1417results about How to "Privacy protection" patented technology

The invention provides an intelligent task alarm rule self-learning method and system based on support priority, and relates to the technical field of intellectualization, and the method comprises the steps: constructing a bidirectional feature extraction network to collect task operation data, and combining historical alarm data to generate a task exception association model; and carrying out migration mapping on the model and a business scene knowledge base by utilizing a migration learning algorithm, generating a task priority quantitative index, and constructing a self-adaptive priority scoring system. And training an alarm rule generator based on the model and a scoring system, generating a multi-level alarm strategy tree, and realizing differential alarm. And the incremental learning engine monitors a task state in real time, dynamically adjusts an alarm response strategy and feeds back data to optimize a rule. A federal learning mechanism is adopted to cooperatively train a model, and the generalization ability and adaptability of alarm rules are improved, so that more accurate and efficient task alarm is realized, the false alarm rate is reduced, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of data processing, and discloses an intelligent water conservancy inspection method, device and equipment and a storage medium. The method comprises the following steps: performing dynamic fusion processing on acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twinborn body; performing agent collaborative analysis on the digital twinborn body to obtain a self-adaptive inspection strategy; performing multi-modal sensing data acquisition on the target area according to the self-adaptive inspection strategy to obtain multi-modal data; performing federated learning processing on the multi-modal data to obtain holographic situation awareness data; performing causal reasoning analysis on the holographic situation awareness data to obtain a dynamic risk map; and inputting the dynamic risk map into a preset deep Q network for prevention and control strategy analysis to obtain an active prevention and control strategy and an inspection report. According to the invention, the efficiency and accuracy of intelligent water conservancy inspection are improved.

The invention relates to the technical field of artificial intelligence and health management, in particular to a health management method and system based on multi-dimensional data analysis, and the method comprises the steps: collecting the physiological, behavior, environment and psychological data of a user, and carrying out the modeling through a recurrent neural network, and generating health state data; extracting a key causal relationship of the health risk based on a causal analysis model of a Bayesian network; comprehensively analyzing health changes through the short-term time sequence model and the long-term trend prediction model, and generating health risk data; a personalized health intervention scheme is designed by using a conditional generative adversarial network, and safe sharing and dynamic management of the scheme are realized through a block chain technology. According to the method, the health risk can be accurately identified, scientific intervention measures can be designed, the health management process is dynamically optimized, powerful technical support is provided for an intelligent old-age nursing scene, and the health management efficiency and effect are improved.

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a park-level adjustable load-oriented resource regulation and control method and system, and the method comprises the steps: formulating an initial energy scheduling scheme according to the predicted energy demand in combination with the output characteristics of distributed energy in a park and the load condition of a power grid; and training a load regulation and control strategy model by adopting a reinforcement learning algorithm based on the initial energy scheduling scheme, and generating a personalized load regulation and control instruction for each energy consumption unit. According to the scheme, the real-time energy consumption data of the plurality of energy consumption units in the park, the output characteristics of the distributed energy and the power grid load condition can be comprehensively considered, a scientific and reasonable energy scheduling and load regulation and control strategy is formulated, and efficient utilization and green low-carbon development of park energy are realized. Meanwhile, the method also needs to have the characteristics of data sharing, accurate prediction, intelligent scheduling, rapid response and the like so as to adapt to complex and changeable energy requirements and environmental protection requirements of the park.

The invention discloses a data security sharing method and system, and relates to the technical field of data security, and the method comprises the steps: receiving to-be-shared data uploaded by a data provider; fragmenting the data by adopting a biologically inspired adaptive fragmentation algorithm; generating a digital fingerprint for each fragment by using a multi-dimensional dynamic Hash algorithm; encrypting data fragments by adopting a post-quantum encryption algorithm and storing the data fragments in a decentralized network; recording the access authority, the digital fingerprint and the storage position information to a block chain security account book; when a data request is received, the access permission is verified; carrying out identity verification by adopting multi-mode biological feature recognition; after the verification is passed, obtaining a digital fingerprint and a storage position from the block chain; acquiring the encrypted fragments from the decentralized network and decrypting the encrypted fragments; verifying the integrity by using the digital fingerprint; and recombining the verified fragments to obtain complete data and returning the complete data to the requester. According to the method, efficient data sharing is realized while the data security is guaranteed, and the data value and privacy protection are effectively balanced.

