CN116933237A - Block chain write permission distribution method and device and computer equipment - Google Patents

Block chain write permission distribution method and device and computer equipment Download PDF

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Publication number
CN116933237A
CN116933237A CN202310414339.5A CN202310414339A CN116933237A CN 116933237 A CN116933237 A CN 116933237A CN 202310414339 A CN202310414339 A CN 202310414339A CN 116933237 A CN116933237 A CN 116933237A
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node
quality parameter
sample data
machine learning
learning model
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张培源
艾欣
吴峥
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Beijing Jinxiu Nianhua Information Technology Co ltd
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Beijing Jinxiu Nianhua Information Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/44—Program or device authentication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F18/00—Pattern recognition
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/27—Regression, e.g. linear or logistic regression
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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    • G06F18/28—Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The application discloses a block chain write permission distribution method, a device and computer equipment, wherein the method comprises the following steps: when event information to be written is stored in local storage positions of a plurality of nodes in the same preset time period, acquiring sample data of each node; selecting a target machine learning model corresponding to first sample data in a preset machine learning model set according to the first sample data corresponding to a first node, wherein the first node is any one of a plurality of nodes; predicting a quality parameter of the first node according to the first sample data and the machine learning model; and determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission for the node corresponding to the final quality parameter.

Description

Block chain write permission distribution method and device and computer equipment
Technical Field
The present application relates to the field of blockchain technologies, and in particular, to a blockchain write permission allocation method, a blockchain write permission allocation device, and a computer device.
Background
In the prior art, when writing data into a blockchain database, the write permission is allocated according to the speed of calculating the result of the difficult problem, and if a certain terminal first solves the result of the difficult problem, the terminal has the write permission of writing data into the blockchain. By adopting the write permission distribution method, write permission is concentrated on a few terminals with high calculation speed, so that the problem of centralization is generated, and the blockchain database can generate great potential safety hazard. Therefore, a new write permission allocation method is needed.
Disclosure of Invention
Therefore, in order to solve the defects in the prior art, the embodiment of the application provides a blockchain writing permission distribution method, a blockchain writing permission distribution device and computer equipment.
According to a first aspect, an embodiment of the application discloses a blockchain write permission allocation method, which comprises the following steps:
when event information to be written is stored in local storage positions of a plurality of nodes in the same preset time period, acquiring sample data of each node;
selecting a target machine learning model corresponding to the first sample data in a preset machine learning model set according to the first sample data corresponding to the first node, wherein the first node is any one of a plurality of nodes;
predicting quality parameters of the first node according to the first sample data and the machine learning model;
and determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission for the node corresponding to the final quality parameter.
Optionally, the first sample data includes labeling data, and selecting, in the preset machine learning model set, a target machine learning model corresponding to the first sample data according to the first sample data corresponding to the first node, specifically including:
and selecting a target machine learning model corresponding to the labeling data in a preset machine learning model set according to the labeling data.
Optionally, determining the final quality parameter according to the quality parameter of each node, a preset rule and a preset algorithm specifically includes:
screening a quality parameter set meeting a preset rule from the quality parameters of each node;
and determining a final quality parameter in the quality parameter set by using a preset algorithm.
Optionally, the preset algorithm includes a random selection and quality allocation rule.
Optionally, when the preset algorithm is a quality allocation rule, determining, by using the preset algorithm, a final quality parameter in the quality parameter set, specifically includes:
calculating the writing probability of each node in the quality parameter set by using a preset algorithm and the quality parameter of each node in the quality parameter set;
and determining the final quality parameter according to the writing probability and the quality parameter in the quality parameter set.
Optionally, the set of preset machine models includes at least two of a linear regression model, a decision tree model, a cluster model, and a text classification model.
According to a second aspect, the embodiment of the application also discloses a blockchain writing permission distribution method device, which comprises the following steps:
the acquisition module is used for acquiring sample data of each node when the event information to be written is stored in the local storage positions of the plurality of nodes within the same preset time period;
the selection module is used for selecting a target machine learning model corresponding to the first sample data in a preset machine learning model set according to the first sample data corresponding to a first node, wherein the first node is any one of a plurality of nodes;
the prediction module is used for predicting quality parameters of the first node according to the first sample data and the machine learning model;
the determining module is used for determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission for the node corresponding to the final quality parameter.
Optionally, the first sample data includes labeling data, and the selection module is specifically configured to:
and selecting a target machine learning model corresponding to the labeling data in a preset machine learning model set according to the labeling data.
According to a third aspect, an embodiment of the present application further discloses a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform steps of a blockchain write permission assignment method as in the first aspect or any of the alternative embodiments of the first aspect.
According to a fourth aspect, an embodiment of the present application also discloses a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the blockchain write permission allocation method as in the first aspect or any of the alternative embodiments of the first aspect.
The technical scheme of the application has the following advantages:
