Multi-channel synchronous temperature measuring device and data processing method thereof

Abstract

The invention discloses a multichannel synchronous temperature measuring device and a data processing method thereof, and the device comprises a multi-modal sensor fusion platform which comprises a laser radar module, a spectral imaging module and an infrared temperature measuring module. Wherein the laser radar module, the multispectral imaging module and the infrared temperature measurement module are fixed through rigid supports, the field overlapping degree is larger than or equal to 80%, and the optical axis alignment deviation is smaller than or equal to 0.1 degree; the synchronous control unit is based on a parallel architecture of an FPGA (Field Programmable Gate Array), integrates a dual-redundancy clock source, and drives multiple channels to synchronously trigger through an optical coupler isolation circuit; and a data fusion processing unit, and a communication and storage unit which supports a multi-mode data transmission protocol and integrates data storage. According to the technical scheme provided by the invention, the problem that the synchronization precision and emissivity adaptive correction problem in a dynamic scene is difficult to improve due to spatial-temporal dislocation of data of a laser radar, a camera and an infrared sensor at present can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of temperature measurement, in particular to a multi-channel synchronous temperature measurement device and a data processing method thereof. BACKGROUND

[0002] Thermal infrared camouflage technology has become one of the core directions of camouflage research due to its strategic value in military concealment, environmental monitoring and other fields. Thermal infrared camouflage matches the infrared characteristics of the target surface with the background environment by regulating the radiation characteristics of the target surface or the thermal coupling effect with the environment, thereby reducing the probability of being identified by infrared detection equipment. However, the evaluation of infrared camouflage effect depends on the accurate measurement and visual analysis of the temperature distribution and dynamic changes of the target surface, which poses a severe challenge to the precision, efficiency and cost of measurement technology.

[0003] At present, two-dimensional temperature field can only obtain the temperature distribution of the target surface, lacking three-dimensional geometric information. At the same time, the temperature measurement accuracy is significantly affected by the emissivity of the target material (such as metal emissivity 0.1-0.3, ceramic 0.8-0.98). And the spatio-temporal misalignment of laser radar, camera and infrared sensor data makes it difficult to improve the synchronization accuracy and emissivity adaptive correction problem in dynamic scenes. SUMMARY

[0004] The main purpose of the present application is to provide a multi-channel synchronous temperature measurement device and a data processing method thereof, which aims to at least solve the technical problem that the spatio-temporal misalignment of laser radar, camera and infrared sensor data makes it difficult to improve the synchronization accuracy and emissivity adaptive correction problem in dynamic scenes in the related art.

[0005] To achieve the above-mentioned purpose, the present application provides a multi-channel synchronous temperature measurement device, which comprises:

[0006] A multi-modal sensor fusion platform, comprising:

[0007] A laser radar module using a 1550nm wavelength laser source and a solid-state MEMS micro-mirror for obtaining three-dimensional spatial point cloud data of the target;

[0008] A spectral imaging module integrating visible light, short-wave infrared and mid-wave infrared band sensors for collecting material reflectivity and surface contamination state; and

[0009] An infrared temperature measurement module using a non-cooled vanadium oxide focal plane array with a temperature measurement range of -40℃ to 1500℃;

[0010] The laser radar module, multi-spectral imaging module and infrared temperature measurement module are fixed by a rigid bracket, the field of view overlap degree is ≥80%, and the optical axis alignment deviation is ≤0.1°;

[0011] Synchronous control unit, based on FPGA parallel architecture, integrated dual redundant clock source, driven multi-channel synchronous trigger through optical coupling isolation circuit;

[0012] Data fusion processing unit, comprising:

[0013] Multi-sensor space-time calibration module, which solves the rotation and translation matrix of the laser radar and the infrared temperature measurement module based on singular value decomposition;

[0014] Emissivity dynamic correction module, which can allow the combination of multi-spectral material classification and laser radar surface normal direction to optimize the emissivity parameter through BP neural network; and

[0015] Three-dimensional temperature field modeling unit, which can project the infrared temperature data to the surface of the laser radar point cloud to generate a pseudo-color heat map;

[0016] Communication and storage unit, supporting multi-modal data transmission protocol, and integrating data storage.

