Video job record tracing system and method based on order information association

Abstract

The application discloses a video operation record tracing system and method based on order information association, relates to the technical field of information management and video data processing, and comprises an order information acquisition module, a video matching module and an association storage module.The order information acquisition module is used for acquiring order information data containing an order identifier and a timestamp.The video matching module is used for matching corresponding video operation record segments based on the timestamp range in the order information.The association storage module is used for establishing an association mapping relationship between the order identifier and the matched video operation record segments and storing the association mapping relationship into a database.The tracing query module is used for extracting associated video records from the database according to input order query conditions and generating a visualized tracing interface.The video operation record tracing system and method based on order information association are based on order-associated videos and trace order query videos.

Description

Technical Field

[0001] This invention relates to the field of information management and video data processing technology, specifically to a video operation record tracing system and method based on order information association. Background Technology

[0002] In modern manufacturing, logistics, and operations management, with the continuous improvement of informatization and visualization, video recording of work sites has gradually become an important means to ensure work quality, support accountability, and strengthen process supervision. Existing technologies typically rely on fixed or mobile camera equipment to continuously capture video of the entire work process and store the captured videos on local servers or cloud platforms. While these video recordings cover key aspects of the work process, they still have significant shortcomings in data organization and retrieval methods.

[0003] Specifically, current video recording systems often lack an effective correlation mechanism with order information. Video data is typically managed based on timestamps or device numbers, while order information resides in a separate business system, failing to establish a hierarchical or indexed correspondence. Therefore, when tracing the operational process of a specific order, managers must rely on time clues, human experience, or vague operational records, manually searching through vast amounts of video data. This is not only time-consuming and labor-intensive but also carries the risk of inaccurate location, missed key information, or false positives, failing to meet the demands for efficient, accurate, and systematic traceability. Furthermore, existing systems lack unified event labeling and process identification capabilities, making it difficult to automatically segment, identify, or tag video segments, further increasing the complexity of queries and limiting the in-depth application of video data in quality management, risk control, and process optimization. Summary of the Invention

[0004] The purpose of this invention is to provide a video job record tracing system and method based on order information association, which traces and queries video based on order-associated videos.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a video job record traceability system based on order information association, the system comprising:

[0006] The order information acquisition module is used to acquire order information data containing order identifiers and timestamps;

[0007] The video matching module is used to match corresponding video job record segments based on the timestamp range in the order information;

[0008] The associated storage module is used to establish an association mapping relationship between the order identifier and the matching video job record segment and store it in the database. This includes extracting the last digit of the order number, extracting the last digit of each associated video segment ID, calculating the absolute value of the two numbers, and if the absolute value does not exceed a set threshold, the video is used as the primary associated video segment; if the absolute value is higher than the set threshold, the video is used as the secondary associated video segment.

[0009] The traceability query module is used to extract related video records from the database based on the input order query conditions and generate a visual traceability interface. In the interface, the primary related video segments are displayed first, and the primary related video segments are displayed before the secondary related video segments. This includes recording the video segments viewed each time after the user completes the order traceability query and loads the video segment list, counting the number of viewed segments, reading the total number of segments in the order, and calculating the browsing coverage rate. If the coverage rate is lower than the set coverage rate threshold, the unviewed high-matching segments are displayed first. If the coverage rate is greater than or equal to the set coverage rate threshold, the viewed segments are displayed first.

[0010] Preferably, the order information acquisition module acquires order information data including order identifiers and timestamps, including:

[0011] Set the number of fields that each order should contain, check the actual number of fields filled in each order, calculate the order information integrity ratio, if the order information integrity ratio is greater than or equal to the set threshold, pass the order information to the video matching module, if the order information integrity ratio is less than the set threshold, mark it as severely obscured information, and wait for supplementation or manual annotation before processing.

[0012] Preferably, the specific formula for calculating the order information integrity ratio from the order information data containing the order identifier and timestamp obtained by the order information acquisition module is as follows:

[0013] R = B / C;

[0014] Where R represents the order information completeness ratio, B represents the number of fields actually filled in each order, and C represents the number of fields that each order should contain.

