Payment data security protection system using encryption technology

Abstract

The invention discloses a payment data security protection system applying an encryption technology, and relates to the technical field of payment data security protection. Comprising an encryption task information acquisition module, an encryption queue prediction evaluation module, a task priority classification module, a dynamic scheduling and real-time monitoring module, an encryption result verification and rescheduling module and an encryption strategy adaptive optimization module. The encryption queue prediction evaluation module is used for analyzing the acquired encryption task information and predicting whether the to-be-encrypted data volume can cause encryption queue blockage or not; and the task priority classification module is used for acquiring and analyzing priority information of each to-be-encrypted data packet under the condition that the to-be-encrypted data volume is predicted to cause the encryption queue to be blocked, and classifying all to-be-encrypted data packets according to an analysis result. According to the invention, the problem of encryption queue blocking in the remote unmanned retail equipment is solved, efficient encryption of data is ensured, and the system processing stability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of payment data security protection, and in particular to a payment data security protection system using encryption technology. Background Art

[0002] Payment data security protection refers to the use of technical and management means to protect sensitive data involved in the payment process (such as bank card numbers, user identity information, transaction records, etc.) from unauthorized access, tampering, leakage and other forms of attacks. In the context of the widespread use of digital payments, payment data has become the main target of cybercrime. Once payment data is stolen or tampered with, it will not only cause user property losses, but may also cause privacy leakage and credit risks. Therefore, effective security protection measures must be taken to ensure the confidentiality, integrity and availability of data. The payment data security protection system using encryption technology encrypts the data transmission, storage and processing processes in all directions, combining asymmetric encryption and symmetric encryption and other technologies to ensure that sensitive information is always in a secure state throughout the life cycle. At the same time, the system adopts dynamic key management, multiple authentication, hierarchical encryption and other measures to effectively resist risks such as man-in-the-middle attacks, data tampering and unauthorized access. Compared with traditional protection methods, encryption technology not only improves the depth and strength of data protection, but also takes into account the performance and availability of the system while ensuring data security, thereby providing users with an efficient, secure and reliable payment environment.

[0003] Existing payment data security protection technologies using encryption technology are protected through multi-level encryption and security mechanisms. First, in the payment data transmission process, the TLS protocol is used for end-to-end encryption to prevent middleman attacks and data eavesdropping; secondly, in the data storage link, symmetric encryption (such as AES) is used to encrypt and store sensitive information, and asymmetric encryption (such as RSA) is used to protect encryption keys, and the key management system (KMS) is used to dynamically update and safely store the keys; in addition, the system will also encrypt the payment data in a hierarchical manner, and determine different encryption strengths according to the sensitivity of the data. In order to prevent unauthorized access, multiple authentication methods (such as two-factor authentication and biometrics) and strict permission control are used to ensure that only authorized users and devices can access the data. Throughout the payment process, real-time data monitoring and security audit mechanisms continue to run, record and analyze all data operations, identify abnormal behaviors and prevent potential threats in a timely manner. These technologies work together to provide all-round security protection for payment data from transmission, storage to processing.

[0004] The prior art has the following deficiencies:

[0005] In remote unmanned retail devices, when the device is not connected to the network, the payment data will be temporarily stored. After the network is restored, the data needs to be encrypted in batches and uploaded to the server. In this case, due to the limited computing resources of the device, the encryption operation queue may be blocked. Especially when the amount of data is large, the remote unmanned retail device cannot efficiently process all the data to be encrypted, resulting in some data not being encrypted or encryption failure. Existing technologies cannot dynamically optimize the processing order of the encryption queue when resources are limited, which leads to the uploading of unencrypted or incorrectly encrypted data. This directly threatens the security of payment data and may lead to the risk of data being intercepted, tampered with or lost. It may also cause transaction data to be incomplete, repeatedly processed or lost, and ultimately lead to financial reconciliation problems and a decrease in user trust.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a payment data security protection system using encryption technology to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a payment data security protection system using encryption technology, including an encryption task information collection module, an encryption queue prediction and evaluation module, a task priority classification module, a dynamic scheduling and real-time monitoring module, an encryption result verification and rescheduling module, and an encryption strategy adaptive optimization module;

[0009] The encryption task information collection module obtains in real time the encryption task information directly associated with the encryption task of the remote unmanned retail equipment after the network is restored;

[0010] The encryption queue prediction and evaluation module analyzes the acquired encryption task information and predicts whether the amount of data to be encrypted will cause the encryption queue to be blocked;

[0011] The task priority classification module obtains and analyzes the priority information of each data packet to be encrypted when it is predicted that the amount of data to be encrypted will cause the encryption queue to be blocked, and classifies all the data packets to be encrypted according to the analysis results;

[0012] The dynamic scheduling and real-time monitoring module allocates all the data packets to be encrypted to different task queues for encryption processing according to the classification results of the task priority classification module, and performs encryption operations in sequence according to the order of the task queues, monitors the progress of encryption tasks in real time, and dynamically adjusts the order of task queues;

[0013] The encryption result verification and rescheduling module verifies the encryption result after the encryption task is completed to verify the integrity and accuracy of the data. If any data that fails the verification is found, it will be rescheduled for encryption processing;

[0014] The encryption strategy adaptive optimization module updates the encryption strategy and task scheduling rules based on the actual execution of encryption tasks, and optimizes the processing efficiency of subsequent encryption tasks.

[0015] Preferably, the encryption queue prediction and evaluation module is used to analyze the acquired encryption task information and predict whether the amount of data to be encrypted will cause the encryption queue to be blocked based on the analysis results. The specific analysis logic is as follows:

[0016] Preprocessing the obtained encrypted task information;

[0017] Extract resource utilization data and task processing data from the preprocessed encrypted task information, and analyze them after extraction to generate an encrypted task load coefficient and a task execution efficiency index, respectively;

[0018] The generated encryption task load coefficient and task execution efficiency index are used to construct a blocking prediction model to generate a blocking prediction coefficient. The generated blocking prediction coefficient is compared with a pre-set blocking prediction coefficient threshold. Based on the comparison result, it is predicted whether the amount of data to be encrypted will cause the encryption queue to be blocked.

