Method and system for optimizing operation of intelligent camera driven by green energy

Abstract

The invention is suitable for the technical field of intelligent monitoring optimization, and provides an operation optimization method and system for an intelligent camera driven by green energy, and the method comprises the steps: determining an image collection frame rate matched with a current power supply state when the current power supply state of imaging equipment is detected to be lower than a set threshold value, and acquiring an image record log of the imaging equipment and a historical operation record of the backside illuminated CMOS image sensor. On the basis of existing green energy driven power supply and image acquisition frame rate adjustment, fine comparison of deviation amplitudes of a current image noise trend and a pixel uniformity trend relative to a standard trend is further introduced. By establishing the reference correction factor and the auxiliary correction factor, effective compensation of image quality reduction caused by performance degradation of the camera and the backside illuminated CMOS image sensor under the condition of low power supply is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring optimization, and particularly relates to a method and system for optimizing the operation of an intelligent camera driven by green energy. Background Art

[0002] An intelligent camera driven by green energy uses renewable energy such as solar energy and wind energy as the power supply source, and cooperates with advanced power management technology to achieve efficient energy utilization and adaptive frame rate adjustment. This camera not only has advantages such as low energy consumption and high endurance, but also can monitor in real time in complex environments, meet the needs of smart cities and security monitoring, while reducing operating costs and environmental pollution.

[0003] In the prior art, an intelligent camera driven by green energy mainly relies on mature power management and dynamic working mode adjustment technology, and adjusts the image acquisition frame rate by real-time monitoring of the power supply state to optimize energy consumption and extend the device endurance time. However, these technologies mainly focus on the monitoring of power parameters and frame rate adjustment, and do not deeply analyze the impact of increased image noise and decreased pixel uniformity caused by performance attenuation of the back-illuminated CMOS image sensor on the image quality under low power supply conditions. This results in that when matching a suitable image acquisition frame rate for low power, the impact brought by the performance attenuation of the back-illuminated CMOS sensor is not fully considered, making the image quality lower than expected and affecting the monitoring effect and system reliability. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the operation of an intelligent camera driven by green energy, aiming to solve the problems raised in the background art.

[0005] The present invention is implemented as follows. A method for optimizing the operation of an intelligent camera driven by green energy, the method includes:

[0006] When it is detected that the current power supply state of the imaging device is lower than the set threshold, determine the image acquisition frame rate matching the current power supply state, and obtain the image recording log of the imaging device and the historical operation record of the back-illuminated CMOS image sensor;

[0007] Analyze the image recording log and the historical operation record, and select a predetermined number, a predetermined time interval, and video recording sub-logs and operation sub-logs that match the current power supply state and environmental parameters therefrom;

[0008] According to the selected video recording sub-logs and operation sub-logs, respectively determine the current image noise trend and the current pixel uniformity trend of the imaging device, and based on the set reference model, determine the standard image noise trend and the standard pixel uniformity trend corresponding to the imaging device;

[0009] Compare whether the deviation magnitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation magnitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. If they are consistent, correct the image acquisition frame rate based on the two deviation magnitudes, the standard image noise trend, and the standard pixel uniformity trend.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the set reference model is a mathematical model preset through experimental data and relevant operating conditions, aiming to determine and reflect the standard image noise trend and the standard pixel uniformity trend of the camera and its back-illuminated CMOS image sensor in the normal performance attenuation state under different power supply states, environmental parameters, and total usage durations, where both the standard image noise trend and the standard pixel uniformity trend are embodied in the form of change curves.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the steps of respectively determining the current image noise trend and the current pixel uniformity trend of the imaging device based on the selected recording sub-log and operating sub-log, and determining the corresponding standard image noise trend and standard pixel uniformity trend of the imaging device based on the set reference model include:

[0012] Based on digital image processing technology, determine the average image noise value of the captured images in each recording sub-log, and draw a first current change curve reflecting the current image noise trend of the imaging device in chronological order;

[0013] Based on statistical distribution analysis technology, determine the average pixel response uniformity value during the operation of the back-illuminated CMOS image sensor in each operating sub-log, and draw a second current change curve reflecting the current pixel uniformity trend of the imaging device in chronological order;

[0014] Retrieve the set reference model, and select from it the standard image noise trend and its corresponding first standard change curve, as well as the standard pixel response uniformity trend and its corresponding second standard change curve that match the current power supply state, environmental parameters, and total usage duration of the camera.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the steps of comparing whether the deviation magnitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation magnitude of the current pixel uniformity trend relative to the standard pixel uniformity trend, and if they are consistent, correcting the image acquisition frame rate based on the two deviation magnitudes, the standard image noise trend, and the standard pixel uniformity trend include:

[0016] Calculate the absolute value of the average slope difference between the first current change curve and the first standard change curve, and calculate the absolute value of the average slope difference between the second current change curve and the second standard change curve;

[0017] Determine whether the difference between two absolute values falls within a predetermined numerical range. If the condition is met, it is determined that the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend;

[0018] After confirming that the two deviation amplitudes are consistent, generate a reference correction factor according to the standard image noise trend and the standard pixel uniformity trend, and generate an auxiliary correction factor according to the two deviation amplitudes. The auxiliary correction factor is used to adjust the reference correction factor;

[0019] Retrieve the preset correction formula, and correct the image acquisition frame rate in combination with the reference correction factor and the auxiliary correction factor.

