Product identification tracking management method based on two-dimensional code

Abstract

The invention discloses a product identification and tracking management method based on a two-dimensional code, and relates to the technical field of product identification and tracking management, and the method specifically comprises the following steps: recording the circulation information of a product through scanning the two-dimensional code when the product passes through each circulation node; extracting all information contained in the circulation information of the product, analyzing each piece of information in the circulation information of the product, and dividing each piece of information in the circulation information of the product into high necessary information and low necessary information according to an analysis result; the highly necessary information is processed and stored preferentially; and continuously monitoring the system load and the handling capacity change condition of the high necessary information, judging whether the system can process the low necessary information or not, and determining whether to process the information to be processed or not according to the judgment result. According to the method, timely processing and storage of key data can be ensured, the system load is reduced, the data processing and storage efficiency is improved, and meanwhile, whether the system can process low-necessity information or not can be dynamically judged, so that resource allocation is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of product identification, tracking and management, and in particular to a product identification, tracking and management method based on a two-dimensional code. Background Art

[0002] Product identification and tracking management is a systematic method that gives each product a unique identifier (such as a QR code) and records and tracks its information in real time throughout its life cycle. This method covers all aspects of the product from production, warehousing, transportation to sales, ensuring that every step has detailed data records. The purpose of product identification and tracking management is to improve supply chain transparency and management efficiency, ensure product quality and safety, prevent counterfeit and shoddy products from entering the market, and provide consumers with traceable product information to enhance their sense of trust; in addition, it also helps companies optimize inventory management, speed up logistics, improve after-sales service, and improve operational decision-making capabilities through data analysis, thereby comprehensively enhancing corporate competitiveness.

[0003] The existing product identification and tracking management method based on QR code generates a QR code containing a unique identifier at the beginning of product production, prints it into a label and attaches it to the product or packaging, and then records the product's circulation process by scanning the QR code in various links such as production, warehousing, transportation and sales. This information is stored in the database to form a complete product tracking record. Consumers and enterprises can obtain product detailed information and circulation records by scanning the QR code to achieve full life cycle management of the product from production to consumption. The specific steps of the existing product identification and tracking management method based on QR code include: first, collect and enter the basic information of the product at the beginning of product production, then generate a unique QR code, and then print the QR code into a label and attach it to the product or packaging; in various links such as production, warehousing, transportation and sales, the product's circulation process is recorded by scanning the QR code. This information is stored in the database in real time to form a complete product tracking record; consumers and enterprises can query the detailed information and circulation records of the product by scanning the QR code to ensure the full life cycle management of the product from production to consumption, while enhancing anti-counterfeiting functions and intelligent management, and improving management efficiency and decision-making capabilities through big data analysis.

[0004] The prior art has the following deficiencies:

[0005] In a large-scale production and circulation environment, since each product has a unique QR code, it means that each product will generate a detailed data record at each circulation node (such as production, warehousing, transportation, sales, etc.), including timestamp, location, status, operator and other information, resulting in the generation of massive data. When the system needs to process and store this huge and complex data, there will be problems such as slow data processing and insufficient storage space. In this case, the response speed and performance of the system may be seriously affected, especially when querying and analyzing data, which is prone to delays and freezes, ultimately affecting the real-time tracking management and decision-making efficiency of the product, and increasing the operating costs and management difficulties of the enterprise.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a product identification, tracking and management method based on a two-dimensional code to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a product identification and tracking management method based on a two-dimensional code, specifically comprising the following steps:

[0009] When the product passes through each circulation node, the circulation information of the product is recorded by scanning the QR code;

[0010] Extract all information contained in the circulation information of the product, analyze each piece of information in the circulation information of the product, and classify each piece of information in the circulation information of the product into high-necessity information and low-necessity information according to the analysis result;

[0011] Prioritize processing and storage of high-necessity information;

[0012] Continuously monitor the changes in system load and the amount of processing of high-necessity information, determine whether the system can process low-necessity information, and decide whether to process the pending information based on the determination results;

[0013] On each circulation node, repeat the above steps while optimizing system performance.

[0014] Preferably, extracting all information contained in the circulation information of the product, analyzing each piece of information in the circulation information of the product, and dividing each piece of information in the circulation information of the product into highly necessary information and less necessary information according to the analysis result, specifically comprises the following steps:

[0015] Extract all the information contained in the product's circulation information;

[0016] Obtaining the operation data and timeliness data of each piece of information in the product circulation information, analyzing the operation data and timeliness data, and generating a circulation complexity coefficient and an information timeliness coefficient respectively;

[0017] A necessity evaluation model is constructed for the generated circulation complexity coefficient and information timeliness coefficient to generate a necessary coefficient. The generated necessary coefficient is compared with a pre-set necessary coefficient threshold to evaluate the necessity of each piece of information in the product circulation information, and based on the evaluation results, each piece of information in the product circulation information is divided into high-necessity information and low-necessity information.

[0018] Preferably, the acquisition logic of the circulation complexity coefficient and the information timeliness coefficient is as follows:

[0019] Extract the operation data of each information in the product circulation information, including the number of times each information is accessed at each circulation node, the weight coefficient of the operation type involved, and the number of changes. The number of times each information is accessed at each circulation node, the weight coefficient of the operation type involved, and the number of changes are marked as , and , Indicates The information in The number of times a circulation node is visited, Indicates The information in The weight coefficient of the operation type involved in each circulation node, Indicates The information in The number of changes of circulation nodes, , , and All are positive integers;

[0020] Calculate the product circulation information The circulation complexity coefficient of information is calculated as follows:

[0021] In the formula, For product circulation information The complexity coefficient of information circulation;

[0022] Extract the timeliness data of each piece of information in the product circulation information, including the access timestamp and reference time of each piece of information at each circulation node, and convert the access timestamp and reference time of each piece of information at each circulation node into and , Indicates The information in The access timestamp of the circulation node, Indicates The base time of the information, that is, The creation time of the information;

[0023] Determine the timeliness attenuation coefficient of each piece of information. After determination, calibrate the timeliness attenuation coefficient of each piece of information as , Indicates The timeliness decay coefficient of each piece of information;

[0024] Calculate the product circulation information The information timeliness coefficient of each piece of information is calculated by the following formula:

[0025] In the formula, For product circulation information The information timeliness coefficient of a piece of information.

