A computer data storage method and system based on the Internet of Things

Abstract

The present application provides a computer data storage method and system based on the Internet of Things, which relates to the field of data storage technology. The data to be stored when the computer executes the target business is read in the terminal device of the Internet of Things, and the attributes of the computer storage path are classified by dynamic file name disguise to obtain the storage attribute node corresponding to the storage strategy on the computer storage path. The storage tag heat when the storage operation is performed on the computer storage path is determined according to the storage attribute node and the private isolation identifier; the storage utility information is hierarchically verified, and the retrieval response cycle when the computer data storage is retrieved is determined by the storage track index; the computer data is synchronously stored according to the storage tag heat and the retrieval response cycle, and the incremental data demand for synchronous storage on the terminal of the Internet of Things is adapted in real time based on the elastic expansion strategy. The present application can perform multi-dimensional isolated storage of the data to be stored in the terminal device of the Internet of Things to improve the retrieval response speed of the data storage.

Description

Technical Field

[0001] The present application relates to the field of data storage technology, and more specifically, to a computer data storage method and system based on the Internet of Things. Background Art

[0002] In the IoT environment, data storage refers to the process of storing, managing, and maintaining the data collected and generated in real time by various IoT devices (such as sensors, cameras, and smart terminals) using appropriate storage media and systems. The data generated by these devices is typically large-scale, high-frequency, and diverse, with high requirements for real-time performance and access speed. To effectively address this high-concurrency and large-scale data storage needs, IoT systems adopt a distributed storage architecture, distributing data across different storage nodes.

[0003] However, existing IoT-based computer data storage often lacks precise management of different data types. Storage policies are often too static to effectively address the dynamic demands of data access frequency, access latency, and storage resources. This leads to low storage efficiency and an inability to adjust storage resource allocation in real time. This can easily lead to wasted or insufficient storage resources, especially as the number of devices and data volumes increase. This impacts overall system performance. Therefore, the industry faces the challenge of implementing multi-layered, isolated storage within IoT devices to improve data retrieval response speeds. Summary of the Invention

[0004] The present application provides a computer data storage method and system based on the Internet of Things, which can perform multi-isolated storage of data to be stored in an Internet of Things terminal device to improve the retrieval response speed of data storage.

[0005] In a first aspect, the present application provides a computer data storage method based on the Internet of Things, the data storage method comprising the following steps:

[0006] Reading data to be stored when a computer executes a target business in an IoT terminal device, isolating and dividing the data to be stored, and obtaining private isolation identifiers corresponding to multiple isolated storage areas;

[0007] Classifying the attributes of the computer storage path through dynamic file name camouflage to obtain a storage attribute node corresponding to the storage policy on the computer storage path, and determining the storage tag heat when performing a storage operation on the computer storage path based on the storage attribute node and the private isolation identifier;

[0008] Acquiring storage utility information on a data cache area in a computer storage path, performing hierarchical verification on the storage utility information to obtain a storage trace index in the computer storage path, and then determining a retrieval response period during computer data storage retrieval based on the storage trace index;

[0009] Computer data is synchronously stored according to the storage tag popularity and the retrieval response cycle, and incremental data requirements for synchronous storage on the Internet of Things terminal are adapted in real time based on an elastic expansion strategy.

[0010] In this embodiment, the data to be stored refers to data information units that are dynamically generated during the computer's execution of a target business and have clear business affiliation and storability.

[0011] In this embodiment, isolating and dividing the data to be stored to obtain private isolation identifiers corresponding to multiple isolated storage areas specifically includes:

[0012] Determining data classification dimensions according to access characteristics of the data to be stored;

[0013] generating a private encrypted identifier matching each isolated storage area according to the data classification dimension;

[0014] The data to be stored is dynamically allocated to the corresponding storage area through the private encryption identifier and an access relationship is established to obtain private isolation identifiers corresponding to multiple isolated storage areas.

[0015] In this embodiment, the private isolation identifier refers to a control unit that identifies the binding relationship between the isolated storage area and its belonging data.

[0016] In this embodiment, determining the storage tag heat when performing a storage operation on a computer storage path according to the storage attribute node and the private isolation identifier specifically includes:

[0017] Determining storage timing characteristics when performing a storage operation on a computer storage path according to the storage attribute node;

[0018] Determining storage heat information when performing a storage operation on a computer storage path according to the private isolation identifier;

[0019] The storage tag heat when performing a storage operation on a computer storage path is determined by using the storage timing characteristics and the storage heat information.

[0020] In this embodiment, the storage tag heat refers to the heat level tag marked at the computer storage path level.

