A method and system for fast data retrieval

Abstract

The present application relates to the technical field of data retrieval, in particular to a data fast retrieval method and system, comprising the following steps: based on the received data retrieval query request, positioning to the target data block and obtaining the access type weight and access frequency count, and generating the data block heat value. In the present application, after receiving the data retrieval query request, the target data block is quickly positioned by analyzing the query content, and the access type and access frequency related information are synchronously extracted, the static access behavior is converted into quantifiable heat value index, the time difference calculation method is introduced to extract the new and old degree of the data block, so that the heat value is no longer a static count result, but a dynamic characteristic expression with time limit for judgment basis. Further, the dynamic heat evaluation value is generated by combining the static heat and time limit factor, the comprehensive judgment of the current importance and activity of the data block is realized, and the data value sorting logic is strengthened.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data retrieval, in particular to a data fast retrieval method and system. BACKGROUND

[0002] The technical field of data retrieval relates to efficient positioning and extraction of target information in massive data, and is widely used in search engines, database systems, content management platforms, big data analysis engines and other systems.

[0003] The existing technology has problems such as significant response delay, passive data scheduling, and rigid resource allocation in the face of data retrieval tasks in high concurrency and large data scenarios. In the current commonly used data retrieval system, the positioning of target data often depends on static index structure and fixed strategy path, ignoring the frequency change and real-time hotness distribution of data access, which makes it difficult to effectively distinguish and schedule hot data and cold data. At the same time, the existing technology is difficult to determine whether some data has current access value, and only uses past total access frequency as the basis for evaluation, which easily leads to unreasonable cache space occupation. Therefore, improvement is needed. SUMMARY

[0004] The purpose of the present application is to solve the problems in the prior art and provide a data fast retrieval method and system.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme, a data fast retrieval method comprising the following steps:

[0006] Based on the received data retrieval query request, the target data block is located and the access type weight and access frequency count are obtained, and the data block hotness value is generated;

[0007] Based on the data block hotness value, the global clock is called to obtain the time difference, the time-sensitive hotness weight is obtained, the time-sensitive hotness weight is operated based on the data block hotness value, and the dynamic hotness evaluation value is obtained;

[0008] Based on the dynamic hotness evaluation value, the migration threshold of the storage level where the data block is located is compared and judged, the level migration decision signal is generated, the target storage level of the data block is matched and determined based on the level migration decision signal, and the data block migration instruction is established;

[0009] Based on the data block migration instruction, the source and destination addresses of data movement are constructed, the target migration path is obtained, the data block is prefetched by the background daemon process based on the target migration path, and the data retrieval optimization layout is established.

[0010] Preferably, the data block hotness value obtaining step is:

[0011] Based on the received data retrieval query request, the data block identification field attached in the query request is parsed, the data block identification field is matched to the global storage mapping table, the access record entry set of the corresponding data block is retrieved, and the access type field sequence and the access timestamp field sequence are formed;

[0012] According to the access type field sequence and the access timestamp field sequence, the access times in the set time window are classified and counted according to the access type, the time sequence set of each type of access is constructed, the change rate is calculated according to the corresponding timestamp, and the access type count mapping table and the access type change rate index table are obtained;

[0013] According to the access type count mapping table and the access type change rate index table, the data block hotness value is calculated.

[0014] Preferably, the time-sensitive hotness weight obtaining step is:

[0015] Based on the data block hotness value, the hotness value generation timestamp contained in the generated record is retrieved, and the standard timestamp of the current time is synchronously obtained. The difference in seconds between the current timestamp and the hotness value generation timestamp is calculated to form the hotness value time difference result.

[0016] According to the hotness value time difference result, the pre-defined time difference reference interval table is matched, and each time difference value and the upper and lower limit range of the interval are contrasted in turn to determine the time decay section number where the hotness value time difference result is located, and the section identification information corresponding to the number is extracted to generate the time decay section identification.

[0017] Based on the time decay section identification, the numerical parameter content of the corresponding section is read from the set time-sensitive decay factor parameter table as the time-sensitive influence factor corresponding to the current data block to generate the time-sensitive hotness weight.

[0018] Preferably, the dynamic hotness evaluation value obtaining step is:

[0019] Based on the time-sensitive hotness weight, the hotness value corresponding to each data block is extracted to obtain the data block hotness value and the time-sensitive hotness weight.

[0020] According to the data block hotness value and the time-sensitive hotness weight, a dynamic hotness input vector group is constructed, all data block indexes are extracted, the data block position corresponding to each group of hotness parameters is labeled, and the hotness saturation threshold and the time-sensitive influence factor are appended as constant items to form a hotness calculation input set.

[0021] Based on the hotness calculation input set, the dynamic hotness evaluation value is calculated.

[0022] Preferably, the hierarchical migration decision signal obtaining step is:

[0023] Based on the dynamic hotness evaluation value, the migration threshold value of the storage level where the data block is located is extracted, the level type identifier corresponding to the migration threshold value is parsed piece by piece, and a comparison-verified dynamic hotness evaluation value and migration threshold value comparison set is generated;

[0024] According to the comparison-verified dynamic hotness evaluation value and migration threshold value comparison set, the numerical difference between the dynamic hotness evaluation value of each data block and the corresponding storage level migration threshold value is calculated one by one, the difference result between the dynamic hotness evaluation value and the migration threshold value is recorded, and the positive and negative states of the difference result are marked respectively, and a difference mark set of the dynamic hotness evaluation value and the migration threshold value is generated;

[0025] Based on the difference mark set of the dynamic hotness evaluation value and the migration threshold value, the data block mark state with a numerical difference greater than or equal to zero in the difference mark set is extracted, and the difference mark state is used as the migration judgment basis. When the mark state is positive, the storage level upshift migration action is triggered, and when the mark state is negative, the storage level downshift migration action is triggered. The direction and category of the migration action are recorded, and a level migration decision signal is generated.

[0026] Preferably, the data block migration instruction obtaining step is:

[0027] Based on the level migration decision signal, the migration action direction and category identifier carried in the level migration decision signal are parsed piece by piece, the target storage level information corresponding to the migration action direction identifier is extracted, and a data block target storage level identifier is generated;

[0028] According to the data block target storage level identifier, the storage unit address and port number corresponding to the target storage level identifier are matched, the capacity state and current available space value of the target storage unit are obtained, it is judged whether the available space value of the target storage unit meets the size requirement of the current data block, and a data block target storage unit effectiveness confirmation result is formed;

[0029] Based on the data block target storage unit effectiveness confirmation result, for the target storage unit whose available space value meets the size requirement of the data block, the data block migration instruction content is combined and constructed, including the unique identifier of the data block, the source storage unit position of the data block, the target storage unit address and port number, and the data block migration priority information and transmission mode mark are attached, and the data block migration instruction is generated.

