Dynamic calibration methods, systems, and camera equipment

Abstract

This application relates to a dynamic calibration method, system, and camera device for imaging equipment. The method includes: controlling the camera device to operate according to multiple preset load modes to simulate dynamic temperature rises during actual operation; the load modes include state changes at different resolutions and activation state changes of illumination components; under the multiple preset load modes, acquiring the raw temperature output by a temperature sensor and a reference temperature of the test environment; fitting the raw temperature to the reference temperature and determining compensation parameters corresponding to the camera device according to a preset compensation model; and using the compensation parameters to perform temperature compensation on the temperature sensor. This method enables dynamic calibration of the temperature sensor in the camera device without disassembling the device, ensuring that the compensation parameters match the dynamic temperature rise during actual operation of the camera device, thus improving the accuracy of temperature measurement.

Description

Technical Field

[0001] This application relates to the field of intelligent camera equipment manufacturing technology, and in particular to a dynamic calibration method, system and camera equipment for camera equipment. Background Technology

[0002] With the expansion of IoT applications, network cameras are integrating various environmental sensors, such as temperature and humidity sensors. Additionally, the cameras themselves can be equipped with infrared illumination components for night vision. When the camera is operating, high-resolution encoding causes a significant temperature rise in the main control chip, and the activation and deactivation of the infrared illumination component also generates a severe thermal shock. This dynamic temperature rise can cause sensor thermal drift, leading to inaccurate measurement data; therefore, sensor calibration is necessary for compensation.

[0003] Currently, the industry commonly uses a disassembly calibration method. This method involves removing the sensor from the camera housing, calibrating it separately in a temperature-controlled chamber, and then embedding the obtained static compensation parameters into the device. However, this method has the following drawbacks: First, the sensor position is prone to shifting during disassembly and assembly, leading to a mismatch between the compensation parameters and the actual operating conditions after assembly. Second, the temperature-controlled environment only provides the sensor's steady-state response characteristics and cannot simulate the dynamic thermal shocks generated by resolution switching, infrared lamp activation and deactivation, etc., during actual operation. Furthermore, if a wired calibration method is used, it will interrupt the video stream, affecting production line testing efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a dynamic calibration method, system, and camera device that can accurately simulate dynamic temperature rise without disassembling the device for calibration, in order to address the above problems.

[0005] In a first aspect, this application provides a dynamic calibration method for a camera device, the camera device including an illumination component and a temperature sensor, the method comprising:

[0006] The camera device is controlled to operate according to a variety of preset load modes to simulate the dynamic temperature rise generated during actual operation. The load modes include different resolution states and different activation states of the lighting components.

[0007] Under the various preset load modes, the raw temperature output by the temperature sensor and the reference temperature of the test environment are collected respectively.

[0008] Based on the fitting of the original temperature and the reference temperature, the compensation parameters corresponding to the dynamic temperature rise are determined according to the preset compensation model.

[0009] The temperature sensor is compensated using the compensation parameters.

[0010] In one embodiment, determining the compensation parameters corresponding to the camera device according to a preset compensation model includes:

[0011] Based on the change in the activation state of the lighting component, a first compensation component related to thermal shock is determined;

[0012] Based on the operating state of the camera device under a stable load, a second compensation component related to the steady-state temperature difference is determined;

[0013] Based on the state change of the resolution, determine the third compensation component related to the step temperature difference;

[0014] The compensation parameters are determined based on the first compensation component, the second compensation component, and the third compensation component.

[0015] In one embodiment, the compensation model includes:

[0016]

[0017] in, To correct the temperature, The original temperature, This refers to the baseline temperature deviation of the temperature sensor relative to the test environment when it is in standby mode. This is the thermal shock coefficient corresponding to the first compensation component. The rate of temperature rise when the lighting component changes its active state. The steady-state coefficient corresponding to the second compensation component, This refers to the steady-state temperature difference of the camera device under stable load. The step coefficient corresponding to the third compensation component. The step temperature difference during resolution switching. The time decay factor, This is a load correction factor related to the real-time power consumption of the camera device.

[0018] In one embodiment, the step of fitting the original temperature to the reference temperature and determining the compensation parameters according to a preset compensation model includes:

[0019] Using the original temperature as input and the reference temperature as target, the compensation parameters in the compensation model are solved to ensure that the deviation between the corrected temperature calculated based on the compensation model and the reference temperature meets a preset convergence condition, thereby determining the compensation parameters. The compensation parameters include... , , , and .

