ERP system data synchronization processing batch optimization adjustment method and device, equipment and storage medium

Abstract

The invention discloses an ERP system data synchronization processing batch optimization adjustment method, device and equipment and a storage medium, and the method comprises the steps: monitoring a network performance parameter, a system load parameter and a data complexity parameter of an ERP system in a current processing period, and carrying out the optimization adjustment of the ERP system data synchronization processing batch based on the network performance parameter, the system load parameter and the data complexity parameter; and calculating to obtain a batch adjustment network factor, a batch adjustment load factor and a batch adjustment complexity factor, and optimizing the reference batch amount of the current processing period through the batch adjustment network factor, the batch adjustment load factor and the batch adjustment complexity factor to obtain the data synchronization processing batch amount of the current processing period. Therefore, by monitoring the network, the system load and the data complexity parameter in real time, calculating the corresponding adjustment factor optimization batch quantity and dynamically adapting the operation state, the fixed batch stiffness is avoided, the data synchronization efficiency is improved, and the system overload is avoided, so that the optimal utilization of resources is realized.

Description

Technical Field

[0001] This application relates to the field of ERP system optimization technology, and more specifically, to an optimization and adjustment method, apparatus, equipment and storage medium for batch data synchronization processing in an ERP system. Background Technology

[0002] Enterprise Resource Planning (ERP) systems are core information systems for modern enterprise management, evolving from single-function modules into integrated management platforms. As enterprises continue to expand and business scenarios become more diverse, they need to coordinate multiple systems such as finance, supply chain, and production management. Data synchronization, as a key link connecting these systems and ensuring business continuity, directly impacts the operational effectiveness of the enterprise.

[0003] The existing ERP system data synchronization solution adopts a batch processing strategy with a fixed batch size. This strategy presets a fixed batch size during the design phase and keeps it unchanged throughout the synchronization process. When various factors fluctuate dynamically in the actual application scenario, the data synchronization process cannot be adapted and adjusted because it remains unchanged. This makes it difficult to match the actual operating status of the system, resulting in low overall data synchronization efficiency. In some cases, improper batch settings may even bring additional burden to the system operation.

[0004] Therefore, how to optimize and flexibly adjust the batch processing of data synchronization in the ERP system to improve data synchronization efficiency and ensure the stability of the ERP system while optimizing resource utilization is a problem that needs to be solved. Summary of the Invention

[0005] In view of the above problems, this application provides an optimization adjustment method, apparatus, equipment and storage medium for batch data synchronization processing in an ERP system, so as to improve data synchronization efficiency and ensure the stability of the ERP system while optimizing resource utilization.

[0006] To achieve the above objectives, the following specific solutions are proposed:

[0007] An optimization and adjustment method for batch data synchronization processing in an ERP system includes:

[0008] Monitor the network performance parameters, system load parameters, and data complexity parameters of the ERP system during the current processing cycle;

[0009] Based on the network performance parameters, the system load parameters, and the data complexity parameters, the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor are calculated respectively.

[0010] The reference batch size for the current processing cycle is optimized by the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor to obtain the data synchronization processing batch size for the current processing cycle, so that the ERP system can use the data synchronization processing batch size to perform data synchronization for the current processing cycle.

[0011] Optionally, the calculation of the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor based on the network performance parameters, the system load parameters, and the data complexity parameters respectively includes:

[0012] Calculate the current network condition score and the optimal network condition score based on the network performance parameters, and calculate the batch adjustment network factor using the current network condition score and the optimal network condition score;

[0013] Calculate the current system load score and the maximum load score based on the system load parameters, and calculate the batch adjustment load factor using the current system load score and the maximum load score;

[0014] The current data complexity score and the highest data complexity score are calculated based on the data complexity parameter, and the batch adjustment complexity factor is calculated using the current data complexity score and the highest data complexity score.

