1172 results about "Resource distribution" patented technology

The invention relates to a computing power network resource scheduling method. The method comprises the following steps: acquiring floating point operation performance parameters and operation states of node processors and energy index data of data centers where the node processors are located, and calculating to generate a node list; constructing a global resource pool based on the list, and generating a resource distribution table containing the total calculation power of the region; obtaining calculation requirements and time delay constraints of the task queue, extracting feature vectors in combination with the resource distribution table, and generating a resource utilization rate table; obtaining network link flow data, predicting a link congestion probability through a long short-term memory network, and generating a flow control strategy table; and finally, updating resource pool network constraints according to the resource utilization rate table and the flow control strategy table, and remapping tasks by taking node effective computing power as a weight to generate a scheduling execution scheme. According to the method, accurate quantitative evaluation of the computing power resources is realized, the matching precision of tasks and the computing power resources is effectively improved, the resource utilization rate of the computing power network can be remarkably improved, and the overall scheduling efficiency and stability of the system are enhanced.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

The invention provides a road disaster detection method and system based on air-ground cooperative unmanned inspection, and the method comprises the steps: obtaining a multi-source road monitoring data set, carrying out the time-space alignment processing of the multi-source road monitoring data set, generating a road monitoring data set integrated with a time-space label, and carrying out the recognition of a road disaster based on a preset multi-mode disaster recognition model. Disaster feature extraction processing is carried out on the road monitoring data set fused with the space-time labels to obtain a road disaster feature set, a disaster detection result set is generated according to the road disaster feature set, the disaster detection result set comprises a plurality of disaster detection units, and an inspection optimization strategy set is generated based on the disaster detection result set. According to the method, the automation degree and the response efficiency of road disaster detection are comprehensively improved under the condition of not depending on artificial experience intervention, and the problems of high misjudgment rate, large monitoring blind area and rigid resource allocation caused by data splitting and time-space asynchronization in a traditional method are solved.

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention provides a multi-mode tailing pond dam break digital twinning emergency deduction and decision optimization method, which comprises the following steps of: firstly, constructing a high-precision three-dimensional scene model, performing simulation calculation on the high-precision three-dimensional tailing pond model based on a finite element model to obtain a dam body stress-strain field, and performing simulation calculation on the dam body stress-strain field based on ANSYS + reinforcement learning. And predicting a dam break probability distribution diagram by using the dam body stress-strain field and the current environment state, generating a rescue scheduling scheme based on the dam break probability distribution diagram and the resource distribution data, building a dynamic virtual deduction scene for exercise, and verifying and optimizing the rescue scheduling scheme according to exercise record data. And a final rescue scheduling scheme is obtained. According to the method, the ANSYS + reinforcement learning hybrid engine is utilized to reinforce the deduction precision, dam break path prediction, optimal resource scheduling and plan self-optimization closed loop are realized, the problems of data fusion distortion, deduction stiffness, decision lag and the like in the traditional technology are solved, and the method is particularly suitable for variable working conditions such as tailing pond seepage field sudden change and extreme weather.

The invention provides a low-orbit satellite resource allocation method and device based on beam and frequency domain collaborative optimization, and the method comprises the steps: dividing the overall bandwidth of a user region into a central sub-band and an edge sub-band through employing a dynamic soft frequency reuse scheme; all ground users are subjected to spatial clustering according to geographic positions and traffic demands, coverage cells are dynamically divided according to clustering results, and balanced service is achieved; on the basis of dynamic cell division, user clusters with high service volume are selected according to priorities, a beam activation set is generated, and a hopping beam matrix is updated; establishing a channel model between a user and a beam and a switching matrix between satellites; constructing a system utility target, and constructing a multi-target optimization problem model by taking the system utility target as a main part and integrating interference, switching overhead and delay penalty; and the multi-dimensional resource collaborative optimization framework based on deep reinforcement learning reconstructs a multi-objective optimization problem model, and outputs a solution of the multi-objective optimization problem model, including an optimal bandwidth and a power allocation result. According to the invention, resource allocation can be carried out on low-orbit satellites.