the application provides a blockchain writing permission distribution method, a blockchain writing permission distribution device and computer equipment, which comprise the following steps: when the event information to be written is stored in the local storage positions of the plurality of nodes in the same preset time period, the fact that the plurality of nodes have the capability of writing the event information into the blockchain is indicated, at the moment, sample data of each node are obtained, and the fact that which node has writing permission is determined according to the sample data; selecting a target machine learning model corresponding to the first sample data in a preset machine learning model set according to the first sample data corresponding to the first node, wherein the first node is any one of a plurality of nodes; secondly, predicting quality parameters of the first node according to the first sample data and the target machine learning model, so that the nodes can be classified or ordered according to the quality parameters corresponding to each node; and finally, determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission to the node corresponding to the quality parameter in the job.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a block chain write permission assignment method according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a specific example of a block chain write permission allocation method apparatus in an embodiment of the present application;
FIG. 3 is a diagram showing a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the application are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the description of the present application, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; the two components can be directly connected or indirectly connected through an intermediate medium, or can be communicated inside the two components, or can be connected wirelessly or in a wired way. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
In addition, the technical features of the different embodiments of the present application described below may be combined with each other as long as they do not collide with each other.
Aiming at the technical problems mentioned in the background art, the embodiment of the application provides a blockchain writing permission distribution method, and particularly referring to fig. 1. The block chain writing authority distribution method is suitable for a block chain system with decentralization, such as a decentralization information propagation network built by a P2P (Peer to Peer) network. Each user joining the blockchain can acquire all event information and participate in the propagation of the event. The data communication and interaction among the blockchain users can realize the propagation and synchronization of various data, such as quality sequencing event information, transaction event information or evaluation event information.
Based on the P2P network, any newly generated event information is transmitted to a plurality of users through the P2P network. Any user monitors new events, can verify event information through means such as a verification function and the like, temporarily stores the event information in a local event pool (a local storage position), simultaneously transmits the event information to other users, and waits for the distribution of blockchain writing permission.
As shown in fig. 1, the method comprises the steps of:
step 101, when event information to be written is stored in local storage positions of a plurality of nodes in the same preset time period, sample data of each node is obtained.
Illustratively, when writing data (accounting), the blockchain system writes data at regular intervals, where the intervals are preset time periods, typically ten minutes. When the event information is stored in the local storage positions of the plurality of nodes, the event information is approved by the plurality of nodes, and the correctness of the event information is demonstrated. It is only necessary to wait for the assignment of write rights to determine which node has write rights (accounting rights).
In the embodiment of the application, the write permission is allocated to each node by taking the sample data as a reference, wherein the sample data can be corresponding data information representing the quality of products or services, and particularly, the sample data can be in the form of time sequence data, structured data, semi-structured data, unstructured data and the like. The data acquisition can be completed by a crawler means or an interface accessed to a corresponding platform.
Step 102, selecting a target machine learning model corresponding to the first sample data in a preset machine learning model set according to the first sample data corresponding to the first node.
The first node is any one of a plurality of nodes.
For example, when selecting the machine learning model, determining the corresponding model according to the first sample data can more accurately select to predict the first sample data, and the selected machine learning model is also more fit with the first sample data.
Specifically, the set of preset machine models includes at least two of a linear regression model, a decision tree model, a cluster model, and a text classification model.
In a preferred embodiment, the first sample data further includes labeling data, and a target machine learning model corresponding to the labeling data is selected from a set of preset machine learning models according to the labeling data.
The labeling data is illustratively a corresponding data type in the first sample data or other data that can identify the sample data information, and specifically, the labeling data can be text data or time sequence data, etc. And determining a corresponding machine learning model according to the types of the annotation data and the corresponding text data.
Step 103, predicting the quality parameter of the first node according to the first sample data and the machine learning model.
Illustratively, after determining the machine learning model, the first sample data is input to the machine learning model to predict the first sample data, so as to obtain the quality parameter corresponding to the first node.
Taking a multiple linear regression model as an example, the mathematical expression of the multiple linear regression model is as follows:
wherein, the multi-index quality feature vector of the product or service sample is denoted as X= [ X ] 1 ,x 2 ,x 3 ,...,x d ]The index weight matrix updated by the regression model is marked as W= [ W ] 1 ,w 2 ,w 3 ,...,w d ]The bias scalar of the regression training is marked as b, and the quality evaluation result of the model prediction is
Step 104, determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission for the node corresponding to the final quality parameter.
For example, after obtaining the quality parameter corresponding to each node, determining a final quality parameter according to a preset rule, where the preset rule may be to assign a write permission to the node with the highest quality parameter. The embodiment of the application does not limit the specific content of the preset rule, and the specific content can be determined by a person skilled in the art according to the actual situation.
In a preferred embodiment, determining the final quality parameter according to the quality parameter of each node, the preset rule and the preset algorithm specifically includes: screening a quality parameter set meeting a preset rule from the quality parameters of each node; and determining a final quality parameter in the quality parameter set by using a preset algorithm.
For example, when the preset rule is to screen a predetermined number of quality parameters satisfying a condition among quality parameters of all nodes, the final quality parameter is determined in the quality parameter set according to a preset algorithm. For example, the first five nodes with the highest quality parameters are selected from all the quality parameters, and then the nodes with the same quality parameters may exist, and then one of the five quality parameters may be selected as the final quality parameter according to a preset algorithm.
