1385 results about "Data mapping" patented technology

The invention provides a dam safety perception fusion association method based on a multi-modal space-time diagram neural network. The method comprises the following steps: dividing a dam into a plurality of structural units, and mapping various data into a three-dimensional coordinate system; a heterogeneous graph structure is defined, and a dynamic adjacency matrix is calculated based on the real-time stress gradient so as to reflect physical connection, mechanical conduction and geological association relationships among nodes; carrying out fusion modeling on multi-source data in the heterogeneous graph structure by utilizing a multi-modal space-time diagram neural network, constructing a causal inference engine based on an output result of the multi-modal space-time diagram neural network, and updating a three-level modeling system through structural equation modeling, anti-factual inference and dynamic weight to obtain the heterogeneous graph structure. According to the method, the dynamic coupling rule among the dam structure, geology and material states is excavated, cross-modal space-time fusion of manual inspection and sensor monitoring data can be realized, the early recognition capability and early warning accuracy of dam potential safety hazards are improved, and the problems of data islands and insufficient relevance in a traditional monitoring method are effectively solved.

The invention relates to the technical field of bridge bearing capacity evaluation, and discloses an existing continuous beam bridge bearing capacity comprehensive evaluation method which comprises the steps that multi-source data are collected to construct a three-dimensional damage voxel field, and an initial finite element model is generated; simulating a heavy load identification control section, and designing an equivalent static load test scheme according to an equivalent internal force principle; executing a field static load test, collecting elastic response data and constructing an actual measurement response vector; calculating errors and iteratively adjusting parameters in a voxel field constraint range to finish model correction; and generating an operation space-time security envelope based on the correction model, and comparing the state in real time to output a control instruction. A three-dimensional damage voxel field is constructed, voxels are used as a normalized data carrier, discrete apparent and internal damage data such as three-dimensional laser scanning point cloud, ultrasonic velocity and rebound strength are mapped into a continuously distributed space field, and the space variability of the rigidity of an existing bridge material can be represented in a refined mode. And the accuracy of describing the physical current situation of the structure by the initial finite element model is improved.

System and methods are disclosed for providing unforgeable telemetry on computer platforms. Mathematical modeling and theorem proving are utilized to guarantee the integrity of telemetry probe execution flow and trigger, thereby preventing circumvention and tampering of logged probe data. In contrast to current state-of-the-art solutions that rely implicitly on the operating environment, this approach provides a sound and complete assurance of telemetry output. The system enables organizations to map unforgeable telemetry probe data to industry and government cybersecurity regulatory controls, ensuring compliance therewith. This invention addresses the shortcomings of existing solutions, including their vulnerability to sophisticated attacks, operational complexity, and inability to provide unforgeable telemetry data, thereby providing a reliable and accurate monitoring output in the presence of cyberattacks on computer platforms.

The invention discloses a personalized computer-aided decision-making method and system fusing multi-modal data, and relates to the field of personalized computer-aided decision-making, and the method comprises the steps: mapping multi-source heterogeneous modal data to a unified semantic embedding space, and obtaining a multi-modal unified representation vector set; carrying out three-layer progressive fusion on a feature layer, a situation layer and a decision layer of the multi-modal data to generate a global decision context vector; based on a cross attention mechanism, outputting a fused context sensing personalized vector; based on the behavior cloning model, outputting probability distribution on all decision options; according to the user feedback operation data, generating a user personalized decision strategy and performing dynamic optimization; and generating a structured decision report containing visual traceability information based on the hierarchical fusion process and decision reasoning logic. End-to-end intelligent generation from multi-source heterogeneous data to personalized decisions is realized, and a standardized process is converted into personalized customized decisions.

The invention provides a coal mine high-precision three-dimensional geological modeling method and system based on multi-source heterogeneous data fusion, and relates to the technical field of coal mine geological modeling. The method comprises the following steps: establishing a unified semantic reference system and a time-space coordinate reference, and generating an alignment anchor point set; mapping the multi-source observation data to form an alignment feature set and calculating a source weight; according to a multi-evidence consistency rule and geomechanical prior inversion, a discontinuous geological structure is obtained; constructing an implicit stratigraphic function and applying a boundary jump condition to solve a stratigraphic boundary set; and taking the stratum boundary as a framework, and executing anisotropic joint estimation to obtain a property distribution model and an uncertainty result. According to the method, integrated modeling of the geological structure and the property of the complex structure area is achieved, and the precision, the stability and the credibility of the three-dimensional geological model are remarkably improved.

