An intelligent collaboration method and system based on mobile Internet

Abstract

The present invention discloses a mobile Internet-based intelligent collaboration method and system, which relates to the field of intelligent collaboration technology, including inputting collaborative task data and extracting task feature data including task complexity, time window and dependency relationship; obtaining historical task records and social network data of participants, generating initial capability vectors through capability assessment neural network model, and combining with collaborative capability vectors generated by graph convolutional neural network for feature fusion, calculating task matching scores, and obtaining participant capability portrait data; based on capability portrait data, screening and sorting participants using preset task allocation rules, and generating task allocation plans; sending task invitations to selected participants, and updating task status according to responses. The present invention realizes the flexibility and adaptability of task allocation, thereby significantly improving the overall collaboration efficiency and task completion quality, and provides an innovative technical solution for the field of intelligent collaboration.

Description

Technical Field

[0001] The present invention relates to the field of intelligent collaboration technology, and in particular to an intelligent collaboration method and system based on mobile Internet. Background Art

[0002] With the rapid development and widespread adoption of mobile internet technology, collaborative office work has become an indispensable component of modern enterprise operations. In recent years, intelligent collaboration technology has evolved from simple task assignment systems to intelligent collaboration platforms integrated with artificial intelligence. Traditional collaboration systems primarily relied on manual judgment and fixed rules for task assignment, followed by the development of semi-automated task assignment systems based on rule engines. Currently, the introduction of deep learning technology enables collaborative systems to deeply mine task features through neural network models and, combined with graph computing techniques, analyze the collaborative relationship network between participants. This has significantly improved the intelligence level of task assignment. However, existing intelligent collaboration systems still have limitations in assessing task complexity, constructing participant capability profiles, and matching tasks with participants. Traditional methods often use a simple feature overlay approach to assess task complexity, which fails to accurately characterize the multidimensional characteristics of tasks. When constructing participant capability models, most only consider historical task completion history, ignoring the impact of social network relationships between participants on collaborative effectiveness. Furthermore, existing systems fail to fully leverage the advantages of graph neural networks to mine task dependencies during task assignment, resulting in inefficient task assignment.

[0003] Major issues with existing technologies include: incomplete task feature extraction, a failure to effectively integrate multi-dimensional features such as task complexity, time windows, and dependencies; overly simplistic participant capability assessment models, a lack of in-depth utilization of social network data; and a single task allocation decision-making mechanism, which fails to achieve intelligent and precise matching between tasks and participants. The mobile internet-based intelligent collaboration method provided by this invention belongs to the field of collaborative office technology and effectively addresses these technical issues by constructing a multi-level task feature extraction mechanism, a participant capability assessment model integrated with social networks, and a task allocation optimization strategy based on graph neural networks. Summary of the Invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides an intelligent collaboration method and system based on mobile Internet, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides an intelligent collaboration method based on the mobile Internet, comprising: inputting collaboration task data; extracting collaboration task feature data based on the collaboration task data; the collaboration task feature data including task complexity data, time window data, and task dependency data;

[0009] Obtain participants' historical task records and social network data, generate participant capability initial vectors through the capability assessment neural network model, and perform feature fusion on the collaboration capability vectors generated by the graph convolutional neural network. Calculate the task matching score based on the task complexity data to obtain participant capability profile data.

[0010] Based on the participant capability profile data, the participants are screened and sorted using preset task allocation rules to generate a task allocation plan;

[0011] Based on the task allocation scheme, task invitations are sent to selected participants, and task statuses are updated according to participants' responses.

[0012] As a preferred solution of the mobile Internet-based intelligent collaboration method of the present invention, wherein: extracting task complexity data includes receiving collaborative task data, and extracting task description text and task time parameters from the collaborative task data according to a preset task feature extraction rule;

[0013] Extracting the time window data includes constructing a task timeline based on the task time parameters to generate the time window data;

[0014] Extracting task dependency data includes processing the collaborative task data using a graph neural network, identifying pre-dependencies between tasks, and generating task dependency data.

