1140 results about "Static data" patented technology

The invention discloses an intelligent customer service system based on an AI large model, and belongs to the technical field of intelligent customer service systems. Through the multi-modal interaction technology, the flexibility of the system and the user experience are improved, the data type is intelligently recognized and correspondingly processed, the language style and emotional intonation of the response content are automatically adjusted according to the mood and intention of the user, the knowledge base integrates static data and dynamic interaction data, and the user experience is improved. Real-time updating and comprehensiveness of knowledge content are ensured, rapid retrieval is performed based on a knowledge base, accurate reply is performed in combination with a large language model, high-quality answers and solutions are generated through rule reasoning and model generation, state information of each round of interaction of a user is recorded and managed by constructing a dialogue state tracker, and the user experience is improved. And precise tracking and updating of the context are realized. For a complex task, the system splits a user request into a plurality of sub-tasks, and the sub-tasks are executed in sequence according to the priority and the dependency relationship, so that the high efficiency and continuity of task processing are ensured.

The invention discloses a power load prediction method based on multi-source heterogeneous data feature fusion, system equipment and a medium, and relates to the technical field of power system load prediction, and the method comprises the steps: obtaining a power load prediction data set, and carrying out the preprocessing; performing input feature processing on the static data, the graph data and the time series data based on a multi-layer perceptron, a graph convolutional network and a gating loop unit network, and fusing the processed features based on a feature splicing method to obtain comprehensive feature information; based on full-connection neural network construction, taking the comprehensive features as input, inputting training set data into a model, and performing iterative optimization according to a model effect to adjust model parameters and hyper-parameters; and based on the parameter optimization adjustment model, using the evaluated optimal model to predict the power load in the future time period. The method is superior to a traditional method in precision and stability, can better adapt to complex and changeable load prediction scenes, and has wide application prospects and remarkable actual value.

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 technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

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 discloses a complex three-dimensional scene twinborn modeling method guided by a geographic knowledge map, belongs to the technical field of digital twinborn, and solves the problems that the existing twinborn scene modeling method mostly focuses on single space dimension fusion and does not consider the multi-dimension fusion requirement, such as the synergistic effect of objects, attributes and the whole scene is not considered, and the modeling effect is poor. The model precision is reduced, and the adaptability is insufficient. The method comprises the following steps: constructing a scene-feature-transition complex three-dimensional scene geographic knowledge graph based on obtained dynamic and static data of each geographic scene in a complex three-dimensional twinborn scene physical space, and updating in real time; and carrying out dynamic fusion modeling on the complex three-dimensional scene through progressive guidance layer by layer from an object level, an attribute level to a scene level by adopting a geographic knowledge graph of the complex three-dimensional scene. The method is used for complex three-dimensional scene twin modeling.

The invention relates to the technical field of agricultural science and technology, in particular to a leaf disease and pest identification method and system based on image processing, and the method comprises the following steps: setting multispectral and thermal imaging equipment, collecting multispectral images and thermal imaging data of a target frequency band, deleting images with insufficient definition and data with excessive noise, obtaining optimized multispectral images, and obtaining the leaf disease and pest identification result. And marking and classifying the data according to the collected date to form a time sequence image data set. According to the method, the network parameters are dynamically adjusted to reflect the state of each stage of leaf growth, so that the pest and disease damage prediction is not only based on static data, but also reflects the actual change of plant growth, and the real-time application value and accuracy of the prediction model are improved; the introduction of state transition learning enables the transition from health to each stage of pest and disease damage to be predicted more accurately, carries out early diagnosis and timely intervention, improves the recognition precision of the specific position of the pest and disease damage, can carry out local processing, and effectively improves the pertinence and economic efficiency of the processing.

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.

An intelligent safety supervision system applied to a ship is provided. An image acquisition module is configured to acquires high-definition images in real time. An automatic recognition module is configured to obtains ship dynamic and static data. A ship server to-performs feature recognition on the ship dynamic and static data to obtain a data processing result, to-transmits the ship dynamic and static data and the data processing result, and receives alarm indication information. An alarm module outputs an alarm. A ship client displays the data processing result, and determines whether to transmit the alarm indication information according to the data processing result. A communication module receives and transmits the ship dynamic and static data and the data processing result. A shore-side supervision system includes a ship safety supervision big data analysis platform for performing secondary feature recognition on the ship dynamic and static data, so as to obtain a secondary data processing result.

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 discloses a city planning information display method and system based on digital twinning, and relates to the technical field of city planning. Static data and dynamic data of a city are collected, a three-dimensional model of the city is constructed through the static data, and the dynamic data and the three-dimensional model are combined to construct a digital twinning model of the city; the method comprises the following steps: setting a random data generation pool to generate random data, importing the random data into a digital twinborn model, and monitoring and optimizing a chaos current situation in the digital twinborn model to obtain an optimization scheme; by constructing a public voting platform, public opinions are imported into city planning to obtain a final optimization scheme; according to the method, the digital twinborn model of the city is constructed, more random scenes are provided for city planning by using the random data generation pool, potential changes and uncertainty of the city are fully simulated, and in combination with public opinions, the perspectiveness and flexibility of the digital twinborn technology in the city planning are improved, and a new direction is brought to the city planning.

