CN117290401A - Data transaction method and system - Google Patents
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Abstract
The present disclosure provides a data transaction method and system, comprising: the source node carries out classification and classification labeling on the first local storage resource to obtain a tag data resource with classification and classification information; the source node generates a data resource query interface based on the tag data resource and uploads the data resource query interface to the server; the requiring node requests a plurality of source nodes in a server; the method comprises the steps that an acquirer node obtains tag data resources corresponding to each source node based on a data resource query interface corresponding to each source node in a plurality of source nodes, and determines a data resource list based on the tag data resources corresponding to each source node; the requiring node determines the target data resource from the list of data resources. Therefore, the needed data resource can be rapidly determined by the demand node, and rapid and efficient data transaction is realized.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of digital networking, in particular to a method and a system suitable for data transaction.
Background
The Internet of Data, i.e. the Internet connecting Data to Data, essentially constructs a set of protocol specifications and frameworks capable of identifying Data and Data services of each node, so that users can acquire the Data and the Data services conveniently.
In the digital networking scheme, when a data demand party and a data provider conduct data transaction, the data demand party needs to check the data resources of all the data providers, and selects one data resource meeting the demand of the data demand party for data transaction and use. In the existing data transaction mode, a data demand party needs to check the data resources of each data provider in turn to select the data resources meeting the demand for data transaction.
However, in the above implementation, in many data scenarios facing a large number of data providers, it is difficult for the data demander to efficiently screen out the required data resources.
Disclosure of Invention
Embodiments described herein provide a data transaction method and system that overcomes the above-referenced problems.
In a first aspect, according to the present disclosure, there is provided a data transaction method comprising:
the method comprises the steps that a number source node carries out classification and grading labeling on a first local storage resource to obtain a tag data resource with classification and grading information, and the number source node is used for describing a data provider;
the source node generates a data resource query interface based on the tag data resource and uploads the data resource query interface to a server;
Requesting a plurality of source nodes by a demand node in the server, wherein the demand node is used for describing a data demand party;
the requiring node obtains the label data resources corresponding to each source node based on the data resource query interface corresponding to each source node in the source nodes, and determines a data resource list based on the label data resources corresponding to each source node;
and the requiring party node determines a target data resource from the data resource list so as to conduct corresponding data transaction of the target data resource through the server and the source node corresponding to the target data resource.
In a second aspect, according to the present disclosure, there is provided a data transaction system comprising: a source node and a demand node;
the number source node is used for classifying, grading and labeling the first local storage resource to obtain a tag data resource with classified and graded information, and the number source node is used for describing a data provider; generating a data resource query interface based on the tag data resource, and uploading the data resource query interface to a server;
the said demand side node, is used for requesting a plurality of said number source nodes in the said server, the said demand side node is used for describing the data demand side; acquiring label data resources corresponding to each source node based on a data resource query interface corresponding to each source node in a plurality of source nodes, and determining a data resource list based on the label data resources corresponding to each source node; and determining a target data resource from the data resource list so as to conduct corresponding data transaction of the target data resource through the number source nodes corresponding to the target data resource by the server.
In a third aspect, there is provided a computer device comprising a memory in which a computer program is stored and a processor which when executing the computer program performs the steps of the data transaction method as in any of the above embodiments.
In a fourth aspect, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of a data transaction method as in any of the above embodiments.
According to the data transaction method provided by the embodiment of the application, the number source node performs classification and grading labeling on the first local storage resource to obtain the tag data resource with classification and grading information, and the number source node is used for describing the data provider; the source node generates a data resource query interface based on the tag data resource and uploads the data resource query interface to the server; the method comprises the steps that a plurality of source nodes are requested by a demand node in a server, and the demand node is used for describing a data demand party; the method comprises the steps that an acquirer node obtains tag data resources corresponding to each source node based on a data resource query interface corresponding to each source node in a plurality of source nodes, and determines a data resource list based on the tag data resources corresponding to each source node; and the demand node determines the target data resource from the data resource list so as to conduct corresponding data transaction of the target data resource through the data source node corresponding to the target data resource by the server. Therefore, each data source node carries out classification and grading marking processing on the local storage resources, so that the demand node can rapidly determine the required data resources in the determined data resource list, and rapid and efficient data transaction is realized.
