Cloud document management method and platform based on block chain technology

Abstract

The invention is applicable to the technical field of cloud document management, and provides a cloud document management method and platform based on a block chain technology, after a user uploads a target document to a specified distributed cloud storage node driven by a block chain, an initial operation priority index of the target document is determined, and a file volume of the target document is acquired; acquiring a historical query log of a user for the specified distributed cloud storage node, determining an access heat interval of the user according to the historical query log, and determining a target node state parameter of the specified distributed cloud storage node for the access heat interval; the finally generated priority index comprehensively considers document characteristics, user behaviors and node performance, so that document scheduling is more intelligent and precise, the resource allocation efficiency and the node service response capability are remarkably improved, and the overall performance and the user experience of the distributed cloud storage system are enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud document management, and in particular to a cloud document management method and platform based on blockchain technology. Background Art

[0002] At present, cloud document management technology has been widely used in various distributed storage systems, providing users with reliable and efficient document storage and access services. Traditional cloud document management methods mainly rely on fixed rules or preset strategies to store and schedule documents. These methods usually determine the order of document operations and resource allocation through simple priority division or initial settings based on document categories. However, these priority setting methods are relatively static and cannot dynamically adapt to complex node operating environments and changes in user needs. In high-concurrency, large-scale document operation scenarios, they are prone to uneven resource allocation or node load imbalance.

[0003] In the prior art, the setting of document operation priority lacks rationality and comprehensiveness, and is often statically set based on a single factor (such as document category or file size), failing to fully consider user behavior, node status, and the actual impact of document operations on system resources. This deficiency results in node resource allocation failing to respond to actual demand in real time when documents are accessed frequently, which may cause system performance to degrade; at the same time, when the node is in poor operating condition, the priority cannot be dynamically adjusted to avoid bottlenecks, affecting the stability of the overall service and user experience. In addition, the lack of simulation analysis of node load conditions makes it impossible to accurately optimize resource scheduling, further exacerbating the waste of system resources and low scheduling efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a cloud document management method and platform based on blockchain technology, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: a cloud document management method based on blockchain technology, the method comprising:

[0006] After the user uploads the target document to the designated distributed cloud storage node driven by the blockchain, the initial operation priority index of the target document is determined, and the file volume of the target document is obtained;

[0007] Obtain the user's historical query log for the specified distributed cloud storage node, determine the user's access heat interval based on the historical query log, and determine the target node state parameter of the specified distributed cloud storage node for the access heat interval;

[0008] Evaluate the difference between the target node state parameters and the preset standard node state model, quantify the state deviation degree, and make a first correction to the initial operation priority index of the target document based on the deviation degree to generate a secondary operation priority index;

[0009] Combined with the file volume of the target document, the load condition of the specified distributed cloud storage node for the target document is simulated, and a deviation analysis is performed between it and the standard load model to obtain the load deviation coefficient. According to the load deviation coefficient, the secondary operation priority index is corrected for the second time to obtain the final operation priority index.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the access heat interval refers to the distribution pattern and concentration trend of the user's query behavior for the target category documents on the specified distributed cloud storage node in the time dimension, specifically including the statistical value of the query frequency and the time period corresponding to the high-frequency access.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the steps of obtaining a user's historical query log for a specified distributed cloud storage node, determining the user's access heat interval according to the historical query log, and determining a target node state parameter of the specified distributed cloud storage node for the access heat interval include:

[0012] Extract user access records from the historical query logs of the specified distributed cloud storage nodes, including timestamps, query categories, query frequency, and query duration data. Generate access frequency curves through statistical analysis of the time dimension, locate access popularity intervals, and identify peak and trough periods of query behavior.

[0013] For the time range of the access popularity interval, dynamically monitor the target node status parameters of the specified distributed cloud storage node, including response time, bandwidth occupancy, load rate and storage capacity occupancy.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the preset standard node status model refers to a node performance benchmark set based on the historical operation data of a specified distributed cloud storage node, including standard value ranges of parameters such as response time, bandwidth occupancy, load rate, and storage capacity occupancy, which is used as a comparison reference for the target node status parameters to evaluate the node operation health status;

[0015] The standard load model refers to a node load reference benchmark established based on the simulation test results of historical load data of a specified distributed cloud storage node, including the average or optimal values ​​of the node's load rate, I / O performance, and bandwidth occupancy under different file volume conditions, which is used to measure the resource consumption level of the target document on the node and perform deviation analysis.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the difference between the target node state parameter and the preset standard node state model is evaluated, the state deviation degree is quantified, and the initial operation priority index of the target document is first corrected based on the deviation degree. The step of generating the secondary operation priority index includes:

[0017] Compare the target node state parameters with the corresponding parameters in the preset standard node state model item by item, calculate the difference value of each parameter, and standardize these difference values ​​using a unified proportional formula;

[0018] Collect the difference values ​​of all parameters, assign weights according to the importance of the parameters, perform weighted comprehensive processing on these difference values, generate quantitative indicators, and set the quantitative indicators as the degree of deviation of the target node state parameters from the preset standard node state model;

[0019] Multiply the quantitative index by the initial operation priority index to obtain the first supplement priority index, and then add the first supplement priority index to the initial operation priority index to generate the secondary operation priority index.

