Intelligent integrated management platform and method for logistics information and storage medium

Abstract

The invention discloses a logistics information intelligent integrated management platform and method and a storage medium, and relates to the technical field of logistics information management, and the platform comprises a data optimization demand analysis module, a data optimization label judgment module, a data compression execution module and a computing power enhancement execution module. According to the invention, targeted processing of the logistics data can be realized. Data which is in a service expansion period and has increased data volume but does not reach the computing power bottleneck can be compressed and optimized, so that the storage space is effectively released, the data management efficiency is improved, the influence of blind compression on key data is avoided, and the reasonability and effectiveness of data storage are improved. And when the data volume is increased sharply and processing is complex, computing power optimization can be triggered in time, it is ensured that the system efficiently processes large-scale complex data, efficient collaborative operation of all links of a logistics information chain is ensured in the data compression link, and the timeliness, accuracy and stability of logistics services are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics information management, and in particular to an intelligent integrated management platform, method and storage medium for logistics information. Background Art

[0002] With the booming e-commerce and the rapid development of the logistics industry, a huge amount of logistics data has been generated. Logistics companies need to efficiently process and analyze a large amount of data to optimize operational processes and improve distribution efficiency. Traditional management methods are difficult to cope with the rapid growth of data volume, and are prone to problems such as insufficient data storage and slow processing speed. Intelligent integrated management of logistics information can meet the growing information needs of the logistics industry and achieve efficient management and optimized utilization of logistics information.

[0003] The prior art, such as the invention patent with announcement number: CN117649166B, is a logistics information management method and system based on big data, which includes the following steps: real-time collection of completed logistics information data and pre-processing of completed logistics information data, and modification of completed logistics information data by modifying and deleting the pre-processed data; at the same time, real-time collection of ongoing logistics information and prediction of ongoing logistics information through data twins, and management of logistics information data by comparing predicted data with actual data.

[0004] The prior art, such as the invention patent with announcement number: CN111861323B, is a logistics information management method and device, storage medium, and computer system. The method includes: obtaining tasks to be picked up, and location information of each task to be picked up; classifying the tasks to be picked up according to the location information, and generating at least one subset of tasks to be processed based on the classification results; in response to a touch operation, generating a task merging processing request based on the subset of tasks to be processed, and sending it to the server side to obtain the electronic certificate of the task merging processing request fed back by the server side.

[0005] From the above solutions, it can be seen that the current logistics information management platform has certain limitations in intelligent integrated management. It focuses more on specific aspects of logistics information processing or the realization of isolated functions. For example, it only focuses on the preprocessing and modification of completed logistics information data, or only optimizes the classification and processing flow of tasks to be picked up. However, in the actual complex logistics operation environment, logistics information management involves dynamic changes in data scale. Therefore, the relatively decentralized and single function-oriented approach is difficult to achieve the ideal state in data optimization and computing power allocation, which may cause waste of data resources, untimely processing of logistics information, and insufficient ability of the system to respond to business changes. The problems restrict the effectiveness of the logistics information management platform in improving the overall efficiency of logistics operations. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent integrated management platform, method and storage medium for logistics information, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides an intelligent integrated management platform for logistics information, comprising: The data optimization demand analysis module is used to collect data scale parameters within a preset monitoring period, simultaneously extract related historical data scale parameters, and analyze the data optimization demand index of the logistics information chain.

[0008] The data optimization label judgment module is used to determine the data optimization label of the logistics information chain according to the data optimization demand index of the logistics information chain, and the data optimization label of the logistics information chain includes no optimization, compression optimization and computing power optimization.

[0009] The data compression execution module is used to obtain the usage parameters of various types of data in the logistics information chain when the data optimization label of the logistics information chain is judged as compression optimization, analyze the compression priority indicators of various types of data, and combine the data optimization demand index of the logistics information chain to compress the data of the logistics information chain.

[0010] The computing power enhancement execution module is used to collect the performance data of the logistics information chain when the data optimization label of the logistics information chain is judged as computing power optimization, and analyze the computing power enhancement mode of the logistics information chain in combination with the data optimization demand index of the logistics information chain, thereby enhancing the computing power of the logistics information chain.

[0011] A second aspect of the present invention provides a method for intelligent integrated management of logistics information, comprising the following steps: S1, within the preset monitoring period, collects data scale parameters in real time, simultaneously extracts related historical data scale parameters, and analyzes the data optimization demand index of the logistics information chain.

[0012] S2. Determine the data optimization label of the logistics information chain according to the data optimization demand index of the logistics information chain. The data optimization label of the logistics information chain includes no optimization, compression optimization and computing power optimization. When the data optimization label of the logistics information chain is determined to be compression optimization, execute S3. When the data optimization label of the logistics information chain is determined to be computing power optimization, execute S4.

[0013] S3, obtains the usage parameters of various types of data in the logistics information chain, analyzes the compression priority indicators of various types of data, and combines the data optimization demand index of the logistics information chain to compress the data of the logistics information chain.

[0014] S4, collects the performance data of the logistics information chain, analyzes the computing power enhancement mode of the logistics information chain, and thereby enhances the computing power of the logistics information chain.

