369 results about "Device Sensor" patented technology

The invention relates to the technical field of wind power generation equipment data processing. The invention provides a time sequence database data compression method, system and device and a storage medium. The method comprises the steps of dynamically partitioning time sequence data based on a preset time window; hybrid coding compression is performed on each data block, and a composite strategy of difference coding and run length coding is adopted for numerical data; a metadata index of the compression blocks is established, the time range, the data feature statistics and the compression parameters of each data block are recorded, and the metadata index comprises extreme value distribution, variance features and data fluctuation frequency indexes; and dynamically adjusting a compression strategy according to a historical data feature analysis result, predicting data fluctuation modes of different equipment sensors through a machine learning model, and automatically selecting an optimal coding combination and compression granularity for subsequent data blocks. The problems that when wind power plant time sequence data are processed through an existing method, time sequence characteristics are difficult to adapt, the storage cost is high and the query efficiency is low are solved.

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

A method includes: obtaining equipment sensor data from sensors in a time series; obtaining equipment optimization goal data from optimization goals in a time series; obtaining historical data on equipment abnormal events and intervention events; obtaining static equipment input parameters; applying time series model to the equipment sensor and optimization data, historical event data, and static equipment input parameters, to obtain predicted equipment sensor data; optimizing and controlling hardware operation based on the obtained predicted equipment sensor data; and providing predicted actions for abnormal event intervention based on the obtained predicted equipment sensor data. Hardware control, with optical mechanisms, into deep space, with a novel blockchained quantum bit communication network can be included, as changes to existing applications of transformer models, including ring-all reduced training on top of data and model parallelism, novel graph modalities, generic chart generations, generated image enabled search and drop ship, and multi-modal car foundation models.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention relates to the technical field of virtual reality, in particular to a centimeter-level VR real-scene real-time synchronous rendering method for multi-source data fusion and physical field coupling. Comprising the following steps: eliminating cloud layer interference and expanding Kalman filtering to improve positioning precision through multi-source acquisition of satellite remote sensing data, meteorological data and an equipment sensor; dynamically adjusting process noise by using Kalman filtering, and complementing delay data; constructing a ray tracing and U-Net noise reduction model, carrying out hierarchical loading of textures in combination with dynamic texture management, modeling dynamic illumination and wind field simulation based on a solar declination angle, and realizing scene rendering; a virtual ground is aligned through WebXR, gesture recognition and a force feedback mechanism are integrated, and fingertip curvature and movement speed features are extracted in combination with a Leap Motion sensor. According to the invention, through multi-source data fusion, efficient rendering and accurate interactive design, the authenticity and real-time synchronization capability of the VR scene are significantly improved.

The invention relates to the technical field of sensors, and discloses a detection and management method and system for purification equipment. The method comprises the following steps: carrying out three-phase coupling dynamic modeling on a gas phase flow state, a particle phase movement track and a sensor phase interface characteristic in the purification equipment to obtain a gas mass transfer characteristic parameter; performing response characteristic analysis on a real-time detection signal of a sensor array in the purification equipment to obtain a sensor calibration compensation parameter; based on the sensor calibration compensation parameters and the gas mass transfer characteristic parameters, deposition kinetics analysis of pollutants on the surface of the sensor is carried out, and equipment operation state evaluation data are obtained; and performing spatial layout optimization on the sensor array according to the equipment operation state evaluation data and the gas flow consistency constraint condition to obtain an optimal layout scheme and generate a staged adaptive management control instruction. According to the invention, self-adaptive adjustment and intelligent switching of the sensor management strategy are realized, and the detection performance and reliability of the purification equipment sensor system are significantly improved.

