Finished product storage intelligent management system for medical instrument production and sales warehouse

Abstract

The invention, which relates to the technical field of data management, discloses an intelligent management system for finished product storage of a medical instrument production and sales warehouse, comprising a regular backup module, an information acquisition module, a warehouse data analysis module, a data transmission analysis module, a transmission window analysis module, a window quality division module and a backup window optimization module. The method comprises the following steps: regularly backing up data of various types of commodity finished products, collecting access and operation information, analyzing and generating a warehouse storage data access index, identifying a transmission imbalance phenomenon in a primary backup window, and generating a network fluctuation index; the quality of the backup window is divided based on the two indexes, the duration of re-backup is optimized for the low-quality backup window, and the data integrity and the transmission efficiency are improved; according to the method, re-backup can be triggered in a low-quality backup window, the duration of the backup window is dynamically adjusted through the backup window optimization module, data loss or transmission failure is avoided, the backup period is dynamically adjusted, and the backup data management requirement of the medical instrument is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, more particularly, the present application relates to a medical device production and sales warehouse commodity finished product storage intelligent management system. BACKGROUND

[0002] With the rapid development of the medical device industry, the production, sales and warehouse management of medical devices have become increasingly complex. As a highly specialized product, medical devices require strict quality control and data management during production and sales, and the storage management of finished products is a key link in the supply chain, directly affecting the availability, safety and market response speed of products.

[0003] The traditional management method mainly relies on a static management mode, which has the following problems: data backup is not timely: in the traditional system, the storage data of medical devices usually relies on fixed backup strategies, which are difficult to dynamically adjust according to access requirements and network status, resulting in problems such as data loss or low backup efficiency. Insufficient monitoring of access behavior: different types of medical devices have different requirements for storage environment and data access, and the traditional system cannot accurately monitor these differentiated needs, which may cause backup delays for high-frequency access goods or resource waste for low-frequency access goods. Transmission performance is unstable: when the backup task is under unstable network conditions, data transmission imbalance or backup failure may occur, and there is a lack of effective network fluctuation monitoring and window optimization strategies. Therefore, a medical device production and sales warehouse commodity finished product storage intelligent management system is proposed to solve the above problems. SUMMARY

[0004] To achieve the above purpose, the present application provides the following technical scheme:

[0005] A medical device production and sales warehouse commodity finished product storage intelligent management system, comprising a periodic backup module, an information acquisition module, a warehouse data analysis module, a data transmission analysis module, a transmission window analysis module, a window quality division module, and a backup window optimization module.

[0006] The periodic backup module is used to periodically backup the storage data of each type of warehouse commodity finished product according to the corresponding preset backup period of each type of warehouse commodity finished product;

[0007] The information acquisition module is used to acquire the access information of each type of warehouse commodity finished product storage data and the running information of the backup operation of the backup window, and obtain the access information set of each type of warehouse commodity finished product storage data and the running information set of the backup operation, respectively;

[0008] The warehouse data analysis module is used to measure the degree of access to the storage data of finished goods in the warehouse of the corresponding type between two backups based on the access information set, and to generate a warehouse storage data access index.

[0009] The data transmission analysis module is used to identify, based on the running information set, whether there is a significant imbalance in the backup transmission of the corresponding type of warehouse finished goods storage data within the initial preset backup window. If there is a significant imbalance in the backup transmission, an activation signal is sent to the transmission window analysis module.

[0010] When the transmission window analysis module receives the activation signal, it performs transmission window analysis based on the runtime information set and generates a network fluctuation index.

[0011] The window quality classification module divides the initially preset backup windows into high-quality backup windows and low-quality backup windows based on the warehouse storage data access index and network fluctuation index.

[0012] The backup window optimization module is used to re-back up stored data that was backed up in a low-quality backup window based on the initial preset backup window duration, and to optimize the window duration for re-backup.

[0013] In a preferred embodiment, the intelligent management system for the storage of finished goods in a medical device manufacturing and sales warehouse further includes a backup cycle adjustment module, which is used to adjust the periodic backup cycle of the storage data of the corresponding type of finished goods in the warehouse according to the warehouse storage data access index.

[0014] In a preferred embodiment, the data transmission analysis module is used to identify, based on the operational information set, whether there is a significant imbalance in the backup transmission of warehouse finished goods storage data of the corresponding type within an initial preset backup window.

[0015] From the running information set, all transmission bandwidth data within the initial preset backup window is obtained, and then arranged in chronological order to obtain the time series of transmission bandwidth data. Then, the difference time series corresponding to the difference between the transmission bandwidth data at two adjacent time points is calculated, the average value and standard deviation of the difference time series are obtained, and the ratio of the average value and standard deviation of the difference time series is calculated to obtain the transmission bandwidth fluctuation value.

