Occupational Data Processing Method and Device

Abstract

The embodiments of this specification provide a method and device for processing occupation data. Among them, a method for processing occupation data includes: generating encrypted data based on a user's occupation access request; sending the encrypted data to a target node in a multi-party computing platform for data fusion of occupation data; generating an occupation identification code for the user according to the user's occupation data and target occupation data obtained based on the encrypted occupation data returned by the target node; determining an entitlement service based on at least one of the user's occupation data and the target occupation data, associating the entitlement service with the occupation identification code, and generating service recommendation data for the entitlement service.

Description

Technical Field

[0001] This document relates to the technical field of data processing, and particularly relates to a method and device for processing occupational data. Background Art

[0002] With the rapid development of Internet technology, data sharing is applied more and more widely in the digital age. For example, in the medical industry, insurance industry, and financial industry, data sharing is achieved through data interconnection between various institutions to establish a fast service processing channel. And the two-dimensional code is an important implementation method of data sharing. It is a readable barcode extended on the basis of the one-dimensional barcode. When a device scans the two-dimensional code and identifies the binary data recorded in the length and width of the barcode, the information contained therein can be obtained. Summary of the Invention

[0003] One or more embodiments of this specification provide a method for processing occupational data, including: generating encrypted data based on a user's occupational access request. Sending the encrypted data to a target node in a multi-party computing platform for data fusion of occupational data. Generating a vocational identification code for the user according to the user's occupational data and the target occupational data obtained based on the encrypted occupational data returned by the target node. Determining an entitlement service based on at least one of the user's occupational data and the target occupational data, associating the entitlement service with the vocational identification code, and generating service recommendation data for the entitlement service.

[0004] One or more embodiments of this specification provide a device for processing occupational data, including: an encrypted data generation module configured to generate encrypted data based on a user's occupational access request. An encrypted data sending module configured to send the encrypted data to a target node in a multi-party computing platform for data fusion of occupational data. An identification code generation module configured to generate a vocational identification code for the user according to the user's occupational data and the target occupational data obtained based on the encrypted occupational data returned by the target node. A service association module configured to determine an entitlement service based on at least one of the user's occupational data and the target occupational data, associate the entitlement service with the vocational identification code, and generate service recommendation data for the entitlement service.

[0005] One or more embodiments of this specification provide an occupational data processing device, including: a processor; and a memory configured to store computer-executable instructions, the computer-executable instructions, when executed, cause the processor to: generate encrypted data based on a user's occupational access request. Send the encrypted data to a target node in a multi-party computing platform for data fusion of occupational data. Generate an occupational identification code for the user according to the user's occupational data and target occupational data obtained based on the encrypted occupational data returned by the target node. Determine an entitlement service based on at least one of the user's occupational data and the target occupational data, associate the entitlement service with the occupational identification code, and generate service recommendation data for the entitlement service.

[0006] One or more embodiments of this specification provide a storage medium for storing computer-executable instructions, the computer-executable instructions, when executed by a processor, implement the following process: generate encrypted data based on a user's occupational access request. Send the encrypted data to a target node in a multi-party computing platform for data fusion of occupational data. Generate an occupational identification code for the user according to the user's occupational data and target occupational data obtained based on the encrypted occupational data returned by the target node. Determine an entitlement service based on at least one of the user's occupational data and the target occupational data, associate the entitlement service with the occupational identification code, and generate service recommendation data for the entitlement service. Description of the Drawings

[0007] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings;

[0008] Figure 1 It is a processing flow chart of an occupational data processing method provided by one or more embodiments of this specification;

[0009] Figure 2 It is a processing flow chart of an occupational data processing method applied to an identification code update scenario provided by one or more embodiments of this specification;

[0010] Figure 3 It is a processing flow chart of an occupational data processing method applied to an occupational training scenario provided by one or more embodiments of this specification;

[0011] Figure 4 It is a schematic diagram of an occupational data processing device provided by one or more embodiments of this specification;

[0012] Figure 5 Schematic diagram of a professional data processing device provided for one or more embodiments of this specification. Detailed implementation manners

[0013] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification with reference to the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0014] An embodiment of a professional data processing method provided by this specification:

[0015] Referring to Figure 1 , which shows a processing flow chart of a professional data processing method provided by this embodiment. Referring to Figure 2 , which shows a processing flow chart of a professional data processing method applied to an identification code update scenario provided by this embodiment. Referring to Figure 3 , which shows a processing flow chart of a professional data processing method applied to a professional training scenario provided by this embodiment.

[0016] Referring to Figure 1 , the professional data processing method provided by this embodiment specifically includes steps S102 to S108.

[0017] Step S102, generating encrypted data based on the user's professional access request.

