483results about How to "Guaranteed privacy" patented technology

The invention relates to the technical field of information security, and discloses a data security management system and method in an intelligent power grid, and the system comprises a dynamic encryption and key management module which is used for carrying out the encryption and key management of the data of the intelligent power grid, determining an encryption algorithm according to the sensitivity of the data, and generating a corresponding key, the dynamic encryption and key management module transmits encrypted data and key information to the adaptive access control module so as to ensure the security of data access; and the self-adaptive access control module is used for managing access permissions of the user and the equipment and performing dynamic authorization according to the attribute label and the context information. Through dynamic encryption and adaptive key management, an encryption algorithm and a key length are adjusted in real time according to data sensitivity and a network environment, a quantum key distribution technology is combined, high security and quantum attack resistance of a key are ensured, and then security and real-time performance of data transmission are ensured.

The invention relates to a power field Text2SQL (Structured Query Language) data analysis method based on LLM (Logical Language Model) and RAG (Random Access Gateway), and solves the defects that a retrieval result contains noise and redundancy and a large model still has a large number of illusion phenomena due to the absence of a vectorization model in a specific field and the singleness of knowledge base types and dimensions in the RAG technology compared with the prior art. The method comprises the following steps: constructing a data set of a Text2SQL data analysis method in the power field; constructing a multi-dimensional text knowledge base of the Text2SQL data analysis method in the power field; fine adjustment of an LLM base of a Text2SQL data analysis method in the electric power field; constructing an RAG retrieval module of the Text2SQL data analysis method in the power field; and constructing an Agent data analysis module of the Text2SQL data analysis method in the power field, and performing data analysis processing. According to the method, related information required by Text2SQL data analysis in the electric power field can be obtained by a large language model through retrieval on the basis of an RAG technology, meanwhile, the privacy and timely updating of the data are ensured by field data stored in a knowledge base form, and the model training cost is also greatly reduced by the LLM-based Text2SQL data analysis method taking a prompt project as a main part; and by setting the mixed retrieval parameters, the retrieval result has relatively high accuracy, and the effect of Text2SQL data analysis is ensured.

The invention provides a multi-data fusion method and system based on edge computing. Belongs to the technical field of edge computing. The method comprises the following steps: receiving heterogeneous resource data from different sensors, equipment and sources through an edge task allocation unit; according to the encryption level corresponding to each task in the heterogeneous resource data, dividing each task data into data groups with identifiers according to a preset segmentation strategy; according to the operation condition of the edge task processor, performing distribution calculation on the data set according to a preset distribution strategy; after the distribution is completed, obtaining an identification sequence of the distribution mode; the distributed data set is calculated and processed through the edge task processor, and after data calculation and processing are completed, the result in the data set is sent back to the edge task distribution unit. Through intelligent scheduling of the edge task distribution unit, data from different sensors and equipment can be received and processed in real time, so that the data processing efficiency and response speed are improved.

The invention relates to the technical field of medical image processing, in particular to a medical image enhancement processing method and system based on deep learning, and the method comprises the steps: selecting an image enhancement model according to whether the number of samples needs to be expanded or not; if the number of samples needs to be expanded, performing image enhancement by adopting a generative adversarial network, otherwise, performing image enhancement by adopting an auto-encoder; a federal learning architecture is adopted to train a model, and the model is trained on a plurality of scattered devices or servers. The generative adversarial network is used to generate diversified medical image samples, and the diversity of the samples is improved. And the auto-encoder extracts key features and generates new samples, so that the diversity of data is increased. Through a federal learning architecture, by using dispersed data sources, the diversity of training samples of the model is enhanced, and meanwhile, data privacy is protected. And the diversity of samples is effectively increased, so that the generalization ability of the model is improved, and the method has higher robustness and accuracy in practical application.

The invention discloses a block chain multi-node secure communication method for an industrial internet, and the method comprises the steps: constructing a multi-chain cooperation mechanism, and obtaining an inter-chain interaction path table; determining a task partition optimization scheme and inter-chain interaction path update data based on the inter-chain interaction path table; matching an adaptive node resource for each task according to a task partition optimization scheme; determining an encryption mechanism between a cross-chain protocol and a block chain according to the security requirement of the task; constructing a multi-level block chain node hierarchical architecture and an intra-chain and inter-chain communication architecture, and formulating a parallel communication scheduling scheme; formulating a dynamic load balancing strategy based on the node resource use condition and the task execution state; and obtaining the task execution progress and the resource use condition of each block chain node, and adjusting the task scheduling and cross-chain communication path. Through the multi-dimensional collaborative design, the execution efficiency, the resource utilization rate and the communication security of the industrial internet are remarkably improved, and the stability and the reliability in a complex multi-chain collaborative scene are ensured.