The invention relates to the technical field of mineral resource management, in particular to a mineral resource dynamic prediction and mining management system which comprises a data perception and fusion layer, a unified digital twinborn model, a dynamic prediction and decision intelligent agent and a visualization and interaction control layer. The data perception and fusion layer collects structured data such as geological exploration and mining environment and market unstructured data, and generates a unified space-time tensor through processing; the unified digital twinborn model generates a dynamic comprehensive mining area situation map containing resource reserve risk economic indicators through a three-dimensional convolutional neural network embedded with an attention mechanism; the dynamic prediction and decision-making agent predicts future reserves and geological risks, and constructs a dual-objective optimization model to generate an optimal mining path equipment scheduling and resource allocation scheme; and the visualization and interaction control layer presents the mining area state and the decision scheme in a three-dimensional manner and provides an interaction interface. According to the invention, the data utilization rate and decision scientificity are improved, the safety risk is reduced, and mine management intellectualization is promoted.

The invention discloses a Direct3D memory model compatible method based on adaptive placeholder resources, which comprises the following steps: establishing resource metadata, accessing a scene library, checking an instruction template and double sandboxes when a DXVK is started, describing related resources by the DXVK through the metadata after a D3D application is started, completing resource mapping and metadata dynamic updating, and allocating the resources to the corresponding sandboxes; the DXVK compiles an application shader code, identifies a resource access instruction, matches an access scene and a check template by combining a pipeline stage and binding slot query metadata, instantiates the check instruction and adds the check instruction to the front of a target instruction; according to the unbound resources, adaptive generation of corresponding occupied resources is carried out, the authority and the state are configured, the unbound resources are bound to a Vulkan descriptor set to replace VKNULLHANDLE, and adaptation of the shader interface and the pipeline state parameters is completed to create PSO (Particle Swarm Optimization); and intercepting an application rendering instruction analysis parameter to construct a command buffer area, submitting the command buffer area to a Vulkan queue to trigger a GPU (Graphic Processing Unit) to execute rendering, and finally completing complete conversion and adaptation from the D3D rendering logic to the Vulkan.

The invention relates to the technical field of wind power generation, and discloses a cross-scale wind power plant wind resource evaluation method based on a numerical mode, comprising the following steps: step 1, acquiring multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme adaptive to the regional terrain; step 3, through a WRF power downscaling method, generating annual scale wind resource data as initial wind resource data; 4, performing partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling the WRF mode and the wind power plant parameterized model; step 6, optimizing WRF lower boundary conditions; 7, simulating the atmospheric flow of the target area, and quantifying the wake flow evolution characteristics of the wind power plant; 8, constructing a wake flow analysis model; and step 9, formulating a collaborative productivity plan of the wind power plant group. The power generation capacity of the wind power plant to be developed can be accurately predicted, and a reliable basis is provided for scientific decision-making of resource distribution development and planning of the million-kilowatt wind power plant.

The invention provides a method and a system for intelligently generating and dynamically adjusting a public emergency plan, which are oriented to the field of public safety emergency. The method comprises the following steps: fusing multi-source heterogeneous data to construct an extensible domain knowledge graph, establishing a standardized emergency instruction library, and carrying out multi-dimensional information labeling; automatic extraction of disaster elements is realized based on an entity recognition model of deep learning; analyzing association rules among the emergency entities through a semantic relationship mining technology; key information such as a disaster chain and resource distribution is rapidly obtained by using a map reasoning mechanism; carrying out cross-department resource collaborative allocation by adopting a multi-objective optimization algorithm to generate an optimal disposal scheme; a high-dynamic adjustment mechanism is constructed, and an emergency plan is dynamically optimized based on situation evolution prediction and real-time monitoring data; and finally, automatic generation and versioning management of the plan are realized. Through multi-mechanism cooperation of knowledge modeling, intelligent element analysis, semantic reasoning, dynamic optimization and predictive adjustment, the emergency plan generation efficiency, situation adaptability and resource allocation rationality are remarkably improved, and support is provided for quick response and scientific decision under emergencies.

