Gas cylinder automatic identification and management system based on image recognition

Abstract

The present invention relates to the field of image recognition technology, specifically to an automatic recognition and management system for gas cylinders based on image recognition, the system comprising: a feature reconstruction module, based on a gas cylinder image, analyzes the damage and missing parts of the outer shell material in the image, uses digital signal processing technology to reconstruct the edge and texture of the image, improves the resolution of the image, and generates a reconstructed feature set. In the present invention, through feature reconstruction, accurate recognition of the damage to the outer shell material of the gas cylinder in the image is achieved, the image clarity is improved, and the detailed analysis and reconstruction of the edge and texture of the image make the damage assessment more accurate, and the degree and location of the damage can be determined in a timely and accurate manner. By enhancing the contrast and clarity of the image features, the recognizability of the damage and defects is improved, and the processing weight is adjusted in real time according to the image quality to maintain the recognition accuracy under different environmental conditions, thereby improving the safety and efficiency of gas cylinder management and helping to predict and monitor the use status and potential risks of gas cylinders.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to an automatic recognition and management system for gas cylinders based on image recognition. Background Art

[0002] Image recognition is a core technology in the field of computer vision, which allows computers to identify and classify objects by analyzing image data. This technology uses various algorithms and models, such as convolutional neural networks (CNNs), to interpret the content in images. Image recognition has a wide range of applications, including security monitoring, autonomous driving, medical image analysis, industrial inspection, etc. With the development of deep learning technology, the accuracy and efficiency of image recognition have been significantly improved, enabling it to perform more precise recognition and analysis in more complex environments.

[0003] Among them, the automatic identification and management system for gas cylinders refers to a system that uses image recognition technology to automatically manage and monitor gas cylinders. This system can identify information such as the label, model, usage status and expiration date of the gas cylinder, greatly improving safety and efficiency. Its uses include tracking the distribution of gas cylinders, monitoring the usage cycle and remaining capacity of gas cylinders, and ensuring the safe storage and transportation of gas cylinders. The application of this technology is particularly important for industrial gas suppliers, hospitals and other places that need to use a large number of gas cylinders.

[0004] Common limitations of existing technologies when processing image data include the inadequacy of algorithms when processing low-quality or complex images, especially in the ability to identify subtle damage and damage in complex backgrounds. Existing technologies lack the ability to flexibly adapt to dynamic environments and fail to adjust processing strategies based on real-time data, resulting in reduced recognition accuracy when the environment changes. These deficiencies may cause misidentification in the safety management of gas cylinders, such as missing severely damaged gas cylinders, increasing safety risks. In medical and industrial applications that rely on gas cylinders, inaccurate recognition may lead to serious gas supply interruptions or safety accidents, affecting normal operations and safety assurance. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an automatic recognition and management system for gas cylinders based on image recognition.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: The automatic identification and management system of gas cylinders based on image recognition includes:

[0007] The feature reconstruction module is based on the cylinder image, analyzes the shell material damage and missing parts in the image, uses digital signal processing technology to reconstruct the edge and texture of the image, improves the image resolution, and generates a reconstruction feature set;

[0008] The feature optimization module extracts the image features of the damaged shell material from the reconstructed feature set, uses image enhancement to refine the structural hierarchy and spatial relationship, adjusts the contrast and clarity of the image features, and obtains optimized feature data;

[0009] The dynamic adjustment module adjusts the processing weight of the optimized feature data in real time according to the image quality, analyzes the damage level and noise level in the image, and dynamically adjusts the weight distribution of the feature reconstruction and optimization process according to the analysis results to obtain the adjusted feature output;

[0010] The behavior analysis module uses the adjusted feature output to monitor the current status and potential risks of the gas cylinders, analyze the interactive behaviors between the gas cylinders, predict the behavior patterns and dynamic changes in the future time period, and generate behavior prediction results.

[0011] As a further solution of the present invention, the step of obtaining the reconstruction feature set is specifically:

[0012] From the original cylinder image In order to identify the damaged and missing parts of the shell material and enhance the contrast, the formula is:

[0013] ;

[0014] Generate damage area identification map ;

[0015] in, represents the original image, represents the damage detection mask, is the contrast adjustment coefficient, For images The standard deviation of

[0016] Identification of the damaged area Perform edge detection and locate the damage boundary. The formula is:

[0017] ;

[0018] Generate edge enhancement map ;

[0019] in, Represents the damaged area identification map, and express The horizontal and vertical gradients, represents edge enhancement map;

[0020] The edge enhancement map For texture analysis and reconstruction, the formula is:

[0021] ;

[0022] Generate reconstruction feature set ;

[0023] in, represents the edge enhancement map, and is the adjustment coefficient that controls texture reconstruction, To avoid division by zero errors, Represents the reconstruction feature set.