[0018] The professional data processing method provided by this embodiment, with the help of a multi-party computing platform, during the process of the user accessing professional services, obtains the user's professional access request and generates encrypted data, and then sends the encrypted data to the target nodes participating in multi-party computing to perform data fusion of professional data at the target nodes. On this basis, decrypts the encrypted professional data returned by the target nodes to obtain the target professional data. Further, generates a professional identification code according to the user's professional data and the target professional data, then determines the rights and interests service based on at least one of the user's professional data and the target professional data, associates the rights and interests service with the professional identification code, and generates service recommendation data for the rights and interests service to perform service recommendation.

[0019] Utilize a multi-party computing platform to ensure the data security of multi-party computing participants, enhance the credibility among multi-party computing participants, reduce the risk of data leakage. At the same time, by introducing a professional identification code, it facilitates the use of users, improves the efficiency of data update, enables users to obtain their professional information and rights and interests information in a timely manner, and thus enhances the user experience.

[0020] It should be noted that this embodiment can be applied to a computing node, where the computing node conducts data transmission with other nodes participating in the multi-party computing platform, and can also be applied to a server for professional services, where the server docks with the multi-party computing platform for data transmission.

[0021] In specific implementation, during the process of a user accessing professional services, a professional access request is generated. After obtaining the user's professional access request, other target nodes participating in the multi-party computing are determined. After that, the execution order among the nodes participating in the multi-party computing platform is determined, such as the computing node with the first execution order, the first target node with the second execution order, the second target node with the third execution order, etc. The public key of the first target node is used to encrypt the user identity data and random data to obtain encrypted data, realizing the security of the data transmission process.

[0022] In an optional implementation manner provided by this embodiment, during the process of generating encrypted data based on the user's professional access request, the following operations are performed:

[0023] Determine the target nodes in the multi-party computing platform according to the user's professional access request;

[0024] Use the public key of any one of the target nodes to encrypt the user identity data and random data to obtain the encrypted data.

[0025] In practical applications, during the process of the computing node sending encrypted data to the first target node, on the one hand, the professional credit data in the user's professional data queried at the computing node can be merged with the random data to generate merged professional data, and then the user identity data in the user's professional data and the merged professional data are encrypted to generate encrypted data for sending. On the other hand, the user identity data and random data can also be directly encrypted to generate encrypted data and sent to the first target node, achieving the high efficiency and flexibility of multi-party computing through diverse data merging methods; the encrypted data in this embodiment can be composed of random data and user identity data, and in addition, the encrypted data can also be composed of random data, user identity data, and professional credit data.

[0026] Among them, the random data is data randomly generated by the computing node using a random algorithm or the like. For the computing node, the random data is known, but for other nodes participating in the multi-party computing platform, it is unknown. The user's occupation data may include user identity data and occupation credit data. The user identity data includes, for example, user identification, geographical location data of the user, etc. The occupation credit data includes, for example, resume data, skill certificate data (such as electrician certificate, driver's license), professional title data, occupation credit data (such as number of positive job reviews, number of jobs), etc. Introducing the random data is to make the occupation data of users among the nodes participating in the multi-party computing platform unknown to other nodes, ensuring data isolation and security.

[0027] In addition, to protect the privacy of the user's occupation data and prevent the user's rights and interests from being violated, an authorization instruction of the user can be obtained before performing multi-party computing on the occupation data. Specifically, in an optional implementation manner provided in this embodiment, in the process of generating encrypted data based on the user's occupation access request, the following steps are executed:

[0028] Send an occupation authorization request to the user based on the user's occupation access request;

[0029] Create an authorization credential according to the occupation authorization instruction returned by the user, and generate the encrypted data based on the authorization credential.

[0030] In the specific execution process, after creating the authorization credential according to the occupation authorization instruction returned by the user, random data can be generated for secure merging of the occupation data; optionally, the random data is generated after the computing node detects that the authorization credential is created.

[0031] Step S104: Send the encrypted data to the target node in the multi-party computing platform for data fusion of the occupation data.

[0032] In practical applications, to ensure the reliability of the connection party and the security and confidentiality of data transmission, data transmission among the computing node, the first target node, the second target node, etc. participating in the multi-party computing platform can be implemented using a virtual private network (such as VPN, Virtual Private Network) to save manpower and material costs, achieve convenient and flexible data transmission connections, and improve the speed and efficiency of data transmission; optionally, data transmission processing among the computing node, the first target node, and the second target node participating in the multi-party computing platform is performed through a virtual private network.

