LLM block chain cloud platform based on binary extended field elliptic curve encryption method and data processing method

Abstract

The invention discloses an LLM block chain cloud platform based on a binary extended field elliptic curve encryption method, and the platform comprises a human resource platform and a block chain at a cloud end, and the human resource platform comprises a user interface layer which is used for providing an exclusive operation and information display space; the internal communication layer is used for connecting and coordinating the user interface layer and the data access layer; the data access layer is used for optimizing interaction with a database; the business logic layer is used for packaging business rules and processes into independent business service components; the data storage layer is used for storing block chain node data and sensitive data; the intelligent contract layer is used for automatic execution of the business logic layer; and the encryption service layer is used for encrypting and decrypting the data in the data storage layer based on a binary expansion field elliptic curve encryption algorithm. The personal privacy information of participants is protected while the human resource cost is reduced, and the safety and convenience of information inspection and acquisition of enterprises, job seekers and third parties are optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of blockchain and relates to an LLM blockchain cloud platform and a data processing method based on binary extended domain elliptic curve cryptography. Background Art

[0002] Internationally, the development of blockchain technology has gone through the 1.0 era dominated by virtual currency, data currency, etc., the 2.0 era dominated by blockchain links, and the 3.0 era dominated by blockchain applications. At present, my country's blockchain market is not mature enough, mainly manifested in:

[0003] (1) Digital currencies are being actively developed, but have not yet been promoted nationwide, and other mature digital currencies in China are rarely heard of;

[0004] (2) The development of blockchain technology is slow, and some key technologies are controlled by foreign giants, such as Ethereum, the leader in smart contracts, Deloitte Rubix, Colu and Consensys, financial application platforms R3 and Chain, IoT application platforms Slock and IBM ADEPT, etc.

[0005] (3) Blockchain application projects are not yet perfect, and mature blockchain application projects are even rarer. They all have some flaws, such as in terms of security, software functions, or frameworks.

[0006] (4) The encryption algorithms used by domestic blockchain systems generally include MD5, SHA series, DH, Elgamal, and RSA asymmetric encryption algorithms. Among them, the MD5 algorithm has been proven to have loopholes by Chinese academicians, and virtual currency transactions have also had very serious security issues internationally.

[0007] (5) With the continuous development of cloud computing technology, due to the advantages of cloud computing technology, such as security, easy scalability, stability, and low cost, all industries in the national economy will use cloud platforms as their basic platforms. The security of the client is all transferred to the cloud platform, which puts higher requirements on the security of the cloud platform. Security issues have shifted from being decentralized to centralized, logically forming a new cloud computing center. One of the characteristics of blockchain technology is decentralization. How to resolve this contradiction is an urgent problem that must be solved.

[0008] (6) The application of the national secret SM2 in blockchain is still in the primary research stage. The "SM2 Elliptic Curve Public Key Cryptography Algorithm" has made the SM2 algorithm quickly become the research focus of many researchers and accelerated its application in actual production and life. In response to the cryptographic algorithm security risks faced by blockchain technology, some scholars have proposed using domestic cryptographic algorithms to replace the cryptographic algorithms in blockchain, thereby eliminating its potential backdoor security risks. However, due to the complexity of the calculations involving elliptic curves, the time complexity of the current algorithm is still very high.

[0009] (7) Large language models (LLMs) are a breakthrough in the field of artificial intelligence. They learn deep representations and patterns of language by pre-training on massive text data. For example, LLMs can improve the way users interact with blockchain data through natural language processing technology, allowing users to query on-chain data in natural language and improve user experience; LLMs can track the job market, analyze information samples, and provide companies and job seekers with accurate job and talent recommendations. Currently, LLMs can assist in the development of smart contracts, improve code writing efficiency and reduce errors, but these models are not specifically trained on smart contract data, so there is still room for improvement in their application in this field.

[0010] The shortcomings of the existing technology include the following two points:

[0011] 1. The shortcomings of the national secret SM2 asymmetric encryption algorithm when applied to blockchain technology.

[0012] 2. The cost of human resources in China is constantly increasing. Many domestic companies have moved their businesses to underdeveloped regions such as Southeast Asia to reduce their costs. In addition, there is a lot of false information on current human resources platforms. For example, job seekers and employers can fabricate false information at will, but it is difficult to identify and verify false information, which makes it difficult for employers to find ideal candidates and for job seekers to discern the true situation of the company. Human resources platforms currently rely on third-party platforms for information verification, such as degree verification and background verification, which makes information acquisition relatively passive. When recommending jobs with big data, there are problems such as information security, personal data leakage, and recommendation accuracy that users believe, which leads to poor user experience on various human resources platforms. Summary of the invention

[0013] The purpose of the present invention is to provide an LLM blockchain cloud platform and data processing method based on binary extended domain elliptic curve cryptography, which can reduce human resource costs while protecting the personal data information of participants, and optimize the security and convenience of information verification and acquisition among enterprises, job seekers, and third-party inspection agencies, increase the success rate of recruitment and job hunting, and enhance the user experience of the platform.

[0014] According to some embodiments of the first aspect of the present invention, an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography includes a cloud-based human resources platform and a blockchain, wherein the blockchain includes multiple blockchain nodes, and the human resources platform includes:

[0015] The user interface layer is used to provide exclusive operation and information display space for job seekers, enterprises and third-party inspections;

[0016] An internal communication layer, used for connecting and coordinating the user interface layer and the data access layer;

[0017] Data access layer, used to optimize the interaction with the database and quickly access data;

[0018] Business logic layer, used to encapsulate business rules and processes into independent business service components;

[0019] The data storage layer is used to store blockchain node data and sensitive data. The blockchain node data is stored in a dispersed manner on each blockchain node; the sensitive data is encrypted and stored using a binary extended domain elliptic curve encryption algorithm;

[0020] Smart contract layer, used for automated execution of business logic layer;

[0021] The encryption service layer is used to encrypt and decrypt the data in the data storage layer based on the binary extended field elliptic curve encryption algorithm.

[0022] According to some embodiments of the first aspect of the present invention, an LLM blockchain cloud platform based on binary extended-domain elliptic curve cryptography, the user interface layer includes a job seeker interface, an enterprise interface and a third-party verification interface; the job seeker interface is used for job seekers to display personal information, the enterprise interface is used for recruiting companies to publish job information, screen resumes and arrange interviews, and the third-party verification interface is used for verification agencies to verify the legal content provided by job seekers and companies.

