Low-light night vision imaging device and method

Abstract

The invention relates to the technical field of monitoring equipment, in particular to a low-light night vision imaging device and method. Performing an actual imaging test on the low-light night vision imaging device based on the recommended imaging control scheme, and obtaining an actual imaging test result of the recommended imaging control scheme; performing comparative analysis on the actual imaging test result of the recommended imaging control scheme and the simulation imaging result; if the comparison result is the first comparison result, directly taking the recommended imaging control scheme as a final imaging control scheme; if the comparison result is the second comparison result, drifting operation parameters of the low-light night vision imaging device in the test working process are obtained; performing fault feature analysis on the low-light night vision imaging device according to the drifting operation parameters of the low-light night vision imaging device in the test working process; and if the low-light night vision imaging device does not have the fault condition, adjusting the recommended imaging control scheme. According to the invention, the reliability and high performance of the low-light night vision imaging device in actual work can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring equipment, and in particular to a low-light-level night vision imaging device and method. Background Art

[0002] In recent years, with the increasing demand for night vision imaging technology in the surveillance field, low-light-level night vision imaging devices have been widely used. However, traditional low-light-level night vision imaging devices mainly rely on manual experience to adjust imaging parameters, and have problems such as low efficiency, unstable imaging quality, and difficulty in fault diagnosis. In addition, due to factors such as aging of internal components and environmental interference, it is also easy to have problems such as reduced imaging quality and system failure, which require professional technicians to maintain and troubleshoot, increasing the cost of use and maintenance difficulty. In order to solve the above problems, in recent years, intelligent control methods based on big data and artificial intelligence technology have gradually emerged and have been applied to the control field of low-light-level night vision imaging devices. For example, by using a big data network to share excellent imaging control schemes, combined with simulation analysis and actual testing, the best imaging control scheme suitable for a specific scene can be quickly found to control the imaging parameters to improve imaging efficiency and quality. At the same time, through real-time monitoring and analysis of the operating parameters of the device, potential faults can be discovered in time, and fault warning information can be generated to avoid long-term system failure caused by faults. In view of this, the present invention proposes a low-light-level night vision imaging device and a control method. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a low-light-level night vision imaging device and method.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: The invention discloses a control method for a low-light-level night vision imaging device, comprising the following steps: Acquire the shared imaging control scheme of the low-light-level night vision imaging device through the big data network, and perform simulation analysis on each shared imaging control scheme to screen out a recommended imaging control scheme from each shared imaging control scheme, and obtain the simulation imaging result of the recommended imaging control scheme; Performing an actual imaging test on the low-light-level night vision imaging device based on the recommended imaging control scheme, and obtaining the actual imaging test result of the recommended imaging control scheme; comparing and analyzing the actual imaging test result of the recommended imaging control scheme with the simulation imaging result to obtain a comparison result; If the comparison result is the first one, indicating that the working performance of the low-light-level night vision imaging device is qualified, the recommended imaging control scheme is directly used as the final imaging control scheme, and the low-light-level night vision imaging device is controlled to perform actual work based on the final imaging control scheme; If the second comparison result indicates that the working performance of the low-light-level night vision imaging device is unqualified, then a test working parameter set of various operating parameters of the low-light-level night vision imaging device during the actual imaging test is obtained; a feature analysis is performed on the test working parameter set to obtain the drift operating parameters of the low-light-level night vision imaging device during the test work process; Performing fault characteristic analysis on the low-light-level night vision imaging device according to the drift operating parameters of the low-light-level night vision imaging device during the test operation; if the low-light-level night vision imaging device has a fault, generating fault warning information, and controlling the low-light-level night vision imaging device to stop working; If there is no fault in the low-light-level night vision imaging device, the recommended imaging control scheme is adjusted to obtain a final imaging control scheme for the low-light-level night vision imaging device, and the low-light-level night vision imaging device is controlled to perform actual work based on the final imaging control scheme.

[0005] Preferably, the shared imaging control scheme of the low-light-level night vision imaging device is obtained through the big data network, and each shared imaging control scheme is simulated and analyzed to screen out a recommended imaging control scheme from each shared imaging control scheme, and obtain the simulation imaging result of the recommended imaging control scheme, specifically: Acquire device model information of the low-light-level night vision imaging device, generate a search tag according to the device model information, search the big data network according to the search tag, and acquire various shared imaging control schemes of the low-light-level night vision imaging device; Acquire size parameter information, electrical parameter information, and preset working environment parameters of the low-light-level night vision imaging device, and construct a three-dimensional simulation system of the low-light-level night vision imaging device based on the size parameter information, electrical parameter information, and preset working environment parameters in combination with three-dimensional simulation software; Various shared imaging control schemes are sequentially introduced into the three-dimensional simulation system to simulate the imaging work of the low-light-level night vision imaging device, and the simulation imaging results after the simulation control imaging according to each shared imaging control scheme are output; the simulation imaging results include the simulation image sequence and image index and the gain change curve; Acquiring working accuracy requirement information of the low-light-level night vision imaging device, and determining a target imaging result of the low-light-level night vision imaging device according to the working accuracy requirement information; Introducing a structural similarity index analysis algorithm, and analyzing the structural similarity index between each simulation imaging result and the target imaging result based on the structural similarity index analysis algorithm; The calculated structural similarity indexes are sorted to obtain the maximum structural similarity index; the simulation imaging result corresponding to the maximum structural similarity index is marked, and a shared imaging control scheme with the marked simulation imaging result is obtained to obtain a recommended imaging control scheme for the low-light-level night vision imaging device; The recommended imaging control scheme and the simulated imaging result obtained by performing simulated imaging with the recommended imaging control scheme are compressed, bundled and outputted.

