CN114091062A - Occupational data processing method and device - Google Patents

Abstract

The embodiment of the specification provides a method and a device for processing occupational data, wherein the method for processing the occupational data comprises the following steps: generating encrypted data based on a professional access request of a user; sending the encrypted data to a target node in a multi-party computing platform to perform data fusion of professional data; generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node; determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

Description

Occupational data processing method and device

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing professional data.

Background

With the rapid development of internet technology, data sharing is more and more widely applied in the digital era, such as the medical industry, the insurance industry and the financial industry, data sharing is realized through data intercommunication among various organizations, a rapid service processing channel is established, two-dimensional codes are an important realization mode of data sharing, bar codes with readability are expanded on the basis of one-dimensional bar codes, equipment scans the two-dimensional codes, and information contained in the bar codes can be obtained through recognizing binary data recorded in the length and the width of the bar codes.

Disclosure of Invention

One or more embodiments of the present specification provide a method of occupational data processing, including: encrypted data is generated based on a professional access request of a user. And sending the encrypted data to a target node in the multi-party computing platform to perform data fusion of the professional data. And generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node. Determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

One or more embodiments of the present specification provide a vocational data processing apparatus comprising: and the encrypted data generation module is configured to generate encrypted data based on the professional access request of the user. And the encrypted data sending module is configured to send the encrypted data to a target node in the multi-party computing platform so as to perform data fusion of the professional data. And the identification code generation module is configured to generate the professional identification code of the user according to the professional data of the user and target professional data obtained based on the encrypted professional data returned by the target node. A service association module configured to determine a equity service based on at least one of the user professional data and the target professional data, associate the equity service to the professional identification code, and generate service recommendation data for the equity service.

One or more embodiments of the present specification provide a professional data processing apparatus including: a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to: encrypted data is generated based on a professional access request of a user. And sending the encrypted data to a target node in the multi-party computing platform to perform data fusion of the professional data. And generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node. Determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

One or more embodiments of the present specification provide a storage medium storing computer-executable instructions that, when executed by a processor, implement the following flow: generating encrypted data based on the user's professional access request. And sending the encrypted data to a target node in the multi-party computing platform to perform data fusion of the professional data. And generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node. Determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

Drawings

In order to more clearly illustrate one or more embodiments or technical solutions in the prior art in the present specification, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive exercise;

FIG. 1 is a process flow diagram of a method for processing occupational data according to one or more embodiments of the present disclosure;

fig. 2 is a processing flow diagram of a professional data processing method applied to an identification code update scenario according to one or more embodiments of the present disclosure;

FIG. 3 is a flowchart of a processing method of vocational data applied to a vocational training scenario, according to one or more embodiments of the present disclosure;

FIG. 4 is a schematic diagram of a vocational data processing apparatus provided in one or more embodiments of the present disclosure;

fig. 5 is a schematic structural diagram of a professional data processing device according to one or more embodiments of the present disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step, shall fall within the scope of protection of this document.

An embodiment of a method for processing occupational data provided by the present specification:

referring to fig. 1, which shows a processing flow chart of a professional data processing method provided in this embodiment, referring to fig. 2, which shows a processing flow chart of a professional data processing method applied to an identification code update scenario provided in this embodiment, referring to fig. 3, which shows a processing flow chart of a professional data processing method applied to a professional training scenario provided in this embodiment.

Referring to fig. 1, the method for processing professional data provided in this embodiment specifically includes steps S102 to S108.

Step S102, generating encrypted data based on the professional access request of the user.

In the professional data processing method provided by this embodiment, with the help of a multi-party computing platform, in the process of accessing professional services by a user, a professional access request of the user is acquired, encrypted data is generated, the encrypted data is further sent to a target node participating in multi-party computing, so that data fusion of the professional data is performed at the target node according to the encrypted data, on the basis, the encrypted professional data returned by the target node is decrypted, target professional data is obtained, further, a professional identification code is generated according to the user professional data and the target professional data, then, a equity service is determined based on at least one of the user professional data and the target professional data, the equity service is associated with the professional identification code, and service recommendation data of the equity service is generated to perform service recommendation.

