2015 results about "Integrated systems" patented technology

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 unmanned aerial vehicle control, and discloses an unmanned aerial vehicle group scheduling and simulation integrated system based on digital twinning and deep reinforcement learning, and the system comprises a digital twinning mapping module, a deep reinforcement learning task scheduling module, a heterogeneous interface adaptive module, a fault prediction and self-healing module, and a scenarized strategy optimization module. Through an unmanned aerial vehicle group intelligent scheduling and simulation integrated technology fusing digital twinning and deep reinforcement learning, all links of unmanned aerial vehicle group management and control can be comprehensively covered, common task scheduling and path planning indexes are concerned, and factors such as heterogeneous unmanned aerial vehicle compatibility, fault prediction and self-healing are also included; and the collaborative process of scheduling and simulation is deeply optimized, so that a system operation result more accurately reflects the real operation condition of the unmanned aerial vehicle group, and a more scientific basis is provided for intelligent scheduling and reliable management of the unmanned aerial vehicle group.

The invention discloses a deep and far sea wind power foundation platform and load integrated system under extreme environment conditions, and belongs to the technical field of offshore wind power platforms. The system comprises a basic platform body, a multi-dimensional monitoring subsystem, an environment-load coupling analysis subsystem, an intelligent regulation and control execution subsystem, a safety early warning decision subsystem and a shore-based collaborative management subsystem. Through synchronous acquisition of extreme marine environment, basic platform structure load and operation state data, distributed multi-type sensor layout and high-frequency sampling, data acquisition comprehensiveness and timeliness are realized, monitoring data are fused, a coupling model is constructed, 24-hour load change is predicted, and the real-time performance of the system is improved. According to the method, the attitude of the platform is dynamically adjusted, bearing configuration is optimized, emergency protection is started, the limitation of traditional separation analysis and fixed regulation and control is broken through, and the risk disposal and operation and maintenance efficiency is improved through risk quantification and grading early warning, remote monitoring and intelligent scheduling of a shore-based collaborative management subsystem and four-level early warning accurate matching coping strategies.

The invention provides a water-wind-light integrated system optimization scheduling method considering source load uncertainty. Based on historical output data of wind power, photovoltaic and hydropower, constructing a three-dimensional joint probability density function, and generating multiple groups of typical scheduling scenes; establishing a water-wind-light integrated dispatching optimization model; solving the scheduling optimization model by adopting an improved NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; and carrying out source load sensitivity analysis based on the optimal solution set, identifying the influence weight of the load fluctuation rate and the wind-solar prediction error on the system operation, and dynamically adjusting the hydropower output strategy according to the influence weight so as to improve the stability and adaptability of the system under the extreme load change. The method can effectively cope with the dual uncertainty of wind power, photovoltaic output and load demand, and improves the scheduling feasibility and renewable energy utilization rate of the water-wind-light integrated system.

The invention relates to the field of underwater acoustic communication and detection, in particular to a waveform optimization method and system for an underwater acoustic detection and communication integrated system. According to the method, a constant modulus phase is taken as a constraint, a reserved subcarrier phase is taken as an optimization variable, and a multi-objective function for joint optimization of a peak-to-average power ratio and a non-periodic autocorrelation integral sidelobe ratio is constructed; a population is initialized, an objective function is evaluated, and a parent population is obtained through non-dominated sorting and crowding distance calculation; generating filial generations through selection, crossover and variation, combining the filial generations with the parent generations, and updating sorting and crowding distance to maintain population diversity; introducing a local gradient descent refinement mechanism, carrying out gradient iteration adjustment on an optimized individual under a constant modulus phase condition, and adaptively adjusting a crossover probability, a mutation probability and a phase variation scale according to a diversity evaluation result; and outputting a Pareto optimal solution set after the maximum number of iterations is reached, and obtaining an optimized sounding and communication integrated orthogonal frequency division multiplexing signal, so that the underwater communication and detection comprehensive performance can be effectively improved.

The invention relates to the technical field of electric digital data processing, and discloses a multi-source data automatic processing and service integration system based on artificial intelligence driving. Comprising the steps that a multi-source data access module achieves concurrent data access and caching, an AI driving data preprocessing module completes field automatic mapping through a data isomerism adaptation algorithm, and an intelligent data fusion module calculates a multi-source data fusion weight through a dynamic weight distribution algorithm; the service integration module realizes standardization of fusion data, and the monitoring scheduling module monitors each module in real time. High-concurrency multi-protocol data access is achieved through multi-source data automatic processing and service integration and a distributed Kafka cluster, automatic data mapping and precise data cleaning are completed through an improved algorithm depending on an AI driving module, the data fusion precision is improved in combination with a dynamic weight algorithm, the service integration flexibility is enhanced through multi-protocol API packaging and load balancing, and the service integration efficiency is improved. And stable system operation and maintenance are guaranteed.

