624results about How to "Lower latency" patented technology

The invention relates to an electric power marketing business abnormity real-time detection method and system based on stream-oriented computation, and belongs to the technical field of electric power system optimizing.The method comprises the steps that data snapshots are extracted from an electric power marketing business system, difference comparison is conducted on the data snapshots and historical snapshots of an intermediate library, and standardized increment events are generated and stored; capturing an incremental event in real time through a data change capturing tool and pushing the incremental event to a message queue; a streaming computation engine consumes the event stream, sequentially performs data cleaning, association with a static dimension table and sliding window statistical feature calculation, and constructs a feature vector; and performing parallel analysis and weighted fusion on the feature vectors based on a business rule base and an online machine learning model to generate a comprehensive risk score, and outputting an abnormal event when the score exceeds a threshold value. According to the method, the problems of exception identification lagging and complex work order process in a traditional batch processing mode are solved, the crossing of the business risk from hour-level detection to minute-level real-time perception is realized, and the timeliness and accuracy of power marketing risk management and control are improved.

The invention provides a lightweight large model operation method based on end-side deployment. The lightweight large model operation method comprises the following steps: acquiring equipment operation data and storing a model file in a mixed precision format according to quantization precision supported by terminal equipment; determining a mixing precision quantification strategy and carrying out strategy analysis on the equipment operation data; a Key-Value cache file obtained through model reasoning in the historical dialogue is reserved; calculating a semantic embedding vector of the second user request, calculating a cosine similarity between the semantic embedding vector and the first V vector, and judging whether the second user request has a similar intention or not according to a calculation result; and if yes, performing incremental reasoning on the second user request by multiplexing the Key-Value cache file. By monitoring the state of the terminal equipment in real time, dynamically adjusting the quantization level, multiplexing the cache data of the semantic related requests and merging batch request reasoning, the effects of balancing the energy consumption and performance of the terminal equipment, reducing repeated calculation and improving the service time of the equipment and the user experience are achieved.

The invention discloses a real-time software deformation interaction method based on a physical engine in an environment. The real-time software deformation interaction method comprises the following steps: S1, establishing a virtual environment and a software model; s2, constructing an interactive perception and collision detection network; s3, receiving and analyzing a multi-modal interaction instruction; s4, real-time deformation calculation based on a physical engine; s5, performing multi-user collaborative state synchronization and rendering feedback; and S6, carrying out fault state simulation on the software object. According to the method, real-time and high-precision simulation of software deformation is realized through deep fusion of software dynamics calculation of a physical engine and an interaction instruction stream, and through hierarchical physical attribute binding and client prediction, the network synchronization data volume is greatly reduced while the millimeter-level deformation precision is ensured, so that the network synchronization efficiency is improved. And low-delay and high-consistency experience under multi-person cooperative operation is ensured.

The invention belongs to the technical field of data center network and clock synchronization, and discloses a dynamic time synchronization path optimization method and system based on topology awareness. The invention provides a closed-loop framework of state awareness-path optimization-resource assurance-dynamic adjustment and optimization: in the first stage, network topology, link load and PTP message performance data are periodically acquired through an SDN controller, and a global state view is constructed; in the second stage, the optimal path is dynamically calculated for the PTP synchronous flow through a multi-constraint optimization algorithm with the purpose of minimizing transmission delay jitter, and deterministic transmission is achieved in combination with bandwidth reservation and a priority queue; and meanwhile, a seamless switching mechanism of building first and disassembling second is introduced to adapt to the network state change in real time. On the basis of multiplexing existing hardware, the time synchronization error is remarkably reduced, the transmission stability and the dynamic adaptability are improved, microsecond-level time synchronization can be efficiently achieved, and the method is suitable for high-precision service scenes.

The invention relates to an industrial quality inspection data cooperative transmission method driven by image recognition, in particular to the field of artificial intelligence, which analyzes video frames in real time through a lightweight neural network, accurately locates and extracts key areas and features in industrial product images, constructs a dynamic semantic scene graph to understand contents and divide priorities, and improves the quality inspection efficiency. The core of the method is that non-uniform intelligent coding is carried out on video streams according to semantic importance of contents, high-fidelity transmission of key information such as defects is ensured, non-key areas are greatly compressed to save bandwidth, scheduling is carried out in the transmission process according to priorities, confidence feedback based on a cloud recognition result is introduced, closed-loop control is formed, and high-fidelity transmission is realized. According to the method, the front-end analysis model, the coding strategy and the network path are adaptively optimized, so that low-delay and high-reliability visual quality inspection data transmission and accurate identification can be continuously and stably realized in a complex industrial network environment, and the overall efficiency and the intelligent level of online quality inspection are remarkably improved.

