Market value management diagnosis and analysis method and system based on DER model

Abstract

The invention discloses a market value management diagnosis and analysis method and system based on a DER model, and relates to the technical field of data analysis and processing. The method comprises the following steps of data timeliness analysis, model data flow response inspection and market value management diagnosis result output. According to the method, the data timeliness analysis result is obtained by acquiring the multi-dimensional information data and introducing the data synchronism test, and further, the adaptability of the model to the dynamic data stream is effectively improved by introducing the data stream response test and hysteresis determination mechanism for the DER model. On the basis, whether the market value management diagnosis result is output or not is determined according to the obtained model hysteresis judgment result, visualization is carried out, a set of complete automatic system is constructed, a traditional manual calculation and comparison mode is replaced, and the processing speed and throughput of large-scale enterprise data are greatly improved. And the final score can be traced to the quantile position of the specific index in the industry, so that the interpretability of the model is technically enhanced.

Description

Technical Field

[0001] This invention relates to the field of data analysis and processing technology, and in particular to a market value management diagnosis and analysis method and system based on the DER model. Background Technology

[0002] Market capitalization management, as a crucial tool for enterprises to achieve strategic goals and improve capital efficiency, is gradually becoming a core component of listed company governance systems. The introduction of the DER (Diagnosis-Elevate-Realize) model is of great significance. Through this model, enterprises can conduct systematic diagnoses at different stages of market capitalization management, identify internal and external factors affecting market capitalization performance, and formulate targeted improvement measures. Under existing technological approaches, multi-dimensional data related to the company's market capitalization is collected from multiple data sources (such as financial statements, market data, and industry information), and this data is preprocessed to ensure accuracy and consistency. Based on this, the established DER model outputs market capitalization management diagnostic results to optimize market capitalization management effectiveness.

[0003] For example, Chinese patent application CN116245652A discloses a financial statement data analysis method, system, and storage medium adapted to computing resources, including: extracting features from multiple dimensions of data in corporate financial statements to obtain multiple feature datasets and performing derivative processing; further analyzing different machine learning models carried on computing nodes based on the resource occupancy status and data volume information of multiple computing nodes; outputting the contribution of the data in the feature datasets to the company's market value and summarizing and providing feedback.

[0004] The above-mentioned technology has at least the following technical problems: In market capitalization management practices based on the DER model, the DER model typically relies on comprehensive analysis of historical multi-dimensional information to complete market capitalization diagnosis, value assessment, and response strategy formulation. However, existing DER models generally employ batch data collection, cleaning, and modeling methods, making it difficult to capture the rapid dynamics of the capital market in a timely manner. This results in a significant lag in the model's handling of rapidly changing market environments. In other words, the diagnostic and assessment results generated by the model based on lagging data often fail to accurately reflect the real-time market state, leading to delayed market capitalization management decisions and consequently affecting the effectiveness of these decisions.

[0005] Another consideration is that data distribution in dynamic data stream environments often drifts, and traditional DER models lack the ability to learn online and iterate rapidly from real-time data during market capitalization management, further amplifying the problem of insufficient data timeliness. Due to the lack of effective utilization of real-time data, DER models lag in market capitalization deviation diagnosis, value assessment, and strategy formulation, leading to distorted or delayed implementation of market capitalization management strategies, thereby reducing the overall flexibility and effectiveness of market capitalization management. Summary of the Invention

[0006] To address the technical problem of low accuracy in market capitalization management results due to the lag in response to dynamic data streams in existing technologies, this invention provides a market capitalization management diagnosis and analysis method and system based on the DER model. The technical solution is as follows: On the one hand, a market capitalization management diagnosis and analysis method based on the DER model is provided. This method includes: acquiring multi-dimensional information data to reflect the overall information of the market capitalization management process; performing data synchronization tests on the multi-dimensional information data to obtain data timeliness analysis results to measure the timeliness of multi-dimensional information data in the market capitalization management process; determining whether there is a need for data flow processing intervention based on the acquired data timeliness analysis results to improve the synchronization between the data source and the processing chain by intervening in the performance of the multi-dimensional information data processing link; if so, performing a model data flow response test on the DER model used to output the market capitalization management diagnosis results after data flow processing intervention to obtain a model lag judgment result to measure the effectiveness of the DER model in responding to dynamic data flow; otherwise, directly acquiring the model lag judgment result; determining whether to output the market capitalization management diagnosis results and visualize them based on the acquired model lag judgment result; if so, sending an instruction to visualize the market capitalization management judgment results; otherwise, performing model parallel processing optimization to improve the computational efficiency and response speed of the DER model.

[0007] On the other hand, a market capitalization management diagnosis and analysis system based on the DER model is provided, including: a data timeliness analysis module, a model data flow response verification module, and a market capitalization management diagnosis result output module. The data timeliness analysis module is used to acquire multi-dimensional information data reflecting the overall information of the market capitalization management process, perform data synchronization verification on the multi-dimensional information data, and obtain data timeliness analysis results to measure the timeliness of multi-dimensional information data in the market capitalization management process. The model data flow response verification module is used to determine whether there is a need for data flow processing intervention based on the acquired data timeliness analysis results. This intervention can improve the synchronization between the data source and the processing chain by intervening in the performance of the multi-dimensional information data processing link. If so, after data flow processing intervention, the DER model used to output the market capitalization management diagnosis results is tested for model data flow response to obtain a model lag judgment result to measure the effectiveness of the DER model in responding to dynamic data flow. Otherwise, the model lag judgment result is directly obtained. The market capitalization management diagnosis result output module is used to determine whether to output the market capitalization management diagnosis results and visualize them based on the acquired model lag judgment results. If so, an instruction to visualize the market capitalization management judgment results is sent. Otherwise, the model parallel processing is optimized to improve the computational efficiency and response speed of the DER model.

