137results about How to "Quick response to changes" patented technology

The embodiment of the invention relates to the field of chip verification, and discloses an automatic chip verification method, system and device and a medium. Generating a register model according to the custom register table; generating a register test case based on the register attribute; performing simulation operation on the register model and the register test case according to a simulation configuration file, and collecting coverage rate information in a simulation result; the simulation configuration file comprises simulation times, test case names and coverage rate collection targets. Compared with a traditional manual operation mode, the method has the advantages that the change of the register can be quickly responded, the test case is automatically updated, and the continuity and integrity of the verification process are ensured. According to the invention, high automation of the chip verification process is realized, manual intervention is reduced, verification comprehensiveness and resource utilization rate are improved, and more reliable verification guarantee is provided for chip design. The technical problem of low chip verification efficiency can be at least solved.

The invention discloses an adjustment method and system for 5G network slices, and relates to the field of 5G networks, and the method comprises the steps: obtaining historical demand information of a target service; determining an initial network slicing strategy based on the historical demand information; determining an initial network slice based on the initial network slice strategy; monitoring performance data of each initial network slice; obtaining priority information of a target service corresponding to each network slice; the initial network slice is adjusted based on the performance data and the priority information to determine an optimized network slice.

The invention discloses a mobile game scene interaction intelligent optimization method based on a generative adversarial network, and aims to improve the game experience of players. Comprising the following steps: S1, collecting behavior data and interaction information of a player in a mobile game, and generating a preliminary behavior feature model; s2, constructing a generative adversarial network model; s3, generating diversified game scene interaction examples according to the trained generator; s4, evaluating and optimizing the generated scene interaction example by using a discriminator; s5, real-time feedback of the player is received, and the behavior feature model of the player is updated; s6, dynamically adjusting a generation strategy based on the updated behavior feature model, and optimizing a scene interaction effect; s7, environment data are integrated, scene interaction is optimized in real time, and the immersion of players is enhanced; and S8, providing user-defined scene interaction options for the players, and allowing the players to adjust according to personal preferences. The interactive experience and game fun of players can be effectively improved.

The invention relates to the technical field of robot control instruction analysis, in particular to a robot control instruction analysis system based on multi-algorithm fusion, which comprises a data collection module, an environment map analysis module, a global path analysis module and a moving trajectory analysis module, wherein the data collection module collects state data of a robot, surrounding environment data of the robot and a target position; the environment map analysis module determines an environment occupation grid map according to the environment data; the global path analysis module determines a global path according to an environment occupation grid map, a target position, self state data and robot motion constraint parameters; the running track analysis module determines a smooth track according to the global path and the state data of the running track analysis module and detects an environment occupation grid map, if a dynamic obstacle avoidance object exists in the environment occupation grid map, the environment occupation grid map is updated, the smooth track is modified according to the environment occupation grid map updated in real time, and the dynamic obstacle avoidance object is obtained. And the final robot moving trajectory is obtained.

The invention belongs to the field of medical information systems, and provides a medical institution full-process doctor-seeing path planning method and system based on artificial intelligence, and the method comprises the steps: modeling each doctor-seeing link in a medical institution into a directed graph; the node features and the edge weights of the directed graph serve as input, the node features are updated through a graph neural network, and the weight of each node is dynamically generated; a queuing theory model is adopted for modeling according to the queuing condition of each node, and the average waiting time of each node is calculated; designing a multi-objective optimization function, and taking balance of the shortest path and the minimum waiting time as an optimization objective to generate an optimal doctor-seeing path scheme; the resource state of each node is dynamically monitored based on a real-time feedback mechanism of the Internet of Things equipment and a hospital information system, and when it is monitored that the resource state or waiting time of the node is changed, the graph neural network and the queuing theory model are recalled to update node features and priority weights.

