80 results about "Decision error" patented technology

The invention discloses a reservoir multi-target optimization scheduling decision-making method based on digital twinning and AI simulation, and relates to the technical field of reservoir scheduling, the method effectively solves the problems of data fragmentation and information redundancy in traditional scheduling by constructing a multi-source data fused reservoir digital twinning body, realizes accurate virtual-real mapping of reservoir working conditions, and improves the scheduling efficiency. A high-reliability simulation basis is provided for subsequent optimization scheduling, and decision errors caused by data errors or model deviation are avoided; four core targets of flood control, power generation, ecology and water supply are considered, an engineering safety bottom line and rigidity requirements are defined through a layered constraint system, constraint satisfaction degree screening is enhanced by means of an improved multi-target optimization algorithm, and target weights can be dynamically adjusted according to a real-time scene. And after the non-dominated scheme set is optimized and output, candidate schemes are screened in combination with real-time demand quantitative scores.

The invention relates to the technical field of financial science and technology and data processing, in particular to an intelligent financial financing decision-making system of a distributed data weaving framework, which comprises the following modules connected in sequence: a distributed data weaving module comprising a multi-source heterogeneous data access unit; a dynamic knitting execution unit; the intelligent decision module comprises a dual-channel risk assessment unit; a financing scheme optimization unit; the dynamic risk control module comprises a model drift adaptive unit; and the anti-fact early warning unit is used for generating an associated virtual unfavorable scene set, and evaluating the robustness output early warning level of the financing scheme through the antagonism verification network. The application of transfer learning enables the model to automatically adapt to changes of enterprise and industry environments, thereby improving the stability and robustness of the risk control model, and reducing decision errors caused by outdated models.

The invention provides an active power distribution network prediction-scheduling integration method based on half-end-to-end, and the method comprises the steps: (1), obtaining photovoltaic output, a load power historical data set, a meteorological data set and power distribution network parameter information, and carrying out the initialization; (2) respectively establishing mathematical models of a prediction layer, a day-ahead scheduling layer and a decision evaluation layer, establishing a half-end-to-end model basic architecture based on the mathematical models, and performing photovoltaic output and load power prediction by the prediction layer; in the day-ahead scheduling layer, a day-ahead scheduling model is constructed according to resource elements contained in the active power distribution network; the decision evaluation layer calculates a day-ahead scheduling decision error and constructs a mixed loss function; (3) training a half-end-to-end model based on photovoltaic output, a load power historical data set, a meteorological data set and power distribution network parameter information to perform model parameter optimization; and (4) inputting day-ahead meteorological data to the half-end-to-end model obtained by training in the step (3), and obtaining an optimal day-ahead scheduling optimization strategy of the power distribution network.

The invention provides a vehicle auxiliary driving test scene generation method and device, equipment and a medium, and relates to the technical field of vehicles, and the method comprises the steps: obtaining the real-time state data and environment data of a vehicle; calculating a dynamic security threshold based on the real-time state data and the environmental data; the dynamic safety threshold value represents the safety risk of vehicle auxiliary driving in a vehicle real-time state dynamic change scene; under the condition that the dynamic safety threshold triggers and adjusts the complexity of the vehicle aided driving scene, guiding a preset test scene generation model to generate a test scene of vehicle aided driving based on the dynamic safety threshold; the test scene is matched with the safety risk. According to the invention, the safety risk can be fed back in real time based on the dynamic safety threshold so as to reduce the braking decision error, thereby improving the accuracy and comprehensiveness of verifying the safety of the auxiliary driving system.

The invention discloses a low-speed unmanned vehicle artificial intelligence decision and performance evaluation system, and belongs to the technical field of low-speed unmanned vehicle scheduling, and the system comprises a generation module which is used for generating a scene novelty index through monitoring the instantaneous offset of an environment parameter; the topology module is used for constructing a dynamic scene topology according to the scene novelty index, and the dynamic scene topology comprises nodes and edge weights generated based on the scene novelty index; the decision-making module is used for converting the edge weight into a decision-making rule according to the dynamic scene topology and generating a decision-making strategy; the performance calculation module is used for tracking the deviation between the vehicle motion parameter and the control instruction during the execution of the decision strategy to calculate a performance disturbance coefficient; and a topology calibration module. The method can effectively adapt to unrecorded edge scenes such as sudden congestion and severe weather superposition, improves the generalization ability of a system to complex scenes exceeding a pre-training range, and reduces decision errors caused by insufficient scene adaptation.

