A financial informationization data processing method based on encryption

Abstract

The application discloses a kind of based on encryption financial informatization data processing method, it is related to financial informatization data processing technical field, specifically includes the following steps: to the key consumption coefficient and key supplement coefficient of generation constructs key consumption evaluation model, generates key exhaustion risk coefficient, predicts in high-frequency financial data acquisition task the consumption degree of key in key pool whether it needs to be supplemented, and according to the prediction result respectively generates supplement trigger signal and supplement non-trigger signal;In the case where generating supplement trigger signal, continuously acquire the key exhaustion risk coefficient of several subsequent generations, generate different levels of key supplement signal, and according to the different levels of key supplement signal generated respectively take corresponding key supplement measures.The application solves the problem of key pool resource exhaustion in high-frequency financial data acquisition task, through real-time monitoring and dynamic replenishment strategy, ensures the continuity and data security of encryption task.

Description

Technical Field

[0001] This invention relates to the field of financial information data processing technology, and specifically to an encryption-based financial information data processing method. Background Technology

[0002] Financial informatization refers to the digital, automated, and intelligent transformation of all aspects of financial management through modern information technology (such as computers, networks, and cloud computing) to achieve efficient collection, processing, analysis, and management of financial data, thereby supporting corporate decision-making and improving financial management efficiency. However, with the increasing level of financial informatization, the scale of corporate data has expanded dramatically, and the types of data have become increasingly complex. This data not only includes internal accounting records and budget preparation but also sensitive information related to external customers and suppliers, making data processing a core element in ensuring the efficient operation of the system. Simultaneously, the high value and sensitivity of financial data make it a primary target for cyberattacks. Data leakage or tampering can cause serious economic losses and reputational crises for enterprises. Therefore, processing financial informatization data based on encryption is crucial. By adopting encryption technology, strong security can be provided at every stage of data collection, transmission, storage, and analysis, not only preventing the theft of sensitive information but also ensuring the integrity and authenticity of the data, laying the foundation for enterprises to build a secure and reliable financial management system in the wave of informatization.

[0003] Existing encryption-based financial information data processing technologies primarily ensure data security and integrity through multi-layered encryption mechanisms across four stages: data acquisition, transmission, storage, and processing. In the data acquisition stage, the system performs preliminary encryption on financial data using encryption sensors or client software, for example, employing symmetric encryption algorithms (such as AES) for rapid encryption to ensure the original data is not stolen during acquisition. In the data transmission stage, asymmetric encryption (such as RSA) and digital certificates are used to establish a secure channel, combined with transport layer security protocols (such as TLS / SSL) for double encryption, preventing interception or tampering during network transmission. In the data storage stage, hash algorithms (such as SHA-256) are used to generate digests of the financial data for storage. Simultaneously, distributed encryption technology is employed on the data storage blocks, splitting the data into multiple encrypted fragments stored across distributed storage nodes to reduce the risk of single-point attacks. In the data processing stage, dynamic encryption algorithms and access control technology dynamically allocate decryption keys based on user identity and operational needs, allowing only authorized users to partially decrypt and perform calculations on the data, ensuring the principle of minimal data exposure. In addition, existing technologies are combined with blockchain technology to achieve traceability and immutability of data processing through smart contract execution and encrypted ledger recording of transaction data, thereby further enhancing the security and credibility of financial data.

[0004] The existing technology has the following shortcomings:

[0005] In high-frequency financial data acquisition tasks, each piece of data needs to be encrypted with a key before processing. The dynamic allocation of keys typically relies on a pre-loaded key pool for management. In this scenario, if data acquisition tasks are intensive and encryption calls are frequent, and the key pool's update speed cannot keep up with the demand, key resources may be quickly exhausted, preventing subsequent data from being encrypted. Existing encryption-based financial data processing technologies lack real-time monitoring and dynamic update mechanisms for key pool resource management, failing to predict and replenish key pool usage in advance, thus leading to acquisition task interruptions. This interruption directly results in some data remaining unencrypted, affecting data integrity and transmission security, further severely impacting the accuracy of financial data and the efficient operation of the system, and potentially causing delays in subsequent data analysis and decision-making.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide an encrypted financial information data processing method to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for processing encrypted financial information data, specifically comprising the following steps:

[0009] In high-frequency financial data acquisition tasks, the key pool is monitored in real time during the financial data acquisition process. The key call frequency, the number of remaining keys and the key replenishment delay are recorded. The consumption of the key pool is also monitored in real time to see if it reaches the preset security threshold.

[0010] When the consumption of the key pool is detected to reach the preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is obtained in real time, and after obtaining it, it is analyzed to generate key consumption coefficient and key replenishment coefficient respectively.

[0011] A key consumption assessment model is constructed based on the generated key consumption coefficient and key replenishment coefficient to generate a key exhaustion risk coefficient. After generation, the coefficient is analyzed to predict whether the key consumption in the key pool needs to be replenished in high-frequency financial data collection tasks. Based on the prediction results, replenishment trigger signals and replenishment non-trigger signals are generated respectively.

[0012] When a supplementary trigger signal is generated, several key exhaustion risk coefficients generated subsequently are continuously acquired and analyzed to generate key supplementary signals of different levels. Corresponding key supplementary measures are then taken according to the generated key supplementary signals of different levels.

[0013] The status of the key pool is continuously monitored, and key management parameters and allocation strategies are dynamically adjusted based on real-time monitoring results. Historical data is also stored and analyzed to optimize key replenishment frequency and allocation strategies.

[0014] Preferably, when the key pool consumption reaches a preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is acquired in real time, and analyzed to generate key consumption coefficients and key replenishment coefficients, specifically including the following steps:

[0015] When the consumption of the key pool is detected to reach the preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is obtained in real time and preprocessed after acquisition.

[0016] Extract key consumption monitoring information and key replenishment response performance information from the preprocessed key pool consumption status information;

[0017] The extracted key consumption monitoring information and key replenishment response performance information are analyzed to generate key consumption coefficients and key replenishment coefficients, respectively.