The foregoing description is only an overview of the technical solutions of the embodiments of the present application, and may be implemented according to the content of the specification, so that the technical means of the embodiments of the present application can be more clearly understood, and the following detailed description of the present application will be presented in order to make the foregoing and other objects, features and advantages of the embodiments of the present application more understandable.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following brief description of the drawings of the embodiments will be given, it being understood that the drawings described below relate only to some embodiments of the present disclosure, not to limitations of the present disclosure, in which:
fig. 1 is a flow chart of a data transaction method provided in the present disclosure.
Fig. 2 is a schematic diagram of a node transaction of a digital network provided by the present disclosure.
Fig. 3 is a schematic flow chart of a transaction between a demand node and a source node provided in the present disclosure.
Fig. 4 is a schematic structural diagram of a data transaction system provided in the present disclosure.
Fig. 5 is a schematic structural diagram of a computer device provided in the present disclosure.
It is noted that the elements in the drawings are schematic and are not drawn to scale.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings. It will be apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be made by those skilled in the art based on the described embodiments of the present disclosure without the need for creative efforts, are also within the scope of the protection of the present disclosure.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the presently disclosed subject matter belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. As used herein, a statement that two or more parts are "connected" or "coupled" together shall mean that the parts are joined together either directly or joined through one or more intermediate parts.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of the phrase "an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: there are three cases, a, B, a and B simultaneously. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship. Terms such as "first" and "second" are used merely to distinguish one component (or portion of a component) from another component (or another portion of a component).
In the description of the present application, unless otherwise indicated, the meaning of "plurality" means two or more (including two), and similarly, "plural sets" means two or more (including two).
In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a flow chart of a data transaction method according to an embodiment of the disclosure. As shown in fig. 1, the specific process of the data transaction method includes:
s110, the number source node performs classification labeling on the first local storage resource to obtain the tag data resource with classification information.
The source node may be used to describe a data provider, i.e. a provider of data resources required by the demand node. It will be appreciated that the source node may not be identical to the data provider and that the source node may act as a data processor between the data provider and the data demander.
The first local storage resource is obtained by adding local data to the number source node in advance by the data provider, and the local data can include, but is not limited to, CSV text, mySQL data source text, HBASE data source text and the like.
The data resource is value data (information) capable of representing one data, namely, the first local storage resource is one or more value data capable of representing local data, and the tag data resource is one or more value data capable of representing corresponding data.
The classification and grading labeling is to classify and label the first local storage resource. For example, the first local storage resource is classified into a class 1, a class 2 and a class 3, and there may be a secondary classification and a tertiary classification under each class, for example, the secondary classification corresponding to the class 1 is classified into a class 11 and a class 12, and the tertiary classification corresponding to the class 1 is classified into a class 111 and a class 112.
In some embodiments, the source node performs classification and classification labeling on the first local storage resource to obtain a tag data resource with classification and classification information, including:
the digital source node performs classification and grading marking on the first local storage resource by adopting a preset matching algorithm based on the data information, the data characteristics and the privacy protection level, and determines a classification and grading label to which the first local storage resource belongs; the digital source node sends classification labels to which the first local storage resources belong and label keywords corresponding to each classification label to the digital networking server, so that the digital networking server performs calibration verification on the classification labels to which the first local storage resources belong based on the label keywords corresponding to each classification label; and the digital source node receives the calibration verification result sent by the digital networking server, and corrects the classification label to which the first local storage resource belongs based on the calibration verification result to obtain the label data resource with the classification information.
The number source node can classify, classify and mark the first local storage resources by adopting a preset matching algorithm based on the data information, the data characteristics and the privacy protection level. For example, the first local storage resource is divided into: personal information, financial industry information, power industry information, carrier industry information, medical industry information, etc., each classification may have a secondary or tertiary classification, such as personal information/personal identity information, personal information/personal nature information/personal occupation information, medical industry information/patient identity information, etc.
The data resource features also have corresponding classification information, for example, the classification corresponding to the personal identification information containing the identification card number is personal information/personal identification information/identification card number, the classification information of the data asset is obtained by summarizing the classification of the internal features of the data asset, for example, the classification of 50% of features under a certain data resource is divided into subclasses under the personal information/personal identification information, and then the data resource is divided into personal identification information.
According to the data privacy protection level, the data resource characteristics and the data resources are divided into a first level, a second level, a third level, a fourth level and a fifth level, and the higher the numerical value is, the higher the privacy protection level is, and the more important the data security is. The classification and grading information can refer to national standard files or industry standard files, and can be divided according to business scenes.
