A low-temperature image recognition method applied to the cold chain industry

Abstract

The present invention discloses a low-temperature image recognition method applied to the cold chain industry, comprising: using a security inspection machine to obtain first image information of goods, and pre-processing the first image information to generate second image information with enhanced clarity; determining the identification features of the goods based on the second image information, and comparing and matching the second image information with the standard identification features of goods in a preset database to confirm the identity of the goods; recording the location information of the goods after the identity is confirmed, and associating it with the identity of the goods; during the movement of the goods, automatically triggering a video surveillance device to capture third image information based on the location information; using the third image information to verify the identity information of the goods, and recording the operation process to form an operation record; by integrating the operation record with the business management system, the entire process of cold chain goods operation can be traced. The present invention solves the problem of identifying and tracking goods in low-temperature environments, significantly improves the recognition accuracy, and improves the transparency and security of cold chain logistics operations.

Description

Technical Field

[0001] The present invention belongs to the technical field of cold chain logistics monitoring, and specifically relates to a low-temperature image recognition method applied to the cold chain industry. Background Art

[0002] Ensuring cargo safety and traceability is crucial in the cold chain logistics industry. Traditional methods for cargo inspection and tracking often rely on manual labor, which is inefficient and prone to human error. Technological advancements have made it possible to employ image recognition technology to enhance the intelligence of cold chain logistics. However, image recognition in low-temperature environments faces numerous challenges, such as image quality degradation and blurred features caused by frost on the cargo surface.

[0003] Existing image recognition methods are often unable to effectively extract clear features of goods in low-temperature environments, resulting in reduced recognition accuracy, which in turn affects the safety inspection and full traceability of goods. Summary of the Invention

[0004] The present invention aims to provide a low-temperature image recognition method for the cold chain industry, significantly improving the accuracy of identifying goods under low-temperature conditions. By using a security inspection machine to capture and preprocess cargo images, the image clarity can be enhanced, allowing accurate identification of cargo features, thereby resolving the issues raised in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a low-temperature image recognition method applied to the cold chain industry, comprising the following steps:

[0006] Use a security inspection machine to obtain first image information of the goods to be inspected; preprocess the first image information to generate second image information with enhanced clarity; determine the goods identification characteristics based on the second image information; compare and match the goods identification characteristics with the standard goods identification characteristics in a preset database to confirm the identity of the goods, record the location information of the goods after the identity is confirmed, and associate the location information with the identity of the goods; during the movement of the goods, automatically trigger the video surveillance equipment to capture third image information based on the location information; use the third image information to verify the identity information of the goods, and record the operation process, combine the operation process with the identity information of the goods to form an operation record; by integrating the operation record with the business management system, the entire cold chain goods operation process can be traced.

[0007] Preferably, after using the security inspection machine to obtain the first image information of the goods to be inspected, the method further includes: applying image enhancement means to the first image information to obtain an optimized image; extracting contour features of the goods from the optimized image, and comparing the contour features with data in a known goods contour feature library to identify the type of goods; recording the identification result of the goods type, and combining it with the location information of the goods, and updating the goods tracking database based on the identification result and location information.

[0008] Preferably, the first image information is preprocessed to generate second image information with enhanced clarity, including: adjusting the brightness of the first image information to obtain an image with uniform brightness; performing denoising on the brightness-adjusted image to remove disordered pixels to obtain a pure image; using edge enhancement technology to enhance the boundaries of objects in the denoised image, and refining the shape of the goods based on the edge enhancement result to obtain second image information with enhanced clarity; storing and marking the second image information with enhanced clarity for use in the goods identification and tracking process.

[0009] Preferably, determining the cargo identification features based on the second image information includes: extracting significant visual elements of the cargo from the second image information with enhanced clarity as cargo identification features; establishing a feature template based on the cargo identification features for subsequent cargo comparison; using the feature template to match and confirm the cargo in the second image information to verify the identity of the cargo; recording the location information and timestamp of the cargo based on the result of the matching confirmation to maintain continuous tracking of the cargo in the cold chain logistics; integrating the location information and timestamp with other relevant information of the cargo to form a complete cargo tracking record for querying the cargo status at any time.