[0023] According to some embodiments of the first aspect of the present invention, an LLM blockchain cloud platform based on binary extended-domain elliptic curve cryptography, the internal communication layer adopts a microservice architecture, and the microservice architecture is used to split the functions of the human resources platform into multiple independent small services, each of which corresponds to a specific business function; the internal communication layer includes an API gateway, and the API gateway is used to manage and route external requests to different small services, and the API gateway adopts a service bus communication mechanism.

[0024] According to some embodiments of the first aspect of the present invention, an LLM blockchain cloud platform based on binary extended-domain elliptic curve cryptography, the human resources platform uses message queue technology to process asynchronous tasks.

[0025] According to some embodiments of the first aspect of the present invention, an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography, the data access layer uses ORM tools and database connection pool technology to interact with the database, the data access layer includes data access objects, storage databases and data access strategies, the data access objects are used to encapsulate database operations, and the storage database is used to select a suitable relational database or non-relational database according to the needs of the human resources platform.

[0026] According to an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography in some embodiments of the first aspect of the present invention, the data storage layer adopts blockchain technology and elliptic curve encryption algorithm, and the formula of the elliptic curve encryption algorithm is as follows:

[0027] y 2 +xy=x 3 +ax 2 +b (1)

[0028] Where b≠0; the elliptic curve is generally used in the blockchain encryption process to generate the public key address through point multiplication. The point multiplication k*P is implemented by repeated point addition, where k is an integer and k≠0; for a point P(x,y) on the elliptic curve, if two P points are superimposed, the calculation process is shown in Formula 2:

[0029]

[0030] The encrypted content is defined as a point P on the binary extended elliptic curve. The encrypted content can be obtained by performing operations of equations (1) and (2).

[0031] According to some embodiments of the first aspect of the present invention, an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography, the smart contract layer deploys smart contracts on the blockchain, the smart contract layer includes a smart contract virtual machine, a contract execution engine and a storage manager, the smart contract virtual machine provides an execution environment for the operation of the smart contract, the contract execution engine is used to execute the code in the smart contract, and the storage manager is used to manage the data storage of the smart contract.

[0032] According to some embodiments of the first aspect of the present invention, an LLM blockchain cloud platform based on binary extended-domain elliptic curve cryptography, the encryption service layer uses an encryption algorithm to encrypt and decrypt data for generating, storing, distributing and updating keys.

[0033] According to some embodiments of the second aspect of the present invention, a LLM blockchain cloud platform data processing method is applied to the LLM blockchain cloud platform described in the first aspect of the claim, comprising the following steps:

[0034] Step 1: Build a human resources platform and blockchain. The blockchain is built based on the binary extended domain elliptic curve algorithm, and the front end of the human resources platform is built. The human resources platform is closely integrated with the binary extended domain elliptic curve encrypted blockchain to achieve secure data storage and interaction;

[0035] Step 2: The participant registers an account on the human resources platform, and the human resources platform returns the key sk generated by the national encryption SM2 to the participant;

[0036] Step 3: The participant uses the public key PK of the human resources platform to encrypt the uploaded data to obtain the ciphertext, and then uses the key sk to digitally sign the uploaded information, and then uploads the ciphertext and the digital signature to the blockchain. The participant joins the blockchain and becomes a blockchain node;

[0037] Step 4: The HR platform processes the uploaded data to adapt to the input requirements of LLM. The data remains encrypted during the LLM processing. The processed data is input into the LLM model for training. The LLM model parses the natural language text input by the participants and generates personalized recommendation results based on the rules of the business logic layer. The recommendation results processed by the business logic layer are encrypted again using a semi-homomorphic encryption algorithm to ensure the security of the data during storage and transmission.

[0038] Step 5: The HR platform encrypts the obtained data with the public key of the participants, generates a secure database using blockchain, and stores the data of each category in the database;

[0039] Step 6: The human resources platform uses blockchain technology to implement user identity authentication and authority management, and sends the corresponding information to each participant in ciphertext. The participants use their own private keys to decrypt the ciphertext and obtain the information results they need.

[0040] According to an LLM blockchain cloud platform data processing method of some embodiments of the second aspect of the present invention, the blockchain is composed of multiple blocks, each block contains the hash value of the previous block, forming a chain structure; the data of the i-th block is D i , whose hash value is h i , then the hash value calculation formula for the i-th block is:

[0041] h i =H(D i ||h i-1 ) (3)

[0042] where h i-1 is the hash value of the previous block, D iIncludes transaction records, timestamps, and other relevant information of the current block.

[0043] According to some embodiments of the first aspect and the second aspect of the present invention, at least the following beneficial effects are achieved: using a binary extended domain elliptic curve algorithm to replace the internationally used MD5, SHA series, DH, and RSA asymmetric algorithms to link enterprises, job seekers, and third-party verification parties, and using the characteristics of blockchain technology to allow enterprises, job seekers, and third parties to be informed of any information changes in real time, without the presence of false information; and optimizing the steps for ordinary human resources platform users (job seekers, enterprises) to conduct various information verifications through third-party platforms; the algorithm can help the platform track information on changes in job requirements of various employers and job seekers, and increase the success rate of recruitment; combined with the LLM large language model, the platform can deeply understand user behavior, preferences, and feedback, analyze user queries and feedback, and use this information to find information that matches user interests in large-scale knowledge graphs, and generate personalized recommendation lists based on matching results and user feedback. LLM can analyze a large amount of recruitment data and market information, predict industry trends and skill requirements, and help companies formulate recruitment strategies, etc. It reduces human resource costs while protecting the personal data of participants, and optimizes the security and convenience of information verification and acquisition among enterprises, job seekers, and third-party inspection agencies, increasing the success rate of recruitment and job hunting, and improving the user experience of the platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0045] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0046] Figure 1 This is a schematic diagram of the system model provided by the present invention.

[0047] Figure 2 This is a structural diagram of the human resources platform provided by the present invention.

[0048] Figure 3 The present invention provides a schematic diagram of the system flow. DETAILED DESCRIPTION

[0049] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0050] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as commonly understood by ordinary technicians in the technical field to which this application belongs.

[0051] In the present invention, unless otherwise specified, the directional words used, such as "up, down, top, bottom", usually refer to the directions shown in the drawings, or to the components themselves in the vertical, perpendicular or gravity directions; similarly, for ease of understanding and description, "inside and outside" refer to the inside and outside relative to the outline of each component itself, but the above-mentioned directional words are not used to limit the present invention.

