Method, system, device and storage medium for visualizing dynamic tracking of multi-source indicators

Abstract

This invention discloses a method, system, device, and storage medium for dynamic tracking of multi-source indicators. The method includes: standardizing multi-source business and financial data from different insurance business systems; simultaneously calculating core insurance expense difference indicators and multi-dimensional derived analysis indicators based on the standardized insurance business and financial data; visually displaying the core insurance expense difference indicators, multi-dimensional derived analysis indicators, related deviation information, and attribution conclusions in real time; and monitoring the real-time visualized content according to insurance business early warning rules, and generating early warning information when anomalies are detected. This invention solves the problems of scattered business and financial data, inconsistent definitions, low efficiency and high error rate of manual processing, limitations of single-dimensional indicator calculation, and delayed monitoring and early warning. It improves the ability to integrate and standardize multi-source data, enhances the depth of simultaneous calculation and analysis of multi-dimensional indicators, and improves the timeliness of dynamic visualization tracking and early warning.

Description

Technical Field

[0001] This invention relates to the field of data processing and visualization technology, and in particular to a method, system, device and storage medium for dynamic tracking of visualized multi-source indicators. Background Technology

[0002] With the increasing demand for refined management in the insurance industry, the premium difference indicator has become a core criterion for measuring business profitability and risk control. Currently, business and financial data are scattered across multiple heterogeneous systems, including core business, financial accounting, and channel management. Data formats and indicator definitions lack unified standards, and there are significant differences in dimension definitions, hierarchical divisions, and expense type classifications between different systems. Manual data processing and cleaning are not only inefficient but also prone to errors. Furthermore, existing technologies often focus on single-dimensional indicator calculations, lacking standardized calculation systems for derived indicators such as year-on-year, month-on-month, growth amount, growth rate, and cross-dimensional rankings, making it difficult to comprehensively depict the changing trends of premium differences. In addition, traditional monitoring models rely on multi-system queries and aggregations, lacking real-time dynamic visualization platforms, making it impossible to intuitively present indicator achievement and deviation. Deviation attribution analysis still relies on manual traceability, resulting in weak early warning mechanisms and delayed risk identification and intervention in premium differences. This leads to deficiencies in the management process, such as data chaos, incomplete dimensions, and passive tracking.

[0003] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method, system, device, and storage medium for dynamic tracking of visualized multi-source indicators.

[0005] Firstly, the present invention provides a method for dynamically tracking visual multi-source indicators, the technical solution of which is as follows: Acquire multi-source business and financial data from different insurance business systems; The multi-source business and financial data are standardized to obtain standardized insurance business and financial data; Based on the standardized insurance industry financial data, core insurance expense difference indicators and multi-dimensional derivative analysis indicators are calculated simultaneously; wherein, the multi-dimensional derivative analysis indicators include at least: year-on-year indicators, month-on-month indicators, growth rate indicators and cross-dimensional ranking indicators. The core insurance premium difference indicator and the multi-dimensional derived analysis indicator, as well as the deviation information and attribution conclusions related to the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, are displayed in real time through visualization. According to the preset insurance business early warning rules, the content of the real-time visualization is dynamically monitored, and an early warning message is generated when an anomaly is detected.

[0006] The beneficial effects of the visual multi-source indicator dynamic tracking method of the present invention are as follows: The method of this invention acquires and standardizes multi-source business and financial data from different insurance business systems, simultaneously calculates core insurance expense difference indicators and multi-dimensional derived analysis indicators including year-on-year, month-on-month, growth rate and cross-dimensional ranking, and visualizes the indicators, deviation information and attribution conclusions in real time. It also dynamically monitors and generates early warning information according to preset rules, solving the problems of scattered business and financial data, inconsistent standards, low efficiency and high error rate of manual processing, limitations of single-dimensional indicator calculation and lagging monitoring and early warning. It improves the ability to integrate and standardize multi-source data, enhances the depth of synchronous calculation and analysis of multi-dimensional indicators, and improves the timeliness of dynamic visualization tracking and early warning.

[0007] Based on the above scheme, the visual multi-source indicator dynamic tracking method of the present invention can be further improved as follows.

[0008] In one optional approach, the different insurance business systems include at least: an insurance core business system, a financial accounting system, a channel management system, and an expense reimbursement system; the step of acquiring multi-source business and financial data from different insurance business systems includes: The system collects multi-source business and financial data from the core insurance business system, the financial accounting system, the channel management system, and the expense reimbursement system through a pre-configured data interface. The data interface includes an API interface, a direct database connection interface, and a middleware interface, and supports the collection of structured and semi-structured data.

[0009] The advantages of adopting the above optional methods are: by configuring APIs, direct database connections and middleware interfaces, structured and semi-structured data can be automatically collected from the core insurance business, financial accounting, channel management and expense reimbursement systems, realizing unified access to multi-source heterogeneous data and improving the scope and compatibility of data collection.

[0010] In one alternative approach, the step of standardizing the multi-source business and financial data to obtain standardized insurance business and financial data includes: The multi-source business and financial data is subjected to operations such as removing duplicate data, filling in missing data, and correcting abnormal data to obtain cleaned multi-source business and financial data. The cleaned multi-source business and financial data are subjected to dimension unification processing according to the preset indicator caliber specifications to obtain multi-source business and financial data after dimension unification processing; the dimension unification processing includes unifying indicator definitions, organizational levels, platform classifications and expense types; The multi-source business and financial data that has undergone dimensional unification processing is converted into a unified data format to obtain the standardized insurance business and financial data.

[0011] The beneficial effects of adopting the above optional methods are: further deduplication, missing data, and skew correction cleaning of multi-source data; dimension alignment and format conversion according to unified indicator standards; elimination of definition differences between systems; and formation of a standardized data foundation with consistent standards.

[0012] In one alternative approach, the step of simultaneously calculating the core insurance premium difference indicator and multi-dimensional derivative analysis indicators based on the standardized insurance industry financial data includes: The system calls a preset indicator calculation rule library and uses the standardized insurance industry financial data to calculate the core insurance expense difference indicator, which includes expense difference rate, absolute expense difference, and expense difference contribution of different dimensions. A dynamic indicator weighting model is adopted to simultaneously calculate the multi-dimensional derivative analysis indicators based on the standardized insurance industry financial data and the core insurance expense difference indicators. The multi-dimensional derivative analysis indicators include year-on-year indicators, month-on-month indicators, growth rate indicators, and cross-dimensional ranking indicators.

[0013] The advantages of using the above optional method are: further calling the preset rule base to calculate the fee difference rate, absolute fee difference and contribution of each dimension, and using a dynamic weight model to synchronously generate year-on-year, month-on-month, growth rate and cross-dimensional ranking indicators, so as to realize the parallel processing of core and derived indicators.

[0014] In one optional approach, the step of employing a dynamic indicator weighting model to simultaneously calculate the multi-dimensional derived analysis indicators based on the standardized insurance industry financial data and the core insurance expense difference indicator includes: Based on the time characteristic data in the standardized insurance industry financial data, the current insurance business cycle is identified; Based on the insurance business cycle, query the preset cycle-weight mapping relationship in the dynamic indicator weight model to determine the set of calculation dimensions corresponding to the multi-dimensional derivative analysis indicators; Obtain the corresponding initial weight for each computational dimension in the set of computational dimensions; Based on the business data priority weighting algorithm, the data from the core insurance business system, the financial accounting system, the channel management system and the expense reimbursement system in the standardized insurance business financial data are valued and a data priority factor is generated. The initial weights corresponding to each calculation dimension are corrected based on the data priority factor to generate dynamic weights for each calculation dimension. Using the dynamic weights of each calculation dimension, the standardized insurance industry financial data, and the core insurance expense difference indicator, the year-on-year indicator, the month-on-month indicator, the growth rate indicator, and the cross-dimensional ranking indicator are calculated simultaneously.

