139results about How to "Strong timeliness" patented technology

The invention provides a water conservancy industry large model agent application system, and belongs to the field of intelligent water conservancy, and the system comprises a large language model base unit which is used for providing a standard interface to interact with an open source or non-open source LLM, and carrying out the response operation of a user demand; a training data set of the business knowledge base unit is used for accumulating knowledge training LLM, and a knowledge base data set is used for supporting customized demands and expressing customized businesses as knowledge for intelligent agents to call; the water conservancy assembly library unit special for the large model is used for providing a pipe accommodating platform for all assemblies special for the large model; the water conservancy large model service decision support system unit is used for assembling business components into an application system and providing an operation tool to adjust the assembling process to form an applicable system; and the knowledge accumulation unit is used for extracting the system knowledge generated by the water conservancy large model service decision support system unit. The water conservancy application system architecture provided by the invention has complete functions, can be flexible and changeable along with businesses, and is short and long in service cycle.

The invention discloses an electric power knowledge question-answering system construction method and system combined with engineering ethical analysis, and the construction method comprises the steps: constructing all modules, and carrying out the fine adjustment of a large language model based on a P-Tuning method, so as to construct an electric power system question-answering knowledge base; the question understanding and classification step is responsible for receiving input questions of a user and performing semantic analysis, technical classification and ethical risk identification on the input questions; searching background knowledge; carrying out engineering ethical analysis; generating questions and answers; and a feedback and improvement step: entering a model tuning stage, and carrying out adaptive tuning on the model by adopting a reinforcement learning framework. According to the method, comprehensive engineering ethical analysis can be carried out on technical schemes and operation suggestions, finally, answers generated by the system are accurate and feasible in technology and have ethical compliance at the same time, it is ensured that a user can balance technical requirements and ethical responsibility in practical application, the intelligent level of an electric power knowledge question-answering system is remarkably improved, and the user experience is improved. And important support is provided for safe operation and sustainable development of the power industry.

The invention discloses a hydropower station risk real-time prediction method based on a causal attention mechanism and graph learning. The method comprises the following steps: collecting risk-related real-time data of a target hydropower station; constructing an interaction graph to capture the interaction of the coupled risk and the characteristics of the risk in development and evolution; the weight is updated by adopting an online tread gradient, and the influence of a loss function on the model prediction precision is reduced; building a ConvLSTM network in which a risk causal relationship module is embedded; quantizing a space-time causal relationship among all grids in the risk feature map by adopting transfer entropy TE; a prediction module based on joint feature guidance is constructed, and space and position attributes of occurrence and transmission of different risks of the hydropower station are enhanced in a ConvLSTM network prediction layer; and obtaining a hydropower station risk prediction result. According to the method, the capture concentration degree of risk characteristics is effectively improved, and a new thought scheme is provided for online real-time monitoring-prediction-updating of the risk of the hydropower station.

The invention discloses a satellite networking constellation-based space-ground cooperative remote sensing agent system, which comprises a ground station and a satellite networking constellation, and is characterized in that the satellite networking constellation is divided into link establishment satellites and non-link establishment satellites according to link establishment conditions with the ground station; in the system, in response to a user input or sudden disaster event, a ground station generates an instruction file by using a ground large model and uploads the instruction file to a link establishment satellite; the link building satellite analyzes the instruction file and judges whether the link building satellite itself is a main responsibility satellite or not, if not, the link building satellite transmits the instruction file to the main responsibility satellite, and the main responsibility satellite generates a corresponding task plan through a satellite-borne large model; the main responsibility satellite generates a corresponding calculation request and / or shooting request based on the actual conditions of the main responsibility satellite and the other satellites, sends the calculation request and / or shooting request to the corresponding other satellites, receives returned calculation results and / or shooting results, generates a result file based on all the calculation results and / or shooting results, and downloads the result file to the ground station through the link building satellite; and the ground station analyzes and visualizes the result file.

