2086 results about "Mobile app" patented technology

Systems and methods for generating a web or mobile application by providing a front-end graphics editor to specify a user interface design; applying AI to generate human-readable front-end code for the user interface design as a co-pilot with a user in the user interface design; and applying a large language model (LLM) transformer to transform the generated human-readable front-end code into a machine-readable format and build the web or mobile application.

A system and method are provided for providing advisory notifications to mobile applications. The method includes interfacing the server device with at least one endpoint within an enterprise system and storing a model trained by a machine learning engine to automatically determine advisory notifications relevant to client data sets stored by the endpoint(s) and / or the at least one endpoint. The method also includes determining a current state of a client account, using the model to determine an advisory notification for the client account based on the current state, referring to a set of rules to determine when to provide the advisory notification in the mobile application, and in what portion of the mobile application to display the notification; and sending the advisory notification via the communications module to a client device to display the advisory notification in the mobile application.

A system and method are provided for providing advisory notifications to mobile applications. The method includes interfacing the server device with at least one endpoint within an enterprise system and storing a model trained by a machine learning engine to automatically determine advisory notifications relevant to client data sets stored by the endpoint(s) and / or the at least one endpoint. The method also includes determining a current state of a client account, using the model to determine an advisory notification for the client account based on the current state, referring to a set of rules to determine when to provide the advisory notification in the mobile application, and in what portion of the mobile application to display the notification; and sending the advisory notification via the communications module to a client device to display the advisory notification in the mobile application.

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

The invention discloses a hospital information intelligent analysis and decision-making system based on a multi-modal large model, and relates to the technical field of hospital information analysis and decision-making. The system comprises a data acquisition module, a preprocessing and fusion module, a large model construction and training module, an intelligent analysis module, a decision support module, a knowledge graph construction and application module, a data security and privacy protection module, a system evaluation and optimization module, a multi-hospital cooperation and data sharing module, a mobile application module and the like, and all the modules cooperate to realize hospital information intelligent processing and decision making. The system integrates multi-modal data, assists in precise diagnosis, recommends a treatment scheme, predicts resource demands, monitors medical quality, assists medical research, manages patient health and the like, comprehensively improves the intelligent level of hospitals, optimizes medical services and benefits doctors and patients.

The invention discloses a cognitive impairment assessment system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal data through a wearable device, a mobile application and an online tool; s2, carrying out preprocessing on the multi-modal data; s3, comprehensively analyzing the preprocessed data, evaluating cognitive competence, and identifying a cognitive impaired field; s4, a rehabilitation training plan is generated through a neural evolution algorithm, and the training content, sequence, difficulty and type are dynamically adjusted; s5, providing cognitive training and adjusting tasks in real time through virtual reality and augmented reality technologies; s6, predicting cognitive load and psychological state, and dynamically adjusting the intervention plan; and S7, protecting data privacy by using a block chain technology, monitoring a training effect, and generating a progress report. According to the method, the intervention plan and the privacy protection technology are dynamically adjusted through multi-modal data analysis based on general artificial intelligence, and a personalized and real-time cognitive impairment assessment and intervention scheme is provided.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

A system for improving employee engagement and automating the integration of business processes. The system includes: a user interaction module configured to receive multimodal inputs, including natural language text and voice requests from employees via enterprise portals, mobile applications, voice-activated devices, and collaboration platforms, and to generate real-time responses via conversational output channels; a context processor that is operationally coupled with the user interaction module, wherein the context processor stores the interaction history, employee preferences, organizational role metadata, and task results in both short-term and long-term memory and dynamically derives context for controlling responses and initiating workflows; A natural language and intent processor that is communicatively linked to the context processor. The intent engine consists of large language models trained on company-specific lexicons to perform intent detection, entity extraction, ambiguity resolution, and sentiment or urgency classification from employee queries; a workflow orchestration layer in communication with the intent engine and the context processor, wherein the workflow orchestration layer includes a rule-based and AI-powered process execution engine configured to automatically initiate, route, and complete cross-functional business tasks, including approvals, escalations, and compliance checks, with workflows defined using modular templates that include conditional logic and time-based triggers; Webhooks and authentication protocols provide secure interoperability between the workflow orchestration layer and external enterprise platforms; a feedback and learning module that is operationally coupled with the workflow orchestration layer and the natural language understanding engine, wherein the feedback and learning module is configured to analyze task completion rates, response accuracy, latency metrics, and user feedback signals, and to retrain underlying language and workflow models for adaptive improvement in real time; and an administration console with role-based access controls, compliance dashboards, audit trails and interfaces for workflow configuration, whereby the administration console enables authorized personnel to monitor system operations, adjust interaction rules and enforce data protection restrictions across departments.