[0015] The advantages of adopting the above optional methods are: further identifying insurance business cycles based on time characteristics, determining the calculation dimension by query cycle-weight mapping, evaluating data value and correcting weights through business data priority algorithms, so that the indicator calculation adapts to the business rhythm and improves accuracy.

[0016] In one optional approach, the step of real-time visualization of the core insurance premium difference indicator, the multi-dimensional derived analysis indicator, and the deviation information and attribution conclusions related to the core insurance premium difference indicator and the multi-dimensional derived analysis indicator includes: The actual value of the core insurance premium difference indicator is compared with the preset target value of the core insurance premium difference indicator to generate the first deviation information corresponding to the core insurance premium difference indicator; The actual values ​​of the year-on-year indicator, the month-on-month indicator, the growth rate indicator, and the cross-dimensional ranking indicator are compared with their respective preset target values ​​to generate multiple second deviation information corresponding to the multi-dimensional derived analysis indicators. Based on a preset attribution rule base, the first deviation information and the multiple second deviation information are analyzed respectively to generate a first attribution conclusion corresponding to the core insurance premium difference index and multiple second attribution conclusions corresponding to the multi-dimensional derived analysis index. A real-time dynamically updated visualization interface is constructed, and the actual values ​​of the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, the first deviation information and the multiple second deviation information, as well as the first attribution conclusion and the multiple second attribution conclusions are synchronously displayed in the visualization interface.

[0017] The beneficial effects of adopting the above optional methods are as follows: the actual indicator values ​​are further compared with the target values ​​to generate deviation information, the cause of deviation is analyzed using the attribution rule base, and the indicator values, deviations and conclusions are displayed synchronously on the dynamic interface to achieve a visual presentation of the tracking results and causes.

[0018] In one optional approach, the step of dynamically monitoring the content of the real-time visualized display according to preset insurance business early warning rules, and generating early warning information when an anomaly is detected, includes: Set the insurance business early warning rules, which include threshold rules and volatility rules; When the first deviation information or the second deviation information is detected and triggers the threshold rule or the volatility rule, it is determined to be an abnormal state; The warning information is generated based on the abnormal state, and the warning information is pushed through a preset message push interface.

[0019] The advantages of adopting the above optional approach are: further setting threshold and volatility dual rules to monitor deviation information, automatically generating early warnings when anomalies are triggered and pushing them through the message interface, thereby realizing automatic identification and timely notification of risk status.

[0020] Secondly, this invention provides a visual multi-source indicator dynamic tracking system, the technical solution of which is as follows: The data acquisition module is used to acquire multi-source business and financial data from different insurance business systems; The processing module is used to standardize the multi-source business financial data to obtain standardized insurance business financial data; The calculation module is used to simultaneously calculate the core insurance expense difference indicator and multi-dimensional derivative analysis indicators based on the standardized insurance industry financial data; wherein, the multi-dimensional derivative analysis indicators include at least: year-on-year indicators, month-on-month indicators, growth rate indicators and cross-dimensional ranking indicators. The display module is used to visualize the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, as well as the deviation information and attribution conclusions related to the core insurance premium difference indicator and the multi-dimensional derived analysis indicator in real time. The monitoring module is used to dynamically monitor the content of the real-time visualization display according to the preset insurance business early warning rules, and generate early warning information when an anomaly is detected.

[0021] The beneficial effects of the visual multi-source indicator dynamic tracking system of the present invention are as follows: The system of this invention acquires and standardizes multi-source business and financial data from different insurance business systems, simultaneously calculates core insurance expense difference indicators and multi-dimensional derived analysis indicators including year-on-year, month-on-month, growth rate, and cross-dimensional ranking, and displays the indicators, deviation information, and attribution conclusions in real time. It also dynamically monitors and generates early warning information according to preset rules. This solves the problems of scattered business and financial data, inconsistent standards, low efficiency and high error rate of manual processing, limitations of single-dimensional indicator calculation, and lagging monitoring and early warning. It improves the ability to integrate and standardize multi-source data, enhances the depth of synchronous calculation and analysis of multi-dimensional indicators, and improves the timeliness of dynamic visualization tracking and early warning.

[0022] Thirdly, the technical solution of an electronic device according to the present invention is as follows: It includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the visual multi-source indicator dynamic tracking method of the present invention.

[0023] Fourthly, the technical solution of a computer-readable storage medium provided by the present invention is as follows: The computer-readable storage medium stores instructions that, when read, cause the computer-readable storage medium to perform the steps of the visual multi-source indicator dynamic tracking method of the present invention.

[0024] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0025] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a flowchart illustrating an embodiment of a visual multi-source indicator dynamic tracking method of the present invention; Figure 2 This is a schematic diagram of the overall process; Figure 3 This is a schematic diagram of an embodiment of a visual multi-source indicator dynamic tracking system according to the present invention; Figure 4 This is a schematic diagram of an embodiment of an electronic device according to the present invention. Detailed Implementation

[0026] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0027] Figure 1 This diagram illustrates a flowchart of an embodiment of a dynamic tracking method for visualized multi-source indicators provided by the present invention. This method can be executed by electronic devices such as terminal devices or servers. The terminal device can be any fixed or mobile terminal, such as user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, or wearable device. The server can be a single server or a server cluster consisting of multiple servers. Any electronic device can implement the dynamic tracking method for visualized multi-source indicators by having its processor call computer-readable instructions stored in its memory. Figure 1 As shown, it includes the following steps: S1. Obtain multi-source business and financial data from different insurance business systems.

[0028] Here, "different insurance business systems" refers to the collection of software systems within an insurance institution used to handle different business functions. For example, a life insurance company might have a core business system for handling policy underwriting and management, a financial accounting system for handling accounting and reporting, a channel management system for managing agency sales channels, and an expense reimbursement system for handling employee expense reimbursements. "Multi-source business and financial data" refers to the collection of raw data related to business operations and financial management obtained from different insurance business systems. For example, premium income data for the "Anxin Life Insurance" product in July 2025 obtained from the life insurance company's core business system, monthly commission expense data obtained from the financial accounting system, agent channel sales personnel data obtained from the channel management system, and office expense reimbursement data obtained from the expense reimbursement system.

[0029] S2. Standardize the multi-source business financial data to obtain standardized insurance business financial data.

[0030] Standardized insurance financial data refers to business financial data with a consistent and standardized format and definition, formed by cleaning, standardizing and converting multi-source financial data. For example, it includes data that has duplicate records removed, missing institution codes filled in, expense types uniformly classified as "fixed expenses" or "variable expenses", and then converted to JSON format before storage.

[0031] S3. Based on the standardized insurance industry financial data, simultaneously calculate the core insurance expense difference indicator and multi-dimensional derivative analysis indicators; wherein, the multi-dimensional derivative analysis indicators include at least: year-on-year indicators, month-on-month indicators, growth rate indicators and cross-dimensional ranking indicators.

