An artificial intelligence-based financial indicator data processing system and method

Abstract

The application relates to a financial index data processing system and method, and relates to the technical field of electric power. The system comprises a financial index data analysis platform, specifically comprising: a multi-source financial data integration module, which generates a financial index knowledge graph; an artificial intelligence model analysis module, which generates a preset model and uses the preset model to predict an electric benefit index corresponding to a current electricity price fluctuation index to obtain a current financial index; an intelligent reasoning engine module, which extracts a plurality of target entities of entity features in the current electric benefit index and the current electricity price fluctuation index; according to an association path in the financial index knowledge graph, the plurality of target entities are reasoned to obtain a plurality of first target paths; and a financial report generation module, which generates a first financial analysis report according to the current financial index and the plurality of first target paths.

Description

Technical Field

[0001] This disclosure relates to the field of power financial data processing technology, and in particular to a financial indicator data processing system and method based on artificial intelligence. Background Technology

[0002] With the continuous development of the power system, the collection and analysis of financial indicator data has become increasingly important. However, the power industry currently faces the challenge of integrating and analyzing massive amounts of data in its operation and management. Related technologies rely on manual processing, which is inefficient and prone to errors. Currently, financial indicator data analysis platforms based on simple static rules are gradually being adopted. These platforms struggle to adapt to complex and ever-changing business scenarios and lack the ability to uncover and analyze the inherent relationships between various financial indicators. Consequently, the financial statements generated by these platforms have a single dimension and poor reasoning logic. When financial users rely on these financial statements for account supervision, the error rate is high and the supervision efficiency is low, resulting in a poor user experience. Summary of the Invention

[0003] This invention provides a financial indicator data processing system and method to at least solve the technical problems of high error rate and low regulatory efficiency in related technologies' financial indicator data analysis platforms. The technical solution of this invention is as follows:

[0004] According to a first aspect of the present invention, a financial indicator data processing system is provided. The system includes: a financial indicator data analysis platform; the financial indicator data analysis platform includes a multi-source financial data integration module, an artificial intelligence model analysis module, an intelligent inference engine module, and a financial report generation module. The multi-source financial data integration module is configured to: generate a financial indicator knowledge graph based on multi-dimensional financial indicator data from multiple databases; the financial indicator knowledge graph includes entities representing the characteristics of financial indicators, financial events representing the financial indicators, and the association paths between entities and financial events; the financial indicators include electricity price fluctuation indicators and electricity efficiency indicators; the artificial intelligence model analysis module is configured to: perform reinforcement training using historical electricity price fluctuation indicators, historical financial events, and historical electricity efficiency indicators as training samples to obtain a preset model. The system employs a preset model to predict the electricity benefit index corresponding to the current electricity price fluctuation index, thereby obtaining the current electricity benefit index. The preset model is a reinforcement learning model used to predict the electricity benefit index under the influence of corresponding financial events, where the corresponding financial events are those with a higher probability of occurrence than the preset probability under the corresponding electricity price fluctuation index. The intelligent reasoning engine module is configured to: extract multiple target entities with entity features from the current electricity benefit index and the current electricity price fluctuation index; and reason about the multiple target entities based on the association paths in the financial indicator knowledge graph to obtain multiple first target paths, which include multiple first target financial events corresponding to the multiple target entities. The financial report generation module is configured to generate a first financial analysis report based on the current financial index and the multiple first target paths. The first financial analysis report includes the correlation trend between the current electricity benefit index, the current electricity price fluctuation index, and the first target financial events.

[0005] The aforementioned multi-source financial data integration module, artificial intelligence model analysis module, intelligent inference engine module, and financial report generation module are interconnected via communication. The multi-source financial data integration module connects to multiple databases.

[0006] The preset probability is the probability of being located before the preset position, or the highest probability.

[0007] The system also includes: a financial analysis platform supervision unit and a financial supervision terminal.

[0008] The aforementioned databases include a front-end business management database, a financial database, and an electricity price database.

[0009] The aforementioned electricity price volatility indicators include one or more of the following: statistical volatility indicators (e.g., price volatility), frequency and persistence indicators (frequency of price fluctuations, duration of high / low price fluctuations), peaks (peak factor representing the ratio of peak price to average price, value at risk, conditional value at risk), risk indicators, and electricity price forecast error indicators (deviation between predicted and actual electricity prices).

[0010] Electricity benefit indicators include one or more of the following: efficiency indicators (electricity consumption per unit output / output, electricity consumption per unit area, equipment / system efficiency, power factor), economic indicators (cost per kilowatt-hour, electricity expenditure as a percentage of total expenditure, energy-saving benefits, demand response benefits, investment payback period / net present value / internal rate of return), load characteristic indicators, which indirectly reflect benefits (load factor, peak-valley electricity consumption ratio), management and behavioral indicators (demand response participation / potential, energy-saving target completion rate), and environmental benefit indicators (carbon emissions per unit output / output, renewable energy consumption ratio).

[0011] The aforementioned entities include physical entities (such as equipment entities like substations / generators / lines, and route entities), abstract entities (such as market entities like electricity price types / policy documents, and policy entities), and indicator entities (such as indicator parameters like grid loss rate / renewable energy penetration rate / load fluctuation coefficient).

[0012] In one implementation, the intelligent inference engine module is further configured to: display multiple first target paths on the front-end interface, so that the user account can select a second target path from the multiple first target paths displayed on the front-end interface; receive the second target path returned by the front-end interface; and output the path reasoning process of the second target path in a visual form according to the inference chain based on the financial indicator knowledge graph. The financial report generation module is further configured to: generate a second financial analysis report based on the second target path; the second financial analysis report includes the correlation trend between the current electricity benefit indicator, the current electricity price fluctuation indicator, and the second target financial event in the second target path.

[0013] In another implementation, the financial indicator data analysis platform also includes an interactive response interface module that communicates with other modules. This interactive response interface module is configured to review the first and / or second financial analysis reports; send the reviewed financial analysis reports to the target user terminal, allowing the user on the target user terminal to revise or confirm the reviewed financial analysis reports, and then send the revised or confirmed financial analysis reports to the financial report generation module. The financial report generation module is further configured to generate a target financial analysis report based on the revised or confirmed financial analysis reports. The review of the first and / or second financial analysis reports includes one or more of the following: format validation of the text and / or image formats in the first and / or second financial analysis reports; sensitive word filtering and replacement of preset sensitive words included in the first and / or second financial analysis reports; and marking revisions in the first and / or second financial analysis reports that do not conform to preset logical conditions.

