Data hierarchical caching and intelligent continuous transmission method and device in network disconnection environment

Abstract

The invention relates to a data hierarchical caching and intelligent continuous transmission method and device in an off-network environment, and the method comprises the steps: obtaining to-be-transmitted data, carrying out the hierarchical evaluation based on the importance degree and timeliness of the data, and obtaining a corresponding priority data group; monitoring a network bandwidth state in real time, recording an interruption moment and triggering a hierarchical cache mechanism when network interruption is detected, and setting a corresponding storage area; distributing the priority data group to a corresponding storage area for caching to obtain a final cache area; when network recovery is detected, intelligent continuous transmission is performed on the priority data set according to the cache region, and a continuous transmission report is generated; detecting a network bandwidth state in real time, performing dynamic continuous transmission adjustment on the priority data set, and marking the priority data set after continuous transmission is completed to obtain a marked data set; and performing integrity verification on the marked data group, and generating a transmission state report in combination with the continuous transmission report. The method can adapt to different network environments and data transmission requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of data hierarchical caching and intelligent resume transmission, and in particular to a method and device for data hierarchical caching and intelligent resume transmission in a network disconnection environment. Background Art

[0002] With the rapid development of information technology and the increasing demand for data transmission, data transmission in a network environment has become an important part of modern communication systems. However, in practical applications, the instability and interruption of network connections still exist, which poses a huge challenge to the timely and complete transmission of data. Especially in some critical business scenarios, the reliability and timeliness of data transmission are directly related to the overall performance of the system and user experience. Therefore, how to ensure the effective transmission of data in a network disconnection environment and realize intelligent retransmission after the network is restored has become one of the current research focuses. Traditional data transmission methods usually only consider the network connection status, but ignore the importance and timeliness of the data itself, which may lead to the failure to transmit critical data in a timely manner when network resources are limited. Summary of the invention

[0003] The main purpose of the present invention is to provide a method and device for hierarchical data caching and intelligent transmission in a disconnected environment, which can adapt to different network environments and data transmission requirements.

[0004] To achieve the above object, the present invention provides a method for hierarchical data caching and intelligent data transfer in a network disconnected environment, comprising: Obtain the data to be transmitted, conduct hierarchical evaluation based on the importance and timeliness of the data, and obtain the corresponding priority data group; Monitor the network bandwidth status in real time. When a network interruption is detected, record the interruption time, trigger the hierarchical cache mechanism, and set the corresponding storage area. Allocating the priority data group to the corresponding storage area for caching to obtain a final cache area; When network recovery is detected, the priority data group is intelligently resumed according to the cache area, and a resume report is generated; Real-time detection of network bandwidth status, dynamic retransmission adjustment of the priority data group, and marking of the priority data group after retransmission completion to obtain a marked data group; An integrity check is performed on the marked data group, and a transmission status report is generated in combination with the resume transmission report.

[0005] Furthermore, the data to be transmitted is obtained, and a hierarchical evaluation is performed based on the importance and timeliness of the data to obtain a corresponding priority data group, including: Extracting attributes of the data to be transmitted to obtain a data creation timestamp and a data service type identifier; Classify and encode the data to be transmitted according to a preset service classification rule base and the data service type identifier to obtain an identification data record; Obtain the current timestamp and perform a difference calculation with the data creation timestamp to obtain the data existence time, perform a weight calculation on the data existence time according to a preset timeliness benchmark threshold, and obtain a timeliness weight coefficient; Standardizing and scoring the identification data record and the timeliness weight coefficient to obtain a comprehensive importance score; The comprehensive importance scores are prioritized and grouped according to a preset importance score interval threshold group to obtain the priority data group including a high priority group, a medium priority group and a low priority group.

[0006] Furthermore, the step of standardizing the scoring of the identification data record and the timeliness weight coefficient to obtain a comprehensive importance score further includes: Quantifying the importance of the identified data record to obtain a data service importance score; Performing timeliness calculation on the timeliness weight coefficient to obtain a timeliness score; Perform multi-dimensional analysis of the current system environment according to the preset environmental factor assessment rules to obtain environmental factor scores; Performing weighted fusion on the data service importance score, the timeliness score and the environmental factor score to obtain an initial comprehensive score; The initial comprehensive score is normalized and mapped to a range between 0 and 1 to obtain a mapping initial value; Selecting a preset mapping parameter, performing mapping iterative calculation on the mapping initial value, and obtaining a dynamic adjustment factor; Importance calculation is performed based on the initial comprehensive score and the dynamic adjustment factor to obtain the comprehensive importance score.

[0007] Furthermore, the real-time monitoring of the network bandwidth status, when a network interruption is detected, records the interruption time and triggers the hierarchical cache mechanism, and sets the corresponding storage area, including: Periodically detecting the network bandwidth status to obtain network status parameters; Calculating network quality based on the network status parameters to obtain a network quality score; Performing a threshold comparison on the network quality score to determine whether the network is interrupted; When a network interruption is detected, the interruption time and timing data are recorded, and the duration of the network interruption is calculated; Classifying the duration of the network interruption based on a preset duration threshold to obtain an interruption level; Matching the interrupt level with a preset cache policy library to obtain a corresponding hierarchical cache policy; Initialize the cache management module based on the hierarchical cache strategy and set corresponding data access and storage rules; Establishing a cache index structure based on the data access and the storage rules; The corresponding storage area is set according to the cache index structure to complete the triggering and initialization of the hierarchical cache mechanism.

[0008] Further, the allocating the priority data group to the corresponding storage area for caching to obtain a final cache area includes: Calculating storage requirements for the priority data group to obtain corresponding storage requirements; Partitioning the storage area according to the storage demand to obtain a corresponding plurality of storage partitions; Performing an access speed test on the storage partition to obtain an access speed value; Sorting the storage partitions according to the access speed values ​​to obtain a speed grade sequence; Matching the priority data group with the speed grade sequence to obtain a priority storage partition correspondence relationship; Writing the priority data group into the corresponding storage partition according to the priority storage partition correspondence to obtain an initial cache area; Calculating the storage space usage rate of the initial cache area to obtain the usage rate of each storage partition; Redistributing space of the initial cache area according to a preset cyclic coverage strategy and the usage rate to obtain an optimized cache area; The optimized cache area is monitored in real time, and when the storage capacity of the optimized cache area reaches a preset capacity threshold, data compression encoding processing is performed on the optimized cache area to obtain compressed cache data; The compressed cache data is rewritten into the optimized cache area to obtain the final cache area.

[0009] Furthermore, the calculation of the storage space usage rate of the initial cache area to obtain the usage rate of each storage partition includes: Performing multi-scale spatial division on the initial cache area to obtain scale grids of different scales; Performing occupancy calculation on each scale grid to obtain the number of occupied grids at each scale; The fractal dimension of the occupied grid number is calculated by a preset box counting method to obtain a spatial complexity index; Dividing the initial cache area into storage blocks of fixed size, and calculating the data distribution probability for each storage block to obtain a block-level data distribution probability; Calculating the entropy value of the block-level data distribution probability using a preset entropy formula to obtain data distribution entropy; Performing nonlinear mapping on the storage partition according to the space complexity index to obtain a preliminary utilization rate; Correcting the preliminary usage rate according to the data distribution entropy to obtain a corrected usage rate; Performing access frequency statistics on the storage partition to obtain an access heat map; The modified usage rate is comprehensively counted according to the access heat map to obtain the usage rate of each storage partition.

