A cloud document management method and platform based on blockchain technology

Abstract

The application is suitable for the technical field of cloud document management, and provides a cloud document management method and platform based on blockchain technology, after a user uploads a target document to a specified distributed cloud storage node driven by a blockchain, an initial operation priority index of the target document is determined, and a file volume of the target document is obtained; a historical query log of the user for the specified distributed cloud storage node is obtained, an access heat interval of the user is determined according to the historical query log, and a target node state parameter of the specified distributed cloud storage node for the access heat interval is determined. The priority index finally generated by the application comprehensively considers document characteristics, user behavior and node performance, so that document scheduling is more intelligent and accurate, the resource allocation efficiency and node service response capability are significantly improved, and the overall performance and user experience of the distributed cloud storage system are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of cloud document management, and particularly relates to a cloud document management method and platform based on blockchain technology. BACKGROUND

[0002] At present, cloud document management technology has been widely applied in various distributed storage systems, providing reliable and efficient document storage and access services for users. Traditional cloud document management methods mainly rely on fixed rules or preset strategies for document storage and scheduling. These methods usually determine the operation order and resource allocation of documents through simple priority division or initial setting based on document categories. However, these priority setting methods are relatively static and cannot dynamically adapt to complex node running environments and user demand changes. In high-concurrency and large-scale document operation scenarios, it is easy to cause uneven resource allocation or node load imbalance problems.

[0003] In the prior art, the setting of document operation priority lacks rationality and comprehensiveness, and is often based on a single factor (such as document category or file size) for static setting, without fully considering user behavior, node state and the actual impact of document operation on system resources. This deficiency leads to that the node resource allocation cannot respond to the actual demand in real time when the document is frequently accessed, which may cause system performance degradation; at the same time, in the case of poor node running condition, the priority cannot be dynamically adjusted to avoid bottlenecks, which affects the stability of the overall service and user experience. In addition, the lack of simulation analysis of node load conditions makes the resource scheduling unable to be accurately optimized, further exacerbating the waste of system resources and low scheduling efficiency. SUMMARY

[0004] The purpose of the present application is to provide a cloud document management method and platform based on blockchain technology, which aims to solve the problems proposed in the background art.

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

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

[0007] The historical query log of the user for the specified distributed cloud storage node is obtained, and the access heat interval of the user is determined according to the historical query log, and the target node state parameter of the specified distributed cloud storage node for the access heat interval is determined;

[0008] The target node state parameter is differentially evaluated with a preset standard node state model, a state deviation degree is quantified, a first correction is made to an initial operation priority index of the target document based on the deviation degree, and a secondary operation priority index is generated;

[0009] In combination with a file volume of the target document, a load condition of the specified distributed cloud storage node for the target document is simulated, and deviation analysis is made on the load condition with a standard load model to obtain a load deviation coefficient, a second correction is made to the secondary operation priority index according to the load deviation coefficient, and a final operation priority index is obtained.

[0010] As a further limitation of the technical scheme of the embodiment of the application, the access heat interval refers to the distribution rule and concentration trend of the query behavior of the user on the target category document in the time dimension on the specified distributed cloud storage node, and specifically includes a statistical value of the query frequency and a time period corresponding to the high-frequency access.

[0011] As a further limitation of the technical scheme of the embodiment of the application, the historical query log of the user for the specified distributed cloud storage node is obtained, and the access heat interval of the user is determined according to the historical query log, and the step of determining the target node state parameter of the specified distributed cloud storage node for the access heat interval includes:

[0012] The user access record is extracted from the historical query log of the specified distributed cloud storage node, including the data of the timestamp, the query category, the query frequency and the query duration, the access frequency curve is generated through the statistical analysis in the time dimension, the access heat interval is located, and the peak and trough periods of the query behavior are determined;

[0013] The target node state parameter of the specified distributed cloud storage node is dynamically monitored for the time range of the access heat interval, including the response time, the bandwidth occupancy rate, the load rate and the storage capacity occupancy rate.