[0016] The minimum value in the time series of transmission bandwidth data is compared with a preset standard threshold, and the transmission bandwidth fluctuation value is compared with a preset standard fluctuation range. If the minimum value in the time series of transmission bandwidth data is greater than or equal to the preset standard threshold and the transmission bandwidth fluctuation value falls within the preset standard fluctuation range, an equilibrium signal is issued. If the minimum value in the time series of transmission bandwidth data is not greater than or equal to the preset standard threshold and the transmission bandwidth fluctuation value falls within the preset standard fluctuation range, an imbalance signal is issued. When an imbalance signal is issued, it is identified that there is a significant imbalance in the backup transmission of the corresponding type of warehouse goods finished product storage data within the initial preset backup window.

[0017] In a preferred embodiment, the logic for obtaining the warehouse storage data access index is as follows:

[0018] The following feature indicators were extracted from the access information set:

[0019] Average access frequency per unit time Total amount of data accessed The proportion of abnormal accesses to total accesses The variance of the access time distribution used to measure the uniformity of access. The percentage of times the actual number of visits exceeded the preset capacity. Based on the extracted feature indicators, a warehouse storage data access index is generated, with the following formula:

[0020] ;

[0021] ;

[0022] ;

[0023] This indicates the warehouse storage data access index. Indicates the preset capacity. This indicates that the warehouse stores data access value one. This indicates the second value for accessing warehouse stored data. , , , , All are preset non-zero weight factors, and the sum of all weight factors is one.

[0024] In a preferred embodiment, the variance of the access time distribution used to measure access uniformity... The acquisition logic is as follows:

[0025] Collect all access times between the two backups to form a time series. ,in Let be the time of the i-th access, and n be the total number of accesses. Then, divide the total time span between the two backups into m equal-width time intervals, and obtain the width of each time interval as ΔT:

[0026] Record the number of visits in each time period to form a visit frequency distribution sequence. ,in This represents the number of visits in the i-th time period. Then, the variance is calculated using the visit frequency distribution sequence to obtain the variance of the visit time distribution used to measure the uniformity of visits. .

[0027] In a preferred embodiment, the logic for obtaining the network volatility index is as follows:

[0028] The following core network operation data are obtained from the set of operational information acquired within the transmission window:

[0029] Instantaneous bandwidth is the available bandwidth per unit of time. Delay fluctuation refers to the difference in delay between consecutive time points. The percentage of data packets lost per unit time out of the total number of data packets sent is called the packet loss ratio. Network utilization is the ratio of actual network bandwidth used per unit time to available bandwidth. t represents the index of each unit of time within the transmission window;

[0030] Then, the following characteristic indicators within the transmission window are calculated:

[0031] Bandwidth fluctuation coefficient : ; This represents the average bandwidth within the transmission window, where k is the total number of units of time within the transmission window.

[0032] Delay variation coefficient : ; This represents the average delay within the transmission window;

[0033] Network utilization stability coefficient : ; This represents the average network utilization within the transmission window;

[0034] Packet loss ratio uniformity coefficient : ; Indicates the percentage of all packet losses Standard deviation;

[0035] The formula for calculating the network volatility index is: ; Indicates network volatility index, This represents a preset constant.

[0036] In a preferred embodiment, when initially dividing the backup window into high-quality backup windows and low-quality backup windows, fuzzy logic is used to take the warehouse storage data access index and network fluctuation index as input data, and the initial preset backup window division type as the output data of the fuzzy logic.

[0037] In a preferred embodiment, optimizing the window duration for re-backup refers to:

[0038] The initial preset backup window is categorized as a low-quality backup window. The time required to retrieve the initial preset backup window corresponding to the warehouse product type and its stored data is determined by the specific warehouse product type. The optimized formula for calculating the window duration of the re-backup is as follows:

[0039] ; This is a preset non-zero optimization factor used to control the optimization magnitude. To optimize the window duration for re-backup.

[0040] In a preferred embodiment, the usage logic of the backup cycle adjustment module is as follows:

[0041] The frequency of regularly backing up the storage data of finished goods in the warehouse. and warehouse storage data access index Then perform the following calculations:

[0042] ; The preset non-zero adjustment factor, The periodic backup cycle for the adjusted storage data of finished goods in the warehouse.

[0043] The technical effects and advantages of this invention are as follows:

[0044] This invention achieves comprehensive backup of warehouse storage data for different types of medical devices through a periodic backup module and an information acquisition module, and dynamically collects data access and network operation status to ensure the real-time performance and integrity of the backup process. A re-backup is triggered during low-quality backup windows, and the backup window duration is dynamically adjusted through a backup window optimization module to avoid data loss or transmission failure. The dynamic adjustment of the backup cycle meets the stringent data management requirements of medical devices.

[0045] This invention improves the system's adaptability by quantifying data access characteristics (indirectly) and network environment characteristics (directly) to achieve backup window partitioning and optimization. The modular design dynamically responds to data access fluctuations and network performance changes, adjusting backup task duration and cycles to adapt to actual conditions and avoid system overload or low transmission efficiency. Through data transmission analysis and transmission window analysis modules, the system can detect transmission bandwidth fluctuations and minimum values ​​in real time during the initial backup window, determining if there are significant imbalances in backup transmission. Upon receiving an imbalance signal, the system optimizes the backup task's transmission rate and window duration to ensure the backup task is completed as expected.