[0010] Furthermore, when the network recovery is detected, the priority data group is intelligently resumed according to the cache area, and a resume report is generated, including: Periodically detecting the network bandwidth status, and triggering an intelligent resuming program when the network is detected to be restored; Reading the storage location information of the priority data group in the cache area, performing read sorting, and generating a resume transmission queue; Performing data packet numbering and fragmentation processing on the retransmission queue to obtain a set of data fragments to be transmitted; Calculate the optimal transmission concurrency number and the size of a single shard based on the data shard set to obtain the transmission control parameters; Concurrently transmitting the data slice set based on the transmission control parameter, and recording the transmission status of each data slice in real time to obtain initial resume transmission information; When it is detected that the data segment transmission fails, the data segment is re-added to the retransmission queue and the number of retries is recorded; The initial resume transmission information is integrated with the number of retries to obtain the resume transmission report.

[0011] Furthermore, the real-time detection of the network bandwidth status, the dynamic retransmission adjustment of the priority data group, and the marking of the priority data group after the retransmission is completed to obtain the marked data group include: Performing periodic sampling and measurement on the network bandwidth status to obtain bandwidth sampling data; Calculating network bandwidth characteristics based on the bandwidth sampling data to obtain bandwidth statistical characteristics; According to the bandwidth statistical characteristics, the preset adjustment strategy is matched to obtain the current transmission adjustment strategy; Adjust the transmission rate and concurrency of the priority data group based on the current transmission adjustment strategy to obtain adjusted transmission parameters; According to the transmission adjustment parameter, the priority data group is adjusted and transmitted in blocks to obtain transmission data blocks; Performing checksum calculation on each of the transmission data blocks to obtain data block checksum information; Sending the data block check information and the transmission data block to a receiving end to obtain a sending result; Receive the transmission confirmation information returned by the receiving end to obtain the transmission status; Marking the successfully transmitted priority data group according to the transmission status to obtain the marked data group; removing the marked data group from the priority data group to obtain an updated priority data group; The transmission process is repeated for the update priority data group until the transmission is completed.

[0012] Further, the performing integrity check on the marked data group and generating a transmission status report in combination with the resumed transmission report includes: Performing a checksum calculation on each of the marked data groups to obtain a checksum value, and comparing it with a preset standard value to obtain an integrity check result; Performing a success rate analysis on the integrity check result to obtain a corresponding overall transmission status; Marking the marked data group for verification failure according to the overall transmission status to obtain a data group to be retransmitted; Retransmitting the data group to be retransmitted based on a preset upper limit of the number of retransmissions to obtain a retransmission result; Identify the data group to be retransmitted that exceeds the upper limit of the number of retransmissions according to the retransmission result, obtain a failed data group, process the failed data group according to a preset degradation strategy, and update the overall transmission state to obtain a corresponding updated transmission state; Performing transmission statistics on the marked data group based on the updated transmission status to obtain corresponding transmission statistical information; Classify and summarize the transmission anomalies of the transmission statistical information to obtain corresponding transmission anomaly information; The transmission resume report, the transmission statistics information and the transmission exception information are integrated to obtain the transmission status report.

[0013] The present invention also provides a data hierarchical caching and intelligent retransmission device in a disconnected network environment, which is applied to any of the above data hierarchical caching and intelligent retransmission methods in a disconnected network environment, including: A collection module, which is used to obtain the data to be transmitted, and to perform a hierarchical evaluation based on the importance and timeliness of the data to obtain a corresponding priority data group; An analysis module, which is used to monitor the network bandwidth status in real time, and when a network interruption is detected, records the interruption time and triggers the hierarchical cache mechanism, and sets a corresponding storage area; An association module, the association module is used to allocate the priority data group to the corresponding storage area for caching, to obtain a final cache area; A processing module, the processing module is used for intelligently retransmitting the priority data group according to the cache area and generating a retransmission report when a network recovery is detected; A control module, the control module is used to detect the network bandwidth status in real time, and dynamically adjust the priority data group for continued transmission, and mark the priority data group for which continued transmission is completed to obtain a marked data group; An execution module is used to perform integrity check on the marked data group and generate a transmission status report in combination with the resume transmission report.

[0014] The present invention provides a method and device for hierarchical data caching and intelligent retransmission in a network disconnected environment, which has the following beneficial effects: By performing hierarchical evaluation based on the importance and timeliness of data, intelligent classification of the data to be transmitted is achieved, ensuring that key data can be transmitted first when network resources are limited, and effectively solving the problem of traditional methods ignoring data characteristics. A hierarchical cache mechanism is adopted to allocate storage areas according to data priority, so as to achieve the rational use of storage resources, avoid the risk of key data loss and storage space waste, and improve the efficiency of data management. By real-time monitoring of the network bandwidth status and dynamic retransmission adjustment of the priority data group, adaptive optimization of the transmission process is achieved, the transmission efficiency after network recovery is improved, and the problem of lack of flexibility of the traditional retransmission method is solved. The integrity check of the marked data group is performed, and the transmission status report is generated in combination with the retransmission report, and a comprehensive data transmission monitoring mechanism is established, which effectively improves the reliability and traceability of the transmission process. Through intelligent retransmission and dynamic adjustment strategies, the system can better adapt to different network environments and data transmission requirements, and improve the overall transmission efficiency and system reliability. The present invention comprehensively considers data characteristics, network status and storage resources, realizes intelligent data management and transmission in a disconnected environment, and provides a more reliable and efficient solution for data transmission in various application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a data hierarchical caching and intelligent retransmission method in a network disconnection environment provided by the present invention; Figure 2 It is a structural diagram of a data hierarchical caching and intelligent retransmission device in a network disconnection environment provided by the present invention.

[0016] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Reference Figure 1 As shown, the present invention provides 1. a data hierarchical caching and intelligent retransmission method in a network disconnected environment, characterized in that it includes: Step S1: Obtain the data to be transmitted, perform hierarchical evaluation based on the importance and timeliness of the data, and obtain the corresponding priority data group; Step S2: monitor the network bandwidth status in real time. When a network interruption is detected, record the interruption time and trigger the hierarchical cache mechanism, and set the corresponding storage area; Step S3: Allocate the priority data group to the corresponding storage area for caching to obtain a final cache area; Step S4: when the network is detected to be restored, the priority data group is intelligently resumed according to the cache area, and a resume report is generated; Step S5: Detect the network bandwidth status in real time, dynamically adjust the priority data group for continued transmission, and mark the priority data group for which continued transmission is completed to obtain a marked data group; Step S6: Perform integrity check on the marked data group, and generate a transmission status report in combination with the resume transmission report.

[0020] Based on the above steps, the detailed process is as follows: Step S1: Establish a data interface to connect various data source systems. Set a data acquisition cycle to acquire the data to be transmitted regularly or in real time. Perform preliminary cleaning and formatting on the acquired data to ensure data consistency.

[0021] Establish data importance assessment rules, including: Data type weight (such as key business data, general business data, log data, etc.), data source weight (such as core system, auxiliary system, etc.), and data impact scope (such as global impact, local impact, etc.).

[0022] Apply the evaluation rules to each piece of data and calculate the importance score.

[0023] Establish timeliness evaluation criteria, such as real-time requirements (data that needs to be processed immediately), quasi-real-time requirements (data that can be delayed), and batch processing requirements (data that can be processed later). Assign a timeliness label to each piece of data.

[0024] Design a comprehensive scoring algorithm that combines the importance score and timeliness label to calculate the comprehensive priority score for each piece of data.

[0025] Define priority levels, such as high, medium, and low, or multiple levels with more detailed details. Divide the data into corresponding priority groups based on the comprehensive priority scores. Within each priority group, sort by scores.

[0026] Assign a unique identifier to each priority group. Record statistics such as the amount of data for each group, average priority score, etc. Generate metadata for priority data groups, including group identification, list of included data, priority information, etc.

[0027] Step S2: Monitor the network bandwidth status in real time. When a network interruption is detected, record the interruption time, trigger the hierarchical cache mechanism, and set the corresponding storage area.