The multi-party computing platform is utilized to provide guarantee for data security of the multi-party computing participants, reliability among the multi-party computing participants is enhanced, data leakage risks are reduced, and meanwhile, occupation identification codes are introduced to facilitate use of users, so that data updating efficiency is improved, users can obtain own occupation information and rights and interests information in time, and use experience of the users is improved.

It should be noted that the present embodiment may be applied to a computing node, where the computing node performs data transmission with other nodes participating in a multi-party computing platform, and may also be applied to a server of professional service, where the server performs data transmission with respect to the multi-party computing platform.

In specific implementation, in the process of accessing the professional service by the user, a professional access request is generated, after the professional access request of the user is obtained, other target nodes participating in multi-party computing are determined, then, execution sequences among the nodes participating in the multi-party computing platform are determined, for example, a computing node in a first execution sequence, a first target node in a second execution sequence, a second target node in a third execution sequence and the like, the public key of the first target node is used for encrypting user identity data and random data to obtain encrypted data, and the security of the data transmission process is realized.

In an optional implementation manner provided by this embodiment, in the process of generating the encrypted data based on the professional access request of the user, the following operations are performed:

determining a target node in the multi-party computing platform according to a professional access request of a user;

and encrypting the user identity data and the random data by using the public key of any one of the target nodes to obtain the encrypted data.

In practical application, in the process that the computing node sends the encrypted data to the first target node, on one hand, professional credit data in user professional data inquired by the computing node and random data can be combined to generate combined professional data, and then, user identity data in the user professional data and the combined professional data are encrypted to generate encrypted data to be sent, on the other hand, the user identity data and the random data can be directly encrypted to generate the encrypted data to be sent to the first target node, and high efficiency and flexibility of multi-party computing are achieved in a diversified data combination mode; the encrypted data may be composed of both random data and user identity data, and in addition, the encrypted data may be composed of three of random data, user identity data, and professional credit data.

The random data is data randomly generated by a computing node by using a random algorithm and the like, the random data is known to the computing node and is unknown to other nodes participating in the multi-party computing platform, and the user professional data can comprise user identity data and professional credit data, the user identity data is user identification data, user location data and the like, the professional credit data is resume data, skill certificate data (e.g. electrician certificate and driver certificate), job title data, professional credit data (e.g. number of good work scores and work times) and the like; random data is introduced to make occupational data of users among nodes participating in the multi-party computing platform unknown to other nodes, and isolation and safety of the data are guaranteed.

In addition, in order to ensure the professional data privacy of the user and prevent the rights and interests of the user from being violated, the authorization instruction of the user may be obtained before performing the multiparty computation of the professional data, and specifically, in an optional implementation manner provided by this embodiment, in the process of generating the encrypted data based on the professional access request of the user, the following steps are performed:

sending a professional authorization request to the user based on the professional access request of the user;

and creating an authorization certificate according to the professional authorization instruction returned by the user, and generating the encrypted data based on the authorization certificate.

In a specific execution process, after an authorization voucher is created according to a professional authorization instruction returned by a user, random data can be generated so as to safely merge professional data; optionally, the random data is generated after the computing node detects that the authorization credential is created.

And step S104, sending the encrypted data to a target node in a multi-party computing platform so as to perform data fusion of professional data.

In practical application, in order to ensure the reliability of a connecting party and the safety and confidentiality of data transmission, data transmission among the computing nodes, the first target node, the second target node and other nodes participating in a multi-party computing platform can be realized by adopting a Virtual Private Network (such as a VPN), so that the labor and material cost is saved, the data transmission connection is convenient and flexible, and the speed and efficiency of data transmission are improved; optionally, data transmission processing is performed among the computing node, the first target node, and the second target node participating in the multi-party computing platform through a virtual private network.

In specific implementation, on the basis that a target node in the multi-party computing platform is determined according to the professional access request of the user, the public key of any one of the target nodes is used for encrypting the user identity data and the random data to obtain encrypted data, the encrypted data can be sent to a first target node in a first execution sequence according to a first execution sequence included in the multi-party computing protocol, and specifically, the encrypted data is sent to a first target node corresponding to the public key according to the multi-party computing protocol.

