430 results about "Data collaboration" patented technology

The invention discloses a big data privacy protection modeling method and system based on federated learning and a block chain, and relates to the technical field of privacy protection and joint modeling. According to the method, homomorphic encryption, differential privacy, federated learning, secure multi-party computing and block chain technologies are fused, big data privacy protection and joint modeling are realized, encryption and dimensionality reduction are performed on original data through homomorphic encryption and differential privacy, an encrypted training sample of secure privacy is generated, a local model is trained on an encrypted data set through federated learning, and a big data privacy protection result is obtained. The method comprises the following steps: calculating aggregation parameters by using security multiple parties, constructing a verification network in combination with a block chain, ensuring credibility and integrity of model training, and finally, adding noise optimization performance for a global model by using differential privacy, testing generalization ability through cross validation, and determining a deployable privacy protection joint learning model, thereby breaking traditional data islands, promoting cross-mechanism data cooperation, and improving the privacy protection performance. Big data values are released, and data protection regulations and privacy requirements are met.

The invention discloses an enamel product and an intelligent spraying system thereof, and belongs to the technical field of enamel product spraying, and the enamel product comprises a high-precision visual guidance module, an environment intelligent control module and an auxiliary function module, the intelligent environment control module is used for monitoring and dynamically regulating and controlling parameters such as temperature and humidity and dust concentration of a spraying environment in real time, ensuring stable coating quality and reducing energy consumption, and the auxiliary function module is used for realizing accurate spraying track planning and quality control through process connection, safety guarantee and data collaboration. And high efficiency and reliability of the spraying process are guaranteed. On the basis that spraying of the enamel product is achieved, the quality of the whole process is improved, and comprehensive benefits can be optimized.

The invention discloses a data collaborative directory management method and system, and relates to the technical field of government affair informatization, and the method comprises the steps: generating a directory snapshot containing a global hash value; calculating a directory node hash value of each edge directory node based on the directory snapshot identifier; eliminating clock drift interference through time sequence alignment and dynamic tolerance filtering; inputting the Hash difference time sequence into an isolated forest model to judge a substantial change node; based on the difference entry number and the historical calling weight, combining a dual-threshold rule and an online dichotomy model to hierarchically synchronize requirements; according to a grading result, matching an incremental push mode or a full pull mode, and constructing a synchronous transaction context containing an exponential backoff retry mechanism; synchronous operation is executed through the two-stage state model, and compensation rollback is triggered when the synchronous operation fails; and calculating a health index of the substantially changed node, dynamically selecting a self-healing action and optimizing system parameters. The problem of misjudgment caused by time sequence drift is effectively solved, the synchronization efficiency is improved, and the consistency of directory versions is guaranteed.

The invention relates to the technical field of data analysis, in particular to a city physical examination accurate analysis method and system based on AI multi-modal data collaboration, and the method comprises the following steps: constructing a self-adaptive fractal space-time grid, and mapping a city multi-modal data source into a space-time coding vector with a fractal dimension; establishing a physical field driven cooperative resonance network, and extracting an abnormal cooperative mode exceeding a normal resonance threshold; calculating a modal entropy chain value according to the real-time metabolic rate of the urban system, and generating an optimal weight matrix through a non-equilibrium thermodynamic model; and fusing the abnormal cooperation mode and the dynamic weight matrix, and generating a space-time causal map for displaying a fault source and a propagation path by using a causal discovery algorithm. According to the method, the multi-modal data collaboration and causal analysis technology is utilized, the emergency response capability of an urban system in the case of sudden failures is improved, the stability of urban operation is guaranteed, and the space-time causal atlas provides comprehensive visual information for the failure propagation process.