[0026] Preferably, the first The circulation complexity coefficient and information timeliness coefficient of each piece of information are used to build a necessity evaluation model, and the first product circulation information is generated by weighted summation. Necessary coefficient of information , and compare the generated necessary coefficient with the preset necessary coefficient threshold Compare and evaluate the necessity of each piece of information in the product circulation information, and divide each piece of information in the product circulation information into high-necessity information and low-necessity information according to the evaluation results. The specific division is as follows:

[0027] like , the product circulation information The necessity of a piece of information is low, and the information is classified as low necessity information;

[0028] like , the product circulation information The necessity of a piece of information is high, and the information is classified as high necessity information.

[0029] Preferably, high-necessity information is processed and stored preferentially, which specifically includes the following steps: after high-necessity information is identified, the system marks the information as high-priority data and immediately triggers the data processing module to process it, including verification, conversion, and cleaning steps to ensure the integrity and accuracy of the data; after processing, the high-necessity information is stored in a high-performance database or distributed storage system, and a regular backup mechanism and multi-copy storage strategy are set up to ensure data security and availability; at the same time, the system records the processing and storage status of the high-necessity information in real time in the log for subsequent query and audit use.

[0030] Preferably, continuously monitoring the changes in the system load and the processing volume of the high-necessity information, determining whether the system can process the low-necessity information, and determining whether to process the information to be processed based on the determination result, specifically includes the following steps:

[0031] Obtaining system load data when the system processes and stores high-necessity information and data on the amount of high-necessity information processed;

[0032] Analyze the acquired system load data and high-necessary information processing volume data to generate a system load tolerance index and a high-necessary information processing volume variation coefficient respectively;

[0033] Determine the system load bearing index threshold and the high necessary information processing volume change coefficient threshold;

[0034] The generated system load bearing index and high necessary information processing volume change coefficient are compared with the system load bearing index threshold and high necessary information processing volume change coefficient threshold respectively to determine whether the system can process low necessary information. The specific determination is as follows:

[0035] If the generated system load bearing index is greater than the system load bearing index threshold and the generated high necessary information processing amount change coefficient is less than the high necessary information processing amount change coefficient threshold, then the system can process low necessary information;

[0036] If the generated system load bearing index is greater than the system load bearing index threshold and the generated high necessary information processing amount change coefficient is less than the high necessary information processing amount change coefficient threshold, the system cannot process the low necessary information.

[0037] Preferably, the logic for obtaining the system load bearing index is as follows:

[0038] Obtain the system load data when the system is processing and storing high-necessity information, including the system's CPU usage, memory usage, and disk usage at different times within T when the system is processing and storing high-necessity information. The system's CPU usage, memory usage, and disk usage at different times within T when the system is processing and storing high-necessity information are calibrated as , and , The number indicating the CPU usage, memory usage, and disk usage of the system at different times during the time T when the system is processing and storing high-necessity information. , is a positive integer;

[0039] Calculate the system load bearing index. The specific calculation formula is as follows:

[0040]

[0041] In the formula, is the system load index;

[0042] The logic for obtaining the coefficient of variation of high necessary information processing volume is as follows:

[0043] Obtain the processing volume data of high-necessity information when the system processes and stores high-necessity information, including the processing queue length and corresponding processing time at each moment in a certain period of time when the system processes and stores high-necessity information, and use the function to convert the processing queue length and corresponding processing time at each moment in a certain period of time when the system processes and stores high-necessity information into time series and ,in is a time variable, and the time period is defined as ;

[0044] Use the processing queue length and processing time The derivative of the calculation of the processing volume change rate , according to the formula: ;

[0045] Calculate the coefficient of variation of high necessary information processing volume. The specific calculation formula is as follows:

[0046]

[0047] In the formula, The coefficient of variation of the required information processing volume is high.

[0048] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0049] 1. The present invention can effectively solve the problem of processing and storing massive amounts of data generated in large-scale production and circulation environments. First, by analyzing and classifying product circulation information, the information is divided into high-necessity information and low-necessity information, and high-necessity information is processed and stored first. This method ensures the timely processing and storage of key data, reduces system load, and improves data processing and storage efficiency. The system monitors the changes in the processing volume of high-necessity information in real time, and dynamically determines whether the system can process low-necessity information, thereby optimizing resource allocation, avoiding system overload, and improving overall response speed and performance.

[0050] 2. The present invention collects and analyzes the load data of the system in real time when processing high-necessary information, accurately assesses the current load status and processing capacity of the system, and through these indexes, the system can dynamically adjust the processing strategy to ensure that low-necessary information is processed when the system load is low and the processing pressure is low, thereby avoiding processing too much data under high load conditions, preventing the system from slowing down response speed and performance degradation. This not only improves the stability and reliability of the system, but also reduces delays and freezes during data query and analysis.