[0010] Preferably, the confirmation of the cargo identity includes: searching for corresponding cargo standard identification features in a preset database based on the cargo identification features; comparing the cargo identification features with the found cargo standard identification features to confirm the identity of the cargo; if the cargo identification features match the cargo standard identification features, recording the identity information of the cargo; combining the cargo identity information with the cargo location information to form a cargo status record; updating the cargo status record to the business management system to achieve real-time tracking of the cargo status.

[0011] Preferably, associating the location information with the identity of the goods includes: obtaining the geographic coordinate information of the current location of the goods, combining the geographic coordinate information with the identity information of the goods to form a location identifier; associating the location identifier with the operation record of the goods to record the movement trajectory of the goods; updating the status information of the goods according to the location identifier to reflect the latest location of the goods, and synchronizing the status information of the goods to the business management system.

[0012] Preferably, the video surveillance device is automatically triggered to capture the third image information based on the position information, including: when the position information of the goods changes, the video surveillance device is triggered to start; the video surveillance device captures a real-time image of the environment around the goods according to the position information as the third image information; the third image information is saved and combined with the position information to form a dynamic monitoring record; the dynamic monitoring record is merged with the operation record of the goods to achieve comprehensive monitoring of the goods movement process, and the merged record is sent to the business management system to achieve real-time visualization of the goods movement process.

[0013] Preferably, using the third image information to verify the identity information of the goods includes: analyzing the goods features in the third image information to verify the identity information of the goods; comparing the goods features in the third image information with the identity information of the goods to confirm the consistency of the goods, and recording the comparison result; combining the comparison result with the operation record of the goods to form an operation verification record, and sending the operation verification record to the business management system to achieve traceability of the goods operation process.

[0014] Preferably, the operation process is combined with the identity information of the goods to form an operation record, including: integrating the operation verification record with the identity information of the goods to form operation record entries, arranging the operation record entries in chronological order to show the operation history of the goods; combining the operation record entries with the location information of the goods to form a comprehensive operation record, and updating the comprehensive operation record to the business management system; providing a query interface for the comprehensive operation record through the business management system to achieve transparent management of the goods operation process.

[0015] Preferably, the operation records are integrated with the business management system, including: integrating the comprehensive operation records with other relevant information in the business management system to form a complete cargo file; retrieving the operation records in the cargo file through the query function provided by the business management system; reconstructing the movement path and operation process of the cargo based on the timestamp and location information of the operation records; generating a full-process visualization report of the cargo from warehousing to delivery, and utilizing the full-process visualization report to achieve traceability and management of the entire cold chain cargo operation process.

[0016] Technical effects and advantages of the present invention: Compared with the existing technology, the low-temperature image recognition method for the cold chain industry proposed in the present invention has the following advantages:

[0017] This paper proposes a low-temperature image recognition method suitable for cold chain logistics, which can significantly improve the accuracy of cargo identification under low-temperature conditions. By using a security inspection machine to capture and preprocess cargo images, the image clarity can be enhanced, allowing the cargo's identification features to be accurately identified. Furthermore, combined with video surveillance and a business management system, operational recording and traceability of the entire cargo process, from warehousing to shipment, is achieved, improving the transparency and security of cold chain logistics operations. This method can effectively solve the problem of cargo identification and tracking in low-temperature environments, enhancing the efficiency and reliability of the entire cold chain management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a low-temperature image recognition method applied to the cold chain industry of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] The present invention provides a low-temperature image recognition method applied to the cold chain industry, such as Figure 1 As shown, the following steps are included:

[0021] Step 1: Using a security inspection machine to obtain first image information of the goods to be inspected; further comprising:

[0022] Applying image enhancement means to the first image information to obtain an optimized image; extracting contour features of the cargo from the optimized image, comparing the contour features with data in a library of known cargo contour features to identify the cargo type; recording the cargo type identification result and combining it with the cargo location information, and updating the cargo tracking database based on the identification result and the location information.

[0023] Specifically, the first image information is obtained using security inspection machines installed at key locations throughout the cold chain logistics process. These machines can be fixed or mobile, with the appropriate type selected based on the specific application scenario.

