1864 results about "Complete data" patented technology

The invention belongs to the technical field of artificial intelligence and big data, and discloses a customer data processing and insight system based on a big language model. The system is composed of a multi-source data access module, a data preprocessing and label fusion module, a large language model semantic understanding module, a knowledge enhancement and semantic linkage module, an insight generation and visualization module, an intelligent strategy output module and a feedback learning and self-optimization module. According to the method, multi-source heterogeneous data such as texts, voices and structured behaviors are integrated, and the deep semantic analysis capability of a large language model is combined, so that global modeling of customer behaviors and intentions is realized; a multi-modal synchronous acquisition and standardization mechanism eliminates data format barriers, and a dynamic label mechanism adapts to context changes, so that the system can capture deep semantic association in customer expression, and compared with a traditional keyword matching method, the semantic understanding accuracy is improved by more than 40%, and a more complete data base is provided for insight generation.

The invention discloses a power system source network interaction data security encryption verification method, system, device and medium, and belongs to the technical field of power system communication encryption, and the method comprises the following steps: a source side device and a network side device complete identity authentication through a bidirectional digital certificate verification mechanism; and an identity binding label is generated in combination with the device identifier, the timestamp and the position information. And after the authentication is passed, the two parties perform key negotiation based on an elliptic curve algorithm, and generate a temporary encryption key in combination with an authentication result. A source side device carries out structural packaging on original interaction data, adds a random number, a timestamp and a hash abstract, then encrypts the original interaction data in an AES-GCM mode, and carries out signature binding on key fields through digital signatures to form a complete data packet. And a receiving end performs compliance judgment on the timestamp and the random number in the data packet, and after the timestamp and the random number are confirmed to be in an effective time window and are not replayed, signature verification, decryption processing and Hash consistency verification are sequentially completed, so that closed-loop processing of a data flow is ensured.

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention discloses a large model data distributed management method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: segmenting large model training data to obtain a plurality of data blocks, and determining a predicted access frequency based on a long short-term memory network model and the historical access frequency of the data blocks; caching the large model training data corresponding to the data block to a corresponding data cache layer by utilizing the predicted access frequency; constructing a resource portrait by using the static attribute and the dynamic index of the GPU node, and allocating the large model training task to a target GPU node by using a preset hybrid strategy and the resource portrait based on the predicted access frequency and the storage position corresponding to the data block in the data cache layer; when it is monitored that the large model training task on the target GPU node is executed, periodic snapshot is conducted on the large model training task through a distributed snapshot algorithm, and the obtained complete data state is stored in a distributed storage center. And the resource vacancy rate is reduced.

The invention relates to the technical field of electrical equipment analysis, and particularly discloses an electrical equipment operation state monitoring system and method, and the method comprises the steps: the system is deployed at an electrical equipment site, integrates a low-power-consumption processor and an FPGA acceleration module, and is used for collecting multi-source data in real time, and executing lightweight model calculation and local early warning; and storing full data and operating a global analysis model, and communicating with the edge computing node to realize data collaborative analysis. According to the electrical equipment operation state monitoring system and method provided by the embodiment of the invention, through edge computing lightweight deployment, data preprocessing and local early warning are completed on the equipment site, and high efficiency of monitoring response and low-power-consumption hardware adaptation are realized; by means of an electromagnetic interference dynamic evaluation module, an acquisition strategy is intelligently adjusted, noise is suppressed, the reliability of multi-source data is guaranteed, the efficiency, precision and intelligent capability of electrical equipment state monitoring are remarkably improved, and technical support is provided for reliable operation of power system equipment.

The invention relates to the technical field of metadata generation, and discloses a data platform metadata automatic generation method based on a large model, which comprises the following steps of: firstly, performing unified standardization and preliminary grammatical analysis on an original SQL (Structured Query Language) code to obtain a structured intermediate representation; on the basis, preliminary blood relationship analysis based on rules is carried out, and simple column references are quickly identified and processed. For complex expressions which are difficult to accurately analyze by a traditional method, code snippets and context information of the complex expressions are accurately extracted and submitted to a large language model for deep semantic understanding and complex blood relationship analysis. And finally, integrating the complex consanguinity analyzed by the large model with the initial consanguinity list to form a comprehensive and accurate field-level consanguinity, and further generating complete data platform metadata. In this way, the defect that a traditional analysis tool understands complex semantics is effectively overcome, and the accuracy and integrity of metadata generation are remarkably improved.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention discloses a visual algorithm self-training method based on multi-agent collaborative optimization, and the method comprises the following steps: constructing a multi-agent system architecture which comprises a user interaction layer, an intelligent scheduling layer, an A2A protocol communication layer and a professional agent cluster layer; the user interaction layer analyzes a user task intention and generates an execution plan; the scheduling agent calls the professional agent to complete data processing, model construction, training, testing and deployment; a task process is coordinated through a standardized communication mechanism, and task execution is supported by combining an MCP tool set, a knowledge base module and a memory system; and when the task fails, automatically executing rescheduling operation, and finally outputting a self-training result. According to the method, the development efficiency, the self-adaptability and the intelligent level are remarkably improved, and the method is suitable for computer vision tasks such as industrial detection, intelligent security and protection and automatic driving.