The invention discloses a panel lamp light source integration system based on a flexible circuit board, and relates to the technical field of panel lamp light source integration. Generating an initial design drawing of the flexible circuit board containing a plurality of layers of conductive circuits and a plurality of light source bonding pads; the channel configuration module configures a driving control channel for the bonding pad and establishes a mapping table with the lighting function area; the thermal load optimization module calculates power consumption and thermal load distribution according to the mapping table and the performance parameters and adjusts routing topology to generate a final design drawing; and the manufacturing and assembling module is used for manufacturing a circuit board according to the drawing and mounting a micro light source to finish assembling. Wiring is matched with the shape of a panel and the characteristics of a light source during planning, heat distribution optimization is achieved in the layout stage, the number of times of modification is reduced, and local overheating is restrained. According to the invention, high-fit compact integration of the flexible circuit board and the panel lamp is realized, and the assembly efficiency and the operation stability are improved.

The invention relates to the technical field of food quality detection, in particular to a tea oil authenticity identification method based on an in-situ mass spectrum-machine learning coupling technology. Comprising the following steps: S1, preparing a sample; s2, acquiring mass spectrum data; s3, feature extraction: performing dynamic binning processing on the mass spectrum data, extracting feature peak information, and performing time dimension feature aggregation to form an initial feature matrix; s4, data preprocessing; s5, training a machine learning model; and S6, intelligent identification and visual output: inputting the mass spectrum original data of the tea oil sample to be detected into the trained multi-model integrated system, outputting an identification result through weighted voting or an optimal model strategy, and giving a confidence interval to quantify the reliability of the result.

The embodiment of the invention provides a satellite communication and navigation integrated system, which comprises a baseband subsystem and a processor subsystem, the baseband subsystem comprises a front-end processing module, a low-orbit communication module, a navigation processing module and a decoder module, the front-end processing module is configured to preprocess an acquired satellite signal and output a baseband signal, and the decoder module is configured to decode the baseband signal. The baseband signal comprises a navigation signal and a low-orbit communication signal; the navigation processing module is configured to process the navigation signal to obtain a first target signal; the low-orbit communication module is configured to perform signal processing on the low-orbit communication signal to obtain a second target signal; the decoder module is configured to decode the first target signal and the second target signal to obtain navigation data and communication data; and the processor subsystem is configured to perform resource scheduling on the front-end processing module, the navigation processing module, the low-orbit communication module and the decoder module based on the navigation data and the communication data. According to the invention, the integrated requirement of the navigation chip on navigation and communication can be realized.

The invention relates to the field of traffic engineering, and discloses an urban road network toughness management platform and evaluation method based on dynamic cascade failure, and the platform comprises a disturbance scene simulation and random capacity degradation module, a dynamic cascade failure process simulation module, and a multi-dimensional toughness index calculation and uncertainty evaluation module. A toughness three-dimensional vector is formed by integrating system robustness, restorability and performance accumulated loss, and a time-varying dynamic traffic distribution model considering road network structure change is used for depicting a cascade failure dynamic process; a random variable is introduced into a damaged road section capacity index, and a new evaluation method is provided for solving the defect that the existing toughness evaluation method is difficult to comprehensively quantify the robustness and the restorability of a road network. A failure diffusion mechanism in an abnormal scene is truly restored, and an effective method is provided for overcoming the double defects that a traditional model is insufficient in quantification of travel behavior dynamics and a system recovery process.

The invention relates to the technical field of drug research and development, in particular to a target AKT1 drug molecule discovery and delivery integrated system and method based on deep learning. Aiming at the problems of molecular design, optimization and delivery link separation and low research and development efficiency of drugs in the prior art, the system constructs a multi-module collaborative framework, and comprises a target analysis module for analyzing a target structure and formulating a generation strategy; the molecule generation and optimization module is used for generating and optimizing candidate molecules in combination with the generation model and reinforcement learning; the delivery scheme design module is used for matching a delivery carrier based on molecular physicochemical properties; and a verification module that predicts and evaluates the molecule-deliverer combination using molecular docking and ADMET. An evaluation result of the verification module is fed back to the molecule generation and optimization module to form a closed-loop optimization mechanism, so that an automatic process from target analysis to output of candidate drug molecules and matched delivery schemes thereof is realized. Compared with the prior art, the efficiency and success rate of early drug discovery can be improved.

