Intelligent photographing light box automatic shooting system based on order number driving

Abstract

The application discloses an order number driving-based intelligent photographing light box automatic shooting system and relates to the technical field of automatic shooting image communication.The order number driving-based intelligent photographing light box automatic shooting system comprises a double-node transmission flow direction characteristic monitoring module, a double-node transmission strategy effect monitoring module and an image transmission efficiency monitoring module.Through double-node transmission flow direction characteristic analysis, the application determines whether to adopt a double-node transmission flow direction strategy based on the double-node transmission flow direction characteristic analysis result, determines whether to adopt link bandwidth dynamic adjustment based on the double-node transmission flow direction strategy effect checking result, and finally determines whether to adopt image transmission efficiency optimization based on the image data transmission efficiency evaluation result, so that the image data transmission timeliness is improved, and the problem of low timeliness of the intelligent photographing light box automatic shooting in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of automated image communication technology, and in particular to an automated shooting system for intelligent photo light boxes driven by order numbers. Background Technology

[0002] In the automated shooting process of the smart photo lightbox driven by order number, the automated shooting system first connects to the enterprise order management system, such as an ERP system, through an interface. The system automatically retrieves pending order data, including order numbers, from enterprise resource planning (ERP) systems. The order information includes the product model, quantity, and shooting requirements (such as angle, background, and resolution), and stores the order number as a unique identifier in the local database. Next, the automated shooting system retrieves the corresponding preset shooting parameter templates (such as light brightness and color temperature) from the database based on the order number. Simultaneously, it triggers the initialization process of the intelligent photo lightbox. The lightbox initiates a self-check via its built-in PLC controller, confirming that the lighting module, background motor, camera, and conveyor belt are functioning correctly. Then, the automated shooting system drives the conveyor belt to transport the previously sorted products associated with the order numbers to the positioning area within the lightbox. Once the photoelectric sensor at the bottom of the lightbox detects the product's arrival, it sends a signal to the automated shooting control center, stopping the conveyor belt. Simultaneously, a mechanical positioning device (such as a pneumatic pusher) calibrates the product to the shooting reference position. Afterward, the automated shooting control center automatically adjusts the lightbox status according to preset parameters: adjusting the LED lights in different areas via the dimming module. The brightness and color temperature of the LED (light-emitting diode) are controlled by a motor to switch the background (such as white, gray, etc.) and control the camera gimbal to adjust its position according to a preset angle sequence (such as front, 45° side, etc.). After each angle adjustment, multiple cameras are triggered to focus and take pictures. The image data is transmitted to the local storage server in real time and simultaneously uploaded to the cloud server for subsequent process operations. Then, the automated shooting system associates the captured pictures with the order number for storage and calls the image quality detection module to automatically inspect the images.

[0003] In the automated shooting process of a smart photo lightbox, existing technology first places the product to be photographed on a platform inside the lightbox. After a pressure sensor or photoelectric sensor under the platform detects the product, it triggers a positioning mechanism (such as a pneumatic pusher or electric positioning block) to calibrate the product to a preset shooting reference position, ensuring the object is within a suitable area between the top white light panel and the bottom backlight panel for subsequent lighting coverage. Next, a connection is established between a terminal device (such as a mobile phone) and the photo lightbox's Bluetooth module, enabling command interaction between the terminal and the central processing unit. Then, the terminal device sends a start command, which is transmitted via Bluetooth to the central processing unit, which then controls the lifting... The voltage circuit is activated and supplies power to the white light panel on the top, illuminating the subject and ensuring clear visibility of details. Next, based on preset image requirements, a background color command is sent to the central processing unit (CPU) via the terminal device. The CPU controls the backlight panel on the bottom to switch between different colors (such as red and green) to create a suitable image background. Then, a shooting command is sent via the terminal device, and the CPU coordinates to maintain stable illumination of the white light panel and a constant background color of the backlight panel, completing image acquisition. Finally, the acquired image is retrieved from the terminal device. If adjustments are needed, the background color or lighting parameter adjustment command can be sent repeatedly to trigger shooting again.

[0004] For example, the image communication device announced in Chinese invention patent application CN102035964B includes: First, the sending and receiving of IFP packets (Internet Facsimile Protocol Packets) is realized through T.38 communication function; then, the counting unit in the packet control unit performs data statistics on the received IFP packets, recording the total number of received packets and the number of lost packets respectively; subsequently, when the image communication control unit receives a request message from the sending device, its internal calculation unit calculates the IFP packet loss rate based on the ratio of the number of lost packets counted by the counting unit to the total number of received packets; finally, the response signal return unit of the image communication control unit performs a dual response operation for the request message, on the one hand returning a retraining negative signal to the sending device of the image information, and on the other hand simultaneously returning a message confirmation signal to the sending device, completing a complete communication interaction process.

[0005] For example, the Chinese invention patent application CN102577373B, concerning an image transmitting device and an image communication system, includes the following: First, the measurement unit of the image transmitting device starts working and performs real-time measurement on busy status-related information (such as channel occupancy rate, signal interference level, etc.) of the communication channel used for wireless communication of the device, acquiring basic data that reflects the current busy / idle status of the channel; then, the determination unit of the device receives the measurement results output by the measurement unit and uses these results to determine the actual status of the communication channel (such as whether it is in a high-load busy state, whether it has stable transmission conditions, etc.); subsequently, the transmitting unit of the image transmitting device, in conjunction with the channel status conclusion obtained by the determination unit, transmits image data wirelessly, ensuring that the image data is transmitted under the appropriate channel status.

[0006] The above-mentioned technology has at least the following technical problems:

[0007] In the automated shooting process of the smart photo lightbox driven by order number, the image data generated by multiple cameras taking pictures in sequence according to a preset angle needs to be transmitted to both the local storage server (company's backend database) and the cloud server simultaneously. The image transmission link needs to carry both data flows at the same time. In addition, because a single image has a large amount of image data to meet the requirements of product detail presentation (such as high resolution, high color depth, etc.), the data from multiple cameras taking pictures in sequence will be further accumulated on this basis. This causes the transmission link, which originally needs to carry both flows, to bear the additional pressure of accumulated data. As a result, the preset bandwidth of the transmission link is difficult to match the actual needs of dual data transmission, which leads to a decrease in image data transmission rate and data packets queuing for transmission. Ultimately, this results in low timeliness of image data transmission, which is a problem of low timeliness in the automated shooting of smart photo lightboxes. Summary of the Invention

[0008] To address the issue of low timeliness in automated shooting of intelligent photo light boxes in existing technologies, this invention provides an automated shooting system for intelligent photo light boxes driven by order numbers. The system includes: a dual-node transmission flow characteristic monitoring module, a dual-node transmission strategy effect monitoring module, and an image transmission efficiency monitoring module. The dual-node transmission flow characteristic monitoring module performs dual-node transmission flow characteristic analysis during the acquisition of automated shooting data based on order numbers. Based on the analysis results, it determines whether a dual-node transmission flow strategy is needed. This strategy reduces the concurrent bandwidth usage pressure on the transmission link, ensuring that the shooting data for each order accurately matches the dual-node transmission rhythm. The dual-node transmission strategy effect monitoring module monitors the image transmission efficiency during the dual-node transmission process. After the point-to-point transmission flow strategy is completed, the effect of the dual-node transmission flow strategy is verified. Based on the obtained dual-node transmission flow strategy effect verification results, it is determined whether dynamic adjustment of link bandwidth is required. Dynamic adjustment of link bandwidth is used to improve the effective bandwidth of the link, ensuring that the image data corresponding to the order number can still be transmitted to the dual nodes completely and in a timely manner after the bandwidth adjustment. The image transmission efficiency monitoring module is used to evaluate the image data transmission efficiency after the dual-node transmission flow feature analysis is qualified. Based on the obtained image data transmission efficiency evaluation results, it is determined whether image transmission efficiency optimization is required. Image transmission efficiency optimization is used to improve the image data transmission rate and reduce transmission latency fluctuations, ensuring that the image data associated with the order number can be transmitted quickly and stably, meeting the timely processing requirements of image data driven by orders.