[0024] As a further solution of the present invention, the step of obtaining the optimized feature data is specifically as follows:

[0025] Reconstruct the feature set from The image features of the damaged shell material are extracted and improved by the weighted average method. The formula is:

[0026] ;

[0027] Generate initial feature data ;

[0028] in, represents the weight factor, represent The Features, represents the total number of features;

[0029] The initial feature data To enhance the image, refine the structure level and spatial relationship, the formula is:

[0030] ;

[0031] Generate enhanced feature data ;

[0032] in, represents the enhancement adjustment coefficient;

[0033] The enhanced feature data To adjust the contrast and clarity, the formula is:

[0034] ;

[0035] Normalize the eigenvalue range to , generate optimized feature data ;

[0036] in, represents enhanced feature data, and Respectively represent The minimum and maximum values ​​of .

[0037] As a further solution of the present invention, the step of obtaining the adjusted feature output is specifically:

[0038] Analyzing the optimization feature data The damage level and noise level in the formula are:

[0039] ;

[0040] Calculates damage level and noise level and generates damage level and noise analysis results ;

[0041] in, represents the effect of adjusting the damage level, represents the effect of adjusting the noise data, express The standard deviation of represents the normalization constant;

[0042] According to the damage level and the noise analysis results , dynamically adjust the weight distribution of feature reconstruction and optimization process, using the formula:

[0043] ;

[0044] Determine the current weight distribution ;

[0045] in, represents the weight adjustment coefficient;

[0046] Apply the current weight distribution Re-adjust and optimize feature data , using the formula:

[0047] ;

[0048] Generate adjusted feature output ;

[0049] in, Represents element-wise multiplication.

[0050] As a further solution of the present invention, the steps of monitoring the current status and potential risks of the gas cylinder are specifically:

[0051] Using the adjusted feature output Calculate the basic state description using the formula:

[0052] ;

[0053] Aggregate all features to generate preliminary status assessment results ;

[0054] in, represents the adjusted feature output, represents the number of features, Representative Features

[0055] Based on preliminary status assessment , apply variance analysis to improve risk sensitivity, using the formula:

[0056] ;

[0057] Calculate the average value for each feature Deviations from the previous state can generate detailed state analysis results. ;

[0058] in, represents the average value of the feature;

[0059] Utilize the refined state analysis results To assess potential risks, use the formula:

[0060] ;

[0061] Capture the biggest risk points, obtain the current status of gas cylinders and potential risk assessment ;

[0062] in, Represents the current status of the gas cylinder and potential risk assessment.

[0063] As a further solution of the present invention, the steps of obtaining the behavior prediction results are specifically as follows:

[0064] Based on the current status and potential risks of the cylinders , using the formula:

[0065] ;

[0066] Calculate the average status of all gas cylinders and generate current status monitoring results ;

[0067] in, Indicates the number of gas cylinders, Representative Risk assessment of each gas cylinder;

[0068] Analyze the current status monitoring results The difference with the risk assessment of multiple cylinders is that the formula is used:

[0069] ;

[0070] Calculate the behavioral differences between cylinders and generate interactive behavior analysis results ;

[0071] in, and Representative and Risk assessment of each gas cylinder;

[0072] According to the interactive behavior analysis results , predicting behavior patterns and dynamic changes in future time periods, using the formula:

[0073] ;

[0074] Generate behavior prediction results ;

[0075] in, Represents the results of interactive behavior analysis. represents the decay constant, Indicates time.