[0014] In another implementation, the system also includes a financial analysis platform monitoring unit that communicates with the financial indicator data analysis platform; the financial analysis platform monitoring unit is used to determine the operational stability of the financial indicator data analysis platform; the financial analysis platform monitoring unit includes a security and stability assessment module, a database connection monitoring and analysis module, a revision tracking and analysis module, and a request processing detection module;

[0015] The safety and stability assessment module is configured to detect target operating data within the financial indicator data analysis platform at a preset detection frequency, fit the target operating data to obtain a target fitting curve, and determine the stability operation result based on the curve trend of the target fitting curve. The stability operation result includes a safety and stability normal signal or a safety and stability alarm signal.

[0016] The database docking monitoring and analysis module is configured to monitor and analyze the docking status between the multi-source financial data integration module and each database during the detection period when a safe, stable and normal signal is received, and generate a docking qualified signal or a docking abnormal signal through analysis.

[0017] The revised tracking and analysis module is configured to analyze the user's interaction efficiency with the financial indicator data analysis platform during the detection period when a qualified docking signal is received, so as to generate an interaction abnormal signal or an interaction normal signal.

[0018] The request processing detection module is configured to analyze the query request processing load of the financial indicator data analysis platform during the detection period when a normal interaction signal is received, so as to generate a high processing load signal or a low processing load signal.

[0019] In another implementation, the security and stability assessment module is further configured to: obtain the total number of network attacks on the financial indicator data analysis platform during the detection period, and mark the total number of network attacks as a network characteristic value; and obtain the total number of crashes and paralysises of the financial indicator data analysis platform during the detection period, and mark the total number of crashes and paralysises as a paralysis characteristic value; if the network characteristic value is greater than or equal to a first preset threshold and / or the paralysis characteristic value is greater than or equal to a second preset threshold, a security and stability alarm signal is generated; if the network characteristic value is less than the first preset threshold and the paralysis characteristic value is less than the second preset threshold, the network characteristic value, the paralysis characteristic value, the intervention time value, and the intervention risk value are weighted and summed to obtain a security and stability assessment coefficient; if the security and stability assessment coefficient is greater than or equal to a preset security and stability assessment coefficient threshold, a security and stability alarm signal is generated; if the security and stability assessment coefficient is less than the preset security and stability assessment coefficient threshold, a security and stability normal signal is generated.

[0020] In another implementation, the database docking monitoring and analysis module is specifically configured to: mark any database as a stable abnormal database if the total disconnection time value or total disconnection frequency value of any database in multiple databases exceeds the corresponding preset disconnection threshold; generate a docking anomaly signal if a stable abnormal database exists in multiple databases during the detection period; generate a disconnection anomaly signal if no stable abnormal database exists in multiple databases during the detection period, calculate the weighted sum of the disconnection time measurement value and disconnection frequency measurement value of the stable abnormal database to obtain a disconnection monitoring evaluation value; generate a docking anomaly signal if the disconnection monitoring evaluation value is greater than or equal to the preset disconnection monitoring evaluation threshold; and generate a docking qualified signal if the disconnection monitoring evaluation value is less than the preset disconnection monitoring evaluation threshold.

[0021] In another implementation, the revision tracking and analysis module is specifically configured to: acquire the number of interactions with non-compliant durations during the detection period; calculate the ratio of the number of interactions to the total number of command analysis durations to obtain the non-compliant time-frequency value; mark the ratio of the command analysis duration of each interaction to the corresponding preset command analysis duration threshold as the command occupation duration; and calculate the average of all command occupation durations during the detection period to obtain the command occupation evaluation value; if the non-compliant time-frequency value is greater than or equal to the time-frequency threshold, and / or the command occupation evaluation value is greater than or equal to the preset evaluation threshold, a revision abnormal signal is generated; otherwise, a revision normal signal is generated.

[0022] In another implementation, the request processing detection module is specifically configured to obtain the total number of query requests received by the financial indicator data analysis platform during the detection period, and mark the total number of query requests as the load detection value. If the load detection value is greater than or equal to a preset load detection threshold, a high load signal is generated; if the load detection value is less than the preset load detection threshold, the load detection value, the delayed detection value, and the waiting status value are weighted and summed to obtain a load assessment value. If the load assessment value is greater than or equal to a preset load assessment threshold, a high load signal is generated; if the load assessment value is less than the preset load assessment threshold, a low load signal is generated.

[0023] According to a second aspect of the present invention, a method for processing financial indicator data is provided, applied to a financial indicator data processing system. The system includes a financial indicator knowledge graph and a preset model. The financial indicator knowledge graph includes entities representing the characteristics of financial indicators, financial events representing the financial indicators, and the association paths between entities and financial events. The financial indicators include electricity price fluctuation indicators and electricity efficiency indicators. The preset model is a reinforcement learning model used to predict the electricity efficiency indicators under the influence of corresponding financial events, where the corresponding financial events are those with a higher probability of occurrence than a preset probability under the corresponding electricity price fluctuation indicators. The financial indicator knowledge graph is based on data from multiple databases. The method generates multi-dimensional financial indicator data; the method includes: obtaining the current electricity price fluctuation indicator and inputting the current electricity price fluctuation indicator into a preset model to obtain the current electricity benefit indicator; extracting multiple target entities with entity features from the current electricity benefit indicator and the current electricity price fluctuation indicator; reasoning about the multiple target entities based on the association paths in the financial indicator knowledge graph to obtain multiple first target paths, the multiple first target paths including multiple first target financial events corresponding to the multiple target entities; generating a first financial analysis report based on the current financial indicator and the multiple first target paths; the first financial analysis report includes the correlation trend between the current electricity benefit indicator, the current electricity price fluctuation indicator and the first target financial events.

[0024] Multiple primary target paths include multiple target entities and multiple primary target financial events corresponding to the multiple target entities.

[0025] In one implementation, the method further includes: determining the stability of the financial indicator data analysis platform; and, if the financial indicator data analysis platform is determined to be in a stable operating state, acquiring multi-dimensional financial indicator data, acquiring training samples, and acquiring current electricity price fluctuation indicators; the training samples include historical electricity price fluctuation indicators, historical financial events, and historical electricity benefit indicators.

[0026] The above-mentioned determination that the financial indicator data analysis platform is in a stable operating state can be achieved by uniformly executing one or more of the following modules: security and stability assessment module, database connection monitoring and analysis module, revision tracking and analysis module, and request processing detection module.

[0027] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which instructions are stored, which, when executed by a processor of an electronic device, enable the electronic device to perform a financial indicator data processing method as described in the first aspect and any possible implementation thereof.

[0028] According to a fourth aspect of the present disclosure, a computer program product is provided, the computer program product including computer instructions that, when executed on an electronic device, cause the electronic device to perform the financial indicator data processing method of the first aspect and any possible implementation thereof.