[0021] In this embodiment, performing hierarchical verification on the storage utility information to obtain a storage trace index in the computer storage path specifically includes:

[0022] determining a hierarchical verification indicator for data storage based on the storage utility information;

[0023] Determining the boundaries of hot and cold data in the storage trajectory according to the hierarchical verification indicator;

[0024] Dynamically verify metadata features in computer storage paths and generate storage trajectory rules;

[0025] A storage trace index in a computer storage path is determined according to the cold and hot data boundary and the storage trace rule.

[0026] In this embodiment, the storage trace index refers to a structured table entry that records the status, location, and activity of various types of data in the storage path.

[0027] In this embodiment, determining the retrieval response period during computer data storage retrieval using the storage trace index specifically includes:

[0028] Extracting search path features during computer data storage retrieval from the storage trajectory index;

[0029] Determining a search attribute descriptor for computer data storage retrieval using the search path characteristics;

[0030] The retrieval attribute descriptor determines the retrieval response period during computer data storage retrieval.

[0031] In a second aspect, the present application provides a computer data storage system based on the Internet of Things, for executing a computer data storage method based on the Internet of Things, the data storage system comprising:

[0032] An isolation and partitioning module is used to read the data to be stored when the computer executes the target business in the Internet of Things terminal device, isolate and partition the data to be stored, and obtain private isolation identifiers corresponding to multiple isolated storage areas;

[0033] an attribute classification module for classifying the attributes of the computer storage path through dynamic file name masquerading, obtaining a storage attribute node corresponding to the storage policy on the computer storage path, and determining the storage tag heat when performing a storage operation on the computer storage path based on the storage attribute node and the private isolation identifier;

[0034] a hierarchical verification module, configured to obtain storage utility information on a data cache area in a computer storage path, perform hierarchical verification on the storage utility information, obtain a storage trace index in the computer storage path, and further determine a retrieval response period during computer data storage retrieval based on the storage trace index;

[0035] The synchronous storage module is used to synchronously store computer data according to the storage tag popularity and the retrieval response cycle, and to adapt to the incremental data requirements of synchronous storage on the Internet of Things terminal in real time based on the elastic expansion strategy.

[0036] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0037] The method comprises the following steps: reading data to be stored when a computer executes a target business in an Internet of Things terminal device, isolating and dividing the data to be stored, and obtaining private isolation identifiers corresponding to multiple isolated storage areas; classifying the attributes of a computer storage path through dynamic file name camouflage, obtaining a storage attribute node corresponding to a storage policy on the computer storage path, and determining the storage tag heat when a storage operation is performed on the computer storage path according to the storage attribute node and the private isolation identifier; obtaining storage utility information on a data cache area in the computer storage path, performing hierarchical verification on the storage utility information, obtaining a storage track index in the computer storage path, and then determining a retrieval response cycle during computer data storage retrieval through the storage track index; synchronously storing computer data according to the storage tag heat and the retrieval response cycle, and adapting to the incremental data demand for synchronous storage on the Internet of Things terminal in real time based on an elastic expansion strategy.

[0038] It can be seen from this that in this application, dynamic storage strategies and real-time resource expansion mechanisms can be used in computer data storage to finely manage the storage process; among them, by accurately isolating and dividing the data to be stored, personalized storage management can be performed on the data according to the characteristics and access requirements of the data, which can ensure the security and privacy of the data, while optimizing the access efficiency of data storage and avoiding unnecessary resource waste; dynamic file name disguise and storage attribute node generation enable storage paths to be flexibly classified according to different storage strategies, avoiding the bottleneck caused by the static nature of traditional storage paths, and improving the management accuracy of storage paths, so that the storage of hot data and cold data can be effectively distinguished, thereby improving data access efficiency and system response speed; by performing hierarchical verification of storage utility information and combining it with the generation of storage trajectory indexes, the storage history and usage patterns of data can be accurately analyzed, thereby helping the system predict future data access needs, not only optimizing the allocation of storage space, but also improving the data retrieval response speed, so that the system can respond to changing access needs more intelligently. The combination of synchronous storage and elastic expansion strategies enables the system to adjust storage resource allocation in real time according to the popularity of data tags and the retrieval response cycle to cope with the ever-changing incremental data needs in IoT terminals. By dynamically allocating storage resources, resource waste or shortage is avoided, ensuring efficient data storage and system reliability.