[0015] Optionally, the network performance parameters include network latency, bandwidth utilization, and packet loss rate;

[0016] Calculate the current network condition score and the optimal network condition score based on the network performance parameters, and calculate the batch adjustment network factor using the current network condition score and the optimal network condition score, including:

[0017] Based on the network latency value, the latency score is calculated using the following formula:

[0018]

[0019] in, For the aforementioned delay score, The network latency value, The maximum acceptable network latency threshold;

[0020] Subtract the bandwidth utilization rate from 1 to obtain the bandwidth score;

[0021] Based on the packet loss rate, the packet loss score is calculated using the following formula:

[0022]

[0023] in, Score the packet loss. The packet loss rate is... This is the maximum acceptable packet loss rate threshold.

[0024] The following formula is used to calculate the current network condition score and the optimal network condition score:

[0025]

[0026] in, Rate the current network condition. Score the bandwidth. , and These are the first weight coefficient, the second weight coefficient, and the third weight coefficient of the network factor, respectively. , The optimal network condition score;

[0027] Divide the current network condition score by the optimal network condition score to obtain the batch adjustment network factor.

[0028] Optionally, the system load parameters include CPU utilization, memory utilization, thread pool utilization, and processing queue length;

[0029] Calculate the current system load score and maximum load score based on the system load parameters, and calculate the batch adjustment load factor using the current system load score and the maximum load score, including:

[0030] Divide the length of the processing queue by the maximum capacity of the processing queue to obtain the processing queue utilization rate.

[0031] Calculate the current system load score and the maximum load score using the following formula:

[0032]

[0033] in, Score the current system load. The CPU utilization rate, The memory usage rate, The thread pool utilization rate, The processing queue utilization rate, , , and These are the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient of the load factor, respectively. , The maximum load fraction;

[0034] Divide the current system load score by the maximum load score to obtain the batch adjustment load factor.

[0035] Optionally, the data complexity parameters include: the number of fields of the business object in the data to be synchronized, the nesting level of the data structure of the business object, the number of associated objects of the business object, the data type contained in the data to be synchronized, and the number of business rules of the business object;

[0036] Calculate the current data complexity score and the highest data complexity score based on the data complexity parameter, and calculate the batch adjustment complexity factor using the current data complexity score and the highest data complexity score, including:

[0037] Based on the number of fields in the business object, the field number complexity score is calculated using the following formula:

[0038]

[0039] in, Score the complexity of the number of fields. The number of fields in the business object. Number of baseline fields;

[0040] Based on the nesting level of the data structure of the business object, the nesting level complexity score is calculated using the following formula:

[0041]

[0042] in, Score the complexity of the nested levels. The data structure nesting hierarchy of the business object. This represents the maximum nesting level.

[0043] Based on the number of associated objects of the business object, the complexity score of the associated objects is calculated using the following formula:

[0044]

[0045] in, Score the complexity of the associated objects. The number of associated objects of the business object. The number of associated objects is the baseline.

[0046] Based on the data types contained in the data to be synchronized, the data type complexity score is calculated using the following formula:

[0047]

[0048] in, Score the complexity of the data type. Let i be the number of objects of the i-th data type. The unit weight for the i-th data type, This represents the sum of the weights of the largest type.

[0049] Based on the number of business rules for the business object, the business rule complexity score is calculated using the following formula:

[0050]

[0051] in, Score the complexity of the associated objects. The number of business rules for the business object. The number of baseline rules;

[0052] Calculate the current data complexity score and the highest data complexity score using the following formula:

[0053]

[0054] in, Score the complexity of the current data. The highest data complexity score, , , , and These are the first, second, third, fourth, and fifth weighting coefficients of the complexity factor, respectively. ;

[0055] Divide the current data complexity score by the highest data complexity score to obtain the batch adjustment complexity factor.

[0056] Optionally, the reference batch size for the current processing cycle is optimized using the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor to obtain the data synchronization processing batch size for the current processing cycle, including:

[0057] The batch size for data synchronization processing in the current processing cycle is calculated using the following formula:

[0058]

[0059] in, This is the reference batch size for the current processing cycle. To synchronize the batch size of data for the current processing cycle, , and These are the weighting coefficients for network factors, system load factors, and complexity factors, respectively. Adjust the network factor for the batch. Adjust the load factor for the batch. Adjust the complexity factor for the batch.

