AI logistics order visual identification detection system

Abstract

The invention discloses an AI logistics order visual identification detection system, and the system comprises an image collection and preprocessing module which is used for obtaining a high-quality image of a logistics label, and carrying out the preliminary preprocessing of the high-quality image; the multi-modal data fusion module is used for integrating various data sources and carrying out multi-angle and multi-level verification on the logistics label; the graph calculation and label information analysis module is used for carrying out structured representation and analysis on the logistics label information; the dynamic behavior modeling and anomaly detection module is used for simulating the life cycle of a logistics order, detecting anomaly in real time and predicting a state; the path planning and task distribution module is used for optimizing order distribution and path planning in the logistics network; and the distributed calculation and feedback module is used for processing the data flow of the logistics system in real time and generating optimization feedback. According to the invention, the accuracy and real-time performance of visual identification of the logistics order are improved, and the anomaly detection and prediction capability in the logistics process is optimized.

Description

Technical Field

[0001] The present invention relates to the field of visual recognition technology, and in particular to an AI logistics order visual recognition detection system. Background Art

[0002] With the development of e-commerce and smart logistics, the logistics industry faces significant challenges, particularly in order processing, product tracking, inventory management, and delivery efficiency. Modern logistics systems rely on massive flows of information, goods, and capital to ensure a smooth supply chain. However, with the increasing number of orders and processing complexity, potential problems in the logistics process, such as order errors, damage, or loss, have seriously impacted the efficiency and reliability of the logistics system. Therefore, improving the accuracy of logistics order processing and anomaly detection capabilities has become a key issue that the logistics industry urgently needs to address.

[0003] Traditional methods for identifying and detecting anomalies in logistics orders rely primarily on technologies such as barcodes, RFID tags, or manual data entry. Barcodes and RFID tags are commonly used to identify items in the logistics process, effectively enabling tracking and management. However, these technologies rely primarily on static information and are not well adapted to situations where labels are damaged, contaminated, or obscured. When logistics tags become damaged, contaminated, or unreadable, existing barcode and RFID-based identification systems often fail to function properly, resulting in inaccurate logistics information and, in turn, impacting order tracking, delivery, and accuracy.

[0004] Furthermore, existing visual recognition methods for logistics orders mostly rely on single-image recognition technology, using traditional machine learning algorithms for processing. Recognition effectiveness is limited by image quality and environmental factors. This is particularly true in complex real-world environments, where image acquisition is susceptible to factors such as lighting, occlusion, and label quality, significantly reducing recognition accuracy. Traditional visual recognition technology struggles to fully adapt to the demands of complex environments, especially when faced with a variety of label materials, textures, and reflective properties, making accurate identification and detection difficult.

[0005] To overcome these problems, in recent years, academia and industry have begun to explore more intelligent solutions, such as multimodal detection methods that combine deep learning and computer vision. However, existing technologies still have some significant shortcomings. First, although deep learning-based visual recognition systems can improve recognition accuracy, they often require higher computing resources when processing large-scale orders. In particular, when image data and multimodal feature data are involved, the computational complexity is high, resulting in lower system processing efficiency. Second, deep learning algorithms rely on large amounts of labeled data and have poor adaptability to new types of labels, making them unable to flexibly respond to changing logistics environments and label styles.

[0006] Therefore, how to provide an AI logistics order visual recognition and detection system is an urgent problem that technicians in this field need to solve. Summary of the Invention

[0007] One purpose of the present invention is to propose an AI logistics order visual recognition and detection system. The present invention provides an AI logistics order visual recognition and detection method based on quantum vision processing, adaptive optimization algorithm and multimodal data fusion, aiming to solve the problems of low accuracy, low efficiency and poor robustness of existing logistics order recognition technology in processing complex environments, and to improve the recognition accuracy of logistics orders under different conditions and the real-time processing capability of the system.

[0008] The AI ​​logistics order visual recognition and detection system according to an embodiment of the present invention includes:

[0009] Image acquisition and preprocessing module, used to obtain high-quality images of logistics labels and perform preliminary preprocessing;

[0010] Multimodal data fusion module, used to integrate multiple data sources and conduct multi-angle and multi-level verification of logistics labels;

[0011] Graph computing and label information parsing module, used for structured representation and parsing of logistics label information;

[0012] Dynamic behavior modeling and anomaly detection module, used to simulate the logistics order life cycle, detect anomalies in real time and predict status;

[0013] Path planning and task allocation module, used to optimize order allocation and path planning in the logistics network;

[0014] Distributed computing and feedback module, used to process data flow of logistics system in real time and generate optimization feedback.

[0015] Optionally, modules can be connected using the following methods:

[0016] S1. Use quantum vision processing technology to capture images of logistics labels, simulate photon behavior through quantum states to improve image resolution and detail capture capabilities, and output image data;

[0017] S2. Use a meta-learning-based adaptive parameter optimization algorithm to dynamically adjust the image acquisition device parameters, process the image data, and output the optimized image;

[0018] S3. Utilize a multi-camera array to perform multi-view data fusion on the optimized image. Verify the label area using physical property-assisted detection technology combined with the material texture and spectral reflectance characteristics of the logistics label to generate multimodal feature data for the label area.