The invention discloses a photovoltaic power station distributed construction management method and system based on load analysis, and the method comprises the steps: outputting a dynamic load intensity thermodynamic diagram according to the terrain elevation data, meteorological historical data and assembly mechanical parameters of a photovoltaic power station planning region; based on the dynamic load strength thermodynamic diagram, generating a construction zoning scheme for wind pressure resistance optimization; outputting a conflict-free construction resource allocation matrix according to the construction partitioning scheme; on the basis of the construction resource allocation matrix, construction parameters are dynamically corrected by adopting an adaptive particle swarm optimization algorithm, and an anti-deformation construction instruction set is generated; and driving the construction machinery to execute operation according to the anti-deformation construction instruction set, and iteratively updating the global load distribution map by using the reinforcement learning model to form a closed-loop construction management link. By utilizing the embodiment of the invention, the balance of construction precision and efficiency in a complex environment can be realized by fusing multi-source data, optimizing partition planning, intelligently scheduling equipment and controlling closed-loop quality.

The invention relates to the technical field of environment monitoring, in particular to a farmland environment intelligent monitoring system based on multi-source data fusion, which comprises a multi-source data acquisition module for synchronously acquiring original data; the dynamic compensation preprocessing module is used for receiving the original data and generating standard data with aligned time axes through a dynamic timestamp compensation mechanism; the spatial-temporal feature decoupling module is used for performing multi-scale feature extraction on the standardized data and separating spatial distribution features, time sequence features and environment coupling features; the multi-modal fusion analysis module is used for fusing the spatial distribution characteristics, the time sequence characteristics and the environment coupling characteristics by adopting an evidence chain fusion algorithm to generate farmland environment state evaluation parameters; and the edge decision optimization module is used for dynamically adjusting a resource allocation strategy of an edge computing node according to the farmland environment state evaluation parameters. Priority processing of key tasks is achieved under limited computing power, and the response efficiency and the resource utilization rate of the system are effectively improved.

The invention relates to the technical field of construction risk early warning, in particular to a BIM-based building construction risk early warning method and system, and the method comprises the following steps: constructing an incidence matrix between risk nodes based on time and space dimensions for the risk factor of each node in a construction process, calculating the risk propagation intensity value between the nodes, and calculating the risk propagation intensity value between the nodes; and judging the risk association relationship between the nodes, performing grading processing on the risk coupling degree according to the propagation rate, and outputting a risk coupling matrix value. According to the method, a high-risk area is extracted by combining spatial distribution data, dynamic partition management is achieved, dynamic influences of environmental variables such as temperature, humidity and vibration are quantified, the complex relation between environmental conditions and risk states is revealed, the accuracy and real-time performance of risk assessment are optimized, and the risk assessment accuracy is improved by analyzing the risk transfer trend and propagation path changes. And dynamically adjusting node priorities and resource allocation, updating the influence of newly added risk signals in real time, and realizing full-process dynamic optimization of construction risk early warning.

The invention provides an information resource distribution method and system applied to a dynamic network environment, and the method comprises the steps: firstly obtaining a real-time state feature set of a target distribution network, determining a resource distribution constraint condition set in a current network environment based on the real-time state feature set, carrying out the priority analysis of a real-time resource request sequence of a target user group, and obtaining a real-time resource distribution constraint condition set; obtaining resource timeliness grade distribution and a request path optimization feature set, generating a dynamic distribution strategy parameter set matched with a resource distribution constraint condition, triggering a resource redirection operation, and enabling the resource request response rate of the target user group to be improved to a preset threshold interval; and the information resource distribution efficiency and stability in the dynamic network are improved.

The invention provides a local cache resource optimization method and system combined with a node state, and relates to the technical field of cloud storage optimizing.The method comprises the steps that firstly, a state index set, including indexes such as resource use, security levels, survivability and transmission cost, of multiple nodes in a distributed network is obtained; calculating a target cache value of the current node to the target information resource according to the target cache value, generating a local cache decision parameter after correction of a dynamic adjustment factor, if the local cache decision parameter exceeds a node cache trigger threshold value, storing the target information resource into a local cache of the current node and generating a cache resource distribution update instruction, and updating the cache resource distribution update instruction. And adjusting the adjacent node cache synchronization strategy according to the cache resource distribution updating instruction, thereby realizing the cooperative operation of the cache resources, and improving the resource utilization rate and access efficiency of the distributed network.