[0060] Optionally, the method further includes:

[0061] If the batch adjustment load factor is greater than the high threshold of the load factor or the batch adjustment complexity factor is greater than the high threshold of the complexity factor, then the batch size of the data synchronization processing is multiplied by the reduction coefficient, and the result is used as the reference batch size for the next processing cycle.

[0062] If the batch adjustment load factor is less than the low threshold of the load factor and the batch adjustment complexity factor is less than the low threshold of the complexity factor, then the batch size of the data synchronization processing is multiplied by the amplification coefficient, and the result is used as the reference batch size for the next processing cycle.

[0063] If the batch adjustment load factor is not greater than the high threshold of the load factor and the batch adjustment complexity factor is not greater than the high threshold of the complexity factor, and the first condition is met, then the data synchronization processing batch size is used as the reference batch size for the next processing cycle, wherein the first condition is that the batch adjustment load factor is not less than the low threshold of the load factor or the batch adjustment complexity factor is not less than the low threshold of the complexity factor.

[0064] An optimization and adjustment device for batch data synchronization processing in an ERP system, comprising:

[0065] The parameter monitoring module is used to monitor the network performance parameters, system load parameters, and data complexity parameters of the ERP system in the current processing cycle.

[0066] The batch adjustment factor calculation module is used to calculate the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor based on the network performance parameters, the system load parameters, and the data complexity parameters, respectively.

[0067] The batch size optimization module is used to optimize the reference batch size of the current processing cycle through the batch adjustment network factor, the batch adjustment load factor and the batch adjustment complexity factor to obtain the data synchronization processing batch size of the current processing cycle, so that the ERP system can use the data synchronization processing batch size to perform data synchronization for the current processing cycle.

[0068] Optionally, the batch adjustment factor calculation module includes:

[0069] The batch adjustment network factor calculation module is used to calculate the current network status score and the optimal network status score based on the network performance parameters, and to calculate the batch adjustment network factor using the current network status score and the optimal network status score.

[0070] The batch adjustment load factor calculation module is used to calculate the current system load score and the maximum load score based on the system load parameters, and to calculate the batch adjustment load factor using the current system load score and the maximum load score.

[0071] The batch adjustment complexity factor calculation module is used to calculate the current data complexity score and the highest data complexity score based on the data complexity parameter, and to calculate the batch adjustment complexity factor using the current data complexity score and the highest data complexity score.

[0072] Optionally, the network performance parameters include network latency, bandwidth utilization, and packet loss rate;

[0073] The batch adjustment network factor calculation module includes:

[0074] The latency score calculation module is used to calculate the latency score based on the network latency value using the following formula:

[0075]

[0076] in, For the aforementioned delay score, The network latency value, The maximum acceptable network latency threshold;

[0077] The bandwidth score calculation module is used to subtract the bandwidth utilization rate from 1 to obtain the bandwidth score.

[0078] The packet loss score calculation module is used to calculate the packet loss score based on the packet loss rate using the following formula:

[0079]

[0080] in, Score the packet loss. The packet loss rate is... This is the maximum acceptable packet loss rate threshold.

[0081] The network condition score calculation module uses the following formula to calculate the current network condition score and the optimal network condition score:

[0082]

[0083] in, Rate the current network condition. Score the bandwidth. , and These are the first weight coefficient, the second weight coefficient, and the third weight coefficient of the network factor, respectively. , The optimal network condition score;

[0084] The batch adjustment network factor determination module is used to divide the current network condition score by the optimal network condition score to obtain the batch adjustment network factor.

[0085] Optionally, the system load parameters include CPU utilization, memory utilization, thread pool utilization, and processing queue length;

[0086] The batch adjustment load factor calculation module includes:

[0087] The processing queue utilization calculation module is used to divide the length of the processing queue by the maximum capacity of the processing queue to obtain the processing queue utilization rate.

[0088] The load score calculation module is used to calculate the current system load score and the maximum load score using the following formula:

[0089]

[0090] in, Score the current system load. The CPU utilization rate, The memory usage rate, The thread pool utilization rate, The processing queue utilization rate, , , and These are the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient of the load factor, respectively. , The maximum load fraction;

[0091] The batch adjustment load factor determination module is used to divide the current system load score by the maximum load score to obtain the batch adjustment load factor.