[0020] As a further limitation of the technical solution of the embodiment of the present invention, the preset correction formula is: , where F corr refers to the corrected image acquisition frame rate, F init refers to the uncorrected image acquisition frame rate, C1 refers to the reference correction factor, K1 is the adjustment coefficient corresponding to the reference correction factor, C2 refers to the auxiliary correction factor, and K2 refers to the adjustment coefficient corresponding to the auxiliary correction factor;

[0021] In the preset correction formula, , where S1 refers to the average slope of the first standard change curve, and S2 refers to the average slope of the second standard change curve;

[0022] , where S 1，current refers to the average slope of the first current change curve, and S 2，current refers to the average slope of the second current change curve.

[0023] The design basis of the preset correction formula is as follows:

[0024] First, in the normal performance degradation state, the standard image noise trend of the camera and its back-illuminated CMOS image sensor gradually rises (i.e., the noise increases), while the standard pixel uniformity trend gradually decreases (i.e., the uniformity deteriorates). Based on this phenomenon, we use a set reference model to pre-calculate the average slopes of these two standard trends, denoted as S1 (the average slope of the standard image noise trend) and S2 (the average slope of the standard pixel uniformity trend), and since S2 is negative (the standard pixel uniformity trend will gradually decrease), its absolute value is taken. Finally, combining the absolute value of S2, S1, and the constant 1 (to ensure that the correction factor is not zero) forms a reference correction factor C1, that is .

[0025] Secondly, the current image noise trend and the current pixel uniformity trend are obtained through real-time monitoring, and their average slopes are denoted as S 1，current and S 2，current . Calculate the deviation magnitudes between the current trends and the standard trends, which are respectively and . In the previous determination, if the absolute value difference of the two average slope differences falls within a predetermined numerical range, it indicates that the deviation magnitudes of the current image noise trend and the pixel uniformity trend are consistent, indicating that the camera and the sensor are in a state of accelerated performance decline. To reflect this impact, we sum the two deviation values and take the average to obtain an auxiliary correction factor C2, that is , and then use this auxiliary correction factor C2 to adjust the reference correction factor C1

[0026] An intelligent camera operation optimization system driven by green energy, the system includes: a data acquisition module, a data screening module, a trend determination module, and a capture frame rate correction module, where

[0027] The data acquisition module is used to determine the image capture frame rate matching the current power supply state of the imaging device and obtain the image recording log of the imaging device and the historical operation record of the back-illuminated CMOS image sensor when it detects that the current power supply state of the imaging device is lower than the set threshold

[0028] The data screening module is used to analyze the image recording log and the historical operation record, and select the recorded sub-logs and operation sub-logs with a predetermined number, a predetermined time interval, and matching the current power supply state and environmental parameters

[0029] The trend determination module is used to respectively determine the current image noise trend and the current pixel uniformity trend of the imaging device based on the selected recorded sub-logs and operation sub-logs, and determine the corresponding standard image noise trend and standard pixel uniformity trend of the imaging device based on the set reference model

[0030] The capture frame rate correction module is used to compare whether the deviation magnitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation magnitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. If they are consistent, the image capture frame rate is corrected based on the two deviation magnitudes, the standard image noise trend, and the standard pixel uniformity trend

[0031] As a further limitation of the technical solution of the embodiment of the present invention, the set reference model is a mathematical model preset through experimental data and relevant operating conditions, aiming to determine and reflect the standard image noise trend and the standard pixel uniformity trend of the camera and its back-illuminated CMOS image sensor in the normal performance attenuation state under the conditions of different power supply states, environmental parameters, and total usage duration, wherein both the standard image noise trend and the standard pixel uniformity trend are embodied in the form of change curves.

[0032] As a further limitation of the technical solution of the embodiment of the present invention, the trend determination module specifically includes:

[0033] The video recording sub-log analysis unit is used to determine the average image noise value of the captured image in each video recording sub-log based on digital image processing technology, and draw a first current change curve reflecting the current image noise trend of the imaging device in chronological order;

[0034] The operation sub-log analysis unit is used to determine the average pixel response uniformity value of the operation process of the back-illuminated CMOS image sensor in each operation sub-log based on statistical distribution analysis technology, and draw a second current change curve reflecting the current pixel uniformity trend of the imaging device in chronological order;

[0035] The reference model application unit is used to retrieve the set reference model, and select the standard image noise trend and its corresponding first standard change curve, as well as the standard pixel response uniformity trend and its corresponding second standard change curve that match the current power supply state, environmental parameters, and total usage duration of the camera.

[0036] As a further limitation of the technical solution of the embodiment of the present invention, the acquisition frame rate correction module specifically includes:

[0037] The absolute value calculation unit is used to calculate the absolute value of the average slope difference between the first current change curve and the first standard change curve, and calculate the absolute value of the average slope difference between the second current change curve and the second standard change curve;

[0038] The consistency judgment unit is used to judge whether the difference between the two absolute values falls within a predetermined numerical range. If the condition is met, it is determined that the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend;

[0039] The correction factor generation unit is used to generate a reference correction factor according to the standard image noise trend and the standard pixel uniformity trend, and generate an auxiliary correction factor according to the two deviation amplitudes. The auxiliary correction factor is used to adjust the reference correction factor;

[0040] The acquisition frame rate correction unit is used to retrieve a preset correction formula and correct the image acquisition frame rate in combination with a reference correction factor and an auxiliary correction factor.

[0041] As a further limitation of the technical solution of the embodiment of the present invention, the preset correction formula is: , where F corr refers to the corrected image acquisition frame rate, F init refers to the uncorrected image acquisition frame rate, C1 refers to the reference correction factor, K1 is the adjustment coefficient corresponding to the reference correction factor, C2 refers to the auxiliary correction factor, and K2 refers to the adjustment coefficient corresponding to the auxiliary correction factor;

[0042] In the preset correction formula, , where S1 refers to the average slope of the first standard change curve, and S2 refers to the average slope of the second standard change curve;

[0043] , where S 1，current refers to the average slope of the first current change curve, and S 2，current refers to the average slope of the second current change curve.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] On the basis of the existing power supply driven by green energy and the adjustment of the image acquisition frame rate, the present invention further introduces a fine comparison of the deviation amplitude of the current image noise trend and pixel uniformity trend relative to the standard trend. By establishing a reference correction factor and an auxiliary correction factor, the present invention realizes an effective compensation for the decline in image quality caused by the performance attenuation of the camera and its back-illuminated CMOS image sensor under low power supply conditions.

