Blockchain-based data resource management method, device and equipment, and storage medium

Abstract

Embodiments of the present application disclose a kind of data resource management method, device and equipment based on blockchain and storage medium, and specifically disclose: the use amount of multiple resources by node in blockchain network is monitored;Based on historical resource usage data, and the real-time usage amount monitored, dynamic simulation model is constructed;Dynamic simulation model is used to predict the demand amount of resource by node in future time period;Historical resource usage data is the use amount of resource by node monitored in history;Using machine learning algorithm, based on the resource demand amount predicted by dynamic simulation model, the real-time resource load data of node and the preset performance index, iteratively generate resource allocation decision;According to resource allocation decision, corresponding resource is allocated for node, and the allocation record of resource allocation decision is stored in the distributed ledger of blockchain network;Wherein, allocation record includes resource allocation content, resource allocation decision time and the identification information of node involved in resource allocation.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and in particular to a blockchain-based data resource management method, apparatus, device, and storage medium. Background Technology

[0002] Currently, data resource management based on blockchain typically relies on cloud service provider infrastructure solutions and general blockchain node scheduling strategies. The cloud service provider infrastructure solution involves the provider offering basic resources such as computing, storage, and network. Enterprise users do not need to manage complex hardware and software themselves and can meet their basic data management needs by applying for and using blockchain services. The general blockchain node scheduling strategy involves using a pre-defined, general-purpose resource scheduling algorithm to monitor and statistically analyze the CPU, memory, and other resource usage of each node in the blockchain network to achieve basic resource allocation.

[0003] However, both solutions have significant technical problems: cloud service provider infrastructure solutions mainly complete the fixed allocation of data resources, which is difficult to cope with the elastic changes in business needs. They generally lack the ability to securely control the resource scheduling process and trace data, and cannot guarantee the traceability of data resources throughout their entire lifecycle. On the other hand, general blockchain node scheduling strategies do not consider the dynamic resource needs of the blockchain network, which makes it impossible to perform precise elastic scaling based on real-time load and historical data. This can easily lead to uneven resource allocation or low utilization efficiency, affecting the performance of the entire blockchain system. Summary of the Invention

[0004] The main objective of this invention is to provide a data resource management method, apparatus, device, and storage medium based on blockchain, aiming to solve the problems of existing blockchain-based data resource management methods, which rely entirely on the fixed resource allocation mode and general static scheduling algorithm of cloud service providers, resulting in inflexible resource allocation, lack of security traceability of the scheduling process, low resource utilization efficiency, unstable overall performance, and difficulty in meeting dynamic business needs.

[0005] In a first aspect, embodiments of the present invention provide a blockchain-based data resource management method, applied to a blockchain network composed of multiple nodes, wherein the blockchain network is of the type of public chain, private chain, or consortium chain; the method includes: Monitor the usage of various resources by nodes in the blockchain network; these resources include at least computing resources, memory resources, storage resources, and network bandwidth resources. A dynamic simulation model is constructed based on historical resource usage data and monitored real-time usage. The dynamic simulation model is used to predict the resource demand of the node in a future time period. The historical resource usage data refers to the historically monitored usage of the node on the resource. A machine learning algorithm is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the nodes, and preset performance indicators. The machine learning algorithm is optimized based on the historical resource usage data and real-time performance data. The real-time performance data includes at least one of the task processing throughput and response time of the nodes. Based on the resource allocation decision, corresponding resources are allocated to the node, and the allocation record of the resource allocation decision is stored in the distributed ledger of the blockchain network; wherein, the allocation record includes the resource allocation content, the resource allocation decision time, and the identification information of the nodes involved in the resource allocation.

[0006] Secondly, embodiments of the present invention provide a blockchain-based data resource management device, applied to a blockchain network composed of multiple nodes, wherein the blockchain network is of the type of public chain, private chain, or consortium chain; the device includes: The monitoring module is used to monitor the usage of various resources by nodes in the blockchain network; the resources include at least computing resources, memory resources, storage resources, and network bandwidth resources. A construction module is used to build a dynamic simulation model based on historical resource usage data and monitored real-time usage; the dynamic simulation model is used to predict the node's demand for the resource in a future time period; the historical resource usage data is the historically monitored usage of the resource by the node. An optimization module is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the node, and preset performance indicators using a machine learning algorithm; wherein, the machine learning algorithm optimizes based on the historical resource usage data and real-time performance data; the real-time performance data includes at least one of the node's task processing throughput and response time; The allocation module is used to allocate corresponding resources to the node according to the resource allocation decision, and store the allocation record of the resource allocation decision in the distributed ledger of the blockchain network; wherein, the allocation record includes resource allocation content, resource allocation decision time, and identification information of the nodes involved in the resource allocation.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method described in the first aspect above.

