A natural language processing technology integrated management device and its use method

Abstract

The present invention relates to the field of natural language processing technology, and in particular to a natural language processing technology comprehensive management device and a method for using the same, comprising the following steps: S1: task initialization: configuring a task input data source, including text, table, voice and image multimodal data; setting task attributes, including task type, urgency, complexity and maximum allowable delay time; calculating task priority through a task priority calculation module; S2: task allocation and resource scheduling: sorting task queues from high to low according to priority; in the present invention, by adopting edge computing, dynamic resource adjustment, real-time feedback mechanism, multimodal data fusion and other technologies, the response capability of a natural language processing device to tasks with high real-time requirements in enterprise management is optimized, the real-time response capability of the system under high load conditions is significantly improved, the impact of delays on task execution is reduced, and user satisfaction is improved.

Description

Technical Field

[0001] The present invention relates to the field of natural language processing technology, and in particular to a natural language processing technology comprehensive management device and a use method thereof. Background Art

[0002] The natural language processing technology integrated management device is an intelligent system or device that uses natural language processing technology to perform integrated management tasks. It combines the core functions of natural language processing (such as text analysis, semantic understanding, automatic generation, etc.) with management needs to achieve efficient management and intelligent support for data, tasks, processes or user interactions; The natural language processing technology integrated management device is an intelligent tool that deeply integrates NLP technology into various management tasks. It is widely used in data analysis, task execution, human-computer interaction and other fields. It improves the automation and intelligence level of traditional management systems through its powerful semantic understanding and generation capabilities, and provides innovative technical support for enterprises, scientific research institutions, public services and other fields. When the natural language processing technology integrated management device is applied in enterprise management, the customer service and real-time scheduling scenarios in enterprise management require the system to respond quickly. However, the NLP device may not respond in time due to computational complexity or network delays, and cannot meet the real-time requirements of key tasks, thus reducing user satisfaction. Therefore, a natural language processing technology integrated management device and a method for using the same are proposed to address the above problems. Summary of the invention

[0003] The purpose of the present invention is to provide a natural language processing technology comprehensive management device and a method of using the same, so as to solve the problem that when the natural language processing technology comprehensive management device is applied to enterprise management, customer service and real-time scheduling scenarios in enterprise management require a quick response from the system, while the NLP device may not respond in time due to computational complexity or network delays, and cannot meet the real-time requirements of key tasks, thereby reducing user satisfaction.

[0004] To achieve the above object, the present invention provides the following technical solutions: A natural language processing technology integrated management device and a method for using the same, comprising the following steps: S1: Task initialization: configure the task input data source, including text, table, voice and image multimodal data; Set task properties, including task type, urgency, complexity, and maximum allowed delay time; Calculate task priority through task priority calculation module ; S2: Task allocation and resource scheduling: sort the task queue from high to low priority; According to the complexity and real-time requirements of the task, the task is assigned to the edge computing node or the core computing module. The assignment rules are as follows: Edge computing nodes handle tasks with high real-time requirements and low complexity; The core computing module handles tasks with lower real-time requirements but higher complexity; S3: Task execution: Edge nodes handle lightweight tasks, including real-time question answering, voice command parsing, and simple text classification; The core modules handle complex tasks, including document summarization, multimodal data fusion, and sentiment analysis; During task execution, the computing resource utilization is monitored in real time, and the allocation ratio is dynamically adjusted according to the task queue; S4: Real-time feedback and optimization: After the task is completed, the system automatically collects user feedback and prioritizes feedback The calculation formula is:

[0005] In the formula, Rate user satisfaction, and Influence weights and scores for tasks, is the task response time; Adjust the task queue priority according to the feedback priority and re-optimize the execution parameters of low-satisfaction tasks; S5: Model update and optimization: Analyze the processing records of inefficient tasks and add new data to train the model; Update core module models through transfer learning to improve the understanding of industry-specific terminology and complex expressions; The lightweight model of the edge node is optimized using distillation learning, model pruning, and quantization techniques to ensure low resource usage while maintaining performance.

[0006] As a further optimization of the present invention, the task priority The calculation formula is:

[0007] In the formula, and are the weights of urgency and complexity, Rate the urgency of the task, Score the complexity of the task, The maximum delay allowed for a task.

[0008] As a further optimization of the present invention, in the task allocation and resource scheduling step, the resource ratio allocated to the edge computing node is Calculated by the following formula:

[0009] In the formula, and are the weights of low-complexity and high-complexity tasks, and is the number of low-complexity and high-complexity tasks, is the current execution time of the task, The average execution time of a task, and It is an adjustment coefficient used to balance the flexibility of resource allocation and system stability.

[0010] As a further optimization of the present invention, the calculation weight of the task priority is and Dynamically adjust according to business scenarios to meet diverse task management needs.

