271results about "Pyrometry for temperature profile" patented technology

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention relates to the technical field of quality detection, in particular to a gas atomization powder sphericity degree multi-dimensional online analysis system and a method thereof.The gas atomization powder sphericity degree multi-dimensional online analysis system comprises a dynamic dual-mode imaging module, a transverse wind field auxiliary detection unit and a multi-dimensional feature fusion analysis module; the transverse wind field auxiliary detection unit is used for applying controllable transverse wind power through an airflow nozzle orthogonal to the powder falling direction and measuring the deflection track of gas atomization powder ball particles in combination with a laser displacement sensor. The online calibration module is used for spraying standardized spherical particles to the powder flow according to a preset period; the traditional density detection depends on destructive sampling and off-line measurement, the efficiency is low, and the whole production line particles cannot be covered; through an orthogonal wind field trajectory inversion technology, a density value and an internal defect mark are synchronously output through non-contact dynamic measurement, 100% lossless online full inspection of a production line is realized, and sampling limitation and aging bottleneck of a traditional means are broken through.

The invention relates to an industrial gas tank storage place early warning method and system. The method comprises the following steps: acquiring image time sequence data at least representing temperature field distribution of a target monitoring area, generating a temperature image sequence, determining area temperature change characteristics and tank body surface reflection characteristics in the area, and generating reflection area probability distribution of the target monitoring area to determine initial gas leakage probability data of the target monitoring area; generating baseline feature data at least representing background thermal field distribution of the target monitoring area; and performing association processing on the initial gas leakage probability data and the baseline feature data to generate fusion time sequence features and determine a gas leakage diffusion weight so as to correct the initial gas leakage probability data to obtain corrected gas leakage probability data. By adopting the method, the gas leakage probability can be accurately judged by comprehensively utilizing temperature field time sequence information, tank body surface reflection characteristics and background thermal field distribution characteristics under the condition of a complex background thermal field, so that early warning is realized.

A method for analyzing images includes obtaining a visible light image which depicts a structure and a thermal image which depicts the structure, implementing one or more first machine-learned models to identify a class associated with the structure in the visible light image, based on the visible light image, implementing one or more second machine-learned models to identify one or more objects associated with the structure in the visible light image, based on the visible light image and the class associated with the structure, determining a temperature associated with each object among the one or more objects, based on the thermal image, evaluating for each object among the one or more objects, whether a temperature value associated with a respective object among the one or more objects satisfies a temperature criteria associated with the respective object, and providing an output based on the evaluating.

The invention discloses a three-dimensional combustion multi-parameter field inversion method based on two-dimensional infrared imaging and hyperspectral light field imaging, and belongs to the technical field of flame combustion detection. The invention aims to solve the problem of low reconstruction precision caused by limited spontaneous radiation information and poor low-temperature region signal sensitivity in the existing combustion flow field temperature field reconstruction. Comprising the following steps: obtaining a flame two-dimensional infrared temperature distribution image and a flame hyperspectral light field image; a two-dimensional blackbody radiation intensity curve and a three-dimensional blackbody radiation intensity curve are obtained through calibration according to the blackbody radiation law; combining the corresponding image gray values to obtain two-dimensional infrared radiation intensity, three-dimensional visible radiation intensity and three-dimensional infrared radiation intensity; determining a temperature threshold value of the high-temperature area; constructing a multispectral radiation source term matrix equation, and reconstructing a three-dimensional temperature field; obtaining a separated flame hyperspectral light field image, and reconstructing a low-temperature region and a reconstructed temperature field; and combining with a high-temperature region reconstruction temperature field to obtain a whole temperature field. The method is used for three-dimensional combustion multi-parameter field inversion.

The invention belongs to the technical field of nondestructive testing, and relates to a method for imaging residual oil in a powder metallurgy component based on pulse thermal excitation, which comprises the following steps: applying preheating disturbance pulse to a target area to generate a pre-excitation thermal field; applying a main detection pulse to the same target area at an interval of a preset time lag, and generating a transient composite hot spot; obtaining the whole process from formation to cooling dissipation of the transient composite hot spot, and generating a dynamic thermal field image sequence; images in a specific time window are extracted, and a key stage hot spot evolution image set is generated; the physical state of residual oil in the target area is judged, and a residual oil state label is generated; performing mapping association on the residual oil state label generated at each detection position and the two-dimensional coordinate of the residual oil state label to generate a residual oil state visual image; according to the invention, the problem of missing detection or misjudgment caused by difficulty in effectively amplifying weak thermal physical property difference between oil liquids in different states due to single excitation mode in the existing thermal imaging technology is solved.

