414 results about "Data splitting" patented technology

The invention relates to the technical field of construction engineering cost management, and discloses an intelligent engineering cost calculation system based on multi-source heterogeneous data fusion, and the system comprises a multi-source data collection module which is used for collecting structured data and unstructured data from a design file, a market database, a construction monitoring system, a contract document, and a historical project library; and the heterogeneous data fusion module is connected with the multi-source data acquisition module and analyzes the risk terms in the contract text by adopting a natural language processing technology. According to the invention, the multi-source data acquisition module is used for widely collecting data in multiple aspects of design, market, construction, contract and the like, the problems of data splitting and information isolated island in traditional cost management are solved, integration of multi-source heterogeneous data is realized, and the heterogeneous data fusion module utilizes advanced technologies of natural language processing, image recognition and the like, so that the cost management efficiency is improved. Contract texts and design drawings can be efficiently analyzed, the processing capacity of unstructured data is improved, and the error rate and omission rate of manual interpretation are reduced.

The invention discloses an enterprise multi-modal data intelligent processing system fused with an RAG technology and an intelligent processing method of the enterprise multi-modal data intelligent processing system, and relates to the technical field of enterprise-level multi-modal data intelligent processing. And the data processing module is configured to respectively process the structured data and the unstructured data through the dynamic heterogeneous encoder and output unified semantic representation by adopting a cross-modal adversarial alignment mechanism. According to the enterprise multi-modal data intelligent processing system fused with the RAG technology, the problem of enterprise multi-modal data splitting is solved through dynamic adversarial semantic alignment and a stepped fusion mechanism. Semantic gaps are eliminated through self-adaptive convergence of cross-modal features in a hidden space, deep association of heterogeneous data is achieved based on concept mapping and credibility arbitration of an ontology network, key information of unstructured data is accurately extracted and converted into structured knowledge, and the accuracy of cross-modal association analysis and decision reliability are improved.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention discloses a supply chain collaborative management method and system for a whole-industry link platform, and the method comprises the steps: constructing a distributed supply chain data warehouse through employing a block chain and lightweight encryption; generating a multi-dimensional matching degree score table to recommend suppliers; decrypting and storing the encrypted bidding file, and calculating a comprehensive score according to a quantitative index and an expert qualitative index; constructing a hierarchical prediction model, and outputting a three-dimensional prediction result; calculating a supply-demand gap according to a supply-demand elastic coefficient coupling algorithm, automatically generating an order, and synchronizing the order to each end in real time; building a supply chain digital twinborn model, and dynamically planning an optimal transportation route through an intelligent fusion road condition algorithm; early warning information is automatically sent to generate a coping strategy, and full-link operation data are integrated for visual display; the objective of the invention is to solve the problems of existing supply chain data splitting, low supplier matching efficiency, low demand prediction precision, rigid logistics planning, risk monitoring lagging and poor collaboration of all links.

The invention provides a hydropower station AI supervision system and method based on a multi-modal large model, and relates to the technical field of intelligent hydropower. The system comprises a multi-modal data acquisition module, a cross-modal space-time alignment module, a multi-modal feature extraction module, a multi-modal large model processing module and an intelligent reasoning and decision module. A neural differential equation model is introduced to carry out space-time alignment on asynchronous sensing data, networks such as Vision Transformer, MelCNN, TCN and the like are utilized to extract multi-modal features, cross-modal fusion analysis is realized by combining a local and global attention mechanism and dynamic weight distribution, and equipment abnormality is further reasoned based on a reconstruction error, a mahalanobis distance and a knowledge graph and a maintenance strategy is generated. According to the method, high-precision anomaly detection, fault root cause positioning and dynamic maintenance optimization of key equipment of the hydropower station are realized, diagnosis errors caused by traditional manual inspection and data splitting are avoided, and the operation and maintenance intelligence level and the equipment operation reliability are improved.