The invention relates to the technical field of chronic disease information management, in particular to a chronic disease information management method which comprises the following steps: S1, acquiring physiological index data, medication record data and behavioral habit data of a patient through a multi-source data acquisition module; s2, performing feature extraction on the multi-source data by using a data analysis module to generate multi-dimensional health assessment parameters; and S3, dynamically comparing the health assessment parameters with a preset assessment value through a risk early warning module, triggering hierarchical early warning when an abnormal parameter combination is detected, realizing multi-modal data deep mining by using an intelligent analysis module, performing association analysis on wearable device sensor data, an electronic medical record and a medication compliance record, and determining whether the health assessment parameters are abnormal or not. An individualized medication portrait of the chronic disease patient is constructed, parameters such as gene detection and metabolic indexes are combined, through medicine-disease conflict detection and dose self-adaptive calculation, an accurate medication adjustment strategy and a chronic disease patient health behavior guidance scheme are generated, and individual differences are fully reflected.

The invention discloses an augmented reality and digital product fusion display system, which can identify preset identifiers such as a two-dimensional code, a graphic code and a radio frequency identification tag through an identifier identification module, is integrated on an intelligent terminal or augmented reality equipment, is convenient for a user to operate at any time, improves the use convenience, and improves the user experience. The three-dimensional model database stores appearance structures, internal components and interactive function data, provides comprehensive product information for users and meets diversified requirements, the scene fusion calculation module realizes accurate positioning and reasonable proportion zooming of a three-dimensional model in a real scene by means of a virtual reality fusion degree calculation formula, dislocation and disproportionality are avoided, and the user experience is improved. The interactive display module supports multi-mode interaction modes such as gestures, voice instructions and equipment sensors, rich and natural interactive experience is provided, the real-time interaction function enables a user to see the influence of operation on the model in real time, interactivity and feedback are enhanced, and the digital product display effect and the user experience are integrally improved.

The invention relates to the technical field of intelligent agricultural planting and breeding, and discloses a digital intelligent agricultural planting and breeding combined system which comprises a multi-source sensing module used for collecting environment data, biological sign data and equipment operation data through a planting area sensor, a breeding area sensor and a circular processing equipment sensor; the data verification module is used for dynamically verifying the collected data based on a preset agronomic mechanism model; the hierarchical digital twinning module is used for carrying out collaborative decision making in a three-level architecture of the edge computing node, the local server and the cloud platform and generating an optimization instruction; a virtual-real decision engine module; and a closed loop execution network module. According to the invention, a lightweight twinborn model is deployed through the edge layer to realize millisecond-level equipment control and guarantee timely response of extreme working conditions, the field area layer coordinates planting and breeding material circulation through a dynamic optimization algorithm, and the cloud layer integrates climate data to generate a carbon sink strategy of block chain evidence storage. And the real-time decision-making precision, the resource utilization efficiency and the carbon economic value are improved through three-level cooperation.

The invention relates to the technical field of data management, and discloses a dynamic error management and tracing method for flow line production data, and the method comprises the following steps: collecting multi-source dynamic data in a flow line production process, including equipment sensor data, production parameter data and product quality data; performing real-time dynamic error detection processing based on the multi-source dynamic data, and generating dynamic error characteristic data through a sliding window algorithm and an abnormal value analysis technology; in the dynamic error management and tracing process of flow line production data, equipment sensor data, production parameter data and product quality data are acquired in real time through a multi-source dynamic data acquisition technology, and data integration is performed by using a timestamp alignment algorithm, so that the synchronism and integrity of error detection data are ensured; meanwhile, through real-time dynamic error detection processing, abnormal fluctuation in the production process can be recognized in time, and the real-time performance and reliability of error detection are guaranteed.

The invention provides a wind power plant equipment modeling and operation and maintenance management and control method and device based on digital twinning, and the method comprises the steps: restoring a fan entity based on a 1: 1 three-dimensional modeling technology, integrating multi-source data, including equipment sensor, video monitoring and GIS topographic data, and achieving the real-time mapping of a physical field and a virtual field through the construction of a high-precision digital twinning model. And the intelligent decision support module predicts wind resource distribution by using a dynamic boundary layer model, optimizes a grid-connected control strategy in combination with a virtual inertia simulation technology, and guarantees the transient stability of the power grid in a new energy fluctuation scene. And the full-life-cycle management platform supports full-process management from site selection planning, operation monitoring to fault diagnosis, and realizes multi-service data fusion display and interactive operation and maintenance strategy verification through a three-dimensional visual large screen.

