3830 results about "Integrated systems" patented technology

Blockchain synchronization and point-of-sale integration systems are provided in a packet-switched computer network. The POS interfaces through the network with a consumer smartphone and a blockchain validation device. The POS shares a session identifier with the blockchain validation device by transmitting a graphic encoding of a payment request and the session identifier to the smartphone. The camera of the smartphone is configured with an image of the graphical encoding. A payment instruction is encoded to conform to the payment request including the session identifier and is transmitted to the blockchain validation device. The blockchain validation device performs a verification of the correctness and authenticity of the payment instruction encoded in the data record. The POS device receives a confirmation that the correctness and authenticity of the payment instruction encoded in the data record to confirm it has been verified.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The invention relates to the technical field of ship propulsion, and discloses an energy-saving control method for a pre-rotating guide wheel of a ship propulsion system and a fluid optimization system. According to the invention, through a multi-dimensional cooperative control strategy and fluid structure optimization, an integrated system with dynamic prerotation compensation, self-adaptive flow regulation and vortex suppression functions is constructed. The control method comprises a multi-physical field sensor array, a guide wheel angle dynamic compensation algorithm and an energy efficiency optimization decision model, a deep learning algorithm is adopted to establish a mapping relation between a propeller wake flow field and guide wheel parameters, and 0.1-second-level dynamic response is achieved. According to the fluid optimization system, an asymmetric variable-curvature guide vane structure is innovatively designed, the optimal space distribution of guide vanes is determined through three-dimensional flow field simulation, and a flow guide channel with the pressure gradient self-adaptive characteristic is formed. A real ship verifies that the propulsive energy consumption can be reduced by 10%-25%, the propulsive efficiency can be improved by 15%-30%, the cavitation effect is effectively restrained, vibration noise is reduced, and remarkable economic benefits and environmental protection values are achieved.

The invention discloses an unmanned aerial vehicle monitoring and countering integrated system, and relates to the technical field of unmanned aerial vehicle monitoring, and the system comprises a sensing module which is used for carrying out the monitoring of an environment through a plurality of sensors, and obtaining original data; the fusion module is used for processing the original data by adopting a data fusion algorithm to obtain target information; the identification module is used for outputting the type and behavior mode of the unmanned aerial vehicle; the analysis module is used for evaluating the type and behavior mode of the unmanned aerial vehicle by adopting a dynamic threat evaluation method and generating early warning information; the strategy module is used for formulating a dynamic countering strategy; the execution module is used for implementing corresponding countering measures and monitoring the countering effect in real time; and the recording module is used for recording all results in the whole process. Through the technical means of multi-sensor cooperative monitoring, data fusion processing, classification identification, dynamic threat assessment, game decision making, intelligent countering and the like, omnibearing perception, accurate identification, intelligent assessment and efficient disposal of the unmanned aerial vehicle are realized.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention relates to the technical field of continuous integration and continuous delivery, and discloses a Jenkins continuous integration method based on Kubernetes. According to the method, a Jenkins server is deployed in a Kubernetes cluster to serve as a control plane, a construction environment is created through a preset multi-container Pod template, and a Pod containing a plurality of containers is dynamically created based on the Pod template, so that each container executes different construction tasks. Containers in the Pod realize collaboration through sharing a network namespace and a storage volume, and a Jenkins server monitors task execution states of the containers in the Pod and performs dynamic scheduling according to task priorities and cluster load states. And after the task is completed, automatically destroying the Pod and releasing cluster resources. According to the method and the device, dynamic expansion and contraction of a construction environment and efficient parallel execution of tasks are realized, the problem that a traditional Jenkins static resource pool is difficult to deal with load changes is solved, the resource utilization rate is improved, the expandability of the system is enhanced, and an efficient and reliable solution is provided for an enterprise-level continuous integration system.

The invention belongs to the technical field of data twinning, and discloses a digital twinning integrated system which comprises the following steps: acquiring multi-source heterogeneous data, and performing compression processing, fusion optimization and standard integration processing on the multi-source heterogeneous data to obtain a corresponding standardized digital twinning set; constructing a corresponding high-precision digital twinning scene based on the obtained standardized digital twinning data set; performing scene compression and scene optimization processing on the high-precision digital twinning scene to obtain a corresponding lightweight digital twinning scene packet; building a corresponding digital twinning platform based on the lightweight digital twinning scene packet; carrying out diversified service release and authority management on the digital twinborn platform to obtain a corresponding standardized digital twinborn service set; performing real-time monitoring and performance analysis on a digital twinborn service process based on the standardized digital twinborn service set, and performing feedback adjustment on a corresponding digital twinborn platform based on the standardized digital twinborn service set; the application efficiency and the application range of the digital twinning service are remarkably improved.