[0032] Among them, the core expense difference indicator refers to a key assessment indicator used to measure the gap between insurance business expenses and expected expenses; for example, the core expense difference indicator used to assess the expense control of the "Anxin Life Insurance" product includes the expense difference rate, absolute expense difference, and expense contribution by sales channel. Multi-dimensional derived analysis indicators refer to analytical indicators calculated from multiple dimensions such as time, institution, and channel, based on the core expense difference indicator; for example, based on the monthly expense difference data of the "Anxin Life Insurance" product, the year-on-year change rate, month-on-month change rate, growth rate, and expense ranking among all products can be further calculated. Year-on-year indicators refer to relative change indicators obtained by comparing current data with data from the same period of the previous year; for example, calculating the expense difference rate of "Anxin Life Insurance" in July 2025 and comparing it with the expense difference rate in July 2024 to obtain the percentage change. Month-on-month indicators refer to the relative change obtained by comparing the current period's data with the data of the previous adjacent period; for example, calculating the expense difference rate of "Anxin Life Insurance" in July 2025 and comparing it with the expense difference rate in June 2025 to obtain the percentage change. Growth rate indicators refer to the quantitative indicators that reflect the magnitude or speed of growth of a certain indicator within a certain period; for example, calculating the absolute increase in expense difference of "Anxin Life Insurance" products in the second quarter of 2025 compared to the first quarter, and the percentage of this increase relative to the base of the first quarter. Cross-dimensional ranking indicators refer to the results obtained by ranking and comparing the values ​​of the same indicator under different business dimensions; for example, ranking the expense difference rates of all life insurance products of the company, such as "Anxin Life Insurance" and "Health Worry-Free Insurance," from low to high in the current month, with "Anxin Life Insurance" ranking fifth.

[0033] S4. The core insurance premium difference indicator and the multi-dimensional derived analysis indicator, as well as the deviation information and attribution conclusions related to the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, are displayed in real time through visualization.

[0034] Deviation information refers to the quantitative description of the difference between the actual value of an indicator and the preset target value. For example, the actual expense difference rate of the "Anxin Life Insurance" product this month is 12.5%, and the budgeted target value is 11.0%, resulting in a deviation rate of +1.5 percentage points. Attribution conclusions refer to the structured explanations derived from the automatic analysis of the main causes of the indicator deviation. For example, analysis reveals that the deviation in the expense difference rate of "Anxin Life Insurance" mainly stems from excessive sales expenses in the agency channel, thus generating the textual conclusion that "excessive agency channel expenses caused the expense difference rate to deviate from the target by 1.5%." Real-time visualization refers to the process of dynamically and instantly presenting data indicators, deviations, and attribution information through a graphical interface. For example, on the management dashboard, the current expense difference rate of "Anxin Life Insurance" is dynamically displayed as 12.5% ​​in the form of digital cards, and a bar chart is used to highlight the deviation from the target value.

[0035] S5. Based on the preset insurance business early warning rules, dynamically monitor the content of the real-time visualization display, and generate early warning information when an anomaly is detected.

[0036] Among them, insurance business early warning rules refer to a pre-set set of logical conditions used to determine whether indicator data is in an abnormal state; for example, two rules are set for the expense difference rate indicator: a threshold early warning is triggered when the actual value exceeds the target value by 0.5 percentage points; and a volatility early warning is triggered when the daily fluctuation exceeds 5%. Real-time visualization refers to the collection of all information elements dynamically presented on the visualization interface; for example, the actual expense difference rate of "Anxin Life Insurance" (12.5%), target value (11.0%), deviation rate (+1.5%), attribution conclusion text, and monthly trend line chart displayed on the management dashboard. Dynamic monitoring refers to the process of continuously and automatically checking real-time changing data or states according to preset rules; for example, continuously comparing the real-time calculated value of the "Anxin Life Insurance" expense difference rate with the preset early warning threshold. Warning information refers to the notification content automatically generated to alert to risks when abnormal status is detected by dynamic monitoring; for example, when the deviation of the premium difference rate of "Anxin Life Insurance" is detected to trigger the threshold rule, a warning message is generated with the content "Anxin Life Insurance premium difference rate exceeds the standard warning, current deviation +1.5%".

[0037] The technical solution of this embodiment acquires multi-source business and financial data from different insurance business systems and performs standardized processing. It simultaneously calculates core insurance expense difference indicators and multi-dimensional derived analysis indicators, including year-on-year, month-on-month, growth rate, and cross-dimensional ranking. The indicators, deviation information, and attribution conclusions are visualized in real time, and early warning information is dynamically monitored and generated according to preset rules. This solves the problems of scattered business and financial data, inconsistent standards, low efficiency and high error rate of manual processing, limitations of single-dimensional indicator calculation, and lag in monitoring and early warning. It improves the ability to integrate and standardize multi-source data, enhances the depth of synchronous calculation and analysis of multi-dimensional indicators, and improves the timeliness of dynamic visualization tracking and early warning.

[0038] In one optional approach, the different insurance business systems include at least: an insurance core business system, a financial accounting system, a channel management system, and an expense reimbursement system; S1 specifically includes: The system collects multi-source business and financial data from the core insurance business system, the financial accounting system, the channel management system, and the expense reimbursement system through a pre-configured data interface. The data interface includes an API interface, a direct database connection interface, and a middleware interface, and supports the collection of structured and semi-structured data.

[0039] The insurance core business system refers to the main operational management system used by insurance companies to handle core policy business, including underwriting, policy maintenance, and claims processes; for example, the system used to process "Anxin Life Insurance" applications, calculate premiums, manage policy status, and process claims. The financial accounting system refers to the professional system used by insurance companies for financial accounting, cost accounting, budget management, and generating statutory financial statements; for example, the system used to record "Anxin Life Insurance" premium income, accrue reserves, calculate sales commission expenses, and generate profit and loss statements. The channel management system refers to the system used by insurance companies to manage, support, and evaluate the operational performance of various sales channels; for example, the platform used to manage the qualifications of agents selling "Anxin Life Insurance," track account manager performance, and calculate channel commissions. The expense reimbursement system refers to the internal management system used by insurance companies to process, review, pay, and control the budget for expenses incurred by employees for official business activities; for example, the system where employees submit travel expense reimbursement forms for product promotion activities, which are then processed online and paid by the finance department.

[0040] Among these, a data interface refers to a standardized connection method used for data exchange and communication between different software systems or modules; for example, a program call channel established between the core business system and the financial accounting system for daily synchronization of premium income data. An API interface refers to a service interface defined by the Application Programming Interface specification, allowing different software systems to exchange data or functions via network calls; for example, a channel management system provides a set of standard HTTP APIs, allowing the retrieval of daily sales data for a specified channel by sending specific requests. A direct database connection interface refers to an interface method that directly connects to the database of the other system, using query languages ​​to read or write data; for example, directly connecting to the database of the financial accounting system via the JDBC protocol to execute SQL queries to obtain monthly expense details data. A middleware interface refers to an interface method that achieves loosely coupled data exchange between systems through middleware software such as message queues and enterprise service buses; for example, an expense reimbursement system publishes approved reimbursement form data to a message queue, from which the data acquisition module subscribes to and consumes this data. Structured data refers to data with a predefined data model, fixed format, and clear field relationships, typically stored in database tables; for example, data with complete fields such as policy number, insured name, insurance type, premium amount, and effective date collected from the "Policy Information Table" of the core business system database. Semi-structured data refers to data that, while not possessing a strict relational table structure, includes labels, tags, or hierarchical structures to separate data elements; for example, a JSON-formatted log file exported from a channel management system, containing nested channel information, sales records, and performance indicators.

[0041] Among the above optional methods, structured and semi-structured data can be automatically collected from insurance core business, financial accounting, channel management and expense reimbursement systems by configuring APIs, direct database connections and middleware interfaces, so as to achieve unified access to multi-source heterogeneous data and improve the scope and compatibility of data collection.