[0015] Preferably, the video matching module matches the corresponding video job record segments based on the timestamp range in the order information. This includes dividing each video segment into a 10-second window, calculating the frame interval of image changes in the video within each window, calculating the image change rate, setting an image change rate threshold, and if the image change rate is higher than the set image change rate threshold, determining that the video is an active action segment. Within the order time range, all segments with image change rates higher than the set image change rate threshold are searched and selected as target segments. The corresponding segment start and end times, video ID, and speed score are then output.

[0016] Preferably, the video matching module calculates the image change rate in the corresponding video job record segment based on the timestamp range in the order information using the following specific formula:

[0017] s = g / t;

[0018] Where s represents the rate of image change, g represents the frame interval of image change in the video, and t represents the length of the time period during which the change occurs.

[0019] Preferably, the traceability query module extracts relevant video records from the database based on the input order query conditions and generates a visual traceability interface. The specific formula for calculating the coverage is as follows:

[0020] A = X / Y;

[0021] Where A represents coverage, X represents the number of viewed items, and Y represents the total number of order segments.

[0022] Preferably, the video matching module calculates the frame interval of image changes in the video based on the timestamp range in the order information by matching the corresponding video job record segment. The specific method includes comparing the gray values ​​of each pair of adjacent frames in the video pixel by pixel at the same position, calculating the difference in gray values, taking the absolute value, summing these difference values ​​at all pixel positions to obtain the intensity of image change between the two frames, repeating this calculation throughout the entire video frame sequence, and accumulating the amount of change between all adjacent frames to obtain the total amount of image change in the entire video within a time window, which is the frame interval of image changes in the video.

[0023] Preferably, the visual traceability interface includes a video preview window, an order details area, a keyframe snapshot area, and a timeline label navigation component.

[0024] Preferably, the database adopts a primary key-secondary key structure, storing the primary associated video segments and order numbers in the primary table, storing the secondary associated video segments in the secondary table, and setting association weights for each.

[0025] A method for tracing video job records based on order information association, employing the aforementioned video job record tracing system based on order information association, the method comprising:

[0026] S1. Obtain order information data containing order identifier and timestamp;

[0027] S2. Match the corresponding video job record segment based on the timestamp range in the order information;

[0028] S3. Establish an association mapping relationship between the order identifier and the matching video job record segment and store it in the database;

[0029] S4. Extract relevant video records from the database based on the input order query conditions and generate a visual traceability interface.

[0030] As can be seen from the above technical solution, the present invention has the following beneficial effects:

[0031] This video operation record traceability system and method based on order information association obtains order information data containing order identifiers and timestamps through an order information acquisition module. A video matching module matches corresponding video operation record segments based on the timestamp range in the order information. An association storage module establishes an association mapping relationship between the order identifier and the matched video operation record segments and stores it in a database. A traceability query module extracts associated video records from the database based on input order query conditions and generates a visual traceability interface. This effectively breaks down data barriers between the business system and the video system, enabling rapid location and reverse traceability from orders to videos. It can quickly extract corresponding video segments from the database based on structured query conditions, significantly shortening search time and reducing the risk of human error. It can intuitively display video content related to orders and can overlay functions such as event tags and timeline navigation, improving user operational efficiency and management experience. It achieves comprehensive backtracking of key processes, operators, product quality, and other elements, and is suitable for scenarios such as quality auditing, anomaly analysis, and after-sales traceability, improving production transparency and risk control capabilities. It has strong versatility and scalability. Attached Figure Description

[0032] Figure 1 This is a connection diagram of the system modules of the present invention;

[0033] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Figure 1 As shown, the present invention provides a video job record traceability system based on order information association, characterized in that the system includes:

[0035] The order information acquisition module is used to acquire order information data containing order identifiers and timestamps;

[0036] The video matching module is used to match corresponding video job record segments based on the timestamp range in the order information;

[0037] The associated storage module is used to establish an association mapping relationship between the order identifier and the matching video job record segment and store it in the database. This includes extracting the last digit of the order number, extracting the last digit of each associated video segment ID, calculating the absolute value of the two numbers, and if the absolute value does not exceed a set threshold, the video is used as the primary associated video segment; if the absolute value is higher than the set threshold, the video is used as the secondary associated video segment.