[0024] The acquired first image information is preprocessed, including but not limited to image enhancement measures such as brightness adjustment, denoising and edge enhancement, to improve the quality and clarity of the image.

[0025] Brightness adjustment: Increase the overall or local brightness of an image to make details more visible.

[0026] Denoising: Use appropriate algorithms to remove noise from the image and improve the purity of the image.

[0027] Edge enhancement: Algorithms are used to strengthen the boundaries of objects in the image, making the outline of the goods more clearly visible.

[0028] Extract the contour features of the goods from the pre-processed images. These features include the shape, size, and texture of the goods, which are used in the subsequent recognition process. Computer vision algorithms, such as edge detection and contour extraction, are used to automatically extract these features.

[0029] The extracted contour features are compared with the data in the pre-established known cargo contour feature library to identify the type of cargo. The feature library contains standard contour features of different types of cargo for comparison.

[0030] The identified cargo type is recorded and combined with the cargo location information to form cargo status information. The cargo location information can be obtained through RFID tags, GPS positioning or other location tracking technologies.

[0031] The cargo tracking database is updated based on the cargo type identification and location information to ensure that the database contains the latest cargo status information. This information will be used for subsequent cargo tracking and management.

[0032] Through the above steps, goods can be accurately identified and their location tracked in low-temperature environments, improving the transparency and security of cold chain logistics operations. This method significantly improves the accuracy of cargo identification and, through integration with business management systems, enables visual management of goods from inbound to outbound delivery.

[0033] Step 2: pre-processing the first image information to generate second image information with enhanced definition;

[0034] The method further includes: adjusting the brightness of the first image information to obtain an image with uniform brightness; performing denoising on the brightness-adjusted image to remove disordered pixels to obtain a pure image; using edge enhancement technology to enhance the boundaries of objects in the denoised image, and refining the shape of the goods based on the edge enhancement results to obtain a second image information with enhanced clarity; and storing and marking the second image information with enhanced clarity for use in the goods identification and tracking process. The details are as follows:

[0035] Adjust the brightness of the first image information: obtain the first image information captured by the security inspection machine.

[0036] Use image processing software or algorithms to adjust image brightness to ensure uniform brightness across the entire image and improve overall visibility. Image brightness and contrast can be adjusted automatically or manually. Considering changing lighting conditions in low-temperature environments, dynamic brightness adjustment may be necessary to accommodate varying lighting conditions.

[0037] Perform denoising on the brightness-adjusted image: After brightness adjustment, perform denoising on the image to remove random pixels and reduce interference. Use an appropriate denoising algorithm, such as a median filter, Gaussian filter, or more complex deep learning-based methods to remove noise from the image.

[0038] Use edge enhancement techniques to strengthen the boundaries of objects in the denoised image: Apply edge detection algorithms, such as Canny edge detection and Sobel operators, to strengthen the boundaries of objects in the image. Edge enhancement helps highlight the outlines of objects, which is crucial for subsequent cargo shape recognition.

[0039] Refine the shape of the product based on the edge enhancement results: Based on the enhanced boundary information, the shape features of the product are refined to obtain a clearer and more accurate product outline. Morphological operations (such as dilation and erosion) may be used to further optimize the outline.

[0040] Obtaining a second image with enhanced clarity: After the above steps, a second image with uniform brightness, denoised noise, and clear edges is obtained. These processed images will serve as the basis for subsequent image analysis.

[0041] Storing and tagging the enhanced second image: This function saves the processed second image to a designated data storage area and tags it for easy identification and tracking. Tags can include information such as the image's source, processing date and time, and processing steps for easier management and tracking.

[0042] The above steps ensure high-quality images in low-temperature environments, providing a solid foundation for subsequent cargo identification and tracking. This image processing method helps improve image clarity and contrast, thereby enhancing the extraction of cargo identification features and ultimately improving the safety and efficiency of the entire cold chain logistics process.

[0043] Step 3: Determine the identification characteristics of the goods based on the second image information;

[0044] Further including: extracting significant visual elements of the goods from the second image information with enhanced clarity as goods identification features; establishing a feature template based on the goods identification features for subsequent goods comparison; using the feature template to match and confirm the goods in the second image information to verify the identity of the goods; based on the results of the matching confirmation, recording the location information and timestamp of the goods to maintain continuous tracking of the goods in the cold chain logistics; integrating the location information and timestamp with other relevant information of the goods to form a complete goods tracking record for querying the status of the goods at any time.

