Financial consultation efficient feedback system

Abstract

The invention belongs to the field of financial consultation, and particularly relates to a financial consultation efficient feedback system which comprises a customer information analysis module, the customer information analysis module is connected with a demand classification module, the demand classification module is connected with a data integration module, and the data integration module is connected with an intelligent recommendation module. The intelligent recommendation module is connected with a real-time feedback module, the real-time feedback module is connected with a preprocessing and caching module and a multi-thread processing module, the real-time feedback module is further connected with a risk assessment module, the risk assessment module is connected with a financial optimization suggestion module, and the financial optimization suggestion module is connected with a historical consultation recording module. The multi-thread processing module is connected with a multi-dimensional feedback effect evaluation module, the multi-thread processing module comprises a dynamic load balance scheduling unit, the dynamic load balance scheduling unit is connected with a thread self-adaption unit, efficient real-time feedback can be achieved, reliable risk control is provided, and the customer experience feeling is improved.

Description

Technical Field

[0001] This invention relates to the field of financial consulting technology, and in particular to an efficient feedback system for financial consulting. Background Technology

[0002] Financial consulting refers to the act of natural or legal persons with professional knowledge in finance, accounting, and related fields providing business solutions, planning, and guidance to clients upon commission. The meaning of financial consulting should be very broad, encompassing both commissioned professional financial consulting and ancillary financial consulting services subordinate to overall management consulting services. Therefore, financial consulting can be broadly defined as: management consulting services related to asset management, securities investment, and other financial aspects provided by professional institutions such as consulting firms, securities companies, and investment banks to clients and investors; in other words, all consulting activities related to finance fall under the broad definition of financial consulting.

[0003] In existing technologies, the feedback time for clients' financial consulting tasks is long and inefficient. Therefore, we propose an efficient feedback system for financial consulting to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies, such as long feedback times and low efficiency in providing financial advice to clients, and to propose an efficient feedback system for financial advice.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A high-efficiency feedback system for financial consulting includes a customer information analysis module connected to a demand classification module, which in turn is connected to a data integration module. The data integration module is connected to an intelligent recommendation module, which is connected to a real-time feedback module. The real-time feedback module is connected to a preprocessing and caching module and a multi-threaded processing module. The real-time feedback module is also connected to a risk assessment module, which is connected to a financial optimization suggestion module and a historical consultation record module. The multi-threaded processing module is connected to a multi-dimensional feedback effect evaluation module. The multi-threaded processing module includes a dynamic load balancing scheduling unit connected to a thread adaptive unit and a parallel task optimization unit. The preprocessing and caching module includes an intelligent prediction pre-caching unit connected to an adaptive preprocessing optimization unit and an intelligent cache eviction unit.

[0007] Preferably, the customer information parsing module parses the input information and extracts key information, including the question type and data format, to prepare for subsequent classification. The parsed information is sent to the demand classification module, which classifies the questions into different categories according to preset classification rules. The classified information then enters the data integration module.

[0008] Preferably, the data integration module is used to collect data related to customer problems from internal databases or external data sources and integrate them to provide comprehensive data support for subsequent recommendations and analysis. The integrated data is sent to the intelligent recommendation module, which recommends the most suitable solutions or expert consultants based on the category of customer problems and data characteristics, quickly providing customers with preliminary consultation directions. The recommendation results are then sent to the real-time feedback module.

[0009] Preferably, the real-time feedback module quickly generates and sends feedback information to the customer, the risk assessment module performs risk analysis on the recommended solution, identifies potential risk points, and based on the risk assessment results, the financial optimization suggestion module generates optimization suggestions to further improve the recommended solution. The optimized solution and customer feedback information are stored in the historical consultation record module for subsequent learning and analysis to improve system performance.

[0010] Preferably, the multi-dimensional feedback effect evaluation module assesses the quality of the feedback by analyzing it from multiple dimensions, including customer satisfaction and semantic fit, to provide data support for system optimization.

[0011] Preferably, the dynamic load balancing scheduling unit is used to monitor the load of each thread in real time, and to prioritize the allocation of new tasks to threads with lighter loads, so as to avoid some threads being overloaded while other threads are idle, thereby improving the overall throughput.