[0038] The traceability query module is used to extract related video records from the database based on the input order query conditions and generate a visual traceability interface. In the interface, the primary related video segments are displayed first, and the primary related video segments are displayed before the secondary related video segments. This includes recording the video segments viewed each time after the user completes the order traceability query and loads the video segment list, counting the number of viewed segments, reading the total number of segments in the order, and calculating the browsing coverage rate. If the coverage rate is lower than the set coverage rate threshold, the unviewed high-matching segments are displayed first. If the coverage rate is greater than or equal to the set coverage rate threshold, the viewed segments are displayed first.

[0039] The system uses orders as the core information source, collecting data including order identifiers and corresponding timestamps through the order information acquisition module. The video matching module uses timestamps as the matching criterion, retrieving and filtering video clips matching the time period in the video job records to achieve initial association. Subsequently, the association storage module further refines the matching results, comparing the last digit of the order number and the video clip ID, calculating the absolute value to determine the matching strength. If the absolute value is less than or equal to a threshold, it is marked as the primary associated video clip; if it is greater than the threshold, it is stored as a secondary associated clip, thus constructing a structured database mapping. The traceability query module retrieves database data after the user enters order information, displays video records in a visual interface, and calculates the ratio of the number of clips viewed by the user to all clips. The display strategy is adjusted based on a set coverage threshold: when the coverage is insufficient, the system recommends unviewed videos with high matching degrees to improve traceability; when the coverage meets the threshold, it prioritizes displaying clips previously viewed by the user, improving query efficiency and user experience.

[0040] This invention effectively improves the traceability efficiency and matching accuracy of job records by accurately matching and classifying order identifiers with video recordings across multiple dimensions. Utilizing the numerical similarity between the last two digits of the order number and the video ID as a supplementary matching criterion enhances the rationality and reliability of the association results. Simultaneously, the visual query interface dynamically adjusts its display strategy to ensure users can quickly access key recording segments, especially prioritizing users to view missed videos when coverage is insufficient, which facilitates comprehensive tracing. Furthermore, recording user click behavior and calculating browsing coverage effectively improves the system's interactivity and intelligent recommendation capabilities, optimizing the overall user experience.

[0041] The order information retrieval module obtains order information data containing order identifiers and timestamps, including:

[0042] Set the number of fields that each order should contain, check the actual number of fields filled in each order, calculate the order information integrity ratio, if the order information integrity ratio is greater than or equal to the set threshold, pass the order information to the video matching module, if the order information integrity ratio is less than the set threshold, mark it as severely obscured information, and wait for supplementation or manual annotation before processing.

[0043] In this implementation, the order information acquisition module is not only responsible for collecting basic order data, but also introduces an information integrity verification mechanism to ensure the data quality of subsequent modules. The specific process is as follows: First, the system presets a "standard value for the number of fields," which defines the standard number of fields that each valid order should contain, such as order number, customer name, start time, end time, and job type. After receiving each order record, the system automatically counts the actual number of fields filled in and compares it with the standard value to calculate the "order information integrity ratio." This ratio is the proportion obtained by dividing the actual number of fields filled in by the total number of fields set. A completeness threshold is set (the default value can be 80%) to distinguish whether the order data is complete enough for automatic video matching. When the ratio is greater than or equal to the threshold, it indicates that the order data is basically complete, and the system passes it to the video matching module for further processing; if it is lower than the threshold, the system marks the order as "severely obscured information" and suspends its subsequent processing until missing fields are supplemented or repaired through manual review and labeling mechanisms before resuming the matching operation. The threshold setting can be dynamically adjusted based on historical processing success rates; the initial recommended value is generally between 70% and 90%, determined by the fault tolerance requirements of the business scenario. This design effectively improves the stability of overall data processing and avoids matching errors or system anomalies caused by incomplete information.

[0044] By introducing a verification mechanism for the order information integrity ratio, this invention establishes a quality threshold at the initial stage of the system, effectively blocking low-quality or incomplete order data from flowing into downstream modules, avoiding wasted computing resources and incorrect matching. This strategy improves the system's robustness and data processing efficiency, while also providing a processing channel for subsequent data completion or manual annotation, enhancing the overall system's fault tolerance and intelligence level. Furthermore, the integrity threshold, as a key parameter, offers good flexibility and configurability, allowing users to dynamically adjust it according to their industry application characteristics, which is beneficial for its application in various business scenarios.