[0039] In summary, the technical solution adopted in this application can perform multi-dimensional isolated storage of data to be stored in IoT terminal devices to improve the retrieval response speed of data storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 This is an exemplary flow chart of a computer data storage method based on the Internet of Things provided by this application;

[0042] Figure 2 This is a schematic diagram of a process for determining a storage attribute node according to the present application;

[0043] Figure 3 is a schematic diagram of a process for determining a storage trajectory index according to the present application;

[0044] Figure 4 This is a module structure diagram of a computer data storage system based on the Internet of Things provided in this application. DETAILED DESCRIPTION

[0045] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0046] An embodiment of the present application provides a computer data storage method and system based on the Internet of Things. The core of the method and system is to read the data to be stored when the computer executes the target business in the Internet of Things terminal device, isolate and divide the data to be stored, and obtain private isolation identifiers corresponding to multiple isolated storage areas; classify the attributes of the computer storage path through dynamic file name camouflage, obtain the storage attribute node corresponding to the storage policy on the computer storage path, and determine the storage tag heat when performing the storage operation on the computer storage path according to the storage attribute node and the private isolation identifier; obtain the storage utility information on the data cache area in the computer storage path, perform hierarchical verification on the storage utility information, obtain the storage track index in the computer storage path, and then determine the retrieval response cycle when retrieving computer data through the storage track index; synchronize the computer data according to the storage tag heat and the retrieval response cycle, and adapt the incremental data demand for synchronized storage on the Internet of Things terminal in real time based on the elastic expansion strategy.

[0047] Example 1: In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown in the figure, this figure is an exemplary flow chart of a computer data storage method based on the Internet of Things according to this embodiment of the present application, and the data storage method includes the following steps:

[0048] In step S1, the data to be stored when the computer executes the target business is read in the Internet of Things terminal device, the data to be stored is isolated and divided, and private isolation identifiers corresponding to multiple isolated storage areas are obtained.

[0049] In specific implementation, the following method can be used to read the data to be stored when the computer executes the target business in the IoT terminal device, namely: first, the execution events of the operating system process or service are monitored through the computer's business scheduling module, the business trigger point is captured, and the output data stream generated by the business is intercepted through the middleware interface; then, the built-in data capture engine is used to read structured or unstructured data including sensor acquisition values, control instruction results, intermediate state files, log fragments, etc.; during the reading process, the system automatically records metadata such as data source, generation time, and belonging module, and binds the data to the business context to initially form an identifiable data unit, which is used as the data to be stored.

[0050] It should be noted that, in this application, the data to be stored refers to data information units that are dynamically generated during the computer's execution of the target business and have clear business affiliation and storability.

[0051] In this embodiment, isolating and dividing the data to be stored to obtain private isolation identifiers corresponding to multiple isolated storage areas can be achieved by using the following steps:

[0052] Determining data classification dimensions according to access characteristics of the data to be stored;

[0053] generating a private encrypted identifier matching each isolated storage area according to the data classification dimension;

[0054] The data to be stored is dynamically allocated to the corresponding storage area through the private encryption identifier and an access relationship is established to obtain private isolation identifiers corresponding to multiple isolated storage areas.

[0055] In a specific implementation, after capturing the data to be stored, the accompanying metadata is read. This metadata includes: generation module, data type, access frequency, read / write permissions, timestamp, and data usage. This metadata is then input into a feature analysis model as feature factors. This feature analysis model can be based on a decision tree, rule engine, or preset policy library to extract dimensions from the data. The extracted dimensions include: data usage (control / monitoring / logging); access frequency (high frequency / low frequency); and security level (public / restricted / private). The primary and secondary dimensions for partitioning storage areas are determined from these extracted dimensions to form data classification dimensions. Then, the fields in the data classification dimensions are hashed and encrypted using a device-specific key (e.g., AES or SM4 algorithm) to generate a unique encrypted identifier for each isolated storage area, i.e., a private encrypted identifier. The private encrypted identifier can be constructed using a structure consisting of a service ID, a type tag, and a security level, though this is not limited here. This private encrypted identifier is guaranteed to be unique within the terminal device and does not leak the original classification information. Finally, a storage directory or logical partition is created that corresponds to the private encryption identifier, and the corresponding read and write permissions and access interface are registered for the private encryption identifier in the internal access control table. When data is written, an indexing mechanism automatically directs each data block to a matching isolation area (such as a different folder, virtual volume, encrypted partition, or container memory). Finally, the path to each storage area is bound to its encryption identifier, resulting in private isolation identifiers corresponding to multiple isolated storage areas.

[0056] It should be noted that, in this application, the isolated storage area represents a physical storage unit with independent access rights and data ownership; the access characteristics of the data to be stored represent a set of behavioral attributes consisting of access frequency, access time, and permission level; the data classification dimension refers to a set of features used to distinguish the data to be stored; the private encryption identifier refers to a security identifier that ensures the security and non-tamperability of data partition access; the private isolation identifier refers to a control unit that identifies the binding relationship between the isolated storage area and the data to which it belongs.

[0057] In step S2, the computer storage path is attribute-classified through dynamic file name camouflage to obtain a storage attribute node corresponding to the storage policy on the computer storage path, and the storage tag heat when performing a storage operation on the computer storage path is determined based on the storage attribute node and the private isolation identifier.