The invention discloses a law shield AI intelligent case handling all-in-one machine system based on a large language model, and relates to the technical field of law artificial intelligence and judicial informatization, the system comprises an integrated terminal device, a case semantic modeling module, a class case knowledge engine, a risk prediction module, a large language model service interface and an intelligent document generation module; the system constructs a case semantic graph through multi-modal information fusion and a graph neural network, performs legal rule path matching and similarity reasoning based on a class case database, outputs structured legal suggestions and standard legal instruments in combination with user context recognition and large language model multi-round generation capability, and realizes dynamic updating of the semantic graph. The method improves the automation, structuring and interpretability of case processing, and is suitable for intelligent case handling scenes such as legal assistance, litigation assistance and judicial mediation.

The invention relates to the technical field of multi-source heterogeneous data fusion of territorial space planning, and particularly discloses a territorial space planning multi-source heterogeneous data intelligent integration system based on semantic analysis. The system comprises a data acquisition and identification module, a semantic density balance degree analysis module, a boundary fuzzy infectivity analysis module, a comprehensive reliability evaluation module and a semantic calibration feedback module. Starting from overall balance and local boundary stability of semantic distribution, semantic conflicts and leakage problems in multi-source data are accurately recognized, reliability evaluation is performed in combination with a deep learning model, semantic dynamic calibration is realized by using knowledge graph reasoning and transfer learning, and a closed-loop feedback mechanism is formed.

The invention relates to the technical field of water and fertilizer control, in particular to a water and fertilizer integrated control system and method based on the Internet of Things. The method comprises the following steps of obtaining environment measured data, historical environment data and regional geographic information of a farmland region, performing spatial modeling and time sequence learning on the farmland region, and constructing a farmland digital twinborn model; simulating the state of an area without sensors by using a farmland digital twinborn model to obtain virtual sensing node data; collecting multispectral image data of crops, and extracting physiological feature data of the crops; and constructing a crop physiological state model by utilizing the environment measured data, the virtual sensing node data and the crop physiological feature data, and generating crop physiological state evaluation data. Through multi-source data fusion and three-dimensional visualization integration, data-driven accurate regulation and control and whole-process intelligent management of the water and fertilizer integrated system are realized, and the intelligent level of irrigation and fertilization and the resource utilization efficiency are comprehensively improved.

The invention relates to the technical field of crack detection, in particular to a building crack intelligent detection and prediction method based on multi-modal data fusion, which comprises the following steps: constructing a multi-source heterogeneous data set including an image data set, a thermal imaging data set and a vibration data set; simulating complex environment interference through a GAN (Generative Adversarial Network), and dynamically enhancing image training data; designing a double-branch multi-mode fusion network, and extracting the edge and texture features of the crack; designing a multi-modal fusion branch, and carrying out feature alignment and weighted fusion on vision, thermal imaging and vibration data; and based on a long short-term memory network LSTM and a structural mechanics model, a crack trend prediction module is developed, and early warning is provided for building structure safety. According to the method, intelligent monitoring and early warning of building structure safety are realized by constructing a multi-source heterogeneous data set, enhancing image training data through a GAN, extracting features by using a double-branch multi-mode fusion network, predicting a crack trend based on an LSTM and a structural mechanical model, and finally integrating system deployment.

The invention relates to the technical field of storage batteries, in particular to a storage battery monitoring, detection, treatment and evaluation integrated system and a storage battery management method, and the system comprises a plurality of accompanying modules, a collection power distribution control module, a storage battery management capacity checking host and a remote platform; the internal resistance analysis test of the system is based on multi-frequency-point alternating current excitation (0.5 Hz-7. 5kHz), a complex impedance spectrum of not less than 20 frequency points can be constructed, and the internal polarization characteristics of the battery are analyzed; the system adopts a feed network type discharge capacity checking, so that discharge electric energy is fed back to a power grid, and the capacity detection precision is improved to 98%; according to the system, EIS data and an LSTM neural network are fused, and a battery state of health (SOH) prediction model is established; the system is based on a DSP digital power supply technology, and can realize 50A active equalization topology.