[0015] A third aspect of the present invention provides a computer-readable storage medium for storing a program, which, when executed by a processor, implements an intelligent integrated management method for logistics information.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects: (1) The present invention provides an intelligent integrated management platform, method and storage medium for logistics information, which can accurately evaluate the data optimization demand index of the logistics information chain, and then scientifically determine the data optimization label based on the index to achieve targeted processing of logistics data. For data that is in the business expansion period and has an increasing amount of data but has not yet reached the computing power bottleneck, compression optimization can be performed to effectively release storage space and improve data management efficiency. When the amount of data increases dramatically and the processing is complex, computing power optimization can be triggered in time to ensure that the system efficiently processes large-scale complex data. In the data compression link, blind compression that affects key data is avoided to improve the rationality and effectiveness of data storage. When computing power is enhanced, it can not only improve computing power through moderate adjustment when demand is low, but also perform deep hardware adjustments when demand is high to fully solve the problem of insufficient computing power, ensure efficient and coordinated operation of all links in the logistics information chain, and greatly improve the timeliness, accuracy and stability of logistics services.

[0017] (2) The present invention helps to clearly understand the data status of the logistics information chain by determining the data optimization label of the logistics information chain, implement targeted adjustment methods for different statuses, avoid unnecessary resource investment, improve data storage and management efficiency, and ensure that the logistics information chain can still operate efficiently and smoothly when facing a sharp increase in data volume and a significant increase in processing complexity, thereby ensuring the smooth development of logistics business and improving overall operating efficiency.

[0018] (3) The present invention analyzes the compression priority indexes of various types of data, thereby compressing the data of the logistics information chain, making the data compression work more targeted and scientific, improving the utilization rate of data storage, and alleviating the storage pressure caused by the increase in data volume. At the same time, it avoids the problem of over-compression of key data affecting data accuracy and system performance. In addition, during the compression process, the data optimization demand index of the logistics information chain is combined to reasonably determine the remaining storage capacity required to ensure that the remaining capacity of the compressed storage space can meet the current demand without excessive compression causing difficulties in subsequent data management. It helps to optimize the data management process of the logistics information chain and improve the overall data management efficiency.

[0019] (4) The present invention analyzes the computing power enhancement mode of the logistics information chain and enhances the computing power of the logistics information chain, thereby achieving an accurate assessment of the computing power demand and ensuring that the logistics information chain can obtain matching computing power support when facing different degrees of data processing pressure. This improves the speed and accuracy of logistics information processing, avoids business freezes or delays caused by insufficient computing power, optimizes resource allocation, avoids over-investment or waste of resources, effectively reduces operating costs, and improves data processing efficiency.

[0020] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the platform module of the present invention.

[0022] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the embodiment of the present invention provides an intelligent integrated management platform for logistics information, including: The data optimization demand analysis module is used to collect data scale parameters within a preset monitoring period, simultaneously extract related historical data scale parameters, and analyze the data optimization demand index of the logistics information chain.

[0025] In this embodiment, the data scale parameters include the total number of data records in each logistics sub-data chain in the logistics information chain, the total data storage capacity, the total amount of new data, and the data growth rate.

[0026] It should be noted that the data scale parameters are collected through the monitoring tools of the information management system of the logistics information chain.

[0027] The total number of data records refers to the sum of the number of data records contained in each logistics sub-data chain. It intuitively reflects the richness of data in the logistics information chain. By counting it, we can understand the scale of data accumulated by different logistics sub-data chains.

[0028] The total data storage capacity refers to the total amount of storage space occupied by each logistics sub-data chain, which clarifies the overall occupancy of the logistics information chain in terms of storage data.

[0029] The total amount of new data refers to the sum of the newly added data in each logistics sub-data chain in the logistics information chain during the monitoring period. It can reflect the activity of logistics business activities and the speed of data update.

[0030] The data growth rate refers to the speed at which data in the logistics information chain grows. It is obtained by dividing the total amount of new data during the monitoring period by the total amount of initial data, and reflects the data growth trend of the logistics information chain.

[0031] In this embodiment, the data optimization demand index of the logistics information chain is analyzed, and the specific analysis process is as follows: The associated historical data scale parameters include the total amount of historical new data and the historical data growth rate of the logistics information chain.

[0032] It should be understood that the associated historical data refers to the historical data scale parameters corresponding to each logistics sub-data chain in the logistics information chain within the next monitoring period.

[0033] It should be noted that the associated historical data is directly extracted from the database.

[0034] The rated storage parameters of the logistics information chain stored in the database are extracted, including the maximum number of data records of the logistics information chain, the rated capacity of data storage, the reference total amount of new data and the reference data growth rate.

[0035] Based on the data scale parameter, the associated historical data scale parameter and the rated storage parameter of the logistics information chain, the data optimization demand index of the logistics information chain is analyzed and processed, and the data optimization demand index of the logistics information chain is used to characterize the degree of data optimization demand of the logistics information chain.

