346 results about "Adaptive system" patented technology

The invention relates to the technical field of data processing, and provides an intelligent risk control method for carbon asset transaction, which is used for solving the problems of data island, response lag, low efficiency, safety risk and the like in traditional carbon transaction. According to the invention, standardized access of a carbon emission permit trading system, a registration system and enterprise internal data is realized by constructing a carbon asset data middle platform, integrating a block chain and an RPA technology, a price fluctuation rate and a quota gap risk are calculated in real time based on Apache Flink, a carbon price trend is predicted through an LSTM + Attention model, and an intelligent contract is triggered to generate a hedging instruction; a three-level risk prevention and control system is constructed by adopting an ABAC model and biological characteristic verification, a quota regulation path is optimized through a graph neural network, automatic performance is driven, strategy self-adaption is realized in combination with PPO reinforcement learning and BERT policy analysis, and the problems of data islands, response lag, low efficiency and security risks in traditional carbon transactions are systematically solved.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications—such as capabilities, objects, mappings, policies, and workflows—as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

The invention discloses an intelligent photovoltaic grid-connected adaptive system based on multi-modal sensing and predictive analysis, and the system comprises a multi-modal sensing unit which is used for collecting the multi-source information of a photovoltaic system; the predictive fault analysis module is used for predicting potential system faults based on the multi-source data; the self-adaptive phase-frequency synchronous control unit is used for dynamically adjusting the output parameters of the photovoltaic inverter, so that the output of the photovoltaic inverter is matched with the parameters of the power grid; the comprehensive decision engine is used for generating a grid-connected or off-grid instruction; the self-repairing execution unit is used for generating a specific response scheme and controlling related equipment to execute based on the fault early warning and the strategy instruction of the comprehensive decision engine; and the remote monitoring and manual intervention interface is used for providing a monitoring interface. Through cooperative work of multi-modal data perception, predictive fault analysis and adaptive control, safe, efficient and intelligent grid connection of the photovoltaic system and the power grid is realized, and the reliability, safety and economical efficiency of the photovoltaic power generation system are improved.

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

The invention relates to the technical field of artificial intelligence education, in particular to an intelligent homework tutoring system and method based on multi-modal interaction and adaptive learning, and the system comprises a multi-modal input analysis module, a cognitive state dynamic evaluation module, an intelligent decision explanation engine, and a learning effect visualization closed-loop module. The method has the beneficial effects that a three-dimensional adaptive system of explanation granularity-presentation form-interaction frequency is used, and teaching strategies such as visual derivation, concept metaphor and the like can be automatically matched according to cognitive styles of students. Secondly, a'backtracking reinforcement-lateral expansion 'double-intervention mechanism is innovatively used, and the recurrence rate of similar errors is effectively reduced through error real-time detection and correlation knowledge contrast teaching. And finally, constructing a dynamic knowledge graph and an interactive learning report, realizing visual tracking of a learning path, and helping students to establish a systematic knowledge framework.

The present disclosure relates to methods and systems based on a transformer model, in which an elastic transformer service system, referred to herein as an online lexical adaptation system (OTAS), is involved that accommodates diversified user requests with fluctuating query loads, while optimizing output accuracy and runtime delay. The OTAS uses lexical adaptation techniques that involve adding cue lexical elements to improve accuracy and removing redundant lexical elements to accelerate reasoning. To handle fluctuating query loads and diversified user requests, the OTAS also applies a perceptual selective batch processing algorithm for online lexical adaptive design. In a system service process, OTAS firstly processes incoming queries with similar requirements in batches to improve ingress throughput. Then, in order to balance between the overhead of lexical element growth and the potential of improving accuracy, OTAS adaptively adjusts lexical element execution settings by solving an optimization problem.

The invention relates to the technical field of agent systems, and provides an MCP agent-oriented dynamic tool discovery and adaptive system construction method, which comprises the following steps of: designing a uniform interface to register tools, and storing tool data through a dictionary structure; environment parameters are collected, external input and context information are received, three types of data are fused, task types are judged according to priorities, and task requirements are analyzed; according to the capability description during tool registration, the suitability of task execution is judged, a correlation score is calculated, and tools with the score lower than a preset score are screened; comprehensive calculation is carried out by combining the correlation score of the tool, the cost and the delay value, and the tool for executing the task is selected according to the score; according to the selected tool and the current operation environment, resources needed by tasks and an execution sequence are judged, and an execution chain is automatically constructed; and recording and collecting feedback data, evaluating an execution result, and iterating tool parameters.