In this embodiment, the classification and classification labeling modes include, but are not limited to: manual labeling, rule labeling and manual auditing.
Wherein, manual labeling: by means of manual mode, classifying and grading manual labeling is carried out on the characteristics in the data resources and the data resources, and a proper classifying and grading label is selected for labeling. The data resources ABC are labeled (classified: personal information/personal identity information; classified: level 3), for example, manually, and the feature ID Card in the data resources ABC is labeled (classified: personal information/personal identity information/identification Card number; classified: level 3).
Rule marking: firstly, defining a rule for classifying, grading and labeling, after defining the rule, reading data information of the data resource, and identifying and labeling the data resource and the characteristics according to the rule. If the personal basic information rule and the characteristic identification Card number rule of the data resource ABC are hit after the data of the data resource ABC are read, the data resource ABC is marked (classified as personal information/personal identification information; classified as 3 grade), and the characteristic ID Card in the data resource ABC is marked (classified as personal information/personal identification information/identification Card number; classified as 3 grade).
Rule marking + manual auditing: firstly, identifying and marking are carried out based on a rule marking mode, and then, manual confirmation and correction are carried out.
Each class hierarchical label corresponds to a label keyword. The label keywords can display the classification marks, the classification marks and the label marks corresponding to the classification labels, and the classification marks, the classification marks and the label marks can be displayed in different display forms such as letters, numbers or characters, so that identification distinction is facilitated.
The preset matching algorithm comprises the following steps: a data-based canonical matching algorithm, a data-based keyword matching algorithm, and a feature name-based matching algorithm.
The digital networking server performs calibration verification on the classification hierarchical labels based on the label keywords, verifies the matching degree between the classification hierarchical labels and the corresponding first local storage resources based on the label keywords, thus, the problem of mismatch between the first local storage resource and the class hierarchy label to which it belongs is avoided.
The calibration verification result returned to the acquirer node by the digital networking server may include: verification passing information and verification failing information. Verification pass information may include: the verification is performed through indication and label association data, wherein the label association data is a backup classification hierarchical label determined by the data networking server based on corresponding first local storage resources. The calibration verification failure information may include: the verification is not through indication and tag renaming data, the tag renaming data is a classified hierarchical tag redetermined by the data networking server based on the corresponding first local storage resource.
Correcting the classification and grading label to which the first local storage resource belongs based on the calibration verification result to obtain a label data resource with classification and grading information, which can comprise: when the calibration verification result is the verification passing information, the classification and grading label which the first local storage resource belongs to can be replaced based on the label association information, or fine adjustment (such as replacing part fonts or marks) is performed on the classification and grading label which the first local storage resource belongs to based on the label association information, or the classification and grading label which the first local storage resource belongs to is continuously maintained, so that the label data resource with the classification and grading information is obtained.
Correcting the classification and grading label to which the first local storage resource belongs based on the calibration verification result to obtain a label data resource with classification and grading information, which can comprise: and when the calibration verification result is that the verification fails, the classification and grading label which the first local storage resource belongs to can be replaced based on the label renaming information, so that the label data resource with the classification and grading information is obtained. Therefore, the adaptation degree between the first local storage resource and the corresponding classification hierarchical label is effectively improved.
In some embodiments, the determining, by the source node, a classification label to which the first local storage resource belongs by classifying and grading the first local storage resource by using a preset matching algorithm based on the data information, the data feature and the privacy protection level includes:
and the data source node adopts a data-based regular matching algorithm, performs regular matching on the first local storage resource based on the data information, the data characteristics and the privacy protection level, and determines a classification hierarchical label to which the first local storage resource belongs.
For example, based on the data information, the data characteristics and the privacy protection level, the content of the data is subjected to regular matching, and when a certain data conforming to the regular matching in the first local storage resource reaches a set threshold value, the data is considered to conform to the classification hierarchical label corresponding to the rule.
Or the number source node adopts a keyword matching algorithm based on data, performs keyword matching on the first local storage resource based on the data information, the data characteristics and the privacy protection level, and determines a classification hierarchical label to which the first local storage resource belongs.
