489 results about "Data cloud" patented technology

The invention discloses a battery full life cycle intelligent management method and system based on a large model, and the method comprises the following steps: S1, collecting the operation data of a battery, and carrying out the preprocessing; s2, inputting the pre-processed sample into a pre-trained Transform encoder model, and extracting a time sequence and cross-stage characteristics; s3, executing model reasoning, outputting a state index, and generating a battery state vector; s4, identifying an abnormal category and a position in combination with the working condition information; s5, generating a dynamically optimized battery management strategy based on the battery state index and the abnormity identification result; s6, deploying a lightweight model at the edge device, executing local reasoning and uploading data; s7, the cloud updates the model through self-supervised training and issues the model; and S8, repeatedly executing the steps S1 to S7, and carrying out optimized closed-loop management. According to the invention, large model modeling and an edge cloud cooperation mechanism are fused, and intelligent sensing and dynamic management of the whole life cycle of the battery are realized.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The invention relates to the technical field of network security, in particular to a network security situation real-time monitoring method and system. The method comprises the following steps: acquiring virtual machine running state data and physical host deployment data, and performing distributed probe deployment in a cloud computing environment according to the virtual machine running state data and the physical host deployment data to obtain monitoring probe data, wherein the monitoring probe data comprises lightweight virtual machine probe data, physical host monitoring probe data and network flow probe data; according to the monitoring probe data, performing global identification mapping on the corresponding relationship between the virtual machine and the physical host so as to obtain identification mapping data; obtaining tenant security data; and performing streaming hierarchical processing on the tenant security data according to the identifier mapping data to obtain tenant isolation data. Through the design of the distributed probe, a virtual machine, a physical host and network flow are covered, comprehensive data can be collected in real time, and a monitoring blind area is eliminated.

The invention relates to the technical field of medical rehabilitation monitoring, in particular to a rehabilitation cloud platform system for cardiopulmonary function monitoring. According to the system, an intelligent monitoring terminal collects physiological data; the cloud computing platform adopts wavelet threshold denoising and an index moving average method to remove noise, combines dynamic time warping to achieve multi-physiological signal time sequence alignment, constructs a double-flow model, generates a health state index and a comprehensive health risk index, introduces a multi-scale branch and differential attention mechanism, and achieves the multi-scale health state index and the comprehensive health risk index. High-precision anomaly detection is realized through prediction deviation analysis, multi-target reinforcement learning is adopted to balance health improvement and risk control, and a personalized training scheme is generated; the remote medical support module supports a doctor to monitor patient data in real time and intervene in an adjustment scheme; the user interaction end provides a real-time monitoring data visualization and individuation rehabilitation plan. Through multi-modal data fusion, time sequence synchronous optimization and adaptive reinforcement learning, accurate monitoring, dynamic rehabilitation optimization and remote medical collaboration are realized.

The invention discloses an encryption protection method for network data backup and recovery, which relates to the technical field of data protection, and comprises the following steps: respectively implementing differential backup strategies by adopting a local, cloud and remote three-terminal collaborative backup architecture; and performing local data backup by a combined scheme of complete backup and incremental backup to generate a chained hash value sequence. Three-terminal key isolation and dynamic derivation are adopted to guarantee data security, an independent key system is adopted locally, cloud and remotely, a chain type key is generated locally based on a hash chain, version independent keys are generated at the cloud and remotely, and data security of other terminals is not affected by key leakage of any storage terminal. The recovery priority is automatically switched from local to cloud to remote, the local decrypts incremental data layer by layer through a hash chain, the cloud directly calls an independent key of a target version and uses a synthetic backup complete copy in remote, and the disaster recovery efficiency is improved through layered recovery and synthetic backup.

The invention discloses a cache data security protection system based on cloud computing, particularly relates to the field of security protection, and comprises a data acquisition module, a cloud storage module, a verification execution module, a security processing module and a protection early warning module. The data acquisition module acquires cache data in the platform in real time, and the cloud storage module adopts a distributed architecture to safely store various types of data, and performs classified preprocessing and encrypted storage; the verification execution module identifies the safety signal, generates an execution signaling, and transmits the execution signaling to the execution node through the encryption channel; the security processing module is used for cleaning, desensitizing and encrypting input and recorded information and converting the input and recorded information into calculation processing data, and the protection early warning module is used for integrating a data source, constructing an analysis data set, detecting abnormal behaviors through an algorithm and a rule engine and generating a security signal; in the security comparison stage, the to-be-approved data is compared in real time, the operation risk is evaluated, the signal data is transmitted through an encryption channel, and a full-link log is stored in the block chain.