[0092] Optionally, the data complexity parameters include: the number of fields of the business object in the data to be synchronized, the nesting level of the data structure of the business object, the number of associated objects of the business object, the data type contained in the data to be synchronized, and the number of business rules of the business object;

[0093] The batch adjustment complexity factor calculation module includes:

[0094] The field quantity complexity score calculation module is used to calculate the field quantity complexity score based on the number of fields of the business object using the following formula:

[0095]

[0096] in, Score the complexity of the number of fields. The number of fields in the business object. Number of baseline fields;

[0097] Based on the nesting level of the data structure of the business object, the nesting level complexity score is calculated using the following formula:

[0098]

[0099] in, Score the complexity of the nested levels. The data structure nesting hierarchy of the business object. This represents the maximum nesting level.

[0100] The associated object complexity score calculation module is used to calculate the associated object complexity score based on the number of associated objects of the business object using the following formula:

[0101]

[0102] in, Score the complexity of the associated objects. The number of associated objects of the business object. The number of associated objects is the baseline.

[0103] The data type complexity score calculation module is used to calculate the data type complexity score based on the data type contained in the data to be synchronized using the following formula:

[0104]

[0105] in, Score the complexity of the data type. Let i be the number of objects of the i-th data type. The unit weight for the i-th data type, This represents the sum of the weights of the largest type.

[0106] The business rule complexity score calculation module is used to calculate the business rule complexity score based on the number of business rules of the business object using the following formula:

[0107]

[0108] in, Score the complexity of the associated objects. The number of business rules for the business object. The number of baseline rules;

[0109] The complexity score calculation module is used to calculate the current data complexity score and the highest data complexity score using the following formula:

[0110]

[0111] in, Score the complexity of the current data. The highest data complexity score, , , , and These are the first, second, third, fourth, and fifth weighting coefficients of the complexity factor, respectively. ;

[0112] The batch adjustment complexity factor determination module is used to divide the current data complexity score by the highest data complexity score to obtain the batch adjustment complexity factor.

[0113] Optionally, the batch size optimization module includes:

[0114] The batch size calculation module is used to calculate the data synchronization batch size for the current processing cycle using the following formula:

[0115]

[0116] in, This is the reference batch size for the current processing cycle. To synchronize the batch size of data for the current processing cycle, , and These are the weighting coefficients for network factors, system load factors, and complexity factors, respectively. Adjust the network factor for the batch. Adjust the load factor for the batch. Adjust the complexity factor for the batch.

[0117] Optionally, the device may also include:

[0118] The next reference batch quantity first determination module is used to multiply the data synchronization processing batch quantity by the reduction coefficient if the batch adjustment load factor is greater than the load factor high threshold or the batch adjustment complexity factor is greater than the complexity factor high threshold, and the result is used as the reference batch quantity for the next processing cycle.

[0119] The next reference batch size second determination module is used to multiply the data synchronization processing batch size by the amplification coefficient if the batch adjustment load factor is less than the load factor low threshold and the batch adjustment complexity factor is less than the complexity factor low threshold, and the result is used as the reference batch size for the next processing cycle.

[0120] The third module for determining the next reference batch size is used to determine the data synchronization processing batch size as the reference batch size for the next processing cycle if the batch adjustment load factor is not greater than the high threshold of the load factor, and the batch adjustment complexity factor is not greater than the high threshold of the complexity factor, and the first condition is met.

[0121] An optimization and adjustment device for batch data synchronization processing in an ERP system, comprising a memory and a processor;

[0122] The memory is used to store programs;

[0123] The processor is used to execute the program to implement the various steps of the ERP system data synchronization processing batch optimization and adjustment method as described above.

[0124] A storage medium storing a computer program, which, when executed by a processor, implements the steps of the optimization and adjustment method for batch data synchronization processing in an ERP system as described above.