The invention relates to the technical field of intelligent forming, in particular to an intelligent adjusting type medical cotton ball forming method and system.The intelligent adjusting type medical cotton ball forming method comprises the following steps that pressure data are collected through a distributed pressure sensor array, pressure feature vectors are generated through Gaussian filtering denoising, a pressure change trend is analyzed based on the feature vectors, and a gradient adjusting instruction is output; and controlling the valve to adjust air pressure to obtain compensation parameters, driving the servo motor to adjust a mold gap, collecting offset data to generate a feedback signal, and inputting a neural network to evaluate a forming result in combination with feedback and adjustment instructions. According to the method, a distributed sensor array collects data, multi-dimensional denoising improves feature extraction, a dynamic function analyzes a pressure trend, an offset adjustment parameter is identified, closed-loop compensation is realized through cooperative control, a displacement sensor feeds back gap change, parameter modeling optimizes precision consistency, a convolutional network extracts time sequence features, and a self-adaptive system is constructed through fusion analysis. And intelligent prediction and optimization of the forming quality are realized.

The invention relates to the technical field of data processing, in particular to an artificial intelligence-assisted password security policy dynamic adaptive adjustment method, which comprises the following steps of: acquiring login operation data to generate an encryption feature gradient; the aggregation server generates a dynamically updated federal reference model through a double-channel model updating mechanism, wherein a long-term model updating channel is fused with historical parameters to maintain system stability, and a short-term behavior time window channel dynamically adjusts recent gradient weight based on a time decay function. A graph computation engine generates a spatio-temporal composite risk score. And the risk decision engine generates a third-level response instruction. A risk misjudgment event is verified based on a response result, high-value training samples are screened to drive incremental training, space-time sensitive parameters are finely adjusted by a meta-learning optimizer, and noise is injected into a quantum gradient jammer to defend attacks. Optimized parameters are synchronized to a system module, and a closed-loop adaptive system is formed by combining homomorphic encryption transmission, model digital watermarking, quantum source hardware binding and anti-quantum signature.

The invention relates to the technical field of thermal management control, and particularly discloses a thermal management controller regulation and control method and system based on dynamic thermal inertia prediction. According to the method, the dynamic thermal inertia model is constructed through system identification, and the future temperature field evolution and thermal accumulation risks of the system are accurately predicted. On the basis, a multi-layer nested compensation strategy composed of pre-compensation, main compensation and residual compensation is generated in three dimensions of a compensation angle, a compensation order and a compensation domain, and prospective accurate intervention is achieved. In order to evaluate the control effect, an artifact intensity entropy value is introduced as a quantitative index, and a model and a strategy are corrected in real time through an outer ring optimization mechanism to form self-correction. When continuous thermal unbalance caused by hardware design defects is detected, the system can further start hardware cooperative regulation and control, local heat capacity and a heat dissipation path are optimized fundamentally, cross-level cooperative optimization from a control algorithm to a physical structure is achieved, and finally high-precision, low-energy-consumption and self-adaptive system-level thermal management is achieved.

The invention relates to the technical field of computer data processing, and particularly provides an MCU low-power-consumption firmware upgrading method based on energy self-adaption, which comprises the following steps of: establishing a communication link between an upgrading initiating end and MCU equipment, and acquiring energy state parameters in real time through an energy monitoring terminal arranged in the MCU equipment; according to a preset energy state threshold interval, the comprehensive value of the energy state parameters is divided into a low energy state, a medium energy state and a high energy state; dynamically adjusting an upgrading strategy parameter of the current firmware according to a preset adjustment rule based on the current energy state level; and transmitting target firmware data to the MCU equipment through the communication link based on the dynamically adjusted upgrading strategy parameters, and executing firmware upgrading operation until upgrading is completed. According to the scheme, an adaptive system of energy state perception-strategy dynamic adjustment-hardware collaborative optimization is constructed, and the problems of high power consumption and unbalanced efficiency and reliability caused by the fact that the energy state is not combined in the prior art are effectively solved.

The invention discloses a machine room water-cooled air conditioner adaptive control method based on big data analysis, which relates to the technical field of adaptive regulation and control, is used for improving the problem of overlong response time caused by fixed thermal inertia, and comprises the following steps of: performing network deployment on distributed optical fiber sensing, screening environmental data in a water-cooled air conditioner adaptive system by utilizing a sliding time window algorithm, and determining the response time of the water-cooled air conditioner. Noise is suppressed, a thermal inertia compensation mechanism is entered, an LSTM neural network is constructed based on initial system thermal inertia to predict thermal disturbance of a plurality of subsequent control periods, and a space-time alignment compensation matrix is introduced to suppress harmonic oscillation in the thermal inertia compensation mechanism; the predicted thermal disturbance and the chilled water flow in the air conditioner are integrated, a double-closed-loop correction method is used for generating thermal compensation correction, gain switching is conducted on the double-closed-loop correction method, and synchronous alignment correction is conducted on asynchronously-collected data packets through a time sequence alignment engine; and setting a bending window and carrying out characterization fusion to judge an environment data acquisition point and an abnormal acquisition response rate thereof.