[0020] In one embodiment, the method further includes:

[0021] When the calibration time interval reaches a preset period, or when the real-time temperature drift of the temperature sensor exceeds a preset threshold, at least one of the compensation parameters is corrected.

[0022] Secondly, this application also provides a dynamic calibration system for camera equipment, comprising:

[0023] The camera device includes an illumination component and a temperature sensor, and the system includes:

[0024] The working condition simulation module is used to control the camera device to operate according to a variety of preset load modes to simulate the dynamic temperature rise generated during actual work. The load modes include different resolution states and / or different activation states of the lighting components.

[0025] The data acquisition module is used to acquire the raw temperature output by the temperature sensor and the reference temperature of the test environment under the various preset load modes, respectively.

[0026] The parameter calculation module is used to fit the original temperature and the reference temperature, and determine the compensation parameters corresponding to the dynamic temperature rise according to the preset compensation model.

[0027] The temperature compensation module uses the compensation parameters to perform temperature compensation on the temperature sensor.

[0028] In one embodiment, the system further includes a wireless communication module that interacts with the camera device via the LightLink protocol.

[0029] Thirdly, this application also provides a camera device, including:

[0030] An illumination component is disposed inside the camera device;

[0031] A temperature sensor is installed inside the camera device;

[0032] A memory storing a computer program for storing the computer program and compensation parameters generated by the method as described in any of the first aspects;

[0033] The processor is configured to, while executing the computer program, correct the data collected by the temperature sensor using the compensation parameters.

[0034] In one embodiment, the camera device further includes a gradient thermal conductivity substrate, on which the temperature sensor is disposed, and the gradient thermal conductivity substrate is used to reduce the dynamic temperature difference inside the camera device.

[0035] In one embodiment, the gradient thermal conductivity substrate includes a copper pillar array and a carbon nanotube radiating layer. The copper pillar array is disposed in the region where the temperature sensor is located, and the carbon nanotube radiating layer is disposed in the edge region of the substrate. The thermal conductivity of the gradient thermal conductivity substrate decreases in a gradient from the copper pillar array to the carbon nanotube radiating layer.

[0036] The aforementioned dynamic calibration method, system, and camera for video equipment simulate different operating conditions of the camera in actual operation through preset load modes. This allows the calibration process to cover dynamic operating conditions without disassembling the device, avoiding sensor position shift caused by disassembly and improving the dynamic response capability of sensor temperature compensation. By incorporating various factors that cause temperature rise into the compensation model, the compensated temperature is closer to the true value, thereby improving the image quality and operational stability of the video equipment under different working environments. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is an application environment diagram of the dynamic calibration method for a camera device in one embodiment;

[0039] Figure 2 This is a flowchart illustrating a dynamic calibration method for a camera device in one embodiment;

[0040] Figure 3 This is a structural block diagram of a dynamic calibration system for a camera device in one embodiment;

[0041] Figure 4 This is a schematic diagram of the gradient thermal conductivity substrate in one embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] The dynamic calibration method for camera equipment provided in this application embodiment can be applied to, for example... Figure 1The application environment is shown. The camera device 100 is connected to the calibration controller 110. During the calibration phase, the camera device 100 is placed in the calibration environment, which is equipped with a reference temperature sensor 120. The calibration controller 110 communicates with the camera device via a wireless communication module, sending control commands to the camera device to operate according to a preset load mode, and receiving raw temperature data collected by the camera device's internal temperature sensor and reference temperature data collected by the reference temperature sensor. Based on the raw and reference temperatures, the calibration controller 110 generates compensation parameters and writes these parameters into the memory of the camera device 100.

[0044] In one exemplary embodiment, a dynamic calibration method for a camera device is provided. In this embodiment, the camera device includes a housing, an illumination component disposed within the housing, and a temperature sensor. The illumination component is used for supplemental lighting in low-light environments, such as an infrared lamp (IR lamp); the temperature sensor is used to collect temperature data inside the camera device, providing temperature data for functions such as image noise reduction, autofocus, and illumination component control.

[0045] Applying this method to Figure 1 Taking the calibration controller in the example, as an example, Figure 2 As shown, the method includes the following steps:

[0046] Step S201: Control the camera device to operate according to a variety of preset load modes to simulate the dynamic temperature rise generated during actual operation. The load modes include different resolution states and different activation states of the lighting components.