[0011] As further optimized content of the present invention, the lightweight model deployed by the edge node adopts the following optimization method: Parameter pruning technology reduces redundant parameters; Distillation learning extracts knowledge from the core model; Model quantization converts floating-point weights into integers to improve operating efficiency.

[0012] As further optimized content of the present invention, the core computing module uniformly processes the input text, image, and voice data through a multimodal data fusion algorithm to generate a comprehensive analysis report; after the task is completed, the real-time feedback module dynamically adjusts the task reallocation and parameter optimization according to the priority of user feedback.

[0013] As further optimized content of the present invention, the user feedback module records the processing parameters of low-satisfaction tasks, and updates the language model of the core module through transfer learning technology to improve the adaptability to specific task scenarios.

[0014] As further optimized content of the present invention, among others: the core computing module supports distributed processing of complex tasks, reduces latency and improves processing efficiency by task segmentation and result aggregation.

[0015] As a further optimization of the present invention, the system monitors the following performance indicators at each stage of task execution: Average task response time; User satisfaction rating; System resource utilization and adjust scheduling strategies in real time based on monitoring data.

[0016] As further optimized content of the present invention, it includes: Main control module: used to coordinate task allocation and resource scheduling; Priority calculation module: calculates task priority based on urgency, complexity and allowed delay time; Edge computing module: deploy lightweight models to handle tasks with high real-time requirements; Core computing module: handles complex tasks, including multimodal data fusion and advanced sentiment analysis; User feedback module: collect user feedback and adjust task priorities; Model optimization module: Continuously optimize the model through transfer learning and model pruning technology.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, by adopting edge computing, dynamic resource adjustment, real-time feedback mechanism, multimodal data fusion and other technologies, the response capability of the natural language processing device to tasks with high real-time requirements in enterprise management is optimized, the real-time response capability of the system under high load is significantly improved, and the impact of delay on task execution is reduced, thereby improving user satisfaction; 2. In the present invention, through the collaborative work of edge computing and core computing modules, the system can dynamically allocate resources according to the complexity and real-time requirements of the task, quickly assign low-complexity tasks to edge nodes for processing, and hand over complex tasks to the core module for processing. This flexible resource scheduling mechanism significantly reduces the delay in task execution and ensures that key tasks can respond quickly, especially in scenarios such as customer service and real-time scheduling; 3. In the present invention, real-time feedback adjustment and multimodal data fusion technology are adopted. The system can monitor the task execution status in real time and dynamically optimize resource allocation according to feedback. When the task execution time exceeds expectations, the system will automatically adjust the task priority and allocate more computing resources, thereby reducing the problem of untimely response caused by computing complexity or network delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method for using a natural language processing technology integrated management device of the present invention; Figure 2 This is a system block diagram of a natural language processing technology comprehensive management device of the present invention. DETAILED DESCRIPTION

[0019] See also Figure 1-2 , the present invention provides a technical solution: A natural language processing technology integrated management device and a method for using the same, comprising the following steps: S1: Task initialization: configure the task input data source, including text, table, voice and image multimodal data; Set task properties, including task type, urgency, complexity, and maximum allowed delay time; Calculate task priority through task priority calculation module ; S2: Task allocation and resource scheduling: sort the task queue from high to low priority; According to the complexity and real-time requirements of the task, the task is assigned to the edge computing node or the core computing module. The assignment rules are as follows: Edge computing nodes handle tasks with high real-time requirements and low complexity; The core computing module handles tasks with lower real-time requirements but higher complexity; S3: Task execution: Edge nodes handle lightweight tasks, including real-time question answering, voice command parsing, and simple text classification; The core modules handle complex tasks, including document summarization, multimodal data fusion, and sentiment analysis; During task execution, the computing resource utilization is monitored in real time, and the allocation ratio is dynamically adjusted according to the task queue; S4: Real-time feedback and optimization: After the task is completed, the system automatically collects user feedback and prioritizes feedback The calculation formula is:

[0020] In the formula, Rate user satisfaction, and Influence weights and scores for tasks, is the task response time; Adjust the task queue priority according to the feedback priority and re-optimize the execution parameters of low-satisfaction tasks; S5: Model update and optimization: Analyze the processing records of inefficient tasks and add new data to train the model; Update core module models through transfer learning to improve the understanding of industry-specific terminology and complex expressions; The lightweight model of the edge node is optimized using distillation learning, model pruning and quantization technology to ensure low resource usage while maintaining performance. The complete process of task initialization, allocation and scheduling, execution, feedback and optimization, and model update ensures that tasks can be executed efficiently and quickly, while improving resource utilization and reducing unnecessary waste of computing resources. Through priority calculation and dynamic adjustment, the system can adapt to various task requirements and improve the real-time response capability and task processing efficiency of the overall system.

