Mine digital asset storage management method

Abstract

The invention discloses a mine digital asset storage management method, which comprises the following steps of: extracting characteristics of mine data, dividing the mine data into hot data, warm data and cold data according to the characteristics, selecting different storage devices for the data, and dynamically adjusting migration of the data among the different storage devices based on access frequency and business requirements; future data storage requirements are predicted through a machine learning algorithm, and storage resources are scheduled; performing full life cycle management on the data, performing business value evaluation on the data, and adjusting storage priority and resource allocation based on the data value; carrying out full-life-cycle encryption on the data, and recording data operation through a block chain technology; energy consumption of the storage equipment is monitored in real time, and storage energy efficiency and carbon footprint management are adjusted. The problems of low storage efficiency, high storage cost and insufficient energy consumption optimization caused by data type diversity, access frequency difference and insufficient storage resource utilization in mine data management can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of mine data management, and in particular to a mine digital asset storage management method. Background Art

[0002] In the process of mine production and operation, with the improvement of informatization and automation, massive data are continuously generated. These data cover multiple fields such as mine equipment operation, environmental monitoring, production management, etc., and include real-time monitoring data, historical records, analysis reports and other forms. The effective management of these data is not only related to the production efficiency of the mine, but also involves cost control and safety assurance. However, existing mine data management methods face many challenges in dealing with these large-scale and diverse data.

[0003] First, the diversity of data types leads to different storage requirements. Data with high access frequency, such as real-time equipment monitoring data, requires higher access speed and performance support, while historical data and archived data have low access frequency and do not require high-performance storage devices, but occupy a large amount of storage space. Existing management methods often fail to distinguish between different data types, resulting in high-performance storage devices being occupied by data with low access frequency, resulting in serious waste of resources.

[0004] Secondly, as the size of mine data continues to grow, storage costs have risen sharply. Traditional static storage solutions lack flexibility and cannot dynamically adjust storage strategies based on real-time data needs. In addition, existing storage systems lack effective management of data lifecycles, and low-frequency access data cannot be migrated to low-cost storage devices in a timely manner, further exacerbating the waste of storage resources. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a mine digital asset storage management method, which can improve the problems of low storage efficiency, high storage cost and insufficient energy consumption optimization in mine data management caused by the diversity of data types, differences in access frequency and insufficient utilization of storage resources.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a method for storing and managing digital assets in a mine, comprising the following steps:

[0007] S1. Extracting the characteristics of mining data, including access frequency, business contribution, data size and real-time requirements;

[0008] S2. Classify the mine data into three categories: hot data, warm data and cold data according to the characteristics, and select different storage devices for each type of data, wherein the hot data is stored in a high-performance storage device, the warm data is stored in a hybrid storage device, and the cold data is stored in a low-cost storage device;

[0009] S3: Dynamically adjust data migration between different storage devices based on access frequency and business needs;

[0010] S4, predict future data storage needs through machine learning algorithms and schedule storage resources;

[0011] S5. Manage the entire life cycle of data, which includes the creation, activation, archiving and destruction stages of data;

[0012] S6. Evaluate the business value of data based on access frequency, business contribution, and real-time requirements, and adjust storage priority and resource allocation based on data value;

[0013] S7. Encrypt data throughout its life cycle and record data operations through blockchain technology;

[0014] S8. Monitor the energy consumption of storage devices in real time and adjust storage energy efficiency and carbon footprint management.

[0015] Furthermore, the data storage device level uses the formula OK, A i (t) represents the access frequency of the i-th data, S j represents the performance of the jth storage device, η i represents the business contribution weight of the i-th data, C j represents the storage cost of the jth storage device, E j represents the device energy consumption of the jth storage device, λ is the energy efficiency optimization weight, N represents the total number of data, and t represents the time.

[0016] Furthermore, the decision rule for data migration between different storage devices is:

[0017]

[0018] Among them, A i (t) represents the access frequency of data, A max Indicates the upper threshold of access frequency, A threshold Indicates the lower threshold of access frequency, D i (t+1) represents the classification of the i-th data at time t+1, HotData represents hot data, WarmData represents warm data, and ColdData represents cold data.

[0019] Furthermore, step S4 specifically includes:

[0020] S4.1. Historical data analysis: Use historical access records to build a time series dataset of access frequency;

[0021] S4.2, LSTM neural network training: Train the LSTM neural network based on the time series data set of access frequency to learn the long-term dependency of data access;

[0022] S4.3, access demand prediction: input the data access history for a period of time, and output the predicted value of the data access frequency in the next k steps;

[0023] S4.4, Resource demand prediction: Based on the predicted access demand, calculate the resource usage of different storage devices in the future, and pre-allocate the corresponding bandwidth, IO resources and storage space among different storage devices;

[0024] S4.5, Dynamic Scheduling: Based on the predicted results of resource demand, storage resources are automatically adjusted through dynamic programming algorithms.

