498 results about "Information silo" patented technology

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention discloses an order-driven cross-factory collaborative production system, and relates to the technical field of intelligent manufacturing and supply chain collaboration, and the system obtains the productivity data, logistics cost and tax policies of a plurality of production bases such as Ningbo, Thailand and America in real time, and carries out the intelligent splitting and distribution of orders through a multi-base productivity game algorithm. And dynamic optimal matching of the order and the productivity is realized. Meanwhile, the system integrates WMS inventory data and third-party logistics real-time quotation, a transportation scheme with the lowest total cost is generated by adopting a genetic algorithm, and cross-border logistics and tax expenditure are remarkably reduced. The system overcomes the problems of information isolated island, response lag, extensive cost control and the like in traditional multi-factory production, realizes global productivity collaborative optimization and supply chain integrated intelligent decision, and improves the enterprise order performance efficiency and the overall resource utilization rate.

The invention provides an enterprise financial comprehensive management system based on an intelligent technology, and relates to the technical field of financial management. The accounting intelligent subsystem is used for acquiring financial data, performing semantic analysis and conversion on the financial data through a preset rule and a multi-stage data mapping mechanism, generating a standardized accounting service instruction and issuing the standardized accounting service instruction to an automatic process engine, thereby automatically generating a financial document meeting an accounting criterion; the financial management subsystem is used for integrally realizing execution of backlog management, compliance guidance manual query and financial data structured storage according to a preset standardized process; and the business service subsystem is used for providing tax-related business service, asset management service, fund management service and operation management service processing and guidance for enterprises through modular design. The invention aims to improve the operation efficiency, reduce the error rate, break information islands, realize effective integration and utilization of resources, and provide powerful support for decision making of enterprises.

The invention provides a multi-park energy consumption prediction scheduling method based on digital twinning and a control system, and systematically solves the problem of the pain point of multi-park energy consumption management by constructing a technical chain from data perception to closed-loop optimization. The method comprises the following steps: firstly, by constructing a global unified digital twinborn model, standardized integration of dispersed and heterogeneous park assets and data is realized, and the problem of information islands is solved; secondly, prediction is carried out by adopting federated learning, cross-park knowledge sharing and joint modeling are realized on the premise of ensuring data privacy and security of each park, and the prediction precision of a single park under the condition of limited data is remarkably improved; and finally, through a'prediction-decision-execution-update 'closed-loop process, traditional passive and static energy consumption management is converted into active and dynamic prediction scheduling, so that the energy consumption peak can be stabilized prospectively, the energy distribution can be optimized, and the comprehensive energy consumption cost and carbon emission can be effectively reduced.

The invention relates to the technical field of supply chain management, in particular to a digital visual management system and method applied to a building material supply chain. The method comprises the following steps: acquiring real-time position data, material attribute data and time sequence node data; performing data preprocessing and integration on the real-time position data, the material attribute data and the time sequence node data to obtain standard building material supply chain data; obtaining a construction drawing; basic building materials of the standard building material supply chain data are extracted based on the construction drawing, wherein the basic building materials comprise concrete, reinforcing steel bars and building blocks; building pouring abnormity monitoring is carried out based on the concrete, the steel bars and the building blocks, and pouring abnormity monitoring data is generated. Through multi-source data integration, ternary coupling analysis, scheduling optimization and three-dimensional dynamic visualization, the problems of information island, scheduling conflict and low management efficiency of the building material supply chain are solved, and intelligent, efficient and transparent management of the supply chain is realized.

The invention relates to the technical field of the Internet of Things, and discloses a campus smart life terminal comprehensive management and control system based on big data, and the system comprises a data collection module, a big data analysis module, a comprehensive management and control module and an execution feedback module. Operation data of different systems are integrated in real time, cross-platform data sharing and consistency of a canteen, an access control and an energy terminal are guaranteed, the problem of information isolated island caused by independent operation of terminal data is avoided, meanwhile, association analysis is carried out on collected environmental factors and user behavior information, the risk of terminal operation abnormity and resource conflict can be found in real time, and the user experience is improved. The method is advantaged in that high efficiency and reliability of terminal cooperation management are guaranteed, fault response delay is further reduced, a shunting scheme is automatically generated when sudden demand increase occurs, a condition of student waiting time prolonging is prevented, and dynamic adaptive capability and service quality of the campus life terminal are guaranteed.