[0019] S4. Based on multimodal feature data, a graph computing framework is used to structure the logistics tag information. Graph isomorphism detection is used to identify the geometric shape and positional features of the logistics tags. Heuristic search is then used to optimize graph structure matching to generate a key information graph of the logistics tags.

[0020] S5. Use pattern matching and rule engines to extract key content from the logistics label key information graph, correct and complete the deformed or damaged parts of the label, and generate complete label information;

[0021] S6. Based on the complete label information, cellular automata are used to model the logistics order flow state, defining the dynamic behavior rules of orders during generation, sorting, and transportation. Abnormal states are predicted by iteratively updating the cellular state and combining the state changes of the logistics labels.

[0022] S7: Use the graph attention mechanism to analyze order anomaly status in real time, identify potential causes and propagation paths of order anomalies, and output optimization and adjustment strategies;

[0023] S8. Based on the optimization and adjustment strategy, the Ray distributed computing framework is used to perform large-scale parallel processing of real-time data of the logistics system, coordinate the collaborative operation of edge devices and cloud computing, and combine dynamic task allocation algorithms with quantum computing optimization technology to complete path planning for multi-order and multi-node networks, generate a globally optimized logistics execution plan, and provide real-time feedback.

[0024] Optionally, S1 includes the following specific steps:

[0025] S11. Based on the quantum optics model, a quantum state model of logistics tag photon propagation is established, and a photon propagation simulation model is output:

[0026] ψ(x,t)=A·e i(kx-ωt) ;

[0027] Among them, ψ(x,t) represents the wave function of photon propagation, A is the amplitude of the photon, k is the wave number, ω is the angular frequency, x is the spatial coordinate, and t is the time coordinate;

[0028] S12. Based on the photon propagation simulation model, the optical characteristics of the logistics label are modeled, and the lighting conditions are optimized using the quantum interference phenomenon. The lighting conditions are adjusted based on the quantum interference phenomenon and the light intensity distribution data is output:

[0029] I=I0·(1+cos(Δφ));

[0030] Where I is the light intensity, I0 is the incident light intensity, and Δφ is the optical path difference;

[0031] S13. Perform coherent signal enhancement processing on the light intensity distribution data, adopt a quantum state decoding method, reduce noise interference through phase compensation, and output phase image data after signal enhancement:

[0032] φ c =-Δφ;

[0033] Among them, φ c is the compensation phase, -Δφ is the optical path difference in interference;

[0034] S14. Based on the phase image data after signal enhancement, calculate the edge sharpness of the logistics label and output the sharpened image of the label:

[0035]

[0036] Among them, G is the gradient value, and are the rates of change of light intensity in the horizontal and vertical directions, respectively;

[0037] S15. Enhance the contrast of the sharpened image of the label under a complex background to generate a high-resolution image with optimized contrast:

[0038]

[0039] Among them, C is contrast, I max and I min are the maximum light intensity and minimum light intensity of the image area respectively;

[0040] S16 outputs high-resolution images to meet the multi-scenario requirements of logistics labels in reflective, low-light, and dynamic background environments.

[0041] Optionally, S2 includes the following specific steps:

[0042] S21. Define the image acquisition task set and output the task set requirements;

[0043] S22. Establish an optimization objective function according to the task set requirements and output an initial optimization objective function;

[0044] S23. Based on the initial optimization objective function, train through the meta-learning framework and define the meta-gradient update rule:

[0045]

[0046] Among them, θ is the meta-learning optimization parameter, c is the learning rate, is the gradient of the objective function, L i () is the optimization objective function, n is the set of equipment parameters;

[0047] S24. Based on the meta-learning optimization parameters θ, fine-tune the model in a real-time environment with a small amount of collected data:

[0048]

[0049] Among them, θ′ i is the fine-tuned parameter, β is the fine-tuning learning rate;

[0050] S25. Dynamically adjust the focal length, exposure time, and sensor sensitivity of the image acquisition device using the fine-tuned parameters, collect image data, and output an optimized image.

[0051] S26, performing quality evaluation on the optimized image;

[0052] S27. According to the quality evaluation value, iteratively adjust the meta-learning optimization parameters and output the final optimized parameters and image.

[0053] Optionally, S3 includes the following specific steps:

[0054] S31. Use a multi-camera array to collect multi-view data of the optimized image, define an imaging model of the camera array, and output multi-view image coordinate data:

[0055] P = K[R|t];

[0056] Among them, P is the image coordinate matrix, K is the camera intrinsic parameter matrix, R and t are the camera rotation matrix and translation matrix respectively;

[0057] S32, extracting feature points from the image based on the multi-view image coordinate data;

[0058] S33. Match the multi-view feature points using a geometric correction method, calculate the matching error for each pair of feature points, and output the corrected feature matching data:

[0059] ε=||F i -F j ||;

[0060] Among them, F i and F j are feature descriptors in multiple views, and ε is the Euclidean distance between feature points;

[0061] S34. Based on the feature matching data, the material texture of the stream label is extracted by combining physical property-assisted detection technology:

[0062]

[0063] Among them, T(u,v) is the frequency domain representation of the material, I(x,y) is the image intensity value, (u,v) is the frequency domain coordinate, and the output material texture feature data;

[0064] S35. Calculate the verification score of the logistics label area based on the material texture feature data and the spectral reflectance characteristics:

[0065]

[0066] Among them, V s For verification scoring, T k (u,v) is the frequency domain eigenvalue of the kth material, w k is the weight factor, n is the number of material features, and the verified logistics label area features are output;

[0067] S36. Utilize the regional features of logistics labels, fuse multi-view feature data with physical characteristics, and generate multimodal feature data.