[0025] Furthermore, the calculation formula for the predicted value of data access frequency in step S4.3 is:

[0026] in, represents the predicted value of the data access frequency of the i-th data at the future time t+k, and n represents the length of the input historical data;

[0027] The calculation formula of the dynamic programming algorithm in step S4.5 is:

[0028] Among them, R j represents the available storage resources of the jth storage device, C j represents the storage cost of the jth storage device, E j represents the energy consumption of the jth storage device, λ represents the energy efficiency optimization coefficient, which is used to control the weight of energy consumption in the optimization; M represents the number of storage devices, and t represents the time.

[0029] Furthermore, step S5 specifically includes:

[0030] S5.1, Data creation stage: After the new data is generated, it is immediately stored in a high-performance storage device according to the real-time requirements and initial business contribution;

[0031] S5.2, Data Active Phase: Real-time monitoring of data access and business demand changes. If the data access frequency is greater than the threshold A max And the real-time requirement is high, so the data is kept in a high-performance storage device;

[0032] S5.3, Data archiving stage: When the access frequency of data is lower than the threshold A max , and when the business contribution of the data decreases, the system will automatically migrate the data to a low-cost storage device;

[0033] S5.4, Data destruction phase: When the data storage time exceeds the set life cycle or there is no further business demand, the data is securely destroyed.

[0034] Furthermore, step S6 specifically includes:

[0035] S6.1 Data evaluation model construction: Build a data value evaluation model based on access frequency, business contribution, real-time requirements and data size:

[0036] S6.2. Priority adjustment rules: adjust the priority of data storage based on the data value assessment results; S6.3. Resource allocation adjustment: allocate high-value data to high-performance storage devices and low-value data to hybrid storage devices or low-cost storage devices based on the priority of data storage.

[0037] Furthermore, the data value assessment model is: V(D i )=α·A i (t)+β·V i +γ·R i +δ·S i , where α, β, γ, and δ represent weight parameters, V(D i ) represents data D i The business value of A i (t) represents the access frequency of the i-th data, R i represents the real-time requirement of the i-th data, S i Indicates the data size of the i-th data, V i Indicates the business contribution of the i-th data.

[0038] Furthermore, the resource allocation rule in step S6.3 is: Among them, P(D i ) represents data D i The priority of V(D i ) represents data D i The business value of V high Indicates the upper threshold of data priority, V low Indicates the lower threshold of data priority.

[0039] Furthermore, in step S8, the energy consumption calculation formula of the storage device is: E j (t) = P j ·U j (t)+μ·T j , where P j Represents the power consumption of the storage device, U j (t) represents the usage rate of storage devices, T jrepresents the temperature adjustment factor, and μ represents the energy efficiency optimization coefficient.

[0040] The beneficial effects of the present invention are:

[0041] (1) The present invention realizes differentiated management of hot data, warm data and cold data through intelligent data classification and hierarchical storage strategy. Data is automatically allocated to appropriate storage levels according to access frequency, business contribution and real-time requirements. Data with high access frequency is stored in high-performance devices, while data with low access frequency is migrated to low-cost devices. This improves the response speed of the system and reduces unnecessary high-performance storage resource occupation, thereby greatly improving the efficiency and flexibility of the storage system.

[0042] (2) Through full life cycle data management and dynamic migration strategies, the present invention can dynamically adjust the allocation of data between different storage levels according to changes in data access frequency and business needs. The system can automatically archive data with low access frequency to low-cost storage devices, reduce the occupancy of high-performance storage resources, ensure data accessibility, and significantly reduce storage costs. In particular, it saves investment in storage infrastructure when processing large-scale data.

[0043] (3) The present invention reduces the energy consumption of the storage system by real-time monitoring of the energy consumption of the storage device and dynamically adjusting the working state of the device according to the device load. For storage devices that are in a low-load state for a long time, the system further reduces unnecessary energy consumption by enabling low-power or sleep mode. In addition, through carbon footprint calculation and management, storage devices with high energy efficiency and low carbon emissions are given priority, effectively reducing the carbon emissions of the entire storage system and achieving green and environmentally friendly storage management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0045] Figure 1 It is a flow chart of the mine digital asset storage management method of the present invention. DETAILED DESCRIPTION

[0046] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0047] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0048] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] As shown in the figure, the mine digital asset storage management method of the present invention includes the following steps:

[0050] S1. Extract the characteristics of mining data, including access frequency, business contribution, data size and real-time requirements.

[0051] It should be noted that a large amount of data will be generated during the production, monitoring and management of mines, including equipment sensor data, real-time production data, historical operation and maintenance records, environmental monitoring data, etc. In order to effectively manage these data, the key features of the data are first extracted. i (t): records the access frequency of each data set at different time points, which is used to measure the usage frequency of the data. Business Contribution V i :Score the data based on its actual impact on mine production and management. Data with high business contribution is often more important for decision support or key equipment monitoring. Data size S i : Record the physical size of the data in order to reasonably allocate storage space. Real-time requirements R i :Define different real-time requirements according to the time sensitivity of the data. The above features are represented by the feature vector X i ={A i (t),V i ,S i ,Ri The feature extraction process is automatically completed based on the existing data acquisition module and feature analysis module.

