36901 results about "Fully automated" patented technology

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

A fully-automatic unpacking machine (100), comprising a conveying device (1), transferring and holding-clamping devices (3), a ton bag grabbing device (4) and a bag breaking device (5). The conveying device (1) is docked with an external apparatus, and is used for conveying ton bag materials to a rear end device. When a ton bag is moved to a first station, the two transferring and holding-clamping devices (3) get close to each other so as to clamp the ton bag at the first station and place same at a second station; the ton bag grabbing device (4) grabs the ton bag located at the second station to move same to be above a feeding port (512), and the ton bag grabbing device (4) drives the ton bag to move downwards, such that a bag breaking cutter (52) punctures the bottom of the ton bag to form an opening; and a material in the ton bag flows into a discharging bin (511) through the opening and is sent out through a discharging outlet (513). Thus, one bag breaking and discharging cycle is completed.

The invention belongs to the technical field of information, and relates to a multi-level dynamic intelligent automatic data processing and information evaluation method and system. The method comprises the steps that a database environment operation data set is constructed, targeted training and fine adjustment are conducted on a large language model in combination with reinforcement learning, and full-automatic data analysis, query and information extraction of multiple tables in a database are achieved; a multi-level agent interaction and cooperation framework is established, a scheduling agent is responsible for global decision making and strategy planning, an operation agent is responsible for execution of specific tasks, and the scheduling agent and the operation agent share information and coordinate the tasks to form a highly cooperative whole; and establishing a cyclic supervision and error correction mechanism, scheduling the intelligent agent to learn a normative workflow example of the target working environment and track the state of the operation intelligent agent in real time, identifying the behavior of the operation intelligent agent deviating from a predetermined path, and taking corrective measures. According to the method, high efficiency, intelligence and automation of data processing are realized, and the method has remarkable practical value and innovative significance.

The invention relates to the technical field of image processing, and discloses a scoliosis angle intelligent measurement method and system based on spine CT three-dimensional reconstruction. The method comprises the following steps: carrying out adaptive threshold segmentation and gray optimization processing on a spine CT sequence image to obtain a standardized preprocessed image, and carrying out adaptive bone tissue region identification and boundary enhancement processing to obtain spine skeleton segmentation data; performing three-dimensional grid construction and curved surface smoothing processing on the spine skeleton segmentation data to obtain spine three-dimensional model data; carrying out centrum gap identification and key point positioning processing on the spine three-dimensional model data to obtain a centrum feature data set, and carrying out spine midline curve construction and end centrum marking processing to obtain Cobb angle data; and performing three-dimensional space reconstruction and multi-plane projection processing on the Cobb angle data to obtain a measurement result report. According to the method and the device, full-automatic processing from acquisition of the CT image to generation of the final measurement result report is realized on the premise of ensuring the measurement accuracy.

Embodiments of the present disclosure provide a report generation method and system, an electronic device, and a storage medium. The report generation method comprises: acquiring requirement text information of a report to be generated in a target application scenario; performing requirement intent recognition on the requirement text information, to acquire a report parameter matching the report to be generated; on the basis of the target application scenario, selecting and arranging a preset intelligent agent, to obtain an intelligent agent call chain; on the basis of the report parameter, performing chain-type calling on the preset intelligent agent in the intelligent agent call chain, to generate a report matching the requirement text information. The present method can automatically and quickly generate a report matching a user requirement, effectively increasing report generation efficiency. Using the present method to generate a report does not require manual data acquisition and analysis; efficiency is higher, and costs are lower.

The present invention relates to the field of ship design, and in particular to an automatic three-dimensional modeling method, system and device based on a two-dimensional ship CAD drawing. The method includes obtaining and parsing ship CAD drawing data, extracting metadata from the drawing, and identifying primitives; generating images through rendering technology for unidentified complex primitive areas, and performing preprocessing; inputting the preprocessed images into an object detection model, generating detection frames and category annotations using a deep learning algorithm, and performing coordinate conversion to accurately calibrate the positions of primitives; extracting geometric information, structural attributes and material data based on object detection results and primitive information, performing parametric expression conversion, and generating parametric model data of the ship structure; inputting the generated parametric data into a modeling instruction template, generating a ship CAD modeling instruction sequence, and executing the instructions through a CAD system to automatically construct a three-dimensional model of the ship. The present invention can realize the fully automated conversion from two-dimensional CAD drawings to three-dimensional modeling, thereby improving the efficiency and accuracy of ship design.