[0068] Optionally, S4 includes the following specific steps:

[0069] S41. Construct a graph structure model of logistics label information based on multimodal feature data;

[0070] S42. Use the graph isomorphism detection method to analyze the constructed graph structure model and obtain the graph isomorphism matrix:

[0071]

[0072] Among them, d(v i ,v j ) is the node v i and node v j The distance metric between ij is the similarity measure between nodes;

[0073] S43. Perform eigendecomposition on the graph isomorphism matrix to obtain its eigenvalues ​​and eigenvectors, and output feature information of the graph;

[0074] S44. Combining the extracted feature information and graph similarity, the graph isomorphism matrix is ​​optimized through a heuristic search algorithm to obtain the optimized graph structure:

[0075]

[0076] Among them, M′ ij is the optimized graph isomorphism matrix, ||·|| represents the Euclidean distance, and G′ is the output optimized graph structure;

[0077] S45. Identify the geometric shape and position information of the logistics label based on the optimized graph structure, represent the geometric features of the label area using a geometric feature descriptor, and output the geometric shape and position information of the logistics label;

[0078] S46. Generate a key information graph of the logistics label based on the geometric shape and position information of the logistics label and the isomorphism of the graph.

[0079] Optionally, S6 includes the following specific steps:

[0080] S61. Based on the generated logistics label key information graph, identify the content of the label area through a pattern matching algorithm and define a feature set of the label content;

[0081] S62: Using a rule engine to analyze and match the feature set of the tag content to generate a matching rule set. The rule engine matches the tag according to a predetermined rule template and outputs a matching rule set.

[0082] S63. Correct and complete the deformed or damaged portion of the label according to the matched rule set, repair the damaged label information using the correction model, and output the corrected label information.

[0083] S64. Based on the corrected label information, use an anomaly detection algorithm to re-verify the label information, define an anomaly detection model, calculate an anomaly detection score for each label item, and output an anomaly score set;

[0084] A={a1,a2,…,a q};

[0085] Among them, A is the anomaly detection model, a i is the i-th detection item, indicating the probability score of abnormality;

[0086] S65. Based on the obtained abnormality score set, use statistical analysis methods to evaluate the abnormality degree of the label:

[0087]

[0088] Among them, F(α) is the abnormality evaluation function, α i is the anomaly score of the i-th label item, and m is the total number of label regions;

[0089] S66: Based on the obtained abnormality assessment results, subsequent abnormality adjustment and optimization are performed, and the final corrected label information is output.

[0090] Optionally, the step S8 includes the following specific steps:

[0091] S81. Based on the output order anomaly analysis results, calculate the anomaly impact factor of each order, model the dependencies between orders through the graph attention mechanism, and obtain the degree of anomaly impact of each order;

[0092] S82. Perform a weighted average of the abnormal impact factors of each order to obtain an overall abnormal impact factor;

[0093] S83. Set a dynamic threshold based on the obtained overall abnormality impact factor. If the dynamic threshold is exceeded, it is determined to be a system abnormality and an optimization adjustment strategy is triggered;

[0094] S84. Calculate the optimized path information for each order based on the optimization adjustment strategy, use the Ray distributed computing framework to parallelize each order path in the system, and adjust the path based on the abnormal impact factor and optimization target of each order.

[0095] S85. Based on the optimization path information and combined with quantum computing optimization technology, further refine the system to comprehensively evaluate the resource consumption and time cost of each order path in the system, and ultimately generate the optimal execution path for each order;

[0096] S86. Generate a global logistics execution plan based on the optimal execution path for each order. Apply the global logistics execution plan to the logistics system to guide the status adjustment of orders during generation, sorting, and transportation, and perform corrections and optimizations through a real-time feedback mechanism.