[0052] S2. According to the characteristics, the mining data is divided into three categories: hot data, warm data and cold data, and different storage devices are selected for each type of data. Hot data is stored in high-performance storage devices, warm data is stored in hybrid storage devices, and cold data is stored in low-cost storage devices.

[0053] It should be noted that the system classifies different types of data based on the extracted data features (including access frequency, business contribution, data size, and real-time requirements), and selects the appropriate storage tier based on the classification results. The classified data is stored in the corresponding devices to ensure that the utilization efficiency of storage resources is optimized while meeting business needs. The division of storage tiers and the allocation of storage resources are conducive to ensuring a balance between high performance and low cost.

[0054] Data classification includes: according to the data characteristics of access frequency, business contribution, data size, and real-time requirements, the data is divided into three categories: hot data, warm data, and cold data using the k-means clustering algorithm or the threshold-based method. Among them, hot data refers to data with high access frequency, large business contribution, and high real-time requirements. The characteristics of hot data are that they usually have an important impact on real-time decision-making and equipment monitoring in mining production. Warm data refers to data with medium access frequency and business contribution. Warm data may be regularly used business analysis data or equipment operation and maintenance data. Cold data refers to data with low access frequency, historical or archived data. Cold data is more of an archived record that needs to be retained but is used less frequently.

[0055] Selection of storage devices: The classified data is stored in the corresponding storage devices, and different levels of storage systems are selected according to the characteristics of the data. For example, hot data is stored in high-performance storage devices (such as solid-state drives NVMeSSD) to ensure fast access to data and low latency. High-performance storage devices have extremely high I / O performance and are suitable for data with high access frequency and high real-time requirements. Warm data is stored in hybrid storage devices (such as a storage system that combines SSD and HDD). The system strikes a balance between performance and cost and is suitable for data with medium access frequency. Cold data is stored in low-cost storage devices (such as cloud cold storage or tape storage). This type of storage device has extremely low cost and is suitable for long-term archival data preservation.

[0056] Optimization of storage resource allocation:

[0057] The data storage device level uses the formula OK, A i (t) represents the access frequency of the i-th data, S jrepresents the performance of the jth storage device, usually measured by the I / O speed and capacity of the device; η i Represents the weight of business contribution. The higher the business contribution of data, the higher the i The bigger; C j represents the storage cost of the jth storage device, E j represents the device energy consumption of the jth storage device, λ is the energy efficiency optimization weight, which is used to weigh the balance between storage performance and energy consumption, t represents the time, and N represents the data size. This formula ensures that data with high access frequency and high business contribution is stored on storage devices with higher performance, while data with low access frequency and low business contribution is stored on devices with lower cost. This strategy achieves a reasonable balance between performance, storage cost, and energy consumption.

[0058] This embodiment also dynamically adjusts the allocation of data in storage devices by continuously monitoring the access frequency of data and business needs. Specifically, the system periodically evaluates the usage of storage resources and updates the storage policy based on the changes in data access to adapt to changes in business needs. The dynamic adjustment process includes regularly detecting the performance and load of storage devices and reallocating resources based on predicted future access needs. This mechanism ensures that even when data access patterns change, the system can adjust storage allocation in a timely manner to maintain efficient storage management.

[0059] S3: Dynamically adjust data migration between different storage devices based on access frequency and business needs.

[0060] It should be noted that the decision rules for data migration between different storage devices are:

[0061]

[0062] Among them, A i (t) represents the access frequency of the i-th data, A max Indicates the upper threshold of access frequency, A threshold Indicates the lower threshold of access frequency, D i (t+1) represents the classification of the i-th data at time t+1, HotData represents hot data, WarmData represents warm data, and ColdData represents cold data.

[0063] In order to ensure efficient use of data storage resources, the system needs to dynamically adjust the migration of data between different storage devices based on real-time data access and business needs. By monitoring the frequency of data access and changes in its business importance, the system can migrate high-frequency data from low-performance devices to high-performance devices, and migrate low-frequency data from high-performance devices to low-cost devices, thereby achieving optimal resource allocation.

[0064] Monitoring of data access frequency and business needs:

[0065] In this embodiment, the system monitors the access frequency of data and its changing trend in real time. The access frequency of each data set is counted within a specific time interval and stored in the access log. The change in access frequency will serve as the basis for the system to dynamically adjust the storage level. At the same time, the system also evaluates the business needs of each data in real time. The business needs are scored based on the impact of the data on mine production decisions, equipment management, and production scheduling. Changes in business needs will also directly affect the data migration strategy between different storage devices.