The invention discloses a hierarchical traffic prediction-based multi-terminal video display adaptive method, which comprises the following steps of: acquiring network traffic, user behaviors and equipment state information in real time by constructing a multi-dimensional feature acquisition module, and generating a dynamic feature matrix containing traffic, interaction and performance features; processing the feature matrix by using a deep learning model, and generating and dynamically adjusting a network traffic grading prediction result; formulating an optimized video content distribution strategy based on a prediction result and equipment performance parameters, and coordinating multi-terminal resource distribution; through user behavior analysis and network state monitoring, a self-adaptive video display control strategy is generated, dynamic adaptation of video resolution, frame rate and playing logic is realized, a full-link collaborative optimization module is constructed, terminal, network and application layer resource allocation is coordinated, a video transmission and display strategy is optimized, and network flow prediction and multi-terminal interaction requirements are combined to realize multi-terminal interaction. And generating a dynamic video layout and content switching strategy.

The invention relates to a GPU resource space-time slicing and dynamic reconstruction virtualization method and system based on AI training, and the method comprises the steps: receiving an AI training task, obtaining the task feature data of the AI training task, and transmitting the task feature data to a task prediction model; the task prediction model outputs a training demand portrait of the current AI training task based on the task feature data, and the training demand portrait comprises an estimated computing power demand, a peak video memory demand, an expected running time length and a task priority; based on the training demand portrait, a preset AI scheduler generates a vGPU slicing strategy corresponding to the AI training task according to the current GPU resource distribution condition; operation information of each vGPU is obtained in real time and updated to the GPU resource pool, and the computing power or the video memory size of the vGPU in operation is adjusted in real time according to the training demand portrait. The method has the advantages that the dynamic allocation capacity of the GPU load resources is improved, and therefore the task training efficiency of the GPU is improved.

The invention relates to a method for setting power loss of a mountain photovoltaic array. The method comprises the following steps of: establishing a coupling causal spectrum of multi-source factors including terrain, angle, sunlight, temperature, dust and wiring mode to component output; identifying a dominant loss path based on an identification method of a Bayesian network; outputting a structure weight matrix of the power loss, and identifying an inclination angle gt; a temperature gt; a set of influence factors of the wiring topology pair setting; calculating a power loss response index of each component based on the loss path weight; forming a response index thermodynamic diagram by using a space weight interpolation algorithm, and performing array space sensitive partitioning; the high response area is used as a preferential setting target, and the low response area enters an observation maintenance mechanism; by constructing the response index and the correction response potential function based on multi-factor coupling, the power response potential of the component to the setting operation can be accurately identified, the problems of average adjustment and low-efficiency resource allocation in a traditional setting method are effectively avoided, and it is ensured that setting resources are intensively input into a high-response area.

The invention belongs to the technical field of agricultural Internet of Things, and discloses an Internet of Things-based intelligent agricultural cloud platform data acquisition and management system, which comprises the following steps of: acquiring plant rhizosphere microenvironment information, generating rhizosphere original data and performing correlation analysis to obtain rhizosphere electrochemical interaction data; collecting canopy microclimate information, and performing temperature and humidity gradient quantification processing and pore dynamic response characteristic extraction to obtain pore response characteristic data; collecting field illumination information, and carrying out multispectral distribution analysis and photoperiod sensitivity differentiation analysis to obtain a crop photoperiod sensitivity spectrum; performing multi-dimensional association fusion to generate crop physiological status comprehensive evaluation data; agricultural measure analysis and resource allocation optimization are carried out, and finally a resource allocation instruction is generated and agricultural operation scheduling is carried out; according to the invention, intelligent acquisition, analysis and management of data in each link of agricultural production are realized, and the utilization efficiency of agricultural resources and the production accuracy are improved.

