3190 results about "Collection system" patented technology

The invention relates to the field of information management, and discloses a constructional engineering information management system, which comprises a data acquisition module, an intelligent analysis module, a risk prediction module, a resource optimization module and a feedback learning module, the data acquisition module is used for acquiring multi-source data related to constructional engineering; the intelligent analysis module carries out fusion, preprocessing and feature extraction on the collected data; the risk prediction module predicts possible risks in the future by using a statistical analysis model; the resource optimization module dynamically adjusts and optimizes manpower, equipment, materials and process arrangement of the construction site according to the risk prediction result; and the feedback learning module is used for collecting a system execution result, risk prediction accuracy and user feedback, and recording an optimized execution result. The method has the advantage of realizing efficient engineering management and decision support.

A system for dynamically scaling microservices in cloud environments, including: a predictive scaling module that uses machine learning algorithms to analyze historical and real-time workload data and predict resource requirements; an auto-scaling decision engine configured to determine and execute dynamic scaling actions based on predictions and current system performance; a cloud orchestration module that deploys and manages microservice instances in cloud environments to ensure optimal resource allocation; an intelligent load balancer that dynamically redistributes incoming requests across microservice instances to avoid bottlenecks and maintain service reliability; and a real-time monitoring module configured to continuously collect system performance data, detect workload fluctuations, and provide feedback for adaptive scaling.

The invention discloses a self-adaptive encryption system based on AI intelligent security management, which relates to the technical field of information security, and comprises a data acquisition module, a risk assessment module, an encryption algorithm selection module, a key generation and management module, an encryption execution module and an encryption effect feedback module, the data collection module is used for collecting various data in a system operation environment, including but not limited to network flow data, equipment state data and user behavior data; and the risk assessment module analyzes the collected data based on an AI algorithm and assesses the security risk level faced by the current system. The operation environment data is comprehensively collected through the data acquisition module, the security risk level is evaluated in real time through the risk evaluation module based on the AI algorithm, the adaptive encryption algorithm can be dynamically selected according to the risk, the defect that a traditional encryption system is fixed in strategy is overcome, and encryption pertinence and effectiveness are improved.

The invention discloses a mechanical arm precision control method based on a neural network, and relates to the field of mechanical arm precision control, and the method comprises the steps: S1, constructing a multi-source sensing data collection system of a mechanical arm system, and obtaining a joint control instruction, tail end track feedback, an environment state and stress data in real time; s2, constructing a time sequence prediction model based on a mask encoder, processing high-dimensional dynamic error data by adopting a mask encoder structure, adaptively dividing time sequences of different sampling frequencies through a multi-patch projection module, and introducing an arbitrary variable attention mechanism to capture time sequence dependence and heterogeneity between variables; s3, training the model by adopting a composite loss function; and S4, the mechanical arm target pose error point estimation output by the trained model is injected into a mechanical arm control instruction, the motion track of the mechanical arm is compensated in real time, and the mechanical arm is driven to execute high-precision motion control. The problems that in the prior art, efficient and accurate modeling of a multivariable time sequence is difficult, and scheduling optimization is insufficient are solved.

A data collection system is disclosed to be communicatively coupled to a data platform configured to perform security monitoring of a compute environment. A sub-kernel component of the data collection system may collect sub-kernel data accessible from a sub-kernel space below an operating system of a compute resource in the compute environment. The data collection system may manipulate the collected sub-kernel data to prepare the sub-kernel data to be used by the data platform in performing the security monitoring of the compute environment. The data collection system may then communicate the manipulated sub-kernel data to the data platform. Corresponding methods, systems, and products for compute environment security monitoring using data collected from a sub-kernel space are also disclosed.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention discloses a railway catenary reliability evaluation method based on a Markov chain and a dynamic Bayesian network, and the method achieves the systematic monitoring and fault early warning of the operation state of catenary equipment through the construction of a catenary monitoring data collection system, and the utilization of multi-source data integration, multi-node multivariable modeling and hierarchical time scale monitoring. According to the scheme, comprehensive acquisition and analysis of key parameters of a contact network system are ensured by integrating various factors such as meteorology, load and equipment state. Through combined application of the dynamic Bayesian network and the Markov chain, multi-node and multi-variable modeling is carried out, and the dynamic influence of factors such as equipment aging, load fluctuation and external environment change on the system reliability can be accurately captured. In addition, by refining the time scale, performing high-frequency sampling on short-term variables such as weather and load fluctuation, and performing low-frequency monitoring on long-term factors such as equipment aging and corrosion, the state of the contact network is finely evaluated on different time scales.