[0012] Preferably, the thread adaptive unit is used to set a time window. If the average task delay exceeds the threshold within the window, the number of threads is increased, and vice versa. This dynamically adapts to load fluctuations and ensures the system's high efficiency and stability. The parallel task optimization unit is used to analyze the task flow, identify the longest critical path, and break it down into parallelizable subtasks to reduce task dependencies, fully explore the potential of multi-threaded parallelism, and accelerate task completion.

[0013] Preferably, the intelligent prediction and pre-caching unit uses machine learning algorithms to analyze user behavior and historical data, predict possible user requests, and cache relevant data and calculation results in advance to reduce user waiting time and improve response speed.

[0014] Preferably, the adaptive preprocessing optimization unit dynamically adjusts the processing strategy based on real-time feedback, optimizes the preprocessing process in real time according to resource usage and task priority, reduces redundant calculations, and improves system resource utilization.

[0015] Preferably, the intelligent cache eviction unit introduces a machine learning model to evaluate the "value" of cached data, including access frequency and data importance, to achieve a more intelligent cache eviction mechanism, prioritize the retention of high-value data, improve cache hit rate, and optimize system performance.

[0016] The beneficial effects of the efficient feedback system for financial consulting described in this invention are as follows:

[0017] Achieving efficient real-time feedback: Through intelligent predictive pre-caching, adaptive pre-processing optimization, and intelligent cache eviction units in the pre-processing and caching modules, combined with dynamic load balancing scheduling, thread adaptation, and parallel task optimization in the multi-threaded processing module, the system can quickly respond to customer inquiries, reduce waiting time, and improve service efficiency.

[0018] Provides reliable risk control: The risk assessment module conducts in-depth analysis of recommended solutions to identify potential risk points, and the financial optimization suggestion module generates optimization suggestions based on this to improve the solution, ensuring the reliability and security of the consulting advice and helping clients avoid risks.

[0019] Data-driven optimization cycle: The historical consultation record module stores consultation data, and the multi-dimensional feedback effect evaluation module analyzes feedback quality from multiple dimensions such as customer satisfaction and semantic fit, providing data support for system optimization, realizing data-driven continuous improvement, and enhancing consultation quality.

[0020] Exceptional customer experience: The system optimizes services from multiple dimensions such as efficiency, accuracy, and ease of use, bringing customers a fast, personalized, and reliable financial consulting experience, enhancing customer satisfaction and trust.

[0021] This invention enables efficient real-time feedback, provides reliable risk control, and improves customer experience. Attached Figure Description

[0022] Figure 1 This is a block diagram illustrating the working principle of an efficient feedback system for financial consulting proposed in this invention.

[0023] Figure 2 This is a block diagram of the multi-threaded processing module of a high-efficiency financial consulting feedback system proposed in this invention.

[0024] Figure 3 This is a block diagram of the preprocessing and caching module of a high-efficiency financial consulting feedback system proposed in this invention.

[0025] Figure 4 This is a block diagram illustrating the working principle of Embodiment 4 of the efficient feedback system for financial consulting proposed in this invention.

[0026] Figure 5This is a block diagram illustrating the working principle of a fifth embodiment of the efficient feedback system for financial consulting proposed in this invention. Detailed Implementation

[0027] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0028] Example 1

[0029] Reference Figures 1-3 A high-efficiency feedback system for financial consulting includes a customer information analysis module, which is connected to a demand classification module. The demand classification module is connected to a data integration module, which is connected to an intelligent recommendation module. The intelligent recommendation module is connected to a real-time feedback module, which is connected to a preprocessing and caching module and a multi-threaded processing module. The real-time feedback module is also connected to a risk assessment module, which is connected to a financial optimization suggestion module. The financial optimization suggestion module is connected to a historical consulting record module. The multi-threaded processing module is connected to a multi-dimensional feedback effect evaluation module. The multi-threaded processing module includes a dynamic load balancing scheduling unit, which is connected to a thread adaptive unit, and the thread adaptive unit is connected to a parallel task optimization unit. The preprocessing and caching module includes an intelligent prediction pre-caching unit, which is connected to an adaptive preprocessing optimization unit, and the adaptive preprocessing optimization unit is connected to an intelligent cache eviction unit.

