294 results about "Computing center" patented technology

The invention relates to a bridge and tunnel disease detection method and system based on unmanned aerial vehicle-edge computing collaboration, and solves the problem that the detection efficiency is limited due to the lack of systematic design of a collaboration mechanism of an unmanned aerial vehicle and edge computing. The method comprises the following steps that: a distributed edge computing node fuses multi-source monitoring data to obtain a health index, compares the health index with a multi-level threshold value, generates a message containing space coordinates, levels and characteristics when the health index is abnormal, and transmits the message to an edge computing center; the center screens adaptive unmanned aerial vehicles, plans an optimal path, dispatches collected data, and preliminarily screens diseases through a parallel model; determining disease complexity and types in combination with abnormal features, and establishing a collaborative detection unit to specially collect multi-source data; centimeter-level positioning is realized through BIM registration and SLAM, and an accurate detection report is generated. The method has the following effects: accurate scheduling, real-time processing and centimeter-level positioning of disease detection are realized, an intelligent detection closed loop is constructed, and the accuracy and efficiency of bridge and tunnel operation and maintenance are greatly improved.

The invention belongs to the technical field of railway inspection, and particularly relates to a space-air-ground integrated railway power transformation and distribution intelligent inspection method and system which comprises a sensing layer, a platform layer, a data and intelligent center, an application layer and a user and presentation layer. The sensing layer is composed of an aerial inspection layer, a ground inspection layer and a fixed monitoring layer; the platform layer is based on a cloud computing center and edge computing node collaborative architecture, combines data and an intelligent center, integrates multi-source heterogeneous data through a big data platform, carries an AI algorithm engine to realize automatic identification of equipment defects, analyzes images, synchronously predicts the images, and realizes automatic identification of the equipment defects. And the application layer is combined to complete the functions of automatic generation of an inspection report, automatic distribution of a maintenance work order, equipment health state evaluation and the like. According to the method, functions of railway power place inspection, power supply line inspection, power supply and distribution facility operation and maintenance emergency and the like can be realized, deep fusion analysis of multi-dimensional data is realized, and meanwhile, the problems of insufficient training samples, sparse defect types and the like of an existing AI model are solved.

The invention relates to the technical field of spacecraft orbit control and space safety, and discloses a space debris orbit prediction and avoidance method, which comprises the following steps that: a ground computing center screens candidate targets and generates enhanced data packets for uploading; the spaceborne computer combines real-time navigation data to carry out geometric feature screening, and an instant high-risk target list is generated; controlling the star sensor to execute optical observation, and resolving an optical measurement relative orbit state by utilizing displacement superposition and Kalman filtering; selecting a high confidence coefficient or a conservative probability threshold according to the optical measurement state acquisition condition, and generating an avoidance instruction when the collision probability exceeds the limit; and the propulsion system responds to the instruction to execute avoidance, verifies the effect by confirming the semi-major axis variation and executes track recovery by selecting an aircraft. According to the method, the ground-air collaborative screening and shift superposition enhancement technology is adopted, the problems that the low-precision ephemeris false alarm rate is high and the detection capability of a satellite-borne sensor on a dark and weak target is insufficient are solved, and the accuracy of evasion decision making and the execution reliability are improved.

The invention provides a computing power metering method and device of an intelligent computing center cloud platform based on a multi-dimensional performance index, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a first task type of a first computing power operation task; s2, determining a plurality of resource indexes corresponding to the first task type and a weight coefficient corresponding to each resource index; s3, collecting a parameter value corresponding to each resource index in each of the N periods; s4, calculating a computing power value corresponding to the first computing power operation task based on the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods; and S5, generating a first verification data packet based on the first task identifier of the first computing power operation task, the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods. The computing power credibility of the intelligent computing center cloud platform can be greatly improved.

The invention provides an edge calculation optimization method for a seawater parameter monitoring Internet of Things system, and belongs to the technical field of edge calculation, and the method comprises the steps: constructing a seawater parameter monitoring sensor network, and calculating an energy consumption evaluation reference value based on a data change rate and a disorder degree; performing feature extraction on the seawater parameter data; deploying a double-model collaborative architecture comprising a terminal lightweight model and a center complete model; setting a dynamic energy consumption threshold judgment mechanism to allocate calculation tasks; executing lightweight edge calculation at the sensing nodes; transmitting the processed data through a low-power wide area network; the calculation center verifies and fuses the data; constructing a seawater parameter space-time distribution model; dynamically adjusting an energy consumption evaluation reference value by using a Pareto optimal solution set method; and periodically evaluating system energy consumption efficiency and updating model parameters. According to the method, dynamic balance of energy consumption and calculation efficiency of the seawater parameter monitoring system is realized, and the continuous operation capability and the monitoring reliability of the system are remarkably improved.