[0008] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention, by acquiring and analyzing the synchronization and timeliness of multi-dimensional information data, ensures real-time consistency between the data source and the processing chain, enabling dynamic monitoring and evaluation of the entire market capitalization management process. When a timeliness defect is detected in the data stream, a data stream processing intervention mechanism is triggered to improve the synchronization between the data source and the processing chain, thereby ensuring the real-time nature and reliability of the input data. At the model level, by performing data stream response testing on the DER model, the model lag judgment result is obtained, ensuring the accuracy of the diagnostic results and enabling timely parallel processing optimization when the model lag is too large, improving the model's computational efficiency and response speed. Finally, under the condition of meeting the timeliness and responsiveness requirements, the market capitalization management diagnostic results are output and visualized. In summary, through dual optimization of the data processing chain and the model response chain, balancing real-time performance, stability, and interpretability, the scientific rigor and practical value of market capitalization management diagnosis are effectively improved.

[0009] 2. By quantitatively characterizing and analyzing the timeliness of multi-dimensional information data, the system can determine whether data stream processing intervention is needed based on the acquired data timeliness analysis results. When no intervention is needed, adaptive testing of the DER model's response capability is achieved through dynamic mapping and adjustment of the response verification frequency and verification window. When intervention is required, data link performance is dynamically optimized through adjustment of data stream transmission rate and incremental update frequency. This avoids unnecessary link operations, ensures timely intervention when latency exists, and comprehensively assesses DER model performance by combining quantitative indicators of model response and data stream processing. This achieves closed-loop optimization of data link and model performance, significantly improving the real-time performance, stability, and reliability of market capitalization management diagnostics.

[0010] 3. By combining response test indicators with model lag test intervals, accurate model lag determination results can be obtained. Based on the determination results, it is determined whether to output market capitalization management diagnostic results and visualize them. When the results are satisfactory, the test strategy is optimized by combining period and frequency mapping. When the results are lagging, the computational performance of the DER model is improved through dynamic intervention of parallelism and task partitioning granularity. After optimization, closed-loop verification is performed. If lag still exists, a fault tolerance mechanism is triggered to re-input data. This achieves a synergistic improvement in the reliability of diagnostic results, test efficiency, and model computational performance, significantly enhancing the real-time performance, stability, and application value of market capitalization management diagnosis. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating the market capitalization management diagnosis and analysis method based on the DER model provided in this embodiment of the invention; Figure 2 A flowchart illustrating the data synchronization test of the market value management diagnosis and analysis method based on the DER model provided in this embodiment of the invention; Figure 3 A flowchart illustrating the process of obtaining the model lag determination result for the market value management diagnosis and analysis method based on the DER model provided in this embodiment of the invention; Figure 4 A schematic diagram of the structure of a market value management diagnosis and analysis system based on the DER model provided in an embodiment of the present invention; Figure 5 This is one of the report interface diagrams of the market value management diagnosis and analysis system based on the DER model provided in the embodiments of the present invention. Detailed Implementation

[0013] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0014] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0015] The DER theory (Diagnose-Elevate-Realize) is a systematic theory in the field of market capitalization management. It primarily improves a company's market capitalization health, liquidity, and reasonable premium through three modules. These three modules work synergistically to help companies achieve comprehensive optimization and long-term value enhancement from a market capitalization management perspective.

[0016] The core objective of the diagnostic module is to comprehensively assess a company's market capitalization using quantitative indicators, similar to a "physical examination," to identify the root causes of problems. This stage of the assessment covers two main dimensions: transactional and non-transactional. Transactional indicators, such as relative stock price performance, shareholder structure, and liquidity, help determine the company's relative strength in the market; non-transactional indicators focus on the company's visibility among the general public and institutional investors. Through a comprehensive score of these indicators, companies can understand the health of their market capitalization and decide whether further improvements or adjustments are needed based on the score.

[0017] The enhancement module focuses on improving a company's market capitalization health based on diagnostic results, utilizing a series of compliance tools. Specifically, this module systematically implements market capitalization management measures through five overt strategies: information disclosure compliance, equity incentives, ESG (Environmental, Social, and Governance), financial and investor relations, and mergers and acquisitions. Each strategy has its unique role: information disclosure compliance helps companies establish information transparency and enhances institutional investor trust; equity incentives enhance governance premium by aligning the interests of the management team and shareholders; ESG improves the company's social responsibility image in the capital market, attracting international investors; financial and investor relations enhance investor attention through segmented communication strategies targeting retail and institutional investors; and mergers and acquisitions improve the company's valuation and market competitiveness through resource integration and cross-industry transformation.

[0018] The core objective of this module is to ensure, through long-term operation, that market capitalization stably reflects a company's intrinsic value and achieves a reasonable market premium. Unlike short-term market fluctuations and speculation, the DER theory advocates that companies should achieve a reasonable premium of 20%-30% through steady market capitalization management. This process not only ensures the rationality of the company's valuation but also guarantees smooth liquidity and high capital efficiency. Through long-term market capitalization management, companies can avoid market bubbles, achieve stable growth, and ultimately deliver sustainable returns for shareholders.