The invention discloses an intelligent fitness method and system. The intelligent bodybuilding method comprises the steps that the bodybuilding equipment receives a bodybuilding situation instruction and provides a corresponding motion mode for a user; the fitness equipment collects fitness data of the fitness equipment and feeds back the fitness data to the terminal; the terminal collects the fitness data and sends the fitness data to the cloud server; the cloud server processes the fitness data based on a pre-trained model and returns an interaction command to the terminal according to a processing result; and the terminal sends the interaction command returned by the cloud server to the fitness equipment, so that the fitness equipment updates the exercise mode of the fitness equipment. According to the technical scheme, efficient transmission and comprehensive analysis of the fitness data can be realized through interaction of the fitness equipment, the terminal and the cloud server, and a proper fitness situation instruction can be given by utilizing the pre-trained model in combination with the fitness condition of the user, so that the exercise mode is dynamically adjusted, and the user experience is improved. Personalized and intelligent fitness services and fitness guidance are provided for the user, and the fitness experience of the user is improved.

The invention relates to the technical field of lithium battery energy storage and temperature control, in particular to an electronic radiator intelligent temperature control system based on a lithium battery energy storage cabinet. The system comprises an edge temperature sensing module, a cloud thermal management strategy module, a cloud clustering analysis module, a thermal field topological optimization module, a multi-source thermal feature fusion module, a dynamic temperature control strategy generation module and a grading strategy issuing execution module. An edge temperature sensing module collects real-time temperature data of multiple monitoring points and an edge side preliminary temperature control scene judgment result; the cloud heat management strategy module receives battery pack topology connection data; the cloud clustering analysis module carries out fuzzy clustering on the data to obtain a temperature state and scene discrimination clustering center; the thermal field topological optimization module optimizes a temperature state clustering center in combination with topological data; the multi-source thermal feature fusion module fuses the two clustering centers to form multi-source fusion feature representation; the dynamic temperature control strategy generation module determines a temperature control strategy identifier accordingly, and the grading strategy issuing execution module matches and distributes a gradient temperature control strategy to a heat dissipation actuator.

The invention relates to the technical field of power system safety monitoring, in particular to an electricity utilization potential safety hazard supervision method and system based on an electricity utilization data cloud platform. Multi-dimensional electricity utilization data are collected in real time and preprocessed, and the preprocessed data are subjected to multi-source fusion in combination with a data fusion algorithm; carrying out hidden danger detection on the fused data by utilizing a multi-layer intelligent learning model; a dynamic scoring mechanism is introduced, multi-dimensional scoring is performed on a detection result, hidden danger risk levels are divided, a multi-level early warning mechanism is triggered based on the risk levels, and automatic emergency response is implemented; potential electricity utilization safety hazards can be recognized in time, early warning is achieved, and lagged detection and processing of a traditional method are avoided. By means of intelligent hidden danger detection and dynamic risk assessment, the monitoring accuracy and efficiency are improved, human intervention is reduced, and the timeliness of emergency response is improved.

The invention provides a cloud control robot method and system, and relates to an intelligent control technology, a robot terminal obtains environment data and object state information based on a sensor module, and uploads the environment data and the object state information to a cloud server based on a communication module, and the cloud server determines an object specification according to the object state information and sends the object specification to a cloud server. The cloud server determines a constraint road section according to the environmental data, calculates a track offset value according to the object specification and the road section parameters, generates a control instruction according to the grabbing point and the track offset value, sends the control instruction to the robot terminal, and obtains real-time state data when the robot terminal executes the control instruction; and the cloud server generates action adjustment information based on the real-time state data to perform instruction updating on the robot terminal, so that more efficient and more flexible object carrying control on the robot can be realized.

The invention relates to a distributed fault positioning system and method for a power transmission and distribution line, and the system comprises the power transmission and distribution line, an acquisition unit group, a collection unit, a communication base station, a cloud server, and a data terminal. The acquisition unit group is used for monitoring current, self state parameters and the like of the power transmission and distribution line, the collection unit is used for receiving and sending communication data with the acquisition unit group, the communication base station is used for receiving and sending communication data with the collection unit, and the cloud server is used for communicating with the communication base station and the data terminal. And the data terminal is equipment with a communication function at the user side. According to the scheme, the application range of a line fault positioning system is expanded, including mountainous areas without mobile communication network coverage, a mode of collecting and installing equipment distributed calculation is adopted, the accuracy and efficiency of fault positioning are improved, the line patrol time is saved, the time of line power restoration is shortened, and the reliability of a power supply system is effectively ensured.