[0047] It is understood that the purpose of setting different load modes is to change the heat generated by the internal heat source of the camera device by altering its operating conditions, thereby simulating the dynamic temperature rise generated during actual operation. Therefore, the load mode includes at least one mode capable of changing the internal power consumption state of the camera device. For example, different load modes may include the workload of the image processing unit, including resolution, frame rate, encoding complexity, or changes in the activation state of the lighting components, or changes in the activation state of other functional modules, etc.

[0048] Changing the load pattern can be a combination of changing one or more of the above conditions.

[0049] Step S202: Under various preset load modes, the raw temperature output by the temperature sensor and the reference temperature of the test environment are collected respectively.

[0050] The raw temperature is the uncorrected measurement output by the internal temperature sensor of the camera device, which includes both ambient temperature and interference components from internal heat sources. The reference temperature of the test environment is the temperature value collected by a reference temperature sensor located outside the camera device. This sensor is unaffected by internal heat sources, and its measurement is considered the true temperature value. The difference between the two is the temperature difference that needs to be compensated.

[0051] Under each load mode, the raw temperature and reference temperature are collected synchronously to obtain multiple sets of corresponding relationships.

[0052] Step S203: Fit the original temperature and the reference temperature, and determine the compensation parameters corresponding to the dynamic temperature rise according to the preset compensation model.

[0053] The compensation model is a pre-built model used to describe the mapping relationship between the output of the temperature sensor and the actual temperature. The compensation parameters are the undetermined coefficients in the compensation model, and their specific values ​​need to be determined through the fitting process.

[0054] It is understandable that this model contains multiple function terms characterizing the temperature rise characteristics of the camera device under different operating conditions, such as thermal shock, steady-state temperature rise, and thermal inertia decay. Specifically, it may include: function terms related to heat source intensity, used to characterize the impact of the heat generated by the main heat source on the temperature sensor under different load modes, which can be quantified by parameters such as power consumption and computing resource utilization; function terms related to thermal shock response, used to characterize the instantaneous response of the temperature sensor due to thermal inertia when the temperature changes abruptly, which can be described by the temperature rise rate, shock response function, or step response curve; and function terms related to thermal accumulation effect, used to characterize the process of gradually accumulating heat inside the device under continuous working conditions, which can be described by models such as time integration terms and accumulated power consumption.

[0055] Specifically, the fitting process uses the reference temperature as the target value and the original temperature as the input data. A nonlinear fitting method is used to identify the parameters of the compensation model, so as to minimize the error between the compensated temperature output by the model and the reference temperature, thereby obtaining the compensation parameters.

[0056] Step S204: Perform temperature compensation on the temperature sensor using compensation parameters.

[0057] After calibration, the compensation parameters determined through the above steps are written into the camera device's memory. During camera operation, the stored compensation parameters are read, and the real-time data from the temperature sensor is compensated, outputting the compensated temperature value.

[0058] The dynamic calibration method in this embodiment simulates the dynamic temperature rise of the camera device under different operating conditions by running a preset load mode, replacing the traditional disassembly calibration method. This avoids sensor position shift caused by disassembly. By establishing a compensation model based on different temperature rise factors under multiple load modes, the compensated temperature can be closer to the true value, eliminating thermal drift error.

[0059] In one embodiment, in step S201 above, controlling the camera device to operate according to multiple preset load modes can be achieved in the following ways:

[0060] Different load modes are numbered and set as a timing sequence. For example, the load mode sequence includes: controlling the camera device to sequentially switch from a first resolution to a second resolution, then to a third resolution, and maintaining this switch for a preset duration after each resolution change, causing the image processing chip's heat generation to rise accordingly with the step change in computational load. Simultaneously, at specific time points, the illumination component is controlled to switch from an off state to an on state to simulate the instantaneous thermal shock generated when night vision mode is activated. For example, the camera device can be controlled to sequentially switch resolutions in the order of 720p→1080p→4K, running continuously for a first duration at each resolution; when running at 4K resolution, the infrared illumination lamp is controlled to switch from an off state to an on state, maintaining this for a second duration before turning off, to simulate the process of night vision mode being activated and deactivated.

[0061] By combining the resolution switching and illumination component activation states mentioned above, the dynamic temperature rise process generated by the camera device in actual use due to user adjustment of resolution, changes in ambient illumination, and switching of night vision mode can be reproduced during the calibration phase. This includes steady-state temperature rise, instantaneous thermal shock, and the cumulative effect of thermal inertia.