The invention relates to the technical field of temperature monitoring, in particular to a severe pneumonia patient body surface temperature distribution monitoring method based on thermal imaging. The method comprises the following steps: solving a dynamic deformation field of the body surface of a patient relative to a standard three-dimensional body surface model through a minimized deformation energy function, and generating an initial monitoring area by using the dynamic deformation field and an internal parameter matrix of a thermal imager; an active contour model is adopted to optimize the boundary of the initial monitoring area, and a self-adaptive optimization monitoring area matched with the current temperature distribution characteristics is generated; if the credibility of the temperature feature vector is low, the temperature feature vector is compensated in combination with vascular active drug infusion information, and a pathophysiological residual feature sequence is obtained. According to the method, an initial monitoring area capable of tracking a target anatomical structure in real time is generated, the spatial consistency and anatomical accuracy of data acquisition are ensured, and a temperature feature sequence for precise time sequence analysis can be generated.

A metrology system may measure thermal conductance across an interface between a first material layer bonded to a second material layer. The first material layer may include a first outward facing and the second material layer may include a second outward facing surface. The system may include a heating source which provides periodically varying heat across the first outward facing surface, and a heat sink in contact with the second outward facing surface. The system may further include a first thermal measurement device configured to measure a temperature of the first outward facing surface, and a second thermal measurement device configured to measure a temperature of the second outward facing surface. The system may generate, based on a plurality of measurements acquired over time, a thermal conductance measurement across the interface between the first material layer bonded to the second material layer.

The invention relates to the technical field of power failure prediction, and discloses a power equipment thermal failure monitoring and early warning system fused with infrared image processing. According to the invention, the infrared image acquisition module acquires and standardizes equipment temperature field data, and the electrical parameter synchronous acquisition module acquires load current and environment temperature in real time; the hot spot dynamic analysis module identifies and tracks an overheat area based on a dynamic threshold value, extracts spatio-temporal evolution characteristics of the overheat area, and comprehensively calculates a fault hot spot intensity factor capable of representing an early fault; the electrical fault diagnosis module starts deep diagnosis when the factor is abnormal, and accurately estimates the contact resistance value of the key part through a nonlinear recursive algorithm; and the comprehensive early warning module performs fusion evaluation on the multi-source information, and generates multi-level early warning information based on a layering strategy. According to the method, post-event alarm is converted into beforehand intervention, the sudden failure risk of the equipment is remarkably reduced, and accurate and reliable decision support is provided for predictive maintenance.

The invention relates to the technical field of image recognition, in particular to an abnormal security scene monitoring method and system for infrared thermal imaging, and the method comprises the steps: collecting a picture of a monitoring region through an infrared thermal imager, building a video frame sequence, and constructing a Gaussian mixture model for each pixel point; performing spatial analysis on the video frame sequence, establishing a foreground patch, and determining artificial environment spatial interference of each pixel point in the current frame through the foreground patch; performing time analysis on the video frame sequence in combination with the artificial environment space-time interference to obtain the artificial environment space-time interference of each pixel point in the current frame; evaluating space-time interference of an artificial environment by using a Gaussian mixture model, and judging and marking whether each pixel point in the current frame is in a candidate state or not; and presetting a decision threshold, counting the number of continuous frames with candidate state marks, comparing the number of continuous frames with the decision threshold, and distinguishing normal and abnormal environmental changes. Benign environment changes and real threats are effectively distinguished, and a large number of invalid alarms are prevented from being generated.

Presented are smart testing systems for detecting leaks in electrochemical devices, methods for making / using such testing systems, and memory-stored instructions for automating operation of such systems. A method of detecting a leak in an electrochemical device includes positioning a temperature-controlled (TC) screen with an electrothermal device at a predefined distance from an infrared camera to define therebetween a test envelope. A testing system controller commands the electrothermal device to modify the TC screen's operating temperature to a predefined screen testing temperature. After positioning an electrochemical device in the test envelope, between the TC screen and infrared camera, the infrared camera captures infrared images of the electrochemical device within the test envelope, showing the device's housing located in front of the TC screen. The system controller then analyzes the captured infrared images of the electrochemical device to determine if a gas leak is present in the device housing.