The invention discloses a geological disaster risk intelligent pre-judgment method based on deep learning, and relates to the technical field of geological disaster monitoring and early warning. According to the method, high-precision alignment and feature extraction can be automatically performed on monitoring data with different temporal-spatial resolutions and different physical meanings, such as optical remote sensing, radar measurement, laser point cloud and the like, uniform and information-rich representations are generated, a solid data foundation is laid for subsequent accurate prediction, and the limitation of data splitting application in a traditional method is overcome; a space-time diagram with slope units as nodes is constructed, an attention mechanism diagram convolutional network with hydrological directivity introduced is utilized, and the model can accurately describe the spatial propagation process that slope substances migrate and accumulate along with a confluence path and block a river channel under the rainfall condition; meanwhile, the time sequence module effectively learns the influence of the past hydrological state on the future evolution trend.

The invention relates to a gold mine air-ground-in-well rapid collaborative exploration method, belongs to the field of geological exploration, and aims to solve the technical problems of data splitting, low target area positioning precision, insufficient deep prediction capability and the like in traditional gold mine exploration. By integrating air remote sensing, surface geological survey and underground detection data, a multi-source data standardization processing flow is established, and a progressive exploration system from wide-area screening to accurate positioning is realized by combining geological, geophysical and geochemical comprehensive analysis methods. The system adopts a dynamic feedback mechanism to optimize an exploration process, forms closed-loop analysis and prediction, improves exploration efficiency and accuracy, and reduces resource waste and exploration cost. The method is suitable for gold ore resource exploration under complex geological conditions, has remarkable economic benefits and application value, and can be widely applied to the field of mineral resource development.

The invention provides a drug identification information efficient query method and system based on distributed cache. The method comprises the following steps: firstly, receiving and analyzing a drug identifier query request to generate a dynamic cache routing key, then positioning a target cache node, then matching a cache data block corresponding to a drug unique code in the target cache node, if matching succeeds, extracting a drug full life cycle record in the cache data block, and sending the drug full life cycle record to the target cache node; if the matching fails, initiating a cross-node retrieval request to the drug block chain database to return drug source data, splitting the drug source data into a core attribute set and an extended track set, and finally executing a dynamic combination operation on the core attribute set and the extended track set to generate a drug tracing result set and returning the drug tracing result set to the user terminal; according to the technical scheme provided by the invention, the query fluctuation defect of a single storage architecture is eliminated from the source, so that the whole system simultaneously meets three drug traceability core requirements of high concurrency, cross-mechanism and tamper-proofing.

The invention discloses a coal mine underground equipment personnel collaborative safety monitoring and risk early warning method, which comprises the following steps: deploying a sensing network, and collecting and processing personnel data and equipment data; evaluating the health state of the equipment based on the equipment data; cleaning the personnel data and then constructing a trajectory model; performing risk coupling analysis based on the three-dimensional risk identification model to obtain a comprehensive risk value; and performing grading response based on the comprehensive risk value and judging whether to perform model optimization or not. The invention further discloses a coal mine underground equipment personnel collaborative safety monitoring and risk early warning system which comprises a sensing layer, the sensing layer is connected with an analysis layer through a network layer, the analysis layer is connected with an execution layer, and the execution layer is connected with the sensing layer. The invention discloses a coal mine underground equipment personnel collaborative safety monitoring and risk early warning method and system, solves the technical bottlenecks of data splitting, response lag and poor environmental adaptability of a traditional monitoring system, and reduces the accident false alarm rate by more than 35% through spatial coupling analysis.

The invention relates to the field of emerging software and information technology service, in particular to an artificial intelligence-based application system integrated management method, which comprises the following steps of: analyzing an API (Application Program Interface) log, structured data, an unstructured document, business work order data and business image data by pre-training a large model, and generating a unified semantic representation in combination with multi-modal contrast learning; a knowledge distillation technology is adopted to migrate the large model capability to a lightweight neural network, and a domain knowledge base is generated based on fine tuning of business work order data; a user multi-mode instruction is analyzed, a DAG service agent template library is matched, and an execution chain is dynamically combined based on a reinforcement learning strategy, so that text processing, multi-system collaborative reasoning and RPA service process automation are realized; and collecting an execution log, and updating lightweight network parameters and an intelligent agent strategy library through an online distillation algorithm to form a closed-loop optimization mechanism. According to the method, the service response efficiency can be improved, and the problems of enterprise multi-service system data splitting and process stiffness are solved.