The invention belongs to the technical field of multi-physics field coupling simulation, and particularly relates to a multi-physics field coupling Fluent simulation analysis integrated system, which comprises a data preprocessing module used for carrying out data integration, grid division and boundary condition setting on a multi-physics field coupling problem; the multi-physical field coupling module comprises a fluid-structure coupling sub-module, a thermal-force coupling sub-module, an electromagnetic-fluid coupling sub-module and the like, and interaction simulation among different physical fields is realized respectively; the calculating and solving module is used for calculating and solving a multi-physics field coupling problem based on a Fluent software platform and coordinating the calculating sequence and the time step length of each physics field; and the post-processing and visualization module is used for performing post-processing analysis and visualization display on the simulation result. The method effectively solves the problems that a traditional system is low in data interaction efficiency, insufficient in grid precision and poor in calculation coordination, and has the advantages that simulation precision is improved, resource consumption is reduced, and system expansibility is enhanced.

The invention discloses a fuel cell hydrogen circulation cooling integrated system, and relates to the technical field of fuel cells, the fuel cell hydrogen circulation cooling integrated system comprises a hydrogen circulation cooling management center, the hydrogen circulation cooling management center is in communication connection with the following modules: a multi-source state sensing module, which is used for collecting multi-dimensional state data of a hydrogen circulation-cooling system in real time, extracting state characteristics, and sending the state characteristics to a control module; and forming a state feature sequence table. The multi-dimensional state data of the hydrogen circulation-cooling system is collected, the dynamic cooperative control strategy of the reinforcement learning decision module is combined, the precision and stability of temperature control are remarkably improved, the Markov decision process model is used for predicting the instantaneous heat production change caused by load sudden change, and the stability of the system is improved. And through combined adjustment of hydrogen circulation and a cooling system, temperature fluctuation is rapidly restrained, compared with traditional PID control, the temperature overshoot is reduced under the dynamic working condition, it is ensured that the galvanic pile works within the optimal temperature range all the time, the service life of the galvanic pile is prolonged, and the energy conversion efficiency is improved.

A system for managing financial portfolio as well as for recommending personalized investment strategies, is disclosed. The system includes a first computing unit having an application interface, communicably connected to a central controller. The central controller includes a back-end server. The backend server includes a data receiving component adapted to receive the input data-sets from the first computing unit and real time data-sets from a plurality of data-sources. The backend server further includes a data analysis module adapted to process the real-time data to generate one or more actionable insights. The backend server furthermore includes a contextually intelligent portfolio management module adapted to utilize one or more contextual data related to the user, to monitors the user's investments and asset portfolios, and generate contextually relevant portfolio information for the user in a real-time. The backend server additionally includes a financial strategy implementation module adapted to utilize the actionable insights in combination with the contextually relevant portfolio information of the user to generate personalized investment strategies and recommendations for each user. In operation, a user generates and / or formulate at least one input query based at least in part on one or more input data-sets related to the user's financial portfolio. Thereafter, the input datasets are received at the back-end server which in turn are processed by the financial strategy implementation module in combination with one or more actionable insights generated by the data analysis module of the back-end server to identify a response to the input query, and / or provide one or more personalized investment related recommendation. The identified information and / or recommendation is elicited as a response to the input query and is presented and / or visualized on an output component of the first computing unit.

The invention relates to the technical field of clothing manufacturing, in particular to an AI scheduling optimization method for clothing flexible production and an ERP integrated system. Comprising the following steps: multi-source heterogeneous data acquisition and fusion: acquiring real-time equipment state data of a clothing production line through Internet of Things equipment, wherein the real-time equipment state data comprises a sewing machine rotating speed, a cutting bed load rate and a quality inspection station abnormal signal; obtaining an order delivery period, a material inventory and a BOM process path through an ERP system; the equipment data and the order data are stored in a distributed database after being aligned through timestamps; dynamic priority modeling: constructing a dynamic priority calculation model based on the order emergency degree, the production line bottleneck process load threshold and the material neat rate, and generating an order-insertable process-level priority queue; according to the scheme, the limitation of a traditional method can be broken through, and the modern production requirements of flexibility and high aging are met.