The invention relates to the technical field of metallurgical chemical equipment monitoring, and particularly discloses a gas purification heat exchanger online diagnosis system and method based on multi-source data fusion, an infrared thermal imaging data acquisition unit adopts an explosion-proof uncooled infrared focal plane detector and is used for continuously acquiring temperature field distribution infrared video streams on the outer surface of a heat exchanger; the process parameter sensing unit comprises a temperature sensor, a pressure sensor and a flow sensor, and all sensor signals are accessed to a DCS (Distributed Control System) through a PROFIBUS-DP bus; the data integration and processing unit is an industrial server deployed in a control room, acquires process parameter time sequence data from a DCS system in real time through an OPC UA protocol, and receives an infrared video stream through a private network; according to the method, accurate identification and positioning of early hidden faults are realized, the situation that small diseases drag large disasters is avoided, the diagnosis result visualization degree is high, and the operation and maintenance efficiency and the response speed are improved.

A system for monitoring a switchgear includes an infrared camera; a processing unit; and an output unit. The camera acquires a first infrared image. The processing unit determines a pixel in the first image with a maximum temperature and uses a second number of pixels to determine a temperature interval. The processing unit sorts the pixels in descending order of temperature to determine a threshold temperature and determines that a hot spot exists in the switchgear using the temperature interval for the first infrared image. The output unit is configured to output an indication of a fault in the switchgear when the determination has been made that a hot spot exists in the switchgear.

The invention discloses an active infrared thermal imaging thermal diffusion parameter inversion method and system based on a physical information neural network, and belongs to the technical field of infrared thermal imaging nondestructive testing and thermophysical parameter identification. In order to solve the problems that an active infrared thermal imaging method is difficult to steadily invert spatial distribution thermal diffusion parameters under the condition of only single-side surface temperature measurement and is sensitive to measurement noise, the method comprises the following steps: collecting surface space-time temperature field data of a tested piece under thermal excitation, and calculating a relative temperature field; normalizing the space-time coordinates, and constructing a multi-output physical information neural network; the equivalent surface heat conduction equation and the boundary and initial conditions thereof serve as a loss function of a physical constraint embedded neural network, and a Huber loss function is adopted to calculate a data fitting item; a neural network is optimized through joint training, a global volume thermal diffusion coefficient, a global equivalent thermal loss coefficient, a relative thermal diffusion coefficient field and a relative thermal loss coefficient field of spatial distribution are synchronously inverted, and a high signal-to-noise ratio temperature field is reconstructed. The method is used for material surface nondestructive testing.

The invention discloses a marking method and system for a geothermal abnormal target area, and the method comprises the steps: generating a surface temperature distribution diagram according to the remote sensing thermal infrared data of a target area, and marking a first-stage geothermal abnormal target area; a temperature measurement network is arranged in the first-stage geothermal abnormal target area, ground temperature measurement data are collected, and a shallow ground temperature distribution diagram is generated; marking a secondary geothermal abnormal target area by combining the shallow ground temperature distribution diagram and the ground surface temperature distribution diagram; and obtaining geothermal information of the second-stage geothermal abnormal target area, marking a drilling position according to the geothermal information, drilling the drilling position, collecting deep geological data, and inputting the deep geological data into a pre-trained geothermal geological model to obtain a target geothermal abnormal target area output by the geothermal geological model. According to the method, through comprehensive analysis and processing of multiple data, the accuracy and reliability of marking of the geothermal abnormal target area are improved.

The invention relates to the technical field of hydrogen energy storage and transportation safety, and discloses a hydrogen leakage high-precision detection system based on multistage sensing fusion. Comprising a multi-stage sensor data acquisition module, a data preprocessing module, a data analysis module, a hydrogen leakage risk assessment module, a self-adaptive adjustment module, an emergency risk processing module, an execution control module and a closed-loop monitoring feedback module, the data pre-processing module pre-processes the collected data; the data analysis module performs three-level risk value calculation based on the pre-processed data; the hydrogen leakage risk assessment module performs grading rating; the self-adaptive adjustment module generates a self-adaptive control instruction; the execution control module receives and executes the control instruction, the closed-loop monitoring feedback module carries out real-time monitoring and effect evaluation on an instruction execution result of the execution control module, and if the instruction execution result does not reach the standard, a secondary disposal process is triggered.

The invention discloses an outer wall hollowing detection method and device based on thermal infrared and facade point cloud fusion and a storage medium, relates to the technical field of building structure safety detection and intelligent sensing, and discloses the outer wall hollowing detection method based on thermal infrared and facade point cloud fusion. Comprising the steps that a thermal infrared image collected by a thermal infrared camera for a to-be-detected outer wall and a vertical face laser point cloud collected by a laser radar for the to-be-detected outer wall are acquired; determining a temperature abnormal area in the thermal infrared image and an outer wall point cloud subset corresponding to the temperature abnormal area in the facade laser point cloud; performing reference surface fitting on the outer wall point cloud subset data, constructing an outer wall normal deformation model reflecting concave-convex characteristics of a wall surface based on a fitting result, and extracting a local deformation abnormal region from the outer wall normal deformation model; and fusing the detection results of the temperature abnormal region and the local deformation abnormal region, and judging a target outer wall hollowing detection result. The technical effect of improving the outer wall hollowing detection accuracy is achieved.