[0076] Compared with the prior art, the advantages and positive effects of the present invention are:

[0077] In the present invention, accurate identification of the damage to the material of the gas cylinder shell in the image is achieved through feature reconstruction, and the image clarity is improved. The detailed analysis and reconstruction of the image edges and textures make the damage assessment more accurate, and the extent and location of the damage can be determined in a timely and accurate manner. By enhancing the contrast and clarity of the image features, the recognizability of damage and defects is significantly improved. In addition, the processing weight is adjusted in real time according to the image quality to maintain the recognition accuracy of the system under different environmental conditions, thereby improving the safety and efficiency of gas cylinder management, supporting efficient management decisions, and helping to predict and monitor the use status and potential risks of gas cylinders. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 is a system flow chart of the present invention;

[0079] Figure 2 A flow chart of the acquisition steps of the feature set reconstruction of the present invention;

[0080] Figure 3 A flow chart of the steps for obtaining the optimized feature data of the present invention;

[0081] Figure 4 A flow chart of the steps for obtaining the adjusted feature output of the present invention;

[0082] Figure 5 The figure is a flow chart of the steps for obtaining the behavior prediction results of the present invention. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0084] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0085] Example 1: Please refer to Figure 1 , the gas cylinder automatic identification and management system based on image recognition includes:

[0086] The feature reconstruction module is based on the cylinder image, analyzes the shell material damage and missing parts in the image, uses digital signal processing technology to reconstruct the edge and texture of the image, improves the image resolution, and generates a reconstruction feature set;

[0087] The feature optimization module extracts the image features of the damaged shell material from the reconstructed feature set, uses image enhancement to refine the structural hierarchy and spatial relationship, adjusts the contrast and clarity of the image features, and obtains optimized feature data;

[0088] The dynamic adjustment module adjusts the processing weight of the optimized feature data in real time according to the image quality, analyzes the damage level and noise level in the image, and dynamically adjusts the weight distribution of the feature reconstruction and optimization process according to the analysis results to obtain the adjusted feature output;

[0089] The behavior analysis module uses the adjusted feature output to monitor the current status and potential risks of gas cylinders, analyze the interactive behaviors between gas cylinders, predict the behavior patterns and dynamic changes in future time periods, and generate behavior prediction results.

[0090] The reconstructed feature set specifically includes image edges and texture features. The optimized feature data includes structural hierarchy, spatial relationship, contrast, and clarity. The adjusted feature output includes feature reconstruction weights and optimization process weights. The behavior prediction results specifically refer to interactive behaviors, risk levels, and dynamic changes.

[0091] See also Figure 2, the specific steps for obtaining the reconstructed feature set are:

[0092] From the original cylinder image In order to identify the damaged and missing parts of the shell material and enhance the contrast, the formula is:

[0093] ;

[0094] Generate damage area identification map ;

[0095] in, represents the original image, represents the damage detection mask, is the contrast adjustment coefficient, For images The standard deviation of

[0096] Damage area identification Perform edge detection and locate the damage boundary. The formula is:

[0097] ;

[0098] Generate edge enhancement map ;

[0099] in, Represents the damaged area identification map, and express The horizontal and vertical gradients, represents edge enhancement map;

[0100] Edge Enhancement For texture analysis and reconstruction, the formula is:

[0101] ;

[0102] Generate reconstruction feature set ;

[0103] in, represents the edge enhancement map, and is the adjustment coefficient that controls texture reconstruction, To avoid division by zero errors, Represents the reconstruction feature set.

[0104] formula:

[0105] ;

[0106] in:

[0107] : The intensity value of the original image, assumed to be 150 (the image pixel value range is 0-255)

[0108] : Damage detection mask, set to 1.0 (full mask, i.e. damage is fully visible)

[0109] : Contrast adjustment factor, set to 0.5 (this is a commonly used medium value)

[0110] : The standard deviation of the image is set to 30 (a common value in general image processing)

[0111] Calculation process:

[0112] To calculate the standard deviation adjustment: Use the formula ;

[0113] Calculate the denominator: ;

[0114] Calculate the entire formula: ;

[0115] Example results:

[0116] What you get The value is 9.375, which reflects the visibility of the damaged area after contrast adjustment. The value indicates that the damaged part is more prominent in the image and easier to identify.

[0117] formula:

[0118] ;

[0119] in:

[0120] and The horizontal and vertical gradients are assumed to be 10 and 15.

[0121] Calculation process:

[0122] Compute the sum of the squares of the horizontal and vertical gradients: ;

[0123] Calculate the value under the square root: ;

[0124] Example results:

[0125] What you get The value is 18.027, which indicates the edge strength at the boundary of the damaged area. The value indicates that the edge is clear, which is helpful for subsequent feature extraction.

[0126] formula:

[0127] ;

[0128] in:

[0129] and Adjustment coefficient, set to 0.8 and 0.2;

[0130] : A small positive number to prevent division by zero errors, set to 0.1;

[0131] : Calculated from the previous step, it is 18.027.