[0009] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0010] 1. By performing dual-node transmission flow characteristic analysis, the results are used to determine whether a dual-node transmission flow strategy is needed. This helps to identify problems with insufficient dual-node transmission coordination in advance, reducing the surge in concurrent pressure on the transmission link caused by the direct superposition of dual data flows. The dual-node transmission flow strategy is used to reduce the concurrent bandwidth occupancy pressure on the transmission link, ensuring the orderliness and stability of image data transmission to the dual nodes. After the dual-node transmission flow strategy is implemented, its effectiveness is verified. The results are used to determine whether dynamic adjustment of link bandwidth is needed, helping to promptly identify problems with insufficient link bandwidth adaptation after the strategy is implemented, reducing packet queuing problems caused by bandwidth not matching accumulated data transmission demand, and ensuring the timeliness of dual-node data transmission. After the dual-node transmission flow characteristic analysis is qualified, image data transmission efficiency is evaluated. The results are used to determine whether image transmission efficiency optimization is needed, helping to solve the problem of low transmission timeliness caused by the superposition of multi-camera image data.

[0011] 2. By selectively using the dual-node transmission flow coordination index and CPU load utilization coefficient as multi-dimensional transmission status quantification data items, the system can comprehensively capture the real-time load status and transmission coordination of image data received by both local storage servers and cloud servers. This avoids the limitation of a single parameter only evaluating one dimension of dual-node transmission. By weighting and coupling the multi-dimensional transmission status quantification data items with the corresponding multi-dimensional transmission status influencing parameters, a dual-node transmission flow adaptability index is obtained. This index comprehensively considers the combined impact of dual-node transmission coordination and load status on transmission adaptability, avoiding bias caused by single data item evaluation. The dual-node transmission flow adaptability index more objectively reflects whether dual nodes are adapted to image data transmission requirements. Based on the dual-node transmission flow adaptability index, the system can determine whether to dynamically adjust the link bandwidth. This helps to specifically solve the problem of overlapping transmission link pressure and difficulty in matching the cumulative image data transmission requirements of multiple cameras caused by insufficient dual-node coordination or excessive load. This ensures the stability and timeliness of subsequent dual-node image data transmission and provides a reliable data evaluation basis for the efficient transmission of image data after automated shooting by multiple cameras.

[0012] 3. By selectively choosing the dual-node transmission flow adaptability coefficient, transmission delay fluctuation coefficient, and data transmission integrity verification rate as multi-dimensional transmission efficiency data items, this approach comprehensively captures the adaptability, link stability, and data integrity of dual-node image transmission. This avoids the limitation of a single parameter only evaluating one dimension of transmission. By weighting and coupling the multi-dimensional transmission efficiency data items with their corresponding transmission efficiency influencing parameters, an image data transmission efficiency evaluation index is obtained. This comprehensively considers the differentiated impact of each dimension on transmission efficiency, avoiding biases caused by single data item evaluations. The evaluation index objectively and comprehensively measures the overall transmission efficiency level of the dual nodes, determining whether the image data transmission efficiency evaluation index exceeds the preset transmission efficiency. If the threshold is met, the image transmission operation continues and monitoring continues. If the image data transmission efficiency evaluation index is not greater than the preset transmission efficiency qualified threshold, the image data transmission efficiency evaluation index is further judged to be greater than the preset transmission efficiency base threshold. If it is, the corresponding image is marked as a lightly compressed image and lightly compressed based on a lossless compression algorithm. Otherwise, the corresponding image is marked as a deeply compressed image and deeply compressed based on a lossy compression algorithm. It can selectively choose to prioritize reducing image quality or image data volume to reduce image quality waste or transmission congestion caused by image compression processing, and ensure the efficiency, stability and data integrity of dual-node image data transmission. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the structure of the intelligent photo lightbox automated shooting system based on order number driven provided in an embodiment of the present invention;

[0015] Figure 2 This is a flowchart summarizing the overall process of the intelligent photo lightbox automated shooting system driven by order number provided in this embodiment of the invention.

[0016] Figure 3 This is a logic diagram of the dual-node transmission flow strategy of the intelligent photo lightbox automated shooting system based on order number driven, provided in an embodiment of the present invention.

[0017] Figure 4 This is an interface diagram of the order information recognition system of the intelligent photo lightbox automated shooting system based on order number driven, provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] like Figure 1 The diagram shown is a structural schematic of an automated shooting system for an intelligent photo lightbox driven by order numbers, provided in an embodiment of the present invention. The automated shooting system includes: a dual-node transmission flow characteristic monitoring module, a dual-node transmission strategy effect monitoring module, and an image transmission efficiency monitoring module.

[0020] The dual-node transmission flow characteristic monitoring module is used to analyze the dual-node transmission flow characteristics during the automated shooting data acquisition process of the photo lightbox based on the order number. Based on the obtained dual-node transmission flow characteristic analysis results, it determines whether a dual-node transmission flow strategy needs to be adopted. The dual-node transmission flow strategy is used to reduce the pressure on the concurrent bandwidth of the transmission link and ensure the orderliness and stability of the transmission of image data bound to the order number to the dual nodes, ensuring that the shooting data corresponding to each order can accurately match the dual-node transmission rhythm. By monitoring the dual-node transmission flow characteristic analysis results, it helps to alleviate the problem of low transmission timeliness from the source and ensure the orderliness of the initial stage of dual-node transmission.

[0021] The dual-node transmission strategy effect monitoring module is used to verify the effect of the dual-node transmission flow strategy after it ends. Based on the obtained dual-node transmission flow strategy effect verification results, it determines whether dynamic adjustment of link bandwidth is required. Dynamic adjustment of link bandwidth is used to improve the effective bandwidth of the link, ensuring that the image data corresponding to the order number can still be transmitted to the dual nodes completely and in a timely manner after the bandwidth adjustment. By monitoring the dual-node transmission flow strategy effect verification results, it helps to reduce the data packet queuing problem caused by the bandwidth not matching the transmission demand of accumulated image data, further compensates for the impact of dual flow superposition on transmission timeliness, and ensures the integrity and timeliness of dual-node data reception.

[0022] The image transmission efficiency monitoring module is used to evaluate the image data transmission efficiency after the dual-node transmission flow characteristic analysis is qualified. Based on the obtained image data transmission efficiency evaluation results, it determines whether image transmission efficiency optimization is needed. Image transmission efficiency optimization is used to improve the image data transmission rate and reduce transmission latency fluctuations, ensuring that image data associated with the order number can be transmitted quickly and stably, meeting the timely processing requirements of image data driven by orders. By monitoring the image data transmission efficiency evaluation results, it helps to optimize and directly improve the transmission rate and reduce latency, effectively solving the problem of low transmission timeliness caused by the superposition of dual transmission flows and data accumulation, and adapting to the high-frequency data transmission requirements of intelligent photo light boxes with multiple cameras for automated shooting.

[0023] It should be noted that the automated shooting system for intelligent photo light boxes based on order number driven provided in this application was designed with a database that stores various settings data. The database includes, but is not limited to, preset dual-node transmission collaboration threshold, preset load balancing threshold, preset local server storage response latency threshold, preset maximum transmission link bandwidth threshold, preset maximum number of dynamic allocations of link bandwidth, and preset transmission efficiency qualification threshold. The preset values ​​are set directly by technical personnel.