[0008] Fourthly, embodiments of the present invention provide a computer-readable storage medium for storing computer-executable instructions that, when executed by a processor, implement the steps of the method described in the first aspect above.

[0009] Fifthly, embodiments of the present invention provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0010] The at least one technical solution provided by the embodiments of the present invention can achieve the following technical effects: In this embodiment of the invention, the usage of various resources by nodes in the blockchain network is first monitored. These resources include at least computing resources, memory resources, storage resources, and network bandwidth resources. Then, based on historical resource usage data and the monitored real-time usage, a dynamic simulation model is constructed to predict the resource demand of nodes in the future. Based on this, a machine learning algorithm is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the nodes, and preset performance indicators. The machine learning algorithm is continuously optimized based on historical resource usage data and real-time performance data. After obtaining the resource allocation decision, corresponding resources are allocated to the nodes according to the decision, and the allocation record is stored in the distributed ledger of the blockchain network, recording the resource allocation content, decision time, and identification information of the involved nodes.

[0011] This invention enables resource demand prediction through dynamic simulation models and iterative decision generation using machine learning algorithms. This achieves precise elastic scaling based on real-time load and historical data, resolving the issues of uneven resource allocation and low utilization efficiency caused by general scheduling strategies that fail to consider dynamic demands. By storing resource allocation records in a blockchain distributed ledger, this invention achieves secure control and full lifecycle data traceability of the resource scheduling process, overcoming the technical deficiency of cloud service solutions lacking traceability. Through the collaborative process of monitoring, prediction, intelligent decision-making, and blockchain notarization, it effectively addresses the problem that existing technologies cannot simultaneously achieve elastic resource scheduling and trusted traceability management. Attached Figure Description

[0012] Figure 1 This is one of the flowcharts illustrating a blockchain-based data resource management method provided in an embodiment of the present invention; Figure 2 A second schematic flowchart illustrating a blockchain-based data resource management method provided in an embodiment of the present invention; Figure 3 A schematic diagram of the architecture of a blockchain-based data resource management method provided in an embodiment of the present invention; Figure 4 A schematic diagram of the module composition of a blockchain-based data resource management device 400 provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the embodiments of the present invention.

[0014] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0015] Please see Figure 1 , Figure 1 This is one of the flowcharts illustrating a resource allocation method provided in an embodiment of the present invention. It is applied to a blockchain network composed of multiple nodes, where the blockchain network is one of a public chain, a private chain, or a consortium chain. Figure 1 As shown, the method includes the following steps: Step 102: Monitor the usage of various resources by nodes in the blockchain network; resources include at least computing resources, memory resources, storage resources, and network bandwidth resources.

[0016] Step 104: Based on historical resource usage data and monitored real-time usage, construct a dynamic simulation model; the dynamic simulation model is used to predict the resource demand of nodes in the future time period; historical resource usage data refers to the resource usage of nodes monitored in the past.

[0017] Step 106: Using a machine learning algorithm, based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the nodes, and the preset performance indicators, resource allocation decisions are generated iteratively; wherein, the machine learning algorithm is optimized based on historical resource usage data and real-time performance data; real-time performance data includes at least one of the node's task processing throughput and response time.

[0018] Step 108: Based on the resource allocation decision, allocate corresponding resources to the nodes and store the allocation record of the resource allocation decision in the distributed ledger of the blockchain network; wherein, the allocation record includes the resource allocation content, the resource allocation decision time, and the identification information of the nodes involved in the resource allocation.

[0019] In this embodiment of the invention, the usage of various resources by nodes in the blockchain network can be monitored first. The blockchain network can consist of multiple nodes. The type of blockchain network can be a public chain, a private chain, or a consortium chain. The choice of blockchain network type depends on the specific application scenario's requirements for access control, performance, and decentralization. For example, in a business collaboration scenario requiring the participation of multiple organizations but also demanding a certain level of privacy protection, a consortium chain can be chosen as the underlying framework. Since different types of networks may have different node responsibilities, trust models, and resource access patterns, determining the blockchain network type is the foundation for all subsequent resource management strategies.

[0020] When monitoring the usage of various resources by each node in a blockchain network, the monitored resources can include at least four categories: computing resources, memory resources, storage resources, and network bandwidth resources. Computing resources primarily refer to CPU utilization and processing power, reflecting the load of nodes executing smart contracts, performing encryption and decryption operations, and verifying transactions—computational intensive tasks. Memory resources refer to the usage of random access memory, affecting the speed of temporary data storage and access when nodes process transaction data. Storage resources primarily refer to the usage of persistent storage devices, used to store blockchain ledger data, state data, and possible off-chain data. Network bandwidth resources refer to the uplink and downlink bandwidth used by nodes when communicating with other nodes in the blockchain network, synchronizing block data, and broadcasting transactions.