[0132] Calculation process:

[0133] calculate ;

[0134] Compute cosine and sine functions: ;

[0135] Calculate the fractional part: ;

[0136] Overall formula calculation: ;

[0137] =Example results:

[0138] What you get The value is 262.5616, which indicates the strength of the feature set after texture reconstruction. The value indicates that the reconstructed texture features are clear and suitable for further analysis.

[0139] See also Figure 3 , the specific steps for obtaining optimized feature data are:

[0140] From the reconstruction feature set The image features of the damaged shell material are extracted and improved by the weighted average method. The formula is:

[0141] ;

[0142] Generate initial feature data ;

[0143] in, represents the weight factor, represent The Features, represents the total number of features;

[0144] For the initial feature data To enhance the image, refine the structure level and spatial relationship, the formula is:

[0145] ;

[0146] Generate enhanced feature data ;

[0147] in, represents the enhancement adjustment coefficient;

[0148] Enhanced feature data To adjust the contrast and clarity, the formula is:

[0149] ;

[0150] Normalize the eigenvalue range to , generate optimized feature data ;

[0151] in, represents enhanced feature data, and Respectively represent The minimum and maximum values ​​of .

[0152] Formula analysis:

[0153] ;

[0154] The formula is a weighted average formula, which is used to extract the features from multiple images. Extract a comprehensive initial feature ,in is the weight of each feature, indicating the importance of the feature.

[0155] Calculation process:

[0156] Suppose we have 3 image features They are ;

[0157] The corresponding weight yes ;

[0158] Compute the weighted sum: 1400;

[0159] Calculate the weighted sum: ;

[0160] Calculate the weighted average: ;

[0161] This result It represents the comprehensive image feature value, reflecting the weighted influence of all features.

[0162] ;

[0163] Formula is used to enhance image features , enhancing details and contrast by combining a logarithmic function and a power function. is the enhancement coefficient.

[0164] Calculation process:

[0165] Using the previous step calculated ;

[0166] Assumptions

[0167] calculate ;

[0168] ;

[0169] result Represents the enhanced feature values, showing better visual contrast and details of the features.

[0170] Formula analysis:

[0171] ;

[0172] The formula is used to enhance the feature data Standardized to range, making it more suitable for subsequent processing.

[0173] Calculation process:

[0174] Using the previous step calculated .

[0175] Assume that in a set of enhanced features, Minimum value of , the maximum value ;

[0176] calculate ;

[0177] result is the normalized feature value, indicating the position of the enhanced feature relative to the minimum and maximum values.

[0178] See also Figure 4 , the steps to obtain the adjusted feature output are as follows:

[0179] Analyze and optimize feature data The damage level and noise level in the formula are:

[0180] ;

[0181] Calculates damage level and noise level and generates damage level and noise analysis results ;

[0182] in, represents the effect of adjusting the damage level, represents the effect of adjusting the noise data, express The standard deviation of represents the normalization constant;

[0183] According to the damage level and noise analysis results , dynamically adjust the weight distribution of feature reconstruction and optimization process, using the formula:

[0184] ;

[0185] Determine the current weight distribution ;

[0186] in, represents the weight adjustment coefficient;

[0187] Apply current weight distribution Re-adjust and optimize feature data , using the formula:

[0188] ;

[0189] Generate adjusted feature output ;

[0190] in, Represents element-wise multiplication.

[0191] formula:

[0192] ;

[0193] Parameter description and derivation:

[0194] and are preset coefficients used to adjust the impact of impairment level and noise level.

[0195] Represents optimized feature data, assuming a value of 0.5.

[0196] represent The standard deviation of is assumed to be 0.1, indicating that the variation of the feature data is small.

[0197] Calculation example:

[0198] Assumptions:

[0199] ;

[0200] ;

[0201] ;

[0202] ;

[0203] ;

[0204] calculate:

[0205] ;

[0206] ;

[0207] ;

[0208] Analysis:

[0209] It means that with the given parameters, the comprehensive evaluation value of damage level and noise level is 0.119, which is a low evaluation value, indicating that the degree of damage is not high.

[0210] formula:

[0211] ;

[0212] Parameter description and derivation:

[0213] is the weight adjustment coefficient, set to 1, representing the standard weight adjustment;

[0214] Known to be 0.119.