The invention discloses a wireless communication network data secure transmission method and system, and relates to the technical field of secure transmission. During system operation, agricultural monitoring data are collected in real time through various sensor devices arranged in an agricultural environment, encryption processing is performed by using a symmetric encryption algorithm, and a dynamic key length adjustment mechanism is supported; a random number generator is integrated, unified management is performed on key generation, distribution, updating and destruction in a system, a timestamp or a random serial number is embedded into each data packet, the uniqueness of the timestamp or the serial number is verified at a receiving end, so that replay attacks are resisted, and the data are comprehensively analyzed and evaluated according to a set algorithm, so that the replay attack is prevented. The method comprises the following steps: acquiring a security data transmission performance index SDTP through calculation, generating a monitoring report, detecting abnormal conditions in a data transmission process, including channel quality reduction, data loss or verification failure, and providing a visual monitoring result.

The invention relates to a multi-client collaborative SAR image target identification method based on federated learning, and the method comprises the steps: collecting SAR image data, carrying out the preprocessing, generating a corresponding target identification tag, and obtaining a data set with a tag; building a federal learning framework, initializing and distributing global model parameters to each client; distributing local private data to each client according to the data set with the label, performing local model training by each client by using the local private data, and uploading updated model parameters to a server; the server aggregates the model parameters uploaded by all the clients, updates global model parameters, and distributes the updated global model parameters to all the clients; repeating the steps until the global model converges; and testing the trained global model, and selecting the tested global model to carry out SAR image target identification. Compared with the prior art, the method has the advantages that the accuracy of the model is effectively maintained while privacy is protected.

According to the dynamic stress analysis system and method for the fabricated beam string structure, the strain, vibration, displacement, temperature, soil pressure and construction load of the structure are comprehensively monitored by integrating various sensors, the safety of data is ensured through encryption transmission, sensitive data are protected by adopting a role-based access control mechanism, and the reliability of the system is improved. In the data processing process, a stress prediction model is established by cleaning, calibrating and synchronizing data, extracting key vibration characteristics, considering temperature influence and using finite element analysis to simulate structural response, and is used for identifying a potential damage area and predicting the residual service life of the structure, so that maintenance measures can be taken in time, the safety of the structure is ensured, and the service life of the structure is prolonged. And the health management efficiency and accuracy of the fabricated beam string structure are improved.

The invention discloses a privacy data protection method and device, a storage medium and a computer program product, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining local privacy data, and screening the local privacy data to obtain a plaintext prompt word corresponding to a to-be-trained small language model; the encryption and decryption model encrypts the plaintext cue word, generates an encrypted perturbed cue word, and sends the encrypted perturbed cue word to the second device, so that the second device generates a perturbed thinking chain according to the encrypted perturbed cue word based on the large language model, and sends the perturbed thinking chain to the first device; the encryption and decryption model decrypts the disturbed thinking chain to obtain a decrypted thinking chain; and training a to-be-trained small language model based on the decrypted thinking chain to obtain a trained small language model. Through the encryption and decryption model, under the condition that the data privacy of the first equipment is ensured, the small privacy data protection of the first equipment guided by the large language model of the second equipment is completed.

The invention discloses an automatic input vital sign detection method and system, and relates to the technical field of medical information processing, and the method comprises the steps: collecting vital sign data of a target individual, and carrying out the synchronous processing; a vital sign data set is formed through cleaning and multi-channel data fusion; uploading the encrypted data to a medical information system to finish classified storage and updating of the data; performing feature extraction and analysis to generate a health risk prediction result; health management suggestions are generated, and data display is completed through a dynamic feedback mechanism. According to the method, the privacy and the integrity of the vital sign data are guaranteed through a dynamic updating and verification mechanism in a mode of combining symmetric encryption and asymmetric encryption, and the risks of data leakage, data tampering and version management disorder in a traditional system are overcome. By introducing an intelligent analysis method combining a classification model and a regression model, the limitation of traditional single-dimensional static assessment is solved, and a comprehensive health risk report is generated by integrating logic association among multi-modal feature data.