The invention discloses a Transform large model training method based on cloud edge collaboration. The method comprises the following steps: S1, constructing a data set; s2, preprocessing the data set; s3, segmenting the preprocessed data set by using a data parallelism strategy, and performing distributed training by combining data parallelism and model parallelism technologies; s4, monitoring computing resources and network conditions of the edge equipment and the cloud server in real time, dynamically adjusting a data distribution strategy, and optimizing computing load distribution; s5, a multi-agent dominant actor-commentator algorithm is combined with a multi-arm tiger machine strategy, and the layered structure of the Transform model is adjusted in real time; s6, performing partial model reasoning tasks on the edge equipment in combination with the edge reasoning capability; s7, protecting data privacy by adopting a federal learning strategy and utilizing a differential privacy technology; and S8, performing hardware acceleration by utilizing heterogeneous computing resources. According to the method, a cloud edge cooperative computing and intelligent scheduling method is adopted, and large model training and reasoning with efficient privacy protection are realized.

The invention provides a security management and retrieval method based on Internet of Vehicles data, and the method comprises the steps: carrying out the cooperative training of a global key management model through a federated learning technology on the premise of not leaking the original data of all parties, and achieving a security key management scheme under the participation of multiple parties through a model parameter aggregation and updating mechanism; when data retrieval is needed, a user submits a retrieval request and identity authentication information, and after a system verifies the identity of the user, a ciphertext data identifier meeting conditions is matched in an attribute index according to a keyword and an authorization attribute of the request; the system acquires corresponding ciphertext data from a distributed database according to the ciphertext data identifier, and directly performs keyword matching calculation on the ciphertext data through a secure multi-party computing protocol under the condition of not decrypting the data; and according to the mined security threats and abnormal modes, a data encryption strategy and an access control strategy are dynamically adjusted, authority limitation or access blocking is performed on suspicious users, and the security protection capability of the system is improved.

The invention discloses an internet data security protection method and system based on an intelligent algorithm, and relates to the technical field of security protection. During system operation, user behavior data are collected in real time through log recording, a sensor and an API interface, the real-time operation behavior of a user is monitored, the interaction mode and transaction behavior of the user on a platform are analyzed, and the security of the user is improved. Calculating and acquiring a user behavior safety coefficient Yhxw, a data transmission safety coefficient Sjcs and a risk protection response coefficient Fxfh, identifying potential fraudulent behaviors, performing dynamic risk assessment by combining user historical behaviors, a transaction amount, a geographic position and data of payment equipment, performing threshold comparison to obtain an assessment scheme, and during login and transaction, combining multiple identity verification means to obtain a risk protection result; according to the method, user identity verification is carried out, security of login behaviors is dynamically monitored, network traffic and platform logs are analyzed, abnormal access and malicious requests or DDOS attacks are detected in real time, and abnormal behaviors or attacks are automatically identified and prevented.

The invention relates to a transportation agent intelligent cargo tracking method, device and equipment and a storage medium. The method comprises the following steps: carrying out multi-source sensor data acquisition on goods and a transport vehicle to obtain an original data set, and carrying out time synchronization and Kalman filtering processing to obtain a fused state vector; performing feature extraction through a deep cargo tracking network to obtain a feature vector; performing distributed model training based on the feature vectors to obtain a global cargo state detection model; inputting the collected real-time data set into a global cargo state detection model, and performing multi-stage anomaly detection processing to obtain an anomaly detection result; and performing trajectory prediction and multi-target optimization by using a space-time diagram neural network model according to an anomaly detection result and a historical transportation route to obtain a target transportation route. According to the embodiment of the invention, comprehensive goods tracking of multi-source data and dynamic optimization paths can be integrated to cope with complex and changeable logistics environments, and more accurate, safer and more efficient goods tracking services are provided.