[0097] The beneficial effects of the present invention are:

[0098] This invention uses a series of innovative technologies such as quantum vision processing, meta-learning algorithms, multi-perspective data fusion, and graph computing frameworks to not only solve the defects in accuracy, efficiency, and adaptability of existing logistics order recognition technologies, but also greatly improves the intelligence level and real-time response capabilities of the system, providing an efficient, robust, and flexible solution for the field of intelligent logistics, with significant technical advantages and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0100] Figure 1 This is the overall flow chart of the AI ​​logistics order visual recognition and detection system proposed by the present invention;

[0101] Figure 2 This is a data processing flow chart of the AI ​​logistics order visual recognition and detection system proposed by the present invention; DETAILED DESCRIPTION

[0102] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0103] refer to Figure 1-2 , AI logistics order visual recognition and detection system, including:

[0104] Image acquisition and preprocessing module, used to obtain high-quality images of logistics labels and perform preliminary preprocessing;

[0105] Multimodal data fusion module, used to integrate multiple data sources and conduct multi-angle and multi-level verification of logistics labels;

[0106] Graph computing and label information parsing module, used for structured representation and parsing of logistics label information;

[0107] Dynamic behavior modeling and anomaly detection module, used to simulate the logistics order life cycle, detect anomalies in real time and predict status;

[0108] Path planning and task allocation module, used to optimize order allocation and path planning in the logistics network;

[0109] Distributed computing and feedback module, used to process data flow of logistics system in real time and generate optimization feedback.

[0110] In this embodiment, the modules are connected through the following methods:

[0111] S1. Use quantum vision processing technology to capture images of logistics labels, simulate photon behavior through quantum states to improve image resolution and detail capture capabilities, and output image data;

[0112] S2. Use a meta-learning-based adaptive parameter optimization algorithm to dynamically adjust the image acquisition device parameters, process the image data, and output the optimized image;

[0113] S3. Utilize a multi-camera array to perform multi-view data fusion on the optimized image. Verify the label area using physical property-assisted detection technology combined with the material texture and spectral reflectance characteristics of the logistics label to generate multimodal feature data for the label area.

[0114] S4. Based on multimodal feature data, a graph computing framework is used to structure the logistics tag information. Graph isomorphism detection is used to identify the geometric shape and positional features of the logistics tags. Heuristic search is then used to optimize graph structure matching to generate a key information graph of the logistics tags.

[0115] S5. Use pattern matching and rule engines to extract key content from the logistics label key information graph, correct and complete the deformed or damaged parts of the label, and generate complete label information;

[0116] S6. Based on the complete label information, cellular automata are used to model the logistics order flow state, defining the dynamic behavior rules of orders during generation, sorting, and transportation. Abnormal states are predicted by iteratively updating the cellular state and combining the state changes of the logistics labels.

[0117] S7: Use the graph attention mechanism to analyze order anomaly status in real time, identify potential causes and propagation paths of order anomalies, and output optimization and adjustment strategies;

[0118] S8. Based on the optimization and adjustment strategy, the Ray distributed computing framework is used to perform large-scale parallel processing of real-time data of the logistics system, coordinate the collaborative operation of edge devices and cloud computing, and combine dynamic task allocation algorithms with quantum computing optimization technology to complete path planning for multi-order and multi-node networks, generate a globally optimized logistics execution plan, and provide real-time feedback.

[0119] In this embodiment, S1 includes the following specific steps:

[0120] S11. Based on the quantum optics model, a quantum state model of logistics tag photon propagation is established, and a photon propagation simulation model is output:

[0121] ψ(x,t)=A·e i(kx-ωt) ;

[0122] Among them, ψ(x,t) represents the wave function of photon propagation, A is the amplitude of the photon, k is the wave number, ω is the angular frequency, x is the spatial coordinate, and t is the time coordinate;

[0123] S12. Based on the photon propagation simulation model, the optical characteristics of the logistics label are modeled, and the lighting conditions are optimized using the quantum interference phenomenon. The lighting conditions are adjusted based on the quantum interference phenomenon and the light intensity distribution data is output:

[0124] I=I0·(1+cos(Δφ));

[0125] Where I is the light intensity, I0 is the incident light intensity, and Δφ is the optical path difference;

[0126] S13. Perform coherent signal enhancement processing on the light intensity distribution data, adopt a quantum state decoding method, reduce noise interference through phase compensation, and output phase image data after signal enhancement:

[0127] φ c =-Δφ;

[0128] Among them, φ cis the compensation phase, -Δφ is the optical path difference in interference;

[0129] S14. Based on the phase image data after signal enhancement, calculate the edge sharpness of the logistics label and output the sharpened image of the label:

[0130]

[0131] Among them, G is the gradient value, and are the rates of change of light intensity in the horizontal and vertical directions, respectively;

[0132] S15. Enhance the contrast of the sharpened image of the label under a complex background to generate a high-resolution image with optimized contrast:

[0133]

[0134] Among them, C is contrast, I max and I min are the maximum light intensity and minimum light intensity of the image area respectively;

[0135] S16 outputs high-resolution images to meet the multi-scenario requirements of logistics labels in reflective, low-light, and dynamic background environments.

[0136] In this embodiment, S2 includes the following specific steps:

[0137] S21. Define the image acquisition task set and output the task set requirements;

[0138] S22. Establish an optimization objective function according to the task set requirements and output an initial optimization objective function;

[0139] S23. Based on the initial optimization objective function, train through the meta-learning framework and define the meta-gradient update rule:

[0140]

[0141] Among them, θ is the meta-learning optimization parameter, c is the learning rate, is the gradient of the objective function, L i () is the optimization objective function, n is the set of equipment parameters;

[0142] S24. Based on the meta-learning optimization parameters θ, fine-tune the model in a real-time environment with a small amount of collected data:

[0143]

[0144] Among them, θ′ i is the fine-tuned parameter, β is the fine-tuning learning rate;

[0145] S25. Dynamically adjust the focal length, exposure time, and sensor sensitivity of the image acquisition device using the fine-tuned parameters, collect image data, and output an optimized image.

