Universal multifunctional decision-making system and method

Abstract

The invention relates to the technical field of intelligent decision making, and discloses a universal multifunctional decision making system and method, and the system comprises an information sharing subsystem and a decision making subsystem. The information sharing subsystem comprises a personal information sharing module and an inter-group information sharing module; the decision-making subsystem comprises a task acquisition module and a task decision-making module; the task collection module is used for collecting to-be-distributed tasks; the task decision module comprises a task analysis unit, a personnel analysis unit and a matching unit; the task analysis unit is used for dividing a to-be-distributed task and obtaining a plurality of sub-tasks in a time sequence; the personnel analysis unit is used for determining a task undertaking time zone and a task undertaking type of an employee; and the matching unit is used for matching the sub-tasks with the employees according to the time sequence and generating task allocation suggestions. According to the invention, the enterprise task allocation scheme can be intelligently set and optimized in combination with the actual conditions of employees, the functions are rich, the versatility is high, and the cooperative interaction of enterprise work can be realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent decision-making technology, and in particular to a universal multifunctional decision-making system and method. Background Art

[0002] Today, digitalization is accelerating the entry of enterprises into the era of high-frequency competition. The exponential development of technology, the accelerated evolution of industrial changes and the endless global disruption will continue to change people's lives and the development trajectory of enterprises, and will accelerate the triggering of a new wave of digital transformation - no enterprise is without digital transformation.

[0003] However, at present, there are still some problems in the process of enterprise digitalization that need to be solved urgently. Although most enterprises have introduced different types of digital management systems such as OA systems, PM systems, CRM systems, and ERP systems, most of these systems manage process work according to the rules set by the enterprise or perform centralized online management of data, but cannot use digital analysis to assist enterprises in optimizing their management decisions, and cannot provide intelligent decision-making suggestions for enterprise management optimization and employee experience improvement.

[0004] For example, in the allocation of work tasks in an enterprise, it is often distributed by the management or applied for by employees themselves. However, due to subjective decision-making bias and subjective cognitive bias (such as incorrect estimation of employee task volume, incorrect statistics of employee skills, etc.), unreasonable task allocation will occur, which will affect the team's work efficiency. In addition, when there are many departments in charge, the work skills of many employees are limited by the information barriers between departments and cannot be fully utilized. It is difficult to find suitable employees to undertake some work tasks in time. In particular, in the context of big data, when making work task allocation decisions, the conventional allocation decision-making method for employees as an individual subject is no longer reliable enough. Summary of the invention

[0005] The present invention aims to provide a universal multifunctional decision-making system and method, which can intelligently set and optimize the enterprise task allocation plan based on the actual situation of employees, and has rich functions and strong versatility, which helps to achieve synergy and efficiency improvement in enterprise work.

[0006] To achieve the above objectives, the present invention provides the following basic solutions.

[0007] Solution 1

[0008] A general multifunctional decision-making system, comprising an information sharing subsystem and a decision-making subsystem; the information sharing subsystem comprises a personal information sharing module and an inter-group information sharing module; the personal information sharing module is used to automatically collect personal task information of employees and allow employees to upload personal task information; the inter-group information sharing module is used to collect personnel arrangement information, task arrangement information and historical project information of various departments and groups in the enterprise;

[0009] The decision-making subsystem includes a task collection module and a task decision module; the task collection module is used to collect tasks to be assigned; the task decision module includes a task analysis unit, a personnel analysis unit and a matching unit; the task analysis unit is used to divide the tasks to be assigned according to time sequence and by section, and obtain multiple subtasks in time sequence; the personnel analysis unit is used to determine the employee's task acceptance time zone and task acceptance type based on personal task information and data collected by the inter-group information sharing module; the matching unit is used to match subtasks with employees in time sequence according to the analysis results of the task analysis unit and the personnel analysis unit, and generate task allocation suggestions.

[0010] Furthermore, the personal task information includes the employee's current task data and completed task data; the current task data includes the planned completion time of the task; and the personnel analysis unit is also used to quantify the task carrying capacity of each employee based on the employee's completed task data.

