962 results about "Static data" patented technology

The invention discloses a low-altitude aircraft take-off and landing platform site selection optimization method. The method comprises the steps that real-time dynamic data and static GIS data including urban traffic flow data, meteorological data, landform data, environment data and POI data are acquired; constructing an urban three-dimensional digital model according to the static GIS data, and constructing a digital twin model according to the three-dimensional digital model and the real-time dynamic data; constructing environment constraint conditions and safety constraint conditions of candidate take-off and landing platform positions in the digital twin model; performing multi-objective optimization on the digital twin model through a particle swarm optimization algorithm, and generating an optimal candidate take-off and landing platform site selection scheme set meeting environment constraint conditions and safety constraint conditions; and dynamically updating the digital twin model according to real-time data feedback, and dynamically adjusting the site selection scheme of the take-off and landing platform. According to the method, the virtual city model is constructed through the digital twin, an accurate simulation environment and real-time feedback are provided for site selection optimization, and the accuracy and feasibility of a site selection scheme are improved.

The invention relates to the field of distributed machine learning, provides a big data-oriented distributed model training optimization method, and solves the problems of load imbalance, low resource utilization rate, large communication overhead, insufficient fault-tolerant efficiency and the like caused by data fragmentation staticization in the prior art. Load balancing is realized through intelligent clustering and overlapping control; the multi-dimensional heterogeneous resource evaluation model monitors calculation / storage / network indexes in real time, and realizes adaptive scheduling in combination with a task prediction and optimization algorithm; the hierarchical gradient synchronization mechanism adopts a tree-shaped parameter server and a dynamic compression technology, so that the communication traffic is reduced by 50%, and the precision loss is less than 0.8%; the incremental checkpoint system uses erasure code coding and parallel recovery to shorten the fault recovery time from 15 minutes to within 2 minutes, the resource utilization rate reaches 85% or above, the convergence speed is improved by 30%-40% in ResNet, BERT and other model training, and the large-scale training efficiency and the system stability are remarkably optimized.

The invention relates to the technical field of data analysis, in particular to an intelligent talent data matching system and method based on big data, and the system comprises an insight analysis module, a portrait construction and management module, a matching and recommendation module, a strategy support module and an intelligent assistant module. Compared with the prior art, the method depends on a static database and keyword matching, so that skill ecology which evolves rapidly is difficult to capture, and talent evaluation and post requirements are disjointed; according to the scheme, a dynamically updated skill knowledge graph is constructed, a talent portrait and post model is continuously optimized in combination with a real-time data flow, and deep matching calculation is performed by applying a multi-strategy intelligent engine; especially in the rapid iteration field of high and new technology and the like, the system can automatically identify emerging skill association, and it is ensured that the recommendation result always reflects the real market demand.

The invention provides a method and a system for monitoring and predicting the health state of a storage battery based on multi-modal feature fusion, and relates to the technical field of storage battery monitoring, and the method comprises the steps: obtaining multi-modal data through periodically collecting static data of the storage battery and collecting dynamic data of the storage battery in real time; and then the method is realized through the steps of data monitoring, shared bottom layer feature extraction, SOH prediction, RUL prediction, multi-target joint optimization and the like. According to the method, multi-modal feature data generated by the storage battery can be monitored and analyzed on line, a multi-modal feature fusion and multi-target joint prediction and optimization mechanism is introduced, and SOH and RUL are predicted by adopting independent branches based on an SOH prediction model and an RUL prediction model, so that the health state of the storage battery is predicted; according to the method, automatic analysis on multi-modal mass data of the battery can be realized, the workload is reduced, the analysis efficiency is improved, the accuracy of a prediction result can be improved, and meanwhile, a constructed feature sharing mechanism can reduce the calculation complexity and improve the prediction efficiency.