The invention provides a method and electronic equipment for monitoring a fracturing crack by using an active high-frequency water hammer pressure wave. The method comprises the steps that active water hammer pressure waves are emitted to a target well; echo information corresponding to the active water hammer pressure waves is received; based on the echo information, evaluating a fracturing effect to obtain a fracturing monitoring result; wherein the fracturing monitoring result comprises the geometric dimension of a fracturing crack. According to the scheme, the fracturing effect can be accurately evaluated, and the method has the advantages of being low in cost, easy and convenient to operate, high in timeliness and large in monitoring depth.

The invention belongs to the technical field of power system data analysis, and relates to a power utilization habit-based power utilization abnormal behavior real-time analysis method and system, and the method comprises the steps: obtaining a historical power utilization data sample of a charging pile user, and carrying out the calculation to obtain a power utilization behavior habit interval; acquiring real-time power consumption data of the charging pile user according to a preset period, judging whether the real-time power consumption data is in the power consumption behavior habit interval, and if so, not giving an alarm; otherwise, giving an alarm; the advantages of wide coverage, high timeliness and high accuracy of power data are utilized, and the power utilization habit of the user is obtained through data analysis. Real-time power utilization data and historical power utilization habits are compared, abnormal power utilization behaviors are recognized, abnormal power utilization alarms are given out, and a charging pile operator is assisted in effective management, prevention and control; maintenance and upgrade plans of the charging piles are reasonably arranged, and usability of the charging piles is ensured. An electric power company can adjust an electric power supply strategy in advance, optimizes a power grid load, and avoids overlarge power grid pressure caused by a charging peak.

The invention provides a new energy primary frequency modulation acquisition method and device based on self-correlation frequency fitting, and the method comprises the steps: carrying out the digital-to-analog conversion and low-pass filtering of a three-phase current / voltage signal of a secondary side loop of a power grid in a primary frequency modulation system of a new energy station, describing a self-correlation function of the current / voltage signal after low-pass filtering through a fitting model, and carrying out the self-correlation frequency fitting. Constructing a loss function to represent a residual sum of squares between the current / voltage signal autocorrelation function and the fitting model, and minimizing the loss function by using a nonlinear least square method; and taking the frequency obtained through the fitting model as the estimated power grid frequency. According to the method, the self-correlation function is used for frequency fitting, the power grid frequency can be rapidly solved, the precision is high, the robustness is good, and the timeliness is high.

The invention provides a building group seismic loss probability assessment method based on seismic oscillation intensity random field simulation, and belongs to the technical field of seismic loss assessment. The method provided by the invention comprises the following steps: firstly simulating seismic oscillation of a region by adopting a seismic oscillation intensity random field simulation technology to obtain seismic oscillation intensity spatial distribution of different sites, and then estimating loss probability distribution of each building by utilizing a building anti-seismic performance model and a building vulnerability database. According to the method, the occurrence probability of extreme earthquake loss of a large-scale building group can be evaluated more quickly and accurately, and important support is provided for earthquake risk evaluation and post-disaster emergency decision, so that the disaster resistance and recovery capability of a city are enhanced, and the overall toughness of the city is improved.

The invention relates to an offshore wind power plant reactive voltage regulation method based on deep learning, and the method comprises the steps: collecting the historical operation data of an offshore wind power plant, constructing an LSTM neural network model, training the LSTM neural network model through employing the preprocessed historical operation data of the offshore wind power plant, adjusting the weight and bias in a network, and obtaining the reactive voltage of the offshore wind power plant. And obtaining operation data of the offshore wind power plant in real time, inputting the operation data obtained in real time into the trained LSTM neural network model to obtain a predicted value of a reactive power demand and a grid-connected point bus voltage amplitude, and according to the predicted value of the reactive power demand and the grid-connected point bus voltage amplitude, combining an operation constraint and a control objective function of the wind power plant. And dynamically adjusting the reactive voltage by adopting a hybrid optimization algorithm. According to the invention, the time sequence characteristics and dynamic changes of the wind power plant can be accurately captured, so that the reactive power and voltage of the offshore wind power plant can be accurately predicted and adjusted.