[0045] The details are as follows:

[0046] Extracting salient visual elements of the goods from the clarity-enhanced second image: Image processing techniques and machine learning algorithms are used to extract salient visual elements of the goods from the pre-processed second image. These salient visual elements may include, but are not limited to, the goods' color, shape, barcode, QR code, printed text, and trademark images. Feature detectors (such as SIFT, SURF, and ORB) are used to locate these feature points and extract their descriptors.

[0047] Based on the identified product features, a feature template is created: A feature template is constructed based on the extracted salient visual elements. This template describes the unique appearance of the product. The feature template can be a set of feature vectors or a specific feature representation, such as a hash table or feature tree. To improve matching accuracy, multiple feature extraction methods can be used and combined to create a comprehensive feature template.

[0048] Using the feature template, the goods in the second image information are matched and confirmed: the feature template is applied to the subsequently captured image to identify and confirm the identity of the goods. This process usually involves a feature matching algorithm:

[0049] For example, FLANN (Fast Library for Approximate Nearest Ne i ghbors) or brute force matching algorithm. For each newly acquired image, its features are extracted and compared with the existing feature templates to find the closest match.

[0050] Based on the matching confirmation result, the location information and timestamp of the goods are recorded: When the goods are successfully matched, the coordinates of the goods in the current image and the timestamp of the image capture are recorded. The location information can be pixel coordinates or converted into actual spatial coordinates. The timestamp is used to record the moment the goods were identified, which is very important for tracking the movement of goods.

[0051] Integrate the location information and timestamp with other relevant information about the goods: Integrate the location information, timestamp, and other relevant information about the goods (such as the goods ID, shipper information, destination, etc.). This information can be stored in a database or data structure for easy query and management.

[0052] Create a complete cargo tracking record for immediate tracking of shipment status: Build a tracking system that generates a complete cargo tracking record based on the above information. This tracking record should include the entire shipment's path from origin to destination, including status updates at all intermediate nodes. Users can query the system to obtain the shipment's current location, estimated time of arrival, and other relevant information.

[0053] By following these steps, image processing technology can be effectively used to identify and track goods, ensuring the continuity and safety of goods throughout the cold chain logistics process. Such a system can not only improve logistics efficiency, but also reduce human error and increase transparency throughout the supply chain.

[0054] Step 4: Compare and match the cargo identification features with the standard cargo identification features in a preset database to confirm the cargo identity, record the location information of the cargo after the identity is confirmed, and associate the location information with the cargo identity;

[0055] The above-mentioned confirmation of the identity of the goods further includes: searching for corresponding standard identification features of the goods in a preset database based on the identification features of the goods; comparing the identification features of the goods with the found standard identification features of the goods to confirm the identity of the goods; if the identification features of the goods match the standard identification features of the goods, recording the identity information of the goods; combining the identity information of the goods with the location information of the goods to form a record of the status of the goods; updating the record of the status of the goods to the business management system to realize real-time tracking of the status of the goods.

[0056] The above-mentioned associating the location information with the cargo identity further includes: obtaining the geographic coordinate information of the cargo's current location, combining the geographic coordinate information with the cargo's identity information to form a location identifier; associating the location identifier with the cargo's operation record to record the cargo's movement trajectory; updating the cargo's status information based on the location identifier to reflect the cargo's latest location, and synchronizing the cargo's status information to the business management system. The details are as follows:

[0057] According to the cargo identification features, the corresponding cargo standard identification features are searched in the preset database:

[0058] The product identification features extracted from the product image are input into a pre-set database for query. The pre-set database stores standard identification features for various products, such as barcodes, QR codes, trademark patterns, colors, shapes, etc. The database query process may require the use of an efficient indexing mechanism to quickly locate similar feature templates.

