1892 results about "Text messaging" patented technology

The invention discloses an abnormal short message behavior detection method and system based on multi-dimensional feature fusion, and relates to the related technical field of short message security detection.The method comprises the steps that spatial and temporal distribution features, semantic association maps and equipment behavior fingerprints of short message interaction are collected, a dynamic feature pool is configured, and a cross-modal feature sequence is extracted; cascade identification is carried out; the feature fusion weight matrix is dynamically adjusted, an abnormal probability score is generated, and when the abnormal probability score exceeds a dynamic abnormal probability threshold, a multi-stage verification mechanism is triggered; and matching a time sequence mode at an edge computing node, dynamically generating a verification code triggering threshold value, performing interactive risk verification, and determining an abnormal short message behavior mark. The technical problems of insufficient detection timeliness and adaptability and high false alarm and missing report rate caused by single detection dimension and difficulty in identifying novel complex abnormal short message behaviors in the prior art are solved, and the technical effects of reducing the false alarm and missing report rate of short message anomaly detection and improving the detection timeliness and adaptability are achieved.

A wearable device that communicates with a host device can be used to initiate a communication functionality of the host device (e.g., telephone calls, text messages). The wearable device can obtain user input indicating a recipient of the communication and in some instances content for the communication and can provide an instruction to the host device. The host device can use the indicated recipient and content to initiate communication and where applicable to send the content. Recipients and / or content can be selected from predefined lists available on the wearable device.

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The present disclosure provides methods and systems for generative question answering for construction project document. The method comprises: receiving a question from a user via a message interface, wherein the question is related to information from a plurality of construction project documents; identifying, using one or more large language models, one or more relevant documents from the plurality of construction project documents and one or more chunks relevant to the question, and generating an answer based at least in part on the one or more relevant documents and one or more chunks; and providing the answer and one or more links to the one or more relevant documents in a text message in the message interface.

The invention relates to the technical field of seepage monitoring, and discloses a seepage monitoring system based on a hydraulic engineering project. The system collects water level, flow velocity, seepage flow, soil humidity, soil temperature and soil pressure data through a data collection module, a data analysis module calculates standard soil humidity, standard water flow velocity, standard soil pressure, standard soil temperature and standard seepage flow according to the data, establishes a mathematical model, calculates seepage abnormal indexes, and calculates the seepage abnormal indexes according to the standard soil humidity, the standard water flow velocity, the standard soil pressure, the standard soil temperature and the standard seepage flow. The risk assessment and early warning module performs threshold judgment on the seepage abnormal indexes, sends out an overrun signal when an overrun condition occurs, predicts a future seepage trend by using a moving average method at the moment, generates a future seepage trend broken line graph and a seepage risk assessment report, and uniformly transmits the future seepage trend broken line graph and the seepage risk assessment report to the emergency response module; and when the emergency response module receives the overrun signal, an emergency plan is started, and related managers are informed of the emergency plan, the future seepage trend broken line graph and the seepage risk assessment report in a short message mode.

The invention discloses an AI intelligent collaborative monitoring management system for a multi-index water volume system, and relates to the technical field of monitoring management, the collaborative monitoring management system comprises a data acquisition module, a water volume system analysis module, a collaborative monitoring module, an emergency early warning module, an early warning scheme module and a data visualization module, all the modules are connected through electric signals. According to the AI intelligent collaborative monitoring management system for the multi-index water volume system, abnormal changes in water quality, water volume and water ecological indexes are identified through anomaly detection, an early warning mechanism is triggered according to the detected anomaly, early warning information is sent to management personnel through various modes such as a visual interface, a short message and an email, and the management personnel can carry out early warning on the water quality, the water volume and the water ecological indexes. According to the invention, the time interval from the occurrence of the abnormity to the discovery of the abnormity is greatly shortened, and valuable response time is provided for managers, so that further deterioration of the abnormity is effectively restrained, and the safety and sustainable utilization of water resources are guaranteed.