The invention relates to the technical field of intelligent parking, in particular to a parking assisting method and system, a vehicle, computer equipment and a storage medium. The parking assisting method comprises the steps of obtaining environment data collected by a current vehicle; updating real-time parking space state information of the current parking lot by using the environment data; wherein the current parking lot represents the parking lot where the current vehicle is currently located; and the updated parking space state information is shared. By adopting the method, the parking lot information can be updated through the vehicle acquisition data, so that the reliability of the parking space state information is improved, and the timeliness of the parking space state information is ensured.

The invention belongs to the technical field of electric power big data, and particularly relates to a carbon emission monitoring method and system based on electric power big data. According to the method, the power related data of the target area is obtained from the data center, the time sequence data set is constructed and input into the electric carbon calculation model trained based on the ARDL-ECM model, the energy consumption amount is rapidly predicted, and then direct carbon emission is determined. Meanwhile, indirect implicit carbon emission is calculated in combination with the called-in power data and the power emission factor, and the final carbon emission is comprehensively obtained. According to the scheme, high efficiency, low cost and wide applicability of carbon emission monitoring are realized by utilizing the real-time and high-frequency characteristics of electric power big data and combining the prediction capability of a machine learning model.

The invention discloses a real-time electric power carbon emission metering method based on an enterprise electric power carbon emission metering platform, and the method comprises the steps: calculating the real-time electric power carbon emission of an electric terminal from the carbon emission of electric power flowing into a power plant and a province; comprising the steps of power plant direct carbon emission calculation, power plant direct carbon emission factor calculation, inter-provincial inflow power indirect carbon emission calculation, node carbon emission factor calculation, regional carbon emission factor calculation and terminal power utilization carbon emission. Real-time carbon emission measurement is carried out on the power utilization terminal, and more powerful underlying data is provided for government carbon supervision and enterprise carbon emission reduction.

The utility model belongs to the technical field of track detection, and particularly discloses a tunnel detection vehicle. The tunnel detection vehicle comprises a vehicle body, and a tunnel three-dimensional detection device, a tunnel appearance detection device, a tunnel internal structure detection device, a synchronous positioning device and a main control device which are arranged on the vehicle body, the tunnel three-dimensional detection device, the tunnel appearance detection device, the tunnel internal structure detection device and the synchronous positioning device are all in communication connection with the master control device. The tunnel detection vehicle not only can complete the inspection work of the tunnel of the urban rail transit line in a short time, is high in detection efficiency, but also can realize intelligent comprehensive detection on various diseases and faults, and is high in detection accuracy.

The invention belongs to the technical field of medicines, and particularly discloses a biomarker for detecting kidney fibrosis related diseases and application of the biomarker. The biomarker comprises serum copper cyanin, or the serum copper cyanin and serum creatinine. Compared with other methods for diagnosing renal fibrosis, the serum cupreuloplasmin has the following three advantages: 1, compared with a conventional diagnostic marker (such as serum creatinine), the cupreuloplasmin can detect up-regulation in the serum of a patient suffering from early chronic kidney diseases, and can be combined with an existing mature diagnostic marker for detection; secondly, compared with renal tissue pathologic biopsy, the use of the copper cyanin as the diagnostic marker has non-invasiveness, and only a small amount or even a trace amount of patient serum is needed, so that the harm to the body health of the patient is avoided; and thirdly, compared with a nuclear magnetic resonance method for analyzing the metabolic components of the urine, the method for detecting the markers in the serum is lower in cost, more flexible, high in timeliness and easier to popularize.

The invention relates to the technical field of ESG evaluation, in particular to a listed company ESG evaluation method, which comprises the following steps of: establishing a set of weighted total scores of ESG evaluation layer by layer by taking underlying basic data as an entry point and through weight distribution and data identification four-level indexes, and rating according to the weighted total scores. AI, language recognition and other means are adopted in the weighting calculation and rating process to ensure the data accuracy, then the final data accuracy is ensured, and a scientific and accurate strategy basis is provided for ESG investment.