[0059] Compare the cargo identification features with the found cargo standard identification features to confirm the cargo's identity:

[0060] A feature matching algorithm (such as FLANN or brute force matching) is used to compare the extracted cargo identification features with the standard identification features stored in the database. If the feature matching score exceeds a predetermined threshold, the cargo is considered a match. Other information, such as cargo size and weight, may also be incorporated to improve matching accuracy.

[0061] If the cargo identification characteristics match the standard cargo identification characteristics, the cargo identity information is recorded:

[0062] Once the identity of the goods is confirmed, the identity information of the goods is recorded, such as the goods number, batch number, production date, etc. The identity information can be directly obtained from the database or a pre-defined field.

[0063] The cargo identity information is combined with the cargo location information to form a cargo status record:

[0064] Obtain the geographic coordinates of the cargo's current location, which can be obtained using GPS or other positioning technology. Combine this geographic coordinate information with the cargo's identity information to create a location identifier. The location identifier should contain sufficient information to uniquely identify the cargo's current location.

[0065] The location identifier is associated with the cargo operation record to record the cargo movement trajectory:

[0066] Record every movement of cargo, including loading, unloading, and transportation, and associate it with a location identifier. Operation records should include timestamps, operation types, and operator information. Movement traces can help track the historical location and status changes of cargo.

[0067] Update the status information of the goods according to the location identifier to reflect the latest location of the goods:

[0068] Update the cargo status information regularly or as needed to ensure that the latest recorded location is accurate. The status information should also include additional information such as the cargo's temperature condition and whether it is damaged.

[0069] Synchronize the status information of the goods to the business management system:

[0070] Synchronize cargo status records into the business management system to enable real-time tracking of cargo status. The business management system should include permission control to ensure that only authorized users can access sensitive information. Through the business management system, users can query the status, location, and history of cargo at any time.

[0071] Through the above steps, it is possible to effectively manage and monitor goods in cold chain logistics, ensuring the safety and quality of goods. In addition, such a system can also help logistics companies improve efficiency, reduce losses, and meet customers' real-time tracking needs.

[0072] Step 5: During the movement of the goods, automatically triggering the video surveillance device to capture the third image information according to the location information;

[0073] Further including: when the location information of the cargo changes, triggering the video surveillance equipment to start; the video surveillance equipment captures a real-time image of the cargo surroundings based on the location information as third image information; saving the third image information and combining it with the location information to form a dynamic monitoring record; merging the dynamic monitoring record with the cargo operation record to achieve comprehensive monitoring of the cargo movement process, and sending the merged record to the business management system to achieve real-time visualization of the cargo movement process. The details are as follows:

[0074] When the location information of the goods changes, the video surveillance equipment is triggered to start:

[0075] The location of the goods is monitored. Any changes in the detected location are considered movement. Real-time location information is obtained using RFID tags, GPS, or other location tracking technologies. When the location information changes, a trigger signal is sent to the video surveillance device via Internet of Things (IoT) technology or wireless communication protocols (such as Wi-Fi, Bluetooth, and LoRaWAN).

[0076] The video surveillance device captures a real-time image of the environment surrounding the cargo according to the location information as the third image information:

[0077] Upon receiving a trigger signal, video surveillance equipment activates immediately, capturing real-time images of the cargo's surroundings. These devices can be fixed cameras or mobile surveillance devices, such as drones or mobile robots equipped with cameras. Image capture should ensure full coverage of the area surrounding the cargo to capture the complete environment.

[0078] The third image information is saved and combined with the location information to form a dynamic monitoring record:

[0079] Store captured real-time images locally or on a cloud server, along with timestamps and location information. Dynamic monitoring records should include, but are not limited to, image files, timestamps, and location coordinates. These records can be stored in a standard format for easy processing and retrieval.

[0080] The dynamic monitoring records are combined with the cargo operation records to achieve comprehensive monitoring of the cargo movement process:

[0081] Combine dynamic monitoring records with previously recorded cargo operation records (such as loading, unloading, and transshipment). This creates a complete cargo movement history, including cargo location changes, operation details, and corresponding real-time images. The combined record should clearly show the status and location of the cargo at every stage of the cold chain logistics process.