An offshore wind power plant current collection system optimization method considering cable type selection comprises the following steps: S1, acquiring wind power plant basic data including fan and transformer substation coordinates, fan rated power, multi-specification cable electrical and cost parameters and the like; s2, on the basis of the basic data, generating a candidate cable set by using clustering partitions and a distance threshold value, completing candidate set reduction, and synchronously generating a cable crossing avoidance constraint pair set; s3, an optimization model with the minimum total cost of the whole life cycle is established, the double-side ring path does not need scene variables, and the composite ring path needs to integrate scene driving variables; s4, carrying out path accelerated solution, carrying out hot start on a double-sided ring, carrying out cross constraint simplification, and carrying out additional fault set dimension reduction on a composite ring to obtain an initial scheme; and S5, performing full fault set N-1 verification on the initial scheme, bringing a violation scene into a fault set for resolution until the scheme meets an N-1 criterion, and outputting a final result. According to the method, topology and cable type selection integrated optimization is realized, and the solving quality and efficiency are considered.

The invention, which relates to the technical field of traffic safety dispersion, discloses an anti-vehicle-blocking traffic safety dispersion method for reconstruction and expansion of an expressway, and the method comprises the following components: S1, constructing a multi-dimensional information collection system, S2, establishing an information intelligent transmission network, S3, carrying out a traffic jam prediction algorithm, and S4, carrying out precise early warning and intelligent guidance. According to the invention, rapid and efficient transmission of information is realized through the converged communication technology, the communication technology not only improves the speed and stability of data transmission, but also can realize intelligent switching in different signal environments, thereby providing timely and reliable traffic information for a traffic management center, enabling the traffic management center to make a response rapidly, and improving the traffic management efficiency. Accurate early warning and intelligent guidance are implemented, and traffic jam and safety accidents are effectively avoided.

The invention discloses a source-load interaction method based on an electricity utilization information acquisition system, and relates to the field of power distribution network operation and optimization. The method comprises the following four steps: S1, power consumption data acquisition: dynamically acquiring user power consumption, distributed energy output and power grid state data through intelligent equipment, and ensuring real-time accuracy; s2, load characteristic analysis: constructing a quantitative model to analyze characteristics such as a user load elastic coefficient and adjustable potential, establishing a supply and demand balance evaluation system in combination with distributed energy fluctuation, and determining peak and valley time periods and gaps; s3, generating an interactive strategy, and generating and issuing dynamic strategies such as electricity price excitation, load regulation and energy scheduling based on an analysis result; and S4, executing and feeding back the strategy, monitoring the execution effect in real time, and optimizing the strategy through a closed loop mechanism. According to the method, the problems of staticization, single mode and low efficiency of a traditional method are solved, the power grid supply and demand balance capability, the renewable energy consumption rate and the strategy accuracy are improved, and low-carbon and intelligent operation of the power distribution network is supported.

The invention relates to the technical field of power system simulation, in particular to a new energy power system simulation method, which comprises the following steps: S1, constructing a multi-dimensional information acquisition system, S2, constructing a new energy station model, S3, constructing a power grid control system model, S4, constructing a typical transient scene model, and S5, carrying out optimization solution and model correction. A full-chain model covering a new energy station, a power grid control system and a typical transient scene is constructed, and parameter optimization and a multi-objective decision technology are combined, so that the dynamic characteristics of the new energy power system are accurately simulated.