The invention relates to the technical field of intelligent traffic systems, in particular to a linear intelligent monitoring system for a bridge fabrication machine, which comprises a multi-source sensing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the multi-source sensing module comprises strain sensors, inclinometers, GPS positioning units and machine vision units; the edge computing node is internally provided with a self-adaptive data fusion algorithm and is used for denoising sensor data in real time; the cloud digital twin platform is used for synchronizing the BIM design model and generating a millimeter-level linear deviation early warning signal; the PLC control interface is used for receiving the early warning signal and automatically adjusting parameters of the template system and the walking mechanism; according to the method, environmental noise interference is effectively eliminated through multi-source sensor fusion and a self-adaptive data filtering technology, the monitoring precision is improved to a millimeter level, real-time comparison of construction data and a design model is realized based on a BIM and digital twinning dynamic interaction system, and the timeliness and accuracy of linear control are remarkably improved.

The invention belongs to the technical field of floor support plate construction, and particularly discloses a disassembly-free bottom die steel bar truss floor support plate construction system based on an intelligent construction site, which comprises a BIM (Building Information Modeling) model driving module, a construction progress control module, an Internet of Things sensing module, a visual detection module, an evidence storage module and a cloud platform, the BIM model driving module constructs a three-dimensional model and maps the three-dimensional model to field positioning; the construction progress control module dynamically simulates the process through four-dimensional coupling; the Internet of Things sensing module collects welding current, stress and temperature and humidity data in real time and carries out early warning. The visual inspection module inspects quality parameters such as seam splicing and lap joint based on unmanned aerial vehicle inspection; the evidence storage module encrypts and stores construction data; and the cloud platform integrates the whole-process instruction. Through cooperation of multiple modules, high-precision positioning, process dynamic simulation and data full-process management of floor support plate construction are realized, and the construction precision and the progress management and control efficiency are remarkably improved.

The invention belongs to the technical field of intelligent retrieval, and discloses an archive data intelligent retrieval method and system based on a multi-modal large model. The method comprises the following steps that a cloud data center builds an archive data intelligent retrieval platform, deploys a block chain network and builds an archive data intelligent retrieval engine; the cloud data center is used for carrying out data processing on the real-time multi-modal file big data; the cloud data center is used for generating a knowledge graph according to a plurality of real-time retrieval themes; the cloud data center is used for carrying out data clustering; the cloud data center uses a block chain network to carry out distributed storage; the user terminal is used for uploading real-time query data; the cloud data center is used for performing intelligent retrieval by using an archive data intelligent retrieval engine; and the cloud data center visually displays the target archive data. According to the method, the problems of low retrieval efficiency, insufficient accuracy, weak multi-modal data processing capability and insufficient security in the prior art are solved.

The invention relates to the technical field of quality management, in particular to a concrete building quality monitoring system and method. The problems that in the prior art, concrete building quality monitoring mainly depends on manual sampling inspection and regular inspection modes, and potential quality hazards in a concrete building structure are difficult to find and solve are solved. The optimal sensor deployment scheme is generated through the sensor deployment unit in the cloud computing platform, the sensors are deployed according to the deployment scheme, the data acquisition module acquires key parameters of the concrete building through the sensors, and the data transmission module transmits the acquired data. And the cloud computing platform performs safety assessment on the acquired data through an internal quality assessment unit so as to obtain intelligent monitoring and management of the concrete building. Potential safety hazards of the concrete building on the construction site can be found in time so that measures can be taken for repairing in advance, meanwhile, the maintenance cost and the management cost of the concrete building can be reduced, and the management efficiency is improved.

The invention provides a TFT instrument data interaction and cloud collaborative management system, and relates to the technical field of TFT display, and the system comprises a communication collection module which is used for deploying a plurality of integrated thin film transistor display screen units in a transport vehicle, and each unit collects the operation parameter data of the vehicle in real time through a vehicle-mounted sensor network; and the cloud receiving module is used for receiving and analyzing the operation parameters of the transport vehicle, determining the vehicle position, the operation state, the real-time parameter information content and the display mode position which need to be displayed on the monitoring interface, and mapping related data into interface elements to generate a motorcade monitoring interface. According to the invention, real-time data intelligent analysis and cloud cooperation are realized, full-link visualization and remote optimization of the vehicle state are realized, the operation efficiency is improved, and the maintenance cost is reduced.