After the target node in the multi-party computing platform is determined, the multi-party computing protocol may be generated based on an execution sequence among nodes participating in the multi-party computing platform, that is: a compute node in a first execution order, a first target node in a second execution order, a second target node in a third execution order, etc.; in addition, the multi-party computing protocol can also be generated firstly, and then the execution sequence among the nodes participating in the multi-party computing platform is determined according to the generated multi-party computing protocol.

On this basis, the first target node performs data fusion of professional data after receiving the encrypted data, and in an optional implementation manner provided by this embodiment, in the process of performing data fusion of professional data, the following operations are performed:

the first target node is decrypted by using a private key to obtain the user identity data and the random data;

inquiring first occupational data according to the user identity data, and carrying out data combination on the inquired first occupational data and the random data to obtain first combined data;

and encrypting the user identity data and the first combined data by using a public key of a second target node, and sending the first encrypted professional data obtained by encryption to the second target node.

For example, the computing node a encrypts the user identity data and the random data r by using the public key of the first target node B, sends the obtained encrypted data to the first target node B, the first target node B decrypts the encrypted data by using the private key to obtain the user identity data and the random data r, the first target node B queries the first occupational data B1 through the user identity data, and combines the queried B1 and the random data r to form B1+ r, the first target node B encrypts the user identity data and the first combined data B1+ r by using the public key of the second target node C, and sends the encrypted first encrypted occupational data to the second target node C.

It should be noted that, the second target node is unknown to the data component of the first merged data, that is: the specific data information of the first merged data is unknown, the second target node only needs to merge the queried second occupational data with the first merged data and then encrypt the merged data, similarly, the data components of the subsequent second merged data and the specific data information are unknown to the node receiving the second merged data, the nodes participating in the multi-party calculation can only know the occupational data after the data fusion of the occupational data, and the specific component structure of the occupational data after the data fusion is unknown, so that the isolation and the safety of data transmission are ensured.

Further, on the basis that the public key of the second target node is used for encrypting the user identity data and the first combined data and sending the encrypted first encrypted professional data to the second target node, the second target node can also use the private key to decrypt the first encrypted data and then perform data fusion of the professional data. In an optional implementation manner provided by this embodiment, the data fusion of the vocational data further includes:

the second target node decrypts by using a private key to obtain the user identity data and the first combined data;

inquiring second occupational data according to the user identity data, and performing data merging on the inquired second occupational data and the first merged data to obtain second merged data;

judging whether a next target node exists or not;

if not, encrypting the user identity data and the second combined data by using a public key of the computing node, and returning second encrypted professional data obtained by encryption to the computing node;

if yes, the public key of the next target node is used for encrypting the user identity data and the merged data, encrypted professional data obtained through encryption is sent to the next target node, and the next target node operation is returned to be judged whether or not.

In the above example, after receiving the first encrypted professional data sent by the first target node B, the second target node C decrypts the first encrypted professional data by using the private key to obtain the user identity data and the first merged data B1+ r, then queries the second professional data of the second target node C according to the user identity data, merges the queried second professional data C1 into B1+ r to obtain the second merged data C1+ B1+ r, and encrypts the user identity data and C1+ B1+ r by using the public key of the computing node a if the next target node does not exist, and returns the encrypted second encrypted professional data to the computing node a.

Further, on the basis that the public key of the computing node is used for encrypting the user identity data and the second combined data and the encrypted second encrypted professional data is returned to the computing node, the computing node can decrypt the second encrypted professional data to obtain the target professional data, namely: and acquiring the occupational data of the user subjected to data fusion from the first target node and the second target node. In an optional implementation manner provided by this embodiment, after encrypting the user identity data and the second merged data by using the public key of the computing node, and returning the second encrypted professional data obtained by the encryption to the computing node, the following operations are performed:

decrypting the second encrypted professional data through a private key of the computing node to obtain the user identity data and the second merged data;

and extracting the target occupational data from the second combined data according to the random data.

In the above example, after receiving the second encrypted professional data sent by the second target node C, the computing node a decrypts the second encrypted professional data by using the private key of the computing node a to obtain the user identity data and the second merged data C1+ B1+ r, and since the random data r is generated for the computing node a, the computing node a is known about the random data r, and the computing node a may extract the target professional data C1+ B1 from the second merged data C1+ B1+ r according to the known random data r.