The embodiment of the invention provides a dynamic knowledge graph-driven cross-department government affair data collaboration method and a dynamic knowledge graph-driven cross-department government affair data collaboration system. The method is applied to the technical field of computer science and technology, and comprises the steps of obtaining cross-department government affair service data and corresponding policy rule data, constructing a data knowledge graph and a policy knowledge graph, performing incremental updating and consistency verification on the knowledge graphs based on preset event information, and extracting a newest service state and a newest policy rule. A minimum necessary data view and an authorization range are generated through joint reasoning, efficient and safe collaboration of cross-department government affair data is achieved on the basis, and accurate authorization and compliance management of data access are achieved. According to the scheme, only necessary data is provided and the access permission is strictly controlled in cross-department government affair data collaboration, so that the data sharing efficiency, security and compliance are remarkably improved, and meanwhile, the risks of unauthorized access and redundant data transmission are reduced.

The invention discloses a multi-dimensional data collaborative analysis early warning method based on intelligent operation and maintenance of a power transmission network, and relates to the technical field of power transmission network early warning, and the method comprises the steps: collecting target data of target equipment, and obtaining sudden change data of a power load in a system; performing correlation analysis on the instant characteristics of the electrical quantity and the dynamic change of the power grid structure in combination with the sudden change data of the power load to obtain correlation factors capable of predicting a fault; according to the power transmission network intelligent operation and maintenance early warning method, the problem that fault early warning is inaccurate and not timely due to insufficient data collaboration and early warning response lag in power transmission network operation and maintenance is solved, and the effect of improving the early warning precision and response speed of power transmission network intelligent operation and maintenance is achieved.

The invention relates to the technical field of remote sensing data processing and water environment monitoring, and discloses a black and odorous water body intelligent identification method based on a multi-source remote sensing image, and the method comprises the steps: firstly obtaining a first high-resolution remote sensing image, and extracting a global water body mask through a TransUNet model; secondly, removing non-target plaques based on geometric features and a basic spectrum rule; then, calculating a black and odorous water body index by using the second multispectral remote sensing image, and screening suspected black and odorous water bodies by combining adaptive threshold segmentation; further, performing fine classification confirmation on the suspected target by using a third high-frequency remote sensing image and an Officient Net model; and finally performing fusion output on the identification result. Through multi-source data collaboration and a coarse screening-fine classification multi-stage strategy, the limitation of insufficient spatial-temporal resolution of a single data source is overcome, and the positioning precision and the anti-interference capability of urban black and odorous water body recognition are improved.

The invention discloses an emergency rescue data privacy protection and collaborative analysis system based on federated learning, and belongs to the technical field of emergency rescue data privacy protection and collaborative analysis. A dynamic trust evaluation module; the federated learning privacy protection engine module is used for realizing cross-domain data collaborative training by adopting a secure multi-party computing model; and the block chain intelligent contract module is used for deploying a data access control strategy and a credible auditing rule. According to the method, a global trusted environment is established through an end-side cloud three-level trust chain, the reliability of equipment is quantified through dynamic trust evaluation, data collaboration of privacy protection is realized through federated learning, an auditing strategy is automatically executed through an intelligent contract, an efficient encryption technology for guaranteeing safety and effectively reducing resource consumption is adopted, and the urban emergency data safety problem is solved.

The invention relates to a real-time monitoring method for soil remediation by utilizing multi-spectrum of an unmanned aerial vehicle and sensing coupling of the Internet of Things, and belongs to the technical field of soil environment monitoring and remediation. The method comprises the following steps: constructing a space grid model of a monitoring area; a space-air-ground integrated monitoring network is arranged, image data are obtained through multi-spectral remote sensing of an unmanned aerial vehicle, and soil environment parameters are collected through a ground Internet of Things sensor; preprocessing and fusing the multi-source spatio-temporal data, and establishing a spatio-temporal matching model; constructing an inversion model of the soil heavy metal content, the organic matter content and the pollutant degradation degree based on the fusion data; and the repair efficiency is dynamically calculated and visualized, and real-time evaluation and early warning of the repair process are realized. According to the invention, space-air-ground data collaboration is realized, the real-time performance, accuracy and space coverage of monitoring are remarkably improved, the defects of high cost, low efficiency and limited data dimension of a traditional method are overcome, and whole-process and intelligent decision support is provided for soil remediation.