[0030] Preferably, the target migration path obtaining step is:

[0031] Based on the data block migration instruction, the storage system management interface is called, the source storage unit address where the data block is currently located and the destination storage unit address after migration are obtained, and the source and destination address set of data movement is formed;

[0032] According to the source and destination address set of the data movement, the path query of the source storage unit address and the destination storage unit address is performed, the associated network nodes and link information in the path are extracted, the data transmission link node sequence from the source storage unit address to the destination storage unit address is determined, and the target migration path is formed.

[0033] Preferably, the data retrieval optimization layout obtaining step is:

[0034] Based on the target migration path, a background daemon is called, the link node sequence recorded in the target migration path is sequentially traversed, data block prefetching on each node is performed, the data blocks are gradually cached to the intermediate nodes in the order of the data transmission link node sequence, the cache layout index table is updated to indicate the new layout position of the data blocks, and the data retrieval optimization layout is established.

[0035] The application also provides a data fast retrieval system, comprising:

[0036] The query identification module is configured to locate to the target data block based on the received data retrieval query request, obtain the access type weight and the access frequency count, and generate a data block heat value.

[0037] The heat evaluation module is configured to obtain a time difference by calling a global clock based on the data block heat value, obtain a timeliness heat weight, and obtain a dynamic heat evaluation value by performing operation based on the timeliness heat weight and the data block heat value.

[0038] The hierarchical decision module is configured to compare and judge the dynamic heat evaluation value with a migration threshold of a storage hierarchy where the data block is located, generate a hierarchical migration decision signal, match and determine a target storage hierarchy of the data block based on the hierarchical migration decision signal, and establish a data block migration instruction.

[0039] The data prefetching module is configured to construct source and destination addresses of data movement based on the data block migration instruction, obtain a target migration path, perform data block prefetching by a background daemon based on the target migration path, and establish a data retrieval optimization layout.

[0040] Compared with the prior art, the application has the following advantages and positive effects:

[0041] In the present application, after receiving the data retrieval query request, the target data block is quickly located by analyzing the query content, and the access type and access frequency related information are synchronously extracted, the static access behavior is converted into quantifiable heat value index, the time difference calculation method is introduced to extract the new and old degree of the data block, so that the heat value is no longer a static counting result, but a dynamic characteristic expression with time limit judgment basis. Further, the dynamic heat evaluation value is generated by combining the static heat and the time limit factor, the comprehensive judgment of the current importance and activity of the data block is realized, and the data value sorting logic is strengthened. On this basis, the dynamic heat evaluation value is compared with the migration threshold of the storage level, the decision signal is constructed to complete the migration trigger judgment between different storage levels, and the linkage mechanism of heat and storage strategy is opened. Through the clear positioning of the target storage level of the data block, the source and destination address path is constructed, the background daemon process is introduced for prefetching before migration execution, the data is adjusted and laid out in advance on the migration path, so that the subsequent data retrieval process has higher hit rate and response speed. In summary, the present application improves the retrieval efficiency, reduces the access delay, optimizes the storage load allocation, and provides continuous support for high-concurrency retrieval requirements in large-scale data environment. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The present application is a schematic diagram of the steps. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application, and do not limit the present application.

[0044] Please refer to Figure 1 The present application provides a technical scheme, a data fast retrieval method, comprising the following steps:

[0045] Based on the received data retrieval query request, the target data block is located and the access type weight and access frequency count are obtained, and the data block heat value is generated;

[0046] Based on the data block heat value, the time difference is obtained by calling the global clock, the timeliness heat weight is obtained, the dynamic heat evaluation value is obtained by operating the timeliness heat weight and the data block heat value;

[0047] Based on the dynamic heat evaluation value, the migration threshold of the storage level where the data block is located is compared and judged, the level migration decision signal is generated, the target storage level of the data block is matched and determined based on the level migration decision signal, and the data block migration instruction is established;

[0048] Based on the data block migration instruction, the source and destination addresses of data movement are constructed, the target migration path is obtained, the data block is prefetched by the background daemon process based on the target migration path, and the data retrieval optimization layout is established.

[0049] The data block hotness value acquisition step is:

[0050] Based on the received data retrieval query request, the data block identification field attached in the query request is parsed, the data block identification field is matched to the global storage mapping table, the access record entry set of the corresponding data block is retrieved, the access type field sequence and the access timestamp field sequence are formed;

[0051] According to the access type field sequence and the access timestamp field sequence, the access times in the set time window are classified and counted according to the access type, the time sequence set of each type of access is constructed, and the change rate is calculated according to the corresponding timestamp, to obtain the access type count mapping table and the access type change rate index table;

[0052] According to the access type count mapping table and the access type change rate index table, the data block hotness value is calculated, and the calculation formula is:

[0053] ;

[0054] Wherein, is the data block hotness value, is the total number of access types, is the current access type serial number, is the type factor of the first type of access, is the total number of access records of the first type of access, is the current timestamp, is the timestamp of the first access in the first type of access, is a time decay constant, is the relative change rate of the first type of access in the time window, is a change rate adjustment coefficient.

[0055] Specifically, based on the received data retrieval query request, the system first calls a query parser, which performs lexical analysis and syntax analysis on the request string, such as a structured query language (SQL) statement SELECT data FROM table WHERE block_id='7A3F9B', according to predefined syntax rules, extracts the data block identifier field '7A3F9B' as the key index, and then uses this identifier field as the key value to perform a quick search in the global storage mapping table in the resident memory. The global storage mapping table uses a hash table structure, with the key being the data block identifier and the value being a composite structure containing a physical location pointer and an access log index, such as {'7A3F9B': {'location':'ssd_array_02 / volume_3 / offset_8192', 'log_index': 'access_log_2023_q4:10245'}}. Through the physical location pointer, the data block can be directly located, and the access log index points to the log file recording all historical access operations of the data block and the starting line number. The system then opens the corresponding log file according to the index and sequentially scans from the specified line number, loading each access record related to '7A3F9B' such as {'timestamp': 1696154400, 'access_type':'read', 'user': 'app_server_01'} into the temporary buffer area. Finally, the system traverses all record entries in the buffer area, extracts the access_type field and timestamp field respectively, organizes them into two independent sequences in chronological order, and forms the access type field sequence and access timestamp field sequence.