The invention discloses an intelligent task allocation method and related equipment. The method comprises the steps of receiving multi-format task description information, extracting multi-dimensional attributes to generate a task vector, constructing an executive vector containing multiple capability dimensions, matching an optimal main executive through a multi-layer perception model, and performing real-time monitoring and adjustment. According to the scheme, the existing defects are specifically solved: multi-format information is analyzed to extract multi-dimensional attributes, so that a system comprehensively understands the context of a task, and the understanding insufficiency of a traditional method is avoided; constructing a multi-dimensional executive capacity vector and combining a scoring model to realize accurate matching of a task and an executive, and solving the problem of unreasonable distribution caused by asymmetry of man-machine capacity; the execution state is monitored in real time, redistribution is triggered, a dynamic feedback closed loop is formed, and the defect that the dynamic performance of a labor division mechanism is insufficient is overcome, so that the task execution flexibility and reliability are improved, resource waste and decision errors are reduced, and the execution efficiency is improved.

The invention relates to the technical field of active safety of forklifts, and discloses a driving assistance method for stability adjustment of a forklift, which comprises the following steps: acquiring input parameters of the forklift for driving assistance; acquiring a fuzzy set corresponding to the input parameter; establishing a fuzzy rule base for logical reasoning based on the fuzzy set of the input parameters; constructing a decision function through a weighted average method to calculate a driving mode control value, and optimizing a weight through minimizing a decision error based on supervised learning; outputting a corresponding driving mode according to the driving mode control value; the invention further discloses a driving assistance system for forklift stability adjustment. The driving assistance system comprises a road surface recognition module, a forklift state acquisition module, a parameter fuzzification module, a fuzzy rule base storage module, a driving mode decision module and a driving mode execution module. By monitoring a plurality of input parameters, the overall performance and the adaptive capacity of the forklift system are improved, and efficient operation and safe operation of the forklift system in a dynamic complex environment are ensured.

The invention provides an autonomous operation obstacle avoidance method and system for a low-altitude aircraft, and the method is executed by the low-altitude aircraft, and comprises the following steps: S1, data acquisition: collecting data through a multi-source heterogeneous sensor group carried by the low-altitude aircraft, airspace control information, real-time meteorological data and collaborative sensing data of other aircrafts sent by the ground sensing base station are received through the air-ground communication interface; preprocessing the collected original data, wherein the preprocessing comprises data format standardization, abnormal value elimination and time synchronization; and S2, data fusion processing: carrying out credibility evaluation on the preprocessed multi-source heterogeneous sensing data and collaborative sensing data. According to the invention, through three-dimensional credibility evaluation and an air-ground data compensation mechanism, the perception precision is greatly optimized, the perception data efficiency is improved in complex environments such as low illumination, strong electromagnetic interference, rainstorm and the like, and obstacle avoidance decision errors caused by data distortion of a single sensor are avoided.

The invention discloses an intelligent decision-making method for a cerebral ischemia drug administration time window, and relates to the technical field of intelligent decision-making. The method comprises the following steps: S1, acquiring parameters, and setting a cerebral ischemia drug configuration scheme; s2, extracting a medicine characteristic parameter set and a medicine taking influence parameter set; s3, calculating a drug combination risk parameter set; s4, performing hierarchical classification processing on the drug combination risk parameter set, and performing drug correlation analysis; s5, performing weighted fusion on a correlation analysis result in combination with a drug influence attenuation rule, generating a drug administration time window evaluation parameter, and adding a label; and S6, according to the parameters, the labels and the configuration scheme, generating a cerebral ischemia drug administration time window intelligent decision scheme, and performing storage and backup. According to the method, by combining directional extraction of the drug characteristic parameter set and the drug taking influence parameter set with Pearson correlation algorithm screening, decision errors caused by data deviation are avoided.