[0052] See the examples Figure 1-Figure 3 As shown, an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography includes a human resources platform, a blockchain and various participants. Assuming there are n participants here, the participants will obtain a key based on SM2 after registering an account on the human resources platform, encrypt the information and upload it to the blockchain, and participate in the calculation of the blockchain as a blockchain node. The human resources platform, as a cloud platform of the system, provides participants with registration, job search, recruitment and other functions, while integrating blockchain technology to ensure the authenticity and reliability of data. The blockchain is integrated into the human resources platform for information storage verification, in which the binary extended domain elliptic curve algorithm is used instead of the internationally used MD5, SHA series and DH and RSA asymmetric algorithms to enhance the security of the blockchain. The server receives the encrypted data, performs necessary decryption, applies the collaborative filtering algorithm to generate recommendations, and returns the results to the user.

[0053] The above-mentioned human resources platform is also called the human resources cloud platform, or cloud platform for short.

[0054] like Figure 2 The human resources platform structure diagram shown in the figure includes, from top to bottom, the user interface layer, internal communication layer, data access layer, business logic layer, data storage layer, smart contract layer, and encryption service layer.

[0055] The user interface layer of the human resources platform covers the job seeker interface, enterprise interface and third-party verification interface, providing exclusive operation and information display space for different types of users.

[0056] The job seeker interface is simple and intuitive, allowing job seekers to quickly register, log in and complete their resumes. Here, job seekers can clearly display their skills, work experience, educational background and other information, and can easily search and apply for positions of interest. The interface will highlight job recommendations, job search progress tracking and other functions, allowing job seekers to keep track of their job search dynamics at any time.

[0057] The enterprise interface provides recruiting companies with efficient recruitment management tools. Companies can publish job information, screen resumes, arrange interviews, etc. The interface will display key data such as the number of job applications received by the company and the degree of talent matching, helping companies to better select talents.

[0058] The third-party verification interface provides a professional platform for independent verification agencies. They can verify the legal content provided by job seekers and companies. The legal content of job seekers is: education, certificates and other information, and the legal content of companies is: business license, etc., and feedback the results to the cloud platform to ensure the authenticity and reliability of the information.

[0059] In order to provide users with the best experience, the user interface layer adopts responsive design and modern front-end technology. Whether users access the cloud platform on different devices such as computers, tablets or mobile phones, the interface can automatically adapt to the screen size and present a consistent layout and functions. Modern front-end technology ensures the smoothness, beauty and interactivity of the interface, and improves the user's operational convenience. Through these designs and technologies, the human resources platform is committed to providing efficient, convenient and consistent service experience for all types of users.

[0060] In the HR platform, the internal communication layer plays a key role in connection and coordination. By adopting microservice architecture and API gateway, the cloud platform achieves high flexibility and scalability.

[0061] The microservice architecture splits the functions of the human resources platform into multiple independent small services, each of which focuses on a specific business function. This design makes development, deployment, and maintenance more efficient. For example, job recommendation services, resume management services, recruitment process services, etc. can be developed and upgraded independently without affecting the stability of the entire cloud platform. The API gateway serves as a unified entrance, responsible for managing and routing external requests to different microservices. It provides security authentication, flow control, protocol conversion and other functions to ensure that communication between services is secure and reliable.

[0062] As a communication mechanism, the service bus enables efficient communication between services. It is like an information highway, allowing microservices to quickly exchange data and requests. The service bus can be implemented using a variety of technologies, such as message middleware, RPC framework, etc. Through the service bus, different services can be easily integrated and collaborated, improving the overall performance and response speed of the cloud platform.

[0063] At the same time, the human resources platform uses message queue technology to handle asynchronous tasks. When there are time-consuming operations, such as sending email notifications and data synchronization, these tasks can be put into the message queue and processed asynchronously by a dedicated consumer process. This can avoid blocking the main thread and improve the throughput and responsiveness of the system. For example, when a job seeker submits a resume, the system can return a response immediately, and the task of sending a confirmation email can be put into the message queue and processed asynchronously in the background. The message queue can also provide reliable message delivery guarantees to ensure that tasks are not lost. Through these technical means, the internal communication layer of the human resources platform realizes efficient and secure inter-service communication, providing strong support for the stable operation and sustainable development of the cloud platform.

[0064] In the human resources platform, the data access layer plays a vital role. It uses ORM tools and database connection pool technology to optimize the interaction with the database, ensure fast data access, and achieve efficient and stable data management and interaction.

[0065] The ORM (Object-Relational Mapping) tool plays a key role in the data access layer. It establishes a mapping relationship between objects in object-oriented programming languages ​​and tables in relational databases. Developers can perform database operations by operating objects without having to directly write complex SQL statements. This greatly improves development efficiency and also enhances the maintainability of the code. For example, when it is necessary to query job applicants' information, developers can directly use object-oriented method calls, and the ORM tool will automatically convert it into the corresponding SQL query statement and execute it.

[0066] The database connection pool technology further optimizes the interaction performance with the database. It creates a certain number of database connections in advance and saves these connections in the connection pool. When there is a data access request, the available connection can be directly obtained from the connection pool without re-establishing the connection every time. This can significantly reduce the time overhead of connection establishment and improve the speed of data access. At the same time, the connection pool can also manage and monitor the connection to ensure the effective use of the connection and the reasonable allocation of resources.

[0067] The data access layer includes data access objects, storage databases, and data access strategies. Data access objects are encapsulations of database operations, providing a series of methods for operations such as addition, deletion, modification, and query. The storage database is the actual storage medium for data, and you can choose a suitable relational database or non-relational database based on the needs of the cloud platform. The data access strategy defines how to automatically prepare and process data, and the cache optimization strategy covers data access and management, such as selecting the appropriate cache level and cache size, and how to effectively use memory and distributed cache systems. By reasonably designing and using these components, the data access layer can ensure fast access to data and provide a solid foundation for the stable operation and efficient service of the human resources platform.

[0068] In the human resources platform, the business logic layer plays a core role, which determines the business functions and processes of the cloud platform. Through microservice design, the business logic layer encapsulates complex business rules and processes into independent business service components, greatly improving the flexibility and maintainability of the system.

[0069] Microservice design enables each business service component to focus on a specific business function, such as job seeker management service, recruitment process service, performance evaluation service, etc. This modular design allows the development team to independently develop, test and deploy each business service component without affecting the functions of other parts. At the same time, when business requirements change, only the corresponding business service component needs to be modified without large-scale reconstruction of the entire system. This not only improves development efficiency, but also reduces maintenance costs.