[0029] The technical solution provided by the embodiments of the present invention brings at least the following beneficial effects: In the financial indicator data processing system of this application, a financial indicator knowledge graph of the correlation between multi-dimensional financial indicators is first constructed, and a reinforcement learning model of the correlation between electricity price fluctuation indicators and electricity benefit indicators is also trained, i.e., a preset model. Based on this preset model, the current electricity benefit indicator of the current electricity price fluctuation indicator can be intelligently and accurately predicted. Based on the financial indicator knowledge graph, financial events related to target entities in the current electricity price fluctuation indicator and the current electricity benefit indicator are inferred to form a first target path. Based on the current financial indicators and the first target path, a first financial analysis report representing the mutual influence between various financial indicator influencing factors is generated. This allows the financial analysis report to accurately reflect the mutual influence trend between different financial indicator influencing factors, thereby helping financial users to more effectively supervise the power financial situation based on the financial report and improve the efficiency of financial supervision.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0032] Figure 1 This is a block diagram of a financial indicator data processing system according to an exemplary embodiment;

[0033] Figure 2This is a flowchart illustrating a financial indicator data processing method according to an exemplary embodiment. Detailed Implementation

[0034] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0035] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0036] like Figure 1 As shown, the financial indicator data processing system includes a financial indicator data analysis platform 1; the financial indicator data analysis platform 1 includes a multi-source financial data integration module 11, an artificial intelligence model analysis module 12, an intelligent inference engine module 13, and a financial report generation module 14; the multi-source financial data integration module 11 is connected to multiple databases; the multi-source financial data integration module 11, the artificial intelligence model analysis module 12, the intelligent inference engine module 13, and the financial report generation module 14 are connected through communication.

[0037] In some embodiments, the above-mentioned financial indicator data processing system is also referred to as an artificial intelligence-based multi-dimensional analysis system for financial indicator data. The system includes a financial indicator data analysis platform 1, a financial analysis platform supervision unit 2, and a financial supervision terminal 3. The financial indicator data analysis platform 1 includes a multi-source financial data integration module 11, an artificial intelligence model analysis module 12, an intelligent inference engine module 13, a financial report generation module 14, and an interactive response interface module 15.

[0038] The multi-source financial data integration module 11 is configured to generate a financial indicator knowledge graph based on multi-dimensional financial indicator data from multiple databases. The financial indicator knowledge graph includes entities that represent the characteristics of financial indicators, financial events that affect financial indicators, and the association paths between entities and financial events. The financial indicators include electricity price fluctuation indicators and electricity efficiency indicators.

[0039] For example, the multi-source financial data integration module 11 connects to multiple databases, including front-end business management, finance, and electricity price databases. It extracts structured data through entity extraction and financial event extraction techniques to construct a financial indicator knowledge graph. The artificial intelligence model analysis module 12 trains specialized models for multiple scenarios, including electricity price fluctuation indicators and electricity efficiency indicators, based on pre-trained large models (such as the GPT series) and combined with user feedback reinforcement learning algorithms (RLHF) and prompt learning algorithms. It receives the financial indicator knowledge graph and user query information (such as analyzing the reasons for electricity price fluctuation indicators) and generates preliminary prediction results (such as "rising supply chain costs" and "impact of policy adjustments") through the trained model.

[0040] The artificial intelligence model analysis module 12 is configured to: perform reinforcement training using historical electricity price fluctuation indicators, historical financial events, and historical electricity benefit indicators as training samples to obtain a preset model; use the preset model to predict the electricity benefit indicator corresponding to the current electricity price fluctuation indicator to obtain the current electricity benefit indicator; the preset model is a reinforcement learning model used to predict the electricity benefit indicator under the influence of the corresponding financial event, where the corresponding financial event is a financial event with a higher probability of occurrence than the preset probability under the corresponding electricity price fluctuation indicator.

[0041] The intelligent reasoning engine module 13 is configured to: extract multiple target entities with entity characteristics from the current electricity efficiency index and the current electricity price fluctuation index; and reason about the multiple target entities based on the association paths in the financial indicator knowledge graph to obtain multiple first target paths, which include multiple first target financial events corresponding to the multiple target entities.

[0042] The financial report generation module 14 is configured to generate a first financial analysis report based on current financial indicators and multiple first target paths; the first financial analysis report includes the correlation trend between current electricity efficiency indicators, current electricity price fluctuation indicators and first target financial events.

[0043] The preset probability is the probability of being located before the preset position, or the highest probability.

[0044] The system also includes: financial analysis platform supervision unit 2 and financial supervision terminal 3.

[0045] The aforementioned databases include a front-end business management database, a financial database, and an electricity price database.

[0046] The aforementioned electricity price volatility indicators include one or more of the following: statistical volatility indicators (e.g., price volatility), frequency and persistence indicators (frequency of price fluctuations, duration of high / low price fluctuations), peaks (peak factor representing the ratio of peak price to average price, value at risk, conditional value at risk), risk indicators, and electricity price forecast error indicators (deviation between predicted and actual electricity prices).

[0047] Electricity benefit indicators include one or more of the following: efficiency indicators (electricity consumption per unit output / output, electricity consumption per unit area, equipment / system efficiency, power factor), economic indicators (cost per kilowatt-hour, electricity expenditure as a percentage of total expenditure, energy-saving benefits, demand response benefits, investment payback period / net present value / internal rate of return), load characteristic indicators, which indirectly reflect benefits (load factor, peak-valley electricity consumption ratio), management and behavioral indicators (demand response participation / potential, energy-saving target completion rate), and environmental benefit indicators (carbon emissions per unit output / output, renewable energy consumption ratio).

[0048] The aforementioned entities include physical entities (such as equipment entities like substations / generators / lines, and route entities), abstract entities (such as market entities like electricity price types / policy documents, and policy entities), and indicator entities (such as indicator parameters like grid loss rate / renewable energy penetration rate / load fluctuation coefficient).

