419results about How to "Accurate response" patented technology

The invention relates to the technical field of electric power energy storage management, in particular to an independent electrochemical energy storage integrated management system, which comprises a load prediction analysis module, a charge and discharge response control module, a dynamic load threshold adjustment module, an energy storage response optimization module, a PID (Proportion Integration Differentiation) adjustment module and an abnormal state monitoring module. According to the method, the change rate and fluctuation amplitude of the load are adjusted in real time, the power demand change is accurately predicted, the stability and response speed of the system are improved, the fluctuation period and sudden change value of the load are analyzed, the demand trend is accurately predicted, the charging and discharging rate is adjusted, the actual demand is matched, the energy utilization efficiency is improved, the service life of equipment is prolonged, and the response threshold value is dynamically adjusted; the operation time interval is optimized, so that the system is maintained in an optimal state under variable loads, the energy consumption and the operation and maintenance cost are reduced, power output and equipment states are monitored in real time, abnormities are handled in time, safety and stability of the system are ensured, and the overall efficiency and reliability of the power system are remarkably improved.

The invention discloses an intelligent navigation system and method for a ship in the technical field of ship navigation, and the system comprises a sensing module which is used for collecting the surrounding environment information and the state information of the ship in real time; the data processing and fusing module is used for preprocessing, calibrating and fusing the surrounding environment information and the self state information of the sensing module; the route optimization module is used for formulating a navigation plan and a collision avoidance strategy; the navigation control module is used for autonomously controlling the ship according to the output of the ship route optimization module; and the communication monitoring module monitors the navigation state of the ship and the change of the surrounding environment through real-time communication between the ship and a shore-based control center, other ships and a maritime affair management department. According to the method, data of different sensing units are associated, and the associated data are fused, so that mutual verification and complementation can be realized, and the accuracy and reliability of target identification are improved.

The invention provides a large language model intelligent interaction method and system with memory management capability in the technical field of artificial intelligence. The method comprises the following steps: S1, creating a work memory module, a long-term memory module, a memory fusion module and an intelligent interaction module; s2, acquiring external knowledge and integrating the external knowledge into a long-term memory module; s3, the intelligent interaction module obtains an intention sequence based on a user query statement and sends the intention sequence to a work memory module; s4, the memory fusion module retrieves context information from the long-term memory module based on the user query statement and sends the context information to the working memory module; s5, the work memory module constructs a work memory sending intelligent interaction module based on the intention sequence, the context information and the system instruction; s6, the intelligent interaction module outputs a query result based on the working memory; s7, a memory fusion module extracts semantic memory and plot memory from user query statements and query results and stores the semantic memory and plot memory in a long-term memory module. The method has the advantage that the man-machine interaction performance is greatly improved.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention discloses a power business scheduling method and system based on 5G communication grading and classification, and relates to the technical field of 5G slicing. According to the method, the prediction model and the real-time monitoring data are utilized to effectively predict the network flow and dynamically adjust the network resources according to different levels of power business requirements, so that the power business with high priority can quickly obtain necessary network support, the quality of the overall network service and the operation efficiency of the power business are improved, and the network flow is effectively predicted. And network resource allocation is optimized through the graph neural network, accurate analysis and response to power service grading are realized, predictability and stability of the network are enhanced, and more complex power service application is supported.

The invention relates to the technical field of vehicle control, in particular to an unmanned vehicle control method based on deep learning, and the method comprises the following steps: collecting the multi-modal data of weak participants in a road environment in real time, and recognizing the weak participants; the behaviors of the weak participants at the next moment are predicted through multi-dimensional information fusion, and behavior intention prediction of the weak participants is carried out; the path planning and control strategy of the vehicle is adjusted in real time through the self-adaptive decision control module; and when the predicted behavior intention of the weak participant has high uncertainty or potential risk, an emergency avoidance mechanism is automatically triggered, the risk level of the current traffic environment is dynamically evaluated, an optimal avoidance path is generated, and emergency stop or detour operation is executed. According to the method, double guarantees of high-dimensional analysis of behavior intentions of participants and real-time control of vehicles are combined, and the driving safety in a complex traffic scene is ensured.