The invention discloses a building engineering multi-dimensional monitoring system based on panoramic imaging, and relates to the technical field of building engineering monitoring and sensing. Comprising a panoramic image acquisition module, an image splicing and panoramic reconstruction module, a multi-source data synchronous acquisition module, an image-data fusion construction module, an image-physical association modeling module and a risk identification and intelligent response module, an image acquisition array formed by high-resolution cameras is installed at key positions of a building structure, images of all the cameras are obtained, and an original image sequence covering a monitoring area is output. According to the invention, through fusion of the panoramic image and the multi-source sensing data, visualization, intelligent identification and risk prediction of the structure state are realized, a closed-loop monitoring system is constructed, the problems of single traditional monitoring means, data splitting and the like are solved, and the safety monitoring intelligent level of constructional engineering is improved.

The invention relates to the technical field of rural space governance and intelligent decision making, in particular to a rural space planning method and related equipment, and the method comprises the steps: obtaining multi-source data of a target rural, the multi-source data comprising land data, population data and rural economic data of the target rural; performing fusion processing of space-time alignment on the multi-source data to obtain fusion data; constructing a spatio-temporal knowledge graph based on the fused data, and extracting entity attributes and spatial relationships; and inputting the entity attributes and the spatial relationship into a pre-trained planning optimization model, iteratively optimizing a rural space matching strategy according to rigid constraints, and outputting an optimal planning result, the technical problems that in the prior art, rural planning faces data splitting, element analysis isolation, planning scheme staticization and actual development requirements are disjointed, the decision making process depends on subjective experience, and economic-ecological benefit tradeoff is difficult to quantify are effectively solved.

The invention discloses an electronic medical record intelligent coding method, device and system, and a storage medium. The method comprises the following steps: respectively carrying out entity extraction, feature coding and homomorphic encryption transmission on an electronic medical record text, a medical image and structured inspection data; according to the preprocessed data, performing multi-modal feature alignment, ICD semantic mapping and real-time medical insurance rule injection through a dynamic knowledge fusion coding engine; generating a three-dimensional visual evidence chain of the text evidence, the image evidence and the policy evidence through the interpretable traceability matrix; through a multi-role cooperative workflow engine, generating a code according to a confidence threshold routing AI, and auditing the code to a clinician or a professional coder; rare disease data are synthesized through an adversarial training enhancement module, and policy mutation is simulated for model adaptability training. By adopting the technical scheme of the invention, the problems of multi-modal data splitting, policy lag and non-traceability in the prior art are solved.

The invention discloses a compression method and device for generic semiconductor industry operation and maintenance data, electronic equipment and a medium, and relates to the technical field of data compression. The method comprises the following steps: acquiring to-be-compressed generic semiconductor industrial operation and maintenance data; identifying the time sequence of the generic semiconductor industrial operation and maintenance data to be compressed, and judging whether the generic semiconductor industrial operation and maintenance data is time sequence data or not according to the time sequence; and if the to-be-compressed generic semiconductor industrial operation and maintenance data is time series data, splitting the to-be-compressed generic semiconductor industrial operation and maintenance data into a time data set and a feature data set, and compressing the time data set and the feature data set respectively. According to the compression method, different compression strategies are adopted for different types of data, the compression efficiency of the data is improved, and the balance between the compression rate and the calculation efficiency is optimized.

The embodiment of the invention discloses a security deployment method and device for enterprise-level applications and a medium, and relates to the technical field of security deployment, and the method comprises the steps: obtaining application topological graph data generated by a preset visual development platform, so as to split the application topological graph data into an atomized micro-service unit set through a quantization deconstruction engine, generating a micro-service dependency tree; scanning the target deployment environment, extracting an environment feature fingerprint of the target deployment environment, and generating an environment adaptation layer code according to the environment feature fingerprint; injecting an environment adaptation layer code into each atomized micro-service unit to generate an environment perception type micro-service package, and constructing a cross-cloud communication link matrix based on the micro-service dependency tree; and based on the environment-aware micro-service packet and the cross-cloud communication link matrix, executing a deployment verification test in the sandbox environment, generating a deployment verification report, and according to the deployment verification report, distributing the environment-aware micro-service packet to a target deployment environment node.