The invention discloses a heterogeneous federal fine tuning language model construction method and system based on differential privacy, and the method comprises the steps: a parameter server firstly carries out the clustering of clients with similar capabilities, carries out the sensing clustering according to resources, obtains a larger-rank adaptive matrix for each class according to the class with the large computing power of the computing resources, and carries out the calculation of the resources; therefore, the flexible rank of the low-rank adaptive matrix is realized, and the fine tuning performance is enhanced. In each class, privacy federal fine tuning is adopted, including two parts of noise distribution with sensitivity perception and regular term optimization, so that better training efficiency and reduced noise influence are obtained. Between each class, heterogeneous low rank adaptive matrix aggregation is performed through a knowledge distillation technique, including knowledge aggregation and selection of high quality knowledge to enhance the local model. According to the method, strict privacy guarantee can be provided, the influence of differential privacy on fine tuning of the low-rank adaptive matrix can be relieved in a heterogeneous environment, and higher efficiency and accuracy are realized.

The invention belongs to the field of privacy protection data, and provides a privacy protection data exchange method and system under a zero-trust network architecture in order to solve the problem that a traditional scheme cannot cope with the privacy leakage risk through data analysis. The privacy protection data exchange method under the zero-trust network architecture comprises the following steps: performing tagging processing on user behavior data, equipment state data and operation environment data; executing multi-dimensional dynamic risk assessment, and calculating a comprehensive risk score; adjusting privacy budget and noise intensity according to a comprehensive risk scoring result, and protecting a data query result in combination with an adaptive differential privacy mechanism; performing dynamic data desensitization based on information entropy on a data query result; embedding a self-evolved data watermark into the data after the data query result desensitization processing is completed; the embedded watermark data is exchanged through the encrypted transmission channel, and the transmission process is monitored in real time, so that the security and integrity of the data exchange under the zero-trust network architecture are ensured.

The invention relates to a lattice-based specified server searchable encryption optimization method, device and equipment and a medium, and the method comprises the following steps: a receiver mechanism calculates and determines # imgabs0 # imgabs1 # according to a system public parameter pp, a public key pkr = Ar of the receiver mechanism, a private key skr = Tr of the receiver mechanism and a keyword w 'belongs to {0, 1} * to be searched, and a SamplePre (Ar, Tr, uw', sigma) algorithm is called to generate a trap door ew '; and the cloud server matches keyword information in the ciphertext and the trap door according to the ciphertext Cw = (c1, c2, c3), the trap door ew'and a private key skc = ec of the cloud server, and sends a matching result to a user client corresponding to a receiver mechanism to complete lattice-based specified server searchable encryption optimization. The system is convenient to manage and maintain, the specified server can centrally manage encrypted data and search trap doors, and maintenance and management of the system are facilitated.

The invention relates to a digital media content processing method, device and system. The method comprises the following steps: performing logic semantic analysis on digital media data to be detected to obtain digital media logic analysis data; carrying out illegal content identification on identification in the digital media logic analysis data to obtain media word identification data and media logic identification data; under the condition that the media word identification data or the media logic identification data represents that violation information is recognized in the digital media data to be detected, abnormal word filtering is conducted on the digital media data to be detected according to the media word identification data, and initial digital media content filtering data is obtained; performing context exception filtering on the initial digital media content filtering data according to the media logic identification data to obtain target digital media content filtering data; and performing asymmetric content encryption on the target digital media content filtering data to obtain detected digital media data. The efficiency of monitoring and identifying the abnormal content of the digital media can be improved.

The invention discloses a real-name system information management method supporting multi-level authentication, and the method comprises the following steps: collecting basic identity information of a user, and storing the basic identity information through an asymmetric encryption algorithm; according to the use demand and the access authority requirement of the user, generating a multi-level authentication hierarchy; dividing the real-name system information into a basic information area, an authority information area and a sensitive information area according to the sensitivity of the real-name system information; carrying out distributed storage on real-name system information, and carrying out hierarchical encryption on data of different information areas by using a hierarchical encryption strategy; based on a multi-level authentication result and in combination with an access control strategy, authority distribution is carried out on the user, and controlled access of real-name system information is carried out under multi-dimensional verification; user information operation behaviors are monitored in real time, user behavior characteristics are analyzed based on an anomaly detection model, and when risk behaviors are recognized, an additional verification process is triggered or user operation is directly limited. According to the invention, safer, more flexible and more efficient real-name system information management is realized.