The invention provides a reservoir inherent frequency downhole in-situ measurement method based on neural network filtering. The method comprises the steps that (1) pulse shock waves are generated through a continuous high-voltage pulse discharging device to induce reservoir rock mass vibration; (2) acquiring natural vibration noise signal data of the continuous high-voltage pulse discharge device during pulse discharge at the wellhead; (3) acquiring a background noise signal of the continuous high-voltage pulse discharge device in a static state in a shaft and feedback vibration signal data during pulse discharge; (4) filtering and noise reduction are carried out on the high-noise background data; (5) carrying out vibration signal analysis; (6) obtaining a Hilbert marginal spectrum, and determining a correlation coefficient between each intrinsic mode and an original vibration signal; and (7) determining the inherent frequency of the target reservoir. According to the method, the inherent frequency measurement of the target reservoir can be directly carried out underground, the problem that the underground inherent frequency measurement is difficult or the indoor inherent frequency test is inaccurate is effectively solved, and the underground in-situ efficient acquisition of the inherent frequency of the reservoir is realized.

The invention relates to the technical field of AI digital humans, and particularly discloses an AI digital human autonomous learning method and system based on deep learning, and the method comprises the steps: collecting user interaction behavior data and operation maintenance data of application equipment in an application scene in real time; performing optimization training on the built-in function model of the AI digital human by using the latest collected user interaction behavior data and the operation maintenance data of the application equipment to obtain an AI digital human autonomous learning result; the method and the device are used for improving the quality and the effect of interaction between AI digital people and users and providing services which are more suitable for user expectations. And the functions of the AI digital human can be continuously improved, so that the AI digital human has higher adaptability and competitiveness.

The invention discloses a salt-alkali tolerant bacterial agent and a preparation method and application thereof. The salt-alkali tolerant bacterial agent comprises Bacillus proteolyticus XZT198. The deposit number of the strain is CCTCC M 20232321. The strain has salt-alkali tolerance and can tolerate salt stress caused by sodium chloride concentration ≤10% and alkali stress caused by pH ≤9.0. The salt-alkali tolerant bacterial agent can significantly reduce the pH of saline-alkali soil, improve soil microbial activity, significantly improve salinized soil, enhance the stress resistance of crops in saline-alkali land, promote crop growth, and increase the germination rate and yield of crops. The preparation method comprises: grinding a corn fermentation liquid product and mixing it with the fermentation liquid of Bacillus proteolyticus XZT198 to obtain the salt-alkali tolerant bacterial agent. The preparation method is simple, can retain the activity of the salt-alkali tolerant bacterial agent to the greatest extent, and improve the use effect of the salt-alkali tolerant bacterial agent.

The invention provides an improved adaptive ant colony path planning method and system, and the method comprises the steps: S1, scanning a global map environment through a laser radar, carrying out the modeling, building a three-dimensional point cloud image, and carrying out the obstacle expansion and map rasterization; s2, initializing ant colony parameters, and performing grid search; s3, performing optimization processing on the state transition probability by utilizing the trained neural network model during searching; s4, performing path search planning according to the optimized state transition probability until the ant reaches a final target point; and S5, repeatedly executing the step S4 until the maximum number of iterations is reached, and completing all path planning. According to the method, the trained neural network model is utilized to optimize the state transition probability, the optimized algorithm can quickly find the optimal path in the iteration process, and the method has higher timeliness and better path planning capability.

The invention discloses an evaluation method, device and equipment for oil gas clean production conditions and a medium. The method comprises the steps of determining monitoring data of an oil and gas production waste liquid temporary storage pool in a target mining right area of a to-be-evaluated oil and gas field through high-resolution remote sensing data based on feature information of the oil and gas production waste liquid temporary storage pool; determining a clean production evaluation index of the target mine right area according to the monitoring data of the oil and gas production waste liquid temporary storage pool in the target mine right area; and determining a clean production evaluation result of the to-be-evaluated oil and gas field according to the clean production evaluation index of each mine right area in the to-be-evaluated oil and gas field. According to the method, a clean production condition evaluation result is provided, the clean production condition in the oil and gas field production process can be reflected, and an objective, quantitative and high-timeliness evaluation method is provided for clean production in the oil and gas field production process.