[0125] By employing the aforementioned technical solution, this application monitors the network performance parameters, system load parameters, and data complexity parameters of the ERP system during the current processing cycle. Based on these parameters, it calculates batch adjustment network factors, batch adjustment load factors, and batch adjustment complexity factors. These factors are then used to optimize the reference batch size for the current processing cycle, resulting in the current data synchronization batch size. This batch size is then used by the ERP system for data synchronization during the current processing cycle. Therefore, by real-time monitoring of network, system load, and data complexity parameters, and calculating corresponding adjustment factors to optimize the batch size, the system dynamically adapts to the operating state, avoids rigid fixed batches, improves data synchronization efficiency, and prevents system overload, thereby achieving optimal resource utilization. Attached Figure Description

[0126] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0127] Figure 1 This is a schematic diagram illustrating a process for optimizing and adjusting batches of data synchronization processing in an ERP system, as provided in an embodiment of this application.

[0128] Figure 2 A flowchart illustrating the process of adjusting the reference batch size for the next processing cycle is provided in this embodiment of the application.

[0129] Figure 3 A schematic diagram of a device structure for optimizing and adjusting batches of data synchronization processing in an ERP system, provided in an embodiment of this application;

[0130] Figure 4 This is a schematic diagram of the structure of a device for optimizing and adjusting batches of data synchronization processing in an ERP system, provided as an embodiment of this application. Detailed Implementation

[0131] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0132] The proposed solution can be implemented based on a terminal with data processing capabilities, such as a computer, cloud, or server. This terminal can monitor various operational performance and business data parameters of the ERP system within the ERP system.

[0133] Next, combined Figure 1 The optimization and adjustment method for batch data synchronization processing in the ERP system of this application may include the following steps:

[0134] Step S110: Monitor the network performance parameters, system load parameters, and data complexity parameters of the ERP system during the current processing cycle.

[0135] Specifically, each time the ERP system optimizes and adjusts the batch size for synchronization, it needs to monitor and obtain various parameters of the current processing cycle. These parameters include network performance parameters such as network latency, bandwidth utilization, and packet loss rate; system load parameters such as CPU utilization, memory utilization, thread pool utilization, and processing queue length; and data complexity parameters such as the number of fields in the business objects in the data to be synchronized, the nesting level of the data structure of the business objects, the number of associated objects of the business objects, the data types contained in the data to be synchronized, and the number of business rules of the business objects.

[0136] Understandably, network performance parameters directly determine the smoothness of data transmission and have a positive impact on batch size. Lower network latency means shorter round-trip data transmission time, supporting continuous transmission of larger batches of data; lower bandwidth utilization means more remaining network capacity, accommodating more concurrent data transmission; lower packet loss rate means higher data transmission integrity, eliminating the need for frequent retransmissions and reducing the overhead of batch splitting. For example, when network conditions are favorable (low latency, low bandwidth usage, low packet loss), batch size can be significantly increased to maximize synchronization efficiency; when network conditions are poor (high latency, high bandwidth usage, high packet loss), the network factor decreases, and the batch size decreases accordingly, avoiding data congestion or loss due to transmission bottlenecks.

[0137] System load parameters reflect the system's hardware and task scheduling capacity, and have an inverse impact on batch size. Higher CPU and memory utilization means less remaining computing and storage resources; excessively large batch sizes can lead to resource exhaustion and processing delays. High thread pool utilization indicates that active threads are nearing their limit, and adding large batches of tasks can cause thread blocking. Longer processing queues indicate a backlog of tasks, and excessively large batch sizes exacerbate queue congestion. High system load indicates greater system pressure, requiring controlled gain during batch size calculations. Conversely, low system load results in a smaller load factor, allowing for larger batch sizes to fully utilize idle resources and avoid resource idleness.

[0138] Data complexity determines the processing difficulty of a single batch of data and has a reverse effect on batch size. The more fields and the deeper the nesting, the longer the data parsing and structuring processing takes. A large number of related objects requires additional handling of cross-object relationship logic; parsing complex data types (such as JSON and collections) is more costly than parsing basic types; and a large number of business rules necessitates more validation and computation operations. Excessive batch size can easily lead to timeouts in individual batch processing, prolonged consumption of system resources, and impact on other business operations. Therefore, a high complexity factor limits batch size growth, while when the data structure is simple and there are few business rules, the batch size can be appropriately increased to improve overall synchronization efficiency through batch processing, balancing processing difficulty and synchronization speed.