The invention relates to the technical field of data desensitization, in particular to an electronic commerce platform information security desensitization scheme analysis system based on artificial intelligence, which comprises a data classification module, an access evaluation module, a dynamic desensitization module and a security audit module. According to the method, the data of the e-commerce platform is intelligently classified and evaluated by adopting an artificial intelligence technology, dynamic desensitization and finer access control of the data are realized, various data access requirements can be dynamically met by evaluating the sensitivity level of the file and the security level of the access request, data processing is performed according to requirements, and the data processing efficiency is improved. The security and privacy of user data are effectively protected, through automation of security audit, the recognition efficiency of abnormal behaviors is improved, the transparency and traceability of overall data management are enhanced, through real-time analysis and response, the risk of data leakage is reduced, and the practical value of data and the fluency of service operation are kept.

The invention discloses an intelligent security and protection monitoring method and system based on image recognition. The method comprises the following steps: S1, acquiring multi-modal data; s2, performing preprocessing to generate complete target information; s3, performing real-time detection on the generated complete target information, and performing primary processing on a conventional scene; s4, based on an improved visual SLAM technology, obtaining three-dimensional information of multiple cameras; s5, when the edge device detects a potential abnormal condition, performing abnormal behavior analysis on the data by using a Swin Transform model, and optimizing a detection result in combination with the three-dimensional information of the multiple cameras; s6, performing adaptive learning on historical data by using a depth deterministic strategy gradient algorithm, and dynamically adjusting a threshold value of abnormal behavior detection; and S7, after an abnormal condition is detected, processing data through differential privacy protection and an end-to-end encryption technology, automatically controlling security and protection equipment, and giving a real-time alarm. According to the invention, image recognition and deep learning technologies are fully utilized, and accurate real-time monitoring in a complex scene is realized.

The invention discloses an Internet of Vehicles multi-parameter monitoring method and system based on 5G multi-access edge computing. The method comprises the following steps: collecting multi-source heterogeneous data in real time by using a vehicle-mounted sensor; transmitting the collected multi-source heterogeneous data to the nearest edge computing node in real time by using a 5G network; preprocessing the multi-source heterogeneous data at an edge computing node; inputting the fused data into a local model deployed on an edge node, and carrying out real-time monitoring and real-time evaluation on a vehicle state and a driver state; the obtained evaluation result is uploaded to a cloud platform, and the cloud platform dynamically adjusts a driving assistance decision and a control instruction according to the vehicle driving working condition and the environment data to generate an optimal decision result; and issuing the optimal decision result to a vehicle-mounted execution unit through a 5G network. According to the method, key parameters such as the vehicle speed and the physiological state of a driver are monitored, and the real-time performance and accuracy of a monitoring system are improved.

The invention discloses an artificial intelligence psychological assessment method and system based on multi-modal input. The method comprises the following steps that S10, a personal psychological model used for psychological assessment is established and trained; s20, receiving a psychological assessment request initiated by a user, and carrying out conversation with the user through the personal psychological model; s30, in the dialogue process with the user, multi-modal data of the user is obtained; and S40, processing and analyzing the multi-modal data of the user, and performing mental health assessment on the user. The method can acquire and process various types of data of the user, such as texts, voices, facial expressions, physiological signals and the like. The multi-modal data fusion mode can provide more comprehensive and accurate psychological state information, so that the accuracy of psychological assessment is improved. According to the method, a personalized psychological model is established and trained for each user, and a more targeted psychological assessment service can be provided.

The invention provides a low-altitude industry chain information intelligent cooperation and airspace management platform based on federal learning. Through cluster ad hoc network and decentralized federated learning, the method is divided into three stages of data acquisition, data preprocessing and analysis application, low-altitude scene data communication of dynamic topology and static topology is realized, and the efficient communication requirement in a low-altitude operation scene is met. According to the system, differential data transmission strategies are adopted, optimization design is carried out on a single machine, a co-task cluster, a cooperative cluster and a non-cooperative cluster, and the communication efficiency and the resource utilization rate are improved. In the data preprocessing stage, federated learning aggregation of multi-source data fusion, data anonymization and decentralization is combined, and data privacy protection and safe sharing of three parties of industry, university and research are ensured. In the analysis and application stage, low-altitude aircraft task scheduling, airspace flow control and environmental condition evaluation are realized through aircraft analysis, airspace analysis, environmental analysis and digital twinborn technologies.

