Self-adaptive deformation correction and identification system of flexible curved bar code

Abstract

The invention relates to the technical field of automatic identification, and particularly discloses a self-adaptive deformation correction and identification system for a flexible curved surface bar code, which can accurately capture a complex deformation condition by monitoring the three-dimensional shape change and strain distribution of a bar code area on a flexible surface in real time, and can calculate a curvature characteristic value by using a K-means clustering algorithm, so as to realize the self-adaptive deformation correction and identification of the flexible curved surface bar code. Strain data are analyzed through Haar wavelet transform to determine deformation characteristic values, so that the deformation degree of the bar code and the influence of the deformation degree on the recognition process are comprehensively evaluated, a random forest model is constructed based on the characteristic values for deformation degree prediction and deformation score output, high-precision recognition is ensured, and when uncorrectable serious deformation is detected, the recognition accuracy is improved. The system automatically triggers an alarm mechanism, including sound alarm, visual signal and network notification, to inform management personnel in time, so that the stability and long-term reliability of the system are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic identification, and particularly to an adaptive deformation correction and recognition system for flexible curved surface barcodes. Background Art

[0002] With the increasing demand for barcode recognition in industries such as logistics, manufacturing, and retail, traditional flat barcodes can no longer meet the requirements for applications on flexible curved surfaces. The application scenarios of flexible curved surface barcodes are becoming increasingly extensive, including but not limited to packaging, identification of medical devices, and personal electronic products. However, barcodes in these application scenarios often face recognition challenges due to bending, folding, or other physical deformations of the substrate material. Therefore, developing a system that can adapt to and correct these deformations has become an important research direction. Such a system not only needs to have high-precision three-dimensional shape measurement capabilities but also be able to monitor and evaluate the strain distribution of the material in real time to ensure accurate barcode recognition even under complex deformation conditions.

[0003] The existing technologies have the following deficiencies: Although there are currently various technologies and methods attempting to solve the deformation problem of flexible curved surface barcodes, they still have significant limitations. Traditional methods usually rely on single-dimensional data acquisition methods, such as only using two-dimensional image processing technology for analysis, which leads to incomplete and inaccurate capture of complex deformation patterns. In addition, existing deformation correction algorithms are mostly based on preset models or parameters and are difficult to adapt to actual changes under different materials and environments, thus affecting the recognition accuracy. At the same time, the lack of an effective early warning mechanism to timely detect and handle serious deformations that cannot be corrected makes the system respond slowly in the face of emergencies, increasing the risk of recognition failure. These limitations indicate that there is still much room for improvement in the flexibility, accuracy, and reliability of existing technologies, and a more comprehensive and intelligent technical solution is urgently needed to address various challenges in flexible curved surface barcode recognition. Summary of the Invention

[0004] The purpose of the present invention is to provide an adaptive deformation correction and recognition system for flexible curved surface barcodes to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An adaptive deformation correction and recognition system for flexible curved surface barcodes, comprising: A data acquisition module, which monitors the three-dimensional shape changes of the barcode area on the flexible surface in real time, obtains curvature distribution data, and directly measures the strain distribution on the material to obtain strain data; Flexible curved surface barcode recognition module, which determines the curvature eigenvalue according to the change degree of the curvature distribution, calculates the deformation eigenvalue according to the abnormal degree of the strain distribution, and identifies the deformation degree according to the curvature eigenvalue and deformation eigenvalue of the flexible curved surface barcode; Flexible curved surface barcode correction module, which judges whether there is serious deformation that cannot be corrected according to the recognition result of the deformation degree. If there is serious deformation that cannot be corrected, it automatically triggers the alarm mechanism.

[0006] As a further scheme of the present invention: evaluate the local deformation characteristics in the barcode area according to the change degree of the curvature distribution, specifically including: Real-time collect the curvature distribution data of the flexible curved surface barcode, calculate the curvature eigenvalue according to the change degree of the curvature distribution of the flexible curved surface barcode, and judge whether the curvature eigenvalue of the flexible curved surface barcode is greater than or equal to the preset threshold. If so, there is local deformation in the barcode area. If not, there is no local deformation in the barcode area.