[0015] As a preferred solution of the mobile Internet-based intelligent collaboration method of the present invention, the generating of the participant capability initial vector includes:

[0016] Obtain the participant's historical task records and participant social network data, and extract the participant's basic feature data from the participant's historical task records. The participant's basic feature data includes task completion rate, task quality score, and task response time. The calculation formula for extracting the participant's basic feature data is as follows:

[0017]

[0018]

[0019] Among them, TCR is the task completion rate, N c is the number of completed tasks, N t is the total number of tasks, TQS is the task quality score, Q i The quality score of the i-th task, w i is the task weight coefficient, TRT is the task response time, T e is the task response end time, T s is the task release time, and N is the total number of tasks.

[0020] As a preferred solution of the mobile Internet-based intelligent collaboration method of the present invention, the feature fusion includes:

[0021] Construct a deep neural network model, input the basic feature data of the participants into the deep neural network model, generate the initial vector of the participant's ability, and obtain the calculation formula of the initial vector of the participant's ability as follows:

[0022] v init =σ(W2·ReLU(W1·x+b1)+b2)

[0023] Among them, v init is the participant's initial ability vector, x is the participant's basic feature data, W1 and W2 are weight matrices, b1 and b2 are bias vectors, and σ is the tanh activation function;

[0024] Construct a capability assessment neural network model, input the participant social network data into the graph convolutional neural network to generate a collaboration capability vector, and generate the collaboration capability vector through feature aggregation. The calculation formula for feature aggregation is as follows:

[0025]

[0026] in, is the adjacency matrix with self-loop added, A is the adjacency matrix of the social network, I is the identity matrix, is the degree matrix, H (l+1) is the l+1th layer node feature matrix, H (l) is the feature matrix of the l-th layer node, W (l) is the learnable weight matrix, σ1 is the ReLU activation function;

[0027] The initial capability vector of the participant is fused with the collaborative capability vector to generate the comprehensive capability vector of the participant. The calculation formula for feature fusion is as follows:

[0028] v f =λ·v init +(1-λ)·vc

[0029] λ=sigmoid(MLP([v init ||v c ]))

[0030] Among them, v f is the comprehensive ability vector of the participants, v c is the collaborative capability vector, and λ is the fusion weight coefficient.

[0031] As a preferred solution of the mobile Internet-based intelligent collaboration method of the present invention, wherein: the obtaining of participant capability portrait data includes:

[0032] According to the task complexity data in the collaborative task feature data, the comprehensive ability vectors of the participants are weighted to generate a task matching score. The weighted calculation formula is as follows:

[0033]

[0034] Among them, s is the task matching score, f(c i ) is the complexity adjustment function, which is used to adjust the score according to the complexity of the task. is the i-th component of the comprehensive ability vector of the participant, w i is the weight coefficient of the i-th capability dimension, c i is the i-th component of the task complexity index, α and β are adjustment parameters;

[0035] The task matching score and the comprehensive ability vector of the participant are combined to form the participant ability profile data, that is, p = [v f ,s], where p is the participant's ability portrait data.

[0036] As a preferred solution of the mobile Internet-based intelligent collaboration method of the present invention, the screening and sorting of participants includes:

[0037] Extract participants with free time periods based on time window data, and filter the list of participants whose task load is less than the preset parallel upper limit;

[0038] Calculating the task linkage degree for each participant in the participant list according to the task dependency data, wherein the task linkage degree is obtained by weighting the similarity between the current task and the participant's historical completed tasks;

[0039] Performing a weighted combination of the task linkage degree and the task matching degree score to obtain a comprehensive participant score, sorting the participant list based on the comprehensive participant score, and selecting N participants with the highest comprehensive scores as candidate participants;

[0040] The task allocation plan includes a task ID, a candidate participant ID, an expected start time, and an expected end time.

[0041] As a preferred solution of the mobile Internet-based intelligent collaboration method of the present invention, the updating of the task status according to the response of the participants includes:

[0042] Sending task invitation information to the candidate participant via the mobile Internet according to the candidate participant ID in the task allocation plan, wherein the task invitation information includes a task description, an expected start time and an expected end time;

[0043] receiving response data from the candidate participant, the response data including an acceptance flag or a rejection flag, and reselecting a candidate participant when the response data including the rejection flag is returned;

[0044] After receiving the response data including the acceptance identifier, a task execution record is created, which includes the task status identifier, the actual start time and the expected completion time, and the task execution record is stored in the participant's historical task record.