It should be noted that the above examples are only convenient for those skilled in the art to understand, and the data forms formed by merging are not necessarily B1+ r, C1+ B1+ r, a1+ C1+ B1+ r, for example, professional data can also be merged by using data aggregation, aggregation algorithm, and coding algorithm, and the present embodiment does not specifically limit the operation process of merging and the data forms formed by merging.

And step S106, generating the occupation identification code of the user according to the occupation data of the user and the target occupation data obtained based on the encrypted occupation data returned by the target node.

On the basis that the second encrypted professional data is decrypted through the private key of the computing node to obtain the user identity data and the second merged data, and the target professional data is extracted from the second merged data according to the random data, the embodiment generates the professional identification code of the user according to the user professional data inquired at the computing node by using the user identity data and the extracted target professional data, so that the professional data can be shared, checked and stored conveniently.

During specific implementation, in order to provide better professional service for a user, the user can be provided with a plan of a professional development direction, the planned professional development direction, user identity data and target professional data are jointly coded, and a professional identification code is obtained, so that the user can carry out corresponding skill training and learning according to the planned professional development direction, and further the professional skill of the user is perfected. In an optional implementation manner provided by this embodiment, in a process of generating a career identification code of a user according to user career data and target career data obtained based on encrypted career data returned by a target node, the following operations are performed:

determining a professional channel of the user based on professional grade information and professional credit data contained in the user professional data and the target professional data;

and according to a preset coding rule, coding the user occupation data, the target occupation data and the occupation channel to obtain the occupation identification code.

In a specific execution process, the user professional data and the target professional data may include professional grade information and/or professional credit information, the professional grade information includes grade certificate information examined by the user, obtained titles and the like, the professional credit data includes job goodness, job times, professional skill reality and the like, a professional channel (such as a professional development direction) can be provided for the user by using the professional grade information and/or the professional credit information of the user, and then the user professional data, the target professional data and the professional channel related to the user are coded according to a preset coding rule to obtain a professional identification code, so that the user can clearly recognize the user, the user can be better perfected by using the professional channel, and user experience is improved.

Step S108, determining equity services based on at least one of the user occupation data and the target occupation data, associating the equity services to the occupation identification codes, and generating service recommendation data of the equity services.

In practical application, in order to provide a better professional service for a user, the user can acquire social welfare and the like in time while knowing his work history and professional channels, on the basis of generating a professional identification code of the user according to the user professional data and target professional data obtained based on encrypted professional data returned by a target node, a equity service can be determined, and the equity service is associated with the professional identification code of the user so as to recommend the service.

The service recommendation data of the equity service in this embodiment includes the service recommendation data displayed after being checked through a training link or a training control of professional training and an information link or an information control of welfare information, and may also include the service recommendation data of professional training or welfare information, that is: in the process of displaying the professional service page to the user, the service recommendation data and the professional identification code of the equity service are displayed at the same time, the user can perform professional training or benefit information viewing through the link or the control, and the service recommendation data of the professional training or the service recommendation data of the benefit information can be directly displayed on the professional service page.

For the equity service, because the nodes participating in the multi-party computing platform may store the occupational data of the user, the equity service may be determined based on the occupational data of the user, and the equity service may be from occupational institutions corresponding to the computing nodes or from occupational institutions corresponding to the first target node and/or the second target node; optionally, the equity service is provided by a professional organization corresponding to the computing node, the first target node and/or the second target node; through diversified sources of the equity service, diversified requirements of users are met, the users can timely, comprehensively and comprehensively master the equity service, and the professional equity of the users is guaranteed.

In specific implementation, after the user acquires the professional identification code and the service recommendation data of the equity service, the user can check the user professional information displayed after the professional identification code is identified in the process of accessing the user. In an optional implementation manner provided by this embodiment, after the identification instruction for the professional identification code is detected, the following operations are performed:

acquiring an access request submitted by the user aiming at the professional identification code, and decoding the professional identification code;

and returning the user occupation information obtained by decoding to the user for displaying according to the preset display rule.