The invention relates to the technical field of transaction data verification, in particular to a transaction data verification method and system. The method comprises the following steps: carrying out transaction data acquisition on block chain network nodes to obtain an original transaction data set; performing adaptive heterogeneous fragmentation on the original transaction data set to obtain a fragmented transaction data set; performing verification weight coefficient distribution on the fragment transaction data set to obtain a weighted fragment transaction data set; performing multi-level verification engine deployment on the verification nodes to obtain a layered verification node network; performing asynchronous message queue mechanism configuration on the hierarchical verification node network to obtain a parallel verification network cluster; and performing load balancing scheduling on the weighted fragment transaction data set based on the parallel verification network cluster to obtain a transaction verification task sequence. According to the invention, the throughput and transaction confirmation speed of the block chain system can be effectively improved.

The invention relates to the technical field of continuous casting equipment, and discloses a crystallizer control method and system based on proportional integral differential, and the method comprises the steps: converting analog signals of liquid level height and wall surface temperature into analog control signals, and adjusting parameters of a cooling water inlet of a crystallizer; constructing a cooling water database according to the flow and temperature of the generated cooling water and the heat transfer information of each area in the crystallizer; calculating proportional control output which is in direct proportion to the difference value, calculating integral of past errors, predicting changes of the errors, combining the outputs of all the parts, and calculating a final control signal; according to the calculation result of the final control signal, the flow and the temperature of cooling water are adjusted, and the liquid level height and the wall surface temperature in the crystallizer reach set values. The system comprises a database construction module, an intelligent control module and a result evaluation module. According to the invention, the operation performance of the crystallizer is effectively maintained, and support is provided for further improvement.

The invention provides a resonator phase noise suppression method based on Kalman filtering. The resonator phase noise suppression method comprises the following implementation steps: sampling displacement of a resonator; calculating a priori error matrix and a priori estimation value of each sampling; calculating the Kalman gain of each sampling; calculating an error matrix of each sampling; and obtaining a resonator phase noise suppression result. According to the method, suppression of the resonator phase noise in the mechanical resonant device is realized based on Kalman filtering, and the method is suitable for various noise types including high-frequency noise, can flexibly cope with different working environments and noise types, and has relatively good adaptability; based on the recursive calculation characteristic of Kalman filtering, state estimation can be updated every time new data arrives, processing of all historical data is not needed, meanwhile, dynamic estimation and optimization are carried out on input signals in combination with a prediction model and noise observation, a filter can rapidly respond to the change of signal frequency, and the accuracy and the reliability of the method are improved. And an efficient real-time adjustment capability is provided.

The invention relates to a multi-language intelligent question and answer method and device, equipment and a medium in the technical field of e-commerce, and the method comprises the steps: obtaining a question text provided by a user, and determining a question language of the question text; when the question language is consistent with a knowledge language corresponding to the question and answer knowledge base, determining a pre-stored question matched with the question text in the question and answer knowledge base, and taking an answer text corresponding to the pre-stored question as a target answer text; when the question language is inconsistent with the knowledge language, translating the question text into a target question text corresponding to the knowledge language, determining a pre-stored question matched with the target question text in a question and answer knowledge base, and translating an answer text corresponding to the pre-stored question into a target answer text corresponding to the knowledge language; and sending the target answer text to the user. Only one question and answer knowledge base of a specific language needs to be maintained, and the management and maintenance workload of the question and answer knowledge base is remarkably reduced.

The invention belongs to the technical field of vehicles, and particularly relates to a steering angle module chassis and a vehicle. The steering angle module chassis comprises a frame platform, a steering angle module and a hydraulic interconnection suspension system. The steering angle modules are arranged at the four corners of the vehicle frame platform and correspond to the left front steering angle module, the right front steering angle module, the left rear steering angle module and the right rear steering angle module respectively. Each steering angle module comprises a vibration reduction sliding column assembly. The hydraulic interconnection suspension system is arranged on the vehicle frame platform and connected to the vibration reduction sliding column assemblies in the steering angle modules so as to be used for adjusting and controlling the posture of the vehicle body. According to the hydraulic interconnection suspension system, the interconnection relation and internal hydraulic pressure of the vibration reduction sliding column assemblies in the four steering angle modules can be changed, four-wheel movement interconnection is achieved, the change response to the road surface is better and more rapid, then the inclination angle of a vehicle body is controlled, and the purpose of regulating and controlling the posture of the vehicle body is achieved.