The invention relates to the technical field of wireless, and provides a target sensing method, device and equipment, a storage medium and a program product, the target sensing method is applied to network equipment, and the method comprises the following steps: determining type information of a sensing target in a sensing result based on the sensing result and RID information under the condition that the sensing result and the RID information exist; wherein the sensing result is obtained by performing target sensing by the network equipment; the RID information is sent by the RID receiver. According to the invention, under the condition of determining that the sensing result and the RID information exist, the RID information can be used as the auxiliary information to assist the base station to carry out data processing in the sensing result, and the type information of the sensing target in the sensing result is accurately determined, so that the sensing result and the RID information are fused, and the sensing accuracy is improved. The method can solve the problem that the general sensing integrated system is easy to mistakenly recognize the target type when detecting the target, thereby improving the credibility of the detection result, and guaranteeing the reliability of the general sensing integrated system.

The invention belongs to the technical field of precision machining and in-situ measurement, and discloses an in-situ measurement and bidirectional tool setting integrated system and method for an internal thread grinding machine. Various system errors and random errors are effectively eliminated through a non-hardware error compensation model and a high-fidelity data processing algorithm, in-situ measurement of the internal thread is completed in a modularized mode, and high-precision and high-efficiency in-situ automatic measurement of key parameters such as the pitch diameter, the thread pitch and the thread angle of the thread is achieved. The system can be directly integrated on the internal thread grinding machine, measurement is rapidly completed after machining or between procedures, workpieces do not need to be disassembled, secondary clamping errors are effectively avoided, and the detection efficiency and consistency are greatly improved.

The invention belongs to the technical field of automatic control, discloses an integrated system based on magnetic drive conveying, automatic assembly and automatic detection, and aims to solve the contradiction between the requirements for high efficiency, flexibility and integration of a production line in the field of new energy and fragmentation, conveying limitation, heavy manual dependence, the machining rhythm as long as 40 s, large precision fluctuation and lack of an automatic detection closed loop in the prior art. The system integrates a magnetic drive conveying module, a bending module, a riveting module, a code printing module, a multi-dimensional detection module and the like, and realizes the automation of the whole process from conveying, processing, code printing, detection to management and control by overall planning the time sequence coordination of the whole process through a PLC (Programmable Logic Controller); traditional conveying limitation is broken through through magnetic field driving, multi-machine-type production is adapted, a driving module provides stable pressure to guarantee machining accuracy, unqualified product accumulation is avoided through multi-dimensional detection, the single cycle beat is smaller than or equal to 4.1 s, the production efficiency and the product percent of pass are greatly improved, and manpower and quality control cost is remarkably reduced.

The present application relates to a kind of plate type ballastless track bed expansion joint glue injection and leveling integrated system, including control system, glue pipeline system, mobile platform, glue injection execution system, wherein glue pipeline system is applied to the pressure of glue body storage tank, force glue liquid to be transported along pipeline to the end of glue injection execution system, glue injection execution system is by six-axis mechanical arm control unit according to expansion joint space coordinate planning six-axis mechanical arm path information and forms six-axis mechanical arm action instruction, realizes six-axis mechanical arm end injection head to expansion joint and carries out glue injection operation.The present application is by setting glue injection execution system and glue pipeline system on mobile platform, can guarantee according to the length direction of expansion joint and carry out glue injection and according to stable glue injection quantity realize the glue injection of expansion joint, reduce labor intensity, improve the glue injection quality of expansion joint.

The embodiment of the invention provides a non-performing asset complaint and dispatch integrated system and method. The system comprises a case import and initialization module, an intelligent case division and strategy matching module, a disposal execution module, a mediation stage module, a litigation stage module, a case settlement and filing module and a data analysis and optimization module. Data interconnection and intercommunication among a financial institution, an outsourcing institution, a mediation center / law office and a court can be realized, case information of the whole process is collected in a unified manner, and informatization management of the urging and complaint business of the whole process including case distribution, collection, online mediation, electronic signing, file filing, litigation result follow-up and the like is realized. The method draws the dynamic image of the case to formulate a proper disposal strategy, timely allocates the appropriate disposal strategy to the corresponding institutions to carry out disposal work, provides digital intelligent services of one-stop follow-up, full-process scheduling and full-chain solution for the vast institutions, improves the case circulation efficiency, saves judicial resources, optimizes the clearing and receiving effect, and comprehensively reduces the cost and increases the efficiency.