Specifically, the computing node decodes the stored professional identification code, returns the user professional information obtained by decoding to the user, and displays the professional identification code according to a display rule pre-configured by the user, in addition, the preset display rule can also be configured by the computing node to realize the management of the professional identification code, and in the process of configuring the preset display rule, the displayed field, namely the desensitization rule, can be set, and the checking authority can also be set; optionally, the preset display rule includes at least one of: desensitization rules, permission rules; the privacy requirements of the user are met, the information safety of the user is guaranteed, and the use experience of the user is improved.

In an actual professional training scene, the professional identification code of the user can be used as a sign-in certificate in the process of carrying out a professional training task, after the user arrives at a professional training place, scanning of the professional identification code is carried out through code scanning equipment configured at the training place, geographic position information carried by scanning is obtained, information verification is carried out by displaying the professional information of the user, and it is ensured that the actual training staff information is consistent with the displayed professional information of the user.

In an alternative implementation manner provided by this embodiment, during the process of performing the vocational training task by the user, the following operations are performed:

acquiring geographical position information carried by code scanning equipment for scanning the professional identification code;

judging whether the geographic position contained in the address position information is consistent with the position of a vocational training task of a vocational channel;

if the training records are consistent, generating training records of professional training tasks, and updating the training records to the professional identification codes;

if not, no treatment is carried out.

It is supplementary to be needed, the occupational information of the user can change in the job period, in order to enable the user to obtain the occupational information in time, an update mechanism of the occupational identification code and the service recommendation data is introduced, in the process of updating the occupational identification code, the occupational identification code can be updated according to the training record or the learning record of the user, the occupational period of the user can also be updated according to the training record or the learning record of the user, the updating is also performed according to the occupational period of the user, the occupational information of the user is ensured not to be omitted, and the information is displayed to the user in time. In an optional implementation manner provided by this embodiment, in the process of updating the career identification code and the service recommendation data of the user, the following operations are performed:

updating the career identification code and the service recommendation data according to the career period of the user;

and displaying the updated occupation identification code and the service recommendation data according to the preset display rule configured by the user.

The career cycle can be a work record cycle of the user or a career grade cycle of the user, the updated career identification code and the updated service recommendation data are displayed according to a preset display rule configured by the user, and information safety of the user in the process of sharing the career identification code and showing others is guaranteed.

In addition, the occupation identification code and the service recommendation data can be comprehensively acquired through the steps S102 to S108, and the occupation data can be displayed to the user in multiple directions.

It should be noted that, in this embodiment, the user professional data is obtained by querying a computing node, and is not sent to the first target node in the process of multiparty computing, in addition, the computing node may also combine professional credit data and random data included in the user professional data, encrypt the combined professional data obtained by combining with user identity data, generate encrypted data, and send the encrypted data to the first target node for data fusion of the professional data, specifically, the method includes: generating encrypted data based on a professional access request of a user; sending the encrypted data to a target node in a multi-party computing platform to perform data fusion of professional data; generating the occupation identification code of the user according to target occupation data and user occupation data obtained by the encrypted occupation data returned by the target node; determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

The following description will further explain the professional data processing method provided in this embodiment by taking an application of the professional data processing method provided in this embodiment in an identification code update scenario as an example, and referring to fig. 2, the professional data processing method applied in the identification code update scenario specifically includes the following steps.

Step S202, determining a target node in the multi-party computing platform according to the professional access request of the user.

Step S204, the public key of the target node is used for encrypting the user identity data and the random data to obtain encrypted data.

Step S206, sending the encrypted data to the target node according to the multi-party computing protocol, so that the target node can decrypt the encrypted data by using the private key to obtain the user identity data and the random data.

Then, the target node inquires the occupational data according to the user identity data, and carries out data combination on the occupational data obtained through inquiry and the random data to obtain combined data;

then judging whether a next target node exists or not; if not, encrypting the user identity data and the combined data by using the public key of the computing node, and returning encrypted professional data obtained by encryption to the computing node; if so, encrypting the user identity data and the combined data by using the public key of the next target node, and sending encrypted professional data obtained by encryption to the next target node;

and the next target node decrypts by using the private key to obtain user identity data and merged data, queries next occupational data according to the user identity data, performs data merging on the queried next occupational data and the merged data to obtain next merged data, and returns to execute the operation of judging whether the next target node exists.