The invention belongs to the technical field of complication prevention, and particularly relates to a rehabilitation equipment integrated management and data interaction system based on a mobile terminal, which comprises a multi-source data acquisition module, a data integration and preprocessing module, an intelligent analysis module and a risk early warning module, data islands are cracked through a multi-source heterogeneous data fusion architecture, and an extensible access layer is designed; the system is compatible with medical systems such as HIS, LIS and PACS; a Bluetooth module, a Wi-Fi module and a 5G module are embedded and directly connected with rehabilitation equipment such as a power bicycle and an inspiratory muscle training instrument; cross-institution data collaboration is realized through a federated learning framework, for example, local training sub-models of hospitals and global parameter aggregation of a central server are realized, model generalization is improved on the premise of privacy protection, 'preoperative, intra-operative and post-operative link splitting 'is broken, and a perioperative period full-link data set is constructed.

The invention relates to the technical field of big data, in particular to a warehouse management analysis method and system based on big data and a medium, comprising heterogeneous data real-time acquisition, stream batch fusion processing, AI intelligent decision making, digital twinborn verification and hierarchical storage scheduling. Compared with the prior art in which a distributed data acquisition and delay batch processing scheme is adopted, which leads to serious data islands and lagging of decision basis, the scheme adopts an edge protocol conversion gateway and flow batch fusion processing architecture, uniformly accesses real-time data flow of industrial equipment through MQTT, captures service system changes in combination with DebeziumCDC, realizes millisecond data integration through Kafka, and realizes real-time data flow access through MQTT. Dynamic feature calculation and deep model training are synchronously completed by means of a Flink-Spark hybrid engine; the method has the advantages of total-factor real-time mapping and cross-domain data collaboration, gets through information barriers of an equipment layer, a business layer and an environment layer, and provides millisecond-level fresh global data views for storage decisions.

The invention discloses a multi-relation extraction error propagation optimization method and device based on data collaborative enhancement, and the method comprises the steps: generating extended training data through grammar recombination and adversarial samples, carrying out the positioning and weighted sampling of a low-frequency relation according to an entity pair relative position, and obtaining an upper relation data set; and training an upper relation classifier based on the upper relation data set, and performing prediction through the trained upper relation classifier to obtain a prediction result and confidence distribution thereof. According to the method, through text enhancement and a weighted sampling strategy for relative position positioning based on entities, the problem of unbalanced data distribution is effectively relieved, the modeling capability of the upper relation classifier for long-tail distribution is remarkably improved, and therefore systematic optimization of the upper relation classifier for low-frequency relation recognition accuracy is achieved.

The invention relates to the technical field of electrical digital data processing, and discloses a health data processing and alarming method for a family monitoring group, which comprises the following steps of: establishing a dynamic baseline for individualized long-term stable habits and a short-term baseline for reflecting a recent mode for a multi-mode data stream of a monitored person; two processing paths are executed in parallel, one processing path generates a system steady-state entropy value based on double-baseline difference aggregation so as to identify a long-term system evolution trend, and the other processing path performs multi-dimensional resonance judgment based on a difference value between an original data point and a short-term baseline so as to perceive a transient system impact event; according to the invention, by constructing a double-pedigree collaborative monitoring framework of slow-varying risks and acute events, mutually independent trend analysis and transaction detection in a traditional data processing mode are integrated into an integral processing structure with internal data collaboration and function complementation, and the problem of inherent information omission of a single processing mode is solved.