[0028] Deploy network monitoring probes to continuously collect network performance indicators. Set the sampling frequency, such as once per second or once per minute. Collect key indicators, including upstream and downstream bandwidth utilization, network latency, packet loss rate, and connection status.

[0029] Define network status assessment rules, such as normal (bandwidth utilization <80%, latency <100ms, packet loss rate <1%), warning (bandwidth utilization 80%-95%, latency 100ms-500ms, packet loss rate 1%-5%), severe (bandwidth utilization >95%, latency >500ms, packet loss rate >5%), and interrupted (unable to establish connection or multiple consecutive severe states).

[0030] Compare the current network status with the interruption definition in real time. When an interruption is detected, immediately record the exact timestamp of the interruption, the last successfully transmitted data packet information, and the historical data of the network status before the interruption. Send a trigger signal to the cache management module. Pass the interruption time information and the last transmission status.

[0031] Dynamically allocate cache storage areas according to preset storage policies. Consider factors including available storage space, estimated interruption duration, and data priority distribution. Allocate corresponding storage space ratios for data of different priorities. Generate a storage area mapping table containing the starting address, size, and corresponding data priority of each area. Continuously monitor network status during caching. Prepare to detect network recovery conditions so as to switch back to normal transmission mode in time.

[0032] Step S3: Based on the priority data group, a preset cache strategy is adopted to write the data to be transmitted into the corresponding storage area to complete data cache.

[0033] Design cache strategies based on the characteristics of priority data groups, including high-priority data (priority cache, allocation of the most reliable storage area), medium-priority data (balanced storage, trade-off between reliability and capacity), and low-priority data (large-capacity storage, acceptable lower reliability).

[0034] The storage area mapping table generated in step S2 is read to allocate a corresponding storage area to each priority data group.

[0035] Process the data groups in order of priority, from high to low. For each data group, read the data group metadata, extract a single piece of data in the order of priority within the group, write the data to the allocated storage area, and update the storage area usage status. Verify each write operation to ensure data integrity. Record the cache location, timestamp, and verification information of each piece of data. Update the data group metadata and mark the cached data. Get the final cache area.

[0036] Step S4: Continuously monitor the network status and set the network recovery judgment standard (such as multiple consecutive samplings showing that the network indicators have returned to normal). Once the network recovery is detected, the retransmission mechanism is triggered immediately. Read the cache area mapping table and data group metadata to obtain the priority and storage location information of the data to be retransmitted. Process each data group in order from high to low priority. For each data group, extract a single data according to the priority within the group and retransmit it. During the transmission process, record the transmission status of each data in real time, including the start time, completion time, transmission speed, number of retries, etc. For the successfully transmitted data, update its status in the data group metadata. In the event of a transmission failure, apply the retry strategy and record the reason for the failure. Dynamically adjust the transmission order and optimize the transmission strategy of subsequent data according to the real-time network status and transmission success rate. Generate a real-time retransmission report containing information such as the amount of data transmitted, the amount of remaining data, the average transmission speed, and the estimated completion time. Regularly update the retransmission report to provide managers with real-time transmission progress.

[0037] Step S5: Deploy the bandwidth monitoring module to continuously collect network bandwidth utilization, available bandwidth and other indicators. Set the sampling frequency, such as once per second or once per minute. Define bandwidth status evaluation rules, such as high speed (available bandwidth > 90%), medium speed (available bandwidth 50%-90%), and low speed (available bandwidth < 50%). Dynamically adjust the data transmission strategy based on the real-time bandwidth status.

[0038] At high speeds, increase the number of concurrent transmissions to improve transmission efficiency. At medium speeds, maintain a balanced transmission strategy. At low speeds, reduce the number of concurrent transmissions to prioritize the transmission of high-priority data. Update the transmission progress of each priority data group in real time.

[0039] When all data transmission of a data group is completed, it is marked as "completed". The metadata of the data group is updated to record the completion time, total amount of data transmitted, average transmission speed and other information. A list of marked data groups is generated, including all data groups that have completed transmission and their related statistical information. The transmission status of unfinished data groups is continuously monitored, and the transmission order and resource allocation are dynamically adjusted according to the network conditions and the amount of remaining data.

[0040] Step S6: Perform integrity check on each data group marked as "completed". Read the original checksum information in the data group metadata. Recalculate the checksum value of the transmitted data and compare it with the original checksum information. If any inconsistency is found, mark the data group as "needs to be retransmitted" and re-add it to the transmission queue. For data groups that pass the check, update their status to "verification completed". Summarize the transmission and verification results of all data groups. Combined with the retransmission report generated in step S4, integrate various statistical data during the transmission process. Generate a detailed transmission status report, including the total number of transmissions.

[0041] The present invention provides a data hierarchical caching and intelligent retransmission method in a disconnected network environment. By performing hierarchical evaluation based on the importance and timeliness of data, the intelligent classification of the data to be transmitted is realized, ensuring that key data can be transmitted preferentially when network resources are limited, and effectively solving the problem that the traditional method ignores data characteristics. A hierarchical caching mechanism is adopted to allocate storage areas according to data priority, so as to realize the rational use of storage resources, avoid the risk of key data loss and storage space waste, and improve the efficiency of data management. By real-time monitoring of the network bandwidth status and dynamic retransmission adjustment of the priority data group, adaptive optimization of the transmission process is realized, the transmission efficiency after network recovery is improved, and the problem of lack of flexibility of the traditional retransmission method is solved. The integrity check of the marked data group is performed, and the transmission status report is generated in combination with the retransmission report, and a comprehensive data transmission monitoring mechanism is established, which effectively improves the reliability and traceability of the transmission process. Through intelligent retransmission and dynamic adjustment strategies, the system can better adapt to different network environments and data transmission requirements, and improve the overall transmission efficiency and system reliability. The present invention comprehensively considers data characteristics, network status and storage resources, realizes intelligent data management and transmission in a disconnected network environment, and provides a more reliable and efficient solution for data transmission in various application scenarios.

[0042] In one embodiment, the data to be transmitted is obtained, and a hierarchical evaluation is performed based on the importance and timeliness of the data to obtain a corresponding priority data group, including: Attributes of the data to be transmitted are extracted to obtain the data creation timestamp and data business type identifier. During the attribute extraction process, the metadata information of the data file is parsed to extract key attributes such as the data creation time and business type. These attributes are the basis for subsequent data classification evaluation.

[0043] The data to be transmitted is classified and coded according to the preset business classification rule base and data business type identification to obtain the identification data record. The business classification rule base contains the importance weights and classification standards of different business types. According to the extracted business type identification, it is matched against the rule base and the corresponding classification code is assigned to each data. This process ensures the consistency and traceability of data classification.

[0044] Get the current timestamp and calculate the difference with the data creation timestamp to get the data existence time. Perform weight calculation on the data existence time according to the preset timeliness benchmark threshold to get the timeliness weight coefficient. The timeliness benchmark threshold is pre-set according to the timeliness requirements of different business types. By comparing the data existence time with the corresponding timeliness benchmark threshold, calculate the weight coefficient reflecting the timeliness of the data. This step takes into account the time sensitivity of the data and gives a higher weight to data with high timeliness requirements.

[0045] Standardized scoring is performed on the identification data records and timeliness weight coefficients to obtain a comprehensive importance score. The standardized scoring process uses a standardized algorithm to unify the scoring indicators of different dimensions to the same scale. Taking into account the business importance and timeliness of the data, the comprehensive importance score of each data is obtained through weighted calculation. This score reflects the overall importance of the data at the current point in time.

[0046] The comprehensive importance scores are prioritized and grouped according to the preset importance score interval threshold groups to obtain priority data groups including high priority group, medium priority group and low priority group. The importance score interval threshold groups are pre-defined based on historical data statistics and business needs. The comprehensive importance score of each data is compared with these thresholds to divide the data into corresponding priority groups. This grouping method provides clear priority guidance for subsequent data transmission and caching strategies.