The invention discloses a photovoltaic power station risk prediction method, device and equipment, a medium and a program product, and the method comprises the steps: obtaining multi-dimensional data, collected by an information collection system, of a photovoltaic power station, processing the multi-dimensional data, obtaining event data, feature data and fault result data, and carrying out the processing of the event data, the feature data and the fault result data; constructing a mapping matrix based on a mapping relation among the event data, the feature data and the fault result data, analyzing a fault logic path of the photovoltaic power station according to the mapping matrix, constructing a risk prediction model based on an analysis result, and inputting real-time monitoring data of the photovoltaic power station into the risk prediction model. According to the method, the accuracy of risk prediction of the photovoltaic power station is effectively improved, accurate prediction of potential risks is realized, the timeliness of risk identification is ensured, and the safety of the photovoltaic power station is improved.

Waste collection and processing systems for use in low-gravity environments include a toilet having a bowl and a lid, the toilet configured to receive solid human waste; a liquid waste collection system comprising a liquid waste receptacle configured to receive liquid human waste; a first waste tank fluidly coupled to the toilet; a second waste tank fluidly coupled to the liquid waste collection system; a third waste tank fluidly coupled to the liquid waste collection system, wherein the first waste tank, the second waste tank, and the third waste tank are each configured to be operated between reclamation modes of operation and collection modes of operation; and a waste outlet configured to selectively receive an airflow from each of the first waste tank, the second waste tank, and the third waste tank.

The invention discloses an optical fiber vibration signal time identification and classification method, system and device based on a Mam-YOLOv10 neural network and a medium, and belongs to the technical field of optical fiber communication, and the method comprises the steps: obtaining OTDR signals of a plurality of targets based on an optical fiber vibration signal collection system, and carrying out the denoising of the OTDR signals; performing short-time Fourier transform on the de-noised OTDR signal to obtain a two-dimensional time-frequency diagram of the vibration signal; based on the two-dimensional time-frequency diagram, multi-scale features of the optical fiber vibration signals are extracted; and inputting the multi-scale features into a Mam-YOLOv10 neural network containing a Mama attention block to complete identification and classification of the optical fiber vibration signals. According to the method, the influence of interference fading on the pattern recognition accuracy is effectively reduced, meanwhile, the hardware complexity is reduced, the attention weight is automatically adjusted according to different fault scenes and data characteristics, and the accuracy of optical fiber fault recognition and classification is improved.

The invention belongs to the technical field of computers, and discloses a computing power dynamic segmentation method and system, computer equipment and a storage medium, and the method comprises the steps: collecting system historical load data and a task execution mode, and predicting a future resource use condition by adopting a machine learning algorithm model; monitoring the overall load data of the system; searching available computing resources in the system; if the overall load data of the system exceeds an overall load threshold value, the resource demand of a single task exceeds a resource tension threshold value and / or the future resource use condition exceeds the computing power of the system, triggering a computing power segmentation decision; matching the compatibility between the available computing resources and resource parameters of a to-be-executed task, and obtaining a compatibility result; according to the compatibility result, the available computing resources and resource parameters of the to-be-executed task, a segmentation strategy is formulated; and redistributing the available computing resources according to the segmentation strategy. The technical problems of low resource utilization rate and long waiting time existing in computing power segmentation can be solved.

The invention discloses a cross-border e-commerce supply chain data analysis method and system based on artificial intelligence, and belongs to the technical field of cross-border e-commerce supply chains, and the method specifically comprises the steps: constructing a full-link and region-tagged data collection system, covering basic sales data and associated data, and filling missing values through an ETL tool unified format and an AI model; the method comprises the following steps: generating four types of core indexes through an AI model based on a full-link and regional tagging data acquisition system, and quantifying the current state of the regional popularity of a target commodity according to the four types of core indexes; meanwhile, a multi-dimensional data set of preview behaviors and regional features is constructed, and a data processing principle is set; evaluating an expected state of the target commodity based on a multi-dimensional data set for constructing the preview behavior and the regional feature; and adjusting the supply chain link of the target commodity based on the target region according to the comprehensive analysis of the current state of the popularity of the target commodity region and the expected state of the target commodity.