[0070] The LLM integration layer introduces a large pre-trained language model, which brings powerful intelligent functions to the cloud platform. The large pre-trained language model has powerful language understanding and generation capabilities by learning a large amount of text data. In the human resources platform, it can analyze and understand user input through natural language processing technology, extract key information, and provide users with personalized recommendations and intelligent search functions based on this information.

[0071] For example, when job seekers enter their skills and interests, the LLM integration layer can recommend suitable positions for them based on this information. At the same time, in terms of intelligent search, users can describe their needs through natural language, such as "looking for software development positions in Shanghai", and the LLM integration layer can understand the user's intention and retrieve eligible job information from the database.

[0072] The workflow engine is responsible for managing and monitoring the execution of business processes to ensure the smooth progress of business activities. In the human resources platform, business processes usually include recruitment processes, onboarding processes, performance evaluation processes, etc. The workflow engine can define and execute these processes to ensure that each link is carried out according to the predetermined rules.

[0073] For example, in the recruitment process, the workflow engine can manage resume screening, interview arrangements, hiring decisions, etc. It can automatically send notifications to relevant personnel to remind them to complete their respective tasks. At the same time, the workflow engine can also monitor the execution of the process, discover and solve problems in a timely manner, and ensure the efficient implementation of the recruitment process.

[0074] In short, the business logic layer provides powerful business functions and intelligent services for the human resources platform through microservice design, LLM integration, workflow engine and other technical means. It enables the cloud platform to better meet user needs, improve work efficiency, and provide better human resources services for enterprises and job seekers.

[0075] In the data storage layer of the human resources platform, blockchain technology and elliptic curve encryption algorithm are innovatively used to provide solid protection for the security and reliability of data.

[0076] The introduction of blockchain technology has brought important changes to the storage of key data. The distributed ledger feature of blockchain allows data to be stored in multiple nodes without a single control center. This means that it is difficult for anyone to tamper with the data, ensuring that the data cannot be tampered with. At the same time, every transaction and data record on the blockchain is open and transparent, and all participants can view and verify the authenticity of the data, which enhances the transparency of the data. In the human resources platform, key information such as job applicants' academic certificates and work experience can be stored on the blockchain to ensure the authenticity and reliability of this information, providing an accurate basis for corporate recruitment and talent assessment.

[0077] For sensitive data, the cloud platform uses elliptic curve encryption algorithm for encrypted storage. Elliptic curve encryption algorithm is an advanced encryption technology with high security and efficiency. By encrypting sensitive data, users' data can be effectively protected. For example, sensitive data such as job applicants' personal identity information and contact information are encrypted when stored, and only authorized users with corresponding decryption keys can access these data, thus achieving data protection. At the same time, when performing data calculations, encrypted data can be processed through secure computing technology to ensure that data will not be leaked during the calculation process, further enhancing data security.

[0078] In short, the data storage layer of the human resources platform provides users with a safe and reliable data storage environment through the combination of blockchain technology and elliptic curve encryption algorithm.

[0079] In the human resources platform, the smart contract layer plays a vital role, bringing higher efficiency and security to the business operations of the cloud platform.

[0080] A smart contract is a computer protocol that transmits, verifies or executes a contract in an information-based manner. By deploying smart contracts on the blockchain, the human resources platform is able to automate the execution of business logic. This means that once the conditions preset in the contract are met, the relevant operations will be automatically triggered without human intervention. For example, in the recruitment process, when the conditions of the job seeker meet the company's job requirements, the smart contract can automatically trigger the interview arrangement process, greatly improving the efficiency of the operation.

[0081] The smart contract layer includes the smart contract virtual machine, the contract execution engine, and the storage manager. The smart contract virtual machine provides an independent execution environment for the operation of smart contracts. It can ensure that the execution of the contract is not interfered with by external factors, and ensure the stability and reliability of the contract execution. The contract execution engine is responsible for the specific execution of the code in the smart contract. It will operate according to the predetermined steps based on the logic and input data of the contract and output the results. The storage manager is responsible for managing the data storage of the smart contract. It can ensure that the contract can read and write data securely during the execution process, while ensuring the consistency and integrity of the data.

[0082] Through these components of the smart contract layer, the human resources platform can achieve more efficient and secure business operations. The tamper-proof and automatic execution characteristics of smart contracts make business processes more transparent and trustworthy. At the same time, the automated execution of smart contracts also reduces the risk of human error and fraud, and improves the security of the cloud platform. In addition, smart contracts can also be combined with other blockchain technologies, such as encryption algorithms and distributed ledgers, to further enhance the data security and personal data protection of the cloud platform. In short, the smart contract layer provides strong technical support for the development of the human resources platform.

[0083] In the human resources platform, the encryption service layer plays a key role in safeguarding data security, providing reliable protection for data on the cloud platform throughout its entire life cycle.

[0084] Key management in the encryption service layer is the basis for ensuring data security. The key is like the only key to open the treasure house of data and must be properly managed. The key management system is responsible for generating, storing, distributing and updating keys. In the human resources platform, different types of data may require different levels of key protection. For example, sensitive personal information of job seekers may require high-strength encryption keys, while some public job information can use relatively weak keys. Through strict key management, ensure that only authorized users can access the corresponding data to prevent security risks caused by key leakage.

[0085] The encryption service is the core function of the encryption service layer. It uses advanced encryption algorithms to encrypt and decrypt data. Encryption services can play a role in all stages of data storage, transmission and processing. When data is stored in the database, the encryption service will encrypt it to ensure that the data cannot be easily read even if the database is illegally accessed. During data transmission, the encryption service can ensure that data is securely transmitted in the network to prevent eavesdropping or tampering. At the same time, the encryption service can also provide flexible encryption methods such as symmetric encryption and asymmetric encryption according to different business needs to meet data security requirements in different scenarios.

[0086] Elliptic curve operations are also an important part of the encryption service layer. Elliptic curve cryptography is an efficient and secure encryption technology that is widely used in modern cryptography. In the human resources platform, elliptic curve operations can be used in scenarios such as digital signatures and key exchange. Through elliptic curve operations, the cloud platform can implement data integrity verification and identity authentication, ensure the source of data is reliable, and prevent forgery and tampering. At the same time, the efficiency of the elliptic curve cryptography algorithm also enables the cloud platform to maintain high performance when processing large amounts of data.