[0082] Send the combined records to the business management system to achieve real-time visualization of the cargo movement process:

[0083] The combined records are sent to the business management system via the network to ensure real-time updates of cargo status and location information. The business management system should have a user-friendly interface that allows managers to view the cargo's real-time location, historical trajectory, and related operation records. Visualization features such as map views and timeline views are also required to provide an intuitive understanding of cargo status and movement.

[0084] Through the above steps, comprehensive monitoring and real-time visualization of the cargo movement process in cold chain logistics can be achieved, which helps to improve logistics efficiency, ensure cargo safety, and meet customers' real-time tracking needs for cargo status.

[0085] Step 6: Verify the identity information of the goods using the third image information, record the operation process, and combine the operation process with the identity information of the goods to form an operation record;

[0086] The above-mentioned use of the third image information to verify the identity information of the goods further includes: analyzing the goods characteristics in the third image information to verify the identity information of the goods; comparing the goods characteristics in the third image information with the identity information of the goods to confirm the consistency of the goods, and recording the comparison result; combining the comparison result with the operation record of the goods to form an operation verification record, and sending the operation verification record to the business management system to achieve traceability of the goods operation process.

[0087] The above-mentioned combining the operation process with the identity information of the goods to form an operation record further includes: integrating the operation verification record with the identity information of the goods to form operation record entries, arranging the operation record entries in chronological order to display the operation history of the goods; combining the operation record entries with the location information of the goods to form a comprehensive operation record, updating the comprehensive operation record to the business management system; providing a query interface for the comprehensive operation record through the business management system to achieve transparent management of the goods operation process. The details are as follows:

[0088] Analyze the cargo features in the third image information:

[0089] Analyze the features of the goods in the third image information captured by the video surveillance equipment. These features may include the shape, color, barcode, QR code, etc. Use image processing algorithms such as edge detection and color segmentation to extract these features.

[0090] Compare the features of the goods in the third image information with the identity information of the goods:

[0091] Compare the cargo features extracted from the third image information with the previously confirmed cargo identity information to verify the consistency of the cargo. Use a feature matching algorithm (such as SIFT, SURF, ORB, etc.) to compare the feature points in the image with the features recorded in the cargo identity information.

[0092] Record the comparison results:

[0093] Record the results of the comparison process, including the number of successfully matched feature points, the matching score, and other information. Any discrepancies should also be recorded for subsequent analysis. Combine the comparison results with the handling records of the goods to form a handling verification record.

[0094] Integrate the comparison results with the cargo operation records:

[0095] Combine the comparison results with the cargo operation records to form an operation verification record. The operation record should include detailed information on the cargo loading, unloading, transportation and other operations.

[0096] Create an operation verification record:

[0097] Operation verification records should include comparison results, cargo operation time, location, operator information, etc. Records should be clear and easy to access.

[0098] Send operation verification records to the business management system:

[0099] The operation verification records are sent to the business management system via the network to ensure real-time updates of the cargo status and operation history. The business management system should support real-time updates and storage of historical records.

[0100] Achieve traceability of cargo operation process:

[0101] The business management system should provide a query interface to allow users to query the operation records and comparison results of goods to achieve traceability of the goods operation process.

[0102] Integrate the operation verification record with the identity information of the goods to form an operation record entry:

[0103] Integrate the operation verification record with the cargo identity information to form a detailed operation record entry. The operation record entry should include the cargo identity information, operation record, comparison results, etc.

[0104] Arrange the operation record entries in chronological order to show the operation history of the goods:

[0105] Arrange the operation log entries in chronological order to form a historical record of the goods' operations. This allows for a clear view of the entire operational process from goods entering the warehouse to leaving the warehouse. Combine the operation log entries with the goods' location information to form a comprehensive operation record. This comprehensive operation record should include information such as the goods' movement trajectory and location changes.

[0106] Update the comprehensive operation record to the business management system:

[0107] Comprehensive operation records should be updated regularly or as needed to the business management system to ensure real-time and accurate records. The business management system should provide a query interface for these comprehensive operation records to achieve transparent management of the cargo handling process. The business management system should provide an easy-to-use query interface that allows users to conveniently query comprehensive cargo operation records. The query interface should support multi-criteria queries, such as filtering information by time, location, and operation type.