The invention relates to a vehicle fault prediction method and device and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data of a vehicle, the multi-source heterogeneous data comprising at least two types of equipment sensing data, driving data and environment data of the vehicle; performing feature extraction on different data in the multi-source heterogeneous data based on a mode corresponding to each type to obtain a plurality of feature vectors; and obtaining the fault risk of the vehicle in the future time period through a prediction model based on the plurality of feature vectors. According to the method, by integrating the equipment sensing data, the driving data and the environment data, information richer than that of a single data source can be provided, the running state of the vehicle can be comprehensively evaluated, and therefore potential faults can be predicted more accurately.

The invention discloses a thermal power generating unit operation and maintenance decision digital twinning test system. The system comprises a physical layer which is composed of thermal power generating unit equipment, a sensor and edge computing equipment; the data acquisition and transmission layer comprises a plurality of sensor interfaces and transmits the acquired data to a cloud or a local server in real time; according to the data twinborn model layer, sub-models of all devices of the thermal power generating unit are built based on physical modeling, the sub-models are optimized and integrated through a machine learning algorithm, and a digital twinborn model is built; the test and optimization decision layer is used for evaluating an operation and maintenance decision according to a simulation result and selecting an optimal operation and maintenance strategy; and a man-machine interaction layer. The invention belongs to the technical field of generator set operation and maintenance, and particularly provides a method for solving the problems of high data fusion difficulty, lack of real-time performance and verification mechanism, insufficient operation and maintenance optimization capability and the like in the prior art.

The embodiment of the invention provides a form generation method, device and system based on AI enhancement and a storage medium, and relates to the technical field of form generation. The method comprises the steps of generating form structure metadata according to a natural language processing technology, a reinforcement learning algorithm and / or a type security verification rule; according to the form structure metadata, the equipment sensor data and / or the user visual behavior data, the rendering priority of each field is determined; and generating a target form according to the rendering priority and the equipment parameters. By generating the form structure metadata which is reasonable in structure and conforms to the type safety specification, the automation degree and reliability of form design are improved. By fusing form structure metadata, device sensor data and user visual behavior data, a rendering strategy can reflect device characteristics and user attention distribution. The target form is generated by combining the rendering priority with the equipment parameters, so that the form layout is adaptively matched with the terminal equipment, and the loading performance and the interaction experience of the form are improved.

The invention discloses a parallel double-filtering pressure-reducing valve redundancy control system with one used and one standby, which comprises a main filtering pressure-reducing valve and a standby filtering pressure-reducing valve which are oppositely arranged, a switching valve, a sensor module and a controller, and is characterized in that the main filtering pressure-reducing valve comprises a first air inlet and a first air outlet which are communicated with each other; the standby filtering pressure reducing valve comprises a second air inlet and a second air outlet which are communicated with each other, the first air inlet and the second air inlet are both communicated with an air source pipeline, the first air outlet and the second air outlet are communicated with an input port of the switching valve, an output port of the switching valve is used for being connected with downstream equipment, and the sensor module is arranged on the main filtering pressure reducing valve. The sensor module is used for monitoring the pressure condition of the main filtering pressure reducing valve, the controller is connected with the main filtering pressure reducing valve, the standby filtering pressure reducing valve and the sensor module, and the controller can automatically switch a gas path flowing through the device between the main filtering pressure reducing valve and the standby filtering pressure reducing valve according to the pressure condition. The system has the advantages of seamless switching, quick response and the like.