[0047] This embodiment achieves accurate classification of data by extracting attributes and classifying and encoding the data to be transmitted, laying the foundation for subsequent priority evaluation. Based on timeliness weight calculation and standardized scoring, the system can dynamically evaluate the importance of data to ensure that key data with high timeliness is given priority. The use of preset importance score interval threshold groups for priority sorting and grouping makes the data priority division more scientific and reasonable, effectively improving the utilization efficiency of network resources. By optimizing the data transmission sequence, not only the transmission success rate of key data is improved, but also the data backlog after network recovery is reduced, greatly improving the overall performance and reliability of the system.

[0048] In one embodiment, the identification data record and the timeliness weight coefficient are standardized and scored to obtain a comprehensive importance score, which also includes: The importance of the identified data records is quantified to obtain the data business importance score. This quantification process is based on the preset business importance evaluation rules, taking into account factors such as the business type, usage frequency, and relevance of the data, and assigning an importance value to each data record.

[0049] The timeliness weight coefficient is calculated to obtain a timeliness score. The timeliness calculation uses a predefined timeliness function, which considers the time attributes of the data such as the generation time, validity period, and update cycle, and converts the timeliness weight coefficient into a standardized timeliness score.

[0050] The system analyzes the current system environment in multiple dimensions according to the preset environmental factor evaluation rules to obtain the environmental factor score. The evaluation rules cover multiple dimensions such as network status, storage capacity, and processing power. Through the comprehensive evaluation of indicators in each dimension, a quantitative score reflecting the current system environment is generated.

[0051] The data service importance score, timeliness score and environmental factor score are weighted and integrated to obtain the initial comprehensive score. The integration process adopts a preset weight distribution scheme to ensure that the contribution of each factor to the final score matches its actual importance.

[0052] The initial comprehensive score is normalized and mapped to the interval between 0 and 1 to obtain the initial value of the logistic map. The standardization process uses the maximum and minimum value normalization method to ensure that the scores of different scales can be compared in the same interval.

[0053] Select the preset Logistic mapping parameters, perform Logistic mapping iterative calculation on the initial value of Logistic mapping, and obtain the dynamic adjustment factor. Logistic mapping introduces nonlinear characteristics, enhances the sensitivity of the scoring system to boundary conditions, and improves the discrimination of the scoring.

[0054] Importance calculation is performed based on the initial comprehensive score and dynamic adjustment factor to obtain a comprehensive importance score. Importance calculation uses a predefined calculation formula to nonlinearly combine the initial comprehensive score and the dynamic adjustment factor to generate the final comprehensive importance score. This score fully considers the business importance, timeliness and current system environment of the data, and provides a reliable quantitative basis for subsequent data caching and retransmission decisions.

[0055] This embodiment achieves a comprehensive quantification of data value by scoring identification data records in multiple dimensions, including business importance, timeliness, and environmental factors, and provides a reliable basis for caching decisions. The introduction of Logistic mapping iterative calculation enhances the sensitivity of the scoring system to boundary conditions and improves the discrimination of the scores. The weighted fusion and nonlinear combination methods are used to ensure that the impact of each evaluation factor on the final score matches its actual importance. Standardization processing and 0-1 interval mapping allow scores of different scales to be compared under unified standards. This comprehensive scoring mechanism can effectively identify and prioritize key data, improve data management efficiency and resource utilization in disconnected environments, while ensuring the intelligence and accuracy of data retransmission, and significantly enhance the system's adaptability and performance in complex network environments.

[0056] In one embodiment, the network bandwidth status is monitored in real time. When a network interruption is detected, the interruption time is recorded and a hierarchical cache mechanism is triggered, and a corresponding storage area is set, including: Perform periodic detection on the network bandwidth status to obtain network status parameters. Network status parameters include current bandwidth, delay, packet loss rate and other indicators. The interval of periodic detection is set according to actual needs, and the setting duration is from a few seconds to a few minutes.

[0057] The network quality is calculated based on the network status parameters to obtain the network quality score. The network quality score is a comprehensive evaluation of the network status parameters. It uses a weighted calculation method to assign different weights to each parameter according to its importance, and finally obtains a score between 0 and 100.

[0058] Compare the network quality score with the threshold to determine whether the network is disconnected. Set a network quality score threshold. When the score is lower than the threshold, it is considered a network interruption. The selection of the threshold needs to balance sensitivity and stability, and is set between 20-30 points.

[0059] When a network interruption is detected, the interruption time and timing data are recorded, and the duration of the network interruption is calculated. The interruption time is accurate to milliseconds, and the timing data is used for subsequent duration calculations. The duration calculation uses a real-time update mechanism, which is updated once per second.

[0060] The duration of network interruption is graded based on the preset duration threshold to obtain the interruption level. The duration threshold can be set to multiple levels, such as 1 minute, 5 minutes, 30 minutes, etc., corresponding to different interruption levels. The interruption level reflects the severity of the network interruption and is used for the subsequent caching strategy selection.

[0061] The interrupt level is matched with the preset cache policy library to obtain the corresponding hierarchical cache policy. The cache policy library contains multiple predefined cache policies, each corresponding to different interrupt levels and data types. The policy matching process takes into account factors such as interrupt level, data importance, and system resources.

[0062] Initialize the cache management module based on the hierarchical cache strategy and set the corresponding data access and storage rules. The initialization settings include parameters such as cache space allocation and cache update frequency. The data access and storage rules define the cache priority, retention time, and access rights for different types of data.

[0063] The cache index structure is established based on data access and storage rules. The cache index structure is implemented using a multi-level hash table to support fast search and update. The index structure contains information such as data identification, storage location, and timestamp, which facilitates subsequent data management and retransmission operations.

[0064] The corresponding storage area is set according to the cache index structure to complete the triggering and initialization of the hierarchical cache mechanism. The storage area division takes into account the data type, importance and access frequency, and adopts a hierarchical storage strategy. High-priority data is stored in the fast access area, and low-priority data can be stored on slower storage media. After initialization is completed, the hierarchical cache mechanism starts to run, realizing intelligent data management and retransmission preparation in a disconnected environment.

[0065] This embodiment can detect network interruptions in a timely manner by real-time monitoring of the network bandwidth status and performing dynamic evaluation, thereby improving the system's response speed and accuracy to network changes. The interruption classification mechanism based on the preset duration threshold realizes the refined processing of network interruptions of different severity and enhances the adaptability of the system. The hierarchical cache strategy and adaptive data access storage rules are adopted to effectively improve the data management efficiency and resource utilization in the network disconnection environment. The cache index of the multi-level hash table structure realizes fast data search and update, and optimizes the cache performance. The hierarchical storage strategy reasonably allocates storage resources according to the importance and access frequency of the data, while ensuring the availability of key data, it improves the overall storage efficiency. These measures jointly ensure the reliability and availability of data in the network disconnection environment, lay the foundation for the subsequent intelligent retransmission, and significantly improve the system's operating efficiency and user experience in an unstable network environment.

[0066] In one embodiment, the priority data groups are allocated to corresponding storage areas for caching to obtain a final cache area, including: The storage demand calculation is performed on the priority data group to obtain the corresponding storage demand. The storage demand calculation takes into account factors such as data size, growth rate, and reserved space, and uses a dynamic prediction algorithm to ensure accurate estimation of the storage demand of data of different priorities.

[0067] The storage area is partitioned according to the storage demand to obtain multiple corresponding storage partitions. The partition operation adopts an adaptive partitioning strategy to dynamically adjust the partition size and number according to the ratio of storage demand and available storage space to ensure the reasonable allocation of storage resources.