And S208, decrypting the encrypted professional data returned by the target node through the private key of the computing node to obtain user identity data and merged data, and extracting the target professional data from the merged data according to the random data.

Step S210, determining the professional channel of the user based on the professional grade information and the professional credit data contained in the user professional data and the target professional data.

And S212, coding the user occupation data, the target occupation data and the occupation channel according to a preset coding rule to obtain the occupation identification code.

Step S214, determining the equity service based on at least one of the user professional data and the target professional data, associating the equity service with the professional identification code, and generating service recommendation data of the equity service.

And S216, updating the career identification code and the service recommendation data according to the career period of the user, and displaying the updated career identification code and the service recommendation data according to a preset display rule configured by the user.

The following further describes the vocational data processing method provided in this embodiment by taking an application of the vocational data processing method provided in this embodiment to a vocational training scenario as an example, and referring to fig. 3, the vocational data processing method applied to the vocational training scenario specifically includes the following steps.

Step S302, a professional authorization request is sent to the user based on the professional access request of the user.

And step S304, creating an authorization certificate according to the professional authorization instruction returned by the user, and generating encrypted data based on the authorization certificate.

And step S306, sending the encrypted data to a target node in the multi-party computing platform so as to perform data fusion of professional data.

Step S308, determining the professional channel of the user according to the user professional data obtained from the encrypted professional data returned from the target node and the professional grade information and the professional credit data contained in the target professional data.

And S310, according to a preset coding rule, coding the user occupation data, the target occupation data and the occupation channel to obtain the occupation identification code.

Step S312, based on at least one of the user professional data and the target professional data, the equity service is determined, the equity service is associated with the professional identification code, and service recommendation data of the equity service is generated.

And step S314, acquiring geographical position information carried by the code scanning equipment for scanning the professional identification code.

Step S316, judging whether the geographic position contained in the address position information is consistent with the position of a professional training task of a professional channel;

if yes, go to step S318;

if not, no treatment is carried out.

And step S318, generating a training record of the vocational training task, and updating the training record to the vocational identification code.

In summary, in the professional data processing method provided in this embodiment, a target node in a multi-party computing platform is determined according to a professional access request of a user, user identity data and random data are encrypted by using a public key of any one of the target nodes to obtain encrypted data, and then the encrypted data is sent to a first target node corresponding to the public key according to a multi-party computing protocol;

secondly, a first target node decrypts by using a private key to obtain user identity data and the random data, inquires first occupational data according to the user identity data, merges the inquired first occupational data with the random data to obtain first merged data, encrypts the user identity data and the first merged data by using a public key of a second target node, sends the encrypted first encrypted occupational data to a second target node, decrypts by using the private key to obtain the user identity data and the first merged data, inquires the second occupational data according to the user identity data, merges the inquired second occupational data with the first merged data to obtain second merged data, judges whether a next target node exists or not, and encrypts the user identity data and the second merged data by using the public key of a computing node if the second occupational data does not exist in the next target node, the second encrypted professional data obtained through encryption is returned to the computing node;

thirdly, decrypting the second encrypted professional data through a private key of the computing node to obtain user identity data and second merged data, extracting target professional data from the second merged data according to random data, then determining a professional channel of the user according to professional grade information and professional credit data contained in the user professional data and the target professional data, and coding the user professional data, the target professional data and the professional channel according to a preset coding rule to obtain a professional identification code;

and finally, the occupation identification code and the service recommendation data are updated according to the occupation period of the user, the updated occupation identification code and the updated service recommendation data are displayed according to a preset display rule configured by the user, so that a multiparty computing platform is utilized, the data security of the multiparty computing participants is guaranteed, the reliability among the multiparty computing participants is enhanced, the data leakage risk is reduced, meanwhile, the occupation identification code is introduced, the use of the user is facilitated, the data updating efficiency is improved, the user can timely acquire own occupation information and rights and interests information, and the use experience of the user is further improved.

The embodiment of the occupation data processing device provided by the specification is as follows:

in the above embodiment, a method for processing professional data is provided, and a corresponding device for processing professional data is also provided, which is described below with reference to the accompanying drawings.