The invention discloses a self-adaptive system for industrial line-scan digital camera imaging, and relates to the technical field of imaging calibration, and the system comprises a machine vision collection module which is used for obtaining an image obtained by collecting a detected object through an industrial line-scan digital camera in a continuous steel belt production detection process; the control algorithm module is used for analyzing the image to obtain an analysis result; the communication control module is used for generating a target instruction for controlling the three-dimensional electric adjusting structure to move according to the analysis result; and the electrical structure adjusting module is used for adjusting the moving state of the three-dimensional electric adjusting structure according to the target instruction, so that the industrial line-scan digital camera acquires the image of the measured object at the target position. According to the invention, the control algorithm module performs optimization in real time according to the adjusted state while automatic correction is performed, the quality of the output image is ensured, and when the quality of the output image meets the design requirement, self-adaption is stopped.

The invention discloses an electric power steering fault diagnosis and self-adaption system based on torque offset, which relates to the technical field of fault diagnosis and comprises a data acquisition unit, a model establishment unit, a torque offset evaluation unit, a fault judgment unit and a power output adjustment unit. According to the method, the return-to-power torque model is constructed based on the vehicle dynamics law and experience adjustment, the model parameters are optimized to obtain the return-to-power torque model, meanwhile, the real-time output torque of the EPS system is calculated according to the steering wheel torque signal, the deviation coefficient of the EPS system is comprehensively calculated, and the accuracy of the EPS system is improved. The deviation coefficient is evaluated according to a preset fault grading interval, a multi-grade fault judgment mechanism is established according to the change rule of the deviation coefficient, dynamic health evaluation of the EPS system motor performance is achieved, the evaluation serves as the basis of control strategy adjustment, the output assistance value can be dynamically adjusted, return control can be enhanced or a safety mode can be switched, and the reliability of the EPS system motor performance is improved. Therefore, the redundancy control capability and the driving safety are improved.

The invention discloses a self-adaptive system and method for end-side cloud dynamic task scheduling of a power distribution internet of things, and relates to the technical field of cloud computing regulation and control, and the method comprises the steps: collecting power distribution network environment data and worker operation data through a power distribution network equipment sensor connected through a gateway; carrying out preprocessing and feature extraction on the collected data to generate a standardized data stream; dynamically distributing calculation tasks to a local gateway or a cloud server according to a worker request type and an equipment load state; dynamically adjusting a resource allocation strategy based on the network bandwidth of the power distribution network, the computing power of the equipment and the task priority; monitoring data transmission safety and equipment state abnormity of the cloud computing environment of the power distribution network in real time, and triggering a protection mechanism; and displaying the data processing result, the task state and the security alarm information through a gateway interface. The method has the advantages that by dynamically distributing local and cloud computing resources, the task processing efficiency and the system adaptability under the power distribution network multi-device cooperation scene are remarkably improved.

An artificial intelligence-based adaptive interference reduction system (100), comprising: a signal acquisition unit (102) configured to acquire and digitize raw signals from an environment via one or more sensors; an interference detection module (104) comprising a processor configured to implement a Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model for identifying and categorizing interference patterns based on frequency, amplitude and spectral properties; an adaptive filter control module (106) configured to implement reinforcement learning-based adaptive filtering by dynamically adjusting the filter parameters based on detected interference conditions; a machine learning module (108) configured to implement a hierarchical deep neural network (DNN) model trained on historical interference patterns to predict and prevent interference in real time; and a feedback control module (110) configured to monitor and evaluate the interference reduction performance and to provide iterative adjustment signals to the adaptive filter control module (106).