[0056] According to the access type field sequence and the access timestamp field sequence obtained in the previous step, the system sets a dynamically adjusted time window, the size of which is determined according to the current load and data access mode of the system, for example, 15 minutes during peak transaction time and extended to 60 minutes during off-peak time. The system takes the current time as the base point, and traces back all access records within this time window, and groups the records according to the access type, such as'read', 'write', 'update', and then accurately counts the number of records in each group, thereby constructing an access type count mapping table with the structure {'read': 210, 'write': 35, 'update': 12}. At the same time, in order to calculate the access change rate, the system further divides the set time window into two consecutive and equal length subintervals, namely the recent subinterval and the prior subinterval, for example, a 60-minute time window is divided into the last 0-30 minutes and the previous 31-60 minutes. The system respectively counts the number of occurrences of each access type in the two subintervals, denoted as Count_recent and Count_prior, and applies the formula (Count_recent-Count_prior) / (Count_prior+1) to calculate the relative change rate, where the denominator plus 1 is to avoid the error of dividing by zero. Finally, the change rate results of various types of access are stored in the access type change rate index table, such as {'read': 0.15, 'write': -0.2, 'update': 0.5}, obtaining the access type count mapping table and the access type change rate index table.

[0057] Formula: The formula has the benefit of comprehensively evaluating the three core dimensions of data block hotness through a unified framework: the inherent cost of access behavior, the temporal contiguity of access behavior, and the future trend of access behavior. Specifically, by introducing the parameter, it distinguishes the consumption difference of different access types (such as read, write) on system resources; through the exponential decay term , it ensures that the more recent access contributes more to the hotness value, which conforms to the principle of locality of data access; through the trend adjustment term , it makes the hotness evaluation have foresight, can amplify the hotness of data blocks with rising access frequency, and vice versa, thereby realizing dynamic, accurate and predictive quantitative evaluation of data hotness, providing high-quality input basis for subsequent storage layering decisions.

[0058] is a constant predefined in the system, which represents all kinds of data access operations that the system can identify and process, and this value is determined when the system architecture is designed, and the calculation of all data blocks remains consistent. For example, a typical database system may define four access types as read, write, update, and delete, so the value of is 4.

[0059] is the type factor of the th access type, which quantifies the inherent impact of different types of access operations on system resources and business importance. Its setting is not subjective, but based on the analysis of historical performance data of the system. The specific steps are as follows: first, continuously monitor and record the average CPU cycles consumed by each type of access operation (such as read, write) during execution , the number of disk I / O operations , and the occupied network bandwidth , then normalize the collected resource consumption data to the interval [0, 1] by maximum and minimum normalization, the normalization formula is: , finally, according to the business requirements, allocate weights to different resource types , , , and the sum is 1), and calculate the final type factor of each access type by weighted summation, the calculation formula is: For example, after monitoring, the resource consumption of write operation is generally higher than that of read operation, after setting the weight , the type factor of read operation is calculated as , and the type factor of write operation is calculated as .

[0060] is the total number of the th access record, which directly comes from the access type count mapping table generated in the previous step, and represents the total frequency of a specific type of data access within a specified time window. This parameter is a direct reflection of the recent activity level of the data block. For example, from the access type count mapping table {'read': 210, 'write': 35}, we can know that for read operation , the total number of accesses , and for write operation , the total number of accesses .

[0061] The current timestamp is obtained by the system in real time by calling the global clock service each time the heat calculation is performed. It is usually expressed as the total number of seconds or milliseconds since a certain fixed point in time (such as the Unix epoch). It provides a unified and dynamic time base for heat calculation, ensuring the accuracy of the time decay effect calculation. For example, in a calculation, the current timestamp obtained by the system is 1696158000.

[0062] For the first The first in class access The timestamp of each access is extracted from the access timestamp field sequence generated in the previous steps. It records the time when each specific access operation occurred and is the basic data for calculating access proximateness. For example, for a read operation of a data block, its three most recent access timestamps are 1696157995, 1696157980, and 1696157950, respectively.

[0063] As a time decay constant, this parameter controls the rate at which the influence of historical access records decays over time. Its value is set based on the half-life of data access, i.e., the probability and timeframe for a data block to be accessed again after it has been accessed. By analyzing the historical access intervals of a large amount of data, statistically analyzing their probability distribution, and fitting an exponential decay model, a suitable value is determined. The goal is to maintain the impact of data access frequency at a high level within its typical reuse cycle, while decreasing rapidly outside of that cycle. For example, if analysis shows that the average reuse cycle of data is 30 minutes (1800 seconds), it is expected that its frequency contribution will decay to its original value at this point. Then it can be made Calculations yielded .

[0064] For the first The relative rate of change of access type within the time window, which comes directly from the access type change rate index table generated in the previous steps, reflects the short-term trend of data block access frequency. It is a key indicator for assessing whether the data block's popularity is in an upward or downward phase. For example, from the access type change rate index table {'read': 0.15, 'write': -0.2}, we can see the relative rate of change of read operations. The relative rate of change of write operations .

[0065] This is the rate of change adjustment coefficient. This parameter is used to adjust the sensitivity of the heat calculation to the trend of changes in access frequency. Its value is calibrated through multiple rounds of simulation tests in a pre-production environment, where different... The value is used to observe the improvement in the accuracy of migration decisions and data retrieval performance of the system, and to select the option that achieves the best balance between increasing the hit rate of hot data and avoiding invalid migration of cold data. Values, for example, as found through testing when When set to 0.8, the overall system performance is optimal, therefore it is determined to be [the optimal value]. .

[0066] Calculation process:

[0067] The access patterns within the most recent 60-minute time window are as follows:

[0068] Read operation ( ): , Access timestamp from current time The relative rate of change was 10 seconds and 600 seconds, respectively. .

[0069] Write operation ( ): , Access timestamp from current time For 300 seconds, relative rate of change .

[0070] System parameters: , .