[0046] This invention utilizes a backup cycle adjustment module and a window quality segmentation module to rationally plan the backup task sequence, avoiding resource contention during high-load backup tasks. Based on fuzzy logic, it comprehensively analyzes the warehouse storage data access index and network fluctuation index to accurately classify backup window types, providing a scientific basis for backup optimization. Low warehouse storage data access indices and low network fluctuation indices serve as system early warning signals, indicating access conflicts or network fluctuations, allowing for proactive optimization measures. During the backup process, the system can monitor access and transmission status in real time and dynamically adjust task parameters to ensure the stability and flexibility of the backup system.

[0047] The intelligent backup strategy of this invention meets the stringent requirements of the medical device industry for data security and traceability, and can satisfy auditing and regulatory needs. Through dynamic optimization of backup tasks, the system ensures data timeliness and integrity, providing robust warehouse data management support for medical device production and sales. By dynamically adjusting the backup cycle and re-backup window duration, unnecessary backup tasks and redundant transmissions are reduced, lowering storage and bandwidth operating costs. This invention achieves a fully automated process from data acquisition and analysis to optimization, reducing the need for manual intervention and improving operational efficiency. Attached Figure Description

[0048] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0049] Fig. 1 This is a schematic diagram of an intelligent management system for storing finished products in a medical device production and sales warehouse, according to Embodiment 1 of the present invention.

[0050] Fig. 2 This is a schematic diagram of an intelligent management system for storing finished products in a medical device production and sales warehouse, according to Embodiment 2 of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0052] Reference Figs. 1-2 The following examples were obtained:

[0053] Example 1

[0054] A smart management system for the storage of finished goods in a medical device manufacturing and sales warehouse includes a periodic backup module, an information acquisition module, a warehouse data analysis module, a data transmission analysis module, a transmission window analysis module, a window quality classification module, and a backup window optimization module; and communication connections between the periodic backup module, the information acquisition module, the warehouse data analysis module, the data transmission analysis module, the transmission window analysis module, the window quality classification module, and the backup window optimization module.

[0055] The periodic backup module is used to perform periodic backups of the stored data of finished goods in each type of warehouse according to the preset backup cycle. This ensures that the data of finished goods in different types of warehouses are reliably backed up according to the preset cycle, reducing the risk of data loss. The preset cycle enables the orderly arrangement of backup tasks, avoids conflicts in resource usage between backup tasks, complies with the backup management regulations of the enterprise or industry, and meets audit or traceability requirements.

[0056] The information acquisition module is used to collect access information of finished goods storage data in various types of warehouses, as well as the operation information of backup operations in the backup window. It obtains access information sets and backup operation information sets for finished goods storage data in various types of warehouses, respectively. It provides basic data on warehouse data access behavior and backup operation status, providing a reliable basis for subsequent analysis modules. It collects backup and access information in real time, promptly captures data transmission problems and abnormal access fluctuations, and provides support for optimizing backup strategies.

[0057] The warehouse data analysis module measures the access level of warehouse finished goods between two backups based on the access information set, generating a warehouse storage data access index. Through access level index analysis, it quantifies the access frequency and balance of warehouse data, providing an indirect basis for backup window quality assessment. In cases of high access frequency or uneven access, the backup time point can be dynamically adjusted to avoid data conflicts or performance bottlenecks, enabling backup tasks to adapt to access characteristics and thus improving the overall backup efficiency of the system.

[0058] The data transmission analysis module is used to identify, based on the runtime information set, whether there is a significant imbalance in the backup transmission of finished goods storage data of the corresponding type of warehouse within the initial preset backup window. If a significant imbalance exists, an activation signal is sent to the transmission window analysis module. This captures the imbalance in transmission performance within the initial backup window, avoiding inefficient transmission or missing data. By sending an activation signal to the transmission window analysis module, the problem can be further analyzed and the transmission mechanism optimized to ensure the stable operation of the final backup task and reduce the risk of data transmission failure.

[0059] When the transmission window analysis module receives the activation signal, it performs transmission window analysis based on the running information set to generate a network fluctuation index. This quantifies the degree of network fluctuation, intuitively reflects the impact of network conditions on backup tasks, provides a basis for subsequent window division and optimization, avoids backup failures caused by low-quality network environments, and ensures that backup tasks can adapt to dynamically changing transmission conditions through real-time analysis of network status.

[0060] The window quality classification module divides the initially preset backup windows into high-quality backup windows and low-quality backup windows based on the warehouse storage data access index and network fluctuation index. It clarifies the quality category of the backup windows, provides a target and basis for optimizing low-quality backup windows, and ensures that high-quality backup windows complete their tasks first by dividing the windows. This avoids blindly re-backing up after high-quality backup windows, reducing resource waste. Low-quality backup windows are then re-backed up to ensure the integrity of data transmission.