[0108] Through the above steps, comprehensive monitoring and real-time visualization of the cargo handling process in cold chain logistics can be achieved, helping to improve logistics efficiency, ensure cargo safety, and meet customers' needs for real-time tracking of cargo status. At the same time, such a system can also provide traceability of the cargo handling process, helping to improve the transparency and reliability of the entire logistics chain.

[0109] Step 7: By integrating the operation records with the business management system, the entire cold chain cargo operation process can be traced. Further steps include:

[0110] Integrate the comprehensive operation records with other relevant information in the business management system to form a complete cargo file; retrieve the operation records in the cargo file through the query function provided by the business management system; reconstruct the cargo movement path and operation process based on the timestamp and location information of the operation records; generate a full-process visualization report of the cargo from warehousing to delivery, and use the full-process visualization report to achieve traceability and management of the entire cold chain cargo operation process. The details are as follows:

[0111] Integrate the comprehensive operation records with other relevant information in the business management system to form a complete cargo file and collect relevant data:

[0112] Collect basic information about the goods, such as their name, specifications, production date, and expiration date. Integrate the comprehensive operation records obtained in step 6, including the operation record entries, location information, and comparison results. Obtain information about the goods' status at different stages, such as environmental parameters like temperature and humidity, as well as any quality inspection reports.

[0113] Establish a cargo file: Create a file for each cargo in the business management system, integrating all the relevant information collected above. The cargo file should be well organized so that the information is easy to find and manage.

[0114] Develop a query function: Develop a query function in the business management system to allow users to search for cargo files based on different criteria. Query criteria can include cargo number, batch number, operation date range, etc.

[0115] Retrieve Operation Records: Users can search for operation records for a specific item by entering the appropriate criteria through the query function. The query results should display all operation record entries for the item, including information such as timestamp, operation type, and operator.

[0116] Reconstruct movement paths: Use the timestamps and location information in the operation records to map the movement paths of goods in the supply chain. GIS (Geographic Information System) technology can be used to assist in constructing a visual representation of the movement paths.

[0117] Reconstruct the operational process: Based on the chronological order and specific content of the operation records, sort out the complete operational process from goods entering the warehouse to leaving the warehouse. This should include the operations of all key nodes, such as receiving, storage, picking, packaging, and shipping.

[0118] Design a visual report template: Create a template that shows the entire process and movement path of the goods. The report template should include charts, maps, and other graphical elements to present the information intuitively.

[0119] Generate a visual report: Utilize the data and information collated above to automatically generate a visual report of the entire process. The report should include a flow chart of the cargo's operations, a map of its movement paths, and a timeline of key operations.

[0120] Traceability management: With fully visualized reports, you can clearly understand the status of goods at every stage of the supply chain. If any problems arise, you can quickly locate the specific time and location of the problem and take appropriate measures.

[0121] Optimized Management: Based on full-process visual reporting, potential problems and improvement opportunities can be identified to help optimize the overall process. Improvement plans can be developed for frequently problematic links to improve the efficiency and quality of the entire cold chain process.

[0122] Through the above steps, effective traceability and management of the entire cold chain cargo operation process can be achieved. Specifically, the business management system not only stores and manages detailed cargo information but also provides powerful query and analysis capabilities, helping managers to understand cargo status in real time, identify issues promptly, and respond swiftly. Furthermore, comprehensive visual reporting plays a vital role in enhancing transparency throughout the supply chain, strengthening customer trust, and mitigating risks. Such a system not only helps meet regulatory requirements but also helps companies improve operational efficiency and service levels.