[0062] In an exemplary embodiment, compensation parameters corresponding to the camera device are determined according to a preset compensation model, specifically including:

[0063] Step S301: Determine the first compensation component related to thermal shock based on the change in the activation state of the lighting component.

[0064] Among them, thermal shock can be generated when the lighting component is turned on instantaneously, characterized by a high rate of temperature rise and a short duration. The first compensation component is used to quantify the impact of the thermal shock on the temperature rise of the sensor.

[0065] Step S302: Determine the second compensation component related to the steady-state temperature difference based on the operating state of the camera device under a stable load.

[0066] Among them, stable load refers to the image processing chip generating continuous heat when the camera device is running continuously under constant working conditions such as fixed resolution and fixed frame rate, resulting in a stable temperature difference between the internal temperature of the device and the external environment.

[0067] Step S303: Determine the third compensation component related to the step temperature difference based on the state change of the resolution.

[0068] It is understandable that when the resolution is switched, the computational load of the image processing chip changes, resulting in a step change in its heat generation.

[0069] Step S304: Determine the compensation parameters based on the first compensation component, the second compensation component, and the third compensation component.

[0070] In this embodiment, the sensor's compensation temperature is decomposed into three different types: thermal shock, steady-state temperature difference, and step temperature difference, and corresponding compensation components are established. This allows the compensation parameters to be differentiated for the causes of temperature rise under different operating conditions, thereby effectively improving the accuracy of temperature compensation.

[0071] In an exemplary embodiment, the above compensation model can take the following form:

[0072]

[0073] in, To correct the temperature, The original temperature, This refers to the baseline temperature deviation of the temperature sensor relative to the test environment when it is in standby mode. This is the thermal shock coefficient corresponding to the first compensation component. The rate of temperature rise when the lighting component changes its active state. The steady-state coefficient corresponding to the second compensation component, This refers to the steady-state temperature difference of the camera device under stable load. The step coefficient corresponding to the third compensation component. The step temperature difference during resolution switching. The time decay factor, This is a load correction factor related to the real-time power consumption of the camera device.

[0074] It should also be noted that, for the thermal shock coefficient of the first compensation component, due to the time step... Since they are fixed values, they can be combined into one item. .

[0075] Furthermore, in the above formula, and It can be defined as:

[0076]

[0077]

[0078] in, Let be the time constant, t be the current time, k be the preset load factor, and P be the real-time power consumption of the camera device. This represents the maximum power consumption.

[0079] In one embodiment, based on the above compensation model, fitting the original temperature and the reference temperature, and determining the compensation parameters according to the preset compensation model, can be implemented in the following manner:

[0080] Using the original temperature as the input value and the reference temperature as the target value, the compensation parameters in the compensation model are solved to ensure that the deviation between the corrected temperature calculated based on the compensation model and the reference temperature meets the preset convergence conditions, thereby determining the compensation parameters.

[0081] Specifically, for each sampling time i, the original temperature and the rate of temperature rise are... steady-state temperature difference Step temperature difference Substituting the real-time power consumption P and time t into the model, the corrected temperature is calculated. Establish the objective function, for example... That is, the sum of squared errors between the corrected temperature and the reference temperature at all sampling points is taken as the objective to be minimized.

[0082] Nonlinear optimization algorithms, such as the least squares method, are used to iteratively solve the objective function J until it converges to a preset threshold. The undetermined coefficients obtained at this point are the compensation parameters. The compensation parameters include... , , , and ,for and This also includes the load factor k and the time constant. .

[0083] The above fitting process is completed during the calibration stage, and the compensation parameters obtained from the fitting are written into the non-volatile memory of the camera device.

[0084] In one embodiment, the method further includes a self-maintenance step for dynamically correcting stored compensation parameters during routine operation of the camera device. Specifically, when the calibration time interval reaches a preset period, or when the real-time temperature drift of the temperature sensor exceeds a preset threshold, at least one parameter among the compensation parameters is corrected.