The invention provides an HC-MOGA-based agent task collaborative scheduling method in an industrial internet, and aims to solve the key problems of low scheduling efficiency, poor system robustness and the like caused by heterogeneous types of equipment (subsequent modeling is an agent), complex task dependence and non-uniform resource distribution in an industrial site. According to the method, a heterogeneous agent scheduling model with task identification, resource matching and dependent modeling capabilities is constructed for a multi-stage operation task cooperatively completed by a data acquisition agent and an operation type agent in an industrial internet environment. An HC-MOGA (heterogeneous constraint multi-objective genetic algorithm) is provided, multi-dimensional chromosome coding, a layered mapping mechanism, a dual-stage variation strategy and a dynamic constraint repair mechanism are introduced, and a dual-objective optimization model fusing work completion income and comprehensive resource consumption and potential loss is established. The method is suitable for industrial internet typical application scenes such as intelligent manufacturing, automatic production lines and multi-agent task collaborative management.

The invention discloses a charging user behavior pattern analysis system and method, and relates to the technical field of electric vehicle charging management, and the method comprises the steps: obtaining and building a multi-source data pool, fusing the multi-source data pool, analyzing a charging behavior pattern of a user, calculating the abnormal behavior probability, building a dynamic scheduling rule according to the charging behavior pattern of the user, and carrying out the dynamic scheduling according to the abnormal behavior probability. According to the method, through multi-source data collection and analysis, the user charging behavior is accurately predicted, resource distribution is dynamically optimized, the charging station operation efficiency and the user satisfaction degree are improved, virtual power plant integration is promoted, and the power generation efficiency is improved. According to the method, a vehicle-to-power grid strategy optimization module is constructed, the user participation power grid peak regulation potential is quantified, regional energy scheduling is optimized, in addition, an anomaly detection and response mechanism is provided, safety and stability are ensured, and the charging service is promoted to develop towards the efficient, intelligent and sustainable direction.

Systems and methods disclosed herein automatically detect, analyze, and mitigate anomalous resource distribution among artificial intelligence (AI)-based agents within a distributed computational network. The system receives a resource allocation request specifying computational resources, agent parameters, and performance objectives for a network of agents. A first AI model set monitors agent activity by tracking resource consumption and behavioral deviations from baseline profiles. The system compares resource usage of agents with historical norms and / or predetermined thresholds to generate an anomaly score for each agent. A second AI model set aggregates scores to construct a multi-dimensional data structure that indicates the comparison and anomaly score. The system ranks agents by anomaly severity to isolate agents with high scores (e.g., misaligned agents), and reallocates resources and updates access privileges for the misaligned agents.

The invention relates to the technical field of audio control, and discloses a low-delay audio input switching method and system, a storage medium and equipment, and the method comprises the steps: carrying out the parallel pre-initialization of a plurality of pieces of audio input equipment, and building an independent parallel audio data cache for each piece of equipment; monitoring the state of each audio input device and the audio stream quality in real time, and judging whether switching is triggered or not based on a multi-factor decision model; after the switching decision is triggered, seamless audio data stream switching is executed, and format unification processing and cross fade-in and fade-out transition are included; a unified equipment operation interface is provided through the hardware abstraction layer, and system resources are optimized and managed; a delay sensing closed-loop control mechanism is constructed, processing delay of each link is monitored in real time, a caching strategy, a processing algorithm and resource allocation parameters are dynamically adjusted, self-adaptive balance of low delay and high tone quality is achieved, and through the method, quick and smooth switching of audio input equipment is achieved, delay is remarkably reduced, and real-time audio experience is improved.

The invention relates to the technical field of industrial data, discloses a mining equipment state monitoring method and system based on the Internet of Things, and effectively solves the problem of insufficient model generalization ability caused by scarcity of fault samples of mining equipment. The virtual sample generation technology expands the available training data volume by 3-5 times, so that the early fault detection rate is improved to 85% or above. The self-adaptive feature selection mechanism reduces the consumption of computing resources by more than 30%, and maintains the integrity of key fault features at the same time. The multi-stage early warning system realizes accurate grading of fault severity, so that the maintenance resource distribution efficiency is improved by about 40%. The closed-loop optimization mechanism enables the model to continuously evolve in the operation process, and the annual false alarm rate is reduced by about 15%. The explainable diagnosis report provides a clear technical basis for field maintenance, and the average troubleshooting time is shortened by about 50%.