The embodiment of the invention provides a data processing method and device, equipment and a storage medium, which are used for realizing rapidness and stability of distributed storage. The method is applied to the fields of block chains, big data, cloud technologies, artificial intelligence and the like, and comprises the steps that a data writing request sent by a client is received, and the data writing request is used for indicating data modification of N copies of target data; a consistency rule of the target storage pool is obtained, the consistency rule is used for indicating that the execution of the feedback data write request to the client is successful when response replies returned by M storage nodes are received, and M is a positive integer smaller than or equal to N; sending a data writing request to the N storage nodes to enable the N storage nodes to execute a writing operation of the target data; counting response replies fed back by the N storage nodes; when the response reply meets the consistency rule of the target storage pool, a response message is replied to the client side, and the response message is used for indicating that the data writing request is executed successfully.

The invention discloses a self-learning power transmission line fault detection system based on cloud-edge cooperation, which comprises an edge end, a cloud end and a user end, and is characterized in that the edge end collects an image sample, obtains a fault identification result and a confidence score by using a lightweight edge end detection model, judges whether an anomaly or a defect exists based on the confidence score, and sends the anomaly or the defect to the user end; when abnormity or defects exist, the side end generates alarm information and transmits the alarm information, a fault identification result and an image sample to the cloud end; the cloud side forwards the alarm information and the image sample to the user side, and if the user side carries out fault label marking and submits the fault label to the cloud side, the cloud side forms training data based on the fault label submitted by the user side; and if the user side does not submit the marked image sample to the cloud side, the cloud side forms training data based on a high-precision integrated model fault identification analysis result, and the cloud side performs incremental training on a lightweight edge detection model stored by the cloud side by using the training data to obtain model parameters and sends the model parameters to the edge side so as to iteratively optimize the model of the edge side.

The invention provides an edge diagnosis and maintenance system and method for industrial equipment. The system comprises a data acquisition module, a cloud collaboration module and at least one edge calculation module, the data acquisition module is used for acquiring multi-mode real-time operation data of industrial equipment; the cloud collaboration module is used for performing incremental training on the initial diagnosis model and the initial knowledge graph based on the multi-modal real-time operation data to obtain a target diagnosis model and a target knowledge graph, and issuing the target diagnosis model and the target knowledge graph to the edge calculation module; and the edge calculation module is used for determining whether the industrial equipment has a fault based on the multi-modal real-time operation data and the target diagnosis model, and if so, determining a fault position and a maintenance suggestion of the industrial equipment based on the multi-modal real-time operation data and the target knowledge graph. According to the invention, by setting the edge-cloud architecture, the accuracy and real-time performance of fault diagnosis and maintenance are improved.

The invention discloses a gas safety intelligent inspection method and system, and belongs to the crossing field of artificial intelligence technology and safety production management. The method comprises the following steps: acquiring image data through a field data acquisition terminal; a preliminary hidden danger identification module in the cloud server innovatively adopts a scene perception target detection model and a scene reasoning multi-label model for collaborative analysis; and the comprehensive risk assessment module performs deep correlation analysis on the hidden dangers based on a preset gas safety knowledge graph, and determines a quantitative comprehensive risk value by calculating a basic risk accumulated value of each hidden danger and a correlation risk increment generated by a correlated hidden danger pair. According to the method, the defects that traditional inspection depends on subjective experience and composite risks are difficult to quantify are overcome, amplification risks caused by concurrence of multiple hidden dangers can be accurately identified and quantitatively evaluated, continuous self-improvement of system capability is realized through a closed-loop mechanism, and the intelligent level of gas safety management is remarkably improved.

The invention provides a science and technology project data cloud processing method based on Internet of Things data fusion. The method comprises the steps of collecting multi-source science and technology project data, performing standardized preprocessing, constructing an initial data graph, and generating time sequence data; weight redistribution is carried out on the multi-source science and technology project data through a dynamic credibility adjustment mechanism; modeling a time-sensitive interaction relationship between science and technology projects by adopting a dynamic graph neural network, and generating a dynamic graph embedding representation; constructing a causal relationship network based on causal reasoning, determining an initial influence score, eliminating external interference factors based on the initial influence score, and calculating a real influence score; the method comprises the following steps: constructing a time dynamic model, predicting the influence of a science and technology project in a future time step, constructing a closed-loop optimization mechanism based on a prediction result and historical data, and dynamically adjusting parameters of the time dynamic model to adapt to the change of science and technology project data.