[0068] Perform access speed test on storage partitions to obtain access speed value. The access speed test uses a combination of random read and write and sequential read and write methods, and takes the average value after multiple tests to eliminate the influence of accidental factors and obtain accurate access speed evaluation.

[0069] The storage partitions are sorted according to the access speed values ​​to obtain a speed grade sequence. The sorting process uses a quick sort algorithm to arrange the storage partitions from high to low according to the access speed, forming a clear speed grade sequence to provide a basis for subsequent data allocation.

[0070] The priority data group is matched with the speed grade sequence to obtain the priority storage partition correspondence. The matching process takes into account the data priority and access frequency. High priority and high-frequency access data are allocated to high-speed storage partitions, and low priority data are allocated to slower storage partitions, so as to achieve efficient use of storage resources.

[0071] According to the corresponding relationship of the priority storage partitions, the priority data group is written into the corresponding storage partition to obtain the initial cache area. The writing process adopts parallel writing technology to improve the efficiency of data writing. At the same time, a data index structure is established to facilitate subsequent rapid retrieval and access.

[0072] The storage space utilization rate of the initial cache area is calculated to obtain the utilization rate of each storage partition. The utilization rate calculation adopts a real-time monitoring mechanism to regularly update the utilization of each partition to provide a basis for subsequent space optimization.

[0073] The initial cache area is reallocated according to the preset cyclic overwrite strategy and usage rate to obtain the optimized cache area. The cyclic overwrite strategy defines the overwrite rules for data of different priorities. High-priority data is not overwritten, and the storage space of low-priority data can be dynamically adjusted according to the usage rate. During the space reallocation process, data integrity and consistency are considered to ensure that important data will not be lost.

[0074] The optimized cache area is monitored in real time. When the storage capacity of the optimized cache area reaches the preset capacity threshold, the optimized cache area is compressed and encoded to obtain compressed cache data. Data compression uses an adaptive compression algorithm to select an appropriate compression method based on the data type and find a balance between compression rate and decompression speed. The preset capacity threshold is usually set to 80%-90% of the total storage space to reserve sufficient operating space.

[0075] The compressed cache data is rewritten into the optimized cache area to obtain the final cache area. During the rewriting process, an incremental update strategy is adopted to update only the changed data blocks to reduce the impact of the write operation on system performance.

[0076] This embodiment achieves reasonable allocation of storage resources and improves storage space utilization efficiency by performing storage demand calculation and adaptive partitioning on priority data groups. Access speed testing and sorting mechanisms are adopted to ensure that high-priority data is stored in high-speed partitions, thereby optimizing data access performance. Dynamic monitoring and space reallocation strategies ensure flexible adjustment of storage space to meet the storage requirements of different data types. Real-time monitoring combined with an adaptive compression algorithm effectively increases storage capacity while ensuring data integrity. The incremental update strategy reduces the impact of data rewriting on system performance and ensures the stability of the cache system. The overall solution realizes efficient storage, fast access and intelligent management of data in a disconnected environment, improves system reliability and data security, and lays the foundation for data continuation in a disconnected environment.

[0077] In one embodiment, the storage space usage rate of the initial cache area is calculated to obtain the usage rate of each storage partition, including: Perform multi-scale spatial division on the initial cache area to obtain scale grids of different scales. Multi-scale spatial division is to grid the cache area at different granularities to capture the spatial characteristics of data distribution. This division method describes the spatial distribution of data at multiple levels from macro to micro, which helps to analyze the usage of storage space more comprehensively.

[0078] Perform occupancy calculation on each scale grid to obtain the number of occupied grids at each scale. During the occupancy calculation process, the number of grids actually occupied by data at each scale is counted, which reflects the spatial distribution density of data at different scales.

[0079] The fractal dimension of the number of occupied grids is calculated by the preset box counting method to obtain the spatial complexity index. The box counting method is a commonly used fractal dimension calculation method. By analyzing the changing pattern of the number of occupied grids at different scales, an index describing the complexity of data spatial distribution is obtained. This index can quantify the spatial complexity of data distribution and provide an important reference for the subsequent calculation of storage space utilization.

[0080] The initial cache area is divided into fixed-size storage blocks, and the data distribution probability is calculated for each storage block to obtain the block-level data distribution probability. This step divides the cache area into finer-grained storage units and analyzes the distribution of data in each storage block to obtain more accurate data distribution information.

[0081] The entropy of the block-level data distribution probability is calculated using the preset Shannon entropy formula to obtain the data distribution entropy. Shannon entropy is an important indicator for describing uncertainty in information theory, and is used here to quantify the uniformity of data distribution. The higher the data distribution entropy, the more uniform the data distribution; conversely, the more concentrated the data distribution.

[0082] According to the spatial complexity index, nonlinear mapping is performed on the storage partitions to obtain preliminary utilization rates. Nonlinear mapping takes into account the spatial complexity of data distribution and converts indicators such as fractal dimension into preliminary storage space utilization estimates. This mapping method can reflect the impact of non-uniform data distribution on storage space utilization efficiency.

[0083] The preliminary utilization rate is corrected according to the data distribution entropy to obtain the corrected utilization rate. This step takes the uniformity of data distribution into consideration and adjusts the preliminary utilization rate. The more uniform the data distribution, the higher the actual utilization rate of the storage space may be; conversely, concentrated data may lead to overuse of certain storage areas.

[0084] The access frequency of the storage partitions is counted to obtain the access heat map. The access heat map reflects the usage frequency of different storage partitions. This information is crucial for evaluating the actual utilization efficiency of storage space.

[0085] The modified utilization rate is comprehensively counted according to the access heat map to obtain the utilization rate of each storage partition. The data distribution characteristics are combined with the actual access mode to obtain a more accurate and meaningful storage space utilization evaluation result.

[0086] This embodiment can comprehensively capture the spatial distribution characteristics of data in the cache area through multi-scale space division and box counting method fractal dimension calculation, describe the data distribution from multiple levels from macro to micro, and provide an accurate spatial complexity index for storage space utilization calculation. The data distribution entropy obtained by combining Shannon entropy calculation can accurately quantify the uniformity of data distribution, further improving the accuracy of utilization evaluation. Through nonlinear mapping and data distribution entropy correction, the impact of the non-uniformity of data distribution on the efficiency of storage space utilization is effectively considered, making the utilization evaluation closer to the actual situation. Finally, combined with the access heat map for comprehensive statistics, the data distribution characteristics are combined with the actual access mode, and the storage partition utilization evaluation results obtained are more accurate and meaningful, providing a reliable basis for subsequent data management and storage optimization, and helping to improve the performance and efficiency of the overall storage system.

[0087] In one embodiment, when network recovery is detected, priority data groups are intelligently resumed according to the cache area, and a resume report is generated, including: The network bandwidth status is periodically checked, either by periodic polling or event triggering. The detection methods include sending a small probe packet and measuring the round-trip time, or directly querying the network interface status. When the network is detected to be restored, that is, the network connection is reestablished and the bandwidth reaches the preset threshold, the intelligent retransmission program is triggered. Periodic detection ensures timely response to changes in network status and creates conditions for data retransmission.

[0088] Read the storage location information of the priority data group in the cache area, including metadata such as the physical address, size, and priority of the data. Sort this information according to the predefined priority strategy to generate a resume queue. The sorting strategy considers factors such as the importance, timeliness, and size of the data to ensure that the most critical data is transmitted first.

[0089] The data packets in the retransmission queue are numbered and fragmented. The numbering process assigns a unique identifier to each data packet to facilitate tracking and management during transmission. The fragmentation process cuts large data packets into small pieces suitable for network transmission, improving transmission efficiency and reducing the risk of network congestion. After the processing is completed, a set of data fragments to be transmitted is obtained, each with a number and metadata information.

