Shipment label detection method and system, computer equipment and storage medium

Abstract

The invention relates to a shipment label detection method and system, computer equipment and a storage medium, and the method comprises the steps: receiving a warehouse-out request of finished product packaging completion, generating a warehouse-out list with a unique identifier, carrying out the OQC quality detection of a finished product through the quality detection logic, updating the state information corresponding to the finished product passing the quality detection into a warehouse-in state, and storing the state information in the warehouse-in state. The encasement information is generated and associated to the ex-warehouse list; scanning internal label information of the finished product and a client label image, and analyzing the client label image by using an image recognition algorithm to obtain corresponding finished product key information; comparing the finished product key information with the internal label information through a rule matching algorithm, and generating a corresponding label verification message according to a comparison result; and after the label verification is completed, automatically triggering a finished product ex-warehouse process, and updating state information corresponding to the ex-warehouse finished product in the ex-warehouse list. The method has the effect of improving the label information management efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of industrial automation detection, and in particular to a shipping label detection method, system, computer equipment and storage medium. Background Art

[0002] At present, in the process of industrial manufacturing and logistics management, the accuracy of shipping labels is crucial to ensure the integrity of product outbound information and the smoothness of logistics links. The common shipping label inspection process usually relies on manual verification and comparison of label information, but this method has the problems of low efficiency and high error rate, especially when processing large-volume orders. The manual verification method is difficult to meet the needs of efficient and accurate outbound management. Existing automated inspection systems usually identify and verify label information through rule matching or simple OCR technology, but in actual applications, label information may lead to inaccurate comparison results due to input errors, character format differences, and diverse customer needs, making it difficult to achieve efficient management of label information.

[0003] The above-mentioned existing technical solutions have the following defects: the existing shipping label detection technology is insufficient in the ability to identify and compare label information, and the degree of automation in status update and synchronization management is low, resulting in low efficiency in label information management, so there is room for improvement. Summary of the invention

[0004] In order to improve the management efficiency of label information, the present application provides a shipping label detection method, system, computer device and storage medium.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A shipping label detection method, the shipping label detection method comprising: Receive the outbound request for finished product packaging, generate a uniquely identified outbound order, perform OQC quality inspection on the finished product using quality inspection logic, update the status information corresponding to the finished product that passes the quality inspection to the warehousing status, and generate packing information associated with the outbound order; Scan the internal label information and customer label image of the finished product, and use the image recognition algorithm to analyze the customer label image to obtain the corresponding key information of the finished product; Compare the finished product key information with the internal label information through a rule matching algorithm, and generate a corresponding label verification message according to the comparison result; After the label verification is completed, the finished product delivery process is automatically triggered, and the status information corresponding to the delivered finished product is updated in the delivery order.

[0006] By adopting the above technical solution, by receiving the outbound request for finished product packaging, a uniquely identified outbound order is generated, and a unique tracking identifier can be generated for each outbound request, thereby achieving the accuracy and traceability of outbound management; by using quality inspection logic to perform OQC quality inspection on the finished product, and updating the status information corresponding to the finished product that has passed the quality inspection to the warehouse status, it can be ensured that only the finished products that meet the preset quality standards are marked as qualified, thereby improving the reliability of product outbound quality; by scanning the internal label information of the finished product and the customer label image, and using the image recognition algorithm to parse the customer label image to obtain the corresponding finished product key information, the key data in the customer label can be quickly parsed, thereby reducing manual operation time and improving data processing efficiency; the finished product key information is compared with the internal label information through the rule matching algorithm, and the corresponding label verification message is generated according to the comparison result, which can verify the consistency of the finished product information with the internal records, thereby reducing logistics problems caused by label errors.

[0007] In one example, the present application may be further configured as follows: the quality inspection logic is used to perform OQC quality inspection on the finished product, and the status information corresponding to the finished product that passes the quality inspection is updated to the storage status, specifically including: According to the quality inspection logic, the appearance, packaging integrity and randomly sampled functional performance of the finished product are inspected item by item, and the corresponding inspection results are recorded; The test result is compared with a preset quality test standard, and when the test result reaches the quality test standard, the status information of the finished product is updated to a warehousing status.