The invention discloses a holographic display digital human speech recognition enhancement method based on multi-modal interactive learning, and the method comprises the following steps: S1, collecting and preprocessing data, and generating a multi-modal feature matrix; s2, inputting the multi-modal feature matrix into a self-adaptive multi-modal interaction kernel to generate multi-modal interaction features; s3, inputting the multi-modal interaction features into an emotion convolution block to generate semantic-emotion mapping features; s4, inputting the semantic-emotion mapping features into a cross attention generator, and outputting multi-modal fusion features; s5, inputting the multi-modal fusion features into a multi-modal fusion-decomposition two-way network, and generating multi-modal features after decoupling optimization; s6, inputting the multi-modal features into a holographic-speech symbiosis optimization module to generate speech recognition and holographic display data; and S7, performing parameter optimization by applying self-supervised learning and comparative learning strategies to realize self-adaptive adjustment. According to the invention, the enhancement of holographic display digital human speech recognition is realized by using a multi-mode interactive learning method.

The invention discloses a transaction platform management system based on cloud service, and relates to the technical field of cloud computing, and the system comprises the following components: a user behavior analysis module which collects the browsing track, purchase record and evaluation information of a user through an e-commerce platform, collects the attribute data and sales data of a commodity, and sends the collected data to a server; the user behavior analysis module accurately extracts key features of user activeness and commodity popularity to provide powerful support for personalized recommendation, the intelligent recommendation module generates a personalized recommendation list based on real-time behavior data and interests of the user, and the intelligent customer service module realizes personalized recommendation through natural language processing and machine learning technologies. According to the system, the user problem is quickly and accurately responded, the customer service efficiency and the user satisfaction degree are remarkably improved, the predictive maintenance module identifies potential risks and abnormities in advance through real-time data analysis, an early warning and solution scheme is provided for an administrator, and the system failure rate and the maintenance cost are effectively reduced.

The invention discloses a virtual reality interaction method for constructing multi-modal fusion based on application scene features, and relates to the technical field of virtual reality. In order to solve the technical problem that in the prior art, when an existing multi-modal fusion technology is used for processing fusion of different modal data, the characteristic difference between different modals is large, the technical scheme provided by the invention comprises the following steps: acquiring and preprocessing multi-modal data; constructing a cross-modal knowledge enhancement network, extracting initial features, and enhancing the consistency and difference between visual and auditory features; important context information in fusion features is optimized and enhanced based on an optimization network of frequency spectrum and space attention; constructing an edge guide learning network, and combining the edge guide learning network to the tactile data; the visual, auditory and tactile features are fused and applied to a virtual reality scene; and optimizing the model through structural similarity loss, mean square error loss, significance weight loss and edge loss. The method is suitable for cooperative work of multi-modal features in a complex environment.

The invention provides a data security traceability method and system based on artificial intelligence, relates to the field of data security, and is used for solving the problems of relatively poor data security traceability accuracy and response delay in the prior art. The method comprises the following steps: updating a user behavior attention model, and carrying out multi-dimensional aggregation analysis on all data flow paths comprising abnormal nodes and abnormal edges; determining the path with the highest comprehensive score value in all the data flow paths as a target abnormal path; performing data security traceability analysis on the target abnormal path to obtain a traceability analysis result; and determining a chain creation rule, taking each data circulation event on the target abnormal path as a block in combination with the chain creation rule, connecting the data circulation events in series to form a traceability chain, and starting a corresponding safety response strategy according to a traceability analysis result, the safety response strategy being obtained through self-adaptive generation according to the traceability chain. According to the technical scheme provided by the invention, the accuracy of data security traceability is improved.

The invention discloses a standardized electrical switch cabinet detection method based on the Internet of Things, and relates to the technical field of electrical switch cabinet detection, and the method specifically comprises the following steps: obtaining the core information of all abnormal data generated by all sensors in an electrical switch cabinet in real time, and carrying out the analysis after obtaining, evaluating the priority of each abnormal data in the electrical switch cabinet with the high current leakage phenomenon, and dividing each abnormal data into low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data according to an evaluation result; and according to the division result of each abnormal data, constructing an abnormal data hierarchical sorting mechanism, and respectively adopting different sorting and processing measures for low-priority abnormal data, medium-priority abnormal data and high-priority abnormal data. According to the method, the problem of unreasonable priority processing of the abnormal data in a high-current leakage scene is solved, quick response and accurate detection are realized, and the fault processing efficiency and the system reliability are improved.