[0017] In an embodiment of the present application, the laser radar module can allow dynamic scanning mode switching, including:

[0018] For moving targets, high-speed scanning is enabled, with a frame rate ≥ 100 Hz and a point cloud density ≥ 50 points / m 2 For static targets, high-precision mode is enabled, with a frame rate ≥ 30 Hz and a point cloud density ≥ 200 points / m 2 ;

[0019] Wherein, during mode switching, the synchronous control unit automatically adjusts the trigger frequency.

[0020] In an embodiment of the present application, the multi-spectral imaging module further comprises:

[0021] Built-in blackbody and diffuse whiteboard, automatically performing flat field correction every 30 minutes; and

[0022] Split-beam prism co-axial unit for sharing the same optical path for visible light, short-wave infrared and mid-wave infrared through a split-beam prism, and ensuring imaging consistency based on focal length deviation compensation algorithm.

[0023] In an embodiment of the present application, the infrared temperature measurement module further comprises a correction sub-module, which can be used to perform the steps of:

[0024] Using a blackbody radiation source to calibrate the gain and offset parameters of each pixel at low and high temperature points;

[0025] Real-time acquisition of environmental temperature data, dynamic compensation of temperature drift through piecewise cubic spline interpolation algorithm;

[0026] Based on the multi-spectral material classification results, assigning initial values of emissivity to each region.

[0027] In an embodiment of the present application, the synchronization control unit further comprises a timestamp injection module, which injects the FPGA counter value when the ADC conversion is completed, and aligns the laser radar scanning start signal, the multi-spectral exposure pulse and the infrared sampling time through a cross-correlation algorithm;

[0028] In an embodiment of the present application, the data fusion processing unit further comprises an abnormal temperature detection module, a heat conduction prediction module and a dynamic visualization module,

[0029] The abnormal temperature detection module is based on a convolutional neural network to identify local hot spots or cold areas in the temperature field;

[0030] The heat conduction prediction module can allow the combination of finite element analysis and LSTM time series model to predict the temperature distribution trend in the next 5 seconds; and

[0031] The dynamic visualization module can be used to generate a pseudo-color three-dimensional heat map to support real-time interactive viewing by a visualization device.

[0032] The present application also provides a multi-channel synchronous temperature measurement data processing method, which comprises:

[0033] Step S1: generating three-phase synchronous pulses based on FPGA to trigger laser radar scanning, multi-spectral exposure and infrared sampling, respectively;

[0034] Step S2: calibrating and aligning the laser radar module, the spectral imaging module and the infrared temperature measurement module, which comprises:

[0035] Step S201, laser radar-infrared geometric alignment, based on SVD decomposition to solve the rotation and translation matrix;

[0036] Step S202, multi-spectral-infrared feature matching, using an improved ORB algorithm to match feature points;

[0037] Step S3: initializing the emissivity, optimizing the optimal emissivity by combining the multi-spectral reflectivity and surface normal through a BP neural network;

[0038] Step S4: dynamic heat conduction prediction and early warning, which comprises the steps of:

[0039] Step S401, multi-physical field modeling, which solves the spatio-temporal evolution of the temperature field by coupling the heat conduction equation and the fluid dynamics model;

[0040] Step S402, time series prediction fusion, which inputs the historical temperature sequence through an LSTM network and outputs the predicted value in the next 5 seconds;

[0041] Step S5: realizing multi-threaded non-blocking communication based on a message queue.

[0042] The application also provides an electronic device comprising:

[0043] one or more processors; and a memory storing computer program instructions that, when executed, cause the processors to perform the steps of the multi-channel synchronous temperature measurement data processing method as described in the above embodiments.

[0044] The application also provides a computer-readable storage medium storing a computer program, wherein the program, when executed by a processor, implements the multi-channel synchronous temperature measurement data processing method as described in the above embodiments.