[0139] Step S120: Calculate the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor based on network performance parameters, system load parameters, and data complexity parameters, respectively.

[0140] The batch adjustment network factor is a standardized parameter used to quantify the current network transmission adaptability. It is calculated by weighting network latency, bandwidth utilization, and packet loss rate to obtain a score of the current network condition, which is then compared with the score of the best network condition. A higher batch adjustment network factor indicates stronger network transmission smoothness and stability, and better adaptability to larger data synchronization batches; conversely, a lower value requires reducing the batch size to avoid transmission risks.

[0141] Batch load factor is a standardized metric used to measure the pressure on system resources. Specifically, it is calculated by weighting CPU utilization, memory utilization, thread pool utilization, and processing queue length to determine the current load score, which is then compared to the system's maximum load capacity score. A higher batch load factor indicates more strained system resources, requiring limiting batch size to avoid overload; a lower value indicates more idle resources, allowing for increased batch size to utilize resources more effectively.

[0142] The batch adjustment complexity factor is a standardized parameter characterizing the difficulty of processing a single batch of data. Specifically, it can be calculated by weighting five types of data complexity parameters, such as the number of fields and nesting levels, and then comparing the current complexity score to the highest complexity score. A higher batch adjustment complexity factor means a higher cost per batch of data processing, requiring control of batch size to prevent timeouts; a lower factor allows for larger batch sizes to improve synchronization efficiency.

[0143] Step S130: Optimize the reference batch size for the current processing cycle by adjusting the network factor, load factor, and complexity factor to obtain the data synchronization processing batch size for the current processing cycle, so that the ERP system can use the data synchronization processing batch size to perform data synchronization for the current processing cycle.

[0144] Specifically, during the optimization of the reference batch size, the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor are all positively correlated with the data synchronization processing batch size. That is, the data synchronization processing batch size increases with the increase of the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor.

[0145] Understandably, by adjusting the data synchronization batch size using factors such as network, load, and complexity, the rigidity of fixed batches can be avoided. This allows for flexible adjustment based on network connectivity, system resource availability, and data processing difficulty, thereby maximizing system resource utilization while ensuring data synchronization stability and balancing synchronization speed with operational safety.

[0146] The ERP system data synchronization batch optimization method provided in this embodiment monitors the network performance parameters, system load parameters, and data complexity parameters of the ERP system in the current processing cycle. Based on these parameters, it calculates batch adjustment network factors, batch adjustment load factors, and batch adjustment complexity factors. These factors are then used to optimize the reference batch size for the current processing cycle, resulting in the current data synchronization batch size for the ERP system to use. Therefore, by real-time monitoring of network, system load, and data complexity parameters, and calculating corresponding adjustment factors to optimize the batch size, the method dynamically adapts to the operating state, avoids rigid fixed batches, improves data synchronization efficiency, and prevents system overload, thus achieving optimal resource utilization.

[0147] In some embodiments of this application, the process of calculating the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor based on network performance parameters, system load parameters, and data complexity parameters, respectively, in step S120 is described. This process may include the following three steps:

[0148] First, calculate the current network condition score and the optimal network condition score based on the network performance parameters, and then calculate the batch adjustment network factor using the current network condition score and the optimal network condition score.

[0149] Specifically, the process of calculating batch adjustment network factors based on network performance parameters including network latency, bandwidth utilization, and packet loss rate can include:

[0150] S11. Calculate the latency score based on the network latency value using the following formula:

[0151]

[0152] in, To delay the scoring, This represents the network latency value. This is the maximum acceptable network latency threshold.

[0153] Understandably, delayed scoring The value range is 0-1, when the network latency value The smaller the value, the larger the latency score, which in turn leads to a higher current network condition score, a larger batch adjustment network factor, and guides the data synchronization processing to increase the batch size for adjustment. Conversely, a larger network latency value... The larger the value, the smaller the batch size for data synchronization processing should be adjusted. When the network latency value... Exceeding the maximum acceptable network latency threshold After that, it indicates severe network congestion, the latency score is 0, and it will not continue to decrease.