The invention belongs to the technical field of crossing of artificial intelligence and computer system architecture, particularly relates to a computing power center equipment power supply intelligent system of a knowledge graph, and aims to solve the problem of low energy efficiency caused by extensive power supply management, unbalanced energy consumption and lagged load response. The system comprises a task analysis module, a dynamic load prediction module, a multi-source power supply scheduling module, an equipment-level power supply control module and a feedback optimization module, through load prediction and multi-source power supply cooperative scheduling driven by task semantics, voltage and current dynamic adjustment and sleep wake-up control are achieved, and a strategy is optimized online based on operation feedback. The scheme improves energy efficiency, equipment life and system stability.

The invention provides a heterogeneous computing power normalization metering method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a parameter value of a first related parameter of a first GPU and a parameter value of a second related parameter of a first TPU, the first GPU and the first TPU are used for executing a first computing power operation task; s2, determining a first weighting coefficient of the first GPU, and determining a second weighting coefficient of the first TPU; s3, based on the first weighting coefficient, the parameter value of the first related parameter and a preset reference value, calculating to obtain a first computing power value of the first GPU; s4, based on the second weighting coefficient, the parameter value of the second related parameter and a preset reference value, calculating to obtain a second calculation force value of the first TPU; and S5, based on the first computing power value and the second computing power value, calculating to obtain a third computing power value of the first computing power operation task. According to the method, the computing power metering accuracy of the computing power operation task is greatly improved.

The invention provides a method and a device for dynamically metering computing power based on computing power operation tasks of an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing cloud. The method for dynamically metering the computing power of the intelligent computing center cloud platform based on the computing power operation task comprises the following steps: S1, acquiring a parameter value of first operation data of a first GPU in a first time period, wherein the first GPU is a GPU for operating a first computing power operation task; s2, determining a first task type of the first computing power operation task based on a parameter value of the first operation data; s3, determining a weighting coefficient corresponding to the first task type; and S4, based on the weighting coefficient and the parameter value of the first operation data, calculating to obtain the calculation power value of the first calculation power operation task in the first time period. According to the method, the computing power metering accuracy of the computing power operation task in the intelligent computing center cloud platform can be greatly improved.

Disclosed in the present invention are a method for constructing a channel fingerprint twin, and a system. In the present invention, a coarse-grained channel fingerprint and a fine-grained channel fingerprint are respectively regarded as a physical object and a digital twin object, and image super-resolution technology is used to construct a relationship between the coarse-grained channel fingerprint and the fine-grained channel fingerprint. In the present invention, on the basis of variational inference and a re-parameterization theory, an evidence lower bound of a fine-grained channel fingerprint twin likelihood is derived to serve as an objective function, and the coarse-grained channel fingerprint is introduced as side information to design a conditional generative diffusion model for generating the fine-grained channel fingerprint, wherein the conditional generative diffusion model can be deployed in a core computing center of a channel fingerprint twin. In addition, in the present invention, a one-shot pruning algorithm and multi-objective knowledge distillation technology are further introduced to acquire a lightweight conditional generative diffusion model. The method for constructing a channel fingerprint twin provided in the present invention not only ensures the reconstruction accuracy, but also has relatively strong scalability and generalization capability in wireless communication scenarios with different fine-grained channel fingerprints.

The invention provides an intelligent computing center path distribution method, a data transmission method and a network system. According to the technical scheme, based on the time sequence characteristics of the AI training data flow and the characteristic that each set communication library only selects one communication algorithm to drive data flow transmission in the same communication period, the data flow identification capable of identifying the data flow and the communication algorithm corresponding to the data flow identification are obtained, and path planning is carried out based on the communication algorithm. In this way, during actual communication, data streams driven by different communication algorithms on the same path cannot be transmitted at the same time, the link congestion risk is reduced, and idle links are reserved for subsequent data streams of the same communication algorithm; different links are allocated to data flow identifiers with the same source IP address or the same destination IP address corresponding to the same communication algorithm in the same set communication domain, multi-path load sharing is carried out, data flow dispersed transmission based on the same communication algorithm drive is ensured, the congestion risk of a single link is further reduced, and the transmission efficiency is improved. The communication efficiency of an intelligent computing center network is expected to be improved.