The invention discloses a graph database query method based on runtime filtering, which belongs to the technical field of graph data, and comprises the following steps: analyzing a graph database query request to obtain an original physical execution plan; identifying each connection operator meeting a runtime filter generation condition as a target connection operator, and verifying that a scanning operator capable of receiving the runtime filter exists at the upstream of the detection side of each target connection operator; a runtime filter transmission channel is established, and the output end of the construction side of each target connection operator is connected to the input end of the corresponding scanning operator; during operation, the construction side operator completes data processing and then sends the data to the runtime filter, and the corresponding scanning operator receives the runtime filter, executes optimized scanning by using the structured storage characteristics of the graph storage engine, and outputs graph elements matched with the runtime filter. According to the method, the RuntimeFilter is introduced into the physical execution plan of the graph database query request, so that the data scanning amount can be greatly reduced, and the query speed is improved.

The invention discloses a project side end data fragmentation recombination and service breakpoint resuming method in a weak network environment, and the method comprises the steps: receiving original service data collected on site through a project side end, carrying out the fragmentation according to the division of a construction region and a time interval, and generating a fragmentation data package with a time-space identifier and a check code; verifying the received fragmented data packet, and generating a fragmented cache set with a processing progress; when all fragments of a certain region-time fragment in the fragment cache set are received, the preprocessing progress of the corresponding service is read from the persistent file, and remaining service processing continues to be executed from the progress interruption position; and integrating the recombined original data of each region-time slice, the business processing result, the breakpoint resume record and the fragment transmission log, and generating a business data file containing a complete data chain and a processing track. By utilizing the embodiment of the invention, the data transmission efficiency and the service continuity in a weak network environment can be improved, and the reliability requirement of a project side end scene is met.

The invention discloses a bar code and RFID dual-mode fused tamper-proof data storage system, and relates to the field of data security storage, which comprises the following steps: uploading complete data to be stored into a scanning monitoring platform, splitting the data into static data and dynamic data, writing the static data into a bar code module, and writing the dynamic data into the bar code module; randomly distributed initial fluorescent particles are generated on the surface of the bar code data label through a scanning monitoring platform, a unique initial optical fingerprint is generated, if a bar code module is tried to be written or modified, the distribution position of the initial fluorescent particles is modified, an abnormal optical fingerprint is obtained, and an alarm is given; and writing the dynamic data into the RFID module, generating a dynamic key in each communication, and performing cross validation on the hash value based on the static data and the dynamic data. The method has the advantages that a three-dimensional protection system is constructed through a dual tamper-proof mechanism of a physical layer of fluorescent particle displacement and a password layer of a dynamic key in combination with cross validation of a hash tree, and the tamper-proof capability is improved.

The invention discloses a fixed dual-runway FOD detection method based on multi-source circumferential scanning, and belongs to the technical field of data fusion detection, and the method specifically comprises the steps: synchronously obtaining dual-runway multi-polarization echo data through a fixed circumferential scanning rada; performing time-varying clutter suppression by combining an adaptive clutter cancellation method matched with a dynamic background template and a runway material feature library, and generating a time-frequency domain three-dimensional feature matrix through time-frequency analysis; synchronously collecting and preprocessing image data, and marking a target area through a constant false alarm rate algorithm and image detection; a multi-source data geographic coordinate mapping model is established, data registration and cross validation are completed under a runway physical coordinate system, and real-time tracking of a real FOD target is realized based on radar and image fusion data through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features; according to the method, the FOD detection accuracy and the position identification precision are improved through multi-source data fusion, the false alarm rate is effectively reduced, and a guarantee is provided for safe operation of an airport.