Referring to fig. 4, a schematic diagram of an occupational data processing device according to the present embodiment is shown.

Since the device embodiments correspond to the method embodiments, the description is relatively simple, and the relevant portions may refer to the corresponding description of the method embodiments provided above. The device embodiments described below are merely illustrative.

The embodiment provides an occupation data processing device, including:

an encrypted data generation module 402 configured to generate encrypted data based on a professional access request of a user;

an encrypted data sending module 404 configured to send the encrypted data to a target node in the multi-party computing platform for data fusion of professional data;

an identification code generation module 406, configured to generate a professional identification code of the user according to the professional data of the user and target professional data obtained based on the encrypted professional data returned by the target node;

a service association module 408 configured to determine a equity service based on at least one of the user professional data and the target professional data, associate the equity service to the professional identification code, and generate service recommendation data for the equity service.

An embodiment of a professional data processing device provided in this specification is as follows:

on the basis of the same technical concept, corresponding to the above-described professional data processing method, one or more embodiments of the present specification further provide a professional data processing device, where the professional data processing device is configured to execute the above-described professional data processing method, and fig. 5 is a schematic structural diagram of a professional data processing device provided in one or more embodiments of the present specification.

The embodiment provides a professional data processing device, including:

as shown in fig. 5, professional data processing equipment may have large differences due to different configurations or performances, and may include one or more processors 501 and a memory 502, where the memory 502 may store one or more stored applications or data. Memory 502 may be, among other things, transient or persistent storage. The application programs stored in memory 502 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a professional data processing facility. Still further, the processor 501 may be configured to communicate with the memory 502 to execute a series of computer-executable instructions in the memory 502 on the professional data processing device. The professional data processing apparatus may also comprise one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input/output interfaces 505, one or more keyboards 506, etc.

In a particular embodiment, the professional data processing apparatus comprises a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may comprise one or more modules, and each module may comprise a series of computer-executable instructions for the professional data processing apparatus, and the one or more programs configured to be executed by the one or more processors comprise computer-executable instructions for:

generating encrypted data based on a professional access request of a user;

sending the encrypted data to a target node in a multi-party computing platform to perform data fusion of professional data;

generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node;

determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

An embodiment of a storage medium provided in this specification is as follows:

on the basis of the same technical concept, one or more embodiments of the present specification further provide a storage medium corresponding to the aforementioned professional data processing method.

The present embodiment provides a storage medium for storing computer-executable instructions, which when executed by a processor implement the following procedures:

generating encrypted data based on a professional access request of a user;

sending the encrypted data to a target node in a multi-party computing platform to perform data fusion of professional data;

generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node;

determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

It should be noted that the embodiment related to the storage medium in this specification and the embodiment related to the professional data processing method in this specification are based on the same inventive concept, and therefore, specific implementation of this embodiment may refer to implementation of the foregoing corresponding method, and repeated details are not described herein.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 30 s of the 20 th century, improvements in a technology could clearly be distinguished between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is an integrated circuit whose Logic functions are determined by a user programming the Device. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium that stores computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: the ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in multiple software and/or hardware when implementing the embodiments of the present description.

One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.

The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of this document shall be included in the scope of the claims of this document.

Claims (15)

1. A method of occupational data processing, comprising:

generating encrypted data based on a professional access request of a user;

sending the encrypted data to a target node in a multi-party computing platform to perform data fusion of professional data;

generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node;

determining equity services based on at least one of the user professional data and the target professional data, associating the equity services to the professional identification codes, and generating service recommendation data for the equity services.

2. The professional data processing method of claim 1, wherein the generating of the encrypted data based on the professional access request of the user comprises:

determining a target node in the multi-party computing platform according to the professional access request of the user;

and encrypting the user identity data and the random data by using the public key of any one of the target nodes to obtain the encrypted data.