[0046] The reference correction factor reflects the expected image noise and pixel uniformity trend of the device in the normal performance attenuation state, while the auxiliary correction factor dynamically reflects the deviation between the current actual state and the standard trend. The combination of the two corrects the initial image acquisition frame rate in real time, not only ensuring the stable and clear image quality while reducing energy consumption, but also accurately reflecting the decline state of the sensor, realizing dynamic and adaptive frame rate optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flowchart of the method provided by the embodiment of the present invention;

[0048] Figure 2 is a flowchart of analyzing the image noise trend and pixel uniformity trend in the method provided by the embodiment of the present invention;

[0049] Figure 3Flow chart for correcting the image acquisition frame rate in the method provided by the embodiments of the present invention;

[0050] Figure 4 Application architecture diagram of the system provided by the embodiments of the present invention;

[0051] Figure 5 Structural block diagram of the trend determination module in the system provided by the embodiments of the present invention;

[0052] Figure 6 Structural block diagram of the acquisition frame rate correction module in the system provided by the embodiments of the present invention. Detailed implementation manners

[0053] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] Figure 1 The flow chart of the method provided by the embodiments of the present invention is shown.

[0055] Specifically, an intelligent camera operation optimization method driven by green energy, the method specifically includes the following steps:

[0056] Step S100, when it is detected that the current power supply state of the imaging device is lower than the set threshold, determine the image acquisition frame rate matching the current power supply state, and obtain the image recording log of the imaging device and the historical operation record of the back-illuminated CMOS image sensor.

[0057] In the embodiments of the present invention, the "current power supply state" refers to parameters such as voltage, current or remaining power collected in real time by the built-in power management module, and the "set threshold" is the lowest power supply standard determined in advance according to experimental data and on-site application requirements. When it is detected that the camera is in a low-light environment and the power supply state is lower than this threshold, the system automatically determines an image acquisition frame rate matching the current power supply state according to mature existing technologies, and this step has been widely used in existing power management and dynamic working mode adjustment.

[0058] Collect two types of data through the internal logging module: one is the image recording log, and the other is the historical operation record of the back-illuminated CMOS image sensor. The image recording log includes at least the acquisition time, the image acquisition frame rate, the power supply status, the ambient light parameters, and the image quality indicators (such as the image noise level); while the historical operation record records at least the key operation parameters such as the data acquisition time, the sensor operating temperature, voltage, dark current, and pixel response uniformity. The main reason for choosing the back-illuminated CMOS image sensor is that it has higher sensitivity and lower noise performance under low light conditions, and the impact of its performance degradation on the image quality is more significant, which facilitates accurately evaluating the attenuation of the sensor through the analysis of the historical operation record and providing a reliable data basis for the subsequent intelligent correction of the image acquisition frame rate.

[0059] Further, the method for optimizing the operation of the green energy-driven intelligent camera further includes the following steps:

[0060] Step S200, parse the image recording log and the historical operation record, and select the recorded sub-logs and operation sub-logs with a predetermined number, a predetermined time interval, and matching the current power supply status and environmental parameters.

[0061] In the embodiment of the present invention, step S200 involves parsing the image recording log and the historical operation record, and selecting the recorded sub-logs and operation sub-logs with a predetermined number, a predetermined time interval, and matching the current power supply status and environmental parameters. The purpose of selecting these sub-logs is to ensure that the data used for subsequent trend analysis has a high degree of standardization and comparability.

[0062] Specifically, all selected recorded sub-logs and operation sub-logs not only have the same intercepted time length, but also the time interval between two adjacent sub-logs must be the same, ensuring that each pair of recorded sub-logs and operation sub-logs strictly corresponds in time. In this way, the operation status of the intelligent camera and its back-illuminated CMOS image sensor under specific power supply and environmental conditions can be accurately reflected, providing a reliable data basis for the subsequent correction of the image acquisition frame rate based on the deviation between the real-time data and the standard trend.

[0063] Further, the method for optimizing the operation of the green energy-driven intelligent camera further includes the following steps:

[0064] Step S300, based on the selected recorded sub-logs and operation sub-logs, respectively determine the current image noise trend and the current pixel uniformity trend of the imaging device, and based on the set reference model, determine the corresponding standard image noise trend and standard pixel uniformity trend of the imaging device.

[0065] The set reference model is a mathematical model preset through experimental data and relevant operating conditions, aiming to determine and reflect the standard image noise trend and standard pixel uniformity trend of the camera and its back-illuminated CMOS image sensor under normal performance attenuation conditions according to different power supply states, environmental parameters, and total usage duration. Both the standard image noise trend and the standard pixel uniformity trend are reflected in the form of change curves.

[0066] Specifically, Figure 2 The flowchart for analyzing the image noise trend and pixel uniformity trend is shown.