The invention relates to a data query analysis method based on a natural language, which comprises the following steps of: analyzing a natural language query request input by a user into a dynamic semantic intention tensor which comprises an operation type, a comparison relationship, a time constraint and a data role semantic dimension, and dynamically combining according to task semantics to form a complete data query and analysis operation expression; mapping the natural language intention to a controllable data operation space of a database, and generating a preliminary data operation chain; performing side effect perception processing on the operation tensor, predicting a potential execution consequence of the operation, generating a side effect tensor for simulating a side effect of the database operation, and if any index in the side effect tensor exceeds a preset threshold value, performing local correction on the operation tensor; according to the method, a complex natural language query task is disassembled into a plurality of minimum semantic units by adopting a step-by-step semantic resolution mechanism, each unit is mapped into a specific tensor operation, and a dependency relationship between operation units is dynamically reasoned to generate an operation execution chain.

The invention discloses a distributed supply chain traceability system and method supported by a block chain, and relates to the field of data analysis, and the system comprises a collection module which is used for capturing the original data of a supply chain in real time through an Internet of Things sensor, and cleaning the data; the processing module is used for constructing a distributed account book based on a HyperledgerFabric architecture, performing hash encryption on the data, performing uplink storage on the data, packaging the data into blocks according to a time sequence, and completing data verification between nodes through a BFT consensus mechanism; according to the invention, full-link data of production, logistics and the like are captured in real time through the Internet of Things sensor and encrypted and chained, so that the data cannot be tampered and can be traced in the whole process, and the problems of easy data tampering and many breakpoints in traditional traceability are solved.

The invention provides a vibration signal space-time reconstruction method based on a multi-modal condition diffusion model, and relates to the technical field of vibration signal reconstruction, and the method comprises the steps: firstly collecting structure vibration response through multiple sensors, constructing a multi-dimensional vibration signal matrix, and automatically recognizing a space continuous missing region and a time random missing region; performing coarse reconstruction on the missing region by adopting self-adaptive multi-scale interpolation so as to recover the basic trend and frequency band characteristics of the signal; a pseudo-missing mask is further applied to complete data, a training sample is constructed through a self-supervision strategy, and the model is guided to learn spatio-temporal correlation features and missing modes; in a training stage, a diffusion model is used as a generation framework, Gaussian noise disturbance is applied to a missing region, four types of condition embedding of time, space, trend and frequency domain are introduced in a denoising inversion process, signal periodicity, multi-sensor space coupling, low-frequency change and a physical frequency spectrum structure are respectively described, and the noise is reduced; and high-fidelity signal reconstruction under multi-modal information joint constraint is realized.

The invention discloses an automatic monitoring system and method for hydrological data and a storage medium. The automatic monitoring system comprises a multi-modal data acquisition module, an edge intelligent processing module and a cloud platform. The multi-modal data acquisition module acquires water level, water quality and meteorological data in real time through a radar water level gauge, a multi-parameter water quality meter and a rain gauge, and transmits the data to the edge intelligent processing module; the module extracts and aligns spatio-temporal features through a multi-scale spatio-temporal convolution unit, an uncertainty quantization unit is fused with Bayesian to calculate confidence, and complete data or abstract features are selectively transmitted to a cloud according to an abnormal classification judgment result; the cloud platform fuses multi-source features through a space-time attention mechanism, dynamically adjusts an early warning threshold based on a near-end strategy optimization algorithm, aggregates edge node model parameters through a federated learning framework in combination with a homomorphic encryption technology, updates a global model, and then synchronizes the global model to edge nodes to form closed-loop optimization.

The invention discloses a storage cluster, a system, a data processing method, equipment, a medium and a product, and relates to the technical field of data storage. A plurality of devices are arranged in a controller unit, a switch unit and an extended memory unit; a plurality of switches of the switch unit are correspondingly connected with a plurality of extended memories of the extended memory unit, and copy data of a plurality of controllers are respectively stored in the extended memories. The technical problem that the high availability of a storage system is poor due to the fact that the expansibility of the number of controllers and the number of data copies is poor is solved, and the purposes that memory cache data of the controllers are decoupled from DRAM memories in the controllers to external extended memories, deployment operation is simplified, N-1 controllers are allowed to fail at the same time, and the reliability of the controllers is improved are achieved. The data access processing can be completed under the condition that the M-1 switches simultaneously fail and / or the extended memories corresponding to the P-1 data copies fail, so that the technical effect of high availability of the storage system is improved.