[0123] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A low-temperature image recognition method applied to the cold chain industry, characterized in that: The following steps are involved: Using a security inspection machine to obtain first image information of the goods to be inspected; preprocessing the first image information to generate second image information with enhanced definition; determining a cargo identification feature based on the second image information; Comparing and matching the cargo identification features with standard cargo identification features in a preset database to confirm the cargo identity, recording the location information of the cargo after the identity is confirmed, and associating the location information with the cargo identity; During the movement of the cargo, the video surveillance device is automatically triggered to capture the third image information based on the location information, specifically including: when the location information of the cargo changes, the video surveillance device is triggered to start; the video surveillance device captures a real-time image of the environment surrounding the cargo as the third image information based on the location information; the third image information is stored and combined with the location information to form a dynamic monitoring record; the dynamic monitoring record is merged with the operation record of the cargo to achieve comprehensive monitoring of the cargo movement process, and the merged record is sent to the business management system to achieve real-time visualization of the cargo movement process; Verifying the identity information of the goods using the third image information, recording the operation process, and combining the operation process with the identity information of the goods to form an operation record; By integrating the operation records with the business management system, the entire cold chain cargo operation process can be traced, specifically including: integrating the comprehensive operation records with other relevant information in the business management system to form a complete cargo file; retrieving the operation records in the cargo file through the query function provided by the business management system; reconstructing the cargo movement path and operation process based on the timestamp and location information of the operation records; generating a full-process visual report of the cargo from warehousing to delivery, and using the full-process visual report to achieve traceability and management of the entire cold chain cargo operation process; After obtaining the first image information of the goods to be inspected by the security inspection machine, the method further includes: Applying an image enhancement method to the first image information to obtain an optimized image; Extracting contour features of the cargo from the optimized image, and comparing the contour features with data in a known cargo contour feature library to identify the cargo type; Recording the identification result of the cargo type and combining it with the location information of the cargo, and updating the cargo tracking database based on the identification result and the location information; Preprocessing the first image information to generate second image information with enhanced clarity includes: performing brightness adjustment on the first image information to obtain an image with uniform brightness; Perform denoising on the brightness-adjusted image to remove cluttered pixels and obtain a pure image. Using edge enhancement technology to enhance the boundaries of objects in the denoised image, and refining the shape of the goods based on the edge enhancement results to obtain second image information with enhanced clarity; The second image information with enhanced clarity is stored and marked for use in cargo identification and tracking; Determining the cargo identification feature according to the second image information includes: Extracting significant visual elements of the goods from the second image information with enhanced clarity as identification features of the goods; Based on the cargo identification features, a feature template is established for subsequent cargo comparison; Using the feature template, matching and confirming the goods in the second image information to verify the identity of the goods; Based on the matching confirmation result, the location information and timestamp of the goods are recorded to maintain the continuous tracking of the goods in the cold chain logistics; Integrate the location information and timestamp with other relevant information about the goods to form a complete cargo tracking record for checking the status of the goods at any time; Confirming the identity of the goods includes: According to the cargo identification feature, searching for the corresponding cargo standard identification feature in a preset database; Comparing the cargo identification features with the found standard cargo identification features to confirm the identity of the cargo; If the cargo identification characteristics match the standard cargo identification characteristics, the cargo identity information is recorded; Combining the cargo identity information with the cargo location information to form a cargo status record; updating the cargo status record to the business management system to achieve real-time tracking of the cargo status; Associating the location information with the cargo identity, including: Obtaining geographic coordinate information of the current location of the cargo, and combining the geographic coordinate information with the identity information of the cargo to form a location identifier; The location identifier is associated with the operation record of the goods to record the movement trajectory of the goods; the status information of the goods is updated according to the location identifier to reflect the latest location of the goods, and the status information of the goods is synchronized to the business management system.

2. A low-temperature image recognition method applied to the cold chain industry according to claim 1, characterized in that: Verifying the identity information of the goods using the third image information includes: analyzing features of the goods in the third image information to verify the identity information of the goods; comparing the features of the goods in the third image information with the identity information of the goods to confirm the consistency of the goods, and recording the comparison result; The comparison result is combined with the operation record of the goods to form an operation verification record, and the operation verification record is sent to the business management system to achieve traceability of the goods operation process.

3. A low-temperature image recognition method applied to the cold chain industry according to claim 2, characterized in that: The operation process is combined with the identity information of the goods to form an operation record, including: Integrating the operation verification record with the identity information of the goods to form operation record entries, and arranging the operation record entries in chronological order to display the operation history of the goods; combining the operation record entries with the location information of the goods to form a comprehensive operation record, and updating the comprehensive operation record to the business management system; The business management system provides a query interface for the comprehensive operation records, thereby realizing transparent management of the cargo operation process.

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