The invention discloses an intelligent growth monitoring and analysis method and system for mariculture, and the method comprises the following steps: firstly, collecting breeding environment data in real time through a first sensor group; and acquiring growth data of the breeding object through the second sensor group. Then, the cultivation environment data and the growth data are transmitted to a cloud platform by using a data acquisition system, and the data are preprocessed by the cloud platform to generate a standardized data model; based on the standardized data, a deep learning algorithm is used for analyzing the growth trend of the breeding object and predicting the growth change in a period of time in the future. Through combining the breeding environment and the growth data, the cloud platform performs risk assessment, generates early warning information in real time, and identifies potential risk factors. According to the early warning information, the cloud platform automatically adjusts breeding environment parameters and management measures, and healthy growth of breeding objects is ensured. According to the method, intelligent decision support is provided for breeding management, and the breeding efficiency and the growth quality of bred objects are improved.

The invention relates to the technical field of bearing fault monitoring, and discloses a large-scale equipment bearing fault intelligent monitoring method, which comprises the following steps: S1, real-time data acquisition: deploying multi-source sensing equipment, acquiring bearing operation parameters in real time, setting a data cloud platform access layer, and integrating flaw detection data by adopting an OPC UA protocol; the invention further provides a large equipment bearing fault intelligent monitoring system which comprises a three-axis vibration acceleration sensing module, a temperature monitoring module, a lubricating oil online monitoring module, an installation state monitoring module, an environment auxiliary monitoring module and a data integration and intelligent analysis module. And bearing fault pre-judgment is realized, interpretation errors caused by manual intervention are reduced, complex working conditions can be adapted, the stability and reliability of a monitoring system are ensured, and thus the influence of equipment faults on production activities is reduced.

The invention discloses a distributed RID receiving system based on edge multi-source fusion, which relates to the technical field of low-altitude supervision, and is characterized in that a distributed RID receiving node cluster adopts a fixed node and maneuvering node collaborative grid deployment scheme to capture and preliminarily analyze low-altitude aircraft RID signals in real time, and node data is output after the low-altitude aircraft RID signals are processed by a primary fusion algorithm; the edge multi-source fusion processing unit is deployed in each management and control partition, converges node data in the area, completes calibration, de-duplication and noise reduction through a secondary fusion algorithm, and outputs structured RID data; the cloud management and control platform realizes global data overall planning, situation visualization, intelligent decision making and instruction issuing through a three-level fusion algorithm, and is linked with the urban low-altitude scheduling system to form closed-loop management and control; the clock synchronization module provides nanosecond time reference for the whole system. According to the method, the problems of incomplete coverage and insufficient data processing precision of a traditional RID receiving scheme are solved, and global sensing, accurate data processing and closed-loop management and control of the low-altitude aircraft are realized.

The invention discloses a monitoring system for energy storage of an all-vanadium redox flow battery. The monitoring system comprises a multi-dimensional data acquisition sensing device, an intelligent analysis and prediction system, a big data cloud platform, a user interface and intelligent alarm system and a remote control and automatic optimization system, the multi-dimensional data acquisition sensing device is integrated with a plurality of acquisition sensors to acquire multi-dimensional real-time data of the battery system, the intelligent analysis and prediction system performs intelligent analysis on the data acquired in real time by using a big data analysis and machine learning algorithm, and the big data cloud platform performs centralized data storage and management. The user interface and intelligent alarm system provides a friendly user interface, and the remote control and automatic optimization system supports a remote control function. The invention relates to the technical field of energy storage, and in the scheme, various sensors and intelligent analysis algorithms are integrated, the operation data of the energy storage system are acquired and processed in real time, and efficient, safe and reliable operation of the energy storage system is ensured.