[0051] 3. The present invention repeats the above steps on each circulation node, and further improves the data processing efficiency and system response speed by optimizing algorithms and performance tuning. The system can maintain efficient and stable operation in large-scale production and circulation environments, realize comprehensive product tracking management, and improve supply chain transparency and management efficiency. At the same time, through distributed storage systems and regular backup mechanisms, data security and availability are ensured, reducing enterprise operating costs and management difficulties, and enhancing enterprise competitiveness. These technical effects and advantages combined significantly improve the data processing capabilities and management efficiency of enterprises in large-scale production and circulation environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0053] Figure 1 The present invention is a flowchart of a product identification, tracking and management method based on a QR code. DETAILED DESCRIPTION

[0054] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0055] The present invention provides Figure 1 The product identification and tracking management method based on a QR code shown in the figure specifically includes the following steps:

[0056] When the product passes through each circulation node, the circulation information of the product is recorded by scanning the QR code, including timestamp, location, status and operator information;

[0057] When a product passes through each circulation node, the system records the circulation information of the product by scanning the QR code. This QR code contains the unique identification of the product. When scanning, the system automatically obtains and records the specific information of the product at the node, including the timestamp of the scanning time, the specific location of the product, the current status of the product (for example, to be inspected, shipped out, etc.), and the identity information of the staff who operate the product. This information is transmitted to the central database in real time for storage for subsequent query and analysis.

[0058] Distribution nodes refer to the important links or locations that a product passes through during the entire supply chain process. These nodes can include different workstations on the production line, storage locations in the warehouse, transfer stations during transportation, distribution centers, and final sales points. At each distribution node, the QR code can be scanned and recorded in a variety of ways, including using handheld scanning devices, fixed scanners, cameras on smartphones or tablets, and even scanning systems on automated assembly lines. Each scan will automatically collect and record relevant distribution information to ensure the integrity of product information and real-time tracking.

[0059] Extract all information contained in the circulation information of the product, analyze each piece of information in the circulation information of the product, and classify each piece of information in the circulation information of the product into high-necessity information and low-necessity information according to the analysis result;

[0060] In this embodiment, all information contained in the circulation information of the product is extracted, each piece of information in the circulation information of the product is analyzed, and each piece of information in the circulation information of the product is divided into high-necessity information and low-necessity information according to the analysis result, which specifically includes the following steps:

[0061] Extract all the information contained in the product's circulation information;

[0062] Extracting all the information contained in the circulation information of a product can be achieved in the following ways: first, deploy a QR code scanning device at each circulation node to obtain the unique identification of the product by scanning the QR code; then, use Internet of Things (IoT) devices and sensors to collect product-related data in real time, including location, timestamp, operation type (such as loading, unloading, inspection, etc.), environmental conditions (such as temperature, humidity, etc.) and operator information; all collected data are transmitted to the central database via a wireless network or wired network, and data collection software (such as ETL tools) is used to integrate and pre-process the data to ensure the accuracy and consistency of the data; in addition, the relevant information of production, warehousing, transportation and sales can be automatically synchronized by integrating the enterprise resource planning (ERP) system and the warehouse management system (WMS), and the API interface and data exchange standards (such as JSON, XML) are used for real-time transmission and update of data, and finally unified management and analysis are carried out through a data analysis platform (such as a big data platform or a cloud computing platform), so as to comprehensively extract and integrate the circulation information of the product.

[0063] Obtaining the operation data and timeliness data of each piece of information in the product circulation information, analyzing the operation data and timeliness data, and generating a circulation complexity coefficient and an information timeliness coefficient respectively;

[0064] A necessity evaluation model is constructed for the generated circulation complexity coefficient and information timeliness coefficient to generate a necessary coefficient. The generated necessary coefficient is compared with a pre-set necessary coefficient threshold to evaluate the necessity of each piece of information in the product circulation information, and based on the evaluation results, each piece of information in the product circulation information is divided into high-necessity information and low-necessity information.

[0065] The pre-set threshold of the necessary coefficient can be determined in the following ways: first, collect historical data, including the operation data and sensitivity data of different products at various circulation nodes, as well as the corresponding circulation complexity coefficient and information criticality index; then, use data analysis and machine learning algorithms (such as cluster analysis, regression analysis) to analyze the historical data to identify the characteristics and patterns of high-necessary information and low-necessary information; then, according to the analysis results and business needs, set the initial threshold, and verify and optimize it through simulation or A / B testing to ensure that the threshold can effectively distinguish between high-necessary information and low-necessary information; on this basis, build a necessity assessment model, and use optimization algorithms (such as genetic algorithms, particle swarm algorithms) to continuously adjust and optimize the threshold to achieve the best classification effect; finally, solidify the optimized necessary coefficient threshold into the system, manage it through configuration files or database parameters, and continuously monitor and adjust it during actual operation to ensure the applicability and accuracy of the threshold, so as to achieve accurate assessment and classification of the necessity of product circulation information.

[0066] In this embodiment, the logic for obtaining the circulation complexity coefficient and the information timeliness coefficient is as follows:

[0067] Extract the operation data of each information in the product circulation information, including the number of times each information is accessed at each circulation node, the weight coefficient of the operation type involved, and the number of changes. The number of times each information is accessed at each circulation node, the weight coefficient of the operation type involved, and the number of changes are marked as , and , Indicates The information in The number of times a circulation node is visited, Indicates The information in The weight coefficient of the operation type involved in each circulation node, Indicates The information in The number of changes of circulation nodes, , , and All are positive integers;

[0068] Extracting the operational data of each piece of information in product circulation information can be achieved in the following ways: first, deploy a QR code scanning device on each circulation node to obtain the unique identification of the product by scanning the QR code; then, use the system log to record the details of each time the information is accessed, including the access time and the operation type, such as reading, modifying or deleting; the system automatically assigns corresponding weight coefficients according to different operation types, with the weight of the read operation being 1, the weight of the modify operation being 2, and the weight of the delete operation being 3; in addition, the number of times the information changes is recorded through the timestamp in the log, and the frequency of changes in the status or position of the information in each circulation node is counted; all data are transmitted to the central database via a wireless network or a wired network, and data integration and preprocessing are performed using data acquisition and processing software (such as ETL tools) to ensure the accuracy and consistency of the data, and finally unified management and analysis are performed through a data analysis platform, so as to comprehensively extract and integrate the operational data of each piece of information.