A machine learning-based fraud detection system (100) for financial transactions, comprising the following: a data collection and preprocessing module configured to capture transaction data from financial institutions, payment gateways and bank modules, and to perform data cleansing, normalization and format conversion; a module for creating user profiles and behavioral analyses, configured to create dynamic user profiles based on historical transaction patterns, spending habits, device usage, and geographic location; an anomaly detection and risk assessment module configured to use machine learning algorithms to detect deviations from normal transaction patterns and assign a dynamic risk rating to each transaction; a fraud detection module configured to classify transactions as legitimate or fraudulent based on supervised and unsupervised learning techniques, including decision trees, support vector machines (SVMs), and deep learning models; a rule-based and hybrid fraud detection module configured to integrate predefined rule-based fraud detection mechanisms with AI-driven anomaly detection to improve fraud detection accuracy; a real-time fraud detection and warning module configured to monitor transactions in real time and generate security alerts via SMS, email or mobile app when suspicious activity is detected; a transaction verification and multi-factor authentication (MFA) module configured to implement adaptive authentication measures, including biometric verification, one-time passwords (OTP), and challenge-response authentication for high-risk transactions; a self-learning and model-updating module configured to dynamically update fraud detection models using reinforcement learning and federated learning techniques; a data encryption and security module configured to protect transaction data using encryption techniques, including AES-256 and RSA, to prevent unauthorized access and data breaches; and A dashboard and reporting module configured to provide real-time analytics, visualizations, and fraud detection reports for financial institutions to monitor, track, and mitigate fraudulent activity.

The invention relates to a method for intelligently pushing commodity display according to user behavior habits, and belongs to the technical field of artificial intelligence and electronic commerce recommendation systems. Aiming at the problems of low recommendation accuracy and insufficient real-time performance caused by dynamic change of user behaviors in the existing commodity pushing technology, the method comprises the following steps of: acquiring multi-dimensional data such as user browsing tracks, click preferences, purchase records and page staying duration, and constructing a dynamic user portrait in combination with a time sequence analysis and clustering algorithm; fusing real-time behavior feedback by adopting an improved collaborative filtering algorithm, mining potential association between behavior characteristics and commodity attributes through a deep learning model, and establishing an adaptive weight adjustment mechanism; and finally, a personalized commodity sorting strategy is generated based on the current scene and behavior trend prediction of the user, and dynamic optimization of the pushed content is realized. The method can be applied to an e-commerce platform, an advertisement putting system and a mobile application, the recommendation accuracy, the user conversion efficiency and the platform sales volume are remarkably improved, and meanwhile computing resource consumption is reduced.

Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

Disclosed are a three-dimensional laser point cloud-based method and system for in-depth vegetation management in transmission corridors. The method comprises: collecting multi-source data by means of an unmanned aerial vehicle- and helicopter-mounted multi-sensor system, and preprocessing the multi-source data; using the processed multi-source data to create a digitalized power grid corridor and construct a vegetation management analysis model; on the basis of data analyzed by the vegetation management analysis model and geofencing technology, designing a hazard vegetation clearing-cost-compensation prediction model; and developing a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows. The described method achieves high-precision and high-coverage data collection, precisely identifies transmission line digital twins and hazard vegetation, standardizes and automates the management of hazard vegetation clearing and related compensation, and develops a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows, thereby achieving the end-to-end digital and mobile management of vegetation management. The organic combination of steps comprehensively improves the intelligence and efficiency of transmission line vegetation management.

An IoT-based intelligent room management system for hotels and resorts, consisting of: a variety of IoT-enabled devices in individual hotel rooms, with each device configured to control a specific room function, including lighting, air conditioning, entertainment systems, and security systems; a centralized management platform that is operationally coupled with the multitude of IoT-enabled devices and configured to communicate with the devices via a wireless network, with the centralized management platform collecting real-time data on room settings, occupancy status, and device performance; a guest interface that includes a mobile application or voice-activated assistant and is configured to allow guests to customize and control room functions, with the guest interface communicating with the central management platform to implement changes to room settings according to user preferences; An automatic occupancy detection system integrated into IoT-enabled devices and the central management platform. The system detects the presence or absence of guests in the room and adjusts room settings, including lighting and air conditioning, to optimize energy consumption when the room is unoccupied; a keyless access system that is operationally linked to the central management platform and provides guests with access to their assigned rooms via smartphones or wearable devices using wireless communication protocols; and A resource management module integrated into the central management platform, providing real-time insights into room occupancy, energy consumption and equipment status, enabling predictive maintenance and optimizing resource allocation within the hotel or resort.