The invention relates to the technical field of electric vehicles, and discloses a full-automatic AGV charging collaborative optimization system which comprises an energy consumption prediction module, a consensus mechanism design module and an AGV scheduling optimization module. The energy consumption prediction module is used for acquiring a pose topological feature vector and a potential margin attenuation gradient, constructing a multi-dimensional input vector, and generating a dynamic energy consumption prediction value based on an energy consumption track residual model of multi-physics field coupling; the consensus mechanism design module is used for constructing and verifying a dynamic consensus factor and providing a priority decision basis; the AGV scheduling optimization module is used for discretizing an operation space of a battery swap station, generating a space-time folding unit, constructing a space-time conflict resolution atlas, and generating an optimal battery swap sequence and a collision avoidance path; through multi-module cooperation, efficient cooperation of multi-AGV charging and battery swap is realized, and congestion of the battery swap station and resource waste are avoided.

The present disclosure relates generally to computer-implemented systems and techniques for leveraging a virtual decentralized database to enable user identity authentication and verification of user activity and associated behaviors on open internet and public web-based and native applications. Techniques are disclosed for user identification profile onboarding using biometrics, frictionless CAPTCHA (Completely Automatic Public Turing Tests To Tell Humans And Computers Apart) processes, passive data capture of physiological and behaviors using device sensors, and cryptographic methods of storing user engagement activity as immutable data files for secure distribution on a decentralized virtual private network. Such technologies operate to establish a high confidence level for user authenticity, communication origin, engagement outcome transparency and clarity of intent for users active on social, commercial, e-commerce and information sharing platforms. More specifically, an unobtrusive passive data capture method of authentication and verification on a secure VPN supports the organic, spontaneous and original experience audiences desire while simultaneously eliminating opportunities for unscrupulous actors to exploit open internet exchanges by inserting fraudulent user accounts, automated bots and other measures that mimic or impersonate live humans, which ultimately undermines user confidence in data safety and erodes mutual trust between people, companies and institutions.

The invention relates to the technical field of semiconductors, and discloses a full-temperature-zone polynomial digital temperature compensation method and system based on an SOI chip, and the method comprises the steps: obtaining the temperature frequency data of a preset temperature zone of a target SOI chip, fitting a full-temperature-zone curve, extracting a critical point of the temperature zone, dividing a segmented compensation interval according to the critical point, and obtaining the temperature frequency data of the preset temperature zone of the target SOI chip; the method comprises the following steps: calculating frequency drift amounts under different voltages to construct a full-automatic test framework, acquiring an environment temperature parameter and an instantaneous temperature value by using the framework, analyzing a temperature compensation vector of a segmented compensation interval, further monitoring a signal frequency interval output by a chip, extracting a phase noise parameter, calculating a frequency offset error amount with a preset value, and calculating the frequency offset error amount. And analyzing an error source, optimizing a polynomial compensation order, and collecting temperature drift suppression data to generate a full temperature region compensation report. According to the invention, the performance stability of the chip in a complex environment can be improved.

The invention discloses a data quality intelligent auditing system and method based on a dynamic rule base, and belongs to the technical field of data auditing, and the system comprises a rule base construction module which is used for analyzing business scene parameters through a scene analysis unit according to business scene demands and data type features to generate a rule configuration instruction; the multi-source monitoring engine module is connected to the rule base construction module and is used for collecting multi-source data in real time and loading corresponding checking rules; the automatic verification execution module is used for executing normalized quality verification on the multi-source data based on the verification rule base; and the feedback optimization module analyzes a rule hit rate and a false alarm rate in a verification result through a reinforcement learning algorithm, and dynamically iteratively updates a rule threshold value and a logic combination in the rule base. By constructing a full-automatic process of rule generation, execution, feedback and updating, the problems that a traditional system depends on manual intervention, response is slow, the industry average rule updating period is 3-7 days, and real-time updating is achieved through the scheme are solved.