The invention discloses a core rock sampling automatic identification system and method based on image processing and machine learning. The system comprises a drilling assembly, a sensor assembly, a measurement and control lamp assembly, an identification assembly, a relay assembly and a control and display assembly. The method comprises the steps that core rock drilling is conducted, and a core rock sample is obtained through extraction; core rock sample images are collected, and parameters in the drilling process are monitored; sorting physical and appearance characteristics of the obtained core rock sample and the core rock sample image, and storing the physical and appearance characteristics in a database; preprocessing the acquired core rock sample image, and extracting key features; training by using a machine learning algorithm to obtain a core rock category recognition model; and applying the core rock category identification model obtained by training to newly obtained core rock sample image data to obtain classification and identification results. According to the method, the sensor, image processing and the convolutional neural network are combined, automatic and real-time rock core recognition is realized, the recognition efficiency and accuracy are improved, and the method can be flexibly applied to a field environment.

The embodiment of the invention provides a software test recommendation method and device, equipment and a storage medium. The method comprises the following steps: performing feature extraction on a technical document of to-be-tested software to obtain test features of the to-be-tested software; according to a similarity score between the test feature and a historical test feature corresponding to a historical test demand, screening out a target historical test demand meeting a condition from the historical test demand; performing feature screening on the test features corresponding to the target historical test requirements based on an association rule analysis algorithm to obtain target test features; performing clustering processing on the target test features based on a preset clustering dimension to obtain software test suggestion information under the preset clustering dimension; and recommending the software test suggestion information to a user. According to the embodiment of the invention, quality fluctuation caused by experience and ability level differences of testers can be avoided, the systematicness and consistency of test suggestions are improved, and the coverage of the test suggestions is improved.

The invention relates to the field of micro-control units, and discloses an MCU remote real-time debugging method which comprises the following steps: S1, initializing an API management module on an MCU, and loading an API function for reading a plurality of memory addresses; s2, designing and implementing a communication protocol for transmitting a variable address needing to be checked and a corresponding variable value in real time; s3, the external equipment analyzes a map file generated after compiling, and extracts variable names and corresponding memory address mapping; s4, receiving a variable address sent by external equipment in real time through a communication protocol, and reading a corresponding variable value from the MCU memory by using an API function according to the received variable address; s5, implementing intelligent filtering based on the change frequency, the change amplitude and the historical importance of the variable; and S6, when the variable meets a preset triggering condition, automatically executing a response operation. The invention provides an efficient, safe and intelligent MCU remote real-time debugging method, and the debugging efficiency and reliability of the system are improved.

The invention discloses a data security compliance evaluation and identification method and system, and relates to the technical field of data security compliance evaluation and identification, and the method comprises the steps: collecting regulation text data through an API interface, and carrying out the preprocessing of the regulation text data through a natural language processing library NLTK, and obtaining formatted regulation text data; mapping each word in the formatted law and regulation text data into a fixed-length vector by adopting a pre-calculation word embedding method, and generating a digital vector; marking important terms in the digital vector by adopting a named entity recognition NER method to obtain a key concept text; establishing an evaluation model based on a machine learning algorithm, inputting the digital vector into the evaluation model, calculating and outputting an evaluation value by adopting a weighted average method, setting an evaluation threshold value, comparing the evaluation threshold value with the evaluation value, judging whether the digital vector is compliant or not, and obtaining an evaluation result; and constructing a recognition model based on an anomaly detection algorithm, and inputting the key concept text into the recognition model.

The invention relates to the technical field of motor control and parameter identification, and discloses a permanent magnet synchronous motor parameter identification method based on a self-adaptive forgetting factor, which comprises the following steps of: acquiring an input signal and an output signal of a permanent magnet synchronous motor in real time, and constructing a regression vector by using the acquired input signal and output signal; calculating the information entropy of the current moment according to the constructed regression vector; performing normalization processing on the information entropy, and calculating a forgetting factor at the current moment according to the normalized information entropy; and substituting the forgetting factor into a parameter updating formula of a recursive least square method for parameter identification. According to the method disclosed by the invention, the adaptive capacity to dynamic change of motor parameters can be improved, the interference of noise on identification precision is effectively suppressed, the credibility of an identification result is improved, and the robustness and long-term stability of a control system are ensured.