The invention provides a computing power routing gateway configuration method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the steps: S1, employing an agent module to collect the operation state data of a computing power routing gateway deployed on the same server node as the agent module, the operation state data of the computing power routing gateway is reported to the analysis service module; s2, the analysis service module analyzes the received operation state data of the computing power routing gateway, judges whether the computing power routing gateway has a crash risk or not based on an analysis result, takes the computing power routing gateway with the crash risk as a to-be-adjusted computing power routing gateway, and sends the to-be-adjusted computing power routing gateway to the computing power routing gateway; determining a configuration adjustment operation executed by the computing power routing gateway to be adjusted; and S3, the analysis service module calls the intelligent computing center cloud platform management system to perform configuration adjustment on the computing power routing gateway to be adjusted based on the configuration adjustment operation.

The invention discloses an edge end AI computing task scheduling method and device for cloud edge collaboration, and relates to the technical field of edge computing, and the method comprises the steps: continuously obtaining a real-time task set, and carrying out the classification and priority sorting of each randomly generated task sequence, and obtaining a sorted sequence; task scheduling is carried out according to each sorting sequence, and corresponding task execution time is recorded; predicting each sorting sequence corresponding to a given to-be-allocated task set through the first model, and selecting the sorting sequence with the minimum task execution time prediction value to perform task scheduling; and predicting the newly added to-be-allocated task set through the second model to obtain an optimal task queue, and performing task scheduling according to the optimal task queue. According to the method, the task waiting time is prolonged, the resource utilization rate of the edge server is improved, and the problem that in existing edge computing, when a user and a cloud computing center send a large number of task requests, huge burden and energy consumption are brought to the edge server is solved.

The invention belongs to the technical field of flow measurement, and discloses a data-mechanism driven porous pore plate design method, which comprises the following steps: firstly, constructing a parameterized mechanism model, and associating performance indexes of a porous pore plate with design variables and working condition variables; then training a data-driven agent model based on an orthogonal experiment and CFD simulation data, and establishing nonlinear mapping of input and output; using an optimization algorithm to reversely search an optimal structure parameter combination by taking user performance requirements as a target; and finally, iteratively calculating the specific apertures of the center hole and the function hole in combination with a momentum equilibrium mechanism model. According to the method, the fluid mechanics mechanism and machine learning are fused, the limitation that traditional pure CFD simulation is time-consuming and the empirical formula is poor in universality is overcome, rapid, accurate and automatic design of the porous plate is achieved, and the design efficiency and reliability are remarkably improved.

The invention relates to the technical field of computing collaboration, and particularly provides a computing power center scheduling method and device considering power-computing power combination, and the method comprises the steps: obtaining the information entropy of a computing power center and the energy entropy of a power system; substituting the information entropy of the computing power center and the energy entropy of the power system into a pre-constructed day-ahead power-computing collaborative optimization model and solving to obtain an optimization result; and performing day-ahead optimization scheduling on the computing power center based on the optimization result. According to the technical scheme provided by the invention, the schedulability of the calculation power task is quantified by defining the information entropy, the space-time supply and demand difference of the power system is represented by the energy entropy, the scheduling optimization model is constructed, the electric calculation cooperative scheduling is realized, the power-calculation power unified cooperative scheduling is realized, the energy consumption is reduced, and the calculation power utilization rate is improved.

The invention discloses an automatic alarm system of an air cushion type belt conveyor. The automatic alarm system comprises a sensing layer, a control layer and an execution layer. The sensing layer constructs a multi-dimensional sensing network through an air film thickness sensor, an air pressure sensor, a belt state sensor, an air chamber airflow audio analysis module, a motor power fluctuation detection module and a distributed optical fiber sensing module. The control layer adopts an edge-cloud computing collaborative architecture, an edge computing node is responsible for real-time data preprocessing, the fog computing layer runs a digital twin model to complete data fusion and state simulation, and a cloud computing center stores historical data and optimizes an algorithm and is provided with a three-level early warning and adaptive sensing mechanism. The execution layer executes parameter adjustment, sound-light alarm or emergency shutdown according to instructions of the control layer. Comprehensive monitoring, intelligent diagnosis and automatic regulation and control of the operation state of the air cushion type belt conveyor are achieved, and the operation reliability and the maintenance efficiency of equipment are remarkably improved.