[0018] Preferably, the logic for obtaining the key consumption coefficient is as follows:

[0019] Extract key consumption monitoring information from the preprocessed key pool consumption status information. Specifically, this includes the number of keys actually invoked at different times within a certain period during high-frequency financial data collection tasks, the rate of decrease in the number of keys in the key pool, and the number of data collection tasks that completed encryption processing. These are then labeled as NC. m DK m and PR m NC m DK represents the number of keys actually invoked at time m within a certain period during a high-frequency financial data acquisition task. m PR represents the rate of change of the decrease in the number of keys in the key pool at time m within a certain period during a high-frequency financial data acquisition task. m This represents the number of data collection tasks that complete encryption processing at time m within a certain period of time in a high-frequency financial data collection task, where m = 1, 2, 3, ..., g, and g is a positive integer;

[0020] The key consumption coefficient is calculated using the following formula:

[0021]

[0022] In the formula, KCC is the key consumption coefficient.

[0023] Preferably, the logic for obtaining the key supplementation coefficient is as follows:

[0024] Extract key replenishment response performance information from the preprocessed key pool consumption status information. Specifically, this includes the number of keys added to the key pool after key replenishment operations are completed at different times within a certain period during a high-frequency financial data acquisition task, the number of times key replenishment operations are triggered, and the rate at which keys are added to the key pool. These are then denoted as QS. n FT n and RS n QS n FT represents the number of new keys added to the key pool after the key replenishment operation at time n within a certain period of time during a high-frequency financial data acquisition task. n RS represents the number of times a key replenishment operation is triggered at time n within a certain period of time during a high-frequency financial data acquisition task. n This represents the rate at which new keys are added to the key pool at time n within a certain period during a high-frequency financial data acquisition task, where n = 1, 2, 3, ..., u, and u is a positive integer;

[0025] The key supplementation coefficient is calculated using the following formula:

[0026]

[0027] In the formula, KSC is the key supplement coefficient.

[0028] Preferably, a key consumption evaluation model is constructed based on the generated key consumption coefficient KCC and key replenishment coefficient KSC, and a key exhaustion risk coefficient is generated by weighted summation. The specific calculation formula is as follows:

[0029] KDR=ω1*KCC-ω2*KSC

[0030] In the formula, KDR is the key depletion risk coefficient, ω1 and ω2 are the non-zero weight coefficients of the key consumption coefficient KCC and the key replenishment coefficient KSC, respectively, and ω1+ω2=1.

[0031] Preferably, a pre-set key depletion risk threshold KDR is determined. yuzhi After determination, it is compared with the generated key depletion risk coefficient KDR. Based on the comparison result, it is predicted whether the key consumption in the key pool needs to be replenished in the high-frequency financial data collection task. Based on the prediction result, a replenishment trigger signal and a replenishment non-trigger signal are generated respectively. The specific comparison analysis is as follows:

[0032] If KDR≤KDR yuzhiIn high-frequency financial data acquisition tasks, the key consumption in the key pool does not need to be replenished, and a supplementary non-trigger signal is generated.

[0033] If KDR>KDR yuzhi In high-frequency financial data acquisition tasks, the key pool needs to be replenished to compensate for the depletion of keys, and a replenishment trigger signal needs to be generated.

[0034] Preferably, when a supplementary trigger signal is generated, several subsequently generated key exhaustion risk coefficients are continuously acquired and recalibrated as KDR. x x is the number of several key exhaustion risk coefficients subsequently generated in the case of generating a supplementary trigger signal, x = 1, 2, 3, ..., d, where d is a positive integer;

[0035] Calculate the key exhaustion risk coefficient (KDR) for several subsequently generated keys. x The average value of KDR is calculated according to the formula:

[0036] Calculate the key exhaustion risk coefficient (KDR) for several subsequently generated keys. x Standard deviation KDR σ According to the formula:

[0037] Preferably, a preset average threshold is determined for several key exhaustion risk coefficients subsequently generated in the event of generating a supplementary trigger signal. and preset standard deviation threshold After determination, it is compared with the average value of several key exhaustion risk coefficients, KDR. - Sum of standard deviations and KDR σ A comparison is performed, and different levels of key supplementation signals are generated based on the comparison results. Corresponding key supplementation measures are then taken according to the different levels of key supplementation signals generated. The specific comparison analysis is as follows:

[0038] like and The first-level key replenishment signal is generated, and the key replenishment measures taken are as follows: the emergency replenishment process is initiated, resources are allocated for replenishment with priority, the replenishment frequency is increased, and the key pool is restored to a security level.

[0039] like and The second-level key supplementation signal is generated, and the key supplementation measures adopted are: increasing the supplementation frequency, optimizing the supplementation strategy to reduce the risk of volatility, while ensuring that the supplementation does not exceed the actual needs.

[0040] like and The third-level key replenishment signal is generated, and the key replenishment measures adopted are: maintaining the existing replenishment strategy, maintaining the current replenishment frequency, and ensuring that the key pool can stably support subsequent tasks;

[0041] like and The fourth-level key supplement signal is generated, and the key supplement measures adopted are: maintaining a low-frequency supplement strategy to avoid resource waste.

[0042] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0043] 1. This invention solves the problems of rapid key resource consumption and untimely replenishment in traditional financial information data processing methods by real-time monitoring and dynamic management of the key pool's usage status. This is particularly relevant in high-frequency financial data acquisition tasks, where a delayed key pool update can lead to task interruptions and security risks. By extracting key pool consumption status information and generating key consumption and replenishment coefficients, the system can assess the key pool's consumption level and replenishment capacity in real time. This allows for early prediction of whether key replenishment is needed, preventing data acquisition tasks from failing due to key pool resource depletion and ensuring the continuity and security of data processing.

[0044] 2. This invention, by constructing a key consumption assessment model and a key exhaustion risk coefficient, quantifies the consumption and replenishment efficiency of the key pool. Based on changes in the risk coefficient, it dynamically generates replenishment trigger signals and replenishment signals of different levels, ensuring timely replenishment of the key pool under high load. For replenishment signals of different levels, the system dynamically adjusts the replenishment frequency and allocation strategy, flexibly adjusting the replenishment priority and resource allocation according to the urgency of the task and resource needs. This intelligent and flexible replenishment mechanism not only ensures the stable operation of the key pool but also minimizes unnecessary resource waste and improves resource utilization efficiency.