The invention belongs to the technical field of engineering progress tracking and early warning, and particularly discloses an AI-based engineering progress tracking and early warning method and system. According to the method, the problem of information islands in multi-modal data integration of a traditional method is solved by constructing the engineering space-time knowledge graph, and the comprehensiveness of construction site state sensing is improved; an intelligent mechanism of deviation analysis and trend deduction is established, and accurate prediction of the engineering state evolution trend is realized through multi-dimensional comprehensive evaluation of geometric morphology, construction process topology, resource in-place time sequence and quality compliance attributes; through structured early warning generation and multi-level risk conduction path modeling, the risk source and the influence range thereof are clarified, and a subsequent construction scheme is optimized in combination with a dynamic adjustment strategy, so that the scientificity and execution efficiency of project progress management are improved.

The invention relates to the field of traffic control, in particular to an intelligent network connection inductive control platform for road traffic safety facilities. The traffic data acquisition module is used for acquiring a data flow of a traffic sensor and outputting traffic data with a traffic data source identifier through an identification technology; the traffic data processing module is used for obtaining standardized traffic parameters according to a traffic data source identifier adaptive analysis protocol; evaluating a data reliability index according to the index system; the situation fusion module is used for forming a vehicle driving track through a graph neural network, generating a global traffic situation map by using a data reliability index, and constructing a traffic network digital twinborn model; and the decision and control module is used for analyzing traffic states from the traffic network digital twin model, predicting traffic events and collision risks and generating traffic control instructions. According to the platform, through the equipment fingerprint and protocol reverse technology, an information island is broken, the comprehensiveness and high credibility of a data source are ensured, and the road traffic safety and passing efficiency are remarkably improved.

The invention discloses an automobile part multi-source fusion tracing method and system based on a distributed block chain. Firstly, a block chain network and a tracing platform are constructed; a microscopic visual feature image of the surface of the automobile part is collected, the texture type is judged, features are extracted, electromagnetic field intensity distribution features are extracted in combination with an RFID tag, and electromagnetic fingerprints are recognized through a neural network; secondly, multi-source feature data and production and transportation information are bound with unique identifiers and then stored in a distributed block chain network, and a five-layer Merkel tree storage structure is adopted to ensure that the data cannot be tampered; and finally, constructing a tracing query module of a three-layer architecture, and automatically managing data writing and query through an intelligent contract. According to the invention, through combination of multi-feature fusion and the block chain technology, the problems of information island and weak traceability of a traditional traceability system are solved, credible traceability and authenticity identification of automobile parts in the whole life cycle are realized, and the supply chain data security and query efficiency are improved.

The invention relates to the technical field related to medical systems, in particular to a medical teaching and research integrated system based on a common bottom layer, and aims to break through information islands among medical treatment, teaching and scientific research and realize closed-loop linkage and dynamic knowledge conversion of a whole process. The system comprises a diagnosis and treatment decision subsystem, a scientific research queue management subsystem and a teaching subsystem, integrates data through a three-in-one data engine, and realizes information sharing and cooperative work. The diagnosis and treatment decision subsystem provides diagnosis and treatment support for severe diseases based on an expert thinking chain. And the scientific research queue management subsystem is responsible for screening cases, sorting data, implementing quality control and statistical analysis, and adjusting an expert thinking chain according to scientific research results. And the teaching subsystem carries out teaching by using a standardized case presentation mode. Through a feedback closed loop of clinical discovery, scientific research verification, knowledge updating and teaching feedback, rapid propagation and application of medical knowledge are promoted, and the medical service quality and the medical education level are improved.

The invention relates to a power grid project multi-dimensional digital supervision method based on big data, and belongs to the technical field of power grid project digital supervision, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-dimensional data, and constructing a supervision system through four supervision layers and a digital twinborn body; s2, multi-dimensional supervision indexes are intelligently calculated, and a supervision dimension index system is constructed; s3, risk dynamic early warning and root cause tracing; and S4, decision optimization and closed-loop control. According to the method, multi-source heterogeneous data integration is carried out by fusing multi-dimensional data sources of BIM model, GIS positioning, Internet of Things sensing data, environment monitoring and business system data, so that single data source deviation is avoided, information islands are broken, multi-target collaborative optimization is realized, a'supervision-decision-execution 'closed loop is realized, an optimal regulation and control strategy is convenient to calculate, and the system performance is improved. An optimal regulation and control strategy can be generated based on the reinforcement learning model, and an early warning instruction is directly connected to a field terminal, so that the power grid project monitoring and early warning efficiency can be effectively improved.