This invention relates to a method and system for recalling recommended products in a second-hand e-commerce scenario. The method includes: acquiring real-time user behavior data; extracting the user's identity identifier and the identity identifier of the object product in the user's current behavior based on the real-time user behavior data; acquiring co-occurring products that form a co-occurring product pair with the object product from a data source; calculating the similarity between the object product and each co-occurring product; and sorting the multiple co-occurring products in descending order of similarity, selecting a predetermined number of co-occurring products at the top of the sorting as the recall products. This invention achieves real-time updates of the similarity of each product by storing the user's real-time behavior and promptly deleting expired user behavior, ensuring that the recommendation results are more relevant to the user's current or recent interests.

The present invention provides a slide rule for quickly calculating the attenuation of electromagnetic wave two-line propagation, including a Fresnel zone calculation tool and a two-line propagation attenuation calculation tool; a set of calculation tools for the attenuation of electromagnetic wave signal two-line propagation includes two calculation tools, which are respectively used to calculate the attenuation of two-line propagation signals and the Fresnel zone distance, wherein the Fresnel zone calculation tool is used to determine whether to apply a two-line propagation model to calculate the attenuation, and the two-line propagation attenuation calculation tool is used to calculate the specific attenuation value. Using this set of tools, the conditions for applying two-line propagation and the values ​​of the two-line propagation signal attenuation can be quickly determined. This set of tools can help combat support personnel save calculation time, quickly calculate which model to use to calculate the attenuation of signal propagation and the transmission power of the signal after two-line propagation, greatly improve efficiency, and have significant military benefits.

The present invention discloses a monitoring and early warning device capable of preventing bridge rollover, comprising a collection end, which collects bridge physical data; a collaboration unit, which detects bridge physical data; and an early warning unit, which warns of bridge status; wherein the collection end provides real-time collection of various physical data of the bridge, the collaboration unit provides real-time monitoring of various physical data of the bridge, the early warning unit generates an early warning report based on the collection end and the collaboration unit data, and performs timely bridge early warning, and relates to the technical field of bridge monitoring and early warning. The present invention establishes a monitoring network above the bridge and monitors the bridge and the vehicles traveling above the bridge, so as to monitor the operation status of the bridge (including bridge balance, bridge posture, and bridge load-bearing, etc.) in real time, identify abnormal bridge weighing in the future period, and provide timely alarms and responses to ensure the safe operation of the bridge and avoid the problem of bridge overturning.

The invention relates to the technical field of composite current collector production, in particular to an adapter welding method of a composite current collector. According to the invention, the outer side of the blank area of the composite current collector is folded inwards to form the folded B-surface metal layer, so that the folded blank area is obtained; and covering the turned blank area with a transfer metal foil, and welding in a pole piece welding area, so that the A-surface metal layer of the composite current collector, the turned B-surface metal layer and the transfer metal foil are welded together to obtain the composite current collector after transfer welding. According to the invention, the A / B surface metal layer of the composite current collector is conducted only by adopting the single-surface metal foil, so that the waste of the foil is reduced, and meanwhile, the resistance performance is better than that of a transfer welding scheme without using the metal foil.

The invention provides a mountain road flood disaster identification method and equipment based on a low-altitude digital image, and relates to the technical field of disaster identification. The method comprises the following steps: collecting a current low-altitude digital image and current point cloud data of a target mountain road; extracting a preset detection index and a water damage area of the current low-altitude digital image, and calculating a membership degree of the preset detection index and a preset water damage disaster situation level; inputting the current point cloud data into a pre-constructed three-dimensional terrain model to obtain a point cloud identification index; and according to the point cloud identification index, the membership degree and the water damage area, determining water damage disaster information of the target mountain road. According to the method, the low-altitude digital image and the point cloud data are cooperated to realize identification of water damage and hidden dangers, and the problem that hidden structure damage cannot be detected in a traditional method is solved. Moreover, the membership degree is calculated and combined with the point cloud identification index to determine the flood disaster information of the target mountain road, so that the error caused by single information identification can be avoided, and the identification accuracy is improved.