[0045] In summary, the application provides a multi-channel synchronous temperature measurement device and a data processing method thereof, which realizes μs-level synchronous triggering of three channels of laser radar, multi-spectrum and infrared through FPGA parallel architecture and double-redundant clock sources, with a timestamp accuracy of ±10ns, solving the problem of millisecond-level time misplacement of traditional single-channel or asynchronous multi-channel. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only show some embodiments of the application, and for those skilled in the art, other drawings can be obtained from the structures shown in the drawings without creative labor.

[0047] Figure 1 A module schematic diagram of an embodiment of a multi-channel synchronous temperature measurement device provided by the application.

[0048] Figure 2 A flowchart of an embodiment of a multi-channel synchronous temperature measurement data processing method provided by the application.

[0049] Figure 3 An exemplary structural schematic diagram of an electronic device according to the application.

[0050] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0051] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application.

[0052] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative positional relationship, movement condition, etc. between components in a certain posture, and if the certain posture changes, the directional indications also change accordingly.

[0053] In addition, if the embodiments of the present application involve descriptions such as "first", "second", etc., the descriptions of "first", "second", etc. are only for description purposes, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features.

[0054] If "and / or" or "and / or" appears throughout the text, it means that the three parallel schemes include "A and / or B", which includes A scheme, or B scheme, or A and B scheme. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the scope of protection claimed by the present application.

[0055] Please refer to Figure 1 The present application discloses a multi-channel synchronous temperature measuring device, which can be used to improve the problem that the synchronization accuracy and emissivity adaptive correction in dynamic scene caused by the spatio-temporal misplacement of laser radar, camera and infrared sensor data are difficult to improve.

[0056] Specifically, the multi-channel synchronous temperature measuring device disclosed by the present application at least includes a multi-modal sensor fusion platform, a synchronous control unit, a data fusion processing unit and a communication and storage unit.

[0057] The multi-modal sensor fusion platform includes a laser radar module, a multi-spectral imaging module and an infrared temperature measurement module. The laser radar module uses a 1550nm wavelength laser source and a solid-state MEMS micro-mirror, which can quickly and accurately obtain three-dimensional spatial point cloud data of the target, and provide accurate spatial information for subsequent temperature field modeling.

[0058] At the same time, in this embodiment, the laser radar module can dynamically switch the scanning mode according to the target state, enable high-speed scanning for moving targets, with a frame rate ≥ 100Hz and a point cloud density ≥ 50 points / m 2 , ensuring accurate tracking of fast-moving targets. Enable high-precision mode for static targets, with a frame rate of ≥30Hz and a point cloud density of ≥200 points / m 2 , to obtain more detailed static target space features.

[0059] The multispectral imaging module integrates visible light, short-wave infrared and medium-wave infrared band sensors, which can be used to collect reflectivity information of the target material and monitor the pollution status of the target surface by analyzing the spectra of different bands.

[0060] The multispectral imaging module includes a built-in blackbody and diffuse whiteboard, and automatically performs flat-field calibration every 30 minutes to ensure the accuracy and stability of the collected data. Furthermore, the module allows for the integration of visible light, shortwave infrared, and medium-wave infrared into a single optical path through a pre-set beamsplitting prism coaxial unit, while maintaining consistent imaging across all bands using a focal length deviation compensation algorithm.

[0061] Furthermore, the infrared temperature measurement module utilizes an uncooled vanadium oxide focal plane array (VFA) with a wide temperature measurement range of -40°C to 1500°C, capable of simultaneously acquiring the two-dimensional temperature distribution of the target surface. A correction submodule is incorporated within the infrared temperature measurement module, utilizing a blackbody radiation source to calibrate the gain and offset parameters of each pixel at both low and high temperature points, allowing for real-time acquisition of ambient temperature data. Furthermore, a piecewise cubic spline interpolation algorithm dynamically compensates for temperature drift, and an initial emissivity value is assigned to each region based on multispectral material classification results, effectively improving temperature measurement accuracy.