[0030] In this embodiment, the customer information parsing module parses the input information, extracting key information, including the problem type and data format, to prepare for subsequent classification. The parsed information is sent to the demand classification module, which categorizes the problems into different types according to preset classification rules. The categorized information then enters the data integration module, which collects and integrates data related to customer problems from internal databases or external data sources to provide comprehensive data support for subsequent recommendations and analysis. The integrated data is sent to the intelligent recommendation module, which recommends the most suitable solution or expert consultant based on the customer problem category and data characteristics, quickly providing the customer with an initial consultation direction. The recommendation results enter the real-time feedback module, which quickly generates and sends feedback information to the customer. The risk assessment module performs risk analysis on the recommended solution, identifying potential risk points. Based on the risk assessment results, the financial optimization suggestion module generates optimization suggestions to further improve the recommended solution. The optimized solution and customer feedback information are stored in the historical consultation record module for subsequent learning and analysis to improve system performance. The multi-dimensional feedback effect evaluation module evaluates the quality of feedback, analyzing it from multiple dimensions such as customer satisfaction and semantic fit, to provide the system with the best results. The system provides data support for optimization. The dynamic load balancing scheduling unit monitors the load of each thread in real time, prioritizing the allocation of new tasks to threads with lighter loads to avoid overloading some threads while idling others, thus improving overall throughput. The thread adaptive unit sets time windows; if the average task latency exceeds a threshold within the window, the number of threads is increased, and vice versa, dynamically adapting to load fluctuations to ensure efficient and stable system performance. The parallel task optimization unit analyzes the task flow, identifies the longest critical path, breaks it down into parallelizable subtasks, reduces task dependencies, fully explores the potential of multi-threaded parallelism, and accelerates task completion. The intelligent prediction and pre-caching unit uses machine learning algorithms to analyze user behavior and historical data, predicts possible user requests, and caches relevant data and calculation results in advance, reducing user waiting time and improving response speed. The adaptive preprocessing optimization unit dynamically adjusts processing strategies based on real-time feedback, optimizing the preprocessing process in real time according to resource usage and task priority, reducing redundant calculations, and improving system resource utilization. The intelligent cache eviction unit introduces machine learning models to evaluate the "value" of cached data, including access frequency and data importance, to achieve a more intelligent cache eviction mechanism, prioritizing the retention of high-value data, improving cache hit rate, and optimizing system performance.

[0031] Example 2

[0032] An efficient feedback system for financial consulting includes a customer information analysis module, which is connected to a demand classification module. The demand classification module is connected to a data integration module, which is connected to an intelligent recommendation module. The intelligent recommendation module is connected to a real-time feedback module, which is connected to a preprocessing and caching module and a multi-threaded processing module. The real-time feedback module is also connected to a risk assessment module, which is connected to a financial optimization suggestion module, which is connected to a historical consultation record module. The multi-threaded processing module is connected to a multi-dimensional feedback effect evaluation module. The multi-threaded processing module includes a dynamic load balancing scheduling unit, which is connected to a thread adaptive unit, which is connected to a parallel task optimization unit. The preprocessing and caching module includes an intelligent prediction pre-caching unit, which is connected to an adaptive preprocessing optimization unit, which is connected to an intelligent cache eviction unit. The real-time feedback module is also connected to a personalized push module, which pushes relevant information and knowledge based on customer interests or question categories to increase customer understanding of financial knowledge and improve customer satisfaction.