[0066] In the decision rule for migration, A max Indicates the upper threshold of access frequency. Data above this value will be classified as hot data and migrated to the high-performance storage layer. threshold Indicates the lower threshold of access frequency, that is, the threshold for data migration from the warm storage layer to the cold storage layer. Data below this value will be stored in the cold storage layer. threshold i (t)≤A max When the data is in a state of being accessed, it is divided into warm data and stored in medium-performance storage devices. This dynamic migration mechanism based on access frequency ensures that high-frequency access data can be migrated to high-performance devices in a timely manner, improving the overall response speed of the system while reducing the situation where low-frequency data occupies high-performance storage resources.

[0067] Execution of dynamic migration:

[0068] In this embodiment, the system periodically performs data migration operations. Specifically, the system sets a periodic scheduling task to re-evaluate the access frequency and business needs of all data sets at fixed time intervals. According to the above decision rules, the system moves the data that needs to be migrated between different storage layers. Migrate from hot data to warm data layer: When the access frequency of a hot data decreases and the business demand decreases below the set threshold, the system will automatically migrate the data from the high-performance storage device to the warm storage device. This reduces the occupation of high-performance storage resources and frees up space for other high-priority data. Migrate from warm data to cold data layer: If the access frequency of a certain data decreases further and is no longer frequently accessed for a long time, the system will archive it to the cold storage device. Cold storage devices are low in cost and are suitable for storing historical data and infrequently accessed data. Migrate from cold data to warm / hot data layer: When the data in the cold storage device is suddenly frequently accessed or the business demand increases, the system will migrate the data back to the warm storage layer or hot storage layer to ensure fast access to the data.

[0069] Load balancing in dynamic migration: ​

[0070] During the data migration process, the system also needs to balance the load of the storage device. In this embodiment, the system monitors the current load status of each storage device (including storage capacity, IO load, bandwidth usage, etc.), and gives priority to devices with lower loads for storage when migrating data. This strategy can effectively avoid the overload problem of storage devices and ensure that the overall performance of the storage system is not affected during the data migration process.

[0071] Logging of data migration process:

[0072] In order to ensure the traceability of data operations, each data migration operation in this embodiment is recorded in detail in a log file. The log content includes: i The log records provide important basis for subsequent system performance analysis and troubleshooting.

[0073] S4. Predict future data storage needs through machine learning algorithms and schedule storage resources.

[0074] Step S4 specifically includes:

[0075] S4.1. Historical data analysis: Use historical access records to build a time series dataset of access frequency.

[0076] S4.2. LSTM neural network training: Train the LSTM neural network based on the time series dataset of access frequency to learn the long-term dependency of data access.

[0077] S4.3, access demand prediction: input the data access history for a period of time and output the predicted value of the data access frequency in the next k steps.

[0078] in, It represents the predicted value of the data access frequency at the future time t+k, i represents the data sequence, t represents the time, and n represents the length of the input historical data.

[0079] S4.4. Resource demand prediction: Based on the predicted access demand, calculate the resource usage of different storage devices in the future, and pre-allocate the corresponding bandwidth, IO resources and storage space among different storage devices.

[0080] S4.5, Dynamic Scheduling: Based on the predicted results of resource demand, storage resources are automatically adjusted through dynamic programming algorithms.

[0081] The calculation formula of the dynamic programming algorithm is: Among them, Rj represents the available storage resources of the jth storage device, C j represents the storage cost of the jth storage device, E j represents the energy consumption of the jth storage device, λ represents the energy efficiency optimization coefficient, which is used to control the weight of energy consumption in the optimization; M represents the number of storage devices, and t represents the time.

[0082] In this embodiment, it is first necessary to analyze the access history of the data and collect data D i The access frequency A at time t i (t), and organize it into a time series data set. The time series of historical data can reflect the changing laws of data access patterns, and these patterns provide basic input for subsequent prediction models. Data access records are collected regularly, and the data access frequency recorded in each time interval will be stored in the system database. The length n of the time series depends on the availability of historical data and the requirements of model training. In order to effectively predict future access needs of data, an LSTM neural network is used in this embodiment. The LSTM neural network can process time series data and is suitable for data access patterns with long-term dependencies. The input of the LSTM neural network is the time series A of data access i (t),A i (t-1),…,A i (tn), which is the data access frequency in the past n time steps. The output is the predicted value of data access in the next k time steps

[0083] In this way, the system can generate future access prediction data within a period of time, which provides a basis for the advance scheduling of storage resources. After completing the training of the LSTM neural network, the system generates a forecast value for data access demand in the next k time steps by inputting new historical access data into the model. The prediction result represents the access frequency of each data set in the next k time steps. Through this prediction result, the system can determine which data will be frequently accessed in the future and which data may have a lower access frequency. Based on these predictions, the system can allocate more storage resources for high-frequency access data in advance, or migrate low-frequency data to low-cost storage devices to optimize the overall utilization of storage resources.

[0084] Based on the future data access requirements predicted by the LSTM neural network, the system calculates the resource usage of different storage tiers in the future. The system calculates the resource requirements of each type of data on each storage device in the future, especially the storage capacity and IO load requirements, based on the predicted access requirements. For data with a higher access frequency, the system will pre-allocate more bandwidth, IO resources, and storage space to ensure data access performance under high load conditions in the future.