[0087] In short, the encryption service layer of the human resources platform provides all-round security protection for data on the cloud platform through key management, encryption services, elliptic curve operations and other functions. Throughout the life cycle of the data, the encryption service layer always sticks to its post to ensure the security and integrity of the data, laying a solid foundation for the stable operation of the cloud platform and the trust of users.

[0088] like Figure 3 As shown, a LLM blockchain cloud platform data processing method includes the following steps:

[0089] Step 1: Generation of the human resources platform and blockchain; wherein step 1 includes the following sub-steps:

[0090] Step 1.1: Build the blockchain part.

[0091] When building the blockchain, we use an innovative binary extended domain elliptic curve algorithm to replace the internationally used MD5, SHA series, DH and RSA asymmetric algorithms. The security of MD5 and SHA series algorithms faces many challenges in the current complex network environment. The DH algorithm and RSA asymmetric algorithm also have certain potential risks. The binary extended domain elliptic curve algorithm is based on complex mathematical principles and provides higher security than other public key encryption algorithms with the same key length, greatly enhancing the security of the blockchain. The specific formulas are shown in Equation 1 and Equation 2. Its encryption and decryption process is more difficult to crack. Under the same security level, the required key length is shorter, reducing the computing and storage costs. Through this algorithm, a solid security protection is provided for the blockchain, effectively resisting various potential attacks and ensuring the integrity, confidentiality and immutability of data.

[0092] y 2 +xy=x 3 +ax 2 +b (1)

[0093] Formula 1 is the equation of the binary extended domain elliptic curve, where b≠0. In the blockchain encryption process, the elliptic curve is generally used to generate the public key address through point multiplication. The point multiplication k*P is implemented by repeated point addition, where k is an integer and k≠0. For a point P(x,y) on the elliptic curve, if two points P are superimposed, the calculation process is shown in Formula 2.

[0094]

[0095] The encrypted content is defined as a point P on the binary extended elliptic curve. The encrypted content can be obtained by performing operations of equations (1) and (2) to protect confidential information and further transmit it.

[0096] Step 1.2: Build the front-end of the human resources platform.

[0097] The front-end of the human resources platform needs to meet a series of important functions. The first is the registration function, where users can easily create their own accounts and provide necessary personal information. The login function ensures that users can access their accounts safely. The job search function provides a platform for job seekers to showcase their talents. They can upload resumes and fill in job application intentions. The recruitment function provides companies with a channel to publish recruitment information and screen candidates. Through careful design and development, the front-end of the cloud platform is simple and easy to use, with complete functions to meet the needs of different users.

[0098] Step 1.3: Combine the HR platform with the improved blockchain and open it to participants.

[0099] The human resources platform is closely integrated with the blockchain encrypted by binary extended domain elliptic curve to further improve the security of relevant information during transmission and reception, avoid the unstable factors of traditional encryption algorithms that are easy to crack and easy to attack, and realize the safe storage and interaction of data. The blockchain consists of multiple blocks, each of which contains the hash value of the previous block, forming a chain structure. Assume that the data of the i-th block is D i , whose hash value is h i , then the hash value calculation formula for the i-th block is:

[0100] h i =H(D i ||h i-1 ) (3)

[0101] where h i-1 is the hash value of the previous block, D i Including the transaction records, timestamps and other relevant information of the current block. The hash value is mapped through a hash function to obtain the unique identifier of the block. The hash function is collision-resistant, that is, it is difficult to find two different inputs D1 and D2 such that H(D1)=H(D2). The distributed ledger technology of the blockchain ensures the authenticity and integrity of human resources data and prevents data from being tampered with. At the same time, the encryption technology of the blockchain is used to establish a secure digital identity for users to ensure the security of their data and information. The combined cloud platform is open to all kinds of participants, including job seekers, recruiting companies, training institutions, etc. They can interact safely and efficiently on the cloud platform, share human resources information, and realize the rational flow and optimal allocation of talents. Through this combination, new models and opportunities are brought to human resources management, promoting the development and progress of the industry.

[0102] Step 2: Participants register an account; the specific implementation of step 2 includes the following sub-steps:

[0103] Step 2.1: Participants send the corresponding information to the human resources platform to register an account.

[0104] In this step, all kinds of participants, including job seekers, recruiting companies, training institutions, etc., prepare their own relevant information, such as personal basic information, company qualification information, etc., and send this information to the human resources platform through a secure network channel. When sending information, participants should ensure the accuracy and completeness of the information so that they can use the various functions of the cloud platform smoothly in the future. For job seekers, they may need to provide information such as educational background, work experience, and skill certificates; recruiting companies need to provide company names, industry fields, recruitment needs, etc.; training institutions need to submit training courses, teaching staff and other materials. This information will become their important identification and basis on the human resources platform.

[0105] Step 2.2: The human resources platform returns the key sk generated by the national encryption SM2 to each participant.

[0106] After receiving the registration information of the participants, the human resources platform will go through a strict review and verification process to ensure the authenticity and legality of the information. Once the review is passed, the cloud platform will use the national secret SM2 algorithm to generate a unique key sk and return it to the corresponding participant. National secret SM2 is an advanced domestic encryption algorithm with high security and reliability. By assigning each participant a unique key sk, the information security and data protection of the participants on the cloud platform can be effectively guaranteed. Participants can use this key to encrypt and decrypt data to ensure that their sensitive information is not stolen or tampered with during transmission and storage. At the same time, the key sk also provides a basis for identity authentication and authorization for various operations of participants on the cloud platform, ensuring that only legitimate users can access and use the corresponding functions. In this way, the human resources platform creates a safe and reliable interactive environment for participants.

[0107] Step 3: Participants use the binary extended-field elliptic curve encryption algorithm to encrypt the information and upload it to the blockchain; the specific implementation of step 3 includes the following sub-steps:

[0108] Step 3.1: Participants encrypt job information, job application information, third-party institution degree and other verification information using the public key PK of the human resources platform to obtain ciphertext to ensure that the information will not be eavesdropped or modified during transmission.

[0109] In this step, the participants fully realized the importance of information security. Whether it is the job information released by the recruiting company, the job application information provided by the job seeker, or the degree verification information provided by the third-party agency, they all carry key value. In order to ensure that this information is not eavesdropped or tampered with during transmission, the participants use the public key PK provided by the human resources platform for encryption. Public key encryption technology is like a solid lock that protects the information tightly. Only the recipient with the corresponding private key can unlock the lock and read the information content. In this way, even if the information is intercepted by others during transmission on the network, they will not be able to understand the meaning, which effectively guarantees the confidentiality and integrity of the information.