[0045] 3. By continuously monitoring the key pool's status and analyzing historical data, this invention optimizes the key replenishment frequency and allocation strategy. Based on the storage and analysis of historical data, the system can continuously optimize the replenishment strategy and dynamically adjust the replenishment frequency according to different task loads, ensuring the key pool remains in optimal condition under various environments. This functionality not only improves the system's response speed but also enhances the intelligence level of key pool management, enabling the system to adaptively adjust resource allocation, thereby improving the overall efficiency and security of financial data collection tasks. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0047] Figure 1 This is a flowchart illustrating an encrypted financial information data processing method according to the present invention. Detailed Implementation

[0048] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0049] This invention provides, for example Figure 1 The method for processing encrypted financial information data, as shown, specifically includes the following steps:

[0050] In high-frequency financial data acquisition tasks, the key pool is monitored in real time during the financial data acquisition process. The key call frequency, the number of remaining keys and the key replenishment delay are recorded. The consumption of the key pool is also monitored in real time to see if it reaches the preset security threshold.

[0051] To monitor the key usage status of the key pool in real time during financial data collection, an embedded software module can continuously monitor the key pool resources. Whenever a data collection task requires encryption, the software module automatically records the key number used, the key call time, and the execution frequency of the encryption operation. This information can be stored in system logs or a dedicated database to obtain the key pool call frequency, remaining key quantity, and key replenishment delay in real time. The key call frequency can be counted by the number of key uses per unit time, the remaining key quantity can be automatically refreshed with each key pool update operation, and the key replenishment delay is calculated by recording the timestamp of the key pool resource update and the time difference between replenishment completion. By regularly collecting and recording this data, the system can clearly reflect the key pool usage status and ensure timely detection of potential risks of key pool resource consumption.

[0052] Once the real-time monitoring module has collected the key pool usage data, the next step is to monitor the key pool's consumption in real time. A dynamic threshold detection mechanism can be set to determine if the key pool is about to run out of resources. For example, the software can be set to perform a periodic check (e.g., every minute or hour) to assess the remaining resources of the key pool. If the number of remaining keys falls below a preset threshold, or if the key usage frequency is too high and the replenishment rate cannot keep up, the system will automatically trigger an alarm and enter replenishment mode. The detection process will dynamically adjust according to the key pool consumption rate, ensuring that necessary replenishment measures are taken before resource usage reaches a preset security value, preventing task interruptions or data encryption failures.

[0053] The preset security threshold can be determined through historical data analysis and dynamic adjustment based on task load. First, the average key consumption rate in historical financial data collection tasks can be analyzed, and the standard key usage value for each collection task over a certain period can be calculated. Second, the key pool consumption can be categorized according to the type of collection task (e.g., high-frequency trading, batch trading, etc.), and different thresholds can be set for different types of tasks. For example, the key consumption rate of high-frequency trading tasks may be faster, so a lower security threshold can be set, while a higher threshold can be set for low-frequency trading tasks. To adapt to the needs of different workloads, the threshold can be adjusted based on real-time load at the start of the task, thereby improving the responsiveness to the key pool usage status.

[0054] This design effectively addresses the issue of key pool resources being rapidly depleted due to frequent encryption operations in high-frequency financial data acquisition tasks, preventing data encryption failures caused by insufficient key pool resources. By monitoring the key pool usage status in real time, the system can dynamically acquire key pool resource consumption and provide early warnings and replenishment based on actual consumption. Furthermore, by setting reasonable security thresholds, the system can take timely supplementary measures when key pool resources are nearing depletion, preventing interruptions in encryption operations and risks to data security. This approach ensures the continuity of financial data acquisition tasks and the integrity of data encryption, solving the core problem in existing technologies where "untimely key pool updates and unmonitored resource consumption lead to task interruptions," thus providing a more secure and stable guarantee for financial management.

[0055] When the consumption of the key pool is detected to reach the preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is obtained in real time, and after obtaining it, it is analyzed to generate key consumption coefficient and key replenishment coefficient respectively.

[0056] In this embodiment, when the consumption of the key pool is detected to reach a preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is acquired in real time, and then analyzed to generate key consumption coefficients and key replenishment coefficients, specifically including the following steps:

[0057] When the consumption of the key pool is detected to reach the preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is obtained in real time and preprocessed after acquisition.

[0058] To achieve the goal of "real-time acquisition of key pool consumption status information during high-frequency financial data acquisition tasks when the key pool consumption reaches a preset security threshold," a monitoring mechanism based on real-time data stream processing can be employed. Specifically, monitoring software is embedded in the encryption module of the data acquisition task. This software tracks the usage of each key in the key pool in real time. Data such as the remaining number of keys, the number of key calls, and the incremental key consumption are collected periodically and sent to the central monitoring system in real time. The monitoring system sets a threshold; when the key pool consumption reaches the predetermined security threshold, the system immediately triggers an alarm and begins recording relevant key consumption status information, such as the frequency and timing of key calls. The system can also dynamically adjust the data acquisition frequency based on the workload of the acquisition task to ensure efficient acquisition of real-time key pool consumption information even under increased data acquisition pressure.

[0059] Preprocessing is crucial to ensure that the acquired key pool consumption status information accurately and clearly reflects the actual usage of the key pool, eliminating any noise or irrelevant data. Specific preprocessing steps include data cleaning, outlier removal, and normalization. First, the real-time acquired key pool consumption information is cleaned to remove irrelevant fields or abnormal data caused by equipment malfunctions or acquisition errors. Next, algorithms detect and remove outliers; for example, if the key call frequency is abnormally high or low within a certain time period, this may indicate acquisition errors or system failures and needs to be removed from the dataset. Finally, the data is normalized to ensure comparability of data from different time periods or under different workloads, thus making the subsequent analysis results more reliable. These preprocessing steps can be implemented using data processing software modules to ensure that the key pool consumption status information has high quality and high reliability during analysis.

[0060] Extract key consumption monitoring information and key replenishment response performance information from the preprocessed key pool consumption status information;

[0061] To extract key consumption monitoring and key replenishment response performance information from preprocessed key pool consumption status information, an automated software module based on data tagging and feature extraction can be designed. First, the preprocessed data is typically stored in a structured database, such as a relational database or a time-series database. The software module can extract relevant field data from the database via SQL queries or API interfaces. During extraction, key consumption monitoring information can be obtained by looking up features such as key call frequency, consumption increment, and the number of encryption requests per unit time. These feature values ​​reflect the consumption trend and rate of the key pool. For key replenishment response performance information, the software module can extract the number of keys replenished each time, the replenishment trigger time, and the actual replenishment completion time from the replenishment log. By calculating the replenishment latency and replenishment amount, the replenishment response efficiency can be evaluated. Through this extraction method, the software can automatically extract the required monitoring and response performance information according to preset conditions, ensuring the accuracy of subsequent analysis and decision-making.