[0011] Furthermore, when matching subtasks with employees in chronological order, the following steps are included: based on the timing of the subtasks, first match them with the employee's task acceptance time zone, and filter to obtain an initial matching pair; then, based on the initial matching pair, match them with the employee's task acceptance type, and filter to obtain a target matching pair.

[0012] Furthermore, the initial matching pair is a many-to-many matching pair, and the target matching pair is a one-to-many matching pair; after the target matching pair is screened, the task carrying capacity occupied by the subtask is also evaluated, and employees with more task carrying capacity margin are selected to match the subtask.

[0013] Furthermore, when quantifying the task carrying capacity of each employee and evaluating the task carrying capacity occupied by subtasks, a preset neural network model is used for quantitative evaluation; the preset neural network model is provided with an input layer, a self-attention network layer, an RNN network layer, a first fully connected layer, a Relu function layer, a second fully connected layer and an output layer.

[0014] Furthermore, when quantifying the task carrying capacity of each employee, the preset neural network model takes as input the employee's completed task data, and outputs the threshold of the employee's task carrying capacity; when evaluating the task carrying capacity occupied by a subtask, the preset neural network model takes as input the employee's completed task data and subtask data, and outputs the proportion of the subtask in the employee's task carrying capacity.

[0015] Furthermore, when dividing the tasks to be assigned, the task analysis unit adopts a preset classification model for division; the preset classification model is a CNN-RNN joint model; and before dividing the tasks to be assigned, an initial judgment is also made on the tasks to be assigned; the initial judgment includes: searching for target words in the data of the tasks to be assigned, and if the search result is that the number of target word matches is less than 2, directly treating the task to be assigned as a subtask and no longer using the preset classification model for division.

[0016] Furthermore, the information sharing subsystem also includes a device information sharing module; the device information sharing module is used to collect personal device parameters equipped by each employee in the enterprise, as well as common device parameters shared by each employee in the enterprise.

[0017] Furthermore, the task analysis unit is also used to quantify the equipment carrying capacity required for each subtask, and to attach a first equipment condition label to each subtask; the personnel analysis unit is also used to quantify the equipment carrying capacity of each employee based on personal equipment parameters, and to attach a second equipment condition label to each employee. At the same time, the general equipment parameters are quantified in real time to obtain a third equipment condition label; when matching subtasks with employees, the matching unit also uses the first equipment condition label as a benchmark to match the second equipment condition label and the third equipment condition label.

[0018] Solution 2

[0019] A general multifunctional decision-making method, using a general multifunctional decision-making system as described in Scheme 1 to assist in enterprise management decision-making; comprising the following steps:

[0020] The personal information sharing module collects employees' personal task information and allows employees to upload their personal task information; the inter-group information sharing module collects personnel arrangement information, task arrangement information and historical project information of each department and group in the enterprise;

[0021] The task collection module collects tasks to be assigned; the task analysis unit divides the tasks to be assigned by time sequence and by section, and obtains multiple subtasks in time sequence; the personnel analysis unit determines the employee's task acceptance time zone and task acceptance type based on personal task information and data collected by the inter-group information sharing module; the matching unit matches subtasks with employees in time sequence based on the analysis results of the task analysis unit and the personnel analysis unit, and generates task allocation suggestions.

[0022] The working principle and advantages of the present invention are:

[0023] The present invention provides a general multifunctional decision-making system and method, which can quantitatively analyze the task carrying capacity of employees in combination with their actual conditions, and can intuitively display the task processing capabilities and adapted task types of each employee. In addition, the scheme can also automatically collect tasks to be assigned and parse the tasks, and match the parsed subtasks with employees. It can use intelligent data analysis to set task allocation plans for enterprises, which helps to achieve synergy and efficiency in enterprise work. In particular, firstly, before the matching decision process, the scheme specially sets up a preset neural network model to mine and quantify the task carrying capacity, which can provide objective and reliable data reference for matching decisions; in the matching decision process, the scheme sets up a multi-level matching strategy, including from the initial matching pair to the target matching pair, and advanced screening of the target matching pair, which can analyze and obtain the employees who are most suitable for the subtasks, and ensure that the task carrying capacity of each employee is not overloaded, and the task allocation decision is more reasonable and effective.