[0062] For example, this allows full-frame data to be acquired at both low and high temperatures by using a blackbody source.

[0063] Calculate the gain coefficient G for each pixel ij and the offset coefficient O ij

[0064]

[0065] O ij =V low -G ij ·T low

[0066] Through real-time correction formula: Perform correction processing. Among them, V high and V low Characterizes the voltage digital value output by the sensor pixel at high and low temperatures; V raw Represents the raw voltage digital value collected in real time; and Tij characterize the corrected temperature value; T high and T low characterize the set high and low temperature points.

[0067] It should be noted that the above laser radar module, multispectral imaging module and infrared temperature measurement module are fixed and stabilized by a rigid support, ensuring that the field of view overlap is ≥ 80%, the optical axis alignment deviation is ≤ 0.1°, and ensuring that the data collected by each sensor has high consistency in space.

[0068] In actual use, the laser radar module, spectral imaging module and infrared temperature measurement module can be installed on a rigid support to ensure that the field of view overlap reaches ≥ 80% and the optical axis alignment deviation is controlled within ≤ 0.1°. Through high-precision installation process and calibration tools, the spatial position and angle of each module are ensured to be accurate, laying a foundation for subsequent synchronous measurement and data fusion.

[0069] In some embodiments, the synchronous control unit is constructed based on a field programmable gate array (FPGA) parallel architecture, and integrates a double-redundancy clock source to ensure the stability and reliability of the system clock. The synchronous control unit can drive multi-channel synchronous triggering through an optical coupling isolation circuit, realize accurate synchronization between channels, and ensure that the trigger phase difference is minimal.

[0070] The synchronous control unit also includes a timestamp injection module that injects an FPGA counter value when the ADC conversion is complete, aligns the laser radar scanning start signal, multispectral exposure pulse and infrared sampling time through a cross-correlation algorithm, and updates the noise covariance every 10 ms using a Kalman filter model to further improve the synchronization accuracy and ensure accurate matching of sensor data in the time dimension.

[0071] Specifically, in actual circuit connection and debugging, the FPGA of the synchronous control unit can be connected with the signal output interface of each sensor module to ensure that the electrical performance of the signal transmission line is good. The optical coupling isolation circuit is connected to realize effective isolation of digital and analog signals and improve the anti-interference ability of the system. At the same time, the double-redundancy clock source needs to be configured and debugged to ensure stable operation of the system clock.

[0072] Further, the data fusion processing unit comprises a multi-sensor space-time calibration module, an emissivity dynamic correction module, and a three-dimensional temperature field modeling unit. The multi-sensor space-time calibration module can allow accurate solution of the rotation and translation matrix of the laser radar and the infrared temperature measurement module based on singular value decomposition (SVD), and accurate registration of the two in space. At the same time, through coordinate system conversion, SVD decomposition is performed on the feature points of the point cloud and the infrared image, and a cubic spline interpolation is used to align the time axis of the multi-spectral and infrared data, to ensure the consistency of the multi-sensor data in space and time.

[0073] Specifically, in the actual operation process of the data fusion processing unit, Zhang's calibration method combined with SVD decomposition can be used to collect 100 groups of corresponding feature points of laser radar point cloud (X, Y, Z) and infrared image (u, v) through a checkerboard target (500*500mm, checkerboard interval 20mm), to construct a 3D-2D corresponding matrix, to obtain a rotation matrix R and a translation vector T through SVD decomposition, and to perform iterative optimization through an ICP algorithm to RMS<0.8mm.

[0074] The emissivity dynamic correction module can allow optimization of the emissivity parameter through a BP neural network combined with multi-spectral material classification and laser radar surface normal direction. The emissivity dynamic correction module is based on multi-spectral SVM classification and laser radar surface normal, and constructs a BP neural network. The training set of the BP neural network contains 200+ material samples, and the corrected emissivity Δε≤±0.02. Through the use of advanced algorithms in multi-spectral material classification, various types of materials can be accurately identified, providing a basis for accurate correction of the emissivity.