The invention discloses a knowledge graph-based old building reconstruction scheme automatic recommendation method. The method comprises the following steps of S1, obtaining and preprocessing old building house data; s2, extracting a semantic entity and attribute relationship from a transformation case library, a building specification library and a construction scheme library, and constructing a knowledge graph; s3, mapping the house data to knowledge graph entity nodes, and executing graph embedding to generate building semantic representation; s4, constructing a graph neural network, calculating node semantic relevancy, and generating a building state vector; s5, inputting the building state vector into the reinforcement learning decision network, and optimizing the strategy to obtain an optimal transformation action; s6, screening reconstruction measures from the knowledge graph according to the optimal reconstruction action, and performing scoring to form candidate schemes; and S7, sorting the candidate schemes, selecting the scheme with the highest score, and recommending and updating the knowledge graph. According to the method, intelligent generation and self-optimization recommendation of the old building reconstruction scheme are realized, and the reconstruction efficiency of the scheme is remarkably improved.

Apparatus and method for generating an output using an AI-PII model. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor receive personally identifiable information (PII) data, receive one or more model constraints, map, using an AI-PII model, the PII data to at least a data schema as a function of the one or more model constraints by identifying at least a PII datum of the PII data, categorizing the at least a PII datum to one or more categories of a plurality of categories, and mapping the PII data to the at least a data schema, modify the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints, and generate an output as a function of the refinement datum and data schema.

The invention relates to a smart hospital collaborative diagnosis and treatment management method and system, and the method comprises the steps: receiving the appointment registration information of a patient, building a data mapping relation with a hospital information system based on the appointment registration information, and generating a corresponding treatment identifier; after the patient arrives at the hospital, information of the sign-in patient is collected, a sign-in sequence is established, and according to appointment registration information and diagnosis and treatment service data parameters corresponding to the sign-in patient, a treatment serial number is dynamically distributed and a waiting prompt is pushed; after the doctor receives the diagnosis, generating an examination item list based on the preliminary diagnosis information input by the doctor, obtaining the real-time state and queuing data of equipment required by each examination in the examination item list, constructing an execution time window of examination items, generating an optimal examination sequence, and pushing an examination guide path to the patient terminal; and after the patient finishes seeing a doctor, generating a personalized propaganda and education task list according to the diagnosis and treatment information of each stage, and pushing the personalized propaganda and education task list to the patient terminal. The method has the effect of improving the hospital operation efficiency.

The invention provides a paper file information input method, system and device and a medium, and belongs to the technical field of file information processing. The method comprises the steps of collecting a paper file image, performing preprocessing in sequence to obtain a preprocessed image, and performing region segmentation on the preprocessed image by using a target detection algorithm to generate a region coordinate mapping table; generating a file template by using a predefined file template library according to the region coordinate mapping table; performing printing form recognition and handwritten form recognition on characters in the image by using a sequence generation model to generate a text sequence, and recognizing a table structure by using a two-dimensional convolutional network; according to the text sequence and the table structure data, a named entity recognition model is used for labeling field types, data formats are checked and corrected according to template rules, errors are corrected in combination with context semantic similarity, and structured data are generated; and mapping the structured data to an information system database table according to a template field mapping rule, and inputting the structured data into an information system.

A computer-security architecture removes activation authority from client-facing purchase flows. A server records a digital entitlement in an INACTIVE state and issues a presentation artifact. A verification service accepts messages only from trusted origins carrying freshness-bounded evidence and validates using (i) stateless keyed recomputation over a time-windowed payload, (ii) confirmation that token-rail authorization data maps to a registered association within a policy freshness bound, or (iii) a server-maintained session. Activation occurs only on an authoritative commit path that enforces no activation without a durable audit record and effective-once behavior: either a single ACID transaction that conditionally updates state and writes an insert-only audit in the same commit, or an append-only activation ledger with idempotent materialization. Access controls deny issuance-facing code permission to set ACTIVE. Expiration is timer-free via batch or TTL evaluation.