[0071] Calculate read operation ( ) Contribution of heat value :

[0072] Calculate the sum of time decay terms:

[0073] ;

[0074] Calculate the trend adjustment term:

[0075] ;

[0076] calculate :

[0077] ;

[0078] Compute write operations ( ) Contribution of heat value :

[0079] Calculate the sum of time decay terms:

[0080] ;

[0081] Calculate the trend adjustment term:

[0082] ;

[0083] Calculate :

[0084] ;

[0085] Calculate the final data block hotness value :

[0086] ;

[0087] The result shows that the comprehensive hotness value of the data block is 3.5778, which is a quantitative score combining access type, access timeliness and access trend. This value will be used as the basis for subsequent dynamic hotness evaluation, and compared with the migration threshold of the storage hierarchy. If the value is higher than the upward migration threshold of the current hierarchy, it indicates that the data block is becoming more and more active and needs to be migrated to a higher performance storage medium. Conversely, if it is lower than the downward migration threshold, it indicates that its access value is decreasing and should be migrated to a lower cost storage hierarchy.

[0088] The acquisition step of timeliness hotness weight is:

[0089] Based on the data block hotness value, retrieve the hotness value generation timestamp contained in the generation record, and synchronously acquire the standard timestamp of the current time, calculate the difference in seconds between the current timestamp and the hotness value generation timestamp, and form the hotness value time difference result;

[0090] According to the hotness value time difference result, match the predefined time difference reference interval table, and sequentially compare each time difference value with the upper and lower limit range of the interval to determine the time decay section number where the hotness value time difference result is located, and extract the section identification information corresponding to the number to generate the time decay section identification;

[0091] Based on the time decay section identification, read the numerical parameter content of the corresponding section from the set timeliness decay factor parameter table as the timeliness influence factor corresponding to the current data block, and generate the timeliness hotness weight.

[0092] Specifically, based on the data block hotness value record obtained in the last calculation period, for example, {'block_id': '7A3F9B', 'hotness': 3.5778, 'timestamp': 1696158000}, the system first accurately extracts the hotness value generation timestamp from the record, that is, 1696158000, which is a standard Unix timestamp, indicating the precise moment when the hotness value calculation is completed. Then, the system initiates a query request to the Network Time Protocol (NTP) synchronized local system clock service to obtain the standard timestamp of the current time, for example, 1696161600. Subsequently, the system performs a subtraction operation, that is, 1696161600-1696158000, to calculate the total number of seconds between the two timestamps, resulting in a difference of 3600 seconds. This difference is encapsulated as the hotness value time difference result.

[0093] According to the hotness value time difference result, that is, 3600 seconds, the system will next match with a predefined time difference reference interval table, which is established by statistical analysis of the historical data block life cycle of the system. The specific establishment process is as follows: analyze the time interval distribution of data blocks from becoming hot for the first time to a significant decline in access frequency in the past few months, and use the K-Means clustering algorithm to divide these time intervals into four representative categories. The centroid of each category defines the typical timeliness of the section, thereby determining the boundaries of the interval. For example, the table structure is {(section number, interval lower limit (seconds), interval upper limit (seconds), section identification information),...}, and the specific entries are {(1, 0, 600, 'extremely high timeliness'), (2, 601, 3600, 'high timeliness'), (3, 3601, 86400,'medium timeliness'), (4, 86401, 604800, 'low timeliness')}. The system will start an iterative matching process to compare the hotness value time difference result 3600 seconds with each interval in the table in turn. First, compare 3600 with the first section [0, 600], which does not match. Then, compare 3600 with the second section [601, 3600], which falls exactly on the closed upper limit of the interval, and is determined to be a successful match. The system immediately stops iteration, locks the matched section number as 2, and extracts the corresponding section identification information "high timeliness" from the entry to generate the time decay section identification.

[0094] Based on the time decay segment identifier, i.e., "high timeliness," the system looks up the corresponding value from a statically configured timeliness decay factor parameter table. This parameter table maps qualitative timeliness descriptions to quantitative weighting factors. Its content is set based on a series of simulation experiments: in a sandbox isolated from the production environment, multiple data migration strategies are run for different storage tiers and data access modes. Each strategy uses a different combination of decay factors. By evaluating the average data retrieval latency and storage cost of each strategy within a specific time period, the system selects the strategy that maximizes the overall benefit function: benefit = (α * retrieval performance improvement) - (β * storage cost). The set of attenuation factor values ​​that is maximized (in this case, α and β) is used as the final configuration, where α and β are weighting coefficients set according to business priority. For example, α=0.7 and β=0.3. The final parameter representation is as follows: {'Extremely High Timeliness': 1.0, 'High Timeliness': 0.85, 'Medium Timeliness': 0.5, 'Low Timeliness': 0.2}. The system uses "High Timeliness" as the key to query this parameter table and directly obtains the corresponding value 0.85. This value is determined as the timeliness impact factor corresponding to the current data block and is directly used as the final result output to generate the timeliness heat weight.

[0095] The steps to obtain dynamic heat assessment values ​​are as follows:

[0096] Based on the timeliness popularity weight, the popularity value corresponding to each data block is extracted to obtain the data block popularity value and timeliness popularity weight;

[0097] Based on the data block popularity value and the timeliness popularity weight, a dynamic popularity input vector group is constructed, all data block indices are extracted, and the data block position corresponding to each set of popularity parameters is marked. At the same time, the popularity saturation threshold and timeliness influence factor are added as constant terms to form a popularity calculation input set.

[0098] Based on the input set for heat calculation, the dynamic heat assessment value is calculated using the following formula:

[0099] ;

[0100] in, This is a dynamic heat assessment value. The data block heat value represents the number of data blocks. The original heat of each data block, The weighting is based on timeliness and popularity, indicating the weight of the first... The current time decay status of each data block As a time-sensitive influencing factor, This represents the heat saturation threshold.

[0101] Specifically, based on the timeliness hotness weight generated by the system for each data block, for example, the timeliness hotness weight corresponding to the data block '7A3F9B' is 0.85, the system starts a data aggregation process, which first traverses the list of all data block identifiers that need to be evaluated for hotness. For each data block identifier in the list, the system uses it as a key value to query the data block hotness cache area in memory. This cache area is a key-value storage structure that stores records such as {'block_id': '7A3F9B', 'hotness': 3.5778, 'timestamp': 1696158000}. The system extracts the data block hotness value, i.e. 3.5778, from it. Then, the system pairs the data block hotness value just extracted with the timeliness hotness weight corresponding to the data block to form a temporary data structure containing the data block identifier, data block hotness value, and timeliness hotness weight, such as {'block_id': '7A3F9B', 'Q': 3.5778, 'Z': 0.85}. This operation is performed for all data blocks to be evaluated, ultimately forming a set containing all data block hotness values and timeliness hotness weights.