The invention belongs to the technical field of medical material management, and particularly relates to a medical material intelligent supply chain management scheme and system based on multi-modal AI. According to the invention, through multi-modal data fusion, full-life-cycle and full-link tracking of materials is realized, information islands are eliminated, management transparency is improved, unexpected consumption and physical damage risks are identified in an early stage, a large amount of overstock or clinical supply interruption is avoided, operation cost is reduced, and based on a dynamic loss threshold, replenishment and cross-warehouse allocation can be realized just in need. The inventory turnover rate and the supply guarantee rate are considered, environment monitoring and validity period information are processed, it is ensured that temperature and humidity sensitive materials are stored under the optimal condition, losses caused by expiration or environment abnormity are reduced, the waste rate and the excessive stockpiling risk are comprehensively reduced, meanwhile, timely availability of key materials is clinically guaranteed, and the service life of the materials is prolonged. And the economic benefit and safety of the medical material supply chain are improved.

The invention provides a digital twin smart park security and protection management system based on artificial intelligence, and relates to the field of security and protection management, and the system comprises a multi-mode sensing network which is used for collecting park environment data through a multi-mode sensor network, and carrying out the preprocessing of the data, and obtaining an environment data set; the digital twin modeling module is used for constructing a park three-dimensional virtual model based on the environment data set; the semantic alignment module is used for defining a dynamic security semantic rule based on the three-dimensional virtual model and carrying out semantic alignment of multi-modal features on the environment data set; the threat assessment module is used for performing threat assessment on the aligned semantics based on a knowledge graph and a Bayesian network; and the resource scheduling execution end is used for generating a disposal plan according to the evaluation result and realizing security response through an execution terminal. The method is used for solving the problems of information isolated island, response lag, high false alarm rate, fuzzy situation awareness and the like of a park security system in the prior art.

The invention discloses a multi-modal big data intelligent cleaning and fusion method based on a knowledge graph and deep learning, and relates to the technical field of artificial intelligence and multi-modal data processing. According to the method, a cross-modal semantic similarity matrix is constructed through a multi-modal semantic association mining algorithm, the problem that in a traditional method, an effective semantic association mechanism is lacked between modal data is effectively solved, the quality and consistency of the data are remarkably improved, and a hierarchical feature extraction and weighted fusion strategy is further adopted, so that the method is more efficient and efficient. The multi-modal data is subjected to feature extraction by using a convolutional neural network and a Transform model, and the fusion weight of each modal feature is dynamically adjusted through an attention mechanism, so that the information island phenomenon is effectively solved, the quality and effectiveness of the fusion feature are remarkably improved, and through a semantic consistency verification mechanism and a continuous learning updating mechanism, the accuracy of the feature fusion is improved. High quality of fusion features is ensured, the model can adapt to new data in real time, and adaptability and expansibility of the model are remarkably improved.

The invention discloses an intelligent prediction and transaction management system for carbon quotas and carbon assets, and relates to the technical field of carbon transaction systems. The intelligent prediction and transaction management system for the carbon quota and the carbon assets comprises a carbon transaction mode portrait construction module, a dynamic module matching and initializing module, a multi-source data fusion and dynamic updating module, a hybrid prediction model operation module and a dynamic transaction strategy generation and optimization module. Through cooperative work of the five modules, enterprise carbon transaction whole-process intelligent management is realized. The portrait construction module accurately classifies enterprise modes and provides a basis for dynamic matching; a module library and a parameter initialization mechanism ensure strategy customization flexibility and deployment efficiency; the multi-source data fusion module breaks an information island and improves the input quality of a prediction model; outputting a multi-time scale prediction result and a risk index by the hybrid prediction model in combination with a statistics and machine learning method; and the dynamic strategy module automatically generates and optimizes a transaction strategy according to the prediction result and the enterprise risk preference.