[0049] For example, the intelligent inference engine module 13 performs multi-path inference on the preliminary prediction results (generating 3-5 inference chains), responds to multiple user selection results, selects the optimal path (such as "policy adjustment as the main cause"), preferably uses the self-consistency method to generate multiple inference chains and conduct majority voting; and outputs the inference chains in a visual form (such as a flowchart); the financial report generation module 14 calls the dynamic template library, automatically fills in the indicator data and inference results, generates the initial analysis results (i.e., the first financial analysis report) including trend charts and attribution analysis, and performs format verification and sensitive word filtering on the initial analysis results through the verification engine, and marks the parts that need manual revision, and generates the final draft after review and output. User feedback on the report revision (such as adjusting the chart type) is provided through the interactive response interface module 15. The interactive response interface module 15 receives user instructions, triggers model re-analysis or expands the query scope, thereby realizing dynamic updating of the report. Thus, by integrating multi-source data, adaptive models, and self-consistent reasoning technology, intelligent processing of scenarios such as electricity price fluctuation index attribution and electricity benefit index correlation analysis is achieved. Furthermore, the adoption of dynamic templates and multi-round review mechanisms improves the efficiency and accuracy of report generation, providing technical support for the digital transformation of the power industry. Specifically, by integrating generative large models, self-consistent reasoning chains, and multi-source data extraction technology, the problems of scattered data, single analysis dimensions, and low report generation efficiency in the power industry are solved.

[0050] Through the above implementation methods, the financial indicator data processing system of this application first constructs a financial indicator knowledge graph showing the relationships between multi-dimensional financial indicators, and simultaneously trains a reinforcement learning model for the relationship between electricity price fluctuation indicators and electricity benefit indicators, i.e., a preset model. Based on this preset model, it can intelligently and accurately predict the current electricity benefit indicator of the current electricity price fluctuation indicator. Then, based on the financial indicator knowledge graph, it infers the financial events related to target entities in the current electricity price fluctuation indicator and the current electricity benefit indicator to form a first target path. Thus, based on the current financial indicators and the first target path, a first financial analysis report representing the mutual influence between various financial indicator influencing factors is generated. This allows the financial analysis report to accurately reflect the mutual influence trends between different financial indicator influencing factors, thereby helping financial users to more effectively supervise the power financial situation based on this financial report and improve the efficiency of financial supervision.

[0051] In one implementation, the intelligent reasoning engine module 13 is further configured to: display multiple first target paths on the front-end interface, so that the user account can select a second target path from the multiple first target paths displayed on the front-end interface; receive the second target path returned by the front-end interface; and output the path reasoning process of the second target path in a visual form according to the reasoning chain based on the financial indicator knowledge graph. The financial report generation module 14 is further configured to: generate a second financial analysis report based on the second target path; the second financial analysis report includes the correlation trend between the current electricity benefit indicator, the current electricity price fluctuation indicator, and the second target financial event in the second target path.

[0052] In another embodiment, the financial indicator data analysis platform 1 further includes an interactive response interface module 15 that communicates with other modules. The interactive response interface module 15 is configured to perform an audit operation on the first financial analysis report and / or the second financial analysis report; send the audited financial analysis report to the target user terminal so that the user on the target user terminal can revise or confirm the audited financial analysis report on the target user terminal, and send the revised or confirmed financial analysis report to the financial report generation module 14. The financial report generation module 14 is further configured to generate a target financial analysis report based on the revised or confirmed financial analysis report. The audit operation on the first financial analysis report and / or the second financial analysis report includes one or more of the following: performing format verification on the text format and / or image format in the first financial analysis report and / or the second financial analysis report; performing sensitive word filtering and replacement operations on the preset sensitive words included in the first financial analysis report and / or the second financial analysis report; and marking the revised content in the first financial analysis report and / or the second financial analysis report that does not conform to the preset logical conditions.

[0053] In another embodiment, the system further includes a financial analysis platform monitoring unit 2 that is communicatively connected to the financial indicator data analysis platform 1; the financial analysis platform monitoring unit 2 is used to determine the operational stability of the financial indicator data analysis platform 1.

[0054] like Figure 1 As shown, the system can also be equipped with a financial analysis platform supervision unit 2, which includes one or more of the following: a security and stability assessment module 21, a database connection monitoring and analysis module 22, a revision tracking and analysis module 23, and a request processing detection module 24.

[0055] The financial analysis platform monitoring unit 2 is used to ensure the stability of the financial indicator data analysis platform 1, so as to ensure that the multi-dimensional financial indicator data can be obtained under normal conditions.

[0056] The safety and stability assessment module 21 is configured to detect the target operating data in the financial indicator data analysis platform 1 according to a preset detection frequency, fit the target operating data to obtain a target fitting curve, and determine the stability operation result based on the curve trend of the target fitting curve. The stability operation result includes a safety and stability normal signal or a safety and stability alarm signal.

[0057] The database docking monitoring and analysis module 22 is configured to monitor and analyze the docking status between the multi-source financial data integration module 11 and each database during the detection period when a safe, stable and normal signal is received, and generate a docking qualified signal or a docking abnormal signal through analysis.

[0058] The revised tracking and analysis module 23 is configured to analyze the user's interaction efficiency with the financial indicator data analysis platform 1 during the detection period when a qualified docking signal is received, so as to generate an interaction abnormal signal or an interaction normal signal.

[0059] The request processing detection module 24 is configured to analyze the query request processing load status of the financial indicator data analysis platform 1 during the detection period when it receives a normal interaction signal, so as to generate a high processing load signal or a low processing load signal.

[0060] As one implementation method, such as Figure 1 The system shown can also be equipped with a financial supervision terminal 3.

[0061] The financial analysis platform monitoring unit 2 comprehensively monitors the operation of the financial indicator data analysis platform 1. The monitoring unit 2 includes a security and stability assessment module 21 and a database connection monitoring and analysis module 22. The security and stability assessment module 21 assesses the operational security and stability of the financial indicator data analysis platform 1 during the testing period (preferably 25 days), generating a normal security and stability signal or a security and stability alarm signal accordingly. Furthermore, the security and stability alarm signal is sent to the financial monitoring terminal 3. Upon receiving the alarm signal, the financial monitoring terminal 3 issues a corresponding warning to remind supervisory personnel to take appropriate security optimization and improvement measures for the financial indicator data analysis platform 1, strengthen subsequent operational supervision of the platform, and ensure its safe and stable operation.

[0062] In another embodiment, the security and stability assessment module 21 is further configured to: obtain the total number of network attacks received by the financial indicator data analysis platform 1 during the detection period, and mark the total number of network attacks as a network characteristic value; and obtain the total number of crashes and paralysises of the financial indicator data analysis platform 1 during the detection period, and mark the total number of crashes and paralysises as a paralysis characteristic value; if the network characteristic value is greater than or equal to a first preset threshold and / or the paralysis characteristic value is greater than or equal to a second preset threshold, a security and stability alarm signal is generated; if the network characteristic value is less than the first preset threshold and the paralysis characteristic value is less than the second preset threshold, the network characteristic value, the paralysis characteristic value, the intervention time value, and the intervention risk value are weighted and summed to obtain a security and stability assessment coefficient; if the security and stability assessment coefficient is greater than or equal to a preset security and stability assessment coefficient threshold, a security and stability alarm signal is generated; if the security and stability assessment coefficient is less than the preset security and stability assessment coefficient threshold, a security and stability normal signal is generated.