[0110] Step 3.2: The participant uses the key sk obtained in step 2.2 to digitally sign the job information or job search information he or she publishes to ensure the integrity of the information and the correctness of the identity.

[0111] Digital signatures play a vital role in the authenticity and reliability of information. Participants use the key sk obtained in step 2.2 to digitally sign the job information or job application information they publish. This digital signature is like a fingerprint of the information, uniquely identifying the source and integrity of the information. Through the digital signature, the receiver can verify whether the information has been tampered with during transmission, and also confirm whether the sender of the information is correct. If the information is tampered with, the digital signature will not be verified, thus reminding the receiver to pay attention to the security issues of the information.

[0112] Step 3.3: The participant uploads the ciphertext and the digital signature to the blockchain.

[0113] Participants upload the encrypted ciphertext and digital signature to the blockchain. As a distributed ledger technology, blockchain has the characteristics of decentralization, immutability, and traceability. Uploading information to the blockchain is equivalent to storing the information in a highly secure safe. Once the information is recorded on the blockchain, it is difficult to tamper with or delete. At the same time, the information on the blockchain can be viewed and verified by all participants, which improves the transparency and credibility of the information.

[0114] Step 3.4: Participants join the blockchain as blockchain nodes to assist blockchain technology, thereby speeding up the computing power of the blockchain.

[0115] Participants are not only information providers, they can also join the blockchain and become blockchain nodes. As nodes, participants can provide computing power and storage space for the blockchain network. Through the joint collaboration of multiple nodes, the computing power of the blockchain is enhanced and the transaction processing speed is accelerated. In addition, more node participation can also improve the stability and security of the blockchain network and prevent single point failures and attacks. The active participation of participants provides strong support for the development and application of blockchain technology.

[0116] Step 4: The cloud platform processes the data; the specific implementation of step 4 includes the following sub-steps:

[0117] Step 4.1: The cloud platform extracts encrypted personal information and job requirements data from the blockchain, which may include job seekers’ resumes, skills, work experience, and company recruitment information.

[0118] The human resources platform has begun to play its core role, carefully extracting valuable encrypted data from the blockchain. These data include the carefully prepared resumes of job seekers, which record in detail their skills, expertise, rich work experience and other personal growth trajectories. At the same time, the recruitment information released by the company is also included, such as job requirements, salary, development prospects and other key contents.

[0119] Step 4.2: The cloud platform cleans and formats the extracted data to adapt to the input requirements of LLM.

[0120] The extracted data is not directly usable, and the cloud platform needs to perform detailed cleaning and formatting on it. During the cleaning process, noise data, erroneous information, and duplicate content are removed to ensure the accuracy and purity of the data. Then, it is formatted according to the specific input requirements of the large language model (LLM). Different types of data, such as text (text string), numbers (text string), dates (text string), expected salary (numeric number), etc., are organized into formats that meet actual needs so that LLM can efficiently understand and process this information. For the current platform, we use the powerful reasoning and analysis capabilities of LLM to give matching job information based on the text description information of the job seeker's resume. The whole process is based on natural language instructions, namely Prompt, to define the specific tasks that the model needs to perform. For the current task, we can use the following instructions to complete the generation of matching positions:

[0121] “Please generate a list of matching positions based on the available information: [Insert the applicant’s personal information (text string, numerical value or other multimodal data information)].”

[0122] Step 4.3: Use encryption algorithms to ensure security during data transmission and processing. Even during LLM processing, the data remains encrypted.

[0123] During the entire data transmission and processing process, the cloud platform continuously uses encryption algorithms to protect data security. Even when LLM is processing, the data remains encrypted. This encryption measure effectively prevents data from being stolen, tampered with, or leaked during transmission and processing, providing reliable protection for users' information security.

[0124] Step 4.4: Input the processed data into the LLM model. The model is specially trained for the HR platform to adapt to specific business scenarios.

[0125] The carefully processed data is input into the LLM model specially trained for the human resources platform. This model has been trained and optimized with a large amount of data, and has a deep understanding and adaptability to specific business scenarios in the human resources field. It can accurately parse and process job seekers' resume information, companies' recruitment needs, etc., laying the foundation for subsequent job recommendations.

[0126] Step 4.5: The LLM model parses the input natural language text to understand the applicant’s intention, job description, and corporate needs, and extract key information.

[0127] The LLM model demonstrates strong language understanding capabilities and performs in-depth analysis of the input natural language text. It carefully considers the career intentions, skill strengths, and development expectations expressed by job seekers in their resumes, while deeply understanding the specific requirements and expected talent traits put forward by companies in job descriptions. Through this detailed analysis, the model can accurately extract key information and provide a strong basis for generating personalized job recommendations.

[0128] Step 4.6: Based on the applicant’s resume and job requirements, the LLM model uses its language understanding capabilities and combines the rules of the business logic layer to generate personalized job recommendations.

[0129] Based on an in-depth understanding of job seekers' resumes and job requirements, the LLM model gives full play to its language understanding ability and combines the rules formulated in the business logic layer to generate personalized job recommendations for job seekers. These recommendations are not just simple job matching, but also comprehensively consider job seekers' skills, experience, interests and career development plans, as well as corporate culture, development direction and job requirements, to provide job seekers with the most suitable career development opportunities.

[0130] Step 4.7: The generated recommendation results will be passed to the business logic layer, which contains the algorithms and decision logic to implement specific business requirements.

[0131] The generated personalized job recommendation results are passed to the business logic layer. The business logic layer is like the brain of the HR platform, containing various algorithms and decision logic to achieve specific business needs. Here, the recommendation results will be further analyzed and processed to ensure that they meet the platform's business goals and the actual needs of users.

[0132] Step 4.8: The recommendation results processed by the business logic layer are encrypted again using a semi-homomorphic encryption algorithm to ensure the security of the data during storage and transmission.