The invention discloses a double-layer multi-target planning method and system for a bilateral looped network current collection system of an offshore wind plant, and the method comprises the steps: inputting the data of the offshore wind plant into a double-layer planning model of the bilateral looped network current collection system, and enabling an outer-layer model to be used for constructing a plurality of two-dimensional populations composed of weights and the number of clusters, and inputting the two-dimensional populations into an inner-layer model; the inner-layer model is used for grouping the wind turbine generators under different two-dimensional populations by adopting a clustering algorithm, taking minimum cable investment cost and minimum network loss as objective functions and adding constraint conditions to construct a path optimization model so as to carry out path planning on the wind turbine generator groups, solving total cost and outputting the total cost to the outer-layer model; and ending iteration of the outer layer model when the total cost is converged, if not, continuing to output an optimal solution result, and otherwise, continuing to update the two-dimensional population. The invention aims to spontaneously cluster wind turbine generators through a heuristic algorithm and find the optimal path of the bilateral looped network of the wind turbine generators so as to realize accurate planning of the bilateral looped network current collection system of the offshore wind power plant.

The invention belongs to the technical field of spectral measurement, and particularly discloses a non-contact high-temperature detector based on a visible light spectrometer and a full-spectrum method, which comprises a full-spectrum collection system and a spectral analysis system. The output end of the full-spectrum collection system is connected with the input end of the spectral analysis system; the light splitting module is used for acquiring spontaneous radiation light of a to-be-detected target and carrying out dispersion light splitting on the radiation light to obtain visible light full-band spectral data; and the spectral analysis system is used for analyzing and processing the visible light full-band spectral data to obtain the spectrum of the to-be-detected target, and obtaining the temperature based on the spectrum of the to-be-detected target, so that the precision of temperature detection can be remarkably enhanced.

An offline collection system comprises a pipeline for storing metadata of user interface (UI) elements at web pages of a web application. The pipeline comprises crawling uniform resource locators (URLs) of web pages of the web application for content and rendering screenshots of the web pages. The pipeline then prompts a multimodal large language model (LLM) to generate database entries for the web pages comprising UI element metadata derived from the crawled content and rendered screenshots. A response system receives user queries to navigate the web application and augments prompts to an LLM to respond to the user queries with metadata of UI elements relevant to the user queries stored by the offline collection system.

The invention relates to the technical field of rail transit intelligence, and discloses a rail transit intelligent scheduling management method and system, and the method comprises the steps: collecting the entering and exiting data of passengers, the number of people in a waiting area, the train load factor and the platform congestion degree through an automatic fare collection system, a video monitor and a sensor of each station of rail transit; the collected passenger flow data are preprocessed, a box plot about passenger flow distribution is constructed, and sudden passenger flow fluctuation areas are identified in different time windows; a K-means clustering algorithm is adopted to classify passenger flow modes in peak periods, and the distribution type of passenger flow fluctuation is analyzed; a time sequence prediction model is constructed by adopting a Transform model in combination with weather, holidays and festivals and emergencies, the future short-term and medium-and-long-term passenger flow trend is predicted, and the train departure interval is optimized; and based on the predicted passenger flow distribution, a scheduling optimization objective function is constructed, and a reinforcement learning algorithm is combined. The method has the advantage of improving the passenger flow prediction precision in the peak period.

The invention provides a mountain power station current collection system state online monitoring method and device, and relates to the field of data processing. According to the method, distributed optical fiber data, environmental meteorological data and electrical measurement data are acquired to construct a multi-mode time sequence matrix, an operation state diagram is formed through graph structure representation and graph signal processing, a working condition mode cluster is obtained by combining dynamic clustering, a constraint diagram optimization framework is input, thermal, electrical and mechanical constraints are introduced, and an operation state track is generated. And constructing and fusing a non-stationary degradation path library and environment exposure history to form a dynamic degradation evolution model, and finally outputting in-service reliability and predicting residual life by a risk inference engine to realize online monitoring of the state of the current collection system of the mountain power station. By implementing the technical scheme provided by the invention, the accuracy of online monitoring of the state of the current collection system of the mountain power station is improved.