The invention provides an intelligent short message link efficient detection method and system. According to the method, unmanned aerial vehicle cluster deployment is used for forming a dynamic communication topology, and the dynamic communication topology is used for monitoring the time delay mutation rate and the packet loss rate of a damaged short message link in real time and generating a state fluctuation sequence. When it is detected that the transmission time delay gradient exceeds an emergency communication threshold value, an abnormal marking mechanism is triggered, and abnormal association clusters and the space-time coupling relation of the abnormal association clusters are recognized by analyzing the data. Then, the information is bound with timestamps, and a block chain state change event is generated by using a multi-node parallel evidence storage technology; and adjusting the distribution density of the unmanned aerial vehicle according to a verification result, and generating a topology reconstruction factor so as to update a short message transmission path and optimize a communication network. According to the technical scheme provided by the invention, the stability and reliability of efficient detection of the short message link can be improved.

The invention relates to the technical field of short message channel testing, and discloses a short message channel testing configuration method, device and equipment and a storage medium, and the method comprises the following steps: firstly verifying user login information and distributing authority, then extracting and screening channel information from a database based on the authority, and presenting a result in a paging mode. A user can input channel configuration information, and the database is updated after verification. The system then generates an association configuration with the engine user based on the updated channel information. And establishing a TCP / IP Socket communication session and acquiring a session identifier according to the channel selected by the user and the associated configuration. And the system receives the test short message content input by the user, packages the test short message content and the session identifier into a sending request, and sends and records a result through the communication session. According to the method, the channel configuration information can be managed in a centralized manner, a visual user interface is provided, flexible query and screening functions are supported, and an automatic test process can be realized.

The invention discloses a property maintenance prediction system based on big data, and the system comprises the steps: monitoring equipment and environment in real time through an intelligent sensor, transmitting the data to a cloud or local storage through a wireless network, and carrying out the historical data processing and cleaning through a distributed architecture; predicting the equipment fault risk through machine learning, establishing a customized prediction model for different equipment, analyzing the life cycle and predicting the service life of the equipment; for old equipment, constructing a life extension model by using historical fault data; the system generates intelligent decision suggestions, pushes equipment health states to property management personnel, automatically formulates maintenance plans and priorities, and dispatches maintenance personnel; during fault prediction, early warning is given out in real time, related personnel are reminded through a short message or an APP, meanwhile, maintenance operation is recorded, historical data are analyzed, and future maintenance plans and budget are optimized; the system dynamically adjusts the life prediction model of the old equipment by accumulating historical data, and ensures the accuracy and efficiency of equipment health management.

The invention relates to an HTTP (Hyper Text Transport Protocol) short message gateway processing method and device, equipment and a storage medium. The method comprises the following steps: firstly, carrying out multi-dimensional verification on a short message sending request, and then, carrying out auditing and template matching on short message contents. And based on an audit result, considering multiple factors to select an optimal sending channel. Distributing priorities for the short messages according to user characteristics and system states, and putting the short messages into a multi-level feedback queue; and based on the type of the short message, selecting a channel and a user package to carry out charging and quota control. The short message information is stored in a distributed cache system, a sending task is processed through an asynchronous processing queue and a working thread pool, a failover and intelligent retry mechanism is implemented, and efficient and reliable short message sending is ensured. According to the HTTP short message gateway processing scheme, a large number of concurrent requests can be efficiently processed, the sending channel is intelligently selected, the sending priority is flexibly controlled, charging is accurate, and the HTTP short message gateway processing scheme has the fault transfer capability.

The invention relates to an intelligent short message template generation method and system based on a large language AI model, and the method comprises the steps: extracting cross-cultural communication frequency, sentence pattern preference analysis results and screen size adaptation data from social media interaction data and equipment use logs of target audiences, and generating a cultural background distribution matrix; identifying regional culture features in the culture background distribution matrix to obtain a taboo matching result; generating an expression habit preference matrix according to the updated culture background distribution matrix; generating a preliminary short message template according to the expression habit preference matrix and the symbol semantic mapping table, and outputting an adjusted template set conforming to message length control; analyzing the acceptability of the adjusted template set on the language conciseness requirement and the interactive button layout, and generating a new template set; evaluating a text alignment mode of the new template set; and screening candidate short message templates according to the information definition score. According to the invention, the accuracy and acceptability of cross-culture short message communication can be effectively improved, and the participation degree of audiences is enhanced.