The invention discloses a full-automatic packaging equipment self-adaptive control method based on dynamic data acquisition, which acquires dynamic data in real time by configuring a sensor in packaging equipment, and performs cleaning, anomaly detection and trend prediction on the acquired data by using a multi-dimensional data analysis technology, so that the packaging equipment can be automatically controlled. Generating a characteristic parameter set related to the current packaging working condition; according to the characteristic parameter set, dynamically adjusting operation parameters of the equipment, including a speed adjusting parameter, a temperature control parameter and a tension control parameter, so as to realize adaptive control on different packaging materials and specifications; besides, the system can identify potential equipment abnormity or process problems in the packaging process based on a multi-dimensional data analysis result, and generate optimization suggestions for operators to refer to or automatically adjust parameters, so that the consistency of the equipment operation stability and the packaging quality is improved; the full-automatic packaging equipment is suitable for multi-specification and multi-category packaging, the production efficiency is remarkably improved, and the product percent of pass is remarkably increased.

The invention provides a complex task full-automatic processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as large language models, generative models, agents and task intelligent scheduling. The method comprises the following steps: interacting with a target user who puts forward an original task demand to obtain a complete task demand; the complete task demand is split into a plurality of sub-tasks at least comprising a complex sub-task, the complex sub-task refers to a sub-task needing at least two sub-agents to process according to a cooperation process, and a single sub-agent is used for processing a simple sub-task; issuing the complex sub-tasks to corresponding target execution intelligent units, and issuing the simple sub-tasks to corresponding target sub-agents; and summarizing sub-task execution results returned by each target execution intelligent unit and each target sub-agent. According to the method, the execution intelligent unit specially used for processing the complex sub-task is introduced, and the complex sub-task is processed more intensively according to the cooperation process through the at least two sub-agents contained in the execution intelligent unit, so that the task disassembling difficulty is reduced; and a better sub-task processing result can be obtained through the execution intelligent unit integrated by the multiple sub-agents.

The invention relates to a full-automatic injection molding optimization method, device and equipment based on digital twinning, and belongs to the technical field of intelligent manufacturing equipment. Inputting the sensor data into a digital twin model to construct equipment dynamic response simulation, and synchronously comparing the actual mode locking force with the offset of a preset pressure threshold; generating compensation data of mold locking pressure according to the offset, and dynamically correcting an acceleration curve of a servo motor in a glue injection stage on the basis of the displacement of a screw rod; in hot runner control, a thermal interference compensation coefficient is calculated according to a temperature difference value of adjacent temperature zones, and an output power gradient signal of each heating ring is adjusted; the corrected parameter information is transmitted to an equipment PLC through an industrial bus to execute control, and a compensation coefficient threshold value is iteratively updated according to a product weight fluctuation value in the next forming period; closed-loop data interaction of an equipment control system is achieved, and the injection molding process is optimized.

Systems and methods for integrating a continuous glucose sensor 12, including a receiver 14, a medicament delivery device 16, a controller module, and optionally a single point glucose monitor 18 are provided. Integration may be manual, semi-automated and / or fully automated.

The present disclosure provides a self-propelled full-automatic dense-planting vegetable transplanter and a planting control method thereof. The self-propelled full-automatic dense-planting vegetable transplanter includes a seedling taking and throwing mechanism, a box moving device, a crawler-type walking chassis, a seedling guiding device, a soil covering mechanism, a planting mechanism, and a dispersing device. The box moving device is mounted on the crawler-type walking chassis. The dispersing device is located on one side of the box moving device. The seedling taking and throwing mechanism includes a seedling taking and throwing movement mechanism and a linear movement mechanism. The linear movement mechanism is arranged above the box moving device. The seedling taking and throwing movement mechanism is configured to reciprocate between the dispersing device and the box moving device through the linear movement mechanism. The dispersing device is connected to the seedling guiding device.