[0062] The extracted key consumption monitoring information and key replenishment response performance information are analyzed to generate key consumption coefficients and key replenishment coefficients, respectively.

[0063] In this embodiment, the logic for obtaining the key consumption coefficient is as follows:

[0064] Extract key consumption monitoring information from the preprocessed key pool consumption status information. Specifically, this includes the number of keys actually invoked at different times within a certain period during high-frequency financial data collection tasks, the rate of decrease in the number of keys in the key pool, and the number of data collection tasks that completed encryption processing. These are then labeled as NC. m DK m and PR m NC m DK represents the number of keys actually invoked at time m within a certain period during a high-frequency financial data acquisition task. m PR represents the rate of change of the decrease in the number of keys in the key pool at time m within a certain period during a high-frequency financial data acquisition task. m This represents the number of data collection tasks that complete encryption processing at time m within a certain period of time in a high-frequency financial data collection task, where m = 1, 2, 3, ..., g, and g is a positive integer;

[0065] To achieve real-time acquisition of the actual number of keys invoked at different times within a period of time during high-frequency financial data acquisition tasks, the rate of decrease in the number of keys in the key pool, and the number of data acquisition tasks that have completed encryption processing, an event-driven real-time data monitoring system can be designed. First, the number of keys invoked can be monitored by embedding an event listener in the key management module. When an encryption task begins, the system automatically records the number of keys used. Each time an encryption task completes, the number of keys in the key pool is updated. The system can query the database in real time to obtain the remaining number of keys in the key pool and calculate the rate of decrease in keys by comparing it with the previous time. Second, to monitor the encryption processing of data acquisition tasks in real time, integration with the acquisition task management module can be achieved to obtain the processing status of each data acquisition task in real time, including the start and completion times of the encryption tasks. The system periodically updates the status of these tasks in the background and counts the number of tasks completed per unit time. All this information can be calculated and recorded in real time in the background through a scheduled task scheduler (such as a timestamp-based triggering mechanism), ensuring that the data at each moment accurately reflects the use of the key pool and the encryption progress of the data acquisition tasks. This data will be stored in a time-series database for efficient querying and analysis, ensuring that the system can respond in real time and trigger supplementary or other decision-making actions.

[0066] The key consumption coefficient is calculated using the following formula:

[0067]

[0068] In the formula, KCC is the key consumption coefficient.

[0069] The formula for calculating the key consumption coefficient KCC is designed to comprehensively reflect the key pool consumption in high-frequency financial data acquisition tasks. Firstly, each term in the formula represents a different dimension of key pool consumption: NC m This represents the number of keys actually invoked at time m, measuring the immediate usage frequency of the key pool; DK m This reflects the rate of change in the number of keys in the key pool, revealing the rate of key pool consumption; PR m This represents the number of encryption processing tasks completed at each moment, indicating the processing load of the task. The formula first uses... The basic ratio of key consumption rate was calculated to avoid a denominator of zero and to balance the consumption differences between different tasks. Then, This exponential term reinforces the non-linear relationship between key consumption and task processing rate; that is, the relationship between key invocation volume and task processing load grows exponentially, reflecting the sharp increase in key pool consumption under high load. Following this, ln(1+PR) mLogarithmic operations further smooth out the impact of task processing volume, ensuring a relatively stable effect under different acquisition task volumes and preventing excessive amplification of the consumption coefficient when the task volume is too large. Finally, a comprehensive key consumption coefficient is obtained by weighted averaging and combining the consumption at all times, which can accurately reflect the consumption trend of the key pool under the overall task load, providing strong support for subsequent key replenishment decisions.

[0070] The Key Consumption Coefficient (KCC) directly reflects the level of key pool consumption during high-frequency financial data acquisition tasks. A higher KCC value indicates a faster consumption rate per unit time, typically suggesting frequent and high-consumption key calls, especially during high-load acquisition tasks where the consumption rate can rise sharply. When the KCC value exceeds a preset threshold, it means the key pool's resource consumption rate has approached or exceeded its replenishment capacity, requiring timely key replenishment to prevent key exhaustion from causing encryption task interruptions or data security risks. Therefore, KCC, as a core indicator of key pool consumption, allows for accurate prediction of whether key pool resources need replenishment by monitoring its changing trends, thereby ensuring the continuity of data acquisition tasks and encryption security.

[0071] In this embodiment, the logic for obtaining the key supplementation coefficient is as follows:

[0072] Extract key replenishment response performance information from the preprocessed key pool consumption status information. Specifically, this includes the number of keys added to the key pool after key replenishment operations are completed at different times within a certain period during a high-frequency financial data acquisition task, the number of times key replenishment operations are triggered, and the rate at which keys are added to the key pool. These are then denoted as QS. n FT n and RS n QS n FT represents the number of new keys added to the key pool after the key replenishment operation at time n within a certain period of time during a high-frequency financial data acquisition task. n RS represents the number of times a key replenishment operation is triggered at time n within a certain period of time during a high-frequency financial data acquisition task. n This represents the rate at which new keys are added to the key pool at time n within a certain period during a high-frequency financial data acquisition task, where n = 1, 2, 3, ..., u, and u is a positive integer;

[0073] To achieve real-time acquisition of the number of keys added to the key pool, the number of key replenishment operations triggered, and the rate of key additions within the key pool at different times during a period of high-frequency financial data acquisition tasks, an integrated real-time monitoring and data acquisition platform can be designed. First, after a key replenishment operation is completed, the system needs to record detailed information for each operation, including the number of keys replenished and the operation time. This information can be captured by embedding an event listener in the key pool management module; whenever a key replenishment operation is completed, the relevant information will be written to the database or message queue in real time. Second, the number of key replenishment operations triggered can be obtained by monitoring the consumption status of the key pool. Each time a replenishment is triggered, the system records the trigger time and counts the number of replenishment requests within a certain time period. To ensure real-time performance, the system can use a timestamp-based logging and counter mechanism to periodically update and save the trigger count for each moment. Finally, the rate of key additions in the key pool can be calculated by determining the number of keys added per unit time. Specifically, after each replenishment operation is completed, the replenishment time and the number of keys added are recorded, and the addition rate per unit time is calculated. All this data is streamed through real-time data streaming frameworks (such as Apache Kafka and Flume) to ensure the timeliness and accuracy of the information, providing real-time support for subsequent key replenishment decisions. Through these methods, key pool replenishment information can be continuously collected and analyzed during task execution, providing accurate real-time data support for subsequent replenishment decisions.