[0063] In this embodiment, the total number of times the financial indicator data analysis platform 1 is attacked by the network during the detection period is obtained and marked as a network feature value, and the total number of times the financial indicator data analysis platform 1 crashes and becomes paralyzed during the detection period is obtained and marked as a paralysis feature value. The network feature value and the paralysis feature value are compared with the preset network feature threshold and the preset paralysis feature threshold respectively. If the network feature value or the paralysis feature value exceeds the corresponding preset threshold, it indicates that the security and stability of the financial indicator data analysis platform 1 is poor during the detection period, and a security and stability alarm signal is generated.

[0064] In another implementation, the database docking monitoring and analysis module 22 is specifically configured to: mark any database as a stable abnormal database if the total disconnection time value or total disconnection frequency value of any database in multiple databases exceeds the corresponding preset disconnection threshold; generate a docking anomaly signal if a stable abnormal database exists in multiple databases during the detection period; generate a disconnection anomaly signal if no stable abnormal database exists in multiple databases during the detection period, calculate the weighted sum of the disconnection time measurement value and disconnection frequency measurement value of the stable abnormal database to obtain a disconnection monitoring evaluation value; generate a docking anomaly signal if the disconnection monitoring evaluation value is greater than or equal to the preset disconnection monitoring evaluation threshold; and generate a docking qualified signal if the disconnection monitoring evaluation value is less than the preset disconnection monitoring evaluation threshold.

[0065] In this implementation, if neither the network feature value nor the paralysis feature value exceeds the corresponding preset threshold, the detection period is divided into multiple monitoring periods, all of which have the same duration. If the financial indicator data analysis platform 1 is attacked or crashes during the corresponding monitoring period, it indicates that the operation of the financial indicator data analysis platform 1 is hindered during the corresponding monitoring period, and the corresponding monitoring period is marked as an emergency intervention period. If the financial indicator data analysis platform 1 is not attacked or crashes during the corresponding monitoring period, it indicates that the operation of the financial indicator data analysis platform 1 is relatively smooth during the corresponding monitoring period, and the corresponding monitoring period is marked as a safe and stable period.

[0066] Example 1: Taking the financial analysis platform supervision unit 2, which includes a security and stability assessment module 21 and a database connection monitoring and analysis module 22, as an example, the stability of its analysis platform is specifically implemented.

[0067] First, the number of emergency intervention periods within the detection period is obtained and the ratio is calculated with the total number of monitoring periods to obtain the intervention duration value. The number of emergency intervention periods between two adjacent safe and stable periods is marked as the intervention duration value. The intervention duration value is compared with the preset intervention duration threshold. If the intervention duration value exceeds the preset intervention duration threshold, the corresponding intervention duration value is marked as an intervention duration anomaly value.

[0068] Secondly, the number of intervention-related abnormal values ​​obtained during the detection period is marked as the intervention-related risk value. The safety and stability assessment coefficient is obtained by weighted summation of network characteristic values, paralysis characteristic values, intervention time-occupancy values, and intervention-related risk values. That is, the network characteristic values, paralysis characteristic values, intervention time-occupancy values, and intervention-related risk values ​​are each assigned a corresponding preset weight coefficient, and the network characteristic values, paralysis characteristic values, intervention time-occupancy values, and intervention-related risk values ​​are multiplied by the corresponding preset weight coefficients. The sum of the four sets of product results is marked as the safety and stability assessment coefficient. Furthermore, the larger the value of the safety and stability assessment coefficient, the worse the overall safety and stability of the financial indicator data analysis platform 1 is during the detection period.

[0069] Third, the safety and stability assessment coefficient is compared with the preset safety and stability assessment coefficient threshold. If the safety and stability assessment coefficient exceeds the preset safety and stability assessment coefficient threshold, it indicates that the overall safety and stability of the financial indicator data analysis platform 1 is poor during the testing period, and a safety and stability alarm signal is generated. If the safety and stability assessment coefficient does not exceed the preset safety and stability assessment coefficient threshold, it indicates that the overall safety and stability of the financial indicator data analysis platform 1 is good during the testing period, and a safety and stability normal signal is generated.

[0070] Third, the safety and stability assessment module 21 sends a safety and stability normal signal to the database docking monitoring and analysis module 22. When the database docking monitoring and analysis module 22 receives the safety and stability normal signal, it monitors and analyzes the docking status of the multi-source financial data integration module 11 with each database during the detection period, and generates a docking qualified signal or a docking abnormal signal through analysis.

[0071] Fourth, the abnormal connection signal is sent to the financial supervision terminal 3. When the financial supervision terminal 3 receives the abnormal connection signal, it issues a corresponding warning to strengthen the supervision of the connection of all databases in a timely manner, ensure the continuous and stable connection between the financial indicator data analysis platform 1 and all databases, avoid the inability to obtain data and cause the analysis process to stop, and further ensure the smooth operation of the financial indicator data analysis platform 1.

[0072] The specific analysis process of the database integration monitoring and analysis module 22 is as follows:

[0073] All databases connected to the multi-source financial data integration module 11 are obtained, and the corresponding databases are marked as i, where i is a natural number greater than 1; when the connection between the financial indicator data analysis platform 1 and database i is disconnected (i.e., the financial indicator data analysis platform 1 cannot obtain data from database i), the disconnection duration is collected, and the total disconnection duration of all disconnection durations corresponding to database i during the detection period is summed to obtain the total disconnection duration value, and the total number of times the connection of database i is disconnected during the detection period is marked as the total disconnection frequency value;

[0074] The total disconnection time value and total disconnection frequency value are compared with the preset total disconnection time threshold and preset total disconnection frequency threshold respectively. If the total disconnection time value or the total disconnection frequency value exceeds the corresponding preset threshold, it indicates that the connection with database i is unstable during the detection period, and database i is marked as a stable abnormal database. If there is a stable abnormal database during the detection period, it indicates that the database connection performance is poor during the detection period, and a connection abnormality signal is generated.

[0075] Furthermore, if there is no stable database during the detection period, the average of the total disconnection time values ​​of all databases is used to calculate the disconnection time value, and the average of the total disconnection frequency values ​​of all databases is used to calculate the disconnection frequency value.

[0076] The disconnection monitoring evaluation value is obtained by weighted summation of the disconnection time measurement value and the disconnection frequency measurement value. Specifically, the disconnection time measurement value and the disconnection frequency measurement value are assigned corresponding preset weight coefficients, and the disconnection time measurement value and the disconnection frequency measurement value are multiplied by the corresponding preset weight coefficients. The sum of the two sets of product results is marked as the disconnection monitoring evaluation value. Furthermore, the larger the value of the disconnection monitoring evaluation value, the worse the overall database connection performance during the detection period.