The invention discloses an electric power data acquisition control method and device, and a storage medium. The method comprises the following steps: receiving a control instruction; executing a control action according to the type of the control instruction, which comprises any one of the following steps: if the control instruction is a data acquisition instruction, acquiring acquisition data, splitting the acquisition data into a structured data unit by taking a measurement point dimension as a unit, and storing the structured data unit into a task data table; and if the control instruction is a data reporting instruction, obtaining corresponding structured data units in a task data table in batches to generate task data group packets, and reporting the task data group packets to a server. According to the method, integrated fine control of electric power data acquisition, storage and reporting is realized through a measurement point dimension structured data splitting and dynamic packaging mechanism, the data transmission efficiency, the data analysis capability and the acquisition reliability are synchronously improved, and the technical effect of matching of service requirements and storage structures is fundamentally realized.

The invention relates to the technical field of engineering digitization and information, and discloses a water conveyance tunnel BIM model engineering management business application method, which comprises the following steps: carrying out management business carding, and determining an engineering unit hierarchical structure; uniformly coding the data information related to the management service and the hierarchical structure data of the engineering unit to form a service data coding result and an engineering division coding result; creating a water conveyance tunnel BIM model, and endowing model components with the codes to form BIM data carrying code information; inputting the business data and the BIM data carrying the coding information into a digital construction and management system, and associating the business data and the BIM data by using a common coding identifier; based on the association, business management digital applications of investment, progress, quality, safety and the like are carried out in a digital construction and management system. Through standardized data sorting and coding, fusion of the BIM model and engineering management business is realized, the problem of data splitting is solved, and the refinement and digitization level of water conveyance tunnel engineering management is improved.

The invention relates to the technical field of insurance business management, and discloses a multi-level-oriented insurance business index dynamic monitoring and intelligent decision-making system, which comprises a data acquisition module used for acquiring multi-level business data involved in an insurance business process in a multi-source manner, and the multiple levels comprise at least two levels with different management dimensions; and the data processing and standardization module is connected with the data acquisition module and is used for cleaning, converting and standardizing the acquired multi-level business data to generate structured data in a unified format. By constructing a multi-level index system and realizing dynamic monitoring of cross-dimensional business data, and combining intelligent early warning and root cause tracing functions, changes of operating indexes of each level can be linked in real time, so that insurance enterprises can accurately master operating states from high-level management to basic-level business links, quickly identify abnormities and position roots, and the safety of the insurance enterprises is improved. And the limitation of data splitting of each level and each dimension in traditional index monitoring is effectively broken through.

The invention belongs to the technical field of intelligent irrigation, and particularly relates to a dry land terrace intelligent irrigation control system based on multi-modal data, which comprises a data acquisition module, a data analysis and processing module, a water source and water delivery module, an irrigation execution module and a user interface and monitoring module. According to the invention, sudden water deficit (such as late spring coldness) is dealt with through path re-planning by the mobile irrigation unit, the emergency response speed is increased by 70%, and the drought loss rate is reduced by 54% by a synchronous digital twinning alarm strategy. The core breakthrough is that a closed-loop dynamic optimization mechanism of multi-source data acquisition-edge cloud collaboration-execution feedback is formed, and the problem of resource mismatching caused by data splitting of a traditional system is thoroughly solved, so that the emergency capability is improved; through collaborative optimization of an edge calculation layer (for example, more than 50% humidity data is removed in real time in continuous 3 hours) and a cloud decision layer (three-dimensional control matrix output), a valve is dynamically adjusted in combination with gravitational potential energy, so that the irrigation misjudgment rate is reduced.

The invention relates to the technical field of construction quality monitoring, in particular to a construction process quality monitoring method and system based on multi-modal data, and the method comprises the steps: S1, carrying out the omnibearing sensing of a construction pavement region through a multi-modal sensing system, and obtaining real-time monitoring data; and S2, preprocessing the real-time monitoring data and finishing time alignment. When the method is used, through multi-source data acquisition, fusion processing and construction and application of a digital twin model, the state of data splitting in monitoring, analysis and control links in traditional construction monitoring is broken, and the construction efficiency is improved. Full-automatic response from data acquisition to process adjustment is realized, the manual intervention cost is reduced by 70%, the defect identification accuracy is improved to 95% through combination of cross validation of multi-modal data, visual inspection, equipment parameter monitoring and simulation prediction results, the control accuracy of the construction process is enhanced, the occurrence rate of quality problems is reduced by 60%, and the construction efficiency is improved. And the quality control precision is improved.