[0033] In this embodiment, the customer information parsing module parses the input information, extracting key information, including the problem type and data format, to prepare for subsequent classification. The parsed information is sent to the demand classification module, which categorizes the problems into different types according to preset classification rules. The categorized information then enters the data integration module, which collects and integrates data related to customer problems from internal databases or external data sources to provide comprehensive data support for subsequent recommendations and analysis. The integrated data is sent to the intelligent recommendation module, which recommends the most suitable solution or expert consultant based on the customer problem category and data characteristics, quickly providing the customer with an initial consultation direction. The recommendation results enter the real-time feedback module, which quickly generates and sends feedback information to the customer. The risk assessment module performs risk analysis on the recommended solution, identifying potential risk points. Based on the risk assessment results, the financial optimization suggestion module generates optimization suggestions to further improve the recommended solution. The optimized solution and customer feedback information are stored in the historical consultation record module for subsequent learning and analysis to improve system performance. The multi-dimensional feedback effect evaluation module evaluates the quality of feedback, analyzing it from multiple dimensions such as customer satisfaction and semantic fit, to provide the system with the best results. The system provides data support for optimization. The dynamic load balancing scheduling unit monitors the load of each thread in real time, prioritizing the allocation of new tasks to threads with lighter loads to avoid overloading some threads while idling others, thus improving overall throughput. The thread adaptive unit sets time windows; if the average task latency exceeds a threshold within the window, the number of threads is increased, and vice versa, dynamically adapting to load fluctuations to ensure efficient and stable system performance. The parallel task optimization unit analyzes the task flow, identifies the longest critical path, breaks it down into parallelizable subtasks, reduces task dependencies, fully explores the potential of multi-threaded parallelism, and accelerates task completion. The intelligent prediction and pre-caching unit uses machine learning algorithms to analyze user behavior and historical data, predicts possible user requests, and caches relevant data and calculation results in advance, reducing user waiting time and improving response speed. The adaptive preprocessing optimization unit dynamically adjusts processing strategies based on real-time feedback, optimizing the preprocessing process in real time according to resource usage and task priority, reducing redundant calculations, and improving system resource utilization. The intelligent cache eviction unit introduces machine learning models to evaluate the "value" of cached data, including access frequency and data importance, to achieve a more intelligent cache eviction mechanism, prioritizing the retention of high-value data, improving cache hit rate, and optimizing system performance.

[0034] Example 3

[0035] An efficient feedback system for financial consulting includes a customer information analysis module, which is connected to a demand classification module. The demand classification module is connected to a data integration module, which is connected to an intelligent recommendation module. The intelligent recommendation module is connected to a real-time feedback module, which is connected to a preprocessing and caching module and a multi-threaded processing module. The real-time feedback module is also connected to a risk assessment module, which is connected to a financial optimization suggestion module, which is connected to a historical consulting record module. The multi-threaded processing module is connected to a multi-dimensional feedback effect evaluation module. The multi-threaded processing module includes a dynamic load balancing scheduling unit, which is connected to a thread adaptive unit, which is connected to a parallel task optimization unit. The preprocessing and caching module includes an intelligent prediction pre-caching unit, which is connected to an adaptive preprocessing optimization unit, which is connected to an intelligent cache elimination unit. The real-time feedback module is connected to a customer feedback incentive module, which issues points tokens based on blockchain. Customers can earn points rewards for providing valid feedback, and these points can be redeemed for consulting fee discounts or peripheral gifts, forming a positive incentive cycle.