[0008] By adopting the above technical solution, the appearance, packaging integrity and randomly sampled functional performance of the finished products are tested item by item according to the quality inspection logic, and the corresponding test results are recorded, which can comprehensively cover the multi-dimensional quality standards of the finished products, thereby ensuring the qualification of the finished products in appearance and function; by comparing the test results with the preset quality inspection standards, when the test results meet the quality inspection standards, the status information of the finished products is updated to the warehousing status, and the quality status of the finished products can be dynamically updated, thereby realizing the automated management of the outbound link.

[0009] In one example, the present application may be further configured as follows: the step of using an image recognition algorithm to analyze the customer label image to obtain corresponding key information of the finished product specifically includes: Extracting text information and non-text information from the customer label image, and preliminarily parsing the text information and the non-text information to generate label structured data; The label structured data is classified based on the confidence of the parsing process, and the fields with low confidence are parsed again to obtain the key information of the finished product.

[0010] By adopting the above technical solution, by extracting text information and non-text information from the customer label image, and preliminarily parsing the text information and non-text information respectively, to generate label structured data, it is possible to achieve accurate parsing and structured storage of customer label information, thereby providing a high-quality data foundation for subsequent comparison and verification; by classifying the label structured data based on the confidence of the parsing process and performing a secondary analysis on the fields with low confidence, the accuracy of data parsing can be improved, thereby reducing subsequent processing problems caused by erroneous parsing.

[0011] In one example, the present application may be further configured as follows: extracting text information and non-text information from the customer label image, and preliminarily parsing the text information and the non-text information respectively, and generating label structured data specifically includes: Using image recognition technology to extract features from the customer label image, and combining character recognition and image region segmentation technology to extract key image fields; The key fields of the image are standardized by a template matching algorithm to generate the label structured data consistent with the format of the internal label information.

[0012] By adopting the above technical solution, by using image recognition technology to extract features of customer label images, and combining character recognition and image region segmentation technology to extract key image fields, the core information of customer labels can be effectively extracted, thereby improving the efficiency and accuracy of label parsing; the key image fields are standardized through template matching algorithms to generate label structured data that is consistent with the internal label information format, which can eliminate parsing and comparison errors caused by inconsistent formats, thereby ensuring the standardization of label information.

[0013] In one example, the present application may be further configured as follows: comparing the finished product key information with the internal label information by using a rule matching algorithm specifically includes: Dynamically load the comparison rules and set the corresponding field priority for the comparison fields according to the customer requirements associated with the delivery order; According to the field priority, a fuzzy matching algorithm is used to calculate the content similarity of the comparison field, and then fault tolerance processing is performed on character deviations to obtain a corresponding comparison result.

[0014] By adopting the above technical solution, by dynamically loading the comparison rules and setting the corresponding field priority for the comparison field according to the customer needs associated with the delivery order, it is possible to adapt to the customized needs of different customers, thereby improving the flexibility and efficiency of the comparison process; by using the fuzzy matching algorithm to calculate the content similarity of the comparison field according to the field priority, and then performing fault-tolerant processing for character deviations, it is possible to reduce the comparison failures caused by format differences or character errors, thereby improving the success rate of label comparison.

[0015] In one example, the present application can be further configured as follows: after the label verification is completed, the finished product delivery process is triggered, specifically including: If the tag verification message is verified to be passed, the status information of the finished product is updated to the outbound status, and the updated status information is synchronized with the outbound order; In the case where the tag verification message fails verification, detailed error data is recorded and generated.

[0016] By adopting the above technical solution, by updating the status information of the finished product to the outbound status when the label verification message is verified, and synchronizing the updated status information with the outbound order, it is possible to ensure that the status information in the outbound order is consistent with the actual status of the finished product, thereby achieving precise control of the outbound process; by recording and generating detailed error data when the label verification message is verified failed, it is possible to provide data support for the analysis of abnormal problems and manual review, thereby improving the reliability and maintainability of the system.