[0146] S26, performing quality evaluation on the optimized image;

[0147] S27. According to the quality evaluation value, iteratively adjust the meta-learning optimization parameters and output the final optimized parameters and image.

[0148] In this embodiment, S3 includes the following specific steps:

[0149] S31. Use a multi-camera array to collect multi-view data of the optimized image, define an imaging model of the camera array, and output multi-view image coordinate data:

[0150] P = K[R|t];

[0151] Among them, P is the image coordinate matrix, K is the camera intrinsic parameter matrix, R and t are the camera rotation matrix and translation matrix respectively;

[0152] S32, extracting feature points from the image based on the multi-view image coordinate data;

[0153] S33. Match the multi-view feature points using a geometric correction method, calculate the matching error for each pair of feature points, and output the corrected feature matching data:

[0154] ε=||F i -F j ||;

[0155] Among them, F i and F j are feature descriptors in multiple views, and ε is the Euclidean distance between feature points;

[0156] S34. Based on the feature matching data, the material texture of the stream label is extracted by combining physical property-assisted detection technology:

[0157]

[0158] Among them, T(u,v) is the frequency domain representation of the material, I(x,y) is the image intensity value, (u,v) is the frequency domain coordinate, and the output material texture feature data;

[0159] S35. Calculate the verification score of the logistics label area based on the material texture feature data and the spectral reflectance characteristics:

[0160]

[0161] Among them, V sFor verification scoring, T k (u,v) is the frequency domain eigenvalue of the kth material, w k is the weight factor, n is the number of material features, and the verified logistics label area features are output;

[0162] S36. Utilize the regional features of logistics labels, fuse multi-view feature data with physical characteristics, and generate multimodal feature data.

[0163] In this embodiment, S4 includes the following specific steps:

[0164] S41. Construct a graph structure model of logistics label information based on multimodal feature data;

[0165] S42. Use the graph isomorphism detection method to analyze the constructed graph structure model and obtain the graph isomorphism matrix:

[0166]

[0167] Among them, d(v i ,v j ) is the node v i and node v j The distance metric between ij is the similarity measure between nodes;

[0168] S43. Perform eigendecomposition on the graph isomorphism matrix to obtain its eigenvalues ​​and eigenvectors, and output feature information of the graph;

[0169] S44. Combining the extracted feature information and graph similarity, the graph isomorphism matrix is ​​optimized through a heuristic search algorithm to obtain the optimized graph structure:

[0170]

[0171] Among them, M′ ij is the optimized graph isomorphism matrix, ||·|| represents the Euclidean distance, and G′ is the output optimized graph structure;

[0172] S45. Identify the geometric shape and position information of the logistics label based on the optimized graph structure, represent the geometric features of the label area using a geometric feature descriptor, and output the geometric shape and position information of the logistics label;

[0173] S46. Generate a key information graph of the logistics label based on the geometric shape and position information of the logistics label and the isomorphism of the graph.

[0174] In this embodiment, S6 includes the following specific steps:

[0175] S61. Based on the generated logistics label key information graph, identify the content of the label area through a pattern matching algorithm and define a feature set of the label content;

[0176] S62: Using a rule engine to analyze and match the feature set of the tag content to generate a matching rule set. The rule engine matches the tag according to a predetermined rule template and outputs a matching rule set.

[0177] S63. Correct and complete the deformed or damaged portion of the label according to the matched rule set, repair the damaged label information using the correction model, and output the corrected label information.

[0178] S64. Based on the corrected label information, use an anomaly detection algorithm to re-verify the label information, define an anomaly detection model, calculate an anomaly detection score for each label item, and output an anomaly score set;

[0179] A={a1,a2,…,a q};

[0180] Among them, A is the anomaly detection model, a i is the i-th detection item, indicating the probability score of abnormality;

[0181] S65. Based on the obtained abnormality score set, use statistical analysis methods to evaluate the abnormality degree of the label:

[0182]

[0183] Among them, F(α) is the abnormality evaluation function, α i is the anomaly score of the i-th label item, and m is the total number of label regions;

[0184] S66: Based on the obtained abnormality assessment results, subsequent abnormality adjustment and optimization are performed, and the final corrected label information is output.

[0185] In this embodiment, S8 includes the following specific steps:

[0186] S81. Based on the output order anomaly analysis results, calculate the anomaly impact factor of each order, model the dependencies between orders through the graph attention mechanism, and obtain the degree of anomaly impact of each order;

[0187] S82. Perform a weighted average of the abnormal impact factors of each order to obtain an overall abnormal impact factor;

[0188] S83. Set a dynamic threshold based on the obtained overall abnormality impact factor. If the dynamic threshold is exceeded, it is determined to be a system abnormality and an optimization adjustment strategy is triggered;

[0189] S84. Calculate the optimized path information for each order based on the optimization adjustment strategy, use the Ray distributed computing framework to parallelize each order path in the system, and adjust the path based on the abnormal impact factor and optimization target of each order.