[0042] In one alternative approach, S2 specifically includes: The multi-source business and financial data is processed by removing duplicate data, filling in missing data, and correcting abnormal data to obtain cleaned multi-source business and financial data.

[0043] Among these, deduplication refers to the process of identifying and removing identical or redundant records representing the same entity from a dataset; for example, if two identical reimbursement records are found in the collected reimbursement data, one of them is automatically removed. Completing missing data refers to the process of filling in reasonable values ​​for records in the dataset where certain fields are empty or have invalid values, according to rules; for example, if the "sales agency code" field of a premium income record is empty, the code of the branch to which the policy belongs is found based on the agent's number and automatically filled in. Correcting outlier data refers to the process of identifying outliers or erroneous values ​​in the data that clearly do not conform to business logic or statistical regularities, and correcting them to reasonable values; for example, if the amount of an expense record is -10,000 yuan, it is corrected to 1,000 yuan based on the historical average level of this type of expense. Cleaned multi-source business and financial data refers to the original business and financial data whose quality has been improved after deduplication, completion of missing data, and correction of outliers; for example, the premium, expense, channel, and reimbursement data of "Anxin Life Insurance" after the above three steps.

[0044] The cleaned multi-source business and financial data are subjected to dimension unification processing according to the preset indicator caliber specifications to obtain multi-source business and financial data after dimension unification processing; the dimension unification processing includes unifying indicator definitions, organizational levels, platform classifications and expense types.

[0045] The standardization of indicator definitions refers to unified and clear standardized regulations on the definition, calculation logic, statistical dimensions, and data sources of indicators. For example, the "fee difference rate" indicator is uniformly defined as "(actual expenses incurred - budgeted expenses) / earned premiums," and summarized according to the organizational hierarchy of "head office - branch office - sub-branch office." Multi-source business and financial data that has undergone unified dimension processing refers to data that has been standardized and categorized according to unified standards. For example, the names of institutions from different systems in the cleaned data are uniformly mapped to standard institution codes, and expense descriptions are uniformly categorized into standard types.

[0046] Among these, the definition of an indicator refers to a precise description of the business meaning, calculation method, and constituent elements measured by a specific indicator; for example, the "expense difference rate" is defined as the relative ratio used to measure the difference between actual expenses and expected expenses, calculated as (actual expenses - budgeted expenses) / earned premiums. Institutional hierarchy refers to the hierarchical division of the insurance company's internal organizational structure, typically used for data aggregation and drill-down analysis; for example, the institutional hierarchy might be divided into three levels: the head office, provincial branches, and municipal branches. Platform classification refers to the categorization based on different channels or models of insurance business sales or operations; for example, business platforms might be classified as "individual insurance," "bancassurance," and "group insurance." Expense type refers to the categorization based on the nature of the expense or its relationship to business volume; for example, expense types might be classified as "fixed expenses" and "variable expenses."

[0047] The multi-source business and financial data that has undergone dimensional unification processing is converted into a unified data format to obtain the standardized insurance business and financial data.

[0048] Unified data format conversion refers to the process of converting data from different sources and with different formats into a predetermined standard format; for example, converting XML and CSV data from different systems into Parquet columnar storage format.

[0049] Among the above optional methods, further deduplication, missing data filling, and skew correction cleaning are performed on the multi-source data. Dimension alignment and format conversion are carried out according to the unified indicator standard to eliminate the definition differences between the systems and form a standardized data foundation with consistent standards.

[0050] In one alternative approach, S3 specifically includes: The system calls a preset indicator calculation rule library and uses the standardized insurance industry financial data to calculate the core insurance expense difference indicator, which includes expense difference rate, absolute expense difference, and expense difference contribution of different dimensions.

[0051] The indicator calculation rule library refers to a collection of callable programs or configurations that store standardized calculation formulas, logic, and parameters for various indicators; for example, a repository containing code modules or configuration files such as "expense difference rate calculation function" and "year-on-year growth rate calculation function". The expense difference rate refers to the deviation rate between the actual expense rate and the planned expense rate in insurance business, and is a core indicator for measuring the effectiveness of cost control; for example, the actual expense rate for the "Anxin Life Insurance" product this month is 12.5%, the planned expense rate is 11.0%, and the expense difference rate is +1.5% (12.5% ​​- 11.0%). The absolute expense difference refers to the absolute difference between the actual expenses incurred and the budgeted expenses in insurance business; for example, the actual expenses for the "Anxin Life Insurance" product this month are 1.25 million yuan, the budgeted expenses are 1.1 million yuan, and the absolute expense difference is +150,000 yuan. The contribution of the expense difference by dimension refers to the degree of influence or contribution share of the expense difference on the overall expense difference from the perspective of a specific dimension. For example, if it is calculated that the agency channel's expenses exceeded 100,000 yuan this month, while the overall expense difference of the "Anxin Life Insurance" product is 150,000 yuan, then the agency channel's contribution to the overall expense difference is 66.7% (10 / 15).

[0052] Specifically, it calls the preset indicator calculation rule library, uses standardized insurance industry financial data, and calculates the premium difference rate, absolute premium difference, and premium difference contribution of each dimension in parallel.

[0053] ① The formula for calculating the premium difference rate is: FC_R=(AFC-BFC) / EP×100%; FC_R represents the premium difference rate, AFC represents the actual expenses incurred, BFC represents the budgeted expenses, and EP represents the earned premium.

[0054] ② The formula for calculating the absolute cost difference is: AFS = AFC - BFC; AFS represents the absolute cost difference, AFC represents the actual cost incurred, and BFC represents the budgeted cost.

[0055] ③ The formula for calculating the contribution of the cost difference by dimension is: DC=(DFS / AFS)×100%; DC represents the contribution of the cost difference by dimension, DFS represents the cost difference calculated on a specified business dimension (including channels, product lines or institutions), and AFS represents the total absolute cost difference.

[0056] A dynamic indicator weighting model is adopted to simultaneously calculate the multi-dimensional derivative analysis indicators based on the standardized insurance industry financial data and the core insurance expense difference indicators. The multi-dimensional derivative analysis indicators include year-on-year indicators, month-on-month indicators, growth rate indicators, and cross-dimensional ranking indicators.

[0057] Among them, the dynamic indicator weight model refers to a mathematical model or algorithm that can automatically adjust the importance of different dimensions or factors in indicator calculation according to the business context; for example, a calculation model that can automatically increase the weight of the "growth rate indicator" in the comprehensive evaluation based on the identified "business sprint period".

[0058] In the above optional methods, the preset rule base is further called to calculate the fee difference rate, absolute fee difference and contribution of each dimension, and a dynamic weight model is used to generate year-on-year, month-on-month, growth rate and cross-dimensional ranking indicators simultaneously to achieve parallel processing of core and derived indicators.

[0059] In one optional approach, the step of employing a dynamic indicator weighting model to simultaneously calculate the multi-dimensional derived analysis indicators based on the standardized insurance industry financial data and the core insurance expense difference indicator includes: Based on the time characteristic data in the standardized insurance industry financial data, the current insurance business cycle is identified.

[0060] Among them, time characteristic data refers to information in the data used to characterize time attributes or business cycle stages; for example, fields such as "policy effective date," "accounting period," and "month in which expenses are incurred" in the data. The current insurance business cycle refers to the specific stage of the business determined based on the time characteristic data; for example, based on the current date of July 2025, it is determined that the current stage is the "mid-term assessment period of the third quarter."

[0061] Based on the insurance business cycle, query the preset cycle-weight mapping relationship in the dynamic indicator weight model to determine the set of calculation dimensions corresponding to the multi-dimensional derivative analysis indicators.