Specifically, the preset algorithm may include a random selection and quality allocation rule.
When the preset algorithm is a quality allocation rule, determining a final quality parameter in the quality parameter set by using the preset algorithm, wherein the method specifically comprises the following steps:
calculating the writing probability of each node in the quality parameter set by using a preset algorithm and the quality parameter of each node in the quality parameter set; and determining a final quality parameter according to the writing probability and the quality parameter in the quality parameter set.
Illustratively, the quality allocation rule may be calculated according to the following formula:
wherein Q is n And P is the writing probability, wherein the P is the quality parameter of the nth node.
After determining the writing probability, the correspondingThe higher the probability of obtaining the write rights, the higher the final quality parameter is determined.
By executing the method, when the event information to be written is stored in the local storage positions of a plurality of nodes in the same preset time period, the fact that the plurality of nodes have the capability of writing the event information into the blockchain is indicated, sample data of each node is obtained at the moment, and the fact that which node has writing permission is determined according to the sample data; selecting a target machine learning model corresponding to the first sample data in a preset machine learning model set according to the first sample data corresponding to the first node, wherein the first node is any one of a plurality of nodes; secondly, predicting quality parameters of the first node according to the first sample data and the target machine learning model, so that the nodes can be classified or ordered according to the quality parameters corresponding to each node; and finally, determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission to the node corresponding to the quality parameter in the job.
In the above, for the embodiment of the blockchain write permission allocation method provided by the present application, other embodiments of the blockchain write permission allocation method provided by the present application are described below, specifically, see the following.
The embodiment of the application also discloses a device for distributing the write permission of the blockchain, as shown in fig. 2, which comprises:
an obtaining module 201, configured to obtain sample data of each node when event information to be written is stored in local storage locations of a plurality of nodes within the same preset time period;
a selecting module 202, configured to select, from a preset machine learning model set, a target machine learning model corresponding to first sample data according to first sample data corresponding to a first node, where the first node is any one of a plurality of nodes;
a prediction module 203, configured to predict a quality parameter of the first node according to the first sample data and the machine learning model;
the determining module 204 is configured to determine a final quality parameter according to the quality parameter of each node and a preset rule, and allocate a write permission to a node corresponding to the final quality parameter.
As an optional embodiment of the present application, the first sample data includes labeling data, and the selection module is specifically configured to:
and selecting a target machine learning model corresponding to the labeling data in a preset machine learning model set according to the labeling data.
As an optional embodiment of the present application, the selection module is specifically further configured to:
screening a quality parameter set meeting a preset rule from the quality parameters of each node;
and determining a final quality parameter in the quality parameter set by using a preset algorithm.
As an alternative embodiment of the present application, the preset algorithm includes a random selection and quality allocation rule.
As an optional implementation manner of the present application, when the preset algorithm is a quality allocation rule, the selecting module is specifically further configured to:
calculating the writing probability of each node in the quality parameter set by using a preset algorithm and the quality parameter of each node in the quality parameter set;
and determining a final quality parameter according to the writing probability.
As an alternative embodiment of the present application, the set of preset machine models includes at least two of a linear regression model, a decision tree model, a cluster model, and a text classification model.
The functions executed by each component in the blockchain writing permission allocation method device provided by the embodiment of the present application are described in detail in any of the above method embodiments, so that the details are not repeated here.
By executing the device, when the event information to be written is stored in the local storage positions of a plurality of nodes in the same preset time period, the fact that the plurality of nodes have the capability of writing the event information into the blockchain is indicated, sample data of each node is obtained at the moment, and the fact that which node has writing permission is determined according to the sample data; selecting a target machine learning model corresponding to the first sample data in a preset machine learning model set according to the first sample data corresponding to the first node, wherein the first node is any one of a plurality of nodes; secondly, predicting quality parameters of the first node according to the first sample data and the target machine learning model, so that the nodes can be classified or ordered according to the quality parameters corresponding to each node; and finally, determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission to the node corresponding to the quality parameter in the job.
Embodiments of the present application also provide a computer device, as shown in fig. 3, which may include a processor 301 and a memory 302, where the processor 301 and the memory 302 may be connected by a bus or otherwise, and in fig. 3, the connection is exemplified by a bus.
The processor 301 may be a central processing unit (Central Processing Unit, CPU). The processor 301 may also be a chip such as other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination thereof.
The memory 302, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the blockchain write permission assignment method in the embodiment of the present application. The processor 301 executes various functional applications of the processor and data processing, i.e., implements the blockchain write permission allocation method in the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory 302.
Memory 302 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created by the processor 301, etc. In addition, memory 302 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 302 may optionally include memory located remotely from processor 301, such remote memory being connectable to processor 301 through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in memory 302 that, when executed by processor 301, perform the blockchain write permission assignment method in the embodiment shown in fig. 1.
The details of the above computer device may be understood correspondingly with respect to the corresponding relevant descriptions and effects in the embodiment shown in fig. 1, which are not repeated here.
It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, and the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
Although embodiments of the present application have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the application, and such modifications and variations are within the scope of the application as defined by the appended claims.