[0190] S85. Based on the optimization path information and combined with quantum computing optimization technology, further refine the system to comprehensively evaluate the resource consumption and time cost of each order path in the system, and ultimately generate the optimal execution path for each order;

[0191] S86. Generate a global logistics execution plan based on the optimal execution path for each order. Apply the global logistics execution plan to the logistics system to guide the status adjustment of orders during generation, sorting, and transportation, and perform corrections and optimizations through a real-time feedback mechanism.

[0192] Example 1:

[0193] The logistics distribution center involved in this embodiment, as the core warehousing center of a large e-commerce platform, is responsible for the daily processing of millions of orders. The center is equipped with multiple automated sorting equipment, warehousing robots, and transport vehicles. The order generation, picking, sorting, and transportation processes are highly dependent on advanced information technology systems for real-time monitoring and dynamic scheduling. However, with the surge in the number of orders, traditional manual monitoring and image recognition technologies have gradually revealed their limitations: First, the accuracy of order recognition has significantly decreased due to reasons such as damage and stains on logistics labels; second, traditional algorithms are inefficient when processing large-scale parallel data, and are prone to system delays, resulting in delayed prediction and response to abnormal order status. Therefore, how to improve the accuracy of logistics label recognition, accelerate abnormal status prediction, and enhance the overall responsiveness of the system has become an urgent problem that needs to be solved.

[0194] To address these issues, the system utilizes quantum vision processing technology to capture high-precision images of logistics labels during the initial logistics order generation process. This technology simulates the behavior of photons, significantly improving image resolution and enabling accurate recognition of image details, particularly in low light conditions or when labels are damaged.

[0195] Secondly, a meta-learning-based adaptive parameter optimization algorithm processes captured images in real time to optimize image quality. This process dynamically adjusts the camera's shooting parameters, optimizing each logistics label image for clarity and color reproduction. In particular, the system automatically repairs stains and tears on labels, improving the accuracy of subsequent image analysis.

[0196] The system then combines multi-view data fusion technology to perform multimodal processing on image data from different angles, extracting features such as the logistics label's material texture and spectral reflectance. Based on these features, the system uses a graph computing framework for structured representation, accurately identifying the label's geometry and position, and further extracting key logistics information (such as order number and delivery address).

[0197] Next, the system uses pattern matching and rule engine analysis on the extracted label information to repair any deformed or damaged parts of the label, generate complete label information, and use this information to predict the order's flow status. Using a cellular automation model, the system can dynamically simulate the state changes of orders during the generation, sorting, and transportation processes, and predict anomalies.

[0198] Finally, when the system detects an abnormal order status, it uses a graph attention mechanism to analyze the cause and propagation path of the anomaly in real time, automatically outputting an optimization and adjustment strategy, and quickly providing feedback to relevant operators. Combined with the Ray distributed computing framework, the system can efficiently process large amounts of data and perform real-time task scheduling, ensuring that abnormal conditions are promptly responded to and resolved.

[0199] Table 1 Comparison of logistics order anomaly identification and processing efficiency

[0200]

[0201] Table 2 Comparison of logistics label image acquisition and processing time

[0202]

[0203] As can be seen from Table 1, the accuracy of abnormal recognition before the application of the system was 82.5%, and the accuracy after application increased to 98.7%, an improvement of +16.2%. The abnormal response time was 12.5 seconds, and after application it was reduced to 3.2 seconds, an improvement of -9.3 seconds. The order recognition accuracy increased from 85.4% to 97.3%, an increase of 11.9%, which means that the system's ability to identify and process orders has been significantly improved. The overall throughput of the system increased from 8.3 orders / second to 15.6 orders / second, an increase of 87.95%. This improvement shows that through the distributed computing framework and efficient image processing algorithm of the present invention, the system can maintain lower latency and higher concurrency while processing higher order volumes, effectively improving the overall work efficiency of the logistics system.

[0204] It can be seen from Table 2 that the acquisition time of each label image was 0.45 seconds before the application of the system, which was reduced to 0.12 seconds after application, with an improvement of -0.33 seconds. The image optimization processing time was reduced from 0.65 seconds to 0.18 seconds, with an improvement of -0.47 seconds. The label information extraction time was reduced from 1.12 seconds to 0.33 seconds, with an improvement of -0.79 seconds. The overall system processing time was reduced from 2.22 seconds to 0.63 seconds, with an improvement of -1.59 seconds. This shows that the present invention significantly reduces the processing time in multiple links such as image acquisition, processing, and information extraction, and greatly improves the operating efficiency of the logistics system. Especially in the case of high concurrency and large-scale order processing, this time optimization can effectively reduce the bottleneck of the system and improve the real-time and accuracy of order processing.