The invention relates to a power data federated learning privacy protection method based on efficient homomorphic encryption. The method comprises the following steps: step 1, a client initializes a neural network model; step 2, the server side encrypts and updates local neural network model gradient parameters uploaded by the client side; 3, the server side executes gradient average aggregation operation, the server side does not need to decrypt, the encrypted gradient parameters uploaded by the client side are directly calculated, an average value is solved, and therefore global gradient parameters are updated; step 4, after each client receives the updated global gradient, carrying out decryption calculation operation; 5, loading the updated weight parameters of the neural network model into a local neural network model, and performing neural network model training by using a local data set; the problems that an existing encryption technology is large in operand, large in computing resource overhead and low in algorithm efficiency, and privacy leakage exists in the transmission and analysis process of power data can be solved.

The invention discloses an intelligent tuberculosis screening and data analysis system, relates to the technical field of medical data analysis systems, and aims to improve the diagnosis efficiency and accuracy of tuberculosis. The technical key points are that the system comprises a data acquisition module, a preprocessing module, an AI diagnosis module, a multi-modal data fusion module, a data analysis module, an intelligent follow-up visit module and a database management module. By collecting image data and clinical information of a patient, the system adopts a deep learning algorithm to automatically analyze an image, identifies a potential tuberculosis focus, and provides a personalized diagnosis result in combination with a multi-modal data fusion technology. Meanwhile, the system has intelligent follow-up visit and remote monitoring functions, physiological indexes of the patient are obtained in real time through the wearable device, and dynamic illness state tracking and early warning are carried out. According to the method, the accuracy and efficiency of tuberculosis screening can be remarkably improved, precise medical services are provided for patients, and epidemiological prediction and prevention and control in the public health field are supported.

The invention discloses a multi-center inherited metabolic disease information integration platform, and relates to the technical field of medical treatment, according to the multi-center inherited metabolic disease information integration platform, the normalization and availability of data are improved through a data standardization module; data from different centers are integrated into a unified database through the data integration module by utilizing a data fusion technology, redundant data are removed, the problem of heterogeneous data integration is effectively solved, and the integrity and consistency of the data are improved; the privacy protection module avoids data leakage and unauthorized access; through the resource calculation management module, the resource utilization efficiency is effectively improved, and the calculation cost is reduced; and secondly, the storage resources are dynamically adjusted and allocated according to the evaluation result of the storage index Cczs, so that the optimal management of the storage resources is realized, the problems of resource waste and resource insufficiency are avoided, and the efficient operation of the system is ensured.

The invention discloses an accumulation fund management system based on multivariate heterogeneous data fusion and a control method thereof, and relates to the field of accumulation fund management systems, and the system comprises a data access layer which is used for receiving various heterogeneous data sources of a bank end, an enterprise end and an accumulation fund management center end; the data fusion layer is used for performing data cleaning operation, data matching operation and data model construction operation on the heterogeneous data sources; the business processing layer is used for processing the accumulation fund core business; the data analysis layer and the business analysis layer are used for performing data analysis operation based on database storage data and user input data to generate an evaluation result; the security management layer is used for performing authority control operation on the access user, and the security management layer is also used for performing desensitization and encryption operation on the sensitive information; and the user service layer is used for providing mobile phone APP service, official account service and Web portal service.

The invention provides a federated learning-based cloud edge size model collaboration method and system, and electronic equipment, and the method makes full use of cloud edge resources, releases the size model capability: a large-scale model is trained at a cloud end, a small model suitable for a local scene is trained by an edge server and terminal equipment, and through a federated learning mechanism, a large-scale model is trained at the cloud end; and model collaborative optimization among different levels of nodes is realized. Data are available and invisible, data privacy and safety are guaranteed, private data of clients on the edge side do not need to be out of the domain, the cloud side shares the model capacity, the data are available and invisible, and data privacy and safety are guaranteed. The invention provides an efficient, flexible and extensible model training and optimizing method, computing power resources of cloud edges can be fully utilized, user privacy is protected, and a global large model is continuously optimized.