[0020] As a further limitation of the technical solution of the embodiment of the present invention, in combination with the file volume of the target document, the load condition of the target document by the designated distributed cloud storage node is simulated, and a deviation analysis is performed between the load condition and the standard load model to obtain a load deviation coefficient, and the secondary operation priority index is corrected for the second time according to the load deviation coefficient to obtain the final operation priority index. The steps include:

[0021] Combined with the file size of the target document, simulate the load status of the specified distributed cloud storage node when processing the target document, and calculate the resource usage value caused by the target document operation, including load parameters such as load rate, I / O performance and bandwidth consumption;

[0022] Compare the simulated load parameters with the corresponding values ​​in the standard load model item by item, calculate the deviation values, and standardize these deviation values ​​through a unified proportional formula to obtain the standard deviation value;

[0023] Collect the standardized deviation values ​​of all load parameters, and perform weighted comprehensive processing based on the weight of the parameters' impact on the node load to generate a load deviation coefficient;

[0024] The load deviation coefficient is multiplied by the secondary operation priority index to obtain a second supplementary priority index, and then the second supplementary priority index is added to the secondary operation priority index to generate a final operation priority index.

[0025] A cloud document management platform based on blockchain technology, the system includes: an initial priority determination module, a target node state parameter determination module, a secondary priority generation module and a final priority generation module, wherein:

[0026] An initial priority determination module, used to determine the initial operation priority index of the target document and obtain the file volume of the target document after the user uploads the target document to the designated distributed cloud storage node driven by the blockchain;

[0027] The target node state parameter determination module is used to obtain the user's historical query log for the specified distributed cloud storage node, determine the user's access heat interval based on the historical query log, and determine the target node state parameter of the specified distributed cloud storage node for the access heat interval;

[0028] A secondary priority generation module is used to evaluate the difference between the target node state parameters and the preset standard node state model, quantify the state deviation degree, and make a first correction to the initial operation priority index of the target document based on the deviation degree to generate a secondary operation priority index;

[0029] The final priority generation module is used to simulate the load status of the target document on the designated distributed cloud storage node in combination with the file volume of the target document, and to perform deviation analysis with the standard load model to obtain the load deviation coefficient. The secondary operation priority index is corrected for the second time according to the load deviation coefficient to obtain the final operation priority index.

[0030] The access heat interval refers to the distribution pattern and concentration trend of the user's query behavior on the target category documents on the specified distributed cloud storage node in the time dimension, specifically including the statistical value of the query frequency and the time period corresponding to the high-frequency access.

[0031] As a further limitation of the technical solution of the embodiment of the present invention, the target node state parameter determination module specifically includes:

[0032] The data query unit is used to extract user access records from the historical query logs of the specified distributed cloud storage nodes, including timestamp, query category, query frequency and query duration data, generate access frequency curves through statistical analysis of the time dimension, locate access popularity intervals, and identify peak and trough periods of query behavior;

[0033] The target node status parameter acquisition unit is used to dynamically monitor the target node status parameters of the designated distributed cloud storage node according to the time range of the access heat interval, including response time, bandwidth occupancy rate, load rate and storage capacity occupancy rate.

[0034] As a further limitation of the technical solution of the embodiment of the present invention, the preset standard node status model refers to a node performance benchmark set based on the historical operation data of a specified distributed cloud storage node, including standard value ranges of parameters such as response time, bandwidth occupancy, load rate, and storage capacity occupancy, which is used as a comparison reference for the target node status parameters to evaluate the node operation health status;

[0035] The standard load model refers to a node load reference benchmark established based on the simulation test results of historical load data of a specified distributed cloud storage node, including the average or optimal values ​​of the node's load rate, I / O performance, and bandwidth occupancy under different file volume conditions, which is used to measure the resource consumption level of the target document on the node and perform deviation analysis.

[0036] As a further limitation of the technical solution of the embodiment of the present invention, the secondary priority generation module specifically includes:

[0037] A parameter comparison unit is used to compare the target node state parameters with the corresponding parameters in the preset standard node state model item by item, calculate the difference value of each parameter, and standardize these difference values ​​using a unified proportional formula;

[0038] The deviation degree determination unit is used to collect the difference values ​​of all parameters, assign weights according to the importance of the parameters, perform weighted comprehensive processing on these difference values, generate a quantitative index, and set the quantitative index as the deviation degree of the target node state parameter relative to the preset standard node state model;

[0039] The secondary priority generation unit is used to multiply the quantitative index by the initial operation priority index to obtain a first supplementary priority index, and then add the first supplementary priority index to the initial operation priority index to generate a secondary operation priority index.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] It can realize dynamic adjustment of the priority of the whole process of the target document, and optimize the storage and scheduling efficiency of the document in the designated distributed cloud storage node driven by blockchain. By setting the initial operation priority index, the basic importance of the target document can be quickly determined; combined with the user's access popularity range and the evaluation of the target node status parameters, the priority can be dynamically corrected to adapt to the node operation status; the priority adjustment is further refined through the simulation analysis of the load status, realizing real-time response and precise optimization of node resource pressure. The final priority index comprehensively considers the document characteristics, user behavior and node performance, making document scheduling more intelligent and precise, significantly improving the resource allocation efficiency and node service response capability, and helping to enhance the overall performance and user experience of the distributed cloud storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0043] Figure 2 A flow chart of determining target node state parameters in the method provided in an embodiment of the present invention;

[0044] Figure 3 A flow chart of generating a secondary operation priority index in a method provided in an embodiment of the present invention;

[0045] Figure 4 A flowchart of obtaining a final operation priority index in a method provided in an embodiment of the present invention;

[0046] Figure 5 An application architecture diagram of a system provided by an embodiment of the present invention;

[0047] Figure 6 A structural block diagram of a target node state parameter determination module in a system provided by an embodiment of the present invention;

[0048] Figure 7 This is a structural block diagram of a secondary priority generation module in a system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0051] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0052] Specifically, a cloud document management method based on blockchain technology includes the following steps:

[0053] Step S100, after the user uploads the target document to the designated distributed cloud storage node driven by the blockchain, the initial operation priority index of the target document is determined, and the file volume of the target document is obtained.