The invention discloses a multi-source information real-time identification and positioning method for a coal mine rock burst risk area. The method comprises the following steps: integrating a micro-seismic system, a rock noise system, a stress system and a geological system to construct an acquisition network; extracting multi-source characteristic indexes and carrying out space-time unification; establishing a dynamic weighted risk fusion model to calculate a comprehensive risk index RI of a three-dimensional grid unit, wherein the weight of the comprehensive risk index RI is dynamically and adaptively adjusted along with mining; based on the RI value, a high-risk area is identified and positioned through three-dimensional visualization, and graded early warning information is generated; according to the invention, a time-space synchronous multi-source information acquisition network is constructed, and uniform preprocessing and feature extraction are carried out on four types of data of microseism, rock noise, stress and geologic structure, so that deep fusion and collaborative analysis of multi-source information are realized, and an information island phenomenon existing in traditional monitoring is overcome; and the judgment on the rock burst risk is more comprehensive.

The invention discloses a multi-modal data retrieval method and device under dynamic collaboration and a medium, belongs to the technical field of information retrieval and natural language processing, and is used for solving the problems that unstructured data is difficult to process efficiently in traditional data retrieval, an information island phenomenon exists, and the data retrieval efficiency is low. And an effective connection and integration mechanism is lacked between data from different sources. The method comprises the following steps: performing task allocation processing on a corresponding Agent cluster on a question type to be queried to obtain an Agent task list; performing data query processing on the Agent task list under different data sources, and obtaining query result data; performing data primary processing on the query result data to determine initial retrieval data; performing multi-modal consistency check on the initial retrieval data, and integrating verified integrated retrieval data; and performing information strategy generation processing based on a fact verification mode and an explanation enhancement mode on the integrated retrieval data to obtain final answer information.

The invention relates to the technical field of water conservancy project construction, and discloses a water conservancy project construction intelligent inspection method and system based on an unmanned aerial vehicle, and the method comprises a route planning step, an inspection preparation step, a data collection step, a data recognition step, a data uploading step and a closed-loop management step. The system corresponds to the method. According to the invention, through full-automatic inspection of the unmanned aerial vehicle, full-coverage imaging and special detection are carried out on construction key parts and risk areas, so that no-dead-corner inspection of the construction areas is realized; defects are automatically recognized based on an AI recognition system, positions are marked and graded, an inspection log is generated, and the defect recognition efficiency and accuracy are improved; secondly, the ground terminal uploads the data to a supervision platform, the supervision platform carries out filing according to construction nodes and generates a closed-loop tracking list, and an information island is broken; therefore, the supervision accuracy, timeliness and traceability are improved, the visual, quantifiable and traceable supervision requirements of the construction period of the water conservancy project are met, and the construction quality and safety are guaranteed.

The invention relates to the technical field of cross-border trade settlement, and discloses a cross-border trade settlement risk prevention and control system based on a block chain smart contract, and the system comprises a regional network which serves as a distributed account book infrastructure and is used for storing and sharing the transaction data of all participants; the cross-border trade settlement risk prevention and control system comprises a block chain intelligent contract, a trusted data acquisition and uplink module used for acquiring and verifying off-chain entity trade data and uploading the verified trusted data to a local area network, and the off-chain entity trade data comprises logistics information sensed by Internet of Things equipment. The problem of traditional cross-border trade settlement pain points can be effectively solved, and fraud is prevented and controlled in the whole process by means of dual verification and intelligent contracts; a sharing platform is constructed through the block chain, an information island is broken, the settlement efficiency is greatly improved, and data security and traceability are guaranteed.

The invention discloses a building reconstruction and reinforcement whole process monitoring method, device and equipment and a medium, and relates to the field of building reconstruction. According to the method, a digital twinborn-driven closed-loop monitoring system is constructed to link all parties, firstly, a dynamic digital twinborn body is constructed through multi-source data fusion, and macroscopic-microscopic two-dimensional reference data is collected; secondly, a differentiated monitoring scheme is cooperatively designed, and a physical and data-driven dual early warning model is established; the construction stage depends on a perception-transmission-platform-application four-layer system, multi-parameter real-time monitoring, four-level intelligent early warning and linkage regulation and control of the construction process are achieved, the problems of traditional monitoring fragmentation, early warning lag and information isolated island are solved, data of design, construction and monitoring parties can be effectively linked and combined, and the construction efficiency is improved. All parties are pushed to make accurate dynamic decisions under structural state changes generated by construction disturbance, full-period intelligent management and control are achieved, engineering safety and quality controllability are improved, and the method is suitable for various existing building reconstruction and reinforcement projects.