[0021] Monitoring can be implemented through monitoring agents deployed on nodes or by calling the underlying performance interfaces of the node's operating system. During monitoring, data can be collected at fixed time intervals, which can be configured as needed, such as once per second or every five seconds, to ensure real-time performance and accuracy. The collected raw data may include the percentage of CPU idle time during the sampling period, the number of bytes of memory used, remaining disk space, and the number of data packets sent and received by the network interface. This raw data will be further processed, for example, by calculating standardized metrics such as CPU utilization, memory utilization, storage utilization, and network bandwidth utilization, for subsequent unified analysis and comparison. All this real-time monitored data, along with timestamps and node identifiers, will be temporarily cached and sent to the central processing unit, and will also be archived to form historical resource usage data, providing a data foundation for subsequent modeling and prediction.

[0022] In one example, to standardize raw data collected from different nodes and at different durations for effective comparison and analysis, the monitored raw usage can be converted into resource utilization using the following formula:

[0023] in, Represents a node In time period Internal resources The utilization rate. This is a standardized, unitless ratio that allows for a direct comparison of the usage of the same resource at different nodes or during different time periods; It can represent the type of resource, specifically computing resources, memory resources, storage resources, or network bandwidth resources; It can represent a designated, monitored node in a blockchain network; This represents a specific time period for analysis, such as the past 5 minutes or 1 hour.

[0024] In this embodiment of the invention, a dynamic simulation model can be constructed and run while continuously acquiring real-time monitoring data. The dynamic simulation model is constructed primarily based on two types of data. First, there is the historical resource usage data accumulated over a long period, i.e., records of resource usage by previously monitored nodes, forming a time-series database reflecting resource usage patterns. Second, there is the real-time usage data that has just been monitored, reflecting the current latest status. The dynamic simulation model can comprehensively utilize historical and real-time data to predict the demand for various types of resources by each node within a future time period. This prediction can anticipate the likelihood of resource bottlenecks occurring before they actually occur, thereby enabling a shift from passive response to proactive planning and facilitating true elastic scaling.

[0025] In this embodiment of the invention, when constructing a dynamic simulation model, the following prediction process can be executed for each monitored resource, such as CPU resources. First, based on stored historical resource usage data, the usage records of the resource in past periods similar to the time period to be predicted can be retrieved. For example, to predict the CPU demand from 9:00 AM to 10:00 AM tomorrow, historical CPU usage data from 9:00 AM to 10:00 AM every day over the past few weeks or months can be retrieved. Then, by statistically analyzing this historical data, the average usage level of the resource in the same past period can be calculated. This value can represent a predictive benchmark based on historical patterns.

[0026] Then, real-time changes can be introduced to obtain the real-time usage of this resource monitored within a recent specified time period. This specified time period could be the past hour or a few minutes, depending on the drastic nature of business changes. The calculated real-time usage can be compared with the previously determined historical average usage for that period to determine the deviation between the two. This deviation value can quantify the current resource usage pattern, the degree and direction of its deviation from its historical norm. Based on this deviation, an adjustment factor can be determined using a certain algorithm. This adjustment factor is essentially a quantitative indicator reflecting the short-term trend of resource usage changes. If the real-time usage is consistently higher than the historical average, the adjustment factor will be positive, indicating an upward trend in demand; if the real-time usage is consistently lower than the historical average, the adjustment factor will be negative, indicating a downward trend in demand.

[0027] After determining the adjustment factor, the forecasting model can combine historical patterns and real-time trends. Using a weighted summation method, the calculated historical average usage of the resource is superimposed with the calculated adjustment factor reflecting the latest trend, according to a certain weight ratio. The resulting value is then used as the forecast value for the demand of this resource in the future time period.

[0028] In one example, the formula for the above construction and prediction process can be:

[0029] in, It is the target output of the model calculation, representing the resources. In the future Forecasted demand; resources It can be any one of CPU, memory, storage, or bandwidth; parameters Indicates past dates Resources The average usage is a baseline value derived from historical data, reflecting the cyclical and inertial nature of resource use; parameters This is a trend factor calculated based on recent real-time data. It reflects the dynamic changes in current demand that deviate from historical patterns due to unforeseen events, business growth, or other factors. Parameters and These are two weighting parameters, which are configurable coefficients used to adjust historical data. and real-time trend data The proportion it accounts for in the final predicted value. By adjusting... and The size of the variable can control the characteristics of the model. For example, if you want the model to be more stable and more dependent on historical patterns, you can increase the variable size. And reduce If the business environment changes rapidly and the model needs to respond more sensitively to the latest changes, then you can increase the size. And reduce This weighted summation formula effectively combines historical data with real-time trends, significantly improving the accuracy and adaptability of forecasts.