[0014] As a further limitation of the technical scheme of the embodiment of the application, the preset standard node state model refers to a node performance benchmark set based on the historical running data of the specified distributed cloud storage node, including the standard value range of the parameters of the response time, the bandwidth occupancy rate, the load rate and the storage capacity occupancy rate, which is used as a comparison reference of the target node state parameter for evaluating the node running health condition;

[0015] The standard load model refers to a node load reference benchmark established according to the historical load data simulation test result of the specified distributed cloud storage node, including the average value or the best value of the load rate, the I / O performance and the bandwidth occupancy of the node under different file volume conditions, which is used for measuring the resource consumption level of the target document on the node and performing deviation analysis.

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

[0017] The target node state parameter is compared with the corresponding parameter in the preset standard node state model item by item, the difference value of each parameter is calculated, and the difference values are standardized by using a unified proportional formula;

[0018] The difference values of all parameters are collected, the difference values are weighted and integrated according to the importance of the parameters, a quantitative index is generated, and the quantitative index is set as the deviation degree of the target node state parameter relative to the preset standard node state model;

[0019] The quantitative index is multiplied by the initial operation priority index to obtain the first supplementary priority index, and the first supplementary priority index is added to the initial operation priority index to generate the secondary operation priority index.

[0020] As a further limitation of the technical scheme of the embodiment of the present application, the load condition of the specified distributed cloud storage node for the target document is simulated in combination with the file volume of the target document, and deviation analysis is performed on the standard load model to obtain a load deviation coefficient, the secondary operation priority index is second corrected according to the load deviation coefficient to obtain the final operation priority index.

[0021] The load condition of the specified distributed cloud storage node when processing the target document is simulated in combination with the file volume of the target document, the resource occupation value caused by the target document operation is calculated, including the load parameters of the load rate, the I / O performance and the bandwidth consumption;

[0022] The simulated load parameters are compared with the corresponding values in the standard load model item by item, the deviation values are calculated, and the deviation values are standardized by using a unified proportional formula to obtain standard deviation values;

[0023] The standardized deviation values of all load parameters are collected, and a load deviation coefficient is generated by weighted and integrated according to the influence weight of the parameters on the node load;

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

[0025] A cloud document management platform based on blockchain technology, 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:

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

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

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

[0029] The final priority generation module is used to combine the file volume of the target document, simulate the load condition of the designated distributed cloud storage node for the target document, and analyze the deviation of the standard load model to obtain a load deviation coefficient, and correct the secondary operation priority index for the second time according to the load deviation coefficient to obtain a final operation priority index;

[0030] The access heat interval refers to the distribution rule and concentration trend of the user's query behavior on the target category document in the time dimension on the designated distributed cloud storage node, 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 scheme of the embodiment of the application, the target node state parameter determination module specifically comprises:

[0032] The data query unit is used to extract user access records from the historical query log of the designated distributed cloud storage node, including timestamp, query category, query frequency and query duration data, generate an access frequency curve through time dimension statistical analysis, locate the access heat interval, and determine the peak and trough periods of the query behavior;

[0033] The target node state parameter acquisition unit is used to dynamically monitor the target node state parameters of the designated distributed cloud storage node within 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 scheme of the embodiment of the application, the preset standard node state model refers to a node performance benchmark set based on historical running data of the specified distributed cloud storage node, including standard value ranges of parameters such as response time, bandwidth occupancy rate, load rate and storage capacity occupancy rate, which are used as a comparison reference of target node state parameters for evaluating the running health status of the node.

[0035] The standard load model refers to a node load reference benchmark established according to historical load data simulation test results of the specified distributed cloud storage node, including average values or optimal values of load rate, I / O performance and bandwidth occupancy of the node under different file volume conditions, which are 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 scheme of the embodiment of the application, the secondary priority generation module specifically includes:

[0037] The parameter comparison unit is configured to compare the target node state parameters with corresponding parameters in the preset standard node state model item by item, calculate the difference values of each parameter, and standardize these difference values by using a unified proportion formula.

