48 results about "Multidimensional index" patented technology

The invention provides an MES-based solar cell production and manufacturing execution method and system, and the method comprises the steps: collecting equipment states and technological parameters in real time, combining a bar code system to achieve the single-chip-level metadata association, constructing a standardized data architecture, building a multi-dimensional index, converting the expert experience into reasonable rules, and carrying out the calculation of the reasonable rules. The method comprises the following steps: simulating the influence of parameter combination on product performance, triggering an edge computing node to call a knowledge graph to perform parameter optimization and production simulation based on a dynamic threshold early warning mechanism, identifying process improvement opportunities through a batch tracing report and clustering analysis, forming knowledge base iteration update, predicting inventory risks in combination with a production plan and equipment load, and performing production management. According to the method, intelligent purchasing is automatically triggered, raw material distribution is optimized, technological parameter self-optimization, abnormity self-healing, quality traceability and supply chain collaboration are achieved through data driving, the production efficiency, the product consistency and the resource utilization rate are improved, and a whole-process digital solution is provided for photovoltaic manufacturing.

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph-based agent behavior description method, which comprises the following steps of: firstly, analyzing an unstructured agent operation log data stream through a processor, and mapping the unstructured agent operation log data stream into a discretized module access sequence by utilizing a topological structure of a code knowledge graph so as to remove text redundancy; performing multi-dimensional time sequence analysis on the sequence, and calculating resident distribution data representing operation continuity, frequency domain fluctuation data representing switching rhythm and multi-scale coverage extension data representing a traversal range; and then aggregating the data into a fixed-length multi-dimensional index vector, constructing an index node associated with a session entity in a graph database, and writing the vector as a binary structured attribute into a storage field. According to the method, massive unstructured logs are converted into compact graph structured indexes, and quick positioning and direct retrieval of a complex agent operation mode are supported while the storage space is remarkably saved.

The application relates to the field of information technology, in particular to an intelligent processing method and system for new media data. The method comprises the following steps: reading historical new media material data; analyzing the historical new media material data, calculating an availability estimation value of the historical new media material, and sorting and screening the historical new media resource according to the availability estimation value; storing the analyzed historical new media material data in a classified manner, and establishing a multidimensional index of the historical new media material data; receiving a new media content generation demand, converting the new media content generation demand into a new media material matching condition; screening and sorting new media materials based on the new media material matching condition, and selecting the first N new media material data as basic new media material data; and fusing a theme content based on the basic new media material, generating multi-form new media content, and realizing intelligent processing of high-efficiency, high-quality and self-optimizable new media content.

The invention discloses a user dynamic access control method and system based on multi-dimensional data, and belongs to the technical field of software. The invention aims to solve the problems of user access control policy jitter, security vulnerability and difficulty in explaining compliance caused by static threshold, module independent decision and non-explicit modeling user experience cost and compliance risk cost in the prior art. Therefore, the invention provides a dynamic access control method, which comprises the following steps of: constructing a session-level multi-dimensional access context; dividing risk signal detection according to a visual angle; carrying out risk evidence combination and mode recognition; performing risk trajectory and strategy hysteresis control based on a state machine; unifying action arrangement and conflict resolution of the strategy center; strategy execution and session consistency control are carried out; and effect evaluation and strategy evolution based on multi-dimensional indexes are carried out. Compared with the prior art, multi-view evidence combination, session-level trajectory analysis, state machine and constraint solution and other means are introduced, multi-layer integration from a data layer, a logic layer, a time layer to a control layer is achieved, strategy jitter is effectively relieved, user experience is improved, service continuity is guaranteed, and compliance interpretability is enhanced.

The invention discloses a water ecosystem safety assessment method for cascade reservoir development, and relates to the technical field of water ecology and environment assessment. Comprising the following steps: constructing a water ecosystem safety assessment multi-dimensional index system under cascade reservoir development; acquiring data of the cascade reservoirs and performing standardization processing; determining the initial weight of each index, and dynamically adjusting the weight of each index; calculating a static safety index; calculating a dynamic toughness index; calculating a comprehensive safety index according to the static safety index and the dynamic toughness index; and according to the size of the comprehensive index, determining the safety level of the water ecosystem in cascade reservoir development, establishing an early warning mechanism, and identifying the key influence index. According to the method, ecosystem toughness quantification is incorporated into an evaluation system, the cumulative effect and dynamic change of the cascade reservoir can be reflected, comprehensive and dynamic evaluation of the health condition of the watershed water ecosystem under the influence of the cascade reservoir is realized, and a scientific basis is provided for watershed ecological protection and reservoir ecological scheduling.