[0024] In addition, when making task allocation decisions, this solution also collects and analyzes equipment information and quantifies it into equipment carrying capacity, which can further consider the impact of equipment configuration conditions on task processing capabilities and further optimize task allocation decisions. Among them, this solution has made a breakthrough in considering that in the context of big data, the work execution of many enterprises (especially for enterprises related to work tasks and data analysis and processing) is often closely related to cloud resources and hardware resources (for example, the execution of a data analysis task requires not only the participation of employees, but also the computing power support of cloud servers and computer hardware. Cloud and hardware resources have become a key link in task execution, and sufficient cloud and hardware resources are required to support the efficient execution of tasks). Based on this, this solution not only quantifies the task carrying capacity of individual employees, but also quantifies the equipment carrying capacity of the equipment conditions equipped to them, and combines employees and equipment into an integrated task processing unit. When making task allocation decisions, task allocation suggestions are made based on the quantitative evaluation of the task carrying capacity of the overall task processing unit. The suggestion formulation method is more objective and comprehensive, which can provide more realistic and objective decision-making suggestions for enterprises in the context of big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a structural schematic diagram of a general multifunctional decision-making system and method embodiment 1 of the present invention;

[0026] Figure 2 It is a structural diagram of a second embodiment of a general multifunctional decision-making system and method of the present invention. DETAILED DESCRIPTION

[0027] The following is a further detailed description through specific implementation methods.

[0028] Embodiment 1

[0029] The embodiment is basically as shown in the attached Figure 1 Shown: A general multifunctional decision-making system, including an information sharing subsystem and a decision-making subsystem.

[0030] The information sharing subsystem includes a personal information sharing module and an inter-group information sharing module.

[0031] The personal information sharing module is used to automatically collect employees' personal task information and allow employees to upload personal task information. Specifically, the personal task information includes the employee's current task data and completed task data; the current task data includes the planned completion time of the task, basic task information (such as task name, description, goal, expected results, etc.), task-related attachments, etc. The completed task data includes basic task information, time management data (including task start date, deadline, progress tracking, extension status), responsibility allocation data (including task leader, team members, respective responsibilities and roles, etc.), progress status data (including the current completion status of the task, completion status of each stage, progress percentage, etc.) and the working hours data spent by the employee on the completed task.

[0032] The inter-group information sharing module is used to collect personnel arrangement information, task arrangement information and historical project information of each department and group in the enterprise. The information collected by the inter-group information sharing module can be used by the subsequent personnel analysis unit to combine the inter-group information to further confirm the task section that each employee is actually responsible for, which helps to improve the accuracy of the analysis of task carrying capacity.

[0033] The decision-making subsystem includes a task collection module and a task decision module.

[0034] The task collection module is used to collect tasks to be assigned. In this embodiment, the task collection module can establish a two-way information connection with the enterprise PM system (project management system) and the OA system, and capture the information of tasks to be assigned from the PM system and the OA system. At the same time, employees can also upload tasks to be assigned to the task collection module by themselves.

[0035] The task decision module includes a task analysis unit, a personnel analysis unit and a matching unit.

[0036] The task analysis unit is used to divide the tasks to be assigned by time sequence and by section, and obtain multiple subtasks in time sequence. Specifically, when dividing the tasks to be assigned, the task analysis unit uses a preset classification model for division; the preset classification model is a CNN-RNN joint model, which can complete the task division based on the semantic description of the task.

[0037] Moreover, before dividing the tasks to be assigned, an initial judgment is also made on the tasks to be assigned; the initial judgment includes: searching for a target word in the data of the tasks to be assigned, and if the search result shows that the target word matches successfully and the number of target word matches is less than 2, the task to be assigned is directly treated as a subtask, and the preset classification model is no longer used for division. In practical applications, the target word can be customized based on the task situation of the enterprise. In this way, for some daily small tasks (such as filling out a weekly report, etc.), the task division can be completed directly based on the preset target word, without the need to split it into redundant subtasks, which can reduce the data processing volume of the task analysis unit and avoid excessive task splitting and prolonging the workflow.

