146 results about "Decision error" patented technology

The invention provides a multi-core heterogeneous system-oriented startup deployment and cooperative processing method, which comprises the following steps of: when the system runs, obtaining startup codes after a master core and a slave core are electrified, respectively obtaining mirror image files of Linux and RTOS (Real Time Operating System) on the basis of synchronous running and storage partition, and running the mirror image files on the master core and the slave core after moving the mirror image files; and after the heterogeneous operating system is loaded, generating an interrupt signal based on inter-core interrupt and a corresponding register through a CLINT module in the main core, and realizing data communication between the main core and each slave core based on the interrupt signal and the shared memory. By means of the method, after the system is powered on, the multiple cores can synchronously run the starting codes, and time delay and uncertainty possibly caused by one-by-one starting of the single cores are avoided; communication and data transmission among multiple cores are realized through interruption and memory sharing, the synchronism and consistency of data are ensured, and misjudgment and decision errors caused by data delay and inconsistency are avoided, so that the real-time performance of the system is remarkably improved.

The invention discloses a humanoid robot dynamic environment adaptation and behavior optimization method based on artificial intelligence, the humanoid robot dynamic environment adaptation and behavior optimization method has excellent environment adaptation capability, the robot can comprehensively and accurately perceive and understand environment change through a multi-modal environment perception and knowledge graph real-time construction and updating mechanism, and the robot can be used for optimizing the environment change. The self behavior strategy is rapidly adjusted, and the strong adaptive capacity far better than that of a traditional technology is shown in a complex dynamic environment; through fusion of reinforcement learning and a planning algorithm and combination of rich information of a knowledge graph, the robot can quickly make scientific and reasonable behavior decisions when facing various uncertainties and emergencies, and decision errors are effectively avoided; the robot has continuous evolution learning ability, and a continuous learning and optimization mechanism enables the robot to continuously accumulate experience in the operation process, continuously improve and optimize a knowledge system and a decision model of the robot, gradually improve the performance and ability, and better adapt to diversified environments and complex task requirements.

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 discloses a supply chain material data management method and a data management system, and particularly relates to the technical field of data management. Through collecting and centrally storing material data of each link of a supply chain, an inventory turnover rate and residence time are analyzed, a safe inventory level is evaluated, and a purchase plan and inventory control are optimized. In order to solve the possible problem that inventory data is not updated in time or input errors occur, interference of the inventory data on inventory turnover rate calculation is evaluated through real-time analysis and abnormal input monitoring, and the accuracy of the inventory data is ensured; according to the method, the accuracy of the inventory turnover rate calculation result is divided into high, medium and low accuracy levels, and corresponding processing is carried out, so that purchase, production and supply chain decisions of enterprises are optimized, strategic decision errors are reduced, and the overall efficiency and response capability of a supply chain are improved.

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 power distribution network grounding line selection analysis and fault location method based on signal feature identification. The method comprises the following steps: extracting a fault signal of a monitoring point; adopting an AGCN-TCN model to carry out fault identification and classification on the fault signals; local outlier factors of the monitoring points are calculated, fault signals of all the monitoring points are decomposed according to sliding windows, sliding window frequency domain assignments obtained after decomposition serve as frequency domain fault indexes, fault position judgment comprehensive indexes are constructed based on the local outlier factors and the frequency domain fault indexes, and the positions of fault points are judged through the fault position judgment comprehensive indexes; according to the method, the AGCN-TCN model is innovatively constructed for the distributed power distribution network, feature extraction and analysis of fault signals are realized from the angles of space and time, and decision errors caused by faults of single monitoring equipment are avoided; according to the method, a fault position judgment comprehensive index is defined, the fault position is judged from the double angles of the time domain and the frequency domain, and inaccuracy of a single method is avoided.

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.

An urban drainage, regulation and storage facility operation step-by-step decision analysis method based on risk assessment. When information of each decision standard preference for urban drainage, regulation and storage facility operation is completely missing or is unclear, a method combining game theory with feasible weight space is used to aggregate urban drainage, regulation and storage facility operation weights; stochastic multi-criteria decision analysis algorithm and fuzzy optimization models are used to construct a stochastic multi-criteria decision analysis-fuzzy optimization model for the urban drainage, regulation and storage facility operation; an urban drainage, regulation and storage facility operation decision risk assessment model is constructed to quantitatively assess the risk of urban drainage, regulation and storage facility operation decision errors; and multi-stage multi-criteria decision analysis is used to determine step by step information of the weight of each standard of the urban drainage, regulation and storage facility operation, thereby achieving an optimal decision for the urban drainage, regulation and storage facility operation.