[0045] The order information acquisition module calculates the order information integrity ratio from the order information data containing order identifiers and timestamps using the following formula: R = B / C;

[0046] Where R represents the order information completeness ratio, B represents the number of fields actually filled in each order, and C represents the number of fields that each order should contain.

[0047] This implementation method, based on the aforementioned order information integrity assessment, further clarifies the calculation formula for the integrity ratio to ensure a unified and repeatable standard in system implementation. Upon receiving an order, the system first loads the total number of standard fields (C) that the order should contain from a predefined configuration file or database. These fields include, for example, order number, customer information, start time, end time, job content, and operator. Next, the system counts the number of non-empty fields already filled in the order record, denoted as B. The integrity ratio (R) is calculated using the formula R = B / C, and is a precise real value (ranging from 0 to 1) reflecting the completeness of the current order data. This formula is simple and clear, facilitating rapid system implementation and subsequent module calls. The integrity ratio (R) is then compared with a set threshold to determine if sufficient information is available to support video matching. This process serves as the first data quality checkpoint after order information enters the system, effectively ensuring system stability and data consistency.

[0048] By introducing a clear ratio formula R=B / C, the system implements a standardized and quantifiable order information integrity assessment mechanism. This method boasts simple calculation logic and high execution efficiency, making it particularly suitable for high-concurrency, high-data-volume business scenarios, avoiding errors introduced by subjective judgment. Furthermore, this mechanism can be extended into a data quality monitoring system, performing real-time screening and statistical analysis of the input dataset, providing a more stable data foundation for system operation. The explicit indicator calculation method also facilitates cross-module collaboration and problem localization; when processing anomalies occur, the specific missing field can be traced, improving system maintainability.

[0049] The video matching module matches corresponding video job record segments based on the timestamp range in the order information. This includes dividing each video segment into a 10-second window, calculating the frame interval of image changes in the video within each window, calculating the image change rate, setting an image change rate threshold, and determining that the video is an active action segment if the image change rate is higher than the set threshold. The module then searches for all segments within the order time range whose image change rate is higher than the set threshold and uses them as target segments, outputting the start and end times, video ID, and speed score of the corresponding segments.

[0050] In this implementation, the video matching module divides the entire video segment into several equal-length units by constructing a temporal sliding window mechanism. Each window has a duration of 10 seconds, ensuring that fine-grained image changes are captured within a short timeframe. Within each 10-second window, the system extracts consecutive frame images and calculates the inter-frame change interval, i.e., the sum of pixel differences or image histogram differences between adjacent image frames, used to quantify the dynamic range of the video image. Subsequently, the system divides the total frame interval change per unit time by the number of frames to obtain the "image change rate" index. The system presets an image change rate threshold (which can be initially set to an average frame interval change exceeding a specific value, such as Δ>15), used to distinguish static monitoring segments from "active segments" with obvious operations or actions. When the image change rate exceeds this threshold, the system identifies the video within that window as a segment with dense action. Based on this, the system further combines the timestamp range provided by the order to filter all video segments within the range that meet the criteria of an image change rate exceeding the threshold, and uses these as target candidate segments. The output includes the start time of each segment, the video ID, and a rate-based "speed score," which can be used for subsequent sorting and display optimization to improve the accuracy and usability of the matched videos.

[0051] By introducing an image change rate analysis mechanism, this implementation can quickly identify key action segments related to order processing behavior in massive video data, significantly improving the accuracy and contextual relevance of video matching. Compared to matching methods that rely solely on timestamps, this method increases the ability to discriminate dynamic features of videos, effectively filtering irrelevant static images and reducing user browsing interference. The introduction of an image change rate threshold provides an automated screening standard, avoiding subjective judgment and improving system robustness. Simultaneously, the segmented output speed score provides a quantitative basis for subsequent visualization, supporting a high-match-priority recommendation strategy, improving user traceability efficiency and system intelligence.