[0033] In specific implementation, based on the above-mentioned determination of the target node in the multi-party computing platform according to the user's professional access request, and on the basis of encrypting the user identity data and the random data with the public key of any one of the target nodes to obtain the encrypted data, the encrypted data can be sent to the first target node in the first execution order according to the first execution order included in the multi-party computing protocol. Specifically, according to the multi-party computing protocol, the encrypted data is sent to the first target node corresponding to the public key.

[0034] Among them, the multi-party computing protocol can be generated based on the execution order among the nodes participating in the multi-party computing platform after the target node in the above-mentioned multi-party computing platform is determined, that is: the computing node in the first execution order, the first target node in the second execution order, the second target node in the third execution order, etc.; in addition, the multi-party computing protocol can also be generated first, and then the execution order among the nodes participating in the multi-party computing platform is determined according to the generated multi-party computing protocol.

[0035] On this basis, after the first target node receives the encrypted data, it performs data fusion of the professional data. In an optional implementation manner provided in this embodiment, during the process of performing data fusion of the professional data, the following operations are performed:

[0036] The first target node decrypts with the private key to obtain the user identity data and the random data;

[0037] Query the first professional data according to the user identity data, and merge the queried first professional data with the random data to obtain the first merged data;

[0038] Encrypt the user identity data and the first merged data with the public key of the second target node, and send the first encrypted professional data obtained by encryption to the second target node.

[0039] For example, computing node A encrypts the user identity data and the random data r with the public key of the first target node B, and sends the obtained encrypted data to the first target node B. The first target node B decrypts the encrypted data with the private key to obtain the user identity data and the random data r. The first target node B queries the first professional data B1 according to the user identity data, and merges 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 merged data B1 + r with the public key of the second target node C, and sends the first encrypted professional data obtained by encryption to the second target node C.

[0040] It should be noted that the above second target node is unknown about the data composition part of the first merged data, that is, it is unknown about the specific data information of the first merged data. The second target node only needs to merge the queried second occupation data with the first merged data and then encrypt it. Similarly, the data composition part and specific data information of the subsequent second merged data are unknown to the node receiving the second merged data. The nodes participating in the multi-party calculation can only know the occupation data after the data fusion of the occupation data, and the specific composition structure of the occupation data after the data fusion is unknown, so as to ensure the isolation and security of data transmission.

[0041] Furthermore, on the basis of encrypting the user identity data and the first merged data with the public key of the second target node and sending the obtained first encrypted occupation data to the second target node, the second target node can also decrypt the first encrypted data with the private key and then perform data fusion of the occupation data. In an optional implementation manner provided in this embodiment, performing data fusion of the occupation data further includes:

[0042] The second target node decrypts with the private key to obtain the user identity data and the first merged data;

[0043] Query the second occupation data according to the user identity data, and merge the queried second occupation data with the first merged data to obtain the second merged data;

[0044] Judge whether there is a next target node;

[0045] If not, encrypt the user identity data and the second merged data with the public key of the computing node and return the obtained second encrypted occupation data to the computing node;

[0046] If so, encrypt the user identity data and the merged data with the public key of the next target node, send the obtained encrypted occupation data to the next target node, and return to execute the operation of judging whether there is a next target node.

[0047] Continuing with the above example, after the second target node C receives the first encrypted occupation data sent by the first target node B, it decrypts with the private key to obtain the user identity data and the first merged data B1+r. Then, it queries the second occupation data of the second target node C according to the user identity data. The queried second occupation data C1 is merged into B1+r to obtain the second merged data C1+B1+r. If there is no next target node, it encrypts the user identity data and C1+B1+r with the public key of the computing node A and returns the encrypted second encrypted occupation data to the computing node A.

[0048] Furthermore, on the basis of encrypting the user identity data and the second merged data with the public key of the computing node and returning the obtained second encrypted occupation data to the computing node, the computing node can decrypt the second encrypted occupation data to obtain the target occupation data, that is, the occupation data of the user after data fusion obtained from the first target node and the second target node. In an optional implementation manner provided in this embodiment, after encrypting the user identity data and the second merged data with the public key of the computing node and returning the obtained second encrypted occupation data to the computing node, the following operations are performed:

[0049] Decrypt the second encrypted occupation data with the private key of the computing node to obtain the user identity data and the second merged data;

[0050] Extract the target occupation data from the second merged data according to the random data.

[0051] Continuing with the above example, after computing node A receives the second encrypted occupation data sent by the second target node C, it decrypts the second encrypted occupation data with the private key of computing node A to obtain the user identity data and the second merged data C1 + B1 + r. Since the random data r is generated by computing node A, computing node A knows the random data r. Computing node A can extract the target occupation data C1 + B1 from the second merged data C1 + B1 + r according to the known random data r.