[0090] The optimal number of concurrent transmissions and the size of a single fragment are calculated based on the data fragment set to obtain the transmission control parameters. This process involves complex algorithms that take into account factors such as the current network bandwidth, latency, and packet loss rate. Adaptive algorithms are used to dynamically adjust these parameters based on network conditions to achieve optimal transmission efficiency. Accurate settings of transmission control parameters are critical to optimizing retransmission performance.

[0091] Based on the calculated transmission control parameters, the data shard set is transmitted concurrently, and multiple data shards are sent at the same time to make full use of the network bandwidth. During the transmission process, the transmission status of each data shard is recorded in real time, including the start time, completion time, number of retransmissions, etc. These records constitute the initial retransmission information and provide basic data for subsequent retransmission reports.

[0092] Continuously monitor the transmission status of each data segment. When a data segment fails to be transmitted, such as timeout or packet loss caused by network fluctuations, the failed data segment is added to the retransmission queue and the number of retries for the segment is recorded. This mechanism ensures the integrity and reliability of data transmission and ensures successful data transmission in an unstable network environment.

[0093] The initial transfer information is integrated with the number of retries to generate a complete transfer report. The report contains information such as the overall transmission status, success rate, average transmission speed, and detailed transmission records of each data segment. The transfer report provides a comprehensive overview of the data transfer and provides important data support for optimization and problem diagnosis. By analyzing the various indicators in the transfer report, the efficiency and reliability of the transfer process can be evaluated, providing a basis for subsequent optimization.

[0094] This embodiment realizes efficient and reliable data transmission in a network-offline environment by establishing an intelligent data retransmission mechanism. Through the trigger mechanism of periodic network status detection and intelligent retransmission program, the system can respond to network recovery in a timely manner, avoid blindness of data transmission, and improve the timeliness of transmission. By sorting and slicing the storage location information of the priority data group, combined with the predefined priority strategy, the priority transmission of key data is ensured, and the accuracy and rationality of data transmission are improved. By using an adaptive algorithm to dynamically calculate the transmission control parameters, the system can automatically adjust the transmission strategy according to the real-time network status, effectively improving the transmission efficiency. Through concurrent transmission and failure retry mechanism, not only the bandwidth utilization is improved, but also the integrity and reliability of data transmission are guaranteed. The final generated retransmission report provides an important basis for system optimization, making the entire data transmission process more intelligent and efficient.

[0095] In one embodiment, the network bandwidth status is detected in real time, and the priority data group is dynamically adjusted for continued transmission, and the priority data group for which continued transmission is completed is marked to obtain a marked data group, including: The network bandwidth status is periodically sampled and measured to obtain bandwidth sampling data. The time interval of periodic sampling measurement is set to 100 milliseconds, and the sampling content includes network status parameters such as the current network upload rate, download rate, delay time and packet loss rate. The real-time values ​​of these parameters are obtained through network detection packets to form a bandwidth sampling data sequence.

[0096] The network bandwidth characteristics are calculated based on the bandwidth sampling data to obtain the bandwidth statistical characteristics. The bandwidth statistical characteristics include indicators such as average transmission rate, fluctuation range, stability index and congestion probability. The sliding time window method is used in the calculation process. The window size is set to 1 minute. The bandwidth sampling data in the window is statistically analyzed to generate a set of characteristic indicators that reflect the current network status.

[0097] The current transmission adjustment strategy is obtained by matching the bandwidth statistical characteristics with the preset adjustment strategy. The preset adjustment strategy contains multiple network status scenarios and their corresponding transmission parameter configurations. When the bandwidth is sufficient, the high-speed transmission mode is used, when the bandwidth is limited, the conservative transmission mode is enabled, and when the network is unstable, the fault-tolerant transmission mode is used. By calculating the similarity between the bandwidth statistical characteristics and the preset scenarios, the best matching transmission adjustment strategy is selected.

[0098] Based on the current transmission adjustment strategy, the transmission rate and concurrency of the priority data group are adjusted to obtain the adjusted transmission parameters. The transmission parameters include the size of a single data block, the upper limit of the transmission rate, the number of concurrent transmissions, and the retransmission timeout. The data priority level is taken into account during the adjustment process, and high-priority data obtains more transmission resource quotas to ensure that critical data is transmitted first.

[0099] According to the adjusted transmission parameters, the priority data group is divided into blocks for transmission to obtain transmission data blocks. The data block process adopts an adaptive block algorithm to dynamically adjust the block size according to the network status. When the network condition is good, a larger block size is used to improve transmission efficiency; when the network condition deteriorates, the block size is reduced to improve transmission reliability.

[0100] Perform a checksum calculation on each transmission data block to obtain the data block checksum information. The checksum calculation uses the CRC32 algorithm to ensure the integrity of the transmitted data. The checksum information is packaged together with the data block to form a complete transmission unit.

[0101] The data block checksum information and the transmission data block are sent to the receiving end to obtain the transmission result. During the transmission process, a reliable transmission protocol is used to automatically retransmit the data block that failed to be sent. The transmission result contains the transmission status identification of each data block.

[0102] Receive the transmission confirmation information returned by the receiving end and obtain the transmission status. The transmission confirmation information contains the reception status and verification results of the data block. For the data block that failed the verification, it is added to the retransmission queue; for the data block that was successfully transmitted, its transmission completion flag is updated.

[0103] The successfully transmitted priority data groups are marked according to the transmission status to obtain marked data groups. The marking information includes the transmission completion time, data integrity verification results and transmission performance statistics. This information is used for subsequent transmission quality analysis and optimization.

[0104] The marked data group is removed from the priority data group to obtain an updated priority data group. The priority order of the data group is kept unchanged during the removal process to ensure the continuity of priority scheduling in subsequent transmission processes.

[0105] Repeat the transmission process for the updated priority data group until the transmission is completed. During the entire transmission process, the network status changes are continuously monitored and the transmission strategy is dynamically adjusted to achieve reliable data transmission.

[0106] This embodiment can flexibly configure data transmission parameters according to changes in the current network environment and optimize data transmission efficiency by monitoring the network bandwidth status in real time and dynamically adjusting the transmission strategy. The periodic sampling and sliding time window analysis methods are used to accurately grasp the characteristics of the network status, making the adjustment of the transmission strategy more accurate and reliable. By prioritizing and adaptively blocking the data, the priority transmission of important data is guaranteed, while the stability of the transmission is improved. The CRC32 checksum and reliable transmission protocol are used to effectively ensure the integrity and accuracy of data transmission. The marking and removal mechanism of the data completed during transmission makes the retransmission process more efficient and avoids the waste of resources caused by repeated transmission. The dynamic adjustment mechanism during the entire transmission process significantly improves the reliability and efficiency of data transmission in a disconnected environment, and provides a strong guarantee for the intelligent retransmission of data.

[0107] In one embodiment, integrity check is performed on the marked data group, and a transmission status report is generated in combination with the retransmission report, including: The checksum value is calculated for each marked data group and compared with the preset standard value to obtain the integrity check result. The checksum calculation adopts the CRC32 algorithm, and a 32-bit check code is obtained by performing a polynomial division operation on each byte in the data group. The preset standard value is stored in the check field of the data packet header as the basis for the integrity comparison.

[0108] The success rate analysis of the integrity check results is performed to obtain the overall transmission status. The success rate analysis is calculated based on the preset transmission quality evaluation model, which divides the transmission success rate into high-quality transmission intervals (95%-100%), medium-quality transmission intervals (80%-95%), and low-quality transmission intervals (below 80%). The evaluation results reflect the stability of the current transmission environment.

[0109] According to the overall transmission status, the marked data group is marked as failed to verify, and the data group to be retransmitted is obtained. The failed verification mark is realized by adding a retransmission flag in the data group metadata, which contains key information such as the number of retransmissions and the last transmission timestamp. The marking process uses atomic operations to ensure data consistency.