[0007] As a further scheme of the present invention: the acquisition process of the curvature eigenvalue is as follows: Real-time collect the curvature distribution data of the flexible curved surface barcode. For each data point in the collected data set, obtain the curvature value of the corresponding point by fitting a local quadratic surface and extracting its principal curvature, integrate all the obtained curvature values into a curvature set, and apply the K-means clustering algorithm to classify the curvature set. First, select the number of clusters and initialize the centroid positions of each cluster. According to the distance between each data point and each centroid, assign the data points to the nearest cluster and update the centroid positions until the algorithm converges. Divide the data points into several categories according to the curvature values to identify the curvature characteristics of different regions; For each cluster, obtain the curvature eigenvalue of the corresponding cluster by adding the curvature values of all data points belonging to the same cluster and dividing by the total number of data points in the corresponding cluster; calculate the mean value of the curvature eigenvalues of all clusters to obtain the overall curvature eigenvalue of the flexible curved surface barcode.

[0008] As a further scheme of the present invention: evaluate the physical deformation characteristics of the flexible curved surface barcode according to the abnormal degree of the strain distribution, specifically including: Real-time collect the strain data of the flexible curved surface barcode, calculate the deformation eigenvalue according to the abnormal degree of the strain distribution of the flexible curved surface barcode, and judge whether the deformation eigenvalue of the flexible curved surface barcode is greater than or equal to the preset threshold. If so, the flexible curved surface barcode has physical deformation. If not, the flexible curved surface barcode has no physical deformation.

[0009] As a further scheme of the present invention: the acquisition process of the deformation eigenvalue is as follows: Obtain the strain data of the barcode area on the flexible surface in real time, apply Haar wavelet transform to the strain data, decompose the signal into approximation coefficients and detail coefficients at different scales. After multi-layer decomposition, we obtain a set of approximation coefficients and several sets of detail coefficients. Each set of detail coefficients represents the detail change situation at different scales; For each set of detail coefficients, calculate its energy distribution, specifically including: for each element in each set of detail coefficients, obtain its square value and accumulate all these square values to obtain the energy of the corresponding set of detail coefficients; According to the energy distribution of all sets of detail coefficients, synthesize this information in the form of a ratio to calculate the overall deformation eigenvalue. Add a small positive constant to the energy of each set of detail coefficients, multiply all the adjusted energy values, and divide by the sum of all weight factors to obtain the deformation eigenvalue.

[0010] As a further solution of the present invention: the identification of the deformation degree according to the curvature eigenvalue and deformation eigenvalue of the flexible surface barcode specifically includes: Obtain the curvature eigenvalue and deformation eigenvalue of the flexible surface barcode, directly construct the curvature eigenvalue and deformation eigenvalue into a comprehensive feature vector, and use it as the input of the machine learning model. Take minimizing the error between the predicted deformation degree score and the actual deformation degree score as the training objective, train the model, and according to the trained model, output the deformation degree score of the flexible surface barcode. The machine learning model is a random forest model.

[0011] As a further solution of the present invention: the training process of the machine learning model is: In the model training stage, use the random forest algorithm to train the constructed comprehensive feature vector. Improve the prediction accuracy and control overfitting by constructing multiple decision trees and summarizing their results. During the training process, take minimizing the error between the predicted deformation degree identification accuracy score and the actual deformation degree identification accuracy score as the objective function. Continuously adjust the parameters of each decision tree until the best parameter combination is found. Use the trained random forest model to analyze the received new comprehensive feature vector and output the deformation degree score of the flexible surface barcode.

[0012] As a further solution of the present invention: the judgment of whether there is a serious deformation that cannot be corrected specifically includes: Judge whether the deformation degree score of the flexible surface barcode during identification is greater than or equal to the preset threshold. If so, there is a serious deformation that cannot be corrected. If not, there is no serious deformation that cannot be corrected.

[0013] As a further solution of the present invention: the judgment of whether there is a serious deformation that cannot be corrected specifically includes: Determine whether the deformation degree score of the flexible curved surface barcode during recognition is greater than or equal to the preset threshold. If so, there is a serious deformation that cannot be corrected. If not, there is no serious deformation that cannot be corrected.

[0014] Advantages of the present invention: (1) By integrating advanced 3D scanning technology and strain measurement technology, the present invention realizes real-time and accurate monitoring of the barcode area on the flexible surface. It not only captures its complex 3D shape changes but also directly measures the strain distribution on the material, comprehensively reflecting the true physical state of the environment where the barcode is located. Using the K-means clustering algorithm to classify the curvature data and calculate the curvature eigenvalue, and at the same time applying the Haar wavelet transform to analyze the energy of the strain detail coefficients at multiple scales to determine the deformation eigenvalue, so as to systematically evaluate the local and overall deformation conditions of the barcode and its impact on the recognition process. This multi-level data processing and analysis method significantly improves the accuracy and robustness of deformation recognition, providing a solid foundation for subsequent correction measures. Even in the face of complex and unpredictable deformation conditions, the system can still ensure high-precision barcode recognition, greatly enhancing the adaptability and reliability of the system, and ensuring stable and efficient barcode reading and decoding in various application scenarios. In addition, through in-depth analysis of the collected data, the present invention also supports the triggering of an automatic warning mechanism, further ensuring the long-term stable operation and management efficiency of the system.