The invention discloses an intelligent virtual power plant cross-domain collaborative optimization system. The system comprises a data acquisition unit which comprises an energy controller, an intelligent electric meter, a sensor and an edge gateway; the edge calculation unit comprises a federated learning optimization module, and the federated learning optimization module is used for carrying out federated learning optimization; based on a lightweight federated learning node local training model, adopting a differential privacy technology to desensitize data; the cloud collaboration unit comprises a multi-modal large model, and the multi-modal large model is used for aggregating edge model parameters and generating a global optimization strategy; and the execution unit comprises an intelligent contract. According to the method, global optimization of industrial, commercial and resident resources is realized through cross-domain collaborative scheduling, and compared with existing single-group scheduling, the efficiency of the system is improved; the communication overhead is reduced by balancing privacy and efficiency, locally processing sensitive data by the edge computing layer and only transmitting model parameters by the cloud, and the system response time is greatly shortened by executing real-time dynamic response and combining reinforcement learning dynamic adjustment strategies.

The invention relates to the technical field of new energy, in particular to an intelligent charging gun and a charging method. An off-line adaptation algorithm unit identifies CP signal characteristics of a vehicle through a built-in standard library of a CP signal identification module and outputs a vehicle type identifier, and a protocol compatibility algorithm library matches charging protocol characteristic parameters according to the vehicle type identifier; after the protocol conversion unit receives the parameters, voltage thresholds, PWM duty ratios and communication message formats among different protocol standards are converted in real time through a preset mapping table; when the off-line adaptation fails, the system triggers the on-line auxiliary adaptation algorithm unit, and the fault detection module monitors handshake failure, identifies protocol incompatibility characteristics and outputs a fault signal; the data recording and broadcasting module records data in the charging starting process, the cloud communication module uploads the data to the algorithm repairing platform and receives an updating package, and finally the protocol compatibility algorithm library is updated. According to the invention, intelligent conversion and continuous learning of the charging protocol are realized, and charging compatibility and system adaptability are significantly improved.

The invention discloses a fire-fighting smoke exhaust pipeline laying planning system based on an industrial cloud platform, and relates to the technical field of pipeline laying planning, and the system comprises a building information collection module which collects building data; the cloud edge cooperative computing module is used for constructing a smoke exhaust simulation model by using distributed computing resources of an industrial cloud platform and carrying out real-time parameter iteration through edge nodes; the pipeline path planning module is used for generating a plurality of candidate laying paths by using an improved algorithm and carrying out path optimization based on a pipeline resistance coefficient and a construction cost constraint condition; and the scheme verification and optimization module is used for carrying out smoke flow dynamics simulation verification on the optimal path scheme, identifying scheme defects and carrying out iterative optimization on path parameters through a genetic algorithm. According to the invention, through a cloud-edge collaborative intelligent computing architecture, a multi-dimensional constrained dual-target optimization mechanism and a dynamic adaptive path planning and verification system are fused, and collaborative improvement of fire-fighting smoke exhaust pipeline laying planning in accuracy, efficiency and compliance is realized.

The invention provides an air conditioner indoor environment parameter control method and system, and relates to the technical field of air conditioner control. External environment information and internal heat source information such as sunlight intensity data and cloud cover prediction data outside a building and the operation state and real-time power consumption data of large electronic equipment inside the building are obtained; the preset building physical attribute parameters are combined, the predicted total heat load increment of all the areas in the future preset time can be calculated in advance, an adjusting instruction is generated in advance before the indoor environment parameters actually change, the operation state of an air conditioner system executing mechanism is adjusted, accurate and active control can be achieved, and the control efficiency is improved. According to the method, local supercooling or superheating possibly caused by a traditional method is avoided, the comfort consistency of the indoor environment is remarkably improved, meanwhile, the energy utilization efficiency is optimized, and energy waste is effectively reduced.

According to the multi-user hierarchical data cloud storage sharing method and system based on secret sharing provided by the invention, storage protection is provided for an encryption key by using a secret sharing mechanism, so that the security of the key can be effectively ensured; different shared user groups and shared data grading contents are set according to different key shares, and attribute encryption can be equivalently realized; the access control server performs processing such as interpolation, slicing and transposition on an external storage key share, so that proxy re-encryption can be efficiently and equivalently realized; the secret key share is simultaneously used for realizing the processes of shared user group setting, shared data grading encryption, shared user and access control server bidirectional authentication, proxy re-encryption and the like, so that password resources are fully utilized. In addition, a key generation center is not needed, the interaction process of roles in the system is reduced, and the working process is simplified; and only simple Hash operation is needed, so that the generation of password resources is quicker, and the calculation is simpler and more efficient.