[0069] The number of times the information is accessed at each circulation node , refers to the The information in The total number of times product circulation information is read or modified in a circulation node. This data is obtained through system log records. Whenever information is read by the system (such as scanning a QR code to confirm the location or status of a product) or operated (such as modifying information to update the product status), the corresponding operation time and type are recorded in the log. For example, in a storage node, if the information is read 3 times to confirm inventory and modified 2 times to update the inbound and outbound status, the number of information accesses to the node is 5. This number reflects the activity and importance of the information in the node.

[0070] Weight coefficients related to the type of operation , which means that different weight values ​​are assigned according to the complexity and importance of the operation type. The operation types mainly include read, modify and delete. Each operation is assigned a different weight coefficient according to its impact on system load and data accuracy. For example, the read operation is usually only used to view information, and its weight is low, set to 1; the modification operation requires updating information and may involve data consistency issues, so its weight is high, set to 2; the deletion operation removes information and has the greatest impact on the system and data, so its weight is the highest, set to 3. The setting of weight coefficients helps to reflect the different impacts of operation types on information complexity.

[0071] Number of changes , refers to the The information in The number of changes in a circulation node. Whenever the circulation information of a product changes in a node (for example, the location changes, the status is updated), the system log will record the timestamp and specific content of these changes. By calculating these timestamps, the number of times the information has changed in the node can be determined. For example, in a transportation node, the information may be updated multiple times in different states such as departure, passage, and arrival. Assuming that these state changes are recorded 5 times, the number of information changes in the node is 5 times. The number of changes reflects the degree of dynamic change of information in the circulation process and is an important indicator for evaluating information complexity.

[0072] Calculate the product circulation information The circulation complexity coefficient of information is calculated as follows:

[0073] In the formula, For product circulation information The complexity coefficient of information flow.

[0074] Product circulation information The size of the circulation complexity coefficient of an information directly affects the evaluation of the necessity of the information in the entire circulation process. Specifically, the circulation complexity coefficient reflects the activity and operational complexity of the information by comprehensively considering the number of times the information is accessed at each circulation node, the weight coefficient of the operation type involved, and the number of changes. A higher circulation complexity coefficient means that the information is frequently accessed, involves complex operation types, and changes frequently during the circulation process, indicating that it is of high importance in product management, monitoring, and tracking. Therefore, in the necessity evaluation model, information with a higher circulation complexity coefficient is more likely to be classified as high-necessity information, which needs to be processed and stored first to ensure the transparency and accuracy of product circulation, thereby improving supply chain management efficiency and product quality traceability. A lower circulation complexity coefficient indicates that the information is relatively inactive during the circulation process, the operation is simple, and the changes are not frequent. It may be classified as low-necessity information, and the processing priority is relatively low. Through this evaluation method, the allocation of data processing and storage resources can be optimized, and system performance and management efficiency can be improved.

[0075] Extract the timeliness data of each piece of information in the product circulation information, including the access timestamp and reference time of each piece of information at each circulation node, and convert the access timestamp and reference time of each piece of information at each circulation node into and , Indicates The information in The access timestamp of the circulation node, Indicates The base time of the information, that is, The creation time of the information;

[0076] Extracting time-effective data from product circulation information, including the access timestamps and reference times of each piece of information at each circulation node, can be achieved by deploying a QR code scanning device and a timestamp recording system at each circulation node. Every time a product passes through a circulation node, the QR code is scanned to obtain the unique identification of the product, and the timestamp of the operation is automatically recorded. These timestamps and product identification information are transmitted to the central database, and data integration is performed through data acquisition and processing software (such as ETL tools) to ensure data accuracy and consistency. In addition, a reference time can be set during the product production stage, and this time can be used as the creation time of the product, and the reference time information can be recorded and managed through an ERP system or other enterprise management system.

[0077] The access timestamps of each piece of information at each circulation node refer to the specific time points when the product was operated at different circulation nodes. These timestamps are recorded through system logs. Each time a product is scanned or operated, the system generates a log entry to record the specific time and node location of the operation. These timestamp data are transmitted to the central database through the network and sorted and stored by data processing software. The access timestamp can reflect the specific time path of the product in the circulation process and provide basic data for timeliness analysis.

[0078] The reference time is the reference time point set when a product or information is created, usually the time when the product is produced or entered. During the product production stage, the system automatically records the creation time of the product and stores it in the database as the reference time. These reference time data can be managed and accessed in a unified manner through the ERP system or other enterprise management system. The reference time is used to calculate the timeliness of information and serves as a reference point for all access timestamps to help evaluate the time sensitivity of information during circulation.