[0102] Based on the set of data block hotness values and timeliness hotness weights obtained from the previous step, the system first organizes this set into a structured dynamic hotness input vector group, where each vector represents a data block and contains its identifier, hotness value Q, and timeliness hotness weight Z. Then, the system reads two system-level parameters from the global configuration as constant items. The first is the hotness saturation threshold, which is set to prevent the unlimited growth of hotness values from having an excessive impact on the evaluation results. The value of this threshold is determined by the system's daily scheduled analysis of the historical distribution of all data block hotness values over the past 30 days, and the calculation of the 98th percentile. This percentile value is used as the hotness saturation threshold. For example, if the 98th percentile of historical hotness values is 50.0, then the hotness saturation threshold is set to 50.0. The second is the timeliness influence factor, which is used to adjust the contribution of timeliness to the final evaluation value. The value of this factor is determined through simulation testing in an offline environment. In the test, historical access data is used to calculate the simulated cache hit rate of the data migration strategy under a series of candidate influence factor values (e.g. from 0.1 to 1.0, with a step size of 0.1), and the factor value that achieves the highest hit rate is selected. For example, if the test results show that the system performs best when the factor is 0.5, then the timeliness influence factor is set to 0.5. Finally, the system appends these two constant items to the dynamic hotness input vector group to form a hotness calculation input set.

[0103] Formula: The formula is beneficial because it takes into account the original data block hotness value On the basis of the original hotness evaluation formula, a dynamic and nonlinear adjustment factor is introduced to achieve fine correction of the hotness evaluation. Specifically, the exponential term can amplify the original hotness according to the timeliness hotness weight , and the amplification effect is adjusted by the timeliness influence factor . More importantly, the denominator introduces a saturation inhibition mechanism. When the original hotness of a data block is already very high and close to the saturation threshold , the denominator will become large, thereby inhibiting the additional growth caused by timeliness. This design avoids over-amplification of hot data, while enabling timeliness to have a more significant impact on data blocks with medium hotness or those that are "preheating", thereby identifying data with immediate access value.

[0104] is the data block hotness value, representing the inherent access activity of the data block within a specific time window. This value is obtained from the hotness calculation formula in the previous step and is the basis input of this formula. It comprehensively reflects access frequency, access type, and recent access history. In this example, the result obtained by calculating the data block '7A3F9B' in the previous step is used, and the data block hotness value is 3.5778.

[0105] is the timeliness hotness weight, indicating the freshness of the data block hotness value. This value is obtained from the previous step based on the difference between the hotness value generation time and the current time. The value ranges from 0 to 1, with a higher value indicating a more "fresh" hotness value that better reflects the current access pattern. In this example, the result obtained by calculating the data block '7A3F9B' in the previous step is used, and the timeliness hotness weight is 0.85.

[0106] is the timeliness influence factor, a global adjustment coefficient used to control the impact of the timeliness hotness weight on the final evaluation value. This parameter is optimized and determined through backtesting of historical data. The specific steps are as follows: collect all data block access logs and corresponding migration decision records of the system in the past three months, set an evaluation function such as predicting the cache hit rate after migration, then test a series of values (e.g., from 0.1 to 2.0, with a step size of 0.1) in a parameter optimization framework (such as grid search), replace each value into the formula to recalculate the dynamic hotness evaluation value of the historical data and simulate the migration decision, and finally select the that maximizes the evaluation function value (i.e., the highest cache hit rate) as the system parameter. For example, after the above process, the optimal timeliness influence factor is determined to be 0.5. ​​

[0107] is a global constant, which is used to define a "soft upper limit" for the hotness value, to prevent data blocks with extremely high hotness from being pulled too far away from other data blocks after timeliness adjustment, the setting of this threshold is based on statistical analysis of long-term running data of the system, the specific steps are: the system automatically counts all the calculated data block hotness values in the past 30 days every day, forming a huge data set, then calculates the probability density distribution of this data set, and selects a specific percentile point at the tail of the distribution as the threshold, usually selecting the 98th percentile or the 99th percentile, to ensure that only a few extreme hot spot data will touch the saturation zone, for example, the analysis result shows that the 98th percentile of the historical hotness value is 50.0, so the hotness saturation threshold is set to 50.0.

[0108] Calculation process:

[0109] According to the hotness calculation input set, extract the parameters of data block '7A3F9B':

[0110] Data block hotness value .

[0111] Timeliness hotness weight .

[0112] Timeliness influence factor .

[0113] Hotness saturation threshold .

[0114] Substitute the above parameters into the formula for calculation:

[0115] Calculate the hotness saturation influence item in the denominator: ;

[0116] Calculate the exponential part: ;

[0117] Calculate the exponential term, i.e. timeliness adjustment factor: ;

[0118] Calculate the final dynamic hotness evaluation value : ;

[0119] The result shows that after timeliness weighting and saturation inhibition adjustment, the final dynamic hotness evaluation value of data block '7A3F9B' is 5.3195, which is about 48.7% higher than the original hotness value 3.5778, indicating that this data block not only has a certain basic hotness, but also its hotness information is very "fresh", with high potential for immediate access. ​​

[0120] The obtaining step of the tier migration decision signal is:

[0121] Based on the dynamic hotness evaluation value, the migration threshold value of the storage tier where the data block is located is extracted, and the tier type identifier corresponding to the migration threshold value is parsed piece by piece to generate a comparison-verified dynamic hotness evaluation value and migration threshold value comparison set.

[0122] According to the comparison-verified dynamic hotness evaluation value and migration threshold value comparison set, the numerical difference between the dynamic hotness evaluation value of each data block and the corresponding storage tier migration threshold value is calculated one by one, the difference result between the dynamic hotness evaluation value and the migration threshold value is recorded, and the positive and negative states of the difference result are marked respectively to generate a difference mark set of the dynamic hotness evaluation value and the migration threshold value.

[0123] Based on the difference mark set of the dynamic hotness evaluation value and the migration threshold value, the data block mark state with a numerical difference greater than or equal to zero in the difference mark set is extracted, and the difference mark state is used as the migration judgment basis. When the mark state is positive, the storage tier upshift migration action is triggered, and when the mark state is negative, the storage tier downshift migration action is triggered. The direction and category of the migration action are recorded to generate a tier migration decision signal.