[0061] The backup window optimization module is used to re-back up stored data that was backed up in a low-quality backup window, based on the initially preset backup window duration, and optimize the re-backup window duration. By re-backing up, it compensates for backup defects caused by low-quality windows, ensuring data integrity. Adjusting the re-backup window duration matches network fluctuations and data access characteristics, improving transmission efficiency and reliability, optimizing the backup strategy, and enhancing the overall backup performance of the system. It should be noted that extending the backup window can mitigate the impact of network fluctuations. Network fluctuations are dynamic; a short backup window may coincide with a network performance trough, leading to a large number of data transmission failures. Extending the window duration covers peak periods of network performance recovery, allowing the task to complete the required transmission volume within a longer timeframe.

[0062] The retransmission mechanism has more time to complete the retransmission of lost data, and can adapt to the current network conditions during retransmission by adaptively adjusting the transmission rate in existing technologies, thereby improving data integrity. Adaptive adjustment of the transmission rate is an existing mature technology that is widely used in network communication and data transmission fields, and will not be elaborated on further here.

[0063] Instructions for not using adaptive scaling during the initial backup:

[0064] The initial goal is to complete the backup task quickly: Backup tasks typically attempt to transfer data at the maximum rate upon startup to complete the backup operation as quickly as possible. Using adaptive adjustment from the outset might proactively reduce the transfer rate, thus prolonging the backup completion time, which could contradict the task's efficiency objective. Only if significant imbalances in backup transfer are detected during the initial backup will subsequent steps focus more on task stability. If a backup is classified as low-quality, retransferring with adaptively adjusted transfer rates is more reasonable.

[0065] When a backup task starts, the actual performance and transmission capacity of the network may not be fully understood. Direct adaptive adjustment might result in a low transmission rate, failing to fully utilize network resources. Adaptive adjustment requires real-time monitoring of network conditions (such as packet loss rate, latency, and bandwidth utilization) and dynamic adjustment of transmission strategies, which increases computational and communication overhead and may be considered unnecessary in the initial stages. Moreover, if the backup task is expected to complete in a short time, adaptive adjustment may not bring significant benefits and could instead increase algorithm complexity. The backup task is designed to assume that the initial network conditions are good enough that frequent adjustments to the transmission rate are unnecessary. The adaptive adjustment mechanism is only activated to address problems if significant network fluctuations or poor backup quality are detected.

[0066] Extending the backup window allows backup tasks more time to monitor network fluctuations and gradually adjust transmission rates to optimize efficiency. With the extended window, the backup objective shifts from "quick completion" to "ensuring quality and integrity," making adaptive adjustment crucial for balancing transmission rate and network conditions. After initial detection of network fluctuations, the system obtains clearer network performance data (such as bandwidth fluctuations and packet loss rates), providing a reliable reference for adaptive adjustments.

[0067] The data transmission analysis module is used to identify, based on the operational information set, whether there is a significant imbalance in the backup transmission of finished goods storage data for the corresponding type of warehouse goods within the initial preset backup window.

[0068] From the running information set, all transmission bandwidth data within the initial preset backup window is obtained, and then arranged in chronological order to obtain the time series of transmission bandwidth data. Then, the difference time series corresponding to the difference between the transmission bandwidth data at two adjacent time points is calculated, the average value and standard deviation of the difference time series are obtained, and the ratio of the average value and standard deviation of the difference time series is calculated to obtain the transmission bandwidth fluctuation value.

[0069] The minimum value in the time series of transmission bandwidth data is compared with a preset standard threshold, and the transmission bandwidth fluctuation value is compared with a preset standard fluctuation range. If the minimum value in the time series of transmission bandwidth data is greater than or equal to the preset standard threshold and the transmission bandwidth fluctuation value falls within the preset standard fluctuation range, an equilibrium signal is issued. If the minimum value in the time series of transmission bandwidth data is not greater than or equal to the preset standard threshold and the transmission bandwidth fluctuation value falls within the preset standard fluctuation range, an imbalance signal is issued. When an imbalance signal is issued, it is identified that there is a significant imbalance in the backup transmission of the corresponding type of warehouse goods finished product storage data within the initial preset backup window.

[0070] Fluctuations in data transmission can prevent backup tasks from completing within the preset time window, affecting data integrity and backup efficiency. By analyzing transmission bandwidth fluctuations and minimum values, the balance of transmission status can be dynamically assessed, allowing for timely optimization measures to be taken when imbalances occur, ensuring the stability of backup tasks. Practical application scenario: When backing up finished goods data in certain warehouses, sudden access or network congestion can cause drastic fluctuations in transmission bandwidth. The system can identify the problem in advance and make adjustments based on imbalance signals. Under conditions of low transmission bandwidth or large bandwidth fluctuations, the probability of backup task failure increases significantly. By comprehensively analyzing the minimum and fluctuation of bandwidth, it is possible to promptly determine whether to adjust the window or optimize the backup strategy, thereby reducing the risk of transmission failure and improving the backup success rate. Practical application scenario: For the backup of critical medical device data, the triggering of imbalance signals can guide the system to extend the window duration or reallocate bandwidth resources, ensuring the completion of critical data backups.