[0054] In the embodiment of the present invention, the designated distributed cloud storage node driven by blockchain refers to a specific node in a distributed storage system based on blockchain technology. These nodes are interconnected through a blockchain network and are responsible for storing, verifying and managing documents uploaded by users. The characteristics of blockchain enable these nodes to provide data immutability, operational transparency and trusted storage services. When a user uploads a target document, the system will allocate it to a designated storage node based on the document attributes and user needs, and the node will be recorded in the blockchain to ensure traceability and security.

[0055] The initial operation priority index is an indicator used to measure the importance of the target document in storage and access. It is determined based on the basic attributes of the target document, including factors such as file category, user role, and file volume. Common technical methods include weight calculation and rule engine, which generate priority indexes by setting weight values ​​for each attribute and calculating weighted scores. For example, rule engines (such as Drools) can set basic priorities for documents of different categories according to predefined rules, and then dynamically adjust the final score based on file volume and user role.

[0056] The file size of the target document is obtained through the metadata extraction mechanism of the storage node when receiving the document upload. During the file upload process, the system automatically reads the basic properties of the document and generates metadata, including the file size, type, format, etc. This is usually achieved by relying on the standard functions of the file upload interface (such as HTTP multipart request parsing) or the built-in file attribute recording module in the distributed storage network (such as IPFS). The file size information is recorded as one of the core metadata of the target document, which is used for subsequent priority calculation and storage scheduling.

[0057] Furthermore, the cloud document management method based on blockchain technology also includes the following steps:

[0058] Step S200, obtaining the user's historical query log for the specified distributed cloud storage node, and determining the user's access heat interval according to the historical query log, and determining the target node state parameter of the specified distributed cloud storage node for the access heat interval.

[0059] Specifically, Figure 2 A flow chart for determining target node state parameters is shown.

[0060] The process of obtaining the user's historical query log for a specified distributed cloud storage node, determining the user's access heat interval according to the historical query log, and determining the target node state parameter of the specified distributed cloud storage node for the access heat interval specifically includes the following steps:

[0061] Step S201, extracting user access records from the historical query logs of the designated distributed cloud storage node, including data on timestamp, query category, query frequency, and query duration, generating an access frequency curve through statistical analysis of the time dimension, locating the access popularity interval, and clarifying the peak and trough periods of query behavior;

[0062] Step S202, dynamically monitors the target node status parameters of the designated distributed cloud storage node for the time range of the access heat interval, including response time, bandwidth occupancy, load rate and storage capacity occupancy.

[0063] The access heat interval refers to the distribution pattern and concentration trend of the user's query behavior on the target category documents on the specified distributed cloud storage node in the time dimension, specifically including the statistical value of the query frequency and the time period corresponding to the high-frequency access.

[0064] In an embodiment of the present invention, user access records are extracted from the historical query logs of the designated distributed cloud storage node, including data such as timestamp, query category, query frequency, and query duration, and these records are classified and organized according to the time dimension. Through statistical analysis methods, such as time series aggregation and peak detection of query frequency and duration, a complete access frequency curve is generated to reflect the distribution of user query behavior in different time periods. Combined with the analysis results of the access frequency curve, the access heat interval is located, that is, the high-frequency period and low-frequency period of user query behavior are clarified, providing a time range basis for subsequent node status monitoring.

[0065] After determining the access heat interval, dynamically monitor the target node status parameters of the specified distributed cloud storage node within the heat interval. This includes collecting key parameters such as the node's response time, bandwidth occupancy, load rate, and storage capacity occupancy rate through real-time monitoring tools. In order to ensure the accuracy and timeliness of the data, the monitoring tool will sample the target node's status parameters in time-sharing and generate a status parameter data set corresponding to the access heat interval. After processing, these data can clearly describe the operating status and performance of the target node during the high-frequency access period.

[0066] During the entire implementation process, the analysis of historical query logs and the determination of access popularity intervals were completed through statistical and time series analysis methods, while the monitoring of target node status parameters relied on the status acquisition module and real-time monitoring tools of the distributed storage system. The combination of these data provides detailed basic data for further node status evaluation and optimization.

[0067] Furthermore, the cloud document management method based on blockchain technology also includes the following steps:

[0068] Step S300, evaluating the difference between the target node state parameters and the preset standard node state model, quantifying the state deviation degree, and making a first correction to the initial operation priority index of the target document based on the deviation degree to generate a secondary operation priority index.

[0069] Specifically, Figure 3 A flow chart for generating a secondary operation priority index is shown.

[0070] Among them, the difference between the target node state parameters and the preset standard node state model is evaluated to quantify the state deviation degree, and the initial operation priority index of the target document is first corrected based on the deviation degree. The generation of the secondary operation priority index specifically includes the following steps:

[0071] Step S301, comparing the target node state parameters with the corresponding parameters in the preset standard node state model item by item, calculating the difference value of each parameter, and standardizing these difference values ​​using a unified proportional formula;

[0072] Step S302, collecting the difference values ​​of all parameters, assigning weights according to the importance of the parameters, performing weighted comprehensive processing on these difference values, generating a quantitative index, and setting the quantitative index as the degree of deviation of the target node state parameter from the preset standard node state model;

[0073] Step S303, multiplying the quantization index by the initial operation priority index to obtain a first supplementary priority index, and then adding the first supplementary priority index to the initial operation priority index to generate a secondary operation priority index.

[0074] The preset standard node status model refers to a node performance benchmark set based on the historical operation data of a specified distributed cloud storage node, including the standard value ranges of parameters such as response time, bandwidth occupancy, load rate, and storage capacity occupancy, which serves as a comparison reference for the target node status parameters and is used to evaluate the node operation health status.