[0036] In a specific embodiment, the data optimization demand index of the logistics information chain is obtained in the following specific method: , in, Optimize the demand index for data in the logistics information chain, is the total number of data records of the i-th logistics sub-data chain in the logistics information chain, is the total data storage capacity of the i-th logistics sub-data chain in the logistics information chain, is the total amount of new data of the i-th logistics sub-data chain in the logistics information chain, is the data growth rate of the i-th logistics sub-data chain in the logistics information chain, The total amount of historical new data for the logistics information chain. is the historical data growth rate of the logistics information chain, is the maximum number of data records in the logistics information chain, The data storage rated capacity of the logistics information chain, The total amount of new data added for reference in the logistics information chain, is the reference data growth rate of the logistics information chain, i is the number of each logistics sub-data chain, , m is the number of logistics sub-data chains, and e is a natural constant.

[0037] It should be understood that the softplus function is a built-in function in Python. .

[0038] It should be noted that the data optimization demand index of the logistics information chain is obtained by analyzing and processing the data scale parameter and the associated historical data scale parameter, taking into account the mutual influence between them. For example, the increase in the total number of data records often leads to an increase in the total data storage capacity. With the development of logistics business, each new data record requires a certain amount of storage space. When the total number of data records continues to increase, the total data storage capacity occupied will also increase accordingly. The total amount of new data directly affects the change in the total number of data records. The total amount of new data reflects the amount of new data generated in a certain period of time. These new data will continue to accumulate to the total number of data records, causing the total number of data records to continue to grow. The data growth rate is calculated based on the change in the total number of data records. When the total number of data records grows rapidly over a period of time, the data growth rate will increase; conversely, if the total number of data records grows slowly or stagnates, the data growth rate will decrease. The total amount of new data is one of the key factors in calculating the data growth rate. The size of the total amount of new data directly affects the data growth rate. When the total amount of new data is large, the data growth rate will be higher within the same computing cycle; conversely, when the total amount of new data is small, the data growth rate will be lower.

[0039] In a specific embodiment, by analyzing the data optimization demand index of the logistics information chain, the data scale parameters and the associated historical data scale parameters are comprehensively analyzed to provide a numerical basis for future data optimization needs. When the index shows that the data optimization demand is low, the existing data management mode can be maintained to avoid excessive investment of resources. When the index indicates that the data volume is growing faster, compression optimization can be carried out in a timely manner to effectively release storage space, improve data management efficiency, ensure smooth operation of the system, avoid freezes or delays due to insufficient data processing capabilities, and achieve reasonable allocation of data resources, improve the efficiency of coordinated operation of each link in the logistics information chain, and enhance the timeliness, accuracy and stability of logistics services.

[0040] The data optimization label judgment module is used to determine the data optimization label of the logistics information chain according to the data optimization demand index of the logistics information chain, and the data optimization label of the logistics information chain includes no optimization, compression optimization and computing power optimization.

[0041] In this embodiment, the data optimization label of the logistics information chain is determined, and the specific determination process is as follows: Extract the first verification index of the data optimization requirement and the second verification index of the data optimization requirement stored in the database.

[0042] The first verification indicator of data optimization requirements and the second verification indicator of data optimization requirements are pre-set in the database. The first verification indicator of data optimization requirements is used to represent the critical indicator of data compression optimization requirements, and the second verification indicator of data optimization requirements is used to represent the critical indicator of data computing power optimization requirements.

[0043] If the data optimization demand index of the logistics information chain is less than the first verification indicator of the data optimization demand, the data optimization label of the logistics information chain is judged as not requiring optimization.

[0044] If the data optimization demand index of the logistics information chain is less than the first verification index of data optimization demand, it means that the current data status of the logistics information chain is relatively stable, and the expected growth rate of data volume, data storage and processing pressure are all at a low level. The existing data management strategy and resource allocation can better meet the data growth demand for a period of time in the future. There is no obvious demand for data optimization, the data growth rate is relatively slow, the system's storage capacity has not yet faced a tense situation, and there are no outstanding problems in data processing efficiency.

[0045] If the data optimization demand index of the logistics information chain is greater than or equal to the first verification index of the data optimization demand and less than or equal to the second verification index of the data optimization demand, the data optimization label of the logistics information chain is judged as compression optimization.

[0046] If the data optimization demand index of the logistics information chain is greater than or equal to the first verification index of the data optimization demand and less than or equal to the second verification index of the data optimization demand, it means that the logistics information chain is in a stage where the business is gradually expanding or the amount of data begins to accumulate, and the data growth rate has accelerated. Although the system can still operate normally, it has shown certain optimization needs. At this time, the data optimization demand index suggests optimizing data storage through means such as data compression. This can effectively release storage space and improve data management efficiency without large-scale hardware upgrades or computing power increases, thus reserving a certain amount of space for possible further data growth in the future.

[0047] If the data optimization demand index of the logistics information chain is greater than the second verification indicator of the data optimization demand, the data optimization label of the logistics information chain will be judged as computing power optimization.

[0048] If the data optimization demand index of the logistics information chain is greater than the second verification indicator of data optimization demand, it means that the amount of data is showing a sharp growth trend, and the complexity of data processing has also increased significantly. The existing data storage and computing resources can no longer effectively cope with this change. The data optimization demand index indicates that computing power optimization is needed, such as upgrading server hardware configuration, adopting more powerful cloud computing services, or optimizing data processing algorithms, to ensure that the logistics information chain can efficiently process large-scale and complex data and ensure the smooth progress of logistics business.