The invention discloses a multi-layer memory management method and device, a storage medium and electronic equipment, and relates to the technical field of memory management, and the method comprises the steps that a prediction frequency set of storage equipment is obtained through prediction by using a reference information set corresponding to the storage equipment, and the storage equipment comprises a first memory and a second memory; the first delay of the first memory is smaller than the second delay of the second memory; determining a plurality of target sub-data contained in the target data of the storage device, and determining predicted access frequencies corresponding to the plurality of target sub-data based on the predicted frequency set; under the condition that the predicted access frequencies corresponding to the multiple pieces of target sub-data meet a preset splitting condition, splitting the target data into multiple pieces of target sub-data; and storing the target sub-data with the predicted access frequency greater than the frequency threshold in the first memory, and storing the target sub-data with the predicted access frequency less than the frequency threshold in the second memory. The technical problem that a memory management method in the related technology is low in resource utilization rate is solved.

The invention discloses a data security grading and classification management system and method, relates to the technical field of data classification, and solves the problem that an original data management mode cannot guarantee an index rate and encryption strength. A'feature key 'which is deeply bound with a storage position is provided for data encryption, and the potential safety hazard that the key is disjointed from a storage path in a traditional encryption mode is avoided; on the basis, encryption segments are decomposed according to path codes, encryption circles are generated, and a unique encryption structure is constructed by utilizing spatial characteristics such as fan-shaped regions and dense point location distribution, so that the encryption process is closely associated with a data storage path, and the cracking difficulty is improved through complex point location distance design; according to the path-coding-space encryption integrated mechanism, a complete and safe closed loop is formed from data splitting to encrypted storage, and the attack resistance of encrypted data is greatly enhanced.

The invention discloses a two-dimensional code traceability assisted product carbon footprint tracking method, system and device, and relates to the technical field of carbon emission management. The method comprises the following steps: acquiring a full life cycle of a target product and creating a storage chain; generating and binding a unique two-dimensional code; a writing instruction is generated when the two-dimensional code is triggered; calling preposed carbon emission data, and reading environmental parameters of scanning nodes; performing evolutionary analysis in combination with the environmental parameters and the emission data to generate update data; associating the preposed data and then writing into the storage space; and performing carbon footprint tracking management through the two-dimensional code and the updated chain. The technical problems that in a traditional carbon footprint calculation method, due to data splitting, dynamic updating lagging and boundary definition fuzziness, carbon emission accounting is inaccurate, and tracking efficiency is low are solved, and a carbon footprint dynamic evolution mechanism combining a two-dimensional code identification binding period storage chain and environment context parameter real-time collection is adopted. The technical effect of improving the real-time performance, the accuracy and the traceability of the carbon footprint accounting data is achieved.

The invention discloses a digital twin basin early warning data efficient processing method, system and device, and belongs to the technical field of basin early warning. An existing drainage basin early warning data processing scheme cannot meet the real-time early warning requirement. According to the efficient processing method for the early warning data of the digital twin basin, optimized high-risk early warning data are obtained by creating the data splitting model, the template making model, the data repositioning model and the risk focusing model, so that the processing efficiency can be greatly improved, the redundancy of the data is reduced, the real-time early warning requirement is met, and the method is suitable for popularization and application. And the problem of repeated calculation of pain points can be effectively solved, and memory overflow is avoided, so that efficient processing of the digital twinning basin early warning data can be realized. Furthermore, by multiplexing the static geometric grid and storing the dynamic hydrological parameters, repeated calculation of'independently positioning the geometric grid in each time step 'in a traditional scheme is avoided, so that the service requirement of'minute-level response' of drainage basin early warning can be met.

The invention relates to the technical field of power equipment fault detection, in particular to a fault detection method for a cable branch box, and aims to solve the problems that a single monitoring means is generally adopted in a traditional method, corrosion of connecting pieces is accelerated by condensation in the box, and the coupling effects such as insulation performance reduction in a high-temperature environment cannot be effectively recognized. Through multi-modal data acquisition, dynamic threshold adjustment and fusion model analysis, coupling analysis of the cable state and the environmental parameters is realized, the problems of data splitting, early warning lag and positioning fuzziness of a traditional method are solved, and the method has the advantages of integrating multi-dimensional monitoring data, dynamically adjusting a fault judgment standard and improving fault positioning precision.