Autonomous vehicle systems provide improved safety in comparison to human driven vehicles by reducing crashes. However, in future scenarios, the autonomous vehicles have to share the road with human-driven vehicles. But human drivers make decision errors during maneuvers which lead to crashes. Autonomous vehicles therefore have to predict human-initiated safe and unsafe maneuvers to react appropriately for the safety and comfort of its passengers. The present disclosure addresses unresolved problems of the conventional autonomous vehicle systems by providing a stacked autoencoder based singular deep neural network that uses sensor data to predict if any vehicle within its field of view would perform safe or unsafe lane maneuvers into its headway or into any other vehicle's headway. The method of the present disclosure also helps surrounding vehicles to know intention of a target vehicle with the help of vehicle-to-vehicle communication systems in a road driving environment.

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 relates to the technical field of farmland water saving, in particular to a farmland water-saving self-adaptive irrigation system based on the Internet of Things, which comprises a data acquisition module, a crop irrigation sensitivity self-adaptive adjustment module, an intelligent decision module and an intelligent control module, the data acquisition module acquires environmental data of farmland soil in real time; the crop irrigation sensitivity self-adaptive adjustment module evaluates the irrigation sensitivity of crops in the current growth stage in real time, and outputs a sensitivity level and suggested irrigation intensity; the intelligent decision-making module generates an irrigation strategy through a multi-objective optimization algorithm; the intelligent control module executes the irrigation strategy and controls the water pump, the valve and the pipe network flow of the irrigation system. According to the method, the scientificity of irrigation decision and the accuracy of data analysis are improved, and resource waste and decision errors caused by data incompleteness or deviation are effectively 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 an automatic driving behavior decision-making method fusing time sequence Transform and safety constraints, and relates to the technical field of automatic driving decision-making. The method comprises the following steps: constructing an automatic driving mixed traffic scene, wherein the mixed traffic scene comprises an automatic driving vehicle and a man-driven vehicle; a man-driven vehicle model is constructed for a man-driven vehicle in a mixed traffic scene, an IDM model is adopted to control a longitudinal decision, and an MOBIL model is adopted to control a transverse decision. According to the invention, the cost constraint is introduced, so that the automatic driving system can avoid potential dangerous behaviors in a complex environment, and the safety is improved; an action regularization item is introduced into a strategy loss function, so that the stability of the system under an uncertain condition is enhanced, and decision errors caused by external interference are reduced; and meanwhile, a transform time sequence model is introduced to design a network structure, so that the information capturing capability of the model is enhanced, and the decision effectiveness is improved.

The invention provides an energy storage system arbitrage end-to-end method considering battery attenuation cost, and relates to the field of energy storage arbitrage optimization, and the method comprises the steps: building an electricity price prediction model based on a deep neural network, and achieving the hour-by-hour electricity price prediction in the next day; an energy storage system arbitrage optimization model is established, and the attenuation cost of the energy storage battery is modeled as a part of an objective function of the optimization model; establishing an end-to-end model fusing the electricity price prediction model and the energy storage arbitrage optimization model, wherein the energy storage arbitrage optimization model is equivalently converted into a neural network layer structure compatible with the electricity price prediction model; a two-stage training algorithm is established to determine parameters of an end-to-end model, in the first stage, the electricity price prediction model is pre-trained only by using the electricity price prediction error, and in the second stage, the electricity price prediction model is re-trained by using the decision error of energy storage arbitrage optimization. According to the method, convergence of the electricity price prediction model can be accelerated, the decision error influence can be considered, and the overall arbitrage income in the whole life cycle of the energy storage system is optimized.