[0205] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. AI logistics order visual recognition and detection system, characterized by: include: Image acquisition and preprocessing module, used to obtain high-quality images of logistics labels and perform preliminary preprocessing; Multimodal data fusion module, used to integrate multiple data sources and conduct multi-angle and multi-level verification of logistics labels; Graph computing and label information parsing module, used for structured representation and parsing of logistics label information; Dynamic behavior modeling and anomaly detection module, used to simulate the logistics order life cycle, detect anomalies in real time and predict status; Path planning and task allocation module, used to optimize order allocation and path planning in the logistics network; Distributed computing and feedback module, used to process data flow of logistics system in real time and generate optimization feedback.

2. The AI ​​logistics order visual recognition and detection system according to claim 1 is characterized in that: The modules are implemented as follows: S1. Use quantum vision processing technology to capture images of logistics labels, simulate photon behavior through quantum states to improve image resolution and detail capture capabilities, and output image data; S2. Use a meta-learning-based adaptive parameter optimization algorithm to dynamically adjust the image acquisition device parameters, process the image data, and output the optimized image; S3. Utilize a multi-camera array to perform multi-view data fusion on the optimized image. Verify the label area using physical property-assisted detection technology combined with the material texture and spectral reflectance characteristics of the logistics label to generate multimodal feature data for the label area. S4. Based on multimodal feature data, a graph computing framework is used to structure the logistics tag information. Graph isomorphism detection is used to identify the geometric shape and positional features of the logistics tags. Heuristic search is then used to optimize graph structure matching to generate a key information graph of the logistics tags. S5. Use pattern matching and rule engines to extract key content from the logistics label key information graph, correct and complete deformed or damaged parts of the label, and generate complete label information; S6. Based on the complete label information, cellular automata are used to model the logistics order flow state, defining the dynamic behavior rules of orders during generation, sorting, and transportation. Abnormal states are predicted by iteratively updating the cellular state and combining the state changes of the logistics labels. S7: Use the graph attention mechanism to analyze order anomaly status in real time, identify potential causes and propagation paths of order anomalies, and output optimization and adjustment strategies; S8. Based on the optimization and adjustment strategy, the Ray distributed computing framework is used to perform large-scale parallel processing of real-time data of the logistics system, coordinate the collaborative operation of edge devices and cloud computing, and combine dynamic task allocation algorithms with quantum computing optimization technology to complete path planning for multi-order and multi-node networks, generate a globally optimized logistics execution plan, and provide real-time feedback.

3. The AI ​​logistics order visual recognition and detection system according to claim 1 is characterized in that: The S1 includes the following specific steps: S11. Based on the quantum optics model, a quantum state model of logistics tag photon propagation is established, and a photon propagation simulation model is output: ψ(x,t)=A·e i(kx-ωt) 4 Among them, ψ(x,t) represents the wave function of photon propagation, A is the amplitude of the photon, k is the wave number, ω is the angular frequency, x is the spatial coordinate, and t is the time coordinate; S12. Based on the photon propagation simulation model, the optical characteristics of the logistics label are modeled, and the lighting conditions are optimized using the quantum interference phenomenon. The lighting conditions are adjusted based on the quantum interference phenomenon and the light intensity distribution data is output: I=I0·(1+cos(Δφ)); Where I is the light intensity, I0 is the incident light intensity, and Δφ is the optical path difference; S13. Perform coherent signal enhancement processing on the light intensity distribution data, adopt a quantum state decoding method, reduce noise interference through phase compensation, and output phase image data after signal enhancement: f c =-Df; Among them, φ c is the compensation phase, -Δφ is the optical path difference in interference; S14. Based on the phase image data after signal enhancement, calculate the edge sharpness of the logistics label and output the sharpened image of the label: Among them, G is the gradient value, and are the rates of change of light intensity in the horizontal and vertical directions, respectively; S15. Enhance the contrast of the sharpened image of the label under a complex background to generate a high-resolution image with optimized contrast: Among them, C is contrast, I max and I min are the maximum light intensity and minimum light intensity of the image area respectively; S16 outputs high-resolution images to meet the multi-scenario requirements of logistics labels in reflective, low-light, and dynamic background environments.

4. The AI ​​logistics order visual recognition and detection system according to claim 1 is characterized in that: The S2 includes the following specific steps: S21. Define the image acquisition task set and output the task set requirements; S22. Establish an optimization objective function according to the task set requirements and output an initial optimization objective function; S23. Based on the initial optimization objective function, train through the meta-learning framework and define the meta-gradient update rule: Among them, θ is the meta-learning optimization parameter, c is the learning rate, is the gradient of the objective function, L i () is the optimization objective function, n is the set of equipment parameters; S24. Based on the meta-learning optimization parameters θ, fine-tune the model in a real-time environment with a small amount of collected data: Among them, θ′ i is the fine-tuned parameter, β is the fine-tuning learning rate; S25. Dynamically adjust the focal length, exposure time, and sensor sensitivity of the image acquisition device using the fine-tuned parameters, collect image data, and output an optimized image. S26, performing quality evaluation on the optimized image; S27. According to the quality evaluation value, iteratively adjust the meta-learning optimization parameters and output the final optimized parameters and image.