[0067] Among them, based on the selected video recording sub-log and operation sub-log, determining the current image noise trend and current pixel uniformity trend of the imaging device respectively, and determining the corresponding standard image noise trend and standard pixel uniformity trend of the imaging device based on the set reference model specifically includes the following steps:

[0068] Step S301, based on digital image processing technology, determine the average image noise value of the captured images in each video recording sub-log, and draw the first current change curve reflecting the current image noise trend of the imaging device in chronological order;

[0069] Step S302, based on statistical distribution analysis technology, determine the average pixel response uniformity value during the operation of the back-illuminated CMOS image sensor in each operation sub-log, and draw the second current change curve reflecting the current pixel uniformity trend of the imaging device in chronological order;

[0070] Step S303, retrieve the set reference model, and select the standard image noise trend and its corresponding first standard change curve, as well as the standard pixel response uniformity trend and its corresponding second standard change curve that match the current power supply state, environmental parameters, and total usage duration of the camera.

[0071] In the embodiment of the present invention, the set reference model is a mathematical model established in advance by collecting a large amount of historical operation data and combining conditions such as different power supply states, environmental parameters, and total usage duration. This model uses methods such as statistical regression, machine learning, or empirical data to reflect the standard image noise trend and standard pixel uniformity trend of the camera and its back-illuminated CMOS image sensor under normal performance attenuation conditions in the form of change curves, thereby providing a highly comparable and authentic standard benchmark for subsequent performance comparison and calibration.

[0072] In step S301, the system processes the acquired images in each video recording sub-log using digital image processing techniques to calculate the average image noise value of the images. Common methods include statistically analyzing the pixel grayscale of the images and obtaining the standard deviation or other noise metrics as the noise level. Subsequently, the average image noise values in all video recording sub-logs are arranged in chronological order to plot a first current change curve reflecting the current image noise trend of the imaging device. This curve can visually display the change in the noise level of the device over a period of time and reflect the attenuation process of the sensor performance.

[0073] In step S302, the system processes the back-illuminated CMOS image sensor operation data recorded in each operation sub-log using statistical distribution analysis techniques to calculate the average pixel response uniformity value of the sensor in each sub-log. Here, it can be quantified by comparing the variance of each pixel response value or other uniformity metrics. Then, these uniformity values are arranged in chronological order to plot a second current change curve reflecting the current pixel uniformity trend of the imaging device. This curve can reflect the change in sensor uniformity over time and further reflect the decline in sensor performance.

[0074] In step S303, the system retrieves the pre-established set reference model and selects the standard image noise trend and its corresponding first standard change curve, as well as the standard pixel uniformity trend and its corresponding second standard change curve, under conditions matching the current power supply state, environmental parameters, and total usage duration. These standard curves serve as benchmarks, providing the ideal performance indicators of the device in the normal attenuation state. By comparing with the current actual change trend, it can accurately evaluate whether the device has abnormal accelerated attenuation. The significance of selecting these standard curves is that they make the subsequent comparison between real-time data and the standard trend more comparable and reliable, thus providing a scientific basis for the intelligent correction of the image acquisition frame rate.

[0075] Furthermore, the intelligent camera operation optimization method driven by green energy further includes the following steps:

[0076] Step S400, compare whether the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. If they are consistent, correct the image acquisition frame rate based on the two deviation amplitudes, the standard image noise trend, and the standard pixel uniformity trend.

[0077] Specifically, Figure 3 shows the flowchart for correcting the image acquisition frame rate.

[0078] Among them, compare whether the deviation magnitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation magnitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. If they are consistent, the image acquisition frame rate is corrected based on the two deviation magnitudes, the standard image noise trend, and the standard pixel uniformity trend. The specific steps are as follows:

[0079] Step S401, calculate the absolute value of the average slope difference between the first current change curve and the first standard change curve, and calculate the absolute value of the average slope difference between the second current change curve and the second standard change curve;

[0080] Step S402, determine whether the difference between the two absolute values falls within a predetermined numerical range. If the condition is met, it is determined that the deviation magnitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation magnitude of the current pixel uniformity trend relative to the standard pixel uniformity trend;

[0081] Step S403, after confirming that the two deviation magnitudes are consistent, generate a reference correction factor according to the standard image noise trend and the standard pixel uniformity trend, and generate an auxiliary correction factor according to the two deviation magnitudes. The auxiliary correction factor is used to adjust the reference correction factor;

[0082] Step S404, retrieve the preset correction formula, and correct the image acquisition frame rate in combination with the reference correction factor and the auxiliary correction factor.

[0083] The preset correction formula is: , where F corr refers to the corrected image acquisition frame rate, F init refers to the uncorrected image acquisition frame rate, C1 refers to the reference correction factor, K1 is the adjustment coefficient corresponding to the reference correction factor, C2 refers to the auxiliary correction factor, and K2 refers to the adjustment coefficient corresponding to the auxiliary correction factor;

[0084] In the preset correction formula, , where S1 refers to the average slope of the first standard change curve, and S2 refers to the average slope of the second standard change curve;

[0085] , where S 1，current refers to the average slope of the first current change curve, and S 2，current refers to the average slope of the second current change curve.

[0086] The design basis of the preset correction formula is as follows:

[0087] First, in the normal performance degradation state, the standard image noise trend of the camera and its back-illuminated CMOS image sensor gradually increases (i.e., more noise), while the standard pixel uniformity trend gradually decreases (i.e., worse uniformity). Based on this phenomenon, we use a set reference model to pre-calculate the average slopes of these two standard trends, denoted as S1 (the average slope of the standard image noise trend) and S2 (the average slope of the standard pixel uniformity trend). Since S2 is negative (the standard pixel uniformity trend will gradually decrease), its absolute value is taken. Finally, combining the absolute value of S2, S1, and the constant 1 (to ensure that the correction factor is not zero) forms a reference correction factor C1, that is .