For example, based on the data information, the data characteristics and the privacy protection level, keyword matching is performed on the content of the data, and when a certain data meeting the keyword matching in the first local storage resource reaches a set threshold value, the data is considered to meet the classification hierarchical label corresponding to the rule. Keywords defining nationality classification and classification: [ China|Korea|Japanese|Vietnam| … ], wherein when the data of a certain feature in the first local storage resource is China, the rule is hit, and when 80% of the data in the feature hit the rule, the feature is considered to hit the nationality classification label (classification: personal information/personal identity information/nationality; classification: class 3); when more than 50% of the features in a certain data accord with the classification under the personal identity information, the data is considered to belong to the personal identity information classification label (classification: personal information/personal identity information; classification: class 3).
Or the number source node adopts a matching algorithm based on the feature names, performs feature name matching on the first local storage resource based on the data information, the data features and the privacy protection level, and determines the classification hierarchical label to which the first local storage resource belongs.
For example, as defined by the classification and grading rule of mobile phone number: feature name = "Phone Number" or "Telephone Number" or "Phone Number", when the data ABC feature contains "Telephone Number", then the feature is considered to be labeled as a Phone Number classification hierarchy (classification: personal information/personal contact information/Phone Number; hierarchy: level 3); when more than 50% of the features in a certain data accord with the classification under personal contact information, then the data is considered to belong to the personal contact information classification label (classification: personal information/personal contact information; classification: class 3).
Or, the number source node adopts an algorithm combination rule, performs corresponding matching on the first local storage resource based on the data information, the data characteristics and the privacy protection level, determines a classification hierarchical label to which the first local storage resource belongs, and the algorithm combination rule is used for describing a partial combination screening mode of a data-based regular matching algorithm, a data-based keyword matching algorithm and a characteristic name-based matching algorithm.
For example, the data is marked when the first local storage resource satisfies multiple algorithms simultaneously, or only a portion of the algorithms. If the first local storage resource simultaneously meets a data-based regular matching algorithm and a feature name-based matching algorithm, and classification of the two algorithms is the same, classification marking is performed on the data and the features.
Or, the number source node adopts an algorithm mutual exclusion rule, performs corresponding matching on the first local storage resource based on the data information, the data characteristics and the privacy protection level, determines a classification hierarchical label to which the first local storage resource belongs, and the algorithm mutual exclusion rule is used for describing a partial mutual exclusion screening mode of a data-based regular matching algorithm, a data-based keyword matching algorithm and a characteristic name-based matching algorithm.
For example, when the first local storage resource satisfies both the data-based regular matching algorithm and the feature-name-based matching algorithm, and the classification ranks pointed by the two algorithms are different, the classification ranks of the data and the features may be labeled, such as labeling classification rank labels pointed by the data-based regular matching algorithm/the feature-name-based matching algorithm.
S120, the digital source node generates a data resource query interface based on the tag data resource, and uploads the data resource query interface to the digital networking server.
The data resource query interface can return the data resource information containing the classification grading label.
The data source node can generate a data resource query interface for all the tag data resources, or the data source node can generate a shared data resource query interface for a plurality of tag data resources in the same class or with association relation.
S130, the demand node requests a plurality of source nodes in the digital networking server.
Wherein the demand node may be used to describe the data demand party. The acquirer node may request a plurality of source nodes from the digital networking server by sending a node access request to the digital networking server.
S140, the acquirer node obtains the tag data resources corresponding to each source node based on the data resource query interface corresponding to each source node in the plurality of source nodes, and determines a data resource list based on the tag data resources corresponding to each source node.
The data resource list is provided with a plurality of data resources in sequence according to a specified sequence.
In some embodiments, the determining, by the acquirer node, a data resource list based on the tag data resources corresponding to each source node includes:
the requiring party node sorts the plurality of tag data resources based on the time information of the tag data resources corresponding to each source node to obtain an initial resource list; and the requiring party node performs data resource screening on the initial resource list based on the preset time data to obtain a data resource list.
The time information of the tag data resource corresponding to each source node may be an uploading time of the tag data resource to the source node, or a generating time of the tag data resource.
The preset time data is a time period preset by the acquirer node, and the acquirer node can perform data screening on the initial resource list through the time period to obtain a data resource list meeting the requirements.
In addition, the label data resources corresponding to each source node have corresponding resource priority levels, and the resource priority levels corresponding to the label data resources are preset for the source nodes based on the data resource utilization rate.