[0019] The values ​​of the DER theory are reflected in three aspects: long-term focus, legality, and professionalism. First, the DER theory emphasizes that corporate market capitalization management should focus on long-term planning and avoid short-term market speculation. Second, all market capitalization management tools must be compliant and legal, ensuring no insider trading or stock price manipulation occurs. Finally, the DER theory advocates for market capitalization management through quantitative indicators and standardized processes, ensuring that every step is scientific and professional, making market capitalization management more precise and controllable.

[0020] like Figure 1 The flowchart shown is a process for a market capitalization management diagnosis and analysis method based on the DER model provided in an embodiment of the present invention. The method includes the following steps: data timeliness analysis, model data flow response verification, and market capitalization management diagnosis result output.

[0021] refer to Figure 1 The first step of this method is data timeliness analysis, specifically: S1, acquiring multi-dimensional information data reflecting the overall picture of the market capitalization management process through data crawling tools (such as SpiderFlow). This multi-dimensional information data includes, but is not limited to, market data, industry data, and financial reports. This multi-dimensional information data provides a comprehensive perspective on market capitalization management, helping decision-makers fully understand various influencing factors such as the market environment, financial situation, and industry trends, thereby making more accurate and effective decisions and improving the overall level of market capitalization management. Simultaneously, high-quality multi-dimensional information data, as input to the DER model, optimizes the market capitalization diagnostic process, improves the accuracy and credibility of the results, and helps companies make more effective adjustments and strategic decisions in a dynamic market environment.

[0022] As a further solution, the update frequency, latency characteristics, and collection mechanisms of different data sources often differ during the market capitalization management process, leading to asynchrony in these data over time and affecting the accuracy and comparability of the analysis results. Furthermore, the market capitalization management process relies on multiple heterogeneous data sources (such as market data, financial data, and industry data), whose update frequencies and timestamps may vary. Discrepancies in time synchronization between different data sources directly impact the accuracy and timeliness of market capitalization management analysis. Therefore, acquiring multi-dimensional information data to reflect the overall picture of the market capitalization management process also includes: S11, obtain the average deviation of data source updates, which reflects the average deviation between the update timestamps of various types of data in multi-dimensional information data and the baseline update timestamp; that is, the average value of the difference between the update timestamps of various types of data and the baseline update timestamp, obtained through a data stream processing framework (such as Apache Kafka, Apache Flink).

[0023] S12 compares the acquired average deviation of data source updates with the set data source update benchmark interval. The data source update benchmark interval represents the interval corresponding to deviations less than the set data source deviation benchmark value. The set data source deviation benchmark value in the set data source update benchmark interval is obtained from the market capitalization management analysis database. The data source deviation benchmark value can be set to the maximum value of the historical average deviation of data source updates. The market capitalization management analysis database is a database specifically created to store core configuration information when designing the market capitalization management diagnostic and analysis method based on the DER model. This database stores various settings, setting intervals, and mapping sets necessary for the operation of this method, such as the set data source update benchmark interval. These initial settings are not arbitrarily specified, but are calculated based on a large amount of previously accumulated actual test data in the market capitalization management analysis database through methods such as summation and averaging, thus making the initial settings more objective and more reflective of general situations. Of course, considering the complex and ever-changing actual application environment and the new problems that may arise during the operation of this method, these values ​​in the market capitalization management analysis database are not fixed. Technical personnel can manually set, adjust, or fine-tune them at any time according to the specific performance of this method in actual testing, thereby ensuring that this method can be continuously optimized to achieve the best working state.

[0024] S13, if the average deviation of the data source update is within the set data source update reference range, then obtain the update deviation reference deviation degree, which reflects the degree of deviation between the average deviation of the data source update and the set data source deviation reference value; the update deviation reference deviation degree is the result of the ratio calculation of the absolute value of the difference between the average deviation of the data source update and the set data source deviation reference value and the set data source deviation reference value.

[0025] S14: The obtained update deviation benchmark deviation is input into the time alignment granularity down-adjustment mapping set, and the corresponding time alignment granularity down-adjustment intervention value is output. Based on the obtained time alignment granularity down-adjustment intervention value, the initial time alignment granularity in the time alignment process of multi-dimensional information data is reduced. The time alignment granularity down-adjustment mapping set is pre-trained using historical update deviation benchmark deviation and time alignment granularity down-adjustment intervention values ​​set by professionals according to empirical rules, and is used to describe the mapping relationship between update deviation benchmark deviation and time alignment granularity down-adjustment intervention values.

[0026] Reducing the granularity of time alignment means that data is divided into larger time windows during the time alignment process, thereby reducing computational and storage requirements and further improving processing efficiency. This is especially important when dealing with large-scale data or when computing power is limited. At the same time, reducing the granularity of time alignment can maintain data consistency, reduce overly frequent alignment operations, and ensure synchronization between different data sources.

[0027] S15. If the average deviation of the data source update is not within the set data source update baseline range, determine whether the obtained average deviation of the data source update is greater than the set data source update maximum limit. If so, re-acquire multi-dimensional information data. Otherwise, obtain the maximum deviation of the update deviation, which reflects the degree of deviation between the average deviation of the data source update and the set data source update maximum limit. The maximum deviation of the update deviation is the result of the ratio calculation between the absolute value of the difference between the average deviation of the data source update and the set data source update maximum limit and the set data source update maximum limit.