The invention relates to a multi-parameter centralized monitoring method, system and equipment for a noninvasive ventilator and a medium. The method comprises the steps that firstly, original parameter data and coding rules of noninvasive respirators of all brands are obtained, format unification processing is conducted on the original parameter data by constructing a standardized data mapping table, and a parameter data set is obtained; transmitting the parameter data set to a local gateway by adopting a low-power-consumption Bluetooth protocol to generate an alarm data set; analyzing the dynamic change trend of the parameters by combining the parameter data set and the alarm data set, and generating a monitoring report data set; and finally, extracting a continuous abnormal trend based on the monitoring report data set, performing priority ranking according to the emergency degree to generate an intervention instruction, and tracking the processing state of the intervention instruction to obtain a final decision data set. According to the method, data intercommunication of noninvasive respirators of different brands is realized, the hysteresis of manual inspection is avoided, the timeliness and accuracy of abnormal response are improved, and the safety of patients is guaranteed.

The invention discloses a multi-dimensional training method and device for a support vector machine, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the discretization of a training sample data set, and obtaining a discretized data set, the discretized data set comprises a plurality of different attributes, and each attribute corresponds to a plurality of feature vectors; calculating a classification contribution parameter of each attribute feature vector to obtain a plurality of classification contribution parameters; performing data mapping on the plurality of classification contribution degrees by using a kernel function to obtain a target function; and optimizing and training the objective function by using a gradient descent algorithm to obtain a support vector machine model. According to the method, different dimension data are mapped through the kernel function, the data gain weight can be determined, the linear separable effect of the mapped dimension data can be improved, and then the classification and analysis capability of the SVM can be improved.

The invention provides a smart city construction energy intelligent management scheduling regulation and control system, and relates to the field of energy intelligent management, comprising the following steps: the system detects energy physical parameters in real time through an event chain generation module, maps data into event nodes by using an event feature extraction chip, and sends the event nodes to a cloud server; an event chain topology is constructed through a causal association engine, and a nonlinear priority is generated; the responsibility collaboration module creates a scheduling instruction and a responsibility node based on the priority, calculates a responsibility weight through a reverse causal tracing algorithm, triggers cross-system permission degradation when the weight exceeds a threshold, and migrates the operation permission to an associated system; the paradox enhancement module injects controllable disturbance when the event chain conflicts or the responsibility nodes continuously exceed the limit, the disturbance effect is evaluated through the paradox index, the event chain topology is fed back and updated, and dynamic optimization and hidden risk prevention and control of energy dispatching are achieved.

The invention relates to the field of intelligent information processing, in particular to a carbon border adjustment mechanism intelligent auxiliary customs declaration method and system based on natural language processing, and the method comprises the steps: carrying out the real-time translation and key information extraction of CBAM related rule files and unstructured documents; a dynamic CBAM knowledge graph is constructed based on related rule texts, rule updating is monitored in real time through a text classification and event extraction technology, and calculation rules and declaration logic are dynamically adjusted; performing standardization processing on supplier data in different formats, integrating an industry emission factor library, automatically matching a calculation model according to a product type, generating a carbon emission result meeting a CBAM requirement, and mapping the data to a corresponding position of a CBAM declaration form; based on historical declaration data and related rule texts, analysis and compliance risk prediction are carried out by using a natural language processing technology, so that the declaration data are ensured to accord with latest rules, and violation risks caused by rule changes are avoided.

The invention discloses a laser equipment fault prediction and maintenance method based on digital twinning, and the method comprises the following steps: S1, collecting structure parameters, control logic and historical data, and constructing a unified neural network digital twinning model; s2, collecting multi-source operation data in real time in an operation process, and constructing a standardized state data set; s3, mapping the data to a unified neural network digital twin model to realize state synchronization; s4, predicting a future key parameter trend by using an LSTM model; s5, a health score is calculated through an entropy weight method, and the equipment state is quantitatively evaluated; s6, triggering early warning based on the health threshold value, identifying abnormity and generating a maintenance strategy; s7, pushing the strategy to an operation and maintenance system for closed-loop execution; and S8, maintaining feedback for retraining and self-optimizing the unified neural network digital twin model. According to the method, high-fidelity digital twin modeling is realized, so that a prediction-maintenance-feedback-updating intelligent closed-loop system is formed, and the fault prediction accuracy and the maintenance response efficiency of the laser equipment are remarkably improved.