The invention belongs to the technical field of computer server management, and particularly discloses a laboratory collaborative environment-oriented user and data full-life-cycle server management system, which comprises a container virtualization layer, a server management layer and a server management layer, dynamic resource allocation is supported through a state saving-resource reallocation-environment recovery mechanism; the heterogeneous transaction coordination layer adopts a Saga transaction mode to coordinate a heterogeneous system so as to realize cross-system transaction consistency; the data collaboration ecological layer establishes typed management of a personal data volume, a shared software environment volume and a public data set volume, and supports software environment encapsulation sharing and versioning management; and the metadata management layer realizes real-time update and high-performance query of metadata based on a Redis hot update architecture. According to the method, the problems of inflexible resource allocation, incomplete environment isolation, dilemma in authority management and difficulty in data collaboration and recovery in the prior art are solved.

The invention discloses a large-size complex surface type high-speed scanning measurement device based on a white light interference technology and a method thereof, and relates to the technical field of crossing of optical precision measurement and machine vision, and the device comprises an intelligent measurement module which integrates a large-view-field vision camera, a 3D camera and a white light interferometer, the large-view-field vision camera is used for acquiring a global two-dimensional image of a measured object and automatically identifying features to trigger a measurement program, and the 3D camera is used for acquiring three-dimensional point cloud data of the measured object; according to the invention, the intelligent measurement module integrates multiple devices, a large-view-field camera triggering program, point cloud acquisition by the 3D camera and high-precision measurement by the white light interferometer, so that multi-source data collaboration is realized. The dynamic scanning optimization module adjusts step lengths and grid priorities according to curvature regions, improves an algorithm to generate a path, improves scanning efficiency and pertinence, achieves combination of global and local measurement through cooperation of multiple devices, meets the multi-precision requirements of complex surface types, improves measurement efficiency, and guarantees the precision of key regions.

The invention discloses a cross-system data collaborative driving workflow full-link approval method and system, belongs to the technical field of workflow management, and aims to solve the technical problem of how to realize cross-system data collaborative driving and cover a workflow full-link approval method and process. Comprising the steps that a system architecture composed of a unified workflow engine, data collaboration middleware and a plurality of service systems is built; drawing a cross-system workflow full-link approval flow chart in the unified workflow engine; by means of data collaboration middleware, a collaboration mapping relation of all service systems is established, and data format conversion, extraction and integration operations are automatically executed in process execution; and the unified workflow engine generates a process instance, notifies a service system of a subsequent approval node through the data collaboration middleware, sends an approval task to the corresponding approval node, transmits related data through the data collaboration middleware, notifies related personnel, and records various information in a full-link approval process execution process in real time.

The invention relates to the technical field of data processing, and discloses a full-life-cycle-oriented engineering technology data governance method and a full-life-cycle-oriented engineering technology data governance system. The method comprises the steps that project object attributes are divided into an anchor point attribute set and an extended attribute set, a locking mark is added to the anchor point attribute set, the extended attribute set is layered according to stages to obtain a data object meta-model, a dependency relationship mapping table is established according to the data object meta-model to achieve automatic verification when the attributes are changed, and the attribute change is completed. And establishing a spatial topological graph and a process graph to diagnose inconsistent defects, establishing a weighted graph to quantitatively analyze change influences, evaluating data quality according to scene types, and intelligently complementing missing attributes. According to the method, the integrity and consistency of cross-stage transmission of engineering data, the real-time performance and accuracy of multi-professional data collaboration, the depth and automation degree of data quality diagnosis, the scientificity and comprehensiveness of change impact analysis and the flexibility and efficiency of data utilization are improved.

The invention discloses a neurosurgical operation virtual simulation training system based on multi-modal data fusion, and relates to the technical field of operation simulation. The data acquisition and preprocessing module acquires data such as images and force feedback, and standardized storage is performed after filtering and registration; the multi-modal data fusion module performs weighted fusion on the data by using an attention algorithm to generate a three-dimensional model; the virtual operation scene construction module generates a high-fidelity environment through ray tracing and a physical engine; the surgical operation interaction simulation module realizes accurate interaction through positioning tracking and force feedback; the training evaluation and feedback module quantifies operation performance and provides guidance; and the system management and iteration module guarantees operation and supports data updating and optimization. According to the method, a high-reality scene is constructed through multi-modal fusion, interaction and multi-dimensional evaluation help skill improvement, dynamic difficulty and personalized paths are adaptive to different users, collaborative training and privacy data collaboration are supported, operation training standardization is promoted, and the risk of errors in an operation is reduced.