[0024] In this embodiment, the dual-node transmission flow direction feature monitoring module, the dual-node transmission strategy effect monitoring module, and the image transmission efficiency monitoring module collaboratively constitute a full-process monitoring and control system for dual-node image transmission in the intelligent photo lightbox. Together, they address the low transmission timeliness issue caused by the accumulation of image data from multiple cameras and the superposition of dual transmission flows. Through data transmission and process integration, the three modules form a progressive closed-loop control process. The dual-node transmission flow direction feature monitoring module prioritizes analyzing the dual-node transmission coordination status based on the shooting task volume associated with the order number (e.g., the number of image sets to be taken in a single order). This provides an initial decision-making basis for subsequent transmission control that aligns with order business needs, reducing link concurrency pressure and the risk of affecting the timing of order image data transmission. The dual-node transmission strategy effect monitoring module receives the strategy implementation results output by the dual-node transmission flow direction feature monitoring module and improves the dual-node load and link pressure through dual-node transmission strategies. This system ensures that image data requiring priority transmission under order-driven conditions is transmitted, reducing bandwidth compatibility issues and minimizing the impact of transmission delays on subsequent image review and delivery processes. It also provides a more stable bandwidth environment for the subsequent image transmission efficiency monitoring module, reducing the risk of image data transmission efficiency assessment distortion or optimization measures failure due to bandwidth bottlenecks. The image transmission efficiency monitoring module further improves the transmission rate of order-related image data and reduces latency fluctuations, ensuring that image data corresponding to different orders arrives at both nodes on time, compensating for potential efficiency shortcomings of simple strategy control. These three aspects are progressive and interconnected, effectively solving the problem of low timeliness in image data transmission caused by the accumulation of image data captured by multiple cameras according to business needs and the superposition of dual transmission flows under order-driven conditions. This guarantees the orderly, stable, and efficient transmission of order-related image data to both the local storage server and the cloud server.

[0025] like Figure 2 The diagram shown is a general overview flowchart of the intelligent photo lightbox automated shooting system based on order number driven by an embodiment of the present invention. Figure 2It can be seen that during the automated shooting process of the intelligent photo lightbox, dual-node transmission flow characteristic analysis is performed and a dual-node transmission flow coordination index is obtained. It is determined whether the dual-node transmission flow coordination index is less than a preset dual-node transmission coordination threshold. If not, a dual-node transmission flow strategy is adopted; otherwise, image data transmission efficiency is evaluated and an image data transmission efficiency assessment index is obtained. It is determined whether the image data transmission efficiency assessment index is greater than a preset transmission efficiency pass threshold. If yes, image transmission operation continues and monitoring continues; otherwise, it is determined whether the image data transmission efficiency assessment index is greater than a preset transmission efficiency base threshold. If yes, light image compression processing is applied; otherwise, deep image compression processing is applied. After light and deep image compression processing, image data compression effect verification is performed and an image compression effect verification value is obtained. It is determined whether the image compression effect verification value is greater than a preset compression effect pass threshold. If yes, image transmission operation continues and monitoring continues; otherwise, image data fragmentation transmission is adopted. After image data fragmentation transmission, it is determined whether the image data transmission efficiency evaluation is qualified. If yes, image transmission operation continues and monitoring continues; otherwise, a data fragmentation failure alarm is sent.

[0026] Furthermore, dual-node transmission flow characteristic analysis is used to quantify the degree of flow coordination between the local storage server and the cloud server during image data transmission, ensuring good adaptability for synchronous response to transmission requests between the two nodes. The specific process is as follows: Obtain a dual-node transmission flow coordination index to quantify the coordination between the local storage server and the cloud server; determine whether the dual-node transmission flow coordination index is less than a preset dual-node transmission coordination threshold. If so, image data transmission efficiency is evaluated; otherwise, a dual-node transmission flow strategy is adopted. The preset dual-node transmission coordination threshold is represented by the average value of the dual-node transmission flow coordination index over a historical time period; dual-node transmission flow... The synergy index is represented by the ratio of the number of times the dual-node synchronous response image data transmission request is monitored by the dual-node transmission request synchronization monitoring instrument to the total number of transmission requests within a preset dual-node transmission time period. The preset dual-node transmission time period represents the time period for dual-node transmission flow characteristic analysis, and the ratio quantification represents the calculation of the ratio. The dual-node synchronous response image data transmission request represents the process in which the local storage server and the cloud server simultaneously start the data receiving process within the preset response time period when the image data transmission request generated after the camera completes image capture is issued. The preset response time period is shorter than the preset dual-node transmission time period.

[0027] In this embodiment, by analyzing the characteristics of dual-node transmission flow, the synchronous response of dual nodes to transmission requests can be accurately captured by the dual-node transmission request synchronization monitoring instrument. The degree of coordination of dual-node transmission flow is scientifically quantified, which effectively solves the problem of low timeliness of image data transmission caused by the superposition of dual-node transmission flow. It improves the coordination and adaptability of dual nodes in the image data transmission process and the resource utilization efficiency of the transmission link. From the initial stage of the transmission process, it ensures the orderliness and basic stability of the dual-node transmission of image data from the intelligent photo lightbox after automated shooting to the local storage server and the cloud server.

[0028] like Figure 3 The diagram shown is a logic diagram of the dual-node transmission flow strategy of the intelligent photo lightbox automated shooting system based on order number driven by an embodiment of the present invention. Figure 3 The process involves implementing a dual-node transmission flow strategy and obtaining the dual-node load deviation value. It is then determined whether the dual-node load deviation value is not less than a preset load balancing threshold. If so, the corresponding local storage server is marked as a high-priority node, the corresponding cloud server is marked as a low-priority node, and node image data transmission is triggered. Conversely, if the dual-node load deviation value is not less than a preset load balancing threshold, the corresponding local storage server is marked as a low-priority node, the corresponding cloud server is marked as a high-priority node, and node image data transmission is triggered. After the node image data transmission is completed, the effectiveness of the dual-node transmission flow strategy is verified, and the dual-node transmission flow adaptability index is obtained. It is then determined whether the dual-node transmission flow adaptability index is less than a preset dual-node transmission adaptability threshold. If not, dynamic adjustment of the link bandwidth is implemented. After the dynamic adjustment of the link bandwidth is completed, the effectiveness verification of the dual-node transmission flow strategy is determined. If so, image data transmission efficiency is evaluated; otherwise, a link bandwidth dynamic adjustment failure alarm is sent.

[0029] Furthermore, the specific process of the dual-node transmission flow strategy is as follows: Dual-node transmission priority ranking judgment: Determine whether the obtained dual-node load deviation value is not less than a preset load balancing threshold. If so, mark the corresponding local storage server as a high-priority node and the corresponding cloud server as a low-priority node, and trigger node image data transmission. Conversely, mark the corresponding local storage server as a low-priority node and the corresponding cloud server as a high-priority node, and trigger node image data transmission. The preset load balancing threshold is represented by the average value of the dual-node load deviation values ​​over a historical time period. The dual-node load deviation value is represented by quantifying the deviation between the local storage server's storage response latency utilization rate and the cloud server's storage response latency utilization rate during a preset dual-node transmission time period. The local storage server's storage response latency utilization rate is determined by the preset dual-node transmission... Within the transmission period, the average response time monitored by the server storage response monitoring instrument after the local storage server receives image data and completes storage writing is represented by the ratio of the local server storage response latency threshold to the preset local server storage response latency threshold. The cloud server storage response latency utilization rate is represented by the average response time monitored by the server storage response monitoring instrument after the cloud server receives image data and completes storage writing within the preset dual-node transmission period, and the preset cloud server storage response latency threshold is represented by the ratio of the cloud server storage response latency threshold to the preset cloud server storage response latency threshold. Among them, the deviation quantification represents the difference calculation. The preset local server storage response latency threshold is represented by the average of the average response time of the local storage server after receiving image data and completing storage writing within the historical time period. The preset cloud server storage response latency threshold is represented by the average of the average response time of the cloud server storage response latency threshold after receiving image data and completing storage writing within the historical time period.