[0045] In a second aspect, the present invention provides an intelligent collaboration system based on mobile Internet, which includes: a task feature extraction module, a participant capability assessment module, a task allocation optimization module and a task interaction and collaboration module;

[0046] The task feature extraction module is used to input collaborative task data and extract collaborative task feature data based on the collaborative task data, wherein the collaborative task feature data includes task complexity data, time window data and task dependency data;

[0047] The participant capability assessment module is used to obtain the participant's historical task records and social network data, generate the participant's initial capability vector through the capability assessment neural network model, and perform feature fusion with the collaborative capability vector generated by the graph convolutional neural network. The task matching score is calculated based on the task complexity data to obtain the participant capability profile data;

[0048] The task allocation optimization module is used to screen and sort the participants according to the participant capability profile data using preset task allocation rules to generate a task allocation plan;

[0049] The task interactive collaboration module is used to send task invitations to selected participants based on the task allocation plan, and update task status according to the participants' responses.

[0050] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the intelligent collaboration method based on the mobile Internet are implemented.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the steps of the intelligent collaboration method based on the mobile Internet are implemented.

[0052] Compared with the existing technology, the beneficial effects of the present invention are as follows: by constructing a multi-dimensional task feature extraction mechanism, combining preset task feature extraction rules and graph neural network technology, a comprehensive analysis of task features is achieved, providing accurate data support for task allocation; by integrating the capability evaluation neural network model and the graph convolutional neural network, the personal capabilities of the participants and social network data are innovatively integrated to construct a comprehensive participant capability portrait; based on the participant capability portrait data, a multi-dimensional screening and sorting mechanism is adopted, and the concept of task linkage degree is introduced to achieve intelligent matching between tasks and participants; finally, by establishing a complete task invitation and response mechanism, combined with the real-time update function of task status, dynamic management of the task allocation process is achieved; the organic combination of the various steps of the present invention not only improves the accuracy and completeness of task feature extraction, enhances the comprehensiveness of participant capability evaluation, optimizes the rationality of task allocation and collaboration efficiency, but also achieves the flexibility and adaptability of task allocation, thereby significantly improving the overall collaboration efficiency and task completion quality, and providing an innovative technical solution for the field of intelligent collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 A flowchart of a mobile Internet-based intelligent collaboration method and system provided by one embodiment of the present invention;

[0055] Figure 2 This is a diagram of the internal structure of a computer device providing a mobile Internet-based intelligent collaboration method and system according to one embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the above-mentioned objects, features, and advantages of the present invention more easily understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0059] Example 1, with reference to Figure 1-Figure 2 , which is the first embodiment of the present invention, provides an intelligent collaboration method based on mobile Internet, including:

[0060] Before introducing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0061] Graph Convolutional Network: A deep learning model that specializes in processing graph-structured data. It can learn the structured representation of nodes by iteratively aggregating information about nodes and their neighboring nodes in the graph. In this invention, it is mainly used to analyze the collaborative relationships in the social networks of participants and generate vector representations that reflect the collaborative capabilities of participants through feature aggregation.

[0062] Task Linkage Degree: A quantitative indicator used to measure the degree of association between the current task and the participant's historically completed tasks. This indicator is obtained by calculating the similarity of task characteristics and combining it with a weight coefficient. It reflects the participant's expertise and persistence in a specific type of task, and helps to achieve continuity and specialization in task allocation.

[0063] Complexity Adjustment Function: A mathematical function used to dynamically adjust the participant's ability score based on the complexity of the task. This function takes into account the multidimensional complexity indicators of the task and corrects the participant's ability score through nonlinear transformation, so that the final task matching degree more accurately reflects the participant's actual ability to complete a task of a specific complexity.

[0064] This application provides an effective solution to the above-mentioned problems. Next, we will describe in detail how to implement the intelligent collaboration method based on mobile Internet with reference to multiple embodiments.