[0085] The steps for storage resource demand forecasting are as follows:

[0086] For each data D i Predicted visit frequency Accumulate and obtain the total access demand in the future time period. Evaluate the available storage resources of each type of storage device (hot storage, warm storage, cold storage) to determine whether the storage resource allocation needs to be adjusted. In order to further improve the utilization efficiency of storage resources, this embodiment uses a dynamic programming algorithm to schedule and optimize storage resources. In the process of storage resource allocation, the system will take into account factors such as the performance, cost, and energy consumption of the storage device, and through the optimization algorithm, the storage resource allocation can achieve the lowest storage cost and energy consumption while meeting future access needs.

[0087] Through dynamic programming algorithms, the system can allocate storage resources in the best way under future load conditions, ensuring that data with high access demand is fully supported by resources while reducing the resource usage of data with low access demand. In this way, while ensuring performance, it also achieves cost optimization and energy consumption management of the storage system.

[0088] When the system has completed resource demand prediction and scheduling optimization, it will perform dynamic scheduling based on the prediction results. Specific steps include: adjusting the storage level of high-frequency access data and migrating it to high-performance storage devices; reserving sufficient bandwidth, storage space and IO resources for future high-load storage devices; migrating low-frequency access data to low-cost storage devices to reduce the load of high-performance storage; real-time monitoring of the load of storage devices to ensure the effectiveness of prediction and scheduling results.

[0089] S5. Manage data throughout its entire life cycle, which includes the data creation, activation, archiving, and destruction stages.

[0090] Step S5 specifically includes:

[0091] S5.1, Data creation phase: After new data is generated, it is immediately stored in a high-performance storage device based on real-time requirements and initial business contribution.

[0092] S5.2, Data Active Phase: Real-time monitoring of data access and business demand changes. If the data access frequency is greater than the threshold A max And the real-time requirement is high, so the data is kept in a high-performance storage device.

[0093] S5.3, Data archiving stage: When the access frequency of data is lower than the threshold A max , and when the business contribution of the data decreases, the system will automatically migrate the data to a low-cost storage device.

[0094] S5.4, Data destruction phase: When the data storage time exceeds the set life cycle or there is no further business demand, the data is securely destroyed.

[0095] During the data creation phase, a large amount of real-time data is generated during mine production and equipment operation and maintenance. The creation of these new data is the beginning of the life cycle. At this stage, the system stores the data in high-performance or medium-performance storage devices based on the real-time requirements and initial business contribution of the data. High real-time data: such as real-time monitoring data generated by mining equipment sensors, these data usually require immediate access, so they are stored in the hot data layer, that is, high-performance storage devices. Medium real-time data: such as regularly generated report data, the access frequency is slightly lower, but it still has certain real-time requirements, so it is stored in the warm data layer, that is, hybrid storage devices. Data storage decisions at this stage are mainly based on the initial business needs and access frequency of the data.

[0096] When the data enters the active stage, the access frequency and business needs of the data usually reach a high level, which is manifested as high-frequency access and continuous use. In this embodiment, the system monitors the access status and business needs of the data in real time, and dynamically adjusts the storage strategy according to these changes. Data access frequency monitoring: The system monitors the access frequency of each data in real time through log records, and calculates the changing trend of the access frequency. Data with high access frequency and high real-time requirements will be continuously stored in high-performance storage devices to ensure efficient access to the data. Business demand change monitoring: During the data active stage, the system will also evaluate the business contribution of the data, especially the impact on key businesses such as mine production scheduling, equipment management, and environmental monitoring. If business needs change, the system will automatically adjust the storage level of the data to ensure that data of high-priority businesses is stored on appropriate devices. The dynamic adjustment strategy at this stage ensures a balance between performance and resources through continuous monitoring and intelligent decision-making.

[0097] When the access frequency of data drops to threshold A threshold When the access frequency of a data is lower than the threshold value A, the system will identify the data as low-frequency access data and enter the archiving stage. In this embodiment, the system will automatically migrate the low-frequency access data to low-cost storage devices to release high-performance storage resources. Migration condition: When the access frequency of a data is continuously lower than the threshold value A threshold When the business contribution decreases, the system will trigger data migration. Migration process: Data will be migrated from high-performance or medium-performance storage layers to cold storage layers, such as cloud cold storage or tape storage devices. Such devices have low storage costs and are suitable for storing archived data. Archiving operations can effectively reduce the storage cost of the system and ensure that high-performance devices are not occupied by data with low access frequency.

[0098] When the data in the storage system exceeds the set life cycle limit T max , or when the data is no longer needed in the business system, the data will enter the destruction phase. In this embodiment, the system will perform a secure destruction operation on the data to ensure that the data is completely cleared and record the destruction process. Lifecycle expiration: Each data set has a maximum storage period. When the data reaches this period, the system will automatically perform a destruction operation. Destruction process: Data destruction follows a secure destruction process to ensure that deleted data cannot be recovered. The destruction operation will generate corresponding log records, including the data destruction time, destruction method, and related operation information for subsequent tracing. Through a strict destruction process, the system can prevent invalid data from occupying storage resources for a long time and ensure data compliance and security.

