264 results about "Passive detection" patented technology

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention relates to the technical field of wireless signal detection, and discloses a wireless anti-interference signal detection method, device and equipment and a storage medium, and the method comprises the steps: collecting a standardized interference feature vector through a multi-scene wireless signal receiving device; performing interference feature enhancement processing on the standardized interference feature vector to obtain an interference feature sample set; constructing a dual-array cooperative detection system comprising a passive detection array and an active detection array, and performing real-time monitoring to obtain dual-path interference signal monitoring data; performing spatial domain and time-frequency domain dual feature extraction and fusion processing on the dual-path interference signal monitoring data to obtain a comprehensive interference feature; the receiver filtering parameter matrix and the detection threshold vector of the double-array cooperative detection system are dynamically adjusted according to the comprehensive interference characteristics, the method can adapt to changes of different interference environments in a self-adaptive mode, comprehensive coverage of communication spectrums is achieved, and the wireless anti-interference monitoring range and the spectrum sensing capacity are greatly expanded.

The invention relates to the technical field of concrete defect identification, and discloses a concrete defect state identification and passive detection combined control method under a super-tonnage block type spherical hinge, which is used for solving the problem that defect prediction cannot be realized during concrete defect identification, and comprises the following steps: setting sampling points in a concrete structure in a layered manner, acquiring strain monitoring data of each sampling point, judging whether the sampling points have defects or not, if so, performing grouting repair intervention treatment, if not, acquiring risk parameters of concrete defects, and evaluating according to the risk parameters to obtain defect risk indexes; whether the sampling points have the risk of concrete defects or not is judged, if the sampling points have the risk of concrete defects, early warning and vibration equipment parameter adjustment are carried out on the sampling points according to the defect risk indexes, the structure quality safety and the construction efficiency are effectively improved, and the engineering field intelligent management and control degree is improved.

The invention belongs to the technical field of distributed optical fiber sensing and low-altitude security and protection, and particularly relates to an unmanned aerial vehicle long-distance tracking method based on distributed optical fiber vibration sensing. The invention relates to an unmanned aerial vehicle vibration / electromagnetic fusion multi-mode identification method, a special installation formation design, a dispersion compensation and amplitude phase error compensation design, an unmanned aerial vehicle target vibration / electromagnetic fusion multi-mode identification design, a detection capability calculation method of an unmanned aerial vehicle vibration sound pressure-space attenuation law-distributed optical fiber processing equipment detection capability coupling model, a long-distance sensing optical cable and an electromagnetic monitoring node. According to the tracking method, a special optical fiber installation mode, dispersion compensation and phase error compensation of an original phase signal, a vibration / electromagnetic fusion multi-mode identification method, an unmanned aerial vehicle vibration sound pressure-space attenuation model-distributed optical fiber processing equipment detection capability corresponding method and other innovative work are adopted; a mature distributed optical fiber vibration sensing system is utilized to realize long-distance passive detection and target tracking of the low-slow-small unmanned aerial vehicle.

The invention relates to the technical field of particle track microscopic analysis, in particular to a heavy ion track positioning method suitable for tiny seeds. The method mainly comprises the following steps of: scanning biological stacks by using a scanner, processing a scanning image by using drawing software to obtain an image of a single seed hole, acquiring position information of seeds in the seed holes, scanning a CR39 detection sheet by using a VHX microscope, acquiring passive detection information of each seed hole, and superposing the passive detection information with the image processed by the drawing software to obtain a final detection result. The radiation detection condition at the arabidopsis thaliana seeds is analyzed, and data conversion is further carried out to obtain various radiation information of individuals aiming at the arabidopsis thaliana seeds. The method is applicable to batch, rapid and high-precision processing of returned detection piece data. And in the aspect of the batch degree, the whole process is batch processing, and batch pictures and data can be automatically output by utilizing related software, so that the manual participation degree is reduced.