The invention provides an Internet of Things data acquisition and abnormity early warning method and system, and relates to the technical field of Internet of Things data processing, and the method comprises the steps: constructing a digital twin model for a physical entity at a cloud end, and issuing prediction data to an edge node; acquiring sensor data by an edge computing node at a basic frequency, and computing a residual error between real-time data and predicted data and an information entropy of a residual error sequence; dynamically adjusting the data acquisition frequency of the edge node based on a two-dimensional decision space formed by the residual error and the information entropy; and when the residual error and the information entropy both exceed the threshold values, potential abnormity is judged, an edge side abnormity analysis program is triggered, and abnormal event information is uploaded. According to the invention, through cloud edge cooperation and intelligent decision making, the acquisition frequency is reduced in a stationary period, resources are saved, the frequency is improved when an abnormal symptom appears, monitoring is enhanced, and optimal balance between resource consumption and monitoring precision is realized; double criteria are adopted to effectively filter noise interference, and the false alarm rate and the missing report rate are reduced.

The invention discloses a large model reasoning hardware accelerator based on data flow execution, and belongs to the technical field of large model reasoning hardware acceleration. Each node comprises a linear calculation kernel used for sequentially completing query / key / value projection and attention output projection in multi-head attention calculation, dimension raising projection and dimension reduction projection calculation in a feedforward neural network layer, and inverse quantization operation after each projection is output; the multi-head attention calculation kernel is used for completing loading and quantization of query / key / value vectors, multi-head division and cache management, attention score calculation, Softmax normalization, context vector generation and KV cache updating; the RLA kernel is used for completing residual addition, layer normalization, quantization / inverse quantization and nonlinear activation calculation of each layer; the three cores in the same node are located in the same SLR area, and data transmission is carried out through a hardware queue and an HLS blocking mechanism. According to the invention, high hardware utilization rate and low-delay reasoning can be realized.

The invention discloses an intelligent glasses double-target speech enhancement method and system based on a multi-microphone array, terminal equipment and a medium, and relates to the technical field of speech enhancement. The method is applied to the intelligent glasses, and comprises the following steps: collecting noise-containing multichannel original time domain voice signals through a multi-microphone array of the intelligent glasses, and performing time-frequency domain conversion to obtain a frequency spectrum set; frequency domain complex spectrum amplitude-phase characteristics of each channel are extracted, a local time sequence context is introduced, and multi-channel space acoustic characteristics of amplitude-phase decoupling are constructed; and regulating and controlling the double-tone-area complex spectrum through pre-trained deep neural network modeling to obtain a target complex spectrum, and performing inverse time-frequency domain conversion and reconstruction to obtain an enhanced independent wearer voice signal and a preset direction target voice signal. According to the method, double-target voice parallel enhancement and non-target interference suppression are realized, the power consumption is low, the real-time performance is high, the method adapts to the harsh performance constraint of the end side of the intelligent glasses, and the robustness of voice enhancement in a complex noise scene is excellent.

This invention belongs to the field of intelligent agent large language model service technology, and discloses a chip computing system, device and application for intelligent agent large language model. Through the collaborative design of hardware architecture and software scheduling, it systematically solves the core technical challenges faced by intelligent agent large language model services. Specifically, it includes a hardware and software collaborative implementation scheme for resource partitioning, key-value cache management and runtime scheduling of pre-filling, decoding and tool call related workloads in a multi-chip encapsulation and inconsistent memory access environment.

The embodiment of the invention discloses a stereo echo cancellation method and terminal equipment. The method comprises the following steps: acquiring a plurality of first microphone signals and a plurality of loudspeaker signals; respectively carrying out time-frequency conversion, feature extraction, sub-band decomposition and feature fusion on the plurality of first microphone signals and the plurality of loudspeaker signals to obtain low sub-band combined features and high sub-band combined features; inputting the low sub-band combined features and the high sub-band combined features into a neural network model, and outputting a plurality of amplitude spectrum gain factors; a plurality of second microphone signals is generated based on the plurality of amplitude spectrum gain factors and the plurality of first microphone signals. According to the method and the device, the high-low sub-band combined features are obtained through feature fusion of the microphone signals and the loudspeaker signals, the echo-removed microphone signals are generated through the gain factors output by the neural network model, processing such as inter-channel decorrelation is not needed, the complexity of an algorithm is reduced, and the tone quality is improved.