[0077] The disconnection monitoring evaluation value is compared with the preset disconnection monitoring evaluation threshold. If the disconnection monitoring evaluation value exceeds the preset disconnection monitoring evaluation threshold, it indicates that the overall database docking performance during the testing period is poor, and a docking anomaly signal is generated. If the disconnection monitoring evaluation value does not exceed the preset disconnection monitoring evaluation threshold, it indicates that the overall database docking performance during the testing period is good, and a docking qualified signal is generated.

[0078] In another implementation, the revision tracking and analysis module 23 is specifically configured to: acquire the number of interactions with non-compliant durations during the detection period; calculate the ratio of the number of interactions to the total number of command analysis durations to obtain the non-compliant time-frequency value; mark the ratio of the command analysis duration of each interaction to the corresponding preset command analysis duration threshold as the command occupation duration; calculate the average of all command occupation durations during the detection period to obtain the command occupation evaluation value; if the non-compliant time-frequency value is greater than or equal to the time-frequency threshold, and / or the command occupation evaluation value is greater than or equal to the preset evaluation threshold, then a revision abnormal signal is generated; otherwise, a revision normal signal is generated.

[0079] In another implementation, the request processing detection module 24 is specifically configured to obtain the total number of query requests received by the financial indicator data analysis platform 1 during the detection period, and mark the total number of query requests as the load detection value. If the load detection value is greater than or equal to a preset load detection threshold, a high load signal is generated. If the load detection value is less than the preset load detection threshold, the load detection value, the delayed detection value, and the waiting status value are weighted and summed to obtain a load assessment value. If the load assessment value is greater than or equal to a preset load assessment threshold, a high load signal is generated. If the load assessment value is less than the preset load assessment threshold, a low load signal is generated.

[0080] Example 2 differs from Example 1 in that the financial analysis platform monitoring unit 2 further includes a revision tracking analysis module 23. The database docking monitoring and analysis module 22 sends a docking pass signal to the revision tracking analysis module 23. When the revision tracking analysis module 23 receives the docking pass signal, it analyzes the efficiency of the financial indicator data analysis platform 1 in responding to user revision opinions during the detection period, and generates a revision anomaly signal or a revision normal signal accordingly.

[0081] The revision anomaly signal is sent to the financial supervision terminal 3. When the financial supervision terminal 3 receives the revision anomaly signal, it issues a corresponding warning to remind the supervisors to take timely optimization and improvement measures to ensure that the financial indicator data analysis platform 1 responds to user modification opinions in a timely manner and improves the operating efficiency of the financial indicator data analysis platform 1.

[0082] The specific analysis process of its revision tracking analysis module 23 is as follows.

[0083] First, the financial indicator data analysis platform 1 obtains the moment when it receives the revision opinion and marks it as the order receiving moment. It also marks the moment when the revised report is generated and output as the completion moment. The interval between the completion moment and the corresponding order receiving moment is marked as the order analysis duration. The order analysis duration is compared with the corresponding preset order analysis duration threshold. If the order analysis duration exceeds the corresponding preset order analysis duration threshold, the corresponding order analysis duration is marked as an unqualified duration.

[0084] Secondly, the number of non-compliant durations during the detection period is obtained and the ratio of this number to the total number of command analysis durations is calculated to obtain the non-compliant time frequency value. The ratio of the command analysis duration to the corresponding preset command analysis duration threshold is marked as the command occupation duration. The average of all command occupation durations during the detection period is calculated to obtain the command occupation evaluation value.

[0085] Third, the non-compliant time-frequency value and order percentage evaluation value are compared with the preset non-compliant time-frequency threshold and preset order percentage evaluation threshold respectively. If the non-compliant time-frequency value or order percentage evaluation value exceeds the corresponding preset threshold, a revision abnormal signal is generated; if neither the non-compliant time-frequency value nor the order percentage evaluation value exceeds the corresponding preset threshold, a revision normal signal is generated.

[0086] Example 3: The difference between this example and Example 1 and Example 2 is that the financial analysis platform supervision unit 2 also includes a request processing detection module 24. The revision tracking and analysis module 23 sends a revision normal signal to the request processing detection module 24. When the request processing detection module 24 receives the revision normal signal, it analyzes the query request processing load of the financial indicator data analysis platform 1 during the detection period and generates a high processing load signal or a low processing load signal through analysis.

[0087] Furthermore, the high-load signal is sent to the financial supervision terminal 3. When the financial supervision terminal 3 receives the high-load signal, it issues a corresponding warning to remind supervisors to strengthen the subsequent operation supervision of the financial indicator data analysis platform 1 and improve its operating load capacity, thereby ensuring its operating performance and user satisfaction.

[0088] The specific analysis process of the request processing detection module 24 is as follows.

[0089] The total number of query requests received by the financial indicator data analysis platform 1 during the detection period is obtained and marked as the load detection value. The load detection value is compared with the preset load detection threshold. If the load detection value exceeds the preset load detection threshold, it indicates that the request processing load during the detection period is initially high, and a high processing load signal is generated.

[0090] If the load detection value does not exceed the preset load detection threshold, the waiting time of the corresponding query request is collected, and the waiting time is compared with the preset waiting time threshold. The number of query requests whose waiting time exceeds the preset waiting time threshold during the detection period is marked as the delay detection value, and the waiting time of all query requests during the detection period is averaged to obtain the waiting status value.

[0091] The load assessment value is obtained by weighted summation of load detection value, delay handling value, and waiting status value. Specifically, each load detection value, delay handling value, and waiting status value is assigned a corresponding preset weight coefficient, and then each of these values ​​is multiplied by its respective preset weight coefficient. The sum of the three products is then marked as the load assessment value. Furthermore, the larger the load assessment value, the higher the overall request processing load during the detection period.

[0092] The load assessment value is compared with the preset load assessment threshold. If the load assessment value exceeds the preset load assessment threshold, it indicates that the overall request processing load during the detection period is high, and a high load signal is generated. If the load assessment value does not exceed the preset load assessment threshold, it indicates that the overall request processing load during the detection period is low, and a low load signal is generated.