The invention relates to the technical field of power communication network security, in particular to a power communication network risk assessment method based on risk genes and a space-time large model. According to the technical scheme, the power communication network risk assessment method based on the risk gene and the space-time large model comprises the following steps that equipment sensor data, meteorological grid data and service topology data of a power communication transmission network are obtained in real time through a multi-source data acquisition module, and space-time alignment and feature coding are executed; a risk gene map is constructed, the risk gene map comprises a three-layer map structure of an equipment vulnerability gene, an environment threat gene and a business dependence gene, and a cross-space-time risk propagation rule is defined; a multi-modal space-time large model is trained, and a framework combining Transform and a space-time diagram convolutional network is adopted. A biological gene concept is introduced into power communication network risk assessment, gene-level fault traceability is achieved, and risk factors have combinability and interpretability.

The invention relates to the technical field of image detection, can be applied to a financial science and technology business system platform, and discloses a tamper-proof loss assessment image traceability method, device, equipment and medium, and the method comprises the steps: obtaining global positioning data, base station positioning data and equipment sensor data of a loss assessment image; performing fusion processing on the global positioning data, the base station positioning data and the equipment sensor data to obtain a dynamic timestamp; a preset device hardware fingerprint and a preset sensor fingerprint are combined to generate a composite fingerprint; performing asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encryption binding data; performing multiple hash processing on the loss assessment image to obtain hash chain data; and tracing the loss assessment image according to the encrypted binding data and the Hash chain data to obtain a tracing result. Through traceability of shooting information, the phenomenon that the operator does not arrive at the scene for survey and certificate collection and is shot by others can be avoided, the leakage risk of a case is further reduced, and the operation cost of a company is reduced.

To efficiently determine the causes of logistics work accidents, such as accidents occurring during receiving, storing, and sorting at automated logistics bases in the transportation industry.SOLUTION: A system is composed of a cloud server, a WEB server, camera equipment, sensor equipment, a determination function, and various terminals to determine the cause of a logistics operation accident such as an accident at the time of receiving, storing, and sorting at a logistics base. Information on the identified cause of the accident is sent to the responsible person, remote monitors, and workers to improve the cause of the accident, and to utilize the logistics operation accident cause determination system for quick work recovery.SELECTED DRAWING: Figure 1

The invention relates to the technical field of Beidou inspection data processing, and discloses an automatic inspection data acquisition and space-time association method under Beidou space-time reference. The method comprises the following steps: acquiring historical working condition data of inspection equipment based on nanosecond-level time synchronization and centimeter-level space positioning information of a Beidou system, and constructing a space-time dynamic prediction model; acquiring an equipment sensor measurement value, a moving track point set and a timestamp sequence in real time, and outputting a theoretical inspection value through a model; performing space-time multi-dimensional comparison on the theoretical value and the actual value, analyzing a time accumulation error, a space deviation degree and data flow form consistency, and generating a difference feature tensor; inputting the tensor into a geocoding topological reasoning network, simulating anomaly diffusion and generating an anomaly probability cloud picture in combination with topographic map node attributes and equipment distribution, and delineating an anomaly range; polling parameters are dynamically configured according to the cloud atlas, enhanced collection is started for a high-probability abnormal area, and data credibility inspection is executed on peripheral equipment.

The invention relates to the technical field of medical equipment, and discloses a sensor implantation mechanism which is used for realizing repeated use of auxiliary implantation equipment. The sensor implantation mechanism comprises a sensor module and a reusable auxiliary implantation device. The sensor module comprises a packaging box, a module support, a guide needle and a sensor device, the packaging box comprises a bottom shell and a box cover, and the box cover detachably covers the top end of the bottom shell. The reusable auxiliary implantation device comprises an injection structure and a reset structure, and the injection structure can be fixed to the module support. The injection structure can be implanted into the human body along the drive sensor device. The injection structure is provided with a clamping assembly, the clamping assembly is used for clamping the guide needle, and when the injection structure moves in the direction opposite to the injection direction, the injection structure is further used for driving the guide needle to be separated from the sensor device. The reset structure is used for driving the second clamping structure to be separated from the first clamping structure and also used for driving the guide needle to be separated from the clamping assembly.