[0215] Calculation example:

[0216] ;

[0217] ;

[0218] ;

[0219] Analysis:

[0220] It indicates that the adjusted weight is 0.530, which means that this feature has a greater influence in subsequent processing.

[0221] formula:

[0222] ;

[0223] Parameter description and derivation:

[0224] Known to be 0.530.

[0225] Assume it is still 0.5.

[0226] Calculation example:

[0227] ;

[0228] Analysis:

[0229] Indicates that the adjusted feature output is 0.265 Compared with the original data decrease, reflecting the reduced importance of the features after weight adjustment.

[0230] See also Figure 5 The specific steps to monitor the current status and potential risks of gas cylinders are:

[0231] Use the adjusted feature output Calculate the basic state description using the formula:

[0232] ;

[0233] Aggregate all features to generate preliminary status assessment results ;

[0234] in, represents the adjusted feature output, represents the number of features, Representative Features

[0235] Based on preliminary status assessment , apply variance analysis to improve risk sensitivity, using the formula:

[0236] ;

[0237] Calculate the average value for each feature Deviations from the previous state can generate detailed state analysis results. ;

[0238] in, represents the average value of the feature;

[0239] Analyze results using refined states To assess potential risks, use the formula:

[0240] ;

[0241] Capture the biggest risk points, obtain the current status of gas cylinders and potential risk assessment ;

[0242] in, Represents the current status of the gas cylinder and potential risk assessment.

[0243] formula:

[0244] ;

[0245] Suppose there are several The values ​​represent the adjusted feature outputs at the differentiated time points, which are obtained through real-time monitoring and dynamic weight adjustment. , each value represents the characteristic output of the cylinder in a differentiated state.

[0246] Calculation process:

[0247] Given Value is ;

[0248] ;

[0249] result: It is 1.175, which means the sum of comprehensive feature outputs obtained through weighted processing.

[0250] formula:

[0251] ;

[0252] Calculation process:

[0253] average value ;

[0254] Variance calculation:

[0255] ;

[0256] ;

[0257] ;

[0258] result: It is 0.0198, which indicates the statistical dispersion of state changes, and lower values ​​indicate consistency of the state.

[0259] formula:

[0260]

[0261] Calculation process: Since in the example is a single value, so .

[0262] result: The value is 0.0198, which represents the potential level of risk based on dispersion, with lower values ​​generally indicating less risk.

[0263] See also Figure 5 , the steps for obtaining behavior prediction results are as follows:

[0264] Based on the current status of the cylinder and potential risks , using the formula:

[0265] ;

[0266] Calculate the average status of all gas cylinders and generate current status monitoring results ;

[0267] in, Indicates the number of gas cylinders, Representative Risk assessment of each gas cylinder;

[0268] Analyze current status monitoring results The difference with the risk assessment of multiple cylinders is that the formula is used:

[0269] ;

[0270] Calculate the behavioral differences between cylinders and generate interactive behavior analysis results ;

[0271] in, and Representative and Risk assessment of each gas cylinder;

[0272] According to the results of interactive behavior analysis , predicting behavior patterns and dynamic changes in future time periods, using the formula:

[0273] ;

[0274] Generate behavior prediction results ;

[0275] in, Represents the results of interactive behavior analysis. represents the decay constant, Indicates time.

[0276] formula:

[0277] ;

[0278] Assuming there are three gas cylinders, the adjusted risk assessment values ​​are:

[0279] ;

[0280] ;

[0281] ;

[0282] Calculation process:

[0283] ;

[0284] This suggests an average risk estimate of 0.0133.

[0285] formula:

[0286] ;

[0287] Calculation process:

[0288] ;

[0289] This represents an overall behavioral difference of 0.04.

[0290] formula:

[0291] ;

[0292] set up:

[0293] ;

[0294] ;

[0295] Calculation process:

[0296] ;

[0297] This indicates a future behavior prediction value of 0.02424, reflecting the expected change in future behavior patterns based on current interactions.