[0038] The personnel analysis unit is used to determine the employee's task acceptance time zone and task acceptance type based on personal task information and data collected by the inter-group information sharing module. The task acceptance time zone refers to the period of time when the employee can continue to accept tasks, and the task acceptance type is extracted from the completed task data. In this embodiment, the task acceptance type can be divided into information collection type, project planning type, architecture design type, project development type, project testing type, project procurement type, resource management type, risk management type, quality management type, process management type, cost management type, etc. according to the project section. Each task acceptance type can correspond to a series of subtasks.

[0039] The personnel analysis unit is also used to quantify the task carrying capacity of each employee based on the completed task data of the employee.

[0040] The matching unit is used to match subtasks with employees in chronological order according to the analysis results of the task analysis unit and the personnel analysis unit, and generate task allocation suggestions.

[0041] When matching subtasks with employees in time sequence, the following steps are included: based on the time sequence of the subtasks, first match with the employee's task acceptance time zone, and filter to obtain an initial matching pair; then, based on the initial matching pair, match with the employee's task acceptance type, and filter to obtain a target matching pair.

[0042] The initial matching pair is a many-to-many matching (i.e., multiple subtasks are matched with multiple employees respectively), and the target matching pair is a one-to-many matching (i.e., one subtask is matched with multiple employees); after the target matching pair is screened, the task carrying capacity occupied by the subtask is evaluated, and the employee with more task carrying capacity margin is selected as the preferred employee to match the subtask; the remaining employees are selected as alternatives. The generated task allocation suggestion includes each subtask of the task to be assigned and its corresponding preferred employee and alternative employee.

[0043] When quantifying the task carrying capacity of each employee and evaluating the task carrying capacity occupied by the subtasks, a preset neural network model is used for quantitative evaluation.

[0044] The preset neural network model is provided with an input layer, a self-attention network layer, an RNN network layer, a first fully connected layer, a Relu function layer, a second fully connected layer and an output layer. Among them, the self-attention network layer can help the network to accurately capture data features. The RNN network layer can complete the quantification of task carrying capacity in combination with the temporal relationship. The Relu layer is used to introduce nonlinear relationships. The Relu layer can increase the expression ability of the network and realize accurate analysis of complex data.

[0045] When quantifying the task carrying capacity of each employee, the preset neural network model takes as input the completed task data of the employee and outputs the threshold value of the employee's task carrying capacity; when evaluating the task carrying capacity occupied by a subtask, the preset neural network model takes as input the completed task data and subtask data of the employee and outputs the proportion of the subtask in the task carrying capacity of the employee. In addition, when performing quantitative evaluation, the preset neural network model's self-attention network focuses on capturing the working hours data spent by the employee on the completed task.

[0046] In this embodiment, the preset neural network model is also trained using a historical data set, which includes multiple different types of completed tasks (such as business type tasks, daily affairs management tasks, etc.) and the average working hours occupied by the task (the working hours can be obtained using statistical methods).

[0047] This embodiment also provides a general multifunctional decision-making method, which uses a general multifunctional decision-making system as described above to assist in enterprise management decision-making; the method includes the following steps:

[0048] The personal information sharing module collects employees' personal task information and allows employees to upload their personal task information; the inter-group information sharing module collects personnel arrangement information, task arrangement information and historical project information of each department and group in the enterprise;

[0049] The task collection module collects tasks to be assigned; the task analysis unit divides the tasks to be assigned by time sequence and by section, and obtains multiple subtasks in time sequence; the personnel analysis unit determines the employee's task acceptance time zone and task acceptance type based on personal task information and data collected by the inter-group information sharing module; the matching unit matches subtasks with employees in time sequence based on the analysis results of the task analysis unit and the personnel analysis unit, and generates task allocation suggestions.

[0050] The present embodiment provides a general multifunctional decision-making system and method, which can intelligently set and optimize the enterprise task allocation plan based on the actual situation of employees, and has rich functions and strong versatility, which helps to achieve synergy and efficiency improvement in enterprise work.