[0075] The three-dimensional temperature field modeling unit can project the infrared temperature data onto the surface of the laser radar point cloud to generate a pseudo-color heat map. In the three-dimensional temperature field modeling, the corrected infrared temperature data is mapped to the surface of the laser radar point cloud through a thermal radiation projection algorithm, and the reflectivity is compensated by combining a BRDF parameter library to generate a STL format three-dimensional heat map. The BRDF parameter library includes the reflection characteristics of 50 materials.

[0076] The three-dimensional temperature field modeling unit projects the infrared temperature data onto the surface of the laser radar point cloud to generate a pseudo-color heat map, which directly presents the three-dimensional temperature distribution of the target. In the projection process, the surface reflection characteristics are corrected by combining a bidirectional reflectance distribution function to improve the accuracy of temperature field modeling. In addition, the three-dimensional temperature field modeling unit also includes an abnormal temperature detection module, a heat conduction prediction module, and a heat conduction prediction module.

[0077] It can be understood that the abnormal temperature detection module is based on a convolutional neural network to identify local hot spots or cold zones in the temperature field. The heat conduction prediction module can allow the combination of finite element analysis and an LSTM time series model to predict the temperature distribution trend in the next 5 seconds. The dynamic visualization module supports generating pseudo-color three-dimensional heat maps and can be viewed interactively in real time through a visualization device, making it easy for users to intuitively and deeply understand the temperature field information. Among them, the visualization device can be AR / VR, but is not limited to this, and can be determined according to actual needs.

[0078] Further, the communication and storage unit can support a variety of multi-modal data transmission protocols, which can be flexibly selected according to actual needs to ensure that data can be quickly and stably transmitted to the designated device. At the same time, the communication and storage unit integrates a large-capacity data storage function, which can efficiently store the collected multi-channel temperature data and related auxiliary information, facilitating subsequent data query, analysis and processing.

[0079] It is worth mentioning that each module involved in the present embodiment is a logical module. In actual application, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present application, units that are not closely related to solving the technical problems proposed in the present application are not introduced in the present embodiment, but this does not mean that there are no other units in the present embodiment.

[0080] Please refer to Figure 2 The present application also provides a multi-channel synchronous temperature measurement data processing method, which is applied to the multi-channel synchronous temperature measurement device of any of the above embodiments.

[0081] It can be understood that the multi-channel synchronous temperature measurement data processing method provided by the present application includes at least the following steps in the actual execution process.

[0082] First, perform step S1 to generate three-phase synchronous pulses based on FPGA to trigger laser radar scanning, multi-spectral exposure and infrared sampling respectively. Step S1 is used for synchronous triggering and data acquisition.

[0083] Specifically, start the synchronization control unit, and the FPGA generates three-phase synchronous pulses to accurately trigger laser radar scanning, multi-spectral exposure and infrared sampling respectively. The laser radar module automatically switches the scanning mode according to the target state, the spectral imaging module collects the material reflectivity and surface pollution state, and the infrared temperature measurement module synchronously acquires the two-dimensional temperature distribution data of the target surface. The data collected by each sensor is transmitted to the data fusion processing unit through the corresponding interface.

[0084] Secondly, perform step S2 to calibrate and align the laser radar module, spectral imaging module and infrared temperature measurement module. Step S2 is for spatio-temporal calibration and alignment.

[0085] Specifically, in step S2, it includes:

[0086] Step S201, laser radar-infrared geometric alignment, based on SVD decomposition to solve the rotation translation matrix.

[0087] Step S202, multispectral-infrared feature matching, using an improved ORB algorithm to match feature points.

[0088] It can be understood that through the multi-sensor spatio-temporal calibration module, the laser radar and the infrared temperature measurement module are spatially registered based on singular value decomposition to solve the rotation translation matrix. At the same time, the time axis of multispectral and infrared data is aligned using a cubic spline interpolation algorithm to ensure the consistency of multi-sensor data in space and time. In the calibration process, known standard objects or scenes can be used for calibration to improve the accuracy of calibration.