[0006] Preferably, the actual imaging test results of the recommended imaging control scheme are compared and analyzed with the simulation imaging results to obtain the comparison results, specifically: Introducing a structural similarity index analysis algorithm, and calculating a structural similarity index between a simulation imaging result of the recommended imaging control scheme and an actual imaging test result based on the structural similarity index analysis algorithm; Comparing the structural similarity index between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result with a preset index threshold; If the structural similarity index between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result is greater than a preset index threshold, it means that the similarity between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result meets the preset requirement, and a first comparison result is generated; If the structural similarity index between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result is not greater than the preset index threshold, it means that the similarity between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result does not meet the preset requirements, and a second comparison result is generated.

[0007] Preferably, the test working parameter set is subjected to feature analysis to obtain the drift operating parameters of the low-light-level night vision imaging device during the test working process, specifically: Obtaining a recommended imaging control scheme for a low-light-level night vision imaging device, and obtaining a predicted working parameter set of various operating parameters of the low-light-level night vision imaging device according to the recommended imaging control scheme; Constructing predicted working parameter curve graphs of various operating parameters according to predicted working parameter sets of various operating parameters, and constructing tested working parameter working curve graphs of various operating parameters according to tested working parameter sets of various operating parameters; Performing curve coincidence analysis on the test working parameter working curves of various operating parameters and the corresponding predicted working parameter curves to obtain the coincidence between the test working parameter working curves of various operating parameters and the corresponding predicted working parameter curves; Compare the degree of coincidence between the test working parameter working curve diagram of various operating parameters and the corresponding predicted working parameter curve diagram with a preset coincidence degree threshold; The operating parameters corresponding to the overlap degree greater than the preset overlap degree threshold are extracted, and the operating parameters corresponding to the overlap degree greater than the preset overlap degree threshold are calibrated as the drift operating parameters of the low-light level night vision imaging device during the test operation.

[0008] Preferably, a fault characteristic analysis is performed on the low-light-level night vision imaging device according to the drift operating parameters of the low-light-level night vision imaging device during the test operation; if a fault occurs in the low-light-level night vision imaging device, fault warning information is generated, specifically: Retrieve various fault events of low-light-level night vision imaging devices through the big data network, and obtain the fault characteristic parameters and fault causes corresponding to various fault events; The fault causes corresponding to various fault events are converted into codes, so that the fault causes corresponding to various fault events are converted into a string of binary digital codes; Construct a knowledge graph, and divide the knowledge graph into a plurality of sub-graph nodes, and map each binary digital code onto a corresponding sub-graph node; And the fault characteristic parameters corresponding to various fault events are stored in the sub-graph nodes mapped with the corresponding binary digital codes to obtain the fault query knowledge graph; Acquire drift operation parameters of the low-light-level night vision imaging device during the test work process, and import the drift operation parameters of the low-light-level night vision imaging device during the test work process into the fault query knowledge graph; Calculating the Jaccard similarity coefficient value between the drift operation parameter and the fault characteristic parameter in each sub-map node to obtain a plurality of Jaccard similarity coefficient values; comparing each calculated Jaccard similarity coefficient value with a preset threshold value; If there is at least one Jaccard similarity coefficient value greater than the preset threshold, it means that there is a fault in the low-light level night vision imaging device. At this time, the sub-graph node corresponding to the Jaccard similarity coefficient value greater than the preset threshold is marked, and the binary digital code of the marked sub-graph node is obtained. The obtained binary digital code is interpreted to obtain the cause of the failure of the low-light level night vision imaging device, and fault warning information is generated according to the cause of the failure of the low-light level night vision imaging device.

[0009] Preferably, if there is no fault in the low-light-level night vision imaging device, the recommended imaging control scheme is adjusted to obtain the final imaging control scheme of the low-light-level night vision imaging device, specifically: If the calculated Jaccard similarity coefficient values ​​are not greater than the preset threshold, it means that there is no fault in the low-light-level night vision imaging device, and a recommended imaging control scheme for the low-light-level night vision imaging device is obtained; Introducing a particle swarm optimization algorithm and setting an optimization algebra, performing parameter adjustment on the recommended imaging control scheme based on the optimization algebra, and obtaining an adjusted imaging control scheme after each adjustment cycle is completed; Based on the adjusted imaging control scheme, an actual imaging test is performed on the low-light level night vision imaging device. After the actual imaging test results meet the preset requirements and there are no drift operating parameters in the low-light level night vision imaging device, the iterative optimization is stopped and the corresponding adjusted imaging control scheme is output as the final imaging control scheme.

[0010] The present invention also discloses a low-light-level night vision imaging device, which is applied to any one of the control methods of the low-light-level night vision imaging device, and the low-light-level night vision imaging device comprises: A device body and a mounting bracket, wherein the device body and the mounting bracket are hinged to each other; a blocking cover is provided on the top of the device body; An image acquisition module, an imaging module, a data processing and storage module, a control module and a network communication module are arranged inside the device body.

[0011] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: based on the recommended imaging control scheme, an actual imaging test is performed on the low-light-level night vision imaging device, and the actual imaging test results of the recommended imaging control scheme are obtained; the actual imaging test results of the recommended imaging control scheme are compared and analyzed with the simulation imaging results; if it is the first comparison result, the recommended imaging control scheme is directly used as the final imaging control scheme; if it is the second comparison result, the drift operating parameters of the low-light-level night vision imaging device during the test work are obtained; the fault characteristics of the low-light-level night vision imaging device are analyzed according to the drift operating parameters of the low-light-level night vision imaging device during the test work; if there is a fault in the low-light-level night vision imaging device, fault warning information is generated, and the low-light-level night vision imaging device is controlled to stop working; if there is no fault in the low-light-level night vision imaging device, the recommended imaging control scheme is adjusted and processed. The present invention can adaptively generate an imaging control scheme for a low-light-level night vision imaging device, and can timely discover and handle device faults, optimize imaging effects, and ensure the reliability and high performance of the low-light-level night vision imaging device in actual work. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0013] Figure 1 This is the overall method flow chart of the control method of the low-light-level night vision imaging device; Figure 2 This is a partial method flow chart of the control method of the low-light-level night vision imaging device; Figure 3 This is a simplified structural diagram of the low-light-level night vision imaging device. DETAILED DESCRIPTION