The application provides a die cutting and hot stamping equipment intelligent state monitoring and fault diagnosis system and method, and relates to the field of intelligent monitoring.The method comprises the following steps: collecting working parameters of multiple key parts of the die cutting and hot stamping equipment, constructing a time sequence collection window, slidingly collecting the working parameters, and obtaining characteristic information reflecting the equipment state; based on the characteristic information, constructing an anomaly detection model, performing anomaly detection on the equipment state, obtaining an anomaly score, and judging whether the equipment state is abnormal according to the anomaly score; for the characteristic information judged as abnormal, constructing a fault diagnosis model based on the fault type to which the characteristic information belongs, performing fault diagnosis on the equipment state, and generating a diagnosis result; and generating a comprehensive diagnosis and operation and maintenance decision report according to the diagnosis result.The application realizes comprehensive perception of the internal state of a closed host through multi-sensor collaborative monitoring and dynamic time sequence collection, breaks through the limitations of traditional monitoring, and provides accurate data basis for early fault warning and predictive maintenance.

The utility model discloses a kind of TCP protocol conversion LIN protocol communication conversion module, it includes shell, the bottom of the shell is connected with mounting block, LIN protocol interface is provided on the side of module shell, the positive side of module shell is provided with indicator light, power module and TCP protocol interface, wherein the inside of the module shell is also provided with main control chip module, the main control chip module is used to parse and convert LIN protocol data and TCP protocol data;LIN protocol interface is used to connect with external LIN protocol equipment, receives and sends LIN protocol data, the TCP protocol interface is used to connect with external network, data transmission based on TCP protocol. Advantage: it has the advantage that long-distance transmission can be efficiently and stably realized LIN protocol and TCP protocol conversion, and support.

The application discloses a method for automatically improving separation efficiency of an amino graphene preparation system, and relates to the technical field of graphene preparation.The preparation system comprises a graphene preparation detection module, a primary reaction detection module, a middle reaction detection module and a reminding adjustment display module; the graphene preparation detection module, the primary reaction detection module and the middle reaction detection module are used for detecting and controlling reaction temperature, time, reactant quality and other parameters in the process of preparing amino graphene, so as to obtain a first coefficient DYxs1, a second coefficient DYxs2 and a third coefficient DYxs3; the first coefficient DYxs1, the second coefficient DYxs2 and the third coefficient DYxs3 are compared and analyzed with threshold values after being corrected by the reminding adjustment template, an adjustment scheme is obtained, a control command is generated according to the adjustment scheme, accurate control and optimization are performed, and then efficient preparation and separation of amino graphene are realized, and the preparation and separation effect of the amino graphene is improved.

The invention relates to the technical field of medical simulation, in particular to an ablation needle puncture training auxiliary system, and aims to solve the problems of insufficient tactile feedback and low fineness when an existing ablation needle puncture training system simulates a needle tip to penetrate through multiple layers of biological tissues so as to improve the'needle feeling 'cognition and fine operation skills of doctors. The system is characterized by comprising a physical interaction module, a virtual simulation module, a central processing module and a visual presentation module, a high-fidelity virtual tissue model is constructed, real-time multi-mode tactile feedback is combined, and an accurate needle-tissue interaction mechanical simulation and multi-dimensional tactile rendering algorithm is adopted. By the adoption of the technical scheme, the problem of a touch gap is effectively solved, the authenticity and effectiveness of simulation training are improved, and the precision and safety of clinical puncture operation are remarkably improved.