The invention relates to the field of automobile part welding processing, in particular to a tool clamp for integral welding of multiple accessories, the tool clamp comprises a jig frame, a clamp and a detection device, the jig frame is used for placing and positioning the accessories, the clamp is rotatably connected with the jig frame, the clamp is used for fixing the accessories in the jig frame, and the detection device is arranged on the jig frame. The detection device is used for detecting the position of the accessory and controlling the welding robot to conduct welding. The detection device comprises a direct detection device and an indirect detection device, the direct detection device is used for directly detecting the position of the part, the indirect detection device comprises an active detection device and a passive detection device, the passive detection device is connected with the clamp, and the passive detection device is used for detecting the position of the part along with rotation of the clamp; the active detection device is fixedly connected with the jig frame and is driven by the power source to detect the positions of the parts, and the effect of reducing the reject ratio of automobile part welding finished products is achieved.

The invention relates to the technical field of energy-saving door and window machining, and particularly discloses an energy-saving door and window machining manipulator device which comprises a manipulator, a first guide rod, a recording mechanism and a clamping jaw. The recording mechanism is arranged at the top end of the right side of the mechanical arm, and the clamping jaw is arranged at the right end of the recording mechanism. The device effectively overcomes the defect that a traditional detection method cannot quantitatively evaluate the wear process of the sliding rail, can accurately record the change rule of the friction characteristic of the sliding rail under different opening and closing times, visually reflects the wear critical point through the fluctuation characteristic of the corrugated line, and can accurately predict the failure threshold value of a hardware system. Test data not only reveals the corresponding relation between sliding rail abrasion and opening and closing times, but also can provide a scientific basis for optimizing hardware material pairing and improving a lubricating scheme, the durability design level of energy-saving door and window products is remarkably improved, the technology span from passive detection to active prediction is achieved, and the method is suitable for popularization and application. And a durability test result has a higher engineering guidance value.

According to the intelligent frequency spectrum prediction system and method with the dynamic anti-interference function, through the lightweight AI prediction model deployed in the embedded terminal, prospective accurate prediction of the future frequency spectrum hole probability is achieved, and the time delay defect of traditional passive detection is overcome; through an interference identification and classification module, fine identification of an interference source is realized, and a basis is provided for a differential anti-interference strategy; dynamic global optimization of communication parameters is realized through fusion of a utility function decision mechanism of a prediction result, interference information and historical quality and GFSK / QPSK adaptive modulation switching, and optimal balance is obtained between noise immunity and spectrum efficiency; through maintenance of the pre-switching channel pool and a microsecond-level rapid switching mechanism, challenges brought by strong interference burst and high-speed moving scenes are effectively dealt with, and continuity and high reliability of communication are ensured.

The invention relates to the technical field of communication terminals, in particular to an ultra-long-distance aviation airborne 5G communication terminal, which comprises an opportunity signal passive detection module for monitoring multi-source opportunity signals in parallel by adopting a broadband multi-channel radio frequency front end, and performing time delay conjugate multiplication and integral operation on reference channel signals and detection channel signals, so as to obtain an opportunity signal passive detection module; and extracting a cross-correlation peak value. According to the method, the multi-dimensional demand vector is formed by analyzing the mirror image metadata of the virtual machine and the configuration parameters, and the multi-dimensional demand vector is compared with the capability vector of the heterogeneous micro-host item by item, so that the scheduling precision can be improved on the two aspects of architecture consistency and resource adaptability. In the matching logic, matching pairs which do not meet conditions are endowed with negative infinite values, invalid scheduling paths are eliminated, candidate matching pairs meeting the conditions are prioritized by calculating resource matching degrees and affinity scores and integrating the adaptation level of network bandwidth and storage performance, and therefore resource utilization efficiency and scheduling reasonability are guaranteed.

The invention relates to an intelligent analysis method and system for PCBA production data, and relates to the technical field of PCBA data analysis, and the method comprises the steps: collecting real-time production data in a PCBA production process, associating production batches, and forming a real-time production data sequence; constructing a parameter-defect knowledge graph based on historical production data; after a production defect signal is received, taking a production batch as an index, extracting corresponding real-time production data, taking a corresponding defect type node as a starting point for reverse reasoning, and calculating the contribution degree of each upstream node to the defect type node; and positioning a root cause node, determining a key process parameter node in combination with the type, and outputting a process adjustment suggestion. The method solves the problems that traditional PCBA defect analysis depends on experience judgment and lacks causal correlation analysis among different production key elements in production data, so that root cause positioning is fuzzy, the efficiency is low, passive detection is mostly carried out after defects occur, prevention is difficult to carry out in advance, and then the production quality control effect is affected.