[0019] Preferably, the acquisition logic of the encryption task load coefficient and the task execution efficiency index is as follows:

[0020] Extract resource utilization data from the preprocessed encryption task information, including the total number of packets to be encrypted, the size of each packet to be encrypted, the percentage of CPU resources available to the device, and the percentage of memory resources available to the device. The total number of packets to be encrypted, the size of each packet to be encrypted, the percentage of CPU resources available to the device, and the percentage of memory resources available to the device are marked as n, DS, respectively. i , ACR and AMR, DS i represents the size of the i-th data packet to be encrypted, ACR represents the percentage of CPU resources available on the device, AMR represents the percentage of memory resources available on the device, i=1, 2, 3, ..., n, where n is a positive integer;

[0021] Calculate the encryption task load coefficient. The specific calculation formula is as follows:

[0022]

[0023] Where, ETLC is the encryption task load system;

[0024] Extract task processing data from the preprocessed encryption task information, including the number of processed data packets to be encrypted, the processing time of each processed data packet to be encrypted, the size of each processed data packet to be encrypted, and the waiting time of each processed data packet to be encrypted in the queue, and mark the number of processed data packets to be encrypted, the processing time of each processed data packet to be encrypted, the size of each processed data packet to be encrypted, and the waiting time of each processed data packet to be encrypted in the queue as m, PT, and j DS j and WT j , PT j represents the processing time of the jth processed data packet to be encrypted, DS j represents the size of the jth processed data packet to be encrypted, WT j represents the waiting time of the jth processed data packet to be encrypted in the queue, j = 1, 2, 3, ..., m, where m is a positive integer;

[0025] Calculate the task execution efficiency index. The specific calculation formula is as follows:

[0026]

[0027] Where TEEI is the task execution efficiency index.

[0028] Preferably, the generated encryption task load coefficient ETLC and task execution efficiency index TEEI are used to construct a blocking prediction model, a blocking prediction coefficient BPC is generated by weighted summation, and the generated blocking prediction coefficient BPC is compared with a preset blocking prediction coefficient threshold BPC. yuzhii Perform a comparison and predict whether the amount of data to be encrypted will cause the encryption queue to be blocked based on the comparison results. The specific comparison analysis is as follows:

[0029] If BPC≤BPC yuzhii , the amount of data to be encrypted will not cause the encryption queue to be blocked;

[0030] If BPC>BPC yuzhii , the amount of data to be encrypted will cause the encryption queue to be blocked.

[0031] Preferably, the task priority classification module is used to obtain and analyze the priority information of each data packet to be encrypted when it is predicted that the amount of data to be encrypted will cause the encryption queue to be blocked, and classify all data packets to be encrypted according to the analysis results. The specific analysis logic is as follows:

[0032] Obtaining priority information of each data packet to be encrypted, and performing preprocessing after obtaining;

[0033] Analyze the priority information of each pre-processed data packet to be encrypted, and generate a priority index for each data packet to be encrypted.

[0034] Determine a preset priority index threshold, and compare the priority index of each generated data packet to be encrypted with the preset priority index threshold, evaluate the priority of each data packet to be encrypted based on the comparison result, and divide each data packet to be encrypted into a high priority data packet to be encrypted and a low priority data packet to be encrypted based on the evaluation result.

[0035] Preferably, the logic for obtaining the priority index of each data packet to be encrypted is as follows:

[0036] Extract the priority information of each pre-processed data packet to be encrypted, including the processing time of each data packet to be encrypted, the data packet size of each data packet to be encrypted, and the task urgency of each data packet to be encrypted, and mark them as PCT x DS x and UR x , PCT x Indicates the processing time of the xth data packet to be encrypted, DS x Indicates the size of the xth packet to be encrypted, UR x represents the task urgency of the xth data packet to be encrypted, x=1, 2, 3, ..., h, where h is a positive integer;

[0037] Calculate the priority index PIYX of each data packet to be encrypted x , according to the formula:

[0038] Preferably, a preset priority index threshold PIYX is determined yuzhi , and the priority index PIYX of each data packet to be encrypted is generated x With pre-set priority index threshold PIYX yuzhi Compare and evaluate the priority of each data packet to be encrypted according to the comparison result, and divide each data packet to be encrypted into high priority data packets to be encrypted and low priority data packets to be encrypted according to the evaluation result. The specific comparison analysis and division are as follows:

[0039] If PIYX x ≤PIYX yuzhi , the priority of the data packet to be encrypted is low, and the data packet is classified as a low priority data packet to be encrypted;

[0040] If PIYX x >PIYX yuzhi The priority of the data packet to be encrypted is high, and the data packet is classified as a high priority data packet to be encrypted.

[0041] Preferably, according to the classification results of the task priority classification module, high-priority data packets to be encrypted are allocated to the high-priority task queue, and low-priority data packets to be encrypted are allocated to the low-priority task queue, and the data packets in each task queue are sorted from large to small according to the priority index of each data packet to be encrypted; when performing encryption operations, the data packets to be encrypted are first processed in sequence according to the order in the high-priority task queue, and then the data packets to be encrypted in the low-priority task queue are processed after completion; during the task execution process, the resource utilization, task progress and current execution status of the task queue are obtained in real time, and the order of the task queue is dynamically adjusted based on these data. In the event of resource competition, task delay or unbalanced progress, resources are reallocated or the priority order of the task queue is adjusted.