The invention provides a multi-tenant visual large model reasoning resource dynamic allocation and isolation method. An integrated system of a multi-tenant environment, visual large model reasoning, dynamic resource allocation and isolation guarantee is constructed. The system controls resources such as model copy number, video memory quota, batch size, queue weight, bandwidth and the like at tenant level fine granularity, and adjusts priority and quota based on closed-loop feedback of real-time indexes (such as queuing length, delay and throughput). Interference suppression among tasks is realized through priority grading, a hard / soft isolation strategy and a tenant-model copy mapping mechanism in combination with GPU MIG, video memory partitioning, queue scheduling and other technologies. Aiming at the characteristics of high video memory, large input and the like of a visual large model, model loading, batch processing and video memory multiplexing strategies are optimized, and differential resource allocation of heterogeneous models is supported. According to the overall scheme, on the premise that the service quality and isolation are guaranteed, the GPU resource utilization rate is remarkably increased, and the operation cost is reduced.

The invention provides a multi-modal ecological environment monitoring element plug-in integration system and method based on unified objectification modeling. Firstly, an object model and an inheritance system of monitoring equipment, data and an algorithm are constructed; on the basis, a unified logic storage and metadata cataloguing mechanism oriented to heterogeneous databases such as relational databases, non-relational databases and file object databases is designed and realized; the plug-and-play and full-life-cycle management of equipment, data and algorithm models is further realized through a dynamic registration module; common APIs (Application Program Interface) such as object-level addition, deletion, modification and query, space-time association query, concurrent task scheduling and aggregation and the like are externally provided by relying on a unified service bus. And the hybrid storage engine cooperatively manages structured and unstructured data and maintains a topological relation between objects. According to the scheme, object dynamic integration and expansion are completed on the premise that operation is not interrupted, the multi-source heterogeneous data management and cross-modal analysis efficiency is remarkably improved, and the method is suitable for various ecological environment monitoring scenes such as natural reserve, drainage basin pollution supervision and disaster emergency response.

The invention discloses a distributed light storage and charging integrated system, which comprises a photovoltaic power generation unit, an energy storage unit, a charging unit, a power grid interface unit, an energy management controller and a direct current bus, the photovoltaic power generation unit comprises a DC / DC converter, and the photovoltaic power generation unit is connected with the DC bus through the DC / DC converter; the energy storage unit comprises a lithium ion battery pack, a flywheel energy storage device, a bidirectional DC converter and a flywheel controller. The lithium ion battery pack and the flywheel energy storage device are connected to the DC bus through the bidirectional DC converter and the flywheel controller respectively. The charging unit comprises at least one direct-current quick charging pile, at least one alternating-current slow charging pile and a DC / AC inverter, the direct-current quick charging pile is directly connected with the direct-current bus, and the alternating-current slow charging pile is connected with the direct-current bus through the DC / AC inverter; the power grid interface unit comprises an AC / DC converter and is connected with the DC bus through the AC / DC converter. According to the invention, through collaborative design of a modular hardware architecture and an intelligent time-sharing strategy, comprehensive improvement of economy, reliability and functionality of the system is realized.

The invention provides a high-density photoelectric common connection pinboard based on a low-loss silicon nitride grating coupler. The invention aims to overcome the difficulty that the existing photoelectric heterogeneous chip is difficult to integrate, stack and package, and realizes high-density interconnection between the chip and the interposer by using a grating coupling structure and a silicon through hole technology, thereby realizing high-integration low-loss packaging of the heterogeneous chip on the interposer. According to the invention, high-efficiency optical interconnection between the heterogeneous chip and the photoelectric co-connection intermediate layer is realized through the high-efficiency grating coupler, and high-density electrical interconnection between the chip and the photoelectric co-connection intermediate layer is realized through the TSV technology and the RDL technology. Meanwhile, the optical interconnection efficiency between the interposer and the chip is improved by utilizing the electrode patterning advantage of the RDL technology, and the self-alignment between the chip and the interposer is realized. The method has extremely high compatibility to the packaging scene of the existing photoelectric heterogeneous chip, so that the method has great significance in improving the integration density and reducing the interconnection loss of a photoelectric heterogeneous integrated system and promoting the practicability of the photoelectric heterogeneous integrated system.