[0062] The cycle-weight mapping relationship refers to the pre-defined correspondence between different business cycles and the initial weights of the indicator calculation dimensions in the dynamic indicator weight model. For example, the model configuration specifies that the initial weight of the "growth rate indicator" is 0.6 during the "business sprint" cycle. The set of calculation dimensions refers to the entirety of all analytical perspectives involved in the calculation of a multi-dimensional derived analysis indicator. For example, when calculating the "cross-channel ranking" indicator of the "Anxin Life Insurance" premium difference rate, the "individual insurance channel," "bancassurance channel," and "group insurance channel" are involved.

[0063] Obtain the corresponding initial weight for each computational dimension in the set of computational dimensions.

[0064] The initial weight refers to the baseline importance coefficient assigned to each calculation dimension based on the period-weight mapping relationship at the start of dynamic weight calculation; for example, in the current business period, the initial weight assigned to the "year-on-year" dimension in the "growth rate indicator" is 0.5 based on the mapping relationship.

[0065] Based on the business data priority weighting algorithm, the data from the core insurance business system, the financial accounting system, the channel management system and the expense reimbursement system in the standardized insurance business financial data are valued and a data priority factor is generated.

[0066] The business data priority weighting algorithm refers to an algorithm used to evaluate the relative value or credibility of data from different sources in a specific business scenario and output a quantitative factor. For example, the algorithm specifies that the data priority factor for the core business system is 1.0, and the data for the financial system is 0.9. By evaluating the data status of each system this month, the overall data priority factor for this month is calculated to be 0.95. The data priority factor is a value calculated by the business data priority weighting algorithm to correct the initial weights, reflecting the overall quality level of currently available data. For example, if the algorithm evaluates the data quality of each system this month as good, the calculated data priority factor is 0.95.

[0067] Specifically, for each data source, scores are assigned across four evaluation dimensions: data integrity, data accuracy, data timeliness, and business relevance, denoted as C, A, T, and R, respectively. The scores for each dimension are multiplied by their corresponding pre-defined weighting coefficients and summed to obtain the overall value score S for that data source. The calculation formula is S = w_c × C + w_a × A + w_t × T + w_r × R, where w_c, w_a, w_t, and w_r are the weighting coefficients for data integrity, accuracy, timeliness, and business relevance, respectively, and w_c + w_a + w_t + w_r = 1. Based on the inherent importance of each system in the financial analysis of the insurance industry, the core insurance business system, financial... The accounting system, channel management system, and expense reimbursement system each have a basic weighting coefficient, denoted as B_core, B_fin, B_chan, and B_exp. Finally, the data priority factor P is obtained by calculating the weighted average of the comprehensive value scores of all data sources after adjustment of the basic weights. The calculation formula is P=(B_core×S_core+B_fin×S_fin+B_chan×S_chan+B_exp×S_exp) / (B_core+B_fin+B_chan+B_exp), where S_core, S_fin, S_chan, and S_exp are the comprehensive value scores of the corresponding systems.

[0068] The initial weights corresponding to each calculation dimension are corrected based on the data priority factor to generate dynamic weights for each calculation dimension.

[0069] Dynamic weight refers to the importance coefficient used for the final indicator calculation, which is obtained by combining the initial weight with the data priority factor and is adapted to the specific business context. For example, the initial weight of the "year-on-year" dimension is 0.5, which, when multiplied by the data priority factor of 0.95, results in a dynamic weight of 0.475.

[0070] Specifically, the data priority factor P is used as a correction coefficient to linearly adjust the initial weights of each calculation dimension. The calculation formula is W_dynamic = W_initial × α + P × β, where α and β are preset adjustment coefficients that satisfy α + β = 1, used to balance the impact of the initial preset weights on the real-time data quality assessment results; W_initial represents the initial weight; for specific dimensions involving the calculation of cost difference rate, absolute cost difference, and cost difference contribution of each dimension, a dimension sensitivity coefficient γ is introduced based on its calculation sensitivity within the current business cycle. At this time, the calculation formula for dynamic weights is expanded to W_dynami c'=(W_initial×α+P×β)×γ, where the value of γ is calculated by the dynamic indicator weight model based on the historical fluctuation range of the expense difference indicator and the degree of deviation from the business target within the period. Finally, the dynamic weights of all calculation dimensions need to be normalized to ensure that the sum of all weights is 1. The normalization formula is W_final=W_dynamic / Σ(W_dynamic) or W_final=W_dynamic' / Σ(W_dynamic'), where W_final is the dynamic weight used to simultaneously calculate year-on-year indicators, month-on-month indicators, growth rate indicators and cross-dimensional ranking indicators.

[0071] Using the dynamic weights of each calculation dimension, the standardized insurance industry financial data, and the core insurance expense difference indicator, the year-on-year indicator, the month-on-month indicator, the growth rate indicator, and the cross-dimensional ranking indicator are calculated simultaneously.

[0072] Specifically, using the dynamic weights W_final of each calculation dimension, standardized insurance industry financial data, and the calculated core insurance expense difference indicators (including expense difference rate FC_R, absolute expense difference AFS, and expense difference contribution DC of each dimension), the year-on-year indicator YOY, month-on-month indicator MOM, growth rate indicator GR, and cross-dimensional ranking indicator CR are calculated simultaneously.

[0073] ① The formula for calculating the year-on-year (YOY) indicator is: YOY=(V_current-V_prior_year) / V_prior_year×100%, where V_current represents the current indicator value (e.g., current expense ratio FC_R or absolute expense ratio AFS), and V_prior_year represents the corresponding indicator value in the same period of the previous year. During the calculation, the dynamic weight W_final_time of the time dimension is used to weight and integrate the year-on-year results from multiple time slices to reflect the differences in the importance of data in different periods during the business cycle.

[0074] ② The formula for calculating the month-on-month indicator MOM is: MOM=(V_current-V_prior_period) / V_prior_period×100%, where V_prior_period represents the corresponding indicator value of the previous adjacent period (such as the previous month); combined with the dynamic weight W_final_biz of business dimensions (such as channels and products), the month-on-month calculation results across business units are aggregated to generate a comprehensive month-on-month view.

[0075] ③ The calculation of the growth rate indicator GR involves the analysis of the change of absolute expense difference (AFS) within a selected period. The calculation formula is: GR=(AFS_end-AFS_start) / |AFS_start|×100%, where AFS_start and AFS_end represent the absolute expense difference at the beginning and end of the period, respectively. When calculating the growth rate across multiple periods or compound growth rate, the contribution degree of expense difference (DC) adjusted by dynamic weight W_final is introduced as an adjustment factor to make the growth rate calculation more reflective of the impact of the main business drivers.

[0076] ④ The calculation process for the cross-dimensional ranking indicator CR is as follows: Under a specified dimension (such as institution or channel), the values ​​of selected core or derived indicators (such as expense ratio FC_R or expense contribution rate DC for each dimension) are used for ranking. During the ranking, not only the original values ​​of the indicators are used, but also the dynamic weight W_final of the corresponding dimension is introduced as a ranking correction coefficient. Entities in dimensions with higher weights are given a ranking boost, making the ranking results more in line with management priorities. The calculation of all indicators is based on the unified standard of basic values ​​provided by standardized insurance industry financial data, and the calculation logic is adaptively adjusted using dynamic weights.

[0077] Among the above optional methods, insurance business cycles are further identified based on time characteristics, the calculation dimension is determined by query cycle-weight mapping, and the data value is evaluated and the weights are corrected through business data priority algorithm, so that the indicator calculation is adapted to the business rhythm and the accuracy is improved.