The invention discloses an intelligent project implementation management system and method based on multi-source perception fusion, and aims to solve the problems of information isolated island, response lag, experience dependence, extensive management and control and the like in an engineering project. According to the system, multi-source data acquisition is carried out through an Internet of Things sensor, a video monitor, a mobile terminal, a BIM / GIS, a project management platform and the like, and after space-time alignment and standardization processing are carried out, a unified situation awareness view is constructed by adopting a hierarchical fusion strategy; performing real-time intelligent evaluation and prediction on progress, quality, safety, cost and resources based on the fused data; a rectification, scheduling or early warning instruction is automatically generated, and a sensing-analysis-decision-execution closed loop is realized by pushing and linking a field execution system through multiple channels; according to the method, the safety prevention and control, quality control and progress performance capabilities are remarkably improved, the accident rate and cost are reduced, and project management is promoted to be transformed and upgraded to a refined, active and self-evolution direction.

The invention discloses an earthquake service integrated management method, and the method can achieve the deep mining of a complex internal relation between historical earthquake service associated data and the final number of casualty population through the powerful nonlinear fitting capability of deep learning. The prediction precision of the casualty population prediction model trained through a large amount of historical earthquake service associated data and the corresponding casualty population quantity is far higher than that of a traditional method, a reliable basis is provided for scientific decision making, unified data assets with an earthquake event as a core are constructed through associative storage of a prediction result and original service data, and the prediction accuracy of the casualty population prediction model is improved. Therefore, information islands are broken, disaster assessment results are deeply integrated into the whole earthquake service process, and a unified and authoritative data entry is provided for subsequent work such as emergency command, resource scheduling, information issuing and post-disaster assessment.

The invention discloses a building material planning management system based on a BIM technology, and relates to the technical field of BIM, and the system comprises the steps: a data collection module collects the full-process multi-source data of materials from purchasing to using; the unified coding module distributes a unique electronic identification code containing a category, a supplier and a full-process traceability index to each building material; the data storage and processing module cleans and converts the data and forms a standardized full-life-cycle database; the BIM integration module establishes one-to-one correspondence between material entities and BIM model components through electronic identification codes, and full-process data tracing calling and circulation state visualization are achieved; the interaction module provides a multi-terminal interface to support cooperation of multiple participants. According to the invention, the problems of information isolated island and traceability fault of traditional material management are solved, the management and control efficiency and collaboration are improved, and cost reduction and benefit increase of the construction project are facilitated.

The invention belongs to the technical field of bidding document auditing, and provides a multimodal bidding document intention anchoring and rule pluggable auditing method and system in order to solve an auditing blind area caused by an information island. The intention anchoring and rule pluggable auditing method for the multi-modal bidding document comprises the following steps: carrying out analysis and element splitting on an original bidding document file; generating a document structure tree; performing cross-modal information alignment and consistency verification on the document structure tree; analyzing the auditing intention into a structured instruction packet containing a context and guiding subsequent operation, traversing the checked document structure tree to generate a hierarchical outline with a type label, injecting the hierarchical outline and the structured instruction packet together as the context into a cue word, and anchoring the auditing intention based on cue word guidance and closed-loop verification; and calling a rule engine and starting the rule engine to execute auditing operation according to the anchored auditing intention. The bidding document auditing system can improve auditing quality and quickly adapt to changeable business rules and bidding document auditing requirements of different industries and different types.

The invention discloses a transient-time varying-based transformer anti-short circuit capability dynamic checking method and system. According to the method, a high-fidelity digital twinborn model is established by constructing a four-layer database comprising a foundation, a material, a structure and operation parameters. And performing transient electromagnetic-structure coupling simulation by using the reconstructed real short-circuit current waveform driving model to obtain transient electromagnetic force, dynamic deformation and stress distribution prediction results under short-circuit impact. Meanwhile, a multi-axis degradation model covering mechanical, electrical and thermal dimensions is constructed, long-term evolution of equipment is quantified, conventional working conditions are converted into equivalent short-circuit impact times, and a comprehensive capability index is calculated. Finally, the transient impact risk and the long-term degradation degree are coupled, a four-quadrant risk assessment matrix is constructed, a comprehensive risk quantitative index is calculated, and dynamic grading and accurate positioning of the overall risk state of the transformer are achieved. According to the method, the problems of isolated island and single risk dimension of traditional static check information are solved.