The invention relates to a multi-signature method with aggregable and responsibility-traceable public keys, relates to the technical field of block chains, and is used for solving the problems that in a privacy protection block chain system, user identity tracking is difficult, the privacy of signers cannot be guaranteed, and privacy protection and effective supervision cannot be balanced. The multi-signature method comprises the following steps: generating a public and private key pair and public parameters; generating a user key; key distribution; user signature; the combiner aggregates and outputs the signature; verifying signature validity; the tracker tracks the signer. According to the method, the roles of the combiner and the tracker are set, the work of aggregating the signature share is completed by the combiner, and only the signature signed and output by the combiner is recognized as an effective signature. And meanwhile, when some illegal behaviors occur, a participant signer is tracked through a tracker, so that identity tracking of the block chain is effectively realized, and meanwhile, privacy of legal users is ensured.

The invention discloses a cross-chain data penetration type sandbox supervision method and system for ideal lattice attribute encryption on a block chain, and the method comprises the following steps: a third-party platform selects and builds a block chain, configures each node on an alliance chain, and forms a supervision sandbox; the third-party platform carries out sandbox initialization, namely, a public key and a main private key are generated according to an ideal lattice encryption algorithm; the data provider, the data user and the supervision mechanism register identities on the block chain; arranging cross-chain data to be shared by a data provider, and defining an access strategy of the cross-chain data; a data user initiates an access request to the data and submits the access request; after the data user obtains the access authorization, data decryption is carried out by utilizing the attribute private key and the decryption ciphertext of the data user; and the supervision mechanism monitors and checks the flow direction and access condition of the data in real time through the block chain in the supervision sandbox, and identifies an abnormal access mode through an integrated anomaly detection mechanism of the smart contract. According to the method, the privacy and the integrity of the cross-chain data are guaranteed.

The invention discloses a federated learning privacy protection method based on a generative adversarial network and knowledge distillation, and relates to the technical field of privacy protection. Comprising the following steps: S1, local model training; s2, generative adversarial network training; s3, communication in an initialization stage; s4, carrying out distillation training; s5, communication at a distillation stage; and S6, updating the client. According to the method, the generative adversarial network and the knowledge distillation technology are creatively combined, the generative adversarial network model is locally trained at the client, the synthetic data conforming to private data distribution is generated, dependence on a public data set is avoided, knowledge distillation is performed by using the generated synthetic data, and the knowledge distillation efficiency is improved. Knowledge migration without selecting a public data set is realized, and direct exposure of private data distribution is effectively avoided, so that the risk of privacy disclosure is remarkably reduced.

The invention discloses a distributed optimization method for accessing a comprehensive energy micro-grid to a power distribution network by considering electricity-carbon-hydrogen coupling. The method comprises the following steps: step 1, modeling main equipment in the comprehensive energy micro-grid; 2, considering multi-type application of a carbon capture system and hydrogen energy, and modeling the comprehensive energy microgrid; 3, considering the access of the integrated energy microgrid, and modeling the power distribution network; 4, considering that the power distribution network is connected to the micro-grid, connecting the power distribution network and the micro-grid through a connecting line, and enabling the output power of the power distribution network to be equal to the input power of the micro-grid; step 5, constructing a comprehensive energy microgrid-power distribution network distributed optimization scheduling model considering electricity-carbon-hydrogen coupling; step 6, carrying out iterative solution on the optimized scheduling model established in the step 5 by adopting an alternating direction multiplier method algorithm; according to the method, the problem of optimal scheduling of the electricity-carbon-hydrogen coupled comprehensive energy microgrid connected to the power distribution network is solved.

The invention discloses a medical auxiliary diagnosis method and electronic equipment based on quantum federated learning in the technical field of smart medical, and the method comprises the steps: carrying out the medical auxiliary diagnosis based on a pre-trained quantum federated learning model, and the obtaining process of the quantum federated learning model comprises the steps: constructing the quantum federated learning model, comprising a local client and a central server. Repeatedly executing the following steps until a preset training period is reached: in the current training period, training the local client according to the training sample set, and calculating the accuracy of the local client according to the test sample set; the local client sends the accuracy and the final model gradient to the central server after the current training period; the central server updates global model parameters according to the accuracy and the model gradient, and returns the global model parameters to the local client; and the local client updates the local model parameters according to the global model parameters. According to the invention, the accuracy and efficiency of medical auxiliary diagnosis can be improved.