[0030] Specifically, the process of node image data transmission is as follows: High-priority node image data transmission is initiated, and a pause signal is sent to the transmission interface of low-priority nodes. When a transmission completion confirmation signal is detected from the high-priority node at the receiving end, the high-priority node image data transmission is considered to have ended. After the high-priority node image data transmission ends, low-priority node image data transmission is initiated. When a transmission completion confirmation signal is detected from the low-priority node at the receiving end, the low-priority node image data transmission is considered to have ended. After the low-priority node image data transmission ends, the effectiveness of the dual-node transmission flow strategy is verified.

[0031] In this embodiment, the dual-node transmission flow strategy can accurately quantify the load status of the two nodes based on the storage response latency utilization rate, reducing the mismatch of transmission resources caused by ambiguity in the dual-node load judgment. By implementing phased execution of high-priority transmission followed by low-priority transmission, combined with the low-priority node transmission interface pause mechanism, the cumulative link pressure caused by synchronous transmission of the two nodes is effectively reduced, and the data packet queuing phenomenon of accumulated image data from multiple cameras during transmission is reduced. This improves the timeliness of image data transmission to the two nodes and the resource utilization efficiency of the transmission link, ensuring the stability of the local storage server and the cloud server receiving image data in an orderly manner according to their own load status, and providing an efficient data input foundation for subsequent image data storage and processing.

[0032] Furthermore, the effectiveness of the dual-node transmission flow strategy is verified to evaluate the real-time adaptation status of the local storage server and cloud server receiving image data after the implementation of the dual-node transmission flow strategy, ensuring the stability and efficiency of dual-node image data transmission. The specific process is as follows: Obtain multi-dimensional transmission state quantification data items to quantify the real-time load status of the local storage server and cloud server receiving image data. The results of weighted coupling processing of the multi-dimensional transmission state quantification data items and the corresponding multi-dimensional transmission state influence parameters are used as the dual-node transmission flow adaptation index. Here, weighted coupling processing means multiplying and then adding the results. The multi-dimensional transmission state quantification data items include the dual-node transmission flow coordination index and CPU (Central Processing Time) index. The CPU load utilization coefficient is represented by the sum of the CPU load utilization of the local storage server and the CPU load utilization of the cloud server. The local storage server CPU load utilization is quantified by comparing the average CPU utilization of the local storage server (monitored by a real-time server CPU load monitor) for image data reception tasks within a preset dual-node transmission period with a preset local server CPU load threshold. Similarly, the cloud storage server CPU load utilization is quantified by comparing the average CPU utilization of the cloud server (monitored by a real-time server CPU load monitor) for image data reception tasks within a preset dual-node transmission period with a preset cloud server CPU load threshold. The preset local server CPU load threshold is represented by the average CPU utilization of the local storage server for image data reception tasks over a historical time period. The preset cloud server CPU load threshold is represented by the average CPU utilization of the cloud server over a historical time period. The average CPU utilization rate of the server in processing image data reception tasks is represented by the average value. The multi-dimensional transmission status influence parameters include the dual-node transmission flow direction coordination influence value, which is used to quantify the influence weight of the dual-node transmission flow direction coordination index on the dual-node transmission flow direction adaptability index, and the CPU load utilization coefficient influence value, which is used to quantify the influence weight of the CPU load utilization coefficient on the dual-node transmission flow direction adaptability index. When the dual-node transmission flow direction coordination index decreases, it means that the proportion of times the dual nodes synchronously respond to image data transmission requests within the preset dual-node transmission time period decreases, and the consistency of the actions of the dual nodes in the process of receiving image data decreases. When the CPU load utilization coefficient decreases, it indicates that the CPU load pressure of the local storage server and the cloud server in processing image data reception tasks decreases together, and the dual nodes have more sufficient computing resources to respond to data reception needs in a timely manner. When both the dual-node transmission flow direction coordination index and the CPU load utilization coefficient decrease, the dual-node transmission flow direction adaptability index decreases.The system determines whether the dual-node transmission flow adaptability index is less than a preset dual-node transmission adaptability threshold. If so, image data transmission efficiency is evaluated; otherwise, dynamic adjustment of link bandwidth is implemented. The preset dual-node transmission adaptability threshold is represented by the average value of the dual-node transmission flow adaptability index over a historical time period.

[0033] It is important to note that the quantification of dual-node transmission flow adaptability indicators, the calculation of image data transmission efficiency evaluation indicators, and the determination of image compression effect verification values ​​involved in this solution all rely on a mapping group consisting of multiple mapping sets pre-set and stored in a database by professional technicians. This mapping group provides a matching basis for the weight coefficients and preset thresholds of multi-dimensional data items in each quantification process, ensuring the accuracy and reliability of each evaluation result. For example, professional technicians first extract a large amount of historical data in the dual-node image transmission scenario of the smart photo lightbox, covering parameter combinations of three core quantification scenarios: dual-node transmission flow strategy effect verification scenario, including dual-node transmission flow coordination. The evaluation scenario for image data transmission efficiency includes a combination of parameters such as the dual-node transmission flow direction adaptability index, transmission delay fluctuation coefficient, data transmission integrity verification rate, and their corresponding dual-node transmission flow direction adaptability, transmission delay fluctuation coefficient, and data transmission integrity verification rate. The evaluation scenario for image data compression effect involves a combination of parameters such as the image data transmission efficiency evaluation index, image quality retention rate, and compressed data reduction rate, and their corresponding image data transmission efficiency, image quality retention, and compressed data reduction rate.

[0034] Specifically, in the weight quantification stage, technicians assign values ​​to the impact of each data item on the corresponding evaluation results (dual-node transmission flow adaptability index, image data transmission efficiency evaluation index, and image compression effect verification value). They simultaneously record the actual effective values ​​of the weight impact values ​​under various historical scenarios. Through correlation analysis (such as Kendall rank correlation coefficient analysis), abnormal correlation data caused by temporary server failures, abnormal camera shooting, or sudden interference in the transmission link are eliminated. Statistically significant parameter combinations and their corresponding weights are retained. The final integrated mapping group uses a 0-1 value range to represent the weight coefficient percentage, achieving accurate matching of input parameter combinations and corresponding weights under different scenarios (such as multi-camera high-concurrency transmission and high-resolution image transmission). When the system performs dual-node transmission flow strategy effect verification, image data transmission efficiency evaluation, or image data compression effect verification, the appropriate weight impact value can be quickly retrieved from the database mapping group. This ensures that each quantified index objectively reflects the actual transmission status and guarantees the accuracy and reliability of the dual-node transmission flow adaptability index, image data transmission efficiency evaluation index, and image compression effect verification value.

[0035] In this embodiment, the effectiveness verification of the dual-node transmission flow strategy helps to accurately evaluate the actual implementation effect of the dual-node transmission flow strategy, promptly identify the dual-node adaptation imbalance problem that still exists after the strategy is implemented, reduce the continuous impact of adaptation problems on the image data transmission process, ensure the stability and adaptability of image data received by dual nodes, alleviate the link pressure caused by the accumulation of data from multiple cameras and dual transmission flows, improve the timeliness of dual nodes in receiving image data and the resource utilization efficiency of the transmission link, and provide reliable state support for subsequent improvements in image transmission efficiency.