[0014] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0015] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0016] The present invention discloses a control method for a low-light-level night vision imaging device. Figure 1 As shown, the following steps are included: S102: obtaining a shared imaging control scheme of a low-light-level night vision imaging device through a big data network, and performing simulation analysis on each shared imaging control scheme to screen out a recommended imaging control scheme from each shared imaging control scheme, and obtaining a simulation imaging result of the recommended imaging control scheme; S104: performing an actual imaging test on the low-light-level night vision imaging device based on the recommended imaging control scheme, and obtaining an actual imaging test result of the recommended imaging control scheme; comparing and analyzing the actual imaging test result of the recommended imaging control scheme with the simulation imaging result to obtain a comparison result; S106: If the comparison result is the first one, indicating that the working performance of the low-light-level night vision imaging device is qualified, the recommended imaging control scheme is directly used as the final imaging control scheme, and the low-light-level night vision imaging device is controlled to perform actual work based on the final imaging control scheme; S108: If the second comparison result indicates that the working performance of the low-light-level night vision imaging device is unqualified, a test working parameter set of various operating parameters of the low-light-level night vision imaging device during an actual imaging test is obtained; a feature analysis is performed on the test working parameter set to obtain drift operating parameters of the low-light-level night vision imaging device during the test working process; S110: performing a fault characteristic analysis on the low-light-level night vision imaging device according to the drift operating parameters of the low-light-level night vision imaging device during the test operation; if the low-light-level night vision imaging device has a fault, generating fault warning information, and controlling the low-light-level night vision imaging device to stop working; S112: If there is no fault in the low-light-level night vision imaging device, the recommended imaging control scheme is adjusted to obtain a final imaging control scheme for the low-light-level night vision imaging device, and the low-light-level night vision imaging device is controlled to perform actual work based on the final imaging control scheme.

[0017] It should be noted that if it is the first comparison result (similarity index is greater than the preset threshold), it means that the imaging effect in the actual test is similar to the simulation effect, the working performance of the low-light night vision imaging device is qualified, and the recommended imaging control scheme can be directly used as the final scheme for actual work. This comparison can verify the effectiveness of the simulation results in practice and ensure the reliability of the selected scheme.

[0018] If it is the second comparison result (similarity index is not greater than the preset threshold), it indicates that the working performance is unqualified. At this time, the test working parameter set in the actual imaging test process is obtained, and the drift operating parameters are obtained by constructing the predicted working parameter curve and the test working parameter curve and analyzing the overlap between the two. The drift operating parameters reflect the operating parameters that deviate from the theoretical expectations in actual work, and these parameters are the key to further analyzing the device problems.

[0019] If there is a fault, a fault warning message is generated and the device is stopped, which helps to timely discover and avoid the adverse effects caused by the fault. If there is no fault, the recommended imaging control scheme is adjusted and processed, using the particle swarm optimization algorithm and combined with actual imaging tests until the preset requirements are met and there is no drift operating parameter, and the final imaging control scheme is obtained for actual work. This step can optimize the imaging control scheme and improve the imaging quality when the device is fault-free.

[0020] The present invention can adaptively generate an imaging control scheme for a low-light-level night vision imaging device, and can timely detect and handle device failures, optimize imaging effects, and ensure the reliability and high performance of the low-light-level night vision imaging device in actual work.

[0021] Preferably, the shared imaging control scheme of the low-light-level night vision imaging device is obtained through the big data network, and each shared imaging control scheme is simulated and analyzed to screen out a recommended imaging control scheme from each shared imaging control scheme, and obtain the simulation imaging result of the recommended imaging control scheme, specifically: Acquire device model information of the low-light-level night vision imaging device, generate a search tag according to the device model information, search the big data network according to the search tag, and acquire various shared imaging control schemes of the low-light-level night vision imaging device; Acquire size parameter information, electrical parameter information, and preset working environment parameters of the low-light-level night vision imaging device, and construct a three-dimensional simulation system of the low-light-level night vision imaging device based on the size parameter information, electrical parameter information, and preset working environment parameters in combination with three-dimensional simulation software; Various shared imaging control schemes are sequentially introduced into the three-dimensional simulation system to simulate the imaging work of the low-light-level night vision imaging device, and the simulation imaging results after the simulation control imaging according to each shared imaging control scheme are output; the simulation imaging results include the simulation image sequence and image index and the gain change curve; Acquiring working accuracy requirement information of the low-light-level night vision imaging device, and determining a target imaging result of the low-light-level night vision imaging device according to the working accuracy requirement information; Introducing a structural similarity index analysis algorithm, and analyzing the structural similarity index between each simulation imaging result and the target imaging result based on the structural similarity index analysis algorithm; The calculated structural similarity indexes are sorted to obtain the maximum structural similarity index; the simulation imaging result corresponding to the maximum structural similarity index is marked, and a shared imaging control scheme with the marked simulation imaging result is obtained to obtain a recommended imaging control scheme for the low-light-level night vision imaging device; The recommended imaging control scheme and the simulated imaging result obtained by performing simulated imaging with the recommended imaging control scheme are compressed, bundled and outputted.

[0022] Among them, the simulated image sequence contains a series of simulated images, showing the imaging effect of the target at different time points; the simulated image indicators include image brightness, image contrast, image clarity, image noise level, target recognition rate, motion blur degree, etc.