The invention discloses a project cost risk monitoring method and a project cost management platform, and the method comprises the steps: S1, carrying out the real-time collection and standardization processing of multi-source data: collecting dynamic data in real time through an Internet of Things device at a construction site, carrying out the butt joint of a design drawing, a financial system and the like, obtaining static data, and employing a data cleaning, conversion and integration technology; s2, carrying out risk factor identification and correlation analysis based on a knowledge graph; s3, implementing risk quantitative prediction and early warning driven by artificial intelligence; s4, real-time monitoring and automatic verification of contract performance of blockchain enabling are realized; s5, providing dynamic cost adjustment and optimization decision support; through real-time collection and standardization processing of multi-source data, data islands of all parties participating in a project are broken, and accuracy, integrity and timeliness of key data such as construction progress and cost expenditure are ensured. Based on big data analysis and artificial intelligence prediction, more accurate risk assessment and cost prediction are provided for project managers.

The invention belongs to the technical field of data processing, and discloses an information technology analysis system and method based on cloud computing, and the method comprises the steps that a network building module caches static data to nodes nearby a user through a CDN; the resource scheduling module monitors and dynamically allocates resources in real time; the data storage module realizes data redundancy storage; the data acquisition module performs multi-source data acquisition; the data integration module integrates data of different data sources; the data management module manages the whole data process; the data analysis module carries out multi-source data fusion analysis and mines a potential relationship; the result display module provides an intuitive and easy-to-use interface; the security management module guarantees data storage and transmission security; and the monitoring management module deploys a monitoring tool to monitor system operation in real time. According to the method, dynamic allocation and efficient utilization of resources are achieved, deep data analysis is achieved, training is accelerated through distributed computing, automatic model updating is achieved, multi-source data fusion and deep mining are conducted, and a scientific basis is provided for decision making.

The invention discloses an intelligent construction method, system, equipment and medium based on a digital twin tunnel, and aims to construct a high-precision physical entity model by fusing dynamic and static data in real time through multiple types of sensors so as to improve data acquisition comprehensiveness; based on tunnel-level, soil-level and ground building-level multi-level modeling and finite element analysis, fine mechanical stability simulation is realized; multi-dimensional data features are extracted by combining a convolutional neural network algorithm, security risks are evaluated, and a reinforcement scheme is optimized through dynamic iteration; a closed-loop regulation and control mechanism of data monitoring, numerical simulation and dynamic feedback is formed by utilizing a collaborative architecture of a sensing layer, a simulation layer and a display layer, so that environmental disturbance is effectively reduced, and construction safety is guaranteed; according to the method, digital twinning, BIM, finite element analysis and deep learning are fused, real-time monitoring and intelligent optimization of the whole construction process are achieved, efficiency and safety are remarkably improved, and influences on the surrounding environment are reduced.

The invention relates to the technical field of power distribution station supervision, in particular to an intelligent auxiliary and artificial intelligence visual gateway control method and system for a station building, which monitors network state parameters in real time, calculates a network quality score and dynamically selects an optimal transmission path based on a reinforcement learning algorithm. Comprising a high bandwidth mode, a low delay mode and a disaster recovery backup mode, stage processing and dynamic compression are performed on transmission data according to data types and network quality scores, link switching and a local caching mechanism are automatically triggered when a network is abnormal, and finally, multi-gateway load balancing and visual monitoring are realized through a software defined network architecture. The problem of transmission delay or interruption caused by the fact that a static data transmission framework cannot adapt to network state changes in the prior art is effectively solved, and the reliability and the real-time performance of data transmission are remarkably improved through dynamic path optimization, intelligent hierarchical transmission and a fault self-healing mechanism.