[0017] The second object of the invention is achieved by the following technical solutions: A shipping label detection system, the shipping label detection system comprising: The outbound processing module is used to receive the outbound request for finished product packaging, generate a uniquely identified outbound order, perform OQC quality inspection on the finished product using quality inspection logic, update the status information corresponding to the finished product that passes the quality inspection to the warehousing status, and generate packing information associated with the outbound order; The label parsing module is used to scan the internal label information of the finished product and the customer label image, and use the image recognition algorithm to parse the customer label image to obtain the corresponding key information of the finished product; An information comparison module, used to compare the finished product key information with the internal label information through a rule matching algorithm, and generate a corresponding label verification message according to the comparison result; The outbound update module is used to automatically trigger the finished product outbound process after the label verification is completed, and update the status information corresponding to the outbound finished product in the outbound order.

[0018] By adopting the above technical solution, by receiving the outbound request for finished product packaging, a uniquely identified outbound order is generated, and a unique tracking identifier can be generated for each outbound request, thereby achieving the accuracy and traceability of outbound management; by using quality inspection logic to perform OQC quality inspection on the finished product, and updating the status information corresponding to the finished product that has passed the quality inspection to the warehouse status, it can be ensured that only the finished products that meet the preset quality standards are marked as qualified, thereby improving the reliability of product outbound quality; by scanning the internal label information of the finished product and the customer label image, and using the image recognition algorithm to parse the customer label image to obtain the corresponding finished product key information, the key data in the customer label can be quickly parsed, thereby reducing manual operation time and improving data processing efficiency; the finished product key information is compared with the internal label information through the rule matching algorithm, and the corresponding label verification message is generated according to the comparison result, which can verify the consistency of the finished product information with the internal records, thereby reducing logistics problems caused by label errors.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned shipping label detection method when executing the computer program.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned shipping label detection method are implemented.

[0021] In summary, this application includes the following beneficial technical effects: 1. By receiving the outbound request of finished product packaging, a unique outbound order is generated, and a unique tracking mark can be generated for each outbound request, so as to achieve the accuracy and traceability of outbound management; by using the quality inspection logic to perform OQC quality inspection on the finished product, and updating the status information corresponding to the finished product that has passed the quality inspection to the warehouse status, it can ensure that only the finished products that meet the preset quality standards are marked as qualified, thereby improving the reliability of product outbound quality; by scanning the internal label information of the finished product and the customer label image, the customer label image is parsed by the image recognition algorithm to obtain the corresponding key information of the finished product, and the key data in the customer label can be quickly parsed, thereby reducing manual operation time and improving data processing efficiency; the key information of the finished product is compared with the internal label information through the rule matching algorithm, and the corresponding label verification message is generated according to the comparison result, which can verify the consistency between the finished product information and the internal records, thereby reducing logistics problems caused by label errors; 2. By testing the appearance, packaging integrity and randomly sampled functional performance of finished products item by item according to the quality inspection logic and recording the corresponding inspection results, the multi-dimensional quality standards of finished products can be fully covered, thereby ensuring the eligibility of finished products in appearance and function; by comparing the inspection results with the preset quality inspection standards, if the inspection results meet the quality inspection standards, the status information of the finished products will be updated to the warehousing status, and the quality status of the finished products can be dynamically updated, thereby realizing the automated management of the outbound link; 3. By extracting text information and non-text information from customer label images, and preliminarily analyzing the text information and non-text information respectively, label structured data is generated, which can achieve accurate analysis and structured storage of customer label information, thereby providing a high-quality data foundation for subsequent comparison and verification; by classifying label structured data based on the confidence of the parsing process and performing secondary analysis on low-confidence fields, the accuracy of data parsing can be improved, thereby reducing subsequent processing problems caused by erroneous parsing. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a shipping label detection method in one embodiment of the present application; Figure 2 It is a flowchart for implementing step S10 in the shipping label detection method in one embodiment of the present application; Figure 3 is a flowchart for implementing step S20 in the shipping label detection method in one embodiment of the present application; Figure 4 is a flowchart for implementing step S21 in the shipping label detection method in one embodiment of the present application; Figure 5 is a flowchart for implementing step S30 in the shipping label detection method in one embodiment of the present application; Figure 6 is a flowchart for implementing step S40 in the shipping label detection method in one embodiment of the present application; Figure 7 This is a principle block diagram of a shipping label detection system in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a shipping label detection method, which specifically includes the following steps: S10: Receive the outbound request for finished product packaging, generate a uniquely identified outbound order, and use the quality inspection logic to perform OQC quality inspection on the finished products. The status information corresponding to the finished products that pass the quality inspection is updated to the warehousing status, and the packing information is generated and associated with the outbound order.