[0074] The key supplementation coefficient is calculated using the following formula:

[0075]

[0076] In the formula, KSC is the key supplement coefficient.

[0077] The formula for calculating the key replenishment coefficient (KSC) comprehensively evaluates the key pool replenishment efficiency through multiple mathematical operations. First, This part involves increasing the key increment QS n The number of times the supplementary operation is triggered (FT) n Logarithmic smoothing is applied to ensure that the evaluation of the supplementation effect is not overemphasized when the number of triggers is high. The logarithmic function ensures that the impact of the number of supplementations on the overall performance is gradual when the number of triggers is high, which avoids excessive interference from high-frequency supplementation operations on the results. The following sections... Adjust the key replenishment rate RS using exponential operations. n The faster the replenishment rate, the closer the exponential term value is to 1, thus increasing the coefficient value and reflecting the priority of higher replenishment efficiency. Finally, The combined effect of the number of newly added keys and the number of triggers was calculated using the square root. This smooths out the relationship between the number of new keys and the replenishment frequency, avoiding overemphasis on any one factor and ensuring a more balanced evaluation of replenishment efficiency. Through the combination of these mathematical operations, the key replenishment coefficient can not only accurately assess the response speed of replenishment operations but also consider the complex relationship between task load and replenishment effect, ultimately providing a precise replenishment efficiency indicator to guide key pool replenishment decisions.

[0078] The Key Supplementation Coefficient (KSC) directly reflects the efficiency and responsiveness of the key pool in replenishment operations during high-frequency financial data acquisition tasks. A high KSC value indicates that the key pool can replenish quickly and efficiently per unit time, typically meaning timely response and rapid resource recovery. Therefore, a higher KSC value is usually associated with lower key consumption risk, indicating that even under high-load financial data acquisition tasks, the key pool can maintain sufficient resources to avoid encryption task interruption or data security issues. Conversely, a lower KSC value means that the key pool's replenishment operation is less efficient and replenishes resources more slowly, potentially leading to excessive key pool resource consumption under high-frequency task loads, thus triggering the risk of key exhaustion. Therefore, the KSC value is directly related to whether key replenishment is needed. When the KSC value is low, the system needs to issue an early warning and trigger a replenishment operation to avoid task interruption or encryption failure due to excessive key pool consumption.

[0079] A key consumption assessment model is constructed based on the generated key consumption coefficient and key replenishment coefficient to generate a key exhaustion risk coefficient. After generation, the coefficient is analyzed to predict whether the key consumption in the key pool needs to be replenished in high-frequency financial data collection tasks. Based on the prediction results, replenishment trigger signals and replenishment non-trigger signals are generated respectively.

[0080] In this embodiment, a key consumption evaluation model is constructed based on the generated key consumption coefficient KCC and key replenishment coefficient KSC. A key exhaustion risk coefficient is generated by weighted summation. The specific calculation formula is as follows:

[0081] KDR=ω1*KCC-ω2*KSC

[0082] In the formula, KDR is the key depletion risk coefficient, ω1 and ω2 are the non-zero weight coefficients of the key consumption coefficient KCC and the key replenishment coefficient KSC, respectively, and ω1+ω2=1.

[0083] When calculating the key depletion risk coefficient (KDR), a weighted summation model is used to combine the key consumption coefficient (KCC) and the key replenishment coefficient (KSC) to comprehensively evaluate the key pool's consumption status and replenishment capability. ω1 and ω2 are two non-zero weight coefficients, assigning importance to the key consumption coefficient and key replenishment coefficient in the risk assessment, respectively. ω1 represents the weight of the key consumption coefficient in the risk assessment; a higher ω1 value means that in high-frequency data acquisition tasks, the key consumption rate has a greater impact on the risk assessment, and the system will pay more attention to the key pool's consumption status. ω2 represents the weight of the key replenishment coefficient in the assessment; a higher ω2 value indicates that the replenishment rate plays a more important role in the risk assessment, and the system will pay more attention to the efficiency of replenishment capability. The weight coefficients are chosen by setting ω1 + ω2 = 1 so that the sum of their effects is 1, thus balancing the relative importance between consumption and replenishment. Specific weight values ​​can be dynamically adjusted according to the characteristics of the task and risk tolerance to ensure that the key pool's consumption and replenishment status are reasonably assessed, thereby predicting whether a replenishment operation needs to be triggered.

[0084] In this embodiment, a pre-set key depletion risk coefficient threshold KDR is determined. yuzhi After determination, it is compared with the generated key depletion risk coefficient KDR. Based on the comparison result, it is predicted whether the key consumption in the key pool needs to be replenished in the high-frequency financial data collection task. Based on the prediction result, a replenishment trigger signal and a replenishment non-trigger signal are generated respectively. The specific comparison analysis is as follows:

[0085] If KDR≤KDR yuzhi In high-frequency financial data acquisition tasks, the key consumption in the key pool does not need to be replenished, and a supplementary non-trigger signal is generated.

[0086] This situation indicates that the current consumption rate of the key pool is within a controllable range, and the system has assessed that the key consumption level is within the normal range and will not immediately lead to resource exhaustion. This means that the current encryption load of the financial data collection task is well-matched with the key pool's resources, and the need for replenishment is not urgent at the moment. Therefore, the system does not need to activate the replenishment mechanism or generate a replenishment non-trigger signal, avoiding unnecessary resource waste and system burden. This situation typically occurs when the system's encryption demand is stable and the load is even, helping to maintain efficient resource utilization and ensuring that the key pool can continue to serve the data collection task without interfering with normal operation.

[0087] If KDR>KDR yuzhi In high-frequency financial data acquisition tasks, the key pool needs to be replenished to compensate for the depletion of keys, and a replenishment trigger signal needs to be generated.

[0088] This situation indicates that the key pool's consumption rate exceeds the system's capacity, and the key pool is rapidly depleting, potentially leading to an inability to meet the encryption requirements of high-frequency financial data collection tasks at some point in the future. This is typically caused by high-load tasks, frequent encryption operations, or a failure to promptly replenish the key pool. In this case, the system assesses that the key pool needs immediate replenishment to prevent encryption task interruption or data security degradation. Therefore, the system generates a replenishment trigger signal, initiating a replenishment process to dynamically adjust key pool resources, ensuring that the number of keys in the key pool can support the processing needs of subsequent tasks. In this scenario, timely key pool replenishment not only ensures system continuity but also improves task execution efficiency and data security, preventing insufficient resources from affecting the accuracy and timeliness of financial data collection.