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

[0039] The secondary priority generation unit is configured to multiply the quantitative index by the initial operation priority index to obtain a first supplementary priority index, and 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 application has the following beneficial effects:

[0041] The application can realize dynamic adjustment of the full-process priority of the target document, and optimize the storage and scheduling efficiency of the document in the specified distributed cloud storage node driven by the blockchain. By setting the initial operation priority index, the basic importance of the target document can be quickly determined; in combination with the access heat interval of the user and the evaluation of the target node state parameters, the priority is dynamically corrected to adapt to the node running status; through simulation analysis of the load condition, the priority adjustment is further refined, realizing real-time response and precise optimization of the node resource pressure. The finally generated priority index comprehensively considers the document characteristics, user behavior and node performance, making the document scheduling more intelligent and precise, significantly improving the resource allocation efficiency and node service response ability, and helping to enhance the overall performance and user experience of the distributed cloud storage system. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A flow chart of the method provided by the embodiment of the present application is shown.

[0043] Figure 2 A flow chart of determining the target node state parameter in the method provided by the embodiment of the present application is shown.

[0044] Figure 3 A flow chart of generating the secondary operation priority index in the method provided by the embodiment of the present application is shown.

[0045] Figure 4 A flow chart of obtaining the final operation priority index in the method provided by the embodiment of the present application is shown.

[0046] Figure 5 An application architecture diagram of the system provided by the embodiment of the present application is shown.

[0047] Figure 6 A structural block diagram of the target node state parameter determination module in the system provided by the embodiment of the present application is shown.

[0048] Figure 7 A structural block diagram of the secondary priority generation module in the system provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0050] It can be understood that the terms "first", "second", and the like used in the present application can be used herein to describe various elements, but unless specifically stated, these elements are not limited by these terms. These terms are only used to distinguish the first element from another element. For example, without departing from the scope of the present application, the first xx script can be referred to as the second xx script, and similarly, the second xx script can be referred to as the first xx script.

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

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

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

[0054] In the embodiments of the present application, the blockchain-driven designated distributed cloud storage node 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 user-uploaded documents. The characteristics of blockchain enable these nodes to provide data tamper-proofing, 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's attributes and user requirements. This node will be recorded in the blockchain to ensure traceability and security.

[0055] The initial operation priority index is an indicator for measuring the importance of the target document in storage and access. It is determined based on the basic attributes of the target document, including file category, user role, file size, and other factors. Common technical methods include weight calculation and rule engine, which generate a priority index by setting weight values for each attribute and calculating a weighted score. For example, a rule engine (such as Drools) can set a basic priority for different categories of documents according to predefined rules, and then dynamically adjust the final score based on file size 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 attributes of the document and generates metadata, including file size, type, format, etc. This usually relies 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) to achieve it. 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] Further, the cloud document management method based on the blockchain technology further comprises the following steps:

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

[0059] Specifically, Figure 2 A flowchart for determining the target node state parameter is shown.

[0060] Among them, obtaining the historical query log of the user for the designated distributed cloud storage node, and determining the access heat interval of the user according to the historical query log, and determining the target node state parameter of the designated distributed cloud storage node for the access heat interval specifically includes the following steps:

[0061] Step S201: Extract user access records from the historical query logs of the designated distributed cloud storage node, including timestamps, query categories, query frequency, and query duration. Generate an access frequency curve through statistical analysis of the time dimension, locate access popularity intervals, and identify peak and trough periods of query behavior.

[0062] Step S202 , dynamically monitoring target node status parameters of a designated distributed cloud storage node within a time range of the access popularity 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 a 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, including data such as timestamps, query categories, query frequency, and query duration, are extracted from the historical query logs of a specified distributed cloud storage node, 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 and low-frequency periods of user query behavior are clearly identified, providing a time range basis for subsequent node status monitoring.

[0065] After determining the access popularity interval, the target node status parameters of the designated distributed cloud storage nodes within this interval are dynamically monitored. This involves using real-time monitoring tools to collect key parameters such as node response time, bandwidth utilization, load rate, and storage capacity utilization. To ensure data accuracy and timeliness, the monitoring tool samples the target node status parameters on a time-sharing basis and generates a data set of status parameters corresponding to the access popularity interval. After processing, this data clearly describes the target node's operating status and performance during high-frequency access periods.