[0071] The logic for obtaining the warehouse storage data access index is as follows:

[0072] Collect data access records between two backups, including access frequency, access time period, access data volume, number of abnormal accesses, etc., and normalize the data to enable analysis across data at different scales.

[0073] Then, the following feature indicators are extracted from the access information set:

[0074] Average access frequency per unit time Average access frequency is used to reflect the level of access activity;

[0075] Total amount of data accessed After normalization, it is compared with the preset capacity. The ratio reflects the access pressure;

[0076] The percentage of abnormal accesses to total accesses This refers to the percentage of abnormal accesses; a low abnormality rate indicates that data access is normalized.

[0077] Variance of the access time distribution used to measure access uniformity Uniform access leads to lower variance, which can be used to boost the index;

[0078] Percentage of visits exceeding the preset capacity This refers to the access overload ratio, which indicates the overload risk and is used to reduce the index.

[0079] Based on the extracted feature indicators, a warehouse storage data access index is generated, using the following formula:

[0080] ;

[0081] ;

[0082] ;

[0083] This indicates the warehouse storage data access index. Indicates the preset capacity. This indicates that the warehouse stores data access value one. This indicates the second value for accessing warehouse stored data. , , , , All are preset non-zero weight factors, and the sum of all weight factors is one.

[0084] Access frequency is processed using a logarithmic function to smooth out the impact of high access frequencies and avoid exponential anomalies caused by a small number of high-frequency accesses. Access data volume is normalized and its square root is taken to reduce the impact of high access volumes, while also considering the preset capacity. The ratio provides a measure of relative stress. Handling abnormal access rates. The more abnormal accesses, the lower the overall index, reflecting the negative impact of anomalies on access quality. Nonlinear reversal of temporal distribution uniformity. The larger the variance (the less uniform the variance), the larger the denominator, leading to a decrease in the contribution of this term. Negating overload access reflects the direct impact of overload on system performance. Warehouse storage data access index. This comprehensive analysis, which integrates access frequency, total data volume, anomaly rate, time uniformity, and overload risk, fully reflects the access characteristics and quality between two backups. A low warehouse data access index can also serve as an early warning signal, indicating potential data access anomalies, storage capacity pressure, or backup risks, allowing for proactive optimization measures.

[0085] Variance of the access time distribution used to measure access uniformity The acquisition logic is as follows:

[0086] Collect all access times between the two backups to form a time series. ,in Let be the time of the i-th access, and n be the total number of accesses. Then, divide the total time span between the two backups into m equal-width time intervals, and obtain the width of each time interval as ΔT:

[0087] Record the number of visits in each time period to form a visit frequency distribution sequence. ,in This represents the number of visits in the i-th time period. Then, the variance is calculated using the visit frequency distribution sequence to obtain the variance of the visit time distribution used to measure the uniformity of visits. .

[0088] The logic for obtaining the network volatility index is as follows:

[0089] The following core network operation data are obtained from the set of operational information acquired within the transmission window:

[0090] Instantaneous bandwidth is the available bandwidth per unit of time. Delay fluctuation refers to the difference in delay between consecutive time points. The percentage of data packets lost per unit time out of the total number of data packets sent is called the packet loss ratio. Network utilization is the ratio of actual network bandwidth used per unit time to available bandwidth. t represents the index of each unit of time within the transmission window;

[0091] Then, the following characteristic indicators within the transmission window are calculated:

[0092] Bandwidth fluctuation coefficient : ; The bandwidth value represents the average bandwidth within the transmission window, where k is the total number of units of time within the transmission window; the bandwidth fluctuation coefficient reflects the severity of bandwidth fluctuations within the transmission window.

[0093] Delay variation coefficient : ; It represents the average delay within the transmission window; the delay variation coefficient reflects the degree of influence of delay variation within the transmission window.

[0094] Network utilization stability coefficient : ; It represents the average network utilization rate within the transmission window; the utilization stability index reflects the stability of network resource allocation.

[0095] Packet loss ratio uniformity coefficient : ; Indicates the percentage of all packet losses The standard deviation; the uniformity coefficient of packet loss distribution quantifies the distribution characteristics of packet loss.

[0096] The formula for calculating the network volatility index is: ; Indicates network volatility index, This represents a preset constant, a small positive number used to prevent the denominator from being zero.

[0097] and It uses non-linear processing to avoid the linear superposition of weighted averages on the final result, and improves the uniformity coefficient of packet loss ratio. Focusing on the uniformity of packet loss distribution, rather than simply the proportion of packet loss, allows the index to reflect more detailed network performance characteristics, such as the network utilization stability coefficient. This approach emphasizes the fluctuating characteristics of network utilization, reflecting the overall resource stability of the network rather than just the current utilization level. It not only focuses on traditional bandwidth and latency but also incorporates network utilization stability and packet loss distribution uniformity for a comprehensive evaluation of network performance. Nonlinear combinations reduce the impact of extreme values ​​of single factors on the final index, making the NFI more robust. A higher NFI value indicates less network fluctuation and more stable performance within the transmission window. A lower NFI value requires measures such as extending the transmission window.