[0085] For example, when the trigger condition is met, the camera automatically executes a preset load mode to reproduce the calibration phase scene, while simultaneously acquiring the output temperature of the temperature sensor and recording load status and time information. Then, based on the above data, at least one parameter in the stored compensation parameters is corrected. Since there is no external reference temperature sensor during the operation phase, the complete fitting step cannot be repeated. Therefore, other methods can be used for parameter fine-tuning. For example, if a systematic deviation is found between the corrected temperature and the historical record under the same load mode, the attenuation factor in the compensation model can be adjusted. Fine-tuning is performed; if a change is found in the correlation between power consumption and temperature rise, the load factor k is fine-tuned. The corrected compensation parameters replace the original parameters in memory for subsequent compensation calls.

[0086] In this embodiment, by introducing a self-maintenance mechanism, the camera equipment's adaptability to environmental changes, equipment aging, and other factors can be improved, the effective period of calibration parameters can be extended, and the long-term stability of temperature compensation can be enhanced.

[0087] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0088] Based on the same inventive concept, this application also provides a dynamic calibration system for a camera device to implement the dynamic calibration method for the camera device described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more embodiments of the dynamic calibration system for a camera device provided below can be found in the limitations of the dynamic calibration method for the camera device described above, and will not be repeated here.

[0089] In one exemplary embodiment, such as Figure 3 As shown, a dynamic calibration system 400 for a camera device is provided, comprising:

[0090] The working condition simulation module 401 is used to control the camera equipment to operate according to a variety of preset load modes to simulate the dynamic temperature rise generated during actual work. The load modes include different resolution states and / or different activation states of the lighting components.

[0091] The data acquisition module 402 is used to acquire the raw temperature output by the temperature sensor and the reference temperature of the test environment under various preset load modes.

[0092] The parameter calculation module 403 is used to fit the original temperature and the reference temperature, and determine the compensation parameters corresponding to the dynamic temperature rise according to the preset compensation model.

[0093] The temperature compensation module 404 uses compensation parameters to compensate the temperature of the temperature sensor.

[0094] In one embodiment, the system further includes a wireless communication module that interacts with the camera device via the LightLink protocol.

[0095] The LightLink protocol is a low-interference wireless calibration protocol with a bandwidth of 2.4 Mbps. This protocol uses a frame priority scheduling mechanism to prioritize video stream data during calibration data transmission, ensuring that the video stream's frame rate drops by less than 5%. During calibration, the operating condition simulation module sends control commands to the camera device via the wireless communication module, causing it to operate according to a preset load mode; the data acquisition module receives raw temperature data collected by the camera device's internal temperature sensor via the wireless communication module; and the parameter calculation module, after calculating the compensation parameters, sends these parameters to the camera device via the wireless communication module, where the camera device writes them into its non-volatile memory.

[0096] This embodiment utilizes a wireless communication module based on the LightLink protocol to achieve wireless data interaction with the camera device during calibration. This avoids the problems of physical connection and video stream interruption required by traditional wired calibration, significantly improving production line calibration efficiency. Simultaneously, the low-interference characteristics of the LightLink protocol ensure the continuity of the camera device's video stream during calibration, allowing calibration to be completed online while the camera device is operating normally.

[0097] Each module in the dynamic calibration system of the aforementioned camera equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0098] In one exemplary embodiment, a camera device is provided, the camera device comprising:

[0099] Lighting components are installed inside the camera equipment;

[0100] Temperature sensor, located inside the camera device;

[0101] The memory stores a computer program and is used to store the computer program and compensation parameters generated by the dynamic calibration method of any of the above-mentioned camera devices.

[0102] The processor is used to correct the data collected by the temperature sensor using compensation parameters when executing a computer program.

[0103] In one embodiment, the camera device further includes a gradient thermal conductivity substrate, on which the temperature sensor is disposed, and the gradient thermal conductivity substrate is used to reduce the dynamic temperature difference inside the camera device.

[0104] Furthermore, such as Figure 4 As shown, the gradient thermal conductivity substrate includes a copper pillar array 610 and a carbon nanotube radiating layer 620, and multiple temperature sensors 630 are disposed on the substrate. The copper pillar array is disposed in the region where the temperature sensors 630 are located, and the carbon nanotube radiating layer 620 is disposed in the edge region of the substrate. The thermal conductivity of the gradient thermal conductivity substrate decreases in a gradient from the copper pillar array to the carbon nanotube radiating layer.

[0105] It is understandable that copper pillar arrays, due to their high thermal conductivity, can be placed in areas where temperature sensors and the main control chip are located, as well as in areas near other heat sources. The carbon nanotube radiating layer, located at the edge of the substrate, can be used to radiate heat to the housing. The substrate can be designed as a ring, with its thermal conductivity distributed in a gradient from the center to the edge.