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 personalized recommendation method driven by user intention recognition, which comprises the following steps of: collecting multi-dimensional information such as browsing records, click behaviors and comment data of a user, and constructing a user behavior data set; preprocessing the collected user behavior data to obtain a session sequence; the current demand of the user is speculated by analyzing the input text and the behavior mode of the user, and the intention of the user is recognized; constructing a user interest preference prediction model according to the identified user intention; and in combination with real-time feedback of the user, the recommendation model is dynamically updated, and the response speed and the individuation degree of the recommendation system are improved. The problem that a traditional recommendation system only depends on static data and does not comprehensively consider real-time feedback of users is solved, self-adaptive ability is injected for personalized recommendation, intention behaviors of different users in multiple scenes can be analyzed and understood, accurate recommendation service is provided, and recommendation accuracy and user satisfaction are improved.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention relates to the field of power transaction security, in particular to a data security protection method for power transaction. Comprising the following steps: a registration authentication stage: completing identity registration through triple authentication of a physical token, biological feature recognition and a dynamic password and GPS positioning white list verification; in the key initialization stage, an SM2 asymmetric key pair and an SM4 symmetric communication key are dynamically generated according to a transaction time window and main body attributes, and are issued through a point-to-point encryption channel and updated in time; in the data encryption and binding stage, a transaction instruction is encrypted, signed and subjected to double-layer integrity verification; a matching and on-chain registration stage: executing matching after verifying the signature, encrypting a result, writing the result into the block chain, and generating a zero-knowledge proof; and in the settlement and supervision stage, settlement or alarm responsibility investigation is started after decryption verification. According to the invention, the problems of single identity authentication, static key, easy data tampering and the like in the prior art are solved, full-flow security protection of power transaction is realized, and data confidentiality, integrity and traceability are improved.

The invention discloses an enterprise classification management method based on a pollution discharge permission data evaluation index, and relates to the technical field of environment supervision, and the method comprises the following steps: S1, obtaining a real-time monitoring data flow and a pollution discharge permission execution report of an enterprise, and the real-time monitoring data flow collects pollutant concentration data at an hour frequency through an Internet of Things sensor. According to the enterprise classification management method based on the pollution discharge permission data evaluation index, the limitation of a traditional static data evaluation system is broken through by constructing a coupled dual-channel decision engine and a space-time self-adaptive evaluation model, fractal time sequence compression and process logic alignment are performed on monitoring data and declaration data, lossless fusion of multi-source heterogeneous data is realized, and the enterprise classification management method based on the pollution discharge permission data evaluation index is realized. Key management enterprises are highlighted through classification levels, resource intensified management and control are supervised in a centralized manner, and the intervention efficiency on high-pollution emission behaviors is effectively improved.

The invention provides a crop planting suitability dynamic evaluation method and system and a storage medium, and the method comprises the steps: S1, carrying out the time sequence processing of collected NDVI, meteorological data and static data; s2, dividing the phenological period of the target crop into a plurality of stages; s3, constructing a seasonal suitability function for the NDVI and the meteorological data to calculate scores of the data in each stage of a phenological period; s4, determining the score of the static data in each stage according to a preset membership score and grade division; s5, according to the historical NDVI, the meteorological data and the static data, the correlation coefficient of each data and the historical target crop yield and the entropy weight of each data are calculated respectively, and the proportion of the data and the historical target crop yield is adjusted through empirical parameters to obtain the dynamic weight corresponding to each data; and S6, performing weighted summation on the data based on the scores obtained in the steps S3 and S4 in combination with corresponding dynamic weights to obtain a crop planting suitability dynamic index. Therefore, the phenological accuracy of the planting suitability evaluation result of the target crop is improved.

The invention discloses a static database self-adaptive fuzzification desensitization implementation method for highly sensitive data in the power industry. The method comprises the following steps: a data preprocessing module, a sensitivity analysis module, a fuzzification algorithm module, a self-adaptive mechanism module and an effect evaluation and verification module. Compared with the prior art, the method has the advantages that 1, self-adaptive dynamic adjustment is performed: a desensitization strategy is automatically optimized according to data characteristics and use scenes, and privacy protection indexes (such as k-anonymity values) are improved by 30%-50%; 2, multi-dimensional balance: the information loss rate is reduced by 10%-20%, the query accuracy is improved by 5%-10%, and safety and service requirements are considered; 3, system expansibility: a plug-in architecture supports dynamic loading of new algorithms and rules, and a configuration management module realizes strategy real-time adjustment; and 4, compliance guarantee: through a law and regulation rule base and a compliance check algorithm, the compliance is ensured to meet the standard.