The invention discloses a user-defined multi-dimensional analysis configuration method, system and equipment based on a constellation model and a medium, belongs to the technical field of data analysis, and aims to solve the technical problem that basic data such as dimensions, indexes and data models in multi-dimensional analysis are complicated to construct. According to the technical scheme, according to the method, the dimensionality, the index and the incidence relation between the fact table and the dimensionality table configured by the model table are imported, the mapping between the dimensionality tables is automatically imported into the correlation table, the attribute and the model relation, the model maintenance cost is reduced, and the model maintenance time is shortened; ordered model construction is realized through dimensions, indexes, fact tables, dimension tables, data models and self-defined multi-dimensional query, multi-fact-table dimensions screen application configuration scenes of reverse mapping of property of the fact tables, required indexes are flexibly selected from the fact tables through dragging operation, and according to a cascade mode of the indexes and the dimensions, the application configuration scenes of reverse mapping of property of the property of the fact tables are selected to be matched with the property of the property of the property of the property of the property of the property of the property of the property of the property of the property of the property of the property. And automatically screening out the generality dimension for display.

The invention discloses an intelligent screen control system based on a large scenarized model, which belongs to the technical field of intelligent vehicle-mounted devices, and comprises a semantic understanding agent used for identifying user requirements according to user text contents and generating operation requirements; the scenarized interface analysis agent is used for performing analysis according to the screenshot image, identifying an interface menu and interface elements and generating a scenarized operation list; the matching agent is used for matching the operation demand with the scenarized operation list, generating an operation instruction and performing interaction determination on the operation instruction and a user; the simulation interaction agent is used for simulating the operation of a user on a vehicle machine screen according to the operation instruction and converting the simulation operation into an operation sequence which can be executed by a vehicle machine operation system; the multi-mode execution agent is used for sending the operation sequence to the vehicle machine; according to the system, through cooperative work of the multi-agent module, accurate recognition and response to user requirements are achieved, and corresponding vehicle-machine interaction operation is generated.

The invention discloses a big language model tool matching method and system based on a knowledge graph, belongs to the technical field of artificial intelligence, and solves the problem that an API (Application Program Interface) tool is difficult to efficiently manage and accurately select in a real-time information processing process of an existing big language model. According to the method, a multi-source data fusion technology is introduced to collect and sort information of external API tools of different data sources, and a dynamic relation weight adjustment mechanism is utilized to dynamically adjust the weight of the relation between different API nodes according to the actual use condition of the API tools and user feedback; representing the characteristics of each API node through a graph embedding technology based on an intelligent matching algorithm of deep learning, and calculating the matching degree of each candidate API and a request by using a sequence processing model; according to the method, the knowledge graph and the intelligent matching algorithm are combined, an external API tool used by a large language model can be dynamically managed and optimized, and the information processing accuracy and the system response speed are improved.

The invention discloses a robot servo parameter self-adaption method and system based on gravity compensation. The method comprises the steps that before the mechanical arm is installed, instruction debugging is conducted on each joint under different loads, the rotating speed of the joints and the rotating speed of a motor are obtained, control parameters are adjusted and calibrated, and finally the calibration result is stored as joint feature information; modeling a mechanical structure, and calculating dynamic parameters of the mass, the mass center and the motor deflection angle of an arm lever; in the operation process, the load is monitored, and a gravity compensation value is calculated in combination with attitude information; uniformly distributing the compensation value to a control period through a feedforward data channel, and applying the compensation value to a motor and a load as a compensation amount of a torque ring instruction; and the servo system dynamically adjusts the control parameter according to the gravity compensation value and the setting parameter. By implementing the method provided by the embodiment of the invention, the defects in parameter setting and dynamic adaptability of the existing technical scheme can be overcome, and the robustness and performance of the servo system in a variable environment can be improved.