3. The method of professional data processing according to claim 2, wherein said sending said encrypted data to a target node in a multi-party computing platform comprises:

according to a multi-party computing protocol, sending the encrypted data to a first target node corresponding to the public key;

correspondingly, the data fusion of the occupational data comprises the following steps:

the first target node is decrypted by using a private key to obtain the user identity data and the random data;

inquiring first occupational data according to the user identity data, and performing data merging on the inquired first occupational data and the random data to obtain first merged data;

and encrypting the user identity data and the first combined data by using a public key of a second target node, and sending the first encrypted professional data obtained by encryption to the second target node.

4. The method for processing professional data according to claim 3, wherein the data fusion of professional data further comprises:

the second target node decrypts by using a private key to obtain the user identity data and the first combined data;

inquiring second occupational data according to the user identity data, and performing data merging on the inquired second occupational data and the first merged data to obtain second merged data;

judging whether a next target node exists or not;

if not, the public key of the computing node is used for encrypting the user identity data and the second combined data, and the second encrypted professional data obtained through encryption is returned to the computing node.

5. The professional data processing method according to claim 4, wherein the target professional data is obtained by:

decrypting the second encrypted professional data through a private key of the computing node to obtain the user identity data and the second merged data;

and extracting the target occupational data from the second combined data according to the random data.

6. The occupational data processing method according to claim 4, wherein data transmission processing among the computing nodes, the first target node and the second target node in the multi-party computing platform is performed through a virtual private network;

and the equity service is provided by a professional organization corresponding to the compute node, the first target node, and/or the second target node.

7. The occupational data processing method of claim 1, wherein the generating of the occupational identification code of the user according to the user occupational data and target occupational data obtained based on encrypted occupational data returned by the target node comprises:

determining a professional channel of the user according to the user professional data and professional grade information and professional credit data contained in the target professional data;

and according to a preset coding rule, coding the user occupation data, the target occupation data and the occupation channel to obtain the occupation identification code.

8. The professional data processing method according to claim 1, further comprising:

updating the career identification code and the service recommendation data according to the career period of the user;

displaying the updated career identification code and the service recommendation data according to the preset display rule configured by the user;

wherein the preset display rule comprises at least one of: desensitization rules, authority rules.

9. The professional data processing method according to claim 1, wherein if the recognition instruction for the professional identification code is detected, the following steps are performed:

acquiring an access request submitted by the user aiming at the professional identification code, and decoding the professional identification code;

and returning the user occupation information obtained by decoding to the user to display according to a preset display rule configured by the user.

10. The professional data processing method according to claim 1, further comprising:

acquiring geographical position information carried by code scanning equipment for scanning the professional identification code;

judging whether the geographic position contained in the address position information is consistent with the position of a professional training task of a professional channel;

and if the training records are consistent, generating a training record of the professional training task, and updating the training record to the professional identification code.

11. The professional data processing method of claim 1, wherein the generating of the encrypted data based on the professional access request of the user comprises:

sending a professional authorization request to the user based on the user's professional access request;

and creating an authorization voucher according to the professional authorization instruction returned by the user, and generating the encrypted data based on the authorization voucher.

12. The professional data processing method according to claim 11, wherein the encrypted data comprises random data, user identity data and professional credit data;

wherein the random data is generated upon a computing node detecting that the authorization credential was created.

13. A vocational data processing apparatus comprising:

an encrypted data generation module configured to generate encrypted data based on a professional access request of a user;

the encrypted data sending module is configured to send the encrypted data to a target node in a multi-party computing platform so as to perform data fusion of professional data;

the identification code generation module is configured to generate the professional identification code of the user according to the professional data of the user and target professional data obtained based on the encrypted professional data returned by the target node;

a service association module configured to determine a equity service based on at least one of the user professional data and the target professional data, associate the equity service to the professional identification code, and generate service recommendation data for the equity service.

14. An occupational data processing device comprising:

a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to:

generating encrypted data based on a professional access request of a user;

sending the encrypted data to a target node in a multi-party computing platform to perform data fusion of professional data;

generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node;

determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

15. A storage medium storing computer-executable instructions that when executed by a processor implement the following:

generating encrypted data based on a professional access request of a user;

sending the encrypted data to a target node in a multi-party computing platform to perform data fusion of professional data;

generating the occupation identification code of the user according to the user occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node;

determining a equity service based on at least one of the user professional data and the target professional data, associating the equity service to the professional identification code, and generating service recommendation data for the equity service.

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