[0066] Throughout the implementation process, historical query log analysis and access popularity interval determination were completed through statistical and time series analysis methods. Monitoring target node status parameters relied on the distributed storage system's status collection module and real-time monitoring tools. This combined data provided a detailed foundation for further node status assessment and optimization.

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

[0068] Step S300, the target node state parameter is compared with the preset standard node state model, the state deviation degree is quantified, the initial operation priority index of the target document is modified for the first time based on the deviation degree, and a secondary operation priority index is generated.

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

[0070] The target node state parameter is compared with the preset standard node state model, the state deviation degree is quantified, the initial operation priority index of the target document is modified for the first time based on the deviation degree, and a secondary operation priority index is generated, which specifically includes the following steps:

[0071] Step S301, the target node state parameter is compared with the corresponding parameter in the preset standard node state model, the difference value of each parameter is calculated, and the difference values are standardized by using a unified proportion formula;

[0072] Step S302, collect the difference values of all parameters, assign weights according to the importance of the parameters, and perform weighted comprehensive processing on the difference values to 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;

[0073] Step S303, multiply the quantitative index by the initial operation priority index to obtain a first supplementary priority index, and add the first supplementary priority index to the initial operation priority index to generate a secondary operation priority index.

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

[0075] In the embodiment of the application, by comparing the target node state parameter with the corresponding parameter in the preset standard node state model, the real-time state data of the target node is first collected, including response time, bandwidth occupancy rate, load rate and storage capacity occupancy rate. These data are dynamically obtained from the running environment of the specified distributed cloud storage node by a distributed monitoring tool (such as Prometheus or Zabbix), and at the same time, the data are cleaned to remove abnormal values and incomplete records to ensure the accuracy and reliability of the comparison.

[0076] For each parameter, the difference value between the target parameter value and the corresponding parameter value in the standard node state model is calculated, and the formula is: difference value = (target parameter value - standard parameter value) / standard parameter value. To ensure the dimensional consistency of different parameter difference values, the calculated difference values are standardized and normalized to a unified proportion 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 standardized difference values are collected and weighted according to the importance of each parameter to the node running health status. For example, the weights of response time and bandwidth occupancy can be set to higher values because they have a more direct impact on user experience; the weight of storage capacity occupancy is lower because it has less impact on real-time performance. The difference values of each parameter are weighted and integrated to generate a comprehensive quantitative index representing the overall deviation of the target node state from the preset standard node state model. The quantitative index is calculated by the formula and stored as key reference data.

[0078] The quantitative index is used as the quantitative result of the target node state health status 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. Then, 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 influence of the current running state of the target node on the document operation priority.

[0079] This method has good prospects and practical benefits as a reference factor for generating the secondary operation priority index. First, it can accurately quantify the running health status of the target node and allocate resources to the document operation that needs more protection through priority correction. Second, combined with real-time monitoring and dynamic calculation, this method makes the priority adjustment more flexible and responsive, effectively improving the resource utilization efficiency of distributed storage nodes. In addition, this mechanism enhances the response ability to user demand, optimizes the document scheduling and storage management strategy through quantitative evaluation of node state, which helps to achieve more efficient service delivery and resource allocation. Finally, this method has great scalability in high-concurrency and multi-node distributed storage scenarios, and can adapt to complex business scenarios and dynamic changes in the running environment.

[0080] Further, the cloud document management method based on blockchain technology further comprises the following steps:

[0081] Step S400, in combination with the file size of the target document, simulate the load condition of the specified distributed cloud storage node for the target document, and analyze the deviation with the standard load model to obtain a load deviation coefficient, and secondly correct the secondary operation priority index according to the load deviation coefficient to obtain the final operation priority index.

[0082] Specifically, Figure 4 The flowchart for obtaining the final operation priority index is shown.