The invention discloses a hybrid far and near field communication beam and power joint optimization method based on rate splitting, and relates to the technical field of wireless communication and novel multiple access. The designed hybrid near-far field communication beam and power joint optimization method comprises the following four steps: S1, establishing a far-field user and a near-field user channel model in a hybrid near-far field communication scene; s2, splitting data of near-field users into public information streams and private information streams by using a rate splitting multiple access technology; s3, pre-configuring beams of the public information flow and the private information flow of the near-field user by using a zero-forcing algorithm, so that the far-field user occupies the beams of the near-field user to carry out information transmission; and S4, establishing an optimization problem by taking maximization of the reachable sum rate as a target, and designing an iterative optimization algorithm to jointly process beam allocation of the near-field users and transmission power allocation variables of the near-field users and the far-field users and the near-field users. According to the invention, the throughput and connectivity of the communication network can be obviously improved.

The invention discloses an enterprise technology achievement evaluation method and device, equipment and a medium, and relates to the technical field of computers. Comprising the steps of obtaining multi-source data including technical dimension data and market dimension data and related to a target enterprise technical achievement; performing feature fusion operation on the technical dimension data and the market dimension data by using a preset space-time alignment engine and a preset semantic alignment engine to obtain a fused feature vector; constructing a technical node and a market node based on the fusion feature vector, and constructing a dynamic association network map based on the technical node and the market node by using a cross-domain attention network created based on a bidirectional attention mechanism; and analyzing the dynamic association network atlas by using a preset deep learning algorithm to obtain a target analysis result corresponding to the target enterprise technical achievement. Therefore, the problems of data splitting, dynamic deficiency and cross-domain association insufficiency can be solved, and joint intelligent prediction of technology maturity and market suitability is realized.

The invention provides an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) fusion settlement monitoring method based on a double-branch neural network, which comprises the following steps of: converting low-time-resolution InSAR data into a high-resolution time sequence matched with GNSS through an interpolation algorithm, and constructing an input matrix with aligned dimensions; a heterogeneous dual-channel network is adopted to respectively extract InSAR spatial features and GNSS time sequence features, after key information is extracted through dimension reduction processing, fusion data is reconstructed, a data splitting strategy is utilized to train a model and optimize parameters, and finally accurate modeling of a multi-source data nonlinear relation is realized. According to the method, the limitation of traditional linear fusion is broken through, a heterogeneous neural network is designed, multi-source data fusion is realized by using the high spatial resolution feature of the InSAR image and the high temporal resolution feature of the GNSS, the problem of insufficient modeling of a non-linear relationship of heterogeneous data in a traditional method is solved, and the space-time precision and robustness of settlement monitoring are improved.

The invention belongs to the technical field of data processing, and particularly relates to a rock-soil data structured conversion method. According to the method, unstructured data of rock and soil logs is split into minimum information units of entity, attribute and relation clues through information particle deconstruction, standardized entities and relations are generated through clue association network clustering and double-track reasoning model processing, and a knowledge graph is constructed and mapped into structured data; and constructing an initial geologic model by using structured data, extracting core data, converting the core data into an extended IFC format intermediate file containing a geologic special entity, and finally associating a BIM model to realize data fusion. According to the method, structured conversion of unstructured data is realized, a data format barrier is broken through, and the engineering collaboration efficiency is improved.

The invention discloses a park global multi-dimensional real-time digital twinborn collaborative management and control system and method, and belongs to the technical field of smart parks, and the system comprises an equipment mapping unit, a data processing unit, a BIM model updating unit, an instruction correction unit, a collaborative management and control unit and an abnormal report generation unit. The equipment mapping unit is used for constructing a space-time coding mapping table and a material library; the data processing unit is used for collecting and filtering IoT data in real time; the BIM model updating unit is used for dynamically updating the BIM model; the instruction correction unit is used for generating and correcting a preliminary cooperation instruction; the collaborative management and control unit is used for obtaining a final control instruction set; and the exception report generation unit is used for generating a fault root cause report as a collaborative management and control result. According to the method, the fault positioning delay is reduced, the red line violation rate is reduced, and the problems of data splitting, response delay and false report and missing report in the prior art are effectively solved.