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications — such as capabilities, objects, mappings, policies, and workflows — as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

The invention discloses a multi-sensor fusion sensing precision self-adaptive system, and relates to the technical field of radio positioning and multi-sensor fusion sensing, the system integrates multi-source sensing units such as a laser radar and a GPS, establishes a unified reference by means of a space-time synchronization module to complete data alignment, and optimizes data quality through preprocessing means such as filtering and denoising; a dynamic weight fusion module allocates weights according to the real-time quality scores, and a unified sensing result is output by fusing the positioning and speed measuring results; through multi-dimensional index evaluation precision grade, self-adaptive adjustment of sensor parameters and fusion strategies, and balance of precision and energy consumption, a result is output in a standardized format, an operation state is fed back, and an external system is linked. According to the invention, through multi-sensing fusion and dynamic adaptive adjustment, the sensing precision and stability are significantly improved; the method can intelligently adapt to multiple scenes, resists interference, has fault-tolerant capability, adapts to new requirements through self-learning evolution, and provides reliable perception support for automatic driving, unmanned aerial vehicle surveying and mapping and the like.

The invention discloses an IMU online adaptive calibration method and calibration device and an automatic driving system, and relates to the technical field of deep learning, automatic driving and navigation. Comprising the following steps: acquiring original measurement data from an IMU in real time; inputting the original measurement data into an error prediction model to output predicted IMU error parameters, wherein the error prediction model adopts a hybrid neural network architecture, is trained and updated based on an end-to-end framework containing a differentiable physical model, and is configured to execute offline training in an offline stage and execute adaptive updating in an online application stage; and correcting the original measurement data by using the IMU error parameter and executing dead reckoning to generate a calibrated motion state track. According to the invention, a static off-line calibration system is improved into a dynamic on-line adaptive system, so that the long-term robustness and reliability are remarkably improved. A differentiable physical model is introduced as a training bridge to realize an end-to-end physical model training normal form, so that the training precision can be improved and the training convergence can be accelerated.

The invention provides an intelligent leveling and ground pressure self-adaption system of a modularized movable laboratory, and relates to the technical field of laboratory leveling, the system realizes high-precision leveling through six-degree-of-freedom hydraulic support legs, is equipped with pressure and displacement sensors, and is combined with spherical hinge universal joints to compensate ground inclination. And the reliability and the safety of the system are improved through the backup design of the double hydraulic pumps and the energy accumulator. The system further has a ground pressure self-adaption function, the pressure of the supporting legs is dynamically adjusted based on the ground type and a bearing capacity model, local overload is prevented, and an anti-sedimentation strategy is set. In addition, the system integrates technologies of Beidou / GNSS positioning assistance, multi-modal data fusion, block chain traceability and the like, and the leveling efficiency, stability and traceability are enhanced. The system is suitable for mobile laboratory deployment under complex terrains, and has the characteristics of high efficiency, accuracy and reliability.

Adaptive system probe action to minimize input / output dirty data transfers is described. In one or more implementations, a system includes a processor, a memory configured to store data, and a cache configured to store a portion of the data stored in the memory for execution by the processor. The system also includes a cache coherence controller including a cache line history. The cache coherence controller is configured detect a direct memory access request from an input / output device. The direct memory access request is associated with an input / output operation involving the data. The cache coherence controller is further configured to identify a cache line associated with the direct memory access request, and, in response to the cache line history including a dirty data transfer record corresponding to the cache line, selectively send a probe to the cache based on a state of the cache line.

The invention discloses an active congestion avoiding method and system based on flow prediction, and relates to the technical field of network communication, and the method comprises the steps: collecting the network state information of a physical network in real time; dynamically synchronizing and mapping network state information of a physical network to a virtual mirror image by using a digital twinning technology; under the virtual mirror image, traffic prediction is carried out on the network state information based on a time domain convolutional network model, and a traffic prediction result is obtained; the traffic prediction result is transmitted into a scheduling strategy engine for dynamic analysis and evaluation, and an active traffic scheduling strategy for traffic prediction is determined; issuing the active flow scheduling strategy to a physical network for execution; and according to the latest network state information, an active flow scheduling strategy is dynamically adjusted to form a closed-loop adaptive system. According to the method, the virtual mirroring of the network state information is realized by using the digital twin technology, the traffic prediction result is obtained by using the time domain convolutional network model, and the traffic scheduling strategy is actively adjusted before traffic congestion occurs, so that congestion is effectively avoided.

The invention discloses an upper limb rehabilitation intelligent self-adaptive system based on multi-modal fusion and MRD-FES cooperation. The system comprises a multi-modal signal acquisition module, a dynamic intention analysis module, a variable damping precise execution module, a neural remodeling strengthening module and a quantitative effect evaluation module. The tDCS-MRD-FES time sequence cooperation protocol is provided, the cortical excitability is improved through tDCS preprocessing, MRD drives training based on intention, FES closed loop strengthens muscle contraction, and vibration tactile feedback is assisted to strengthen sensory movement integration. And the quantitative effect evaluation module further combines a neuroplasticity index and clinical scale digitalization to realize dynamic optimization of training parameters.