[0124] Specifically, based on the dynamic hotness evaluation value of each data block calculated in the previous step, for example, the dynamic hotness evaluation value of the data block '7A3F9B' is 5.3195, the system first determines the current storage tier of the data block by querying the global storage mapping table, for example, it is determined to be located in the "HDD_Tier" tier, then the system accesses a tier migration threshold table pre-configured by the system administrator according to the performance indicators (such as IOPS, bandwidth, delay) and cost model of each storage medium. The table defines in detail the hotness conditions required for each storage tier to migrate upwards or downwards, and its establishment basis is: long-term monitoring of the average read-write delay of each tier under different loads, the delay control target (for example, the delay of the SSD tier is less than 1 millisecond, and the delay of the HDD tier is less than 15 milliseconds) is converted into the corresponding dynamic hotness evaluation value threshold through historical data regression analysis of performance and hotness. For example, for the "HDD_Tier" tier, the threshold for migrating upwards to the "SSD_Tier" is set to 5.0, and the threshold for migrating downwards to the "Archive_Tier" is set to 1.0. The system extracts these two thresholds and performs a comparison verification to confirm that the tier has both uplink and downlink channels and does not have logical contradictions (such as no uplink threshold for the top tier). Finally, the dynamic hotness evaluation value 5.3195 of the data block '7A3F9B' is combined with the uplink migration threshold 5.0 and the downlink migration threshold 1.0 of its current tier to generate a comparison-verified dynamic hotness evaluation value and migration threshold value comparison set.

[0125] According to the dynamic heat evaluation value after the comparability check and the migration threshold value set, the system starts a decision comparison process for each data block. Taking data block '7A3F9B' as an example, its set is {'F': 5.3195, 'up_threshold': 5.0, 'down_threshold': 1.0}. The system first performs the condition judgment of upward migration, that is, compares whether the dynamic heat evaluation value is greater than or equal to the upward migration threshold value. The difference value is 5.3195-5.0=0.3195. Since the difference value result is positive, it indicates that the heat of the data block has reached the upgrade standard. The system records the positive difference value 0.3195 and a 'positive' label of'suggested upward migration' together, and aborts the further comparison of the data block. For another data block 'F8G9H0', its dynamic heat evaluation value is 0.8, and the upward migration difference value is 0.8-5.0=-4.2, which is negative, indicating that it does not meet the upward migration condition. The system then performs the condition judgment of downward migration, calculates the difference value between it and the downward migration threshold value, which is 0.8-1.0=-0.2. The difference value result is negative, indicating that its heat is lower than the standard of keeping at the current level. The system records the negative difference value -0.2 and a 'negative' label of'suggested downward migration'. For data blocks with dynamic heat evaluation values between the upward and downward migration threshold values, for example, the value is 4.0, the upward migration difference value is negative, and the downward migration difference value is positive, no label is recorded. Through the bidirectional comparison and difference calculation of all data blocks, a difference label set of dynamic heat evaluation value and migration threshold value is generated.

[0126] Based on the difference label set of dynamic heat evaluation value and migration threshold value, the system traverses the set and makes a final migration decision according to the label state recorded therein. For any data block in the set that is assigned a 'positive' label, such as data block '7A3F9B', the system determines that it meets the condition of storage level upgrade because its dynamic heat evaluation value exceeds the upward migration threshold value of the level it is in. The system then generates an upgrade migration action record containing the unique identifier of the data block, the current storage level, and the 'MIGRATE_UP' instruction. For data blocks in the set that are assigned a 'negative' label, such as 'F8G9H0', the system determines that they meet the condition of storage level downgrade because their dynamic heat evaluation values are lower than the downward migration threshold value, thereby generating a downgrade migration action record of the 'MIGRATE_DOWN' instruction containing the corresponding information. For data blocks in the set that are not assigned any label, a 'NO_ACTION' record is generated. The system aggregates all the generated upgrade, downgrade or no-operation action records, and sorts them according to the migration priority (for example, the upgrade priority is higher than the downgrade), to form a structured instruction sequence, and generates a level migration decision signal.

[0127] The obtaining step of the data block migration instruction is:

[0128] Based on the hierarchical migration decision signal, the migration action direction and the class identifier carried in the hierarchical migration decision signal are analyzed piece by piece, the target storage hierarchy information corresponding to the migration action direction identifier is extracted, and the data block target storage hierarchy identifier is generated;

[0129] According to the data block target storage hierarchy identifier, the storage unit address and the port number corresponding to the target storage hierarchy identifier are matched, the capacity state and the current available space value of the target storage unit are obtained, it is judged whether the available space value of the target storage unit meets the size requirement of the current data block, and the data block target storage unit effectiveness confirmation result is formed;

[0130] Based on the data block target storage unit effectiveness confirmation result, for the target storage unit whose available space value meets the data block size requirement, the data block migration instruction content is combined and constructed, including the unique identifier of the data block, the source storage unit position of the data block, the target storage unit address and the port number, and the data block migration priority information and the transmission mode mark are attached, and the data block migration instruction is generated.

[0131] Specifically, based on the hierarchical migration decision signal sequence generated in the previous step, the system starts an instruction analysis program, which processes the records in the sequence piece by piece. For a record, for example, {'block_id': '7A3F9B', 'current_tier': 'HDD_Tier', 'action': 'MIGRATE_UP'}, the analysis program first extracts the migration action direction and the class identifier, that is, "MIGRATE_UP". Then, it queries a pre-constructed hierarchical mapping rule table that defines the storage hierarchy topology relationship. The table is manually created by the system administrator according to the physical deployment and performance gradient, which clearly defines the upper and lower levels of each level, for example, the table specifies that the upper level of "HDD_Tier" is "SSD_Tier" and the lower level is "Archive_Tier". According to the "MIGRATE_UP" identifier, the analysis program looks up the corresponding upper level of "HDD_Tier" in the rule table, and determines that the target storage hierarchy is "SSD_Tier". This information is output as a result, generating the data block target storage hierarchy identifier.

[0132] According to the data block target storage tier identifier, such as "SSD_Tier", the system then queries a detailed storage resource list, which is maintained in real time and contains the status of all physical storage units in the system, including their respective tiers, network addresses, access ports, total capacities, used spaces, and current available spaces. The content of the list is periodically updated by sending query commands to each storage node through a background monitoring process. The system filters all storage units belonging to "SSD_Tier" in the list and determines an optimal target storage unit based on a pre-set load balancing strategy, such as selecting the unit with the highest current available space ratio. For example, the system selects an SSD unit with an address of 192.168.1.100 and a port number of 9000. The unit reports a total capacity of 1TB and a current available space of 200GB. Subsequently, the system queries the metadata information of the data block '7A3F9B' and obtains its size of 4MB. Then, a capacity judgment is performed by comparing the available space of 200GB with the data block size of 4MB. Since 200GB is much larger than 4MB, it is determined that the target storage unit has sufficient space to accommodate the data block. Accordingly, the system generates a confirmation result marked as "valid" to form the data block target storage unit validity confirmation result.