The invention belongs to the technical field of distributed multi-sensor passive detection multi-target tracking. The invention provides a factor graph optimization passive detection multi-target tracking method based on a Gaussian mixture model. According to the embodiment of the invention, the multi-target batch number is distributed by constructing the distributed passive sensor cooperative coordinate system and combining the multi-target identity judgment result between the two sensors; calculating direction finding lines based on two-dimensional observation of a sensor, combining the direction finding lines of the same batch number, obtaining a multi-target position estimation point set through a least square method, and obtaining a multi-target coarse positioning point through weighted fusion; modeling by adopting a Gaussian mixture model, fusing measurement distribution characteristics, solving parameters through an expectation maximization algorithm, and completing solvable conversion of an optimization problem; a factor graph optimization model containing multiple factors is constructed, state estimation is achieved through sliding window optimization, and track association and state updating are completed in combination with the Mahalanobis distance and the Hungary algorithm; and the passive detection multi-target tracking performance of the distributed sensor is effectively improved.

The invention belongs to the technical field of radio countermeasure, and particularly relates to a distributed unmanned aerial vehicle active defense system which comprises an intelligent stationing dynamic optimization module, a multi-mode cognitive interference module, a high-precision three-level clock synchronization module and an elastic ad hoc network communication module which are used for supporting the overall function of the system. The distributed hardware equipment and the edge intelligent decision-making terminal are used for implementing a detection countering function at distributed layout positions; during specific operation and use, a distributed unmanned aerial vehicle active defense system can be provided for an important target with a large area and multiple important parts; the application problems that the arrangement position cannot be accurately selected, a large coverage blind area exists, the synchronous time service precision is low, communication networking is unreliable, serious self-interference is generated on electromagnetic and time service equipment of a partner, and the unmanned aerial vehicle countering effect is poor can be solved. And rapid and automatic passive detection and accurate interference of important targets with a large area and multiple important parts on the low-slow-small unmanned aerial vehicle are realized.

The invention relates to the technical field of drainage pipeline detection, and discloses a drainage pipeline structural defect detection system and method, a medium and a product, a controller actively constructs and generates a coded infrasonic wave signal, and it is ensured that the signal has stable frequency and amplitude characteristics; the infrasonic sound production module performs infrasonic wave emission and processing by using a coded infrasonic wave signal, and enables a low-frequency mechanical vibration signal to be stably propagated along a pipe wall, so that energy attenuation is reduced, and a long-distance pipe section which is difficult to touch in traditional passive detection is covered; the infrasound signal receiving module is used for detecting low-frequency mechanical vibration signals and converting analog signals into initial digital signals, and interference and distortion in the transmission process are reduced. Furthermore, by combining a Fourier transform method and a correlation coefficient analysis method, the problem of high misjudgment rate of a traditional single algorithm is avoided, accurate recognition of the structural defects of the pipeline is ensured, and the requirement for accuracy of structural defect detection of the drainage pipeline under complex working conditions is met.

The invention provides a full-automatic probe position inspection system for a probe card, and the system comprises a morphology collection module which is used for carrying out the three-dimensional morphology scanning of a probe group of the probe card, and obtaining the three-dimensional point cloud data of the probe group; the probe position analysis module is connected to the morphology acquisition module, comprises a machine learning model and is used for processing the three-dimensional point cloud data and generating probe position deviation data and probe health state scores; the compensation execution module is connected to the probe position analysis module and is used for scoring based on the probe position deviation data and the probe health state; calculating an optimal contact position, and driving the carrying platform to move to the position; and the verification module is used for verifying the accuracy of the needle position after the contact is executed at the optimal contact position. Passive detection, predictive maintenance and process closed-loop optimization are crossed, and the test yield, the equipment service life and the manufacturing process level are comprehensively improved through data driving.