The invention discloses a sewage monitoring and early warning method and system, and aims to solve the problems of lagging and high misjudgment rate of traditional monitoring. The multi-modal acquisition module acquires water quality parameters, equipment states and pipe network data in real time through a distributed sensor network, a timestamp is calibrated through linear interpolation of a dynamic analysis engine, spatial features are extracted through concentration gradient calculation, and data are cleaned in combination with Kalman filtering and an isolated forest algorithm. And the intelligent analysis and decision module mines time sequence features based on the LSTM model and outputs pollution level indexes. And the grading response execution module triggers early warning according to three-level threshold values: a first-level APP pushes data, a second-level superposition short message and a third-level telephone alarm and links equipment, a standby purification device is started within 10 seconds, and a WebGL visual diffusion report is synchronously generated. The system constructs an acquisition-analysis-decision-response closed loop which is suitable for industrial and urban pipe network scenes.

The invention discloses a gas user intelligent safety inspection system based on the Internet of Things. According to the invention, through a multi-stage intelligent decision-making mechanism, the active protection capability of gas safety management is significantly improved. A traditional alarm system depends on single sensor threshold triggering, is susceptible to environmental interference and lags in response, while the system can accurately distinguish scenes such as real leakage and cooking interference by fusing multi-dimensional data such as gas concentration, temperature and flame and combining a user behavior mode to dynamically evaluate a risk level. For example, when it is detected that the gas concentration is abnormal but not accompanied by sudden temperature rise or a flame signal, the system automatically lowers the alarm level and only pushes prompt information; and when high-risk leakage is confirmed, the valve is immediately closed in a linkage mode, exhaust equipment is started, synchronous early warning is conducted through multiple channels such as telephones, short messages and a community platform, and it is ensured that the user and rescue strength respond in the golden time.

The invention relates to the technical field of short message auditing and intercepting, in particular to a short message auditing and intercepting system based on artificial intelligence, which performs multi-level semantic feature extraction on short message content by constructing a deep learning architecture mixed by a convolutional neural network and a recurrent neural network. And traditional keywords are effectively identified, and camouflage keywords, homophonic replacement and interference symbols which are easy to bypass are filtered. Furthermore, a short message semantic map is constructed through a multi-head self-attention mechanism, and weighted analysis and abnormal node recognition are carried out by taking sensitive words and key semantic units as map nodes and taking semantic association relationships among the nodes as edges, so that the abnormal short message recognition precision is improved. Besides, a multi-dimensional short message risk assessment mechanism is constructed by combining the historical behavior mode of the sending number, the number reputation score and the incidence relation between the numbers, and a hierarchical auditing strategy is implemented, so that the system processing efficiency and the interception accuracy are effectively improved.

The invention relates to the technical field of short message fraud recognition, and discloses a short message fraud recognition method and system based on machine learning and computer equipment, and the method comprises the steps: carrying out the preprocessing of collected fraud and non-fraud short message samples, and obtaining structured short message data; performing multi-dimensional feature extraction to obtain an initial feature vector; constructing a short message fraud multi-mode knowledge graph; performing feature enhancement to obtain a knowledge enhancement feature vector; inputting the knowledge enhancement feature vector into a plurality of machine learning models for training to obtain an integrated short message fraud recognition model; and performing real-time analysis on the newly received short message based on the integrated short message fraud identification model to obtain a fraud risk prediction result, so that the newly received short message can be quickly analyzed, the real-time requirement in practical application is met, and a risk threshold classification mechanism is adopted, so that the fraud risk prediction result is more interpretable and operable, and the fraud risk prediction accuracy is improved. And subsequent risk management and disposal are facilitated.