[0075] In an embodiment of the present invention, by comparing the target node status parameters with the corresponding parameters in the preset standard node status model item by item, the real-time status data of the target node is first collected, including response time, bandwidth occupancy, load rate and storage capacity occupancy. These data are dynamically obtained from the operating environment of the specified distributed cloud storage node through distributed monitoring tools (such as Prometheus or Zabbix), and the data is cleaned to remove outliers and incomplete records to ensure the accuracy and reliability of the comparison.

[0076] For each parameter, the difference between it and the corresponding parameter in the standard node state model is calculated, and the formula is: Difference value = (target parameter value - standard parameter value) / standard parameter value. In order to ensure the dimensional consistency of the difference values ​​of different parameters, the calculated difference values ​​are standardized and normalized to a uniform scale range (such as -1 to 1). This standardization process usually relies on data normalization tools or standard statistical methods (such as Min-Max Scaling) to eliminate the influence of parameter scale.

[0077] All the standardized difference values ​​are collected and weighted according to the importance of each parameter to the health of the node. For example, the weights of response time and bandwidth utilization can be set to higher values ​​because they have a more direct impact on the user experience; the weight of storage capacity utilization is lower because it has less impact on real-time performance. The difference values ​​of each parameter are weighted and comprehensively processed to generate a comprehensive quantitative index that represents the overall deviation of the target node status from the preset standard node status model. This quantitative index is calculated comprehensively through a formula and stored as key reference data.

[0078] The quantitative index is used as the quantitative result of the health status of the target node to correct the initial operation priority index of the target document. The specific operation is to multiply the quantitative index by the initial operation priority index to obtain the first supplementary priority index. Subsequently, the first supplementary priority index is added to the initial operation priority index to generate the secondary operation priority index. Through this correction method, the secondary operation priority index can dynamically reflect the impact of the current operation status of the target node on the document operation priority.

[0079] Using this as a reference factor for generating the secondary operation priority index has good prospects and practical benefits. First, it can accurately quantify the operating health of the target node, and through priority correction, prioritize resources to document operations that need more protection. Secondly, combined with real-time monitoring and dynamic calculation, this method makes priority adjustment more flexible and dynamic responsive, which can effectively improve the resource utilization efficiency of distributed storage nodes. In addition, this mechanism enhances the responsiveness to user needs, optimizes document scheduling and storage management strategies through quantitative evaluation of node status, and helps to achieve more efficient service delivery and resource allocation. Finally, this method has great scalability in high-concurrency, multi-node distributed storage scenarios, and can adapt to complex business scenarios and dynamically changing operating environments.

[0080] Furthermore, the cloud document management method based on blockchain technology also includes the following steps:

[0081] Step S400, in combination with the file volume of the target document, simulates the load condition of the designated distributed cloud storage node for the target document, and performs deviation analysis on it and the standard load model to obtain the load deviation coefficient, and performs a second correction on the secondary operation priority index according to the load deviation coefficient to obtain the final operation priority index.

[0082] Specifically, Figure 4 A flow chart for obtaining the final operation priority index is shown.

[0083] Among them, combining the file volume of the target document, simulating the load status of the designated distributed cloud storage node for the target document, and performing deviation analysis on it and the standard load model to obtain the load deviation coefficient, and performing a second correction on the secondary operation priority index according to the load deviation coefficient to obtain the final operation priority index specifically includes the following steps:

[0084] Step S401, combining the file volume of the target document, simulating the load status of the designated distributed cloud storage node when processing the target document, and calculating the resource occupation value caused by the target document operation, including load parameters such as load rate, I / O performance and bandwidth consumption;

[0085] Step S402, comparing the load parameters obtained by simulation with the corresponding values ​​in the standard load model item by item, calculating the deviation values, and standardizing these deviation values ​​through a unified proportional formula to obtain the standard deviation value;

[0086] Step S403, collecting standardized deviation values ​​of all load parameters, and performing weighted comprehensive processing according to the weight of the parameters' influence on the node load to generate a load deviation coefficient;

[0087] Step S404, multiplying the load deviation coefficient by the secondary operation priority index to obtain a second supplementary priority index, and then adding the second supplementary priority index to the secondary operation priority index to generate a final operation priority index.

[0088] The standard load model refers to a node load reference benchmark established based on the simulation test results of historical load data of a specified distributed cloud storage node, including the average or optimal values ​​of the node's load rate, I / O performance, and bandwidth occupancy under different file volume conditions, which is used to measure the resource consumption level of the target document on the node and perform deviation analysis.

[0089] In an embodiment of the present invention, after combining the file volume of the target document, the load condition of the designated distributed cloud storage node when processing the target document is first simulated. The resource occupancy value that may be caused by the document operation is calculated through simulation analysis, including key load parameters such as load rate, I / O performance and bandwidth consumption. This simulation process usually relies on load simulation tools or the performance analysis module of the distributed storage node, such as using a dynamic simulation model (such as AnyLogic) to predict the processing performance of the node, or using the node's real-time performance monitoring system to generate estimated load data.

[0090] Compare the simulated load parameters with the corresponding values ​​in the standard load model one by one, and calculate the deviation value of each parameter. The deviation value calculation formula is: deviation value = (simulated value - standard value) / standard value. In order to ensure the consistency of the comparison of different load parameters, a unified proportional formula is used to standardize the deviation value, normalize it to a fixed range (such as -1 to 1), and generate a standard deviation value. This process relies on standard data processing techniques (such as Min-Max Scaling) to ensure the fairness of load parameters in calculation.