The invention discloses an anti-unmanned ship unmanned aerial vehicle swarm dynamic clustering algorithm and a saturation attack path planning method, and belongs to the technical field of path planning. The invention discloses an anti-unmanned ship unmanned aerial vehicle swarm dynamic clustering algorithm and saturation attack path planning method. The method comprises the steps of constructing a multi-dimensional situation awareness index system; constructing a clustering decision function; and establishing a cluster structure dynamic adjustment mechanism, and carrying out collaborative scheduling optimization on the cluster structure. According to the invention, the problem of decision error caused by incapability of timely obtaining accurate information when communication is interrupted in the prior art is solved. According to the method, dynamic changes of the bee colony targets can be more flexibly coped with, it is ensured that each bee colony target is covered with the corresponding interception cluster, even if part of communication is interrupted, all clusters can still make decisions autonomously according to local information, certain interception capacity is maintained, basic interception tasks continue to be executed under the condition that communication is limited, and the communication efficiency is improved. It is ensured that interception preparation is completed before the bee colony reaches the defense target, and defense failure caused by response delay is effectively avoided.

The invention relates to the technical field of semiconductor radio frequency signal processing, and discloses a narrowband signal receiving system and method, and the system comprises a coarse frequency offset value capturing module, a carrier phase locking module, a digital domain carrier tracking module, a timing synchronization and multipath correction module, and a vector amplitude phase correction and equalization module. In the prior art, the technical problems of lock losing, judgment errors and high bit error rate caused by serious multipath interference and frequency offset drifting of narrow-band signals are difficult to solve in complex environments with multipath effect and high frequency offset, such as urban canyons, basements and warehouses. According to the invention, by introducing analog phase locking, digital fine tuning, dual-correlation symbol synchronization based on multi-path sensing and vector amplitude-phase combined compensation overall architecture with cooperation of analog and digital, the problems of synchronization failure and demodulation performance reduction caused by multi-path distortion in the prior art are avoided; and the sensitivity and the overall reliability of radio frequency signal receiving are remarkably improved.

The invention discloses a map construction method and system based on air-ground collaborative fusion air-ground perception, and belongs to the technical field of automatic driving and environment perception. In order to solve the problems that closed-loop feedback only stays at a data layer for correction, perception errors cannot be radically eliminated and system robustness is insufficient in existing air-ground collaborative map construction, perception differences, observation angles, speeds and other perception parameters are accurately bound to form parameterized difference data; the cloud data center unit performs periodic iterative optimization on the end-side semantic understanding model based on the data and issues update parameters to realize error source calibration; according to the method, bidirectional cross validation, unmanned aerial vehicle height adaptive adjustment and a double-weight layered grid fusion algorithm are fused, and the system adopts an end-cloud-end architecture and is matched with 5G + V2X dual-mode communication to guarantee air-ground end-cloud real-time interaction. According to the scheme, the map construction precision and the system robustness are effectively improved, the closed-loop feedback delay is low, the obstacle avoidance decision error rate is remarkably reduced, and the method is suitable for complex unstructured environments such as mine exploration and field rescue.

The invention discloses an energy storage system hazard analysis method and system based on full-link fault sorting, and relates to the technical field of energy storage systems, and the method comprises the steps: defining a hazard scene of an energy storage system; establishing a safety control system of the energy storage system; taking a safety control loop of the whole link of the energy storage system as an object, performing fault sorting on the safety control system, and identifying a problem control behavior of the safety control system; and determining an energy storage system hazard scene corresponding to the problem control behavior of the safety control system. According to the method, key failure events can be identified, and harmful events possibly caused by unsafe fault protection decisions when the failure events occur can be analyzed and obtained from the perspective of component interaction. Meanwhile, safety function classification is carried out on all the components, sorting is carried out from the whole link based on the interaction relation, potential safety hazards still existing in a complex system can be avoided, and the possibility of accidents caused by decision errors is relieved from the mechanism.

The invention relates to the technical field of computers and data processing, can be applied to the fields of medical treatment, finance and the like, and discloses a task processing method and device, computer equipment and a readable storage medium. The method comprises the steps of obtaining current attribute information of a task, and judging whether the task needs to be verified or not according to the current attribute information; if verification is needed, obtaining a current log of the task, and judging whether a query database of the task is an Oracle type or not according to the current log; if the task is the Oracle type, whether the task is in a stuck state or not is judged according to the current log; if the current log is in the stuck state, all historical logs of the task are obtained, and whether the ratio of the current stuck duration of the task to the historical average running duration of the task under the condition of the same query data size exceeds a preset ratio or not is judged according to the current log and all the historical logs; and if the preset ratio is exceeded, stopping the task and then restarting the task. According to the method, the task delay risk can be greatly reduced, so that the decision error risk is reduced.