The invention discloses a federal learning data processing method based on privacy protection, and the method comprises the following steps: S1, a client uses local data to train a generator of a generative adversarial network, and generates virtual data similar to the local data in distribution; s2, setting three privacy protection levels according to the privacy sensitivity of the local data; s3, performing model updating operation by using a homomorphic encryption technology; s4, utilizing a dynamic weight optimization mechanism based on a multi-arm bandit algorithm to obtain a priority processing data set; s5, preliminarily updating the global model, analyzing generated data through a reverse inference detection mechanism, and judging whether a privacy disclosure risk exists or not; s6, the server feeds back the updated global model to the client, and the client continues personalized model training in combination with local data; and S7, generating new virtual data, and carrying out the next round of global model training. According to the method, the generative adversarial network, differential privacy and homomorphic encryption are combined, so that efficient federated learning under privacy protection is realized.

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 human body tumble identification method based on millimeter wave radar perception. The method comprises the following steps: acquiring echo data of various human body actions by using a millimeter wave radar; processing the echo data to respectively obtain point cloud data of human body actions; a DBSCAN clustering method is used for clustering point cloud data, point clouds are divided into different clusters based on a central point, then the obtained point cloud data are input into a deep neural network, space structure and dependency relationship information of the point cloud data is captured, the category of human body actions is recognized according to the space structure and dependency relationship information, and a human body tumble recognition result is output. According to the invention, a deep neural network model fusing a PointNet network and a Transform module is designed, and the model can enhance the description of the spatial relationship of point clouds, thereby improving the judgment of human body tumble recognition, and reducing the false alarm and false alarm rate. According to the invention, human body postures and actions are sensed through millimeter wave radar signals, human body tumble recognition is realized, and the system has the advantages of good anti-interference performance, high recognition accuracy and the like.

A user behavior analysis system based on cloud side-end cooperation of a large language model is used for carrying out information collection, cloud side-end cooperation and user behavior analysis on mobile phones and computer equipment, and comprises an information collection module, a cloud side-end cooperation module and a user behavior analysis module. According to the invention, information acquisition is carried out on electronic data of mobile phone equipment, information privacy collaboration is carried out through the cloud side end module, and then user behavior analysis is carried out based on a large model, so that the defects of low collaboration efficiency, high user behavior identification false alarm rate and the like of an existing user behavior analysis system are overcome; the evidence obtaining efficiency and the user behavior analysis effect are effectively improved by introducing a cloud side-end cooperation and large language model, and the cloud side-end cooperation behavior analysis system which is high in efficiency, high in precision and light in weight is achieved.

The invention relates to the technical field of long and short term memory and natural language processing, and discloses a session processing method and device based on long and short term memory, equipment and a medium. Using the updated large language model to respond to the input verbal skill based on the stored and downloaded long-term memory data to form interactive session information, and storing the interactive session information as short-term memory data in real time; wherein the large language model is updated through federal learning according to the short-term memory data; determining a theme according to the short-term memory data, screening important memory data in the short-term memory data based on a preset priority, and incrementally storing the important memory data as long-term memory data of the corresponding theme; and releasing the long-term memory data subjected to incremental storage to the message queue middleware according to the theme, so that other service line servers subscribing to the theme download the long-term memory data. The problems of memory decline, poor memory consistency and poor integration of a session system are solved.