The invention discloses a wireless authentication and privacy infrastructure (WAPI) seamless roaming optimization system and method for a mobile scene. Data collaboration and intelligent decision are realized through a three-layer structure of a mobile terminal, a WAPI access point and a switching control center. A mobile terminal collects a motion state and channel information in real time, a WAPI access point monitors a wireless condition in an area while meeting a WAPI security authentication requirement, a switching control center integrates data of all parties, a candidate access point utility value is dynamically calculated through a built-in strategy decision unit by adopting reinforcement learning, cognitive radio and robust control technologies, and the candidate access point utility value is calculated through a wireless network. Therefore, optimal roaming switching is realized. A pre-authentication and certificate caching mechanism is introduced into the system before switching, so that the authentication time delay is greatly reduced, and stable network connection and seamless transition during high-speed movement are ensured. According to the invention, the structure is simple, the adaptability is high, the WAPI security requirement is met, and the network roaming performance in a mobile scene is remarkably improved.

The invention relates to the technical field of digitization, in particular to a digitization transformation project operation system and method.The digitization transformation project operation system comprises a data intelligent core layer, an elastic infrastructure layer, a man-machine cooperation layer, a safety compliance layer and a value operation layer, and the data intelligent core layer is used for achieving cross-business-domain data management and AI model full-life-cycle management; the data credibility and the model practicability are ensured; according to the method, the defects of cross-service domain data rights and responsibilities disorder and insufficient sensitive information protection due to adoption of a centralized architecture in traditional data governance, and resource waste caused by AI model development and operation and maintenance splitting are overcome; according to the scheme, the service domain sovereignty is divided through the distributed data governance framework, the field-level authority control is realized in combination with the dynamic desensitization technology, and the AI model full-life-cycle management platform is established, so that efficient collaboration of cross-domain data and automatic recovery of model resources are realized, the data credibility and the model practicability are remarkably improved, and the risk of data abuse is reduced.

The invention relates to the technical field of structure health monitoring, in particular to a wharf structure state judgment method based on time sequence image and vibration data collaboration, which comprises the following steps: synchronously acquiring a time sequence image set and a vibration signal set of a wharf structure, and generating an original double-source data set with time-space alignment; respectively extracting a structure displacement sequence and a vibration mode parameter set through sub-pixel displacement tracking and variational mode decomposition; fusing the two to construct a spatial-temporal characteristic matrix, introducing a temperature field and a water level height to construct an environmental interference compensation factor, performing characteristic enhancement and standardization processing, and outputting a damage sensitive matrix; constructing a hybrid discrimination model by using a physical mechanism constraint layer fusing a structural mechanical control equation and a deep learning classification layer formed by one-dimensional convolution-LSTM, and outputting a structural damage probability value; the method has the advantages of space-time cooperative sensing, environmental interference robustness, high damage discrimination precision, high result interpretability and the like, and is suitable for real-time monitoring and damage identification of a wharf structure in a complex marine environment.

The invention discloses a living body target intelligent identification method based on multi-mode heterogeneous data collaboration, and particularly relates to the technical field of target intelligent identification. Multi-mode heterogeneous data are acquired in real time by using multiple types of sensors, and high-precision timestamps of all modes are recorded to construct multi-mode asynchronous mismatch coefficients; the time sequence mismatch risk caused by collection and transmission asynchronization is dynamically recognized, a cross-modal similarity matrix is constructed, joint modeling is conducted on the cross-modal similarity matrix and a modal feature significance conflict area to calculate a cross-modal feature conflict enhancement coefficient, the enhancement effect of cross-modal feature conflicts is quantitatively evaluated from the feature level, and the time sequence mismatch risk caused by collection and transmission asynchronization is evaluated. The cross-modal feature conflict enhancement coefficient and the multi-modal asynchronous mismatch coefficient form a complementary output multi-modal fusion risk assessment index, the stability and risk level of the current multi-modal fusion state are quantified, and the potential time sequence and feature collaborative failure risk in the fusion process is accurately reflected; and dynamically adjusting the fusion weight of each mode based on a risk assessment result.