[0089] To determine the pre-set key exhaustion risk threshold, it can be dynamically adjusted by analyzing historical data and task load characteristics. First, key consumption data over a certain time range can be collected and analyzed to assess the key pool consumption pattern under different financial data collection task loads. This data includes, but is not limited to, key call frequency, key consumption increment, replenishment operation frequency, and the rate of key change in the key pool. Based on this data, the software can use statistical analysis methods, such as calculating the mean and standard deviation, to assess the normal range of key pool consumption and set an initial risk threshold. To ensure the flexibility of the threshold, the system can also introduce machine learning models or rule-based adjustment mechanisms to dynamically adjust the threshold by monitoring the key pool consumption in real time. For example, when the task load increases or the system experiences an anomaly, the threshold can be automatically lowered to enhance sensitivity to resource exhaustion. By periodically backtesting historical data and optimizing the model, the software can continuously adjust and optimize the key exhaustion risk threshold, ensuring the security of the key pool and the continuity of tasks under different environments.

[0090] When a supplementary trigger signal is generated, several key exhaustion risk coefficients generated subsequently are continuously acquired and analyzed to generate key supplementary signals of different levels. Corresponding key supplementary measures are then taken according to the generated key supplementary signals of different levels.

[0091] In this embodiment, upon generating a supplementary trigger signal, several subsequently generated key exhaustion risk coefficients are continuously acquired and recalibrated as KDR. x x represents the numbers of several key exhaustion risk coefficients subsequently generated under the condition of generating a supplementary trigger signal, x = 1, 2, 3, ..., d, where d is a positive integer;

[0092] To achieve the goal of "continuously acquiring several subsequently generated key exhaustion risk coefficients upon generating a replenishment trigger signal," a real-time data stream processing system can be designed to acquire and monitor changes in the key exhaustion risk coefficient. First, the system should have the capability to calculate the key exhaustion risk coefficient in real time. As soon as the key consumption coefficient and key replenishment coefficient are generated, the system should immediately calculate the key exhaustion risk coefficient and save the value for each generated coefficient. Then, by setting a data acquisition window (e.g., hourly or minutely), the system should periodically check the key pool's consumption status and automatically update the key exhaustion risk coefficient. Each generated key exhaustion risk coefficient value is stored in a database along with historical data, accessible in real-time using the database's query interface. To ensure continuous tracking of the key exhaustion risk coefficient, the software system should implement an automatic recording and triggering mechanism. Whenever a replenishment trigger signal is generated, the system should begin acquiring several subsequent key exhaustion risk coefficients, periodically updating and storing them. In this way, the system can acquire the key pool's consumption status in real time and dynamically adjust based on the latest key exhaustion risk coefficient data, further analyzing whether key replenishment is necessary. This process can store historical data in time-series databases (such as InfluxDB or TimescaleDB) and process and analyze the data streams through real-time stream processing platforms (such as Apache Kafka or Apache Flink), thereby ensuring the real-time nature and accuracy of the data.

[0093] Calculate the key exhaustion risk coefficient (KDR) for several subsequently generated keys. x The average value KDR_ is calculated using the formula:

[0094] Calculate the key exhaustion risk coefficient (KDR) for several subsequently generated keys. x Standard deviation KDR σ According to the formula:

[0095] In this embodiment, a preset average threshold is determined for several key exhaustion risk coefficients subsequently generated when a supplementary trigger signal is generated. and preset standard deviation threshold After determination, the average KDR_ and standard deviation KDR of several key exhaustion risk coefficients are respectively compared. σ A comparison is performed, and different levels of key supplementation signals are generated based on the comparison results. Corresponding key supplementation measures are then taken according to the different levels of key supplementation signals generated. The specific comparison analysis is as follows:

[0096] like and The first-level key replenishment signal is generated, and the key replenishment measures taken are as follows: the emergency replenishment process is initiated, resources are allocated for replenishment with priority, the replenishment frequency is increased, and the key pool is restored to a security level.

[0097] This situation indicates that the key pool is being consumed at a high rate and with significant fluctuations. This suggests that the key pool is in a relatively dangerous state under the current workload, potentially leading to resource depletion within a short period. This could affect the smooth execution of encryption tasks, causing potential service interruptions or data breaches. In this case, the system needs to respond promptly, initiating an emergency replenishment process to ensure the key pool can quickly recover to a secure level, maintaining the continuity of financial data collection tasks and data security.

[0098] Key replenishment measures: An emergency replenishment process is initiated, prioritizing resource allocation for replenishment and increasing the replenishment frequency to ensure the key pool is restored to a secure level. To implement this replenishment measure, a priority resource allocation module can be designed into the software system. When the key depletion risk level is detected to be at the highest level, the system automatically adjusts its resource allocation strategy, prioritizing resource replenishment for the key pool. First, the system increases the replenishment frequency to ensure rapid key pool recovery. This can be achieved by dynamically adjusting the key replenishment cycle, for example, by shortening the time interval between replenishments. Second, the system intelligently allocates more resources for replenishment based on the priority of current tasks, ensuring that high-priority encryption tasks can continue to execute uninterruptedly. This emergency replenishment method can quickly respond to situations with high resource consumption, avoiding data processing stagnation or security vulnerabilities caused by key pool depletion.

[0099] like and The second-level key supplementation signal is generated, and the key supplementation measures adopted are: increasing the supplementation frequency, optimizing the supplementation strategy to reduce the risk of volatility, while ensuring that the supplementation does not exceed the actual needs.

[0100] This situation indicates that the key pool's consumption rate is relatively low, but the consumption is highly volatile. While the overall key pool consumption is still acceptable, its volatility may lead to excessively rapid consumption in the short term, creating a sudden risk. Therefore, the system needs to be adjusted by increasing the replenishment frequency to balance consumption volatility, reduce the risks associated with volatility, and avoid resource shortages when the load increases in the future.

[0101] Key replenishment measures: Increase the replenishment frequency and optimize the replenishment strategy to reduce the risks caused by volatility, while ensuring that replenishment does not exceed actual needs. To achieve this, the key replenishment interval and resource allocation can be dynamically adjusted through software. First, the system can set a suitable replenishment cycle and adjust the replenishment frequency in a timely manner based on real-time key consumption (such as the key call frequency and consumption increment per unit time). To reduce resource waste, the system can use a prediction algorithm to predict the key pool consumption over a period of time based on historical data and current consumption trends, thereby determining the precise amount to replenish. This avoids over-replenishment and resource waste, while ensuring that the key pool can stably support task requirements under conditions of large fluctuations, preventing untimely or excessive replenishment.