The invention discloses a storage intelligent monitoring and checking integrated system and method based on multi-technology fusion, and particularly relates to the technical field of storage monitoring, the system obtains environmental parameters and cargo data in real time through a multi-source data acquisition module, carries out cargo identification and segmentation by using YOLOv7 and Mask R-CNN, and carries out storage monitoring and checking on the cargo. A three-dimensional model is constructed in combination with a Poisson surface reconstruction algorithm to calculate volume weight, an environment anomaly analysis module generates an early warning signal through dynamic threshold and influence level evaluation, and an intelligent decision module fuses data to realize quantity anomaly alarm and multi-level response and outputs a three-dimensional visual inventory report. According to the method, the problems of multiple blind areas, low checking efficiency, slow abnormal response and the like of traditional storage monitoring are solved, full coverage of environment monitoring, high accuracy of goods identification, low error of volume calculation and intelligence of abnormal handling are realized, and the automation and digitization levels of storage management are remarkably improved.

The invention provides an AI-enhanced large mine water-doped concrete material performance prediction and mix proportion design integrated system, aiming at the key problem of large mine water-doped concrete material temperature control, the importance of different influence factors such as raw material-structure-environment and the like is comprehensively and quantitatively evaluated; aiming at a complex raw material system of the large mine water-doped industrial concrete, main features of a performance prediction model such as feature dimension reduction and weight calculation are optimized, and meanwhile, a machine learning algorithm combined with a self-attention mechanism is constructed so as to reduce interference of features weakly related to target performance, and the performance regulation and control capability of the large mine water-doped industrial concrete is enhanced. Furthermore, a multi / super multi-target optimization algorithm containing a constraint system is developed and integrated aiming at targets such as material key performance, carbon emission and cost in the preparation process, and a comprehensive and flexible data-driven solution is provided for temperature control, influence factor evaluation, performance regulation and control and proportion optimization of the large-mine water-doped concrete material. Therefore, the overall decision-making and design process is optimized.

The invention discloses an intelligent port operation vehicle scheduling system and a scheduling robot, and relates to the technical field of intelligent port operation. The problems that in traditional port vehicle scheduling, the manual scheduling response is slow, the error rate is high, resource distribution is uneven, the labor cost is too high, and the efficient and intelligent requirements of modern ports are difficult to meet are solved. The vehicle no-load distance is reduced and the heavy load rate is improved by using a bipartite graph matching strategy, the prediction scheduling module plans in advance, task overstock and vehicle idleness are reduced, the operation efficiency is effectively improved, the constraint processing module optimizes task allocation according to task and vehicle conditions, and the multi-target optimization module gives consideration to the heavy load rate and order dispatching fairness. The scheduling robot integrates system functions, and can adapt to different working environments and realize intelligent scheduling through data acquisition and continuous optimization of an online learning technology, so that the scheduling accuracy and efficiency are improved, the labor cost is reduced, and the overall competitiveness of a port is improved.

The invention discloses a data processing method of an intelligent cold stamping production line integrated with an MES system, and the method comprises the steps: S1, installing at least one fisheye lens for detecting a stamping workpiece, collecting data, preprocessing the data, and building a three-dimensional scene, S2, carrying out the further processing of the preprocessed data in S1 through a flexible sensor network, the method comprises the following steps: S1, carrying out cross-modal fusion and feature extraction on data, S3, transmitting the data extracted in S2 to a digital twinborn body for dynamic updating and prediction, S4, carrying out deformation with the assistance of quantum entanglement and carrying out over-distance early warning, and S5, carrying out data storage and knowledge management. According to the data processing method of the intelligent cold stamping production line integrated with the MES system, a digital twinborn dynamic updating and predicting technology is utilized, quantum-classical hybrid simulation modeling and reinforcement learning algorithms are combined, precise prediction and optimization adjustment can be conducted on mold abrasion, process parameters and the like, the production efficiency and the product quality are improved, and the production cost is reduced. The production cost and the energy consumption are reduced.