[0042] Preferably, after the encryption task is completed, the integrity and accuracy of the completed encrypted data are verified, specifically including verifying the hash value, consistency check code and encryption format of the data packet; the identification information of the data that fails the verification is recorded, and it is re-added to the queue of pending tasks and encrypted again according to the task priority; during the rescheduling process, the position of the data packet that needs to be re-encrypted in the task queue is dynamically adjusted according to the resource status and task priority of the current queue.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] 1. The present invention effectively solves the encryption queue blocking problem that may occur when resources are limited by collecting data directly related to encryption tasks in real time in remote unmanned retail equipment, analyzing and predicting the data. Especially in the batch data encryption scenario after network recovery, the solution constructs a blocking prediction model by generating encryption task load coefficients and task execution efficiency indexes, enabling the system to predict and avoid queue blocking in advance and ensure the smooth execution of encryption tasks. This solution improves the operating efficiency of the system under high load and ensures data security and processing continuity by reasonably allocating resources and optimizing processing order.

[0045] 2. The present invention uses a task priority classification module to classify the data packets to be encrypted into high and low priority according to their priority index, and accurately schedules them by weighted summation. This classification and scheduling method can not only give priority to urgent and important tasks when system resources are tight, but also ensure that the system can still operate efficiently in situations such as resource competition and task delays by real-time monitoring and adjusting the task queue order. After the encryption task is completed, the system can verify the integrity and accuracy of the encryption result, and reschedule the data that has not passed the verification, ensuring the quality and security of the final encryption task.

[0046] 3. The encryption strategy adaptive optimization module of the present invention continuously updates and adjusts the task scheduling rules based on the actual execution of the system to ensure that the efficiency of subsequent task processing is continuously optimized. This adaptive adjustment mechanism enables the system to allocate resources and adjust policies according to real-time environmental changes, ensuring that the system can operate efficiently and stably under different workloads. In addition, each module is implemented through software automation, making the solution highly operable and practical, especially in application scenarios such as unmanned retail equipment, which can significantly improve the stability and security of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 This is a module diagram of the payment data security protection system using encryption technology in the present invention. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0050] The present invention provides Figure 1 The payment data security protection system using encryption technology shown in the figure includes an encryption task information collection module, an encryption queue prediction and evaluation module, a task priority classification module, a dynamic scheduling and real-time monitoring module, an encryption result verification and rescheduling module, and an encryption strategy adaptive optimization module;

[0051] The encryption task information collection module obtains in real time the encryption task information directly associated with the encryption task of the remote unmanned retail equipment after the network is restored;

[0052] After the network of the remote unmanned retail device is restored, the encryption task information directly related to the encryption task can be obtained in real time through software. First, the monitoring program or daemon process in the device continues to run, always monitoring the changes in the device network status. Once the network recovery is detected, the information collection module is triggered to start working. This module can obtain the current encryption task status of the device through regular polling or event-driven mechanism, and communicate with the system log, process monitoring tool, task scheduler, etc. inside the device to ensure real-time acquisition of key information required for encryption tasks.

[0053] When the network is restored, the information collection module will automatically start and interact with the system's core resource manager (such as the task scheduler and resource allocation module) to extract the required encryption task information from these modules. The collection process mainly includes reading the current device's task queue status, system resource usage (such as CPU, memory, bandwidth, etc.) through the API interface or system call, and analyzing the number and characteristics of the current data to be processed. The information collection module can also combine the device's historical operating data (such as past task processing speed, task failure rate, etc.) to provide necessary reference data for subsequent encryption tasks. This process is completely implemented through software without human intervention, ensuring the automation and accuracy of data collection.

[0054] The encryption task information directly associated with the encryption task includes: the number of packets to be encrypted, the size of the packets, the priority of each packet, the computing resources currently available in the system (such as the CPU, memory and bandwidth usage), the scheduling of the task queue, the processing time and success rate of similar tasks in the past, the current network connection status and the data transmission rate. In addition, it also includes the concurrency of the current task execution of the device (whether there are other tasks occupying resources at the same time), the historical log of task processing (used to determine problems that occurred in the past), etc. This information can be extracted in real time through the device's built-in system monitoring module, log analysis tools and resource manager to ensure that encryption tasks can be optimized and scheduled based on comprehensive data.

[0055] The encryption queue prediction and evaluation module analyzes the acquired encryption task information and predicts whether the amount of data to be encrypted will cause the encryption queue to be blocked;

[0056] In this embodiment, the encryption queue prediction and evaluation module is used to analyze the obtained encryption task information and predict whether the amount of data to be encrypted will cause the encryption queue to be blocked based on the analysis results. The specific analysis logic is as follows:

[0057] Preprocessing the obtained encrypted task information;

[0058] Before analyzing the acquired encryption task information, preprocessing is necessary because the raw data may contain noise, inconsistent formats, or redundant information, which will affect the accuracy of subsequent analysis. Preprocessing includes steps such as data cleaning, format standardization, and redundancy filtering. First, data cleaning is used to remove invalid or incomplete data, such as erroneous entries that may appear during the recording process. Then, the format of data from different sources is standardized, such as unifying data units, timestamp formats, and the expression of resource utilization. Finally, redundancy filtering is performed to exclude duplicate or irrelevant data entries to ensure that only key information directly related to the encryption task is retained. These preprocessing steps can be implemented through software such as data processing algorithms, rule matching, and anomaly detection to ensure that the data input to the analysis module is accurate, consistent, and has analytical value.

[0059] Extract resource utilization data and task processing data from the preprocessed encrypted task information, and analyze them after extraction to generate an encrypted task load coefficient and a task execution efficiency index, respectively;

[0060] The generated encryption task load coefficient and task execution efficiency index are used to construct a blocking prediction model to generate a blocking prediction coefficient. The generated blocking prediction coefficient is compared with a pre-set blocking prediction coefficient threshold. Based on the comparison result, it is predicted whether the amount of data to be encrypted will cause the encryption queue to be blocked.