[0051] Embodiment 2

[0052] As attached Figure 2 As shown, a general multifunctional decision-making system, based on the first embodiment, the decision-making subsystem also includes an adjustment module; the adjustment module is used to monitor the task execution status of the employees corresponding to each subtask in real time. If the corresponding employee is unable to execute the subtask (such as employee leave, employee transfer, etc.), the task decision module is immediately linked to regenerate the task allocation suggestion for the subtask.

[0053] Compared with the first embodiment, the general multifunctional decision-making system and method provided in this embodiment can timely update the task allocation suggestions based on the actual execution status of the task, and can realize follow-up decision-making.

[0054] Embodiment 3

[0055] A general multifunctional decision-making system, based on the first embodiment, when evaluating the task carrying capacity occupied by a subtask, that is, after obtaining the proportion of the subtask in the task carrying capacity of the employee output by the preset neural network model, the proportion is fine-tuned based on the task proficiency of the employee for the subtask. The fine-tuning includes: task proficiency × proportion, and obtaining a new proportion.

[0056] If there is no task data consistent with the subtask in the employee's historical task data, the task proficiency is determined to be 1, and no fine-tuning is done. If there is task data consistent with the subtask in the employee's historical task data, the task proficiency is evaluated according to the historical completion times and completion quality of this type of subtask. In this embodiment, task proficiency = 1-(Cn+Dm)%; C and D are weight coefficients, n is the historical completion times, and m is the completion quality; the more historical completion times and the higher the completion quality, the smaller the value of the task proficiency, and the smaller the proportion of the corresponding subtask in the employee's task carrying capacity.

[0057] The general multifunctional decision-making system and method provided in this embodiment, compared with the first embodiment, has a more detailed assessment of task carrying capacity, can more accurately measure the adaptation relationship between different subtasks and different employees, and make more reasonable decisions.

[0058] Embodiment 4

[0059] A universal multifunctional decision-making system, based on the first embodiment, the information sharing subsystem also includes a device information sharing module; the device information sharing module is used to collect personal device parameters equipped by each employee in the enterprise, as well as common device parameters shared by each employee in the enterprise.

[0060] In this embodiment, the personal device parameters include the CPU performance, GPU performance, memory size, hard disk read and write speed, storage capacity, network bandwidth, delay time, key software version (the key software version here can be specified according to the actual needs of the enterprise, such as Python 3.10, SQL Server 2022), number of licenses, interface type, technical support response time, etc. of the office equipment (such as a computer) equipped by the employee.

[0061] Through personal equipment parameters, it is possible to intuitively analyze the equipment conditions of each employee, which in turn helps to more comprehensively and objectively analyze their comprehensive carrying capacity when handling tasks.

[0062] The general equipment parameters include cloud database throughput, cloud database response time, cloud database storage capacity, cloud database availability (here refers to the normal operation time percentage of cloud database services, usually given in the form of SLA), RTO (here refers to an indicator for measuring disaster recovery speed, which indicates the time required from the occurrence of a failure to recovery to normal operation; the shorter the RTO, the faster it can recover to normal operation when a failure occurs), RPO (here refers to an indicator for measuring disaster recovery capability, which indicates the time point at which it can recover to the most recent available backup when a catastrophic failure occurs; the higher the RPO, the closer the time point can be when a failure occurs), network packet loss rate, server CPU utilization, server memory utilization, maximum number of concurrent connections, number of concurrent users of collaboration tools, file synchronization speed, firewall effectiveness, etc.

[0063] Through common equipment parameters, the performance and stability of shared equipment in the enterprise can be intuitively analyzed, so as to better quantify the processing conditions of employees' data processing and collaboration needs.

[0064] The task analysis unit is also used to quantify the equipment carrying capacity required for each subtask and attach a first equipment condition tag to each subtask; the personnel analysis unit is also used to quantify the equipment carrying capacity of each employee based on personal equipment parameters and attach a second equipment condition tag to each employee, and at the same time, it also quantifies the general equipment parameters in real time (i.e., quantifies the equipment carrying capacity of the general equipment) and obtains a third equipment condition tag. When matching subtasks with employees, the matching unit also uses the first equipment condition tag as a reference to match the second equipment condition tag and the third equipment condition tag.