[0052] It should be noted that the above example is only for the convenience of 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, data aggregation, aggregation algorithms, and coding algorithms can also be used to merge occupation data. This embodiment does not specifically limit the merging operation process and the data forms formed by merging.

[0053] Step S106: Generate an occupation identification code for the user according to the user occupation data and the target occupation data obtained based on the encrypted occupation data returned by the target node.

[0054] On the basis of decrypting the second encrypted occupation data with the private key of the computing node to obtain the user identity data and the second merged data, and extracting the target occupation data from the second merged data according to the random data, this embodiment generates an occupation identification code for the user according to the user occupation data queried by the computing node using the user identity data and the extracted target occupation data, so as to facilitate the sharing, viewing, and storage of occupation data.

[0055] In specific implementation, in order to provide users with better career services, a career development direction plan can be provided to users. The planned career development direction is encoded together with the user identity data and the target career data to obtain a career identification code, enabling users to conduct corresponding skill training and learning according to the planned career development direction, thereby improving their professional skills. In an optional implementation manner provided in this embodiment, in the process of generating the user's career identification code based on the user career data and the target career data obtained from the encrypted career data returned based on the target node, the following operations are performed:

[0056] Determine the user's career channels based on the career level information and career credit data included in the user career data and the target career data;

[0057] According to the preset coding rules, perform coding processing on the user career data, the target career data, and the career channels to obtain the career identification code.

[0058] In the specific execution process, the user career data and the target career data may include career level information and / or career credit information. Career level information such as the level certificate information obtained by the user, the professional titles obtained, etc. Career credit data such as the number of positive job reviews, the number of jobs, the authenticity of professional skills, etc. Using the user's career level information and / or career credit information, career channels (such as career development directions) can be provided to users, and then, according to the preset coding rules, the user career data, the target career data, and the career channels related to the user are encoded to obtain the career identification code, so that users can have a clear understanding of themselves, better improve themselves using the career channels, and enhance the user experience.

[0059] Step S108, determine the rights and interests services based on at least one of the user career data and the target career data, associate the rights and interests services with the career identification code, and generate service recommendation data for the rights and interests services.

[0060] In practical applications, in order to provide users with better career services, enabling users to understand their work resumes and career channels while also being able to obtain social welfare in a timely manner, based on the user career data and the target career data obtained from the encrypted career data returned based on the target node to generate the user's career identification code, the rights and interests services can be determined, and the rights and interests services are associated with the user's career identification code for service recommendation.

[0061] The service recommendation data of the rights and interests service in this embodiment includes service recommendation data displayed after viewing through training links or training controls for vocational training, as well as information links or information controls for welfare information. It can also include service recommendation data for vocational training or welfare information, that is, during the process of displaying the vocational service page to the user, the service recommendation data of the rights and interests service and the vocational identification code are displayed simultaneously. The user can view vocational training or welfare information through the link or control, or directly display the service recommendation data of vocational training or welfare information on the vocational service page.

[0062] For the rights and interests service, since each node participating in the multi-party computing platform may store the user's vocational data, based on the user's vocational data, the rights and interests service can be determined. Therefore, the rights and interests service may come from the vocational institution corresponding to the computing node, or may come from the vocational institution corresponding to the first target node and / or the second target node; optionally, the rights and interests service is provided by the vocational institution corresponding to the computing node, the first target node, and / or the second target node; through the diverse sources of the rights and interests service, the diverse needs of users are met, enabling users to comprehensively and timely master the rights and interests service and protecting the users' vocational rights and interests.

[0063] Specifically, when the user obtains the vocational identification code and the service recommendation data of the rights and interests service, during the user's access process, the user can view the user's vocational information displayed after the vocational identification code is recognized. In an optional implementation manner provided in this embodiment, after detecting an identification instruction for the vocational identification code, the following operations are performed:

[0064] Obtain the access request submitted by the user for the vocational identification code, and perform decoding processing on the vocational identification code;

[0065] Return the user's vocational information obtained through decoding processing to the user for display according to the preset display rules.

[0066] Specifically, the computing node decodes the stored vocational identification code and returns the decoded user's vocational information to the user for display according to the display rules pre-configured by the user. In addition, the preset display rules can also be configured by the computing node to manage the vocational identification code. During the configuration of the preset display rules, the displayed fields, that is, the desensitization rules, can be set, or the viewing permissions can be set; optionally, the preset display rules include at least one of the following: desensitization rules, permission rules; to meet the privacy needs of users, protect the information security of users, and improve the user experience.

[0067] In an actual vocational training scenario, the user's vocational identification code can be used as a check-in voucher during the process of performing vocational training tasks. After the user arrives at the vocational training location, the vocational identification code is scanned through the scanning device configured at the training location to obtain the geographical location information carried by the scan, and the user's vocational information is displayed for information verification to ensure that the information of the actual training personnel is consistent with the displayed user vocational information.