[0093] In the above embodiments of this application, during use, the multi-source financial data integration module 11 connects to multiple databases and constructs a financial indicator knowledge graph. The artificial intelligence model analysis module 12 receives the financial indicator knowledge graph and user query information and generates preliminary prediction results through the trained model. The intelligent inference engine module 13 generates multiple inference chains and conducts majority voting. The financial report generation module 14 generates initial analysis results including trend charts and attribution analysis. After review, the final draft is generated and output. User feedback on the report revision is provided through the interactive response interface module 15 to trigger model re-analysis or expand the query scope. By integrating generative large models, self-consistent inference chains, and multi-source data extraction technology, the problems of scattered data, single analysis dimensions, and low report generation efficiency in the power industry are solved. Furthermore, the financial analysis platform supervision unit 2 performs progressive and precise analysis on the security and stability of the financial indicator data analysis platform 1, database connection performance, efficiency of responding to revision opinions, and request processing load status, realizing comprehensive monitoring and timely alarm of the financial indicator data analysis platform 1, significantly reducing the difficulty of operation supervision of the financial indicator data analysis platform 1 and ensuring its operation performance.

[0094] like Figure 2 As shown, the data processing method for financial indicators provided in this application is described in detail.

[0095] S21. Obtain the current electricity price fluctuation index and input the current electricity price fluctuation index into the preset model to obtain the current electricity benefit index.

[0096] S22, extract multiple target entities with entity characteristics from the current electricity efficiency index and the current electricity price fluctuation index.

[0097] S23. Based on the association paths in the financial indicator knowledge graph, reason about multiple target entities to obtain multiple first target paths.

[0098] Multiple primary target paths include multiple primary target financial events corresponding to multiple target entities.

[0099] S24, Generate the first financial analysis report based on current financial indicators and multiple primary target paths.

[0100] The first financial analysis report includes current electricity efficiency indicators, current electricity price volatility indicators, and the correlation trends between the first target financial event.

[0101] Multiple primary target paths include multiple target entities and multiple primary target financial events corresponding to the multiple target entities.

[0102] The aforementioned financial indicator knowledge graph includes entities representing the characteristics of financial indicators, financial events representing the financial indicators, and the association paths between entities and financial events. The financial indicators include electricity price fluctuation indicators and electricity efficiency indicators. The preset model is a reinforcement learning model used to predict the electricity efficiency indicators under the influence of corresponding financial events. The corresponding financial events are those with a higher probability of occurrence than the preset probability under the corresponding electricity price fluctuation indicators. The financial indicator knowledge graph is generated based on multi-dimensional financial indicator data from multiple databases.

[0103] In one implementation, before or during the execution of the above method steps, the following steps are performed to determine the stability of the financial indicator data analysis platform, so that the data on which the report is based is generated by the financial indicator data analysis platform under stable processing conditions.

[0104] First, ensure the stability of the financial indicator data analysis platform.

[0105] Secondly, after confirming that the financial indicator data analysis platform is in a stable operating state, obtain multi-dimensional financial indicator data, training samples, and current electricity price fluctuation indicators.

[0106] The training samples include historical electricity price fluctuation indicators, historical financial events, and historical electricity benefit indicators.

[0107] The above-mentioned determination that the financial indicator data analysis platform is in a stable operating state can be achieved by executing one or more of the following modules in a unified manner: security and stability assessment module 21, database connection monitoring and analysis module 22, revision tracking and analysis module 23, and request processing detection module 24.

[0108] This application also provides a computer-readable storage medium, which, when executed by a processor of a financial indicator data processing device or a power system device, enables the financial indicator data processing device or power system device to perform a financial indicator data processing method as described in any of the possible embodiments above. The same technical effects can be achieved, and to avoid repetition, further details are omitted here.

[0109] This application also provides a computer program product, including a computer program or instructions, which are executed by a processor as a financial indicator data processing method according to any of the possible implementations described above. Furthermore, it achieves the same technical effects, and to avoid repetition, it will not be described again here.

[0110] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0111] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An artificial intelligence-based financial indicator data processing system, characterized by, The system is used for intelligent generation of financial statements, and includes: a financial indicator data analysis platform; the financial indicator data analysis platform includes: The multi-source financial data integration module is configured to generate a financial indicator knowledge graph based on multi-dimensional financial indicator data from multiple databases. The financial indicator knowledge graph includes entities representing financial indicators, financial events representing financial indicators, and the association paths between entities and financial events. The financial indicators include electricity price fluctuation indicators and electricity efficiency indicators. The multiple databases include a front-end business management database, a financial database, and an electricity price database. The artificial intelligence model analysis module is configured to: perform reinforcement learning training based on historical electricity price fluctuation indicators, historical financial events, and historical electricity benefit indicators as training samples to obtain a preset model; use the preset model to predict the electricity benefit indicator corresponding to the current electricity price fluctuation indicator to obtain the current electricity benefit indicator; the preset model is a reinforcement learning model used to predict the electricity benefit indicator under the influence of the corresponding financial event, wherein the corresponding financial event is a financial event with a higher probability of occurrence than a preset probability under the corresponding electricity price fluctuation indicator; The intelligent reasoning engine module is configured to: extract multiple target entities with entity features from the current electricity efficiency index and the current electricity price fluctuation index; and reason about the multiple target entities based on the association paths in the financial indicator knowledge graph to obtain multiple first target paths, wherein the multiple first target paths include multiple first target financial events corresponding to the multiple target entities. The financial report generation module is configured to generate a first financial analysis report based on current financial indicators and the multiple first target paths; the first financial analysis report includes the correlation trend between the current electricity efficiency indicators, the current electricity price fluctuation indicators, and the first target financial events.

2. The financial indicator data processing system according to claim 1, characterized in that, The intelligent inference engine module is also configured as follows: The plurality of first target paths are displayed on the front-end interface so that the user account can select a second target path from the plurality of first target paths displayed on the front-end interface; Receive the second target path returned by the front-end interface; Based on the financial indicator knowledge graph, the path reasoning process of the second target path is output in a visual form according to the reasoning chain; The financial report generation module is further configured to: generate a second financial analysis report based on the current financial indicators and the second target path; the second financial analysis report includes the correlation and influence trend between the current electricity benefit indicator, the current electricity price fluctuation indicator, and the second target financial event in the second target path; the current financial indicators include the current electricity benefit indicator and the current electricity price fluctuation indicator.

3. The financial indicator data processing system according to claim 2, characterized in that, The financial indicator data analysis platform also includes an interactive response interface module that communicates and connects with other modules. The interactive response interface module is configured to: review the first financial analysis report and / or the second financial analysis report; send the reviewed financial analysis report to the target user terminal so that the user of the target user terminal can revise or confirm the reviewed financial analysis report on the target user terminal; and send the revised or confirmed financial analysis report to the financial report generation module. The financial report generation module is also configured to generate a target financial analysis report based on the revised or confirmed financial analysis report. The auditing operation of the first financial analysis report and / or the second financial analysis report includes one or more of the following: performing format verification on the text format and / or image format of the first financial analysis report and / or the second financial analysis report; performing sensitive word filtering and replacement operations on the preset sensitive words included in the first financial analysis report and / or the second financial analysis report; and marking the revised content in the first financial analysis report and / or the second financial analysis report that does not conform to the preset logical conditions.