A system and method are disclosed for an intelligent wellness management platform that integrates multiple wellness dimensions into a unified, adaptive system. Using advanced AI, the platform creates personalized wellness journeys that adapt in real time to users' changing goals and external influences, ensuring relevance and effectiveness. A virtual advisor guides users through goal setting, data entry, and provides continuous support for a seamless experience. The platform also identifies user connections through “orbital convergence,” enabling meaningful interactions and shared experiences to enhance wellness outcomes. With real-time data processing, event-driven architecture, and user feedback, the system continuously updates recommendations, allowing users to manage their wellness journey with personalized guidance. The platform empowers users to customize their wellness paths, set goals and timeframes, and participate in community-driven activities, all while maintaining autonomy and flexibility.

The present invention provides a computer system and a computer-implemented method for adaptive quality management in artificial intelligence (AI) processing. The system comprises a memory unit storing machine-readable instructions and a processor configured to execute the instructions to store, in a persistent correction memory, records of prior corrections to AI outputs; retrieve a correction for a current input based on similarity between the input and stored records; generate, by an audit-trail module, a tamper-evident record of the correction and its application; schedule, by a predictive resource manager, AI workloads in dependence on service-level constraints, carbon-intensity data, and quality metrics; and update, by a feedback controller, retrieval and scheduling parameters based on effectiveness information. The processor thereby enables the persistent correction memory, audit-trail module, predictive resource manager, and feedback controller to operate as a closed-loop adaptive system that reduces recurrence of AI errors, ensures verifiable auditability, and optimizes computational efficiency.

The invention provides a big data advertisement putting method and system based on a cloud platform. The method comprises the following steps of performing multi-source data collection and preprocessing; dynamically constructing a user portrait; performing multi-dimensional analysis on the behavior pattern; target audience subdivision is based on a Gaussian mixture model; dynamically generating personalized advertisement creativity; a step of optimizing a multi-target delivery strategy; a step of real-time bidding and bidding optimization; a step of putting execution and real-time monitoring; a step of effect evaluation and multi-dimensional feedback; and carrying out adaptive system adjustment and iterative optimization. The big data advertisement putting method based on the cloud platform has the advantages that putting precision and efficiency are improved, audiences are subdivided through complex statistics, dynamic creativity is generated, advertisement correlation is enhanced, the predicted click rate is increased by 15-20%, and budget waste is reduced. And the cloud platform supports stream processing and automatic adjustment, so that the system can quickly respond to market changes, and the delay is reduced to a millisecond level.

The invention discloses a self-adaptive system data inspection and anomaly repair method for an intelligent computing center, and relates to the technical field of intelligent computing system operation and maintenance and data processing, and the method comprises the steps: collecting system operation data of a plurality of computing nodes of the intelligent computing center, and carrying out the dynamic weight standardization processing of the system operation data based on a resource consumption proportion; obtaining a weight standardization data matrix; constructing a node correlation degree matrix according to the weight standardized data matrix, and generating a system state expected value through a bidirectional recurrent neural network model; based on the deviation distribution of the system state expected value and the current weight standardized data, an adaptive interval estimation algorithm is adopted to determine an anomaly recognition boundary; and performing data reconstruction on the data exceeding the abnormal recognition boundary by adopting a multi-source cooperative compensation mechanism, and outputting repaired system operation data through inverse weight standardization processing. According to the invention, full-process intelligent inspection and accurate abnormity repair of the operation data of the intelligent calculation center system are realized.

The invention discloses a self-adaptive system for various gas sensors and a parameter calculation method of the self-adaptive system, and belongs to the technical field of gas detection. The system comprises a front-end polarity adaptation unit, a current-voltage conversion unit, an intelligent neural network calculation unit U5 and an analog-to-digital conversion unit U4, wherein the front-end polarity adaptation unit is used for being connected with a gas sensor and receiving a current signal of the gas sensor; the signal subjected to front-end polarity matching and current-voltage conversion is subjected to signal filtering and signal gain amplification through the intelligent neural network calculation unit U5, analog quantity and digital quantity conversion is performed through the analog-to-digital conversion unit U4, the detected gas voltage value of the gas sensor is obtained, and the intelligent neural network calculation unit U5 calculates the current gas concentration. The multi-gas detection device adapts to gas sensors of different manufacturers, different principles and different measuring ranges, and intelligent self-adaption of multi-gas detection is achieved on the premise that the product cost is not increased; and accurate concentrations can be calculated for different gases.