[0133] Based on the data block target storage unit validity confirmation result, which is marked as "valid", the system confirms that it can generate a formal migration instruction for the data block '7A3F9B'. If the confirmation result is "invalid", such as insufficient space in the target storage unit, the system will reselect another target unit or place the migration task in the waiting queue according to the pre-set retry strategy. After confirming the validity, the system begins to combine and construct the data block migration instruction. First, it integrates all necessary information: the unique identifier of the data block '7A3F9B'; the location of the data block source storage unit obtained by querying the global storage mapping table, such as 'hdd_array_01 / volume_2 / offset_4096'; and the target storage unit address 192.168.1.100 and port number 9000 determined in the previous step. Then, the system adds two additional control markers. The first is the data block migration priority information, which is set according to the migration direction, such as "MIGRATE_UP" being assigned a high priority (value 1) and "MIGRATE_DOWN" being assigned a low priority (value 3). The second is the transmission mode marker, which is selected by the system based on the network characteristics of the source and target storage tiers to choose the optimal transmission protocol, such as using the high-speed RDMA protocol for migration from HDD to SSD. Finally, all this information is encapsulated into a standardized data structure to generate the data block migration instruction.

[0134] The target migration path acquisition step is:

[0135] Based on the data block migration instruction, a storage system management interface is called to obtain the source storage unit address where the data block currently locates and the target storage unit address after migration, and a source and target address set of data movement is formed.

[0136] According to the source and target address set of data movement, through path query of the source storage unit address and the target storage unit address, each network node and link information associated in the path are extracted, a data transmission link node sequence from the source storage unit address to the target storage unit address is determined, and a target migration path is formed.

[0137] Specifically, based on the data block migration instruction generated in the previous step, for example, an instruction containing a data block '7A3F9B', a source location 'hdd_array_01 / volume_2 / offset_4096', and a target address '192.168.1.100:9000', the system sends the instruction to a unified storage system management interface. The interface is an API endpoint that provides standardized access services. It receives structured migration instructions as input and is responsible for parsing the field content therein. Specifically, the interface program directly extracts the value of the source storage unit location field from the data structure of the instruction, that is, 'hdd_array_01 / volume_2 / offset_4096', and takes it as the source address. At the same time, the value of the target storage unit address field '192.168.1.100' and the value of the port number field '9000' are extracted, and the two are combined into a network-reachable destination address '192.168.1.100:9000'. This process does not require secondary query or conversion of address information, but only structured data extraction. Finally, the parsed source address and target address are encapsulated into an address pair, for example, {'source': 'hdd_array_01 / volume_2 / offset_4096', 'destination': '192.168.1.100:9000'}. This operation is performed on each migration instruction in batch processing to form a source and target address set of data movement.

[0138] According to the address pair contained in the source and destination address set of data movement, such as {'source': 'hdd_array_01 / volume_2 / offset_4096', 'destination': '192.168.1.100:9000'}, the system calls the network path discovery service, which maintains a global network topology map, which is updated in real time through periodic running of link layer discovery protocol (LLDP) and border gateway protocol (BGP) route information collection, the interconnection relationship and link state of each switch and router in the network, the path query process adopts the improved Dijkstra algorithm, which is not only based on the number of hops, but also the real-time bandwidth utilization and delay of the link, the cost function is defined as cost=(0.6*normalized delay)+(0.4 / normalized available bandwidth), the weights 0.6 and 0.4 are pre-set according to the system's preference for low-latency data migration, the algorithm takes the access switch where the source address is located as the starting point and the access switch where the destination address is located as the end point, and calculates the path with the lowest cost in the network topology map, for example, the calculated path is: source server access switch SW-A1, core switch Core-SW1, aggregation switch AGG-SW2, destination server access switch SW-B3, and finally organizes each network device identifier on this path in order to form a data transmission link node sequence, that is, ['SW-A1', 'Core-SW1', 'AGG-SW2', 'SW-B3'], and the target migration path is obtained.

[0139] The acquisition step of the data retrieval optimization layout is:

[0140] Based on the target migration path, the background daemon process is called to sequentially traverse the link node sequence recorded in the target migration path, and the data block pre-fetching on each node is performed, the data blocks are gradually cached to the intermediate nodes in the order of the data transmission link node sequence, the cache layout index table is updated to indicate the new layout position of the data blocks, and the data retrieval optimization layout is established.

[0141] Specifically, based on the target migration path, for example, ['SW-A1', 'Core-SW1', 'AGG-SW2', 'SW-B3'], a resident background daemon process in the system receives the path and the corresponding data block migration task, and the process then traverses each network node in the path in order. For intermediate nodes in the path (i.e., Core-SW1 and AGG-SW2), the daemon process queries the attributes of the nodes to determine whether they are designated nodes with caching capabilities. The designation is based on the hardware configuration of the nodes (e.g., having on-board solid state disks) and their centrality scores in the network topology. The centrality score is derived by calculating the betweenness centrality of the nodes. Nodes with centrality scores higher than a preset threshold of 0.75 are marked as cache points with high strategic value. The threshold is derived from historical network traffic analysis and is the minimum centrality value that can cover 80% of cross-regional data flows. If node Core-SW1 meets the conditions and its current cache available space is greater than the data block size, the daemon process will first prefetch the data block from the source to the cache of Core-SW1, and then forward it from Core-SW1 to the final destination. After the data block is landed on the final destination, the daemon process updates a global, distributed cache layout index table. The table is a key-value store, with the key being the data block identifier and the value being the list of all storage locations. For example, the entry of '7A3F9B' is updated to ['192.168.1.100:9000', 'Core-SW1_cache_address']. In this way, an optimized data retrieval layout is established.

[0142] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any skilled person in the art can modify or change the above disclosed technical content to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments, without departing from the technical solution of the present application, still falls within the protection scope of the present application.

