983 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.

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 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 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 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 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 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 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 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.

The invention belongs to the technical field of intelligent short message service scheduling, and particularly relates to an intelligent short message scheduling system and method based on 5G network slices. According to the method, the short message service processing efficiency in the 5G network slice environment is effectively improved through accurate prediction and intelligent scheduling of the short message service traffic, the change trend of the short message service traffic can be accurately captured through smooth processing and exponentially weighted moving average processing of the traffic change rate time sequence, and the short message service processing efficiency is improved. Reliable data support is provided for subsequent scheduling decisions, meanwhile, through confidence evaluation and resource utilization evaluation, the predicted demand of short message service flow and the resource utilization condition can be comprehensively considered, the scheduling reasonability and effectiveness are ensured, in addition, the intelligent scheduling mechanism can automatically adjust resource allocation according to the scheduling priority, and the scheduling efficiency is improved. The sending requirement of the high-priority short message service is met, and the processing speed of the short message service and the user experience are further improved.

The invention discloses a multi-channel high-precision water level dynamic real-time monitoring system for a drilling water pumping test, and the system comprises a sensing layer which is used for deploying a multi-source sensor array, inhibiting interference through distributed layout and electromagnetic shielding, collecting multi-dimensional data in real time, and carrying out the preliminary filtering; according to the transmission layer, an optical fiber and twisted pair redundant network is constructed, an EtherCAT protocol is adopted for low-delay transmission, AES-128 encryption tampering prevention is integrated, and edge nodes compress data and verify integrity; the processing layer is used for carrying out real-time preprocessing on edge calculation and switching a standby sensor in abnormal detection; the cloud end predicts the water level trend through Spark Streaming analysis and an LSTM model, distinguishes interference through a random forest algorithm, and outputs calibration data and an analysis report; the application layer is used for constructing a three-dimensional drilling model based on Unity, dynamically displaying water level changes, synchronizing a multi-parameter curve and supporting historical backtracking; the early warning module notifies abnormity through sound and light and short messages, the decision module generates a report and supports multi-drill-hole comparison, and the test strategy is optimized.

A processing system including at least one processor may obtain a text message content from a sender to a recipient, identify additional contextual content based upon a context associated with at least one of: the sender and the recipient, generate a revised text message content based upon the text message content in accordance with the additional contextual content, and transmitting a message to the recipient comprising the revised text message content.

The invention discloses a multi-source environment monitoring and intelligent alarm system and method based on a Palladium platform. The method comprises the following steps: S1, obtaining preprocessed environment monitoring data; s2, forming transmission environment monitoring data; s3, carrying out distributed aggregation and fusion on the transmission environment monitoring data on the central Palladium platform; s4, identifying abnormal fluctuation in the data; s5, generating adjusted alarm threshold data by using a self-learning mechanism; s6, according to the adjusted alarm threshold data, carrying out grading evaluation on the detected environment abnormal events, forming multi-grade risk determination, and generating corresponding intelligent alarm signals; and S7, transmitting the intelligent alarm signal to each edge computing node and a related monitoring terminal, issuing alarm information in a short message, mail, voice broadcast or system interface display mode, and starting a corresponding emergency processing plan. According to the invention, the transmission efficiency of the alarm information can be effectively improved, and the emergency response system can make proper response according to different risk levels.

The invention discloses a multi-user multi-channel message scheduling method, system and device and a storage medium, and the method comprises the steps: creating a sending channel index structure and a message index structure, and creating a group of first-in first-out message cache queues of different channel types for each user; caching the obtained user message to the tail of a message cache queue, mapping the message ID to a message index structure, and mapping a sending channel ID to a sending channel index structure; according to the sending channel index structure, writing a to-be-sent message into a corresponding sending channel from a message cache queue of the user, and deleting a message ID of the to-be-sent message from the message index structure; performing flow velocity detection on each sending channel; and until the ID data in the message index structures of all the users are empty. The short messages submitted by different users have the same sending priority, so that the problem of poor experience such as user interface lagging or long-time non-response in a multi-user request scene in the prior art is solved.

The invention discloses a slope early warning method and system based on a knowledge graph, and relates to the technical field of multi-source monitoring slope early warning, and the method comprises the steps: 1, unifying laser, radar, satellite remote sensing and underground water level monitoring flow into an index-structure-disaster situation graph containing traceability metadata; 2, generating an instability trigger coefficient by using a gradient lifting model driven by a coupling trend and credibility; step 3, automatically outputting short messages, broadcasts and social contact templates, and implanting abstract codes for publishing; 4, comparing the returned text in real time, marking deviation nodes according to graph consistency, and tracing a source link; and step 5, performing Hash solidification on the core early warning segment and spreading the performance to the alliance chain, and writing back the knowledge graph optimization template and channel. According to the scheme, multi-source data alignment is achieved, early warning is accurate and reliable, cross-medium semantic consistency and evidence chain traceability are achieved, and the requirement of the whole mountain disaster prevention process is met.