The application relates to the technical field of data transmission, and discloses an industrial Internet of Things data transmission optimization method in a complex environment, which comprises collecting data transmission parameters and environment parameters of industrial Internet of Things equipment in a complex environment, generating original data transmission data sets and environment parameter data sets; preprocessing the original data transmission data sets and the environment parameter data sets, including data cleaning, normalization and feature extraction, generating preprocessed data transmission feature data and environment features; collecting data transmission parameters and environment parameters of the industrial Internet of Things equipment, and performing data cleaning, normalization and feature extraction on the data transmission parameters and the environment parameters, so as to generate preprocessed feature data, guarantee the accuracy and reliability of data transmission analysis, simultaneously perform timestamp alignment and space correlation processing on the environment parameters and the transmission parameters, perceive the influence of the complex environment, and guarantee the accuracy and integrity of the data transmission optimization basic data.

The application discloses an article recommendation method and related device, and relates to the field of article recommendation. According to the article ID contained in the article recommendation request, a positive query result is obtained. If the positive query meets the first missing level, and the incremental benefit evaluated based on the article recommendation scene data is not less than the first benefit threshold, a semantic vector is generated according to the article key field to recall a first group of candidate articles similar to the current article from a vector library, a second group of candidate articles corresponding to the current article is recalled from other multiple different databases, multi-dimensional feature fusion evaluation and sorting are performed on each candidate article, and the top N articles are selected as recommended articles. The application reduces invalid filling of low-value recommendation requests through information missing level and incremental benefit evaluation. When the positive feature is missing, candidate articles are recalled from the vector library and multiple different databases outside the vector library, multi-dimensional feature fusion evaluation and sorting are performed on each candidate article, and article recommendation is realized.

The invention relates to the technical field of high-speed aircraft health management, and particularly discloses an interpretable aircraft state sensing and predicting system and method. The method comprises the following steps: constructing and training a lightweight anomaly diagnosis network with an expansion perception layer; aircraft dynamics, kinematics and fault mechanism formulas are used as soft constraints to be embedded into the future state estimation network; an SHAP interpretation framework is used for sorting the importance of the input features, and a network structure is optimized accordingly; and finally, accurate diagnosis of abnormal types and positions and rapid prediction of aircraft states at a plurality of moments in the future are realized. According to the method, by introducing three innovation points of an expansion perception layer, physical information embedding and feature importance sorting, the diagnosis accuracy and the model interpretability are remarkably improved, and the high-performance calculation requirement in a scene with limited calculation power is met.

The present application relates to the field of smart meter data acquisition, especially to a CPU occupancy-based meter data acquisition optimization method and system, the method comprising: obtaining the signal strength and working state of concentrator and meter nodes, establishing a data acquisition network model and calculating the load rate to reflect the network busy degree. Vector coding is used to generate initial particle swarm, each particle represents a data acquisition path, the transmission efficiency and communication quality of the path are evaluated, and the path score is obtained by weighted fusion based on the load rate. The particle with the highest score is selected as the global optimal solution to guide the particle swarm learning, the learning ratio is adjusted through the negative exponential function, the parent node vector is updated and iteratively optimized until the score no longer improves, the optimal path is output, and the data acquisition path optimization is completed. The present application dynamically adjusts the weight and optimization strategy, adapts to network changes, reduces the hop count, avoids weak signal links, and improves the transmission efficiency and stability of the data acquisition path.

The invention discloses an end-to-end control method, system, device and product of a humanoid robot. The method comprises the steps of obtaining a multi-modal sensing data set and a task instruction of the humanoid robot in a target scene; constructing a scene adaptation model based on the target scene and the multi-modal perception data set; guiding the scene adaptation model to generate a control instruction based on a preset scene reward function; and generating a control interface based on the multi-modal sensing data set, the task instruction and the control instruction, and performing end-to-end control on the humanoid robot based on the control interface. The working efficiency of the humanoid robot can be improved, and the humanoid robot can be widely applied to the technical field of robots.

This invention provides a redundant array management method and system based on ZNS solid-state drives, comprising: for each stripe, upon receiving a write request, sequentially writing the data to be written into the corresponding data blocks, and overwriting and updating the check data of the check block; positioning the logical actual write pointer to the tail position of the consecutively successfully written data blocks, generating metadata corresponding to each data block, and writing the metadata into a spare area; the logical actual write pointer is used to point to the next writable position within the logical area; in the event of a failure, repositioning the logical actual write pointer based on the metadata in the spare area; based on the repositioning of the logical actual write pointer after the failure, discarding the data blocks outside the logical actual write pointer, restoring the data blocks within the logical actual write pointer, and recalculating and generating check data to complete the redundant array management of the ZNS solid-state drive.