[0068] In an optional implementation manner provided by this embodiment, during the process of the user performing vocational training tasks, the following operations are performed:

[0069] Obtain the geographical location information carried by the scanning device for scanning the vocational identification code;

[0070] Determine whether the geographical location included in the address location information is consistent with the vocational training task location of the vocational channel;

[0071] If they are consistent, generate a training record for the vocational training task and update the training record to the vocational identification code;

[0072] If they are inconsistent, do not perform any processing.

[0073] It should be added that the user's vocational information will change during the vocational cycle. In order to enable the user to obtain vocational information in a timely manner, an update mechanism for the vocational identification code and service recommendation data is introduced. During the process of updating the vocational identification code, it can be updated according to the user's training record or learning record, or according to the user's vocational cycle, or both according to the user's training record or learning record and according to the user's vocational cycle, to ensure that the user's vocational information is not omitted and is displayed to the user in a timely manner. In an optional implementation manner provided by this embodiment, during the process of updating the user's vocational identification code and service recommendation data, the following operations are performed:

[0074] Update the vocational identification code and the service recommendation data according to the user's vocational cycle;

[0075] Display the updated vocational identification code and service recommendation data according to the preset display rules configured by the user.

[0076] Among them, the vocational cycle can be the user's work resume cycle or the user's vocational level cycle. Displaying the updated vocational identification code and service recommendation data according to the preset display rules configured by the user ensures the information security of the user during the process of sharing and presenting the vocational identification code to others.

[0077] In addition, the update process of the occupation identification code and service recommendation data can also comprehensively obtain occupation data through the above steps S102 to S108 and display it to the user in all aspects.

[0078] It should be noted that the user occupation data in this embodiment is obtained by querying at the computing node and is not sent to the first target node during the multi-party computing process. In addition, the computing node can also merge the occupation credit data and random data included in the user occupation data, encrypt the merged occupation data obtained by the merger with the user identity data to generate encrypted data, and send it to the first target node for data fusion of occupation data. Specifically, it includes: generating encrypted data based on the user's occupation access request; sending the encrypted data to the target node in the multi-party computing platform for data fusion of occupation data; generating the occupation identification code of the user based on the target occupation data obtained from the encrypted occupation data returned by the target node and the user occupation data; determining the rights and interests service based on at least one of the user occupation data and the target occupation data, associating the rights and interests service with the occupation identification code, and generating the service recommendation data of the rights and interests service.

[0079] The following takes the application of a method for processing occupation data provided in this embodiment in the scenario of updating the identification code as an example to further illustrate the method for processing occupation data provided in this embodiment. See Figure 2 , the method for processing occupation data applied to the scenario of updating the identification code specifically includes the following steps.

[0080] Step S202, determine the target node in the multi-party computing platform according to the user's occupation access request.

[0081] Step S204, use the public key of the target node to encrypt the user identity data and random data to obtain encrypted data.

[0082] Step S206, send the encrypted data to the target node according to the multi-party computing protocol so that the target node decrypts it with the private key to obtain the user identity data and random data.

[0083] After that, the target node queries the occupation data according to the user identity data, and merges the queried occupation data with the random data to obtain merged data;

[0084] Then, it is determined whether there is a next target node; if not, use the public key of the computing node to encrypt the user identity data and the merged data, and return the encrypted occupation data obtained by the encryption to the computing node; if so, use the public key of the next target node to encrypt the user identity data and the merged data, and send the encrypted occupation data obtained by the encryption to the next target node;

[0085] The next target node decrypts using the private key to obtain user identity data and merged data, queries the next occupation data based on the user identity data, merges the queried next occupation data with the merged data to obtain the next merged data, and returns to perform an operation to determine whether there is a next target node.

[0086] Step S208: Decrypt the encrypted occupation data returned by the target node through the private key of the computing node to obtain user identity data and merged data, and extract the target occupation data from the merged data according to the random data.

[0087] Step S210: Determine the user's occupation channel based on the occupation level information and occupation credit data included in the user occupation data and the target occupation data.

[0088] Step S212: Perform encoding processing on the user occupation data, target occupation data, and occupation channel according to the preset encoding rules to obtain an occupation identification code.

[0089] Step S214: Determine the rights and interests service based on at least one of the user occupation data and the target occupation data, associate the rights and interests service with the occupation identification code, and generate service recommendation data for the rights and interests service.

[0090] Step S216: Update the occupation identification code and the service recommendation data according to the user's occupation cycle, and display the updated occupation identification code and service recommendation data according to the preset display rules configured by the user.