The invention discloses a full-automatic laser welding forming system, which relates to the technical field of laser welding, and comprises a laser generation and light beam modulation module, a material conveying and positioning module, a welding execution module, a visual inspection and monitoring module, an intelligent self-adaptive control module and an energy recovery and reutilization module, the material module accurately conveys and positions materials; the welding module robot is accurate in operation; the visual module detects welding seams; the intelligent module performs self-adaptive control; the energy module recycles energy. The welding quality is improved, and defects are reduced; the production efficiency is improved and the cost is reduced; operation is simple, and manpower requirements are reduced; energy is saved, environment is protected, and green concept is met; real-time monitoring is achieved, and the product quality and the management level are guaranteed.

The invention provides a multi-band radio frequency power amplifier dynamic range adaptive test method and a system thereof. The method comprises the following steps: acquiring an output signal frequency spectrum of a tested radio frequency power amplifier, extracting characteristic frequency points including a center frequency and a bandwidth, matching the characteristic frequency points with a preset frequency band database, and automatically loading a test scheme of a corresponding frequency band. According to the method, the frequency bands are automatically matched through spectrum sensing, the test scheme is loaded, the multi-frequency-band characteristics can be rapidly adapted without manual parameter configuration, index missing test is avoided, and the switching efficiency is improved; dynamically optimizing test parameters by using a genetic algorithm, and accurately capturing a nonlinear distortion critical point while balancing coverage and efficiency; by combining multi-dimensional signal analysis and a deep learning model, the problem of missing detection of traditional single-domain analysis is solved, and the abnormal signal recognition capability is remarkably enhanced; through linear model correction, impedance dynamic matching and index threshold iteration, full automation and high precision of the test are realized, the environmental interference influence is effectively reduced, and a self-adaptive test system is constructed.

The invention relates to the technical field of building steel pipe welding machining, and discloses a steel pipe welding device suitable for constructional engineering, which comprises a mounting table, a mounting groove is formed in the mounting table, and two first bidirectional threaded rods are rotatably connected in the mounting groove; according to the steel pipe welding device suitable for the constructional engineering, two steel pipes are placed in the mounting ring, then the steel pipes are clamped and fixed through a clamping mechanism in the mounting ring, and then the steel pipes are driven to move, so that the steel pipes are welded to the mounting ring, and the steel pipes are welded to the mounting ring. Then the steel pipe groove grinding mechanism is used for automatically grinding grooves of the two steel pipes, the welding mechanism is used for automatically welding the steel pipes after grinding is completed, and meanwhile, the welded weld joints are automatically ground, so that groove grinding and welding integrated full-automatic machining of the steel pipes of different sizes is achieved; and the welding efficiency and the welding effect of the steel pipe are greatly improved.

The invention relates to the technical field of intelligent control of injection molding machines, in particular to a self-adaptive full-automatic spline injection molding system. Comprising an injection molding data acquisition module, a spline model generation module, a self-adaptive control module, a finished product quality detection module and a monitoring and early warning module. The data acquisition module acquires multi-dimensional data in the injection molding process in real time, the spline model generation module adopts deep learning to construct a nonlinear spline model, and the adaptive control module adjusts equipment parameters in real time based on the model. The finished product quality detection module analyzes quality features such as sizes and defects through machine vision and generates a detection report. And the monitoring and early warning module monitors the running state of the equipment in real time, identifies fault features and provides adjustment suggestions. The spline injection molding process is comprehensively optimized, and the production efficiency, the finished product quality and the safety and reliability of equipment operation are remarkably improved.

According to the capacity expansion and contraction method and system, the electronic equipment and the storage medium, the resource demand mode of the target cloud service is predicted by using the prediction model, full-automatic prediction of the resource demand is realized, manual participation is not needed, and the prediction model is trained by a large amount of data, so that the prediction efficiency is improved. Compared with manual work, accurate resource demand prediction data can be output, then automatic capacity expansion or capacity expansion is carried out on the target cloud service according to the predicted capacity expansion or capacity reduction strategy, full-automatic execution of capacity expansion and capacity reduction is achieved, the capacity expansion server and the capacity reduction server are determined based on the server resource occupation amount in the capacity expansion or capacity reduction strategy, and the capacity expansion or capacity reduction efficiency is improved. The full utilization of the resource data in the server is realized, and the service operation cost is reduced to a certain extent.