[0015] (2) When detecting a serious deformation that cannot be corrected, the present invention can intelligently and automatically trigger a multi-level alarm mechanism, including immediate sound alarms, visual signals such as flashing warning lights, and sending notifications to relevant management personnel through the network, ensuring a response to potential problems in the first time. In addition, based on the constructed random forest model, the system deeply analyzes the received new comprehensive feature vector and outputs an accurate deformation degree score, enabling potential problems to be identified and processed in the early stage, effectively preventing recognition failures or errors caused by deformation. This innovation not only significantly improves the response speed and processing efficiency of the system but also optimizes resource management through continuous data monitoring and analysis, minimizing losses caused by deformation to the greatest extent. The design concept of this system emphasizes comprehensive protection and efficient management. By integrating advanced data acquisition, processing technologies and intelligent warning mechanisms, it ensures excellent stability and long-term reliability even in complex and changeable application environments, providing users with a highly reliable and easy-to-manage barcode recognition solution. Description of the drawings

[0016] The present invention will be further described below with reference to the drawings.

[0017] Figure 1 It is a flow block diagram of the adaptive deformation correction and recognition system for the flexible curved surface barcode of the present invention. Detailed Implementation Modes

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0019] Please refer to Figure 1 As shown, the present invention is an adaptive deformation correction and recognition system for flexible curved barcodes, including: A data acquisition module, which continuously monitors the three-dimensional shape changes of the barcode area on the flexible surface, obtains curvature distribution data, and directly measures the strain distribution on the material to obtain strain data; A flexible curved barcode recognition module, which determines the curvature eigenvalue according to the degree of change in the curvature distribution, calculates the deformation eigenvalue according to the degree of abnormality in the strain distribution, and recognizes the degree of deformation according to the curvature eigenvalue and deformation eigenvalue of the flexible curved barcode; A flexible curved barcode correction module, which determines whether there is a serious deformation that cannot be corrected according to the recognition result of the degree of deformation. If there is a serious deformation that cannot be corrected, it automatically triggers an alarm mechanism.

[0020] In the data acquisition module, continuously monitoring the three-dimensional shape changes of the barcode area on the flexible surface, obtaining curvature distribution data, and directly measuring the strain distribution on the material to obtain strain data, specifically including: First, use a three-dimensional scanning device to continuously monitor the three-dimensional shape changes of the barcode area on the flexible surface and obtain accurate spatial coordinate information. These devices construct a three-dimensional model of the barcode surface by projecting structured light or laser and analyzing the reflection pattern, thereby extracting the position of each data point and its corresponding curvature distribution data. This process ensures high-precision capture of the complex geometric shape of the surface where the barcode is located and provides basic data support for subsequent analysis.

[0021] At the same time, directly measure the strain distribution on the material to obtain strain data, using strain gauges. The strain gauges are directly pasted on the material surface and can sensitively respond to the expansion and contraction changes of the material, outputting corresponding electrical signals to represent the local strain situation.

[0022] In the flexible curved barcode recognition module, determine the curvature eigenvalue according to the degree of change in the curvature distribution, calculate the deformation eigenvalue according to the degree of abnormality in the strain distribution, and recognize the degree of deformation according to the curvature eigenvalue and deformation eigenvalue of the flexible curved barcode, specifically including: Collect the curvature distribution data of the flexible curved barcode in real time. Calculate the curvature eigenvalue according to the degree of change in the curvature distribution of the flexible curved barcode. Determine whether the curvature eigenvalue of the flexible curved barcode is greater than or equal to the preset threshold. If so, there is local deformation in the barcode area. If not, there is no local deformation in the barcode area.