[0078] In one alternative approach, S4 specifically includes: The actual value of the core insurance premium difference indicator is compared with the preset target value of the core insurance premium difference indicator to generate the first deviation information corresponding to the core insurance premium difference indicator.

[0079] The target value of the core expense difference indicator refers to the expected or planned value that is pre-set for the core expense difference indicator and serves as a benchmark. For example, the target value for the expense difference rate for the "Anxin Life Insurance" product at the beginning of the year is 11.0%. The first deviation information refers to the quantitative result of the deviation generated after comparing the actual value of the core expense difference indicator with the target value. For example, comparing the actual expense difference rate of "Anxin Life Insurance" of 12.5% ​​with the target value of 11.0%, the first deviation information generated is "deviation rate: +1.5 percentage points".

[0080] The actual values ​​of the year-on-year indicator, the month-on-month indicator, the growth rate indicator, and the cross-dimensional ranking indicator are compared with their respective preset target values ​​to generate multiple second deviation information corresponding to the multi-dimensional derived analysis indicators.

[0081] The second deviation information refers to the set of deviation quantification results generated by comparing the actual values ​​of various multi-dimensional derived analysis indicators with their corresponding target values. For example, the actual values ​​of the year-on-year and month-on-month indicators of the "Anxin Life Insurance" expense difference rate are compared with their respective target values ​​to generate information such as "year-on-year deviation +2%" and "month-on-month deviation -0.5%".

[0082] Based on a preset attribution rule base, the first deviation information and the multiple second deviation information are analyzed respectively to generate a first attribution conclusion corresponding to the core insurance premium difference indicator and multiple second attribution conclusions corresponding to the multi-dimensional derived analysis indicator.

[0083] The first attribution conclusion refers to the structured explanation of the reasons for the deviation of the core insurance expense difference indicator after analyzing the first deviation information based on the attribution rule base. For example, analyzing the first deviation information of "Anxin Life Insurance expense difference rate deviation +1.5%" yields the first attribution conclusion of "mainly due to the sales expenses of the agency channel exceeding the budget by 100,000 yuan". The second attribution conclusion refers to the set of structured explanations of the reasons for the deviation of the multi-dimensional derived analysis indicator after analyzing each second deviation information based on the attribution rule base. For example, analyzing "year-on-year deviation +2%" yields the conclusion of "mainly affected by the low base of the same period last year", and analyzing "month-on-month deviation -0.5%" yields the conclusion of "the cost control measures have begun to show results".

[0084] A real-time dynamically updated visualization interface is constructed, and the actual values ​​of the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, the first deviation information and the multiple second deviation information, as well as the first attribution conclusion and the multiple second attribution conclusions are synchronously displayed in the visualization interface.

[0085] The visual interface refers to a graphical user interface used to present data, charts, and analytical conclusions; for example, a webpage or large screen interface using a dashboard layout that includes number cards, trend line charts, deviation bar charts, and attribution text areas.

[0086] In the above-mentioned optional methods, the actual indicator value is further compared with the target value to generate deviation information, the cause of the deviation is analyzed using the attribution rule base, and the indicator value, deviation and conclusion are displayed synchronously on the dynamic interface to realize the visualization of tracking results and causes.

[0087] In one alternative approach, S5 specifically includes: Set the insurance business early warning rules, which include threshold rules and volatility rules.

[0088] Threshold rules refer to early warning rules that determine whether an indicator is abnormal based on a fixed numerical limit. For example, a rule could be set such that an early warning is triggered when the absolute value of the deviation rate of the "fee spread" indicator exceeds 0.5 percentage points. Volatility rules refer to early warning rules that determine whether an indicator is abnormal based on the magnitude or rate of change of its value over a short period of time. For example, a rule could be set such that an early warning is triggered when the standard deviation of the volatility of the "fee spread" indicator exceeds twice the historical average level over three consecutive calculation periods.

[0089] When the first deviation information or the second deviation information is detected and triggers the threshold rule or the volatility rule, it is determined to be an abnormal state.

[0090] Among them, abnormal status refers to the status marked when the indicator data meets the preset early warning rule conditions; for example, when the premium difference rate deviation of "Anxin Life Insurance" is +1.5%, it triggers the rule of "threshold exceeds 0.5%", and the premium difference status of the product is marked as "abnormal".

[0091] The warning information is generated based on the abnormal state, and the warning information is pushed through a preset message push interface.

[0092] Among them, the message push interface refers to the program interface used to send the generated warning information to external communication platforms or applications; for example, calling the API interface provided by the WeChat Enterprise Open Platform to push the warning information to the chat window of a designated manager in the form of a message card.

[0093] Among the above optional methods, a dual rule of threshold and volatility is further set to monitor deviation information. When an anomaly is triggered, an early warning is automatically generated and pushed through the message interface to achieve automatic identification and timely notification of risk status.

[0094] It should be noted that the visualization multi-source indicator dynamic tracking method provided by this invention is implemented through various technical means to achieve its specific architecture and functional carrier. The complete implementation process is as follows: Figure 2 As shown, the process covers the unified configuration of indicators, data visualization through multiple channels, and a closed-loop management system for completing early warning analysis.

[0095] The visualization dashboard module integrates professional visualization libraries such as DataV and ECharts. The display interface is developed using Vue or React front-end frameworks, enabling it to adapt to different display resolutions. This module periodically retrieves data from a data platform storing standardized insurance industry financial data via pre-configured API interfaces or direct database connections. It supports dynamic data updates and user interaction, thereby achieving real-time dynamic visualization of core insurance expense difference indicators and multi-dimensional derived analysis indicators.

[0096] The proactive push notification function for alerts relies on the message push API provided by the WeChat Work Open Platform. The push process uses an access_token mechanism to complete server-side authentication, with the backend service initiating an HTTP request to call the relevant interface. Push messages support various formats including text, images, and cards, and can be combined with the JSSDK to implement webpage authorization redirection. For sensitive information involving approval processes or risk alerts, encryption and decryption libraries are used to securely process the data, ensuring that alert information can be delivered securely and accurately to the terminals of designated administrators.

[0097] To meet the needs of managers for long-term, immersive monitoring of business performance, a cross-platform desktop application based on the Electron framework is provided. The Electron framework allows for client-side development using web technologies such as HTML, CSS, and JavaScript. Its main process manages the application lifecycle and native window, while the rendering process handles the user interface display. Through the framework's built-in Node.js API and rich community modules, the application can implement complex functions such as file system access and local system notifications. Finally, it is packaged into an executable program that can run independently on Windows, macOS, and Linux operating systems, serving as a personalized data dashboard that can reside permanently on the desktop.

[0098] To achieve secure integration and unified access with multiple heterogeneous systems such as the core insurance business system and the financial accounting system, a unified identity authentication and authorization management technology based on tokens was adopted. Through standardized interfaces, secure single sign-on and fine-grained access control can be achieved across different platforms and applications, thereby securely embedding data dashboards or analysis components into various third-party business system processes.

[0099] An overview of the specific implementation process is as follows: Figure 2 As shown, the first step is the indicator configuration phase. In this phase, a visual indicator management platform is built, allowing business personnel to configure indicator definitions, statistical dimensions, and calculation logic independently. The core of the configuration is establishing a unified business semantic layer, ensuring that the same indicator maintains consistent calculation definitions across different scenarios. The platform can automatically generate underlying SQL queries or big data calculation tasks based on the configuration. Simultaneously, an indicator lineage diagram is constructed to trace the complete data chain from the source business system through cleaning, calculation, and final display, achieving transparent management of data assets and analysis of the impact of changes.