[0023] It should be noted that, first, a search tag is generated according to the device model information of the low-light-level night vision imaging device, and this search tag is used to search in the big data network to obtain various shared imaging control schemes applicable to the low-light-level night vision imaging device. The significance of this step is to use the resource advantages of big data to widely collect control schemes that may be applicable to the device, avoiding the tedious process of designing control schemes from scratch. Based on the size parameter information, electrical parameter information and preset working environment parameters of the low-light-level night vision imaging device, a three-dimensional simulation system is constructed in combination with three-dimensional simulation software (such as SolidWorks). This system can simulate the actual working environment and its own characteristics of the low-light-level night vision imaging device, providing an accurate virtual platform for subsequent simulation imaging work. The various shared imaging control schemes obtained are sequentially imported into the constructed three-dimensional simulation system to perform simulation imaging work. The output simulation imaging results contain information on various aspects such as simulation image sequence, image indicators and gain change curve. This information comprehensively reflects the imaging effect of the low-light-level night vision imaging device under different control schemes, including image quality, gain change and so on. The calculated structural similarity indexes are sorted to find the maximum structural similarity index. The corresponding simulation imaging results are marked, and then the corresponding shared imaging control scheme is obtained to determine the recommended imaging control scheme for the low-light night vision imaging device. This process screens out the imaging control scheme that best meets the working accuracy requirements by comparing the structural similarity index. Finally, the recommended imaging control scheme and the simulation imaging results obtained by the simulation imaging of the scheme are compressed and bundled and output, which is convenient for subsequent actual imaging tests and other operations.

[0024] Through the above series of steps, we can make full use of the shared imaging control scheme resources in the big data network, and combine the various parameters of the low-light-level night vision imaging device itself to build an accurate three-dimensional simulation system for simulated imaging. The structural similarity index analysis algorithm is used to objectively screen out the recommended imaging control scheme closest to the working accuracy requirements and its corresponding simulation imaging results. This process efficiently integrates big data resources and simulation technology, and provides a scientific, objective and practical imaging control scheme screening method for low-light-level night vision imaging devices, which reduces the cost of manual trial and error, improves the efficiency of obtaining high-quality imaging control schemes, and can lay a good foundation for subsequent actual imaging tests and device control based on recommended schemes.

[0025] Preferably, the actual imaging test results of the recommended imaging control scheme are compared and analyzed with the simulation imaging results to obtain a comparison result, such as Figure 2 As shown, specifically: S202: introducing a structural similarity index analysis algorithm, and calculating a structural similarity index between a simulation imaging result of the recommended imaging control scheme and an actual imaging test result based on the structural similarity index analysis algorithm; S204: comparing a structural similarity index between a simulation imaging result of the recommended imaging control scheme and an actual imaging test result with a preset index threshold; S206: If the structural similarity index between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result is greater than a preset index threshold, it means that the similarity between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result meets the preset requirement, and a first comparison result is generated; S208: If the structural similarity index between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result is not greater than the preset index threshold, it means that the similarity between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result does not meet the preset requirements, and a second comparison result is generated.

[0026] It should be noted that the calculated structural similarity index is compared with the preset index threshold. This preset index threshold is a standard value pre-set according to the performance requirements, application scenarios and other factors of the low-light-level night vision imaging device. It represents the lower limit of the acceptable similarity between the simulation imaging results and the actual imaging test results in practical applications. If the structural similarity index between the simulation imaging results of the recommended imaging control scheme and the actual imaging test results is greater than the preset index threshold, this means that the similarity between the two meets the preset requirements. In this case, a first comparison result is generated, which shows that in the actual imaging test, when the low-light-level night vision imaging device works according to the recommended imaging control scheme, its imaging effect is closer to the simulation situation, indicating that the recommended imaging control scheme is relatively reliable in practical applications and the performance of the device is in line with expectations.

[0027] When the structural similarity index between the simulated imaging result of the recommended imaging control scheme and the actual imaging test result is not greater than the preset index threshold, it means that the similarity between the two does not meet the preset requirement. At this time, the second comparison result is generated, which implies that in the actual imaging test, there may be some factors that cause the actual imaging effect to be significantly different from the simulation result, such as the difference between the actual working environment of the device and the simulation environment, the device itself has unconsidered characteristics or faults, etc.

[0028] By calculating the structural similarity index between the simulation imaging results of the recommended imaging control scheme and the actual imaging test results, and comparing them with the preset index threshold to generate different comparison results, the effectiveness of the recommended imaging control scheme in practical applications can be objectively and accurately evaluated. This comparative analysis helps to determine whether the low-light night vision imaging device achieves the expected imaging effect when working according to the recommended scheme, and provides a key basis for further judging whether the device's working performance is qualified and whether there are any faults, so that the device can be adjusted, maintained or troubleshooted in a targeted manner.

[0029] Preferably, the test working parameter set is subjected to feature analysis to obtain the drift operating parameters of the low-light-level night vision imaging device during the test working process, specifically: Obtaining a recommended imaging control scheme for a low-light-level night vision imaging device, and obtaining a predicted working parameter set of various operating parameters of the low-light-level night vision imaging device according to the recommended imaging control scheme; Constructing predicted working parameter curve graphs of various operating parameters according to predicted working parameter sets of various operating parameters, and constructing tested working parameter working curve graphs of various operating parameters according to tested working parameter sets of various operating parameters; Performing curve coincidence analysis on the test working parameter working curves of various operating parameters and the corresponding predicted working parameter curves to obtain the coincidence between the test working parameter working curves of various operating parameters and the corresponding predicted working parameter curves; Compare the degree of coincidence between the test working parameter working curve diagram of various operating parameters and the corresponding predicted working parameter curve diagram with a preset coincidence degree threshold; The operating parameters corresponding to the overlap degree greater than the preset overlap degree threshold are extracted, and the operating parameters corresponding to the overlap degree greater than the preset overlap degree threshold are calibrated as the drift operating parameters of the low-light level night vision imaging device during the test operation.