The invention relates to the technical field of unmanned aerial vehicle intrusion detection, in particular to an unmanned aerial vehicle intrusion detection system based on personnel intrusion alarm, which comprises a user authentication module, an area setting module, an unmanned aerial vehicle control module, a shooting control module, an intrusion trend judgment module, an intrusion identification module, a display terminal, a server and a cloud database. According to the invention, a user can flexibly set a monitoring area after authentication, generates an unmanned aerial vehicle control signal, dispatches an unmanned aerial vehicle in a target aircraft nest base station, accurately determines a dispatchable unmanned aerial vehicle, inputs task data information to the dispatchable unmanned aerial vehicle, photographs personnel in the monitoring area through the dispatchable unmanned aerial vehicle, generates an image set, and sends the image set to the target aircraft nest base station. And then an intrusion trend value is calculated according to the image sets to determine whether a face recognition instruction is triggered or not, and identity recognition judgment is performed according to the face recognition instruction to determine whether an alarm signal is generated or not, so that omnibearing and high-efficiency monitoring of a monitoring area is realized, and the accuracy and reliability of safety monitoring are remarkably improved.

The invention relates to the technical field of temperature regulation and control, in particular to an intelligent temperature regulation and control method and system based on an intelligent building. The method comprises the following steps: performing functional area air flow simulation on intelligent building structure design data to obtain functional area air conditioning flow simulation data; performing air circulation regulation of different functional areas on the functional area air regulation flow simulation data to obtain temperature coupling air circulation regulation data; indoor temperature circulation adjustment compensation is conducted on the temperature coupling air circulation adjustment data according to the human flow real-time monitoring data, and indoor temperature circulation adjustment compensation data are obtained; performing regulation and control logic training optimization on the indoor temperature circulation regulation and compensation data to obtain temperature regulation and control logic optimization data; and performing automatic firmware design based on the temperature regulation and control logic optimization data to obtain temperature regulation and control logic firmware. According to the invention, the temperature regulation and control technology is optimized, so that the temperature regulation and control technology is more perfect.

The invention discloses a distributed photovoltaic power prediction method and device, equipment, a medium and a product, and relates to the technical field of distributed photovoltaic. The method comprises the steps of obtaining a multi-source meteorological matrix; according to the historical power time sequence data, a fuzzy clustering algorithm based on a time sequence form is adopted to carry out regional division on the distributed photovoltaic power stations in the to-be-predicted region; determining an optimal network parameter of each sub-region by adopting a Bayesian method; applying the optimal network parameters to the Transform model of each sub-region to obtain a distributed photovoltaic power prediction model of each sub-region; and inputting the multi-source meteorological matrix in the future prediction time period into the distributed photovoltaic power prediction model of each region, and obtaining a distributed photovoltaic power prediction result of the region to be predicted in the future prediction time period by using the first multilayer sensor. The distributed photovoltaic power prediction method and device can be suitable for distributed photovoltaic power prediction, and the accuracy of distributed photovoltaic power prediction is improved.

The invention relates to an inspection robot global path planning method based on a three-dimensional grid map. The method comprises the following steps: constructing a three-dimensional point cloud map; constructing a three-dimensional occupation grid map; calculating the height difference between each grid and an adjacent grid of the three-dimensional occupied grid map, and marking the current grid and continuous grids in the vertical direction, namely the z direction, as grids with the same attribute; giving current position and target point position information of the robot, and converting the information into serial numbers to which grids belong; and traversing surrounding grids from a starting point by using an A * algorithm until a target point position is searched, and forming a global path from the starting point to the target point. According to the invention, a traditional two-dimensional grid map is replaced by a three-dimensional grid map, so that the obstacle avoidance capability of the ground mobile robot in a complex terrain is remarkably improved, the flexibility of the robot is improved, the safety of the robot in an actual application scene is enhanced, and especially in a dynamically changing environment, the obstacle avoidance capability of the robot is greatly improved. The path can be adjusted in time to avoid collision.

The invention discloses an air cooling unit automatic spraying control system and method capable of reducing backpressure in a segmented mode. The system comprises a data acquisition module, a dirt recognition module, a control module and a spraying module. The data acquisition module is used for acquiring real-time meteorological data and unit operation data, and the unit operation data comprises real-time load data and cooling fin temperature data; the dirt identification module is used for collecting image data of the radiating fins and carrying out dirt identification and positioning on the image data to obtain dirt information; the control module is used for generating a segmented spraying scheme based on the real-time meteorological data, the unit operation data and the dirt information; and the spraying module is used for spraying, cleaning and cooling the cooling fins based on the segmented spraying scheme so as to reduce the back pressure of the air cooling unit. Through regular automatic spraying, cleaning and cooling, the system can effectively reduce the back pressure of the air cooling unit, reduce the energy consumption, improve the operation efficiency of the unit, reduce the requirement for manual cleaning and reduce the maintenance cost.