[0079] Determine the timeliness attenuation coefficient of each piece of information. After determination, calibrate the timeliness attenuation coefficient of each piece of information as , Indicates The timeliness decay coefficient of each piece of information;

[0080] Determining the timeliness decay coefficient of each piece of information can be achieved through a combination of data analysis and business rule setting. First, collect a large amount of historical data, including the access frequency, time interval and changes of information at each circulation node, and then use machine learning algorithms (such as linear regression, decision trees, etc.) to analyze the data and identify the time sensitivity patterns of different types of information. According to the analysis results, set an initial timeliness decay coefficient for each type of information. Next, verify the effectiveness of these coefficients through simulation or A / B testing, and optimize and adjust them according to the test results. In addition, you can also refer to industry standards and expert experience to further adjust and refine the timeliness decay coefficient of each piece of information to ensure its accuracy and applicability.

[0081] The timeliness decay coefficient is a parameter used to evaluate the decay rate of information over time, reflecting the time sensitivity of information. A larger decay coefficient indicates that the timeliness of information is higher, which means that the time of the most recent access contributes more to the timeliness of information, while a smaller decay coefficient indicates that the timeliness of information is lower, and the historical access time has a greater impact on the timeliness of information. In practical applications, the timeliness decay coefficient is used to calculate the timeliness coefficient of information to help evaluate the importance and priority of information in the circulation process. Through system logs and data analysis tools, these coefficients can be automatically obtained and updated, thereby dynamically reflecting the actual timeliness of information and optimizing data processing and decision-making processes.

[0082] Calculate the product circulation information The information timeliness coefficient of each piece of information is calculated by the following formula:

[0083] In the formula, For product circulation information The information timeliness coefficient of a piece of information.

[0084] Product circulation information The size of the information timeliness coefficient of each piece of information directly affects the evaluation of the necessity of the information in the entire circulation process. Specifically, the higher the information timeliness coefficient, the greater the weight of the information in the most recent access time, indicating that it is crucial to the timeliness and accuracy of the current circulation link. Therefore, information with a high timeliness coefficient is considered time-sensitive and must be processed and stored with priority to ensure the transparency and real-time nature of product circulation, thereby responding to market demand and consumer trust in a timely manner. A lower timeliness coefficient indicates that the information has played a smaller role in the past access time and may have a lower influence at the current stage, so it can be arranged for subsequent processing with a lower priority. By combining the circulation complexity coefficient and the information timeliness coefficient, enterprises can more scientifically evaluate and manage the necessity of each piece of information, optimize resource allocation, and improve overall operational efficiency and decision-making capabilities.

[0085] In this embodiment, the first The circulation complexity coefficient and information timeliness coefficient of each piece of information are used to build a necessity evaluation model, and the first product circulation information is generated by weighted summation. Necessary coefficient of information , and compare the generated necessary coefficient with the preset necessary coefficient threshold Compare and evaluate the necessity of each piece of information in the product circulation information, and divide each piece of information in the product circulation information into high-necessity information and low-necessity information according to the evaluation results. The specific division is as follows:

[0086] like , the product circulation information The necessity of a piece of information is low, and the information is classified as low necessity information;

[0087] like , the product circulation information The necessity of a piece of information is high, and the information is classified as high necessity information.

[0088] To generate product circulation information The complexity coefficient of information flow and information timeliness coefficient To build a necessity assessment model, you can set the weight factor and To perform a weighted summation, generate the necessary coefficients for this information The specific method is as follows: First, set the weight factor through data analysis and business needs and , these factors reflect the relative importance of circulation complexity and timeliness in the overall evaluation. Next, use the formula Calculate the necessary coefficients for each message, where and It can be managed and adjusted through configuration files or database parameters according to business rules to ensure the flexibility and accuracy of the evaluation model. Through such weighted summation, the complexity and timeliness of information are comprehensively considered, so as to scientifically evaluate the necessity of information.

[0089] Prioritize processing and storage of high-necessity information;

[0090] In this embodiment, high-necessity information is processed and stored preferentially, specifically including the following steps: after the high-necessity information is identified, the system marks the information as high-priority data and immediately triggers the data processing module to process it, including verification, conversion, and cleaning steps to ensure the integrity and accuracy of the data; after the processing is completed, the high-necessity information is stored in a high-performance database or distributed storage system, and a regular backup mechanism and a multi-copy storage strategy are set up to ensure the security and availability of the data; at the same time, the system records the processing and storage status of the high-necessity information in real time in the log for subsequent query and audit use.

[0091] Prioritize the processing and storage of high-necessity information, which specifically includes the following steps: After the high-necessity information is identified, the system first marks the information as high-priority data and immediately triggers the data processing module for processing. The processing steps include data verification, data conversion and data cleaning to ensure the integrity and accuracy of the data; the verification step will check the correctness and consistency of the data, the conversion step will standardize the data format, and the cleaning step will delete redundant or erroneous data; after processing, the high-necessity information is stored in a high-performance database or distributed storage system that has the ability to read and write quickly; in order to ensure the security and availability of the data, the system will set up a regular backup mechanism and a multi-copy storage strategy to prevent data loss and provide high availability; in addition, the system will record the processing and storage status of the high-necessity information in real time, and generate logs for subsequent queries and audits, thereby ensuring the transparency and traceability of all operations.

[0092] Continuously monitor the changes in system load and the amount of processing of high-necessity information, determine whether the system can process low-necessity information, and decide whether to process the pending information based on the determination results;

[0093] In this embodiment, the system load and the processing amount of high-necessity information are continuously monitored to determine whether the system can process low-necessity information, and whether to process the information to be processed is determined according to the determination result, which specifically includes the following steps:

[0094] Obtaining system load data when the system processes and stores high-necessity information and data on the amount of high-necessity information processed;

[0095] The system load data and the processing volume data of the high-necessary information when the system processes and stores the high-necessary information can be obtained in the following ways: first, use system monitoring tools to collect system load data such as CPU usage, memory occupancy and disk I / O usage in real time. These tools collect system performance indicators regularly through agents installed on each node, and transmit the data to the central monitoring server for storage and analysis; at the same time, use the data processing module to collect the processing volume data of the high-necessary information in real time, including the processing queue length, processing time and processing success rate. These data are embedded in the data processing link through log records and message queues to capture and transmit processing information in real time; the specific acquisition process includes: deploying a log collector on each processing node to record the operations of processing and storing the high-necessary information in log files, and configuring log collection tools such as Logstash or Fluentd to transmit the log data in real time to the Kafka cluster for streaming processing and storage; finally, using a big data analysis platform (such as Hadoop, Spark) to analyze the collected data in real time, extract key indicators, and generate system load and processing volume change reports to help system administrators monitor and optimize system performance.