[0083] In combination with the file size of the target document, simulate the load condition of the specified distributed cloud storage node for the target document, and analyze the deviation with the standard load model to obtain a load deviation coefficient, and secondly correct the secondary operation priority index according to the load deviation coefficient to obtain the final operation priority index, which specifically includes the following steps:

[0084] Step S401, in combination with the file size of the target document, simulate the load condition of the specified distributed cloud storage node when processing the target document, calculate the resource occupation value caused by the target document operation, including load rate, I / O performance and bandwidth consumption load parameters;

[0085] Step S402, 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 proportion formula to obtain the standard deviation values;

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

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

[0088] The standard load model refers to the node load reference benchmark established according to the historical load data simulation test results of the specified distributed cloud storage node, including the average or best value of the load rate, I / O performance and bandwidth occupation of the node under different file size conditions, which is used to measure the resource consumption level of the target document on the node and perform deviation analysis.

[0089] In the embodiment of the present application, after combining the file volume of the target document, the load condition of the specified distributed cloud storage node in processing the target document is first simulated. The resource occupation value that the document operation may cause 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 a load simulation tool or a performance analysis module of the distributed storage node itself, such as using a dynamic simulation model (such as AnyLogic) to predict the processing performance of the node, or using a real-time performance monitoring system of the node to generate estimated load data.

[0090] The simulated load parameters are compared with the corresponding values in the standard load model item by item, and the deviation value of each parameter is calculated. The deviation value calculation formula is: deviation value = (simulated value - standard value) / standard value. To ensure the consistency of comparison of different load parameters, a unified proportional formula is used to standardize the deviation value, which is normalized to a fixed range (such as -1 to 1) to 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, weighted comprehensive processing is performed according to the influence weight of each parameter on the node load. Load rate and I / O performance are usually given higher weight because they have more direct impact on node performance; while bandwidth consumption may have lower weight. Through weighted calculation, a load deviation coefficient is generated as a comprehensive index to measure the resource pressure of the target document on the specified distributed cloud storage node.

[0092] The generated load deviation coefficient is used to modify the secondary operation priority index, 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 load impact of the target document on the node resources, and through comprehensive analysis of the resource occupation of the target document on the current node, a priority result that can be adjusted in real time is generated. Combined with the node running state parameters, the priority index can further reflect the overall health status and resource carrying capacity of the node in a specific time window. When the node running state is tense, the priority index will be dynamically increased to ensure the processing priority of the target document and avoid delay of critical operations; when the node running state is good, the priority index will be appropriately reduced to optimize the balance of system resource scheduling.

[0094] The connection with the running state of the previous node is that the priority index calculation 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 running state parameter provides overall environmental information, and the load deviation coefficient reflects the actual interaction effect between the document and the node by simulating the operation pressure of the specific document. After the combination of the two, not only the current running ability of the node can be evaluated, but also the potential impact of the target document operation on the node can be predicted, so as to realize the dual optimization from the global to the local.

[0095] The benefit of this combination is that it makes the document scheduling more intelligent and refined. Through the dynamic adjustment of the priority index, not only the processing of key documents can be prioritized in high-load situations, but also resource allocation efficiency can be optimized when node resources are relaxed, avoiding resource waste. In addition, this combination also improves the response ability and stability of the system, by coordinating the node running state and the document load pressure, reducing the performance bottleneck caused by sudden high load, and enhancing the overall service quality and user experience of the system. Ultimately, this mechanism provides an efficient, flexible and scalable solution for the running optimization of distributed storage nodes.

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

[0097] In another preferred embodiment provided by the application, a cloud document management platform based on blockchain technology comprises:

[0098] The initial priority determination module 100 is configured 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 specified distributed cloud storage node driven by the blockchain.

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

[0100] The initial operation priority index is an index for measuring the importance of the target document in storage and access, which is determined according to the basic attributes of the target document, including file category, user role, file volume and other factors. Common technical methods include weight calculation and rule engine, which generate priority index by setting weight value for each attribute and calculating weighted score. For example, the rule engine (such as Drools) can set the basic priority of different category documents according to the pre-defined rules, and then dynamically adjust the final score combined with file volume and user role.