[0091] After collecting the standardized deviation values ​​of all load parameters, a weighted comprehensive processing is performed based on the weight of each parameter's impact on the node load. Load rate and I / O performance are usually given higher weights because they have a more direct impact on node performance, while bandwidth consumption may have a lower weight. Through weighted calculation, a load deviation coefficient is generated as a comprehensive indicator to measure the resource pressure of the target document on the specified distributed cloud storage node.

[0092] The secondary operation priority index is corrected using the generated load deviation coefficient, and the load deviation coefficient is multiplied by the secondary operation priority index to generate a second supplementary priority index. Then, the second supplementary priority index is added to the secondary operation priority index to generate a final operation priority index.

[0093] The priority index can dynamically reflect the impact of the target document on the node resource load. By comprehensively analyzing the resource usage of the target document on the current node, a priority result that can be adjusted in real time is generated. Combined with the node operation status parameters, the priority index can further reflect the overall health status and resource carrying capacity of the node within a specific time window. When the node operation status is relatively tense, the priority index will be dynamically increased to ensure the processing priority of the target document and avoid delays in key operations; when the node operation status is good, the priority index will be appropriately reduced to optimize the balance of system resource scheduling.

[0094] The connection with the previous node operation status is that the calculation of the priority index not only considers the node health status (such as response time, bandwidth occupancy, etc.), but also further refines the direct impact of the specific operation of the target document on the node load. The node operation status parameters provide overall environmental information, while the load deviation coefficient reflects the actual interaction effect between the document and the node by simulating the operation pressure of the specific document. The combination of the two can not only evaluate the current operation capacity of the node, but also predict the potential impact of the target document operation on the node, thereby achieving dual optimization from global to local.

[0095] The benefit of this combination is that it makes document scheduling more intelligent and refined. Through the dynamic adjustment of the priority index, it can not only prioritize the processing of key documents under high load conditions, but also optimize resource allocation efficiency when node resources are loose to avoid resource waste. In addition, this combination also improves the responsiveness and stability of the system. By uniformly coordinating the node operation status and document load pressure, it reduces performance bottlenecks caused by sudden high loads and enhances the overall service quality and user experience of the system. Ultimately, this mechanism provides an efficient, flexible and scalable solution for the operation optimization of distributed storage nodes.

[0096] Furthermore, Figure 5 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0097] Among them, in another preferred embodiment provided by the present invention, a cloud document management platform based on blockchain technology includes:

[0098] The initial priority determination module 100 is used to determine the initial operation priority index of the target document and obtain the file size of the target document after the user uploads the target document to the designated distributed cloud storage node driven by the blockchain.

[0099] In the embodiment of the present invention, the designated distributed cloud storage node driven by blockchain refers to a specific node in a distributed storage system based on blockchain technology. These nodes are interconnected through a blockchain network and are responsible for storing, verifying and managing documents uploaded by users. The characteristics of blockchain enable these nodes to provide data immutability, operational transparency and trusted storage services. When a user uploads a target document, the system will allocate it to a designated storage node based on the document attributes and user needs, and the node will be recorded in the blockchain to ensure traceability and security.

[0100] The initial operation priority index is an indicator used to measure the importance of the target document in storage and access. It is determined based on the basic attributes of the target document, including factors such as file category, user role, and file volume. Common technical methods include weight calculation and rule engine, which generate priority indexes by setting weight values ​​for each attribute and calculating weighted scores. For example, rule engines (such as Drools) can set basic priorities for documents of different categories according to predefined rules, and then dynamically adjust the final score based on file volume and user role.

[0101] The file size of the target document is obtained through the metadata extraction mechanism of the storage node when receiving the document upload. During the file upload process, the system automatically reads the basic properties of the document and generates metadata, including the file size, type, format, etc. This is usually achieved by relying on the standard functions of the file upload interface (such as HTTP multipart request parsing) or the built-in file attribute recording module in the distributed storage network (such as IPFS). The file size information is recorded as one of the core metadata of the target document, which is used for subsequent priority calculation and storage scheduling.

[0102] Furthermore, the cloud document management platform based on blockchain technology also includes:

[0103] The target node state parameter determination module 200 is used to obtain the user's historical query log for the specified distributed cloud storage node, determine the user's access heat interval based on the historical query log, and determine the target node state parameter of the specified distributed cloud storage node for the access heat interval.

[0104] Specifically, Figure 6 It shows a structural block diagram of a target node state parameter determination module 200 in a system provided by an embodiment of the present invention.

[0105] Among them, in the preferred implementation manner provided by the present invention, the target node state parameter determination module 200 specifically includes:

[0106] The data query unit 201 is used to extract user access records from the historical query logs of the designated distributed cloud storage node, including the data of timestamp, query category, query frequency and query duration, generate access frequency curves through statistical analysis of the time dimension, locate access popularity intervals, and identify peak and trough periods of query behavior;

[0107] The target node status parameter acquisition unit 202 is used to dynamically monitor the target node status parameters of the designated distributed cloud storage node according to the time range of the access heat interval, including response time, bandwidth occupancy, load rate and storage capacity occupancy.

[0108] The access heat interval refers to the distribution pattern and concentration trend of the user's query behavior on the target category documents on the specified distributed cloud storage node in the time dimension, specifically including the statistical value of the query frequency and the time period corresponding to the high-frequency access.

[0109] In an embodiment of the present invention, user access records are extracted from the historical query logs of the designated distributed cloud storage node, including data such as timestamp, query category, query frequency, and query duration, and these records are classified and organized according to the time dimension. Through statistical analysis methods, such as time series aggregation and peak detection of query frequency and duration, a complete access frequency curve is generated to reflect the distribution of user query behavior in different time periods. Combined with the analysis results of the access frequency curve, the access heat interval is located, that is, the high-frequency period and low-frequency period of user query behavior are clarified, providing a time range basis for subsequent node status monitoring.