[0021] As a technical solution for further implementation of this plan, the task priority The calculation formula is:

[0022] In the formula, and are the weights of urgency and complexity, Rate the urgency of the task, Score the complexity of the task, The maximum delay time allowed for a task can effectively assess the urgency and complexity of the task, ensure that high-priority tasks are processed in a timely manner, and help improve task scheduling efficiency. Especially in scenarios with high real-time requirements such as customer service and real-time scheduling, it can effectively shorten task response time and increase the flexibility and accuracy of system response. As a technical solution for further implementation of this solution, in the task allocation and resource scheduling steps, the resource ratio allocated to the edge computing node is Calculated by the following formula:

[0023] In the formula, and are the weights of low-complexity and high-complexity tasks, and is the number of low-complexity and high-complexity tasks, is the current execution time of the task, The average execution time of a task, and To adjust the coefficient, which is used to balance the flexibility of resource allocation and system stability, the system can dynamically adjust the resource ratio between edge computing nodes and core computing modules according to the complexity and execution time of the task, ensuring that the task is reasonably and efficiently allocated resources, which helps to optimize the system load and improve the efficiency of task processing, while avoiding excessive concentration of resources on a few tasks, reducing delays and bottlenecks. As a technical solution for further implementation of this solution, the calculation weight of the task priority and Dynamic adjustment based on business scenarios to meet diverse task management needs. Dynamic adjustment of calculation weights based on actual business scenarios allows the system to flexibly adapt to task requirements in different scenarios. For example, in customer service scenarios, urgent tasks may need to have a higher priority, while in document processing tasks, complexity weights may be more important. This feature enhances the system's adaptability and adaptability to business scenarios, and improves the accuracy and efficiency of task processing. As a technical solution for further implementation of this solution, the lightweight model deployed in edge nodes adopts the following optimization methods: Parameter pruning technology reduces redundant parameters; Distillation learning extracts knowledge from the core model; Model quantization converts floating-point weights into integers to improve operating efficiency. By adopting lightweight model optimization methods, edge computing nodes can efficiently process tasks with high real-time requirements, such as speech recognition and real-time question and answer, with limited resources. Model optimization not only reduces the occupation of computing resources, but also ensures the response speed and accuracy in real-time task processing. It is particularly suitable for application scenarios of edge devices and low-power devices. As a technical solution for further implementation of this solution, the core computing module uniformly processes the input text, image, and voice data through a multimodal data fusion algorithm to generate a comprehensive analysis report. After the task is completed, the real-time feedback module dynamically adjusts the task reallocation and parameter optimization according to the priority of user feedback. Through multimodal data fusion and real-time feedback mechanism, the system can comprehensively process various types of data and dynamically adjust the task processing strategy according to user feedback. This method can improve the accuracy and efficiency of multimodal task processing, while ensuring that the system can respond to user needs in a timely manner, optimize the task processing process and improve user experience. As a technical solution for further implementation of this solution, the user feedback module records the processing parameters of low-satisfaction tasks and updates the language model of the core module through transfer learning technology to improve the adaptability to specific task scenarios. By recording user feedback and combining transfer learning technology, the system can continuously optimize the language model and improve its understanding of industry-specific tasks. This method enables the system to gradually learn and adapt to new task scenarios, improve the accuracy and efficiency of task execution, and avoid processing errors caused by the diversity of task types; As a technical solution for further implementation of this solution, the core computing module supports distributed processing of complex tasks, reduces latency and improves processing efficiency by task segmentation and result aggregation. Through distributed processing and task segmentation of the core computing module, the system can process multiple tasks in parallel, reducing the total time and latency of task execution. This method is particularly suitable for complex tasks that require a large amount of computing resources, such as document generation and sentiment analysis, which improves processing efficiency and ensures efficient response. As a technical solution for further implementation of this solution, the system monitors the following performance indicators at each stage of task execution: Average task response time; User satisfaction rating; System resource utilization, and adjust the scheduling strategy in real time based on monitoring data. By real-time monitoring of performance indicators, the system can find potential bottlenecks during task execution and adjust resource allocation and scheduling strategies in a timely manner to ensure efficient task completion. This dynamic adjustment mechanism helps optimize system resource usage, reduce task delays, and improve overall user satisfaction; The technical solutions for further implementation of this plan include: Main control module: used to coordinate task allocation and resource scheduling; Priority calculation module: calculates task priority based on urgency, complexity and allowed delay time; Edge computing module: deploy lightweight models to handle tasks with high real-time requirements; Core computing module: handles complex tasks, including multimodal data fusion and advanced sentiment analysis; User feedback module: collect user feedback and adjust task priorities; Model optimization module: continuously optimizes the model through transfer learning and model pruning technology; Through modular design, the device effectively coordinates task allocation, resource scheduling and priority calculation, ensuring that the system can intelligently allocate resources according to the complexity, real-time nature and user needs of the task, thereby improving overall processing efficiency and response speed. The modular architecture improves the scalability and flexibility of the system, can meet the needs of different scenarios, and is easy to optimize according to actual usage.