[0298] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. Gas cylinder automatic identification and management system based on image recognition, characterized in that: The system comprises: The feature reconstruction module is based on the cylinder image, analyzes the shell material damage and missing parts in the image, uses digital signal processing technology to reconstruct the edge and texture of the image, improves the image resolution, and generates a reconstruction feature set; The feature optimization module extracts the image features of the damaged shell material from the reconstructed feature set, uses image enhancement to refine the structural hierarchy and spatial relationship, adjusts the contrast and clarity of the image features, and obtains optimized feature data; The dynamic adjustment module adjusts the processing weight of the optimized feature data in real time according to the image quality, analyzes the damage level and noise level in the image, and dynamically adjusts the weight distribution of the feature reconstruction and optimization process according to the analysis results to obtain the adjusted feature output; The behavior analysis module uses the adjusted feature output to monitor the current status and potential risks of the gas cylinders, analyze the interactive behaviors between the gas cylinders, predict the behavior patterns and dynamic changes in the future time period, and generate behavior prediction results; The steps for obtaining the behavior prediction results are specifically as follows: Based on the current status and potential risks of the cylinders , using the formula: ； Calculate the average status of all gas cylinders and generate current status monitoring results ; in, Indicates the number of gas cylinders, Representative Risk assessment of each gas cylinder; Analyze the current status monitoring results The difference with the risk assessment of multiple cylinders is that the formula is used: ； Calculate the behavioral differences between cylinders and generate interactive behavior analysis results ; in, and Representative and Risk assessment of each gas cylinder; According to the interactive behavior analysis results , predicting behavior patterns and dynamic changes in future time periods, using the formula: ； Generate behavior prediction results ; in, Represents the results of interactive behavior analysis. represents the decay constant, Indicates time.

2. The automatic identification and management system for gas cylinders based on image recognition according to claim 1 is characterized in that: The steps of obtaining the reconstruction feature set are specifically as follows: From the original cylinder image Identify the damaged and missing parts of the shell material and enhance the contrast. The formula is: ； Generate damage area identification map ; in, represents the original image, represents the damage detection mask, is the contrast adjustment coefficient, For images The standard deviation of Identification of the damaged area Perform edge detection and locate the damage boundary. The formula is: ； Generate edge enhancement map ; in, Represents the damaged area identification map, and express The horizontal and vertical gradients, represents edge enhancement map; The edge enhancement map For texture analysis and reconstruction, the formula is: ； Generate reconstruction feature set ; in, represents the edge enhancement map, and is the adjustment coefficient that controls texture reconstruction, To avoid division by zero errors, Represents the reconstruction feature set.

3. The automatic identification and management system for gas cylinders based on image recognition according to claim 2 is characterized in that: The steps for obtaining the optimized feature data are specifically as follows: Reconstruct the feature set from The image features of the damaged shell material are extracted and improved by the weighted average method. The formula is: ； Generate initial feature data ; in, represents the weight factor, represent The Features, represents the total number of features; The initial feature data To enhance the image, refine the structure level and spatial relationship, the formula is: ； Generate enhanced feature data ; in, represents the enhancement adjustment coefficient; The enhanced feature data To adjust the contrast and clarity, the formula is: ； Normalize the eigenvalue range to , generate optimized feature data ; in, represents enhanced feature data, and Respectively represent The minimum and maximum values ​​of .

4. The automatic identification and management system for gas cylinders based on image recognition according to claim 3 is characterized in that: The steps for obtaining the adjusted feature output are specifically as follows: Analyzing the optimization feature data The damage level and noise level in the formula are: ； Calculates damage level and noise level and generates damage level and noise analysis results ; in, represents the effect of adjusting the damage level, represents the effect of adjusting the noise data, express The standard deviation of represents the normalization constant; According to the damage level and the noise analysis results , dynamically adjust the weight distribution of feature reconstruction and optimization process, using the formula: ； Determine the current weight distribution ; in, represents the weight adjustment coefficient; Apply the current weight distribution Re-adjust and optimize feature data , using the formula: ； Generate adjusted feature output ; in, Represents element-wise multiplication.

5. The automatic identification and management system for gas cylinders based on image recognition according to claim 4 is characterized in that: The steps of monitoring the current status and potential risks of the gas cylinder are specifically as follows: Using the adjusted feature output Calculate the basic state description using the formula: ； Aggregate all features to generate preliminary status assessment results ; in, represents the adjusted feature output, represents the number of features, Representative Features Based on preliminary status assessment , apply variance analysis to improve risk sensitivity, using the formula: ； Calculate the average value for each feature Deviations from the previous state can generate detailed state analysis results. ; in, represents the average value of the feature; Utilize the refined state analysis results To assess potential risks, use the formula: ； Capture the biggest risk points, obtain the current status of gas cylinders and potential risk assessment ; in, Represents the current status of the gas cylinder and potential risk assessment.

Images