The invention discloses an RFID-based weather modification rocket projectile operation full-process monitoring and management method and system, and the method comprises the steps: assembling a high-temperature-resistant and impact-resistant RFID tag for each rocket projectile, and writing identification information and operation parameters; the storage position and the transportation track of the ammunition are monitored in real time through a fixed reader-writer network; the matching relation between the rocket projectile and the launcher is verified before launching, and an interception instruction is generated if not; and generating an ammunition management report based on the whole-process collected data and marking an exception. According to the invention, the whole life cycle accurate tracing of the rocket projectile from production to recovery is realized, the operation safety is ensured through a multiple verification mechanism, the meteorological data is fused to construct an intelligent decision system, and the management efficiency and scientificity of the figure operation are significantly improved. The system has the characteristics of quick response, complete data, convenient operation and the like, and provides an innovative solution for modern management of figure operation.

The invention relates to an AI-based subject education auxiliary method and device and a storage medium, which are applied to the technical field of intelligent teaching, and comprise the steps of: introducing a teaching plan script as a macroscopic framework of teaching, ensuring that an AI teaching process is always carried out around teaching design and core targets of teachers, and improving the teaching efficiency. The problem of teaching content deviation possibly occurring in a pure response type system is avoided, meanwhile, microcosmic dynamic adjustment can be carried out in the framework according to real-time feedback of students, and organic combination of teaching structure and personalized self-adaption is achieved. By constructing and dynamically updating the individual knowledge graph for each student, the system can accurately diagnose the weak knowledge points of the students and provide personalized teaching guidance and after-class homework according to the weak knowledge points, thereby realizing accurate and efficient'apportionality teaching '. According to the invention, the whole process of'teaching-learning-evaluation-diagnosis-mending 'is integrated, a complete data-driven teaching closed loop is formed, and the overall teaching quality is systematically improved.

The invention belongs to the technical field of plastic part quality detection, and discloses a plastic part gold stamping mark accurate positioning method and system based on visual identification, and the method comprises the steps: collecting environment illumination parameters, equipment operation parameters and image data, and processing the image data to obtain regional image data; performing edge detection and fracture repair operation on the regional image data to obtain identification edge feature data; calculating to obtain a position deviation quantized value according to the identification edge feature data; based on the position deviation quantized value, in combination with preset inclination direction information, adjusting an image visual angle and correcting an angle deviation to obtain corrected image data; executing edge path tracking on the corrected image data to obtain path complete data; obtaining an identifier positioning result based on the path complete data in combination with the environment illumination parameters and the equipment operation parameters; and carrying out stability evaluation on the identification positioning result, and obtaining a final system identification stability result in combination with preset detection fluctuation characteristics.

The invention relates to the technical field of informatization management systems, and particularly discloses an AI-aid decision-making product full-life-cycle informatization management system which comprises a full-link data sensing module, a digital main line construction module, an AI decision-making center module, a service execution interface module and a man-machine collaborative interaction module. The method comprises the following steps: collecting multi-source heterogeneous data of each stage of a full life cycle of a product through a full-link data sensing module to form a full-amount data sequence with a space-time stamp; and in combination with a data fusion engine, an association graph construction unit and a twinborn model driving unit of the digital main line construction module, data cleaning, alignment, semantic fusion and cross-stage association are realized, a dynamically updated product digital twinborn model is constructed, real-time mapping of a physical entity and a virtual model is achieved, and the real-time mapping of the physical entity and the virtual model is realized. Unified and complete data support is provided for full-link decision making, and the problems of data fragmentation and prominent collaborative barriers of a traditional system are thoroughly solved.

The invention belongs to the field of data quality analysis, and particularly discloses a data set quality analysis and evaluation method, which performs integrity evaluation on a current data set, starts data confidence analysis only after the number of data groups reaches the standard, ensures that confidence evaluation is established on the basis of data with a complete structure, avoids erroneous judgment caused by data missing, and improves the quality of the data set. Meanwhile, during data confidence analysis, dynamic comparison between current data and a local time sequence neighborhood is implemented by constructing an adjacent data set of each data group; meanwhile, a historical reference data set is combined, longitudinal comparison with a historical fluctuation range is carried out, and dual verification of local consistency and historical comparability is fused, so that the anomaly detection sensitivity and the evaluation accuracy are improved to a great extent; and finally, establishing a closed-loop feedback path with an acquisition end when availability judgment is carried out based on a data set integrity and confidence evaluation result, and realizing self-adaptive adjustment of an acquisition strategy according to a risk positioning result.

The invention relates to the technical field of data processing, and particularly provides a multi-source heterogeneous data integration method, system and device and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data, and generating an identification attribute for each piece of heterogeneous data; dividing the heterogeneous data with the same identification attribute into the same data group; converting the heterogeneous data in the data group into standard data in a specified format; based on a predefined integration rule, integrating the plurality of pieces of standard data converted from the data set into one piece of complete data, the integration rule comprising a conflict resolution strategy and / or a data association rule; and storing the complete data to a corresponding position of a database according to a predefined storage strategy. According to the method, effective integration of multi-source heterogeneous data is realized, and the data analysis efficiency and quality are improved.