The invention relates to the technical field of public transportation systems, and particularly discloses a bus departure scheduling method, which comprises the following steps of S1, integrating multi-dimensional data; s2, a dynamic prediction model containing machine learning parameters is adopted to calculate the passenger demand in the future period; s3, calculating the number of required vehicles according to the predicted demand, the vehicle capacity and the dynamic load coefficient; s4, constructing a multi-objective function including energy consumption optimization, and solving the optimal departure interval and route; and S5, according to the real-time data, correcting a scheduling scheme, collecting real-time feedback data through a passenger mobile application, analyzing the emotion and demand of the passenger by using a natural language processing technology, based on feedback intention recognition of an emotion analysis model, constructing a passenger demand knowledge base in combination with historical complaint data, and optimizing a dynamic prediction model and a scheduling strategy. Through technology integration and system innovation, the static and single bottleneck of traditional scheduling is broken through, and an intelligent solution considering efficiency, low carbon and user experience is provided for urban buses.

The invention discloses a mobile application development system based on visual arrangement, one-time code writing is carried out based on a cross-platform development framework of an ArkUI-X framework, a mobile application running on an operating system platform is constructed, and an application interface visual arrangement system provides multiple granularity modules from templates, components to elements. A user is supported to freely arrange an application interface in a mode of dragging and configuring components, the dynamic UI rendering and hot updating engine adjusts the application interface in real time and performs dynamic updating after issuing an application, and an automatic packaging engine of a multi-operating system platform is supported to automatically generate an adaptive application installation package according to characteristics and dependencies of different platforms. An efficient and flexible cross-platform development solution is provided for developers through a mobile application development platform composed of a low-code visual interface arrangement system, an automatic packaging system, dynamic UI rendering and a hot update engine.

An AI-powered augmented reality (AR) system for optimizing product placement and customer loyalty. The system includes: • a user interaction data collection unit configured to monitor consumer behavior in real time in any retail scenario (point-of-sale POS, online e-commerce and shopping areas within a public virtual technology space), with the unit analyzing consumer movement patterns, engagement metrics, eye-tracking and purchase intent; • an intelligent spatial mapping module to build a contextual understanding of high-traffic zones, underutilized areas and high-quality product placements using heatmap analysis, other spatial computer algorithms and the live movement of consumers; • A model-based product placement recommender based on machine learning, which uses one or more DL-based predictive models trained by importing current sales data, historical text interaction data, and consumer preference data to suggest optimal product placement so that customers see the advertisement and are likely to click through it to ensure a conversion. • a customer-centric, real-time AR visualization interface configured to allow customers to experience digitally enhanced product presentations directly via mobile AR applications, smart glasses, or in-store digital kiosks, enabling customers to preview product placements, rotate and resize objects, and visualize interactive advertising content before purchasing; • a customized advertising and sales promotion engine configured to generate augmented reality-based marketing content and provide users with tailored promotions and interactive ads based at least in part on an analysis of consumer sentiment by the system, as well as information on location data or consumer interaction history, wherein the customized advertising and sales promotion engine personalizes the configuration instructions for advertising and sales promotion to encourage consumer interaction and achieve higher sales revenues; • A secure, cloud-based data processing and analytics unit that uses an automated engagement model to link online and in-store sales information with established activity models for product placement decisions. The unit leverages AI-powered consumer insights to continuously recommend product placement changes based on a variety of programmatic retail trends; • a privacy-friendly AI module based on federated learning and on-device AI processing, providing consumer behavior analytics while complying with global data protection regulations and preventing the use of open data; • a processing framework characterized by a low-power mode suitable for AR rendering and AI calculations, and implemented via dynamic voltage scaling and energy management techniques for seamless and sustainable operation; The system according to claim 1, wherein the user interaction data capture unit further comprises an eye-tracking and sentiment analysis sub-module, wherein the sub-module detects facial expressions, emotional reactions, and attention levels of consumers in real time to determine the effectiveness of product placements and advertising strategies.