[0024] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. The above is only a preferred implementation of the present invention. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the protection scope of the present invention.

Claims

1. A method for using a natural language processing technology integrated management device, characterized in that: The following steps are involved: S1: Task initialization: configure the task input data source, including text, table, voice and image multimodal data; Set task properties, including task type, urgency, complexity, and maximum allowed delay time; Calculate task priority through task priority calculation module ; S2: Task allocation and resource scheduling: sort the task queue from high to low priority; According to the complexity and real-time requirements of the task, the task is assigned to the edge computing node or the core computing module. The assignment rules are as follows: Edge computing nodes handle tasks with high real-time requirements and low complexity; The core computing module handles tasks with lower real-time requirements but higher complexity; S3: Task execution: Edge nodes handle lightweight tasks, including real-time question answering, voice command parsing, and simple text classification; The core modules handle complex tasks, including document summarization, multimodal data fusion, and sentiment analysis; During task execution, the computing resource utilization is monitored in real time, and the allocation ratio is dynamically adjusted according to the task queue; S4: Real-time feedback and optimization: After the task is completed, the system automatically collects user feedback and prioritizes feedback The calculation formula is: In the formula, Rate user satisfaction, and Influence weights and scores for tasks, is the task response time; Adjust the task queue priority according to the feedback priority and re-optimize the execution parameters of low-satisfaction tasks; S5: Model update and optimization: Analyze the processing records of inefficient tasks and add new data to train the model; Update core module models through transfer learning to improve the understanding of industry-specific terminology and complex expressions; The lightweight model of the edge node is optimized using distillation learning, model pruning, and quantization techniques to ensure low resource usage while maintaining performance.

2. The method for using the natural language processing technology integrated management device according to claim 1, characterized in that: The task priority The calculation formula is: In the formula, and are the weights of urgency and complexity, Rate the urgency of the task, Score the complexity of the task, The maximum delay allowed for a task.

3. The method for using a natural language processing technology integrated management device according to claim 1, characterized in that: In the task allocation and resource scheduling steps, the resource ratio allocated to the edge computing node Calculated by the following formula: In the formula, and are the weights of low-complexity and high-complexity tasks, and is the number of low-complexity and high-complexity tasks, is the current execution time of the task, The average execution time of a task, and It is an adjustment coefficient used to balance the flexibility of resource allocation and system stability.

4. The method for using a natural language processing technology integrated management device according to claim 2, characterized in that: The calculation weight of the task priority and Dynamically adjust according to business scenarios to meet diverse task management needs.

5. The method for using a natural language processing technology integrated management device according to claim 1, characterized in that: The lightweight model deployed on edge nodes uses the following optimization methods: Parameter pruning technology reduces redundant parameters; Distillation learning extracts knowledge from the core model; Model quantization converts floating-point weights into integers to improve operating efficiency.

6. The method for using a natural language processing technology integrated management device according to claim 1, characterized in that: The core computing module uniformly processes the input text, image, and voice data through a multimodal data fusion algorithm to generate a comprehensive analysis report. After the task is completed, the real-time feedback module dynamically adjusts the task reallocation and parameter optimization according to the priority of user feedback.

7. The method for using a natural language processing technology integrated management device according to claim 1, characterized in that: The user feedback module records the processing parameters of low-satisfaction tasks and updates the language model of the core module through transfer learning technology to improve its adaptability to specific task scenarios.

8. The method for using a natural language processing technology integrated management device according to claim 1, characterized in that: The core computing module supports distributed processing of complex tasks, reducing latency and improving processing efficiency by splitting tasks and aggregating results.

9. The method for using a natural language processing technology integrated management device according to claim 1, characterized in that: The system monitors the following performance indicators at each stage of task execution: Average task response time; User satisfaction rating; System resource utilization and adjust scheduling strategies in real time based on monitoring data.

10. A natural language processing technology integrated management device according to any one of claims 1 to 9, characterized in that: include: Main control module: used to coordinate task allocation and resource scheduling; Priority calculation module: calculates task priority based on urgency, complexity and allowed delay time; Edge computing module: deploy lightweight models to handle tasks with high real-time requirements; Core computing module: handles complex tasks, including multimodal data fusion and advanced sentiment analysis; User feedback module: collect user feedback and adjust task priorities; Model optimization module: Continuously optimize the model through transfer learning and model pruning technology.

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