[0088] Second, the current image noise trend and the current pixel uniformity trend are obtained through real-time monitoring, and their average slopes are denoted as S 1，current and S 2，current . Calculate the deviation magnitudes between the current trends and the standard trends, which are respectively and . In the previous determination, if the absolute value difference of the differences between the two average slopes falls within a predetermined numerical range, it indicates that the deviation magnitudes of the current image noise trend and the pixel uniformity trend are consistent, indicating that the camera and the sensor are in the state of accelerated performance decline. To reflect this influence, we sum and average the two deviation values to obtain an auxiliary correction factor C2, that is , and then use this auxiliary correction factor C2 to adjust the reference correction factor C1.

[0089] In the embodiment of the present invention, in step S401, the system calculates the absolute value of the difference between the average slopes of the first current change curve and the first standard change curve, and the absolute value of the difference between the average slopes of the second current change curve and the second standard change curve. The significance of this step is to quantify the respective deviation magnitudes between the current image noise trend and the pixel uniformity trend and the standard trends, so as to provide a numerical basis for subsequent deviation comparison.

[0090] Next, in step S402, the system determines whether the difference between the above two absolute values falls within a predetermined numerical range. If this condition is met, it can be considered that the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. The principle behind this determination is as follows: Under normal attenuation conditions, the noise and pixel uniformity of a back-illuminated CMOS image sensor change in specific trends; when the sensor experiences rapid performance decay, the deviation amplitudes of both will increase synchronously. If the two deviations are very close numerically, it can indirectly indicate that the sensor is in an accelerated decay state. In addition to this basis, other determination methods can also use, for example, directly comparing the dark current, the change rate of fixed pattern noise (FPN), or a method based on comprehensive scoring of multiple parameters. However, this solution selects the two indicators of noise and uniformity, and their advantage is that they respectively reflect the performance changes of the sensor from the two perspectives of signal quality and pixel consistency, complement each other, and have high representativeness and accuracy.

[0091] In step S403, the system generates a reference correction factor based on the standard image noise trend and the standard pixel uniformity trend, and at the same time generates an auxiliary correction factor based on the two deviation amplitudes. Here, the reference correction factor reflects the expected performance of the device under normal performance decay conditions, while the auxiliary correction factor is used to adjust the reference correction factor to compensate for the additional deviation caused by the accelerated decay of the sensor. The combination of the two can more precisely correct the image acquisition frame rate to ensure reasonable image acquisition conditions even when the device performance deteriorates.

[0092] Finally, in step S404, the system retrieves the preset correction formula and jointly corrects the image acquisition frame rate in combination with the reference correction factor and the auxiliary correction factor. The advantage of using these two factors for joint correction is as follows: On the one hand, the reference correction factor ensures that the correction is based on the standard performance of the sensor in the normal decay state; on the other hand, the auxiliary correction factor dynamically reflects the deviation between the current actual state and the standard state, thereby making real-time corrections to the reference factor. Especially for back-illuminated CMOS image sensors, they perform excellently in low-light environments, but the impact of their performance decay on image quality is extremely obvious. Through this dual correction mechanism, the image acquisition frame rate can be adjusted more precisely, thereby improving the adaptability and reliability of the overall system.

[0093] Furthermore, Figure 4 shows the application architecture diagram of the system provided by the embodiment of the present invention.

[0094] Among them, in another preferred embodiment provided by the present invention, an intelligent camera operation optimization system driven by green energy includes:

[0095] The data acquisition module 100 is configured to determine an image acquisition frame rate matching the current power supply state and obtain the image recording log of the imaging device and the historical operation record of the back-illuminated CMOS image sensor when it is detected that the current power supply state of the imaging device is lower than a set threshold.

[0096] In the embodiment of the present invention, the "current power supply state" refers to parameters such as voltage, current, or remaining battery level collected in real time by the built-in power management module, and the "set threshold" is the minimum power supply standard determined in advance based on experimental data and on-site application requirements. When it is detected that the camera is in a low-light environment and the power supply state is lower than this threshold, the system automatically determines an image acquisition frame rate matching the current power supply state according to mature existing technologies. This step has been widely applied in existing power management and dynamic working mode adjustment.

[0097] Two types of data are collected through the internal log recording module: one is the image recording log, and the other is the historical operation record of the back-illuminated CMOS image sensor. The image recording log at least includes the acquisition time, image acquisition frame rate, power supply state, ambient light parameters, and image quality indicators (such as image noise level); while the historical operation record at least records key operation parameters such as data acquisition time, sensor operating temperature, voltage, dark current, and pixel response uniformity. The main reason for selecting the back-illuminated CMOS image sensor is that it has higher sensitivity and lower noise performance under low-light conditions, and the impact of its performance degradation on image quality is more significant, which is convenient for accurately evaluating the attenuation of the sensor through the analysis of the historical operation record and providing a reliable data basis for the subsequent intelligent correction of the image acquisition frame rate.

[0098] Furthermore, the intelligent camera operation optimization system driven by green energy further includes:

[0099] The data screening module 200 is configured to parse the image recording log and the historical operation record, and select recorded sub-logs and operation sub-logs with a predetermined number, a predetermined time interval, and matching the current power supply state and environmental parameters.

[0100] In the embodiment of the present invention, the data screening module 200 involves parsing the image recording log and the historical operation record, and selecting recorded sub-logs and operation sub-logs with a predetermined number, a predetermined time interval, and matching the current power supply state and environmental parameters. The purpose of selecting these sub-logs is to ensure that the data used for subsequent trend analysis is highly standardized and comparable.

[0101] Specifically, all the selected video recording sub - logs and operation sub - logs not only have the same intercepted time length, but also the time intervals between adjacent sub - logs must be the same, ensuring that each pair of video recording sub - logs and operation sub - logs strictly correspond in time. In this way, the operating states of the intelligent camera and its back - illuminated CMOS image sensor under specific power supply and environmental conditions can be accurately reflected, providing a reliable data basis for subsequent image acquisition frame rate correction based on the deviation between real - time data and the standard trend.