The invention discloses an energy regulation and control method and system of a virtual power plant based on a prediction model, and relates to the field of virtual power plant operation control, and the method comprises the following steps: collecting energy data in the virtual power plant; integrating the wind power data and the load data, and performing feature extraction on the integrated data; based on the predicted power generation data of the distributed energy and the predicted demand data of the load side resources, constructing a preliminary energy dynamic balance prediction model; performing day-ahead load demand and distributed energy prediction by using the preliminary energy dynamic balance prediction model; according to the preliminary scheduling strategy, using a niche grey wolf optimization algorithm to adjust the operation state of each distributed energy and load side resource in real time; dynamically adjusting and optimizing the energy dynamic balance prediction model; and carrying out load demand and distributed energy prediction again by using the optimized energy dynamic balance prediction model. According to the method, the change of the power system can be responded in advance, and more efficient energy scheduling is realized.

The invention provides an innovative personalized medical decision support system, and aims to generate highly-customized medical suggestions by comprehensively analyzing real-time physiological data and historical medical records of a patient in combination with latest clinical guidelines and expert experience. The system adopts an advanced data acquisition technology to monitor patient conditions in real time, uses a data warehouse technology to integrate historical medical data, and deeply mines data values through machine learning and deep learning algorithms. Besides, the system has adaptive learning ability, and can continuously optimize the decision model according to new medical data and user feedback, so as to realize more accurate and timely medical decision support. The design of the system not only improves the quality and efficiency of medical services, but also provides more personalized medical experience for patients, and is expected to bring revolutionary change in the medical field.

The invention provides a rapid in-situ monitoring system for VOCs pollution in underground water and soil gas of a polluted site, and belongs to the technical field of environment monitoring. Comprising a first collection module for collecting VOCs samples in underground water, a second collection module for collecting VOCs samples in soil gas, a collection module connected with the first collection module and the second collection module, an analysis monitoring module connected with the collection module, and a central processing module. VOCs samples in underground water are collected through the first collection module, VOCs samples in soil gas are collected through the second collection module, the collected VOCs samples are collected and stored through the collection module, the concentration and components of the VOCs samples in the collection module are monitored through the analysis monitoring module, and the whole system is arranged near a polluted site. And rapid in-situ monitoring of VOCs pollution in underground water and soil gas of a polluted site can be realized.

The invention discloses a mathematical model-based electric charge prediction, correction and automatic settlement method and system, and relates to the technical field of digital prediction settlement, and the method comprises the steps: collecting electric charge related data, constructing a data set, and carrying out the data cleaning of the data set, so as to guarantee the integrity of the data; constructing a prediction model based on the data set after data cleaning, and predicting and calculating the electric charge through the prediction model; and comparing an output predicted value with an actual value, carrying out anomaly detection and processing on the prediction model, optimizing the prediction model, obtaining an error reason, supplementing an error value, and clearing related electric charges. According to the method, multiple factors such as time change and weather influence are combined, the prediction accuracy is remarkably improved, and by continuously comparing the predicted value and the actual value, the model parameters are continuously optimized, various abnormal conditions are adaptively processed, and the change of the external environment is quickly responded. The method can help a power enterprise to better carry out electricity charge management, and promotes the optimized operation and sustainable development of a whole power system.

The invention relates to the technical field of power grid planning, in particular to a rule engine driven power distribution network planning problem library construction method. The method comprises the following steps: acquiring environment information data of the power distribution network; performing data preprocessing on the power distribution network environment information data to generate standard power distribution network environment information data; performing substation site selection on the power distribution network environment information data to obtain power distribution network substation site selection data; carrying out transformer substation capacity distribution on the power distribution network transformer substation site selection data to generate transformer substation capacity distribution data; performing transformer substation layout according to the transformer substation capacity distribution data and the power distribution network transformer substation site selection data so as to generate transformer substation layout data; and carrying out power transmission line path selection on the standard power distribution network environment information data based on the substation layout data to generate power transmission line route data. Through systematic data integration, flexible rule extraction, dynamic decision monitoring and knowledge management, the accuracy and adaptability of power distribution network planning are improved.