Claims (10)

1. A blockchain write permission assignment method, the method comprising:
when event information to be written is stored in local storage positions of a plurality of nodes in the same preset time period, acquiring sample data of each node;
selecting a target machine learning model corresponding to first sample data in a preset machine learning model set according to the first sample data corresponding to a first node, wherein the first node is any one of a plurality of nodes;
predicting a quality parameter of the first node according to the first sample data and the machine learning model;
and determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission for the node corresponding to the final quality parameter.
2. The method according to claim 1, wherein the first sample data includes labeling data, and the selecting, from a preset machine learning model set, a target machine learning model corresponding to the first sample data according to the first sample data corresponding to the first node specifically includes:
and selecting a target machine learning model corresponding to the labeling data in the preset machine learning model set according to the labeling data.
3. The method according to claim 1, wherein the determining the final quality parameter according to the quality parameter of each node, the preset rule and the preset algorithm specifically comprises:
screening a quality parameter set meeting the preset rule from the quality parameters of each node;
and determining a final quality parameter in the quality parameter set by utilizing the preset algorithm.
4. A method according to claim 3, wherein the pre-set algorithm comprises a random selection and quality allocation rule.
5. The method according to claim 4, wherein when the preset algorithm is a quality allocation rule, the determining, by using the preset algorithm, a final quality parameter in the quality parameter set, specifically includes:
calculating the writing probability of each node in the quality parameter set by using the preset algorithm and the quality parameter of each node in the quality parameter set;
and determining the final quality parameter according to the writing probability and the quality parameter in the quality parameter set.
6. The method of claim 4, wherein the set of pre-set machine models includes at least two of a linear regression model, a decision tree model, a cluster model, and a text classification model.
7. A blockchain write permission allocation method device, characterized in that the device comprises:
the acquisition module is used for acquiring sample data of each node when the event information to be written is stored in the local storage positions of the plurality of nodes within the same preset time period;
a selecting module, configured to select, according to first sample data corresponding to a first node, a target machine learning model corresponding to the first sample data in a preset machine learning model set, where the first node is any one of a plurality of nodes;
a prediction module for predicting a quality parameter of the first node according to the first sample data and the machine learning model;
the determining module is used for determining a final quality parameter according to the quality parameter of each node and a preset rule, and distributing write-in permission for the node corresponding to the final quality parameter.
8. The apparatus of claim 7, wherein the first sample data comprises annotation data, and wherein the selection module is configured to:
and selecting a target machine learning model corresponding to the labeling data in the preset machine learning model set according to the labeling data.
9. A computer device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the blockchain write permission assignment method of any of claims 1-6.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a server implements the blockchain write permission allocation method of any of claims 1-6.
CN202310414339.5A 2023-04-18 2023-04-18 Block chain write permission distribution method and device and computer equipment Pending CN116933237A (en)

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Application Number Priority Date Filing Date Title
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