Claims

1. A method for fast data retrieval, characterized by, The method comprises the following steps: Based on the received data retrieval query request, locate the target data block and obtain the access type weight and access frequency count, and generate the data block hotness value; Based on the data block hotness value, call the global clock to obtain the time difference, obtain the time-sensitive hotness weight, and perform operation based on the time-sensitive hotness weight and the data block hotness value to obtain the dynamic hotness evaluation value; Based on the dynamic hotness evaluation value, compare and judge the migration threshold of the storage level where the data block is located to generate the level migration decision signal, match and determine the target storage level of the data block based on the level migration decision signal, and establish the data block migration instruction; Based on the data block migration instruction, construct the source and destination addresses of data movement, obtain the target migration path, and perform data block prefetching by the background daemon process based on the target migration path to establish data retrieval optimization layout; The data block hotness value obtaining step is: Based on the received data retrieval query request, parse the data block identification field attached in the query request, match the data block identification field to the global storage mapping table, retrieve the access record entry set of the corresponding data block, form the access type field sequence and the access timestamp field sequence; According to the access type field sequence and the access timestamp field sequence, classify and count the access times within the set time window according to the access type, construct the time sequence set of each type of access, and extract the corresponding timestamp to calculate the change rate to obtain the access type count mapping table and the access type change rate index table; According to the access type count mapping table and the access type change rate index table, calculate the data block hotness value; The time-sensitive hotness weight obtaining step is: Based on the data block hotness value, retrieve the hotness value generation timestamp contained in the generated record, and synchronously obtain the standard timestamp of the current time, calculate the difference in seconds between the current timestamp and the hotness value generation timestamp, and form the hotness value time difference result; According to the hotness value time difference result, match the pre-defined time difference reference interval table, and sequentially compare each time difference value with the upper and lower limit ranges of the interval to determine the time decay section number where the hotness value time difference result is located, and extract the section identification information corresponding to the number to generate the time decay section identification; Based on the time decay section identification, read the numerical parameter content of the corresponding section from the set time-sensitive decay factor parameter table as the time-sensitive influence factor corresponding to the current data block to generate the time-sensitive hotness weight; The dynamic hotness evaluation value obtaining step is: Based on the time-sensitive hotness weight, extract the hotness value corresponding to each data block to obtain the data block hotness value and the time-sensitive hotness weight; According to the data block hotness value and the time-sensitive hotness weight, construct a dynamic hotness input vector group, extract all data block indexes, label the data block position corresponding to each group of hotness parameters, and additionally add the hotness saturation threshold and the time-sensitive influence factor as constant terms to form a hotness calculation input set; Based on the hotness calculation input set, calculate the dynamic hotness evaluation value.

2. The method of claim 1, wherein, The level migration decision signal obtaining step is: Based on the dynamic heat evaluation value, the migration threshold value of the storage level where the data block is located is extracted, the level type identifier corresponding to the migration threshold value is parsed piece by piece, and the dynamic heat evaluation value after comparability check and the migration threshold value are generated. The control set; According to the dynamic heat evaluation value after comparability check and the migration threshold value control set, the numerical difference value of the dynamic heat evaluation value of each data block and the corresponding storage level migration threshold value is calculated one by one, the difference value result between the dynamic heat evaluation value and the migration threshold value is recorded, and the positive and negative states of the difference value result are marked respectively, and the difference value mark set of the dynamic heat evaluation value and the migration threshold value is generated; Based on the difference value mark set of the dynamic heat evaluation value and the migration threshold value, the data block mark state with the numerical difference value greater than or equal to zero in the difference value mark set is extracted, and the difference value mark state is taken as the migration judgment basis. When the mark state is positive, the storage level up migration action is triggered, and when the mark state is negative, the storage level down migration action is triggered, and the direction and category of the migration action are recorded, and the level migration decision signal is generated.

3. The method of claim 1, wherein, The acquisition step of the data block migration instruction is: Based on the level migration decision signal, the migration action direction and category identifier carried in the level migration decision signal are parsed piece by piece, the target storage level information corresponding to the migration action direction identifier is extracted, and the data block target storage level identifier is generated; According to the data block target storage level identifier, the storage unit address and port number corresponding to the target storage level identifier are matched, the capacity state and current available space value of the target storage unit are obtained, it is judged whether the available space value of the target storage unit meets the size requirement of the current data block, and the data block target storage unit effectiveness confirmation result is formed; Based on the data block target storage unit effectiveness confirmation result, for the target storage unit whose available space value meets the size requirement of the data block, the data block migration instruction content is combined and constructed, including the unique identifier of the data block, the source storage unit position of the data block, the target storage unit address and port number, and the data block migration priority information and transmission mode mark are attached, and the data block migration instruction is generated.

4. The method of claim 1, wherein, The acquisition step of the target migration path is: Based on the data block migration instruction, the storage system management interface is called, the source storage unit address where the data block is currently located and the destination storage unit address after migration are obtained, and the source and destination address set of data movement is formed; According to the source and destination address set of data movement, the path query of source storage unit address and destination storage unit address is carried out, the network nodes and link information associated in the path are extracted, the data transmission link node sequence from the source storage unit address to the destination storage unit address is determined, and the target migration path is formed.

5. The method of claim 1, wherein, The acquisition step of the data retrieval optimization layout is: Based on the target migration path, the background daemon process is called, the link node sequence recorded in the target migration path is traversed in turn, the data block prefetching on each node is carried out, the data block is gradually cached to the intermediate node according to the order of the data transmission link node sequence, the cache layout index table is updated to indicate the new layout position of the data block, and the data retrieval optimization layout is established.

6. The data quick search system of the data quick search method according to any one of claims 1 to 5, characterized by, It includes: The query identification module retrieves a query request based on the received data, locates to a target data block, and acquires an access type weight and an access frequency count, and generates a data block heat value. The heat evaluation module acquires a time difference by calling a global clock based on the data block heat value, acquires a time effectiveness heat weight, and performs an operation based on the time effectiveness heat weight and the data block heat value to obtain a dynamic heat evaluation value. The hierarchical decision module compares and judges the dynamic heat evaluation value with a migration threshold of a storage hierarchy where the data block is located, generates a hierarchical migration decision signal, matches and determines a target storage hierarchy of the data block based on the hierarchical migration decision signal, and establishes a data block migration instruction. The data prefetching module constructs a source and a destination address of data movement based on the data block migration instruction, acquires a target migration path, performs data block prefetching by a background daemon process based on the target migration path, and establishes a data retrieval optimization layout.

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