any group cognition improvement method based on multi-person collaboration provided in the embodiments of the present invention. Please see the previous embodiments for details and will not be repeated here.

[0061] Figure 5 The specific structural block diagram of the electronic device provided in the embodiment of the present invention is shown, and the electronic device can be used to implement the group cognition improvement method based on multi-person collaboration provided in the above embodiment. The electronic device 500 can be a terminal, a server, etc., wherein the terminal can include a tablet computer, a laptop computer, a personal computer (PC, Personal Computer), a micro processing box, or other devices.

[0062] The RF circuit 510 is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices. The RF circuit 510 may include various existing circuit elements for performing these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, user identity module (SIM) cards, memories, etc. The RF circuit 510 can communicate with various networks such as the Internet, corporate intranets, wireless networks, or communicate with other devices through wireless networks. The above-mentioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The above-mentioned wireless networks may use various communication standards, protocols and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as Institute of Electrical and Electronics Engineers standards IEEE 802.11a, IEEE 802.11b, IEEE802.11g and / or IEEE802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging and short messages, and any other suitable communication protocols, even those that have not yet been developed.

[0063] The memory 520 can be used to store software programs and modules, such as the corresponding program instructions / modules in the above embodiments. The processor 580 executes various functional applications and data processing by running the software programs and modules stored in the memory 520, that is, realizing the functions of taking pictures with the front camera, processing the captured images, and switching the display color of the display content on the display screen. The memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 520 may further include a memory remotely arranged relative to the processor 580, and these remote memories may be connected to the electronic device 500 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0064] The input unit 530 may be used to receive input digital or character information, and generate a keyboard and a mouse related to user settings and function control. The display unit 540 may be used to display information input by the user or information provided to the user and various graphical user interfaces, which may be composed of graphics, text, icons, videos, and any combination thereof. The display unit 540 may include a display panel 541, which may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like.

[0065] The audio circuit 560, the speaker 561, and the microphone 562 can provide an audio interface between the user and the electronic device 500. The audio circuit 560 can transmit the electrical signal converted from the received audio data to the speaker 561, which is converted into a sound signal for output; on the other hand, the microphone 562 converts the collected sound signal into an electrical signal, which is received by the audio circuit 560 and converted into audio data, and then the audio data is output to the processor 580 for processing, and then sent to another terminal through the RF circuit 510, or the audio data is output to the memory 520 for further processing. The audio circuit 560 may also include an earplug jack to provide communication between an external headset and the electronic device 500.

[0066] The electronic device 500 can help the user receive requests, send information, etc. through the transmission module 570 (such as a Wi-Fi module), which provides the user with wireless broadband Internet access. Although the transmission module 570 is shown in the figure, it can be understood that it is not a necessary component of the electronic device 500 and can be omitted as needed without changing the essence of the invention.

[0067] The processor 580 is the control center of the electronic device 500. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 520, and calling data stored in the memory 520, it executes various functions of the electronic device 500 and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 580 may include one or more processing cores; in some embodiments, the processor 580 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 580.

[0068] The electronic device 500 also includes a power supply 590 (such as a battery) for supplying power to various components. In some embodiments, the power supply can be logically connected to the processor 580 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 590 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0069] Although not shown, the electronic device 500 also includes a camera (such as a front camera, a rear camera), a Bluetooth module, etc., which will not be described in detail here. Specifically in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory, and one or more programs, wherein one or more programs are stored in the memory, and are configured to be executed by one or more processors. One or more programs include instructions for performing the following operations: Constructing a dynamic social model; wherein the network nodes in the dynamic social model are team members, and the interactive relationships between the team members are represented as edges connecting the network nodes; Constructing a weight matrix, and updating the weight value of the dynamic social network through the weight matrix; The updated dynamic social network describes the overall activities of the team members.

[0070] In specific implementation, the above modules can be implemented as independent entities, or can be arbitrarily combined and implemented as the same or several entities. The specific implementation of the above modules can be found in the previous method embodiments, which will not be repeated here.

[0071] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling related hardware through instructions, and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present invention provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps of any embodiment of the group cognition enhancement method based on multi-person collaboration provided in an embodiment of the present invention.

[0072] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0073] Since the instructions stored in the storage medium can execute the steps in any embodiment of the group cognition improvement method based on multi-person collaboration provided in the embodiments of the present invention, the beneficial effects that can be achieved by any group cognition improvement method based on multi-person collaboration provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0074] The above is a detailed introduction to a method, device, storage medium and electronic device for improving group cognition based on multi-person collaboration provided in an embodiment of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for improving group cognition based on multi-person collaboration, characterized in that: The method comprises: Constructing a dynamic social model; wherein the network nodes in the dynamic social model are team members, and the interactive relationships between the team members are represented as edges connecting the network nodes; Constructing a weight matrix, and updating the weight value of the dynamic social network through the weight matrix; The updated dynamic social network describes the overall activities of the team members.

2. The method for improving group cognition based on multi-person collaboration according to claim 1 is characterized in that: The weight matrix is: w_ij(t) = α·tanh(I_ij) + β·(C_ij)² + γ·ln(1 + P_ij) Among them, w_ij(t) represents the interaction intensity between node i and node j at time t, I_ij represents the frequency of information exchange, C_ij represents cognitive synergy, P_ij represents task dependence, α, β, γ are weight coefficients and satisfy α + β + γ = 1.

3. The method for improving group cognition based on multi-person collaboration according to claim 1 is characterized in that: The method further comprises: Calculating a social distance function based on the weight matrix; The team state field is obtained through the social distance function and the state vector of each team member.

4. The method for improving group cognition based on multi-person collaboration according to claim 3 is characterized in that: The expression of the team status field is: in, is the state vector of team member individual i at time t, and D(i,j) is the social distance function.

5. The method for improving group cognition based on multi-person collaboration according to claim 3 is characterized in that: The method further comprises: Calculate the norms of individual state fields of team members and team state fields respectively; The difference between the norm of the team state field and the sum of the norms of the individual state fields of the team members is calculated to obtain the emergence effect.

6. The method for improving group cognition based on multi-person collaboration according to claim 5 is characterized in that: The expression of the emergence effect is: E(t) = ||Ψ(t)|| - ∑|| || Among them, when E(t) is a positive value, it means that a positive emergence effect has occurred.

7. The method for improving group cognition based on multi-person collaboration according to claim 1 is characterized in that: The method further comprises: Modeling each of the team members as an intelligent agent, and continuously optimizing the decision-making strategies of the team members through a Q-learning algorithm; The reward function of agent i after taking action a in state s is defined as: R(i,s,a) = α·P(s,a) + β·T(S) + γ·E(t) Among them, P(s,a) is the individual benefit, T(S) is the team benefit, and E(t) is the emergence effect value; Each of the agents updates the Q value table of the agent by the following formula: Q(s,a) = (1-α)Q(s,a) + α[R + γ·maxQ(s',a')] Among them, α is the learning rate, s is the current state, γ is the discount factor, s' is the next state, and a' is the learning rate of the next state.

8. A group cognition improvement device based on multi-person collaboration, characterized in that: include: A construction module, used to construct a dynamic social model; wherein the network nodes in the dynamic social model are team members, and the interactive relationships between the team members are represented as edges connecting the network nodes; A weight calculation module, used to construct a weight matrix, and update the weight value of the dynamic social network through the weight matrix; An updating module is used to describe the overall activities of the team members through the updated dynamic social network.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the group cognition improvement method based on multi-person collaboration as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor and a memory, the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used to execute the steps in the group cognition improvement method based on multi-person collaboration as described in any one of claims 1 to 7.

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