[0058] Preferably, in this embodiment, the computer storage path is attribute-classified by dynamic file name disguise to obtain the storage attribute node corresponding to the storage policy on the computer storage path. Figure 2As shown in FIG, this figure is a schematic diagram of a process for determining a storage attribute node in some embodiments of the present application. In this embodiment, determining a storage attribute node can be implemented by using the following steps:

[0059] In step S21, a dynamic camouflage rule is constructed based on the file attribute characteristics of the computer storage path;

[0060] In step S22, a file name identifier matching the storage policy is generated based on the dynamic camouflage rule;

[0061] In step S23, the storage path is attribute-classified according to the metadata features of the file name identifier;

[0062] In step S24, a storage attribute node corresponding to the storage policy on the computer storage path is generated based on the classification result.

[0063] In the specific implementation, first, read the file meta information under each storage path in the computer, including file size, creation time, modification time, file type, read and write frequency, associated business modules and other features. Based on the read meta information, build a set of variable renaming mapping rules, and use the renaming mapping rules as dynamic camouflage rules. The construction of the renaming mapping rules includes: high-frequency access log files → prefixed with "L_", adding a timestamp suffix; control instruction result files → prefixed with "C_", containing module codes; temporary intermediate data files → adding a ".tmpX" suffix to indicate clearing priority. The dynamic camouflage rules can be dynamically generated through configuration templates or using a policy engine to ensure dynamic matching with actual file access patterns. Then, the dynamic camouflage rules are applied to the file name generator, and each time a file is written or moved to a specific path, it is renamed to a file name identifier with classification semantics. For example, L_20250512_001.log represents a log-type, high-frequency file; C_MOD3_CTRL_102.tmp represents a control file generated by control module 3. The generated file name identifier embeds business attributes, allowing the classification system to directly extract semantic information from the file name. Next, the file name identifier in the storage path is scanned to extract its semantic fields (such as type code, module code, timestamp, and priority code). This is then combined with file metadata (such as size and modification frequency) for rule matching. Based on the matching results, the storage paths are classified into multiple attribute types, including: "log-type paths" for storing frequently updated monitoring records; "command-type paths" for storing real-time control instructions; and "cache-type paths" for storing intermediate temporary data. Finally, for each attribute path type, a storage attribute node is generated. This storage attribute node contains the following fields: path ID, path type, recommended storage policy (such as compression, encryption, or backup priority), and storage quota. This storage attribute node structure is registered as metadata in the path mapping table for query and decision-making by the storage scheduler.

[0064] It should be noted that in this application, dynamic file name camouflage means generating a semantic naming structure based on data access characteristics without changing the file content, thereby providing an implicit label function for data path classification; the storage policy is represented by a set of compression, encryption, backup, and cleanup storage processing rules formulated for different data types; the file attribute characteristics are represented by a set of data characteristics used for classification and identification such as file size, type, creation time, access frequency, and business source; the dynamic camouflage rule represents a naming rule system used to identify data types, uses, and access policies; the file name identifier refers to a structured file name with classification meaning; the metadata feature refers to additional information that describes file attributes, such as creation time, modification records, access rights, and modules to which it belongs; the storage attribute node refers to a structured information unit of the role, function, and data characteristics of a storage path in the storage policy system.

[0065] In this embodiment, determining the storage tag heat when performing a storage operation on a computer storage path according to the storage attribute node and the private isolation identifier can be implemented by the following steps:

[0066] Determining storage timing characteristics when performing a storage operation on a computer storage path according to the storage attribute node;

[0067] Determining storage heat information when performing a storage operation on a computer storage path according to the private isolation identifier;

[0068] The storage tag heat when performing a storage operation on a computer storage path is determined by using the storage timing characteristics and the storage heat information.

[0069] In specific implementation, the system first extracts time-series information for each path from the established storage attribute nodes, including historical write cycles, write intervals, active periods, and data retention time. Specifically, log-type paths write every five minutes; control-type paths write frequently during peak business hours; and cache-type paths have short lifecycles and frequent overwrites. These characteristics can be dynamically generated by counting file modification records or access logs and stored in the path's attribute analysis table. By reading the attribute analysis table, the storage time-series characteristics of storage operations executed on the computer storage path are obtained. Then, based on the data partitions corresponding to the private isolation identifiers, data access frequency, data update counts, read-write ratio, and call counts are calculated, and these heat factors are used as storage heat information. For example, if the region corresponding to the isolation identifier "LOG-HF-001" has 50 writes and 20 reads in the past hour, the system can assign it a heat weight of 0.8 (high heat). This storage heat information can be dynamically updated using an exponential decay model, giving greater weight to recent activity. Finally, a weighted fusion of storage time series features and storage heat information is performed to generate a storage tag heat score for each storage path. This storage tag heat score serves as the storage tag heat when performing storage operations on the computer storage path. Storage tag heat can be implemented as follows: Storage tag heat = α × storage time series features + β × storage heat information, where α and β are policy weight coefficients that can be set based on expert experience and are not limited here. The resulting storage tag heat is normalized to [0, 1]. A higher normalized value indicates greater storage activity on the path. This storage tag heat is ultimately bound to path attributes and used to determine the order of operations such as data priority, pre-read caching, and off-site backup.