[0061] The "pre-set blocking prediction coefficient threshold" can be determined through historical data analysis, simulation testing, and dynamic adjustment. First, the historical operating data of the device under different load conditions can be analyzed to identify the key coefficient value interval when the encryption queue is blocked, and the initial threshold is set based on this. Then, through simulation testing, the system is simulated to run under different data volumes, resource usage, and task complexity, and the threshold is further optimized and adjusted to more accurately reflect the actual operation. Finally, the system can also dynamically adjust the threshold based on real-time data, and continuously optimize it through machine learning or data trend analysis algorithms to ensure that the risk of queue blocking can be accurately predicted in various operating environments. These processes can be fully implemented through software, and combined with algorithms, data analysis tools, and simulation platforms for automated tuning.

[0062] In this embodiment, the logic for obtaining the encryption task load coefficient and the task execution efficiency index is as follows:

[0063] Extract resource utilization data from the preprocessed encryption task information, including the total number of packets to be encrypted, the size of each packet to be encrypted, the percentage of CPU resources available to the device, and the percentage of memory resources available to the device. The total number of packets to be encrypted, the size of each packet to be encrypted, the percentage of CPU resources available to the device, and the percentage of memory resources available to the device are marked as n, DS, respectively. i , ACR and AMR, DS i represents the size of the i-th data packet to be encrypted, ACR represents the percentage of CPU resources available on the device, AMR represents the percentage of memory resources available on the device, i=1, 2, 3, ..., n, where n is a positive integer;

[0064] When extracting resource utilization data from the preprocessed encryption task information, it can be implemented in the following ways: First, the current available resource percentage of the device is obtained in real time through the management module that monitors system resources (such as the CPU and memory management module). The module can directly read the current CPU and memory usage through the operating system API interface, extract these data and format them for storage. The size and total number of packets to be encrypted can be extracted through the task scheduling module or the data queue management module, which can identify the attributes of each task to be processed, including the size of the packet and the number of tasks. During the data extraction process, the system can automatically obtain and summarize this information through polling or event triggering mechanism. After preliminary sorting, the various types of extracted data will be used as input data for calculating the encryption task load factor. The entire extraction process is automatically executed by software to ensure the real-time and accuracy of data collection.

[0065] ACR represents the percentage of CPU resources currently available on the device, and is used to measure the CPU computing power that the device can allocate to encryption tasks at the current point in time. ACR is a unitless value between 0 and 1, without a percentage sign. For example, if ACR is 0.7, it means that 70% of the device's CPU resources are available for encryption tasks. This numerical representation makes it easier to calculate the encryption task load factor. AMR represents the percentage of memory resources currently available on the device, and is used to measure the data storage capacity that the device can use for encryption tasks at the current point in time. AMR is also a unitless value between 0 and 1, without a percentage sign. For example, if AMR is 0.6, it means that 60% of the device's memory resources are available for encryption tasks. This representation of AMR ensures accuracy and consistency when calculating the encryption task load factor.

[0066] Calculate the encryption task load coefficient. The specific calculation formula is as follows:

[0067]

[0068] Where, ETLC is the encryption task load system;

[0069] The larger the encryption task load factor is, the higher the resource usage of the current system is, and the amount of encryption task data to be processed is larger than the available resources of the device. This means that the device may find it difficult to smoothly process all the data to be encrypted under the current conditions, thereby increasing the risk of encryption queue being blocked. Therefore, when the encryption task load factor is large, the probability of encryption queue being blocked is predicted to be higher.

[0070] Extract task processing data from the preprocessed encryption task information, including the number of processed data packets to be encrypted, the processing time of each processed data packet to be encrypted, the size of each processed data packet to be encrypted, and the waiting time of each processed data packet to be encrypted in the queue, and mark the number of processed data packets to be encrypted, the processing time of each processed data packet to be encrypted, the size of each processed data packet to be encrypted, and the waiting time of each processed data packet to be encrypted in the queue as m, PT, and j DS j and WT j , PT j represents the processing time of the jth processed data packet to be encrypted, DS j represents the size of the jth processed data packet to be encrypted, WT j represents the waiting time of the jth processed data packet to be encrypted in the queue, j = 1, 2, 3, ..., m, where m is a positive integer;

[0071] When extracting task processing data from pre-processed encrypted task information, it can be achieved in the following ways: First, the task scheduling system and queue management module will record the processing time of each data packet to be encrypted and the waiting time in the queue. These data can be obtained by monitoring the log file of task execution, the timestamp of task completion, and the queue status manager. The processing time data can be directly read from the task scheduling module, which records the processing time of each task from start to finish. For the data packet size, it can be extracted from the data queue manager, which maintains the task attributes, including the size of the data packet, data format, etc. The waiting time can be calculated by analyzing the timestamp and execution order of the tasks in the queue. The task scheduler will update the time point when each task enters the queue and is processed in real time. Through the integration of these modules, the system can automatically extract and calculate task processing data, providing reliable data support for the subsequent task execution efficiency index analysis.

[0072] Calculate the task execution efficiency index. The specific calculation formula is as follows:

[0073]

[0074] Where TEEI is the task execution efficiency index.

[0075] The lower the task execution efficiency index, the lower the system's task processing efficiency under the current resource configuration, the slower the data packet processing speed and the longer the waiting time. In this case, even if the system has sufficient resources in the short term, the queue may be blocked due to low task processing efficiency. Therefore, when the task execution efficiency index is low, the risk of predicting encryption queue blocking increases significantly.