The invention provides a building information model compliance processing method and device based on artificial intelligence, electronic equipment and a storage medium, and the method comprises the steps: analyzing a standard document through a natural language processing model, and constructing a structured compliance knowledge graph containing a forced parameter, a naming rule, a geometric constraint and a data mapping relation; when the components are loaded, geometric feature vectors of the components are extracted, a hierarchical topological graph is modeled and nested, and a digital twin file containing geometric attributes, a parameter list and risk scores is generated; and matching and comparing the archive with the knowledge graph, dynamically generating a processing strategy sequence, and scheduling a geometric optimization and data encapsulation module to execute adaptive processing. Geometric optimization adopts a deep learning driven non-uniform grid simplification algorithm, key region details are reserved, and topological defects are repaired; and the data packaging realizes intelligent matching and binding from the custom parameters to the standard attribute set through semantic mapping. Through the method and the device, the efficiency, the accuracy and the automation level of cross-platform compliance conversion of the building information model can be remarkably improved.

According to the method, firstly, SAR data are mapped to an optical image domain by using a conditional diffusion model, pseudo-optical images with consistent spatial spectrums are generated, and fusion distortion caused by difference of SAR imaging mechanisms in a traditional method is overcome; secondly, a refined cloud region detection mechanism of a Fmask cloud mask is introduced, a cloud pollution region and a cloudless region are dynamically distinguished in combination with an adversarial training strategy, and the problem that the cloudless region is mistakenly changed in the reconstruction process of an existing method is effectively solved; besides, a multi-source integrated sample data set is constructed in stages, and a training normal form of multi-index joint optimization of PSNR, SSIM and the like is adopted, so that texture detail recovery and spectrum fidelity of a thick cloud coverage area are realized in a complex scene. In downstream application tasks such as land utilization classification and disaster dynamic monitoring, the visual quality and the quantitative index of the cloud removal result have good effects, and reliable technical support is provided for high-precision reconstruction of remote sensing information of a multi-cloud area.

The invention discloses a regional carbon emission prediction method and system based on deep learning, and belongs to carbon emission prediction. The method comprises the following steps: acquiring historical carbon emission time sequence data and multi-dimensional constraint boundary data of a target area, and mapping the historical carbon emission time sequence data into an input feature vector; outputting an initial future carbon emission evolution trajectory vector; identifying and extracting a constraint violation dimension; constructing an input feature compensation tensor; generating an updated input feature vector; inputting the updated input feature vector into the depth space-time prediction model again for recursive prediction; and outputting a target carbon emission trajectory vector meeting the multi-dimensional constraint boundary data and a corresponding feature adjustment parameter set. According to the method, by automatically analyzing the multi-dimensional management constraint and fusing the multi-dimensional management constraint into the iterative optimization closed loop of the prediction model, the problem that the prediction model in the prior art only optimizes mathematical statistical errors and is seriously disjointed with multi-dimensional targets such as cost control and path feasibility in real management is solved.

The invention discloses a coal mine intelligent ventilation optimization regulation and control method and system based on digital twinning, and belongs to the technical field of coal mine safety ventilation. The method comprises the following steps: determining a three-dimensional space topological relation of a coal mine ventilation system based on mine structure data, and mapping real-time ventilation state data to corresponding roadway nodes and branch paths to obtain a twin ventilation state; and performing simulation calculation based on a three-dimensional space topological relation to obtain a theoretical ventilation distribution state, comparing the twin ventilation state with the theoretical ventilation distribution state, determining an air volume deviation region and a gas concentration abnormal region, and obtaining ventilation deviation characteristics to perform virtual rehearsal. Determining an adjustment scheme meeting the ventilation demand constraint and the energy consumption constraint, and generating an optimization regulation and control instruction; and the entity ventilation facility is controlled to execute the adjustment action, and the three-dimensional space topological relation is updated through feedback data. Accurate regulation and dynamic optimization of the ventilation system are achieved, and the safety and the energy-saving effect of coal mine ventilation are improved.