The invention discloses a building low-carbon reconstruction evaluation method based on multi-objective optimization. The method comprises the following steps: acquiring real-time data; obtaining static data; based on the real-time data and the static data, a multi-target evaluation model covering the total carbon emission amount, the transformation cost, the energy-saving income and the investment payback period is constructed, and indoor comfort and project implementation constraint conditions are set for the multi-target evaluation model; an NSGA-II algorithm is adopted to carry out optimization solution on the multi-target evaluation model; and performing automatic screening on the Pareto optimal solution set generated by the NSGA-II algorithm by using an ideal point method to generate a unique recommended transformation scheme, wherein the recommended transformation scheme comprises an optimal low-carbon transformation strategy and various key performance indexes corresponding to the optimal low-carbon transformation strategy. According to the method, the problems of evaluation benchmark distortion, insufficient multi-target collaboration and strong subjectivity of a decision-making process of a current existing building low-carbon reconstruction evaluation method are solved.

The invention provides a storage cluster fault diagnosis method and system based on a spiking neural P calculation model, and the method comprises the steps: collecting operation indexes containing static data and dynamic data from a monitoring system of a storage cluster, defining a neuron for each operation index after preprocessing through the construction and training of the spiking neural P calculation model, and carrying out the fault diagnosis of the storage cluster. Initializing an initial state value of each neuron according to the collected data; and performing state updating and reasoning by using pulse signal propagation and fuzzy reasoning through a trained spiking neural membrane calculation model, judging a fault according to a state value of a neuron, and when the state value of a certain neuron exceeds a set fault threshold value, considering that an index has a fault or a potential fault, and identifying a specific fault type in the storage cluster through state combination of the plurality of neurons. According to the invention, efficient fault diagnosis of the distributed storage system is realized.

The invention relates to a multi-channel parallel virtual machine online migration method and system based on memory layering, and belongs to the technical field of cloud computing. According to the method, a to-be-migrated virtual machine memory is divided into a static data layer and a dynamic data layer, and the static data layer is directly transmitted to a target end through shared storage; the dynamic data layer is divided into a plurality of areas according to memory addresses, independent transmission channels are distributed for parallel migration, and the sizes of the partitions are dynamically adjusted according to the dirty page rate; calculating an iteration convergence factor in real time, and switching to a post-copy mode and backing up dirty pages to a shared storage when continuous iteration is not converged for multiple times; and recovering data from the shared storage when the network fails, and finally finishing the migration of the remaining dirty pages. Through memory hierarchical optimization, parallel transmission acceleration and a network fault-tolerant mechanism, the problems of low efficiency, iteration divergence and service interruption risks of large-memory and high-write-dirty-rate virtual machine migration in an AI scene are solved, the migration speed and success rate are remarkably improved, and continuous execution of an AI task is guaranteed.

The invention discloses an equipment mechanism-oriented energy-saving control scheduling method and system. The method comprises the following steps of collecting static data and operation data of core cold and heat source equipment in a building in real time; an energy consumption mechanism model is established and used for describing the rule that equipment energy consumption changes along with the running state; utilizing the energy consumption mechanism model, the historical operation data, the environment data and the people flow data in the building to predict energy consumption and load demand changes in a target time period through a pre-constructed prediction model; according to the prediction result of the energy consumption and the load demand change, in combination with a set optimization objective function, an optimization algorithm is adopted to carry out optimization solution on the start-stop state, the operation load and the water supply and return temperature of the core cold and heat source equipment, and an optimal scheduling scheme is generated; and converting the optimal scheduling scheme into a control instruction and issuing the control instruction to a corresponding building automation system or equipment control unit. According to the invention, the prediction precision and adaptive capacity of energy-saving control scheduling can be improved.

The invention discloses an urban low-altitude-oriented grid dynamic evaluation and intelligent route planning system, and belongs to the field of urban low-altitude airspace and routes. And the data processing module is used for inputting static data including the task number, the task time, the unmanned aerial vehicle number, the take-off and landing field number and the total airspace, the continuous airspace is divided into grid units by a project, and a quantitative evaluation model with seven factors including the underlying surface, the communication quality, the navigation quality, the meteorological environment, the electromagnetic environment, the noise and the countering as the core is constructed. The airworthiness score of each grid is calculated through normalization processing and weighted summation, a real-time and scientific decision basis is provided for path planning of the unmanned aerial vehicle, crossing from static planning to dynamic evaluation is achieved, and a set of airspace gridding dynamic evaluation and task planning system is provided and implemented. The system aims to cope with flight risks caused by multiple factors such as complex underlying surfaces, dynamic weather and communication navigation interference in urban low-altitude flight, and the safety and reliability of unmanned aerial vehicle logistics are improved.