The invention discloses a data generation method of a photoetching pattern, an electron beam exposure system, a medium and a product, which are applied to the technical field of photoetching. The method is applied to an electron beam exposure system comprising an upper computer and a lower computer. The method comprises the following steps: receiving first original coordinate data and second original coordinate data of a layout transmitted by the upper computer; performing linear interpolation based on the first original coordinate data and the second original coordinate data to obtain a plurality of pieces of original intermediate coordinate data; respectively mapping the first original coordinate data, the second original coordinate data and each piece of original intermediate coordinate data into first mapping coordinate data, second mapping coordinate data and each piece of mapping intermediate coordinate data of a lower computer; and performing exposure operation based on the first mapping coordinate data, the second mapping coordinate data and the mapping intermediate coordinate data. The data transmission quantity between the upper computer and the lower computer can be reduced, the bandwidth occupation and delay of data transmission are reduced, and the exposure efficiency is improved while the exposure precision is guaranteed.

The application relates to a system for realizing rapid mass memory data persistence, data double writing and data pushing in a securities core transaction system, wherein the system comprises a file storage engine, a data analysis engine, a data routing engine, a data persistence engine, a data double writing engine and a data pushing engine, and a plurality of nodes are arranged in the system, and the file storage engine, the data analysis engine, the data routing engine, the data persistence engine, the data double writing engine and the data pushing engine are arranged in the corresponding nodes. The application also relates to a corresponding method. The system and the method for realizing rapid mass memory data persistence, data double writing and data pushing in the securities core transaction system have the advantages that the system is deployed in a cluster mode, the node state is monitored in real time through a master-slave mode, the system reliability is improved, high throughput and low delay are achieved under the premise of high availability, and the data integrity is ensured.

The application relates to an intelligent voice communication system and method for real-time interaction between a device end and a large language model cloud end. It solves the problems of high interaction delay, lack of true full-duplex voice stream support, separation of audio stream and instruction execution, and poor interaction efficiency in the prior art. It comprises a device end and a cloud end. The device end comprises a microcontroller running a FreeRTOS real-time operating system, the microcontroller is connected with a signal acquisition and processing unit, and the microcontroller is connected with an NFC controller for device end interaction and communicates data with the cloud end through a wireless communication module. The cloud end comprises a full-duplex voice gateway, a GPU array, an AI acceleration card and a device perception scheduler. The application has the advantages that natural continuous conversation of voice in and out is realized; user instructions are recognized and executed in real time during the conversation process, voice feedback is returned synchronously, and low-delay, low-power-consumption, high-concurrency full-duplex voice interaction is realized.

The application discloses a cloud-edge coordination 5G networking multi-hop forwarding path dynamic processing method and a storage medium. The ant algorithm is used to optimize the forwarding lines between data base stations, so that the data transmission efficiency of the whole network is improved, and the data transmission delay is reduced. While ensuring the service quality, unnecessary data transmission distance and transfer are reduced, so that the operation and maintenance cost can be effectively reduced. The technical scheme adopted by the above embodiment of the application automatically optimizes path selection through an algorithm, and has good scalability and flexibility.

The application discloses a cotton bale detection method based on multi-modal data timestamp synchronization and GPU / CPU collaborative acceleration, collects multi-modal data, marks timestamps, and performs GPU accelerated distortion correction on cotton bale images; the multi-modal data is synchronized and integrated according to timestamps; a main program adopts Nodelet combined with C++ multi-threading technology to process the reception of multi-modal data and the publication of multi-modal sensor coordinate systems in parallel, the main program running optimizes the transmission efficiency of multi-modal data; a cotton bale detection model is constructed, the optimized multi-modal data are input for training, the cotton bale detection model after training is optimized using TensorRT, and cotton bale images are input for inference, cotton bale target recognition data are obtained and transmitted to a CPU for post-processing operation, and OpenMP is used to accelerate point cloud traversal in the main program to match the cotton bale target recognition data with laser radar point cloud data; the matched multi-modal data are subjected to point cloud feature and plane fitting calculation, and the rectangular frame, pose information and score of the cotton bale target are obtained.