[0089] Further, perform step S3 to initialize the emissivity, output the optimal emissivity by BP neural network combined with multispectral reflectance and surface normal optimization. In step S3, it is used for emissivity correction and temperature field construction.

[0090] It can be understood that the emissivity dynamic correction module optimizes the emissivity parameter through the BP neural network according to the multispectral material classification result and the laser radar surface normal direction. The three-dimensional temperature field modeling unit projects the infrared temperature data to the laser radar point cloud surface, combines the bidirectional reflectance distribution function to correct the surface reflection characteristics, and generates an accurate three-dimensional temperature field pseudo-color heat map. In the modeling process, the algorithm parameters are constantly optimized to improve the accuracy and efficiency of temperature field modeling.

[0091] Further, perform step S4 to perform dynamic heat conduction prediction and early warning.

[0092] Specifically, in step S4, it specifically includes:

[0093] Step S401, multi-physical field modeling, which solves the spatio-temporal evolution of the temperature field by coupling the heat conduction equation and the fluid dynamics model.

[0094] Step S402, time series prediction fusion, which inputs the historical temperature sequence through the LSTM network and outputs the predicted value in the next 5 seconds.

[0095] Further, the abnormal temperature detection module monitors the temperature field in real time based on a convolutional neural network to identify local hot spots or cold zones. The heat conduction prediction module combines finite element analysis and an LSTM time series model to predict the temperature distribution trend in the next 5 seconds. Once an abnormal situation is detected or a possible temperature abnormal change is predicted, an alarm is sent out in time to remind the user to take appropriate measures.

[0096] Finally, step S5 is performed to realize multi-thread non-blocking communication based on a message queue.

[0097] In step S5, a ZeroMQ message queue framework can be used to build a distributed communication system, and a standardized message protocol (such as JSON format) is defined, which includes data type identification, timestamp, channel ID, temperature value, and auxiliary information. Each module thread is connected to the message bus through an independent message socket, where the acquisition thread pushes the raw data to the queue in PUSH mode, and the processing thread subscribes to the queue data in PULL mode. A multi-thread parallel processing mechanism is used, and each thread processes an independent message shard. The queue sets dynamic water level control (high water level 10000, low water level 2000), and when the queue is congested, the flow control strategy is automatically triggered to prioritize processing of critical channel data (such as temperature abnormal alarm).

[0098] The communication and storage unit stores the processed data according to the set storage format and path, and transmits the data to the remote server or other terminal equipment through the selected multi-modal data transmission protocol according to the user's demand, facilitating the user to view, analyze and manage the data. In the data transmission process, data encryption technology is used to ensure the security of the data.

[0099] The step division of the above methods is only for clarity, and in implementation, one step can be combined or some steps can be split and decomposed into multiple steps, as long as the same logical relationship is included, and all are within the protection scope of the patent; adding insignificant modifications or introducing insignificant designs in the algorithm or process, but not changing the core design of the algorithm and process, are within the protection scope of the patent.

[0100] In addition, some embodiments of the present application also provide an electronic device. The electronic device can be various forms of digital computers, such as laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices.

[0101] The electronic device includes one or more processors and a memory having stored therein computer program instructions, which, when executed, cause the processors to perform the steps of the method provided by any one or more embodiments described above. Figure 3 An exemplary structural diagram of the electronic device is disclosed. As shown in Figure 3 The electronic device includes one or more processors 1101, a memory 1102, and an interface for connecting the components, including a high-speed interface and a low-speed interface. The components are interconnected by different buses, and can be mounted on a common motherboard or otherwise, as desired. The processor can process instructions for execution within the electronic device, including instructions stored in the memory or on the memory to display graphical information for a GUI on an external input / output device, such as a display device coupled to the interface. In some other embodiments, multiple processors and / or multiple buses can be employed as desired, along with multiple memories and types of memory. Also, multiple electronic devices can be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In this regard, the components illustrated herein can be implemented with various types of hardware, software, components, systems, methods, and / or computer program products such as those described herein, and the particular implementation of the components is not intended to limit the application described and / or claimed herein.