[0096] Analyze the acquired system load data and high-necessary information processing volume data to generate a system load tolerance index and a high-necessary information processing volume variation coefficient respectively;

[0097] Determine the system load bearing index threshold and the high necessary information processing volume change coefficient threshold;

[0098] Determining the threshold of the system load bearing index and the threshold of the coefficient of variation of the high necessary information processing volume can be achieved in the following ways: first, collect the operating data of the system under different load conditions, including CPU usage, memory occupancy, disk I / O usage, and the processing queue length and processing time of the high necessary information, and record these data in real time through system monitoring tools and data processing modules; then, use big data analysis platforms (such as Hadoop and Spark) to perform statistical analysis and cluster analysis on the collected data to identify key indicators and characteristic values ​​of the system under normal and overloaded conditions; then, based on the analysis results, use machine learning algorithms (such as linear regression and decision trees) to establish a prediction model to determine the threshold of the system load bearing index and the coefficient of variation of the high necessary information processing volume; the specific process includes: data preprocessing, feature selection, model training and verification, and finally set a reasonable threshold range according to the output of the model to ensure that the system can operate efficiently and stably before the system load index reaches the threshold and before the coefficient of variation of the high necessary information processing volume exceeds the threshold; finally, configure these thresholds to the system monitoring and scheduling module, and monitor and adjust them in real time through automated rules to dynamically adapt to the operating status of the system.

[0099] The generated system load bearing index and high necessary information processing volume change coefficient are compared with the system load bearing index threshold and high necessary information processing volume change coefficient threshold respectively to determine whether the system can process low necessary information. The specific determination is as follows:

[0100] If the generated system load bearing index is greater than the system load bearing index threshold and the generated high necessary information processing amount change coefficient is less than the high necessary information processing amount change coefficient threshold, then the system is able to process low necessary information, the system is in a relatively idle state, the resource utilization rate is low, there are sufficient resources to process additional tasks, the system processing high necessary information change rate is within a controllable range, and the system will not be overloaded due to processing additional tasks, therefore, the system is able to process low necessary information;

[0101] If the generated system load bearing index is greater than the system load bearing index threshold and the generated high-necessary information processing volume change coefficient is less than the high-necessary information processing volume change coefficient threshold, the system cannot process low-necessary information, the system load is high, the resource utilization rate is high, or the pressure to process high-necessary information is high, and the system is in a busy state. In order to avoid system overload and ensure priority processing of high-necessary information, the low-necessary information is temporarily stored in the queue to be processed, and the low-necessary information will be processed when the system load is reduced or the pressure to process high-necessary information is reduced.

[0102] In this embodiment, the logic for obtaining the system load bearing index is as follows:

[0103] Obtain the system load data when the system is processing and storing high-necessity information, including the system's CPU usage, memory usage, and disk usage at different times within T when the system is processing and storing high-necessity information. The system's CPU usage, memory usage, and disk usage at different times within T when the system is processing and storing high-necessity information are calibrated as , and , The number indicating the CPU usage, memory usage, and disk usage of the system at different times during the time T when the system is processing and storing high-necessity information. , is a positive integer;

[0104] System load data when the system is processing and storing high-necessity information can be obtained through system monitoring tools (such as Nagios, Zabbix, and Prometheus), including CPU usage, memory occupancy, and disk usage at different times within a time period of T. The specific acquisition methods are as follows: First, install monitoring agents on each server node. These agents will regularly collect system performance data; then, configure monitoring tools to collect data regularly, such as collecting CPU, memory, and disk usage every minute; these data are transmitted to the central monitoring server through the network and stored in a time series database (such as InfluxDB, Prometheus TSDB), recording the performance data of the system at each moment when processing and storing high-necessity information; finally, use data processing and analysis tools (such as Grafana and Kibana) to visualize and analyze the collected data and generate system load reports. These reports help system administrators understand the system load when processing high-necessity information, so as to optimize and adjust it.

[0105] Calculate the system load bearing index. The specific calculation formula is as follows:

[0106]

[0107] In the formula, is the system load index;

[0108] The calculation logic is as follows: By calculating the CPU usage at each moment during the processing and storage of high-necessity information , memory usage and disk usage Collect data and sum up these usage rates to get the total system load at each moment. Take the average of the sum of these total loads to reflect the average load of the system during the entire period. The 100 in the formula is used for normalization, because the full score of CPU, memory, and disk usage is 100%, and after dividing by 100, it is converted into a value between 0 and 1 for unified measurement. Then the average load is squared to amplify the impact of higher loads, and an exponential decay function is used to further smooth the load impact, thereby obtaining the system load tolerance index. The closer the index value is to 1, the lower the system load, and the closer it is to 0, the higher the system load.