The invention provides an enterprise production index monitoring method based on electric power data, and relates to the technical field of enterprise production index monitoring. The method comprises the following steps: acquiring real-time power data, water consumption, heat / gas energy consumption data and equipment sensor data of an enterprise, including vibration, temperature and pressure sensor data; calculating a production trend index and a product energy efficiency index by using the dynamic weight, and generating a comprehensive energy efficiency index CEI in combination with the equipment health coefficient; and when the CEI is lower than a threshold value, fault diagnosis is carried out based on the Bayesian network and early warning is triggered. And meanwhile, the electric power data is stored through a block chain technology, and data credibility verification and exception recheck are realized in combination with financial data cross validation. The system realizes multi-dimensional production state monitoring, has the advantages of dynamic adaptation to production fluctuation, intelligent fault diagnosis, data credibility guarantee and the like, can effectively improve enterprise production management efficiency, reduces energy consumption and fault loss, and is suitable for digital transformation requirements of high-energy-consumption industries such as chemical industry and manufacturing industry.

The invention discloses a guide path display method and device for a virtual scene, computer equipment and a medium, and the method comprises the steps: collecting the current behavior data of a user through a VR equipment sensor, and analyzing the intention of the user according to the current behavior data; according to the user intention and the virtual scene information, a current path is dynamically generated in a path planning mode; and generating a special effect guide path based on the current path, and generating semantic path guide information by adopting a multi-modal semantic fusion mode. According to the invention, visual guidance for user navigation is realized, and the path display intuition and immersion experience are improved.

The invention discloses a dynamic self-adaption and multi-physics field coupled coal roadway impact test system and method, and belongs to the technical field of coal roadway tunneling. The system comprises a coal roadway tunneling model, loading equipment, a sensor module, a multi-strain-rate coupling feedback module, a control module and numerical simulation software. Loading equipment comprises an impact oil cylinder, a thrust oil cylinder and a variable-stiffness spring, and the position of an impact module can be adjusted. The sensor module collects a variety of data. The multi-strain-rate coupling feedback module processes sensor signals. The control module controls all the parts to operate. The impact method comprises the steps of system initialization, data acquisition and processing, state evaluation and instruction generation, loading parameter adjustment and analog calculation, result feedback and optimization and the like. The method can monitor and adjust in real time, considers multi-physical field coupling, accurately evaluates the stability of the coal roadway, improves the tunneling safety and efficiency, and can be widely applied to the field of coal roadway tunneling engineering.

The invention belongs to the technical field of underground space flood prevention, and relates to a multistage linkage flood prevention system and method based on intelligent control. The water level sensor is used for monitoring water level data in real time, the environment sensor is used for monitoring environment information in real time, and the equipment sensor is used for monitoring equipment state information in real time; the multi-stage water retaining device comprises a V-shaped buffer water retaining structure, a liftable flood-proof baffle structure and a turnover type closed flood-proof door structure; the emergency ventilation device is arranged at the top end of the ground building and used for supplying air in a closed state; and the intelligent control module is used for outputting a staged control instruction for the multi-stage water retaining device and an on-off control instruction for the emergency ventilation device through processing of a neural network model based on the water level data, the environment information and the equipment state information. Through intelligent control and multi-stage mechanical structure linkage, the flood control response speed and reliability can be improved.

Systems and methods described herein facilitate the tracking of events relating to property. For example, in certain embodiments, a system includes a property event detection device (e.g., smart home device, sensor device, and so forth) configured to detect activity (e.g., motion, light, sound, and so forth) proximate the property. The system also includes a property event tracking system configured to receive data (e.g., images, videos, facial recognition, movement recognition, object tracking, sound signatures, wireless data streams, and so forth) relating to the activity from the property event detection device, to determine whether the activity relates to a property event (e.g., home service, and so forth) for the property, and to store data relating to the property event in a distributed ledger (e.g., blockchain network) upon determination that the activity relates to the property event for the property.