[0052] The video matching module calculates the image change rate based on the timestamp range in the order information by matching the corresponding video job record segments. The specific formula is: s = g / t;

[0053] Where s represents the rate of image change, g represents the frame interval of image change in the video, and t represents the length of the time period during which the change occurs.

[0054] In this implementation, when the video matching module analyzes image changes in each 10-second video window, it further specifies the use of the formula s=g / t to calculate the image change rate. Here, the variable g represents the frame interval of image changes within the window, i.e., the sum of image differences between all consecutive frames in the statistical window. This value can be obtained through pixel-level difference, image histogram difference, or a scoring model based on structural similarity (SSIM). t represents the length of the time period in which the change occurs, i.e., the fixed 10-second window length in this embodiment, or dynamically assigned based on the actual duration of the inter-frame change in a deformed mode. The s value obtained by this formula reflects the density of image changes, i.e., the intensity of dynamic changes in the video per unit time. This rate is used to compare with an image change rate threshold as a criterion for determining whether a certain time period belongs to an "active action segment." The system filters all video segments with s values ​​higher than the set threshold as target segments, providing high-quality candidate content for the subsequent tracing module, and adds an output speed score as a reference for matching degree ranking.

[0055] The introduction of this formula provides a standardized computational path for the dynamic analysis of system images, making the rate of image change quantifiable, repeatable, and comparable, significantly improving the accuracy of video screening and the consistency of system processing. Using the s=g / t model, the system can flexibly adjust the acquisition or value methods of g and t according to the rhythm of different operation processes, ensuring strong algorithm adaptability and high processing accuracy. Simultaneously, this mechanism facilitates batch processing and high-concurrency computation, making it suitable for deployment in real-time video analysis environments, improving overall operating efficiency and performance.

[0056] The traceability query module extracts relevant video records from the database based on the input order query conditions and generates a visual traceability interface. The specific formula for calculating the coverage rate is: A = X / Y;

[0057] Where A represents coverage, X represents the number of viewed items, and Y represents the total number of order segments.

[0058] In this implementation, the traceability query module records and evaluates each video browsing interaction performed by a user after entering the order traceability interface in real time. The system defines the total number of all associated video segments corresponding to the order as Y, and tracks each user's click, playback, or preview behavior on the segments in real time in the database, counting the number of segments actively accessed by the user, X. The system calculates the coverage rate A according to the formula A=X / Y, which serves as a quantitative indicator of the current user's browsing depth. The coverage rate reflects the completeness of the user's traceability of the order video data and is one of the key judgment criteria for the system's intelligent recommendation strategy. Once calculated, this value is compared with a preset coverage rate threshold. If A is lower than the threshold, the system will prioritize displaying highly matched video segments that the user has not yet accessed in the recommendation area to encourage further traceability; conversely, if A reaches or exceeds the threshold, the system will recommend more clicked segments to improve browsing efficiency and interactive experience.

[0059] This implementation method uses a standardized formula to calculate coverage, enabling quantifiable tracking of user behavior and improving the system's understanding and intelligent response to user tracing actions. Using the A=X / Y model, the system can accurately grasp the progress of tracing, effectively avoiding the risk of incomplete evidence due to missing fragments. Simultaneously, the coverage value is linked to the recommendation strategy, allowing flexible switching between "unviewed priority" and "backtracking reinforcement" display strategies, improving the comprehensiveness and efficiency of user tracing operations. This method also facilitates statistical analysis and user behavior research by system administrators, providing a basis for subsequent system optimization and strategy iteration.

[0060] The video matching module calculates the frame interval of image changes in the video by matching the corresponding video job record segments based on the timestamp range in the order information. The specific method includes comparing the gray values ​​of each pair of adjacent frames in the video pixel by pixel at the same position, calculating the difference in gray values, taking the absolute value, summing these difference values ​​at all pixel positions to obtain the intensity of image change between the two frames, repeating this calculation throughout the entire video frame sequence, and accumulating the amount of change between all adjacent frames to obtain the total amount of image change in the entire video within a time window, which is the frame interval of image changes in the video.