4. The financial indicator data processing system according to any one of claims 1 to 3, characterized in that, The system also includes a financial analysis platform monitoring unit that is communicatively connected to the financial indicator data analysis platform; the financial analysis platform monitoring unit is used to determine the operational stability of the financial indicator data analysis platform; the financial analysis platform monitoring unit includes a security and stability assessment module, a database connection monitoring and analysis module, a revision tracking and analysis module, and a request processing detection module; The safety and stability assessment module is configured to detect target operating data within the financial indicator data analysis platform at a preset detection frequency, fit the target operating data to obtain a target fitting curve, and determine the stability operating result based on the curve trend of the target fitting curve, wherein the stability operating result includes a safety and stability normal signal or a safety and stability alarm signal. The database docking monitoring and analysis module is configured to monitor and analyze the docking status of the multi-source financial data integration module with each database during the detection period when the safe, stable and normal signal is received, and generate a docking qualified signal or a docking abnormal signal through analysis. The revision tracking and analysis module is configured to, upon receiving the docking pass signal, analyze the user's interaction efficiency with the financial indicator data analysis platform during the detection period to generate a revision anomaly signal or a revision normal signal. The request processing detection module is configured to analyze the query request processing load status of the financial indicator data analysis platform during the detection period when it receives a normal interaction signal, so as to generate a high processing load signal or a low processing load signal.

5. The financial indicator data processing system according to claim 4, characterized in that, The security and stability assessment module is also configured to obtain the total number of cyberattacks suffered by the financial indicator data analysis platform during the detection period, and mark the total number of cyberattacks suffered as a network feature value. In addition, the total number of times the financial indicator data analysis platform crashed or became paralyzed during the detection period is obtained and the total number of crashes or becoming paralyzed is marked as a paralysis feature value; If the network feature value is greater than or equal to the first preset threshold and / or the paralysis feature value is greater than or equal to the second preset threshold, then the safety and stability alarm signal is generated. If the network feature value is less than the first preset threshold and the paralysis feature value is less than the second preset threshold, the network feature value, the paralysis feature value, the intervention time value, and the intervention risk value are weighted and summed to obtain the safety and stability evaluation coefficient. If the safety and stability evaluation coefficient is greater than or equal to the preset safety and stability evaluation coefficient threshold, the safety and stability alarm signal is generated; if the safety and stability evaluation coefficient is less than the preset safety and stability evaluation coefficient threshold, the safety and stability normal signal is generated.

6. The financial indicator data processing system according to claim 4, characterized in that, The database docking monitoring and analysis module is specifically configured to: mark any database as a stable abnormal database if the total disconnection time value or total disconnection frequency value of any database in the plurality of databases exceeds the corresponding preset disconnection threshold; generate a docking anomaly signal if a stable abnormal database exists in the plurality of databases during the detection period; generate a disconnection anomaly signal if no stable abnormal database exists in the plurality of databases during the detection period; calculate a weighted sum of the disconnection time measurement value and disconnection frequency measurement value of the stable abnormal database to obtain a disconnection monitoring evaluation value; generate a docking anomaly signal if the disconnection monitoring evaluation value is greater than or equal to the preset disconnection monitoring evaluation threshold; and generate a docking qualified signal if the disconnection monitoring evaluation value is less than the preset disconnection monitoring evaluation threshold.

7. The financial indicator data processing system according to claim 4, characterized in that, The revision tracking and analysis module is specifically configured to obtain the number of interactions with non-compliant durations during the detection period, and calculate the ratio of the number of interactions to the total number of analysis durations to obtain the non-compliant time frequency value. The ratio of the command parsing duration of each interaction to the corresponding preset command parsing duration threshold is marked as the command duration. The average duration of all the specified order periods within the detection period is calculated to obtain the order period evaluation value; if the non-compliance time frequency value is greater than or equal to the time frequency threshold, and / or the order period evaluation value is greater than or equal to the preset evaluation threshold, then the revision abnormal signal is generated; Otherwise, the revised normal signal is generated.

8. The financial indicator data processing system according to claim 4, characterized in that, The request processing detection module is specifically configured to obtain the total number of query requests received by the financial indicator data analysis platform during the detection period, and mark the total number of query requests as a load detection value. If the load detection value is greater than or equal to a preset load detection threshold, a high load signal is generated. If the load detection value is less than the preset load detection threshold, a weighted summation is performed on the load detection value, the delayed processing value, and the waiting status value to obtain a load assessment value. If the load assessment value is greater than or equal to a preset load assessment threshold, a high load signal is generated. If the load assessment value is less than the preset load assessment threshold, a low load signal is generated.

9. A method for processing financial indicator data, characterized in that, This system is applied to a financial indicator data processing system. The system includes a financial indicator knowledge graph and a preset model. The financial indicator knowledge graph includes entities that characterize the features of financial indicators, financial events that affect financial indicators, and the association paths between entities and financial events. The financial indicators include electricity price fluctuation indicators and electricity efficiency indicators. The preset model is a reinforcement learning model used to predict the electricity benefit index under the influence of corresponding financial events, where the corresponding financial events are financial events with a higher probability of occurrence than the preset probability under the corresponding electricity price fluctuation index. The financial indicator knowledge graph is generated based on multi-dimensional financial indicator data from multiple databases; the method includes: Obtain the current electricity price fluctuation index and input the current electricity price fluctuation index into the preset model to obtain the current electricity benefit index; Extract multiple target entities from the entity features of the current electricity efficiency index and the current electricity price fluctuation index; based on the association paths in the financial indicator knowledge graph, reason about the multiple target entities to obtain multiple first target paths, the multiple first target paths including multiple first target financial events corresponding to the multiple target entities; Based on the multiple first target paths, a first financial analysis report is generated; the first financial analysis report includes the current electricity efficiency indicator, the current electricity price fluctuation indicator, and the correlation trend between the first target financial events.

10. The financial indicator data processing method according to claim 9, characterized in that, The financial indicator data processing system also includes a financial indicator data analysis platform, and the method further includes: Determine the stability of the financial indicator data analysis platform; Once the financial indicator data analysis platform is confirmed to be operating stably, multi-dimensional financial indicator data is acquired, training samples are obtained, and the current electricity price fluctuation indicator is acquired. The training samples include historical electricity price fluctuation indicators, historical financial events, and historical electricity benefit indicators.

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