5. The AI ​​logistics order visual recognition and detection system according to claim 1 is characterized in that: The S3 includes the following specific steps: S31. Use a multi-camera array to collect multi-view data of the optimized image, define an imaging model of the camera array, and output multi-view image coordinate data: P = K[R|t]; Among them, P is the image coordinate matrix, K is the camera intrinsic parameter matrix, R and t are the camera rotation matrix and translation matrix respectively; S32, extracting feature points from the image based on the multi-view image coordinate data; S33. Match the multi-view feature points using a geometric correction method, calculate the matching error for each pair of feature points, and output the corrected feature matching data: e=||F i -F j ||; Among them, F i and F j are feature descriptors in multiple views, and ε is the Euclidean distance between feature points; S34. Based on the feature matching data, the material texture of the stream label is extracted by combining physical property-assisted detection technology: Among them, T(u,v) is the frequency domain representation of the material, I(x,y) is the image intensity value, (u,v) is the frequency domain coordinate, and the output material texture feature data; S35. Calculate the verification score of the logistics label area based on the material texture feature data and the spectral reflectance characteristics: Among them, V s For verification scoring, T k (u,v) is the frequency domain eigenvalue of the kth material, w k is the weight factor, n is the number of material features, and the verified logistics label area features are output; S36. Utilize the regional features of logistics labels, fuse multi-view feature data with physical characteristics, and generate multimodal feature data.

6. The AI ​​logistics order visual recognition and detection system according to claim 1 is characterized in that: The S4 includes the following specific steps: S41. Construct a graph structure model of logistics label information based on multimodal feature data; S42. Use the graph isomorphism detection method to analyze the constructed graph structure model and obtain the graph isomorphism matrix: Among them, d(v i ,v j ) is the node v i and node v j The distance metric between ij is the similarity measure between nodes; S43. Perform eigendecomposition on the graph isomorphism matrix to obtain its eigenvalues ​​and eigenvectors, and output feature information of the graph; S44. Combining the extracted feature information and graph similarity, the graph isomorphism matrix is ​​optimized through a heuristic search algorithm to obtain the optimized graph structure: Among them, M′ ij is the optimized graph isomorphism matrix, ||·|| represents the Euclidean distance, and G′ is the output optimized graph structure; S45. Identify the geometric shape and position information of the logistics label based on the optimized graph structure, represent the geometric features of the label area using a geometric feature descriptor, and output the geometric shape and position information of the logistics label; S46. Generate a key information graph of the logistics label based on the geometric shape and position information of the logistics label and the isomorphism of the graph.

7. The AI ​​logistics order visual recognition and detection system according to claim 1 is characterized in that: The S6 comprises the following specific steps: S61. Based on the generated logistics label key information graph, identify the content of the label area through a pattern matching algorithm and define a feature set of the label content; S62: Using a rule engine to analyze and match the feature set of the tag content to generate a matching rule set. The rule engine matches the tag according to a predetermined rule template and outputs a matching rule set. S63. Correct and complete the deformed or damaged portion of the label according to the matched rule set, repair the damaged label information using the correction model, and output the corrected label information. S64. Based on the corrected label information, use an anomaly detection algorithm to re-verify the label information, define an anomaly detection model, calculate an anomaly detection score for each label item, and output an anomaly score set; <h2 style=";text-align:left;direction:ltr">A = {a1,a2,…,a<h2 style=";text-align:left;direction:ltr"> q <h2 style=";text-align:left;direction:ltr">}; Among them, A is the anomaly detection model, a i is the i-th detection item, indicating the probability score of abnormality; S65. Based on the obtained abnormality score set, use statistical analysis methods to evaluate the abnormality degree of the label: Among them, F(α) is the abnormality evaluation function, α i is the anomaly score of the i-th label item, and m is the total number of label regions; S66: Based on the obtained abnormality assessment results, subsequent abnormality adjustment and optimization are performed, and the final corrected label information is output.

8. The AI ​​logistics order visual recognition and detection system according to claim 1 is characterized in that: The S8 comprises the following specific steps: S81. Based on the output order anomaly analysis results, calculate the anomaly impact factor of each order, model the dependencies between orders through the graph attention mechanism, and obtain the degree of anomaly impact of each order; S82. Perform a weighted average of the abnormal impact factors of each order to obtain an overall abnormal impact factor; S83. Set a dynamic threshold based on the obtained overall abnormality impact factor. If the dynamic threshold is exceeded, it is determined to be a system abnormality and an optimization adjustment strategy is triggered; S84. Calculate the optimized path information for each order based on the optimization adjustment strategy, use the Ray distributed computing framework to parallelize each order path in the system, and adjust the path based on the abnormal impact factor and optimization target of each order. S85. Based on the optimization path information and combined with quantum computing optimization technology, further refine the system to comprehensively evaluate the resource consumption and time cost of each order path in the system, and ultimately generate the optimal execution path for each order; S86. Generate a global logistics execution plan based on the optimal execution path for each order. Apply the global logistics execution plan to the logistics system to guide the status adjustment of orders during generation, sorting, and transportation, and perform corrections and optimizations through a real-time feedback mechanism.

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