[0098] When initially dividing the backup windows into high-quality backup windows and low-quality backup windows, fuzzy logic is used to take the warehouse storage data access index and network fluctuation index as input data, and the initial preset backup window division type as the output data of the fuzzy logic.

[0099] Input variables: Warehouse data access index: Represents the stability and load of warehouse data access behavior between two backups. A higher index indicates more even access, lighter load, and less impact on backups. Three fuzzy sets are defined: low, medium, and high. Network fluctuation index: Represents the stability and fluctuation of network performance within the transmission window. A higher index indicates a more stable network environment and less negative impact on backup transmission. Three fuzzy sets are defined: low, medium, and high.

[0100] Output variable: Backup window type: indicates the quality category of the initial preset backup window, defining two fuzzy sets: high quality and low quality.

[0101] Fuzzy set of input variables: Warehouse storage data access index: Low: Low value range, indicating uneven access or high load, which is detrimental to backup. Medium: Medium value range, indicating relatively even access, with some impact on backup. High: High value range, indicating even access, light load, and minimal impact on backup.

[0102] Network volatility index: Low: Severe volatility, poor network performance. Medium: Moderate volatility, average network performance. High: Stable network, good transmission environment.

[0103] Fuzzy set of output variables: High-quality backup window: Meets backup transmission quality requirements, requires no retransmission, and exhibits good data and network performance. Low-quality backup window: Backup transmission quality is unsatisfactory, requiring retransmission and optimization.

[0104] Using the "if-then" rule, the relationship between input and output is defined as follows: High warehouse data access index and high network volatility index: If access behavior is stable and network performance is good, the backup window is high quality. Medium warehouse data access index and high network volatility index: If access behavior is average but network performance is good, the backup window is high quality. High warehouse data access index and medium network volatility index: If access behavior is stable but network performance is average, the backup window is high quality. Medium warehouse data access index and medium network volatility index: If access behavior is average and network performance is average, the backup window is high quality. Low warehouse data access index and high network volatility index: If access behavior is poor but network performance is good, the backup window is low quality. High warehouse data access index and low network volatility index: If access behavior is good but network performance is poor, the backup window is low quality. Low warehouse data access index and medium network volatility index: If access behavior is poor and network performance is average, the backup window is low quality. Low warehouse data access index and low network volatility index: If access behavior is poor and network performance is poor, the backup window is low quality.

[0105] Fuzzification is the process of mapping input data to corresponding fuzzy sets. Based on the numerical ranges of the data access index and network volatility index, membership functions are used to calculate the membership degree of each input variable to the fuzzy set. Commonly used membership functions include trigonometric functions and trapezoidal functions. For example, a data access index value of "medium" corresponds to membership degrees of low (0.2), medium (0.7), and high (0.1). A network volatility index value of "high" corresponds to membership degrees of low (0.1), medium (0.3), and high (0.6).

[0106] Based on fuzzy rules, the input membership degrees are combined to derive the output membership degree: calculate the membership degree of all conditions that meet the rule. The minimum value of the rule result is taken as the output membership degree. For example, the rule for access index (0.7) and high network volatility (0.6) corresponds to a membership degree of 0.6 for a high-quality window. Finally, the fuzzy inference result is transformed into a definite output value, and the backup window type corresponding to the final output result is calculated using a weighted average method or centroid method. Fuzzy logic provides a scientific basis for classifying backup window quality. By combining the warehouse storage data access index and network volatility index, it achieves accurate classification of high-quality and low-quality backup windows, which helps improve the overall reliability and dynamic adaptability of backup tasks.

[0107] It's important to note that while the warehouse storage data access index doesn't directly analyze the situation within the backup window, it reflects the data access characteristics during the time interval between two backups. This characteristic indirectly affects backup efficiency and reliability. The network fluctuation index, on the other hand, is a crucial indicator directly reflecting the network environment quality for backup tasks. Combining both provides important data for optimizing backup duration.

[0108] The purpose of the warehouse storage data access index is to assess the impact of data access behavior. The indirect impact of data access behavior is as follows: if data access is frequent and fluctuates significantly between the time intervals before and after a backup (low warehouse storage data access index value), it indicates that data access behavior during that period is unstable and may interfere with backup operations (e.g., access leading to excessive storage device load or resource contention during transmission). Conversely, if the warehouse storage data access index value is high, it indicates stable data access behavior with minimal impact on the backup task, and optimization can be appropriately implemented (e.g., shortening the backup window duration).

[0109] The relationship between access behavior and backup efficiency: Unstable data access may increase the risk of transmission conflicts or delays during backup, affecting backup efficiency. Based on the level of data access index in the warehouse, it can be inferred whether the current backup window is suitable to maintain the original duration.

[0110] The network volatility index assesses the direct impact of network conditions. Network volatility significantly affects the quality and speed of data transfer during backup. A low network volatility index (severe volatility) may prevent the planned task from being completed within the backup window, requiring an extended window duration. A high network volatility index (stable network) can shorten the window duration and improve the resource utilization of the backup task.