[0091] The following takes the application of the occupation data processing method provided in this embodiment in the occupation training scenario as an example to further illustrate the occupation data processing method provided in this embodiment. See Figure 3 The occupation data processing method applied to the occupation training scenario specifically includes the following steps.

[0092] Step S302: Send an occupation authorization request to the user based on the user's occupation access request.

[0093] Step S304: Create an authorization certificate according to the occupation authorization instruction returned by the user, and generate encrypted data based on the authorization certificate.

[0094] Step S306: Send the encrypted data to the target node in the multi-party computing platform for data fusion of occupation data.

[0095] Step S308: Determine the user's occupation channel according to the occupation level information and occupation credit data included in the user occupation data and the target occupation data obtained from the encrypted occupation data returned by the target node.

[0096] Step S310: Code the user's occupation data, target occupation data, and occupation channels according to the preset coding rules to obtain occupation identification codes.

[0097] Step S312: Determine the rights and interests services based on at least one of the user's occupation data and target occupation data, associate the rights and interests services with the occupation identification codes, and generate service recommendation data for the rights and interests services.

[0098] Step S314: Obtain the geographical location information carried by the scanning device when scanning the occupation identification code.

[0099] Step S316: Determine whether the geographical location included in the address location information is consistent with the location of the occupation training task of the occupation channel;

[0100] If they are consistent, execute Step S318;

[0101] If they are inconsistent, do nothing.

[0102] Step S318: Generate a training record for the occupation training task and update the training record to the occupation identification code.

[0103] In summary, for the occupation data processing method provided in this embodiment, first, determine the target node in the multi-party computing platform according to the user's occupation access request, encrypt the user identity data and random data using the public key of any one of the target nodes to obtain encrypted data, and then send the encrypted data to the first target node corresponding to the public key according to the multi-party computing protocol;

[0104] Secondly, the first target node decrypts using the private key to obtain the user identity data and the random data, queries the first occupation data according to the user identity data, and merges the queried first occupation data with the random data to obtain the first merged data. Encrypt the user identity data and the first merged data using the public key of the second target node, and send the first encrypted occupation data obtained by encryption to the second target node. The second target node decrypts using the private key to obtain the user identity data and the first merged data, queries the second occupation data according to the user identity data, and merges the queried second occupation data with the first merged data to obtain the second merged data. Then, determine whether there is a next target node. If not, encrypt the user identity data and the second merged data using the public key of the computing node, and return the second encrypted occupation data obtained by encryption to the computing node;

[0105] Next, decrypt the second encrypted professional data using the private key of the computing node to obtain user identity data and second merged data. Extract target professional data from the second merged data according to the random data. Then, determine the user's professional channel based on the professional level information and professional credit data included in the user professional data and the target professional data. According to the preset coding rules, perform coding processing on the user professional data, target professional data, and professional channel to obtain a professional identification code;

[0106] Finally, update the professional identification code and service recommendation data according to the user's professional cycle, and display the updated professional identification code and service recommendation data according to the preset display rules configured by the user, so as to use the multi-party computing platform to ensure the data security of multi-party computing participants, enhance the credibility among multi-party computing participants, reduce the risk of data leakage. At the same time, by introducing the professional identification code, it is convenient for users to use, improves the efficiency of data update, enables users to obtain their professional information and rights and interests information in a timely manner, and thus enhances the user experience.

[0107] An embodiment of a professional data processing device provided in this specification is as follows:

[0108] In the above embodiment, a professional data processing method is provided. Correspondingly, a professional data processing device is also provided. The following will be described with reference to the accompanying drawings.

[0109] Refer to Figure 4 , which shows a schematic diagram of a professional data processing device provided in this embodiment.

[0110] Since the device embodiment corresponds to the method embodiment, the description is relatively simple. For the relevant parts, please refer to the corresponding description of the method embodiment provided above. The device embodiments described below are only illustrative.

[0111] This embodiment provides a professional data processing device, including:

[0112] An encrypted data generation module 402, configured to generate encrypted data based on the user's professional access request;

[0113] 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;

[0114] An identification code generation module 406, configured to generate the user's professional identification code according to the user professional data and the target professional data obtained based on the encrypted professional data returned by the target node;

[0115] A service association module 408, configured to determine an entitlement service based on at least one of the user's occupation data and the target occupation data, associate the entitlement service with the occupation identification code, and generate service recommendation data for the entitlement service.

[0116] An embodiment of an occupation data processing device provided in this specification is as follows:

[0117] Corresponding to the above-described occupation data processing method, based on the same technical concept, one or more embodiments of this specification also provide an occupation data processing device, which is used to execute the above-provided occupation data processing method. Figure 5 It is a schematic structural diagram of an occupation data processing device provided by one or more embodiments of this specification.