[0049] The present invention helps to clearly understand the data status of the logistics information chain by determining the data optimization label of the logistics information chain, implement targeted adjustment methods for different statuses, avoid unnecessary resource investment, improve data storage and management efficiency, and ensure that the logistics information chain can still operate efficiently and smoothly when facing a sharp increase in data volume and a significant increase in processing complexity, thereby ensuring the smooth development of logistics business and improving overall operating efficiency.

[0050] The data compression execution module is used to obtain the usage parameters of various types of data in the logistics information chain when the data optimization label of the logistics information chain is judged as compression optimization, analyze the compression priority indicators of various types of data, and combine the data optimization demand index of the logistics information chain to compress the data of the logistics information chain.

[0051] In this embodiment, the compression priority indexes of various types of data are analyzed, and the specific analysis process is as follows: Extract the preset monitoring period duration.

[0052] During the preset monitoring period, the number of times each type of data is read is counted, and the reading frequency of each type of data is calculated based on the length of the monitoring period.

[0053] Count the number of times each type of data is updated, and combine it with the length of the monitoring period to calculate the update frequency of each type of data.

[0054] The total number of times each type of data is cited is counted, and combined with the length of the monitoring period, the usage frequency of each type of data is calculated.

[0055] Synchronously locate the last time each type of data was operated and obtain the unused time of each type of data.

[0056] The reading frequency, updating frequency, usage frequency and unused time of each type of data are combined as the usage parameters of each type of data in the logistics information chain.

[0057] According to the usage parameters of various types of data in the logistics information chain, the compression priority index of each type of data is analyzed and processed to obtain the compression priority index of each type of data, and the compression priority index of each type of data is used to characterize the compression priority degree of each type of data.

[0058] In a specific embodiment, the compression priority index of each type of data is obtained in the following manner: , in, is the j-th type data compression priority index, is the reading frequency of the jth type of data, is the update frequency of the j-th type of data, is the usage frequency of the j-th type of data, is the unused time of the j-th type of data, is the deviation factor corresponding to the unit frequency read, is the deviation factor corresponding to the unit update frequency, is the deviation factor corresponding to the unit frequency, is the deviation factor corresponding to the unused duration of the unit, j is the number of the data type, , n is the number of data types, and e is a natural constant.

[0059] It should be noted that the deviation factor corresponding to the unit read frequency, the deviation factor corresponding to the unit updated frequency, the deviation factor corresponding to the unit usage frequency and the deviation factor corresponding to the unit unused time are directly extracted from the database. In a specific embodiment, the specific extraction method is, for example: by forming a mapping set with the deviation factors corresponding to the unit read frequency, the unit updated frequency, the unit usage frequency and the deviation factors corresponding to each interval of the read frequency, the updated frequency, the usage frequency and the unused time, and inputting the real-time read frequency, the updated frequency, the usage frequency and the unused time into the mapping set, thereby obtaining the corresponding deviation factors corresponding to the unit read frequency, the unit updated frequency, the unit usage frequency and the deviation factors corresponding to the unit unused time.

[0060] It should also be noted that the compression priority indicators of various types of data are obtained by analyzing and processing the usage parameters of various types of data in the logistics information chain, taking into account the interrelationship between these parameters. For example, in general, data with high reading frequency is often more likely to be updated. For example, the order status data in the logistics information chain needs to be updated in time (high update frequency) when the order status changes, such as goods leaving the warehouse, updating the location during transportation, and delivery, because logistics staff and customers frequently query the order progress (high reading frequency). The reading frequency is part of the frequency of use, and a high reading frequency will inevitably increase the frequency of use. However, the frequency of use also covers the application of data in other operations, such as the use in background processing such as data analysis and report generation. When data is frequently read and used for various operations in multiple business scenarios, its frequency of use will increase significantly. For example, logistics cost data is not only read when querying orders, but is also often used in financial statistics and cost analysis, which leads to its high frequency of use. Data with high reading frequency is usually not used for a short time. Frequent reading means that the data is in active use, and new operations are constantly involving the data, so that it will not be unused for a long time. Data that is updated frequently often plays an important role in business processes, and its frequency of use is usually high. Data that is updated frequently has a shorter unused period, because the update operation itself is a use of the data. Frequent updates mean that the data is always being paid attention to and maintained, and will not be idle for a long time. Data with a high usage frequency must have a shorter unused period, and continuous use keeps it in an active state. Data with a low usage frequency will gradually accumulate a longer unused period. Over time, if the data is not used for a long time, its value in the system may decrease, and it may even become redundant data.

[0061] In a specific embodiment, by analyzing the compression priority indexes of various types of data, an accurate data basis is provided for data compression. Prioritizing the compression of data with low usage frequency and long unused time can greatly improve the utilization efficiency of storage resources, effectively alleviate the storage pressure caused by the continuous increase in data volume, and ensure the stable operation and efficient storage capacity of the logistics information chain storage system. At the same time, it avoids the risk of over-compression of key data and maintains the integrity and accuracy of the data.