[0036] In this embodiment, the customer information parsing module parses the input information, extracting key information, including the problem type and data format, to prepare for subsequent classification. The parsed information is sent to the demand classification module, which categorizes the problems into different types according to preset classification rules. The categorized information then enters the data integration module, which collects and integrates data related to customer problems from internal databases or external data sources to provide comprehensive data support for subsequent recommendations and analysis. The integrated data is sent to the intelligent recommendation module, which recommends the most suitable solution or expert consultant based on the customer problem category and data characteristics, quickly providing the customer with an initial consultation direction. The recommendation results enter the real-time feedback module, which quickly generates and sends feedback information to the customer. The risk assessment module performs risk analysis on the recommended solution, identifying potential risk points. Based on the risk assessment results, the financial optimization suggestion module generates optimization suggestions to further improve the recommended solution. The optimized solution and customer feedback information are stored in the historical consultation record module for subsequent learning and analysis to improve system performance. The multi-dimensional feedback effect evaluation module evaluates the quality of feedback, analyzing it from multiple dimensions such as customer satisfaction and semantic fit, to provide the system with the best results. The system provides data support for optimization. The dynamic load balancing scheduling unit monitors the load of each thread in real time, prioritizing the allocation of new tasks to threads with lighter loads to avoid overloading some threads while idling others, thus improving overall throughput. The thread adaptive unit sets time windows; if the average task latency exceeds a threshold within the window, the number of threads is increased, and vice versa, dynamically adapting to load fluctuations to ensure efficient and stable system performance. The parallel task optimization unit analyzes the task flow, identifies the longest critical path, breaks it down into parallelizable subtasks, reduces task dependencies, fully explores the potential of multi-threaded parallelism, and accelerates task completion. The intelligent prediction and pre-caching unit uses machine learning algorithms to analyze user behavior and historical data, predicts possible user requests, and caches relevant data and calculation results in advance, reducing user waiting time and improving response speed. The adaptive preprocessing optimization unit dynamically adjusts processing strategies based on real-time feedback, optimizing the preprocessing process in real time according to resource usage and task priority, reducing redundant calculations, and improving system resource utilization. The intelligent cache eviction unit introduces machine learning models to evaluate the "value" of cached data, including access frequency and data importance, to achieve a more intelligent cache eviction mechanism, prioritizing the retention of high-value data, improving cache hit rate, and optimizing system performance.

[0037] Example 4

[0038] Reference Figure 4A highly efficient feedback system for financial consulting includes a customer information analysis module, which is connected to a demand classification module. The demand classification module is connected to a data integration module, which is connected to an intelligent recommendation module. The intelligent recommendation module is connected to a real-time feedback module, which is connected to a preprocessing and caching module and a multi-threaded processing module. The real-time feedback module is also connected to a risk assessment module, which is connected to a financial optimization suggestion module, which is connected to a historical consultation record module. The multi-threaded processing module is connected to a multi-dimensional feedback effect evaluation module. The multi-threaded processing module includes a dynamic load balancing scheduling unit, which is connected to a thread adaptive unit, which is connected to a parallel task optimization unit. The preprocessing and caching module includes an intelligent prediction pre-caching unit, which is connected to an adaptive preprocessing optimization unit, which is connected to an intelligent cache eviction unit. The historical consultation record module is connected to a dynamic knowledge base optimization module, which automatically extracts and pushes commonly mentioned difficult questions, customer misunderstandings, and excellent solutions from the feedback to the knowledge base. Knowledge base entries are dynamically sorted and updated based on citation rate and positive review rate.

[0039] In this embodiment, the customer information parsing module parses the input information, extracting key information, including the problem type and data format, to prepare for subsequent classification. The parsed information is sent to the demand classification module, which categorizes the problems into different types according to preset classification rules. The categorized information then enters the data integration module, which collects and integrates data related to customer problems from internal databases or external data sources to provide comprehensive data support for subsequent recommendations and analysis. The integrated data is sent to the intelligent recommendation module, which recommends the most suitable solution or expert consultant based on the customer problem category and data characteristics, quickly providing the customer with an initial consultation direction. The recommendation results enter the real-time feedback module, which quickly generates and sends feedback information to the customer. The risk assessment module performs risk analysis on the recommended solution, identifying potential risk points. Based on the risk assessment results, the financial optimization suggestion module generates optimization suggestions to further improve the recommended solution. The optimized solution and customer feedback information are stored in the historical consultation record module for subsequent learning and analysis to improve system performance. The multi-dimensional feedback effect evaluation module evaluates the quality of feedback, analyzing it from multiple dimensions such as customer satisfaction and semantic fit, to provide the system with the best results. The system provides data support for optimization. The dynamic load balancing scheduling unit monitors the load of each thread in real time, prioritizing the allocation of new tasks to threads with lighter loads to avoid overloading some threads while idling others, thus improving overall throughput. The thread adaptive unit sets time windows; if the average task latency exceeds a threshold within the window, the number of threads is increased, and vice versa, dynamically adapting to load fluctuations to ensure efficient and stable system performance. The parallel task optimization unit analyzes the task flow, identifies the longest critical path, breaks it down into parallelizable subtasks, reduces task dependencies, fully explores the potential of multi-threaded parallelism, and accelerates task completion. The intelligent prediction and pre-caching unit uses machine learning algorithms to analyze user behavior and historical data, predicts possible user requests, and caches relevant data and calculation results in advance, reducing user waiting time and improving response speed. The adaptive preprocessing optimization unit dynamically adjusts processing strategies based on real-time feedback, optimizing the preprocessing process in real time according to resource usage and task priority, reducing redundant calculations, and improving system resource utilization. The intelligent cache eviction unit introduces machine learning models to evaluate the "value" of cached data, including access frequency and data importance, to achieve a more intelligent cache eviction mechanism, prioritizing the retention of high-value data, improving cache hit rate, and optimizing system performance.