[0025] Specifically, after receiving the outbound delivery request, the order number, customer identification information and packaging batch information of the finished product in the request content are parsed, and a unique outbound delivery order number is generated through the system's built-in coding rules. This number is generated based on a combination of timestamp and serial number, and is used to mark and track each outbound delivery order. Subsequently, OQC quality inspection is performed on the finished products included in the request, and the appearance and functional performance of the finished products are checked one by one according to the inspection logic to verify whether the finished products meet the quality requirements. For example, electrical performance tests are performed on electronic products, and pressure resistance and sealing tests are performed on industrial products. After all inspections are completed, the inspection results are recorded and the status of the finished products that have passed the inspection is updated to the warehousing status. At the same time, the packing information is generated, and the packing number of the finished product is associated with the outbound delivery order.

[0026] S20: Scan the internal label information of the finished product and the customer label image, and use the image recognition algorithm to analyze the customer label image to obtain the corresponding key information of the finished product.

[0027] Specifically, the internal label information of the finished product and the customer label image are obtained through a scanning device. After the collected image is converted into digital data, the customer label image is preprocessed using an image processing algorithm, including denoising, grayscale, and edge enhancement operations, to ensure that the image quality meets the analysis requirements. Subsequently, the image segmentation algorithm is used to separate different areas in the label image, and the text area and graphic area are extracted. The character content in the text area is extracted through optical character recognition, i.e. OCR technology, and redundant background and interference information are removed. The pattern information of the graphic area, such as a QR code, barcode, or LOGO, is extracted through a feature recognition algorithm. The extracted text information and graphic information are separately processed and stored as independent fields, and are preliminarily associated with the scanned internal label information.

[0028] S30: Compare the finished product key information with the internal label information through a rule matching algorithm, and generate a corresponding label verification message according to the comparison result.

[0029] Specifically, after receiving the finished product key information and internal label information, the contents of the two are compared field by field according to the preset comparison rules, and the item-by-item verification operation is performed according to the rule matching algorithm, including verifying the order number, batch number and other key field contents of the finished product. Fields with consistent comparison results are recorded as successful verification, and items with content differences or unmatched fields are marked as verification failures. A label verification message is generated, including the overall status of the verification and the matching results of specific fields.

[0030] S40: After the label verification is completed, the finished product outbound process is automatically triggered, and the status information corresponding to the outbound finished product is updated in the outbound order.

[0031] Specifically, after the label verification is completed, the verification message is used to determine whether the finished product meets the requirements for delivery. For finished products that pass the verification, their status information is updated to "delivered", and the status information is synchronized to the finished product detail field in the delivery order to ensure that the status record in the delivery order is consistent with the actual status of the finished product. For finished products that fail the verification, their verification status is recorded as "abnormal", and the content that needs to be reviewed is marked in the delivery order.

[0032] In one embodiment, if Figure 2 As shown, in step S10, the finished product is subjected to OQC quality inspection using the quality inspection logic, and the status information corresponding to the finished product that has passed the quality inspection is updated to the storage status, which specifically includes: S11: According to the quality inspection logic, the appearance of the finished product, the integrity of the packaging, and the functional performance of random samples are inspected item by item, and the corresponding test results are recorded.