[0070] It should be noted that, in this application, storage timing characteristics refer to the temporal regularity characteristics of data writing, reading and other operations in a certain path; storage heat information refers to a set of indicators that reflect the current access frequency and read-write density behavior intensity of a specific data partition or path; storage label heat refers to the heat level label marked at the computer storage path level.

[0071] In step S3, storage utility information on the data cache area in the computer storage path is obtained, and the storage utility information is hierarchically verified to obtain a storage trace index in the computer storage path. The retrieval response period during computer data storage retrieval is then determined based on the storage trace index.

[0072] In specific implementations, obtaining storage utility information for data caches within a computer's storage path can be accomplished by monitoring the operating system's cache layer and application write buffering mechanisms. First, the corresponding data cache within the computer's storage path is located. This data cache includes the page cache within the operating system's file system, the application-level data buffer pool, and the intermediate storage area before disk writes. Next, through kernel-level interfaces (such as / proc / meminfo and the / sys / fs directory in Linux) and I / O monitoring modules, metrics such as the number of writes, call frequency, retention time, hit rate, and effective data utilization within the data cache are collected in real time. These metrics are then combined with business-relevance tags to convert these metrics into a set of storage utility information, including data reuse, call value, and update density.

[0073] It should be noted that in this application, the data cache area refers to the buffer area that temporarily resides in the system cache layer or application cache module before the data is officially written to the disk, which is used to improve storage access efficiency; storage utility information refers to a set of attributes that reflect the use value of data in the cache area, including indicators such as hit frequency, residence time, and call density.

[0074] Preferably, in this embodiment, the storage utility information is hierarchically verified to obtain the storage track index in the computer storage path, and the reference Figure 3 As shown in FIG, this figure is a schematic diagram of a process for determining a storage trajectory index in some embodiments of the present application. In this embodiment, determining a storage trajectory index can be implemented by the following steps:

[0075] In step S31, a hierarchical verification index for data storage is determined based on the storage utility information;

[0076] In step S32, the hot and cold data boundaries in the storage trace are determined according to the hierarchical verification index;

[0077] In step S33, the metadata features in the computer storage path are dynamically verified to generate storage trajectory rules;

[0078] In step S34, a storage trace index in a computer storage path is determined according to the hot and cold data boundary and the storage trace rule.

[0079] In specific implementation, key behavioral indicators (KBIs) are first extracted from storage utility information. These include hit frequency, data retention time, access density, and update frequency. Based on pre-defined policy models (e.g., LRU, LFU, and access periodicity), a set of hierarchical validation metrics is constructed to determine the data's value tier. These metrics include: hit frequency ≥ 0.8 and retention time ≤ 10 minutes = high-frequency hot data; low update frequency but long retention time = latent hot data; and long misses = cold data. Next, based on these tiered validation metrics, the data is divided into hot, warm, and cold data areas. A logical boundary is established within the storage path, serving as the hot and cold data boundary within the storage trajectory. The hot and cold data boundary within the storage trajectory can be determined using the following method: the hot data area is defined as the buffer segment for newly written files at the end of the path, while the cold data area is defined as the historical segment with long retention and low access frequency. The hot and cold data boundary can be dynamically updated using a sliding window mechanism or rolling statistical model to continuously respond to changes in data activity. Next, the metadata of all files in the computer's storage path (such as creation time, modification time, number of accesses, file size, module, etc.) is scanned. Combined with the hot and cold data boundary status, a dynamic rule generation strategy is employed, which serves as the storage trajectory rule. This strategy includes: files with recent creation times and frequent access are assigned to the hot data rule path; large files with infrequent access are designated as cold data; and temporary files are automatically assigned to the transition rule. The storage trajectory rule defines the hierarchical position and migration strategy of files in the path. Finally, the hot and cold data boundaries and the storage trajectory rules are integrated. This integration process can be performed using a linear fitting algorithm (not specified here) to construct a storage trajectory index table. Each record in the storage trajectory index table corresponds to a data file's storage trajectory index, including: file name, path location, current temperature status, expected migration direction (hot → cold), and access history summary. This storage trajectory index is structured and stored in memory, refreshed periodically, and accessible to the query engine and scheduling system in real time.