[0036] Furthermore, the specific process of dynamic link bandwidth adjustment is as follows: The dual-node transmission flow adaptability index and effective image data transmission throughput are queried in the input bandwidth adjustment parameter mapping set to obtain the dynamic link bandwidth adjustment coefficient; the effective image data transmission throughput is represented by the ratio of the total amount of effective image data actually transmitted in the data link, monitored by the image data transmission volume monitor, to the duration of the preset dual-node transmission time period monitored by the timer. Effective image data is obtained by processing the data during dual data transmission using a data verification filtering and redundancy removal fusion algorithm, removing data verification packets, non-image redundant data, and transmission... The image data representation after erroneous data processing involves a data verification filtering and redundancy removal fusion algorithm. The specific process is as follows: First, the transmitted data undergoes integrity verification using a cyclic redundancy check algorithm, filtering out data that passes the verification. Then, a data type identification algorithm distinguishes image data from non-image redundant data (such as transmission control commands and temporary verification files), removing the latter. Finally, transmission error data caused by link interference is filtered out, resulting in clean and valid image data. The adjustment step size is used, with the adjustment range corresponding to the dynamic adjustment coefficient of the link bandwidth as the adjustment step size, progressively increasing the basic bandwidth threshold of the transmission link (after each adjustment of the basic bandwidth threshold of the transmission link is completed...). The dual-node transmission flow adaptability index is recalculated. If the dual-node transmission flow adaptability index is still not less than the preset dual-node transmission adaptability threshold, the adjusted transmission link base bandwidth threshold will be used as the initial value for the next adjustment and will be gradually increased. This helps reduce link load fluctuations or resource idleness caused by a one-time large adjustment of bandwidth. By combining small-step adjustments with real-time adaptability verification, it is ensured that the bandwidth increase always matches the actual transmission needs of the dual nodes, guaranteeing the stability of image data transmission. The transmission link base bandwidth threshold is less than the preset maximum transmission link bandwidth threshold. The transmission link base bandwidth threshold represents the value before the dynamic adjustment of the link bandwidth. The link bandwidth monitor tracks the actual bandwidth of the transmission link. The maximum threshold for the preset transmission link bandwidth is set in advance by a preset user. It continuously monitors the dual-node transmission flow adaptability index. When the dual-node transmission flow adaptability index is less than the preset dual-node transmission adaptability threshold, image data transmission efficiency is evaluated. Otherwise, dynamic adjustment of the link bandwidth continues. If the number of dynamic adjustments exceeds the preset maximum number of dynamic allocations, and the dual-node transmission flow adaptability index is still not less than the preset dual-node transmission adaptability threshold, a link bandwidth dynamic allocation failure alarm is sent. The preset maximum number of dynamic allocations is set in advance by a preset user.

[0037] It is important to note that the dynamic adjustment of link bandwidth and the adjustment of image data fragmentation involved in this solution both rely on two core mapping sets that are pre-built and stored in the database by designated personnel. These include a bandwidth adjustment parameter mapping set and a fragmentation adjustment mapping set. The core function of these two mapping sets is to establish a precise correspondence between input parameters and corresponding adjustment decision results. The bandwidth adjustment parameter mapping set is associated with the dual-node transmission flow adaptability index, the effective image data transmission throughput, and the dynamic adjustment coefficient of the link bandwidth. The fragmentation adjustment mapping set is associated with the image data transmission efficiency evaluation index, the available bandwidth of the link during the preset data transmission time period, and the fragmentation adjustment ratio. This provides decision support that can be directly queried and invoked for determining the dynamic adjustment step size of the link bandwidth and the step-by-step optimization of the amount of image data fragmentation, avoiding subjective blindness in the adjustment process.

[0038] Specifically, the formation of the two types of mapping sets relies on the historical data statistical analysis and parameter verification of the dual-node image transmission scenario of the smart photo lightbox. First, a large amount of specific combination data of input parameters and output results corresponding to the two types of mapping sets in actual transmission scenarios is extracted. For the bandwidth adjustment parameter mapping set, it is necessary to collect the combination of dual-node transmission flow adaptability index, effective image data transmission throughput and corresponding link bandwidth dynamic adjustment coefficient. For the segmented adjustment mapping set, it is necessary to collect the combination of image data transmission efficiency evaluation index, available bandwidth of the link during the preset data transmission time period and corresponding segmented adjustment ratio. Then, the input parameters in each type of mapping set are assigned weight values ​​based on their influence on the output results, and the actual effective parameter matching results in each scenario are recorded simultaneously. Then, with the help of correlation analysis (such as Pearson correlation coefficient analysis method), abnormal correlation information caused by temporary server failures and sudden interference in the transmission link is filtered out, and the correspondence between the input parameter combinations and output results with statistical significance is retained. Finally, all effective information is integrated to form the bandwidth adjustment parameter mapping set and the segmented adjustment mapping set, which can be directly queried and the corresponding output results can be obtained through input parameters, ensuring that the link bandwidth adjustment and image data segmentation links can quickly call accurate parameters.

[0039] In this embodiment, by dynamically adjusting the link bandwidth, the problem of insufficient link bandwidth adaptation caused by the accumulation of multi-camera image data and the superposition of dual transmission flows can be addressed. Relying on the bandwidth adjustment parameter mapping set and the step-by-step adjustment mechanism, the transmission link bandwidth can be accurately matched with the actual transmission needs of the two nodes. This helps maintain the stable operation of the transmission link, improves the effective bandwidth utilization of the transmission link and the timeliness of the response of the two nodes to receive image data, reduces image data transmission delay and data packet queuing caused by insufficient bandwidth, and ensures the continuity and stability of the large-capacity image data generated by multiple cameras during the transmission to the two nodes. This enables the local storage server and the cloud server to continuously and efficiently receive image data.

[0040] Furthermore, the image data transmission efficiency assessment quantifies the overall efficiency level of image data transmission between local storage servers and cloud servers, ensuring the high efficiency, stability, and data integrity of dual-node image data transmission. The specific process is as follows: Multi-dimensional transmission efficiency data items are obtained. The results of weighted coupling processing of these data items and their corresponding transmission efficiency influencing parameters are used as the image data transmission efficiency evaluation index to measure overall image data transmission efficiency. The multi-dimensional transmission efficiency data items include the dual-node transmission flow adaptability coefficient, transmission delay fluctuation coefficient, and data transmission integrity verification rate. The dual-node transmission flow adaptability coefficient is represented by the reciprocal of the dual-node transmission flow adaptability index. The transmission delay fluctuation coefficient is represented by the ratio of the preset image data transmission time period to the actual image data transmission time period, used to measure... The degree of latency fluctuation during dual-node image data transmission reflects the stability of the transmission link. The image data transmission duration is represented by the time interval monitored by the time interval timer, from the time the camera completes the capture of a single image to the time interval from when both the local storage server and the cloud server receive the image data and return a reception confirmation signal. The preset image data transmission duration is represented by the average image data transmission duration over a historical period. The data transmission integrity verification rate is represented by the ratio of the total amount of dual-node image data (such as product display detail image data received by the local storage server and order-related user photo image data received by the cloud server) successfully verified based on the cyclic redundancy check algorithm to the total amount of transmitted data during the preset data transmission period. This is used to evaluate the data integrity during dual-node image data transmission.