The invention provides an intelligent power dispatching system based on artificial intelligence. The intelligent electric power dispatching system based on artificial intelligence comprises a data acquisition module used for collecting power grid state, load demand, power generation condition and external environment information in real time through various sensors and intelligent equipment, the sensors comprise a current sensor, a voltage sensor and a meteorological sensor, and the intelligent equipment comprises a power supply module and a power supply module; the intelligent device comprises an intelligent electric meter and a load prediction device, and the data processing module is used for cleaning, analyzing and fusing the collected data. According to the intelligent electric power scheduling system based on artificial intelligence, through real-time data acquisition of various sensors and intelligent equipment and data processing of adaptive filtering and a machine learning algorithm, the system can significantly improve the accuracy of scheduling decisions, reduce the risk of decision errors caused by data acquisition errors or delay, and improve the scheduling accuracy. The innovation not only improves the reliability of the system, but also enhances the response capability to the dynamic change of the power market.

The invention belongs to the technical field of railway multi-source data fusion, and particularly relates to a railway infrastructure multi-source data fusion system and method. According to the method, the time features of each reference data source are analyzed, the data sources with the centralized features and the dispersive features can be accurately recognized, the corresponding fusion rules are formulated accordingly, the scientificity and reasonability of the data fusion process are ensured, abnormal fusion nodes can be found and processed in time, and the fusion efficiency is improved. In addition, the abnormal fusion nodes can be dynamically adjusted according to the fusion deviation value, the continuity and stability of the data fusion process are ensured, the deviation in the data fusion process can be found and corrected in time through real-time monitoring and backtracking analysis, and the data fusion accuracy is improved. Therefore, decision errors caused by data fusion errors are avoided, and accurate and timely data support is provided for maintenance and management of railway infrastructures.

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 provides a replenishment prediction model training method and a replenishment method and device, and relates to the technical field of supply chain intellectualization, and the method comprises the steps: obtaining a training sample and an initial replenishment prediction model; inputting the training data into a prediction unit to predict and predict replenishment data; simulating the predicted replenishment data through a simulation unit, and inputting the simulated replenishment data into a prediction unit for iterative prediction; and repeating the above steps until training is completed, determining a loss value of the initial replenishment prediction model based on the predicted replenishment data and the simulated replenishment data, adjusting the initial replenishment prediction model based on the loss value, and outputting a target replenishment prediction model. The target replenishment prediction model containing the prediction unit and the simulation unit at the same time is generated through training, prediction and simulation can be achieved through the same model, intermediate processes are reduced, decision errors are reduced, finally, the theoretically optimal replenishment decision is directly generated, and meanwhile the generation cost of the replenishment decision can be reduced.

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 present invention discloses a DNA storage method for rapidly reading out short fragments by nanopore sequencing. This method encodes the information sequence using block codes, superimposes the sparse codeword fragments with a known pseudo-random sequence, maps the combined equal-length encoded bit sequences into a data DNA sequence after combination, and constructs a labeled DNA sequence using a pseudo-random sequence accompanied by a short block error-correcting code. For nanopore sequencing, primer recognition and label recognition are used to achieve fast clustering of sequencing reads, a hidden Markov model is constructed, and the forward-backward algorithm is used to infer the maximum symbol probability. The reads within the cluster are subjected to upper-layer bit sequence merging, insertion and deletion errors are corrected, a consistent upper-layer bit sequence is obtained, and the lower-layer bit sequences within the cluster are corrected. The likelihood probability of the consistent sequence is calculated, and soft decision error-correcting decoding is performed to restore the original data. The present invention can effectively utilize nanopore sequencing to rapidly read out short fragment DNA, and its advantage lies in using a probability-based multi-sequence merging method to correct the high insertion and deletion errors of nanopore sequencing.

The invention discloses a carbonate rock formation fluid pressure testing method, and relates to the technical field of formation fluid pressure testing, and the method comprises the steps of S1, data acquisition and equipment installation, S2, test fluid injection, S3, pressure response detection, and S4, data analysis and calculation. According to the testing method, by collecting geological parameters and calculating the permeability anisotropy coefficient, a stratum model which is closer to reality can be constructed, specifically, in a carbonate rock stratum testing area, the permeability difference of different areas is determined based on geological analysis, a reliable basis is provided for subsequent testing, and the testing efficiency is improved. Meanwhile, the measured parameters such as the viscosity and density of the testing fluid and the stratum thickness are comprehensively considered for calculation, the obtained result is closer to the real stratum fluid pressure, accurate data support is provided, the testing precision and accuracy are improved, and decision errors caused by data errors are reduced.