The invention relates to an inter-basin water saving amount transaction and scheduling optimization method, which comprises the following steps of: performing dynamic contrast analysis on water saving behaviors and water resource response on the basis of historical water consumption behaviors, resource regulation and control records and meteorological basic data, judging the autonomy and continuity of the water saving behaviors, and obtaining the right-confirmable water saving amount; according to the watershed water resource tension degree, the water ecology sensitivity and the water transfer cost, a cross-watershed conversion rule is adopted, a conversion scale factor is set, and resource value transverse butt joint of water saving behaviors of different areas is achieved; calculating the flow attenuation, the regional bearing threshold and the water quality compatibility in the allocation path based on the water-saving amount obtained by the water-saving transaction, and determining the available transmission range and the sustainable allocation amount of the water-saving amount; and according to the distribution of the water-saving quotas and the corresponding value grades, carrying out combined screening according to the use priority, the basin compensation relationship and the space-time adaptation requirements, outputting a resource allocation scheme, and forming a cross-basin water-saving-quantity-driven water resource optimal allocation structure.

The invention belongs to the technical field of agricultural supervision, and particularly relates to an intelligent agricultural planning decision-making system based on the Internet of Things, which comprises a planting plan decision-making unit, a resource allocation decision-making unit, an irrigation and fertilization monitoring unit, an agricultural planting management and analysis unit and an agricultural management and control terminal, planting plan decision-making information is generated through the planting plan decision-making unit to improve planting benefits, and monitoring analysis of the resource distribution decision-making unit and the pest and disease damage monitoring decision-making unit is beneficial for achieving precise irrigation and fertilization and timely preventing and treating pest and disease damage. The irrigation and fertilization monitoring unit analyzes the nutrient water content suitability abnormal performance of the corresponding area in the detection period, enhances subsequent irrigation and fertilization supervision when an irrigation and fertilization alarm signal is generated, and comprehensively evaluates the planting management performance of the corresponding area when an irrigation and fertilization qualification signal is generated; and subsequent planting supervision is enhanced when the planting management alarm signal is generated, so that the growth of crops in corresponding areas is ensured, and the intelligent level is high.

The invention discloses a real-time reservation and scheduling system for shared parking, and relates to the technical field of intelligent traffic and smart city management, and the system comprises a demand analysis module which is used for extracting parking lot scene types, time requirements and priority information from user parking request data, generating standardized demand description through dynamic weight distribution, and sending the standardized demand description to a user; the process decomposition module is used for decomposing a parking scheduling process into demand receiving, resource matching and path planning business units according to parking lot scene types and time requirements in the user demand analysis result, and determining a business unit set; according to the real-time reservation and scheduling system for shared parking, through dynamic weight distribution and real-time response optimization, efficient resource distribution and path planning are ensured, and the parking scheduling efficiency and the user experience are remarkably improved.

The invention discloses a complex geological weak surrounding rock tunnel micro-disturbance green blasting construction method which comprises the following steps: acquiring multi-source data through a sensor and geological survey equipment, and extracting a standardized feature set; and constructing a blasting parameter and surrounding rock response prediction model by using a random forest algorithm, inputting explosive load and hole net parameters, and outputting predicted surrounding rock deformation and vibration values. And if the vibration value exceeds the environment-friendly threshold value, adjusting the parameter by adopting a gradient descent algorithm until the requirement is met. And in combination with real-time monitoring data, the surrounding rock response is dynamically updated by using a Kalman filtering algorithm. If the overexcavation probability exceeds the threshold value, the parameters are further optimized through a genetic algorithm. And finally, optimizing resource allocation through a linear programming algorithm, and determining a final blasting scheme. Intelligent regulation and control of blasting parameters of the weak surrounding rock tunnel are achieved, vibration and deformation are effectively controlled, the overexcavation risk is reduced, the construction efficiency and quality are improved, and a new technical scheme is provided for similar projects.