[0061] In this implementation, calculating the image change frame interval is a crucial foundational step for identifying video dynamics, and its core lies in quantifying the degree of visual change between adjacent image frames. The system first extracts video segments frame by frame and processes consecutive frame pairs. For each pair of adjacent frames, the system performs pixel-by-pixel grayscale difference analysis, extracting grayscale values ​​(typically integers between 0 and 255) for each pixel location and then calculating the absolute value of the grayscale difference between the same pixel and the two frames. The system sums these absolute values ​​across the entire image range to obtain the "image change intensity" of that frame pair. Subsequently, the system accumulates the change intensity between all adjacent frames within a set time window (e.g., 10 seconds) to obtain the image change frame interval g within that time period. This value serves as a key input parameter for subsequent image change rate calculations, reflecting the activity level of video scene changes within that time period, facilitating subsequent determination of whether a segment is a valid action segment.

[0062] This image change frame distance calculation method boasts high accuracy and interpretability, capable of meticulously capturing minute image changes in videos. It is particularly suitable for motion recognition needs in scenarios such as industrial monitoring, warehouse operations, and production processes. By employing a pixel-by-pixel grayscale difference method, the system avoids misjudgments caused by color changes, noise interference, or scene transitions, improving the accuracy of active segment recognition. Furthermore, this method does not rely on complex models, making it suitable for efficient operation on edge devices and resource-constrained platforms, achieving rapid response. The frame distance value, as a quantitative indicator, also facilitates subsequent rule threshold setting and algorithm expansion, exhibiting good versatility and compatibility.

[0063] The visual traceability interface includes a video preview window, an order details area, a keyframe snapshot area, and a timeline label navigation component. In this implementation, the visual traceability interface is constructed as a multi-area, multi-module collaborative information display platform, used to intuitively display the corresponding video job records and related information when users query orders. The main body of the interface is the "video preview window," which supports continuous playback, pause, dragging, and speed adjustment of matched segments, providing a visual dynamic playback function. Below or on the side is the "order details area," which displays the basic data of the current order, such as order number, customer name, time range, job type, etc., ensuring that users have a clear understanding of the order information during the traceability process. The keyframe snapshot area uses pre-extracted representative frame images to form a static thumbnail sequence, making it easy for users to quickly browse and locate key segments; the generation of keyframes can be based on image change rate analysis, motion recognition, or model filtering technology. The timeline label navigation component constructs a logical index structure for video segments, marking key events, user browsing history, system recommended segments, etc., as graphical labels. Users can quickly jump to the target segment location by clicking on the labels, thus creating an efficient "non-linear" traceability experience.

[0064] This interface design significantly improves the visualization and interaction efficiency of order traceability. The video preview window provides an immersive work review environment, while the keyframe snapshot area greatly reduces user location time and lowers the barrier to video viewing. The timeline tag navigation mechanism visualizes user interaction behavior and system recognition logic, forming a path-based browsing trajectory, while improving retrieval flexibility and operational convenience. The juxtaposition of information in the order details area and video area helps users maintain information integrity and contextual relevance during viewing, enhancing the overall system's user experience and task efficiency.

[0065] The database employs a primary key-secondary key structure, storing the primary associated video segments and order numbers in the primary table, and the secondary associated video segments in the secondary table, each with its own association weight. In this implementation, to achieve efficient management and storage of multi-level associations between videos and orders, the system uses a primary key-secondary key data modeling approach. In the database design, the system constructs two logically related data tables: a primary table and a secondary table. The primary table uses the order number as the primary key field (PrimaryKey), establishing a one-to-many primary association with video segments that have a high matching degree with that order (i.e., segments where the difference between the image change frame distance and the order number's last digit is less than a threshold), storing the start and end times, video IDs, and the "primary association weight" calculated by the system for these segments. The secondary table uses the combination of the order number and the video segment ID as the secondary key (Composite Key), used to store "secondary associated segments" with relatively low matching degrees but still of reference value, and assigns them a lower "secondary association weight." The master-slave table structure ensures data integrity through primary and foreign keys, while supporting fast indexing and batch queries. This allows the system to retrieve the master and slave segments separately during query, recommendation, and display processes, enabling differentiated data utilization strategies.