The invention discloses a ship path planning method and system, a storage medium and a processor, and belongs to the field of marine ship sailing, and the method comprises the following steps: S1, constructing a dynamic environment model; s2, optimizing a node combination by adopting a DE algorithm, and outputting a global optimization path; s3, based on the real-time sensing data and the global optimization path, evaluating a collision risk through a multi-dimensional index, and outputting a local collision avoidance path; s4, in combination with the dynamic flow field model and the ship energy consumption model, calculating an energy consumption gradient, backtracking along the gradient through a Level Set algorithm, and optimizing and generating an energy optimal path on the basis of the collision avoidance path; and S5, performing safety compliance verification. By adopting the ship path planning method and system, the storage medium and the processor, ship path planning with COLREGs compliance, multi-objective optimization, high energy efficiency and real-time collision avoidance is realized, and the ship path planning method and system are suitable for a complex ocean dynamic environment.

The application discloses a kind of audio and video code stream real-time monitoring and diagnosis method and system, it is related to data monitoring technical field.The method comprises the following steps: constructing causal diagram model, real-time acquisition multidimensional index data;Real-time calculation is carried out to multidimensional index data, to obtain the quantization characteristic value of each index at current time, whether there is preliminary abnormal node according to quantization characteristic value;When there is preliminary abnormal node, the state combination between the two nodes directly connected in causal diagram model is detected, to identify joint abnormal mode, and the abnormal state set is formed based on preliminary abnormal node and joint abnormal mode;Abnormal state set is input into causal diagram model, the probability that all upstream nodes become the root cause of abnormal state set is calculated, and the node or group with the highest probability is determined as the root cause of the present abnormality.

This invention relates to the field of renewable energy output prediction technology, and discloses a method, equipment, and medium for renewable energy output prediction based on multidimensional index correlation analysis. The method acquires historical renewable energy output data and preprocesses the data; establishes a multidimensional index system and performs correlation analysis and screening of the multidimensional indicators to determine key influencing indicators; the correlation analysis includes analyzing the linear and nonlinear relationships between the indicators and renewable energy output; constructs a hybrid prediction model based on convolutional neural networks and long short-term memory networks, using the preprocessed data corresponding to the key influencing indicators as input to train and predict future renewable energy output data; wherein, convolutional neural networks are used to extract the interaction features between multidimensional indicators, and long short-term memory networks are used to capture time-series features. This invention improves prediction accuracy and provides reliable technical support for grid connection of renewable energy with high penetration rates.

The present application relates to a kind of physical knowledge guided model explainability analysis method and diagnostic system, belong to oil well fault diagnosis technical field, method includes: collecting sensor data in dynamometer, data is normalized to obtain preprocessed gray image, as oil well fault diagnosis offline data set;Build the physical knowledge guided oil well working condition intelligent diagnosis model based on improved EffcientNet, well fault diagnosis offline data set is as model input;Using the multi-task collaborative training mechanism based on uncertainty weighting, the parameters of oil well working condition intelligent diagnosis model are optimized, and the optimized oil well working condition intelligent diagnosis model is obtained;Using multidimensional index, the performance of the optimized oil well working condition intelligent diagnosis model is quantitatively evaluated;Real-time monitoring and diagnosis of oil well working condition is carried out, and the output result is responded to abnormality.The present application realizes the accurate and explainable intelligent diagnosis of oil well real-time working condition.

The invention discloses a character sentiment classification task-oriented confrontation test method and evaluation device, and belongs to the technical field of natural language processing. The method is realized through the following steps: importing test task information, and configuring a tested model, a text data set and related parameters; applying disturbance to the text data set based on a selected confrontation algorithm to generate a confrontation sample; performing a multi-dimensional index test on the generated confrontation sample, wherein the multi-dimensional index test comprises a model performance index and a text quality index; and iteratively adjusting the disturbance parameters through a stepping test method, generating an adversarial sample meeting a preset disturbance distance requirement, and recording a complete test result. Through stepping testing and multi-index fusion, high-precision, controllable and reproducible model evaluation is realized, and the problems in the prior art are solved.