The invention provides a water conservancy data informatization management system and method. The method comprises the following steps: constructing an object association map of a water conservancy data entity object and business logic based on basic object data, mapping relation data and business logic data in water conservancy data; performing cryptographic hash operation on nodes and connecting lines in the object association map to obtain a data consanguinity fingerprint with uniqueness and tamper-proof characteristics, and storing the data consanguinity fingerprint and an authority strategy matrix for accessing the water conservancy data into an intelligent contract to obtain an authority control contract of the water conservancy data; capturing a data access request of an access user, and determining a data access permission of the access session according to the permission control contract and the data access request; and performing business scene-oriented controlled visual sharing on the water conservancy data extracted from the object association map based on the data access permission. By adopting the scheme of the invention, a water conservancy data security sharing system considering data dynamic adaptation, multi-attribute permission constraint and full-link credible tracing can be constructed.

The invention provides a knowledge graph-driven medical data agent collaborative governance method, system and device and a medium, and belongs to the technical field of medical data governance, and the method comprises the following steps: constructing a knowledge graph containing a quintuple, and defining mapping relationships among medical data entities, attributes and standards; extracting multi-modal medical data knowledge elements through a structured / unstructured engine, and converting the multi-modal medical data knowledge elements into a quintuple for storage; executing table / field / value domain mapping based on the knowledge graph to generate a data mapping result; binding the quality control rules and the medical specifications to form a quality control quintuple; arranging the task into a directed acyclic execution sequence through an agent group cooperation task; and dynamically updating the knowledge graph and performing version management. Medical data full-link governance, knowledge graph unification heterogeneous data semantics, intelligent agent cooperation automatic processing complex tasks, update guarantee knowledge timeliness and quality control rule binding guarantee data quality are achieved, and medical data governance efficiency and accuracy are improved.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention provides a data retrieval system and method based on a large language model and a readable storage medium. The system comprises a data analysis module, a multi-mode integration module, a semantic retrieval module and a post-processing module which are arranged in sequence. The data analysis module analyzes the original engineering data to generate structured data; the multi-modal integration module maps the structured data to the same semantic vector space to construct a vector database, and establishes a cross-modal association index to construct a multi-modal engineering knowledge graph; the semantic retrieval module is based on a large language model and a retrieval enhancement technology, responds to a natural language query of a user, retrieves related information from a vector database and a multi-modal engineering knowledge graph and generates answers; the post-processing module performs abstract generation on the natural language answers based on a large language model and generates associated recommendation content. According to the method, the query accuracy, the retrieval efficiency and the user query interaction experience can be remarkably improved; and the scheme reuse rate can be improved based on the recommended content.

The invention relates to the technical field of metal composite materials, in particular to a metal composite material production process dynamic simulation method based on digital twinning. Comprising the following steps: constructing a digital twin model comprising a physical entity layer, a virtual model layer and a connection layer, and establishing a Fourier neural operator model embedded with a Navier Stokes equation and a heat conduction equation constraint; real-time molten pool monitoring data are mapped into model input through a connecting layer, and advanced time step deduction is executed by using a deep learning model to predict the state of a molten pool; based on the differentiable characteristic of a physical information neural operator, calculating the gradient of a prediction state and an ideal target by using an automatic differential algorithm, generating a process parameter field through reverse iteration updating, and discretizing the process parameter field into a galvanometer control instruction; and when the risk of aggregation of the reinforced phase particles is predicted to exist, automatically generating a swing scanning strategy to realize active suppression. The method gives consideration to simulation precision and real-time performance.

The invention relates to the technical field of power grid digital checking, in particular to a checking method of a power grid digital design model, which comprises the following steps: S1, acquiring an initial parameter set of a to-be-checked power grid model; s2, performing data standardization processing on the initial parameter set; s3, constructing a three-dimensional topology verification model, and identifying abnormal nodes by using a spatial topology constraint algorithm; s4, mapping the real-time operation data to the three-dimensional topology verification model, and generating a dynamic parameter comparison matrix; s5, executing multi-dimensional check analysis, and generating a multi-dimensional difference report; s6, performing parameter replacement or topology reconstruction on the abnormal nodes, and outputting a final check result; according to the method, various power grid parameters are standardized in a unified manner, the accurate three-dimensional topology verification model is constructed, and automatic identification and quantitative correction of abnormal nodes are realized in combination with real-time data, so that the verification accuracy and operation reliability of the power grid model are remarkably improved.