[0028] S16, the obtained maximum deviation of the update bias is input into the time alignment granularity up-adjustment mapping set, and the corresponding time alignment granularity up-adjustment intervention value is output. Based on the obtained time alignment granularity up-adjustment intervention value, multi-dimensional information data is added to form the initial time alignment granularity in the time alignment process. The time alignment granularity up-adjustment mapping set is pre-trained using historical maximum deviations of the update bias and time alignment granularity up-adjustment intervention values ​​set by professionals based on empirical rules. It is used to describe the mapping relationship between the maximum deviation of the update bias and the time alignment granularity up-adjustment intervention value.

[0029] Increasing the granularity of time alignment means that time alignment will be more refined in data processing, thereby improving the accuracy of data synchronization. For scenarios with high-frequency changes or those requiring timeliness, precise time alignment ensures minimal time differences between data, improving the accuracy of data processing. In market capitalization management scenarios that require rapid responses to market changes, increasing the granularity of time alignment helps to more accurately reflect short-term market fluctuations, thus providing more accurate timeliness analysis.

[0030] S2, perform data synchronization checks on multi-dimensional information data to obtain data timeliness analysis results, thereby measuring the timeliness of multi-dimensional information data in the market value management process; such as Figure 2 The diagram shows a data synchronization verification flowchart for the market capitalization management diagnosis and analysis method based on the DER model provided in this embodiment of the invention. The corresponding logic is as follows: Based on the acquired time synchronization fluctuation index and data synchronization quality index, a data timeliness reflection value is obtained to quantitatively characterize the timeliness of multi-dimensional information data during the market capitalization management process. If the data timeliness reflection value is within the data timeliness set benchmark range, it indicates that the data timeliness analysis result is qualified, and it is determined that there is no need for data stream processing intervention. The model lag judgment result is then obtained. Otherwise, it indicates that the data timeliness analysis result is unqualified, and an instruction for data stream processing intervention is sent. The data stream processing intervention includes data stream transmission rate adjustment and incremental update frequency adjustment. (Reference) Figure 2 The following are the specific steps for verifying the data synchronization of multi-dimensional information data: S21, obtain the time synchronization fluctuation index, which is used to quantify the synchronization fluctuation of different data sources in multi-dimensional information data, and the data synchronization quality index, which is used to quantify the degree of missing data in multi-dimensional information data, through SQL query; specifically, the time synchronization fluctuation index is represented by the standard deviation of the synchronization timestamp of various types of data in multi-dimensional information data and the benchmark synchronization timestamp, and the data synchronization quality index is represented by the proportion of missing data in the multi-dimensional information data source during the data synchronization process to the total data volume.

[0031] S21, after inversely proportionalizing the time synchronization fluctuation index and the data synchronization quality index, weighted coupling is performed using their respective assigned weights to obtain a data timeliness reflection value used to quantify the timeliness of multi-dimensional information data in the market capitalization management process. Inverse proportionalization involves adding one and taking the reciprocal. Weighted coupling involves multiplying the time synchronization fluctuation index and the data synchronization quality index by their respective assigned weights and then adding them together. The assigned weights are the time synchronization fluctuation weight corresponding to the time synchronization fluctuation index and the data synchronization quality weight corresponding to the data synchronization quality index. The time synchronization fluctuation index and the data synchronization quality index are interconnected and mutually influential in the data timeliness reflection value. Specifically, as the data synchronization quality index decreases, the time synchronization fluctuation index decreases, and the data timeliness reflection value increases, indicating enhanced timeliness of multi-dimensional information data in the market capitalization management process.

[0032] S21. The obtained data timeliness reflection value is compared with the data timeliness setting benchmark interval. If the data timeliness reflection value is within the data timeliness setting benchmark interval, it means that the data timeliness analysis result is qualified; otherwise, it means that the data timeliness analysis result is unqualified. The data timeliness setting benchmark interval represents the interval corresponding to the timeliness setting benchmark value. The data timeliness analysis result includes qualified and unqualified timeliness analysis. The timeliness setting benchmark value in the data timeliness setting benchmark interval is obtained from the market value management analysis database. The timeliness setting benchmark value can be set as the average value of historical data timeliness reflection values.

[0033] By combining time-synchronized volatility indicators and data-synchronized quality indicators to quantify the timeliness of multi-dimensional information data in the market capitalization management process, this approach offers a more accurate and dynamic evaluation method compared to existing technologies. It enables comprehensive analysis of data timeliness from multiple perspectives, ensuring the accuracy and completeness of the analysis. Quantifying timeliness effectively avoids the time-consuming process of manually analyzing and checking data for problems. This automated timeliness assessment method makes data processing more efficient, facilitating rapid response to market changes, which is particularly important in dynamic market capitalization management.

[0034] The second step of this method is the model data flow response test, which is specifically as follows: based on the obtained data timeliness analysis results, it is determined whether there is a need for data flow processing intervention to improve the synchronization between the data source and the processing chain by intervening in the performance of the multi-dimensional information data processing link. If so, the model data flow response test is performed on the DER model used to output the market value management diagnostic results after the data flow processing intervention, and the model lag judgment result used to measure the effectiveness of the DER model in responding to dynamic data flow is obtained. Otherwise, the model lag judgment result is obtained directly.

[0035] The specific steps for determining whether there is a need for data stream processing intervention based on the timeliness analysis results of the acquired data are as follows: Q1. Determine whether the obtained data timeliness analysis results are qualified. If so, it is determined that there is no need for data flow processing intervention. Obtain the model lag judgment result used to measure the effectiveness of the DER model in responding to dynamic data flow.