The data resource list is determined by the demand node based on the label data resources corresponding to each source node, and the method can further comprise the following steps: and the acquirer node performs priority ranking on all the tag data resources based on the resource priority level corresponding to each tag data resource to obtain a data resource list, wherein the tag data resources positioned at the front of the list position in the data resource list are higher in the corresponding resource priority level.
S150, the demand node determines target data resources from the data resource list.
The target data resource is determined from the data resource list by the acquirer node, so that corresponding data transaction of the target data resource is performed through the digital network server and the digital source node corresponding to the target data resource, and a digital network node transaction schematic diagram is shown in fig. 2.
In some embodiments, the acquirer node determines the target data resource from the data resource list, including:
the corresponding local search word is matched with each tag keyword by the requiring party node to obtain a target search word; and the acquirer node adopts the target search word to perform data search on the tag data resource with the classification and grading information to obtain the target data resource.
The local search word is generated by the acquirer node based on the search requirement, so that the acquirer node can quickly search the required data resources from the data resource list based on the self requirement, and the data resource screening efficiency of the acquirer node is further improved.
The acquirer node adopts the target search word to perform data search on the tag data resource with the classification and grading information to obtain the target data resource, and the method can comprise the following steps: the method comprises the steps that a target search word is adopted by a acquirer node to conduct data search on tag data resources with classification and grading information, at least two initial data resources are obtained, at least two initial data resources are ordered based on uploading time/generating time/resource priority levels of each initial data resource corresponding to a source node, and a preset number of data resources with latest uploading time/generating time/highest resource priority levels corresponding to the source node are selected to be target data resources, so that the resource practicability of the target data resources is effectively guaranteed.
In this embodiment, the source node performs classification and classification labeling on the first local storage resource to obtain a tag data resource with classification and classification information, where the source node is used for describing a data provider; the digital source node generates a data resource query interface based on the tag data resource and uploads the data resource query interface to the digital networking server; the method comprises the steps that a plurality of source nodes are requested by a demand node in a digital networking server, and the demand node is used for describing a data demand party; the method comprises the steps that an acquirer node obtains tag data resources corresponding to each source node based on a data resource query interface corresponding to each source node in a plurality of source nodes, and determines a data resource list based on the tag data resources corresponding to each source node; and the acquirer node determines the target data resource from the data resource list so as to conduct corresponding data transaction of the target data resource through the source node corresponding to the target data resource by the data networking server. Therefore, each data source node carries out classification and grading marking processing on the local storage resources, so that the demand node can rapidly determine the required data resources in the determined data resource list, and rapid and efficient data transaction is realized.
Based on the description of the above embodiment, the method of this embodiment may further include:
the method comprises the steps that an acquirer node sends a resource access request carrying a resource identifier of a target data resource to a digital networking server, so that the digital networking server carries out security verification on the resource access request, establishes a transaction channel between the acquirer node and a digital source node corresponding to the target data resource after the authentication is passed, and sends the resource access request to the digital source node corresponding to the target data resource; and the demand node receives the data resource packet sent by the source node corresponding to the target data resource based on the transaction channel.
Wherein the data resource packet includes the target data resource. Other data resources associated with the target data resource may also be included in the data resource package.
Therefore, when the data transaction is carried out between the demand node and the source node, the data transaction safety is ensured through the network server, so that the data transaction safety between the demand node and the source node is convenient to improve.
In some embodiments, after the data transaction is performed between the acquirer node and the corresponding source node through the transaction channel, the method of this embodiment may further include:
the requiring node determines the associated data resource of the target data resource from the second local storage resource; and the acquirer node sends the associated data resources of the target data resources to the source nodes corresponding to the target data resources based on the transaction channel.
When determining the associated data resource of the target data resource from the second local storage resource, the acquirer node may select one or more data resources with the same resource uploading time/resource generating time/same resource transaction channel/high resource matching degree as the associated data resource of the target data resource from the second local storage resource. The resource uploading time of the target data resource is the time of uploading the target data resource to the corresponding source nodes.
Therefore, when the demand node carries out data transaction with the number source node, the demand node can actively push the associated data resource to the number source node, so that the transaction viscosity between the demand node and the number source node is convenient to increase.
As shown in fig. 3, in this embodiment, the source nodes classify and label the own data, the source nodes request multiple source nodes, obtain a data resource list containing classifying and grading labeling information, quickly locate classifying and grading data resources meeting the own requirements by searching the classifying and grading, and then perform subsequent operations such as data transaction according to the transaction rules of the digital networking, so as to realize quick and efficient data circulation and transaction flow.