The invention provides a power system transient voltage control method and system. The method comprises the following steps: acquiring initial power system operation state parameters in real time and establishing a transient model; obtaining an initial transient voltage severity index based on the parameters and a preset initial voltage; constructing a physical information neural network based on the transient model and exporting a loss function of the physical information neural network; based on the parameters, a preset reward parameter and a noise network, obtaining an initial Rainbow agent, executing a removal action and calculating corresponding loss; and constructing a total loss function based on the total loss function and training to convergence by taking minimization as a target, and obtaining an intelligent agent to realize the control of the transient voltage of the power system. According to the method and the system for controlling the transient voltage of the electric power system, the electric power system can be rapidly controlled to recover stability, the situation that the transient voltage control decision is not accurate due to the fact that the action value is excessively estimated is avoided, and the situation that the instability degree of the electric power system is aggravated is further prevented.

The invention provides a deep learning DD channel estimation system and method combined with an adaptive CIR truncation mechanism, and the system comprises a pilot frequency estimation module which carries out the channel estimation according to a received pilot frequency sequence, and outputs a pilot frequency assisted channel estimation value; the deep learning prediction module is used for predicting the channel state of the next moment according to the historical channel estimation sequence; the data auxiliary channel estimation module is used for balancing and decoding the data symbols based on the channel state, and obtaining data auxiliary channel estimation after reconstruction; the self-adaptive CIR truncation module takes data-assisted channel estimation as input and outputs a channel estimation value after truncation; and the periodic pilot frequency correction module is used for triggering pilot frequency updating and correcting the pilot frequency assisted channel estimation value. According to the method, the influence of a data judgment error is actively considered, the tap weight is dynamically output by using the lightweight neural network, the historical sequence is intelligently cleaned and cut off, error propagation is effectively inhibited, and the stability and robustness of prediction are improved.

The invention discloses a substation intelligent maintenance aid decision-making method and system based on a digital scene, relates to the technical field of electric power operation and maintenance, adopts a cascade optimization algorithm to train a deep learning model to obtain an intelligent maintenance analysis model, effectively overcomes the defect that a traditional optimization method is easy to fall into local optimum, and improves the maintenance efficiency. The maintenance accuracy of an intelligent maintenance analysis model is improved, visual association is achieved by constructing a substation digital scene with interest points, operation and maintenance personnel do not need to interpret a complex data report or search equipment positions on a two-dimensional drawing any more, the fault positions can be determined more rapidly, and the maintenance efficiency is improved. And the risk of decision errors caused by unsmooth information transmission or experience dependence is obviously reduced.

The pulse width compression PPM modulation method based on superconducting nanowire single-photon detection belongs to the technical field of signal processing. In view of the problems that the prior art cannot reduce the communication capacity, increase the system overhead and solve the ranging accuracy when reducing the communication error rate caused by random jitter. The pulse width in the PPM symbol is compressed at the sending end, the time fluctuation range of the superconducting nanowire single-photon detector response pulse is reduced, and the ranging error caused by time fluctuation is reduced. The time range of the random appearance of photons is compressed, the crosstalk of the superconducting nanowire single-photon detector response pulse to the adjacent time slot is reduced, the symbol decision error probability is reduced, and the communication reliability is improved. The signal light power is limited in a smaller time range, the instantaneous light power in the pulse time period is improved, the light power utilization efficiency is improved at the same transmission power, and a longer transmission distance is achieved. The present application is suitable for the field of communication and is used for improving the transmission distance and the ranging accuracy.

The application provides a vehicle auxiliary driving test scene generation method, device and equipment and a medium, and relates to the technical field of vehicles. The method comprises the following steps: acquiring real-time state data and environment data of a vehicle; calculating a dynamic safety threshold based on the real-time state data and the environment data; the dynamic safety threshold represents the safety risk of vehicle auxiliary driving in a real-time state dynamic change scene of the vehicle; in the case that the dynamic safety threshold triggers adjustment of the complexity of the vehicle auxiliary driving scene, generating a test scene of vehicle auxiliary driving based on the dynamic safety threshold and a preset test scene generation model; and the test scene is adapted to the safety risk. The application can reduce braking decision error by feeding back the safety risk in real time based on the dynamic safety threshold, thereby improving the accuracy and comprehensiveness of verifying the safety of the auxiliary driving system.