[0030] In this embodiment of the invention, the dynamic simulation model can be logically divided into two collaborative units: a load prediction unit and a resource demand prediction unit. The load prediction unit is the first-level prediction unit. Its input is a large amount of historical resource usage data, but its output is not a specific resource quantity, but rather a relatively abstract prediction of the total task volume that the node needs to process within a future time period. This total task volume is a comprehensive indicator representing the workload; for example, it can predict the number of transactions to be processed in the next cycle, the number of smart contract calls to be executed, etc. The load prediction unit can use time series analysis, regression algorithms, etc., to mine the changing patterns of task volume from historical data, such as daily patterns, weekly patterns, etc.

[0031] The resource demand forecasting unit receives the future task volume forecast results output by the load forecasting unit. Internally, the resource demand forecasting unit establishes a mapping model from task volume to specific resource consumption. This mapping model can quantify the standard consumption of various resources per unit of task volume, such as a single transaction. For example, this mapping model can transform abstract task volume indicators into specific resource requirements using predefined or learned resource consumption coefficients from historical data. For instance, it can determine the estimated CPU seconds, memory bytes, storage IOPS, and network bandwidth megabits required to process one thousand transactions. Based on the input task volume forecast, the resource demand forecasting unit multiplies the predicted total task volume by these resource consumption coefficients to determine the specific requirements of each node for computing resources, memory resources, storage resources, and network bandwidth resources to complete the expected task volume. This ensures consistency among the various resource demand forecasts, making the final resource allocation decision more scientific and balanced.

[0032] In this embodiment of the invention, after the dynamic simulation model successfully outputs a prediction of future resource demand, the intelligent decision-making stage can begin. Machine learning algorithms can then be used to generate the final resource allocation decision. The machine learning algorithm can simultaneously consider several key input dimensions: the first input is the future resource demand predicted by the dynamic simulation model, indicating how much resource may be needed in the future; the second input is the real-time resource load data of the nodes, reflecting the current actual resource usage; and the third input is a preset performance indicator, such as requiring CPU utilization to ideally be maintained within the 70%-80% golden range to achieve the best balance between resource efficiency and response latency.

[0033] Machine learning algorithms, based on these multi-dimensional inputs, can iteratively generate resource allocation decisions through complex internal computational models. This generation requires a continuous trial-and-error process, constantly optimizing based on feedback. A preliminary resource allocation scheme can be proposed first, then the possible operating states under this scheme can be simulated to evaluate whether preset performance indicators can be achieved. If not, the scheme is adjusted, and the simulation and evaluation are repeated, continuing this cycle until a satisfactory solution is found, or the current optimal solution is selected after a certain number of iterations. Real-time performance data can include key indicators that directly reflect node processing capacity and user experience, such as node task processing throughput and task response time. Task processing throughput can be the number of tasks or transactions successfully processed per unit time; while task response time can be the time interval from initiating a request to receiving a response. By introducing machine learning, the complex, non-linear mapping relationship between resource usage patterns, allocation strategies, and final performance can be automatically learned and discovered, thereby generating a far superior and more adaptive intelligent scheduling strategy compared to static scheduling strategies based on fixed rules.

[0034] In this embodiment of the invention, the machine learning algorithm used can be a reinforcement learning algorithm. A reinforcement learning algorithm can treat resource allocation decisions as an action and the state changes after the decision is implemented as environmental feedback. The efficiency ratio of each node under the latest resource allocation strategy can be evaluated periodically, and "rewards" or "penalties" can be given based on this efficiency ratio to guide the algorithm to adjust its resource allocation strategy in the direction of optimizing the efficiency ratio.

[0035] In one example, the formula for calculating the efficiency ratio can be:

[0036] in, It is the target to be calculated, representing the node. The efficiency ratio. It is a dimensionless numerical value used to quantify and evaluate the working efficiency of a node under a given resource allocation; parameter The actual output represents the nodes. The actual number of tasks or transactions processed within the previous time period represents an objective and measurable performance result. Parameters The expected output represents the result based on the previous nodes. The allocated resources, combined with the number of tasks that should be able to be processed within this period, as predicted by the empirical model.

[0037] In an embodiment of the present invention, when the efficiency ratio When the value is close to 1, it indicates that the actual performance of the node is basically consistent with the expected performance based on resource input, and the resource allocation may be relatively reasonable; if A value significantly greater than 1 may indicate that the node's actual processing capacity exceeds expectations, perhaps due to surplus resources or exceptionally high node performance; if A value significantly less than 1 is a warning sign, indicating that the allocated resources are insufficient to support the expected workload, the node may have become a performance bottleneck, and task processing is experiencing queuing or delays. The goal of reinforcement learning algorithms is to learn a strategy that keeps the efficiency ratio as stable as possible around an ideal value (usually 1 or slightly below 1 to leave a margin) by continuously trying different resource allocation strategies and observing the resulting efficiency ratios. This clearly transforms the complex resource allocation problem into a quantifiable and optimizable mathematical problem.