[0111] The complementarity between warehouse storage data access index and network volatility index is explained below:

[0112] The warehouse storage data access index and the network volatility index together provide a comprehensive perspective on data access characteristics (indirect) and network environment characteristics (direct):

[0113] Low warehouse data access index: This indicates that backup tasks are significantly affected by preceding and subsequent data access behaviors, and may require a longer backup window to ensure data integrity.

[0114] Low network fluctuation index: This reflects significant fluctuations in the network environment, which directly hinders backup tasks. The backup window needs to be extended to adapt to unstable transmission conditions.

[0115] By combining the warehouse storage data access index (indirectly assessing the impact of access) and the network fluctuation index (directly assessing network fluctuation), the backup window duration can be dynamically adjusted to take into account both data access behavior and network conditions.

[0116] Optimizing the backup window duration refers to: retrieving the initial preset backup window, which is classified as a low-quality backup window, and finding the corresponding warehouse product type for that product type's stored data within that initial preset backup window. The optimized formula for calculating the window duration of the re-backup is as follows:

[0117] ; This is a preset non-zero optimization factor used to control the optimization magnitude. To optimize the backup window duration, when the initial backup window is assessed as low quality, directly using the original window duration may not adapt to dynamic network fluctuations or high-load data access, leading to data transmission failures or incomplete backups. Optimizing the backup window duration better adapts to network and data access conditions, increasing the chances of successful transmission and ensuring complete data backup. Inappropriate backup durations lead to inefficient resource utilization: too short a duration: unable to complete a complete backup, requiring multiple retries and increasing overhead. too long a duration: excessive resource consumption hinders the scheduling of other tasks. Optimizing the backup window duration minimizes resource consumption while meeting backup requirements, improving system efficiency. Data access and network fluctuations are dynamic; backups without optimized durations may not adapt to these changes in real time, impacting backup efficiency. By dynamically optimizing the backup window duration, the system can adjust according to actual conditions, enhancing flexibility and adaptability.

[0118] Example 2

[0119] The intelligent management system for the storage of finished goods in the production and sales warehouse of medical devices also includes a backup cycle adjustment module, which is used to adjust the periodic backup cycle of the storage data of the corresponding type of finished goods in the warehouse according to the warehouse storage data access index.

[0120] Different types of medical devices have different data security requirements, and the backup cycle directly impacts system storage and bandwidth usage: A cycle that is too short frequently consumes storage and network resources, potentially leading to performance degradation or resource waste. A cycle that is too long delays backup data updates, increasing the risk of data loss. Dynamically adjusting the backup cycle balances resource allocation based on the data security storage needs of different device types, improving system efficiency. Furthermore, dynamically adjusting the backup cycle reduces unnecessary backup task frequency, alleviating system pressure and preventing performance degradation or failures caused by excessive backup tasks.

[0121] The backup cycle adjustment module works as follows: it retrieves the storage data of finished goods in the warehouse and sets the backup cycle period. and warehouse storage data access index Then perform the following calculations:

[0122] ; The preset non-zero adjustment factor, The frequency of regular backups for warehouse finished goods storage data, adjusted accordingly. Warehouse storage data access index. This enables long-term monitoring of data access behavior, provides data support for backup strategies, and improves the reliability of the medical device warehouse management system.

[0123] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0124] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply 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 this application.

[0125] 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 implementation should not be considered beyond the scope of this application.

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

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent management system for storing finished goods in a medical device manufacturing and sales warehouse, characterized in that, It includes a regular backup module, an information collection module, a warehouse data analysis module, a data transmission analysis module, a transmission window analysis module, a window quality classification module, and a backup window optimization module; The periodic backup module is used to periodically back up the stored data of finished goods in each type of warehouse according to the preset backup cycle. The information acquisition module is used to collect access information of finished goods storage data of various types of warehouses, as well as the operation information of backup operations in the backup window, to obtain access information sets and backup operation operation information sets of finished goods storage data of various types of warehouses respectively. The warehouse data analysis module is used to measure the degree of access to the storage data of finished goods in the warehouse of the corresponding type between two backups based on the access information set, and to generate a warehouse storage data access index. The data transmission analysis module is used to identify, based on the operating information set, whether there is a significant imbalance in the backup transmission of the corresponding type of warehouse finished goods storage data within the initial preset backup window. If there is a significant imbalance in the backup transmission, an activation signal is sent to the transmission window analysis module. When the transmission window analysis module receives the activation signal, it performs transmission window analysis based on the runtime information set and generates a network fluctuation index. The window quality classification module divides the initially preset backup windows into high-quality backup windows and low-quality backup windows based on the warehouse storage data access index and network fluctuation index. The backup window optimization module is used to re-back up stored data that was backed up in a low-quality backup window based on the initial preset backup window duration, and to optimize the window duration for re-backup.

2. The intelligent management system for storing finished goods in a medical device manufacturing and sales warehouse according to claim 1, characterized in that, It also includes a backup cycle adjustment module, which is used to adjust the periodic backup cycle of the storage data of the corresponding type of warehouse goods based on the warehouse storage data access index.