The invention discloses a passive fault positioning system and method based on SRv6 data plane detection, and the method comprises the steps: deploying a data collection module at an SRv6 network node, passively collecting a passing data message, and adding detection information into the data message; analyzing an SRH extension head in the message to extract detection information, and filtering and processing the detection information to provide formatted data; summarizing detection information in the monitoring data of different nodes in different time periods, and performing association and fusion to form a to-be-analyzed data packet; according to the detection information in the to-be-analyzed data packet, performing fault positioning analysis by using a fault analysis method to lock a fault position; and performing root cause tracing based on the fault positioning position to obtain a fault cause, outputting a fault positioning result and linking a fault processing flow. According to the method, the existing information of the SRv6 data plane is multiplexed to realize non-overhead passive detection, and high-precision and real-time fault positioning of the SRv6 network is realized.

The embodiment of the invention provides a network surveying and mapping behavior anomaly detection method and system based on machine learning, and belongs to the technical field of network surveying and mapping behavior anomaly detection. The method comprises the steps of collecting double-source flow data, and generating a structured log data set through a double-source log fusion engine; performing sub-graph matching calculation to obtain a surveying and mapping behavior deviation degree; generating communication data containing the watermark identifier in a communication path corresponding to the session; and verifying whether an attack event carries the watermark identifier, and generating a network surveying and mapping behavior anomaly detection report. According to the method, the self-adaptive attack behavior model is constructed through the multi-modal feature vector based on the structured log and the graph protocol mapping rule base, the cognitive robustness of protocol camouflage and path drifting is fundamentally enhanced, a detection result real-time verification chain is established, and through cross verification of a watermark carrying state and a behavior track, the detection accuracy is improved. And traditional passive detection is converted into self-proof active defense.

The invention discloses an air outlet anti-leakage and anti-reverse-installation detection method based on image detection, and belongs to the technical field of optical detection. The method comprises the following steps: determining a data acquisition range of image detection in an air outlet area, and delimiting a detection area according to structural parameters of the air outlet; by dividing conventional and error-prone areas, adopting a differential detection strategy and combining a three-dimensional point cloud comparison technology, the unqualified product rate and the detection time consumption of air outlet installation are effectively controlled, the labor cost and the repair rate are reduced, a threshold value is dynamically optimized based on historical data, the recognition precision of a complex area is improved, misjudgment of a difficult point area is solved, and the detection efficiency is improved. Double improvement of detection precision and efficiency is realized; meanwhile, through a three-level risk signal mechanism, conversion from passive detection to active early warning is realized, the risk response time is shortened, the product quality stability is guaranteed, the downtime is reduced, the overall efficiency of a production line is improved, and collaborative optimization of risk management and control and production continuity is realized.

The invention discloses an online quality detection system based on artificial intelligence. The online quality detection system comprises a generative full-dimensional perception and cognition twinborn reproduction layer, an interpretable causal atlas and man-machine enhancement cognition layer, an alliance collaboration and credible value tracing layer and an autonomous evolution and scientific discovery layer. Through the online quality detection system based on artificial intelligence, the problems of passive detection, surface recognition, insufficient adaptability to dynamic changes and the like of a current online quality detection system can be solved, and the intelligent level of the online quality detection system is remarkably improved.

The invention belongs to the technical field of medical care informatics, and discloses a remote central monitoring system for community medical treatment, which comprises a data acquisition and integration module, a physiological state analysis engine and a monitoring information presentation module, and can construct a state-dependent baseline model cluster based on the activity state information of a user, according to the real-time activity state of the user in the monitoring period, the corresponding sub-model is dynamically selected to serve as a judgment reference, and then a continuously-changing physiological stability index is generated by comparing the multi-dimensional statistical characteristics of the real-time physiological data with the deviation degree of the reference; according to the method, by establishing an individualized physiological steady state judgment benchmark which is adaptively adjusted along with the activity situation, the capability of a monitoring system for continuously and quantitatively evaluating the overall stability of a human body as a complex system is improved, and the transformation from passive detection of discrete clinical events to state insight of a risk evolution process is realized.

The invention discloses an unmanned aerial vehicle passive detection signal discrimination method and device based on electromagnetic behavior characteristics, and the method comprises the steps: carrying out the collection of direction-finding data, and carrying out the sampling, and obtaining the observation values of signal intensity and azimuth angles; constructing a linear tracking model for tracking filtering; performing initialization setting; updating a tracking chain state at the current moment by using a linear tracking model; and calculating the likelihood probability and the posterior probability of the electromagnetic behavior characteristics at the current moment by using the updated tracking chain state, and judging the electromagnetic behavior characteristics of the unmanned aerial vehicle at the current moment according to a maximum probability principle to finish passive detection signal judgment at the current moment. According to the method, the electromagnetic behavior characteristics are discriminated based on the probability characteristics of the electromagnetic behaviors, the signal discrimination problem of passive detection of an unmanned aerial vehicle of an unknown model is solved, and the observation precision of an observation value and the signal discrimination precision are improved by designing a reasonable linear tracking filtering model.