Fig. 4 is a schematic structural diagram of a data transaction system according to the present embodiment. The data transaction system may include: a source node 410 and a destination node 420.
The number source node 410 is configured to perform classification and classification labeling on the first local storage resource to obtain a tag data resource with classification and classification information, where the number source node 410 is configured to describe a data provider; and generating a data resource query interface based on the tag data resource, and uploading the data resource query interface to a server.
The requiring node 420 is configured to request a plurality of the source nodes 410 in the server, and the requiring node 420 is configured to describe a data requiring party; acquiring tag data resources corresponding to each source node 410 based on the data resource query interface corresponding to each source node 410 in the plurality of source nodes 410, and determining a data resource list based on the tag data resources corresponding to each source node 410; and determining a target data resource from the data resource list so as to conduct corresponding data transaction of the target data resource through the number source nodes 410 corresponding to the target data resource by the server.
In this embodiment, optionally, the number source node 410 is specifically configured to:
based on data information, data characteristics and privacy protection levels, classifying and marking the first local storage resources by adopting a preset matching algorithm, determining classifying and classifying labels to which the first local storage resources belong, wherein each classifying and classifying label corresponds to a label keyword, and the preset matching algorithm comprises: a data-based regular matching algorithm, a data-based keyword matching algorithm and a feature name-based matching algorithm; sending classification labels to which the first local storage resources belong and label keywords corresponding to each classification label to the server, so that the server performs calibration verification on the classification labels to which the first local storage resources belong based on the label keywords corresponding to each classification label; and receiving a calibration verification result sent by the server, and correcting the classification label to which the first local storage resource belongs based on the calibration verification result to obtain a label data resource with classification information.
In this embodiment, optionally, the number source node 410 is specifically configured to:
Performing regular matching on the first local storage resources based on the data information, the data characteristics and the privacy protection level by adopting a data-based regular matching algorithm, and determining classification hierarchical labels to which the first local storage resources belong; or, a keyword matching algorithm based on data is adopted, the keyword matching is carried out on the first local storage resource based on the data information, the data characteristics and the privacy protection level, and a classification grading label to which the first local storage resource belongs is determined; or, adopting a matching algorithm based on the feature names, and carrying out feature name matching on the first local storage resource based on the data information, the data features and the privacy protection level to determine a classification hierarchical label to which the first local storage resource belongs; or, adopting an algorithm combination rule, namely, correspondingly matching the first local storage resource based on the data information, the data characteristics and the privacy protection level, and determining a classification hierarchical label to which the first local storage resource belongs, wherein the algorithm combination rule is used for describing a partial combination screening mode of a regular data-based matching algorithm, a keyword data-based matching algorithm and a matching algorithm based on a characteristic name; or, adopting an algorithm mutual exclusion rule, carrying out corresponding matching on the first local storage resource based on the data information, the data characteristics and the privacy protection level, and determining a classification hierarchical label to which the first local storage resource belongs, wherein the algorithm mutual exclusion rule is used for describing a partial mutual exclusion screening mode of a regular matching algorithm based on data, a keyword matching algorithm based on data and a matching algorithm based on a characteristic name.
In this embodiment, optionally, the requiring node 420 is specifically configured to:
matching the corresponding local search word with each tag keyword to obtain a target search word, wherein the local search word is generated by the requiring node 420 based on the search requirement; and carrying out data retrieval on the tag data resources with the classification and grading information by adopting the target retrieval words to obtain the target data resources.
In this embodiment, optionally, the requiring node 420 is specifically configured to:
sorting a plurality of tag data resources based on time information of the tag data resources corresponding to each source node 410 to obtain an initial resource list; and based on preset time data, carrying out data resource screening on the initial resource list to obtain the data resource list.
In this embodiment, optionally, the acquirer node 420 is further configured to send a resource access request carrying a resource identifier of the target data resource to the server, so that the server performs security verification on the resource access request, establishes a transaction channel between the acquirer node 420 and the source node 410 corresponding to the target data resource after the verification is passed, and sends the resource access request to the source node 410 corresponding to the target data resource; and receiving a data resource packet sent by the source node 410 corresponding to the target data resource based on the transaction channel, wherein the data resource packet comprises the target data resource.