The invention relates to a method for realizing multi-modal large-model fine-grained privacy grading protection in an edge-cloud collaborative inference system, belongs to the technical field of large-model data security and privacy protection, and aims to solve the problems that privacy grading is rough, inference intermediate features are easy to leak and precision is damaged by protection measures in the prior art. According to the method, multi-modal data such as images and texts are received by edge computing nodes, deep semantic analysis and fine-grained segmentation are performed by using a local pre-trained multi-modal large model, and privacy scores are calculated to divide high and low privacy levels; uploading low-privacy data to a cloud computing center, and locally processing high-privacy data; and selectively shielding a middle layer feature map F generated by the edge based on the correlation degree and the contribution degree, then completing local reasoning, and uploading a result to be fused with the cloud. According to the method, precise fine-grained protection is achieved, the sensitive information leakage risk is remarkably reduced, and meanwhile the model reasoning precision and the system real-time performance are kept to the maximum degree.

The invention provides a heterogeneous computing power resource management method and device for an intelligent computing center cloud platform, and the method comprises the steps: S1, registering a plurality of first computing power resources corresponding to a plurality of different operators in a management platform, and obtaining a plurality of registered second computing power resources; s2, receiving a user use request input by a user on the management platform; and S3, performing matching in the plurality of second computing power resources based on the user use request to obtain at least one third computing power resource, and scheduling the at least one third computing power resource to process the computing power operation task. According to the invention, the complexity of management, operation and maintenance is greatly reduced, and the unified management and scheduling of various heterogeneous computing power resources managed by the intelligent computing center cloud platform are realized.

The invention relates to a novel intelligent calculation center power supply framework which comprises an A-path power supply module, a B-path power supply module, a diesel generator power supply module and a cabinet module. Wherein the A-path power supply module, the B-path power supply module and the diesel generator power supply module are used for supplying power to the cabinet module in a normal operation state, an A-path commercial power supply fault state, a B-path commercial power supply fault state and a double-path commercial power supply fault state respectively, and the cabinet module comprises an AC-DC rectifier module, a super capacitor and a cabinet. And the cabinet module is used for inputting the accessed power supply to the cabinet through the AC-DC rectifier module and the super capacitor. The power supply architecture is mainly formed by connecting the busbars and the dense buses, the connection mode is simple, the field construction amount and fault hidden danger points are greatly reduced, and the safety of the system is improved.

The invention provides an enhanced training method and device for an intelligent computing center cloud platform to perform embedded chain self-reward signal through computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, the method comprises the following steps: S1, predicting a first question based on an initial strategy model to obtain a plurality of predicted answers; s2, generating a first strategy parameter based on the plurality of prediction answers through a preset reference model, wherein the first strategy parameter is used for optimizing parameters in the initial strategy model; s3, calculating an embedded chain score corresponding to each prediction answer in the plurality of prediction answers; s4, generating a second strategy parameter based on the embedded chain score corresponding to each prediction answer in the plurality of prediction answers, wherein the second strategy parameter is used for optimizing parameters in the initial strategy model; and S5, optimizing the initial strategy model based on the first strategy parameter and the second strategy parameter. The application range of the model can be greatly expanded.

The invention relates to the technical field of data processing, and discloses an intelligent computing center energy consumption optimization method and system based on the Internet of Things. The method comprises the steps of collecting storage medium energy consumption parameters and data block access behavior parameters to generate an original data set, calculating a weighted access characteristic value and a cold and hot state transition probability, screening candidate data blocks and calculating an energy consumption benefit evaluation index, matching a renewable energy source supply state to construct a migration task sequence, and obtaining a new energy consumption benefit evaluation index. And executing migration operation and dynamically adjusting the prediction parameters. The problems that in the prior art, data cold and hot state conversion cannot be intelligently predicted, energy consumption benefit quantitative evaluation is lacked, cooperation of data migration and renewable energy source supply is insufficient, and prediction model parameters cannot be adaptively adjusted are solved.