[0102] The electronic device can also include an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103, and the output device 1104 can be connected through a bus or other means, Figure 3 for example, by way of example, by way of example.

[0103] The input device 1103 can receive input digital or character information, and generate key signal input with respect to user settings and function controls of the electronic device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, and the like. The output device 1104 can include a display device, an auxiliary lighting device (e.g., an LED), a tactile feedback device (e.g., a vibration motor), and the like. The display device can include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0104] To provide for interaction with a user, the electronic device can be a computer. The computer has a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0105] In the embodiments of the present application, the computer program / instruction is stored on the computer readable medium, and the computer program / instruction is executed by the processor to implement the steps of the method provided by any one or more of the embodiments. The computer readable medium can be included in the electronic device described in the above embodiments, or can exist separately and not be assembled into the device. The computer readable medium carries one or more computer readable instructions.

[0106] The memory 1102 can be used to store non-transitory software programs, non-transitory computer executable programs and modules. The processor 1101 executes various functions and data processing of the server by running the non-transitory software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more of the embodiments in the present application.

[0107] The memory 1102 can include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required by a function. The data storage area can store data created according to the use of the electronic device. In addition, the memory 1102 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 1102 can optionally include a memory disposed remotely from the processor 1101, and these remote memories can be connected to the electronic device through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0108] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0109] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc-read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0110] Computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0111] In the above-described embodiments, all or a part thereof can be realized by software, hardware, firmware, or any combination thereof. For example, an application specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device can be used. In some embodiments, a software program of the present application can be executed by a processor to implement the above steps or functions. Likewise, a software program of the present application (including related data structures) can be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, or a floppy disk, and the like. In addition, some steps or functions of the present application can be implemented by hardware, such as a circuit that cooperates with a processor to perform the respective steps or functions.

[0112] The computer program product provided by the embodiments of the present application includes one or more computer programs / instructions, which, when executed by a processor, generate all or part of the processes or functions described in the embodiments of the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media sets. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0113] The computer program product of the present application can be a computer program implemented on one or more computers. The program segments / computer instructions can be stored in a tangible computer usable medium, or transmitted from a computer data signal, or floppy disk accompanying the application, and comprise a number of computer readable program segments designed to implement the methods of the application. The medium can be a magnetic disk, optical disk, or any other suitable medium. The program segments can be downloaded over a network from an on-line service provider or a manufacturer. The program segments can also be embodied in a computer readable signal directed to an appropriate computer system, which interprets the signal into computer readable program code.

[0114] The above description is only exemplary implementation of the present application, and is not intended to limit the patent scope of the present application. Any equivalent structural transformation made in the technical concept of the present application, or direct / indirect application in other related technical fields, using the content of the present application specification and drawings, is included in the patent protection scope of the present application.

Claims

1. A multi-channel synchronous temperature measurement device, characterized in that: include: Multimodal sensor fusion platform, which includes: The LiDAR module uses a 1550nm wavelength laser source and a solid-state MEMS micro-vibration mirror to acquire three-dimensional spatial point cloud data of the target; Spectral imaging module, integrating visible light, short-wave infrared and medium-wave infrared band sensors, used to collect material reflectance and surface contamination status; and The infrared temperature measurement module uses an uncooled vanadium oxide focal plane array with a temperature measurement range of -40°C to 1500°C; The laser radar module, multispectral imaging module, and infrared temperature measurement module are fixed by a rigid bracket, with a field of view overlap of ≥80% and an optical axis alignment deviation of ≤0.1°; Synchronous control unit, based on FPGA parallel architecture, integrates dual redundant clock sources, and drives multi-channel synchronous triggering through optocoupler isolation circuits; Data fusion processing unit, including: Multi-sensor spatiotemporal calibration module, which solves the rotation and translation matrices of the lidar and infrared temperature measurement modules based on singular value decomposition; The emissivity dynamic correction module allows combining multispectral material classification with the LiDAR surface normal direction to optimize the emissivity parameters through a BP neural network; and A 3D temperature field modeling unit allows infrared temperature data to be projected onto the surface of the LiDAR point cloud to generate a pseudo-color thermal map; Communication and storage unit, supporting multimodal data transmission protocols and integrated data storage.