In this embodiment, optionally, the requiring node 420 is further configured to determine an associated data resource of the target data resource from a second local storage resource; based on the transaction channel, the associated data resource of the target data resource is sent to the source node 410 corresponding to the target data resource.
The data transaction system provided in the present disclosure may execute the above method embodiment, and the specific implementation principle and technical effects thereof may be referred to the above method embodiment, which is not described herein again.
The embodiment of the application also provides computer equipment. The computer device may be a device corresponding to the acquirer node or the source node. Referring specifically to fig. 5, fig. 5 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device includes a memory 510 and a processor 520 communicatively coupled to each other via a system bus. It should be noted that only computer devices having components 510-520 are shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-ProgrammableGate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 510 includes at least one type of readable storage medium including non-volatile memory (non-volatile memory) or volatile memory, such as flash memory (flash memory), hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random access memory (random accessmemory, RAM), read-only memory (ROM), erasable programmable read-only memory (erasableprogrammable read-only memory, EPROM), electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), programmable read-only memory (programmable read-only memory, PROM), magnetic memory, RAM, optical disk, etc., which may include static or dynamic. In some embodiments, the memory 510 may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 510 may also be an external storage device of a computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, or a Flash Card (Flash Card) provided on the computer device. Of course, memory 510 may also include both internal storage units for computer devices and external storage devices. In this embodiment, the memory 510 is typically used to store an operating system installed on a computer device and various types of application software, such as program codes of the above-described methods. In addition, the memory 510 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 520 is typically used to perform the overall operations of the computer device. In this embodiment, the memory 510 is configured to store program codes or instructions, the program codes include computer operation instructions, and the processor 520 is configured to execute the program codes or instructions stored in the memory 510 or process data, such as the program codes for executing the above-mentioned method.
Herein, the bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, a peripheral component interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus system may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Another embodiment of the present application also provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads computer readable program code stored in a computer readable medium, such that the processor is capable of performing the functional actions specified in each step or combination of steps in the above-described method; a means for generating a functional action specified in each block of the block diagram or a combination of blocks.
The computer readable medium includes, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor system, apparatus or device, or any suitable combination of the foregoing, the memory storing program code or instructions, the program code including computer operating instructions, and the processor executing the program code or instructions of the above-described methods stored by the memory.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The functional units or modules in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all or part of the technical solution contributing to the prior art or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps other than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of first, second, third, etc. does not denote any order, and the words are to be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A method of data transaction, comprising:
the method comprises the steps that a number source node carries out classification and grading labeling on a first local storage resource to obtain a tag data resource with classification and grading information, and the number source node is used for describing a data provider;
the source node generates a data resource query interface based on the tag data resource and uploads the data resource query interface to a server;
requesting a plurality of source nodes by a demand node in the server, wherein the demand node is used for describing a data demand party;
the requiring node obtains the label data resources corresponding to each source node based on the data resource query interface corresponding to each source node in the source nodes, and determines a data resource list based on the label data resources corresponding to each source node;
And the requiring party node determines a target data resource from the data resource list so as to conduct corresponding data transaction of the target data resource through the server and the source node corresponding to the target data resource.
2. The method of claim 1, wherein the source node performs classification hierarchical annotation on the first local storage resource to obtain the tag data resource with classification hierarchical information, and the method comprises:
the digital source node performs classification and classification marking on the first local storage resource by adopting a preset matching algorithm based on data information, data characteristics and privacy protection level, determines classification and classification labels to which the first local storage resource belongs, each classification and classification label corresponds to a label keyword, and the preset matching algorithm comprises: a data-based regular matching algorithm, a data-based keyword matching algorithm and a feature name-based matching algorithm;
the source node sends classification labels to which the first local storage resources belong and label keywords corresponding to the classification labels to the server, so that the server carries out calibration verification on the classification labels to which the first local storage resources belong based on the label keywords corresponding to the classification labels;
And the source node receives the calibration verification result sent by the server, and corrects the classification label to which the first local storage resource belongs based on the calibration verification result to obtain the label data resource with classification information.