The embodiment of the invention relates to the technical field of traffic flow prediction, and provides an anti-poisoning traffic flow prediction method based on federated learning, and the method comprises the steps that a traffic region client trains an anti-poisoning dynamic graph neural network model; and the traffic area client calculates the credibility of the central server and the credibility of other traffic areas. And selecting a centralized aggregation mode or a decentralized aggregation mode for aggregation according to a credibility calculation result. And each traffic area client updates local model parameters according to the corresponding aggregation result to obtain a local anti-poisoning dynamic graph neural network model. According to the method and the system, the interference of malicious clients can be resisted, the traffic knowledge sharing effect between normal clients is ensured, an optimal balance point is found between prediction performance and safety protection, and the safety of model training is improved.

The invention provides a method and a device for an intelligent computing center cloud platform to cache an accelerated diffusion model to generate an image through a computing power computing result, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, the method comprises the following steps: S1, receiving an image generation task sent by a client, the image generation task is used for indicating generation of a first image; s2, inputting the image generation task into a pre-trained diffusion model, wherein the diffusion model comprises a plurality of neural network layers connected in sequence; s3, the image generation task is executed based on each neural network layer in a plurality of sequentially connected neural network layers, the first image is obtained, and when the residual value between the jth neural network layer and the (j-1) th neural network layer is smaller than a preset threshold value, the first image is generated. And the jth neural network layer performs the image generation task based on a preset computing power calculation result cache. According to the invention, the image generation speed can be improved.

The invention discloses an edge cloud collaborative multi-modal data real-time processing method and system. The method mainly comprises the steps that 1) a data sensing system directionally collects multi-modal data; 2) the edge device preprocesses the multi-modal data and uploads the multi-modal data to the cloud computing center; 3) the cloud computing center constructs a multi-modal data model to extract high-order feature vectors of the multi-modal data; 4) the cloud computing center performs data fusion processing on the extracted feature data to obtain fusion feature data with unified dimensions, and transmits the fusion feature data to the edge device; and 5) the edge device performs post-processing and application output on the fusion data. According to the invention, multi-modal data is processed through edge cloud, multi-source information is fused and real-time response is carried out to improve processing efficiency and accuracy, bandwidth pressure and cost are reduced through local preprocessing and edge resource scheduling, and system reliability is enhanced through a distributed architecture and data redundancy.

This invention discloses a rack-mounted CDU liquid cooling system, comprising a compatible heat exchanger, multiple internal circulation units, and an external circulation unit. The compatible heat exchanger has multiple non-interconnected heat exchange tubes and an inner cavity surrounding each heat exchange tube. The multiple internal circulation units are respectively connected to each heat exchange tube to form multiple independent liquid-cooled internal circulations, with at least some internal circulations containing different internal coolants. The external circulation unit is connected to the inner cavity to form a liquid-cooled external circulation, containing external coolant. Compared with existing technologies, the rack-mounted CDU liquid cooling system provided by this invention, through the design of a compatible heat exchanger, connects computing devices using the same internal coolant in series within the same internal circulation unit, and then uses a single external circulation unit to absorb the heat from each internal circulation unit. This effectively accommodates the liquid cooling needs of various computing devices, reduces the complexity of the computing center's liquid cooling system, and lowers the difficulty of expanding and maintaining the computing center.

The invention provides a route selection method and device for a computing power network of an intelligent computing center cloud platform based on a predefined rule, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures. Obtaining a destination address carried in the computing power operation data packet, wherein the destination address is a network address of the target computing power application; s2, the gateway queries a computing power network routing table based on the network address of the target computing power application, and obtains a plurality of candidate next-hop routing addresses corresponding to the network address of the target computing power application and a routing priority of each candidate next-hop routing address; s3, the gateway selects an optimal next-hop routing address from a plurality of candidate next-hop routing addresses corresponding to the network address of the target computing power application based on a predefined rule; and S4, the gateway sends the computing power operation data packet to the optimal next-hop routing address.