[0033] Specifically, when conducting appearance inspection on finished products, observe whether the packaging of the finished products is damaged, contaminated, or the label position is not standardized. The appearance characteristics of the finished product packaging, including color, size, identification labels and other information, are recorded through visual inspection equipment to verify whether it meets the quality requirements; when testing the integrity of the packaging, check whether the weight of the packaging is within the allowable error range through a weighing device, and use a laser scanner to verify the packaging quality, such as whether there is glue leakage, loose sealing and other problems; in functional performance testing, a specified proportion of samples are drawn from the finished products through a preset random sampling algorithm, and electronic testing instruments are used to test whether the functional performance meets the specifications. For example, for electronic products, the circuit continuity, power consumption and signal strength are tested; for mechanical products, the pressure resistance and flexibility of moving parts are tested. After the test is completed, all test results are summarized and stored.

[0034] S12: Compare the test results with the preset quality test standards. If the test results meet the quality test standards, update the status information of the finished product to the storage status.

[0035] Specifically, after the inspection is completed, the inspection results will be compared item by item with the quality inspection standards in a predetermined data format. First, verify whether the size, color and packaging integrity recorded in the appearance inspection are consistent with the standard, and then compare whether the key parameters of the functional performance test, such as power consumption value, circuit on-off time, etc. are within the standard range. When all comparison items meet the preset standards, the finished product status information is updated to "warehouse", and a qualified identification code is generated and recorded in the system; for finished products whose inspection results do not meet the standards, an unqualified record is generated and the corresponding status information is marked as "re-inspection required", and the relevant quality management personnel are notified.

[0036] In one embodiment, if Figure 3 As shown, in step S20, the customer label image is analyzed using an image recognition algorithm to obtain the corresponding key information of the finished product, which specifically includes: S21: extracting text information and non-text information from the customer label image, and preliminarily parsing the text information and non-text information respectively to generate label structured data.

[0037] Specifically, the customer label image is converted into a grayscale image to reduce noise interference, and the label area is separated into multiple sub-areas, such as text area, QR code area and barcode area, through an image segmentation algorithm. For the text area, optical character recognition technology is used to extract character data line by line and remove redundant background information, while recording the extracted character position information to maintain the original format; for the non-text area, the feature extraction algorithm is used to parse the QR code and barcode areas, and the parsing results are converted into corresponding character strings or numerical information. The extracted text and non-text information are stored as independent fields, and these fields are organized into label structured data for subsequent processing.

[0038] S22: Classify the label structured data based on the confidence of the parsing process, and perform secondary parsing on the low-confidence fields to obtain the key information of the finished product.

[0039] Specifically, during the parsing process, a corresponding confidence score is generated for each field. For example, the confidence of the text field extracted by OCR is calculated based on the clarity of character recognition, and the confidence of the numerical field extracted by the QR code parsing algorithm is calculated based on the decoding success rate. When the field confidence is lower than the preset threshold, these fields are classified as "low-confidence fields", and the enhanced recognition module is called to re-parse the low-confidence fields. For example, the recognition accuracy can be improved by adjusting the image contrast or increasing the image sharpening processing. For fields that still cannot generate reliable results after multiple parsing, a "manual processing required" mark is generated, and the parsed high-confidence fields and the updated low-confidence fields are merged into the final product key information.

[0040] In one embodiment, if Figure 4As shown, in step S21, the text information and non-text information in the customer label image are extracted, and the text information and non-text information are preliminarily parsed respectively to generate label structured data, which specifically includes: S211: Use image recognition technology to extract features from customer label images, and combine character recognition and image region segmentation technology to extract key image fields.