[0102] like and The third-level key replenishment signal is generated, and the key replenishment measures adopted are: maintaining the existing replenishment strategy, maintaining the current replenishment frequency, and ensuring that the key pool can stably support subsequent tasks;

[0103] This situation indicates a high key pool consumption rate but low volatility. In this case, key pool consumption is relatively stable; although the consumption rate is fast, the low volatility makes the risk relatively controllable. The system can maintain its existing replenishment strategy to stably support the execution of subsequent tasks, avoiding frequent adjustments to replenishment measures and ensuring that task execution is not affected.

[0104] Key replenishment measures: Maintain the existing replenishment strategy and frequency to ensure the key pool can stably support subsequent tasks. To achieve this, the software system should maintain the current key replenishment strategy. Under this condition, the replenishment cycle can remain at a stable frequency without frequent adjustments. The system will maintain a reasonable replenishment cycle based on the current key consumption rate and historical data to ensure smooth resource replenishment. By setting up an automated replenishment scheduling mechanism, the system decides whether to trigger the next replenishment based on real-time data without excessive intervention. Maintaining this stable replenishment strategy ensures efficient system operation and long-term key pool stability even without drastic fluctuations in resource consumption.

[0105] like and The fourth-level key replenishment signal is generated, and the key replenishment measures adopted are: maintaining a low-frequency replenishment strategy, and triggering replenishment only when the key pool is close to the threshold to avoid resource waste.

[0106] This indicates that the key pool's consumption rate is low and its volatility is minimal. This suggests that the key pool is in a relatively stable state, with smooth resource consumption and no need for frequent replenishment operations. In this case, a low-frequency replenishment strategy can be used to ensure the stability of the key pool under low load, avoid over-replenishing resources, and reduce unnecessary resource waste.

[0107] Key replenishment measures: Maintain a low-frequency replenishment strategy, triggering replenishment only when the key pool approaches a threshold to avoid resource waste. To implement this replenishment measure, the software system should dynamically adjust the replenishment triggering conditions based on real-time monitoring of consumption data. In this case, the system can set a relatively long replenishment cycle, triggering a replenishment operation only when the remaining resources in the key pool approach a safety threshold. By designing a threshold triggering mechanism, the system will periodically monitor the key pool's consumption, and automatically issue a replenishment signal once it approaches the set lower limit. This avoids frequent replenishment operations while ensuring that system resources are not wasted when the task load is low, thus improving the resource utilization of the key pool.

[0108] To determine the preset average and standard deviation thresholds for several key exhaustion risk coefficients generated after a replenishment trigger signal is generated, the following approach can be used: First, estimate the typical consumption pattern of the key pool through historical data analysis. The software system can collect and analyze statistical data on key consumption under different load conditions, such as historical key call frequency, replenishment operation records, and task load. This data can be stored and queried using a time-series database (such as InfluxDB or TimescaleDB), and then statistical analysis methods (such as mean, standard deviation, interquartile range, etc.) can be used to calculate the consumption distribution of the key pool under normal operating conditions. Next, machine learning algorithms (such as regression analysis or cluster analysis) are used to model the historical data, thereby predicting the consumption trend and volatility of the key pool under different load conditions. Based on this, the system can dynamically adjust the thresholds so that the preset average and standard deviation thresholds of key pool consumption can reflect the actual consumption of the current system, rather than relying on static preset values. Finally, these thresholds will serve as the basis for determining whether to trigger a replenishment signal, ensuring that the system can effectively cope with the risk of key resource consumption under high load conditions. At the same time, the threshold setting can be fine-tuned through a feedback mechanism so that it can be automatically optimized when the task load changes.

[0109] The status of the key pool is continuously monitored, and key management parameters and allocation strategies are dynamically adjusted based on real-time monitoring results. Historical data is also stored and analyzed to optimize key replenishment frequency and allocation strategies.

[0110] To continuously monitor the key pool's status and dynamically adjust key management parameters and allocation strategies based on real-time monitoring results, the software system can be designed with a monitoring module based on real-time data acquisition and analysis. This module assesses the key pool's usage status by collecting and analyzing key information such as key pool consumption data, replenishment frequency, and key usage in real time. Real-time data acquisition and analysis can be achieved by integrating real-time monitoring tools (such as Prometheus and Grafana) or stream processing frameworks (such as Apache Kafka and Flink). These tools can continuously acquire data such as task load, key consumption rate, and replenishment response, and dynamically adjust the key pool's replenishment frequency and allocation strategy according to the set policies. In this way, the system can immediately take action to replenish the key pool and optimize the allocation strategy when it detects that the key pool is nearing depletion or experiencing excessive consumption fluctuations, ensuring that the key pool can continuously and stably support high-frequency financial data acquisition tasks.

[0111] Meanwhile, to optimize key replenishment frequency and allocation strategies, the system should periodically store and analyze historical data. By storing information such as key pool consumption patterns and the effectiveness of replenishment strategies, the system can identify the optimal timing and frequency for replenishment. Historical data can be stored long-term in time-series databases (such as InfluxDB or TimescaleDB). The system can use regression analysis, cluster analysis, and other methods to uncover key factors affecting key pool consumption and replenishment. Based on the analysis results of historical data, the system can optimize key management parameters and allocation strategies, adjust the replenishment frequency to avoid over-replenishment or resource waste, and improve the accuracy of replenishment, avoiding resource shortages caused by task load fluctuations. Through such historical data analysis and dynamic optimization, the system can continuously improve the management efficiency of the key pool, ensuring efficient support for the continuity and security of financial data collection tasks.

[0112] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0113] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0114] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0115] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0117] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for processing encrypted financial information data, characterized in that, Specifically, the following steps are included: In high-frequency financial data acquisition tasks, the key pool is monitored in real time during the financial data acquisition process. The key call frequency, the number of remaining keys and the key replenishment delay are recorded. The consumption of the key pool is also monitored in real time to see if it reaches the preset security threshold. When the consumption of the key pool is detected to reach the preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is obtained in real time, and after obtaining it, it is analyzed to generate key consumption coefficient and key replenishment coefficient respectively. A key consumption assessment model is constructed based on the generated key consumption coefficient and key replenishment coefficient to generate a key exhaustion risk coefficient. After generation, the coefficient is analyzed to predict whether the key consumption in the key pool needs to be replenished in high-frequency financial data collection tasks. Based on the prediction results, replenishment trigger signals and replenishment non-trigger signals are generated respectively. When a supplementary trigger signal is generated, several key exhaustion risk coefficients generated subsequently are continuously acquired and analyzed to generate key supplementary signals of different levels. Corresponding key supplementary measures are then taken according to the generated key supplementary signals of different levels. The status of the key pool is continuously monitored, and key management parameters and allocation strategies are dynamically adjusted based on real-time monitoring results. Historical data is also stored and analyzed to optimize key replenishment frequency and allocation strategies.