The invention provides a laboratory equipment access and configuration method and system based on dynamic protocol analysis, and the method comprises the steps: firstly, monitoring a physical connection signal of a port, automatically triggering communication link configuration, calling a preset protocol parameter based on an interface type, and obtaining first identification information of equipment; then, based on a preset protocol mapping table or a dynamic matching rule base, determining a protocol type, and based on the protocol type, performing segmented analysis on the original data stream, identifying an effective frame and checking integrity to obtain a field definition, an instruction template and a communication parameter for binding storage with the unique identifier of the equipment; and finally, automatically generating an instruction according to the communication configuration, performing communication interaction with laboratory equipment, and triggering retransmission or link recovery operation when the communication state is abnormal by monitoring the communication state in real time. In addition, the communication condition of the laboratory equipment is displayed in real time through the interaction platform. According to the method, the deployment cost is reduced through full-automatic identification configuration, and the communication stability is improved through communication link optimization and exception handling.

Disclosed in the present invention is a fully automated log analysis and fault handling system and method based on an NLP large model. The method comprises: collecting a service state, an operating environment, and log files; extracting and generating log summaries from the log files, extracting key information from the log summaries to obtain state information of a service process; if an error is fed back, the NLP large model performing fault analysis on the error by using the service state and the operating environment as prior knowledge, and recording upstream and downstream key information involved in the error; on the basis of the upstream and downstream key information involved in the error, finding a fault cause and providing a fault handling action; and then executing a plan on the basis of the fault handling action. Log collection of the present invention is not limited to a prescribed format, and an NLP large model using a service state and an operating environment as prior knowledge is introduced for fault analysis.

The invention provides a space projection error analysis-combined 4D millimeter wave radar and visual camera automatic calibration method. The method comprises the steps of constructing a sensor internal reference model; designing a composite multifunctional calibration board and a three-dimensional adjustable holder, performing rigid geometrical relationship modeling, and automatically associating physics-features-coordinates; collecting multi-frame synchronization frame pair data, and performing time sequence fine grit alignment and interpolation compensation to obtain a space-time alignment frame pair sequence; extracting cross-modal spatial constraints of the features and automatically screening and matching point pairs to obtain a high-confidence point pair set; performing point-point constraint and point-point error, point-plane error and pixel reprojection error minimization to obtain a standardized error term; and constructing an overall objective function, and performing spatial constraint and time deviation estimation to obtain corrected external parameters and synchronization deviation. According to the invention, through a full-automatic standardized process, the probability of artificial participation and error occurrence is greatly reduced; and multi-modal and multi-scene high-precision calibration is supported, and the flexibility and robustness are greatly improved.

The invention discloses a scoliosis evaluation system and device based on multi-modal information fusion, and belongs to the field of spine health. A back pressure electromyographic signal acquisition module acquires stress electromyographic information of a back muscle group; the thermal imaging module obtains a thermal imaging information graph of a human body; the plantar pressure acquisition module acquires plantar pressure information; the multi-mode data fusion module inputs the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information into the encoder layer, a cross attention mechanism is adopted in the feature matching layer to fuse features from different modules, a classifier model is used for scoliosis evaluation, and classification is conducted according to the scoliosis degree. According to the application, the collaborative mechanism of the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information of the back during scoliosis is utilized, multi-modal data fusion is performed on the stress myoelectricity information, the thermal imaging information graph and the plantar pressure information of the back, full-automatic evaluation is realized through a multi-modal data fusion model, the evaluation accuracy is high, and no radiation is generated.