A stuffed, interactive children's toy for assisting individuals in self-calming and / or self-regulating exercises. The toy may include one or more weights, a microphone, a motor, one or more light emitting diodes (LEDs), and / or one or more speakers. The toy optionally further includes one or more buttons corresponding with one or more emotional states. For example, a red button, a yellow button, and / or a green button. Upon pressing the button, the toy may deliver either a random and / or a preprogrammed intervention. The toy may be used in combination with a children's book. In another example, the toy may be in communication with a mobile application (app) via a Bluetooth connection. In yet another example, the toy may be in communication with a website and / or a remote-control device. The app may be used to record emotional data, physical date, and / or to assign preprogrammed stimuli to the one or more buttons.

Systems and method for automatically generating front-end code for a user interface (UI) design created in a graphical UI editor by receiving a design file key and an access token associated with the design file; using the design file key and access token to retrieve one or more outermost frames within a project; obtaining thumbnails of the one or more frames; enabling user selection of the one or more frames for import and retrieving the selected frames along with their components; retrieving image files used within the one or more frames; mapping the design elements to code templates for one or more front-end frameworks; generating front-end code based on the mapped design elements and code templates; and outputting the generated front-end code in a format suitable for use in a web or mobile application.

The invention provides a power cable comprehensive online monitoring system, and relates to the technical field of data processing, and the system comprises the steps: extracting a partial discharge distribution point set in a multi-dimensional feature fusion data set, and calculating the weight factor of each distribution point; based on the weight factors, a weighted point set concave packet generation algorithm is adopted to construct a partial discharge source probability distribution boundary, and a boundary feature point set is obtained; inputting the boundary feature point set and the multi-dimensional feature fusion data set into a convolution-long and short-term memory hybrid neural network to generate a diagnosis result; and a third-level early warning signal is generated based on a diagnosis result, early warning information is pushed to a remote operation and maintenance terminal and a mobile APP through an MQTT protocol, a digital work order service is automatically triggered to generate a maintenance task, and positioning verification is executed. According to the invention, the accuracy of monitoring and fault diagnosis is improved.

A light therapy device stand includes a base, a motorized extension arm, a receptacle connected to the extension arm and an elongated support bar onto which one or more light therapy devices are connected. A system controller communicates wirelessly with a system remote control and a mobile application which operate the motor of the stand and each light therapy device connected to the stand. The light therapy devices are connected in one or both of a side to side configuration and a top to bottom configuration along the length of the elongated support bar, and the support bar rotates 90 degrees relative to the extension arm to transition each of the plurality of light therapy devices between a vertical orientation and a horizontal orientation.

The invention discloses a cattle and sheep farm feeding safety detection method and system based on real-time monitoring, and the method comprises the steps: installing a flow velocity sensor, a flow sensor and a pressure sensor at key positions of a feed conveying pipeline, obtaining the time sequence data of feed conveying in the pipeline, carrying out the preprocessing and feature extraction of the data, and carrying out the feature extraction of the data; and constructing a feed conveying state prediction model, performing online prediction on the conveying state in real time, and triggering early warning when the deviation between a predicted value and an actual value exceeds a threshold value. And specific abnormal types such as pipeline blockage, leakage or equipment failure can be judged by analyzing the change trend of the abnormal data. And abnormal information is pushed to operation and maintenance personnel through the mobile App to assist in quickly positioning and processing problems. According to the method, the model can be continuously optimized by using new abnormal data, the identification capability of different types of anomalies is improved, a decision basis is provided for pipeline maintenance, and the operation reliability and safety of the feed conveying pipeline are effectively improved.

Systems and methods for enhanced security to log in to a mobile application are provided. A short-range communication antenna of a mobile device can receive a cryptogram from a contactless card, and a processor of the mobile device can verify the cryptogram to identify a customer account associated with the contactless card and verify that a phone number of the mobile device is associated with the customer account. Then, a user interface device of the mobile device can receive enhanced security input data, and the the processor of the mobile device can verify that the enhanced security input data matches enhanced security record data associated with the customer account. When the cryptogram, the phone number of the mobile device, and the enhanced security input data have been verified, systems and methods disclosed here can log in to a mobile application running on the mobile device to access the customer account.