[0030] It should be noted that the recommended imaging control scheme of the low-light night vision imaging device is first obtained, and the predicted working parameter set of various operating parameters of the low-light night vision imaging device is obtained based on this scheme. This predicted working parameter set is a set of operating parameters that should be obtained theoretically according to the recommended imaging control scheme. Then, a predicted working parameter curve of various operating parameters is constructed based on this predicted working parameter set. This curve can intuitively show the changing trend of various operating parameters over time or other variables under ideal conditions (according to the recommended imaging control scheme). A test working parameter working curve of various operating parameters is constructed based on the test working parameter set of various operating parameters. This test working parameter set is a set of operating parameters actually obtained by the low-light night vision imaging device during the actual imaging test process. Then, the test working parameter working curve of various operating parameters and the corresponding predicted working parameter curve are analyzed for curve coincidence to obtain the coincidence between them. The curve coincidence analysis can quantitatively represent the similarity between the operating parameter curve obtained by the actual test and the theoretical prediction curve, which helps to discover the deviation from the theoretical situation in the actual working process. The operating parameters corresponding to the overlap degree greater than the preset overlap degree threshold are extracted, and these operating parameters are calibrated as the drift operating parameters of the low-light-level night vision imaging device during the test work. The determination of the drift operating parameters means finding the operating parameters that have a large deviation from the theoretical situation during the actual test work, and these parameters may be one of the reasons for the large difference between the actual imaging results and the simulation imaging results (i.e., the second comparison result).

[0031] By constructing the predicted working parameter curve and the test working parameter curve, analyzing the overlap between the two and comparing them with the preset threshold to determine the drift operating parameters, it is possible to accurately find out the operating parameters of the low-light night vision imaging device that have a large deviation from the theoretical expectations during the actual test process. This helps to deeply analyze the reasons for the difference between the actual imaging effect and the simulated imaging effect when the low-light night vision imaging device works according to the recommended imaging control scheme, and provides a key basis for the subsequent fault feature analysis, so that the device can be more targeted for troubleshooting, adjustment or optimization operations.

[0032] Preferably, a fault characteristic analysis is performed on the low-light-level night vision imaging device according to the drift operating parameters of the low-light-level night vision imaging device during the test operation; if a fault occurs in the low-light-level night vision imaging device, fault warning information is generated, specifically: Retrieve various fault events of low-light-level night vision imaging devices through the big data network, and obtain the fault characteristic parameters and fault causes corresponding to various fault events; The fault causes corresponding to various fault events are converted into codes, so that the fault causes corresponding to various fault events are converted into a string of binary digital codes; Construct a knowledge graph, and divide the knowledge graph into a plurality of sub-graph nodes, and map each binary digital code onto a corresponding sub-graph node; And the fault characteristic parameters corresponding to various fault events are stored in the sub-graph nodes mapped with the corresponding binary digital codes to obtain the fault query knowledge graph; Acquire drift operation parameters of the low-light-level night vision imaging device during the test work process, and import the drift operation parameters of the low-light-level night vision imaging device during the test work process into the fault query knowledge graph; Calculating the Jaccard similarity coefficient value between the drift operation parameter and the fault characteristic parameter in each sub-map node to obtain a plurality of Jaccard similarity coefficient values; comparing each calculated Jaccard similarity coefficient value with a preset threshold value; If there is at least one Jaccard similarity coefficient value greater than the preset threshold, it means that there is a fault in the low-light level night vision imaging device. At this time, the sub-graph node corresponding to the Jaccard similarity coefficient value greater than the preset threshold is marked, and the binary digital code of the marked sub-graph node is obtained. The obtained binary digital code is interpreted to obtain the cause of the failure of the low-light level night vision imaging device, and fault warning information is generated according to the cause of the failure of the low-light level night vision imaging device.

[0033] It should be noted that first, various fault events of low-light night vision imaging devices are retrieved through the big data network, and the fault characteristic parameters and fault causes corresponding to these fault events are obtained. Then various fault causes are coded and converted into binary digital codes. This coding method helps to unify the fault causes in a data form that is easy to represent in a computer-processed form, preparing for the subsequent construction of a knowledge graph.

[0034] Construct a knowledge graph and divide it into several sub-graph nodes. Map the previously obtained binary digital codes to the corresponding sub-graph nodes, and store the fault characteristic parameters corresponding to various fault events in the sub-graph nodes mapped with the corresponding binary digital codes, thus obtaining a fault query knowledge graph. This knowledge graph integrates various fault-related information, including fault characteristic parameters and fault cause codes, and presents them in a structured manner for easy query and analysis.

[0035] The drift operating parameters of the low-light-level night vision imaging device during the test process are obtained and imported into the fault query knowledge graph. The Jaccard similarity coefficient value between the drift operating parameters and the fault characteristic parameters in each sub-graph node is calculated. The Jaccard similarity coefficient is an indicator for measuring the similarity of two sets, and is used here to measure the similarity between the drift operating parameters and the fault characteristic parameters. After obtaining several Jaccard similarity coefficient values, they are compared with the preset threshold. If there is at least one Jaccard similarity coefficient value greater than the preset threshold, it means that there is a fault in the low-light level night vision imaging device. At this time, the corresponding sub-graph node is marked, its binary digital code is obtained and interpreted to obtain the cause of the fault, and finally the fault warning information is generated according to the cause of the fault. This method can accurately determine whether the device has a fault by comparing the similarity between the drift operating parameters and the fault characteristic parameters, and can obtain the specific cause of the fault, thereby generating corresponding warning information.

[0036] By building a fault query knowledge graph, integrating and structuring fault-related information, and using the Jaccard similarity coefficient to calculate the similarity between drift operating parameters and fault characteristic parameters to determine the fault condition, the low-light-level night vision imaging device can be efficiently and accurately diagnosed. Once a fault is found, the cause of the fault can be obtained and fault warning information can be generated, which helps to timely discover and handle faults in low-light-level night vision imaging devices, improve the reliability and stability of the device, and reduce imaging problems or other adverse effects caused by faults.