[0023] The process of obtaining the curvature eigenvalue is as follows: Collect the curvature distribution data of the flexible curved barcode in real time. For each data point in the collected dataset, obtain the curvature value of the corresponding point by fitting a local quadratic surface and extracting its principal curvature. Integrate all the obtained curvature values into a curvature set. Apply the K-means clustering algorithm to classify the curvature set. First, select the number of clusters and initialize the centroid positions of each cluster. According to the distances between each data point and the centroids, assign the data points to the nearest cluster and update the centroid positions until the algorithm converges. Divide the data points into several categories according to the curvature values to identify the curvature characteristics of different regions. For each cluster, obtain the curvature eigenvalue of the corresponding cluster by adding up the curvature values of all the data points belonging to the same cluster and then dividing by the total number of data points in the corresponding cluster. Calculate the mean value of the curvature eigenvalues of all the clusters to obtain the overall curvature eigenvalue of the flexible curved barcode.

[0024] Collect the strain data of the flexible curved barcode in real time. Calculate the deformation eigenvalue according to the degree of abnormality in the strain distribution of the flexible curved barcode. Determine whether the deformation eigenvalue of the flexible curved barcode is greater than or equal to the preset threshold. If so, there is physical deformation in the flexible curved barcode. If not, there is no physical deformation in the flexible curved barcode.

[0025] The process of obtaining the deformation eigenvalue is as follows: Obtain the strain data of the barcode area on the flexible surface in real time. Apply the Haar wavelet transform to the strain data to decompose the signal into approximation coefficients and detail coefficients at different scales. After performing multi-level decomposition, we obtain a set of approximation coefficients and several sets of detail coefficients. Each set of detail coefficients represents the detail change situation at different scales. For each set of detail coefficients, calculate its energy distribution, specifically including: for each element in each set of detail coefficients, obtain its square value and accumulate all these square values to obtain the energy of the corresponding set of detail coefficients. This process helps us identify the change intensity of the strain data at each scale. According to the energy distribution of all groups of detail coefficients, these information are synthesized in the form of a ratio to calculate the overall deformation eigenvalue. A small positive constant is added to the energy of each group of detail coefficients, and then all the adjusted energy values are multiplied and divided by the sum of all weight factors to obtain the deformation eigenvalue. This method not only takes into account the deformation information at different scales, but also emphasizes the deformation characteristics at certain specific scales by introducing weight factors; It should be noted that: the overall deformation eigenvalue reflects the overall deformation degree and its abnormal characteristics of the strain data. It is obtained by adjusting, multiplying and averaging the energies of all groups of detail coefficients, and can accurately capture the local abnormal characteristics caused by material deformation of the flexible surface barcode. It is applicable to barcode recognition in complex deformation scenarios and can provide reliable input features for subsequent correction algorithms, improving the sensitivity and accuracy of deformation evaluation.

[0026] It should be noted that: in the flexible surface barcode recognition module, first, the curvature distribution data of the barcode area are collected in real time through a three-dimensional scanning or stereo vision system, and the K-means clustering algorithm is applied to calculate the curvature eigenvalue to identify local deformation; at the same time, strain gauges are used to obtain the strain distribution data on the material, and the Haar wavelet transform is used to analyze the energy of detail coefficients at each scale, and the deformation eigenvalue is comprehensively calculated to evaluate the physical deformation. Based on the comparison of the calculated curvature and deformation eigenvalues with the preset threshold, it is judged whether there is a serious deformation that cannot be corrected, and the corresponding alarm mechanism is triggered. This innovative method can not only accurately capture the complex deformation of the barcode on the flexible surface, but also provide reliable data support for subsequent correction measures, significantly improving the barcode recognition accuracy and stability in complex environments, reflecting high technical innovation and practicality.

[0027] In the flexible surface barcode correction module, according to the recognition result of the deformation degree, it is judged whether there is a serious deformation that cannot be corrected. If there is a serious deformation that cannot be corrected, the alarm mechanism is automatically triggered, specifically including: Obtain the curvature eigenvalue and deformation eigenvalue of the flexible surface barcode, directly construct the curvature eigenvalue and deformation eigenvalue into a comprehensive feature vector, and use it as the input of the machine learning model. Taking minimizing the error between the predicted deformation degree score and the actual deformation degree score as the training objective, train the model, and according to the trained model, output the deformation degree score of the flexible surface barcode. The machine learning model is a random forest model.

[0028] In the model training stage, the constructed comprehensive feature vector is trained using the random forest algorithm. By constructing multiple decision trees and aggregating their results, the prediction accuracy is improved and overfitting is controlled. During the training process, minimizing the error between the recognition accuracy score of the predicted deformation degree and the recognition accuracy score of the actual deformation degree is used as the objective function. By continuously adjusting the parameters of each decision tree until the optimal parameter combination is found, the trained random forest model is used to analyze the received new comprehensive feature vector, and the deformation degree score of the flexible curved surface barcode is output.