The invention provides a Q / R dual-adaptive hybrid quantum filtering method for an OTFS (Over the The Over the File System) communication and inductance integrated system, and the method comprises the following steps: constructing an OTFS communication and inductance integrated signal model, and obtaining a target measurement vector; establishing a state vector for describing a target motion state, and constructing a state transition equation and a measurement equation; the method comprises the following steps: constructing a Q / R mixed quantum adaptive adjustment module based on dual-channel features to construct a dual-channel feature input vector, generating a dual-path adjustment factor through a mixed quantum neural network, and constructing an adaptive Q matrix and an adaptive R matrix; and performing adaptive state prediction and updating at each time step by using the adaptive Q and R matrixes constructed in the step 3 to realize tracking of the motion state of the target. According to the method, the hybrid quantum neural network is introduced for driving, collaborative optimization of filter parameters is achieved, and therefore the precision, robustness and response speed of target tracking are remarkably improved in a complex dynamic noise environment.

The invention relates to the field of Internet of Things technology, data analysis and insurance claim settlement, in particular to a security Internet of Things and insurance claim settlement intelligent evaluation integrated system, which comprises a security state data acquisition module, a dynamic security portrait module, an accident traceability and responsibility intelligent division module and an automatic claim settlement evaluation module. According to the method, deep analysis of multi-source heterogeneous environment data and accurate representation of the safety state are achieved, the dynamic safety portrait module can capture the essential topological structure relation of the data by constructing the functions of topological feature extraction, state representation, multi-scale anomaly detection, risk prediction and the like, and high-precision recognition and risk prediction of gradient anomaly are achieved. After an accident occurs, a complete accident data chain can be constructed, the responsibility proportion of each party can be intelligently divided based on objective data, and the compensation amount can be automatically calculated, so that full-process automation from safety monitoring to insurance claim settlement is realized, the anomaly detection accuracy is greatly improved, and potential risks can be found in advance.

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.

The invention discloses an optical flux-inductance-energy integrated system and a transmitted waveform design method, and the method comprises the steps: carrying out the MLS modulation and OFDM modulation of an input original signal, obtaining an MLS waveform and an OFDM waveform, carrying out the amplitude distribution of the two waveforms, and combining the two waveforms according to a time sequence to form an MLS-OFDM AC signal; after the alternating current signal is subjected to digital-to-analog conversion, outputting the alternating current signal according to a set driving peak-to-peak value voltage, superposing a direct current component in the alternating current signal so as to carry out alternating current and direct current amplitude distribution, and outputting an analog electric signal; the transmitter converts analog electric signals into optical signals, and the optical signals are simultaneously connected with the information decoding module, the sensing processing module and the energy collecting module. According to the hybrid MLS-OFDM waveform design scheme combining alternating-current amplitude distribution and alternating-current and direct-current amplitude distribution, flexible balance among communication, perception and energy collection performance in an optical flux-inductance-energy integrated system is achieved, the complexity of the system can be reduced, and the system is made to be lighter and more compact.

The invention discloses a mass concrete construction temperature control anti-cracking integrated system and method, and the system comprises a construction module which is used for constructing a cooling water pipe fine model based on concrete thermodynamic parameters and construction conditions, solving a heat flow coupling control equation through a heat flow coupling algorithm, and determining a concrete internal temperature field and stress field distribution cloud chart; the monitoring module is used for collecting attribute data of concrete; the comparison module is used for comparing the attribute data with the concrete internal temperature field and stress field distribution cloud atlas, correcting model parameters of the heat flow coupling model according to a comparison result, generating a regulation and control instruction and sending the regulation and control instruction to the execution module; the execution module is used for receiving and executing the regulation and control instruction; the regulation and control instruction comprises at least one of temperature regulation and pouring time sequence optimization. A temperature field and a stress field are accurately predicted, complex construction working conditions are matched, mechanical and thermal state changes of the whole life cycle of concrete are captured in real time, temperature control adjustment is effectively conducted, and the construction quality is improved.

The invention relates to the technical field of flexible electronic sensing, and particularly discloses a composite fiber membrane-based capacitive flexible pressure sensor integrated system and a preparation method thereof. The sensor is composed of a composite fiber membrane sensitive layer, an interlayer type electrode structure, a multi-channel signal acquisition module and an intelligent data processing system. Wherein the composite fiber membrane is prepared through an electrostatic spinning technology, polyvinylidene fluoride serves as a matrix, ionic liquid with the mass ratio of 40%-70% is evenly dispersed, a three-dimensional porous structure is formed, and a pressure signal is converted into capacitance change through the double-electric-layer capacitance effect; the signal acquisition module adopts a high-precision capacitance digital converter to cooperatively work with a microcontroller, 9-channel parallel data acquisition is realized, the resolution reaches 6aF, and data is transmitted through an anti-interference bus protocol; and the data processing system dynamically analyzes the pressure-capacitance relationship based on an exponential function model, suppresses noise to be below 0.1 aF RMS in combination with a filtering algorithm, and synchronously generates a visual pressure distribution map.