[0038] In this embodiment of the invention, when using machine learning algorithms to iteratively generate resource allocation decisions, a clear and quantifiable optimization direction can be set, so that the utilization rate of each type of resource by the node approaches the preset target utilization rate for each type of resource. For example, the target utilization rate for CPU can be set to 75%, the target utilization rate for memory to 70%, and the target utilization rate for storage I / O to 60%, etc.

[0039] To achieve this multi-objective optimization, machine learning algorithms, such as reinforcement learning algorithms, iteratively adjust the configuration parameters of various resources allocated to nodes. These resource configuration parameters can be operable and allocable quota indicators that directly determine the upper limit of resources that a node can use, and may include, but are not limited to, the number of CPU cores, memory capacity, storage space, and network bandwidth limits allocated to the node.

[0040] In one example, the formula for the adjustment process could be:

[0041] in, and Representing nodes respectively The resource configuration vectors, both before and after this adjustment, can include specific configuration values ​​such as the number of CPU cores and memory size; parameters This is the learning rate, used to control the size of the step in each adjustment. If... Setting the value too high will lead to overly rapid adjustments, causing resource allocation to fluctuate around the optimal value and become unstable; if... If the setting is too small, the adjustment process will be very slow, and it will take a long time to converge to the optimal configuration. It is a node In recent times Resources Actual utilization rate; For target utilization, for the resources mentioned above The preset ideal usage rate.

[0042] The formula above shows that the new resource allocation equals the old resource allocation plus an adjustment factor. This adjustment factor is proportional to the difference between the current usage state and the target state, and the proportionality coefficient is the learning rate. By iterating through this process, the resource utilization of nodes can be driven to continuously approach the preset target utilization rate, thereby achieving continuous optimization of resource allocation.

[0043] In this embodiment of the invention, after the machine learning algorithm generates a resource allocation decision, the decision can be executed. Based on this decision, corresponding resources can be allocated to each node in the blockchain network. Simultaneously, a complete record of this resource allocation decision can be written to the distributed ledger of the blockchain network. This allocation record contains detailed information about the resource allocation event, typically including the specific content of the resource allocation, the timestamp of the allocation decision, and the identification information of all nodes involved. Recording this information on the blockchain leverages its immutability and traceability to provide audit trail capabilities for the entire resource management process. Every resource allocation is permanently and reliably recorded, achieving secure control and data traceability throughout the entire lifecycle of resource scheduling. In the event of resource disputes or performance issues, the decision-making basis and execution status can be clearly traced back by querying the blockchain ledger.

[0044] In this embodiment of the invention, to ensure the long-term accuracy and effectiveness of the dynamic simulation model and machine learning algorithm, after allocating corresponding resources to nodes according to resource allocation decisions, periodic stress tests can be conducted on the entire blockchain network. Stress testing can involve simulating extreme business scenarios such as high concurrency and large data volumes, applying loads far exceeding normal levels to observe performance under high pressure. After the stress test is completed, test results can be collected, including changes in various performance indicators, resource usage, and whether errors occurred. Then, the predictive accuracy of the dynamic simulation model can be evaluated based on these test results. For example, comparing the resource requirements predicted by the model with the actual resource requirements observed during the stress test will generate a corresponding evaluation report. Based on this evaluation report, the prediction parameters of the dynamic simulation model and the strategy parameters of the machine learning algorithm can be dynamically adjusted in conjunction with real-time monitored node resource usage data. For example, the weight parameters in the above formula can be adjusted. and This is a balancing factor used to adjust the weights of historical and real-time data in prediction. It can be automatically increased if the model is found to be sluggish in responding to recent changes. The value of ; the policy parameters of the machine learning algorithm include the learning rate in the above formula. This is a parameter used to control the magnitude of adjustments to the resource allocation strategy. If resource allocation fluctuations are detected, the learning rate may be lowered. If convergence is found to be too slow, the adjustment may be appropriately increased. This feedback mechanism allows the system to continuously evolve in response to environmental changes, thereby maintaining strong resource management capabilities.

[0045] In one example, see Figure 2 This paper presents a flowchart of a blockchain-based data resource management approach. First, the type of blockchain network is determined, including public, private, or consortium blockchains. Resource usage analysis is then performed on the nodes of the blockchain network, covering computing power, memory capacity, storage space, and network bandwidth. Next, based on the analysis results, a dynamic simulation model of resource usage is established to predict resource demand and potential bottlenecks. Resource allocation is dynamically adjusted based on real-time load and the established resource model. Blockchain technology is used to record the decision-making process for resource allocation and load balancing, and machine learning algorithms are employed to optimize resource allocation strategies. Regular stress tests and performance evaluations are also conducted. Finally, a real-time monitoring system can be implemented to adjust resource management strategies.

[0046] In one example, see Figure 3 This illustrates an overall system architecture diagram. Figure 3 In this architecture, the resource management system is at its core and the central control center, working in coordination with multiple functional modules.