The invention relates to the technical field of muon imaging, in particular to a cosmic ray muon distribution three-dimensional reconstruction method and system.The method comprises the steps that a monitoring environment is built, an object to be detected is placed at the circle center of an annular guide rail, two lifting platforms are fixed to the annular guide rail, supporting frames are installed on the lifting platforms respectively, and detector panels are arranged on the supporting frames; detecting position information left when muons penetrate through a detector panel, calculating muon tracks and muon flux to obtain angular distribution, and establishing point-surface images of the angular distribution and flux distribution of the muons based on the muon tracks; performing weighted summation to obtain a face-to-face image; and compressing Monte Carlo output muon position information by using an auto-encoder, obtaining momentum, time and position information of muons, and extracting angular distribution in a preset area for classifying the angular distribution and the position information according to the interval. The method is suitable for various use scenes, belongs to passive detection, does not generate radioactive contamination, and can replace industrial CT with radioactive contamination in a specific scene.

The invention discloses a PCDN active detection method based on fusion of user behaviors and traffic features, which belongs to the technical field of active detection, and comprises the following steps: 1.1, building a simulation environment, collecting data, deploying a controllable PCDN service simulation environment comprising mainstream PCDN platform node equipment, a user terminal and detection equipment, the method comprises the following steps: non-inductively collecting network traffic and application layer behavior data through a bypass-deployed deep packet inspection engine and traffic mirroring equipment; according to the method, the specific user behavior characteristics of PCDN platform registration, node testing, settlement query and the like which are strongly associated with commercial settlement are extracted, and flow characteristic fusion judgment is combined, so that identification can be completed at the stage that the user registers and deploys PCDN nodes, and PCDN behaviors are found 7-10 days earlier than that of a traditional flow detection method; the problem of passive detection lagging is solved, and network risks caused by PCDN commercial behaviors in a home broadband can be monitored more timely.

The invention relates to the technical field of gas detection, and discloses a power switch gas leakage detection device which comprises a detection cylinder, a temperature sensor is arranged at the upper end of the detection cylinder, an expansion section is arranged in the middle of the detection cylinder, and the expansion section divides the detection cylinder into a detection cavity and a comparison cavity. Air guide pipes are installed in the middle of the detection cavity and the middle of the comparison cavity, the two air guide pipes are fixedly sleeved with limiting rings, the adjacent ends of the two air guide pipes are rotationally connected with a convex air guide head and a concave air guide head respectively, and the rear end of the concave air guide head is fixedly connected with a trigger switch. By arranging the air pressure sensor and the temperature sensor, active detection can be realized, the comparison cavity is arranged during active detection, so that the problem of inaccurate detection caused by different temperature change comparison standards during conventional detection is avoided, and compared with general passive detection, the leakage problem is found in advance, so that the potential safety hazard of use of the power switch is solved as soon as possible.

The application discloses a mining behavior identification method combining active and passive detection technologies, comprising flow side identification, wherein the flow side identification comprises passive detection and active detection, and specifically comprises the following contents: obtaining packet features of mining pool miner communication through passive detection; constructing a packet to send to the mining pool, and analyzing and judging returned data; obtaining IP addresses of known mining pools through the passive detection method, obtaining the mining pool server fingerprint by using JARM, searching for IP addresses of the same fingerprint by using a space survey product, actively sending a request by constructing a data packet, analyzing a response result, and judging the belonging mining pool of the IP. The application combines the active and passive detection technologies, can independently detect possible mining machines and mining pools and mining behaviors while monitoring the flow features of the mining behaviors and the device resource occupation caused by the behaviors, and blocks the high-threat mining behaviors hidden deeply.