[0099] Optimization strategy for data lifecycle management:

[0100] In this embodiment, the formula To dynamically manage the different life cycle stages of data and ensure efficient use of storage resources. Create represents the creation stage, Active represents the active stage, Archived represents the archive stage, and Deleted represents the destruction stage. Through this life cycle management model, the system can dynamically identify the current state of data and perform corresponding storage management and operation adjustments.

[0101] S6. Evaluate the business value of data based on access frequency, business contribution, and real-time requirements, and adjust storage priority and resource allocation based on data value.

[0102] Step S6 specifically includes:

[0103] S6.1 Data evaluation model construction: Build a data value evaluation model based on access frequency, business contribution, real-time requirements and data size.

[0104] The data value assessment model is: V(D i )=α·A i (t)+β·V i +γ·R i +δ·S i , where α, β, γ, and δ represent weight parameters, A i (t) represents the access frequency of the i-th data, R i represents the real-time requirement of the i-th data, S i Indicates the data size of the i-th data, V iIndicates the business contribution of the i-th data. The weight coefficient can be adjusted according to the specific business needs of the mine to reflect the importance of each parameter in different scenarios. For example, real-time monitoring data may have higher requirements for real-time performance, while historical analysis data focuses more on business contribution.

[0105] S6.2. Priority adjustment rules: adjust the data storage priority according to the data value assessment results. S6.3. Resource allocation adjustment: allocate high-value data to high-performance storage devices and low-value data to hybrid storage devices or low-cost storage devices according to the data storage priority.

[0106] The rules for resource allocation are: Among them, P(D i ) represents data D i The priority of V(D i ) represents data D i The business value of V high Indicates the upper threshold of data priority, V low Indicates the lower threshold of data priority.

[0107] Hot data (high priority): business value V(D i ) is higher than the threshold V high The data, which is usually of great significance in mine production, is stored in high-performance storage devices to ensure fast access.

[0108] Warm data (medium priority): Business value V(D i ) is at V low and V high This type of data is stored in medium-performance devices to meet their general access needs.

[0109] Cold data (low priority): Business value V(D i ) is lower than V low Data, usually historical or archived, is stored in low-cost devices.

[0110] After the data priority is divided, the system dynamically adjusts the allocation of storage resources according to the business value and priority of the data. The specific steps are as follows:

[0111] High-priority data (hot data): This data will be preferentially allocated to high-performance storage devices, such as NVMe SSD, to ensure efficient data read and write performance and meet its high access frequency and real-time requirements.

[0112] Medium-priority data (warm data): This type of data is allocated to hybrid storage devices, such as SSD+HDD combined storage systems. Hybrid storage can balance storage performance and cost, and is suitable for data with moderate access frequency and real-time requirements.

[0113] Low-priority data (cold data): For low-priority data, the system will migrate it to low-cost storage devices, such as cloud cold storage or tape storage. Such devices are low-cost and large-capacity, suitable for long-term storage of infrequently accessed data.

[0114] Through the above resource allocation adjustment, this embodiment can optimize the use of storage devices and improve the overall efficiency of the system. The system will also regularly re-evaluate the business value of data according to changes in data access patterns and reallocate storage resources accordingly.

[0115] S7. Encrypt data throughout its entire life cycle and record data operations through blockchain technology.

[0116] S8. Monitor the energy consumption of storage devices in real time and adjust storage energy efficiency and carbon footprint management.

[0117] The energy consumption calculation formula of storage device is: E j (t) = P j ·U j (t)+μ·T j , where P j Represents the power consumption of the storage device, U j (t) represents the usage rate of storage devices, T j represents the temperature adjustment factor, and μ represents the energy efficiency optimization coefficient. During operation, the system monitors the energy consumption status of each storage device in real time, including the power consumption P of the device. j and the current usage rate U j (t). Energy consumption data is collected in real time through the built-in sensor module, and the power consumption changes of each storage node are recorded. Through this formula, the system can evaluate the energy consumption level of different devices in real time according to their usage status, and provide data support for subsequent energy efficiency optimization. Based on real-time energy consumption monitoring data, in this embodiment, the system adopts a dynamic energy efficiency optimization strategy to adjust the working status of the device according to the usage rate and current load of the storage device. The main goal of dynamic energy efficiency optimization is to minimize the energy consumption of the device while ensuring data storage and access performance.

[0118] Load distribution adjustment: When the usage rate of a storage device j is lower than the set load threshold, the system will migrate data to other devices by adjusting resource allocation, thereby reducing the operating frequency of the device or putting it into low power mode.