[0062] In this embodiment, data compression of the logistics information chain is performed, and the specific process is as follows: The demand remaining space ratio corresponding to each data optimization demand index interval stored in the database is extracted, and the demand remaining space ratio corresponding to the interval where the data optimization demand index of the logistics information chain is located is mapped and recorded as the demand remaining space ratio of the logistics information chain.

[0063] The total storage capacity of the logistics information chain is obtained synchronously, and the required remaining storage capacity of the logistics information chain is calculated based on the required remaining space ratio of the logistics information chain.

[0064] The demand surplus storage capacity is obtained by multiplying the total storage capacity of the logistics information chain by the demand surplus space ratio.

[0065] The compressible ratio corresponding to each compression priority index interval stored in the database is extracted, and the compressible ratio corresponding to the interval where the compression priority index of each type of data is located is mapped and extracted, and recorded as the compressible ratio of each type of data.

[0066] Each type of data is compressed in turn according to its compressible ratio, and the remaining storage space capacity of the logistics information chain after each compression is calculated synchronously until the remaining storage space capacity of the logistics information chain is greater than or equal to the required remaining storage capacity of the logistics information chain, and then the compression process is stopped.

[0067] In a specific embodiment, for example, the data optimization demand index of the current logistics information chain is calculated to be 0.45, and the required remaining space ratio extracted by its mapping is 0.6. If the total storage capacity of the logistics information chain is 1000GB, the required remaining storage capacity can be obtained by calculation as 1000GB×0.6=600GB. There are data types such as order data, transportation trajectory data, and cargo inventory data in the logistics information chain. After analysis, the compression priority index of order data is 0.45, and its corresponding compressible ratio is 0.3. When compressing the order data, assuming that the remaining storage space capacity after the first compression is 100GB, the cumulative remaining storage space capacity is 100GB at this time, which does not reach the required remaining storage capacity of 600GB. Other types of data continue to be compressed in sequence according to the compressible ratio until the cumulative remaining storage space capacity is greater than or equal to 600GB and the compression is stopped.

[0068] The present invention analyzes the compression priority indexes of various types of data, thereby compressing the data of the logistics information chain, making the data compression work more targeted and scientific, improving the utilization rate of data storage, and alleviating the storage pressure caused by the increase in data volume. At the same time, the problem of over-compression of key data affecting data accuracy and system performance is avoided. Moreover, in the compression process, the data optimization demand index of the logistics information chain is combined to reasonably determine the remaining storage capacity required to ensure that the remaining capacity of the compressed storage space can meet the current demand without excessive compression causing subsequent data management difficulties. It is helpful to optimize the data management process of the logistics information chain and improve the overall data management efficiency.

[0069] The computing power enhancement execution module is used to collect the performance data of the logistics information chain when the data optimization label of the logistics information chain is judged as computing power optimization, and analyze the computing power enhancement mode of the logistics information chain in combination with the data optimization demand index of the logistics information chain, thereby enhancing the computing power of the logistics information chain.

[0070] In this embodiment, the computing power enhancement mode of the logistics information chain is analyzed, and the specific analysis process is as follows: The computing power enhancement mode of the logistics information chain includes a first enhancement mode and a second enhancement mode.

[0071] According to the performance data of the logistics information chain and the data optimization demand index of the logistics information chain, the computing power enhancement demand index of the logistics information chain is obtained through analysis and processing.

[0072] In a specific embodiment, according to the performance data of the logistics information chain and the data optimization demand index of the logistics information chain, the computing power enhancement demand index of the logistics information chain is analyzed and processed, and the current performance status of the logistics information chain and the data optimization demand of the logistics information chain can be combined to realize accurate and efficient computing power resource planning and management. On the one hand, according to the CPU usage rate, memory occupancy rate, average data processing time and total task throughput in the performance data, the processing efficiency and load of the logistics information chain in actual operation can be analyzed. These performance status information are combined with the data optimization demand index, which reflects the changing trend of the data volume and the potential pressure on the system. Combining these performance status information with the data optimization demand index can accurately assess the actual demand degree of the logistics information chain for computing power enhancement, and can predict the computing power bottleneck in advance, and carry out targeted computing power upgrade or optimization strategy formulation to avoid jamming or delays caused by insufficient computing power, ensure that each link of the logistics information chain can operate efficiently and collaboratively, and improve the timeliness and accuracy of logistics services. At the same time, reasonable computing power planning helps to optimize resource allocation and avoid excessive investment in hardware equipment or cloud computing resources.

[0073] The performance data of the logistics information chain includes the CPU usage, memory occupancy, average data processing time and total task throughput of the logistics information chain.

[0074] It should be noted that the logistics information chain is a complex system that integrates a variety of information technologies and equipment to manage and optimize the entire logistics operation process.

[0075] The performance data of the logistics information chain is collected through the monitoring tools of the information management system of the logistics information chain.

[0076] The total task throughput refers to the total number of various tasks completed in the logistics information chain during the monitoring period. It covers the execution of transmission tasks in various links of the logistics business, from cargo receipt, warehouse management, transportation scheduling to distribution and delivery. For example, in an e-commerce logistics scenario, the total task throughput includes the cumulative sum of all relevant task data, such as the number of orders successfully processed within a certain period of time, the number of operations of completing cargo in and out of the warehouse, the number of transportation vehicles arranged, and the number of packages delivered.