[0076] In this embodiment, the generated encryption task load coefficient ETLC and task execution efficiency index TEEI are used to construct a blocking prediction model, and a blocking prediction coefficient BPC is generated by weighted summation, and the generated blocking prediction coefficient BPC is compared with a preset blocking prediction coefficient threshold BPC. yuzhii Perform a comparison and predict whether the amount of data to be encrypted will cause the encryption queue to be blocked based on the comparison results. The specific comparison analysis is as follows:

[0077] If BPC≤BPC yuzhii , the amount of data to be encrypted will not cause the encryption queue to be blocked, which means that the amount of data to be encrypted will not cause the encryption queue to be blocked under the current resource configuration and processing efficiency. The system can process encryption tasks as planned without additional resource scheduling or priority adjustment. In this case, the device can smoothly complete the encryption task, ensuring data security and the continuity of the processing flow, and reducing the risks caused by encryption delays or task blocking;

[0078] If BPC>BPC yuzhii , the amount of data to be encrypted will cause the encryption queue to be blocked, indicating that the amount of data to be encrypted may exceed the processing capacity of the device, causing the encryption queue to be blocked, which in turn affects the efficiency of task execution. At this time, it is necessary to adjust the priority of the encryption task queue or reallocate system resources to reduce the risk of blocking. If measures are not taken in time, data encryption delays, processing failures, or reduced data security may occur, thereby affecting the overall performance of the system and user experience.

[0079] The generated encryption task load coefficient ETLC and task execution efficiency index TEEI are used to construct a blocking prediction model, and the blocking prediction coefficient BPC is generated by weighted summation. Specifically, the blocking prediction model linearly combines these two coefficients, and the generated blocking prediction coefficient formula is as follows: BPC = ω 1 *ETLC+ω 2 *TEEI; where ω 1 and ω 2are the weight coefficients of the encryption task load coefficient and the task execution efficiency index respectively. The setting of the weight coefficient depends on the system's assessment of the relative importance of these two parameters in the actual encryption task. Generally, if the system resource load has a greater impact on the encryption queue blocking, a larger weight ω will be given to ETLC 1 On the contrary, if the task execution efficiency has a more significant impact on queue blocking, the weight of TEEI ω 2 The specific value of the weight coefficient can be determined by regression analysis of historical data, simulation testing and system tuning to achieve more accurate blocking prediction. Finally, the weighted summation result after combining the weights can generate a comprehensive blocking prediction coefficient to determine whether the current encryption task may cause queue blocking.

[0080] The task priority classification module obtains and analyzes the priority information of each data packet to be encrypted when it is predicted that the amount of data to be encrypted will cause the encryption queue to be blocked, and classifies all the data packets to be encrypted according to the analysis results;

[0081] In this embodiment, the task priority classification module is used to obtain and analyze the priority information of each data packet to be encrypted when it is predicted that the amount of data to be encrypted will cause the encryption queue to be blocked, and classify all the data packets to be encrypted according to the analysis results. The specific analysis logic is as follows:

[0082] Obtaining priority information of each data packet to be encrypted, and performing preprocessing after obtaining;

[0083] Obtaining the priority information of each data packet to be encrypted can be achieved through the task scheduling system and the data packet management module. These modules usually record the processing order, task attributes and system-set priority information of each data packet. Specific acquisition methods may include reading the task scheduling log, analyzing the task queue attributes generated by the system, or calling the task management module through the API interface to obtain the priority information of each data packet in real time. This information can be directly extracted from the device's scheduling strategy to ensure synchronous update and extraction of information without affecting task execution.

[0084] Preprocessing the acquired priority information is necessary because the raw data may have problems such as inconsistent formats, redundant information or noise, and direct use may affect the accuracy of subsequent analysis. The preprocessing steps include data cleaning, format unification and redundancy filtering. First, data cleaning can remove invalid or incomplete records, such as incorrect priority entries or duplicate data. Next, the data from different sources are formatted in a unified manner, such as standardizing the expression of priorities into consistent units or numerical ranges. Finally, redundant filtering is used to eliminate duplicate or unnecessary information to ensure that only the most relevant data enters the subsequent analysis. These preprocessing steps can be automatically completed through software rule settings, format conversion tools and anomaly detection algorithms.

[0085] Analyze the priority information of each pre-processed data packet to be encrypted, and generate a priority index for each data packet to be encrypted.

[0086] Determine a preset priority index threshold, and compare the priority index of each generated data packet to be encrypted with the preset priority index threshold, evaluate the priority of each data packet to be encrypted based on the comparison result, and divide each data packet to be encrypted into a high priority data packet to be encrypted and a low priority data packet to be encrypted based on the evaluation result.

[0087] Determining the pre-set priority index threshold can be achieved through historical data analysis, simulation testing, and dynamic adjustment. First, the historical processing data of the device under different task loads can be analyzed to identify the critical interval between the priority index and the task execution result, and use this as the initial threshold. Then, through simulation testing, the operation of the system under different resource configurations and task complexities is simulated to further optimize the threshold to make it closer to the actual operating environment. Finally, the system can also be dynamically adjusted according to real-time operating data, and the threshold setting is continuously optimized through adaptive algorithms to ensure that the priority of the data packet can be accurately evaluated in various scenarios. The entire process can be automatically implemented through data analysis tools, simulation software, and machine learning algorithms.

[0088] In this embodiment, the logic for obtaining the priority index of each data packet to be encrypted is as follows:

[0089] Extract the priority information of each pre-processed data packet to be encrypted, including the processing time of each data packet to be encrypted, the data packet size of each data packet to be encrypted, and the task urgency of each data packet to be encrypted, and mark them as PCT x DS x and UR x , PCT x Indicates the processing time of the xth data packet to be encrypted, DS x Indicates the size of the xth packet to be encrypted, URx represents the task urgency of the xth data packet to be encrypted, x=1, 2, 3, ..., h, where h is a positive integer;

[0090] When extracting the priority information of each data packet to be encrypted, it can be obtained in the following ways: First, the task scheduling system and the data packet management module will record the processing time, data packet size, task urgency and other information of each data packet to be encrypted. This information is usually automatically recorded by the system when the task is generated and saved in the task queue management module. Specifically, the processing time can be obtained through the task scheduling log and the task execution timestamp; the data packet size can be directly extracted from the data packet attributes, and the data packet management module will maintain this information; the task urgency can be obtained through the task priority setting parameters, which are usually automatically assigned by the system according to the task type or external instructions when the task is created. All of this information can be automatically extracted through the API interface or system call, and processed in a unified format to ensure that it is used in subsequent priority analysis.