[0101] The file volume of the target document is obtained by the storage node through a metadata extraction mechanism when receiving the document upload. During the file upload process, the system automatically reads the basic attributes of the document and generates metadata, including the volume, type, format, etc. of the file. This usually relies 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) to achieve. The file volume information is recorded as one of the core metadata of the target document, which is used for subsequent priority calculation and storage scheduling.

[0102] Further, the cloud document management platform based on the blockchain technology further comprises:

[0103] The target node state parameter determination module 200 is configured to obtain historical query logs of the user for the specified distributed cloud storage node, and determine the access heat interval of the user and the target node state parameter of the specified distributed cloud storage node for the access heat interval according to the historical query logs.

[0104] Specifically, Figure 6 The structure block diagram of the target node state parameter determination module 200 in the system provided by the embodiment of the application is shown.

[0105] In the preferred embodiment provided by the application, the target node state parameter determination module 200 specifically comprises:

[0106] The data query unit 201 is configured 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 an access frequency curve through time dimension statistical analysis, locate the access heat interval, and determine the peak and trough periods of the query behavior;

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

[0108] The access heat interval refers to the distribution rule and concentration trend of the user's query behavior on the target category document 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 the embodiment of the application, user access records are extracted from the historical query log of the specified distributed cloud storage node, including timestamp, query category, query frequency and query duration data, etc. 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, reflecting 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 the trough period of user query behavior are determined, providing a time range basis for subsequent node state monitoring.

[0110] After determining the access heat interval, the target node state parameters of the specified distributed cloud storage node in the heat interval are dynamically monitored. This includes collecting key parameters such as response time, bandwidth occupancy, load rate and storage capacity occupancy of the node through real-time monitoring tools. In order to ensure the accuracy and timeliness of the data, the monitoring tools will perform time sampling on the state parameters of the target node, and generate a state parameter dataset corresponding to the access heat interval. After processing these data, the running status and performance of the target node in the high-frequency access period can be clearly described.

[0111] In the entire implementation process, the analysis of historical query logs and the determination of access heat interval are completed through statistical and time series analysis methods, and the monitoring of target node state parameters relies on the state collection module and real-time monitoring tools of the distributed storage system. These data, combined, provide detailed basic data for further node state evaluation and optimization.

[0112] Further, the cloud document management platform based on the blockchain technology further comprises:

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

[0114] Specifically, Figure 7 The structure block diagram of the secondary priority generation module 300 in the system provided by the embodiment of the application is shown.

[0115] In the preferred embodiment provided by the application, the secondary priority generation module 300 specifically comprises:

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

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

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

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

[0120] In the embodiment of the application, by comparing the target node state parameters with corresponding parameters in the preset standard node state model one by one, real-time state data of the target node is first collected, including response time, bandwidth occupancy rate, load rate and storage capacity occupancy rate. These data are dynamically obtained from the running environment of the specified distributed cloud storage node by a distributed monitoring tool (such as Prometheus or Zabbix), and at the same time, the data are cleaned to remove abnormal values and incomplete records, so as to ensure the accuracy and reliability of the comparison.

[0121] For each parameter, the difference value between the target parameter value and the standard parameter value is calculated, and the formula is: difference value=(target parameter value-standard parameter value) / standard parameter value. To ensure the dimensional consistency of the difference values of different parameters, the calculated difference values are standardized to normalize them to a unified proportional 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 scales.

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

[0123] The quantitative index is used as the quantitative result of the target node state health status to correct the initial operation priority index of the target document. Specifically, the quantitative index is multiplied by the initial operation priority index to obtain the first supplementary priority index. Then, 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 influence of the current running state of the target node on the document operation priority.

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

[0125] Further, the cloud document management platform based on blockchain technology further comprises:

[0126] The final priority generation module 400 is configured to simulate the load condition of the target document on the specified distributed cloud storage node in combination with the file size of the target document, and analyze the deviation from the standard load model to obtain a load deviation coefficient. The load deviation coefficient is used to make a second correction to the secondary operation priority index to obtain a final operation priority index.