The invention discloses a reinforcement learning-based dynamic production scheduling optimization method and an ERP (Enterprise Resource Planning) integrated system, relates to the technical field of intelligent manufacturing and information technologies, and is used for solving the problem that the rapid development of the modern manufacturing industry puts forward higher requirements on production scheduling optimization. The flexibility and the adaptability are insufficient in the face of a real-time changing production environment; the intelligent level of the production process is improved through the multi-dimensional data processing capacity, the multi-dimensional quantization coding technology is adopted, dimension raising or dimension reduction processing is carried out based on the income-cost ratio of cross-domain technical parameters, and it is ensured that a calculation task is transferred to edge equipment and the PLC-level soft real-time requirement is met through edge calculation and lightweight framework deployment. On-time process completion and delay penalty are integrated, a global optimization item is combined, a meta-learning algorithm is used for dynamically adjusting the weight, and balance of delay, energy consumption and a safety target is ensured.

The invention provides a steam extraction energy storage and heat energy storage integrated regulation control method and system, and relates to the technical field, and the method comprises the steps: building a digital twinborn model of a steam extraction energy storage and heat energy storage integrated system, and respectively obtaining corresponding data; establishing a multi-objective optimization model based on the collected data; solving the multi-objective optimization model by using an improved multi-objective differential evolution algorithm in combination with an adaptive variation strategy and a non-dominated sorting mechanism to obtain an optimal adjustment strategy; on the basis of the optimal adjusting strategy, the opening degree of a steam extraction valve of each stage of steam extraction point is adjusted in real time through a distributed control system, and the steam extraction flow is controlled; the inflation power and the deflation power of the compressed air energy storage unit are dynamically adjusted, and meanwhile the charging and discharging power of the flywheel energy storage unit is controlled; the heat charging and discharging rate of the phase change material heat storage unit is adjusted in a self-adaptive mode, and meanwhile the heat charging and discharging flow of the fused salt heat storage unit is controlled.

The invention provides a sensing processing method, a sensing integrated system, electronic equipment and a storage medium, relates to the technical field of communication, and is used for improving the sensing data processing accuracy of the sensing integrated system based on artificial intelligence and the convenience of data transmission between modules. The method is applied to a perception function entity, and comprises the following steps: acquiring a trained artificial intelligence model from a perception server; processing the sensing data based on an artificial intelligence model to obtain a sensing result; and sending the artificial intelligence perception result to the core network element.

This invention introduces a kinetic energy recovery wind-wave integrated system for offshore wind power generation. The system consists of a semi-submersible platform equipped with a fan and an internal wave energy device. The device includes a shell housing a Power Take-Off (PTO) system, featuring a permanent magnet synchronous linear motor and an active controller. The motor's stator is fixed inside the shell, while its mover is connected to a counterweight block outside the stator, linked to the shell's top via a spring. Limiters are installed at both ends of the shell to restrict the counterweight block's movement. This system utilizes the wave energy device to absorb kinetic energy, which otherwise affects wind turbine stability, and converts it into usable electrical energy via the PTO system. This enhances the stability and safety of offshore wind turbine power generation.

The invention provides a self-adaptive robot sensing, guiding and controlling integrated system for a strange environment, and belongs to the field of intelligent unmanned systems. Each robot in the system comprises a sensing device, a sensing module, a navigation module and a control module. The sensing module is a VLM-based semantic mapping device, performs ground feature segmentation and semantic recognition on the environment information, and generates a global semantic map and a ground image embedded with a ground feature image semantic descriptor; the navigation module is a semantic planner based on LLM, adopts LLM to perform task understanding on natural language task description, and reasones a task sequence of a current environment; the control module is a terrain adaptation controller based on a VFM, the VFM is adopted to recognize terrain features from ground images, the terrain features and the real-time state of the robot are mapped into a driving matrix, and robot motion control is achieved. According to the invention, deep cooperation of active perception, autonomous exploration and efficient control of the robot is realized, and task planning and real-time adjustment capabilities of the robot in an unfamiliar environment are enhanced.

In some embodiments, the inventive subject matter is directed to a modular waders system that includes: a main waders garment and a covering unit. The covering unit may have multiple functions expressed in one or more layers, the functions being selected from the group of: durability, waterproofness or water resistance, breathability, insulation, cushioning, thermoregulation, and 2-way or 4-way elasticity. The waders system includes a coupling system that makes the covering unit easily donned or removed by the user as conditions dictate. In some embodiments, the inventive subject matter is directed to waders or other garment having a frontside portion with a mobile device holder comprising a flap, the holder configured to contain or couple to a handheld imaging device having a camera, the flap having one edge portion coupled to the other side so that the flap can reposition from a first position to a second position.