The invention discloses a cloud manufacturing optimization system for a linear cutting workshop. The cloud manufacturing optimization system comprises a user layer, a service center layer, a scheduling entity layer and a user feedback layer, the user layer is provided with a cloud terminal and is used for exchanging data with the service center layer; the business center layer comprises a model center, a data center, a search center, a computing center and a planning center; the scheduling entity layer comprises an enterprise level, a workshop level and a production line level, three levels of automatic scheduling systems exchange data with the service center layer respectively, and distributed automatic scheduling systems are arranged in the three levels of automatic scheduling systems respectively; the user feedback layer feeds back the work summarization condition to the user layer; according to the method, a cloud system of a whole wire cutting product is integrally redesigned by utilizing a new computing mode, a business mode and an application mode of cloud computing according to a design concept of'cloud centralized management of discrete manufacturing resources and on-demand service of overall planning of cloud resources.

The application relates to the field of trip planning, and provides a personalized trip planning method and system based on a large language model and a recall strategy, which comprises the following steps: analyzing and refining demand information on a demand field; screening and calculating a point-of-interest recall score to obtain initial candidate points of interest; clustering the initial candidate points of interest to obtain a first cluster including a cluster boundary, constructing a virtual point of interest to obtain a virtual point of interest and merging the virtual point of interest into the first cluster to obtain a second cluster, calculating a point-of-interest standard score, and screening to obtain a high-recommendation-degree point of interest according to the point-of-interest standard score; obtaining a point-of-interest cluster, inserting the second cluster to obtain a third cluster, calculating a cluster density, rearranging the third cluster to obtain a fourth cluster, calculating a center turning angle, and obtaining a target cluster; obtaining an initial global optimal solution based on a greedy search target function and the target cluster, optimizing the initial global optimal solution based on a multilayer nested target function, and completing the planning of the trip.

The invention discloses a sensor data processing method and device based on a cloud computing center, and relates to the technical field of sensors. The method comprises the following steps: in a cloud computing center, constructing a sensor data processing engine by using an artificial intelligence algorithm, and constructing a distributed storage network and a data processing network based on a layering mechanism; preprocessing the collected sensor data at the edge gateway, and uploading the preprocessed sensor data to a cloud computing center through an encryption channel; and in the cloud computing center, processing the preprocessed sensor data based on a data processing network by using a sensor data processing engine, and carrying out distributed storage on the obtained processed sensor data based on a distributed storage network. The problems of centralized processing bottleneck, poor processing model generalization ability, low task scheduling and resource allocation efficiency and insufficient data security and reliability in the prior art are solved.

The invention discloses a device suitable for new energy power grid analysis and calculation, and particularly relates to the field of oilfield power distribution analysis devices.The device comprises a machine body, an upper cabinet door is arranged at the front end of the machine body, a lower cabinet door with an air inlet is arranged at the bottom end of the upper cabinet door, an exhaust window is installed at the rear end of the machine body, and a cooling plate is installed in the machine body; a supporting plate is installed at the top end of the cooling plate, a computing center is supported at the top end of the supporting plate, the bottom end of the cooling plate is connected with a liquid cooling system, two bearing assemblies are arranged between the top end of the liquid cooling system and the cooling plate, and a plurality of small fans are installed in the cooling plate. And exhaust mechanisms are mounted on the inner walls of the machine body on the two sides of the computing center. According to the invention, the heat dissipation effect of the whole computing device can be improved, and certain energy saving can be carried out through the multi-stage heat dissipation arrangement, so that the stable operation of the computing device is ensured, the internal electronic components are prevented from being influenced by too high temperature, and the computing accuracy is improved.

The invention provides a site classification method and system based on a regional gradient-Vs30 mapping model, electronic equipment and a storage medium, and relates to the technical field of seismic engineering and site classification.The method comprises the steps that a grid where a drilling point in a to-be-measured area is located is used as a center pixel, a nine-square window is constructed, and a Vs30 mapping model is constructed; and calculating elevation change rates of the central pixel and the adjacent pixels in x and y directions based on the digital elevation data, and further obtaining a terrain slope value through a slope formula. A slope value is converted into a soil layer shear wave velocity predicted value by utilizing a pre-constructed regional gradient-Vs30 mapping model, and the model is optimized through local drilling data, so that the regional applicability is improved. And finally, according to the mapping relationship between the interval and the site category, classifying the predicted values and generating a site classification spatial distribution diagram. According to the method, the site classification precision and the engineering applicability are improved.