[0091] Calculate the priority index PIYX of each data packet to be encrypted x , according to the formula:

[0092] The size of the priority index directly reflects the processing priority of each data packet to be encrypted in the task queue. The larger the priority index, the longer the processing time of the data packet, the higher the task urgency, or the smaller the relative size of the data packet. Such data packets should be processed first in the queue. On the contrary, data packets with smaller priority indices usually have lower urgency in task processing or larger data volumes, and can be arranged in subsequent queues when processed. The size of the priority index will affect the system's scheduling decisions for tasks, thereby ensuring that high-priority data packets are given priority processing under limited resource conditions, thereby improving the efficiency of the overall encryption task and reducing the risk of queue blocking due to improper resource allocation.

[0093] In this embodiment, the preset priority index threshold PIYX is determined. yuzhi , and the priority index PIYX of each data packet to be encrypted is generated x With pre-set priority index threshold PIYX yuzhi Compare and evaluate the priority of each data packet to be encrypted according to the comparison result, and divide each data packet to be encrypted into high priority data packets to be encrypted and low priority data packets to be encrypted according to the evaluation result. The specific comparison analysis and division are as follows:

[0094] If PIYX x ≤PIYX yuzhi, the priority of the data packet to be encrypted is low, and the data packet is classified as a low-priority data packet to be encrypted, which means that the priority of the data packet to be encrypted is low. The system will classify it as a low-priority data packet to be encrypted and arrange for delayed processing in the task queue. This arrangement method can ensure that high-priority data packets are encrypted first, thereby avoiding resource waste or task delays caused by processing low-priority tasks;

[0095] If PIYX x >PIYX yuzhi , the priority of the data packet to be encrypted is high, and the data packet is classified as a high-priority data packet to be encrypted, indicating that the priority of the data packet to be encrypted is high. The system will classify it as a high-priority data packet to be encrypted and give priority to processing in the task queue. This strategy ensures that urgent and important data packets can complete the encryption task first when resources are limited, thereby reducing the risk of data encryption delays and improving the overall processing efficiency of the system.

[0096] The dynamic scheduling and real-time monitoring module allocates all the data packets to be encrypted to different task queues for encryption processing according to the classification results of the task priority classification module, and performs encryption operations in sequence according to the order of the task queues, monitors the progress of encryption tasks in real time, and dynamically adjusts the order of task queues;

[0097] In this embodiment, according to the classification results of the task priority classification module, high-priority data packets to be encrypted are allocated to the high-priority task queue, and low-priority data packets to be encrypted are allocated to the low-priority task queue, and the data packets in each task queue are sorted from large to small according to the priority index of each data packet to be encrypted; when performing an encryption operation, the data packets to be encrypted are first processed in sequence according to the order in the high-priority task queue, and then the data packets to be encrypted in the low-priority task queue are processed after completion; during the task execution process, the resource utilization rate, task progress and current execution status of the task queue are obtained in real time, and the order of the task queue is dynamically adjusted based on these data. In the event of resource competition, task delay or unbalanced progress, resources are reallocated or the priority order of the task queue is adjusted to ensure that high-priority tasks are processed in a timely manner and low-priority tasks are postponed in time according to the system status.

[0098] According to the classification results of the task priority classification module, high priority data packets to be encrypted are assigned to high priority task queues and low priority data packets to be encrypted are assigned to low priority task queues. This can be achieved through the priority management module in the task scheduling system. This module will automatically assign data packets to corresponding queues according to different priority levels based on the classification results. In actual operation, the scheduling system will read the priority index of each data packet to be encrypted, determine its priority based on the threshold, and then call the task management interface to add the data packet to the corresponding task queue.

[0099] After the queues are allocated, the data packets in each task queue can be sorted from the highest priority index to the lowest by the queue sorting algorithm. Every time a new task is added to the queue, the system triggers the sorting mechanism, using efficient algorithms such as quick sort or heap sort to ensure that the task queues are sorted according to the priority index. This sorting operation is completed before the task is executed, ensuring that the system can process the data packets in order from high to low priority.

[0100] When performing encryption operations, the system will first process the data packets in the high-priority task queue, and then process the low-priority task queue after processing. This operation can be achieved through the scheduler, which will first scan the high-priority queue, read and process the data packets in it in turn, and ensure that they are executed first. The tasks in the low-priority queue will be processed after the high-priority tasks are completed, and the system schedules these tasks one by one according to the pre-set order.

[0101] During the task execution process, the system can obtain the resource utilization, task progress and current execution status of the task queue in real time through the resource monitoring module. This information is usually obtained through system calls, such as the utilization of CPU, memory and network bandwidth, as well as the time progress and completion status of task execution. The monitoring module aggregates this data and dynamically adjusts the order of the task queue. If the system detects resource competition, task delays or unbalanced progress, the scheduler will call the resource allocation algorithm to adjust the order of tasks in the queue according to real-time data and reallocate system resources to ensure that high-priority tasks are given priority and low-priority tasks are postponed when necessary.