[0040] Example 5

[0041] Reference Figure 5A highly efficient feedback system for financial consulting includes a customer information analysis module, which is connected to a demand classification module. The demand classification module is connected to a data integration module, which is connected to an intelligent recommendation module. The intelligent recommendation module is connected to a real-time feedback module, which is connected to a preprocessing and caching module and a multi-threaded processing module. The real-time feedback module is also connected to a risk assessment module, which is connected to a financial optimization suggestion module, which is connected to a historical consultation record module. The multi-threaded processing module is connected to a multi-dimensional feedback effect evaluation module. The multi-threaded processing module includes a dynamic load balancing scheduling unit, which is connected to a thread adaptive unit, which is connected to a parallel task optimization unit. The preprocessing and caching module includes an intelligent prediction pre-caching unit, which is connected to an adaptive preprocessing optimization unit, which is connected to an intelligent cache eviction unit. The historical consultation record module is connected to a blockchain identity authentication and storage module. The blockchain identity authentication and storage module generates a unique, tamper-proof digital fingerprint (hash value) for each consultation session and feedback, and stores it on the blockchain. This ensures the authenticity, traceability, and non-repudiation of all feedback data.

[0042] In this embodiment, the customer information parsing module parses the input information, extracting key information, including the problem type and data format, to prepare for subsequent classification. The parsed information is sent to the demand classification module, which categorizes the problems into different types according to preset classification rules. The categorized information then enters the data integration module, which collects and integrates data related to customer problems from internal databases or external data sources to provide comprehensive data support for subsequent recommendations and analysis. The integrated data is sent to the intelligent recommendation module, which recommends the most suitable solution or expert consultant based on the customer problem category and data characteristics, quickly providing the customer with an initial consultation direction. The recommendation results enter the real-time feedback module, which quickly generates and sends feedback information to the customer. The risk assessment module performs risk analysis on the recommended solution, identifying potential risk points. Based on the risk assessment results, the financial optimization suggestion module generates optimization suggestions to further improve the recommended solution. The optimized solution and customer feedback information are stored in the historical consultation record module for subsequent learning and analysis to improve system performance. The multi-dimensional feedback effect evaluation module evaluates the quality of feedback, analyzing it from multiple dimensions such as customer satisfaction and semantic fit, to provide the system with the best results. The system provides data support for optimization. The dynamic load balancing scheduling unit monitors the load of each thread in real time, prioritizing the allocation of new tasks to threads with lighter loads to avoid overloading some threads while idling others, thus improving overall throughput. The thread adaptive unit sets time windows; if the average task latency exceeds a threshold within the window, the number of threads is increased, and vice versa, dynamically adapting to load fluctuations to ensure efficient and stable system performance. The parallel task optimization unit analyzes the task flow, identifies the longest critical path, breaks it down into parallelizable subtasks, reduces task dependencies, fully explores the potential of multi-threaded parallelism, and accelerates task completion. The intelligent prediction and pre-caching unit uses machine learning algorithms to analyze user behavior and historical data, predicts possible user requests, and caches relevant data and calculation results in advance, reducing user waiting time and improving response speed. The adaptive preprocessing optimization unit dynamically adjusts processing strategies based on real-time feedback, optimizing the preprocessing process in real time according to resource usage and task priority, reducing redundant calculations, and improving system resource utilization. The intelligent cache eviction unit introduces machine learning models to evaluate the "value" of cached data, including access frequency and data importance, to achieve a more intelligent cache eviction mechanism, prioritizing the retention of high-value data, improving cache hit rate, and optimizing system performance.