The invention discloses a high and cold slope disaster body segmentation method fusing optical and SAR images, and the method comprises the following steps: 1, carrying out the snow coverage normalization processing based on the visible light green light wave band and the short wave infrared wave band of an optical image, and generating an ice and snow mask through an expectation maximization algorithm and a Gaussian mixture model; 2, preprocessing the optical image and the SAR image; step 3, constructing a high and cold slope disaster body segmentation model based on a Transform network; 4, training the segmentation model by using Dice loss and cross entropy loss in a combined manner, and freezing parameters of an image encoder in the training process; and step 5, inputting the high and cold slope remote sensing image into the trained segmentation model, and outputting a segmentation result of the slope disaster body by the segmentation model. According to the method, the problems of large ice and snow interference and poor multi-source data collaboration in high and cold slope disaster body segmentation are solved, and the segmentation precision and the engineering applicability are improved.

The invention discloses a multifunctional modularized track comprehensive detection system, which relates to the technical field of track detection, takes a lightweight moving trolley as a carrier, and integrates a system body, a positioning module, six multifunctional detection modules and a data processing module, and the system body is used for providing a walking platform, power supply and operation control. Control and synchronization of different function detection modules are realized through differential pulse signals; the positioning module is used for realizing position alignment of module data in the system; the multifunctional detection module covers visual inspection, track geometric parameter detection, steel rail profile detection, steel rail crawling displacement detection, turnout area detection and high-precision fastener detection; and the data processing module is used for processing the multi-source detection data and outputting a detection result and a maintenance suggestion. The problems that traditional equipment is single in function, low in detection efficiency and accuracy, high in manpower dependence and poor in data collaboration are solved, and scientificity and efficiency of track maintenance are improved.

The invention relates to the technical field of automobile charging station load systems, in particular to a new energy automobile charging station dynamic load optimization integration system and method based on multi-source data collaboration and elastic compensation. According to the system, charging pile power, meteorological data and user behavior information are fused in real time through a multi-source time sequence data set construction module, an LSTM neural network optimized by an improved sparrow algorithm is adopted to predict the remaining power capacity of a charging station, and Bootstrap resampling is combined to generate a confidence interval to realize safety boundary control; the self-adaptive firefly algorithm dynamically adjusts the charging pile power, establishes a multi-dimensional priority queue including SOC emergency degree, compensation willingness and social value, and guides users to charge in a peak shifting manner; the system comprises a 5G communication gateway, an IGBT power control module and a three-level early warning mechanism, and millisecond-level flexible adjustment of charging power is realized. The utilization rate of the transformer is improved to 91%, the peak-valley load ratio is reduced to 1.7: 1, the user waiting time is shortened by 63.5%, and a dynamic load management system of power grid-station-user collaborative optimization is formed.

The invention discloses a private data security sharing method based on federal learning, particularly relates to the field of private data security protection and sharing, and is used for solving the problems of insufficient model credibility and lack of precision control of data exchange in the existing cross-mechanism data collaboration process. According to the method, homomorphic encryption processing is carried out on local data of all participants, a ciphertext verifiable calculation task is constructed in combination with federated learning, forward propagation and back propagation calculation of a ciphertext are carried out by outsourcing calculation nodes, credible verification is carried out on a calculation process by utilizing zero-knowledge proof, and gradient aggregation and model updating are completed in a ciphertext domain; after model training is completed, based on federal learning model output, intelligent judgment is conducted on the sharing value of local data samples, ciphertext re-encryption and data exchange control are driven, multi-party safe and controllable data circulation is achieved, and therefore on the premise that privacy safety and compliance requirements are guaranteed, cross-mechanism data collaboration efficiency and decision-making precision are improved.