[0106] The composite structure design described above can improve the uniformity of heat distribution inside the camera device without adding additional heat dissipation components, thereby further enhancing the temperature compensation effect.

[0107] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0108] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0109] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A dynamic calibration method for a camera device, characterized in that, The camera device includes an illumination component and a temperature sensor, and the method includes: The camera device is controlled to operate according to a variety of preset load modes to simulate the dynamic temperature rise generated during actual operation. The load modes include different resolution states and different activation states of the lighting components. Under the various preset load modes, the raw temperature output by the temperature sensor and the reference temperature of the test environment are collected respectively. Based on the fitting of the original temperature and the reference temperature, the compensation parameters corresponding to the dynamic temperature rise are determined according to the preset compensation model. The temperature sensor is compensated using the compensation parameters.

2. The method according to claim 1, characterized in that, The step of determining the compensation parameters corresponding to the camera device according to the preset compensation model includes: Based on the change in the activation state of the lighting component, a first compensation component related to thermal shock is determined; Based on the operating state of the camera device under a stable load, a second compensation component related to the steady-state temperature difference is determined; Based on the state change of the resolution, determine the third compensation component related to the step temperature difference; The compensation parameters are determined based on the first compensation component, the second compensation component, and the third compensation component.

3. The method according to claim 2, characterized in that, The compensation model includes: in, To correct the temperature, The original temperature, This refers to the baseline temperature deviation of the temperature sensor relative to the test environment when it is in standby mode. This is the thermal shock coefficient corresponding to the first compensation component. The rate of temperature rise when the lighting component changes its active state. The steady-state coefficient corresponding to the second compensation component, This refers to the steady-state temperature difference of the camera device under stable load. The step coefficient corresponding to the third compensation component. The step temperature difference during resolution switching. The time decay factor, This is a load correction factor related to the real-time power consumption of the camera device.

4. The method according to claim 3, characterized in that, The fitting process based on the original temperature and the reference temperature, and the determination of compensation parameters according to a preset compensation model, includes: Using the original temperature as input and the reference temperature as target, the compensation parameters in the compensation model are solved to ensure that the deviation between the corrected temperature calculated based on the compensation model and the reference temperature meets a preset convergence condition, thereby determining the compensation parameters. The compensation parameters include... , , , and .

5. The method according to claim 1, characterized in that, The method further includes: When the calibration time interval reaches a preset period, or when the real-time temperature drift of the temperature sensor exceeds a preset threshold, at least one of the compensation parameters is corrected.

6. A dynamic calibration system for a camera device, characterized in that, The camera device includes an illumination component and a temperature sensor, and the system includes: The working condition simulation module is used to control the camera device to operate according to a variety of preset load modes to simulate the dynamic temperature rise generated during actual work. The load modes include different resolution states and / or different activation states of the lighting components. The data acquisition module is used to acquire the raw temperature output by the temperature sensor and the reference temperature of the test environment under the various preset load modes, respectively. The parameter calculation module is used to fit the original temperature and the reference temperature, and determine the compensation parameters corresponding to the dynamic temperature rise according to the preset compensation model. The temperature compensation module uses the compensation parameters to perform temperature compensation on the temperature sensor.

7. The system according to claim 6, characterized in that, The system also includes a wireless communication module, which interacts with the camera device via the LightLink protocol.

8. A camera device, characterized in that, include: An illumination component is disposed inside the camera device; A temperature sensor is installed inside the camera device; A memory storing a computer program for storing the computer program and compensation parameters generated by the method as described in any one of claims 1-5; The processor is configured to, while executing the computer program, correct the data collected by the temperature sensor using the compensation parameters.

9. The camera device according to claim 8, characterized in that, The camera device also includes a gradient thermal conductivity substrate, on which the temperature sensor is disposed. The gradient thermal conductivity substrate is used to reduce the dynamic temperature difference inside the camera device.

10. The camera device according to claim 9, characterized in that, The gradient thermal conductivity substrate includes a copper pillar array and a carbon nanotube radiating layer. The copper pillar array is disposed in the area where the temperature sensor is located, and the carbon nanotube radiating layer is disposed in the edge area of ​​the substrate. The thermal conductivity of the gradient thermal conductivity substrate decreases in a gradient from the copper pillar array to the carbon nanotube radiating layer.

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