The invention discloses a fraud phone recognition method and system based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a plurality of fraud numbers from historical fraud data; constructing a multi-modal fraud data graph, taking each piece of multi-modal data of the fraud number as a node, and constructing a connection edge between the nodes according to an association relationship between every two pieces of multi-modal data; constructing and finely adjusting a large language model; multi-modal data of a to-be-recognized fraud number is obtained, a plurality of nodes are selected from the multi-modal fraud data graph, then the multi-modal data of the to-be-recognized fraud number and the multi-modal data of the selected nodes form cue words, the cue words are input into the large language model, and whether the obtained to-be-recognized fraud number relates to fraud or not and reason analysis for judging that the to-be-recognized fraud number is fraud are output. The invention relates to the field of communication, can improve the fraud recognition accuracy and system adaptability through deep fusion and calculation of multi-mode data such as call audio, short message text and call ticket data, and provides powerful technical support for fighting against novel telecommunication network fraud.

The invention provides a short message issuing processing method and system based on scene self-adaption. The method comprises the steps that a three-dimensional communication characteristic spectrum is constructed through a building information model and infrared thermal imaging data, short message request distribution is analyzed and predicted in combination with a signal attenuation field, cross-operator network quality data is integrated to form a converged communication quality field, and finally short message issuing is intelligently scheduled according to the network state and service requirements. According to the method, accurate short message routing optimization based on real-time communication quality and crowd distribution in a complex building environment is realized. According to the technical scheme provided by the invention, the short message delivery rate and timeliness in a complex building environment can be remarkably improved, and particularly, the communication reliability in a crowded area and a signal blind area is remarkably improved.

The invention discloses a light-weight government affair questionnaire intelligent touch and analysis system and method based on a fusion message, and the system comprises an integrated message application platform operation interface which is used for the submission, application, strategy definition and transmission of a customer message; the questionnaire engine is used for generating questionnaires, issuing strategies and performing multi-dimensional statistical analysis on questionnaire results; the intelligent message scheduling engine is used for realizing AI generation of rich media messages, multi-channel intelligent reach distribution, optimal reach mode selection and summary analysis of reach mode results; and the electronic fence data engine is used for processing operator real-time signaling big data and capturing operator numbers and accurate touch short messages in a specified range. An efficient and convenient short message creation and auditing mechanism, flexible and diversified questionnaire construction, intelligent rich media message generation, accurate regional orientation and audience access, intelligent distribution, high interactivity, comprehensive and deep data analysis and report generation can be realized.

The invention provides a 5G message, short message and voice fusion information touch method and system, and relates to the technical field of information processing. The method comprises the following steps: acquiring to-be-sent information content, real-time geographic position information of a user and an initial multi-mode user portrait; optimizing the initial multi-modal user portrait by using the to-be-sent information content and the real-time geographical location information of the user to obtain a target multi-modal user portrait; analyzing the information content to obtain an analysis result, and determining a target communication strategy corresponding to the information content by using a reinforcement learning framework based on the target multi-mode user portrait and the analysis result; based on the target communication strategy, determining a target communication resource allocation scheme according to the real-time geographical location information of the user and the network condition by adopting a dynamic resource allocation algorithm; and based on the target communication resource allocation scheme, executing an information transmission process, and introducing a differential privacy protection mechanism to encrypt the information transmission process. According to the method and the device, the information content reaching accuracy is improved.

The invention discloses a short message template intelligent identification method and system based on semantic similarity calculation, relates to the technical field of information and communication, and realizes automatic matching of a request short message and a template library through semantic vectorization and similarity calculation; semantic extension, structural constraint and a dynamic feedback mechanism are combined, so that the matching accuracy and robustness are improved; an optimization process driven by a multi-level model and category features is introduced, and an identification-feedback-optimization closed-loop system is formed; the system structurally comprises a semantic modeling module, a similarity calculation module, a dynamic feedback module and a template optimization module, automatic and efficient short message template recognition and updating can be achieved, and the requirements for real-time performance and accuracy in a large-scale application scene are met.