[0077] The computing power enhancement demand index of the logistics information chain is used to reflect the data processing difficulty of the logistics information chain and to characterize the degree of computing power enhancement demand of the logistics information chain.

[0078] In a specific embodiment, the computing power enhancement demand index of the logistics information chain is obtained in the following way: Extract reference performance data stored in the database, including reference CPU usage, reference memory usage, reference average data processing time, and reference task total throughput.

[0079] , in, Enhance demand indicators for computing power in the logistics information chain, Optimize the demand index for data in the logistics information chain, is the CPU usage of the logistics information chain, is the memory usage of the logistics information chain, is the average data processing time of the logistics information chain, is the total task throughput of the logistics information chain, For reference CPU usage, For reference memory usage, For reference, the average data processing time is is the total throughput of the reference task.

[0080] It should be understood that the switch function is a built-in function in Python. .

[0081] It should be noted that the computing power enhancement demand index of the logistics information chain is obtained by analyzing and processing the performance data of the logistics information chain and the data optimization demand index of the logistics information chain, taking into account the mutual influence between these parameters. For example, when the number of tasks in the logistics information chain increases, such as a sharp increase in orders during promotional activities, the CPU usage rate may increase because more data computing tasks need to be processed. At the same time, in order to store and process the data related to these tasks, the memory usage rate will also increase accordingly. A high CPU usage rate may cause the system to run slower, thereby affecting the efficiency of memory management and further increasing the memory usage rate. If the average data processing time is long, the number of tasks that can be completed in the same time will decrease, resulting in a decrease in the total task throughput. A high CPU usage rate means that the CPU resources are tight and may not be able to respond to and process all data requests in time. This will increase the waiting time of data processing tasks in the queue, thereby extending the average data processing time. Too high a memory usage rate may limit the number of tasks that the system can process simultaneously, because insufficient memory resources will slow down data loading and exchange, affecting the efficiency of task execution, and thus reducing the total task throughput.

[0082] Extract the preset computing power enhancement requirement verification indicators in the database.

[0083] If the computing power enhancement demand index of the logistics information chain is less than the computing power enhancement demand verification index, the computing power enhancement mode of the logistics information chain is recorded as the first enhancement mode; if the computing power enhancement demand index of the logistics information chain is greater than or equal to the computing power enhancement demand verification index, the computing power enhancement mode of the logistics information chain is recorded as the second enhancement mode.

[0084] When the computing power enhancement mode of the logistics information chain is recorded as the first enhancement mode, the computing power enhancement demand indicator of the logistics information chain is subtracted from the computing power enhancement demand verification indicator to obtain the computing power enhancement demand deviation factor of the logistics information chain, extract the network bandwidth adjustment value corresponding to each demand deviation factor interval stored in the database, and map the network bandwidth adjustment value of the interval in which the computing power enhancement demand deviation factor of the logistics information chain is located, which is recorded as the computing power enhancement adjustment value of the logistics information chain. The network bandwidth of the logistics information chain is adjusted with the computing power enhancement adjustment value of the logistics information chain, thereby enhancing the computing power of the logistics information chain and simultaneously generating an early warning prompt.

[0085] It should be noted that if the computing power enhancement adjustment value of the logistics information chain is greater than the logistics information chain adjustment range, it will be adjusted according to the maximum value that the logistics information chain can bear.

[0086] When the computing power enhancement mode of the logistics information chain is recorded as the second enhancement mode, it is directly adjusted according to the maximum network bandwidth that the logistics information chain can bear, and an early warning prompt is generated simultaneously to remind personnel to adjust the hardware equipment of the logistics information chain.

[0087] It should be noted that if the computing power enhancement demand index of the logistics information chain is less than the computing power enhancement demand verification index, it means that the current data processing difficulty and computing power demand of the logistics information chain are relatively low. On the basis of the existing computing power, relatively small optimization measures such as appropriate adjustment of network bandwidth may be sufficient to meet business needs. At this time, the computing power enhancement demand deviation factor calculated reflects the gap with the critical computing power enhancement demand. According to the deviation factor, the appropriate network bandwidth adjustment value is mapped to enhance the computing power in a relatively gentle and economical way. At the same time, an early warning prompt is generated so that relevant personnel can pay attention to the system operation status and prevent possible performance problems. If the computing power enhancement demand index of the logistics information chain is greater than or equal to the computing power enhancement demand verification index, it means that the logistics information chain faces a high data processing difficulty and strong computing power demand. Conventional network bandwidth adjustment alone can no longer meet the requirements. It is necessary to directly use the maximum network bandwidth that the logistics information chain can bear for adjustment to maximize the use of existing network resources to improve computing power. However, this method may only temporarily relieve the computing power pressure, so an early warning prompt is generated simultaneously to remind personnel to further consider adjusting hardware equipment, such as upgrading servers, increasing memory, or replacing more powerful processors, etc., to fundamentally improve the computing power of the logistics information chain to ensure its efficient and stable operation.