The invention discloses an intelligent metasurface-based wave beam forming robust design method for a communication and induction integrated system, and belongs to the technical field of wireless communication. According to the method, aiming at the problems of imperfect channel state information and uncertainty of a target position in a communication and inductance integrated system, a worst-case robust optimization model is established by taking minimization of base station transmitting power as an optimization target. Firstly, an original non-convex problem is equivalently reconstructed by utilizing positive semidefinite programming; secondly, aiming at semi-infinite constraint introduced by uncertainty, solving by adopting a successive convex approximation method; meanwhile, interval uncertainty constraints are efficiently processed in combination with a sampling method; and finally, solving the coupling relationship among the optimization variables through an alternative optimization strategy. Simulation results show that the method can ensure the service quality of the communication and sensing target under multiple uncertainties, significantly improve the robustness of the system in an uncertain environment, and effectively reduce the transmitting power at the same time.

The invention discloses a distributed ducted unmanned aerial vehicle monitoring and fire fighting integrated system and a control method, and belongs to the technical field of fire fighting. The system comprises a fire monitoring network deployed on the outer wall of the roof of the high-rise building, a fire-fighting unmanned aerial vehicle storeroom arranged correspondingly, a fire-fighting unmanned aerial vehicle with an anti-collision structure and a fire-fighting management platform. The method comprises the steps that a fire monitoring network or an original system triggers alarm, and an unmanned aerial vehicle library is activated; the unmanned aerial vehicle automatically takes off, infrared guidance and image acquisition approach reconnaissance are carried out, and information is returned; after the platform confirms the fire behavior, a window breaking fire extinguishing bomb is instructed to be launched, and a fire extinguishing matrix is built in cooperation with multiple mechanisms as needed; after fire extinguishing, environmental parameters are monitored, and the unmanned aerial vehicle automatically returns and resets. Through distributed monitoring, automatic quick response and multi-machine cooperation, second-level sensing and handling of fire of the high-rise building are achieved, the problems that traditional fire fighting is slow in response, difficult to arrive and poor in single-machine capacity are solved, and the fire is controlled within the golden time of 3-5 minutes.

The invention relates to a precise separation and online detection integrated system for a multi-component complex system, and belongs to the technical field of analysis and detection. The system comprises an intelligent preprocessing and component sensing unit, a self-adaptive precise separation unit, a multi-modal cooperative detection unit, a zero dead volume-intelligent adaptive interface unit and a multi-algorithm fusion intelligent control and data analysis unit, and the five units form a closed-loop cooperative architecture through real-time data interaction. The system adopts a preprocessing-sensing integrated design, a multi-mode switching type separation architecture, a dynamic intelligent chromatographic column, a four-channel collaborative detection system and multi-algorithm fusion regulation. According to the invention, the problems of separation and detection disjunction, narrow adaptability, weak matrix interference suppression capability, low intelligent degree, poor portability and the like in the prior art are effectively solved, and one-time sample injection accurate separation and high-accuracy detection of trace target components with wide polarity range and similar structures can be realized.

The invention discloses a zero-carbon park double-layer collaborative planning system oriented to multi-industry symbiosis, which is characterized in that a park-level shared carbon capture system is deployed on the aspect of technical symbiosis, multiple emission sources of energy and industry are converged, unified and purified, and the investment cost of emission reduction facilities is remarkably reduced; on the energy symbiosis level, an electricity-heat-hydrogen flexible network and a source-network-charge-storage integrated system are constructed, and gradient utilization and efficient interaction of multiple energy flows are achieved; on the aspect of material symbiosis, a deep closed-loop circulation system of industrial byproducts, waste and waste gas is established by optimizing the industrial symbiosis network topology, and the purposes of waste minimization and park net zero carbon emission are achieved. According to the method, the problem of comprehensive zero-carbon planning of the high-energy-consumption park is effectively solved, and the overall energy and resource utilization efficiency is remarkably improved while stable operation of the system is guaranteed.