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 communication power supply adaptive optimization method and system based on intelligent prediction, and relates to the technical field of power grid dispatching, and the method comprises the steps: collecting dynamic data and static data of a communication system, and constructing a load prediction model; on the basis of a load prediction model, combining static data to predict future load, combining dynamic data to calculate available energy of the system, and formulating a multi-path power supply scheduling strategy; and based on the prediction result, processing the prediction error, correcting the prediction result, and adjusting the power supply strategy under the condition of power interruption. The accurate load prediction enables the configuration of the standby power supply and the UPS system to be more reasonable, and reduces the risk of excessive configuration or insufficient configuration. The overall energy utilization efficiency is improved, and unnecessary energy waste is reduced. Efficient, reliable and intelligent operation of a power supply system is achieved, more stable and more economical power supply service is provided for users, and meanwhile a new solution is provided for intelligent management of an electric power system.

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 provides a coal mine digital twin geologic model construction method and system based on precise exploration, and relates to the technical field of coal mine geologic modeling, and the method comprises the steps: respectively obtaining ground static data, roadway dynamic data and coal mining real-time data; preprocessing the ground static data, the roadway dynamic data and the coal mining real-time data to obtain preprocessed data; integrating the preprocessed data, and constructing a three-dimensional dynamic geologic model according to the integrated data; dynamically updating the three-dimensional dynamic geologic model according to the coal mining real-time data so as to ensure the real-time performance and the accuracy of the model; according to the method, the three-dimensional dynamic geologic model is displayed through a three-dimensional visualization technology. Through multi-source data fusion and dynamic updating, transparent management of coal mine geological information is realized, and a powerful guarantee is provided for safe and efficient mining of a coal mine.

The invention discloses a robot dynamic grabbing method based on a sliding window self-attention mechanism, and the method comprises the steps: firstly installing a camera, and calculating a transformation matrix between the camera and a mechanical arm coordinate system based on a hand-eye calibration technology; processing the static data set by adopting affine transformation to generate a grabbed dynamic data set with speed information; then, a dynamic grabbing network model based on a sliding window multi-head self-attention mechanism is constructed, and a loss function optimization model considering the object movement speed and the grabbing posture at the same time is designed; and finally, the trained model is deployed in a robot grabbing environment, a visual servo grabbing control scheme based on the position is designed, and follow-up movement and grabbing action of an end effector of a mechanical arm on a to-be-grabbed object are achieved. According to the dynamic grabbing method of the robot, the grabbing posture and the movement speed of the object can be quickly and accurately predicted based on a group of continuous single-channel depth images collected by the camera, and the grabbing problem of a universal object in a complex dynamic environment is solved.

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 provides a personalized health monitoring method based on multi-instrument data fusion, and aims to solve the problems of non-uniform data standards and poor interoperability of existing multiple health monitoring devices. According to the method, a data standardization protocol is automatically selected and optimized according to data characteristics (such as sampling frequency, data precision and the like) of different devices (such as oximeters, sleep monitors and the like) through a machine learning algorithm, and seamless fusion of data between the devices is ensured. Different from a traditional static data processing mode, the scheme not only improves the collaboration efficiency among the devices, but also effectively solves the problems of inconsistent data formats and information loss. For different types of data generated by multiple devices, a data fusion strategy is automatically identified and optimized by adopting an intelligent fusion algorithm based on deep learning, and information conflict or delay caused by mismatching of data features is avoided.

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 discloses a power scene multi-source and multi-state model reconstruction method, medium and system, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data, and constructing a twin according to the multi-source heterogeneous data; s2, obtaining quasi-static data, and constructing an equipment parameterized twin model according to the quasi-static data; and S3, generalizing the parameterized twinborn model, establishing a physical simulation model, obtaining a simulation result according to the physical simulation model, and mapping the simulation result into the twinborn body. The power grid model of the power scene constructed by the model reconstruction method provided by the invention can perform fusion processing on multi-source heterogeneous three-dimensional space data to form a multi-source multi-state power grid three-dimensional digital model, so that a uniform data structure is provided for rapid construction and later operation and inspection of the power grid, and a power grid system platform can conveniently call and analyze.

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 provides a tunnel congestion node real-time estimation method and system based on digital twinning, and the construction of a digital twinning system physical scene of a tunnel is realized based on a Unity engine and SUMO software by determining the spatial characteristics, traffic information, camera information and the like of the tunnel. And then, dynamic and static data information of the vehicle is obtained through a CornerNet image detection network and a Deepsort tracking algorithm, the driving condition of the lost vehicle is deduced and simulated according to an acceleration distance control car-following model and a safe distance lane changing model, and dynamic target projection and full tracking of the digital twin system are realized based on an improved Hungary algorithm. And finally, node division of vehicles in the tunnel is realized according to the proposed intelligent traffic flow node model, node saturation is introduced, a node coupling congestion identification model is proposed, and tunnel congestion event sensing and congestion node positioning are realized. According to the invention, on one hand, traffic management resumption in the tunnel can be carried out by means of the digital twin system, and the congestion process and reasons are deeply analyzed; and on the other hand, the traffic flow congestion condition in the tunnel can be detected in real time, and the congestion node can be determined, so that the congestion traffic flow can be dredged in advance, and the accident occurrence probability in the tunnel can be reduced. And the method is of great significance to future digital and intelligent traffic flow research.

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.