[0118] An occupation data processing device provided in this embodiment includes:

[0119] As Figure 5 shown, the occupation data processing device may vary greatly due to configuration or performance, and may include one or more processors 501 and a memory 502. One or more application programs or data may be stored in the memory 502. Among them, the memory 502 may be short-term storage or persistent storage. The application programs stored in the memory 502 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the occupation data processing device. Further, the processor 501 may be configured to communicate with the memory 502 and execute a series of computer-executable instructions in the memory 502 on the occupation data processing device. The occupation data processing device may also include 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.

[0120] In a specific embodiment, the occupation data processing device includes a memory and one or more programs, where one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the occupation data processing device, and is configured to be executed by one or more processors. The one or more programs include the following computer-executable instructions:

[0121] Generate encrypted data based on the user's occupation access request;

[0122] Send the encrypted data to a target node in a multi-party computing platform for data fusion of occupation data;

[0123] Generate the occupation identification code of the user based on the user occupation data and the target occupation data obtained from the encrypted occupation data returned based on the target node.

[0124] Determine the rights and interests service based on at least one of the user occupation data and the target occupation data, associate the rights and interests service with the occupation identification code, and generate the service recommendation data of the rights and interests service.

[0125] An embodiment of the storage medium provided in this specification is as follows:

[0126] Corresponding to the above-described occupation data processing method, based on the same technical concept, one or more embodiments of this specification also provide a storage medium.

[0127] The storage medium provided in this embodiment is used to store computer-executable instructions, and the computer-executable instructions, when executed by a processor, implement the following processes:

[0128] Generate encrypted data based on the occupation access request of the user;

[0129] Send the encrypted data to the target node in the multi-party computing platform for data fusion of occupation data;

[0130] Generate the occupation identification code of the user based on the user occupation data and the target occupation data obtained from the encrypted occupation data returned based on the target node.

[0131] Determine the rights and interests service based on at least one of the user occupation data and the target occupation data, associate the rights and interests service with the occupation identification code, and generate the service recommendation data of the rights and interests service.

[0132] It should be noted that the embodiment of the storage medium in this specification and the embodiment of the occupation data processing method in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the corresponding method described above, and the repeated parts will not be elaborated.

[0133] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended 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 the desired results. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.

[0134] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many method flow improvements today can be regarded as direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented using a hardware entity module. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is an integrated circuit whose logical function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system onto a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compilers used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL), and there is not just one type of HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0135] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or the structures within the hardware component.

[0136] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0137] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0138] Those skilled in the art should understand that one or more embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, one or more embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0139] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0140] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0142] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0143] The memory may include non-permanent memory in the form of computer-readable media, 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 computer-readable media.

[0144] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0145] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0146] One or more embodiments of the present specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0147] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0148] The above are only examples of this document and are not intended to limit this document. For those skilled in the art, various changes and modifications can be made to this document. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document shall be included within the scope of the claims of this document.

Claims

1. A method for processing occupational data, comprising: generating encrypted data based on a user's occupational access request; sending the encrypted data to a target node in a multi-party computing platform for data fusion of occupational data; decrypting the encrypted occupational data returned by the target node with the private key of the computing node to obtain user identity data and merged data, and extracting target occupational data from the merged data according to random data; the merged data is obtained by merging occupational data queried based on the user identity data and the random data; generating an occupational identification code for the user according to the user's occupational data and the target occupational data; determining an entitlement service based on at least one of the user's occupational data and the target occupational data, associating the entitlement service with the occupational identification code, and generating service recommendation data for the entitlement service.

2. The method for processing occupational data according to claim 1, wherein the generating encrypted data based on a user's occupational access request comprises: determining a target node in the multi-party computing platform according to the user's occupational access request; encrypting the user identity data and random data with the public key of any one of the target nodes to obtain the encrypted data.

3. The method for processing occupational data according to claim 2, wherein the sending the encrypted data to a target node in the multi-party computing platform comprises: sending the encrypted data to a first target node corresponding to the public key according to a multi-party computing protocol; Correspondingly, the performing data fusion of occupational data comprises: the first target node decrypts with the private key to obtain the user identity data and the random data; querying first occupational data according to the user identity data, and merging the queried first occupational data with the random data to obtain first merged data; encrypting the user identity data and the first merged data with the public key of a second target node, and sending the first encrypted occupational data obtained by encryption to the second target node.