Apparatus and methods for identifying a wireless signal-emitting device are disclosed. The apparatus is configured to sense and measure wireless communication signals from signal-emitting devices in a spectrum. The apparatus is operable to automatically detect a signal of interest from the wireless signal-emitting device and create a signal profile of the signal of interest; compare the signal profile with stored device signal profiles for identification of the wireless signal-emitting device; and calculate signal degradation data for the signal of interest based on information associated with the signal of interest in a static database including noise figure parameters of a wireless signal-emitting device outputting the signal of interest. The signal profile of the signal of interest, profile comparison result, and signal degradation data are stored in the apparatus.

The invention provides a circuit board detection system and method. The method belongs to the technical field of circuit board detection, and comprises the steps that original data of a circuit experiment board are collected through a high-precision sensing network, and the original data comprise static data and dynamic data; the collected data is preprocessed; extracting time-related features from the dynamic data, performing Fourier transform on the dynamic data, extracting frequency-related features, and extracting spatial distribution features from the static data; static and dynamic data of the circuit board are acquired through the high-precision sensing network, and the performance of the circuit board in different working states can be comprehensively captured, so that more comprehensive detection is performed.

The invention discloses an enterprise operation supervision system based on data analysis, relates to the technical field of data analysis, and overcomes the defects of dependence on static data and incapability of capturing a long-term trend by collecting time sequence data D and calculating an inertial behavior vector Hinert and establishing a long-term stable behavior mode of each service node. By calculating the current behavior vector Hcurr and acquiring the behavior deviation vector Dd, whether the service node deviates from long-term inertia can be accurately identified, and the problem that a traditional system lags in abnormal response to enterprise services is avoided. And a comprehensive risk vector Rrisk is calculated in combination with an inertial behavior vector Hinert, so that the defect that an existing enterprise supervision system can only perform static analysis based on current data and cannot pre-judge a business change trend in advance is overcome. By optimizing the scheme Oopt, service adjustment is performed, an operation strategy is accurately optimized, and the problem that a traditional system lacks dynamic adjustment capability and is difficult to intervene in the early stage is solved.

The invention discloses a collision detection method and a computer readable storage medium. The collision detection method comprises the following steps: determining all static components belonging to a preset static type and all dynamic components belonging to a preset dynamic type in a three-dimensional building model; static SDF data are constructed for all static components in the three-dimensional building model; wherein the static SDF data comprises a plurality of static SDF values, and the static SDF values are SDF values from a space point in the three-dimensional building model to the surface of the nearest static component; constructing dynamic SDF data for all dynamic components in the three-dimensional building model; wherein the dynamic SDF data comprises a plurality of dynamic SDF values, the dynamic SDF values are SDF values from a space point in a dynamic influence area to the surface of a dynamic component used for generating the dynamic influence area, and the dynamic influence area is obtained after the boundary of a minimum three-dimensional bounding box of the dynamic component is expanded outwards by a first preset distance; and detecting whether a collision point exists in the three-dimensional building model based on the static SDF data and the dynamic SDF data.

The invention provides a plate forming control method and device based on deep learning, and relates to the technical field of stamping control, and the method comprises the steps: training an original CNN model through a sample data set, and obtaining an intermediate CNN model; the sample data set comprises plate static data and springback data; the plate static data comprises initial size parameters and material parameters; the rebound data comprises a first displacement field after the pressure head is evacuated and the plate is rebounded under different punching depths; determining displacement field compensation values corresponding to the different stamping depths based on a first displacement field error corresponding to the experimental data and the simulation data before the rebounding of the plate under the different stamping depths and a second displacement field error after the rebounding of the plate; adjusting the parameters of the intermediate CNN model by using the displacement field compensation value until the prediction error of the intermediate CNN model is less than a preset threshold, and obtaining a target CNN model; and the plate static data of the to-be-processed plate and the target forming effect are input into the target CNN model, and the target stamping depth is obtained.