[0110] After determining the access heat interval, dynamically monitor the target node status parameters of the specified distributed cloud storage node within the heat interval. This includes collecting key parameters such as the node's response time, bandwidth occupancy, load rate, and storage capacity occupancy rate through real-time monitoring tools. In order to ensure the accuracy and timeliness of the data, the monitoring tool will sample the target node's status parameters in time-sharing and generate a status parameter data set corresponding to the access heat interval. After processing, these data can clearly describe the operating status and performance of the target node during the high-frequency access period.

[0111] During the entire implementation process, the analysis of historical query logs and the determination of access popularity intervals were completed through statistical and time series analysis methods, while the monitoring of target node status parameters relied on the status acquisition module and real-time monitoring tools of the distributed storage system. The combination of these data provides detailed basic data for further node status evaluation and optimization.

[0112] Furthermore, the cloud document management platform based on blockchain technology also includes:

[0113] The secondary priority generation module 300 is used to evaluate the difference between the target node state parameters and the preset standard node state model, quantify the degree of state deviation, make a first correction to the initial operation priority index of the target document based on the degree of deviation, and generate a secondary operation priority index.

[0114] Specifically, Figure 7 It shows a structural block diagram of the secondary priority generation module 300 in the system provided by the embodiment of the present invention.

[0115] In a preferred embodiment of the present invention, the secondary priority generation module 300 specifically includes:

[0116] The parameter comparison unit 301 is used to compare the target node state parameter with the corresponding parameter in the preset standard node state model item by item, calculate the difference value of each parameter, and standardize these difference values ​​using a unified proportional formula;

[0117] The deviation degree determination unit 302 is used to collect the difference values ​​of all parameters, assign weights according to the importance of the parameters, perform weighted comprehensive processing on these difference values, generate a quantitative index, and set the quantitative index as the deviation degree of the target node state parameter relative to the preset standard node state model;

[0118] The secondary priority generation unit 303 is used to multiply the quantization index by the initial operation priority index to obtain a first supplementary priority index, and then add the first supplementary priority index to the initial operation priority index to generate a secondary operation priority index.

[0119] The preset standard node status model refers to a node performance benchmark set based on the historical operation data of a specified distributed cloud storage node, including the standard value ranges of parameters such as response time, bandwidth occupancy, load rate, and storage capacity occupancy, which serves as a comparison reference for the target node status parameters and is used to evaluate the node operation health status.

[0120] In an embodiment of the present invention, by comparing the target node status parameters with the corresponding parameters in the preset standard node status model item by item, the real-time status data of the target node is first collected, including response time, bandwidth occupancy, load rate and storage capacity occupancy. These data are dynamically obtained from the operating environment of the specified distributed cloud storage node through distributed monitoring tools (such as Prometheus or Zabbix), and the data is cleaned to remove outliers and incomplete records to ensure the accuracy and reliability of the comparison.

[0121] For each parameter, the difference between it and the corresponding parameter in the standard node state model is calculated, and the formula is: Difference value = (target parameter value - standard parameter value) / standard parameter value. In order to ensure the dimensional consistency of the difference values ​​of different parameters, the calculated difference values ​​are standardized and normalized to a uniform scale range (such as -1 to 1). This standardization process usually relies on data normalization tools or standard statistical methods (such as Min-Max Scaling) to eliminate the influence of parameter scale.

[0122] All the standardized difference values ​​are collected and weighted according to the importance of each parameter to the health of the node. For example, the weights of response time and bandwidth utilization can be set to higher values ​​because they have a more direct impact on the user experience; the weight of storage capacity utilization is lower because it has less impact on real-time performance. The difference values ​​of each parameter are weighted and comprehensively processed to generate a comprehensive quantitative index that represents the overall deviation of the target node status from the preset standard node status model. This quantitative index is calculated comprehensively through a formula and stored as key reference data.

[0123] The quantitative index is used as the quantitative result of the health status of the target node to correct the initial operation priority index of the target document. The specific operation is to multiply the quantitative index by the initial operation priority index to obtain the first supplementary priority index. Subsequently, the first supplementary priority index is added to the initial operation priority index to generate the secondary operation priority index. Through this correction method, the secondary operation priority index can dynamically reflect the impact of the current operation status of the target node on the document operation priority.

[0124] Using this as a reference factor for generating the secondary operation priority index has good prospects and practical benefits. First, it can accurately quantify the operating health of the target node, and through priority correction, prioritize resources to document operations that need more protection. Secondly, combined with real-time monitoring and dynamic calculation, this method makes priority adjustment more flexible and dynamic responsive, which can effectively improve the resource utilization efficiency of distributed storage nodes. In addition, this mechanism enhances the responsiveness to user needs, optimizes document scheduling and storage management strategies through quantitative evaluation of node status, and helps to achieve more efficient service delivery and resource allocation. Finally, this method has great scalability in high-concurrency, multi-node distributed storage scenarios, and can adapt to complex business scenarios and dynamically changing operating environments.

[0125] Furthermore, the cloud document management platform based on blockchain technology also includes:

[0126] The final priority generation module 400 is used to simulate the load condition of the target document on the designated distributed cloud storage node in combination with the file volume of the target document, and to perform deviation analysis on it compared with the standard load model to obtain a load deviation coefficient, and to perform a second correction on the secondary operation priority index according to the load deviation coefficient to obtain a final operation priority index.