The invention provides a flexible production plan prediction system based on a knowledge graph and a language model, and the system comprises a feature network graph building module which is used for aggregating the historical production data of a flexible factory to obtain feature network graph data; the flexible production large language model establishment module is used for training the natural language model by using the feature network graph data to obtain a flexible production large language model; the flexible production plan model establishing module is used for obtaining a flexible production plan model by adopting a stacking algorithm and training; the knowledge graph establishing module is used for obtaining a knowledge graph according to the feature network graph; and the flexible production plan prediction module is used for calling the flexible production large language model, the flexible production plan model and the knowledge graph by utilizing the language model development framework, and inputting the current customer demand to obtain a comprehensive flexible production plan. And predicting the flexible production plan through the synergistic effect of the two models and the knowledge graph.

The invention provides a large model connection context dialogue method based on an LSTM (Long Short-Term Memory) model, which belongs to the field of intelligent deep learning and comprises the steps of data preprocessing, input sequence coding, hidden state and memory cell initialization, decoding response generation, output sequence decoding, training and optimization, evaluation and adjustment. The method has high accuracy and response speed when processing a complex dialogue scene, and can be widely applied to the fields of intelligent customer service, intelligent assistants and the like.

The invention discloses an intelligent automobile path tracking system for urgent danger avoidance, and particularly relates to the technical field of intelligent automobiles, the system comprises an environment sensing module, the output end of the environment sensing module is connected with a danger assessment module, and the output end of the danger assessment module is connected with a path planning module. The output end of the path planning module is connected with a decision planning module, the output end of the decision planning module is connected with a vehicle control module, and the output end of the vehicle control module is connected with a communication and interaction module. Through cooperative work of all the modules, perception of the surrounding environment of the vehicle, danger assessment, path planning, decision making, vehicle control and communication interaction with the outside are achieved, under the dangerous condition, the system can make a response rapidly, effective danger avoiding measures are taken, the driving safety of the vehicle and the accuracy of path tracking are ensured, and the driving safety of the vehicle is improved. And a comprehensive and reliable guarantee is provided for safe driving of the intelligent automobile.

The invention discloses an abnormity monitoring system based on breathing machine operation parameters. The abnormity monitoring system comprises a data acquisition module, a breathing machine operation monitoring module and an abnormity monitoring module. The invention belongs to the field of data processing, and particularly relates to an anomaly monitoring system based on breathing machine operating parameters, according to the scheme, static delay optimization is constructed by introducing an environment compensation item, dynamic delay compensation is carried out by utilizing fuzzy logic prediction, and the response speed is increased during sudden breathing fluctuation; real-time data compression and expansion are carried out based on the system load weight; the accuracy of abnormity monitoring is improved; abnormal ranges of different operation parameters are dynamically adjusted based on environment compensation factors, input quantization errors and actuator faults are gradually compensated in combination with a dynamic environment compensation design control law, the fault-tolerant capability of external interference is improved, input quantization is jointly processed based on dynamic compensation actuator gain changes, random interference and quantization errors, and the fault tolerance of the actuator is improved. Accurate response to sudden breathing fluctuation is ensured; and the abnormity monitoring effect is improved.

The invention provides a multi-source information fusion method and system in an intelligent charging network, and relates to the technical field of data processing, and the method comprises the steps: obtaining original data; preprocessing the obtained original data to obtain preprocessed data; classifying the preprocessed data according to an information source, and performing unified timestamp synchronization on similar data to generate a preliminary fusion data set; performing association analysis on similar data in the preliminary fusion data set, removing redundant information and performing screening to obtain a plurality of simplified data sets; performing cross-class data association on the simplified data set to generate a multi-dimensional fusion data set; grading information in the multi-dimensional fusion data set to form different grades of charging requests; distributing charging power and time periods for the charging requests with different priorities to obtain an optimized scheduling data set; performing conflict detection and adjustment on the optimized scheduling data set to generate an optimized control data set; according to the invention, good fusion data support can be provided for power grid control.