[0047] The resource management system is integrated with the blockchain technology stack, including: a blockchain network, which serves as an immutable distributed ledger system for recording and managing data, and can be a public chain, a private chain, or a consortium chain; a blockchain layer, which is responsible for executing specific blockchain operations, such as encrypted storage of data, transaction verification, and block generation; and a central control system, which is responsible for receiving, processing, and executing resource management strategies, dynamically adjusting resource allocation, and monitoring system performance and resource usage.

[0048] The resource management system interfaces with the intelligent analysis module. The resource simulation model is used to simulate and predict resource usage, providing support for resource allocation decisions; the machine learning module continuously optimizes resource allocation strategies based on historical data and real-time feedback through machine learning algorithms.

[0049] The resource management system also connects to the basic support layer. The backend service layer carries the core business logic, including resource scheduling, data analysis, and the deployment and operation of machine learning models; the API layer provides a unified interface service for the front-end user interface, external applications, and communication between internal modules; the data layer is used to store system operation data, historical records, etc., and can adopt various storage structures such as relational databases and data lakes.

[0050] In addition, the resource management system is connected to the interactive monitoring module. The monitoring and alarm system monitors the system's operating status and resource utilization in real time and triggers alarms when anomalies occur; the front-end user interface provides users with an intuitive operating interface for system configuration, status monitoring, and viewing blockchain activities.

[0051] In this embodiment of the invention, the usage of various resources by nodes in the blockchain network is first monitored. These resources include at least computing resources, memory resources, storage resources, and network bandwidth resources. Then, based on historical resource usage data and the monitored real-time usage, a dynamic simulation model is constructed to predict the resource demand of nodes in the future. Based on this, a machine learning algorithm is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the nodes, and preset performance indicators. The machine learning algorithm is continuously optimized based on historical resource usage data and real-time performance data. After obtaining the resource allocation decision, corresponding resources are allocated to the nodes according to the decision, and the allocation record is stored in the distributed ledger of the blockchain network, recording the resource allocation content, decision time, and identification information of the involved nodes.

[0052] This invention enables resource demand prediction through dynamic simulation models and iterative decision generation using machine learning algorithms. This achieves precise elastic scaling based on real-time load and historical data, resolving the issues of uneven resource allocation and low utilization efficiency caused by general scheduling strategies that fail to consider dynamic demands. By storing resource allocation records in a blockchain distributed ledger, this invention achieves secure control and full lifecycle data traceability of the resource scheduling process, overcoming the technical deficiency of cloud service solutions lacking traceability. Through the collaborative process of monitoring, prediction, intelligent decision-making, and blockchain notarization, it effectively addresses the problem that existing technologies cannot simultaneously achieve elastic resource scheduling and trusted traceability management.

[0053] Figure 4 The blockchain-based data resource management device 400 shown can achieve Figure 1 The method described in the embodiment achieves the same technical effect, and can be specifically referred to in the above description. Figure 1 The resource allocation method of the illustrated embodiment will not be described in detail here. The blockchain-based data resource management device 400 includes: The monitoring module 401 is used to monitor the usage of various resources by nodes in the blockchain network; the resources include at least computing resources, memory resources, storage resources and network bandwidth resources. The construction module 402 is used to build a dynamic simulation model based on historical resource usage data and monitored real-time usage; the dynamic simulation model is used to predict the node's demand for the resource in a future time period; the historical resource usage data is the historically monitored usage of the resource by the node. The optimization module 403 is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the node, and preset performance indicators using a machine learning algorithm; wherein the machine learning algorithm is optimized based on the historical resource usage data and real-time performance data; the real-time performance data includes at least one of the node's task processing throughput and response time. The allocation module 404 is used to allocate corresponding resources to the node according to the resource allocation decision, and store the allocation record of the resource allocation decision in the distributed ledger of the blockchain network; wherein, the allocation record includes resource allocation content, resource allocation decision time, and identification information of the nodes involved in the resource allocation.

[0054] Optionally, the building module 402 is used for: For each of the resources, the historical usage of each resource is determined based on the historical resource usage data; Based on the deviation between the real-time usage of each resource monitored in the most recent specified time period and the historical usage, an adjustment factor reflecting the changing trend of the usage of each resource is determined. The historical usage of each resource and the adjustment factor are weighted and summed to obtain the predicted demand for the resource in the future time period.

[0055] Optionally, the machine learning algorithm is a reinforcement learning algorithm; the reinforcement learning algorithm optimizes the resource allocation decision by periodically evaluating the efficiency ratio of the node; wherein, the efficiency ratio is the ratio of the number of tasks actually processed by the node to the expected number of tasks processed; the expected number of tasks processed is determined based on the resources allocated to the node.