[0127] The standard load model refers to a node load reference benchmark established based on the simulation test results of historical load data of a specified distributed cloud storage node, including the average or optimal values ​​of the node's load rate, I / O performance, and bandwidth occupancy under different file volume conditions, which is used to measure the resource consumption level of the target document on the node and perform deviation analysis.

[0128] In an embodiment of the present invention, after combining the file volume of the target document, the load condition of the designated distributed cloud storage node when processing the target document is first simulated. The resource occupancy value that may be caused by the document operation is calculated through simulation analysis, including key load parameters such as load rate, I / O performance and bandwidth consumption. This simulation process usually relies on load simulation tools or the performance analysis module of the distributed storage node, such as using a dynamic simulation model (such as AnyLogic) to predict the processing performance of the node, or using the node's real-time performance monitoring system to generate estimated load data.

[0129] Compare the simulated load parameters with the corresponding values ​​in the standard load model one by one, and calculate the deviation value of each parameter. The deviation value calculation formula is: deviation value = (simulated value - standard value) / standard value. In order to ensure the consistency of the comparison of different load parameters, a unified proportional formula is used to standardize the deviation value, normalize it to a fixed range (such as -1 to 1), and generate a standard deviation value. This process relies on standard data processing techniques (such as Min-Max Scaling) to ensure the fairness of load parameters in calculation.

[0130] After collecting the standardized deviation values ​​of all load parameters, a weighted comprehensive processing is performed based on the weight of each parameter's impact on the node load. Load rate and I / O performance are usually given higher weights because they have a more direct impact on node performance, while bandwidth consumption may have a lower weight. Through weighted calculation, a load deviation coefficient is generated as a comprehensive indicator to measure the resource pressure of the target document on the specified distributed cloud storage node.

[0131] The secondary operation priority index is corrected using the generated load deviation coefficient, and the load deviation coefficient is multiplied by the secondary operation priority index to generate a second supplementary priority index. Then, the second supplementary priority index is added to the secondary operation priority index to generate a final operation priority index.

[0132] The priority index can dynamically reflect the impact of the target document on the node resource load. By comprehensively analyzing the resource usage of the target document on the current node, a priority result that can be adjusted in real time is generated. Combined with the node operation status parameters, the priority index can further reflect the overall health status and resource carrying capacity of the node within a specific time window. When the node operation status is relatively tense, the priority index will be dynamically increased to ensure the processing priority of the target document and avoid delays in key operations; when the node operation status is good, the priority index will be appropriately reduced to optimize the balance of system resource scheduling.

[0133] The connection with the previous node operation status is that the calculation of the priority index not only considers the node health status (such as response time, bandwidth occupancy, etc.), but also further refines the direct impact of the specific operation of the target document on the node load. The node operation status parameters provide overall environmental information, while the load deviation coefficient reflects the actual interaction effect between the document and the node by simulating the operation pressure of the specific document. The combination of the two can not only evaluate the current operation capacity of the node, but also predict the potential impact of the target document operation on the node, thereby achieving dual optimization from global to local.

[0134] The benefit of this combination is that it makes document scheduling more intelligent and refined. Through the dynamic adjustment of the priority index, it can not only prioritize the processing of key documents under high load conditions, but also optimize resource allocation efficiency when node resources are loose to avoid resource waste. In addition, this combination also improves the responsiveness and stability of the system. By uniformly coordinating the node operation status and document load pressure, it reduces performance bottlenecks caused by sudden high loads and enhances the overall service quality and user experience of the system. Ultimately, this mechanism provides an efficient, flexible and scalable solution for the operation optimization of distributed storage nodes.

[0135] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0136] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0137] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cloud document management method based on blockchain technology, characterized in that: The method comprises: After the user uploads the target document to the designated distributed cloud storage node driven by the blockchain, the initial operation priority index of the target document is determined, and the file volume of the target document is obtained; Obtain the user's historical query log for the specified distributed cloud storage node, determine the user's access heat interval based on the historical query log, and determine the target node state parameter of the specified distributed cloud storage node for the access heat interval; Evaluate the difference between the target node state parameters and the preset standard node state model, quantify the state deviation degree, and make a first correction to the initial operation priority index of the target document based on the deviation degree to generate a secondary operation priority index; Combined with the file volume of the target document, the load condition of the specified distributed cloud storage node for the target document is simulated, and a deviation analysis is performed between it and the standard load model to obtain the load deviation coefficient. According to the load deviation coefficient, the secondary operation priority index is corrected for the second time to obtain the final operation priority index.

2. The cloud document management method based on blockchain technology according to claim 1 is characterized in that: The access heat interval refers to the distribution pattern and concentration trend of the user's query behavior on the target category documents on the specified distributed cloud storage node in the time dimension, specifically including the statistical value of the query frequency and the time period corresponding to the high-frequency access.

3. The cloud document management method based on blockchain technology according to claim 1 is characterized in that: The steps of obtaining a user's historical query log for a specified distributed cloud storage node, determining the user's access heat interval according to the historical query log, and determining a target node state parameter of the specified distributed cloud storage node for the access heat interval include: Extract user access records from the historical query logs of the specified distributed cloud storage nodes, including timestamps, query categories, query frequency, and query duration data. Generate access frequency curves through statistical analysis of the time dimension, locate access popularity intervals, and identify peak and trough periods of query behavior. For the time range of the access popularity interval, dynamically monitor the target node status parameters of the specified distributed cloud storage node, including response time, bandwidth occupancy, load rate and storage capacity occupancy.