[0065] Figure 1 A flowchart of a method and system for intelligent collaboration based on mobile Internet is shown, including:

[0066] S1: Input collaborative task data, and extract collaborative task feature data based on the collaborative task data, wherein the collaborative task feature data includes task complexity data, time window data, and task dependency data;

[0067] Furthermore, the extracted task complexity data includes,

[0068] Receive collaborative task data, and extract task description text and task time parameters from the collaborative task data according to preset task feature extraction rules;

[0069] The preset task feature extraction rules include task time rules, task keyword rules, task dependency rules, and resource requirement rules;

[0070] Task time rules include:

[0071] Extracting the task start time, task deadline, and task duration based on the task description text, calculating the task available time using the task start time and task deadline, and using the ratio of the task duration to the task available time as a complexity indicator of the time dimension;

[0072] Task keyword rules include:

[0073] Preset the task keyword dictionary and the corresponding weight coefficient, and calculate the complexity index of the task semantic dimension based on the keywords and their frequency in the task description text and the weight coefficient;

[0074] Task dependency rules include:

[0075] Based on the number of pre-dependent tasks of the task description text recognition task, the number of pre-dependent tasks is used as a complexity indicator of the task dependency dimension;

[0076] The resource requirement rules include extracting the number of human resources and equipment resource types required for the task based on the task description text, and calculating the complexity index of the resource dimension based on the number of human resources and equipment resource types;

[0077] The task description text is input into a pre-trained natural language processing model to obtain a task semantic feature vector. A task complexity score is calculated based on the task semantic feature vector. The task complexity score is compared with a preset complexity threshold to generate task complexity data. It should be noted that the natural language processing model used in the present invention is prior art and will not be described in detail here.

[0078] Specifically, the present invention performs weighted summation on the complexity indicators of each dimension mentioned above to obtain a task complexity score, and the weight coefficients of the complexity indicators of each dimension are obtained through training of historical task data; based on the comparison result of the task complexity score and the preset complexity threshold, task complexity data including the task complexity level is generated.

[0079] Furthermore, extracting time window data includes:

[0080] A task timeline is constructed based on the task time parameters to generate time window data.

[0081] Furthermore, the task dependency data is extracted including:

[0082] Use graph neural networks to process collaborative task data, identify pre-dependencies between tasks, and generate task dependency data;

[0083] It should be noted that the graph neural network used to extract task dependency data in the present invention includes but is not limited to any of the following: a graph attention network GAT, wherein the graph attention network GAT calculates the attention coefficient between task nodes, and the attention coefficient of each task node is calculated by the feature vector of the node and its adjacent nodes through the attention mechanism, and the attention coefficient is used to characterize the dependency strength between task nodes; a graph convolution network GCN, wherein the graph convolution network GCN adopts a spatial domain convolution method to perform aggregation operations on the feature vector of each task node and the feature vector of its first-order neighbor node, and extracts the local dependency between task nodes through multi-layer convolution operations; a graph isomorphism network GIN, wherein the graph isomorphism network GIN adopts a learnable neighbor aggregation function, and the neighbor aggregation function includes a trainable parameter ε, and the discrimination of the task node representation is improved by adjusting the value of the parameter ε; a message passing neural network MPNN, wherein the message passing neural network MPNN includes a message passing stage and a readout stage. In the message passing stage, message vectors containing dependency information are passed between task nodes, and in the readout stage, all message vectors are aggregated to generate a global task dependency representation.

[0084] Specifically, the present invention preferably uses a graph attention network GAT to extract task dependency data. The input of the GAT network is the word vector matrix corresponding to the task description text, and the output of the GAT network is the dependency vector of the task node. The numerical value of each element in the dependency vector represents the dependency strength of the corresponding task node pair.

[0085] Furthermore, the task complexity data, the time window data and the task dependency data are combined to form collaborative task feature data.