[0127] The standard load model refers to a node load reference benchmark established according to historical load data simulation test results of the specified distributed cloud storage node, including average or optimal values of load rate, I / O performance and bandwidth occupation of the node under different file volume conditions, for measuring the resource consumption level of the target document on the node and performing deviation analysis.

[0128] In the embodiment of the application, after combining the file volume of the target document, the load condition of the specified distributed cloud storage node when processing the target document is first simulated. The resource occupation values that the document operation may cause are calculated through simulation analysis, including key load parameters such as load rate, I / O performance and bandwidth consumption. This simulation process usually relies on a load simulation tool or a performance analysis module of the distributed storage node itself, such as using a dynamic simulation model (such as AnyLogic) to predict the processing performance of the node, or using a real-time performance monitoring system of the node to generate estimated load data.

[0129] The simulated load parameters are compared with the corresponding values in the standard load model item by item, and the deviation value of each parameter is calculated. The deviation value calculation formula is: deviation value = (simulation value - standard value) / standard value. To ensure the consistency of comparison of different load parameters, the deviation value is standardized by using a unified proportional formula, which is normalized to a fixed range (such as -1 to 1) to 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, weighted comprehensive processing is performed according to the influence weight of each parameter on the node load. Load rate and I / O performance are usually given higher weights because they have more direct influence on node performance; while bandwidth consumption may have a lower weight. Through weighted calculation, a load deviation coefficient is generated as a comprehensive index for measuring the resource pressure of the target document on the specified distributed cloud storage node.

[0131] The generated load deviation coefficient is used to correct the secondary operation priority index, 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 load impact of the target document on the node resources, and generate a priority result that can be adjusted in real time by comprehensively analyzing the resource occupation of the target document on the current node. Combined with the node running state parameters, the priority index can further reflect the overall health status and resource carrying capacity of the node in a specific time window. When the node running state is relatively tense, the priority index will be dynamically increased to ensure the processing priority of the target document and avoid delay of critical operations; when the node running state is good, the priority index will be appropriately reduced to optimize the balance of system resource scheduling.

[0133] The connection with the previous node running state 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 running state 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. After the combination of the two, not only the current running ability of the node can be evaluated, but also the potential impact of the target document operation on the node can be predicted, thereby realizing dual optimization from the global to the local.

[0134] The benefit of this combination is that it makes the document scheduling more intelligent and refined. Through the dynamic adjustment of the priority index, not only can the processing of critical documents be prioritized in high-load situations, but also the resource allocation efficiency can be optimized when the node resources are relaxed, avoiding resource waste. In addition, this combination also improves the response capability and stability of the system, by coordinating the node running state and the document load pressure, reducing the performance bottleneck caused by sudden high load, and enhancing the overall service quality and user experience of the system. Ultimately, this mechanism provides an efficient, flexible and scalable solution for the running optimization of distributed storage nodes.

[0135] It should be understood that although each step in the flowchart of each embodiment of the present application is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps has no strict sequence limitation, and these steps can be executed in other orders. Moreover, at least part of the steps in each embodiment can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part 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. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), 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), etc.

[0137] Any combination of the technical features of the above-mentioned embodiments can be combined. In order to make the description simple, all possible combinations of the technical features in the above-mentioned embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0138] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the present application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

[0139] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

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, an initial operation priority index of the target document is determined, and a file size of the target document is obtained, where the initial operation priority index is determined based on at least a file category of the target document; Obtain the user's historical query log for the specified distributed cloud storage node, determine the user's access popularity interval based on the historical query log, and determine the target node state parameter of the specified distributed cloud storage node for the access popularity interval; Evaluate the difference between the target node state parameters and the preset standard node state model, quantify the state deviation, and make a first correction to the initial operation priority index of the target document based on the deviation to generate a secondary operation priority index; Based on the file size of the target document, the load of the specified distributed cloud storage node for the target document is simulated, and a deviation analysis is performed on the load deviation coefficient compared with the standard load model to obtain the load deviation coefficient. Based on the load deviation coefficient, the secondary operation priority index is revised for the second time to obtain the final operation priority index. The access popularity interval refers to the distribution pattern and concentration trend of users' query behavior for target category documents on a specified distributed cloud storage node in the time dimension, specifically including the statistical value of query frequency and the time period corresponding to high-frequency access; The preset standard node status model refers to a node performance benchmark set based on the historical operating data of a specified distributed cloud storage node, including standard value ranges for parameters such as response time, bandwidth utilization, load rate, and storage capacity utilization, which serves as a comparison reference for the target node status parameters and is used to evaluate the node's operating health; 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.