[0066] This master-slave table structure enhances the system's flexibility and efficiency in storing structured video data. By separating the master and slave tables, the system can prioritize accessing the master table when handling high-precision queries or data analysis tasks, reducing computational burden. When comprehensive scene coverage is required, the slave table provides richer background information, ensuring data integrity. The introduction of a correlation weight mechanism further strengthens the system's quantitative management of segment importance, facilitating subsequent sorting, recommendation logic, and statistical analysis, thus enhancing the system's responsiveness and customization capabilities.

[0067] Order query conditions include multiple fields such as order number, order creation time, job type, and operator number. In this implementation, the system supports multi-field combination queries to improve retrieval accuracy and flexibility when performing order traceability operations. When users enter order query conditions in the visual traceability interface, they can quickly locate a unique order record by entering the "order number"; they can also use the "order creation time" as a time filter to filter all orders within a specified time period, suitable for batch queries and historical backtracking scenarios. The "job type" field is used to distinguish different business processes, such as sorting, packing, and barcode scanning, which helps the system accurately match corresponding video clips and keyframe feature templates. The "operator number" field introduces a personnel dimension, allowing the system to trace job records by person, which is especially suitable for scenarios involving multi-person collaboration or responsibility attribution analysis. During the system query execution process, all input fields are converted into SQL or equivalent semantic query conditions, passed to the database layer for matching and filtering, and combined with timestamps and video association rules to retrieve the corresponding video clips and related data for display.

[0068] This implementation significantly enhances the query flexibility and scenario adaptability of the order traceability system by expanding the dimensions of query conditions. Different fields can be used individually or in combination, supporting users to pinpoint specific locations based on order numbers, as well as initiating fuzzy searches from multiple dimensions such as time, person, and job type, effectively covering various business needs from anomaly investigation to process review. Simultaneously, structured field input simplifies the operation process, improves user experience and system response efficiency, and provides robust support for large-scale order data management and accurate traceability.

[0069] like Figure 2 As shown, a method for tracing video job records based on order information association is also provided. This method employs the aforementioned video job record tracing system based on order information association, and includes:

[0070] S1. Obtain order information data containing order identifier and timestamp;

[0071] S2. Match the corresponding video job record segment based on the timestamp range in the order information;

[0072] S3. Establish an association mapping relationship between the order identifier and the matching video job record segment and store it in the database;

[0073] S4. Extract relevant video records from the database based on the input order query conditions and generate a visual traceability interface.

[0074] This method uses order information as the core of traceability and completes the entire process from data collection to video association and display in four steps. In step S1, the system collects structured order data containing unique identifiers (such as order numbers) and timestamps (creation time, operation time, etc.) through the order information acquisition module, and verifies its integrity to filter incomplete or erroneous records. In step S2, a timestamp range matching algorithm is used to filter all video segments covering the specified time range in the video database, and combined with image change rate analysis methods, active action segments are identified as candidate matching objects. In step S3, the system establishes a mapping relationship between the matching results and the orders, and uses a primary key-secondary key structure to record the primary and secondary associated segments respectively, while setting association weights to construct a hierarchical data mapping model. In step S4, the system receives user-input query conditions (such as order number, job type, operator, etc.), extracts the matched associated videos from the database, and constructs an interactive visual traceability interface through an integrated video preview window, order details area, keyframe snapshot area, and timeline navigation component, supporting quick browsing, navigation, and information comparison operations.

[0075] This method, through a step-by-step process design, makes the order-to-video traceability logic clearer and more standardized, possessing good operability and system implementability. Combining key technology modules such as dynamic image analysis, data integrity assessment, multi-dimensional query filtering, and visualization, it can significantly improve the efficiency and accuracy of order anomaly tracing, responsibility determination, and process review. The primary-secondary correlation and weighting mechanism enhances the expressive power of the system's data model, facilitating subsequent expansion with statistical analysis, intelligent recommendation, and other functions. The entire method is applicable to multiple scenarios such as logistics, manufacturing, e-commerce, and quality control, contributing to the construction of an intelligent, data-driven process management system.