[0037] Preferably, if there is no fault in the low-light-level night vision imaging device, the recommended imaging control scheme is adjusted to obtain the final imaging control scheme of the low-light-level night vision imaging device, specifically: If the calculated Jaccard similarity coefficient values ​​are not greater than the preset threshold, it means that there is no fault in the low-light-level night vision imaging device, and a recommended imaging control scheme for the low-light-level night vision imaging device is obtained; Introducing a particle swarm optimization algorithm and setting an optimization algebra, performing parameter adjustment on the recommended imaging control scheme based on the optimization algebra, and obtaining an adjusted imaging control scheme after each adjustment cycle is completed; Based on the adjusted imaging control scheme, an actual imaging test is performed on the low-light level night vision imaging device. After the actual imaging test results meet the preset requirements and there are no drift operating parameters in the low-light level night vision imaging device, the iterative optimization is stopped and the corresponding adjusted imaging control scheme is output as the final imaging control scheme.

[0038] It should be noted that, first, the comparison result between the Jaccard similarity coefficient value and the preset threshold is used to determine whether the low-light-level night vision imaging device has a fault. When the calculated Jaccard similarity coefficient values ​​are not greater than the preset threshold, it is determined that the device has no fault, and the previously obtained recommended imaging control scheme is obtained. This step is the basis for subsequent adjustment and optimization of the recommended imaging control scheme. The particle swarm optimization algorithm is introduced, and the optimization algebra is set. The particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which finds the optimal solution by simulating group behaviors such as bird flocks foraging. In this process, the parameters of the recommended imaging control scheme are adjusted based on the set optimization algebra. After each adjustment cycle, an adjusted imaging control scheme is obtained. This adjustment process is a process of gradually optimizing the recommended imaging control scheme, aiming to improve the performance of the imaging control scheme. The low-light-level night vision imaging device is subjected to actual imaging tests based on the imaging control scheme obtained after each adjustment. In this process, the actual imaging test results are constantly checked to see if they meet the preset requirements, and whether there are still drift operating parameters in the low-light-level night vision imaging device. Only when the actual imaging test results meet the preset requirements and there is no drift operating parameter, the iterative optimization is stopped, and the corresponding adjusted imaging control scheme is output as the final imaging control scheme. This ensures that the final imaging control scheme can achieve good imaging effects on the low-light night vision imaging device, and the operating parameters of the device are stable and meet the expected requirements.

[0039] After judging that the low-light-level night vision imaging device is fault-free, the particle swarm optimization algorithm is used to adjust and optimize the recommended imaging control scheme, and the final imaging control scheme is determined in combination with actual imaging tests, which can further improve the performance of the imaging control scheme when the device is fault-free. This method can effectively overcome the possible deficiencies of the recommended imaging control scheme, so that the low-light-level night vision imaging device can obtain better imaging effects in actual work, while ensuring the stability of the device operation and reducing imaging errors and unstable factors.

[0040] In addition, the method further comprises the following steps: When the low-light-level night vision imaging device is in actual operation, a signal transmission channel between the low-light-level night vision imaging device and a preset terminal is obtained; And obtaining a signal transmission time cutoff point of an image signal required to be transmitted in a low-light-level night vision imaging device, and obtaining a preset signal transmission time period according to the signal transmission time cutoff point; Acquire the transmission time required for transmitting the image signal required in the low-light-level night vision imaging device to a preset terminal, and divide the preset transmission time period of the signal into a plurality of sub-time periods according to the transmission time; Based on the meteorological software, the real-time environmental characteristic parameters of the signal transmission channel in each sub-time period are obtained; and the optimal environmental characteristic parameters of the image signal required to be transmitted in the low-light night vision imaging device are obtained through the big data network; Introducing a Pearson correlation coefficient algorithm, and calculating a Pearson correlation coefficient value between the optimal environmental characteristic parameter and the real-time environmental characteristic parameter of the signal transmission channel in each sub-time period based on the Pearson correlation coefficient algorithm; Constructing a sorting table, importing the calculated Pearson correlation coefficient values ​​into the sorting table for sorting, and sorting out the maximum Pearson correlation coefficient value; A sub-time period corresponding to the maximum Pearson correlation coefficient value is obtained, and based on the sub-time period corresponding to the maximum Pearson correlation coefficient value, an image signal required to be transmitted in the low-light-level night vision imaging device is transmitted to a preset terminal.

[0041] It should be noted that by determining the relevant parameters of signal transmission, obtaining the environmental characteristic parameters of different time periods and analyzing their correlation with the optimal environmental characteristics, the sub-time period most suitable for transmitting image signals can be accurately found. This method can effectively utilize environmental factors, select the best time during signal transmission, improve the quality and efficiency of image signal transmission between the low-light night vision imaging device and the preset terminal, and reduce signal transmission problems caused by environmental factors, such as signal attenuation and interference.

[0042] In addition, the method further comprises the following steps: When the low-light-level night vision imaging device is in actual operation, noise coefficient data after the low-light-level night vision imaging device transmits the required transmission image signal to the preset terminal is obtained at several time nodes; The B-spline curve method is introduced, the order and node vector of the B-spline curve are set, and each basis function is calculated according to the definition of the B-spline basis function; The noise coefficient data and the corresponding basis function are weightedly summed to obtain a B-spline fitting curve of the noise coefficient data; wherein, during the calculation process, the node vectors and the coefficient parameters of the basis function are adjusted so that the fitting curve is as close as possible to the noise coefficient data points, thereby completing the B-spline curve fitting of the noise coefficients at different time nodes; Acquire image accuracy requirement information of the low-light-level night vision imaging device, determine an upper constraint boundary and a lower constraint boundary according to the image accuracy requirement information; and define the upper constraint boundary and the lower constraint boundary in the B-spline fitting curve; Calculating the total length of the curve in the B-spline fitting curve that is within the upper constraint boundary and the lower constraint boundary, and calculating the total length of the curve in the B-spline fitting curve that is outside the upper constraint boundary and the lower constraint boundary; The ratio processing is performed on the bus segment length of the curve in the B-spline fitting curve that is located within the upper constraint boundary and the lower constraint boundary and the bus segment length of the curve in the B-spline fitting curve that is located outside the upper constraint boundary and the lower constraint boundary, so as to obtain the noise coefficient constraint compliance of the low-light level night vision imaging device during operation; the noise coefficient constraint compliance is compared with a preset compliance threshold value, and if the noise coefficient constraint compliance is greater than the preset compliance threshold value, the low-light level night vision imaging device is controlled to stop image signal transmission and generate warning information.