[0119] Device sleep strategy: For storage devices that are in a low-load state for a long time, the system in this embodiment will set them to sleep mode to suspend unnecessary energy-consuming operations. When the device is in sleep mode, energy consumption is reduced to a minimum, and only the basic operating environment is maintained. The sleep strategy can significantly reduce the total energy consumption of the system, especially for devices that store cold data, where the sleep mode is frequently used.

[0120] Dynamic adjustment of device status: The system will regularly check the device's usage status and energy consumption level, and dynamically adjust the device's power consumption status (such as high-performance mode, low-power mode, or sleep mode). Through the automated energy efficiency optimization mechanism, the system can adjust the device's working status in a timely manner to ensure that energy consumption is effectively controlled when data access demand decreases.

[0121] In order to achieve carbon emission management of the storage system, the system in this embodiment calculates the carbon footprint of each storage device through real-time energy consumption data. The carbon footprint calculation is based on the conversion of device energy consumption into carbon emissions using the following formula: C carbon Represents the total carbon emissions of the system, E j (t) represents the energy consumption of the jth storage device, δ j It represents the carbon emission coefficient of the jth storage device, which is usually determined according to the energy consumption characteristics of the device and the source of electricity.

[0122] This embodiment calculates the total carbon emissions of the storage system, generates carbon footprint reports regularly, and adjusts the overall energy efficiency strategy of the system according to the carbon emissions. For storage devices with higher carbon emissions, the system will give priority to reducing their usage rate or migrating their loads to devices with higher energy efficiency, thereby reducing carbon emissions.

[0123] In order to further reduce the carbon footprint, the system in this embodiment gives priority to green storage devices, that is, storage hardware with low power consumption design and using renewable energy power. When selecting storage devices, the system will comprehensively consider the carbon emission coefficient of the device and its energy efficiency ratio, and give priority to devices that meet environmental protection requirements.

[0124] Low-carbon device allocation strategy: For data with a high access frequency, the system gives priority to green storage devices for allocation to reduce the carbon emissions generated by the frequent reading and writing of such data.

[0125] Cold storage device management: For cold data with low access frequency, the system will migrate it to cold storage devices with environmentally friendly and energy-saving designs (such as data centers powered by renewable energy or low-carbon storage devices), further reducing the environmental burden of the storage system.

[0126] Long-term optimization of energy efficiency and carbon footprint:

[0127] The dynamic energy efficiency optimization and carbon footprint management in this embodiment are not limited to short-term adjustments. The system also regularly evaluates the long-term energy consumption trends of storage devices, analyzes energy consumption and carbon emission changes through historical data, and continuously optimizes the energy efficiency strategy of the storage system.

[0128] Historical energy consumption analysis: The system records the energy consumption data of each device, analyzes the changes in the power consumption of the device at different times, and predicts future energy consumption trends based on this data. In this way, the system can adjust the storage strategy in advance to prevent energy consumption peaks.

[0129] Carbon emission assessment and improvement: Based on the carbon footprint report, the system gradually replaces high-energy consumption equipment and gives priority to green equipment in future hardware procurement to ensure that the mine digital asset management system can maintain a low carbon emission level in the long term.

[0130] In summary, the present invention has the following advantages:

[0131] (1) The present invention realizes differentiated management of hot data, warm data and cold data through intelligent data classification and hierarchical storage strategy. Data is automatically allocated to appropriate storage levels according to access frequency, business contribution and real-time requirements. Data with high access frequency is stored in high-performance devices, while data with low access frequency is migrated to low-cost devices. This improves the response speed of the system and reduces unnecessary high-performance storage resource occupation, thereby greatly improving the efficiency and flexibility of the storage system.

[0132] (2) Through full life cycle data management and dynamic migration strategies, the present invention can dynamically adjust the allocation of data between different storage levels according to changes in data access frequency and business needs. The system can automatically archive data with low access frequency to low-cost storage devices, reduce the occupancy of high-performance storage resources, ensure data accessibility, and significantly reduce storage costs. In particular, it saves investment in storage infrastructure when processing large-scale data.

[0133] (3) The present invention reduces the energy consumption of the storage system by real-time monitoring of the energy consumption of the storage device and dynamically adjusting the working state of the device according to the device load. For storage devices that are in a low-load state for a long time, the system further reduces unnecessary energy consumption by enabling low-power or sleep mode. In addition, through carbon footprint calculation and management, storage devices with high energy efficiency and low carbon emissions are given priority, effectively reducing the carbon emissions of the entire storage system and achieving green and environmentally friendly storage management.

[0134] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for storing and managing digital assets in a mine, characterized in that: The following steps are involved: S1. Extracting the characteristics of mining data, including access frequency, business contribution, data size and real-time requirements; S2. Classify the mine data into three categories: hot data, warm data and cold data according to the characteristics, and select different storage devices for each type of data, wherein the hot data is stored in a high-performance storage device, the warm data is stored in a hybrid storage device, and the cold data is stored in a low-cost storage device; S3: Dynamically adjust data migration between different storage devices based on access frequency and business needs; S4, predict future data storage needs through machine learning algorithms and schedule storage resources; S5. Manage the entire life cycle of data, which includes the creation, activation, archiving and destruction stages of data; S6. Evaluate the business value of data based on access frequency, business contribution, and real-time requirements, and adjust storage priority and resource allocation based on data value; S7. Encrypt data throughout its life cycle and record data operations through blockchain technology; S8. Monitor the energy consumption of storage devices in real time and adjust storage energy efficiency and carbon footprint management.