[0041] Specifically, when extracting features from customer label images, a convolutional neural network is used to extract global features of the image, such as edges, textures, and regional shapes. An image segmentation algorithm is used to separate different regions, such as the rectangular boundary region of a text field and the QR code or LOGO region of a non-text field. The corresponding parsing algorithm is called for each segmented region. For the text field, the OCR algorithm is called to recognize characters line by line. For the QR code region, a QR code parsing algorithm is used to extract the encoded information, and the extracted fields are labeled with their types and positions.

[0042] S212: Standardize the key fields of the image through a template matching algorithm to generate label structured data that is consistent with the internal label information format.

[0043] Specifically, after extracting key fields, the fields are standardized according to preset templates. For example, the date format in the text field is converted through regular expressions, and the "DD / MM / YYYY" format is unified into "YYYY-MM-DD". For the barcode field, the decoded value is padded with zeros according to the number of digits to ensure format consistency. For the QR code field, the encoded information is parsed into a key-value pair structure and converted into a JSON format consistent with the internal label information. After completing field standardization, all fields are organized into a structured data format and stored.

[0044] In one embodiment, if Figure 5 As shown, in step S30, the finished product key information is compared with the internal label information through a rule matching algorithm, specifically including: S31: Dynamically load the comparison rules and set the corresponding field priority for the comparison fields according to the customer requirements associated with the outbound order.

[0045] Specifically, a rule set matching customer needs is loaded from the comparison rule library according to the customer identification information and order type in the outbound delivery note. The rule set includes field priority configuration. For example, the order number and batch number are defined as key fields and are compared first. At the same time, the field comparison method and weight configuration are loaded. For numerical fields, the numerical range matching rule is adopted, and for text fields, the exact matching rule is adopted. The loaded rule set is applied to the subsequent comparison process and the field priority is updated.

[0046] S32: According to the field priority, a fuzzy matching algorithm is used to calculate the content similarity of the comparison field, and then fault tolerance processing is performed for character deviations to obtain a corresponding comparison result.

[0047] Specifically, the comparison fields are matched one by one in order of priority, and the fuzzy matching algorithm is used to calculate the similarity of the contents of the two fields. For example, character similarity is calculated to determine whether there is an acceptable deviation in the field, including character replacement, insertion, or order change, etc. Fields with similarity higher than the set threshold are marked as successfully matched, and fields with similarity lower than the threshold are marked as failed matches and the deviation content is recorded. All comparison results are summarized to generate a comparison report and returned for subsequent processing.

[0048] In one embodiment, if Figure 6 As shown, in step S40, the finished product delivery process is triggered after the label verification is completed, which specifically includes: S41: When the tag verification message indicates that the verification has passed, the status information of the finished product is updated to the outbound status, and the updated status information is synchronized with the outbound order.

[0049] Specifically, after the verification message is confirmed, the status field of the finished product is updated to "outbound", and the outbound status of the corresponding finished product is marked as "outbound" in the outbound delivery note. The status information is synchronized to the associated inventory management system to ensure the consistency of inventory information, and an outbound delivery log record is generated, including outbound delivery time, verification details and operator information.

[0050] S42: When the tag verification message indicates verification failure, detailed error data is recorded and generated.

[0051] Specifically, when the verification fails, the field information of the failed comparison and the corresponding failure reason are extracted, and the field name, failure type and failure details are recorded in the error log. At the same time, an error report file is generated. The report includes the field location, original value and comparison value of the failed verification field and possible cause analysis, such as character mismatch or inconsistent format. The error data is used for manual review and as a basis for improvement.

[0052] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0053] In one embodiment, a shipping label detection system is provided, and the shipping label detection system corresponds to the shipping label detection method in the above embodiment. Figure 7 As shown, the shipping label detection system includes a warehouse processing module, a label parsing module, an information comparison module, and a warehouse update module. The detailed description of each functional module is as follows: The outbound processing module is used to receive the outbound request for finished product packaging, generate a uniquely identified outbound order, and use the quality inspection logic to perform OQC quality inspection on the finished products. The status information corresponding to the finished products that pass the quality inspection is updated to the warehousing status, and the packing information is generated and associated with the outbound order; The label parsing module is used to scan the internal label information of the finished product and the customer label image, and use the image recognition algorithm to parse the customer label image to obtain the corresponding key information of the finished product; The information comparison module is used to compare the key information of the finished product with the internal label information through a rule matching algorithm, and generate a corresponding label verification message according to the comparison result; The outbound update module is used to automatically trigger the finished product outbound process after the label verification is completed, and update the status information corresponding to the outbound finished product in the outbound order.