The invention discloses a safety early warning and alarming system based on a subway underground excavation construction environment, and relates to the technical field of early warning and alarming, the safety early warning and alarming system comprises a safety early warning platform, the safety early warning platform is in communication connection with a data acquisition module, a data transmission module, a data analysis module, an early warning analysis module, an alarm notification module and a visual report module, all the modules are connected through electric signals. By monitoring state information of key parts, performing data processing and analysis in time and capturing subtle changes which easily generate potential safety hazards in time, potential potential safety hazards can be quickly found, and once monitoring data exceed a preset alarm threshold value, the system can immediately trigger an alarm mechanism to give an alarm. Alarm information is sent to related personnel in various modes such as short messages, mails or system notifications, so that risk response time is greatly shortened, decision time is provided for managers, and occurrence risks of safety accidents are effectively reduced.

The invention relates to an AI intelligent short message content generation optimization method and system based on a user portrait, and the method comprises the steps: obtaining interaction record data, dominant feedback and implicit feedback of a user and a short message service, and constructing an initial interest association table of the user for the short message content; extracting high-frequency keyword theme clusters and time distribution of the reply text according to the initial interest association table; extracting semantic association and interaction frequency characteristics of the high-frequency keywords; calculating keyword emotion weight and unsubscribing keyword trigger scene distribution density according to the user enhanced behavior feature set; the appearing time point and the expression form of the unsubscribing keyword are combined; mapping explicit feedback correction semantics and implicit feedback emotion tendencies into interest nodes; triggering a scene distribution rule according to a confirmation willingness and an unsubscribing keyword in the historical reply; and generating a personalized short message matched with the dominant feedback questioning mode and the keyword emotion weight. According to the invention, the individuation degree and the user acceptability of the short message content can be effectively improved, and the unsubscribing rate is reduced.

The invention relates to a computer-implemented method for data processing of a vehicle system (22) of a vehicle (10), comprising the steps: - Capture of a voice input (S10) by a microphone (26) coupled to or integrated into the vehicle system (22); - Conversion of voice input into a text message (S12) by the vehicle system (22); - Transferring the text message (S14) to a computing unit (12); - Generating a response using a machine learning algorithm executed on the computing unit (12), comprising: - Extracting a task list (S16) from the text message comprising at least one task, and - Obtaining information and / or generating commands (S18) for the vehicle system (22) according to the tasks in the task list; - Transmitting the response (S20) to the vehicle system (22); and - Execution of the commands contained in the response by the vehicle system (22) and / or generation of a speech output (S22, 40) from the response to read out the information contained in the response.

The invention relates to the technical field of communication, and provides a short message content compliance generation method and system fusing a semantic large model. The method comprises the following steps: carrying out coarse identification on short message content to be processed by a service provider to obtain a short message type and a user demand; inputting the short message content into a semantic large model for deep semantic analysis, and outputting short message semantic analysis information; performing multi-dimensional compliance comparison on the semantic analysis information of the short message according to the type of the short message and a user demand, and identifying risk description content; and optimizing and adjusting the risk description content according to the short message type and the user demand and the semantic analysis information of the risk content, and generating target short message content. The technical problem that short message content generation in communication service depends on a template, and compliance risks cannot be dynamically identified and avoided according to user requirements is solved, and deep semantic analysis and multi-dimensional risk comparison through a semantic large model are realized; and the intelligent level and compliance guarantee capability of short message content generation in the communication service are improved.

A method and system for securely communicating with third parties who are authorized to control access to funds in a banking account held by a financial institution is disclosed. The method and system are particularly useful when the third party does not have direct access to account controls (for example, through a secure web portal or a secure banking application). While joint account holders, or other authorized users, of the banking account may communicate directly through an account management service, communication with the third party is mediated using a separate messaging service, such as a short messaging service (SMS). An authorization token may also be used as part of the approval process.

The invention relates to an AI intelligent short message pushing method and system based on user behaviors, and the method comprises the steps: obtaining the social relation and interaction frequency of short message pushing target group members from historical behavior data, and obtaining a classification result of an initial short message pushing target group; integrating the behavior data of the group members; determining adjacent groups by analyzing social relations and interaction frequencies of members among the groups to obtain a group feature set; identifying a first-time user according to the group feature set; obtaining a dynamic behavior change trend of the short message pushing target user from the real-time interaction data; if the matching degree of the real-time behavior preference distribution and the group feature set is lower than a preset matching degree threshold value, analyzing a social relation between the user and a new group member; and according to the mapping relationship and the real-time preference distribution, determining the priority and correlation of the push information items. According to the method, accurate description of user behaviors and adaptive optimization of a pushing strategy are realized through multi-level feature modeling, dynamic data updating and a cross-group cooperation mechanism.