[0133] In order to ensure the security of the recommendation results during storage and transmission, the recommendation results processed by the business logic layer are encrypted again using a semi-homomorphic encryption algorithm. This encryption method is a type of encryption algorithm that allows certain specific operations to be performed on ciphertext without affecting data processing, and the decrypted value of the operation result is consistent with the result of the corresponding operation on the plaintext. This feature allows effective operations on encrypted data while protecting the data. It is widely used in secure computing, cloud computing and other fields, providing a higher level of security for the data. Even during data storage and transmission, it can effectively prevent data from being illegally accessed and tampered with, providing users with safe and reliable services. Assuming that recommendation results r1 and r2 are encrypted using a semi-homomorphic encryption algorithm, then r1 and r2 are the plaintexts that need to be paid attention to during the encryption process. First, it is necessary to generate the key required for the encryption process. Large prime numbers p and q are used to calculate the order n, n = p*q, and the next step is to select an integer Used to generate the public key, the public key is (n, g); the private key is λ, λ = lcm(p-1, q-1). During the encryption process, for a given plaintext t, select a random number R and calculate the ciphertext c: c = g m *R n modn 2 The next step is to perform homomorphic addition operations. For the ciphertexts c1 and c2 of the plaintexts r1 and r2, their addition operations can be completed by directly operating on the ciphertexts:

[0134]

[0135] Finally, the encrypted result is decrypted in the decryption stage to get the result of r1+r2:

[0136]

[0137] in

[0138] Step 5: The database stores the original data (before encryption), encrypted data, recommendation results, etc.; wherein the specific implementation of step 5 includes the following sub-steps:

[0139] Step 5.1: The HR platform encrypts the obtained data with the public key of the participants and generates a secure database using blockchain.

[0140] In this step, the human resources platform fully plays its role in security protection. First, the cloud platform obtains various data, including original data (before encryption), encrypted data, and recommendation results generated by the LLM model. In order to ensure the security of this data, the cloud platform uses the public key of the participants to encrypt the data. Public key encryption technology is like a solid lock that can only be unlocked by the corresponding private key. In this way, even if the data is illegally obtained during storage, attackers without the private key cannot understand the content.

[0141] At the same time, blockchain technology is used to generate a secure database. The distributed ledger feature of blockchain makes data storage more secure and reliable. Each data block contains the hash value of the previous data block, forming an unalterable chain. This means that once the data is recorded on the blockchain, it is difficult to tamper with or delete. In addition, the decentralized nature of blockchain also increases the security of data because there is no single central control point that can be attacked. By combining public key encryption with blockchain technology, the human resources platform creates a highly secure database that provides solid protection for users' data.

[0142] Step 5.2: The human resources platform stores data of each category in the database.

[0143] Under the premise of ensuring data security, the human resources platform stores data of various categories in the database. These data include unencrypted data such as original job seeker resumes and corporate recruitment information, as well as encrypted personal information, job demand data and recommendation results. Through classified storage, the cloud platform can manage and retrieve data more efficiently. When users need to query their own information or view recommendation results, the cloud platform can quickly extract the corresponding data from the database and decrypt it using the corresponding private key to provide users with accurate and secure services. At the same time, the data stored in the database can also provide a basis for further analysis and optimization of the cloud platform, helping the cloud platform to continuously improve its service quality and recommendation accuracy.

[0144] Step 6: The human resources platform returns job search analysis results, information verification results, and recommendation results, etc. The specific implementation of step 6 includes the following sub-steps:

[0145] Step 6.1: Use blockchain technology to implement user identity authentication and permission management to ensure that only legitimate users can access sensitive data. Strictly control access to data to ensure that only authorized users or systems can access specific data.

[0146] In this critical step, the human resources platform fully leverages the powerful advantages of blockchain technology to achieve rigorous identity authentication and sophisticated permission management. The distributed ledger characteristics and encryption algorithms of blockchain provide highly reliable protection for user identity verification. When a user attempts to access sensitive data on the cloud platform, the system will first start the identity authentication process. The legitimacy of the user is ensured by comparing the identity information provided by the user with the encrypted identity records stored on the blockchain. Only legitimate users can successfully pass this level and obtain further access to sensitive data.

[0147] At the same time, strict control over access to data is an important part of ensuring data security. The cloud platform sets detailed permission levels based on the roles and needs of different users. For example, job seekers may only be able to access information and recommendation results related to their job search, while corporate recruiters can view information about job seekers that meet their recruitment needs. Through this sophisticated permission management, it is ensured that only authorized users or systems can access specific data, minimizing the risk of illegal access or abuse of data. This not only protects users, but also maintains the data security and stable operation of the entire platform.

[0148] Step 6.2: The human resources platform sends the corresponding information to each participant in encrypted form.

[0149] After strict identity authentication and authority management, the human resources platform begins to send corresponding information to each participant. In order to ensure the security of information during transmission, the cloud platform sends this information in the form of ciphertext. Using advanced encryption algorithms, even if it is intercepted during transmission, it cannot be easily cracked. This encryption measure provides a strong guarantee for the secure transmission of information, ensuring that participants can receive complete, accurate and secure information.

[0150] Step 6.3: Participants use their own private keys to decrypt the ciphertext and finally obtain the information results they need.

[0151] When participants receive ciphertext information from the human resources platform, they can use their own unique private key to decrypt the ciphertext. The private key is obtained by the participants during the registration process and is the only key to unlock the encrypted information. By using the private key for decryption, participants can restore the ciphertext to readable information content, thereby obtaining the job search analysis results, information verification results, and recommendation results they need. This encryption and decryption process ensures the confidentiality and security of information. Only legitimate participants can obtain specific information related to them, providing an accurate and reliable basis for their decision-making and actions in the field of human resources.

[0152] The present invention integrates blockchain and cloud computing technologies. The centralized storage and computing capabilities of cloud computing lack the protection of security and privacy in the data flow process. The decentralized characteristics of blockchain not only make data more authentic and reliable, but also enable the encryption algorithm of binary extended domain elliptic curve to be applied to the data flow process, realizing the secure storage and processing of data in the cloud computing environment, completing the maintenance of data security, effectively resolving the decentralized contradiction between the two systems, building a mutually compatible channel between the two systems, and further increasing the security of the system. It builds a bridge for the two systems, allowing them to be compatible with each other and work together.

[0153] The present invention utilizes the advanced natural language processing capabilities of LLM to understand and analyze a large amount of text data, thereby providing more accurate job matching and personalized recommendation services. Job seekers and recruiters can interact with the system through natural language, which improves the naturalness and convenience of the user experience. At the same time, the integration of LLM also allows the cloud platform to perform automated resume parsing, intelligent question and answer, and sentiment analysis, which greatly improves the efficiency and quality of human resource management. The application of blockchain technology brings the immutability and transparency of data to the cloud platform, ensures the authenticity and reliability of information, and enhances users' trust in the platform. The integration of semi-homomorphic encryption technology provides users with a higher level of data protection, so that sensitive information on the blockchain can also be calculated and analyzed in an encrypted state, which is particularly important when processing sensitive operations such as personal resumes and job applications. After obtaining the data, LLM performs automated data analysis and market trend forecasting to help companies and job seekers grasp industry trends and make more informed decisions. The use of smart contracts further automates the recruitment process and reduces the complexity and cost of human resource management.