The invention relates to an atmospheric concentration field inversion method and system based on a space-time diagram network. The method comprises the following steps: acquiring atmospheric observation data of different platforms; the method comprises the following steps: standardizing and preprocessing atmospheric observation data of different platforms to generate space-time point cloud data in a unified format; mapping the time-space point cloud data into a graph network structure for describing an atmospheric physical transmission process; constructing a hybrid neural network architecture, taking the graph network structure as the input of the hybrid neural network architecture, and training the hybrid neural network architecture to obtain a prediction model; inputting real-time radar observation data into the prediction model to obtain a three-dimensional particulate matter concentration field prediction result; and analyzing the prediction result of the three-dimensional particulate matter concentration field, and evaluating the pollution cause of the current pollution event. According to the technical scheme, a high-fidelity and time-space continuous concentration field can be generated under the condition that observation data is limited, and the accuracy and reliability of an inversion result are greatly improved.

The invention discloses a data coding method and device, electronic equipment, a processor and a storage medium, and relates to the field of large language models. The method comprises the following steps: by taking a group consisting of a plurality of continuous elements in a channel as a unit, performing grouping quantization on reasoning parameters of a neural network model in a reasoning process to obtain a plurality of groups of quantized data, mapping a first group of quantized data into first cache data based on a first adjustment coefficient, and mapping a second group of quantized data into second cache data based on a second adjustment coefficient; the data distribution can be changed due to the first adjustment coefficient, so that the method can adapt to various data types, a flexible mapping mode is provided, and the data distribution of the mapped first cache data can fit the data distribution of the first group of quantized data before quantization. And the cached first cache data is the data after the reasoning parameters are compressed, so that the memory resources required by the reasoning parameter cache, such as weight, key data and value data, are reduced, and the reasoning speed of the large language model is further improved.

The invention discloses a convective cloud in-cloud temperature gradient joint inversion method based on a laser radar, and relates to the technical field of meteorological detection, and the method comprises the steps: synchronously collecting multi-modal meteorological remote sensing data through a multi-modal meteorological remote sensing device; constructing an adversarial unsupervised correction network, inputting the multi-modal meteorological remote sensing data into the adversarial unsupervised correction network, and generating corrected multi-source observation data; mapping the corrected multi-source observation data to a uniform space-time grid to form a space-time aligned multi-modal data set; and based on the vertical profile of the temperature gradient, in combination with the radar reflectivity factor and the fusion feature vector, calculating the vertical distribution of the liquid water content, the water vapor density and the particle effective radius in the convective cloud, and generating a quantitative analysis report of the macro and micro characteristics of the convective cloud. According to the invention, by constructing the antagonistic unsupervised correction network, errors in multi-source observation data can be effectively corrected, and a high-quality data basis is provided for fusion and inversion in meteorological service.

The embodiment of the invention provides an Internet of Things control system and method, electronic equipment, a storage medium and a program product, and relates to the technical field of industrial Internet of Things. The system comprises a connection management module used for establishing and maintaining connections between a plurality of Internet of Things devices and a plurality of MQTT servers corresponding to the Internet of Things devices; the dynamic equipment mapping module is used for mapping the data of the Internet of Things equipment to the MQTT server through the configuration file; and the message queue and network disconnection continuous transmission module is used for putting the message into the tail of the message queue to continuously transmit the message if the MQTT client instance corresponding to the Internet of Things equipment is detected to be in a network disconnection state when the message is published to the MQTT server. According to the scheme, dynamic expansion of heterogeneous equipment in the industrial Internet of Things, parallel butt joint of multiple cloud platforms, network disconnection continuous transmission and two-way communication capability are realized.

The invention relates to the technical field of photovoltaic power generation detection, in particular to an outdoor photovoltaic module attenuation rate test method and test system, and the method comprises the steps: a multi-dimensional environment and operation data collection process; constructing and updating a digital twin reference model; a self-adaptive environment compensation and normalization process; and attenuation rate calculation and state evaluation. Nonlinear measurement errors caused by spectrum red shift / blue shift, large-angle incidence and a temperature hysteresis effect are effectively stripped, and real-time output data are mapped to equivalent performance data under a standard test condition, so that the measurement uncertainty is remarkably reduced, and sensitive capture and accurate calculation of a micro power attenuation trend are realized.