[0110] The data group to be retransmitted is retransmitted based on the preset retransmission limit to obtain the retransmission result. The retransmission limit is set differently according to the data priority. The retransmission limit for the core business data group is 5 times, and the retransmission limit for the non-core business data group is 3 times. The retransmission process adopts the backoff algorithm, and the retransmission interval increases each time.

[0111] According to the retransmission results, the data groups to be retransmitted that exceed the upper limit of the number of retransmissions are identified to obtain the failed data groups. The failed data groups are processed using the preset degradation strategy, which includes data compression, fragmented transmission and other methods. After the degradation processing, the overall transmission status is updated to obtain the updated transmission status. The selection of the degradation strategy is based on multi-dimensional indicators such as data type and network environment.

[0112] Based on the updated transmission status, the transmission statistics of the marked data group are performed to obtain transmission statistics information. The transmission statistics adopt a sliding time window mechanism with a window size of 30 minutes. The statistics include key indicators such as transmission success rate, average transmission delay, and retransmission rate. In the statistical process, the abnormal values ​​are smoothed to ensure the accuracy of the statistical results.

[0113] The transmission statistics are classified and summarized by transmission anomaly to obtain transmission anomaly information. The anomaly classification is matched based on the preset anomaly feature library. The anomaly types include network timeout, data corruption, transmission interruption, etc. The anomaly feature library is continuously optimized and updated through machine learning methods to improve the accuracy of anomaly identification.

[0114] The transmission status report is obtained by integrating the transmission continuation report, transmission statistics and transmission exception information. The status report adopts a hierarchical structure design, including summary layer, detail layer and diagnosis layer. The summary layer displays key performance indicators, the detail layer contains specific transmission records, and the diagnosis layer provides abnormal analysis results. During the report generation process, various types of information are correlated and analyzed to establish a problem tracing mechanism.

[0115] This embodiment achieves the reliability and stability of data transmission in a disconnected environment by performing integrity check on the marked data group and generating a transmission status report in combination with a retransmission report. The method uses the CRC32 algorithm for verification and calculation, and combines the preset transmission quality assessment model for success rate analysis, which can timely detect transmission anomalies and trigger the retransmission mechanism. Through differentiated retransmission strategies and degradation processing mechanisms, the transmission quality of core business data is guaranteed and the fault tolerance of the system is improved. The transmission statistics mechanism based on the sliding time window, combined with the abnormal feature library optimized by machine learning, realizes the accurate identification and classification of transmission anomalies. The status report with a hierarchical structure design establishes a complete problem tracing mechanism, which is convenient for system operation and maintenance personnel to quickly locate and solve transmission problems. While ensuring the reliability of data transmission, the overall solution effectively reduces system resource consumption and improves transmission efficiency through optimized retransmission and degradation strategies.

[0116] Reference Figure 2 As shown, the present invention also provides a data hierarchical caching and intelligent retransmission device in a disconnected network environment, which is applied to any of the above data hierarchical caching and intelligent retransmission methods in a disconnected network environment, including: The acquisition module is used to obtain the data to be transmitted, and to perform hierarchical evaluation based on the importance and timeliness of the data to obtain the corresponding priority data group; Analysis module: The analysis module is used to monitor the network bandwidth status in real time. When a network interruption is detected, it records the interruption time, triggers the hierarchical cache mechanism, and sets the corresponding storage area; An association module, the association module is used to allocate the priority data group to the corresponding storage area for caching, and obtain a final cache area; A processing module, the processing module is used to intelligently resume transmission of the priority data group according to the cache area and generate a resume transmission report when the network is detected to be restored; A control module, the control module is used to detect the network bandwidth status in real time, and dynamically adjust the priority data group for continued transmission, and mark the priority data group for which continued transmission is completed to obtain a marked data group; The execution module is used to perform integrity check on the marked data group and generate a transmission status report in combination with the resume transmission report.

[0117] The present invention provides a data hierarchical caching and intelligent retransmission device in a network disconnection environment. By performing hierarchical evaluation based on the importance and timeliness of data, the intelligent classification of the data to be transmitted is realized, ensuring that key data can be transmitted preferentially when network resources are limited, and effectively solving the problem that the traditional method ignores data characteristics. A hierarchical caching mechanism is adopted to allocate storage areas according to data priority, so as to realize the rational use of storage resources, avoid the risk of key data loss and storage space waste, and improve the efficiency of data management. By real-time monitoring of the network bandwidth status and dynamic retransmission adjustment of the priority data group, adaptive optimization of the transmission process is realized, the transmission efficiency after network recovery is improved, and the problem of lack of flexibility of the traditional retransmission method is solved. The integrity check of the marked data group is performed, and the transmission status report is generated in combination with the retransmission report, and a comprehensive data transmission monitoring mechanism is established, which effectively improves the reliability and traceability of the transmission process. Through intelligent retransmission and dynamic adjustment strategies, the system can better adapt to different network environments and data transmission requirements, and improve the overall transmission efficiency and system reliability. The present invention comprehensively considers data characteristics, network status and storage resources, realizes intelligent data management and transmission in a network disconnection environment, and provides a more reliable and efficient solution for data transmission in various application scenarios.

[0118] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0119] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for hierarchical data caching and intelligent data transfer in a network disconnected environment, characterized in that: include: Obtain the data to be transmitted, conduct hierarchical evaluation based on the importance and timeliness of the data, and obtain the corresponding priority data group; Monitor the network bandwidth status in real time. When a network interruption is detected, record the interruption time, trigger the hierarchical cache mechanism, and set the corresponding storage area. Allocating the priority data group to the corresponding storage area for caching to obtain a final cache area; When network recovery is detected, the priority data group is intelligently resumed according to the cache area, and a resume report is generated; Real-time detection of network bandwidth status, dynamic retransmission adjustment of the priority data group, and marking of the priority data group after retransmission completion to obtain a marked data group; An integrity check is performed on the marked data group, and a transmission status report is generated in combination with the resume transmission report.

2. The method for hierarchical data caching and intelligent retransmission in a disconnected network environment according to claim 1, characterized in that: The data to be transmitted is obtained, and a hierarchical evaluation is performed based on the importance and timeliness of the data to obtain a corresponding priority data group, including: Extracting attributes of the data to be transmitted to obtain a data creation timestamp and a data service type identifier; Classify and encode the data to be transmitted according to a preset service classification rule base and the data service type identifier to obtain an identification data record; Obtain the current timestamp, and perform difference calculation with the data creation timestamp to obtain the data existence time, perform weight calculation on the data existence time according to a preset timeliness benchmark threshold, and obtain a timeliness weight coefficient; Standardizing and scoring the identification data record and the timeliness weight coefficient to obtain a comprehensive importance score; The comprehensive importance scores are prioritized and grouped according to a preset importance score interval threshold group to obtain the priority data group including a high priority group, a medium priority group and a low priority group.

3. The method for hierarchical data caching and intelligent retransmission in a disconnected network environment according to claim 2, characterized in that: The step of performing standardized scoring on the identification data record and the timeliness weight coefficient to obtain a comprehensive importance score further includes: Quantifying the importance of the identified data record to obtain a data service importance score; Performing timeliness calculation on the timeliness weight coefficient to obtain a timeliness score; Perform multi-dimensional analysis of the current system environment according to the preset environmental factor assessment rules to obtain environmental factor scores; Performing weighted fusion on the data service importance score, the timeliness score and the environmental factor score to obtain an initial comprehensive score; The initial comprehensive score is normalized and mapped to a range between 0 and 1 to obtain a mapping initial value; Selecting a preset mapping parameter, performing mapping iterative calculation on the mapping initial value, and obtaining a dynamic adjustment factor; Importance calculation is performed based on the initial comprehensive score and the dynamic adjustment factor to obtain the comprehensive importance score.