[0086] S2: Obtain the participant's historical task records and social network data, generate the participant's initial capability vector through the capability assessment neural network model, and perform feature fusion with the collaborative capability vector generated by the graph convolutional neural network. Calculate the task matching score based on the task complexity data to obtain the participant's capability profile data;

[0087] Furthermore, generating the initial vector of participant capabilities includes:

[0088] Obtain the participant's historical task records and participant social network data, and extract the participant's basic characteristic data from the participant's historical task records. The participant's basic characteristic data includes task completion rate, task quality score, and task response time. The calculation formula for extracting the participant's basic characteristic data is as follows:

[0089]

[0090] Among them, TCR is the task completion rate, N c is the number of completed tasks, N t is the total number of tasks, TQS is the task quality score, Q i The quality score of the i-th task, w i is the task weight coefficient, TRT is the task response time, T e is the task response end time, T s is the task release time, and N is the total number of tasks.

[0091] Furthermore, feature fusion includes,

[0092] Construct a deep neural network model, input the basic feature data of the participants into the deep neural network model, generate the initial vector of the participant's ability, and obtain the calculation formula of the initial vector of the participant's ability as follows:

[0093] v init =σ(W2·ReLU(W1·x+b1)+b2)

[0094] Among them, v initis the participant's initial ability vector, x is the participant's basic feature data, W1 and W2 are weight matrices, b1 and b2 are bias vectors, and σ is the tanh activation function;

[0095] Construct a capability assessment neural network model, input the participant social network data into the graph convolutional neural network to generate a collaboration capability vector, and generate the collaboration capability vector through feature aggregation. The calculation formula for feature aggregation is as follows:

[0096]

[0097] in, is the adjacency matrix with self-loop added, A is the adjacency matrix of the social network, I is the identity matrix, is the degree matrix, H (l+1) is the l+1th layer node feature matrix, H (l) is the feature matrix of the l-th layer node, W (l) is the learnable weight matrix, σ1 is the ReLU activation function;

[0098] The initial capability vector of the participant is fused with the collaborative capability vector to generate the comprehensive capability vector of the participant. The calculation formula for feature fusion is as follows:

[0099] v f =λ·v init +(1-λ)·v c

[0100] λ=sigmoid(MLP([v init ||v c ]))

[0101] Among them, v f is the comprehensive ability vector of the participants, v c is the collaborative capability vector, and λ is the fusion weight coefficient.

[0102] Furthermore, the data on the ability portraits of participants include:

[0103] According to the task complexity data in the collaborative task feature data, the comprehensive ability vectors of the participants are weighted to generate the task matching score. The weighted calculation formula is as follows:

[0104]

[0105] Among them, s is the task matching score, f(c i ) is the complexity adjustment function, which is used to adjust the score according to the complexity of the task. is the comprehensive ability vector v of the participant f The i-th component of w i is the weight coefficient of the i-th capability dimension, ci is the i-th component of the task complexity index, α and β are adjustment parameters;

[0106] The task matching score and the comprehensive ability vector of the participant are combined to form the participant ability profile data, that is, p = [v f ,s], where p is the participant's ability portrait data.

[0107] S3: Based on the participant capability profile data, the participants are screened and sorted using preset task allocation rules to generate a task allocation plan;

[0108] Furthermore, screening and sorting of participants include,

[0109] Extract participants with free time periods based on time window data, and filter the list of participants whose task load is less than the preset parallel upper limit;

[0110] Calculating the task linkage degree for each participant in the participant list according to the task dependency data, wherein the task linkage degree is obtained by weighting the similarity between the current task and the participant's historical completed tasks;

[0111] The task linkage degree and the task matching degree score are weightedly combined to obtain a comprehensive participant score. The participant list is sorted based on the comprehensive participant score, and the N participants with the highest comprehensive scores are selected as candidate participants.

[0112] Furthermore, the task allocation plan includes a task ID, a candidate participant ID, an expected start time, and an expected end time.

[0113] S4: Based on the task allocation plan, task invitations are sent to the selected participants, and task statuses are updated according to responses from the participants.

[0114] Furthermore, updating the task status based on the participant's response includes:

[0115] Sending task invitation information to the candidate participant via the mobile Internet according to the candidate participant ID in the task allocation plan, wherein the task invitation information includes a task description, an expected start time and an expected end time;

[0116] receiving response data from the candidate participant, the response data including an acceptance flag or a rejection flag, and reselecting a candidate participant when the response data including the rejection flag is returned;

[0117] After receiving the response data including the acceptance identifier, a task execution record is created, which includes the task status identifier, the actual start time and the expected completion time, and the task execution record is stored in the participant's historical task record.