The invention discloses a data security protection method, system and device and a storage medium, and relates to the field of data security, and the method comprises the steps that a middleware application server sends prompt information after completing an initialization operation, so that a user can input an initial password; the method comprises the following steps: performing SM3 hash calculation on an initial password and a random salt value, encoding a hash value and the random salt value, storing the encoded hash value and random salt value in a target file, after successful starting, obtaining a password ciphertext of the initial password input by a user and a password plaintext after decryption of the password ciphertext, decoding the hash value and the random salt value, performing SM3 hash calculation on the decoded salt value and the password plaintext, and storing the encrypted password ciphertext and the password plaintext in the target file. The calculation result is compared with the decoded hash value; and judging whether login identification succeeds or not according to a comparison result, and if the login identification succeeds, performing encrypted transmission and encrypted storage operation on the sensitive data by using an encryption algorithm when the instruction input by the user corresponds to the sensitive data to complete security protection of the sensitive data. The data security protection level is improved.

The invention discloses an internal medicine laboratory data sharing and querying system, and relates to the technical field of laboratory data security sharing. A special management and control terminal is arranged to match internal medicine laboratory data of a corresponding patient according to identity information of a pre-queried patient; according to the method, the preset security conversion rule is used for converting the internal medicine laboratory data to obtain the corresponding shared query data, so that the dependence on a key is eliminated, the management cost of the key is saved, and the management difficulty is reduced; in the process of converting the internal medicine laboratory data, the internal medicine laboratory data is intercepted through the preset intercepted character quantity, whether each character in the intercepted data is 8 or 9 or not is judged, a plurality of grouped word strings are intercepted and selected based on judgment, and then the corresponding shared conversion sequence is obtained, so that the security of the data is improved, and the conversion efficiency of the internal medicine laboratory data is improved. And a safer and more reliable guarantee is provided for sharing and querying of internal medicine laboratory data, and the privacy of patients and the safety of medical data are ensured.

The invention provides a trusted data exchange and shared space management system supporting multi-party cooperation. The trusted data exchange and shared space management system supporting multi-party cooperation comprises a data exchange and sharing module used for uploading, exchanging and sharing data by a plurality of users in real time and ensuring data consistency, the data exchange and sharing module realizes data consistency through a conflict detection and solution mechanism under the condition of data conflict, and the data exchange and sharing module is used for realizing data exchange and sharing; and intelligently sorting the updated contents through a priority algorithm. According to the trusted data exchange and sharing space management system supporting multi-party collaboration, through modular design, the efficiency, safety and transparency of multi-party collaboration data exchange and sharing are remarkably improved. The data exchange and sharing module solves the data conflict problem when a plurality of users operate data at the same time through a timestamp mechanism, a priority ranking mechanism and a rollback mechanism, and data consistency and accuracy are effectively guaranteed.

The invention discloses a case data analysis system based on a large model, and the system comprises a data collection module, a preprocessing module, a large model training and optimization module, an analysis module, a visualization and interaction module, a security and privacy protection module, a data preprocessing module with an innovative algorithm, a large model training module with an adaptive learning rate formula, and a disease risk prediction module with an evaluation formula. System functions and performance indexes are clear, equipment and medium requirements are clear, the steps of the method are complete, and high efficiency, accuracy and safety of case data analysis are guaranteed; the case data processing efficiency and accuracy are improved, and powerful support is provided for medical decision making. A large model is accurately analyzed, visual and convenient to check, and a security module guarantees data privacy. The medical quality can be improved, the treatment scheme can be optimized, and the method has important application value.

The invention discloses a multi-protocol Internet of Things system and a multi-protocol Internet of Things soft gateway development method, belongs to the technical field of Internet of Things equipment management, and aims to solve the technical problem of compatibility of different communication protocol equipment accessing an Internet of Things platform. According to the technical scheme, the system comprises a device layer, a soft gateway layer and a cloud Internet of Things platform layer, the device layer transmits tcp device information, udp device information and websocket device information to the soft gateway layer through a protocol analysis plug-in, the analyzed tcp device information, udp device information and websocket device information are obtained, and the cloud Internet of Things platform layer sends the tcp device information, the udp device information and the websocket device information to the soft gateway layer. The soft gateway layer transmits the analyzed tcp device message, the udp device message and the websocket device message to a cloud Internet of Things platform layer through an MQTT protocol, and the cloud Internet of Things platform stores and analyzes the analyzed tcp device message, the udp device message and the websocket device message.