The application provides a code modification method and device based on semantic update fragment generation. The code modification method comprises the following steps: generating a semantic update fragment and a first reconstruction code according to original code and modification description information; performing code modification verification on the semantic update fragment according to the code difference between the original code and the first reconstruction code; when the code modification verification is passed, generating a second reconstruction code by modifying the original code according to the semantic update fragment, and determining whether the code modification of the second code modification model is correct according to the code comparison result between the second reconstruction code and the first reconstruction code; taking the original code, the semantic update fragment and the first reconstruction code corresponding to the second code modification model with code modification errors as model training data to perform model training to obtain a target code modification model; and modifying the to-be-modified code by using the target code modification model to obtain a complete code. The application can reduce inference delay and improve code modification efficiency.

The application relates to the fields of microelectronic packaging and novel display technology, and discloses a packaging method suitable for a hybrid device packaging structure of a MicroLED, which comprises the following steps: preparing a wide-bandgap semiconductor driving chip, a logic control chip and a light-emitting pixel chip; transferring and fixing the wide-bandgap semiconductor driving chip, the logic control chip and the light-emitting pixel chip on the surface of a carrier plate in a coplanar mode; depositing a dielectric layer on the surface of the carrier plate to expose electrode contacts; manufacturing metal wires to build an internal electrical interconnection structure and lead out a peripheral interface; coating a packaging material on the surface of the carrier plate to insulate and coat the internal electrical interconnection structure; curing and cutting to separate an independent pixel packaging body; and assembling the pixel packaging body to a display panel substrate and connecting the peripheral interface to a global power supply network. The application suppresses the hot carrier effect under high-temperature working conditions, solves the problem of thermal failure, reduces the parasitic capacitance of high-frequency driving interconnection, removes potential failure products before macroscopic mounting, and improves the assembly yield of a display panel array.

The application relates to the technical field of aerial video analysis, and discloses a large-scene multi-modal analysis system and method based on a UAV. The method acquires an original video frame sequence from aerial equipment, extracts a scene change period and an object motion trend by calculating an inter-frame motion feature vector, and judges whether a scene is in a stable or dynamic state by means of a state classification model. In the dynamic state, a tracking target position is calculated according to preset parameters, and an analysis target area is adjusted in combination with the motion trend; a camera parameter adjustment instruction is generated according to a deviation amount of the analysis target area from an actual object position, and is output to an executing mechanism; a complexity value is acquired through a scene complexity feedback signal, and when the complexity value exceeds a preset range, an adaptive algorithm is used to update an analysis strategy and output an adjustment signal; the output timing of control and strategy adjustment signals is optimized, the frame sequence and analysis parameter change trend are monitored in real time, preset parameters are dynamically updated to match user requirements, and efficient video analysis processing is realized.

This application relates to the field of interface gateway management technology, and discloses an open application programming interface (API) gateway management system and method. The method includes: responding to a received API call request, extracting the caller's identity information and request context parameters, performing dynamic authentication on the call request, and obtaining the authentication result; obtaining the corresponding rate limiting rules, performing dynamic mixed-mode traffic control on the call request, and obtaining the traffic control result; forwarding the call request to the corresponding backend service, and monitoring the backend service's operating status data in real time during the forwarding process, performing circuit breaker analysis based on the operating status data and preset business semantic rules, and obtaining the circuit breaker control signal; executing the corresponding request processing action according to the circuit breaker control signal, and collecting full-link observable data such as call chain tracing data and network performance data in real time to manage interface calls; this application can effectively intercept replay attacks, abnormal logins from different locations, etc., and improve gateway throughput.

The invention discloses a key value cache processing method and device, equipment and a storage medium, the method is applied to edge equipment in communication connection with remote storage, when a large language model service is used for reasoning, the first capacity of a local key value cache is detected, and when a preset capacity threshold value is reached, the first capacity of the local key value cache is determined. And creating and maintaining a sliding window covering a plurality of key value cache entries in a local memory of the edge device. New tokens are continuously generated along with large language model service reasoning, and local cache content is updated through the sliding window: on one hand, the oldest key value cache entry in the sliding window is removed to release memory space; on the other hand, a first cache entry corresponding to the newly generated token is synchronously obtained from the remote storage and stored in the local memory. According to the invention, communication traffic and delay with remote storage are effectively reduced, and realization of low-memory, low-delay and high-performance LLM reasoning service is facilitated. The technical scheme of the invention can be widely applied to the technical field of artificial intelligence.