The invention relates to a crane operation monitoring method and device, equipment and a storage medium. The method comprises the following steps: collecting signal data of a crane through an edge node equipment sensor, analyzing the signal data into structured frame data, dividing the structured frame data into a plurality of samples to be identified based on a preset time window, determining state score values of the samples to be identified based on a locally deployed self-encoding model, and verifying the state score values of the samples to be identified according to a preset verification rule. The to-be-recognized sample with the state score value smaller than a preset threshold value is verified to obtain a verification result, and if the verification result represents that the crane operation corresponding to the to-be-recognized sample is the operation of an alarm event, alarm information is fed back to a preset user, and end-to-end real-time monitoring and abnormal protection of the crane operation state are achieved; and quick response is ensured when the operation of the crane is abnormal.

The invention relates to the technical field of sensors, and discloses an array type flexible pressure sensor and a data transmission method thereof, and a wearable device, the sensor comprises a flexible transverse conductive layer, a flexible longitudinal conductive layer, a flexible pressure-sensitive material layer, a data selector, a divider resistor and a single-chip microcomputer; the flexible transverse conductive layer is located at the top of the flexible pressure-sensitive material layer; the flexible longitudinal conductive layer is located at the bottom of the flexible pressure-sensitive material layer; a pressure-sensitive sensor is formed at the crossed position of the flexible longitudinal conducting layer and the flexible transverse conducting layer; the data selector is respectively connected with an output port of the flexible transverse conducting layer, an output port of the flexible longitudinal conducting layer and the single chip microcomputer; the divider resistor is connected with an output port of the flexible transverse conductive layer; the single-chip microcomputer is used for controlling the array type flexible pressure sensors to switch rows and columns and collecting voltage values between the pressure-sensitive sensors and the divider resistors. The method can be suitable for fabric pressure measurement and other scenes needing flexible sensing, and occupation of hardware resources is reduced.

The invention discloses a home health management method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to a home health management scene of old people. According to the method, multi-dimensional data such as sleep, behavior habits and physiological indexes are collected through a multi-device sensor, and the accuracy and consistency of the data are ensured through time synchronization and consistency verification processing; then, health state parameters are generated through a pre-trained health assessment model, the change trend of the health state is analyzed, potential abnormal health events are recognized, personalized reminding information is generated through an intelligent triggering mechanism for the abnormal events, and the personalized reminding information is pushed to user interaction equipment in a mode suitable for the cognition and operation ability of old people; and the system is guided to timely respond to health management requirements. Meanwhile, the system continuously monitors the operation feedback of the user, calculates the participation index of the user, and dynamically adjusts the reminding frequency and content to realize the self-optimization of the interaction strategy if the responsivity is insufficient.

The utility model relates to the technical field of sensor mounting brackets, and discloses a mounting bracket of a mechanical equipment sensor, which comprises a positioning frame, and the upper part of the positioning frame is connected with an adjusting frame capable of rotating horizontally through a bolt piece; the buffer assembly comprises a guide rod, the guide rod is connected with the vertical end of the adjusting frame in a penetrating mode, the guide rod is sleeved with a spring, and the buffer assembly is used for elastic buffer supporting and positioning of the mounting plate; and the mounting plate is fixedly connected to one end of the guide rod, and the other end of the mounting plate is used for mounting a sensor. The positioning frame and the adjusting frame are designed, the orientation angle of the adjusting frame can be flexibly adjusted through the arrangement of a horizontal rotation adjustable installation structure, then the installation angle of the sensor can be adaptively and flexibly adjusted, and meanwhile the buffering assembly is arranged between the adjusting frame and the installation plate, so that the installation angle of the sensor can be flexibly adjusted. And the installed sensor can be elastically buffered and protected.