[0043] It should be noted that when the low-light night vision imaging device is actually working, the noise coefficient data after the image signal is transmitted to the preset terminal is collected at multiple time nodes. These data reflect the noise situation in the image signal transmission process at different times and are the basis for subsequent analysis. The B-spline curve method is introduced. First, the order and node vector of the B-spline curve are set. These two parameters determine the basic shape and characteristics of the B-spline curve. Then, each basis function is calculated according to the definition of the B-spline basis function, and then the noise coefficient data is weighted and summed with the corresponding basis function to obtain the B-spline fitting curve. In the calculation process, by adjusting the node vector and the coefficient parameters of the basis function, the curve can better approximate the noise coefficient data point, thereby completing the fitting of the noise coefficient at different time nodes, so that a continuous curve can be used to describe the change trend of the noise coefficient over time. The upper constraint boundary and the lower constraint boundary are determined according to the image accuracy requirement information of the low-light night vision imaging device, and are defined on the B-spline fitting curve. Then, the total length of the curve within the upper and lower constraint boundaries and the total length of the curve outside the boundaries are calculated respectively. This step is to quantify the situation of the curve within and outside the range that meets the image accuracy requirements. The bus segment length within the upper and lower constraint boundaries is processed with the bus segment length outside the boundary to obtain the noise coefficient constraint compliance. This parameter reflects the degree to which the change in the noise coefficient meets the image accuracy requirements. It is compared with the preset compliance threshold. If it is greater than the preset threshold, it means that the fluctuation of the noise coefficient exceeds the acceptable range. At this time, the low-light night vision imaging device is controlled to stop the image signal transmission and generate warning information to avoid the serious degradation of image transmission quality due to noise problems, which affects the normal operation of the low-light night vision imaging device. By obtaining the noise coefficient data of the low-light night vision imaging device after image transmission and performing B-spline curve fitting, combining the image accuracy requirements to determine the constraint boundaries, calculating the noise coefficient constraint compliance and comparing it with the threshold, it is possible to effectively monitor whether the noise coefficient change of the low-light night vision imaging device during operation meets the image accuracy requirements, stop transmission in time and issue warnings to ensure image transmission quality.

[0044] The present invention also discloses a low-light-level night vision imaging device, which is applied to any of the control methods of the low-light-level night vision imaging device described above, such as Figure 3 As shown, the low-light-level night vision imaging device comprises: A device body 1011 and a mounting bracket 1022, wherein the device body and the mounting bracket are hinged to each other; a blocking cover 1033 is provided on the top of the device body; An image acquisition module, an imaging module, a data processing and storage module, a control module and a network communication module are arranged inside the device body.

[0045] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A control method for a low-light-level night vision imaging device, characterized in that: The following steps are involved: Acquire the shared imaging control scheme of the low-light-level night vision imaging device through the big data network, and perform simulation analysis on each shared imaging control scheme to screen out a recommended imaging control scheme from each shared imaging control scheme, and obtain the simulation imaging result of the recommended imaging control scheme; Performing an actual imaging test on the low-light-level night vision imaging device based on the recommended imaging control scheme, and obtaining the actual imaging test result of the recommended imaging control scheme; comparing and analyzing the actual imaging test result of the recommended imaging control scheme with the simulation imaging result to obtain a comparison result; If the comparison result is the first one, indicating that the working performance of the low-light-level night vision imaging device is qualified, the recommended imaging control scheme is directly used as the final imaging control scheme, and the low-light-level night vision imaging device is controlled to perform actual work based on the final imaging control scheme; If the second comparison result indicates that the working performance of the low-light-level night vision imaging device is unqualified, then a test working parameter set of various operating parameters of the low-light-level night vision imaging device during the actual imaging test is obtained; a feature analysis is performed on the test working parameter set to obtain the drift operating parameters of the low-light-level night vision imaging device during the test work process; Perform fault characteristic analysis on the low-light-level night vision imaging device according to the drift operating parameters of the low-light-level night vision imaging device during the test operation; If there is a fault in the low-light-level night vision imaging device, a fault warning message is generated, and the low-light-level night vision imaging device is controlled to stop working; If there is no fault in the low-light-level night vision imaging device, the recommended imaging control scheme is adjusted to obtain a final imaging control scheme for the low-light-level night vision imaging device, and the low-light-level night vision imaging device is controlled to perform actual work based on the final imaging control scheme.

2. The control method of a low-light-level night vision imaging device according to claim 1, characterized in that: The shared imaging control scheme of the low-light-level night vision imaging device is obtained through the big data network, and each shared imaging control scheme is simulated and analyzed to screen out a recommended imaging control scheme from each shared imaging control scheme, and obtain the simulation imaging result of the recommended imaging control scheme, specifically: Acquire device model information of the low-light-level night vision imaging device, generate a search tag according to the device model information, search the big data network according to the search tag, and acquire various shared imaging control schemes of the low-light-level night vision imaging device; Acquire size parameter information, electrical parameter information, and preset working environment parameters of the low-light-level night vision imaging device, and construct a three-dimensional simulation system of the low-light-level night vision imaging device based on the size parameter information, electrical parameter information, and preset working environment parameters in combination with three-dimensional simulation software; Various shared imaging control schemes are sequentially introduced into the three-dimensional simulation system to simulate the imaging work of the low-light-level night vision imaging device, and the simulation imaging results after the simulation control imaging according to each shared imaging control scheme are output; the simulation imaging results include the simulation image sequence and image index and the gain change curve; Acquire working accuracy requirement information of the low-light-level night vision imaging device, and determine a target imaging result of the low-light-level night vision imaging device according to the working accuracy requirement information; Introducing a structural similarity index analysis algorithm, and analyzing the structural similarity index between each simulation imaging result and the target imaging result based on the structural similarity index analysis algorithm; The calculated structural similarity indexes are sorted to obtain the maximum structural similarity index; the simulation imaging result corresponding to the maximum structural similarity index is marked, and a shared imaging control scheme with the marked simulation imaging result is obtained to obtain a recommended imaging control scheme for the low-light-level night vision imaging device; The recommended imaging control scheme and the simulated imaging result obtained by performing simulated imaging with the recommended imaging control scheme are compressed, bundled and outputted.