[0102] The encryption result verification and rescheduling module verifies the encryption result after the encryption task is completed to verify the integrity and accuracy of the data. If any data that fails the verification is found, it will be rescheduled for encryption processing;

[0103] In this embodiment, after the encryption task is completed, the integrity and accuracy of the completed encrypted data are verified, specifically including verifying the hash value, consistency check code and encryption format of the data packet to ensure that the encryption result has not been tampered with or lost; the identification information of the data that has not passed the verification is recorded, and it is re-added to the queue of pending tasks and encrypted again according to the task priority; in the rescheduling process, the position of the data packet that needs to be re-encrypted in the task queue is dynamically adjusted according to the resource status and task priority of the current queue to ensure that critical data is re-encrypted first, while avoiding resource waste and processing delays.

[0104] After the encryption task is completed, the system will first verify the integrity and accuracy of the completed encrypted data. The specific implementation methods include: calculating the hash value of each data packet and comparing it with the pre-stored original hash value to ensure that the data has not been tampered with during the encryption and transmission process. Secondly, the system will also verify the consistency check code of the data packet to ensure that no errors have occurred during the transmission and storage of the data. Finally, verify the encryption format and check whether the data meets the format requirements specified in the encryption protocol, such as AES, RSA and other standards. These verification steps are automatically executed by calling the preset encryption algorithm and verification tools, and generate verification results.

[0105] For data that fails verification, the system automatically records its identification information, including the data packet ID, task tag, and failure reason. Subsequently, this data is reallocated to the pending task queue and reordered according to its identification information and priority. The scheduler ensures that these failed data are processed first in the queue according to the priority rules set in the system. This process is automatically completed by the task management system to ensure that the reallocated tasks receive processing resources according to priority.

[0106] During the rescheduling process, the system continuously monitors the resource usage and task priority of the current queue. Based on the real-time monitoring data, the system automatically adjusts the position of the data packets that need to be re-encrypted in the queue. If insufficient resources or task delays are detected, the system will rearrange the order of tasks based on their importance and priority to ensure that critical data is encrypted first. At the same time, the scheduler will adjust the position of low-priority tasks to reduce resource waste and avoid excessive system load. This scheduling process relies on real-time system data analysis and task priority rules, and makes adjustments without interfering with the execution of existing tasks.

[0107] The encryption strategy adaptive optimization module updates the encryption strategy and task scheduling rules based on the actual execution of encryption tasks, and optimizes the processing efficiency of subsequent encryption tasks.

[0108] The encryption strategy adaptive optimization module can be implemented in the following ways:

[0109] First, the system automatically collects relevant execution data after each encryption task is executed, including task completion time, resource utilization, execution order of task queues, number of rescheduling, etc. This data can be extracted from task logs, resource monitoring systems, and scheduler records. The system will summarize and analyze this data to identify bottlenecks and efficiency issues in the execution of encryption tasks.

[0110] Based on the collected execution data, the system will automatically determine whether the current encryption strategy needs to be optimized through analysis algorithms. For example, if the system detects that some encryption tasks have obvious delays in resource allocation, or the sorting in the task queue does not achieve the expected effect, the system will adjust the task priority division, resource allocation ratio, and queue sorting strategy. The adjustment process may involve reconfiguring the task classification criteria, priority thresholds, and task queue processing order. The entire optimization process will combine historical data and real-time feedback to ensure that the adjusted strategy is more adapted to the current system status and task load.

[0111] As tasks are continuously executed, the system will continue to monitor the effects of policy adjustments and further optimize encryption policies based on the new round of task execution data. The system can continuously adjust scheduling rules through machine learning or adaptive algorithms to gradually adapt to different task loads and resource states. This dynamic update process ensures that the system always maintains optimal processing efficiency when facing changing task requirements, avoiding problems such as policy lag or resource waste.

[0112] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0113] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). 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 containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0114] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0115] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0116] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

[0118] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0119] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. Payment data security protection system using encryption technology, It is characterized in that It includes encryption task information collection module, encryption queue prediction and evaluation module, task priority classification module, dynamic scheduling and real-time monitoring module, encryption result verification and rescheduling module and encryption strategy adaptive optimization module; The encryption task information collection module obtains in real time the encryption task information directly associated with the encryption task of the remote unmanned retail equipment after the network is restored; The encryption queue prediction and evaluation module analyzes the acquired encryption task information and predicts whether the amount of data to be encrypted will cause the encryption queue to be blocked; The task priority classification module obtains and analyzes the priority information of each data packet to be encrypted when it is predicted that the amount of data to be encrypted will cause the encryption queue to be blocked, and classifies all the data packets to be encrypted according to the analysis results; The dynamic scheduling and real-time monitoring module allocates all the data packets to be encrypted to different task queues for encryption processing according to the classification results of the task priority classification module, and performs encryption operations in sequence according to the order of the task queues, monitors the progress of encryption tasks in real time, and dynamically adjusts the order of task queues; The encryption result verification and rescheduling module verifies the encryption result after the encryption task is completed to verify the integrity and accuracy of the data. If any data that fails the verification is found, it will be rescheduled for encryption processing; The encryption strategy adaptive optimization module updates the encryption strategy and task scheduling rules based on the actual execution of encryption tasks, and optimizes the processing efficiency of subsequent encryption tasks.

2. The payment data security protection system using encryption technology according to claim 1, It is characterized in that The encryption queue prediction and evaluation module is used to analyze the obtained encryption task information and predict whether the amount of data to be encrypted will cause the encryption queue to be blocked based on the analysis results. The specific analysis logic is as follows: Preprocessing the obtained encrypted task information; Extract resource utilization data and task processing data from the preprocessed encrypted task information, and analyze them after extraction to generate an encrypted task load coefficient and a task execution efficiency index, respectively; The generated encryption task load coefficient and task execution efficiency index are used to construct a blocking prediction model to generate a blocking prediction coefficient. The generated blocking prediction coefficient is compared with a pre-set blocking prediction coefficient threshold. Based on the comparison result, it is predicted whether the amount of data to be encrypted will cause the encryption queue to be blocked.