2. 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 popularity interval based on the historical query log, and determining a target node state parameter of the specified distributed cloud storage node for the access popularity interval include: Extract user access records from the historical query logs of designated distributed cloud storage nodes, including timestamps, query categories, query frequency, and query duration. Generate access frequency curves through statistical analysis over time, locate access popularity intervals, and identify peak and trough periods of query behavior. Dynamically monitor the target node status parameters of the specified distributed cloud storage node, including response time, bandwidth utilization, load rate, and storage capacity utilization, for the time range of the access popularity interval.

3. The cloud document management method based on blockchain technology according to claim 1 is characterized in that: The steps of evaluating the difference between the target node state parameters and the preset standard node state model, quantifying the degree of state deviation, and making a first correction to the initial operation priority index of the target document based on the degree of deviation, and generating a 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 a quantitative index, and set the quantitative index as the degree of deviation of the target node state parameter from the preset standard node state model; Multiply the quantitative index by the initial operation priority index to obtain the first supplementary priority index, and then add the first supplementary priority index to the initial operation priority index to generate the secondary operation priority index.

4. The cloud document management method based on blockchain technology according to claim 1 is characterized in that: The steps of simulating the load of the target document on a designated distributed cloud storage node based on the file size of the target document and performing deviation analysis on the load compared with the standard load model to obtain a load deviation coefficient are as follows: A second correction is made to the secondary operation priority index based on the load deviation coefficient to obtain the final operation priority index. Based on the target document's file size, simulate the load of a specified distributed cloud storage node when processing the target document, and calculate the resource usage 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 value, 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 the second supplementary priority index is added to the secondary operation priority index to generate a final operation priority index.

5. A cloud document management platform based on blockchain technology, characterized by: The platform 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: an initial priority determination module, configured to determine an initial operation priority index of a target document after the user uploads the target document to a designated distributed cloud storage node driven by the blockchain, and to obtain the file size of the target document, wherein the initial operation priority index is determined based on at least the file category of the target document; A target node state parameter determination module is used to obtain the user's historical query logs for the specified distributed cloud storage node, determine the user's access popularity interval based on the historical query logs, and determine the target node state parameters of the specified distributed cloud storage node for the access popularity 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 degree of state deviation, 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 of the target document on the specified distributed cloud storage node based on the file size of the target document, and perform deviation analysis with the standard load model to obtain the load deviation coefficient. Based on the load deviation coefficient, the secondary operation priority index is corrected for a second time to obtain the final operation priority index. The access popularity interval refers to the distribution pattern and concentration trend of users' query behavior for target category documents on a specified distributed cloud storage node in the time dimension, specifically including the statistical value of query frequency and the time period corresponding to high-frequency access; The preset standard node status model refers to a node performance benchmark set based on the historical operating data of a specified distributed cloud storage node, including standard value ranges for parameters such as response time, bandwidth utilization, load rate, and storage capacity utilization, which serves as a comparison reference for the target node status parameters and is used to evaluate the node's operating health; 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.

6. The cloud document management platform based on blockchain technology according to claim 5 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. Through statistical analysis of the time dimension, it generates an access frequency curve, locates the access popularity interval, and identifies the 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 specified distributed cloud storage node according to the time range of the access popularity interval, including response time, bandwidth occupancy, load rate and storage capacity occupancy.

7. The cloud document management platform based on blockchain technology according to claim 6 is 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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