2. The multi-channel synchronous temperature measurement device according to claim 1, characterized in that: The LiDAR module allows dynamic scanning mode switching, including: Enable high-speed scanning for moving targets, with a frame rate ≥ 100Hz and a point cloud density ≥ 50 points / m 2 , enable high-precision mode for static targets, with a frame rate ≥ 30Hz and a point cloud density ≥ 200 points / m 2 ; When the mode is switched, the synchronous control unit automatically adjusts the trigger frequency.

3. The multi-channel synchronous temperature measurement device according to claim 1, characterized in that: The multispectral imaging module further includes: Built-in blackbody and diffuse white plate, automatically performs flat field correction every 30 minutes; and The beam splitter prism co-optical axis unit is used to allow visible light, short-wave infrared and medium-wave infrared to share the same optical path through the beam splitter prism, and ensure imaging consistency based on the focal length deviation compensation algorithm.

4. The multi-channel synchronous temperature measurement device according to claim 3, characterized in that: The infrared temperature measurement module is further provided with a correction submodule, which can be used to perform the following steps: Use a blackbody radiation source to calibrate the gain and offset parameters of each pixel at low and high temperatures; Real-time collection of ambient temperature data, and dynamic compensation of temperature drift through piecewise cubic spline interpolation algorithm; Based on the multispectral material classification results, an initial emissivity value is assigned to each area.

5. The multi-channel synchronous temperature measurement device according to claim 1, characterized in that: The synchronization control unit also includes a timestamp injection module, which injects the FPGA counter value when the ADC conversion is completed, and aligns the lidar scanning start signal, the multispectral exposure pulse and the infrared sampling time through a cross-correlation algorithm; The noise covariance may be updated every 10 ms through the Kalman filter model.

6. The multi-channel synchronous temperature measurement device according to claim 1, characterized in that: The data fusion processing unit also includes an abnormal temperature detection module, a heat conduction prediction module and a dynamic visualization module. The abnormal temperature detection module is based on a convolutional neural network to identify local hot spots or cold areas in the temperature field; The heat conduction prediction module allows combining finite element analysis with LSTM time series model to predict the temperature distribution trend within the next 5 seconds; and The dynamic visualization module can be used to generate pseudo-color 3D heat maps, supporting real-time interactive viewing on visualization devices.

7. A multi-channel synchronous temperature measurement data processing method, characterized in that: include: Step S1: Generate three-phase synchronous pulses based on FPGA to trigger lidar scanning, multispectral exposure and infrared sampling respectively; Step S2: Calibrate and align the laser radar module, spectral imaging module, and infrared temperature measurement module, which includes: Step S201: LiDAR-IR geometric alignment, solving the rotation and translation matrix based on SVD decomposition; Step S202: multispectral-infrared feature matching, using an improved ORB algorithm to match feature points; Step S3: Initialize the emissivity, and output the optimal emissivity by combining multispectral reflectivity and surface normal optimization through BP neural network; Step S4: Perform dynamic heat conduction prediction and early warning, which includes the following steps: Step S401, multi-physics field modeling, which solves the spatiotemporal evolution of the temperature field by coupling the heat conduction equation with the fluid dynamics model; Step S402: Time series prediction fusion, which inputs the historical temperature sequence through the LSTM network and outputs the predicted value for the next 5 seconds; Step S5: Implement multi-threaded non-blocking communication based on the message queue.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; and A memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the multi-channel synchronous temperature measurement data processing method according to claim 7.

9. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the multi-channel synchronous temperature measurement data processing method described in claim 7 is implemented.

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