3. The payment data security protection system using encryption technology according to claim 2, It is characterized in that The acquisition logic of the encryption task load coefficient and task execution efficiency index is as follows: Extract resource utilization data from the preprocessed encryption task information, including the total number of packets to be encrypted, the size of each packet to be encrypted, the percentage of CPU resources available to the device, and the percentage of memory resources available to the device. The total number of packets to be encrypted, the size of each packet to be encrypted, the percentage of CPU resources available to the device, and the percentage of memory resources available to the device are marked as n, DS, respectively. i , ACR and AMR, DS i represents the size of the i-th data packet to be encrypted, ACR represents the percentage of CPU resources available on the device, AMR represents the percentage of memory resources available on the device, i=1, 2, 3, ..., n, where n is a positive integer; Calculate the encryption task load coefficient. The specific calculation formula is as follows: Where, ETLC is the encryption task load system; Extract task processing data from the preprocessed encryption task information, including the number of processed data packets to be encrypted, the processing time of each processed data packet to be encrypted, the size of each processed data packet to be encrypted, and the waiting time of each processed data packet to be encrypted in the queue, and mark the number of processed data packets to be encrypted, the processing time of each processed data packet to be encrypted, the size of each processed data packet to be encrypted, and the waiting time of each processed data packet to be encrypted in the queue as m, PT, and j DS j and WT j , PT j represents the processing time of the jth processed data packet to be encrypted, DS j represents the size of the jth processed data packet to be encrypted, WT j represents the waiting time of the jth processed data packet to be encrypted in the queue, j = 1, 2, 3, ..., m, where m is a positive integer; Calculate the task execution efficiency index. The specific calculation formula is as follows: Where TEEI is the task execution efficiency index.

4. The payment data security protection system using encryption technology according to claim 3, It is characterized in that The generated encryption task load coefficient ETLC and task execution efficiency index TEEI are used to construct a blocking prediction model, and a blocking prediction coefficient BPC is generated by weighted summation. The generated blocking prediction coefficient BPC is compared with the pre-set blocking prediction coefficient threshold BPC. yuzhii Perform a comparison and predict whether the amount of data to be encrypted will cause the encryption queue to be blocked based on the comparison results. The specific comparison analysis is as follows: If BPC≤BPC yuzhii , the amount of data to be encrypted will not cause the encryption queue to be blocked; If BPC>BPC yuzhii , the amount of data to be encrypted will cause the encryption queue to be blocked.

5. The payment data security protection system using encryption technology according to claim 4, It is characterized in that The task priority classification module is used to obtain and analyze the priority information of each data packet to be encrypted when it is predicted that the amount of data to be encrypted will cause the encryption queue to be blocked, and classify all data packets to be encrypted according to the analysis results. The specific analysis logic is as follows: Obtaining priority information of each data packet to be encrypted, and performing preprocessing after obtaining; Analyze the priority information of each pre-processed data packet to be encrypted, and generate a priority index for each data packet to be encrypted. Determine a preset priority index threshold, and compare the priority index of each generated data packet to be encrypted with the preset priority index threshold, evaluate the priority of each data packet to be encrypted based on the comparison result, and divide each data packet to be encrypted into a high priority data packet to be encrypted and a low priority data packet to be encrypted based on the evaluation result.

6. The payment data security protection system using encryption technology according to claim 5, It is characterized in that The logic for obtaining the priority index of each data packet to be encrypted is as follows: Extract the priority information of each pre-processed data packet to be encrypted, including the processing time of each data packet to be encrypted, the data packet size of each data packet to be encrypted, and the task urgency of each data packet to be encrypted, and mark them as PCT x DS x and UR x , PCT x Indicates the processing time of the xth data packet to be encrypted, DS x Indicates the size of the xth packet to be encrypted, UR x represents the task urgency of the xth data packet to be encrypted, x=1, 2, 3, ..., h, where h is a positive integer; Calculate the priority index PIYX of each data packet to be encrypted x , according to the formula:

7. The payment data security protection system using encryption technology according to claim 6, It is characterized in that Determine the pre-set priority index threshold PIYX yuzhi , and the priority index PIYX of each data packet to be encrypted is generated x With pre-set priority index threshold PIYX yuzhi Compare and evaluate the priority of each data packet to be encrypted according to the comparison result, and divide each data packet to be encrypted into high priority data packets to be encrypted and low priority data packets to be encrypted according to the evaluation result. The specific comparison analysis and division are as follows: If PIYX x ≤PIYX yuzhi , the priority of the data packet to be encrypted is low, and the data packet is classified as a low priority data packet to be encrypted; If PIYX x >PIYX yuzhi The priority of the data packet to be encrypted is high, and the data packet is classified as a high priority data packet to be encrypted.

8. The payment data security protection system using encryption technology according to claim 7, It is characterized in that According to the classification results of the task priority classification module, high-priority data packets to be encrypted are assigned to the high-priority task queue, and low-priority data packets to be encrypted are assigned to the low-priority task queue, and the data packets in each task queue are sorted from large to small according to the priority index of each data packet to be encrypted; when performing encryption operations, the data packets to be encrypted are first processed in the order in the high-priority task queue, and then the data packets to be encrypted in the low-priority task queue are processed after completion; during the task execution process, the resource utilization, task progress and current execution status of the task queue are obtained in real time, and the order of the task queue is dynamically adjusted based on these data. In the event of resource competition, task delay or unbalanced progress, resources are reallocated or the priority order of the task queue is adjusted.

9. The payment data security protection system using encryption technology according to claim 8, It is characterized in that After the encryption task is completed, the integrity and accuracy of the completed encrypted data are verified, including the hash value, consistency check code and encryption format of the data packet; the identification information of the data that fails the verification is recorded and re-added to the queue of pending tasks, and encrypted again according to the task priority; During the rescheduling process, the position of the data packets that need to be re-encrypted in the task queue is dynamically adjusted according to the resource status and task priority of the current queue.

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