[0088] The present invention analyzes the computing power enhancement mode of the logistics information chain and enhances the computing power of the logistics information chain, thereby achieving an accurate assessment of the computing power demand and ensuring that the logistics information chain can obtain matching computing power support when facing different degrees of data processing pressure. It improves the speed and accuracy of logistics information processing, avoids business freezes or delays caused by insufficient computing power, optimizes resource allocation, avoids excessive investment or waste of resources, effectively reduces operating costs, and improves data processing efficiency.

[0089] See also Figure 2 As shown, an embodiment of the present invention provides a method for intelligent integrated management of logistics information, comprising the following steps: S1, within the preset monitoring period, collects data scale parameters in real time, simultaneously extracts related historical data scale parameters, and analyzes the data optimization demand index of the logistics information chain.

[0090] S2. Determine the data optimization label of the logistics information chain according to the data optimization demand index of the logistics information chain. The data optimization label of the logistics information chain includes no optimization, compression optimization and computing power optimization. When the data optimization label of the logistics information chain is determined to be compression optimization, execute S3. When the data optimization label of the logistics information chain is determined to be computing power optimization, execute S4.

[0091] S3, obtains the usage parameters of various types of data in the logistics information chain, analyzes the compression priority indicators of various types of data, and combines the data optimization demand index of the logistics information chain to compress the data of the logistics information chain.

[0092] S4, collects the performance data of the logistics information chain, analyzes the computing power enhancement mode of the logistics information chain, and thereby enhances the computing power of the logistics information chain.

[0093] An embodiment of the present application provides a computer-readable storage medium for storing a program, which, when executed by a processor, implements an intelligent integrated management method for logistics information.

[0094] This embodiment provides an intelligent integrated management platform, method and storage medium for logistics information, which can accurately evaluate the data optimization demand index of the logistics information chain, and then scientifically determine the data optimization label based on the index to achieve targeted processing of logistics data. For data that is in the business expansion period and the data volume is increasing but has not yet reached the computing power bottleneck, compression optimization can be performed to effectively release storage space and improve data management efficiency. When the data volume increases dramatically and the processing is complex, computing power optimization can be triggered in time to ensure that the system efficiently processes large-scale complex data. In the data compression link, avoid blind compression that affects key data and improve the rationality and effectiveness of data storage. When the computing power is enhanced, it can not only improve the computing power through moderate adjustments when the demand is low, but also perform deep hardware adjustments when the demand is high to fully solve the problem of insufficient computing power, ensure the efficient and coordinated operation of all links in the logistics information chain, and greatly improve the timeliness, accuracy and stability of logistics services.

[0095] It should be noted that, in this article, relational terms such as first and second, etc. 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.

[0096] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. An intelligent integrated management platform for logistics information, characterized by: include: The data optimization demand analysis module is used to collect data scale parameters within the preset monitoring period, simultaneously extract the scale parameters of related historical data, and analyze the data optimization demand index of the logistics information chain; A data optimization label judgment module is used to determine the data optimization label of the logistics information chain according to the data optimization demand index of the logistics information chain, and the data optimization label of the logistics information chain includes no optimization, compression optimization and computing power optimization; The data compression execution module is used to obtain the usage parameters of various types of data in the logistics information chain when the data optimization tag of the logistics information chain is determined to be compression optimization, analyze the compression priority index of various types of data, and combine the data optimization demand index of the logistics information chain to perform data compression of the logistics information chain; The computing power enhancement execution module is used to collect the performance data of the logistics information chain when the data optimization label of the logistics information chain is judged as computing power optimization, and analyze the computing power enhancement mode of the logistics information chain in combination with the data optimization demand index of the logistics information chain, thereby enhancing the computing power of the logistics information chain.

2. The intelligent integrated management platform for logistics information according to claim 1 is characterized by: The data scale parameters are collected and the associated historical data scale parameters are extracted simultaneously, wherein: The data scale parameters include the total number of data records in each logistics sub-data chain in the logistics information chain, the total data storage capacity, the total amount of new data, and the data growth rate; The associated historical data scale parameters include the total amount of historical new data and the historical data growth rate of the logistics information chain.

3. The intelligent integrated management platform for logistics information according to claim 2 is characterized by: The data optimization demand index of the logistics information chain is analyzed, and the specific analysis process is as follows: Extract the rated storage parameters of the logistics information chain stored in the database, including the maximum number of data records of the logistics information chain, the rated capacity of data storage, the total reference amount of new data, and the reference data growth rate; Based on the data scale parameter, the associated historical data scale parameter and the rated storage parameter of the logistics information chain, the data optimization demand index of the logistics information chain is analyzed and processed, and the data optimization demand index of the logistics information chain is used to characterize the degree of data optimization demand of the logistics information chain.

4. The intelligent integrated management platform for logistics information according to claim 3 is characterized by: The data optimization label for determining the logistics information chain, the specific determination process is as follows: Extract the first verification index of data optimization requirement and the second verification index of data optimization requirement stored in the database; If the data optimization demand index of the logistics information chain is less than the first verification index of the data optimization demand, the data optimization label of the logistics information chain is judged as not requiring optimization; If the data optimization demand index of the logistics information chain is greater than or equal to the first verification index of the data optimization demand and less than or equal to the second verification index of the data optimization demand, the data optimization label of the logistics information chain is determined to be compression optimization; If the data optimization demand index of the logistics information chain is greater than the second verification indicator of the data optimization demand, the data optimization label of the logistics information chain will be judged as computing power optimization.