[0076] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A video job record tracing system based on order information association, characterized by, The system comprises: An order information acquisition module for acquiring order information data containing order identifiers and timestamps; A video matching module for matching corresponding video job record segments based on the timestamp range in the order information; The method comprises dividing each video into 10-second windows, calculating the image change frame distance in each window, calculating the image change rate, setting an image change rate threshold, judging the video as an active action segment if the image change rate is higher than the set image change rate threshold, finding all segments with image change rates higher than the set image change rate threshold within the order time range as target segments, and outputting the corresponding segment start and end times, video ID, and speed score; An association storage module for establishing an association mapping relationship between the order identifier and the matched video job record segment and storing it in the database, comprising extracting the last digit of the order number, extracting the last digit of each associated video segment ID, calculating the absolute value of the two digits, and if the absolute value does not exceed the set threshold, regarding the video as the primary associated video segment, and if the absolute value is higher than the set threshold, regarding the video as the secondary associated video segment; A trace query module for extracting associated video records from the database according to the input order query condition and generating a visual trace interface, wherein the primary associated video segment is displayed preferentially, and the primary associated video segment is displayed preferentially over the secondary associated video segment, comprising recording each browsed video segment after the user completes the order trace query and loads the video segment list, counting the number of browsed segments, reading the total number of order segments, calculating the browsing coverage rate, and if the coverage rate is lower than the set coverage rate threshold, preferentially displaying the high-matching-degree segments that have not been browsed, and if the coverage rate is greater than or equal to the set coverage rate threshold, preferentially displaying the browsed segments.

2. The video job record tracing system based on order information association according to claim 1, characterized in that: The order information acquisition module acquires order information data containing order identifiers and timestamps, which comprises: Setting the number of fields that each order should contain, checking the number of fields actually filled in each order, calculating the order information integrity ratio, and if the order information integrity ratio is greater than or equal to the set threshold, passing the order information to the video matching module, and if the order information integrity ratio is less than the set threshold, marking it as serious information obstruction and waiting for supplement or manual annotation before processing.

3. The video job record traceability system based on order information association of claim 2, wherein: The specific formula for calculating the order information integrity ratio in the order information acquisition module acquiring order information data containing order identifiers and timestamps is: R = B / C; Where R represents the order information integrity ratio, B represents the number of fields actually filled in each order, and C represents the number of fields that each order should contain.

4. The video job record tracing system based on order information association according to claim 3, characterized in that: The specific formula for calculating the image change rate in the video matching module matching corresponding video job record segments based on the timestamp range in the order information is: s = g / t; Where s represents the image change rate, g represents the image change frame distance in the video, and t represents the length of the time period in which the change occurs.

5. The video job record tracing system based on order information association according to claim 1, characterized in that: The specific formula for calculating the coverage rate in the trace query module extracting associated video records from the database according to the input order query condition and generating a visual trace interface is: A = X / Y; Where A represents the coverage rate, X represents the number of browsed segments, and Y represents the total number of order segments.

6. The video job record tracing system based on order information association according to claim 1, characterized in that: The specific method for matching the image change frame distance in the video based on the timestamp range in the order information comprises: for each pair of adjacent frames in the video, comparing the image gray values at the same position pixel by pixel, calculating the difference of the gray values, taking the absolute value, adding up the difference values of all pixel positions, obtaining the image change intensity between the two frames, repeating the calculation for the entire video frame sequence, and accumulating the change amount between all adjacent frames to obtain the total image change amount of the entire video within a time window, which is the image change frame distance in the video.

7. The video job record traceability system based on order information association of claim 1, wherein: The visualized trace interface comprises a video preview window, an order detail area, a key frame snapshot area, and a time axis label navigation component.

8. The video job record tracing system based on order information association according to claim 1, characterized in that: The database adopts a primary key-secondary key structure, stores the primary associated video segments and order numbers in a primary table, stores the secondary associated video segments in a secondary table, and sets the association weights respectively.

9. A video job record tracing method based on order information association, using the video job record tracing system based on order information association according to any one of claims 1-8, characterized in that, The method comprises: S1, obtaining order information data containing an order identifier and a timestamp; S2, matching a corresponding video job record segment based on the timestamp range in the order information; S3, establishing an association mapping relationship between the order identifier and the matched video job record segment and storing it in a database; S4, extracting the associated video record from the database according to the input order query condition and generating a visualized trace interface.

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