8 results about "Data analyst" patented technology

The invention provides an LLM-based multi-agent financial analysis collaboration method, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the evaluation and fine adjustment of an LLM, registering agents of a header, a financial analyst, a data assistant, a data analyst, a summary, and the like, and building a pool; a natural language submission requirement of the user; the header pipe utilizes an LLM thinking chain to decompose the demand into dependent subtasks and generate a dependency graph, and a first-level team is established according to the vector similarity of task description and metadata of each analyst; the first-level team decomposes subtasks for the second time, and data assistants and analysts are matched according to similarity to form a second-level team; the second-level team calls a tool to complete a task and writes the task into a shared message pool; summarizing an intelligent agent weighted fusion result output strategy; and the header reports and obtains feedback to the user, and triggers an observation-reflection-iterative loop re-planning task if deviation exists. The financial decision-making efficiency, quality and flexibility are improved, and the labor cost is reduced.

The application provides a code visualization method and a related device, and relates to the field of data processing.The method comprises the following steps: obtaining a data conversion script, the data in the data conversion script being in the form of a table, wherein the data conversion script comprises a plurality of variables, one of the plurality of variables belongs to one of a table, a row in the table and a column in the table, generating a blood relationship graph according to the relationship between the plurality of variables in the data conversion script, the blood relationship graph representing the blood relationship between the plurality of variables, and displaying the blood relationship graph on a view interface where the data conversion script is located. According to the embodiment of the application, the process relationship between the variables in the data conversion script is represented by the blood relationship graph, the blood relationship graph is displayed on the view interface where the data conversion script is located, the data analyst can analyze the data process realized by the data conversion script, and thus the data analysis is realized.

This invention relates to an AI-based automated report generation method, system, device, and medium. The method includes: acquiring a user's natural language query request; parsing the natural language query request, generating and executing a first SQL query statement to obtain an initial result set; performing multi-dimensional analysis on the initial result set to generate at least one array of analytical dimensions; executing the corresponding analytical SQL query statement for each array of analytical dimensions to obtain a dimension dataset, and generating a corresponding result array based on a recommended chart type; and formatting the result array to generate a visual analysis report. This method automates the entire report generation process, reducing the manpower required from developers and data analysts, freeing up resources for high-value tasks, and significantly reducing labor costs. Moreover, from input requirements to report output, the process can be completed in seconds, far faster than traditional manual methods, significantly reducing time costs.

The application relates to a visual component interaction method and system based on a retail industry template knowledge base, which uses a large language knowledge base model to connect user needs, automatically performs demand understanding and key indicator extraction matching, and analyzes charts through an industry knowledge base. Users only need to describe the analysis scene or target through natural language, and the system automatically generates chart results while also providing more analysis perspective suggestions and complete analysis report templates, which are more intelligent and automated, helping users quickly discover hidden information and rules in data. At the same time, users can also use interactive controls or natural language to change the corresponding charts to quickly explore data. In the process of generating new demands for business parties, data analysts can generate examples on the spot through system conversation to avoid subsequent ambiguity and rework rate below 10%, thereby improving the overall business analysis efficiency of enterprises.

The application discloses a multi-dimension index data query method and device, equipment and a storage medium. The method comprises the following steps: associating business data with dimension configuration data to obtain dimension-based business data; calculating the index value of each dimension according to the dimension-based business data to obtain index result data; storing the index result data in an HBase, wherein the HBase comprises the corresponding relationship between the dimensions and the indexes; receiving a query request from a client, and querying the corresponding index data in the HBase according to the dimension information and the index information in the query request. The index data query method can read different indexes by using the same index query service interface. When a new space-time dimension index needs to be calculated, the configuration file of the associated dimension and the business data only need to be modified, so that the rapid calculation of the existing indexes can be realized. The development and test workload is reduced, the data can be quickly put online, and the analysis efficiency of the data analysts is improved.

Embodiments of the present invention relate to a novel approach to automatically detect the occurrence of evoked potentials, quantify the attributes of the signal and visualize the effect across a high number of spinal cord epidural stimulation parameters. This new method is designed to automate the current process for performing this task that has been accomplished manually by data analysts through observation of the raw EMG signals, which is laborious and time-consuming as well as being prone to human errors. The proposed method provides fast and accurate framework for activation detection and visualization of the results within five main algorithms.

The invention discloses a robot stacking rhythm optimization and parallel processing method. Comprising the following steps that initialization robot parameter information is established, information collection is carried out, a central scheduling unit produces stacking poses and action paths, the central scheduling unit carries out dynamic task scheduling and distribution, multiple robots carry out parallel execution and real-time optimization, and the stacking robots execute stacking tasks and work completion reset and exception handling are carried out. And the central scheduling unit adopts a heuristic algorithm to perform data analyst on the structured material data set, obtains a stacking pose planning result, and then generates an optimal motion track of the robot according to the stacking pose planning result. According to the method, through full-process beat optimization, the single-task operation time is effectively shortened, meanwhile, the deviation problem in the operation process is solved through a real-time dynamic adjustment mechanism, the beat stability is ensured, and in addition, through parallel processing, efficient collaborative operation of multiple robot units is achieved, and the efficiency advantage of parallel operation of multiple robots is fully exerted.

The present disclosure relates to a risk-oriented data analysis and modeling method, which comprises: in response to a user triggering operation on target metadata, refreshing data in the target metadata into data of a first node; preprocessing the data refreshed into the first node to obtain initial data; and in response to a user data analysis operation on the initial data, analyzing the initial data. Since the data analysis tool is node-based, users or business personnel can assemble different nodes as needed, making data analysis more flexible and efficient. Users can use a visual page to drag and drop nodes, configure the data analysis tool, and make operations simpler. Users only need to understand the basic functions of the analysis nodes, without the need to understand structured query language, thereby reducing the learning cost of users and the difficulty of data analysis, and enabling traditional data analysts to easily perform their duties.