[0102] Furthermore, the intelligent camera operation optimization system driven by green energy further includes:

[0103] A trend determination module 300, configured to respectively determine the current image noise trend and the current pixel uniformity trend of the imaging device based on the selected video recording sub - logs and operation sub - logs, and determine the standard image noise trend and the standard pixel uniformity trend corresponding to the imaging device based on a set reference model.

[0104] The set reference model is a mathematical model preset through experimental data and relevant operating conditions, aiming to determine and reflect the standard image noise trend and the standard pixel uniformity trend of the camera and its back - illuminated CMOS image sensor in the normal performance decay state under different power supply states, environmental parameters, and total usage durations, where both the standard image noise trend and the standard pixel uniformity trend are presented in the form of change curves.

[0105] Specifically, Figure 5 FIG. shows the structural block diagram of the trend determination module 300 in the system provided by the embodiment of the present invention.

[0106] Among them, in the preferred embodiment provided by the present invention, the trend determination module 300 specifically includes:

[0107] A video recording sub - log analysis unit 301, configured to determine the average image noise value of the captured images in each video recording sub - log based on digital image processing technology, and draw a first current change curve reflecting the current image noise trend of the imaging device in chronological order;

[0108] An operation sub - log analysis unit 302, configured to determine the average pixel response uniformity value during the operation of the back - illuminated CMOS image sensor in each operation sub - log based on statistical distribution analysis technology, and draw a second current change curve reflecting the current pixel uniformity trend of the imaging device in chronological order;

[0109] A reference model application unit 303 is configured to retrieve the set reference model, and select a standard image noise trend and its corresponding first standard change curve, as well as a standard pixel response uniformity trend and its corresponding second standard change curve that match the current power supply state, environmental parameters, and total usage duration of the camera from the reference model.

[0110] In an embodiment of the present invention, the set reference model is a mathematical model established in advance by collecting a large amount of historical operation data and combining conditions such as different power supply states, environmental parameters, and total usage duration. This model uses methods such as statistical regression, machine learning, or empirical data-based methods to represent the standard image noise trend and standard pixel uniformity trend of the camera and its back-illuminated CMOS image sensor in a normal performance degradation state in the form of change curves, thereby providing a highly comparable and authentic standard benchmark for subsequent performance comparison and correction.

[0111] The video recording sub-log analysis unit 301 processes the captured images in each video recording sub-log using digital image processing technology to calculate the average image noise value of the images. Common methods include statistically analyzing the pixel grayscale of the images and obtaining the standard deviation or other noise metrics as the noise level. Subsequently, the average image noise values in all video recording sub-logs are arranged in chronological order to plot a first current change curve reflecting the current image noise trend of the imaging device. This curve can visually display the change in the noise level of the device over a period of time and reflect the attenuation process of the sensor performance.

[0112] The operation sub-log analysis unit 302 processes the operation data of the back-illuminated CMOS image sensor recorded in each operation sub-log using statistical distribution analysis technology to calculate the average pixel response uniformity value of the sensor in each sub-log. Here, it can be quantified by comparing the variance of each pixel response value or other uniformity metrics. Then, these uniformity values are arranged in chronological order to plot a second current change curve reflecting the current pixel uniformity trend of the imaging device. This curve can reflect the change in sensor uniformity over time and further reflect the decline in sensor performance.

[0113] The reference model application unit 303 retrieves the pre-established set reference model, and selects from it the standard image noise trend and its corresponding first standard change curve, as well as the standard pixel uniformity trend and its corresponding second standard change curve, under the conditions matching the current power supply state, environmental parameters, and total usage duration. These standard curves serve as benchmarks, providing the ideal performance indicators of the device in the normal attenuation state. By comparing with the current actual change trend, it can accurately evaluate whether the device has abnormal accelerated attenuation. The significance of selecting these standard curves lies in that they make the subsequent comparison between real-time data and standard trends more comparable and reliable, thus providing a scientific basis for the intelligent correction of the image acquisition frame rate.

[0114] Furthermore, the intelligent camera operation optimization system driven by green energy further includes:

[0115] An acquisition frame rate correction module 400, configured to compare whether the deviation magnitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation magnitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. If they are consistent, the image acquisition frame rate is corrected based on the two deviation magnitudes, the standard image noise trend, and the standard pixel uniformity trend.

[0116] Specifically, Figure 6 FIG. shows the structural block diagram of the acquisition frame rate correction module 400 in the system provided by the embodiment of the present invention.

[0117] Among them, in the preferred embodiment provided by the present invention, the acquisition frame rate correction module 400 specifically includes:

[0118] An absolute value calculation unit 401, configured to calculate the absolute value of the average slope difference between the first current change curve and the first standard change curve, and calculate the absolute value of the average slope difference between the second current change curve and the second standard change curve;

[0119] A consistency judgment unit 402, configured to judge whether the difference between the two absolute values falls within a predetermined numerical range. If the condition is met, it is determined that the deviation magnitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation magnitude of the current pixel uniformity trend relative to the standard pixel uniformity trend;

[0120] A correction factor generation unit 403, configured to generate a reference correction factor according to the standard image noise trend and the standard pixel uniformity trend after confirming that the two deviation magnitudes are consistent, and generate an auxiliary correction factor according to the two deviation magnitudes, where the auxiliary correction factor is used to adjust the reference correction factor;

[0121] An acquisition frame rate correction unit 404, configured to retrieve a preset correction formula, and correct the image acquisition frame rate in combination with the reference correction factor and the auxiliary correction factor.