The invention relates to a lightweight network flow storage and management method and system based on flow clustering, and belongs to the field of computer network security, and the method comprises the steps: deploying a Zeek monitoring component and P4 equipment at a network boundary and a core node, and carrying out the structural analysis of a multi-source weblog; based on data source and flow feature clustering and grading processing, dividing into a key flow, a common flow and a secondary flow; hierarchical forwarding and priority scheduling of the logs are realized through Filebeat and Kafka; a hierarchical storage architecture of a core flow layer, a main service flow layer and an archiving flow layer is constructed based on ClickHouse, and efficient retrieval and traceability analysis are realized through a materialized view and a multi-dimensional index. According to the invention, flexible scheduling and grading processing of traffic can be realized in a high-concurrency network environment, the overall throughput efficiency and security situation awareness capability of the system are improved, real-time performance and storage cost control are both considered, and the method is suitable for threat monitoring and event tracing scenes in a large-scale network environment.

The invention relates to the technical field of cold-chain logistics, and provides a cold storage site selection method, device and equipment and a storage medium. The method comprises the following steps: constructing a cold storage site selection evaluation model based on a geographic information system, a multi-dimensional evaluation index of cold storage site selection and a spatial constraint condition; performing spatial clustering on target site selection areas obtained based on the cold storage site selection evaluation model to obtain a plurality of target cold storage alternative areas; and determining a target address of the refrigeration house from the target refrigeration house alternative area based on the refrigeration house demand information, a preset refrigeration house decision strategy and a constructed total cost function. According to the cold storage site selection method, device and equipment and the storage medium, modeling can be carried out through a geographic information system, multi-dimensional indexes and spatial constraints, the alternative areas are screened through spatial clustering, the target address of the cold storage is determined from the target cold storage alternative areas in combination with cold storage requirements and the total cost function, and the rationality of cold storage layout is effectively improved.

The application discloses a kind of solid crystal machine temperature sensitive point selection method, device, equipment and storage medium, belong to equipment manufacturing technical field.The method comprises: obtaining the temperature data of first preset quantity temperature measuring points of each position point of solid crystal machine;The position point of solid crystal machine includes second preset quantity position points;Using improved AP clustering algorithm, in combination with multiple preset cluster numbers, the temperature data of each position point obtained is clustered, and the clustering result corresponding to multiple preset cluster numbers of each position point is obtained;According to multidimensional index, the clustering result corresponding to multiple preset cluster numbers of each position point is analyzed, and target cluster number is determined;Based on the clustering result corresponding to multiple preset cluster numbers of each position point, in combination with target cluster number, determine candidate point set;Temperature sensitive point set is determined from candidate point set using greedy selection algorithm.The effectiveness and accuracy of temperature sensitive point selection can be improved using the technical scheme provided by the application.

The invention discloses a standard system intelligent evaluation system and method based on a database, and relates to the technical field of intelligent evaluation. The system comprises a data acquisition and sub-network construction module, a multi-dimensional index calculation module, a cross-domain reference verification module and an evaluation result generation and output module. According to the method, standard structured data is obtained, a sub-reference network is divided, the intra-domain reference proportion and the core standard reference concentration ratio are calculated, the cross-domain reference technology logic relevance is verified, and a comprehensive matching degree evaluation result and a problem list are generated. According to the method, the defects that the deep structure of the standard reference network is difficult to mine and hidden problems cannot be identified in the existing method are overcome, intelligent and accurate evaluation of the standard system is realized, the technical collaboration and support efficiency is improved, and the high-quality development requirement of the industry is met.