The invention discloses a secure transmission system based on a multimedia short message, and relates to the technical field of communication and network security, and the system comprises a protocol stack security reinforcement module which carries out the hierarchical reconstruction of an MMS protocol stack, comprises a preprocessing layer, is used for intercepting and filtering illegal characters, and verifies the legality of a message structure through a finite-state machine; the analysis layer is used for pre-judging memory requirements based on message types and lengths by adopting a dynamic memory allocation strategy; the execution layer is integrated with a null pointer checking mechanism, and the validity of a pointer is forcibly verified before a protocol stack calls a function; the encryption and signature module is used for carrying out end-to-end encryption on a message body and metadata by adopting a hybrid encryption algorithm and carrying out message integrity verification through a lightweight hash tree; and the vulnerability real-time monitoring module is embedded into an abnormal behavior detection model based on machine learning and is used for dynamically identifying memory occupation abnormity and illegal instruction calling high-risk operation when the protocol stack runs.

The invention provides a multi-dimensional fuel gas alarm system for a workshop production line, and relates to the technical field of fuel gas alarm. The multi-dimensional fuel gas alarm system for the workshop production line comprises a fuel gas sensor module, a positioning module, a valve cut-off module, a data acquisition module, a data processing module, an alarm module and a standby power supply module, the gas sensor module comprises a gas sensor installation unit, a sensor node unit, a wireless communication module and a threshold setting unit. According to different levels of gas concentrations, alarms (early warning, alarm and emergency alarm) of different levels are triggered, and corresponding countermeasures are taken, so that when a gas leakage event occurs, gas supply can be quickly cut off, gas accumulation and potential safety hazards are reduced, and after an alarm signal is sent out, a related person in charge is immediately notified, so that the safety of gas leakage is ensured. And timely response is ensured through various modes of short messages, calls and WeChat, so that potential risks and loss are reduced to the greatest extent.

The invention discloses a wireless heart rate monitoring and short message alarm method based on an Arduino single-chip microcomputer, and particularly relates to the technical field of heart rate monitoring, which comprises the following steps: acquiring environment and user activity information through a multi-source sensor, acquiring a heart rate data priority according to an environment risk score and user activity intensity, and sending the heart rate data priority to the Arduino single-chip microcomputer; future environment and activity conditions are predicted based on the long short-term memory network, sampling frequency and transmission strategies are adjusted in advance, prospective intervention on potential risks is achieved, practicability, accuracy and reliability of the heart rate monitoring system are improved, and the heart rate monitoring system is suitable for wide health monitoring scenes; when the heart rate abnormity is compared with the threshold value, multi-level alarm is triggered, the high-frequency sampling energy consumption of the sensor can be reduced, the data transmission path is evaluated and optimized in combination with the network reliability, the abnormity detection precision and stability are remarkably improved, and the problems that the abnormity is difficult to recognize in time and the power consumption is too high in a single heart rate sensor in a complex environment are solved.

The invention relates to the technical field of network security and data processing, and discloses a violation short message recognition method and system based on deep semantic understanding, and the method comprises the steps: firstly cleaning an original short message, generating a mixed embedding vector through characters, sub-words and pinyin, and carrying out the recognition of the violation short message; then processing through a double-layer detection engine, wherein the first layer utilizes rules and a lightweight model for rapid preliminary screening; in the second layer, for suspected samples, a double-tower fusion neural network architecture is adopted, local and global features are combined, fusion is carried out through a gating unit, and a large language model is input to carry out deep semantic reasoning. The system executes strategies such as interception or flow limiting according to the risk score, and realizes model iteration through a dynamic knowledge base and incremental learning. According to the method, the resource consumption and the detection precision are balanced through the layered architecture, the antagonistic variants are effectively identified by utilizing multi-dimensional feature fusion, and the method has the adaptive evolution capability for a novel violation mode.