[0065] In this embodiment, when quantifying the carrying capacity of the equipment, the task analysis unit and the personnel analysis unit respectively use an MLP (multi-layer perceptron) network based on the tensorflow library to perform quantitative analysis.

[0066] When quantifying the carrying capacity of the equipment equipped by each employee, the MLP network used to quantify personal equipment parameters first collects historical personal equipment parameters as training sets and validation sets for network training and validation. During training, the historical personal equipment parameter data is used as input, and the target variable (i.e., output) is set as a comprehensive score, i.e., the equipment carrying capacity. Then, based on the trained MLP network, the carrying capacity of the equipment equipped by each employee is quantified.

[0067] When quantifying the equipment carrying capacity required for each subtask, the corresponding MLP network is divided into a training set and a verification set by improving the historical data set to perform network training and verification; the improved historical data set contains the data of the historical data set in Example 1, as well as the historical personal equipment parameter data equipped for the completed tasks. During training, the various data of the improved historical data set are used as input, and the target variable (i.e., the output) is set as a comprehensive score, i.e., the equipment carrying capacity. Based on the trained MLP network, the equipment carrying capacity required for each subtask is quantified.

[0068] When quantifying general equipment parameters, the corresponding MLP network first collects historical general equipment parameters as training sets and validation sets for network training and validation. During training, the historical general equipment parameter data is used as input, and the target variable (i.e., output) is set as a comprehensive score, i.e., equipment carrying capacity. Then, based on the trained MLP network, the general equipment parameters are quantified.

[0069] When matching subtasks with employees, the matching unit first uses the first device condition tag as a reference and matches it with the second device condition tag. If the task carrying capacity in the second device condition tag is greater than the task carrying capacity in the first device condition tag, then the subtask corresponding to the first device condition tag is determined to match the employee corresponding to the second device condition tag to form a set of matching pairs, otherwise they are not matched. If they are matched, they are matched with the third device condition tag. If the task carrying capacity in the third device condition is greater than the task carrying capacity in the first device condition tag, and the number of concurrent connections of general devices and the number of concurrent users of collaboration tools under the conditions for the matching pair to be established are both less than the threshold, then the matching pair is established, otherwise it is not established.

[0070] In specific applications, the matching unit may introduce the first device condition tag, the second device condition tag and the third device condition tag on the basis of the initial matching pair or the target matching pair for advanced matching; or first perform matching based on the first device condition tag, the second device condition tag and the third device condition tag, and then screen the initial matching pair and the target matching pair, and output task allocation suggestions based on the final matching pairs; this setting can reduce the overall matching analysis amount while ensuring the accuracy of the matching.

[0071] Compared with the first embodiment, the general multifunctional decision-making system and method provided in this embodiment can further consider the impact of equipment configuration conditions on task processing capabilities when making task allocation decisions; it is particularly suitable for enterprises where work efficiency is highly correlated with hardware configuration conditions, such as big data analysis enterprises, information service enterprises, etc.

[0072] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field are aware of all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement the scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for the ordinary technicians in the relevant field to implement this application. It should be pointed out that for the technicians in this field, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A general multifunctional decision-making system, characterized in that: It includes an information sharing subsystem and a decision-making subsystem; the information sharing subsystem includes a personal information sharing module and an inter-group information sharing module; the personal information sharing module is used to automatically collect personal task information of employees and allow employees to upload personal task information; the inter-group information sharing module is used to collect personnel arrangement information, task arrangement information and historical project information of each department and group in the enterprise; The decision-making subsystem includes a task collection module and a task decision module; the task collection module is used to collect tasks to be assigned; the task decision module includes a task analysis unit, a personnel analysis unit and a matching unit; the task analysis unit is used to divide the tasks to be assigned according to time sequence and by section, and obtain multiple subtasks in time sequence; the personnel analysis unit is used to determine the employee's task acceptance time zone and task acceptance type based on personal task information and data collected by the inter-group information sharing module; the matching unit is used to match subtasks with employees in time sequence according to the analysis results of the task analysis unit and the personnel analysis unit, and generate task allocation suggestions.