[0056] Optionally, the dynamic simulation model includes a load prediction unit and a resource demand prediction unit; The load prediction unit is used to predict the amount of tasks that the node needs to process in a future time period based on the historical resource usage data. The resource demand prediction unit is used to determine the node's demand for computing resources, memory resources, storage resources, and network bandwidth resources based on the task volume predicted by the load prediction unit.

[0057] Optionally, the allocation module 404 is used for: The optimization objective is to approximate the utilization rate of each resource by the node to a preset target utilization rate for each resource. The configuration parameters of various resources allocated to the node are iteratively adjusted to generate an optimized resource allocation decision. The total resource utilization rate is obtained by weighted summation of the node's utilization rates of computing resources, memory resources, storage resources, and network bandwidth resources. The resource configuration parameters are the specific quotas of the resources allocated to the node, including at least one of the following: number of CPU cores, memory capacity, storage space size, and network bandwidth limit.

[0058] Optionally, the device further includes ( Figure 4 (not shown in the image) The testing module 405 is used to perform periodic stress tests on the blockchain network after allocating corresponding resources to the node according to the resource allocation decision, obtain test results, evaluate the prediction accuracy of the dynamic simulation model based on the test results, and generate an accuracy evaluation report. The adjustment module 406 is used to dynamically adjust the prediction parameters of the dynamic simulation model and the strategy parameters of the machine learning algorithm based on the accuracy assessment report and the real-time monitoring of the node's usage of the resources; wherein the prediction parameters include a balance factor for adjusting the weights of historical data and real-time data in the prediction; and the strategy parameters include a learning rate for controlling the adjustment range of the resource allocation strategy.

[0059] In this embodiment of the invention, the usage of various resources by nodes in the blockchain network is first monitored. These resources include at least computing resources, memory resources, storage resources, and network bandwidth resources. Then, based on historical resource usage data and the monitored real-time usage, a dynamic simulation model is constructed to predict the resource demand of nodes in the future. Based on this, a machine learning algorithm is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the nodes, and preset performance indicators. The machine learning algorithm is continuously optimized based on historical resource usage data and real-time performance data. After obtaining the resource allocation decision, corresponding resources are allocated to the nodes according to the decision, and the allocation record is stored in the distributed ledger of the blockchain network, recording the resource allocation content, decision time, and identification information of the involved nodes.

[0060] This invention enables resource demand prediction through dynamic simulation models and iterative decision generation using machine learning algorithms. This achieves precise elastic scaling based on real-time load and historical data, resolving the issues of uneven resource allocation and low utilization efficiency caused by general scheduling strategies that fail to consider dynamic demands. By storing resource allocation records in a blockchain distributed ledger, this invention achieves secure control and full lifecycle data traceability of the resource scheduling process, overcoming the technical deficiency of cloud service solutions lacking traceability. Through the collaborative process of monitoring, prediction, intelligent decision-making, and blockchain notarization, it effectively addresses the problem that existing technologies cannot simultaneously achieve elastic resource scheduling and trusted traceability management.

[0061] Figure 5 This is a schematic diagram of the electronic device provided in an embodiment of the present invention. Please refer to it. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0062] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0063] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0064] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming a non-contiguous transfer configuration at the logical level. The processor executes the program stored in memory and specifically performs the following operations: Monitor the usage of various resources by nodes in the blockchain network; these resources include at least computing resources, memory resources, storage resources, and network bandwidth resources. A dynamic simulation model is constructed based on historical resource usage data and monitored real-time usage. The dynamic simulation model is used to predict the resource demand of the node in a future time period. The historical resource usage data refers to the historically monitored usage of the node on the resource. A machine learning algorithm is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the nodes, and preset performance indicators. The machine learning algorithm is optimized based on the historical resource usage data and real-time performance data. The real-time performance data includes at least one of the task processing throughput and response time of the nodes. Based on the resource allocation decision, corresponding resources are allocated to the node, and the allocation record of the resource allocation decision is stored in the distributed ledger of the blockchain network; wherein, the allocation record includes the resource allocation content, the resource allocation decision time, and the identification information of the nodes involved in the resource allocation.

[0065] The above is as described in the embodiments of the present invention. Figure 1The blockchain-based data resource management method disclosed in the embodiments described above can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in one or more embodiments of the present invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in one or more embodiments of the present invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0066] The electronic device can also perform Figure 1 The blockchain-based data resource management method described in this embodiment of the invention will not be elaborated further here.

[0067] This invention also provides a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by a portable electronic device including multiple applications, enable the portable electronic device to perform... Figure 1 The method of the illustrated embodiment will not be described again here.

[0068] This invention also provides a computer program product stored in a storage medium and executed by at least one processor to implement... Figure 1 The method of the illustrated embodiment will not be described again here.

[0069] Of course, in addition to the software implementation, the electronic device of the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0070] In summary, the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the scope of protection of one or more embodiments of the present invention.