The invention discloses a high-speed and high-precision phase locking method, which can accurately grasp the noise level, phase deviation and attenuation coefficient of a channel through quantum channel estimation, and provides important information for subsequent processing. Signal decomposition allows accurate representation of the signal on a quantum base, laying a foundation for noise suppression (S3) and signal reconstruction. And noise suppression utilizes a quantum mechanism, so that the efficiency and accuracy of signal processing are improved. Adaptive link establishment and time-sharing signal transmission aim at different transmission requirements, and the efficiency of communication link establishment and data transmission is optimized. And link monitoring ensures the stability and reliability of communication. On the whole, the method can improve the security, stability and efficiency of quantum communication, reduces the complexity of quantum signal processing, and provides powerful technical support for practicability and commercialization of quantum communication.

The invention relates to the technical field of intelligent eye medicine boxes, and discloses an intelligent eye medicine box and a control method thereof.The eye medicine box is divided into a plurality of compartments for storing different types of medicine, and a sensor is arranged in each compartment to detect the weight of the medicine in real time; and accurately calculating the residual amount of the medicine by combining the initial weight data of the medicine, the dosage of the medicine currently used by the user, the last time of using the medicine by the user, the weight of the medicine in the last time and the total number of times of using the medicine by the user. Furthermore, the future drug use dosage and use rate are predicted through a machine learning model, the remaining amount is optimized based on the prediction results and the real-time weight data, the optimized remaining amount is obtained, and the optimal remaining amount is calculated according to the optimized remaining amount and the future drug use rate. Specifically, firstly, the accuracy of medicine management is remarkably improved through a smoke and flame identification method, it is ensured that a patient can use medicine on time according to the amount, and the poor treatment effect caused by missed taking or wrong taking is avoided;

The invention relates to the technical field of city management, in particular to a city management method and system based on big data. According to the method, urban management demand information is obtained and converted into overall management numerical value information, resource configuration parameters are scientifically formulated in combination with geographic space distribution information, optimal allocation of resources is achieved, and then corresponding management instructions are triggered based on the configuration parameters and management numerical values. Monitoring the regional state in real time according to the management instruction and evaluating the deviation with the target; finally, the management strategy is dynamically adjusted according to the deviation value, and continuous optimization of the management effect is ensured. According to the method, the change of the urban environment can be quickly responded, potential problems can be predicted, measures can be taken in advance, and the accuracy and efficiency of urban management are remarkably improved.

The invention discloses an on-chain enterprise risk identification method and system based on a credit chain large model. The system comprises a data preprocessing module, a model matching module, a weight parameter loading module, a data operation reasoning module, a risk prediction output module and the like. Large model training and optimization are carried out by integrating industry, region, type and other multi-dimensional data sets, a credit chain vertical type general model and a special model are formed, and the method can adapt to credit and risk prediction evaluation, monitoring and early warning of a single enterprise or an integrated enterprise in different supply chain and industry chain scenes. A more comprehensive, accurate and real-time solution is provided for enterprise management and industry supervision, and remarkable beneficial effects and advantages are achieved.

The invention provides a railway green performance data knowledge extraction method. The method comprises the steps of data collection and preprocessing, knowledge graph construction and data standardized storage. The method comprises the following steps: collecting and classifying key data of a full life cycle of a railway; constructing an ontology by a top-down method, extracting knowledge, and storing the knowledge in a graph database; storing the structured data based on the metadata, sorting and classifying the unstructured data, binding the unstructured data with the knowledge graph, and storing the unstructured data; according to the method, the environment-friendly degree of railway engineering can be improved, information management is optimized, the data driving force of decision support is increased, data sharing and communication are promoted, the sustainability of railway projects is improved, compliance of environmental protection rules and policies is achieved, and the timeliness and dynamic updating capacity of data are improved; not only are knowledge extraction efficiency and accuracy improved, but also monitoring and improvement of key performance indexes of the railway engineering are more scientific and systematic, and energy conservation, consumption reduction and ecological environmental protection of the railway engineering are further promoted.