3. The method according to claim 2, wherein the determining the classification label to which the first local storage resource belongs by the source node using a preset matching algorithm to perform classification marking on the first local storage resource based on the data information, the data feature and the privacy protection level includes:
the source node adopts a regular matching algorithm based on data, carries out regular matching on the first local storage resource based on the data information, the data characteristics and the privacy protection level, and determines a classification label to which the first local storage resource belongs;
or the number source node adopts a keyword matching algorithm based on data, performs keyword matching on the first local storage resource based on the data information, the data characteristics and the privacy protection level, and determines a classification hierarchical label to which the first local storage resource belongs;
Or the source node adopts a matching algorithm based on the feature names, performs feature name matching on the first local storage resource based on the data information, the data features and the privacy protection level, and determines a classification hierarchical label to which the first local storage resource belongs;
or the number source node adopts an algorithm combination rule, and correspondingly matches the first local storage resource based on the data information, the data characteristics and the privacy protection level to determine a classification hierarchical label to which the first local storage resource belongs, wherein the algorithm combination rule is used for describing a partial combination screening mode of a data-based regular matching algorithm, a data-based keyword matching algorithm and a characteristic name-based matching algorithm;
or the number source node adopts an algorithm mutual exclusion rule, and correspondingly matches the first local storage resource based on the data information, the data characteristics and the privacy protection level to determine a classification hierarchical label to which the first local storage resource belongs, wherein the algorithm mutual exclusion rule is used for describing a partial mutual exclusion screening mode of a regular data-based matching algorithm, a keyword data-based matching algorithm and a matching algorithm based on characteristic names.
4. The method of claim 2, wherein the requiring node determines a target data resource from the list of data resources, comprising:
the local search word is generated by the requiring party node based on the search requirement;
and the acquirer node adopts the target search word to perform data search on the tag data resource with the classification and grading information to obtain the target data resource.
5. The method of claim 1, wherein the requiring node determines a data resource list based on the tag data resources corresponding to each of the source nodes, comprising:
the requiring party node sorts a plurality of tag data resources based on time information of the tag data resources corresponding to each source node to obtain an initial resource list;
and the requiring node performs data resource screening on the initial resource list based on preset time data to obtain the data resource list.
6. The method as recited in claim 1, further comprising:
the requiring node sends a resource access request carrying a resource identifier of the target data resource to the server, so that the server performs security verification on the resource access request, establishes a transaction channel between the requiring node and a source node corresponding to the target data resource after verification is passed, and sends the resource access request to the source node corresponding to the target data resource;
And the requiring node receives a data resource packet sent by a source node corresponding to the target data resource based on the transaction channel, wherein the data resource packet comprises the target data resource.
7. The method as recited in claim 6, further comprising:
the requiring node determines the associated data resource of the target data resource from a second local storage resource;
and the acquirer node sends the associated data resources of the target data resources to the source node corresponding to the target data resources based on the transaction channel.
8. A data transaction system, comprising: a source node and a demand node;
the number source node is used for classifying, grading and labeling the first local storage resource to obtain a tag data resource with classified and graded information, and the number source node is used for describing a data provider; generating a data resource query interface based on the tag data resource, and uploading the data resource query interface to a server;
the said demand side node, is used for requesting a plurality of said number source nodes in the said server, the said demand side node is used for describing the data demand side; acquiring label data resources corresponding to each source node based on a data resource query interface corresponding to each source node in a plurality of source nodes, and determining a data resource list based on the label data resources corresponding to each source node; and determining a target data resource from the data resource list so as to conduct corresponding data transaction of the target data resource through the number source nodes corresponding to the target data resource by the server.
9. The system according to claim 8, wherein the source node is specifically configured to:
based on data information, data characteristics and privacy protection levels, classifying and marking the first local storage resources by adopting a preset matching algorithm, determining classifying and classifying labels to which the first local storage resources belong, wherein each classifying and classifying label corresponds to a label keyword, and the preset matching algorithm comprises: a data-based regular matching algorithm, a data-based keyword matching algorithm and a feature name-based matching algorithm;
sending classification labels to which the first local storage resources belong and label keywords corresponding to each classification label to the server, so that the server performs calibration verification on the classification labels to which the first local storage resources belong based on the label keywords corresponding to each classification label;
and receiving a calibration verification result sent by the server, and correcting the classification label to which the first local storage resource belongs based on the calibration verification result to obtain a label data resource with classification information.
10. The system according to claim 9, characterized in that the requiring node is specifically configured to:
matching the corresponding local search word with each tag keyword to obtain a target search word, wherein the local search word is generated by the requiring node based on the search requirement;
and carrying out data retrieval on the tag data resources with the classification and grading information by adopting the target retrieval words to obtain the target data resources.
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