[0041] Specifically, the transmission efficiency impact parameters include the dual-node transmission flow direction adaptation impact value (used to quantify the influence weight of the dual-node transmission flow direction adaptability index on the image data transmission efficiency evaluation index), the transmission delay fluctuation coefficient impact value (used to quantify the influence weight of the transmission delay fluctuation coefficient on the image data transmission efficiency evaluation index), and the data transmission integrity verification impact value (used to quantify the influence weight of the data transmission integrity verification rate on the image data transmission efficiency evaluation index). The preset data transmission time period represents the time period for evaluating image data transmission efficiency. When the transmission delay fluctuation coefficient increases, it indicates that the degree of delay fluctuation during dual-node image data transmission is reduced, the impact of external interference or load fluctuations on the transmission link is reduced, the link stability is significantly enhanced, and problems such as data packet queuing and transmission interruption caused by sudden increases in delay during data transmission are reduced, thereby increasing the amount of effective image data and improving data transmission integrity verification. As the data transmission integrity verification rate increases, the proportion of undamaged and unlost valid data in the image data received by both nodes rises. Combined with the synergistic support of the dual-node transmission flow adaptability coefficient and the stable guarantee of the transmission delay fluctuation coefficient, this ultimately leads to an increase in the image data transmission efficiency evaluation index. The process involves determining whether the image data transmission efficiency evaluation index exceeds a preset transmission efficiency qualification threshold. If so, image transmission operations continue and monitoring is ongoing. The preset transmission efficiency qualification threshold is represented by the average value of the image data transmission efficiency evaluation index over a historical time period. If the image data transmission efficiency evaluation index does not exceed the preset transmission efficiency qualification threshold, the process continues to determine whether the image data transmission efficiency evaluation index exceeds a preset basic transmission efficiency threshold. If so, the corresponding image is marked as a lightly compressed image and compressed using a lossless compression algorithm, such as PNG (Portable Network Objects). Light image compression is performed using lossless compression algorithms such as Graphics (Portable Network Graphics) and FLAC (Free Lossless Audio Codec) image compression. Conversely, images with lower compression are marked as deeply compressed and then subjected to deep image compression based on lossy compression algorithms, such as JPEG (Joint Photographic Experts Group) lossy compression and WebP (WebP Image Format) dynamic compression. The preset transmission efficiency threshold is represented by the average value of image data transmission efficiency evaluation indicators that do not exceed the preset transmission efficiency qualification threshold over a historical period. The preset transmission efficiency threshold does not exceed the preset transmission efficiency qualification threshold. After light and deep image compression are completed, the image data compression effect is verified.

[0042] In this embodiment, by evaluating the efficiency of image data transmission, the core issues of dual-node image transmission in dimensions such as adaptability, latency stability, and data integrity can be accurately identified. This helps to reduce image quality loss or efficiency waste caused by indiscriminate adjustments, improves the dynamic control capability and optimization accuracy of dual-node image data transmission, and ensures the stability of image data transmission to dual nodes, the validity of image data, and the balance between compressed image quality and transmission efficiency in multi-camera high-concurrency shooting scenarios. It solves the problems of low transmission efficiency, significant latency fluctuations, and easy data transmission damage caused by the accumulation of multi-camera image data and the superposition of dual-node transmission flows.

[0043] Furthermore, the specific process for verifying the image data compression effect is as follows: Obtain multi-dimensional compression effect evaluation data items for comprehensively assessing the degree of image quality preservation and transmission adaptability after compression. The results of weighted coupling processing of the multi-dimensional compression effect evaluation data items and their corresponding multi-dimensional compression effect influence parameters are used as the image compression effect verification value. The multi-dimensional compression effect evaluation data items include image data transmission efficiency evaluation indicators, image quality preservation rate, and data reduction rate after compression. The image quality preservation rate is represented by the ratio of the compressed image signal-to-noise ratio to the original image signal-to-noise ratio, used to evaluate the degree of image quality preservation during image data compression. The signal-to-noise ratio (SNR) of the compressed image is expressed as the ratio of the average signal power of the compressed image monitored by the image signal power monitoring instrument to the average noise power of the compressed image monitored by the image noise power monitoring instrument. The original image SNR is expressed as the ratio of the average signal power of the original image monitored by the image signal power monitoring instrument to the average noise power of the original image monitored by the image noise power monitoring instrument. The data reduction rate after compression is quantified by quantifying the deviation between the total amount of original image data monitored by the real-time image data volume monitoring instrument and the total amount of compressed image data, and then quantifying this deviation as a percentage of the total amount of original image data. The results indicate that the image data compression method is used to evaluate the effect of image data compression on reducing image data volume. When the image data transmission efficiency evaluation index increases, it indicates that the compressed image data has stronger adaptability and higher transmission efficiency in the dual-node transmission process. When the image quality preservation rate increases, it means that the compression process reduces the degree of damage to image quality, improving the quality of the compression effect. When the data volume reduction rate after compression increases, it indicates that the compression operation has a more significant effect on reducing the image data volume, further reducing the bandwidth occupancy pressure of the transmission link. These three factors work synergistically from three dimensions: transmission adaptation, image quality preservation, and data reduction, resulting in an increase in the image compression effect verification value. The multi-dimensional compression effect has a significant impact on... The impact parameters include the image data transmission efficiency impact value, which quantifies the influence of the image data transmission efficiency evaluation index on the image compression effect verification value; the image quality retention impact value, which quantifies the influence of the image quality retention rate on the image compression effect verification value; and the compressed data reduction impact value, which quantifies the influence of the compressed data reduction rate on the image compression effect verification value. The system determines whether the image compression effect verification value is greater than a preset compression effect qualification threshold. If it is, the image transmission operation continues and monitoring is maintained; otherwise, image data is transmitted in fragments. The preset compression effect qualification threshold is represented by the average value of the image compression effect verification values ​​over a historical time period.

[0044] In this embodiment, by verifying the image data compression effect, a comprehensive control over the image compression effect can be achieved by relying on multi-dimensional evaluation data items, breaking the limitations of single-dimensional evaluation. This ensures the integrity of the image quality after compression to meet the needs of subsequent image processing and display, while also ensuring that the compressed data has good transmission adaptability to reduce link pressure. At the same time, it reduces transmission stuttering, data loss, or image quality degradation caused by poor compression effect, ensuring the efficiency, stability, and image quality reliability of image data received by local storage servers and cloud servers.

[0045] Furthermore, the specific process of image data fragmented transmission is as follows: Image data transmission efficiency evaluation indicators and the available bandwidth of the link during the preset data transmission time period are queried in the fragmented adjustment mapping set to obtain the fragmented adjustment ratio; the adjustment step size is used as the adjustment magnitude corresponding to the fragmented adjustment ratio to gradually reduce the image data fragment size (after each reduction adjustment, the fragmented bandwidth adaptation is recalculated; if the fragmented bandwidth adaptation is still not greater than the preset fragmented bandwidth adaptation threshold, the adjusted image data fragment size is used as the initial value for the next adjustment to continue gradually reducing the size). This helps to reduce problems such as a sudden increase in the number of fragments, increased transmission management complexity, and increased transmission overhead caused by a one-time large reduction in fragmented data size. By combining small-step adjustment with real-time fragmented bandwidth adaptation verification, it ensures that the image data fragment size accurately matches the actual link size. Available transmission capacity steadily improves the adaptation efficiency of fragmented data and links; the image data to be transmitted is segmented level by level and the corresponding segmentation index table is recorded, and the segmentation bandwidth adaptation is continuously monitored. When the segmentation bandwidth adaptation exceeds the preset segmentation bandwidth adaptation threshold, segmentation is stopped and segmented data transmission is performed. The preset segmentation bandwidth adaptation threshold is represented by the average value of segmentation bandwidth adaptation over a historical time period. The segmentation index table contains a segmentation baseline check code; the segmentation bandwidth adaptation is represented by the result of quantifying the ratio of the segmented data volume to the available bandwidth of the link during the preset data transmission time period. It is used to measure the degree of matching between the current segmented data volume and the actual available transmission capacity of the link. The segmented data volume is represented by the actual data volume of a single image data segment after each level of segmentation, and the available bandwidth of the link represents the bandwidth resources in the transmission link that can actually be used for image data segmented transmission within the preset data transmission time period.