The invention relates to the technical field of tire production, in particular to a winding machine winding contour online detection method, system and device, and the method comprises the steps: building a correlation model; collecting the three-dimensional point coordinates of the single-layer adhesive tape and the wound and compacted multi-layer composite structure and the surface temperature of the tire; acquiring real-time process parameters of the winding machine; according to the three-dimensional point coordinates, the tire surface temperature and the real-time process parameters, constructing a tire body digital twinborn model including geometric dimensions, temperature distribution and a tension state; comparing the carcass digital twin model with various preset standards to judge whether the carcass has defects or not, and judging the type of the defects in response to the carcass having the defects; and adjusting process parameters of the winding machine according to the defect type and the correlation model. Data asynchronous errors are eliminated by synchronously binding three-dimensional point clouds, temperature and process parameters, and a digital twin model fusing geometry-temperature-tension is constructed to replace dispersed detection, so that detection links are reduced, and the detection speed is increased.

The invention provides a high-precision infrared fault detection method, which comprises the steps of setting a plurality of focus positions under each response rate, and collecting data of a preset frame number in a focusing process of each focus position; the data comprises temperature data and image data; processing the acquired image data by using a multi-frame super-resolution reconstruction network, a multi-focus fusion network and a multi-response-rate fusion network to obtain a reconstructed infrared image; and fault detection is realized through a fault diagnosis network by using the reconstructed infrared image and historical temperature data. According to the invention, through multi-frame, multi-focus and multi-response-rate image fusion, high-resolution and high-dynamic-range clear imaging and accurate temperature measurement are realized; by using historical temperature data and a reconstructed infrared image, spatial features and time sequence features are aligned and fused, and intelligent thermal fault diagnosis of industrial equipment is realized.

The application discloses an ethylene cracking furnace tube infrared intelligent analysis system, which comprises, which are connected in sequence, an image acquisition module, a temperature monitoring module, a coking diagnosis module and a health management module; the image acquisition module acquires infrared images of cracking furnace tubes and burners by using an infrared thermal imaging online monitoring system, and establishes a cracking furnace tube infrared image database; the temperature monitoring module extracts and records the outer surface temperature of the ethylene cracking furnace tube from the processed infrared images of the ethylene cracking furnace tube by using an infrared image processing and recognition method; the coking diagnosis module combines the technologies of artificial intelligence and data mining, establishes a coking degree diagnosis and prediction model of the ethylene cracking furnace tube, and realizes early warning and targeted tracking of the coking abnormal conditions; the health management module carries out health evaluation of the cracking furnace tube according to the results obtained by the coking degree diagnosis and prediction model of the ethylene cracking furnace tube, and provides maintenance decision support. The application can realize integrated management of cracking furnace tube detection, diagnosis and maintenance.

The application discloses a kind of encryption control information nonlinear mapping methods based on chip self-heating, it is related to chip circuit's encryption information nonlinear mapping technical field, comprising: chip is divided into several physical area blocks according to two-dimensional matrix form, each physical area block is correspondingly provided with a hotspot unit and a network node, each physical area block corresponds different data representation;Each network node is numbered in disorder;Each array of heat intensity control information obtained according to encryption control information is transmitted to each corresponding network node;For a certain network node, the heat intensity control information received is transmitted to the corresponding hotspot unit, and the hotspot unit generates heat;According to the thermal imaging of chip and the data representation corresponding to each physical area block of chip, the nonlinear mapping result of encryption control information is obtained.The application improves the effectiveness of information mapping, better protects the transmission process of control information, and improves the security of information mapping.

The invention relates to the technical field of IV test systems, in particular to a nondestructive test system and method for an IV tester. The system comprises a control module, a gas circuit module and a detection module, the control module comprises a control panel and a thermal imaging camera, the gas circuit module comprises a triple piece connected with a gas source and a gas pipeline located at the rear end of the triple piece and communicated with probe rows, and the detection module comprises an IV tester and multiple groups of probe rows electrically connected with the IV tester. The probe row comprises a plurality of probe units and a plurality of probe seat sleeves, the probe units are located in the probe seat sleeves, and each probe unit comprises a probe sleeve, a probe core and a replaceable probe head; a transparent pressing plate and an insulating plate are matched with the detection module to work; the movement of the conductive probe core drives the probe head to move, the probe head contacts with a battery piece, and the probe sleeve guides the probe core to keep the moving direction of the probe core stable. According to the operation method, the problems that the contact pressure of a traditional spring probe system is uncontrollable (the damage rate is reduced by 92%), and the contact resistance is unstable are solved.