The invention discloses a mass open source code storage method and system based on atomization and bidirectional indexing, and the method comprises the steps: splitting accessed software code data into unique and immutable code atoms, storing the unique and immutable code atoms into a source code compression storage structure based on content addressing, and constructing an index; the source code compression storage structure comprises three areas: a metadata area, an atomic code pool and a multi-dimensional index area; the metadata area is used for a format version number, storage statistical information, a physical offset address and length of an atomic code pool, and a physical offset address and length of the multi-dimensional index area; the atomic code pool is used for storing the code atoms; the multi-dimensional index area is used for storing a version reconstruction forward index and a code snippet reverse index; the statistical information comprises the number of software code items, the number of software code versions and the total number of code atoms. The method has remarkable advantages and beneficial effects in the aspects of storage efficiency, access performance, analysis capability, system architecture and the like.

The present application relates to a kind of coal gasification plant performance evaluation method and system, the coal gasification plant performance evaluation method is by collecting coal gasification plant operating data, and pre-processing is carried out, establishes standard database, and based on the data in this standard database, constantly process obtains the performance evaluation basic index, performance evaluation comprehensive index and value quantization index of the coal gasification plant, then again using these indexes to do multidimensional performance evaluation to the coal gasification plant, not only automatically realizes to the coal gasification plant real-time collection, and greatly reduces the time-consuming problem of the collection statistics for various operating data, adapts the digital construction requirement of coal chemical industry enterprise, to more low-cost way through multidimensional index realizes the performance real-time evaluation and management to the coal gasification plant, improves the device management efficiency.

The invention discloses a power consumption management priority dynamic evaluation method, which comprises the following steps of: acquiring power grid operation, user behavior and environment data in real time through an Internet of Things terminal, and finishing data cleaning and space-time alignment at an edge computing node; dynamically generating a power grid side power supply margin / node vulnerability, a user side elastic coefficient / urgency index and an environmental economy side carbon cost multi-dimensional index; adopting a deep reinforcement learning model (DQN) to optimize index weights in real time by taking the power grid frequency, the user complaint rate and the operation cost as reward functions; solving a multi-target Pareto optimal solution set through an improved grey wolf algorithm, outputting a load priority sequence, and carrying out blockchain smart contract evidence storage; and finally, dividing a rigid group, an elastic group and a tradable group according to load characteristics to execute differentiated regulation and control. The problems that a traditional evaluation method is poor in dynamic adaptability and low in credibility are solved, quick response, user elastic quantification and regulation and control instruction tampering prevention are achieved, and the emergency response efficiency of a power grid is improved.

The invention provides a multi-dimensional resource dynamic management and efficient access method, terminal equipment and a storage medium. The method comprises the following steps: defining a node data structure of a hierarchical tree; constructing a static hierarchical skeleton structure of the hierarchical tree; dynamically adding resource index nodes; establishing a multi-dimensional index mapping table; establishing a node ID index mapping table; realizing a hierarchical caching strategy; batch cache writing is realized; efficient subsequent node traversal is realized; batch traversal operation is realized; and the cache consistency is ensured. According to the method, dynamic construction of hierarchical data, efficient indexing of multi-dimensional resources, differentiated cache management and rapid traversal of batch data can be realized.

The invention relates to the technical field of carbon emission optimization, in particular to a digital asset-driven carbon neutralization path recommendation method, which comprises the following steps of: acquiring carbon neutralization path records in a digital asset full life cycle, screening frequent combinations, evaluating carbon factors and response efficiency, eliminating invalid paths, extracting intersection segments, optimizing and reserving, and generating a recommendation result. According to the method, high-frequency mode recognition of carbon neutralization path execution behaviors can be achieved through combination frequency statistics and sorting of path execution records in the digital asset life cycle, an evaluation system is constructed based on multi-dimensional indexes such as carbon factor fluctuation, task response time and path execution density, the actual effect of each path combination can be quantified, and the evaluation accuracy is improved. According to the method, low-efficiency or invalid path pairs are screened out through an evaluation result, the accuracy and effectiveness of path recommendation are guaranteed, path intersection segments are extracted in combination with continuous and consistent path number sequences in active digital assets, and path combinations identified as low-quality paths can be eliminated.