[0029] Judge whether the deformation degree score of the flexible curved surface barcode during recognition is greater than or equal to the preset threshold. If so, there is a serious deformation that cannot be corrected. If not, there is no serious deformation that cannot be corrected.

[0030] When it is detected that the flexible curved surface barcode has a serious deformation that cannot be corrected, the system will automatically trigger an early warning mechanism, including emitting an audible alarm and visual signals such as a flashing warning light, and simultaneously notifying relevant management personnel immediately through the network.

[0031] The working principle of the present invention: The present invention includes a data acquisition module, a flexible curved surface barcode recognition module, and a flexible curved surface barcode correction module. The data acquisition module uses a three-dimensional scanning device to continuously monitor the three-dimensional shape changes in the barcode area, obtains accurate spatial coordinate information, and then extracts the position of each data point and its corresponding curvature distribution data, ensuring high-precision capture of the complex geometric shape of the surface where the barcode is located. At the same time, the strain distribution on the material is directly measured to obtain strain data. Strain gauges are used to sensitively respond to the stretching and shrinking changes of the material and output corresponding electrical signals to represent the local strain conditions.

[0032] In the flexible curved surface barcode recognition module, based on the curvature distribution data collected in real time, the curvature value of each data point is calculated by fitting a local quadratic surface and extracting its principal curvature, and the K-means clustering algorithm is applied to classify these curvature values into several clusters. The curvature eigenvalue is determined according to the average curvature value of the data points within each cluster. In addition, the Haar wavelet transform is applied to the strain data to analyze the detail coefficient energy at each scale, and the deformation eigenvalue is comprehensively calculated. By comparing the curvature and deformation eigenvalues with the preset threshold, it is judged whether there is a serious deformation that cannot be corrected, providing a decision basis for subsequent processing.

[0033] The flexible curved surface barcode correction module then performs further operations based on the above recognition results. If the recognition result shows that there is a serious deformation that cannot be corrected, an alarm mechanism will be automatically triggered, including a sound alarm, visual signals such as a flashing warning light, and an immediate notification to relevant management personnel via the network. Specifically, the curvature eigenvalue and the deformation eigenvalue are constructed into a comprehensive feature vector as the input of the random forest model. This model improves the prediction accuracy by summarizing the results of multiple decision trees and is trained with the objective function of minimizing the error between the predicted deformation degree score and the actual deformation degree score. The trained model can analyze the received new comprehensive feature vector and output the deformation degree score of the flexible curved surface barcode, thereby evaluating the possibility of the barcode being correctly recognized under the current conditions.

[0034] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0035] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains a set of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0036] It should be understood that the term "and / or" in this text is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, and the specific meaning can be understood by referring to the context before and after.

[0037] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not indicate the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0038] The above has described in detail one embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. An adaptive deformation correction and recognition system for flexible curved bar codes, characterized in that: include: A data acquisition module, which monitors the three-dimensional shape change of the barcode area on the flexible surface in real time, obtains curvature distribution data, and directly measures the strain distribution on the material to obtain strain data; A flexible curved line strip code recognition module, wherein the flexible curved line strip code recognition module determines a curvature characteristic value according to a change degree of the curvature distribution, calculates a deformation characteristic value according to an abnormal degree of the strain distribution, and recognizes the deformation degree according to the curvature characteristic value and the deformation characteristic value of the flexible curved line strip code; The flexible curved strip shape code correction module determines whether there is an uncorrectable severe deformation according to the deformation degree recognition result, and automatically triggers an alarm mechanism if there is an uncorrectable severe deformation.

2. The adaptive deformation correction and recognition system of flexible curved strip code according to claim 1, characterized in that: According to the degree of change in the curvature distribution, the local deformation characteristics within the barcode area are evaluated, including: The curvature distribution data of the flexible curved bar code is collected in real time, and the curvature characteristic value is calculated according to the degree of change of the curvature distribution of the flexible curved bar code to determine whether the curvature characteristic value of the flexible curved bar code is greater than or equal to a preset threshold. If so, there is local deformation in the bar code area, and if not, there is no local deformation in the bar code area.