[0122] The preset correction formula is as follows: , where F corr refers to the corrected image acquisition frame rate, and F init refers to the uncorrected image acquisition frame rate, C1 refers to the reference correction factor, K1 is the adjustment coefficient corresponding to the reference correction factor, C2 refers to the auxiliary correction factor, and K2 refers to the adjustment coefficient corresponding to the auxiliary correction factor;

[0123] In the preset correction formula, , where S1 refers to the average slope of the first standard change curve, and S2 refers to the average slope of the second standard change curve;

[0124] , where S 1，current refers to the average slope of the first current change curve, and S 2，current refers to the average slope of the second current change curve.

[0125] In the embodiment of the present invention, the absolute value calculation unit 401 calculates the absolute value of the average slope difference between the first current change curve and the first standard change curve, and the absolute value of the average slope difference between the second current change curve and the second standard change curve. The significance of this step is to quantify the respective deviation amplitudes between the current image noise trend and pixel uniformity trend and the standard trends, so as to provide a numerical basis for subsequent deviation comparison.

[0126] Next, the consistency judgment unit 402 judges whether the difference between the above two absolute values falls within a predetermined numerical interval. If this condition is met, it can be considered that the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. The principle of this judgment basis is that under normal attenuation conditions, the noise and pixel uniformity of the back-illuminated CMOS image sensor change in specific trends; when the performance of the sensor decays rapidly, the deviation amplitudes of both will be amplified synchronously. If the two deviations are very close numerically, it can indirectly reflect that the sensor is in an accelerated decay state. In addition to this basis, other judgment methods can also adopt methods such as directly comparing the dark current, the change rate of fixed pattern noise (FPN), or a method based on comprehensive scoring of multiple parameters. However, this solution selects the two indicators of noise and uniformity, and its advantage is that they respectively reflect the performance changes of the sensor from the two perspectives of signal quality and pixel consistency, complement each other, and have high representativeness and accuracy.

[0127] The calibration factor generation unit 403 generates a reference calibration factor based on the standard image noise trend and the standard pixel uniformity trend, and simultaneously generates an auxiliary calibration factor based on the two deviation magnitudes. Here, the reference calibration factor reflects the expected performance of the device under normal performance degradation conditions, while the auxiliary calibration factor is used to adjust the reference calibration factor to compensate for the additional deviation caused by the accelerated degradation of the sensor. The combination of the two can more finely correct the image acquisition frame rate to ensure reasonable image acquisition conditions even when the device performance deteriorates.

[0128] Finally, the acquisition frame rate calibration unit 404 retrieves a preset calibration formula and jointly corrects the image acquisition frame rate in combination with the reference calibration factor and the auxiliary calibration factor. The advantage of using these two factors for joint calibration is as follows: on the one hand, the reference calibration factor ensures that the calibration is based on the standard performance of the sensor in the normal degradation state; on the other hand, the auxiliary calibration factor dynamically reflects the deviation between the current actual state and the standard state, thereby making real-time corrections to the reference factor. Especially for back-illuminated CMOS image sensors, they perform excellently in low-light environments, but the impact of their performance degradation on image quality is extremely obvious. Through this dual calibration mechanism, the image acquisition frame rate can be adjusted more precisely, thereby improving the adaptability and reliability of the overall system.

[0129] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0131] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0132] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0133] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A green energy driven smart camera operation optimization method, characterized in that: The method comprises: When it is detected that the current power supply state of the imaging device is lower than a set threshold, an image acquisition frame rate matching the current power supply state is determined, and an image recording log of the imaging device and a historical operation record of the back-illuminated CMOS image sensor are obtained; Parsing the video recording log and the historical operation log, and selecting the recording sub-logs and operation sub-logs of a predetermined number, predetermined time interval and matching the current power supply status and environmental parameters; According to the selected recording sub-log and operation sub-log, respectively determine the current image noise trend and the current pixel uniformity trend of the imaging device, and based on the set reference model, determine the standard image noise trend and the standard pixel uniformity trend corresponding to the imaging device; Compare whether the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. If they are consistent, correct the image acquisition frame rate based on the two deviation amplitudes and the standard image noise trend and standard pixel uniformity trend.

2. The green energy driven smart camera operation optimization method according to claim 1, characterized in that: The set reference model is a mathematical model pre-set by experimental data and relevant operating conditions, and is intended to determine and reflect the standard image noise trend and standard pixel uniformity trend of the camera and its back-illuminated CMOS image sensor under normal performance attenuation conditions under different power supply states, environmental parameters and total usage time, wherein the standard image noise trend and standard pixel uniformity trend are both reflected in the form of change curves.

3. The green energy driven smart camera operation optimization method according to claim 2, characterized in that: The steps of respectively determining the current image noise trend and the current pixel uniformity trend of the imaging device according to the selected recording sub-log and the operation sub-log, and determining the standard image noise trend and the standard pixel uniformity trend corresponding to the imaging device based on the set reference model include: Determine the average image noise value of the collected images in each recording sub-log based on digital image processing technology, and draw a first current change curve reflecting the current image noise trend of the imaging device in chronological order; Determine an average pixel response uniformity value of the back-illuminated CMOS image sensor operation process in each operation sub-log based on a statistical distribution analysis technique, and draw a second current change curve reflecting a current pixel uniformity trend of the imaging device in chronological order; The set reference model is retrieved, and the standard image noise trend and its corresponding first standard change curve under conditions matching the current power supply state, environmental parameters and total usage time of the camera, as well as the standard pixel response uniformity trend and its corresponding second standard change curve are selected therefrom.