The invention discloses a geological park geological relics information acquisition method and system based on multi-source data, and the method comprises the steps: obtaining a GIS ground surface image, geological relics environment parameters and geological relics updating data, updating the flight path of an unmanned aerial vehicle, executing the flight strategy of the unmanned aerial vehicle, obtaining a low-altitude aerial image, carrying out the data matching and data correction, and obtaining the geological park geological relics information. Determining a data fusion area according to the multi-source correction data, constructing a geological relic multi-source data fusion model based on the multi-source correction data, and inputting to-be-collected geological park multi-source sensing data into the geological relic multi-source data fusion model to obtain geological relic collection information, and determining an acquisition scheme according to the data fusion area and the acquisition time. The method not only can improve the efficiency and accuracy of geological park geological relic information acquisition based on multi-source data, but also has good interpretability, and can be directly applied to a geological park geological relic information acquisition system based on multi-source data.

A vapor collection system that can be used at an extra-terrestrial body is envisioned to collect target gaseous atoms and molecules (material) floating around in a shielded environment at a pressure at or less than a bar. The shielded environment is defined within a cover and skirt arrangement that rests atop granular soil, which in one embodiment is regolith. It is advantageous for the regolith under the cover to be essentially devoid of loose rocks that would reduce mining efficacy and / or the cover from sealing against the regolith surface. Accordingly, certain embodiments envision mining the regolith with a zero tailings arrangement that uses a front plow to clear away loose rocks from where the cover will rest on the regolith and a redeposition blade to collect and redeposit the cleared away loose rocks behind the cover.

The invention relates to the technical field of digital twinning, in particular to a smart park digital twinning construction method and system based on big data, and the method comprises the steps: constructing a multi-source heterogeneous data fusion collection system, deploying an Internet of Things sensor in a smart park to collect data, and carrying out the data preprocessing; constructing a distributed big data storage architecture to store the collected data, and extracting data depth features in combination with machine learning and deep learning algorithms; constructing a park high-precision digital twin three-dimensional model, and accurately associating the extracted data depth features with a model entity; a real-time data monitoring mechanism is set, a message queue and edge calculation are adopted for data transmission and processing, a multi-terminal human-computer interaction interface regular evaluation model is developed, and an optimization algorithm is used for iteratively upgrading the digital twinborn body. Through full-process data driving and model iteration, digital mapping of the smart park is realized, and the management efficiency and decision scientificity of the smart park are improved.

An underdrain filtered water collection system includes: a pair of opposing drainpipes, each drainpipe having a plurality of collection drains; and a diffuser screen. The diffuser screen can slide within an outlet in each of the collection drains. A diffuser screen for a pressure vessel containing filter media includes: a screen pipe; a solid plate coupled to a base of the screen pipe; and a winged diffuser arranged around an outer circumference of the solid plate. The winged diffuser includes a plurality of foldable wings. The screen pipe includes a mesh screen having preconfigured interstices therethrough. Feed water flows through the screen pipe where it is directed by an inner surface of the solid plate to flow in a diffused flow through the interstices of the mesh screen and the wings.

The invention discloses an aquaculture tail water treatment intelligent operation and maintenance system and method based on an unmanned aerial vehicle, and relates to the technical field of aquaculture tail water treatment operation and maintenance, and the system comprises an aquaculture tail water treatment system, an unmanned aerial vehicle collection system, a ground monitoring station, a meteorological monitoring station, a data transmission system, a data analysis and processing system, and a control execution system. Provided is a user interaction system. According to the intelligent operation and maintenance system and method for aquaculture tail water treatment based on the unmanned aerial vehicle, a large-area aquaculture area can be rapidly covered, real-time and comprehensive monitoring of tail water is achieved, the monitoring efficiency and precision are improved, the inspection efficiency of aquaculture tail water treatment is improved, a large amount of data are analyzed and processed through the AI technology, and the system and method are high in practicability. Scientific and reasonable processing strategies can be generated, powerful support is provided for operation and maintenance decisions, and through remote control and automatic execution, manual operation is reduced, processing efficiency is improved, and complex conditions and sudden problems can be dealt with.