[0118] Example 2: This embodiment further provides an intelligent collaboration system based on the mobile Internet, comprising: a task feature extraction module, a participant capability assessment module, a task allocation optimization module, and a task interaction and collaboration module;

[0119] The task feature extraction module is used to input collaborative task data and extract collaborative task feature data based on the collaborative task data, wherein the collaborative task feature data includes task complexity data, time window data and task dependency data;

[0120] The participant capability assessment module is used to obtain the participant's historical task records and social network data, generate the participant's initial capability vector through the capability assessment neural network model, and perform feature fusion with the collaborative capability vector generated by the graph convolutional neural network. The task matching score is calculated based on the task complexity data to obtain the participant's capability profile data;

[0121] The task allocation optimization module is used to screen and sort the participants according to the participant capability profile data using preset task allocation rules to generate a task allocation plan;

[0122] The task interaction and collaboration module is used to send task invitations to selected participants based on the task allocation plan and update the task status according to the participants' responses.

[0123] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a smart collaboration method based on the mobile Internet is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0124] This embodiment further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps: inputting collaborative task data, extracting collaborative task feature data based on the collaborative task data, the collaborative task feature data including task complexity data, time window data, and task dependency data;

[0125] Obtain participants' historical task records and social network data, generate participant capability initial vectors through the capability assessment neural network model, and perform feature fusion on the collaboration capability vectors generated by the graph convolutional neural network. Calculate the task matching score based on the task complexity data to obtain participant capability profile data.

[0126] Based on the participant capability profile data, the participants are screened and sorted using preset task allocation rules to generate a task allocation plan;

[0127] Based on the task allocation scheme, task invitations are sent to selected participants, and task statuses are updated according to participants' responses.

[0128] Example 3, reference Figure 1 - Figure 2 , which is the third embodiment of the present invention. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0129] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0130] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0133] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0134] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. An intelligent collaboration method based on mobile Internet, characterized by: The method includes inputting collaborative task data and extracting collaborative task feature data according to the collaborative task data, wherein the collaborative task feature data includes task complexity data, time window data, and task dependency data; Obtain participants' historical task records and social network data, generate participant capability initial vectors through the capability assessment neural network model, and perform feature fusion on the collaboration capability vectors generated by the graph convolutional neural network. Calculate the task matching score based on the task complexity data to obtain participant capability profile data. Based on the participant capability profile data, the participants are screened and sorted using preset task allocation rules to generate a task allocation plan; Based on the task allocation scheme, task invitations are sent to selected participants, and task statuses are updated according to participants' responses.

2. The mobile Internet-based intelligent collaboration method according to claim 1, wherein: Extracting task complexity data includes receiving collaborative task data, and extracting task description text and task time parameters from the collaborative task data according to preset task feature extraction rules; Extracting the time window data includes constructing a task timeline based on the task time parameters to generate the time window data; Extracting task dependency data includes processing the collaborative task data using a graph neural network, identifying pre-dependencies between tasks, and generating task dependency data.

3. The mobile Internet-based intelligent collaboration method according to claim 2, wherein: Generating the participant capability initial vector includes: Obtain the participant's historical task records and participant social network data, and extract the participant's basic feature data from the participant's historical task records. The participant's basic feature data includes task completion rate, task quality score, and task response time. The calculation formula for extracting the participant's basic feature data is as follows: Among them, TCR is the task completion rate, N c is the number of completed tasks, N t is the total number of tasks, TQS is the task quality score, Q i The quality score of the i-th task, w i is the task weight coefficient, TRT is the task response time, T e is the task response end time, T s is the task release time, and N is the total number of tasks.