The invention relates to the technical field of equipment collaborative scheduling, and discloses a multi-equipment collaborative scheduling processing method and system for mobile transparent display equipment, and the method comprises the steps: collecting the real-time state data of a plurality of mobile transparent display equipment in an exhibition hall, and calculating the task compatibility data between the real-time state data and an exhibition task; deadlock risk prediction is carried out based on the real-time state data and the task compatibility data, and an inter-device space conflict probability and a task execution deadlock risk value are obtained; and performing cooperative control on the plurality of mobile transparent display devices based on the space conflict probability between the devices and the task execution deadlock risk value, and outputting a device movement instruction and a transparency coordination instruction. Continuous and stable operation of the multiple mobile transparent display devices in the exhibition hall is ensured, the optimal visual display effect is ensured, and meanwhile energy waste is avoided.

The application discloses a near-field strong interference suppression method based on spherical wave deconvolution beam forming positioning, which comprises the following steps: performing fine grid division on a near-field scanning area of a linear array; performing spherical wave focusing beam forming on the near-field scanning area and two-dimensional deconvolution beam forming about distance-angle under the condition of unknown sound source position; calculating the distribution entropy characteristics of the deconvolution beam forming result in the angle dimension, and using a preset threshold to correlate the interference positioning of a target; constructing an array flow pattern matrix A of each near-field target l and generating a covariance matrix A; calculating the orthogonal complement space projection matrix of the covariance matrix A, and applying the orthogonal complement space projection matrix to the array element domain covariance matrix C to perform near-field interference suppression; using a far-field plane wave model to perform deconvolution beam forming calculation on the covariance matrix for suppressing near-field interference, so as to obtain passive detection results of the near-field strong interference suppression. The application can realize near-field interference positioning and interference suppression under the condition that the interference position is unknown.

The application discloses an industrial label printing self-adaptive optimization method based on multi-dimensional defect quantification. The method realizes intelligent management and control of label printing quality through task data preparation, image acquisition and parallel diagnosis, defect quantification and shunt decision, quality trend analysis and self-adaptive tuning, and predictive maintenance warning. The core of the method is to construct a defect severity index quantification model, combine the improved YOLOv8 algorithm to realize accurate classification and root diagnosis of defects, and realize real-time fine tuning of printing parameters through a fuzzy PID self-tuning tuning engine, and match a cache level real-time bit-level check to ensure data accuracy. The application breaks through the limitations of traditional passive detection, realizes active optimization and predictive maintenance, greatly improves production efficiency and reduces the rate of defective products, ensures stable label printing quality and accurate data, and is suitable for high-speed industrial label printing scenes.

The utility model discloses a temperature measuring device for an enclosed bus, which comprises a directional antenna, a data concentrator, a transmission module and one or more RFID (Radio Frequency Identification)-based temperature sensors, and the temperature sensors are arranged on a metal flexible connector of the enclosed bus; the directional antenna is installed on the side wall of a closed mother cylinder of the closed bus. The temperature sensor is connected with the data concentrator through the directional antenna, the data concentrator is connected with the transmission module, and the transmission module is connected with the upper computer. The temperature of the metal soft connection part of the enclosed bus is acquired through the RFID-based temperature sensor, and the detected temperature is transmitted to the data concentrator through the directional antenna and is uploaded to the upper computer through the transmission module, so that the temperature measurement of the enclosed bus is realized; wireless passive detection can be realized through the temperature sensor based on the RIFD; detection data can be displayed online in real time through the background of the upper computer.

The invention discloses a chip Trojan horse detection and shielding system and method based on multiple confusion links, and relates to the technical field of integrated circuit safety. According to the method, a plurality of confusion links which are large in structural difference, identical in function and similar in time delay are designed for sensitive critical paths in a chip. A write-only unreadable register is adopted to store link selection information, so that an attacker is prevented from stealing or tampering a currently activated path by means of scanning a chain, debugging an interface and the like; a dual-stage operation mechanism is adopted, a test stage is combined with high-precision delay measurement and anomaly analysis to accurately identify a path in which Trojan is implanted, and a formal stage is dynamically switched to a Trojan-free path based on safety link information stored in a register, so that a chip is ensured to automatically avoid malicious logic during operation, and active defense instead of passive detection is realized. The method is superior to a traditional hardware Trojan horse protection scheme in detection precision, operation safety and implementation cost, and an efficient, reliable and easy-to-integrate solution is provided for chip safety.