The invention provides a sub-region acquisition method of a target tumor and a boron drug concentration spatial-temporal distribution acquisition method, and the method comprises the steps: obtaining the regional distribution of a boron drug enrichment region, a tumor region and a boron drug transportation region corresponding to the target tumor, so as to divide the region occupied by the target tumor into a first sub-region, a second sub-region and a third sub-region; and constructing a boron drug transport model based on the boron drug metabolism characteristics corresponding to the different sub-regions, so as to obtain boron drug concentration spatial and temporal distribution in the target tumor based on the boron drug transport model in combination with the boron drug initial concentration of the region occupied by the target tumor, according to the method provided by the invention, the metabolism condition of the boron drug in the area occupied by the target tumor can be accurately reflected, so that the spatial and temporal distribution of the boron drug concentration in the radiotherapy process can be accurately and conveniently extracted, and a more objective and accurate data basis is provided for subsequent data processing and / or analysis calculation.

The invention discloses a circuit breaker anti-tripping function test method and system, and belongs to the field of power system protection and control, and the test method specifically comprises the following steps: I, extracting circuit breaker data, and constructing an anti-tripping function map through a relation analysis technology; according to the method, the model optimization efficiency is improved, the local optimization problem is avoided, the model can make more accurate response to the state change of the circuit breaker under the actual operation condition, the flexibility and instantaneity of a test system are improved, the sample representativeness is optimized, the anti-tripping test effect is more reliable, the data processing cost is reduced, and the data processing time is shortened. According to the method, errors caused by human intervention are reduced, the intelligent level of a test process is improved, hysteresis of static assessment can be avoided, misjudgment caused by a single factor is reduced, the accuracy of health assessment is improved, the risk management and control capability of the system is enhanced, the possibility of fault occurrence is reduced, and the overall stability of the power system is improved.

The marine alcohol-hydrogen power system comprises a methanol fuel tank, a hydrogen storage tank, a methanol extended-range system, a hydrogen fuel cell, a methanol reforming hydrogen production system, an energy storage system, a driving motor, a speed reduction clutch, a propeller and an inverter power supply / load cabinet, the methanol fuel tank is respectively communicated with the input end of the methanol reforming hydrogen production system and the input end of the methanol reforming hydrogen production system, the hydrogen storage tank is connected with the input end of the hydrogen fuel cell, the output end of the methanol reforming hydrogen production system is connected with the input end of the hydrogen fuel cell, and the output end of the methanol reforming hydrogen production system is connected with the input end of the energy storage system. The output end of the hydrogen fuel cell is connected with the input end of the energy storage system, and the output end of the energy storage system is connected with the inverter power supply / load cabinet and the driving motor. According to the invention, the problems of slow dynamic response, poor stability and the like of energy supply of a single fuel cell can be solved.

The invention discloses a sample pretreatment method for measuring total carbon in titanium tetrachloride and a method for measuring total carbon in titanium tetrachloride, and belongs to the technical field of analytical chemistry. The sample pretreatment method comprises the following steps: carrying out the whole process under the protection of inert gas, adding a titanium tetrachloride sample to be detected into a strong alkali solution containing a catalyst, carrying out sufficient hydrolysis, and carrying out evaporation to dryness to obtain a solid matter of the sample to be detected after hydrolysis. The method for measuring the total carbon in the titanium tetrachloride comprises the following steps: preparing a to-be-measured sample solid matter into to-be-measured sample powder, measuring the total carbon content of the to-be-measured sample powder through a total carbon analyzer, and converting to obtain the total carbon content in a to-be-measured titanium tetrachloride sample. The method can effectively solve the problems that the final measurement result is low due to volatilization and loss of part of components during sample pretreatment for total carbon measurement in the titanium tetrachloride in the prior art; the analysis condition of the whole determination method is harsh, the detection cost is high, and the quality control is difficult.

The invention relates to a digital human personalized emotion interaction method, which belongs to the technical field of emotion calculation, and comprises the following steps: S1, carrying out emotion quantification on interaction input of a user to obtain a pleasure-wake-dominate PAD emotion value in a continuous dimension space; s2, performing emotion state evaluation on the user interaction input emotion to obtain an interaction input emotion evaluation function; s3, calculating a digital human emotion transition probability matrix by using a Bayesian network; s4, calculating an emotional response calculation value of the digital human character corrected by the big five-factor model in the current emotional state; s5, the corrected emotion response passes through an emotion influence factor matrix, and a response with complex emotion features is output; and S6, performing PAD emotional space coordinate calibration on the emotional intensity of the digital person on the six basic emotions to obtain an emotional response of the digital person.