[0154] In terms of technology, the cloud platform of the present invention cleverly integrates advanced technologies such as blockchain, big data and cloud computing. Blockchain technology ensures the immutability and transparency of data, providing a solid trust foundation for the recruitment ecosystem. Big data can conduct in-depth analysis of massive human resources data, dig out valuable information, and provide accurate matching services for enterprises and job seekers. Cloud computing provides the platform with powerful computing and storage capabilities to ensure the efficient operation of the platform. At the same time, by continuously optimizing the binary extended domain elliptic curve cryptography, the security of the platform is improved and the risk of data leakage is reduced.

[0155] Through technological innovation and business expansion, the platform aims to reduce costs and improve economic benefits. Ultimately, it aims to create an efficient, transparent and trustworthy recruitment ecosystem to serve enterprises and job seekers, promote the digital transformation of the human resources industry, and inject new vitality into the sustainable development of the industry.

[0156] Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0157] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0158] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0159] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography, characterized by: It includes a cloud-based human resources platform and a blockchain, wherein the blockchain includes a plurality of blockchain nodes, and the human resources platform includes: The user interface layer is used to provide exclusive operation and information display space for job seekers, enterprises and third-party inspections; An internal communication layer, used for connecting and coordinating the user interface layer and the data access layer; Data access layer, used to optimize the interaction with the database and quickly access data; Business logic layer, used to encapsulate business rules and processes into independent business service components; The data storage layer is used to store blockchain node data and sensitive data. The blockchain node data is stored in a dispersed manner on each blockchain node; the sensitive data is encrypted and stored using a binary extended domain elliptic curve encryption algorithm; Smart contract layer, used for automated execution of business logic layer; The encryption service layer is used to encrypt and decrypt the data in the data storage layer based on the binary extended field elliptic curve encryption algorithm.

2. According to claim 1, an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography is characterized in that: The user interface layer includes a job seeker interface, an enterprise interface and a third-party inspection interface; The job seeker interface is used by job seekers to display their personal information, the enterprise interface is used by recruiting companies to publish job information, screen resumes and arrange interviews, and the third-party verification interface is used by verification agencies to verify the legal content provided by job seekers and companies.

3. According to claim 1, an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography is characterized in that: The internal communication layer adopts a microservice architecture, which is used to split the functions of the human resources platform into multiple independent small services, each of which corresponds to a specific business function; the internal communication layer includes an API gateway, which is used to manage and route external requests to different small services, and the API gateway adopts a service bus communication mechanism.

4. According to claim 3, an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography is characterized in that: The human resources platform uses message queue technology to process asynchronous tasks.

5. According to claim 1, an LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography is characterized in that: The data access layer uses ORM tools and database connection pool technology to interact with the database. The data access layer includes data access objects, storage databases and data access strategies. The data access objects are used to encapsulate database operations, and the storage database is used to select a suitable relational database or non-relational database according to the needs of the human resources platform.

6. The LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography according to claim 1, characterized in that: The data storage layer uses blockchain technology and elliptic curve encryption algorithm. The formula of the elliptic curve encryption algorithm is as follows: y 2 +xy=x 3 +ax 2 +b (1) Where b≠0; the elliptic curve is generally used in the blockchain encryption process to generate the public key address through point multiplication. The point multiplication k*P is implemented by repeated point addition, where k is an integer and k≠0; for a point P(x,y) on the elliptic curve, if two P points are superimposed, the calculation process is shown in Formula 2: The encrypted content is defined as a point P on the binary extended elliptic curve. The encrypted content can be obtained by performing operations of equations (1) and (2).

7. The LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography according to claim 1, characterized in that: The smart contract layer deploys smart contracts on the blockchain. The smart contract layer includes a smart contract virtual machine, a contract execution engine and a storage manager. The smart contract virtual machine provides an execution environment for the operation of the smart contract, the contract execution engine is used to execute the code in the smart contract, and the storage manager is used to manage the data storage of the smart contract.

8. The LLM blockchain cloud platform based on binary extended domain elliptic curve cryptography according to claim 1, characterized in that: The encryption service layer uses encryption algorithms to perform encryption and decryption operations on data, and is used to generate, store, distribute and update keys.

9. A LLM blockchain cloud platform data processing method, characterized in that: The LLM blockchain cloud platform applied to any one of claims 1 to 8 comprises the following steps: Step 1: Build a human resources platform and blockchain. The blockchain is built based on the binary extended domain elliptic curve algorithm, and the front end of the human resources platform is built. The human resources platform is closely integrated with the binary extended domain elliptic curve encrypted blockchain to achieve secure data storage and interaction; Step 2: The participant registers an account on the human resources platform, and the human resources platform returns the key sk generated by the national encryption SM2 to the participant; Step 3: The participant uses the public key PK of the human resources platform to encrypt the uploaded data to obtain the ciphertext, and then uses the key sk to digitally sign the uploaded information, and then uploads the ciphertext and the digital signature to the blockchain. The participant joins the blockchain and becomes a blockchain node; Step 4: The HR platform processes the uploaded data to meet the input requirements of LLM. The data remains encrypted during the LLM processing. The processed data is input into the LLM model for training. The LLM model parses the natural language text input by the participants and generates personalized recommendation results based on the rules of the business logic layer. The recommendation results processed by the business logic layer are encrypted again using a semi-homomorphic encryption algorithm to ensure the security of data during storage and transmission; Step 5: The HR platform encrypts the obtained data with the public key of the participants, generates a secure database using blockchain, and stores the data of each category in the database; Step 6: The human resources platform uses blockchain technology to implement user identity authentication and authority management, and sends the corresponding information to each participant in ciphertext. The participants use their own private keys to decrypt the ciphertext and obtain the information results they need.

10. The LLM blockchain cloud platform data processing method according to claim 9 is characterized in that: The blockchain is composed of multiple blocks, each of which contains the hash value of the previous block, forming a chain structure; the data of the i-th block is D i , whose hash value is h i , then the hash value calculation formula for the i-th block is: h i =H(D i ||h i-1 ) (3) where h i-1 is the hash value of the previous block, D i Includes transaction records, timestamps, and other relevant information of the current block.

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