2. A general multifunctional decision-making system according to claim 1, characterized in that: The personal task information includes the employee's current task data and completed task data; the current task data includes the planned completion time of the task; the personnel analysis unit is also used to quantify the task carrying capacity of each employee based on the employee's completed task data.

3. A general multifunctional decision-making system according to claim 2, characterized in that: When matching subtasks with employees in time sequence, the following steps are included: based on the time sequence of the subtasks, first match with the employee's task acceptance time zone, and filter to obtain an initial matching pair; then, based on the initial matching pair, match with the employee's task acceptance type, and filter to obtain a target matching pair.

4. A general multifunctional decision-making system according to claim 3, characterized in that: The initial matching pair is a many-to-many matching pair, and the target matching pair is a one-to-many matching pair; after the target matching pair is screened, the task carrying capacity occupied by the subtask is also evaluated, and an employee with a larger task carrying capacity margin is selected to match the subtask.

5. A general multifunctional decision-making system according to claim 4, characterized in that: When quantifying the task carrying capacity of each employee and evaluating the task carrying capacity occupied by subtasks, a preset neural network model is used for quantitative evaluation; the preset neural network model is provided with an input layer, a self-attention network layer, an RNN network layer, a first fully connected layer, a Relu function layer, a second fully connected layer and an output layer.

6. A general multifunctional decision-making system according to claim 5, characterized in that: When quantifying the task carrying capacity of each employee, the preset neural network model takes as input the completed task data of the employee, and outputs the threshold value of the employee's task carrying capacity; when evaluating the task carrying capacity occupied by a subtask, the preset neural network model takes as input the completed task data and subtask data of the employee, and outputs the proportion of the subtask in the employee's task carrying capacity.

7. A general multifunctional decision-making system according to claim 1, characterized in that: When dividing the tasks to be assigned, the task analysis unit adopts a preset classification model for division; the preset classification model is a CNN-RNN joint model; and before dividing the tasks to be assigned, an initial judgment is also made on the tasks to be assigned; the initial judgment includes: searching for target words in the data of the tasks to be assigned, and if the search result shows that the number of target word matches is less than 2, directly treating the task to be assigned as a subtask and no longer using the preset classification model for division.

8. A general multifunctional decision-making system according to claim 1, characterized in that: The information sharing subsystem also includes a device information sharing module; the device information sharing module is used to collect personal device parameters equipped by each employee in the enterprise, as well as common device parameters shared by each employee in the enterprise.

9. A general multifunctional decision-making system according to claim 8, characterized in that: The task analysis unit is also used to quantify the equipment carrying capacity required for each subtask and attach a first equipment condition label to each subtask; the personnel analysis unit is also used to quantify the equipment carrying capacity of each employee based on personal equipment parameters and attach a second equipment condition label to each employee. At the same time, the general equipment parameters are quantified in real time to obtain a third equipment condition label; when matching subtasks with employees, the matching unit also uses the first equipment condition label as a reference to match the second equipment condition label and the third equipment condition label.

10. A general multifunctional decision-making method, characterized in that: A general multifunctional decision-making system as claimed in any one of claims 1 to 9 is used to assist in enterprise management decision-making; comprising the following steps: The personal information sharing module collects employees' personal task information and allows employees to upload their personal task information; the inter-group information sharing module collects personnel arrangement information, task arrangement information and historical project information of each department and group in the enterprise; The task collection module collects tasks to be assigned; the task analysis unit divides the tasks to be assigned by time sequence and by section, and obtains multiple subtasks in time sequence; the personnel analysis unit determines the employee's task acceptance time zone and task acceptance type based on personal task information and data collected by the inter-group information sharing module; the matching unit matches subtasks with employees in time sequence based on the analysis results of the task analysis unit and the personnel analysis unit, and generates task allocation suggestions.

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