The invention relates to the technical field of building structure detection, and discloses a building wall hollowing detection method and system. The method comprises the following steps: firstly, performing thermal excitation treatment on a building wall surface, automatically selecting a natural heat source, an artificial heat source or a dual-mode heat source combining the natural heat source and the artificial heat source to perform heat loading on the wall surface based on real-time environmental conditions, and adaptively adjusting the power of the artificial heat source according to a wall surface material and an environmental temperature. And in the heat loading and subsequent cooling process, dynamic infrared thermal image sequence data of the wall surface are collected. And performing thermal gradient three-dimensional reconstruction based on the thermal image sequence data to obtain temperature gradient distribution of the surface and the deep part of the wall body. According to the temperature gradient distribution, the apparent temperature difference delta T, the edge gradient G and the depth attenuation rate beta are calculated, and a multi-dimensional hollowing criterion is established. And identifying a hollowing area by using the criterion, and marking and outputting an identification result. According to the invention, the automation degree, the accuracy and the depth identification capability of building wall hollowing detection can be effectively improved.

The invention provides an infrared measurement method for temperature distribution in a frozen soil fractional congelation and expansion process. The method comprises the steps that frozen soil is placed in a soil column barrel, a temperature sensor is arranged at the position of the frozen soil, an infrared camera is arranged outside the soil column barrel, an infrared temperature measurement window is formed in the soil column barrel at a certain height, a polyethylene thin PE plate is fixedly installed at the position of the infrared temperature measurement window, and a layer of polyvinyl chloride PVC film is pasted on the inner side of the polyethylene thin PE plate. Infrared rays emitted by the infrared camera penetrate through the PE plate and the PVC film to irradiate the frozen soil, the infrared camera receives reflected rays reflected by the frozen soil, and the temperature of the frozen soil is measured according to the reflected rays; the temperature sensor is used for measuring the temperature of some sections of the frozen soil, and the temperature measured by the temperature sensor is used for correcting the temperature, measured by the infrared camera, of the frozen soil. According to the invention, a limited number of temperature sensors and a digital image method are adopted to correct an infrared image, and high-precision temperature measurement of the total cross section of the soil column, especially key positions such as segregation cracks, is realized.

The invention belongs to the technical field of battery changing cabinets, and particularly relates to an Internet of Things energized battery changing cabinet battery state safety early warning method, which comprises the following steps: acquiring a battery surface dynamic heat flow map through an infrared micro thermal imager array; extracting a second derivative inflection point of the voltage relaxation curve in a charge-discharge pause period to represent the gelation front edge of the electrolyte; constructing an improved P2D model in which a phase change rate item is introduced, inverting a dynamic lithium ion diffusion coefficient and correcting SOC estimation; the heat flow map and the corrected SOC are fused to construct a multi-dimensional joint criterion to trigger graded early warning; and data are uploaded to a cloud platform through the Internet of Things module, so that individualized health prediction and remote intervention are realized. According to the technical scheme, the SOC estimation precision and the early warning reliability under the working condition of-10 DEG C to 0 DEG C are remarkably improved, the end-to-end response delay is controlled within 800 milliseconds, and the thermal runaway risk is effectively prevented.

Methods and apparatus for monitoring a processing apparatus are provided. In some embodiments, techniques may include determining, in real-time, a first temperature associated with a first component of the processing apparatus based on one or more measurements obtained from at least one sensor disposed relative to the first component of the processing apparatus; and based at least on the first temperature associated with the first component reaching a first threshold value, performing at least a first preventive measure that mitigates degradation or failure associated with the processing apparatus.

The present disclosure relates to a thermal analysis system and method for a battery system. The thermal analysis system includes a learning data generation device configured to generate second thermal analysis data using first thermal analysis data and a first artificial neural network model. The first thermal analysis data is obtained through numerical thermal analysis of a battery system. The thermal analysis system also includes a model construction device configured to construct a thermal analysis model by using a second artificial neural network model with the first thermal analysis data and the second thermal analysis data as learning data.

Embodiments herein are generally related to apparatus and methodologies for improved screening of important biological states in animals. The apparatus and methodologies comprise the use of high-resolution infrared thermography images collected about an animal to obtain both thermal information and behavioural information about the animal, wherein both thermal and behavioural information can be used to generate a comprehensive thermal prolife value that is indicative of the biologically important state in the animal.