4. The method for processing occupational data according to claim 3, wherein the performing data fusion of occupational data further comprises: the second target node decrypts with the private key to obtain the user identity data and the first merged data; querying second occupational data according to the user identity data, and merging the queried second occupational data with the first merged data to obtain second merged data; judging whether there is a next target node; if not, encrypting the user identity data and the second merged data with the public key of the computing node, and returning the second encrypted occupational data obtained by encryption to the computing node.

5. The method for processing occupational data according to claim 4, wherein the target occupational data is obtained in the following manner: decrypting the second encrypted occupational data with the private key of the computing node to obtain the user identity data and the second merged data; extracting the target occupational data from the second merged data according to the random data.

6. The occupational data processing method according to claim 4, wherein data transmission processing is performed between the computing node, the first target node, and the second target node in the multi-party computing platform through a virtual private network; and the rights and interests service is provided by the occupational institutions corresponding to the computing node, the first target node, and / or the second target node.

7. The occupational data processing method according to claim 1, wherein generating the occupational identification code of the user based on the user's occupational data and the target occupational data obtained based on the encrypted occupational data returned by the target node includes: determining the occupational channel of the user according to the user's occupational data, and the occupational level information and occupational credit data included in the target occupational data; encoding the user's occupational data, the target occupational data, and the occupational channel according to a preset encoding rule to obtain the occupational identification code.

8. The occupational data processing method according to claim 1 further includes: updating the occupational identification code and the service recommendation data according to the occupational cycle of the user; displaying the updated occupational identification code and service recommendation data according to the preset display rule configured by the user; wherein the preset display rule includes at least one of the following: desensitization rule, permission rule.

9. The occupational data processing method according to claim 1, if an identification instruction for the occupational identification code is detected, perform the following steps: obtaining the access request submitted by the user for the occupational identification code and performing decoding processing on the occupational identification code; returning the user's occupational information obtained by the decoding processing to the user for display according to the preset display rule configured by the user.

10. The occupational data processing method according to claim 1 further includes: obtaining the geographical location information carried by the scanning device when scanning the occupational identification code; judging whether the geographical location included in the geographical location information is consistent with the location of the occupational training task of the occupational channel; if they are consistent, generating a training record of the occupational training task and updating the training record to the occupational identification code.

11. The occupational data processing method according to claim 1, wherein generating the encrypted data based on the user's occupational access request includes: sending an occupational authorization request to the user based on the user's occupational access request; creating an authorization certificate according to the occupational authorization instruction returned by the user and generating the encrypted data based on the authorization certificate.

12. The occupational data processing method according to claim 11, wherein the encrypted data includes random data, user identity data, and occupational credit data; Among them, the random data is generated after the computing node detects that the authorization certificate is created.

13. An occupational data processing device includes: an encrypted data generation module configured to generate encrypted data based on the user's occupational access request; an encrypted data sending module configured to send the encrypted data to a target node in the multi-party computing platform for data fusion of occupational data; An identification code generation module, configured to decrypt the encrypted occupation data returned by the target node through the private key of the computing node, obtain user identity data and merged data, and extract target occupation data from the merged data according to random data; the merged data is obtained by merging the occupation data queried based on the user identity data and the random data; generate an occupation identification code for the user according to the user occupation data and the target occupation data; A service association module, configured to determine an entitlement service based on at least one of the user occupation data and the target occupation data, associate the entitlement service with the occupation identification code, and generate service recommendation data for the entitlement service.

14. An occupation data processing device, comprising: A processor; And a memory configured to store computer-executable instructions, the computer-executable instructions, when executed, cause the processor to: Generate encrypted data based on a user's occupation access request; Send the encrypted data to a target node in a multi-party computing platform for data fusion of occupation data; Decrypt the encrypted occupation data returned by the target node through the private key of the computing node, obtain user identity data and merged data, and extract target occupation data from the merged data according to random data; The merged data is obtained by merging the occupation data queried based on the user identity data and the random data; Generate an occupation identification code for the user according to the user occupation data and the target occupation data; Determine an entitlement service based on at least one of the user occupation data and the target occupation data, associate the entitlement service with the occupation identification code, and generate service recommendation data for the entitlement service.

15. A storage medium for storing computer-executable instructions, the computer-executable instructions, when executed by a processor, implement the following process: Generate encrypted data based on a user's occupation access request; Send the encrypted data to a target node in a multi-party computing platform for data fusion of occupation data; Decrypt the encrypted occupation data returned by the target node through the private key of the computing node, obtain user identity data and merged data, and extract target occupation data from the merged data according to random data; The merged data is obtained by merging the occupation data queried based on the user identity data and the random data; Generate an occupation identification code for the user according to the user occupation data and the target occupation data; Determine an entitlement service based on at least one of the user occupation data and the target occupation data, associate the entitlement service with the occupation identification code, and generate service recommendation data for the entitlement service.

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