The invention discloses an automatic parameter adjustment test method for an electromagnetic angular displacement sensor, and the method comprises the steps: injecting a standard angle signal into a to-be-tested sensor, and collecting an output response signal; calculating a deviation value of the sensor parameter based on the output response signal and the standard angle signal, and judging whether the deviation value exceeds a preset threshold value or not; if yes, based on the deviation value, a control instruction used for compensation is generated through a self-adaptive control algorithm, and the mechanical zero position of the to-be-detected sensor or the relative position of an internal electromechanical conversion element is adjusted; based on the adjusted output response signal, the above steps are repeatedly executed, and sensor parameters are iteratively updated; and when the deviation value is lower than a preset threshold value, judging that the output precision meets a preset requirement, storing the current parameter, and completing the parameter adjustment process of the to-be-detected sensor. By constructing a closed-loop control flow of'detection-analysis-adjustment-verification 'and an adaptive control algorithm, full-automatic, high-precision and high-efficiency calibration of the zero position, sensitivity and excitation signal parameters of the electromagnetic angular displacement sensor is realized.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention discloses an AI low-code development component automatic generation method based on a knowledge graph, and relates to the technical field of AI low-code development, and the method comprises the following specific steps: constructing an AI development knowledge graph: constructing a knowledge graph containing a five-layer structure, collecting and preprocessing multi-source data, and extracting various entities and association relationships, so as to obtain an AI development knowledge graph; constructing an ontology structure, importing the ontology structure into a database, and forming a final knowledge graph through manual auditing and machine reasoning optimization; according to the method, the multi-dimensional AI development knowledge graph is constructed, user requirements are analyzed in combination with the optimized natural language processing model, the component configuration scheme is generated by introducing the multi-level reasoning mechanism, and finally the standardized low-code component is automatically generated, so that full-automatic conversion from the user requirements to the low-code component is realized, and the user experience is improved. The defect that an existing low-code platform depends on manual component configuration is overcome, the development period is greatly shortened, and the development cost and the technical threshold are reduced.

The invention provides a full-automatic stacking method and system.The method is completed on the basis of a stacking robot, physical parameter information of goods to be stacked is obtained, a single-layer stacking reference scheme is generated on the basis of the physical parameter information and stacking environment parameters, and a final stacking scheme is generated on the basis of the single-layer stacking reference scheme; based on the area where the goods are located and the target stacking area, an optimal operation path is planned for the stacking robot, and automatic stacking operation is executed according to the final stacking scheme; the optimal stacking scheme can be automatically generated according to the physical characteristics of different cargos, and the method is suitable for stacking operation of cargos of various specifications and has wide application prospects.

The invention discloses an intention understanding tool calling method based on a large model function call technology, and the method comprises the following steps: S1, obtaining a natural language instruction of a user, and carrying out the preprocessing of the natural language instruction; s2, based on the structured instruction fragment and the context dialogue information, constructing a semantic vector for intention recognition, and inputting the semantic vector into a large language model; s3, screening function description information from a function definition library according to an intention recognition result; s4, extracting parameter content from the semantic vector under the constraint of a parameter limiting condition; s5, constructing a call request format conforming to a function call interface specification, and executing a corresponding tool call task; and S6, receiving a function calling result, generating a natural language response text, and outputting the natural language response text to a user interface to complete an instruction response closed loop. According to the method, full-automatic semantic mapping from a natural language instruction to tool function calling is realized, and the analysis accuracy and the calling success rate are improved.

The invention provides a medicine dispensing machine which comprises a frame, and a medicine bottle box for storing ampoule bottles and penicillin bottles, a liquid bag box for storing liquid bags and a suction and injection device hanging frame for conveying suction and injection devices are arranged in the frame. An ampoule bottle opening device for opening an ampoule bottle, a penicillin bottle cover opening device for opening a bottle cover of the penicillin bottle, a penicillin bottle liquid medicine mixing device for mixing and uniformly shaking liquid medicine injected into the ampoule bottle, and a marking device for marking a liquid bag injected with the mixed liquid medicine are arranged in the frame; the frame is internally provided with a sucking and injecting manipulator which is used for clamping a sucking and injecting device and sucking and injecting liquid medicine of an ampoule bottle into a penicillin bottle, all the devices are transferred through a transferring manipulator, full-automatic medicine dispensing is carried out, and the medicine dispensing efficiency is improved.