[0036] It's important to add that in market capitalization management and DER models, the timeliness and dynamic responsiveness of the data stream directly impact the model's predictive accuracy and decision quality. When data timeliness is adequate, the model should undergo regular response checks to ensure its stability amidst data stream fluctuations and changes. By finely adjusting the frequency of response checks and windowing, the model can adapt to market changes and provide accurate market capitalization management decisions. Specifically, the criteria for determining that there is no need for data stream processing intervention also include: Q11, the initial verification frequency setting for model data stream response verification based on response verification frequency; the response verification frequency is the output result corresponding to the response verification frequency mapping model, which is input to the data timeliness reflection value; the response verification frequency mapping model is obtained by pre-training using historical data timeliness reflection values ​​and response verification frequencies set by professionals based on experience rules.

[0037] Q12 inputs the data timeliness reflection value into the response verification window mapping model, and outputs the detection window mapping adjustment step size used to increase the initial response verification window. Based on the increased response verification window, the response verification index is obtained during the DER model's data stream response verification process. The response verification index is de-unitized and includes a model response quantification index, which quantifies the DER model's dynamic response capability to the input data stream, represented by the model's average response time to the input data stream, and a data stream processing quantification index, which quantifies the DER model's processing capability to the input data stream, represented by the model's average memory consumption to the input data stream. The response verification window mapping model is pre-trained using historical data timeliness reflection values ​​and a detection window mapping adjustment step size set by professionals based on empirical rules.

[0038] By adjusting the frequency of response validation and the response validation window, the validation frequency and response speed of the model can be precisely controlled. Regularly validating the model and dynamically adjusting the validation frequency based on the timeliness of the response value helps to promptly identify and correct model response lags, ensuring that the model can adapt to rapid market changes, thereby making decisions in the market capitalization management process more timely and accurate.

[0039] Among them, such as Figure 3 The diagram shows the flowchart for obtaining the model lag determination result of the market value management diagnosis and analysis method based on the DER model provided in this embodiment of the invention. The corresponding logic is as follows: Based on the response test index, a model test response value is obtained to quantify the effectiveness of the DER model in responding to dynamic data streams. The obtained model test response value is compared with the model lag test interval to obtain the model lag determination result. The model lag determination result includes model test pass and model test lag. Model test pass indicates that the model test response value is within the model lag test interval, and model test lag indicates that the model test response value is not within the model lag test interval. The specific steps for obtaining the model lag determination result are as follows: First, based on the assigned weights, the response test index after inverse proportional processing is weighted and coupled to obtain the model test response value used to quantify the effectiveness of the DER model in responding to dynamic data streams. Here, weighting and coupling means multiplying the response test index after inverse proportional processing with the corresponding assigned weight and then adding them together. The inverse proportional processing here is to take the absolute value of the difference between the response test setpoint and the corresponding response test index, add one, and then perform the reciprocal operation. The assigned weights are the model response weight corresponding to the model response quantification index and the data stream processing weight corresponding to the data stream processing quantification index.

[0040] Next, the obtained model test response value is compared with the model lag test interval. If the model test response value is within the model lag test interval, the model lag determination result is recorded as "model test qualified"; otherwise, the model lag determination result is recorded as "model test lagging". The model lag test interval is the interval corresponding to the value greater than the set model test benchmark value. The model lag determination result includes "model test qualified" and "model test lagging". The model test benchmark value in the model lag test interval is obtained from the market value management analysis database, and the model test benchmark value can be set to the maximum value of the historical model test response value.

[0041] Q2, otherwise, it is determined that there is a need for data stream processing intervention, and an instruction for data stream processing intervention is sent, and the model lag judgment result after data stream processing intervention is obtained; data stream processing intervention includes data stream transmission rate adjustment for intervening in the performance of multi-dimensional information data transmission link, and incremental update frequency adjustment for intervening in the performance of multi-dimensional information data processing link.

[0042] It should be noted that the data stream transmission rate adjustment is specifically as follows: the deviation quantification processing of the data timeliness response value and the timeliness setting benchmark value is performed to obtain the deviation of the data timeliness response value; the deviation of the data timeliness response value is the result of the ratio calculation of the absolute value of the difference between the data timeliness response value and the timeliness setting benchmark value and the timeliness setting benchmark value.

[0043] The deviation of data timeliness reflection value is input into the data flow transmission rate intervention model, and the corresponding output is a data flow transmission rate intervention value used to increase the initial data flow transmission rate. This increased data flow transmission rate is then used for the transmission of multi-dimensional information data. The data flow transmission rate intervention model is pre-trained using historical data timeliness reflection value deviations and data flow transmission rate intervention values ​​set by professionals based on empirical rules. Increasing the data transmission rate can prevent data backlog and ensure that data flows into the system on time and continuously. Especially in high-frequency data scenarios, rapid data flow transmission can effectively reduce data processing latency, enabling the system to process and respond in real time. Simultaneously, increasing the data flow transmission rate means that more data can be transmitted simultaneously, i.e., more data sources can be processed at the same time, improving overall processing capacity and enabling timely responses, thus ensuring the timeliness of decision-making.