[0080] It should be noted that in this application, hierarchical verification indicators refer to a set of behavioral parameters used to measure the use value of data in storage; hot and cold data boundaries refer to the data classification boundaries related to access popularity; metadata features represent information describing the status of the file itself; storage trajectory rules refer to a set of decision logic that defines how data should be migrated, resided, and cleaned in the path; storage trajectory indexes refer to structured table entries that record the status, location, and activity of various types of data in the storage path.

[0081] In this embodiment, determining the retrieval response period during computer data storage retrieval using the storage trajectory index can be achieved by using the following steps:

[0082] Extracting search path features during computer data storage retrieval from the storage trajectory index;

[0083] Determining a search attribute descriptor for computer data storage retrieval using the search path characteristics;

[0084] The retrieval attribute descriptor determines the retrieval response period during computer data storage retrieval.

[0085] In specific implementation, the path information, activity status, popularity level, historical access history, and storage tier of each data object recorded in the storage trajectory index are first parsed. Data path features relevant to the current search target are then extracted. These data path features include: the data's physical path (SSD, HDD, remote storage, etc.); hot or cold data marking; the depth and access density of the directory in which it resides; and the distribution of historical search times. These data path features are used as search path features for computer data storage retrieval. Next, a set of search attribute descriptors is generated based on the search path features. These descriptors include: physical media latency level (e.g., SSD is superior to HDD); path complexity level (e.g., high levels of nested directories may result in time-consuming searches); access congestion index (concurrent request density within the path); file popularity level identification (hot / cold label); and recent search success or failure rate. These search attribute descriptors can be generated using rule-based mapping, such as using a configuration table to convert popularity and path depth into a latency score, though this is not a limitation here. Finally, a preset evaluation model (such as a rule-based linear scoring model, a machine learning regression model, or a priority queue scheduling model) is used to comprehensively score the retrieval attribute descriptors. The output value is used as the retrieval response cycle, that is, the time predicted by the system from initiating the request to retrieving the data. For example, if the data is located on the local SSD, has high popularity, and a shallow path, the retrieval response cycle is 20ms; if the data is in remote cold storage, in a deep directory, and has low popularity, the retrieval response cycle can be more than 500ms, which is not limited here.

[0086] It should be noted that, in this application, the retrieval path characteristics represent a set of behavioral attributes such as the depth, hierarchical complexity, heat status, and access frequency of the data path in the storage structure; the retrieval attribute descriptor represents a set of standardized indicators converted from the path characteristics; and the retrieval response cycle represents the expected response time from initiating a data retrieval request to successfully obtaining the data.

[0087] In step S4, the computer data is synchronously stored according to the storage tag popularity and the retrieval response cycle, and the incremental data demand for synchronous storage on the Internet of Things terminal is adapted in real time based on the elastic expansion strategy.

[0088] In this embodiment, computer data is synchronously stored based on the storage tag popularity and the retrieval response period, and the incremental data requirements for synchronous storage on the IoT terminal are adapted in real time based on the elastic expansion strategy. This can be achieved by using the following steps:

[0089] Determining a synchronization storage rule according to the storage tag popularity and the retrieval response period;

[0090] Collect incremental data streams from IoT terminals in real time and extract storage demand characteristics;

[0091] Performing policy matching between the storage demand characteristics and the synchronization storage rules, triggering elastic expansion policy instructions to dynamically allocate storage resources;

[0092] The adaptation relationship between incremental data and storage nodes is generated through the elastic expansion policy instruction, and synchronous storage information of synchronous storage is output.

[0093] In specific implementation, the system first analyzes the popularity of storage tags and the retrieval response cycle to construct synchronization storage rules. When constructing synchronization storage rules, high-priority synchronization storage rules can be set for hot data (high-popularity tags) and data with low retrieval response cycles, ensuring that such data is synchronized to high-performance storage (such as SSDs) in real time. Low-priority rules can be set for cold data (low-popularity tags) and data with high response cycles, allowing it to be synchronized to low-cost storage (such as HDDs or remote storage). These synchronization storage rules are generated based on factors such as available data traffic, storage speed, and latency tolerance. Next, incremental data streams are collected from IoT devices to obtain real-time sensor data, device status updates, and other information. Data collection protocols (such as MQTT and CoAP) are used to transmit this data to a local or cloud location. By analyzing the characteristics of the incremental data stream (such as data size, transmission frequency, and real-time requirements), storage requirements are identified and used as storage demand features. These storage requirements include the need for fast storage of large amounts of time series data and the varying storage priorities and access patterns of data from different sensors. Then, based on the extracted storage demand features and the synchronization storage rules, a policy is matched to automatically select appropriate storage resources. If the demand for incremental data is high and storage capacity is insufficient, the system triggers an elastic scaling policy, dynamically allocating additional storage resources on demand (such as expanding cloud storage capacity or adding local cache nodes). This dynamic allocation of storage resources can be accomplished through automated resource scheduling platforms (such as Kubernetes and OpenStack). If the volume of real-time sensor data increases dramatically during this dynamic allocation process, additional storage nodes can be automatically launched to support data synchronization. For low-frequency, smaller data synchronization needs, data can be synchronized to existing storage resources to avoid resource waste. Finally, elastic scaling policy instructions establish a matching relationship between incremental data and storage nodes. For example, real-time data streams can be synchronized to specific storage nodes, cloud storage, or distributed databases. Synchronized storage information includes the synchronization target path, data storage node, and synchronization time. This information can be fed back to IoT terminals or users in real time via the system monitoring panel, ensuring efficient storage of synchronized data. The synchronization process also selects the optimal storage medium (such as SSD, cloud storage, or cold storage) based on data characteristics, which will not be discussed further here.