2. The method for storing and managing digital assets in a mine according to claim 1, characterized in that: The data storage device level uses the formula OK, A i (t) represents the access frequency of the i-th data, S j represents the performance of the jth storage device, η i represents the business contribution weight of the i-th data, C j represents the storage cost of the jth storage device, E j represents the device energy consumption of the jth storage device, λ is the energy efficiency optimization weight, N represents the total number of data, and t represents the time.

3. The method for storing and managing digital assets in a mine according to claim 1, characterized in that: The decision rules for data migration between different storage devices are: Among them, A i (t) represents the access frequency of data, A max Indicates the upper threshold of access frequency, A threshold Indicates the lower threshold of access frequency, D i (t+1) represents the classification of the i-th data at time t+1, HotData represents hot data, WarmData represents warm data, and ColdData represents cold data.

4. The method for storing and managing digital assets in a mine according to claim 1, characterized in that: Step S4 specifically includes: S4.

1. Historical data analysis: Use historical access records to build a time series dataset of access frequency; S4.2, LSTM neural network training: Train the LSTM neural network based on the time series data set of access frequency to learn the long-term dependency of data access; S4.3, access demand prediction: input the data access history for a period of time, and output the predicted value of the data access frequency in the next k steps; S4.4, Resource demand prediction: Based on the predicted access demand, calculate the resource usage of different storage devices in the future, and pre-allocate the corresponding bandwidth, IO resources and storage space among different storage devices; S4.5, Dynamic Scheduling: Based on the predicted results of resource demand, storage resources are automatically adjusted through dynamic programming algorithms.

5. The method for storing and managing digital assets in a mine according to claim 4, characterized in that: The calculation formula for the predicted value of data access frequency in step S4.3 is: in, represents the predicted value of the data access frequency of the i-th data at the future time t+k, and n represents the length of the input historical data; The calculation formula of the dynamic programming algorithm in step S4.5 is: Among them, R j represents the available storage resources of the jth storage device, C j represents the storage cost of the jth storage device, E j represents the energy consumption of the jth storage device, λ represents the energy efficiency optimization coefficient, which is used to control the weight of energy consumption in the optimization; M represents the number of storage devices, and t represents the time.

6. The method for storing and managing digital assets in a mine according to claim 1, characterized in that: Step S5 specifically includes: S5.1, Data creation stage: After the new data is generated, it is immediately stored in a high-performance storage device according to the real-time requirements and initial business contribution; S5.2, Data Active Phase: Real-time monitoring of data access and business demand changes. If the data access frequency is greater than the threshold A max And the real-time requirement is high, so the data is kept in a high-performance storage device; S5.3, Data archiving stage: When the access frequency of data is lower than the threshold A max , and when the business contribution of the data decreases, the system will automatically migrate the data to a low-cost storage device; S5.4, Data destruction phase: When the data storage time exceeds the set life cycle or there is no further business demand, the data is securely destroyed.

7. The method for storing and managing digital assets in a mine according to claim 1, characterized in that: Step S6 specifically includes: S6.1 Data evaluation model construction: Build a data value evaluation model based on access frequency, business contribution, real-time requirements and data size: S6.

2. Priority adjustment rules: adjust the priority of data storage based on the data value assessment results; S6.

3. Resource allocation adjustment: allocate high-value data to high-performance storage devices and low-value data to hybrid storage devices or low-cost storage devices based on the priority of data storage.

8. The method for storing and managing digital assets in a mine according to claim 7, characterized in that: The data value assessment model is: V(D i )=α·A i (t)+β·V i +γ·R i +δ·S i , where α, β, γ, and δ represent weight parameters, V(D i ) represents data D i The business value of A i (t) represents the access frequency of the i-th data, R i represents the real-time requirement of the i-th data, S i Indicates the data size of the i-th data, V i Indicates the business contribution of the i-th data.

9. The method for storing and managing digital assets in a mine according to claim 7, characterized in that: The rule for resource allocation in step S6.3 is: Among them, P(D i ) represents data D i The priority of V(D i ) represents data D i The business value of V high Indicates the upper threshold of data priority, V low Indicates the lower threshold of data priority.

10. The method for storing and managing digital assets in a mine according to claim 1, characterized in that: In step S8, the energy consumption of the storage device is calculated as follows: j (t) = P j ·U j (t)+μ·T j , where P j Represents the power consumption of the storage device, U j (t) represents the utilization rate of storage devices, T j represents the temperature adjustment factor, and μ represents the energy efficiency optimization coefficient.

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