4. The green energy driven smart camera operation optimization method according to claim 3 is characterized in that: Comparing whether the deviation amplitude of the current image noise trend relative to the standard image noise trend and the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend are consistent, if they are consistent, then correcting the image acquisition frame rate based on the two deviation amplitudes and the standard image noise trend and the standard pixel uniformity trend includes: Calculating an absolute value of an average slope difference between a first current change curve and a first standard change curve, and calculating an absolute value of an average slope difference between a second current change curve and a second standard change curve; Determine whether the difference between the two absolute values ​​falls within a predetermined numerical range. If the condition is met, it is determined that the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. After confirming that the two deviation amplitudes are consistent, a reference correction factor is generated according to the standard image noise trend and the standard pixel uniformity trend, and an auxiliary correction factor is generated according to the two deviation amplitudes. The auxiliary correction factor is used to adjust the reference correction factor. Retrieve the preset correction formula and correct the image acquisition frame rate in combination with the baseline correction factor and the auxiliary correction factor.

5. The green energy driven smart camera operation optimization method according to claim 4, characterized in that: The preset correction formula is: , where F corr Refers to the corrected image acquisition frame rate, F init Refers to the uncorrected image acquisition frame rate, C1 refers to the baseline correction factor, K1 is the adjustment coefficient corresponding to the baseline correction factor, C2 refers to the auxiliary correction factor, and K2 refers to the adjustment coefficient corresponding to the auxiliary correction factor; In the preset correction formula, , where S1 refers to the average slope of the first standard change curve, and S2 refers to the average slope of the second standard change curve; , where S 1，current Refers to the average slope of the first current change curve, S 2，current Refers to the average slope of the second current change curve.

6. A green energy driven intelligent camera operation optimization system, characterized in that: The system includes: a data acquisition module, a data screening module, a trend determination module and an acquisition frame rate correction module, wherein: A data acquisition module, for determining an image acquisition frame rate that matches the current power supply state when it is detected that the current power supply state of the imaging device is lower than a set threshold, and obtaining an image recording log of the imaging device and a historical operation record of the back-illuminated CMOS image sensor; A data screening module, used for parsing the image recording log and the historical operation log, and selecting the recording sub-logs and operation sub-logs of a predetermined number, a predetermined time interval and matching the current power supply status and environmental parameters; A trend determination module, for determining the current image noise trend and the current pixel uniformity trend of the imaging device according to the selected recording sub-log and operation sub-log, and determining the standard image noise trend and the standard pixel uniformity trend corresponding to the imaging device based on the set reference model; The acquisition frame rate correction module is used to compare whether the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend. If they are consistent, the image acquisition frame rate is corrected based on the two deviation amplitudes and the standard image noise trend and the standard pixel uniformity trend.

7. The green energy driven intelligent camera operation optimization system according to claim 6, characterized in that: The set reference model is a mathematical model pre-set by experimental data and relevant operating conditions, and is intended to determine and reflect the standard image noise trend and standard pixel uniformity trend of the camera and its back-illuminated CMOS image sensor under normal performance attenuation conditions under different power supply states, environmental parameters and total usage time, wherein the standard image noise trend and standard pixel uniformity trend are both reflected in the form of change curves.

8. The green energy driven intelligent camera operation optimization system according to claim 7, characterized in that: The trend determination module specifically includes: A recording sub-log analysis unit, used to determine the average image noise value of the collected images in each recording sub-log based on digital image processing technology, and to draw a first current change curve reflecting the current image noise trend of the imaging device in chronological order; an operation sub-log analysis unit, for determining an average pixel response uniformity value of an operation process of the back-illuminated CMOS image sensor in each operation sub-log based on a statistical distribution analysis technique, and drawing a second current change curve reflecting a current pixel uniformity trend of the imaging device in chronological order; The reference model application unit is used to call the set reference model and select the standard image noise trend and its corresponding first standard change curve under the conditions matching the current power supply state, environmental parameters and total usage time of the camera, as well as the standard pixel response uniformity trend and its corresponding second standard change curve.

9. The green energy driven intelligent camera operation optimization system according to claim 8, characterized in that: The acquisition frame rate correction module specifically includes: an absolute value calculation unit, used to calculate the absolute value of the average slope difference between the first current change curve and the first standard change curve, and to calculate the absolute value of the average slope difference between the second current change curve and the second standard change curve; A consistency judgment unit, used to judge whether the difference between the two absolute values ​​falls within a predetermined numerical range, and if the condition is met, it is determined that the deviation amplitude of the current image noise trend relative to the standard image noise trend is consistent with the deviation amplitude of the current pixel uniformity trend relative to the standard pixel uniformity trend; A correction factor generating unit, for generating a reference correction factor according to a standard image noise trend and a standard pixel uniformity trend after confirming that the two deviation amplitudes are consistent, and generating an auxiliary correction factor according to the two deviation amplitudes, wherein the auxiliary correction factor is used to adjust the reference correction factor; The acquisition frame rate correction unit is used to call a preset correction formula and correct the image acquisition frame rate in combination with a reference correction factor and an auxiliary correction factor.

10. The green energy driven intelligent camera operation optimization system according to claim 9, characterized in that: The preset correction formula is: , where F corr Refers to the corrected image acquisition frame rate, F init Refers to the uncorrected image acquisition frame rate, C1 refers to the baseline correction factor, K1 is the adjustment coefficient corresponding to the baseline correction factor, C2 refers to the auxiliary correction factor, and K2 refers to the adjustment coefficient corresponding to the auxiliary correction factor; In the preset correction formula, , where S1 refers to the average slope of the first standard change curve, and S2 refers to the average slope of the second standard change curve; , where S 1，current Refers to the average slope of the first current change curve, S 2，current Refers to the average slope of the second current change curve.

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