The application provides a high-order QAM phase noise suppression method, device and equipment, and belongs to the technical field of signal transmission. The method comprises the following steps: after timing synchronization, Doppler compensation and frame synchronization processing are performed on a received signal, identifying a known pilot symbol block inserted at a preset interval, and extracting a pilot signal and a non-pilot signal in the received signal; performing phase compensation and hard decision on the non-pilot signal to obtain a symbol decision error; adjusting Kalman filter parameters by using pilot observation values in the pilot signal and the symbol decision error; and performing phase compensation on the non-pilot signal by using the adjusted Kalman filter parameters, predicting phase noise at a non-pilot time, and adjusting the Kalman filter parameters by using the pilot observation values and the symbol decision error, so that the method can better adapt to time-varying phase noise characteristics and effectively improve the high-order QAM phase noise suppression capability.

The invention relates to the technical field of intelligent traffic control, and discloses a road network mass flow emergency disposal system and method based on an artificial intelligence large model, and the system comprises a data credible fusion module, a traffic flow prediction module, a safe traffic decision module, a cross-department collaborative scheduling module and a traffic decision optimization module. Performing space-time coupling analysis on a pre-acquired traffic state matrix and road topological graph data to obtain a congestion probability estimated value, performing traffic decision analysis to obtain an initial traffic control scheme, performing multi-department permission allocation in combination with emergency resource position data, generating a multi-department cooperation instruction, performing decision error analysis to obtain a correction parameter, and performing traffic control. Generating an optimized traffic control scheme based on the correction parameters; according to the invention, the road congestion event processing efficiency can be improved.

This application provides an artificial intelligence-based method and system for processing respiratory disease treatment feedback data. By combining the high decision-making performance of a first treatment feedback matching model with the low decision-making error of a second treatment feedback matching model, the accuracy of evaluating the semantic correlation of feedback content between respiratory disease treatment feedback data is significantly improved. By introducing a training cost mechanism and dynamically adjusting the iteration number of each training sequence accordingly, the model's ability to distinguish complex feedback data is effectively enhanced, especially when dealing with counterexample data that differs significantly from the target respiratory disease treatment feedback data and is difficult to distinguish. Thus, not only is the model's convergence speed accelerated, but the iterative parameter interaction training strategy also integrates the advantages of both models. The final target treatment feedback matching model maintains high processing efficiency while significantly reducing decision error.

The application discloses a virtual power plant resource decision optimization management method and system based on big data driving, and relates to the technical field of data management.The virtual power plant resource decision optimization management method and system based on big data driving comprises the following steps: collecting risk component data and operation performance data of a virtual power plant, and storing and constructing a virtual power plant management database after preprocessing; performing data quality risk analysis by evaluating communication continuity, equipment stability and meteorological disturbance, performing risk priority sequencing and data classification labeling according to the risk analysis result; fusing data quality, label influence and time persistence, measuring the adaptation degree between data and supply-demand prediction, and constructing a supply-demand prediction model; and measuring feedback optimization effect in view of prediction error, output deviation, constraint touch and data quality.The application solves the problems of inaccurate data cleaning and resource scheduling decision errors caused by the difficulty in defining the boundary of abnormal data in a virtual power plant.

The invention discloses an intelligent inspection system for a power plant air cooling island, and belongs to the technical field of power plant air cooling island monitoring, and the system comprises the steps: obtaining abnormal inspection variables in real time, carrying out the classification processing, dividing a bottom line variable and an elastic variable, and avoiding decision errors caused by fuzzy dependent variable boundaries; constructing a scheduling task set, setting a priority ordering method, calculating the real-time emergency degree of scheduling tasks, and performing priority ordering and conflict resolution; based on a path anchor point topological graph, generating a candidate path set, setting a candidate pre-judgment method, performing result pre-judgment on each candidate path, generating an inspection instruction based on a comprehensive score, and triggering a responsibility transfer mechanism or a reporting mechanism, so as to realize efficient execution of a scheduling task and timely response of risks.