2. The method for processing encrypted financial information data according to claim 1, characterized in that, When the key pool consumption reaches a preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is acquired in real time and analyzed to generate key consumption coefficients and key replenishment coefficients. The specific steps include: When the consumption of the key pool is detected to reach the preset security threshold, the key pool consumption status information in the high-frequency financial data collection task is obtained in real time and preprocessed after acquisition. Extract key consumption monitoring information and key replenishment response performance information from the preprocessed key pool consumption status information; The extracted key consumption monitoring information and key replenishment response performance information are analyzed to generate key consumption coefficients and key replenishment coefficients, respectively.

3. The method for processing encrypted financial information data according to claim 2, characterized in that, The logic for obtaining the key consumption coefficient is as follows: Extract key consumption monitoring information from the preprocessed key pool consumption status information. Specifically, this includes the number of keys actually invoked at different times within a certain period during high-frequency financial data collection tasks, the rate of decrease in the number of keys in the key pool, and the number of data collection tasks that completed encryption processing. These are then labeled as NC. m DK m and PR m NC m DK represents the number of keys actually invoked at time m within a certain period during a high-frequency financial data acquisition task. m PR represents the rate of change of the decrease in the number of keys in the key pool at time m within a certain period during a high-frequency financial data acquisition task. m This represents the number of data collection tasks that complete encryption processing at time m within a certain period of time in a high-frequency financial data collection task, where m = 1, 2, 3, ..., g, and g is a positive integer; The key consumption coefficient is calculated using the following formula: In the formula, KCC is the key consumption coefficient.

4. The method for processing encrypted financial information data according to claim 3, characterized in that, The logic for obtaining the key supplement coefficient is as follows: Extract key replenishment response performance information from the preprocessed key pool consumption status information. Specifically, this includes the number of keys added to the key pool after key replenishment operations are completed at different times within a certain period during a high-frequency financial data acquisition task, the number of times key replenishment operations are triggered, and the rate at which keys are added to the key pool. These are then denoted as QS. n FT n and RS n QS n FT represents the number of new keys added to the key pool after the key replenishment operation at time n within a certain period of time during a high-frequency financial data acquisition task. n RS represents the number of times a key replenishment operation is triggered at time n within a certain period of time during a high-frequency financial data acquisition task. n This represents the rate at which new keys are added to the key pool at time n within a certain period during a high-frequency financial data acquisition task, where n = 1, 2, 3, ..., u, and u is a positive integer; The key supplementation coefficient is calculated using the following formula: In the formula, KSC is the key supplement coefficient.

5. The method for processing encrypted financial information data according to claim 4, characterized in that, A key consumption evaluation model is constructed based on the generated key consumption coefficient KCC and key replenishment coefficient KSC. A key exhaustion risk coefficient is then generated through weighted summation, as shown in the following formula: KDR=ω1*KCC-ω2*KSC In the formula, KDR is the key depletion risk coefficient, ω1 and ω2 are the non-zero weight coefficients of the key consumption coefficient KCC and the key replenishment coefficient KSC, respectively, and ω1+ω2=1.

6. The method for processing encrypted financial information data according to claim 5, characterized in that, Determine the pre-set key depletion risk threshold KDR yuzhi After determination, it is compared with the generated key depletion risk coefficient KDR. Based on the comparison result, it is predicted whether the key consumption in the key pool needs to be replenished in the high-frequency financial data collection task. Based on the prediction result, a replenishment trigger signal and a replenishment non-trigger signal are generated respectively. The specific comparison analysis is as follows: If KDR≤KDR yuzhi In high-frequency financial data acquisition tasks, the key consumption in the key pool does not need to be replenished, and a supplementary non-trigger signal is generated. If KDR>KDR yuzhi In high-frequency financial data acquisition tasks, the key pool needs to be replenished to meet the consumption level of keys, and a replenishment trigger signal needs to be generated.

7. The method for processing encrypted financial information data according to claim 6, characterized in that, Upon generating a supplementary trigger signal, continuously acquire several subsequently generated key exhaustion risk coefficients and recalibrate them as KDR. x x is the number of several key exhaustion risk coefficients subsequently generated in the case of generating a supplementary trigger signal, x = 1, 2, 3, ..., d, where d is a positive integer; Calculate the key exhaustion risk coefficient (KDR) for several subsequently generated keys. x average KDR - According to the formula: Calculate the key exhaustion risk coefficient (KDR) for several subsequently generated keys. x Standard deviation KDR σ According to the formula:

8. The method for processing encrypted financial information data according to claim 7, characterized in that, Determine a preset average threshold for the risk coefficient of several subsequently generated keys exhaustion in the event of generating a supplementary trigger signal. and preset standard deviation threshold After determination, it is compared with the average value of several key exhaustion risk coefficients, KDR. - Sum of standard deviations and KDR σ A comparison is performed, and different levels of key supplementation signals are generated based on the comparison results. Corresponding key supplementation measures are then taken according to the different levels of key supplementation signals generated. The specific comparison analysis is as follows: like and The first-level key replenishment signal is generated, and the key replenishment measures taken are as follows: the emergency replenishment process is initiated, resources are allocated for replenishment with priority, the replenishment frequency is increased, and the key pool is restored to a security level. like and The second-level key supplementation signal is generated, and the key supplementation measures adopted are: increasing the supplementation frequency, optimizing the supplementation strategy to reduce the risk of volatility, while ensuring that the supplementation does not exceed the actual needs. like and The third-level key replenishment signal is generated, and the key replenishment measures adopted are: maintaining the existing replenishment strategy, maintaining the current replenishment frequency, and ensuring that the key pool can stably support subsequent tasks; like and The fourth-level key supplement signal is generated, and the key supplement measures adopted are: maintaining a low-frequency supplement strategy to avoid resource waste.

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