4. The method for hierarchical data caching and intelligent data transfer in a disconnected network environment according to claim 1, characterized in that: The real-time monitoring of the network bandwidth status, when a network interruption is detected, records the interruption time and triggers the hierarchical cache mechanism, and sets the corresponding storage area, including: Periodically detecting the network bandwidth status to obtain network status parameters; Calculating network quality based on the network status parameters to obtain a network quality score; Performing a threshold comparison on the network quality score to determine whether the network is interrupted; When a network interruption is detected, the interruption time and timing data are recorded, and the duration of the network interruption is calculated; Classifying the duration of the network interruption based on a preset duration threshold to obtain an interruption level; Matching the interrupt level with a preset cache policy library to obtain a corresponding hierarchical cache policy; Initialize the cache management module based on the hierarchical cache strategy and set corresponding data access and storage rules; Establishing a cache index structure based on the data access and the storage rules; The corresponding storage area is set according to the cache index structure to complete the triggering and initialization of the hierarchical cache mechanism.

5. The method for hierarchical data caching and intelligent data transfer in a disconnected network environment according to claim 1, characterized in that: The step of allocating the priority data group to the corresponding storage area for caching to obtain a final cache area includes: Calculating storage requirements for the priority data group to obtain corresponding storage requirements; Partitioning the storage area according to the storage demand to obtain a corresponding plurality of storage partitions; Performing an access speed test on the storage partition to obtain an access speed value; Sorting the storage partitions according to the access speed values ​​to obtain a speed grade sequence; Matching the priority data group with the speed grade sequence to obtain a priority storage partition correspondence relationship; Writing the priority data group into the corresponding storage partition according to the priority storage partition correspondence to obtain an initial cache area; Calculating the storage space usage rate of the initial cache area to obtain the usage rate of each storage partition; Redistributing space of the initial cache area according to a preset cyclic coverage strategy and the usage rate to obtain an optimized cache area; The optimized cache area is monitored in real time, and when the storage capacity of the optimized cache area reaches a preset capacity threshold, data compression encoding processing is performed on the optimized cache area to obtain compressed cache data; The compressed cache data is rewritten into the optimized cache area to obtain the final cache area.

6. The method for hierarchical data caching and intelligent transmission in a disconnected network environment according to claim 5, characterized in that: The calculating of the storage space usage rate of the initial cache area to obtain the usage rate of each storage partition includes: Performing multi-scale spatial division on the initial cache area to obtain scale grids of different scales; Performing occupancy calculation on each scale grid to obtain the number of occupied grids at each scale; The fractal dimension of the occupied grid number is calculated by a preset box counting method to obtain a spatial complexity index; Dividing the initial cache area into storage blocks of fixed size, and calculating the data distribution probability for each storage block to obtain a block-level data distribution probability; Calculating the entropy value of the block-level data distribution probability using a preset entropy formula to obtain data distribution entropy; Performing nonlinear mapping on the storage partition according to the space complexity index to obtain a preliminary utilization rate; Correcting the preliminary usage rate according to the data distribution entropy to obtain a corrected usage rate; Performing access frequency statistics on the storage partition to obtain an access heat map; The modified usage rate is comprehensively counted according to the access heat map to obtain the usage rate of each storage partition.

7. The method for hierarchical data caching and intelligent data transfer in a disconnected network environment according to claim 1, characterized in that: When the network recovery is detected, the priority data group is intelligently resumed according to the cache area, and a resume report is generated, including: Periodically detecting the network bandwidth status, and triggering an intelligent resuming program when the network is detected to be restored; Reading the storage location information of the priority data group in the cache area, performing read sorting, and generating a resume transmission queue; Performing data packet numbering and fragmentation processing on the retransmission queue to obtain a set of data fragments to be transmitted; Calculate the optimal transmission concurrency number and the size of a single shard based on the data shard set to obtain the transmission control parameters; Concurrently transmitting the data slice set based on the transmission control parameter, and recording the transmission status of each data slice in real time to obtain initial resume transmission information; When it is detected that the data segment transmission fails, the data segment is re-added to the retransmission queue and the number of retries is recorded; The initial resume transmission information is integrated with the number of retries to obtain the resume transmission report.

8. The method for hierarchical data caching and intelligent data transfer in a disconnected network environment according to claim 1, characterized in that: The real-time detection of the network bandwidth status, the dynamic retransmission adjustment of the priority data group, and the marking of the priority data group after the retransmission is completed to obtain the marked data group include: Performing periodic sampling and measurement on the network bandwidth status to obtain bandwidth sampling data; Calculating network bandwidth characteristics based on the bandwidth sampling data to obtain bandwidth statistical characteristics; According to the bandwidth statistical characteristics, the preset adjustment strategy is matched to obtain the current transmission adjustment strategy; Adjust the transmission rate and concurrency of the priority data group based on the current transmission adjustment strategy to obtain adjusted transmission parameters; According to the transmission adjustment parameter, the priority data group is adjusted and transmitted in blocks to obtain transmission data blocks; Performing checksum calculation on each of the transmission data blocks to obtain data block checksum information; Sending the data block check information and the transmission data block to a receiving end to obtain a sending result; Receive the transmission confirmation information returned by the receiving end to obtain the transmission status; Marking the successfully transmitted priority data group according to the transmission status to obtain the marked data group; removing the marked data group from the priority data group to obtain an updated priority data group; The transmission process is repeated for the update priority data group until the transmission is completed.

9. The method for hierarchical data caching and intelligent data transfer in a disconnected network environment according to claim 1, characterized in that: The performing integrity check on the marked data group and generating a transmission status report in combination with the resumed transmission report includes: Performing a checksum calculation on each of the marked data groups to obtain a checksum value, and comparing it with a preset standard value to obtain an integrity check result; Performing a success rate analysis on the integrity check result to obtain a corresponding overall transmission status; Marking the marked data group for verification failure according to the overall transmission status to obtain a data group to be retransmitted; Retransmitting the data group to be retransmitted based on a preset upper limit of the number of retransmissions to obtain a retransmission result; Identify the data group to be retransmitted that exceeds the upper limit of the number of retransmissions according to the retransmission result, obtain a failed data group, process the failed data group according to a preset degradation strategy, and update the overall transmission state to obtain a corresponding updated transmission state; Performing transmission statistics on the marked data group based on the updated transmission status to obtain corresponding transmission statistical information; Classify and summarize the transmission anomalies of the transmission statistical information to obtain corresponding transmission anomaly information; The transmission resume report, the transmission statistics information and the transmission exception information are integrated to obtain the transmission status report.

10. A data hierarchical caching and intelligent retransmission device in a disconnected network environment, characterized in that: The data hierarchical caching and intelligent retransmission method in a disconnected network environment as described in any one of claims 1 to 9 above comprises: A collection module, which is used to obtain the data to be transmitted, and to perform a hierarchical evaluation based on the importance and timeliness of the data to obtain a corresponding priority data group; An analysis module, which is used to monitor the network bandwidth status in real time, and when a network interruption is detected, records the interruption time and triggers the hierarchical cache mechanism, and sets a corresponding storage area; An association module, the association module is used to allocate the priority data group to the corresponding storage area for caching, to obtain a final cache area; A processing module, the processing module is used for intelligently retransmitting the priority data group according to the cache area and generating a retransmission report when a network recovery is detected; A control module, the control module is used to detect the network bandwidth status in real time, and dynamically adjust the priority data group for continued transmission, and mark the priority data group for which continued transmission is completed to obtain a marked data group; An execution module is used to perform integrity check on the marked data group and generate a transmission status report in combination with the resume transmission report.

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