4. The mobile Internet-based intelligent collaboration method according to claim 3, wherein: The feature fusion includes: Construct a deep neural network model, input the basic feature data of the participants into the deep neural network model, generate the initial vector of the participant's ability, and obtain the calculation formula of the initial vector of the participant's ability as follows: v init =σ(W2·ReLU(W1·x+b1)+b2) Among them, v init is the participant's initial ability vector, x is the participant's basic feature data, W1 and W2 are weight matrices, b1 and b2 are bias vectors, and σ is the tanh activation function; Construct a capability assessment neural network model, input the participant social network data into the graph convolutional neural network to generate a collaboration capability vector, and generate the collaboration capability vector through feature aggregation. The calculation formula for feature aggregation is as follows: in, is the adjacency matrix with self-loop added, A is the adjacency matrix of the social network, I is the identity matrix, is the degree matrix, H (l+1) is the l+1th layer node feature matrix, H (l) is the feature matrix of the l-th layer node, W (l) is the learnable weight matrix, σ1 is the ReLU activation function; The initial capability vector of the participant is fused with the collaborative capability vector to generate the comprehensive capability vector of the participant. The calculation formula for feature fusion is as follows: v f =λ·v init +(1-λ)·v c λ=sigmoid(MLP([v init ||v c ])) Among them, v f is the comprehensive ability vector of the participants, v c is the collaborative capability vector, and λ is the fusion weight coefficient.

5. The mobile Internet-based intelligent collaboration method according to claim 4, wherein: The participant capability portrait data obtained includes: According to the task complexity data in the collaborative task feature data, the comprehensive ability vectors of the participants are weighted to generate a task matching score. The weighted calculation formula is as follows: Among them, s is the task matching score, f(c i ) is the complexity adjustment function, which is used to adjust the score according to the complexity of the task. is the i-th component of the comprehensive ability vector of the participant, w i is the weight coefficient of the i-th capability dimension, c i is the i-th component of the task complexity index, α and β are adjustment parameters; The task matching score and the comprehensive ability vector of the participant are combined to form the participant ability profile data, that is, p = [v f ,s], where p is the participant’s ability portrait data.

6. The mobile Internet-based intelligent collaboration method according to claim 5, characterized in that: The screening and sorting of participants includes: Extract participants with free time periods based on time window data, and filter the list of participants whose task load is less than the preset parallel upper limit; Calculating the task linkage degree for each participant in the participant list according to the task dependency data, wherein the task linkage degree is obtained by weighting the similarity between the current task and the participant's historical completed tasks; Performing a weighted combination of the task linkage degree and the task matching degree score to obtain a comprehensive participant score, sorting the participant list based on the comprehensive participant score, and selecting N participants with the highest comprehensive scores as candidate participants; The task allocation plan includes a task ID, a candidate participant ID, an expected start time, and an expected end time.

7. The mobile Internet-based intelligent collaboration method according to claim 6, wherein: The updating of the task status according to the participant's response includes: Sending task invitation information to the candidate participant via the mobile Internet according to the candidate participant ID in the task allocation plan, wherein the task invitation information includes a task description, an expected start time and an expected end time; receiving response data from the candidate participant, the response data including an acceptance flag or a rejection flag, and reselecting a candidate participant when the response data including the rejection flag is returned; After receiving the response data including the acceptance identifier, a task execution record is created, which includes the task status identifier, the actual start time and the expected completion time, and the task execution record is stored in the participant's historical task record.

8. A mobile internet-based intelligent collaboration system, based on the mobile internet-based intelligent collaboration method according to any one of claims 1 to 7, characterized in that: It includes task feature extraction module, participant capability assessment module, task allocation optimization module and task interaction and collaboration module; The task feature extraction module is used to input collaborative task data and extract collaborative task feature data based on the collaborative task data, wherein the collaborative task feature data includes task complexity data, time window data and task dependency data; The participant capability assessment module is used to obtain the participant's historical task records and social network data, generate the participant's initial capability vector through the capability assessment neural network model, and perform feature fusion with the collaborative capability vector generated by the graph convolutional neural network. The task matching score is calculated based on the task complexity data to obtain the participant capability profile data; The task allocation optimization module is used to screen and sort the participants according to the participant capability profile data using preset task allocation rules to generate a task allocation plan; The task interactive collaboration module is used to send task invitations to selected participants based on the task allocation plan, and update task status according to the participants' responses.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the mobile Internet-based intelligent collaboration method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the mobile Internet-based intelligent collaboration method according to any one of claims 1 to 7 are implemented.

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