[0054] Optionally, the outbound processing module specifically includes: The appearance and function inspection submodule is used to inspect the appearance, packaging integrity and randomly sampled functional performance of the finished product item by item according to the quality inspection logic, and record the corresponding inspection results; The test result processing submodule is used to compare the test results with the preset quality inspection standards. If the test results meet the quality inspection standards, the status information of the finished product is updated to the storage status.

[0055] Optionally, the tag parsing module specifically includes: The information extraction submodule is used to extract text information and non-text information from the customer label image, and to perform preliminary analysis on the text information and non-text information to generate label structured data; The confidence classification submodule is used to classify the label structured data based on the confidence of the parsing process, and perform secondary parsing on the low-confidence fields to obtain the key information of the finished product.

[0056] Optionally, the information extraction submodule specifically includes: The field segmentation unit is used to extract features from customer label images using image recognition technology, and to extract key fields from images by combining character recognition and image region segmentation technology; The field standardization unit is used to standardize the key fields of the image through the template matching algorithm to generate label structured data consistent with the internal label information format.

[0057] Optionally, the information comparison module specifically includes: The rule loading submodule is used to dynamically load the comparison rules and set the corresponding field priority for the comparison fields according to the customer requirements associated with the outbound order; The fault-tolerant comparison submodule is used to calculate the content similarity of the comparison fields based on the field priority using a fuzzy matching algorithm, and then perform fault-tolerant processing on character deviations to obtain the corresponding comparison results.

[0058] Optionally, the outbound update module specifically includes: The outbound status update submodule is used to update the status information of the finished product to the outbound status when the label verification message indicates that the verification has passed, and synchronize the updated status information with the outbound order; The error recording submodule is used to record and generate detailed error data when the tag verification message fails to be verified.

[0059] For the specific definition of the shipping label detection system, please refer to the definition of the shipping label detection method above, which will not be repeated here. Each module in the above-mentioned shipping label detection system can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0060] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a shipping label detection method is implemented.

[0061] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Receive the outbound request for finished product packaging, generate a uniquely identified outbound order, and use the quality inspection logic to perform OQC quality inspection on the finished products. Update the status information corresponding to the finished products that pass the quality inspection to the warehousing status, and generate packing information associated with the outbound order; Scan the internal label information and customer label image of the finished product, and use the image recognition algorithm to analyze the customer label image to obtain the corresponding key information of the finished product; The key information of the finished product is compared with the internal label information through the rule matching algorithm, and the corresponding label verification message is generated according to the comparison result; After the label verification is completed, the finished product outbound process is automatically triggered, and the status information corresponding to the outbound finished product is updated in the outbound order.

[0062] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Receive the outbound request for finished product packaging, generate a uniquely identified outbound order, and use the quality inspection logic to perform OQC quality inspection on the finished products. Update the status information corresponding to the finished products that pass the quality inspection to the warehousing status, and generate packing information associated with the outbound order; Scan the internal label information and customer label image of the finished product, and use the image recognition algorithm to analyze the customer label image to obtain the corresponding key information of the finished product; The key information of the finished product is compared with the internal label information through the rule matching algorithm, and the corresponding label verification message is generated according to the comparison result; After the label verification is completed, the finished product outbound process is automatically triggered, and the status information corresponding to the outbound finished product is updated in the outbound order.