3. The control method of a low-light-level night vision imaging device according to claim 1, characterized in that: Compare and analyze the actual imaging test results of the recommended imaging control scheme with the simulation imaging results to obtain the comparison results, specifically: Introducing a structural similarity index analysis algorithm, and calculating a structural similarity index between a simulation imaging result of the recommended imaging control scheme and an actual imaging test result based on the structural similarity index analysis algorithm; Comparing the structural similarity index between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result with a preset index threshold; If the structural similarity index between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result is greater than a preset index threshold, it means that the similarity between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result meets the preset requirement, and a first comparison result is generated; If the structural similarity index between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result is not greater than the preset index threshold, it means that the similarity between the simulation imaging result of the recommended imaging control scheme and the actual imaging test result does not meet the preset requirements, and a second comparison result is generated.

4. The control method of a low-light-level night vision imaging device according to claim 1, characterized in that: The characteristic analysis of the test working parameter set is performed to obtain the drift operating parameters of the low-light-level night vision imaging device during the test working process, specifically: Obtaining a recommended imaging control scheme for a low-light-level night vision imaging device, and obtaining a predicted working parameter set of various operating parameters of the low-light-level night vision imaging device according to the recommended imaging control scheme; Constructing predicted working parameter curve graphs of various operating parameters according to predicted working parameter sets of various operating parameters, and constructing tested working parameter working curve graphs of various operating parameters according to tested working parameter sets of various operating parameters; Performing curve coincidence analysis on the test working parameter working curves of various operating parameters and the corresponding predicted working parameter curves to obtain the coincidence between the test working parameter working curves of various operating parameters and the corresponding predicted working parameter curves; Compare the degree of coincidence between the test working parameter working curve diagram of various operating parameters and the corresponding predicted working parameter curve diagram with a preset coincidence degree threshold; The operating parameters corresponding to the overlap degree greater than the preset overlap degree threshold are extracted, and the operating parameters corresponding to the overlap degree greater than the preset overlap degree threshold are calibrated as the drift operating parameters of the low-light level night vision imaging device during the test operation.

5. The control method of a low-light-level night vision imaging device according to claim 1, characterized in that: According to the drift operating parameters of the low-light-level night vision imaging device during the test work, the fault characteristics of the low-light-level night vision imaging device are analyzed; if the low-light-level night vision imaging device has a fault, a fault warning information is generated, specifically: Retrieve various fault events of low-light-level night vision imaging devices through the big data network, and obtain the fault characteristic parameters and fault causes corresponding to various fault events; The fault causes corresponding to various fault events are converted into codes, so that the fault causes corresponding to various fault events are converted into a string of binary digital codes; Construct a knowledge graph, and divide the knowledge graph into a plurality of sub-graph nodes, and map each binary digital code onto a corresponding sub-graph node; And the fault characteristic parameters corresponding to various fault events are stored in the sub-graph nodes mapped with the corresponding binary digital codes to obtain the fault query knowledge graph; Acquire drift operation parameters of the low-light-level night vision imaging device during the test work process, and import the drift operation parameters of the low-light-level night vision imaging device during the test work process into the fault query knowledge graph; Calculating the Jaccard similarity coefficient value between the drift operation parameter and the fault characteristic parameter in each sub-map node to obtain a plurality of Jaccard similarity coefficient values; Compare each calculated Jaccard similarity coefficient value with a preset threshold; If there is at least one Jaccard similarity coefficient value greater than the preset threshold, it means that there is a fault in the low-light level night vision imaging device. At this time, the sub-graph node corresponding to the Jaccard similarity coefficient value greater than the preset threshold is marked, and the binary digital code of the marked sub-graph node is obtained. The obtained binary digital code is interpreted to obtain the cause of the failure of the low-light level night vision imaging device, and fault warning information is generated according to the cause of the failure of the low-light level night vision imaging device.

6. The control method of a low-light-level night vision imaging device according to claim 5, characterized in that: If there is no fault in the low-light-level night vision imaging device, the recommended imaging control scheme is adjusted to obtain the final imaging control scheme of the low-light-level night vision imaging device, which is specifically: If the calculated Jaccard similarity coefficient values ​​are not greater than the preset threshold, it means that there is no fault in the low-light-level night vision imaging device, and a recommended imaging control scheme for the low-light-level night vision imaging device is obtained; Introducing a particle swarm optimization algorithm and setting an optimization algebra, performing parameter adjustment on the recommended imaging control scheme based on the optimization algebra, and obtaining an adjusted imaging control scheme after each adjustment cycle is completed; Based on the adjusted imaging control scheme, an actual imaging test is performed on the low-light level night vision imaging device. After the actual imaging test results meet the preset requirements and there are no drift operating parameters in the low-light level night vision imaging device, the iterative optimization is stopped and the corresponding adjusted imaging control scheme is output as the final imaging control scheme.

7. A low-light-level night vision imaging device, applied to the control method of the low-light-level night vision imaging device according to any one of claims 1 to 6, characterized in that: The low-light-level night vision imaging device comprises: A device body and a mounting bracket, wherein the device body and the mounting bracket are hinged to each other; a blocking cover is provided on the top of the device body; An image acquisition module, an imaging module, a data processing and storage module, a control module and a network communication module are arranged inside the device body.

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