The invention relates to the technical field of automatic control, in particular to a dynamic obstacle avoidance method for a balance robot. The method comprises the following steps: calculating a pitch angle and an angular velocity based on collected data in the operation process of a robot, further constructing a curvature offset function and an attitude resonance exponential function, and generating an attitude resonance index through the attitude resonance exponential function; designing an attitude dissipation control factor based on the curvature offset function and the attitude resonance index; generating a differential control signal based on the curvature offset function, the attitude dissipation control factor and the attitude resonance index; and mapping the differential control signal to form a differential driving instruction. The problems that an existing robot is prone to attitude instability, response lag, path decision errors and the like when the obstacle environment changes or turns are solved, and the problems that a dynamic adjusting mechanism is lacked to cope with sudden state changes, and the control precision and stability are insufficient are solved.

The invention belongs to the technical field of intelligent driving, and particularly relates to a driving decision-making method under a blocked intersection, which comprises the following steps of: establishing a main vehicle dynamic visible range model through static geometrical information of a blocked object and a sensing range of a sensor, and solving hypothetical vehicle state information of each road at the intersection through the dynamic visible range model and a road structure. Environmental state input information is formed by detecting environmental vehicle state information and virtual vehicle state information, and the driving behavior of the host vehicle is determined on the basis of the environmental state input information. The virtual vehicle is generated based on the shielding object in the shielding state, and the driving strategy is formed based on the virtual vehicle, so that decision errors caused by the real environment information in the shielding state can be effectively avoided, the accident risk is effectively reduced, global consideration of the decision model on the uncertainty of the environment information and driving behavior robustness decision making are realized, and the driving experience is improved. And the traffic safety and efficiency of the intelligent automobile when the environmental information perception is insufficient are improved.

Shared information generation technique including key generation rate and soft-decision error correction in CV-QKD is disclosed. A receiver is configured to: set reference bit positions for basis reconciliation according to the reference bit positions with a transmitter, to generate sifted key quantization data; performs hard decision on a part of the sifted key quantization data to generate hard-decision data; perform bit position synchronization decision whether bit position synchronization is established; and in response to establishment of bit position synchronization, performs soft-decision error correction processing to generate shared information.

The application relates to the technical field of road marking, in particular to a road marking drawing method and system based on image recognition. The method comprises the following steps: acquiring a historical trajectory data set, a road design parameter set and an environment image set of a target road; analyzing the historical trajectory data set to determine a trajectory repetition degree; determining a geometric constraint condition according to the road design parameter set; determining a dangerous source distribution feature according to the environment image set; and determining a marking scheme according to the trajectory repetition degree, the geometric constraint condition and the dangerous source distribution feature. The method is helpful for improving the marking visible distance under extreme conditions, reducing out-of-control accidents caused by overspeed, reducing the contradiction between the danger source avoidance space and the road side, generating the marking scheme, avoiding that the compensation strength exceeds the physical limit of the lane, and reducing the decision error rate at the marking mutation.

The present application relates to the technical field of digital signal processing, and proposes a terahertz wireless transmission carrier phase recovery system and method based on tree structure Parzen estimation optimization, which comprises a blind phase estimation module, used for setting multiple test phases, calculating the decision error under each test phase, and obtaining the original phase estimation value of the current signal; a cyclic slip unwrapping module, used for selecting the continuous branch of the current original phase estimation value according to the phase estimation result of the previous moment, eliminating phase mutation, inhibiting cyclic slip, and completing phase compensation; and a parameter optimization module based on tree structure Parzen estimation, used for optimizing the test phase number and the sliding window length in the blind phase search according to the performance index after the carrier phase recovery, and obtaining the final carrier phase recovery parameter. The present application realizes low-complexity and strong-robustness carrier phase recovery, and significantly improves the stability of the terahertz wireless transmission system under strong phase noise.

The application discloses a compensation method for CDR channel asymmetry in a high-speed transmission system i and a decision error e i of a current received symbol, attempts to match a continuous received decision bit stream with a special bit sequence pattern to calculate inter-symbol interference ISI, and then uses the calculated inter-symbol interference ISI to compensate for the asymmetry of the channel The application provides a compensation method for CDR channel asymmetry in a high-speed transmission system, which is simple to implement, has a faster convergence speed and better performance.