[0063] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0064] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0065] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A shipping label detection method, characterized in that: The shipping label detection method comprises: Receive the outbound request for finished product packaging, generate a uniquely identified outbound order, perform OQC quality inspection on the finished product using quality inspection logic, update the status information corresponding to the finished product that passes the quality inspection to the warehousing status, and generate packing information associated with the outbound order; Scan the internal label information and customer label image of the finished product, and use the image recognition algorithm to analyze the customer label image to obtain the corresponding key information of the finished product; Compare the finished product key information with the internal label information through a rule matching algorithm, and generate a corresponding label verification message according to the comparison result; After the label verification is completed, the finished product delivery process is automatically triggered, and the status information corresponding to the delivered finished product is updated in the delivery order.

2. The shipping label detection method according to claim 1, characterized in that: The using of quality inspection logic to perform OQC quality inspection on the finished product, and updating the status information corresponding to the finished product that has passed the quality inspection to a storage status, specifically includes: According to the quality inspection logic, the appearance, packaging integrity and randomly sampled functional performance of the finished product are inspected item by item, and the corresponding inspection results are recorded; The test result is compared with a preset quality test standard, and when the test result reaches the quality test standard, the status information of the finished product is updated to a warehousing status.

3. The shipping label detection method according to claim 1, characterized in that: The method of using an image recognition algorithm to analyze the customer label image to obtain corresponding key information of the finished product specifically includes: Extracting text information and non-text information from the customer label image, and preliminarily parsing the text information and the non-text information to generate label structured data; The label structured data is classified based on the confidence of the parsing process, and the fields with low confidence are parsed again to obtain the key information of the finished product.

4. The method for detecting shipping labels according to claim 3, characterized in that: The step of extracting text information and non-text information from the customer label image, and preliminarily parsing the text information and the non-text information to generate label structured data specifically includes: Using image recognition technology to extract features from the customer label image, and combining character recognition and image region segmentation technology to extract key image fields; The key fields of the image are standardized by a template matching algorithm to generate the label structured data consistent with the format of the internal label information.

5. The shipping label detection method according to claim 1, characterized in that: The comparing the finished product key information with the internal label information by using a rule matching algorithm specifically includes: Dynamically load the comparison rules and set the corresponding field priority for the comparison fields according to the customer requirements associated with the delivery order; According to the field priority, a fuzzy matching algorithm is used to calculate the content similarity of the comparison field, and then fault tolerance processing is performed on character deviations to obtain a corresponding comparison result.

6. The method for detecting shipping labels according to claim 1, characterized in that: After the label verification is completed, the finished product delivery process is triggered, which specifically includes: If the tag verification message is verified to be passed, the status information of the finished product is updated to the outbound status, and the updated status information is synchronized with the outbound order; In the case where the tag verification message fails verification, detailed error data is recorded and generated.

7. A shipping label detection system, characterized in that: The shipping label detection system includes: The outbound processing module is used to receive the outbound request for finished product packaging, generate a uniquely identified outbound order, perform OQC quality inspection on the finished product using quality inspection logic, update the status information corresponding to the finished product that passes the quality inspection to the warehousing status, and generate packing information associated with the outbound order; The label parsing module is used to scan the internal label information of the finished product and the customer label image, and use the image recognition algorithm to parse the customer label image to obtain the corresponding key information of the finished product; An information comparison module, used to compare the finished product key information with the internal label information through a rule matching algorithm, and generate a corresponding label verification message according to the comparison result; The outbound update module is used to automatically trigger the finished product outbound process after the label verification is completed, and update the status information corresponding to the outbound finished product in the outbound order.

8. The shipping label detection system according to claim 7, characterized in that: The outbound processing module specifically includes: The appearance and function inspection submodule is used to inspect the appearance, packaging integrity and randomly sampled functional performance of the finished product item by item according to the quality inspection logic, and record the corresponding inspection results; The test result processing submodule is used to compare the test result with the preset quality test standard, and when the test result reaches the quality test standard, update the status information of the finished product to the storage status.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the shipping label detection method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the shipping label detection method according to any one of claims 1 to 6 are implemented.

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