The invention provides a water conservancy equipment remote control method and system for water conservancy informatization, and the method comprises the following steps: connecting water conservancy equipment to the Internet of Things, collecting equipment operation data through a sensor, and uploading the data to a cloud server; establishing a state monitoring model of the water conservancy equipment by applying a machine learning algorithm based on the collected equipment operation data; the water conservancy equipment is remotely controlled through a mobile application or a Web interface; in combination with real-time weather data, water demand prediction and equipment state information, an operation strategy of the water conservancy equipment is automatically adjusted; the information is presented to a manager through a data visualization technology; and feedback information of remote control and scheduling is collected regularly, and the system is optimized and adjusted. According to the invention, through real-time monitoring, accurate prediction, intelligent scheduling, data fusion, visual display and continuous optimization, efficient, stable and intelligent water conservancy equipment management is realized, and the operation efficiency and safety of a water conservancy project are greatly improved.

The invention discloses a ruins protection area tourist diversion system and method based on dynamic bearing capacity. The diversion system comprises a data acquisition module, a bearing capacity calculation module, a diversion strategy optimization module, a tourist guide module and a feedback adjustment module. Real-time tourist data and environment information including tourist flow, residence time, meteorological conditions, site physical conditions and the like are collected; calculating the dynamic bearing capacity of the current ruin area, and comprehensively considering factors such as tourist load and environmental influence; a fuzzy logic reasoning method is adopted to evaluate tourist density and define a congestion degree grade: an IPSO algorithm is adopted to optimize tourist shunting paths, the shunting paths are generated and tourists are guided, and accurate shunting is carried out by adopting modes of intelligent identification, dynamic indication boards, mobile applications and the like; based on historical data and a real-time monitoring result, error correction is carried out on bearing capacity prediction, and the prediction accuracy is improved.

The invention discloses an intelligent monitoring system and method based on the 5G Internet of Things, and relates to the technical field of intelligent monitoring, and the system comprises a data collection module, a data processing platform, a cloud server, an alarm module, a monitoring center and a mobile APP, and is used for solving the defects in the aspects of customer behavior prediction and supermarket safety in the prior art. A cloud platform management tool of the cloud server is used for monitoring the system state and the application program performance in real time, quickly responding to problems and regularly executing data cleaning and backup operations, so that the storage space is prevented from being excessively occupied; according to the method, time series, circulating network model deep learning and Internet of Things technologies are adopted, real-time monitoring and alarm mechanisms are combined, customer action tracks and customer behaviors are predicted and analyzed, appropriate supermarket layout and marketing strategies are formulated, meanwhile, abnormal behaviors can be rapidly and accurately recognized and processed, and the service life of the supermarket is prolonged. Therefore, the safety and management efficiency of supermarkets and supermarkets are effectively improved.

Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

One or more computing devices, systems, and / or methods for providing multi-screen navigation for an application such as a mobile application are provided. An instance of the application is hosted on a mobile device. A primary display interface is populated with a first screen of the application for display through the mobile device. In response to detecting a trigger through the application, a secondary display interface is generated and populated with a second screen of the application for display through the mobile device while the primary display interface is being displayed through the mobile device. A first backstack of the application is utilized to track user navigation amongst screens of the application through the primary display interface. A second backstack of the application is utilized to track user navigation amongst the screens of the application through the secondary display interface.

The invention relates to the technical field of computers, in particular to a mobile application large model risk automatic assessment method based on improved BERT, and the method comprises the following steps: S1, a test preparation stage; s2, realizing an automatic question answering whole process of the large model of the mobile application version based on an interactive automatic question asking method for simulating click by integrated group control equipment; s3, realizing batch acquisition of test result text data and picture information based on a dynamic and static data acquisition engine of the integrated group control equipment; s4, based on the risk study and judgment model of the improved BERT, performing safety risk judgment and classification on the test result, and performing evaluation and scoring according to a safety risk evaluation rule; and S5, uniformly and properly storing the assessment result and the test result screenshots. The method improves the accuracy and reliability of an output result discrimination algorithm, and establishes a comprehensive security risk assessment system.

Example aspects include techniques for creating an augmented reality filter on a mobile device. These techniques may include receiving, in response to a selection of a primary augment reality filter template (ARFT), a primary ARFT package corresponding to the primary ARFT, and receiving, in response to the selection of a secondary ARFT, a secondary ARFT package corresponding to the secondary ARFT. In addition, the techniques may include loading the primary ARFT package and the secondary ARFT package to form a combined ARFT, and modifying, based on user input received via a user interface, the combined ARFT to create a customized augmented reality filter (CARF). Further, the techniques may include generating, for the CARF via the development engine, a configuration file that causes the mobile application to add custom perceptive information to visual data.