The invention provides a multi-sensor fusion and environment feature recognition method and system for digital twin equipment, and the method comprises the steps: constructing a digital twin model of the equipment in a visual platform, and setting a self-updating mechanism; deploying a multi-sensor array at a key installation part of the equipment physical entity, collecting multi-source sensing data and uploading the multi-source sensing data to a twin platform; carrying out noise reduction processing on the multi-source sensing data and completing data fusion to obtain fused data; constructing a diffusion model corresponding to a noise scheduling strategy, generating new sensing data and a corresponding label, and forming analysis data; a multi-channel deep learning model is constructed to extract feature parameters from analysis data, regression prediction is carried out, and an identification result is obtained; the system is integrated to a twin platform and is combined with a control decision function. According to the invention, real-time regression prediction of environment characteristic parameters based on sensing data is realized, deep fusion of environment perception and control decision of equipment is promoted, and perception capability and adaptive adjustment capability of a control decision system of the equipment are improved.

The invention relates to an application method of a GPU (Graphics Processing Unit) in design rule inspection in the field of integrated circuits, in particular to a layout design rule inspection method, system and equipment, a medium and a product. The layout design rule checking method comprises the following steps: inputting initial layout data, analyzing a design rule checking task to be executed, and marking the priority and complexity level of the task; the method comprises the following steps: dividing layout data into a plurality of data blocks according to regions on the basis of regional characteristics of the layout data, distributing tasks and corresponding data blocks to a GPU computing core according to task priorities and complexity levels, and distributing new data blocks according to the overall load condition of the GPU computing core after the GPU computing core completes data block processing, and repeating the steps until all tasks are executed, and outputting a layout design rule check result. The system, the computer equipment, the computer readable storage medium and the computer program product have the same beneficial effects as the layout design rule checking method.

The invention provides a data weaving platform-oriented self-service data preparation method, which comprises the following steps of: accessing original data and associated structured metadata of a target data source into a data weaving platform to generate standardized accessed original data and associated structured metadata; based on a semantic model of the data weaving platform, performing classified semantic annotation on the associated structured metadata, and positioning a transfer node of the associated structured metadata to generate a data blood relationship record; performing bidirectional quality verification on the data consanguinity record according to semantics and path continuity, and matching with a federated governance rule of a data weaving platform to generate a metadata governance list; and generating a unified metadata directory based on the metadata governance list, so that a user of the data knitting platform can obtain key segments containing complete data consanguinity records. According to the method, the problem of insufficient accuracy and efficiency in self-service data preparation can be effectively relieved, and the efficient preparation requirement of a data weaving platform for multi-source data is met.

The invention relates to the technical field of computers and the technical field of intelligent monitoring of forest resources, and particularly discloses a multi-level forest resource monitoring method and system. The method comprises the following steps: collecting computing power of edge nodes, storing and communicating state information, performing normalization processing, and constructing task resource mapping in combination with a forest resource monitoring task set; constructing a scheduling optimization model based on the mapping, executing multi-constraint scoring and iterative optimization, and generating a task-node matching structure; compressing the central graph neural network model according to a matching result and deploying the central graph neural network model to edge nodes; selecting a communication mode in combination with the communication state, and configuring channel resources; executing local reasoning and asynchronously returning data; and finally, extracting features to complete data aggregation, feeding back task weights and node scores, and updating a scheduling strategy in a closed loop. According to the method, the global monitoring scheduling efficiency and the system stability under the environment of uneven resource distribution and communication fluctuation are effectively improved.

The invention discloses a communication method of vehicle-mounted TBOX equipment, which relates to the technical field of equipment communication and comprises the following steps of: initializing a communication module of the vehicle-mounted TBOX equipment; performing channel selection by using a communication module to obtain channel information; performing spectrum allocation according to the channel information to obtain spectrum information; performing data encryption transmission according to the frequency spectrum information to obtain encrypted data; performing integrity verification on the encrypted data to obtain complete data; performing network load prediction according to the complete data to obtain a prediction result; adjusting the bandwidth according to the prediction result to obtain bandwidth data; and performing data packet priority management according to the bandwidth data. According to the method, the prediction accuracy and the overall communication efficiency are improved, the data transmission sequence can be flexibly adjusted according to the real-time network environment, the transmission reliability of key data is improved, and the risk of important information loss or delay caused by sudden network congestion is reduced.