The invention discloses a geological disaster early warning information rapid publishing system and a priority-based accurate pushing method, and relates to the technical field of geological disaster monitoring and early warning. According to the method, the deep learning model, the block chain technology and the multi-channel dynamic pushing strategy are integrated, so that high-precision prediction, safe storage and accurate distribution of geological disasters are realized, the convolutional neural network and the long-short-term memory network are utilized to process multi-source data, and the prediction accuracy is improved; the block chain is adopted to ensure that the information is not tampered and traceable; the information pushing priority is dynamically adjusted according to risk assessment and network conditions, rapid and accurate information distribution is achieved through multiple channels such as short messages, mobile applications and satellite communication, the response speed and reliability of the early warning system are improved, and powerful technical support is provided for effective disaster prevention and reduction.

The invention discloses a bedside skin flap blood flow monitoring and early warning device and computing equipment, and the device comprises a skin flap tissue perfusion monitoring module which comprises a probe housing, a microcirculation blood flow velocity sensor, a tissue oxygenation degree sensor and a flexible connecting arm; the data acquisition and transmission module is used for receiving the blood flow velocity data and the oxygenation degree data through a data line and carrying out analog-to-digital conversion and data packaging; the central processing unit comprises a data storage module, a data analysis module and an early warning module; the data analysis module comprises a baseline establishment sub-module, an anomaly detection sub-module and a trend prediction sub-module; and the early warning module is used for displaying a red alarm through a bedside display screen and sending a short message alarm containing the patient name, the bed number, the early warning type and the severity information to a medical staff mobile terminal through the wireless notification module. According to the invention, the abnormal condition of skin flap blood perfusion can be found and predicted in time, and the workload of medical staff is reduced.

The invention provides a safe short message link invoice generation system and method based on a bank intranet, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining an available invoicing amount interval based on the change condition of a centralized verification risk time period in an effective time interval of an invoicing short message link at a current time period under different invoicing amounts, and when the matching invoice amount is not in the available invoice amount interval, according to the change condition of the matching invoice in different concentrated verification risk time periods and the invoice amount influence condition of the different concentrated verification risk time periods on the future time period, the matching invoice amount is calculated according to the change condition of the matching invoice in the different concentrated verification risk time periods. According to the generation processing method for determining the effective time interval of the short message link invoice of the server of the bank intranet in the current time period, the congestion condition of the server for checking and downloading the invoice is avoided.

An electromechanical Smart Lock is unlocked by the Smart Lock timely receiving a time sensitive access-code that solves a cryptographic hash algorithm (e.g., SHA-3 or the like) running on that Smart Lock. To enable this functionality, the Smart Lock knows at least three items of information: status of a countdown timer, its cryptographic hash algorithm, and its unique identifier. Further, the Smart Lock may have various elements for receiving the time sensitive access-code, such as, but not limited to, a QR code camera, a barcode scanner, a camera, a microphone, a keypad, and / or optionally, an antenna(s) / radio(s) for RFID / NFC inputs to the Smart Lock. Server(s) may also know the same three items of information for each given Smart Lock; and the Server(s) may provide time sensitive access-codes to (authorized) user(s) and / or to their devices. Users may request, receive, and / or send the time sensitive access-codes via text messaging (or other elements).

The invention relates to the technical field of information security, can be applied to the medical field and the financial science and technology field, and discloses a short message privacy protection method, device and equipment based on a block chain, and a storage medium, which are applied to payment verification and transaction confirmation scenes or remote medical consultation scenes. The method comprises the following steps: generating a sender private key and a sender public key in a trusted execution environment, and recording the sender public key in a public key warehouse on a block chain through a smart contract; when a short message sending request is received, generating an elliptic curve key pair, performing key verification, and generating a key negotiation parameter and a temporary session key; performing hierarchical encryption and assembly on the short message content, the temporary session key, the receiver identifier and the sender identifier to generate an encrypted short message packet; calculating a hash value of the encrypted short message packet as a short message identifier; and sending the encrypted short message packet to a receiver terminal for hierarchical decryption and verification. According to the invention, the security of short message privacy is improved.