The invention relates to an emotion recognition method based on a millimeter-wave radar and a robot. The method comprises the following steps: receiving a transmitting signal of the millimeter-wave radar and a corresponding echo signal, converting the transmitting signal and the corresponding echo signal into a point cloud format, and removing static reflection to obtain dynamic point cloud data and a timestamp of a surrounding environment; grouping the dynamic point cloud data into clusters; obtaining a centroid estimation value of the target, and tracking the centroid estimation value; sending the millimeter wave signal which horizontally moves at a constant speed to a target person, and receiving echo signals which are reflected by the target person and are uniformly spaced; point spread function optimization is carried out, and SAR image reconstruction is carried out by using a fast frequency domain near-field imaging method; calculating to obtain phase change of the human body signal, and separating to obtain respiration and heartbeat signals of the target person; and extracting emotion related features to obtain the current emotion of the target person. Compared with the prior art, the specific position of the human body can be accurately recognized, and non-contact privacy protection emotion recognition can be remotely achieved.

The invention relates to integrated airborne communication antenna equipment and a communication method. The method comprises the following steps: carrying out CSI data acquisition and space-time focus modulation on a communication channel to obtain CSI space-time characteristic data; performing residual vector quantization operation to generate a quantized data stream, and performing recursive coding processing to obtain a target data stream; mapping the target data stream to a three-dimensional tensor space, constructing a real value tensor model, and performing iterative operation to obtain a CSI feature vector; performing fractional order wavelet transform decomposition on the CSI feature vector to obtain a radio frequency feature set, and inputting the radio frequency feature set into a federal fractional scattering network for feature processing to obtain an identity authentication result; and calculating an amplitude-phase weight coefficient of the antenna array, generating a beam parameter, and performing compensation matrix operation on the beam parameter according to the CSI feature vector to obtain a pre-coded signal. According to the invention, the dynamic adjustment of the antenna beam parameters is realized, and the accuracy and anti-interference capability of beam forming are improved.

The invention relates to a DID-based block chain data sharing and security verification method. The method comprises the following steps: a user generates a unique real-name DID through multi-factor identity authentication, generates multiple groups of public and private key pairs in an application process, and stores private key fragments in a decentralized network by adopting a secret sharing technology; a real-name DID is used as a unique user account on the block chain, and data sharing and security verification are carried out in combination with a deep learning model and an adaptive zero-knowledge proof mechanism; when the private key is lost, a public and private key pair is regenerated through biological characteristics and equipment fingerprint verification; and cross-chain data sharing and security verification are realized by using an intelligent contract and an inter-chain communication protocol, and identity authentication and data operation processes of the user on different block chains are simplified.

The invention relates to a multi-modal emotion recognition method based on zero-knowledge machine learning, and belongs to the crossing field of artificial intelligence and information security. Key features of local face images, voice signals and posture data multi-modal data are extracted through an attention module, then the key features are transmitted to a server after homomorphic encryption, training of a convolutional neural network (CNN) model is carried out in an encryption domain, and finally a result is sent back to the local for decryption and emotion recognition. The method has the advantages that by using the homomorphic encryption technology, in the whole data processing and transmission process, privacy data of a user is always kept in an encrypted state, and due to fusion of multi-modal features, the emotion recognition accuracy is improved, the robustness of the system in a complex environment is enhanced, the emotion recognition result is more stable and reliable, and the user experience is improved. The method can be widely applied to multiple fields of intelligent monitoring systems, user sentiment analysis, human-computer interaction, mental health monitoring and the like.

The invention discloses an accurate electric power theft detection method based on multi-modal Mamba. The method comprises the following steps: S1, carrying out adaptive multi-scale data preprocessing, and carrying out denoising and normalization processing on input power time sequence data by adopting an adaptive score standardization method and a local abnormal factor technology so as to improve data quality; s2, a multi-mode Mama feature extraction layer is adopted; s3, a federated learning training layer: constructing a secure federated learning framework, realizing cooperative training among a plurality of power supply areas, and introducing a differential privacy mechanism at the same time to protect privacy of model parameters and training data; s4, an intelligent confrontation defense layer; s5, an adaptive classification decision system; s6, a lightweight optimization strategy; efficient and safe cooperative detection is realized through innovative fusion of a multi-modal Mamba model and a federated learning framework; through an intelligent confrontation system and lightweight optimization, the robustness and practicability of the model are improved; according to the method, the detection accuracy, the calculation efficiency, the privacy protection and the like are remarkably improved.