The invention provides a heterogeneous model knowledge migration method based on federated learning. The method comprises the following steps: a data collaborative preparation stage; screening local data, performing multi-dimensional labeling, and performing cooperative training and federal modeling preparation on a plurality of participants; a privacy initialization stage; privacy protection processing is carried out on local data, and local model parameters are initialized; a self-learning stage; each participant completes localized updating of the model parameters by using the local data set; a mutual learning stage; the server extracts valuable knowledge fragments from the knowledge representations uploaded by the participants through the super network for mixing, and updates model parameters of the participants; a server-side super network module is updated and iteratively cycled; and the server side aggregates the valuable knowledge fragments, updates the super network parameters and then returns to the step S3, and loop iteration is carried out until the model converges. The problems that computing resources of participants are limited, generalization is insufficient due to model heterogeneity, and a knowledge migration technology cannot adapt to a heterogeneous model architecture are solved.

The invention discloses a chromatographic data optimization processing method for a liquid chromatograph, and relates to the technical field, and the method comprises the following steps: collecting an original signal flow, carrying out preliminary peak detection, and generating an original peak list; extracting modal exclusive characteristics of each chromatographic peak; converting the modal exclusive features into a unified multi-dimensional semantic feature vector; calculating a time interval weight, and calculating a semantic relevancy weight based on the multi-dimensional semantic feature vector; correcting the time interval weight, generating a network edge weight, and constructing a peak correlation topology network by taking the multi-dimensional semantic feature vector as a node; identifying node clusters connected with high edge weights, and aggregating the node clusters into candidate compound entities; tracking a node signal intensity change track, and adding a conflict mark; conflict resolution arbitration is carried out on the candidate compound entities with the added conflict marks, dominant detector evidence is output, and an analysis report is generated. According to the method, the problems of poor data collaboration of multiple detectors, inaccurate peak identification and association and isolated data processing flow are effectively solved.

The invention discloses a geographical intelligent data base station, which comprises a data sensing layer, a communication transmission layer, a management and calculation layer and a storage layer, and relates to the technical field of data communication. According to the geographical intelligent data base station, the intelligent data base station is arranged, the function boundary of a traditional base station is broken through, data localization storage, preprocessing and multi-modal analysis are achieved at the hierarchy of the base station, real-time decision feedback and data collaboration between distributed nodes are supported, and through a built-in self-adaptive algorithm, the intelligent data base station is obtained. Different scene requirements can be dynamically matched to complete tasks such as predictive analysis, anomaly detection and intelligent optimization, closed-loop data processing ecology is formed, response efficiency and system robustness are remarkably improved, infrastructure-level support is provided for intelligent internet-of-things application in a complex environment, various environmental factors are associated to establish an evaluation model, and the evaluation efficiency is improved. Data analysis and resource allocation are efficiently realized, and an execution strategy is timely generated for regulation and control.

The invention discloses a building industry multi-subject collaborative full life cycle carbon emission management system and method, and relates to the technical field of building industry carbon emission management. The system comprises a carbon perception and data collaboration module, a carbon simulation and deduction module, a carbon optimization and collaborative regulation and control module, a carbon accounting and auditing traceability module and a carbon decision and knowledge generation module. Multi-source heterogeneous data is converted into a carbon flow unit with time, space and responsibility subject attributes, data checking, locking and compliance sharing are achieved by means of a multi-subject data collaboration pool, carbon data barriers among all responsibility subjects are broken through, the problems of fragmentization of carbon data collection and lack of collaboration are solved, and the data collection efficiency is improved. And meanwhile, the carbon accounting and audit traceability module completes multi-dimensional accounting based on the carbon flow unit, and establishes a digital carbon chain pointing to an original data source and a responsibility subject, so that full-link traceability of carbon emission data is realized.