3. The adaptive deformation correction and recognition system of flexible curved strip code according to claim 1, characterized in that: The process of obtaining the curvature characteristic value is as follows: The curvature distribution data of the flexible curved barcode is collected in real time. For each data point in the collected data set, a local quadratic surface is fitted and its principal curvature is extracted to obtain the curvature value of the corresponding point. All the obtained curvature values ​​are integrated into a curvature set. The curvature set is classified by applying the K-means clustering algorithm. First, the number of clusters is selected and the centroid position of each cluster is initialized. According to the distance between each data point and each centroid, the data point is assigned to the nearest cluster, and the centroid position is updated until the algorithm converges. The data points are divided into several categories according to the curvature value to identify the curvature characteristics of different areas. For each cluster, the curvature characteristic value of the corresponding cluster is obtained by adding the curvature values ​​of all data points belonging to the same cluster and dividing it by the total number of data points in the corresponding cluster; The curvature eigenvalues ​​of all clusters are averaged to obtain the overall curvature eigenvalue of the flexible curved strip code.

4. The adaptive deformation correction and recognition system of flexible curved strip code according to claim 1, characterized in that: According to the abnormal degree of strain distribution, the physical deformation characteristics of the flexible curved strip code are evaluated, including: The strain data of the flexible curved strip code is collected in real time, and the deformation characteristic value is calculated according to the abnormal degree of the strain distribution of the flexible curved strip code to determine whether the deformation characteristic value of the flexible curved strip code is greater than or equal to a preset threshold. If so, the flexible curved strip code has physical deformation, and if not, the flexible curved strip code has no physical deformation.

5. The adaptive deformation correction and recognition system of flexible curved bar code according to claim 1, characterized in that: The process of obtaining the deformation feature value is as follows: The strain data of the barcode area on the flexible surface is acquired in real time. The Haar wavelet transform is applied to the strain data to decompose the signal into approximate coefficients and detail coefficients at different scales. After multi-layer decomposition, we obtain a set of approximate coefficients and several sets of detail coefficients. Each set of detail coefficients represents the detail changes at different scales. For each group of detail coefficients, calculating its energy distribution, specifically comprising: for each element in each group of detail coefficients, obtaining its square value and accumulating all these square values, so as to obtain the energy of the corresponding group of detail coefficients; According to the energy distribution of all groups of detail coefficients, this information is integrated in the form of a ratio to calculate the overall deformation eigenvalue. A small positive constant is added to the energy of each group of detail coefficients, all adjusted energy values ​​are multiplied, and divided by the sum of all weight factors to obtain the deformation eigenvalue.

6. The adaptive deformation correction and recognition system of flexible curved bar code according to claim 1, characterized in that: The identification of the deformation degree according to the curvature characteristic value and the deformation characteristic value of the flexible curved line bar code specifically includes: The curvature eigenvalue and deformation eigenvalue of the flexible curved strip code are obtained, and the curvature eigenvalue and the deformation eigenvalue are directly constructed into a comprehensive feature vector as the input of the machine learning model. The model is trained with minimizing the error between the predicted deformation degree score and the actual deformation degree score as the training goal. According to the trained model, the deformation degree score of the flexible curved strip code is output, and the machine learning model is a random forest model.

7. The adaptive deformation correction and recognition system of flexible curved strip code according to claim 6, characterized in that: The training process of the machine learning model is: In the model training stage, the constructed comprehensive feature vector is trained using the random forest algorithm. Multiple decision trees are constructed and their results are summarized to improve the prediction accuracy and control overfitting. During the training process, the objective function is to minimize the error between the predicted deformation degree recognition accuracy score and the actual deformation degree recognition accuracy score. The parameters of each decision tree are continuously adjusted until the optimal parameter combination is found. The trained random forest model is used to analyze the received new comprehensive feature vector and output the deformation degree score of the flexible curve strip code.

8. The adaptive deformation correction and recognition system of flexible curved strip code according to claim 1, characterized in that: The determining whether there is an uncorrectable severe deformation specifically includes: It is determined whether the deformation degree score of the flexible curved strip code during identification is greater than or equal to a preset threshold value. If so, there is an uncorrectable severe deformation; if not, there is no uncorrectable severe deformation.

9. The adaptive deformation correction and recognition system of flexible curved strip code according to claim 1, characterized in that: If there is a severe deformation that cannot be corrected, the alarm mechanism is automatically triggered, specifically including: When uncorrectable severe deformation of the flexible curved bar code is detected, the system will automatically trigger an early warning mechanism, including sound alarms and visual signals such as flashing warning lights, and instantly notify relevant managers through the network.

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