[0044] Incremental update frequency adjustment specifically involves inputting the deviation of data timeliness reflection values ​​into the incremental update frequency intervention model. The corresponding output is an incremental update frequency intervention value used to increase the initial incremental update frequency. This increased incremental update frequency is then used to update multi-dimensional information data. The incremental update frequency intervention model is pre-trained using historical data timeliness reflection value deviations and incremental update frequency intervention values ​​set by professionals based on empirical rules. Increasing the incremental update frequency ensures continuous data updates within a short period, thereby improving the model's responsiveness to market and external data fluctuations. Frequent incremental updates can promptly reflect real-time market changes, improving the timeliness and accuracy of decision-making.

[0045] The third step of this method is the output of market capitalization management diagnostic results. Specifically, based on the obtained model lag assessment results, it is determined whether to output and visualize the market capitalization management diagnostic results. If so, an instruction to visualize the market capitalization management assessment results is sent; otherwise, model parallel processing optimization is performed to improve the computational efficiency and response speed of the DER model. The specific process for determining whether to output and visualize the market capitalization management diagnostic results based on the obtained model lag assessment results is as follows: If the model lag determination result is that the model verification is qualified, then send a command to visualize the market value management determination result and continuously monitor whether the model verification response value is less than the set model verification benchmark value.

[0046] It should also be added that the instruction to visualize the market value management judgment results is included, followed by: The model verification response values ​​are input into the data synchronization verification cycle mapping set and the model data flow response verification cycle mapping set, and the corresponding output data synchronization verification cycle step size and model data flow response verification cycle step size are output. Data synchronization verification is performed on multi-dimensional information data using the data synchronization verification cycle step size. Model data flow response verification is performed on the DER model using the model data flow response verification cycle step size, and the initial verification frequency within the model data flow response verification cycle is set based on the obtained optimized verification frequency. The optimized verification frequency is the output result corresponding to the model corresponding to the optimized verification frequency mapping model, which is input into the model verification response values. Both the data synchronization test cycle mapping set and the model data flow response test cycle mapping set are pre-built in the market value management analysis database. The data synchronization test cycle mapping set is pre-trained using historical model test response values ​​and data synchronization test cycle step sizes set by professionals based on empirical rules, and is used to describe the mapping relationship between model test response values ​​and data synchronization test cycle step sizes. The model data flow response test cycle mapping set is pre-trained using historical model test response values ​​and model data flow response test cycle step sizes set by professionals based on empirical rules, and is used to describe the mapping relationship between model test response values ​​and model data flow response test cycle step sizes. The optimized verification frequency mapping model is pre-trained using historical model test response values ​​and optimized verification frequencies set by professionals based on empirical rules.

[0047] If the obtained model lag determination result is model test lag, then a command for performing model parallel processing optimization is sent. The specific process for performing model parallel processing optimization is as follows: First, obtain the model test response value deviation, which is used to quantify the degree of deviation between the model test response value and the set model test benchmark value. The model test response value deviation is the result of the ratio of the absolute value of the difference between the model test response value and the set model test benchmark value to the set model test benchmark value.

[0048] Secondly, the deviation of the model test response value is input into the parallelism intervention mapping table and the task partitioning granularity intervention mapping table, respectively, to output the parallelism increase intervention value used to increase the initial parallelism and the task partitioning granularity decrease intervention value used to decrease the initial task partitioning granularity. The parallelism intervention mapping table is pre-trained using historical model test response value deviation and parallelism increase intervention values ​​set by professionals based on empirical rules, and is used to describe the mapping relationship between the model test response value deviation and the parallelism increase intervention value. The task partitioning granularity intervention mapping table is pre-trained using historical model test response value deviation and task partitioning granularity decrease intervention values ​​set by professionals based on empirical rules, and is used to describe the mapping relationship between the model test response value deviation and the task partitioning granularity decrease intervention value.

[0049] By increasing the parallelism of the model, multiple computational tasks can be executed simultaneously, significantly improving data processing speed, which is crucial for handling large-scale data streams and complex computational tasks. Reducing the granularity of task partitioning means breaking down large tasks into more and smaller subtasks, enabling finer-grained parallel computing. This makes the computation process more refined and flexible, further improving responsiveness and efficiency when processing data. In dynamic market environments, highly parallel models can update rapidly as data changes, providing real-time decision support and improving the responsiveness of market capitalization management. Reducing task granularity means each computational unit processes a smaller amount of data, making the computation process more refined. This helps adjust computational strategies based on real-time data in dynamic data streams, improving the model's ability and accuracy in handling complex data streams.

[0050] Next, the model data flow response is verified based on the adjusted parallelism and task partitioning granularity. The model data flow response verification is used to re-obtain the model lag determination results.

[0051] Then, determine whether the re-acquired model lag judgment result is still a model verification lag. If so, send a model data stream response verification failure prompt and re-enter multi-dimensional information data. Otherwise, send a command to visualize the market value management judgment result.

[0052] The decision to output and visualize market value management diagnostic results is dynamically determined based on the model's lag assessment results. This ensures the reliability of the output results while avoiding interference from unqualified model results in decision-making.

[0053] like Figure 4 The diagram shown is a structural schematic of the market value management diagnosis and analysis system based on the DER model provided in this embodiment of the invention. The market value management diagnosis and analysis system based on the DER model provided in this embodiment of the invention includes: a data timeliness analysis module, a model data flow response verification module, and a market value management diagnosis result output module.

[0054] The data timeliness analysis module is used to acquire multi-dimensional information data that reflects the overall information of the market value management process, to perform data synchronization verification on the multi-dimensional information data, and to obtain data timeliness analysis results to measure the timeliness of multi-dimensional information data in the market value management process.