[0109] System load index The size of directly affects whether the system can process low-essential information. The closer the value is to 1, the lower the system load is, the lower the resource utilization is, and the system has enough CPU, memory, and disk I / O capacity to handle additional tasks. At this time, the system can process less necessary information. On the contrary, when The closer the value is to 0, the higher the system load, the higher the resource usage, and the system is busy. In order to avoid system overload and performance degradation, the system should prioritize processing of high-necessity information and temporarily store low-necessity information in the queue to be processed to ensure stable system operation and timely processing of high-necessity information. Thresholds can dynamically determine the system load condition, thereby reasonably allocating processing tasks and optimizing system performance.

[0110] The logic for obtaining the coefficient of variation of high necessary information processing volume is as follows:

[0111] Obtain the processing volume data of high-necessity information when the system processes and stores high-necessity information, including the processing queue length and corresponding processing time at each moment in a certain period of time when the system processes and stores high-necessity information, and use the function to convert the processing queue length and corresponding processing time at each moment in a certain period of time when the system processes and stores high-necessity information into time series and ,in is a time variable, and the time period is defined as ;

[0112] Data processing and monitoring software (such as Apache Kafka, Logstash, Prometheus, etc.) can be used to obtain the system's high-necessity information processing volume data when processing and storing high-necessity information, including the processing queue length and corresponding processing time at each moment in a certain period of time. The specific acquisition method is as follows: First, integrate the logging function in the data processing module, and use Logstash to collect the logs generated by the system when processing high-necessity information in real time. These logs record the processing queue length and processing time at each moment; then, use Kafka to transmit these real-time log data to the central data warehouse and store them in a time series database (such as Prometheus TSDB); then, use Prometheus to pull these time series data regularly to ensure that the collection frequency is high enough to capture the data at every moment in the processing process; finally, use data visualization tools such as Grafana to display and analyze the collected data, and generate time series of processing queue length and processing time, which is convenient for system administrators to monitor and optimize system performance.

[0113] Use the processing queue length and processing time The derivative of the calculation of the processing volume change rate , according to the formula: ;

[0114] Calculate the coefficient of variation of high necessary information processing volume. The specific calculation formula is as follows:

[0115]

[0116] In the formula, The coefficient of variation of the amount of necessary information processing is high;

[0117] The calculation logic is as follows: This formula calculates the change in the system's processing volume when processing and storing high-necessary information by means of definite integral. It represents the rate of change of the processing queue length relative to the processing time, that is, Time processing queue length With processing time The rate of change of The absolute value of the integral can be measured over the entire time period Fluctuation of internal processing volume, the integral result is divided by the length of the time period Take the average value to get the average intensity of the processing volume change, and finally generate the high necessary information processing volume change coefficient , reflecting the changes in the pressure of the system to process high-necessary information. The higher the value, the greater the change in the system processing pressure.

[0118] High necessary information processing capacity variation coefficient The size of directly affects whether the system can process low-essential information. When the value is high, it means that the system's processing volume fluctuates greatly during the processing of high-necessity information, and the system resources and processing capacity are under great pressure, which means that the system needs to concentrate resources on processing high-necessity information and cannot allocate enough resources to process low-necessity information; on the contrary, when When the value is low, it means that the system's processing capacity changes little during the processing of high-necessary information, and the system resources and processing capacity are relatively stable and sufficient. At this time, the system has enough spare capacity to process low-necessary information. Therefore, by setting a reasonable The threshold can determine whether the system is currently capable of processing low-essential information, ensuring that the system allocates resources reasonably while running efficiently and avoiding overload.

[0119] Repeat the above steps on each circulation node, while optimizing system performance and improving data processing and storage efficiency.

[0120] Repeat the above steps on each circulation node, and optimize the system performance at the same time, which specifically includes the following steps: deploy a QR code scanning device and a data processing module on each circulation node to record and collect the access timestamp and status data of each information in the product circulation information in real time; analyze each information in the product circulation information on each circulation node, generate a circulation complexity coefficient and an information timeliness coefficient, and calculate the necessity index; give priority to processing and storing high-necessity information, monitor the system load and the changes in the processing volume of high-necessity information in real time, and determine whether the system can process low-necessity information; dynamically adjust the processing strategy according to the system load tolerance index and the coefficient of change of the processing volume of high-necessity information; improve data processing efficiency and system response speed by optimizing algorithms and performance tuning, ensure that the system can run stably and efficiently in large-scale production and circulation environments, monitor and analyze the performance parameters of each circulation node in real time, promptly discover and solve performance bottlenecks, and achieve continuous optimization of the performance of the entire system.

[0121] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0122] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0123] It should be noted that, in this article, if there are first and second, etc., relational terms are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0124] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0125] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0126] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0127] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0130] If the functions are implemented in the form of 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 the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0131] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A product identification and tracking management method based on QR code, characterized in that: The specific steps include: When the product passes through each circulation node, the circulation information of the product is recorded by scanning the QR code; Extract all information contained in the circulation information of the product, analyze each piece of information in the circulation information of the product, and classify each piece of information in the circulation information of the product into high-necessity information and low-necessity information according to the analysis result; Prioritize processing and storage of high-necessity information; Continuously monitor the changes in system load and the amount of processing of high-necessity information, determine whether the system can process low-necessity information, and decide whether to process the pending information based on the determination results; On each circulation node, repeat the above steps while optimizing system performance.

2. A product identification, tracking and management method based on a two-dimensional code according to claim 1, characterized in that: Extract all information contained in the circulation information of the product, analyze each piece of information in the circulation information of the product, and divide each piece of information in the circulation information of the product into high-necessity information and low-necessity information according to the analysis result, specifically including the following steps: Extract all the information contained in the product's circulation information; Obtaining the operation data and timeliness data of each piece of information in the product circulation information, analyzing the operation data and timeliness data, and generating a circulation complexity coefficient and an information timeliness coefficient respectively; A necessity evaluation model is constructed for the generated circulation complexity coefficient and information timeliness coefficient to generate a necessary coefficient. The generated necessary coefficient is compared with a pre-set necessary coefficient threshold to evaluate the necessity of each piece of information in the product circulation information, and based on the evaluation results, each piece of information in the product circulation information is divided into high-necessity information and low-necessity information.