5. The intelligent integrated management platform for logistics information according to claim 1 is characterized by: The specific analysis process of analyzing the compression priority index of each type of data is as follows: Extract the preset monitoring period duration; During the preset monitoring period, the number of times each type of data is read is counted, and the reading frequency of each type of data is calculated based on the duration of the monitoring period; Count the number of times each type of data is updated, and calculate the update frequency of each type of data based on the monitoring period; Count the total number of times each type of data is referenced, and calculate the usage frequency of each type of data based on the monitoring period; Synchronously locate the last time each type of data was operated, and obtain the unused time of each type of data; The reading frequency, updating frequency, usage frequency and unused time of each type of data are combined as the usage parameters of each type of data in the logistics information chain; According to the usage parameters of various types of data in the logistics information chain, the compression priority index of each type of data is analyzed and processed to obtain the compression priority index of each type of data, and the compression priority index of each type of data is used to characterize the compression priority degree of each type of data.

6. The intelligent integrated management platform for logistics information according to claim 5 is characterized by: The specific process of data compression of the logistics information chain is as follows: Extract the demand remaining space ratio corresponding to each data optimization demand index interval stored in the database, and map the demand remaining space ratio corresponding to the interval where the data optimization demand index of the logistics information chain is located, and record it as the demand remaining space ratio of the logistics information chain; The total storage capacity of the logistics information chain is obtained simultaneously, and the required remaining storage capacity of the logistics information chain is calculated based on the required remaining space ratio of the logistics information chain; Extract the compressible ratio corresponding to each compression priority index interval stored in the database, and map and extract the compressible ratio corresponding to the interval where the compression priority index of each type of data is located, and record it as the compressible ratio of each type of data; Each type of data is compressed in turn according to its compressible ratio, and the remaining storage space capacity of the logistics information chain after each compression is calculated synchronously until the remaining storage space capacity of the logistics information chain is greater than or equal to the required remaining storage capacity of the logistics information chain, and then the compression process is stopped.

7. The intelligent integrated management platform for logistics information according to claim 1 is characterized by: The specific analysis process of the computing power enhancement mode of analyzing the logistics information chain is as follows: The computing power enhancement mode of the logistics information chain includes a first enhancement mode and a second enhancement mode; According to the performance data of the logistics information chain and the data optimization demand index of the logistics information chain, the computing power enhancement demand index of the logistics information chain is obtained through analysis and processing; The performance data of the logistics information chain includes the CPU usage rate, memory occupancy rate, average data processing time and total task throughput of the logistics information chain; The computing power enhancement demand index of the logistics information chain is used to reflect the data processing difficulty of the logistics information chain and to characterize the degree of computing power enhancement demand of the logistics information chain; Extract the computing power enhancement demand verification indicators preset in the database; If the computing power enhancement demand index of the logistics information chain is less than the computing power enhancement demand verification index, the computing power enhancement mode of the logistics information chain is recorded as the first enhancement mode; if the computing power enhancement demand index of the logistics information chain is greater than or equal to the computing power enhancement demand verification index, the computing power enhancement mode of the logistics information chain is recorded as the second enhancement mode.

8. The intelligent integrated management platform for logistics information according to claim 3 is characterized by: The data optimization demand index of the logistics information chain is specifically obtained as follows: ， in, Optimize the demand index for data in the logistics information chain, is the total number of data records of the i-th logistics sub-data chain in the logistics information chain, is the total data storage capacity of the i-th logistics sub-data chain in the logistics information chain, is the total amount of new data of the i-th logistics sub-data chain in the logistics information chain, is the data growth rate of the i-th logistics sub-data chain in the logistics information chain, The total amount of historical new data for the logistics information chain. is the historical data growth rate of the logistics information chain, is the maximum number of data records in the logistics information chain, The data storage rated capacity of the logistics information chain, The total amount of new data added for reference in the logistics information chain, is the reference data growth rate of the logistics information chain, i is the number of each logistics sub-data chain, , m is the number of logistics sub-data chains, and e is a natural constant.

9. A method applied to a logistics information intelligent integrated management platform as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: S1, within the preset monitoring period, real-time data scale parameters are collected, and related historical data scale parameters are extracted simultaneously to analyze the data optimization demand index of the logistics information chain; S2, according to the data optimization demand index of the logistics information chain, determine the data optimization label of the logistics information chain, the data optimization label of the logistics information chain includes no optimization, compression optimization and computing power optimization, when the data optimization label of the logistics information chain is determined to be compression optimization, execute S3, when the data optimization label of the logistics information chain is determined to be computing power optimization, execute S4; S3, obtaining the usage parameters of various types of data in the logistics information chain, analyzing the compression priority index of various types of data, combining the data optimization demand index of the logistics information chain, and thus compressing the data of the logistics information chain; S4, collects the performance data of the logistics information chain, analyzes the computing power enhancement mode of the logistics information chain, and thereby enhances the computing power of the logistics information chain.

10. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the method as claimed in claim 9 is implemented.

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