[0055] The model data flow response verification module is used to determine whether there is a need for data flow processing intervention based on the timeliness analysis results of the acquired data. This intervention aims to improve the synchronization between the data source and the processing chain by intervening in the performance of the multi-dimensional information data processing link. If so, the model data flow response verification is performed on the DER model used to output the market value management diagnostic results after the data flow processing intervention is carried out. The model lag judgment result is obtained to measure the effectiveness of the DER model in responding to dynamic data flow. Otherwise, the model lag judgment result is obtained directly.

[0056] The market value management diagnostic result output module is used to determine whether to output and visualize the market value management diagnostic result based on the obtained model lag judgment result. If so, it sends a command to visualize the market value management judgment result; otherwise, it performs model parallel processing optimization to improve the computational efficiency and response speed of the DER model.

[0057] like Figure 5 The image shown is one of the report interface diagrams of the market value management diagnosis and analysis system based on the DER model provided in this embodiment of the invention; by Figure 5 As can be seen, the daily market value provided in this embodiment of the invention includes four modules: diagnosis, report, knowledge base, and my profile. Taking the report module as an example, this interface can be used to display the number of companies followed and their specific information.

[0058] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0060] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0061] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0062] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0063] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0064] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for diagnosis and analysis of market capitalization management based on a DER model, characterized in that, The method comprises the following steps: acquiring multi-dimensional information data reflecting the panoramic information of the market value management process, performing data synchronization verification on the multi-dimensional information data, obtaining data timeliness analysis results to measure the timeliness of the multi-dimensional information data in the market value management process; based on the obtained data timeliness analysis results, determining whether there is a need for data stream processing intervention to improve the synchronization of the data source and the processing chain by intervening in the performance of the multi-dimensional information data processing link, if yes, performing model data stream response verification on the DER model for outputting the market value management diagnosis results after data stream processing intervention, obtaining model lag judgment results for measuring the response effectiveness of the DER model to dynamic data stream, otherwise, directly obtaining the model lag judgment results; determining whether to output the market value management diagnosis results and perform visualization according to the obtained model lag judgment results, if yes, sending an instruction for visualizing the market value management diagnosis results, otherwise, performing model parallel processing optimization to improve the calculation efficiency and response speed of the DER model.

2. The DER model based market value management diagnostic and analysis method of claim 1, wherein, The acquisition of multi-dimensional information data reflecting the panoramic information of the market value management process further comprises the following steps: acquiring a data source update average deviation for reflecting the average deviation degree between the update time stamps of various data in the multi-dimensional information data and the reference update time stamps; comparing the acquired data source update average deviation with a set data source update reference interval, wherein the data source update reference interval represents an interval corresponding to a set data source deviation reference value; if the data source update average deviation is within the set data source update reference interval, acquiring an update deviation reference deviation for reflecting the deviation degree between the data source update average deviation and the set data source deviation reference value; inputting the obtained update deviation reference deviation into a time alignment granularity down mapping set, corresponding to outputting a time alignment granularity down intervention value, and adjusting the initial time alignment granularity in the time alignment processing process of the multi-dimensional information data according to the obtained time alignment granularity down intervention value; if the data source update average deviation is not within the set data source update reference interval, determining whether the acquired data source update average deviation is greater than a set data source update maximum limit value, if yes, re-acquiring the multi-dimensional information data, otherwise, acquiring an update deviation maximum deviation for reflecting the deviation degree between the data source update average deviation and the set data source update maximum limit value; inputting the obtained update deviation maximum deviation into a time alignment granularity up mapping set, corresponding to outputting a time alignment granularity up intervention value, and adjusting the initial time alignment granularity in the time alignment processing process of the multi-dimensional information data according to the obtained time alignment granularity up intervention value.

3. The DER model based market value management diagnostic and analysis method of claim 1, wherein, The data synchronization verification on the multi-dimensional information data comprises the following steps: acquiring a time synchronization fluctuation index for quantifying the synchronization fluctuation of different data sources in the multi-dimensional information data, and a data synchronization quality index for quantifying the missing degree of the multi-dimensional information data; The time synchronization fluctuation index and the data synchronization quality index are inversely proportional processed, and are coupled by assigning corresponding weights to obtain a data timeliness reflection value for quantitatively representing the timeliness of the multi-dimensional information data in the market value management process; If the data timeliness reflection value is within the data timeliness setting reference interval, it indicates that the data timeliness analysis result is timeliness analysis qualified, otherwise, it indicates that the data timeliness analysis result is timeliness analysis unqualified; The data timeliness setting reference interval represents an interval greater than the timeliness setting reference value, and the data timeliness analysis result includes timeliness analysis qualified and timeliness analysis unqualified.

4. The DER model based market value management diagnostic and analysis method of claim 3, wherein, The specific steps for determining whether there is a need for data stream processing intervention based on the obtained data timeliness analysis result are as follows: If the obtained data timeliness analysis result is timeliness analysis qualified, it is determined that there is no need for data stream processing intervention, and a model lag judgment result for measuring the effectiveness of the DER model in responding to dynamic data streams is obtained; Otherwise, it is determined that there is a need for data stream processing intervention, an instruction for data stream processing intervention is sent, and a model lag judgment result after data stream processing intervention is obtained; The data stream processing intervention includes data stream transmission rate adjustment for intervening in the performance of the multi-dimensional information data transmission link, and incremental update frequency adjustment for intervening in the performance of the multi-dimensional information data processing link.