[0100] The second step is the multi-source display stage, presenting data analysis results to users in different scenarios through various formats. The core goal of the large-screen display is to achieve a comprehensive overview on a single screen, using numbers, charts, and color-coded indicators (red, yellow, and blue) for layout. Data is updated promptly, focusing on the dynamic changes of key indicators, and the interactive design is simple, aiming to allow managers to quickly grasp the overall business situation. When fixed reports cannot meet the needs of ad-hoc, cross-dimensional analysis, an intelligent question-and-answer robot tool is provided. The robot understands the manager's query intent through natural language interaction, automatically links and retrieves relevant data, solving the last-mile problem of quickly locating information in complex data environments. For mobile office scenarios, a mobile H5 page was designed with extremely concise information presentation, focusing on the three to five most critical indicators, and using caching technology to achieve instant page loading, providing efficient support for decision-makers on the go. To break down data barriers between systems, data dashboards or analysis components are embedded into the workflows of third-party business systems such as office automation systems and expense control management systems in the form of iframes, micro-frontends, or low-code components, enabling data to appear seamlessly in specific business scenarios, assisting decision-making, and realizing data following the user. In addition to accessing it through a browser, a standalone desktop application is also provided, which managers can deploy on a second screen in the office to achieve 24 / 7 constant display, real-time monitoring of business status, and to meet personalized needs for long-term, immersive monitoring and decision-making.

[0101] The third step is the early warning analysis phase, which shifts the management model from passive querying to proactive intervention. This phase supports configuring various early warning rules, including threshold rules, volatility rules, and anomaly detection models. When the monitoring module detects a deviation in an indicator that triggers any early warning rule, it determines the situation as abnormal and automatically pushes early warning information through multiple channels such as WeChat and email, with links to detailed analysis links included in the messages. The key aspect is forming a closed-loop management system; the complete process includes receiving alerts, viewing analysis details, recording processing feedback, and subsequently optimizing early warning rules. Advanced features support root cause analysis, automatically linking and drilling down to display key business dimension data that caused the deviation when an early warning is triggered, thereby accelerating the problem localization and resolution process.

[0102] Figure 3 This diagram illustrates the structure of an embodiment of a visual multi-source indicator dynamic tracking system 200 provided by the present invention. Figure 3 As shown, the visualized multi-source indicator dynamic tracking system 200 includes: The data acquisition module 201 is used to acquire multi-source business and financial data from different insurance business systems; Processing module 202 is used to standardize the multi-source business financial data to obtain standardized insurance business financial data; The calculation module 203 is used to simultaneously calculate the core insurance expense difference indicator and multi-dimensional derivative analysis indicators based on the standardized insurance industry financial data; wherein, the multi-dimensional derivative analysis indicators include at least: year-on-year indicators, month-on-month indicators, growth rate indicators and cross-dimensional ranking indicators. The display module 204 is used to visualize the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, as well as the deviation information and attribution conclusions related to the core insurance premium difference indicator and the multi-dimensional derived analysis indicator in real time. The monitoring module 205 is used to dynamically monitor the content of the real-time visualization display according to the preset insurance business early warning rules, and generate early warning information when an anomaly is detected.

[0103] In one alternative approach, the different insurance business systems include at least: an insurance core business system, a financial accounting system, a channel management system, and an expense reimbursement system; the data collection module 201 is specifically used for: The system collects multi-source business and financial data from the core insurance business system, the financial accounting system, the channel management system, and the expense reimbursement system through a pre-configured data interface. The data interface includes an API interface, a direct database connection interface, and a middleware interface, and supports the collection of structured and semi-structured data.

[0104] In an alternative embodiment, the processing module 202 is specifically used for: The multi-source business and financial data is subjected to operations such as removing duplicate data, filling in missing data, and correcting abnormal data to obtain cleaned multi-source business and financial data. The cleaned multi-source business and financial data are subjected to dimension unification processing according to the preset indicator caliber specifications to obtain multi-source business and financial data after dimension unification processing; the dimension unification processing includes unifying indicator definitions, organizational levels, platform classifications and expense types; The multi-source business and financial data that has undergone dimensional unification processing is converted into a unified data format to obtain the standardized insurance business and financial data.

[0105] In an alternative embodiment, the computing module 203 is specifically used for: The system calls a preset indicator calculation rule library and uses the standardized insurance industry financial data to calculate the core insurance expense difference indicator, which includes expense difference rate, absolute expense difference, and expense difference contribution of different dimensions. A dynamic indicator weighting model is adopted to simultaneously calculate the multi-dimensional derivative analysis indicators based on the standardized insurance industry financial data and the core insurance expense difference indicators. The multi-dimensional derivative analysis indicators include year-on-year indicators, month-on-month indicators, growth rate indicators, and cross-dimensional ranking indicators.

[0106] In an alternative embodiment, the computing module 203 is specifically used for: Based on the time characteristic data in the standardized insurance industry financial data, the current insurance business cycle is identified; Based on the insurance business cycle, query the preset cycle-weight mapping relationship in the dynamic indicator weight model to determine the set of calculation dimensions corresponding to the multi-dimensional derivative analysis indicators; Obtain the corresponding initial weight for each computational dimension in the set of computational dimensions; Based on the business data priority weighting algorithm, the data from the core insurance business system, the financial accounting system, the channel management system and the expense reimbursement system in the standardized insurance business financial data are valued and a data priority factor is generated. The initial weights corresponding to each calculation dimension are corrected based on the data priority factor to generate dynamic weights for each calculation dimension. Using the dynamic weights of each calculation dimension, the standardized insurance industry financial data, and the core insurance expense difference indicator, the year-on-year indicator, the month-on-month indicator, the growth rate indicator, and the cross-dimensional ranking indicator are calculated simultaneously.

[0107] In an alternative embodiment, the display module 204 is specifically used for: The actual value of the core insurance premium difference indicator is compared with the preset target value of the core insurance premium difference indicator to generate the first deviation information corresponding to the core insurance premium difference indicator; The actual values ​​of the year-on-year indicator, the month-on-month indicator, the growth rate indicator, and the cross-dimensional ranking indicator are compared with their respective preset target values ​​to generate multiple second deviation information corresponding to the multi-dimensional derived analysis indicators. Based on a preset attribution rule base, the first deviation information and the multiple second deviation information are analyzed respectively to generate a first attribution conclusion corresponding to the core insurance premium difference index and multiple second attribution conclusions corresponding to the multi-dimensional derived analysis index. A real-time dynamically updated visualization interface is constructed, and the actual values ​​of the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, the first deviation information and the multiple second deviation information, as well as the first attribution conclusion and the multiple second attribution conclusions are synchronously displayed in the visualization interface.

[0108] In an alternative embodiment, the monitoring module 205 is specifically used for: Set the insurance business early warning rules, which include threshold rules and volatility rules; When the first deviation information or the second deviation information is detected and triggers the threshold rule or the volatility rule, it is determined to be an abnormal state; The warning information is generated based on the abnormal state, and the warning information is pushed through a preset message push interface.

[0109] It should be noted that the beneficial effects of the visualized multi-source indicator dynamic tracking system 200 provided in the above embodiments are the same as those of the visualized multi-source indicator dynamic tracking method described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0110] The visual multi-source indicator dynamic tracking system 200 of the present invention can be a computer program (including program code) running on a computer device. For example, the visual multi-source indicator dynamic tracking system 200 of the present invention is an application software that can be used to execute the corresponding steps in the visual multi-source indicator dynamic tracking method of the present invention.