The invention discloses a heterogeneous aircraft integrated operation risk assessment method based on airspace capacity, and the method comprises the steps: S1, decomposing a global operation airspace into K grid airspaces, and building a heterogeneous aircraft integrated operation airspace scene; s2, calculating the unstructured traffic operation risk of the heterogeneous aircrafts in the single grid airspace; s3, calculating the structured traffic operation risk of the heterogeneous aircrafts in the single grid airspace; s4, calculating the integrated operation overall risk of the heterogeneous aircrafts in the single grid airspace; and S5, calculating the operation risks of the K grid airspaces in the global airspace. According to the method, the evaluation time consumption of the integrated operation risk evaluation of the heterogeneous aircraft can be effectively shortened, and the robustness is improved while the risk evaluation calculation complexity is reduced.

The invention discloses a defect severity prediction method based on developer modification history, and belongs to the technical field of software development. Comprising the steps of extracting modified historical data and defect report data, constructing an original data set, preprocessing the original data set, establishing a standard data set according to a file-defect-developer mapping relation, and constructing a developer-file weighted directed network based on the standard data set. Obtaining a file-file weighted directed network through weighted projection based on the developer-file weighted directed network, calculating a structure entropy score of a file node based on the file-file weighted directed network, and extracting a software measurement index of each source code file; and constructing a comprehensive feature set based on the structure entropy score, the software measurement index and the defect label combination, and training and verifying a machine learning model based on the comprehensive feature set to realize the defect severity prediction of the file. According to the invention, the accuracy and stability of defect severity prediction are improved from the perspective of history modification by developers.

The present invention provides a data closed-loop optimization processing method for enterprise data, and relates to the technical field of big data analysis. The method includes collecting processing operation information data of different types of enterprise data, establishing a processing operation information database for enterprise data; extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing conversion characteristics to form data processing conversion guidance information; extracting enterprise data processing information from the processing operation information database, and performing statistical analysis on the data processing information based on data processing result characteristics to form data processing result guidance information; performing closed-loop optimization analysis based on the data processing conversion guidance information and the data processing result guidance information to form closed-loop optimization guidance data. The method optimizes, analyzes and judges by establishing a data processing information database of the closed-loop of target enterprise data, so as to achieve overall real-time and reasonable optimization of data processing.

The present invention discloses a network switching method, device, equipment and medium based on an adaptive data model. The method belongs to the field of Internet of Things technology, and includes: starting RSRP monitoring to obtain the RSRP value of the working environment of the Internet of Things terminal; judging whether to start ping monitoring according to the RSRP value; if ping monitoring is started, performing network disconnection switching operation and RSRP monitoring according to the monitoring result of ping monitoring; if a network switching strategy issued by the Internet of Things platform based on an adaptive data model is received, performing network switching operation according to the network switching strategy. The present application can perform network disconnection switching operation through RSRP monitoring and ping monitoring. When receiving the network switching strategy issued by the Internet of Things platform based on the adaptive data model, an adaptive network switching operation can be performed according to the network switching strategy, without the need for staff to debug and replace SIM cards on site, which is not only time-effective, but also time-saving and labor-saving.

The application discloses a filtering method for pulse width measurement, which firstly determines whether the pulse signal frequency in a first specified time period is stable, if stable, corrects the pulse width value according to the real-time cycle width, real-time high level width and real-time low level width obtained by real-time measurement, and determines whether the signal state of the current measurement pulse is full high, full low or normal according to the corrected pulse width value, then determines the number of the three signal states in a second specified time period, and determines the real-time signal state as the state with the largest number; if unstable, determines the real-time signal state according to the level for capturing the input pin in a third specified time period; finally, determines the output pulse width value according to the real-time signal state.