5. The DER model based market value management diagnostic and analysis method of claim 4, wherein, After determining that there is no need for data stream processing intervention, the following steps are further included: Initial check frequency setting for model data stream response verification based on response check frequency; The response check frequency is the output result corresponding to the input of the data timeliness reflection value into the response check frequency mapping model; The data timeliness reflection value is input into the response verification window mapping model, and the detection window mapping up adjustment step for adjusting the initial response verification window is output, and the response verification index of the DER model in the model data stream response verification process is obtained according to the adjusted response verification window; The response verification index includes a model response quantitative index for quantitatively reflecting the dynamic response capability of the DER model to the input data stream, and a data stream processing quantitative index for quantitatively reflecting the data stream processing capability of the DER model.

6. The DER model based market value management diagnostic and analysis method of claim 4, wherein, The data stream transmission rate adjustment is as follows: The data timeliness reflection value and the timeliness setting reference value are subjected to deviation quantification processing to obtain a data timeliness reflection value deviation; The data timeliness reflection value deviation is input into the data stream transmission rate intervention model, and the data stream transmission rate intervention value for adjusting the initial data stream transmission rate is output, and the multi-dimensional information data is transmitted at the adjusted data stream transmission rate; The incremental update frequency adjustment is as follows:

7. The DER model based market value management diagnostic and analysis method of claim 5, wherein, The data timeliness reflection value deviation is input into the incremental update frequency intervention model, and the incremental update frequency intervention value for adjusting the initial incremental update frequency is output, and the multi-dimensional information data is updated at the adjusted incremental update frequency. The specific steps for obtaining the model lag judgment result are as follows: The response test indicators after the inverse proportional processing are respectively coupled by weighting based on the assigned weights to obtain a model test reflection value for quantitatively representing the response effectiveness of the DER model to the dynamic data flow; The model test reflection value is compared with a model lag test interval, if the model test reflection value is within the model lag test interval, the model lag test result is recorded as model test qualified, otherwise, the model lag test result is recorded as model test lag; The model lag test interval is greater than an interval corresponding to a set model test reference value; The model lag test result includes the model test qualified and the model test lag.

8. The DER model based market value management diagnostic and analysis method of claim 7, wherein, The specific process of determining whether to output the market value management diagnosis result and visualizing according to the obtained model lag test result is as follows: If the obtained model lag test result is the model test qualified, an instruction for visualizing the market value management diagnosis result is sent, and whether the model test reflection value is less than the set model test reference value is continuously monitored; If the obtained model lag test result is the model test lag, an instruction for performing model parallel processing optimization is sent; The instruction for visualizing the market value management diagnosis result further includes: The model test reflection value is input into a data synchronization test period mapping set and a model data flow response test period mapping set, and corresponding data synchronization test period steps and model data flow response test period steps are output; The multi-dimensional information data is subjected to data synchronization test at the data synchronization test period steps; The DER model is subjected to model data flow response test at the model data flow response test period steps, and an initial calibration frequency setting within the model data flow response test period is performed based on an obtained optimization calibration frequency, the optimization calibration frequency being an output result corresponding to inputting the model test reflection value into an optimization calibration frequency mapping model.

9. The DER model based market value management diagnostic and analysis method of claim 8, wherein, The specific process of performing model parallel processing optimization is as follows: A model test reflection value deviation is obtained for quantitatively measuring the deviation between the model test reflection value and the set model test reference value; The model test reflection value deviation is input into a parallel degree intervention mapping table and a task division granularity intervention mapping table, respectively, to output a parallel degree up intervention value for adjusting an initial parallel degree and a task division granularity down intervention value for adjusting an initial task division granularity, respectively; Model data flow response test verification is performed according to the adjusted parallel degree and task division granularity, the model data flow response test verification being used to reacquire the model lag test result; It is judged whether the reacquired model lag test result is still the model test lag, if yes, a model data flow response test verification failure prompt is sent, and the multi-dimensional information data is re-input, otherwise, the instruction for visualizing the market value management diagnosis result is sent.

10. A system for diagnosis and analysis of equity management based on the DER model, applying the method for diagnosis and analysis of equity management based on the DER model according to any one of claims 1 to 9, characterized in that, It includes: A data timeliness analysis module, a model data flow response test module, and a market value management diagnosis result output module; The data timeliness analysis module is used to acquire multi-dimensional information data for reflecting panoramic information of a market value management process, perform data synchronization test on the multi-dimensional information data, and obtain a data timeliness analysis result to measure the timeliness of the multi-dimensional information data in the market value management process. The model data stream response inspection module is configured to determine whether there is a need for data stream processing intervention based on the acquired data timeliness analysis result to improve the synchronization of the data source and the processing chain by intervening in the performance of the multi-dimensional information data processing link, if yes, then the DER model for outputting the market value management diagnosis result is subjected to model data stream response inspection after data stream processing intervention, and a model lag judgment result for measuring the response effectiveness of the DER model to the dynamic data stream is obtained, otherwise, the model lag judgment result is directly acquired; The market value management diagnosis result output module is configured to determine whether to output the market value management diagnosis result and visualize it according to the acquired model lag judgment result, if yes, then an instruction for visualizing the market value management diagnosis result is sent, otherwise, the model parallel processing optimization is performed to improve the calculation efficiency and response speed of the DER model.

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