[0071] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0072] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined in this embodiment of the invention, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0073] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0074] The embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. A data resource management method based on blockchain, characterized in that, The method is applied to a blockchain network composed of multiple nodes, wherein the blockchain network is one of a public chain, a private chain, or a consortium chain; the method includes: Monitor the usage of various resources by nodes in the blockchain network; these resources include at least computing resources, memory resources, storage resources, and network bandwidth resources. A dynamic simulation model is constructed based on historical resource usage data and monitored real-time usage. The dynamic simulation model is used to predict the resource demand of the node in a future time period. The historical resource usage data refers to the historically monitored usage of the node on the resource. A machine learning algorithm is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the nodes, and preset performance indicators. The machine learning algorithm is optimized based on the historical resource usage data and real-time performance data. The real-time performance data includes at least one of the task processing throughput and response time of the nodes. Based on the resource allocation decision, corresponding resources are allocated to the node, and the allocation record of the resource allocation decision is stored in the distributed ledger of the blockchain network; wherein, the allocation record includes the resource allocation content, the resource allocation decision time, and the identification information of the nodes involved in the resource allocation.

2. The method according to claim 1, characterized in that, The step of constructing a dynamic simulation model based on the monitored usage of the resources includes: For each of the resources, the historical usage of each resource is determined based on the historical resource usage data; Based on the deviation between the real-time usage of each resource monitored in the most recent specified time period and the historical usage, an adjustment factor reflecting the changing trend of the usage of each resource is determined. The historical usage of each resource and the adjustment factor are weighted and summed to obtain the predicted demand for the resource in the future time period.

3. The method according to claim 1, characterized in that, The machine learning algorithm is a reinforcement learning algorithm; the reinforcement learning algorithm optimizes the resource allocation decision by periodically evaluating the efficiency ratio of the node; wherein, the efficiency ratio is the ratio of the number of tasks actually processed by the node to the expected number of tasks processed; the expected number of tasks processed is determined based on the resources allocated to the node.

4. The method according to claim 1, characterized in that, The dynamic simulation model includes a load prediction unit and a resource demand prediction unit; The load prediction unit is used to predict the amount of tasks that the node needs to process in a future time period based on the historical resource usage data. The resource demand prediction unit is used to determine the node's demand for computing resources, memory resources, storage resources, and network bandwidth resources based on the task volume predicted by the load prediction unit.

5. The method according to claim 1, characterized in that, The method of iteratively generating resource allocation decisions using machine learning algorithms includes: The optimization objective is to approximate the utilization rate of each resource by the node to a preset target utilization rate for each resource. The configuration parameters of various resources allocated to the node are iteratively adjusted to generate an optimized resource allocation decision. The total resource utilization rate is obtained by weighted summation of the node's utilization rates of computing resources, memory resources, storage resources, and network bandwidth resources. The resource configuration parameters are the specific quotas of the resources allocated to the node, including at least one of the following: number of CPU cores, memory capacity, storage space size, and network bandwidth limit.

6. The method according to claim 1, characterized in that, After allocating corresponding resources to the node based on the resource allocation decision, the method further includes: Regular stress tests are conducted on the blockchain network to obtain test results. Based on the test results, the predictive accuracy of the dynamic simulation model is evaluated, and an accuracy evaluation report is generated. Based on the accuracy assessment report and the real-time monitoring of the node's resource usage, the prediction parameters of the dynamic simulation model and the strategy parameters of the machine learning algorithm are dynamically adjusted; wherein, the prediction parameters include a balance factor for adjusting the weight of historical data and real-time data in the prediction; the strategy parameters include a learning rate for controlling the adjustment range of the resource allocation strategy.

7. A data resource management device based on blockchain, characterized in that, An apparatus applicable to a blockchain network composed of multiple nodes, wherein the blockchain network is one of a public blockchain, a private blockchain, or a consortium blockchain; the apparatus includes: The monitoring module is used to monitor the usage of various resources by nodes in the blockchain network; the resources include at least computing resources, memory resources, storage resources, and network bandwidth resources. A construction module is used to build a dynamic simulation model based on historical resource usage data and monitored real-time usage; the dynamic simulation model is used to predict the node's demand for the resource in a future time period; the historical resource usage data is the historically monitored usage of the resource by the node. An optimization module is used to iteratively generate resource allocation decisions based on the resource demand predicted by the dynamic simulation model, the real-time resource load data of the node, and preset performance indicators using a machine learning algorithm; wherein, the machine learning algorithm optimizes based on the historical resource usage data and real-time performance data; the real-time performance data includes at least one of the node's task processing throughput and response time; The allocation module is used to allocate corresponding resources to the node according to the resource allocation decision, and store the allocation record of the resource allocation decision in the distributed ledger of the blockchain network; wherein, the allocation record includes resource allocation content, resource allocation decision time, and identification information of the nodes involved in the resource allocation.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store computer-executable instructions that, when executed by a processor, implement the steps of the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1 to 6.

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