[0111] In some embodiments, the visual multi-source indicator dynamic tracking system 200 of the present invention can be implemented in a combination of hardware and software. As an example, the visual multi-source indicator dynamic tracking system 200 of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the visual multi-source indicator dynamic tracking method of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0112] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0113] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned methods for dynamic tracking of visual multi-source indicators. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the method for dynamic tracking of visual multi-source indicators shown in any embodiment of the present invention by calling the computer program.

[0114] In one alternative embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0115] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0116] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0117] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0118] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0119] Among them, electronic devices can also be terminal devices. A terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0120] It should be noted that, Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0121] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned methods for dynamic tracking of visual multi-source indicators.

[0122] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0123] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned dynamic tracking method for visualized multi-source indicators.

[0124] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0126] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0127] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0128] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0129] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0130] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0131] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for dynamic tracking of visualized multi-source indicators, characterized in that, include: Acquire multi-source business and financial data from different insurance business systems; The multi-source business and financial data are standardized to obtain standardized insurance business and financial data; Based on the standardized insurance industry financial data, core insurance expense difference indicators and multi-dimensional derivative analysis indicators are calculated simultaneously; wherein, the multi-dimensional derivative analysis indicators include at least: year-on-year indicators, month-on-month indicators, growth rate indicators and cross-dimensional ranking indicators. The core insurance premium difference indicator and the multi-dimensional derived analysis indicator, as well as the deviation information and attribution conclusions related to the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, are displayed in real time through visualization. According to the preset insurance business early warning rules, the content of the real-time visualization is dynamically monitored, and an early warning message is generated when an anomaly is detected.

2. The method for dynamic tracking of visualized multi-source indicators according to claim 1, characterized in that, The different insurance business systems include at least: an insurance core business system, a financial accounting system, a channel management system, and an expense reimbursement system; the step of obtaining multi-source business and financial data from different insurance business systems includes: The system collects multi-source business and financial data from the core insurance business system, the financial accounting system, the channel management system, and the expense reimbursement system through a pre-configured data interface. The data interface includes an API interface, a direct database connection interface, and a middleware interface, and supports the collection of structured and semi-structured data.

3. The method for dynamic tracking of visualized multi-source indicators according to claim 2, characterized in that, The step of standardizing the multi-source business and financial data to obtain standardized insurance business and financial data includes: The multi-source business and financial data is subjected to operations such as removing duplicate data, filling in missing data, and correcting abnormal data to obtain cleaned multi-source business and financial data. The cleaned multi-source business and financial data are subjected to dimension unification processing according to the preset indicator caliber specifications to obtain multi-source business and financial data after dimension unification processing; the dimension unification processing includes unifying indicator definitions, organizational levels, platform classifications and expense types; The multi-source business and financial data that has undergone dimensional unification processing is converted into a unified data format to obtain the standardized insurance business and financial data.

4. The method for dynamic tracking of visualized multi-source indicators according to claim 2, characterized in that, The steps for simultaneously calculating core insurance expense differential indicators and multi-dimensional derived analysis indicators based on the standardized insurance industry financial data include: The system calls a preset indicator calculation rule library and uses the standardized insurance industry financial data to calculate the core insurance expense difference indicator, which includes expense difference rate, absolute expense difference, and expense difference contribution of different dimensions. A dynamic indicator weighting model is adopted to simultaneously calculate the multi-dimensional derivative analysis indicators based on the standardized insurance industry financial data and the core insurance expense difference indicators. The multi-dimensional derivative analysis indicators include year-on-year indicators, month-on-month indicators, growth rate indicators, and cross-dimensional ranking indicators.

5. The method for dynamic tracking of visualized multi-source indicators according to claim 4, characterized in that, The step of employing a dynamic indicator weighting model to simultaneously calculate the multi-dimensional derived analysis indicators based on the standardized insurance industry financial data and the core insurance expense difference indicator includes: Based on the time characteristic data in the standardized insurance industry financial data, the current insurance business cycle is identified; Based on the insurance business cycle, query the preset cycle-weight mapping relationship in the dynamic indicator weight model to determine the set of calculation dimensions corresponding to the multi-dimensional derivative analysis indicators; Obtain the corresponding initial weight for each computational dimension in the set of computational dimensions; Based on the business data priority weighting algorithm, the data from the core insurance business system, the financial accounting system, the channel management system and the expense reimbursement system in the standardized insurance business financial data are valued and a data priority factor is generated. The initial weights corresponding to each calculation dimension are corrected based on the data priority factor to generate dynamic weights for each calculation dimension. Using the dynamic weights of each calculation dimension, the standardized insurance industry financial data, and the core insurance expense difference indicator, the year-on-year indicator, the month-on-month indicator, the growth rate indicator, and the cross-dimensional ranking indicator are calculated simultaneously.

6. The method for dynamic tracking of visualized multi-source indicators according to claim 5, characterized in that, The step of real-time visualization of the core insurance premium difference indicator, the multi-dimensional derived analysis indicator, and the deviation information and attribution conclusions related to the core insurance premium difference indicator and the multi-dimensional derived analysis indicator includes: The actual value of the core insurance premium difference indicator is compared with the preset target value of the core insurance premium difference indicator to generate the first deviation information corresponding to the core insurance premium difference indicator; The actual values ​​of the year-on-year indicator, the month-on-month indicator, the growth rate indicator, and the cross-dimensional ranking indicator are compared with their respective preset target values ​​to generate multiple second deviation information corresponding to the multi-dimensional derived analysis indicators. Based on a preset attribution rule base, the first deviation information and the multiple second deviation information are analyzed respectively to generate a first attribution conclusion corresponding to the core insurance premium difference index and multiple second attribution conclusions corresponding to the multi-dimensional derived analysis index. A real-time dynamically updated visualization interface is constructed, and the actual values ​​of the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, the first deviation information and the multiple second deviation information, as well as the first attribution conclusion and the multiple second attribution conclusions are synchronously displayed in the visualization interface.

7. The method for dynamic tracking of visualized multi-source indicators according to claim 6, characterized in that, The step of dynamically monitoring the content of the real-time visualized display according to preset insurance business early warning rules, and generating early warning information when an anomaly is detected, includes: Set the insurance business early warning rules, which include threshold rules and volatility rules; When the first deviation information or the second deviation information is detected and triggers the threshold rule or the volatility rule, it is determined to be an abnormal state; The warning information is generated based on the abnormal state, and the warning information is pushed through a preset message push interface.

8. A visual multi-source indicator dynamic tracking system, characterized in that, include: The data acquisition module is used to acquire multi-source business and financial data from different insurance business systems; The processing module is used to standardize the multi-source business financial data to obtain standardized insurance business financial data; The calculation module is used to simultaneously calculate the core insurance expense difference indicator and multi-dimensional derivative analysis indicators based on the standardized insurance industry financial data; wherein, the multi-dimensional derivative analysis indicators include at least: year-on-year indicators, month-on-month indicators, growth rate indicators and cross-dimensional ranking indicators. The display module is used to visualize the core insurance premium difference indicator and the multi-dimensional derived analysis indicator, as well as the deviation information and attribution conclusions related to the core insurance premium difference indicator and the multi-dimensional derived analysis indicator in real time. The monitoring module is used to dynamically monitor the content of the real-time visualization display according to the preset insurance business early warning rules, and generate early warning information when an anomaly is detected.

9. An electronic device, characterized in that, The electronic device includes a processor coupled to a memory, the memory storing at least one computer program, which is loaded and executed by the processor to enable the electronic device to implement the visual multi-source indicator dynamic tracking method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which, when executed by a processor, implements the visualization multi-source indicator dynamic tracking method as described in any one of claims 1 to 7.

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