The invention relates to the technical field of cold storage door control, and discloses a cold storage door opening and closing control method which comprises the following steps: acquiring multi-mode sensing information related to operation of a cold storage door system; driving a digital twinborn model based on the multi-mode sensing information, and analyzing the current state and predicting the future state of the cold storage door system by using the digital twinborn model; based on the multi-mode sensing information and the analysis and prediction result of the digital twinborn model, a deep reinforcement learning algorithm is adopted to generate a cooperative control instruction for the cold storage door opening and closing motion and the operation of at least one energy-saving device; and executing the cooperative control instruction. Energy flow is accurately simulated and predicted by using a high-fidelity digital twinborn model, and the actual traffic demand is accurately judged in combination with multi-modal perception, so that a deep reinforcement learning agent can generate a cooperative control instruction with the lowest comprehensive energy consumption as a target, and active intelligent energy saving is realized.

The invention discloses a low-carbon optimal scheduling method based on source-grid-load-storage integration, and relates to the technical field of energy scheduling, and the method comprises the steps: obtaining historical power demands, renewable energy power generation, energy storage equipment states and power grid load data based on an environment sensor of a source-grid-load-storage integrated system; obtaining historical time sequence characteristic parameter data of the operation mode of the source-network-load-storage integrated system, and determining an interval vector range of the operation mode of the source-network-load-storage integrated system; acquiring real-time data; obtaining a real-time operation vector matrix; matching degree verification is carried out according to the real-time operation vector matrix and the interval vector range of the operation modes of the source-network-load-storage integrated system, and the operation mode with the maximum matching degree is screened out to serve as a scheduling scheme. According to the invention, through source-grid-load-storage integrated operation mode matching, the scheduling scheme is optimized, and the use efficiency of the power grid load and the energy storage equipment is improved.

Blockchain synchronization and point-of-sale integration systems are provided in a packet-switched computer network. The POS interfaces through the network with a consumer smartphone and a blockchain validation device. The POS shares a session identifier with the blockchain validation device by transmitting a graphic encoding of a payment request and the session identifier to the smartphone. The camera of the smartphone is configured with an image of the graphical encoding. A payment instruction is encoded to conform to the payment request including the session identifier and is transmitted to the blockchain validation device. The blockchain validation device performs a verification of the correctness and authenticity of the payment instruction encoded in the data record. The POS device receives a confirmation that the correctness and authenticity of the payment instruction encoded in the data record to confirm it has been verified.

An integrated system for detecting dam breaches, as an early warning system and for evacuation assistance, consisting of: A geoinformatics analysis module comprising a modeling processor with a graphics processing unit, configured to: analyze spatial, hydrological, and topographic data of a dam and its downstream area; generate hydrological models using probable maximum rainfall (PMP) data to determine probable maximum flood discharge (PMF); generate hydraulic models to simulate flood propagation and determine flood depth, flood extent, flow velocity, and flood arrival time; create flood vulnerability models, vulnerability models, and flood hotspot models using a multi-criteria decision-making (MCDM) framework; and generate evacuation plans for high-risk zones based on the models. A sensor monitoring subsystem includes: a distributed fiber optic sensor (DFOS) configured to monitor the structural load and deformation of a dam, and a radar level gauge configured to measure the water level in the reservoir; a microcontroller unit configured to receive and process sensor data from the sensor monitoring subsystem, to detect dam breaches based on abnormal structural movements detected by the DFOS or sudden drops in water level below predefined thresholds detected by the radar level gauge; a communication module configured to transmit warning messages when dam breaches are detected; a warning system configured to: send SMS notifications to residents in downstream high-risk zones via a GSM communication module, generate real-time alerts via an IoT platform on user devices, and activate siren systems in villages to issue loud alarms and voice announcements; and an evacuation assistance platform configured to provide real-time evacuation assistance in emergencies due to dam breaks, displaying site maps with nearby emergency shelters, evacuation routes, hospitals, government buildings and aid centers, showing hydrological layers including flood extent, water depth, flow velocity and arrival time of the water, and enabling real-time location tracking in relation to safety zones and flood-prone areas.