The invention discloses a weak communication distributed photovoltaic inverter reactive power adaptive control method, which relates to the field of electric power, and comprises the following steps: S1, defining problems and parameters: determining power limitation, power factor limitation and voltage limitation of a photovoltaic inverter; s2, discretizing the output level: discretizing the photovoltaic output level into a plurality of nodes to form a first-order spline; s3, constructing an optimization problem: defining an objective function, minimizing the use of reactive power and the reduction amount of active power and adding constraint conditions, S4, solving the optimization problem: solving the problem by using a convex optimization solver to obtain nodes of a piecewise linear control function; s5, application of the control function: applying the optimized control function to the photovoltaic inverter; and S6, regularly updating parameters. The method is quick in response, reactive power use is optimized, the convex optimization problem is solved, the method adapts to an unbalanced power grid through central coordination control, voltage support is improved, and system loss is reduced.

The invention discloses an intelligent control method, system, equipment and medium for dynamic adjustment of supporting legs of a bridging crane, and relates to the technical field of automation in the field of engineering mechanical equipment. The method comprises the steps that the sum of bearing reaction borne by the supporting legs of the to-be-controlled bridging crane under the heavy load working condition is calculated on the basis of the amplitude and the hoisting weight of a crown block cross beam; according to the sum of the bearing reaction force, obtaining the real-time bearing reaction force and the real-time constant reaction force pressure of the truss supporting leg in the to-be-controlled bridging crane; and acquiring the oil cylinder pressure of a truss supporting leg in the to-be-controlled bridging crane and comparing the real-time constant counterforce pressure so as to control the power on and power off of a corresponding electromagnetic valve in the constant counterforce oil pressure system. The overturning condition of the bridging crane due to uneven load in the operation process can be effectively prevented, the supporting leg adjusting time is shortened through the intelligent control and constant counter-force technology, and the operation efficiency of the bridging crane is improved.

The invention relates to a dynamic adjustment method for PCS power of an energy storage EMS system, and the method comprises the steps: employing an Nth monitoring time node to an (N + M) th monitoring time node as a demand calculation window, employing a slip frequency mode and a weighted moving average algorithm to calculate the actual power demand of an initial monitoring time node of a next demand calculation window, the actual power demand of the (N + M + 1) th monitoring time node is calculated, and one monitoring time node is slid each time. In addition, a PID control algorithm is adopted in specific PCS power adjustment, an EMS detects the power of a power grid meter in real time, once the power of the power grid meter is about to exceed the demand, the PCS power meter is controlled in time, the energy storage charging power is reduced, closed-loop control is achieved, the definition of the actual power demand is accurate, and the energy storage charging efficiency is improved. And the PCS power can be controlled according to the actual situation in the aspect of actual power control, the change of the energy storage EMS system can be quickly responded, and the stability of the system can be kept.

The invention discloses an auxiliary driving control method and device through a construction road, equipment and a storage medium, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining cloud construction information and perceived road condition information; performing credibility judgment on the cloud construction information based on the sensed road condition information; when the judgment result is credible, performing collision prediction on the vehicle based on the sensed road condition information and the cloud construction information; and when the collision prediction result is that collision possibly occurs, auxiliary control is performed on the vehicle, and the change of a construction road can be quickly responded, so that accidents are reduced and the driving experience is improved.

The present invention relates to an Internet of Things-based power peak-shaving system, which aims to improve the peak-shaving capacity and efficiency of a new energy power system; the system combines advanced machine learning technology and Internet of Things infrastructure to achieve accurate monitoring and efficient management of the power grid operation status; the core components of the system include a prediction module, an Internet of Things module, a peak-shaving module and an energy storage module; the prediction module uses a machine learning algorithm to predict the new energy generation capacity and the load demand of the power grid; the Internet of Things module includes sensors and intelligent metering devices installed at key nodes of the power grid to collect power grid operation data in real time. The peak-shaving module receives data from the prediction module and the Internet of Things module, and uses a mixed integer linear programming model to determine the optimal peak-shaving strategy; finally, the energy storage module, including battery energy storage and pumped storage submodules, adjusts charging and discharging according to the instructions of the peak-shaving module to support the load and supply balance of the power grid.