[0043] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A highly efficient feedback system for financial consulting, characterized in that, The system includes a customer information parsing module, which is connected to a demand classification module. The demand classification module is connected to a data integration module, which is connected to an intelligent recommendation module. The intelligent recommendation module is connected to a real-time feedback module, which is connected to a preprocessing and caching module and a multi-threaded processing module. The real-time feedback module is also connected to a risk assessment module, which is connected to a financial optimization suggestion module and a historical consultation record module. The multi-threaded processing module is connected to a multi-dimensional feedback effect evaluation module. The multi-threaded processing module includes a dynamic load balancing scheduling unit, which is connected to a thread adaptive unit and a parallel task optimization unit. The preprocessing and caching module includes an intelligent prediction pre-caching unit, which is connected to an adaptive preprocessing optimization unit and an intelligent cache eviction unit.

2. The efficient feedback system for financial consulting according to claim 1, characterized in that, The customer information parsing module parses the input information and extracts key information, including the question type and data format, to prepare for subsequent classification. The parsed information is sent to the demand classification module, which classifies the questions into different categories according to preset classification rules. The classified information then enters the data integration module.

3. The efficient feedback system for financial consulting according to claim 2, characterized in that, The data integration module is used to collect data related to customer issues from internal databases or external data sources and integrate it to provide comprehensive data support for subsequent recommendations and analysis. The integrated data is sent to the intelligent recommendation module, which recommends the most suitable solutions or expert consultants based on the category of customer issues and data characteristics, quickly providing customers with initial consultation directions. The recommendation results are then sent to the real-time feedback module.

4. The efficient feedback system for financial consulting according to claim 3, characterized in that, The real-time feedback module quickly generates and sends feedback information to the customer. The risk assessment module performs risk analysis on the recommended solution, identifies potential risk points, and based on the risk assessment results, the financial optimization suggestion module generates optimization suggestions to further improve the recommended solution. The optimized solution and customer feedback information are stored in the historical consultation record module for subsequent learning and analysis to improve system performance.

5. The efficient feedback system for financial consulting according to claim 4, characterized in that, The multi-dimensional feedback effect evaluation module assesses the quality of feedback by analyzing it from multiple dimensions, including customer satisfaction and semantic fit, providing data support for system optimization.

6. The efficient feedback system for financial consulting according to claim 5, characterized in that, The dynamic load balancing scheduling unit is used to monitor the load of each thread in real time, and prioritize the allocation of new tasks to threads with lighter loads to avoid some threads being overloaded while other threads are idle, thereby improving the overall throughput.

7. The efficient feedback system for financial consulting according to claim 6, characterized in that, The thread adaptive unit is used to set a time window. If the average task delay exceeds the threshold within the window, the number of threads is increased, and vice versa. This dynamically adapts to load fluctuations and ensures the system's high efficiency and stability. The parallel task optimization unit is used to analyze the task flow, identify the longest critical path, and break it down into parallelizable subtasks to reduce task dependencies, fully explore the potential of multi-threaded parallelism, and accelerate task completion.

8. The efficient feedback system for financial consulting according to claim 7, characterized in that, The intelligent prediction and pre-caching unit uses machine learning algorithms to analyze user behavior and historical data, predict possible user requests, and cache relevant data and calculation results in advance to reduce user waiting time and improve response speed.

9. The efficient feedback system for financial consulting according to claim 8, characterized in that, The adaptive preprocessing optimization unit dynamically adjusts the processing strategy based on real-time feedback, optimizes the preprocessing process in real time according to resource usage and task priority, reduces redundant calculations, and improves system resource utilization.

10. The efficient feedback system for financial consulting according to claim 9, characterized in that, The intelligent cache eviction unit introduces a machine learning model to evaluate the "value" of cached data, including access frequency and data importance, to achieve a more intelligent cache eviction mechanism, prioritizing the retention of high-value data, improving cache hit rate, and optimizing system performance.

Images