The application discloses a kind of DevOps platform monitoring cycle intelligent regulation and control methods, first, system multidimensional index data stream is acquired, forms instant multidimensional vector by stream computing, window processing, feature combination etc., to judge system state. Then, the emergency degree is determined by quantifying event information using event level model. Then, the confidence vector is obtained by operating the preset rule base, the corresponding scheduling strategy is triggered, and the resources are allocated according to fuzzy logic and event emergency degree. At the same time, the event priority is determined by combining expert experience and fuzzy logic, and the flexible management plan is executed and the result is evaluated. Finally, the rule weight ratio is adjusted dynamically according to the rule base to optimize the judgment of system multidimensional index. The application realizes dynamic regulation and control of monitoring cycle, accurate evaluation of system and event, intelligent scheduling and disposal, and improves the stability, efficiency and adaptability of DevOps platform.

The application discloses a kind of urban waterlogging resilience evaluation methods based on multidimensional index and coupling model, belong to urban waterlogging prevention and resilience assessment technical field.The method includes: building the resilience evaluation index system covering three dimensions of city space, lifeline engineering and rapid recovery;Establish a two-dimensional coupled hydraulic model, simulate different rainfall scenarios, output dynamic waterlogging parameters;Using comprehensive weighting method and TOPSIS model to quantify the resilience score of each spatial unit;Identify low resilience aggregation area and locate key impact indicators;According to the identification results, targeted improvement measures are developed, and the effect of the measures is verified by the coupling model closed loop.The application solves the problems of one-sided index system, lack of dynamic simulation and lack of governance closed loop in existing evaluation methods, and realizes high-precision, dynamic evaluation and scientific optimization of urban waterlogging resilience.

The invention discloses a construction intelligent supervision-oriented multi-source knowledge collaborative query method and system, relates to the field of knowledge base management, and is used for improving the accuracy of construction quality and safety field specification clause query. The method comprises the steps of obtaining multi-source data of the construction quality and safety field; writing the multi-source data into a knowledge base, and constructing a multi-dimensional index for the multi-source data; performing fine tuning training on a pre-trained large language model LLM by using the multi-source data to obtain a target LLM adaptive to the construction quality and safety field; performing incremental updating on the target LLM and the multi-dimensional index by utilizing the updated or incremental data; and receiving a query request of a user, and generating a query result by integrating understanding of the target LLM on the query request and a matching result of the query request on the multi-dimensional index. According to the invention, the standardization, intelligence and real-time level of construction quality and safety management are effectively improved.

The embodiment of the present application relates to a kind of processing method and device for brain efficiency prediction based on multidimensional index, the method comprises: the multidimensional index set corresponding to multidimensional index data set for brain efficiency prediction is set to obtain;A deep learning model for brain efficiency prediction is constructed as corresponding brain efficiency prediction model;Through the way of Chinese and Western medicine examination and brain efficiency expert evaluation to pre-recruited volunteer population, model data set is constructed and is recorded as corresponding first data set;Brain efficiency prediction model is trained based on the first data set;After model training ends, brain efficiency index prediction is carried out according to the multidimensional index set of the measured person based on brain efficiency prediction model. Through the present application, the prediction efficiency and prediction accuracy can be improved.

The invention discloses a low-efficiency land intelligent identification and redevelopment potential evaluation method and system. The method comprises the steps of obtaining land parcel attribute data; performing nonlinear correlation analysis on the attribute data based on the multi-dimensional index system to identify the low-efficiency land, including: preliminarily identifying whether the land is the low-efficiency land; determining space units belonging to low-efficiency land and optimizing and identifying low-efficiency types; fine recognition of low-efficiency sub-regions caused by mismatching of minimum unit structures in space units of low-efficiency land; constructing a potential evaluation index system to quantify the low-efficiency land evaluation redevelopment potential, and analyzing the redevelopment priority of the low-efficiency land evaluation plot through an entropy weight method and a multi-criterion decision; and integrating a geographic information system platform, realizing visual display of low-efficiency land use, performing policy simulation based on the redevelopment priority, outputting a simulation result and screening an optimal redevelopment mode. According to the invention, accurate identification, classification evaluation and redevelopment potential dynamic analysis of low-efficiency land can be realized.