[0046] Specifically, the process of fragmented data transmission is as follows: Based on the fragment index table, image data fragments are transmitted to the receiving server in ascending order of the fragment sequence number recorded in the fragment index table. The receiving server continuously monitors the comparison between the checksum of each fragment and the base checksum of the fragment in the fragment index table. When the data transmission time of a fragment exceeds the preset maximum fragment reception time, if the fragment data is still not received by the receiving server, the fragment data is retransmitted; otherwise, the fragmented data transmission continues while monitoring continues. Retransmission of fragment data means that the sending end retrieves the original data of that fragment from the fragment index table and clears the original data. After a transmission failure record is received, the corresponding fragment data is retransmitted to the receiving server in the order of the original fragment sequence number corresponding to the fragment index table. The number of times fragment data is retransmitted shall not exceed the preset maximum number of retransmissions, which shall be set in advance by a preset user. The preset maximum fragment reception time represents the duration corresponding to the time period for fragment data transmission. After the image data fragment transmission is completed, the image data transmission efficiency evaluation index is reacquired. If the image data transmission efficiency evaluation index is still not greater than the preset transmission efficiency qualified threshold, a data fragment failure alarm is sent. Otherwise, the image transmission operation continues and monitoring continues.

[0047] In this embodiment, by transmitting image data in segments, dynamic adaptation between image data and transmission link resources can be achieved even when image compression is inadequate. This effectively solves the transmission adaptation problem caused by poor compression of large-size image data. Relying on the segmented adjustment mapping set and the step-by-step adjustment mechanism, the utilization rate of available bandwidth of the link is improved, transmission congestion and resource waste are reduced, and the orderliness, integrity and reliability of image data segmented transmission are ensured. The risk of transmission failure due to transmission timeout or data loss is reduced, and ultimately, it is ensured that large-capacity image data accumulated by multiple cameras can still be transmitted stably and efficiently to the local storage server and the cloud server when link resources are limited or compression is insufficient, providing reliable support for dual-node data reception and subsequent business processing.

[0048] like Figure 4 The image shown is an interface diagram of the order information recognition system of the intelligent photo lightbox automated shooting system based on order number driven according to an embodiment of the present invention. Figure 4It can be seen that, driven by the order number, the automated shooting status of the unique item corresponding to the order number and the efficiency adjustment of image data upload can be monitored in real time. After the order information recognition system identifies the corresponding order number, it quickly retrieves the preset shooting parameters of the corresponding item from the background database. Based on the retrieved preset parameters, the camera focus and white balance parameters are adjusted. After the shooting parameters are adjusted, the automated shooting command is triggered, and the image data transmission efficiency and network status are monitored in real time, realizing full-process visual monitoring from order number association, product location recognition, automated shooting to image data upload and transmission.

[0049] In summary, by conducting dual-node transmission flow characteristic analysis and determining whether a dual-node transmission flow strategy is needed based on the obtained analysis results, it is helpful to identify problems of insufficient dual-node transmission coordination in advance, reduce the sudden increase in transmission link concurrency pressure caused by the direct superposition of dual data flows, and reduce the pressure of concurrent bandwidth occupation on the transmission link. The dual-node transmission flow strategy is used to reduce the pressure of concurrent bandwidth occupation on the transmission link and ensure the orderliness and stability of image data transmission to the dual nodes. After the dual-node transmission flow strategy is implemented, the effect of the dual-node transmission flow strategy is verified. Based on the obtained effect verification results, it is determined whether dynamic adjustment of link bandwidth is needed. This helps to promptly identify link bandwidth mismatch problems that still exist after the strategy is implemented, reduce data packet queuing problems caused by bandwidth not matching the accumulated data transmission demand, and ensure the timeliness of dual-node data transmission. After the dual-node transmission flow characteristic analysis is qualified, the image data transmission efficiency is evaluated. Based on the obtained image data transmission efficiency evaluation results, it is determined whether image transmission efficiency optimization is needed, which helps to solve the problem of low transmission timeliness caused by the superposition of multi-camera image data.

[0050] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0051] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0052] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0053] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0054] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0055] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0056] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An automated shooting system for intelligent photo lightboxes driven by order numbers, characterized in that: include: Dual-node transmission flow characteristic monitoring module, dual-node transmission strategy effect monitoring module, and image transmission efficiency monitoring module: The dual-node transmission flow direction feature monitoring module is used to perform dual-node transmission flow direction feature analysis during the process of obtaining automated shooting data of the photo lightbox based on the order number. Based on the obtained dual-node transmission flow direction feature analysis results, it is determined whether a dual-node transmission flow direction strategy needs to be adopted. The dual-node transmission flow direction strategy is used to reduce the pressure of concurrent bandwidth occupation of the transmission link and ensure that the shooting data corresponding to each order can accurately match the dual-node transmission rhythm. The dual-node transmission strategy effect monitoring module is used to verify the effect of the dual-node transmission flow strategy after it ends. Based on the obtained dual-node transmission flow strategy effect verification result, it determines whether dynamic adjustment of link bandwidth is required. The dynamic adjustment of link bandwidth is used to improve the effective bandwidth of the link and ensure that the image data corresponding to the order number can still be transmitted to the dual nodes completely and in a timely manner after the bandwidth adjustment. The image transmission efficiency monitoring module is used to evaluate the image data transmission efficiency after the dual-node transmission flow characteristic analysis is qualified. Based on the obtained image data transmission efficiency evaluation results, it determines whether image transmission efficiency optimization is needed. The image transmission efficiency optimization is used to improve the image data transmission rate and reduce transmission delay fluctuations, ensuring that the image data associated with the order number can be transmitted quickly and stably, meeting the timely processing requirements of image data driven by orders.

2. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 1, characterized in that, The dual-node transmission flow characteristic analysis is used to ensure that the two nodes have good adaptability in synchronously responding to transmission requests. The specific process is as follows: Obtain a dual-node transmission flow coordination index to quantify the coordination of transmission flow between local storage servers and cloud servers; Determine whether the dual-node transmission flow coordination index is less than the preset dual-node transmission coordination threshold. If it is, then evaluate the image data transmission efficiency; otherwise, adopt the dual-node transmission flow strategy. The dual-node transmission flow coordination index is represented by the ratio of the number of times the dual nodes synchronously respond to image data transmission requests to the total number of transmission requests within a preset dual-node transmission time period, where the preset dual-node transmission time period represents the time period for performing dual-node transmission flow characteristic analysis.

3. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 2, characterized in that, The specific process of the dual-node transmission flow strategy is as follows: Determine whether the obtained dual-node load deviation value is not less than the preset load balancing threshold. If it is, mark the corresponding local storage server as a high-priority node and the corresponding cloud server as a low-priority node, and trigger node image data transmission. Otherwise, mark the corresponding local storage server as a low-priority node and the corresponding cloud server as a high-priority node, and trigger node image data transmission. The dual-node load deviation value is represented by quantifying the deviation between the local storage server's storage response latency utilization rate and the cloud server's storage response latency utilization rate during a preset dual-node transmission time period.

4. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 3, characterized in that, The specific process of transmitting the node image data is as follows: Initiate high-priority node image data transmission and simultaneously send a pause signal to the transmission interface of low-priority nodes. When the high-priority node at the receiving end sends a transmission completion confirmation signal, it is determined that the high-priority node image data transmission has ended. After the high-priority node image data transmission is completed, the low-priority node image data transmission is started. When the low-priority node at the receiving end sends a transmission completion confirmation signal, it is determined that the low-priority node image data transmission has ended. After the low-priority node image data transmission is completed, the effectiveness of the dual-node transmission flow strategy is verified.

5. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 4, characterized in that, The effectiveness verification of the dual-node transmission flow strategy is used to ensure the stability and efficiency of dual-node image data transmission. The specific process is as follows: Obtain multi-dimensional transmission status quantification data items for quantifying the real-time load status of image data received by two nodes, local storage server and cloud server. The result of weighted coupling processing of multi-dimensional transmission status quantification data items and corresponding multi-dimensional transmission status influence parameters is used as the dual-node transmission flow adaptability index. Determine whether the dual-node transmission flow adaptability index is less than the preset dual-node transmission adaptability threshold. If so, evaluate the image data transmission efficiency; otherwise, dynamically adjust the link bandwidth. The multi-dimensional transmission status quantification data items include the dual-node transmission flow coordination index and the CPU load utilization coefficient. The multi-dimensional transmission status influence parameters include the dual-node transmission flow direction coordination influence value, which quantifies the influence weight of the dual-node transmission flow direction coordination index on the dual-node transmission flow direction adaptability index, and the CPU load utilization coefficient influence value, which quantifies the influence weight of the CPU load utilization coefficient on the dual-node transmission flow direction adaptability index.

6. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 5, characterized in that, The specific process of dynamically adjusting the link bandwidth is as follows: The dual-node transmission flow adaptability index and effective image data transmission throughput input bandwidth adjustment parameter are queried in a centralized manner to obtain the dynamic adjustment coefficient of the link bandwidth. The effective image data transmission throughput is represented by the ratio of the total amount of effective image data actually transmitted in the data link to the duration of the preset dual-node transmission time period. The basic bandwidth threshold of the transmission link is gradually increased by using the adjustment range corresponding to the dynamic adjustment coefficient of the link bandwidth as the adjustment step size. The system continuously monitors the dual-node transmission flow adaptability index. When the dual-node transmission flow adaptability index is less than the preset dual-node transmission adaptability threshold, the image data transmission efficiency is evaluated. Otherwise, the link bandwidth is dynamically adjusted. If the number of dynamic link bandwidth adjustments exceeds the preset maximum number of dynamic link bandwidth allocations, and the dual-node transmission flow adaptability index is still not less than the preset dual-node transmission adaptability threshold, a link bandwidth dynamic allocation failure alarm is sent.

7. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 1, characterized in that, The image data transmission efficiency evaluation is used to ensure the high efficiency, stability, and data integrity of dual-node image data transmission. The specific process is as follows: The multidimensional transmission efficiency data item is obtained, and the result of weighted coupling processing of the multidimensional transmission efficiency data item and the corresponding transmission efficiency influencing parameters is used as the image data transmission efficiency evaluation index to measure the overall image data transmission efficiency. Determine whether the image data transmission efficiency evaluation index is greater than the preset transmission efficiency qualification threshold. If so, continue to execute the image transmission operation and continuously monitor it. If the image data transmission efficiency evaluation index is not greater than the preset transmission efficiency qualified threshold, then it is further determined whether the image data transmission efficiency evaluation index is greater than the preset transmission efficiency basic threshold. If it is, the corresponding image is marked as a lightly compressed image and lightly compressed. Otherwise, the corresponding image is marked as a deeply compressed image and deeply compressed. After light and deep image compression processing are completed, the image data compression effect is verified. The multidimensional transmission efficiency data items include the dual-node transmission flow adaptability coefficient, transmission delay fluctuation coefficient, and data transmission integrity verification rate. The transmission delay fluctuation coefficient is used to measure the stability of the transmission link during the dual-node image data transmission process; The data transmission integrity verification rate is used to evaluate the data integrity during the dual-node image data transmission process. The transmission efficiency impact parameters include the dual-node transmission flow direction adaptation impact value, which quantifies the impact weight of the dual-node transmission flow direction adaptability index on the image data transmission efficiency evaluation index; the transmission delay fluctuation coefficient impact value, which quantifies the impact weight of the transmission delay fluctuation coefficient on the image data transmission efficiency evaluation index; and the data transmission integrity verification impact value, which quantifies the impact weight of the data transmission integrity verification rate on the image data transmission efficiency evaluation index.

8. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 7, characterized in that, The specific process for verifying the image data compression effect is as follows: Obtain multi-dimensional compression effect evaluation data items for comprehensively evaluating the image quality preservation and transmission adaptability of compressed images. The result of weighted coupling processing of the multi-dimensional compression effect evaluation data items and the corresponding multi-dimensional compression effect influence parameters is used as the image compression effect verification value to measure whether the image data compression effect meets the dual-node transmission adaptability requirements. The multi-dimensional compression effect influence parameters include the image data transmission efficiency influence value used to quantify the influence weight of the image data transmission efficiency evaluation index on the image compression effect verification value, the image quality retention influence value used to quantify the influence weight of the image quality retention rate on the image compression effect verification value, and the compressed data reduction influence value used to quantify the influence weight of the compressed data reduction rate on the image compression effect verification value. Determine if the image compression effect verification value is greater than the preset compression effect qualification threshold. If it is, continue to perform the image transmission operation and continue to monitor. Otherwise, take the form of image data fragment transmission. The multi-dimensional compression effect evaluation data items include image data transmission efficiency evaluation indicators, image quality retention rate, and data volume reduction rate after compression. The image quality retention rate is used to evaluate the degree of image quality retention during image data compression. The compressed data reduction rate is used to evaluate the effect of image data compression on reducing the amount of image data.

9. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 8, characterized in that, The specific process of transmitting image data in segments is as follows: The image data transmission efficiency evaluation index and the available bandwidth of the link during the preset data transmission time period are queried in the segment adjustment mapping set to obtain the segment adjustment ratio. The adjustment step size is used as the adjustment range corresponding to the segment adjustment ratio, and the amount of image data segment data is reduced step by step. The image data to be transmitted is segmented level by level and the corresponding segmentation index table is recorded. The segmentation bandwidth adaptability is continuously monitored. When the segmentation bandwidth adaptability is greater than the preset segmentation bandwidth adaptability threshold, segmentation is stopped and segmented data transmission is performed. The fragment bandwidth adaptability is used to measure the degree of matching between the current fragment data volume and the actual available transmission capacity of the link.

10. The automated shooting system for intelligent photo lightboxes driven by order numbers according to claim 9, characterized in that, The specific process of the fragmented data transmission is as follows: Based on the fragment index table, the image data fragments are transmitted to the receiving server in ascending order of the fragment sequence number recorded in the fragment index table. When it is detected that the data transmission time of the fragment exceeds the preset maximum fragment reception time, if the fragment data is still not received by the receiving server, the fragment data is retransmitted; otherwise, the fragment data transmission continues and monitoring continues. After clearing the original transmission failure record in the receiving server, the corresponding fragment data is transmitted to the receiving server again in the order of the original fragment sequence number corresponding to the retransmission fragment in the fragment index table. After the image data fragmentation transmission is completed, the image data transmission efficiency evaluation index is reacquired. If the image data transmission efficiency evaluation index is still not greater than the preset transmission efficiency qualified threshold, a data fragmentation failure alarm is sent; otherwise, the image transmission operation continues and monitoring continues.

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