[0094] It should be noted that, in this application, elastic expansion strategy refers to a strategy that automatically adjusts storage resource configuration to meet real-time storage needs; incremental data demand refers to the amount of new or changed data of IoT terminal devices within a specific time period; synchronization storage rules refer to the rules that determine data synchronization priority and target storage media; incremental data streams refer to data streams generated in real time by IoT terminals or devices and that need to be stored over time; storage demand characteristics refer to the storage capacity requirements, storage frequency, access priority, and real-time requirement attributes of data; synchronization storage information refers to all relevant information generated during the data synchronization process, including target storage path, storage node, synchronization time, data type and synchronization status, which are used to track and manage the data synchronization process.

[0095] It can be seen from this that in this application, dynamic storage strategies and real-time resource expansion mechanisms can be used in computer data storage to finely manage the storage process; among them, by accurately isolating and dividing the data to be stored, personalized storage management can be performed on the data according to the characteristics and access requirements of the data, which can ensure the security and privacy of the data, while optimizing the access efficiency of data storage and avoiding unnecessary resource waste; dynamic file name disguise and storage attribute node generation enable storage paths to be flexibly classified according to different storage strategies, avoiding the bottleneck caused by the static nature of traditional storage paths, and improving the management accuracy of storage paths, so that the storage of hot data and cold data can be effectively distinguished, thereby improving data access efficiency and system response speed; by performing hierarchical verification of storage utility information and combining it with the generation of storage trajectory indexes, the storage history and usage patterns of data can be accurately analyzed, thereby helping the system predict future data access needs, not only optimizing the allocation of storage space, but also improving the data retrieval response speed, so that the system can respond to changing access needs more intelligently. The combination of synchronous storage and elastic expansion strategies enables the system to adjust storage resource allocation in real time according to the popularity of data tags and the retrieval response cycle to cope with the ever-changing incremental data needs in IoT terminals. By dynamically allocating storage resources, resource waste or shortage is avoided, ensuring efficient data storage and system reliability.

[0096] In summary, the technical solution adopted in this application can perform multi-dimensional isolated storage of data to be stored in IoT terminal devices to improve the retrieval response speed of data storage.

[0097] In the second embodiment, the present application provides a computer data storage system based on the Internet of Things, referring to Figure 4 As shown in FIG, this figure is a module structure diagram of a computer data storage system based on the Internet of Things according to this embodiment of the present application, and the data storage system includes:

[0098] The isolation and partitioning module 100 is used to read the data to be stored when the computer executes the target business in the Internet of Things terminal device, isolate and partition the data to be stored, and obtain private isolation identifiers corresponding to multiple isolated storage areas;

[0099] The attribute classification module 200 is used to classify the attributes of the computer storage path through dynamic file name masquerading, obtain the storage attribute node corresponding to the storage policy on the computer storage path, and determine the storage tag popularity when performing a storage operation on the computer storage path based on the storage attribute node and the private isolation identifier;

[0100] A hierarchical verification module 300 is configured to obtain storage utility information on a data cache in a computer storage path, perform hierarchical verification on the storage utility information, obtain a storage trace index in the computer storage path, and further determine a retrieval response period during a computer data storage retrieval based on the storage trace index;