The invention discloses an optical fiber sensing-Beidou fusion dam safety multi-dimensional early warning system and method and application. The method comprises the steps that Beidou high-precision three-dimensional coordinate time sequences of key measuring points of a dam body and a near dam, optical fiber continuous strain fields / temperature fields (distributed) in the dam body, strain / temperature point values (quasi-distributed) of key parts and basic static information are collected; unifying a geodetic measurement coordinate system and NTP / PTP time reference, and preprocessing data; on the basis of a dam refined FEM assimilation engine, Beidou displacement is used as dynamic constraint, optical fiber data is used as verification observation quantity, EKF correction parameters are used for inverting a global stress-strain field, and Bayesian optimization is carried out to realize model evolution when residual errors are abnormal; and an M-HWI is constructed, four-level threshold values of normal-attention-early warning-alarm are compared, a response is triggered, early warning is pushed, and an abnormal region is visualized. According to the method, information islands are eliminated, the instability process is tracked in a refined manner, and the early warning accuracy, the diagnosis globality and the long-term monitoring reliability are improved.

The invention discloses a fan alarm intelligent processing method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the duplicate removal and screening of an original alarm data set of a wind turbine generator, and obtaining a fault alarm set; classifying the fault type alarm set according to an affiliation system, and performing time aggregation on fault type alarms under each subsystem to form a plurality of alarm event clusters; for all the alarm event clusters, performing retrieval and reasoning based on a pre-constructed knowledge graph and a historical processing case library, and generating a fault type alarm sequence; retrieving a plurality of measuring point variables corresponding to each fault alarm based on the knowledge graph, and respectively calculating a time sequence statistical feature set of the monitoring data corresponding to each measuring point variable; and performing reset condition reasoning analysis on each fault type alarm in the fault type alarm sequence, and generating a reset operation suggestion for each fault type alarm. According to the method, the problems of artificial experience dependence and information isolated island can be overcome, and the automation level and decision accuracy of fan alarm processing are improved.

The invention discloses an assembly type maritime work component management system based on BIM. The system comprises a BIM model management module, a component production management module, a component transportation management module and a component installation management module. The BIM model management module is used for creating, importing, updating and visualizing a BIM model of the assembly type maritime work component; the invention relates to the technical field of maritime engineering management. According to the BIM-based fabricated maritime work component management system, through a BIM model management module, full-process management is performed on fabricated maritime work components from model creation and introduction in a design stage to model updating and visual display in a construction process. All the modules interact through data interfaces, and a cloud computing architecture and a B / S mode are combined, so that all participants of a project can share information such as the size, the material and the progress of components in real time, an information island is broken, the problem of unsmooth information circulation in a traditional management mode is solved, and the information transmission efficiency and accuracy are greatly improved.

The invention discloses an intelligent monitoring system for multi-source data fusion of a biomass intelligent power plant, relates to the technical field of intelligent monitoring, and solves the problems proposed in the background technology. The biomass multi-source data acquisition module is used for acquiring biomass fuel characteristic data, equipment operation data, environment-friendly emission data and raw material supply chain data in real time; the data preprocessing and order reduction module is used for carrying out denoising, normalization and coupling order reduction processing on the biomass fuel characteristic data and the equipment operation data; the multi-source data dynamic fusion module is used for carrying out weighted fusion on the multi-source data according to the fuel heat value, the water content and the environmental protection weight; the biomass combustion optimization diagnosis module is used for constructing a deep learning model based on the fusion data and outputting combustion efficiency optimization parameters and a fault diagnosis result; according to the method, multi-dimensional data of fuel characteristics, equipment operation, environmental protection emission and the like are integrated, through links of preprocessing, order reduction, fusion and the like, an information island phenomenon is eliminated, and scientificity and accuracy of decision making are improved.

The invention is suitable for the technical field of intelligent property management, and provides an intelligent property management system based on multi-technology fusion, which comprises an infrastructure module, a user access module, a data fusion module, a data support module, an intelligent property module and an intelligent decision module. The infrastructure module collects data through equipment such as a sensor and performs edge calculation preprocessing; the user access module provides a multi-port entrance and adopts unified identity authentication and authorization; the data fusion module integrates multi-source heterogeneous data and constructs a user portrait; the data support module realizes distributed data storage and standardized management; the intelligent property management module integrates multiple management functions and realizes automation of a business process; and the intelligent decision-making module performs fault prediction, demand analysis and the like by using data analysis and machine learning. According to the invention, information islands are broken, the equipment monitoring capability and the service individuation level are improved, data sharing and business collaboration are realized, the management efficiency is improved, the cost is reduced, and intelligent decision support is provided for property management.