The invention relates to a short message fraud detection method based on semantic and behavior feature fusion. The method comprises the following steps of short message collection, short message processing, feature extraction, fraud short message recognition and fraud short message submission. According to the method, the semantic features and the behavior features of the short messages are fused, and the overall detection precision and robustness are effectively improved.

The invention relates to the technical field of satellite communication, and discloses a satellite communication method, electronic equipment, a storage medium and a program product. In the method, by taking electronic equipment as a mobile phone, the mobile phone corresponds to a plurality of directional diagrams, in a satellite communication process, the actual beam coverage range of the plurality of directional diagrams in the mobile phone at the current moment is determined, and then a first directional diagram of which the actual beam coverage range covers a satellite is selected from the plurality of directional diagrams; and establishing communication connection with the satellite based on the first directional diagram so as to execute services such as conversation and short messages. In the method, because the mobile phone has a plurality of directional diagrams, when the relative position relationship between the mobile phone and the satellite changes or the attitude of the mobile phone changes, the first directional diagram can be automatically selected from the plurality of directional diagrams, so that the communication connection with the satellite is established based on the first directional diagram, and the position or attitude of the mobile phone does not need to be changed again. Therefore, the satellite communication experience of the user can be improved.

The invention discloses a short message template classification and automatic matching method and system based on an AI technology, and relates to the technical field of information and communication. A pre-training language model is introduced to carry out separated semantic modeling on a short message request and a template text, and a request side vector group and a template side vector group are extracted respectively; constructing a semantic neighborhood graph based on the request side vector, and performing dynamic correction by taking the template vector as a reference to form a request-template joint semantic graph; node feature propagation is carried out on a graph structure by utilizing a graph convolutional neural network, a migration probability matrix from a request to a template category and template category feedback information are generated, then semantic representation of the request and a template is optimized, a category weight adjustment table is generated by a system in combination with category response strength and a semantic offset condition, and a template category is obtained. And carrying out dynamic correction on template category representation, carrying out matching calculation on optimized vectors through a joint matching module, determining an optimal template, and completing parameter filling and message sending.

Operating effectively in an increasingly prolific Internet of Things environment, one needs the ability to message, whether device status, actions taken, recommendations for actions or other information to those with a need to know and support responses to actionable messages. Texts and e-mail are limited; they can neither provide other outputs, be targeted in intelligent fashion nor selectively relayed or contain execution instructions for target devices. The present invention is an Extended-Capability Messaging Appliance (ECMA) that communicates with other IoT devices that provides these capabilities and can include AI. ECMA and non-ECMA AI elements can constitute a Hybrid AI Backbone. The Hybrid AI Integrator receives results from multiple A elements and develops one or more conclusions. An ECMA may be configured as a Body-Area Network for individuals or a Business-Area Network for industry and other commercial applications with an AI Agent called BANDIT that processes requests from a user.

The invention provides a verification code acquisition system and method, an intelligent agent, a gateway, a platform and a storage medium, and relates to the technical field of artificial intelligence. The verification code acquisition system comprises an AI (artificial intelligence) agent, a short message gateway and a service platform associated with the AI agent, and is characterized in that the AI agent is connected with the short message gateway, and the service platform is connected with the short message gateway. And the AI agent sends an identity verification request carrying the first identifier to an application platform corresponding to the target application in response to an input calling operation for the target application. The first identifier is associated with the AI agent, and the identity verification request is used for requesting the application platform to send a verification code carrying the first identifier to the short message gateway. And the short message gateway sends the verification code carrying the first identifier to the service platform based on the first identifier. And the service platform sends the verification code to the AI agent based on the first identifier. And the AI agent executes an identity verification operation for the target application based on the verification code, so that the processing efficiency of the AI agent for processing the task associated with the target application is improved through dynamic identity verification of the application platform.

The invention relates to an automobile staging post-loan overdue risk prediction method and system based on AI time sequence analysis, and belongs to the field of financial risk control and artificial intelligence. According to the method, time sequence modeling is carried out on historical repayment records, account behavior characteristics and macroeconomic indicators of a loan person, and long-term and short-term dependency relationships of behavior changes of the loan person are captured by using an LSTM network, so that dynamic prediction of an overdue risk after loan is realized. On the basis of the risk score, the system automatically triggers a grading intervention strategy according to a set threshold value, the grading intervention strategy comprises short message reminding, manual return visit and intelligent staging adjustment, and real-time early warning and automatic intervention of post-loan risks are achieved. According to the method, the accuracy and timeliness of post-loan risk identification can be remarkably improved, the asset loss rate is reduced, and the method has relatively high applicability and popularization value in an automobile financial staged scene.