4. The cloud document management method based on blockchain technology according to claim 1 is characterized in that: The preset standard node status model refers to a node performance benchmark set based on the historical operation data of a specified distributed cloud storage node, including the standard value ranges of parameters such as response time, bandwidth occupancy, load rate, and storage capacity occupancy, which is used as a comparison reference for the target node status parameters to evaluate the node operation health status; The standard load model refers to a node load reference benchmark established based on the simulation test results of historical load data of a specified distributed cloud storage node, including the average or optimal values ​​of the node's load rate, I / O performance, and bandwidth occupancy under different file volume conditions, which is used to measure the resource consumption level of the target document on the node and perform deviation analysis.

5. The cloud document management method based on blockchain technology according to claim 4 is characterized in that: The difference between the target node state parameter and the preset standard node state model is evaluated to quantify the state deviation degree, and the initial operation priority index of the target document is first corrected based on the deviation degree. The steps of generating the secondary operation priority index include: Compare the target node state parameters with the corresponding parameters in the preset standard node state model item by item, calculate the difference value of each parameter, and standardize these difference values ​​using a unified proportional formula; Collect the difference values ​​of all parameters, assign weights according to the importance of the parameters, perform weighted comprehensive processing on these difference values, generate quantitative indicators, and set the quantitative indicators as the degree of deviation of the target node state parameters from the preset standard node state model; Multiply the quantitative index by the initial operation priority index to obtain the first supplement priority index, and then add the first supplement priority index to the initial operation priority index to generate the secondary operation priority index.

6. The cloud document management method based on blockchain technology according to claim 1 is characterized in that: Combined with the file volume of the target document, the load status of the designated distributed cloud storage node for the target document is simulated, and the load deviation coefficient is analyzed with the standard load model to obtain the load deviation coefficient. The secondary operation priority index is corrected for the second time according to the load deviation coefficient. The steps of obtaining the final operation priority index include: Combined with the file size of the target document, simulate the load status of the specified distributed cloud storage node when processing the target document, and calculate the resource usage value caused by the target document operation, including load parameters such as load rate, I / O performance and bandwidth consumption; Compare the simulated load parameters with the corresponding values ​​in the standard load model item by item, calculate the deviation values, and standardize these deviation values ​​through a unified proportional formula to obtain the standard deviation value; Collect the standardized deviation values ​​of all load parameters, and perform weighted comprehensive processing based on the weight of the parameters' impact on the node load to generate a load deviation coefficient; The load deviation coefficient is multiplied by the secondary operation priority index to obtain a second supplementary priority index, and then the second supplementary priority index is added to the secondary operation priority index to generate a final operation priority index.

7. A cloud document management platform based on blockchain technology, characterized in that: The system comprises: an initial priority determination module, a target node state parameter determination module, a secondary priority generation module and a final priority generation module, wherein: An initial priority determination module, used to determine the initial operation priority index of the target document and obtain the file volume of the target document after the user uploads the target document to the designated distributed cloud storage node driven by the blockchain; The target node state parameter determination module is used to obtain the user's historical query log for the specified distributed cloud storage node, determine the user's access heat interval based on the historical query log, and determine the target node state parameter of the specified distributed cloud storage node for the access heat interval; A secondary priority generation module is used to evaluate the difference between the target node state parameters and the preset standard node state model, quantify the state deviation degree, and make a first correction to the initial operation priority index of the target document based on the deviation degree to generate a secondary operation priority index; The final priority generation module is used to simulate the load status of the target document on the designated distributed cloud storage node in combination with the file volume of the target document, and to perform deviation analysis on the load status with the standard load model to obtain the load deviation coefficient. The secondary operation priority index is corrected for the second time according to the load deviation coefficient to obtain the final operation priority index. The access heat interval refers to the distribution pattern and concentration trend of the user's query behavior on the target category documents on the specified distributed cloud storage node in the time dimension, specifically including the statistical value of the query frequency and the time period corresponding to the high-frequency access.

8. The cloud document management platform based on blockchain technology according to claim 7 is characterized in that: The target node state parameter determination module specifically includes: The data query unit is used to extract user access records from the historical query logs of the specified distributed cloud storage node, including timestamp, query category, query frequency and query duration data, generate access frequency curves through statistical analysis of the time dimension, locate access popularity intervals, and identify peak and trough periods of query behavior; The target node status parameter acquisition unit is used to dynamically monitor the target node status parameters of the designated distributed cloud storage node according to the time range of the access heat interval, including response time, bandwidth occupancy, load rate and storage capacity occupancy.

9. The cloud document management platform based on blockchain technology according to claim 8, characterized in that: The preset standard node status model refers to a node performance benchmark set based on the historical operation data of a specified distributed cloud storage node, including the standard value ranges of parameters such as response time, bandwidth occupancy, load rate, and storage capacity occupancy, which is used as a comparison reference for the target node status parameters to evaluate the node operation health status; The standard load model refers to a node load reference benchmark established based on the simulation test results of historical load data of a specified distributed cloud storage node, including the average or optimal values ​​of the node's load rate, I / O performance, and bandwidth occupancy under different file volume conditions, which is used to measure the resource consumption level of the target document on the node and perform deviation analysis.

10. The cloud document management platform based on blockchain technology according to claim 9, characterized in that: The secondary priority generation module specifically includes: A parameter comparison unit is used to compare the target node state parameters with the corresponding parameters in the preset standard node state model item by item, calculate the difference value of each parameter, and standardize these difference values ​​using a unified proportional formula; The deviation degree determination unit is used to collect the difference values ​​of all parameters, assign weights according to the importance of the parameters, perform weighted comprehensive processing on these difference values, generate a quantitative index, and set the quantitative index as the deviation degree of the target node state parameter relative to the preset standard node state model; The secondary priority generation unit is used to multiply the quantitative index by the initial operation priority index to obtain a first supplementary priority index, and then add the first supplementary priority index to the initial operation priority index to generate a secondary operation priority index.

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