3. The intelligent management system for storing finished goods in a medical device production and sales warehouse according to claim 2, characterized in that, The data transmission analysis module is used to identify, based on the operational information set, whether there is a significant imbalance in the backup transmission of finished goods storage data for the corresponding type of warehouse goods within the initial preset backup window. From the running information set, all transmission bandwidth data within the initial preset backup window is obtained, and then arranged in chronological order to obtain the time series of transmission bandwidth data. Then, the difference time series corresponding to the difference between the transmission bandwidth data at two adjacent time points is calculated, the average value and standard deviation of the difference time series are obtained, and the ratio of the average value and standard deviation of the difference time series is calculated to obtain the transmission bandwidth fluctuation value. The minimum value in the time series of transmission bandwidth data is compared with a preset standard threshold, and the transmission bandwidth fluctuation value is compared with a preset standard fluctuation range. If the minimum value in the time series of transmission bandwidth data is greater than or equal to the preset standard threshold and the transmission bandwidth fluctuation value falls within the preset standard fluctuation range, an equilibrium signal is issued. If the minimum value in the time series of transmission bandwidth data is not greater than or equal to the preset standard threshold and the transmission bandwidth fluctuation value falls within the preset standard fluctuation range, an imbalance signal is issued. When an imbalance signal is issued, it is identified that there is a significant imbalance in the backup transmission of the corresponding type of warehouse goods finished product storage data within the initial preset backup window.

4. The intelligent management system for storing finished goods in a medical device production and sales warehouse according to claim 3, characterized in that, The logic for obtaining the warehouse storage data access index is as follows: The following feature indicators were extracted from the access information set: Average access frequency per unit time Total amount of data accessed The proportion of abnormal accesses to total accesses The variance of the access time distribution used to measure the uniformity of access. The percentage of times the actual number of visits exceeded the preset capacity. Based on the extracted feature indicators, a warehouse storage data access index is generated, with the following formula: ； ； ； This indicates the warehouse storage data access index. Indicates the preset capacity. This indicates that the warehouse stores data access value one. This indicates the second value for accessing warehouse stored data. , , , , All are preset non-zero weight factors, and the sum of all weight factors is one.

5. The intelligent management system for storing finished goods in a medical device production and sales warehouse according to claim 4, characterized in that, Variance of the access time distribution used to measure access uniformity The acquisition logic is as follows: Collect all access times between the two backups to form a time series. ,in Let be the time of the i-th access, and n be the total number of accesses. Then, divide the total time span between the two backups into m equal-width time intervals, and obtain the width of each time interval as ΔT: Record the number of visits in each time period to form a visit frequency distribution sequence. ,in This represents the number of visits in the i-th time period. Then, the variance is calculated using the visit frequency distribution sequence to obtain the variance of the visit time distribution used to measure the uniformity of visits. .

6. The intelligent management system for storing finished goods in a medical device production and sales warehouse according to claim 5, characterized in that, The logic for obtaining the network volatility index is as follows: The following core network operation data are obtained from the set of operational information acquired within the transmission window: Instantaneous bandwidth is the available bandwidth per unit of time. Delay fluctuation refers to the difference in delay between consecutive time points. The percentage of data packets lost per unit time out of the total number of data packets sent is called the packet loss ratio. Network utilization is the ratio of actual network bandwidth used per unit time to available bandwidth. t represents the index of each unit of time within the transmission window; Then, the following characteristic indicators within the transmission window are calculated: Bandwidth fluctuation coefficient : ; This represents the average bandwidth within the transmission window, where k is the total number of units of time within the transmission window. Delay variation coefficient : ; This represents the average delay within the transmission window; Network utilization stability coefficient : ; This represents the average network utilization within the transmission window; Packet loss ratio uniformity coefficient : ; Indicates the percentage of all packet losses Standard deviation; The formula for calculating the network volatility index is: ; Indicates network volatility index, This represents a preset constant.

7. The intelligent management system for storing finished goods in a medical device production and sales warehouse according to claim 6, characterized in that, When initially dividing the backup windows into high-quality backup windows and low-quality backup windows, fuzzy logic is used to take the warehouse storage data access index and network fluctuation index as input data, and the initial preset backup window division type as the output data of the fuzzy logic.

8. The intelligent management system for storing finished goods in a medical device production and sales warehouse according to claim 7, characterized in that, Optimizing the window duration for re-backup refers to: The initial preset backup window is categorized as a low-quality backup window. The time required to retrieve the initial preset backup window corresponding to the warehouse product type and its stored data is determined by the specific warehouse product type. The optimized formula for calculating the window duration of the re-backup is as follows: ; This is a preset non-zero optimization factor used to control the optimization magnitude. To optimize the window duration for re-backup.

9. The intelligent management system for storing finished goods in a medical device production and sales warehouse according to claim 8, characterized in that, The usage logic of the backup cycle adjustment module is as follows: The frequency of regularly backing up the storage data of finished goods in the warehouse. and warehouse storage data access index Then perform the following calculations: ; The preset non-zero adjustment factor, The periodic backup cycle for the adjusted storage data of finished goods in the warehouse.

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