The invention discloses an indoor water tank sediment pushing characteristic acquisition system and method, the system mainly comprises a water tank main body, a buffer unit, a drainage adjusting unit, a sediment throwing device, an imaging unit, an adjustable support and a data processing unit, and according to the method, non-contact data acquisition is carried out through a high-definition camera and a three-dimensional terrain measuring instrument; automatic identification, parameter extraction and migration speed calculation of landforms such as sand waves are achieved, the sediment bed surface is not touched in the whole process, damage to the bed surface form caused by traditional contact type measurement is eliminated, and the authenticity and reliability of data are ensured. Meanwhile, the buffer design of the combination of the flow slowing plate and the multi-stage water baffle is adopted, the kinetic energy of water flow is effectively dissipated through the graded water outlet structure, the impact force of injected water flow is greatly reduced, the bed surface is prevented from being scoured in the initial water injection stage, and the stability of the initial test condition is powerfully protected.

The invention discloses a radio frequency energy and solar energy hybrid energy supply system and device, and belongs to the field of energy collection and energy supply, and the system comprises a hybrid energy collection circuit and an output voltage stabilizing circuit. The hybrid energy collection circuit comprises a solar energy collection circuit and a radio frequency energy collection circuit. And after the solar energy collection circuit and the radio frequency energy collection circuit are connected with two diodes, the two diodes are connected in parallel to the output voltage stabilizing circuit. The two energy sources can be collected and managed at the same time, the efficiency of the energy collection system is improved, and the stability of the system is improved. Meanwhile, the system is provided with a self-starting module, an external power supply is not needed, the cost is reduced, and the compatibility and flexibility of the system are improved. The level output value can be flexibly set by adjusting the resistance value of the resistor, and the charging voltage of the super capacitor can also be set by the resistance value, so that the flexibility of the system is improved.

The invention belongs to the technical field of energy conversion equipment and solid resource pyrolysis conversion utilization, and discloses continuous replacement type pyrolysis equipment and a pyrolysis method thereof. The continuous replacement type pyrolysis equipment comprises a feeding system, a microwave generating system is arranged on one side of the feeding system, a carbon residue collecting system is arranged at the bottom of the feeding system, a continuous replacement system is installed in the microwave generating system, and a reaction system is installed in the continuous replacement system. One side, far away from the feeding system, of the reaction system is connected with a product collecting system; through the synergistic effect of microwave pyrolysis and dynamic replacement design, pyrolysis of crushed materials and tar cracking are integrated into a continuous process, efficient energy conversion is achieved, high-added-value products such as liquid fuel / chemicals and pyrolysis carbon residues are finally produced, and reduction, harmlessness and resourceful treatment of solid resources is promoted.

The invention provides an intelligent collection method for deep sea polymetallic nodules. The method comprises the following steps: S1, capturing a tuberculosis image through a seabed camera of a mining vehicle; s2, carrying out graying processing on the acquired image; s3, carrying out binarization processing on the preprocessed tuberculosis image; s4, expanding the processed tuberculosis image based on a morphological reconstruction method; s5, determining the particle size and shape of the tuberculosis; s6, comparing with existing tuberculosis data, and determining the quality of the tuberculosis; s8, selecting the optimal advancing route of the mining vehicle, and determining the working condition of the advancing route; s9, further adjusting jet parameters according to the determined information; s10, collecting the tuberculosis washed out by the jet flow; s11, lifting the collected tuberculosis to a collection ship; s12, carrying out collection result detection on the mining area; and S13, if the collection result does not reach the standard, continuing collection, and if the collection result reaches the standard, ending collection. Through the steps, the intelligent and efficient collection system for the deep sea polymetallic nodules is formed.