The invention discloses an energy system capacity interval configuration method, system, equipment and medium, and belongs to the technical field of renewable energy system planning and operation, and the method comprises the steps: obtaining the boundary parameters of an energy system, constructing a static installation and dynamic operation coupling optimization model, solving a multi-target non-inferior solution set, and calculating a comprehensive score. And identifying a performance inflection point, defining a robust optimal interval, and outputting an optimal scheme cluster. According to the method, a static installation and dynamic operation collaborative optimization framework is provided, traditional decoupling limitation is broken through, deep interaction of planning and operation is captured, and global optimality is improved. The system cost is quantified by adjusting the cost multi-dimensional index, and economy and operation safety are both considered. In combination with trend mutation recognition and statistical robustness verification, a robust optimal capacity interval and an optimal scheme cluster are output, the adaptability of planning to uncertainty and the anti-risk level are enhanced, and scientific support is provided for decision making.

The invention relates to a star-level power supply station intelligent management and control method based on deep learning. The method comprises the following steps: S1, collecting multi-dimensional index data of a power supply station; s2, constructing a management knowledge graph of the power supply station according to the collected multi-dimensional index data of the power supply station, and extracting key features by using an NLP technology; s3, according to the extracted key features, obtaining real-time comprehensive scores, star-level suggestions and weak item diagnosis based on a dynamic star-level scoring model; s4, based on the current comprehensive score, rectification record and external environment data of the power supply station, predicting a score trend according to a state conversion prediction model, and pre-judging a state change risk in combination with a rule engine; the potential satellite lowering / picking risk is warned in advance; and S5, recommending the optimal rectification case of the power supply station of the same kind based on collaborative filtering and reinforcement learning according to the state transition prediction model star reduction / star picking risk in combination with weak item diagnosis and according to the intelligent rectification recommendation model. The management and control efficiency and reliability of the power supply station are effectively improved.

The invention discloses a model interpretation-based binary code similarity detection robustness evaluation method, which comprises the following steps of: training a mixed regression model as a locally interpretable agent model, and introducing a fusion lasso regularization item to approximate a local decision boundary of a target model; according to the method, the internal structure and parameter information of the target model do not need to be obtained, black box evaluation is achieved, and the limitation of an existing gradient-based method in a real black box scene is solved. According to the method, the proxy model is used for obtaining the feature importance weight, the instruction feature mapping module is innovatively introduced, the module can process basic block or graph node features of different granularities and accurately map the basic block or graph node features to specific instructions, direct positioning from model decision making to key instructions is achieved, and the method is high in practicability and easy to popularize. According to the method, the evaluation result is quantified through multi-dimensional indexes such as the attack success rate and the disturbance efficiency, so that the evaluation conclusion is more scientific and reliable.

The invention discloses an urban inland inundation toughness evaluation method based on a multi-dimensional index and a coupling model, and belongs to the technical field of urban inland inundation prevention and toughness evaluation. The method comprises the following steps: constructing a toughness evaluation index system covering three dimensions of urban space, lifeline engineering and rapid recovery; establishing a two-dimensional coupling hydraulic model, simulating different rainfall scenes, and outputting dynamic waterlogging parameters; quantifying the toughness score of each space unit by adopting a comprehensive weighting method and a TOPSIS model; identifying a low-toughness aggregation area and positioning key influence indexes; and formulating targeted improvement measures according to an identification result, and verifying the effect of the measures in a closed-loop manner by means of a coupling model. According to the method, the problems of one-sided index system, lack of dynamic simulation and lack of treatment closed loop in the existing evaluation method are solved, and high-precision and dynamic evaluation and scientific optimization of urban waterlogging toughness are realized.

The embodiment of the invention discloses a dynamic test quality evaluation method for a power distribution Internet of Things terminal, and relates to the technical field of smart power grids, and the method comprises the steps: constructing a multi-dimensional index system, and generating the initial weight of each index; in a preset test scene, dynamically testing the power distribution Internet of Things terminal to obtain real-time test data, and dynamically adjusting the initial weight according to the real-time test data and the characteristics of the test scene to obtain a dynamic weight; calculating based on the dynamic weight and the real-time test data to obtain a comprehensive evaluation result, and obtaining a defect diagnosis result; updating the diagnosis results to a knowledge base, and performing feedback optimization on the dynamic weight adjustment process or the defect diagnosis process by using the knowledge base; according to the method, the accuracy and pertinence of evaluation can be improved, intelligent evaluation and self-optimization are realized, and the defect diagnosis capability is enhanced, so that the operation reliability of the intelligent power grid is improved.