The invention discloses a wind power ultra-short-term prediction method based on time fusion Transform, and the method comprises the following steps: 1, collecting historical wind power data, numerical weather prediction (NWP) data and corresponding static data of a wind power plant, carrying out the coding of the data, and constructing an input vector; 2, constructing a data processing module, and performing feature extraction on historical wind power data, NWP data and static data to obtain different data representations; 3, constructing a time fusion coding module, carrying out time fusion coding, and enhancing time features represented by data; 4, constructing an output module, and completing the construction of a prediction model; and 5, training the prediction model by using related data to obtain optimal main parameters of the model. The method can achieve the ultra-short-term prediction of the wind power, and improves the prediction accuracy.

The invention relates to the technical field of medical assistant systems, and discloses a comprehensive intelligent medical assistant system, which comprises an information acquisition unit used for acquiring user state information; the user state information comprises user static data, dynamic data and feedback data; the model construction unit is used for performing feature extraction according to the user state information and establishing data association indexes of physiology, environment and behaviors; the information processing unit is used for automatically capturing symptom characteristic information and carrying out diagnostic analysis on the input information in combination with user state information; the pushing unit is used for performing local decision processing on the analysis result, forming a diagnosis path diagram and pushing the diagnosis path diagram to a user side; and the learning optimization unit is used for optimizing a recommendation strategy according to the feedback data and integrating long-term health features to carry out optimization suggestions. According to the invention, the service mode of medical health is remodeled, the unique health characteristics of each user can be more accurately understood, the service quality and credibility of a medical assistant system are improved, and the occupancy rate of medical resources is reduced.

The invention discloses a coal spontaneous combustion early warning method based on deep learning and physical constraint. The coal spontaneous combustion early warning method comprises the following steps: acquiring coal static data including composition data, physical structure data and temperature rising speed and time sequence gas data including various gas concentrations and the current coal temperature; respectively inputting the processed static data input and time sequence gas data into a deep multi-layer perceptron module and a Transformer encoder module capable of learning a position scaling factor to obtain a first static feature and a first dynamic feature; inputting the features into a gating attention fusion module to obtain fused features, and predicting a critical temperature and a pyrolysis temperature corresponding to spontaneous combustion of the coal; in the training stage of the coal spontaneous combustion early warning model, the total loss comprises physical constraint loss and mean absolute error loss, and the physical constraint loss comprises temperature sequence constraint loss and mass conservation constraint loss; and the temperature range, the early warning grade and the chemical evolution stage are obtained through the critical temperature, the pyrolysis temperature, the coal temperature and a preset threshold value.

The invention discloses an intelligent dynamic prediction method for a coal and gas outburst dangerous area of a working face, and the method comprises the steps: building an integrated early warning information analysis platform, dynamically collecting key parameters in a mine, and constructing a dynamic data set; and calculating a coal seam firmness factor and a gas emission factor by utilizing a data fusion technology and combining historical data and regression analysis. Furthermore, in combination with a static data set, such as geological and ventilation system distribution data, a multi-parameter coupling prediction model is constructed. The model is trained and verified through a machine learning method so as to predict the coal and gas outburst risk of the working face in real time. The system automatically triggers early warning according to a model prediction result and a preset threshold value, a scientific basis is provided for coal mine safety management, and coal and gas outburst accidents are effectively prevented.

The invention discloses an automatic page generation method of a software construction platform based on a large language model, which comprises the following steps of: extracting a scene, a page and constraint description from a natural language demand, and generating JSON (JavaScript Object Notation) content containing a data model, a query condition, a table column and static data through the large language model; according to component type constraints and verification rules, the JSON is mapped to components and combinations of buttons, labels, texts, pictures, pull-down selection, multi-choice, single choice, tables, dates and the like of the code-free construction platform; and automatically filling a component set based on the upper and lower partition templates, and injecting static data and verification logic. Through industry knowledge adaptation, component semantic binding and dynamic constraint, it is ensured that the page conforms to field specifications and interaction logic, the learning cost is reduced, manual intervention is reduced, and the method is suitable for the fields of smart cities, natural resources, urban and rural residence construction, emergency management, enterprise digital transformation and the like.