3. The product identification, tracking and management method based on a two-dimensional code according to claim 2, characterized in that: The logic for obtaining the circulation complexity coefficient and information timeliness coefficient is as follows: Extract the operation data of each information in the product circulation information, including the number of times each information is accessed at each circulation node, the weight coefficient of the operation type involved, and the number of changes. The number of times each information is accessed at each circulation node, the weight coefficient of the operation type involved, and the number of changes are marked as , and , Indicates The information in The number of times a circulation node is visited, Indicates The information in The weight coefficient of the operation type involved in each circulation node, Indicates The information in The number of changes of circulation nodes, , , and All are positive integers; Calculate the product circulation information The circulation complexity coefficient of information is calculated by the formula (1): (1) In formula (1), For product circulation information The complexity coefficient of information circulation; Extract the timeliness data of each piece of information in the product circulation information, including the access timestamp and reference time of each piece of information at each circulation node, and convert the access timestamp and reference time of each piece of information at each circulation node into and , Indicates The information in The access timestamp of the circulation node, Indicates The base time of the information, that is, The creation time of the information; Determine the timeliness attenuation coefficient of each piece of information. After determination, calibrate the timeliness attenuation coefficient of each piece of information as , Indicates The timeliness decay coefficient of each piece of information; Calculate the product circulation information The information timeliness coefficient of each piece of information is calculated by the formula (2): (2) In formula (2), For product circulation information The information timeliness coefficient of a piece of information.

4. The product identification, tracking and management method based on a two-dimensional code according to claim 3 is characterized in that: For the generated product circulation information The circulation complexity coefficient and information timeliness coefficient of each piece of information are used to build a necessity evaluation model, and the first product circulation information is generated by weighted summation. Necessary coefficient of information , and compare the generated necessary coefficient with the preset necessary coefficient threshold Compare and evaluate the necessity of each piece of information in the product circulation information, and divide each piece of information in the product circulation information into high-necessity information and low-necessity information according to the evaluation results. The specific division is as follows: like , the product circulation information The necessity of a piece of information is low, and the information is classified as low necessity information; like , the product circulation information The necessity of a piece of information is high, and the information is classified as high necessity information.

5. The product identification, tracking and management method based on QR code according to claim 1, characterized in that: Prioritize the processing and storage of highly necessary information, which specifically includes the following steps: After the highly necessary information is identified, the system marks the information as high-priority data and immediately triggers the data processing module to process it, including verification, conversion, and cleaning steps to ensure the integrity and accuracy of the data; after processing, the highly necessary information is stored in a high-performance database or distributed storage system, and a regular backup mechanism and multi-copy storage strategy are set up to ensure data security and availability; at the same time, the system records the processing and storage status of the highly necessary information in real time in the log for subsequent query and audit use.

6. The product identification, tracking and management method based on QR code according to claim 1, characterized in that: Continuously monitor the changes in system load and the amount of processing of high-necessity information, determine whether the system can process low-necessity information, and decide whether to process the pending information based on the determination result, including the following steps: Obtaining system load data when the system processes and stores high-necessity information and data on the amount of high-necessity information processed; Analyze the acquired system load data and high-necessary information processing volume data to generate a system load tolerance index and a high-necessary information processing volume variation coefficient respectively; Determine the system load bearing index threshold and the high necessary information processing volume change coefficient threshold; The generated system load bearing index and high necessary information processing volume change coefficient are compared with the system load bearing index threshold and high necessary information processing volume change coefficient threshold respectively to determine whether the system can process low necessary information. The specific determination is as follows: If the generated system load bearing index is greater than the system load bearing index threshold and the generated high necessary information processing amount change coefficient is less than the high necessary information processing amount change coefficient threshold, then the system can process low necessary information; If the generated system load bearing index is greater than the system load bearing index threshold and the generated high necessary information processing amount change coefficient is less than the high necessary information processing amount change coefficient threshold, the system cannot process the low necessary information.

7. The product identification, tracking and management method based on QR code according to claim 6, characterized in that: The logic for obtaining the system load bearing index is as follows: Obtain the system load data when the system is processing and storing high-necessity information, including the system's CPU usage, memory usage, and disk usage at different times within T when the system is processing and storing high-necessity information. The system's CPU usage, memory usage, and disk usage at different times within T when the system is processing and storing high-necessity information are calibrated as , and , The number indicating the CPU usage, memory usage, and disk usage of the system at different times during the time T when the system is processing and storing high-necessity information. , is a positive integer; Calculate the system load bearing index. The specific calculation formula is as follows: (3) In formula (3), is the system load index; The logic for obtaining the coefficient of variation of high necessary information processing volume is as follows: Obtain the processing volume data of high-necessity information when the system processes and stores high-necessity information, including the processing queue length and corresponding processing time at each moment in a certain period of time when the system processes and stores high-necessity information, and use the function to convert the processing queue length and corresponding processing time at each moment in a certain period of time when the system processes and stores high-necessity information into time series and ,in is a time variable, and the time period is defined as ; Use the processing queue length and processing time The derivative of the calculation of the processing volume change rate ; Calculate the coefficient of variation of the necessary information processing amount. The specific calculation formula is as follows: (4) In formula (4), The coefficient of variation of the required information processing volume is high.

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