The invention relates to the technical field of power grid coordination control, and provides a coordination control method for a smart power grid, which comprises the following steps: S1, collecting voltage, current, frequency and load data in real time by deploying sensors and monitoring equipment, S2, processing the data by using a big data analysis technology, extracting load trend and fault early warning information, and carrying out early warning on the load trend and the fault early warning information. Real-time data collection is realized by deploying sensors and monitoring equipment, data analysis and load prediction are performed by using big data and a machine learning technology, an optimized objective function is constructed, fine coordination and a global optimal solution are realized by using a multi-agent system and a distributed optimization algorithm, and meanwhile, energy privacy is protected. The reliability of the power grid is improved through intelligent fault detection and a quick response mechanism; according to the technical scheme, interaction between the power grid and users is enhanced, demand side management is achieved, an efficient safety monitoring system is established, multi-level protection measures are taken, safe and stable operation of the power grid is ensured, and the problem that optimization and coordination are not comprehensive in the prior art is solved.

The invention provides a wireless communication and computing resource collaborative optimization method and system. The method provided by the invention comprises the following steps: establishing an initialized global model matched with a business demand for each service provider, and matching service resources of each service provider according to the business demand; establishing a service agent corresponding to each service provider; each service agent generates an optimal strategy based on the corresponding service resources and the state information of other service providers; sending the initialized global model to a distribution client according to an optimal strategy; each distribution client trains the received to-be-trained model based on the distributed service resources, and sends the trained model to the corresponding service provider; and aggregating the received trained models by each service agent to obtain a global model of the corresponding service. The method and the system provided by the invention are used for realizing collaborative optimization of wireless communication and computing resources and improving the overall performance and the resource utilization efficiency of federated learning.

The invention discloses a security risk assessment system and method based on the Internet of Things, and relates to the technical field of digital information transmission. According to the security risk assessment method based on the Internet of Things, whether hardware equipment can work normally or not is judged, and if the hardware equipment cannot work normally, early warning prompt is carried out; if the hardware equipment can work normally, evaluating the vulnerability degree of the Internet of Things platform; according to the method, the security risk of the Internet of Things platform is evaluated, the security risk level is obtained, and equipment faults and potential security vulnerabilities are found in time through systematic hardware and software evaluation. By judging the normal working state of hardware equipment, early warning can be effectively carried out, occurrence of security events can be reduced, and the vulnerability degree, the recovery capability and the data security of the platform can be comprehensively known, so that scientific risk level assessment is provided for enterprises, the enterprises are helped to formulate corresponding security measures and optimization strategies, security risks are effectively reduced, and the safety of the enterprises is improved. And the user data and privacy are protected.

The invention discloses a multi-object and multi-model deployment and control early warning system. The multi-object multi-model deployment and control early warning system comprises a multi-source data fusion module, an efficient algorithm and model optimization module, a real-time data processing and analysis module, a multi-model cooperation and decision module, an adaptive learning and evolution module and a security and privacy protection module. And a more comprehensive and deep judgment result is provided, so that the police officers can make more accurate decisions in the operation process. The multi-model structure of the system realizes cooperative work through the support of each module, and finally achieves the goals of accurate data, timely analysis and reliable early warning. And due to the close relationship between each module and multiple models, the system has higher decision-making ability and adaptability when facing a complex dynamic environment.

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.

The invention discloses an environmental monitoring system based on an optical sensor, and relates to the technical field of environmental monitoring, and the system comprises the following components: a quantum entanglement module, an optical sensor integration module, a quantum measurement and data processing module, and a data transmission and sharing module. By integrating the quantum entanglement module, the optical sensor integration module and the quantum measurement and data processing module, high-precision and real-time monitoring of environmental parameters is realized, the quantum entanglement technology provides measurement precision and anti-interference capability superior to those of a traditional optical sensor for the system, the accuracy and reliability of data are ensured, and meanwhile, the quantum entanglement technology can be used for monitoring the environmental parameters in real time. Due to the design of high sensitivity and wide dynamic range of the optical sensor, the system can stably work under various complex environment conditions, and the efficiency and accuracy of environment monitoring are effectively improved.