[0101] The synchronous storage module 400 is used to synchronously store computer data based on the storage tag popularity and the retrieval response cycle, and to adapt to the incremental data requirements of synchronous storage on the Internet of Things terminal in real time based on the elastic expansion strategy.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, magnetic disk storage, or magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0104] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A computer data storage method based on the Internet of Things, characterized in that: The data storage method comprises the following steps: Reading data to be stored when a computer executes a target business in an IoT terminal device, isolating and dividing the data to be stored, and obtaining private isolation identifiers corresponding to multiple isolated storage areas; Classifying the attributes of the computer storage path through dynamic file name camouflage to obtain a storage attribute node corresponding to the storage policy on the computer storage path, and determining the storage tag heat when performing a storage operation on the computer storage path based on the storage attribute node and the private isolation identifier; The computer storage path is attribute-classified by dynamic file name disguise, and the storage attribute nodes corresponding to the storage policy on the computer storage path are obtained, specifically including: Construct dynamic camouflage rules based on the file attribute characteristics of the computer storage path; Generating a file name identifier that matches a storage policy based on the dynamic camouflage rule; Attribute classification of the storage path according to metadata features of the file name identifier; Generate a storage attribute node corresponding to the storage policy on the computer storage path based on the classification result; The dynamic file name disguise means generating a semantic naming structure based on data access characteristics without changing the file content, thereby providing an implicit labeling function for data path classification; Acquiring storage utility information on a data cache area in a computer storage path, performing hierarchical verification on the storage utility information to obtain a storage trace index in the computer storage path, and then determining a retrieval response period during computer data storage retrieval based on the storage trace index; The step of performing hierarchical verification on the storage utility information to obtain a storage trace index in the computer storage path specifically includes: determining a hierarchical verification indicator for data storage based on the storage utility information; Determining the boundaries of hot and cold data in the storage trajectory according to the hierarchical verification indicator; Dynamically verify metadata features in computer storage paths and generate storage trajectory rules; Determine a storage track index in a computer storage path according to the hot and cold data boundary and the storage track rule; The hierarchical verification indicators are a set of behavioral parameters used to measure the value of data in storage. Computer data is synchronously stored according to the storage tag popularity and the retrieval response cycle, and incremental data requirements for synchronous storage on the Internet of Things terminal are adapted in real time based on an elastic expansion strategy.

2. A computer data storage method based on the Internet of Things according to claim 1, characterized in that: The data to be stored refers to data information units that are dynamically generated during the computer's execution of the target business and have clear business affiliation and storability.

3. The computer data storage method based on the Internet of Things according to claim 1, characterized in that: Isolating and dividing the data to be stored to obtain private isolation identifiers corresponding to multiple isolated storage areas specifically includes: Determining data classification dimensions according to access characteristics of the data to be stored; generating a private encrypted identifier matching each isolated storage area according to the data classification dimension; The data to be stored is dynamically allocated to the corresponding storage area through the private encryption identifier and an access relationship is established to obtain private isolation identifiers corresponding to multiple isolated storage areas.

4. The computer data storage method based on the Internet of Things according to claim 1, characterized in that: The private isolation identifier refers to a control unit that identifies the binding relationship between the isolated storage area and its belonging data.

5. The computer data storage method based on the Internet of Things according to claim 1, characterized in that: Determining the storage tag heat when performing a storage operation on a computer storage path according to the storage attribute node and the private isolation identifier specifically includes: Determining storage timing characteristics when performing a storage operation on a computer storage path according to the storage attribute node; Determining storage heat information when performing a storage operation on a computer storage path according to the private isolation identifier; The storage tag heat when performing a storage operation on a computer storage path is determined by using the storage timing characteristics and the storage heat information.

6. The computer data storage method based on the Internet of Things according to claim 1, characterized in that: The storage tag heat refers to the heat level label marked at the computer storage path level.

7. The computer data storage method based on the Internet of Things according to claim 1, characterized in that: The storage track index refers to a structured table entry that records the status, location, and activity of various types of data in the storage path.

8. The computer data storage method based on the Internet of Things according to claim 1, characterized in that: Determining the retrieval response period during computer data storage retrieval using the storage trajectory index specifically includes: Extracting search path features during computer data storage retrieval from the storage trajectory index; Determining a search attribute descriptor for computer data storage retrieval using the search path characteristics; The retrieval attribute descriptor determines the retrieval response period during computer data storage retrieval.

9. A computer data storage system based on the Internet of Things, used to execute the computer data storage method based on the Internet of Things according to any one of claims 1 to 8, characterized in that: The data storage system comprises: An isolation and partitioning module is used to read the data to be stored when the computer executes the target business in the Internet of Things terminal device, isolate and partition the data to be stored, and obtain private isolation identifiers corresponding to multiple isolated storage areas; an attribute classification module for classifying the attributes of the computer storage path through dynamic file name masquerading, obtaining a storage attribute node corresponding to the storage policy on the computer storage path, and determining the storage tag heat when performing a storage operation on the computer storage path based on the storage attribute node and the private isolation identifier; a hierarchical verification module, configured to obtain storage utility information on a data cache area in a computer storage path, perform hierarchical verification on the storage utility information, obtain a storage trace index in the computer storage path, and further determine a retrieval response period during computer data storage retrieval based on the storage trace index; The synchronous storage module is used to synchronously store computer data according to the storage tag popularity and the retrieval response cycle, and to adapt to the incremental data requirements of synchronous storage on the Internet of Things terminal in real time based on the elastic expansion strategy.

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