[0154] S12. Subtract the bandwidth utilization rate from 1 to obtain the bandwidth score.

[0155] Understandably, higher bandwidth utilization indicates less remaining network capacity, allowing for smaller concurrent data transmissions. Therefore, higher bandwidth utilization guides adjustments to reduce data synchronization batch sizes (higher bandwidth utilization → bandwidth score). The smaller the value, the lower the current network condition score, which guides the adjustment of the batch size of data synchronization processing (the smaller the network factor, the smaller the batch size of data synchronization processing). Conversely, the smaller the bandwidth utilization, the greater the batch size of data synchronization processing.

[0156] S13. Calculate the packet loss score based on the packet loss rate using the following formula:

[0157]

[0158] in, Score for packet loss. For packet loss rate, This is the maximum acceptable packet loss rate threshold.

[0159] Understandable is packet loss scoring. The value range is 0-1, when the packet loss rate The smaller the value, the higher the packet loss score, which in turn leads to a higher current network condition score. This results in a larger batch adjustment network factor, guiding the data synchronization processing to increase the batch size for adjustments. Conversely, a smaller value indicates a lower packet loss rate. The higher the value, the smaller the batch size for data synchronization processing should be adjusted. When the packet loss rate... Exceeding the maximum acceptable packet loss rate threshold This indicates that significant data loss occurred during data transmission, resulting in a packet loss score of 0, which will not decrease further.

[0160] S14. Calculate the current network condition score and the optimal network condition score using the following formula:

[0161]

[0162] in, Rate the current network condition. Rate bandwidth , and These are the first weight coefficient, the second weight coefficient, and the third weight coefficient of the network factor, respectively. It can be obtained through testing. The optimal network condition score.

[0163] Understandably, the optimal network state score This value is obtained when network latency reaches its optimal level (e.g., below 50ms), bandwidth utilization is low (e.g., below 30%), and there is no packet loss. For example, when... , , When the values ​​are 0.4, 0.4, and 0.2 respectively, =0.4+0.7*0.4+0.2=0.88.

[0164] S15. Divide the current network condition score by the optimal network condition score to obtain the batch adjustment network factor.

[0165] Second, calculate the current system load score and maximum load score based on the system load parameters, and then calculate the batch adjustment load factor using the current system load score and maximum load score.

[0166] Specifically, the process of calculating the batch load adjustment factor based on system load parameters including CPU utilization, memory utilization, thread pool utilization, and processing queue length can include:

[0167] S21. Divide the length of the processing queue by the maximum capacity of the processing queue to obtain the processing queue utilization rate.

[0168] Specifically, the processing queue utilization rate can represent the degree of utilization of queue resources. The longer the processing queue is, the higher the processing queue utilization rate. The backlog of tasks to be processed and the excessive batch volume will exacerbate queue congestion.

[0169] S22. Calculate the current system load score and the maximum load score using the following formula:

[0170]

[0171] in, Score the current system load. For CPU utilization, For memory usage, For thread pool utilization, To handle queue utilization, , , and These are the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient of the load factor, respectively. The weighting coefficients can be obtained through testing. The maximum load score represents the critical load capacity for safe system operation. The maximum load capacity is 85% for CPU utilization, 85% for memory utilization, 90% for thread pool utilization, and 80% for queue length.

[0172] S23. Divide the current system load score by the maximum load score to obtain the batch adjustment load factor.

[0173] It should be noted that system load has an inverse effect on batch size, and the batch adjusts the load factor. Since the value is positive, the weighting coefficient corresponding to the batch adjustment load factor is negative when calculating the batch size of data synchronously.

[0174] Third, calculate the current data complexity score and the highest data complexity score based on the data complexity parameter, and calculate the batch adjustment complexity factor using the current data complexity score and the highest data complexity score.

[0175] Specifically, the process of calculating the batch adjustment complexity factor based on system load parameters including the number of fields of business objects in the data to be synchronized, the nesting level of the data structure of the business objects, the number of associated objects of the business objects, the data types contained in the data to be synchronized, and the number of business rules of the business objects can include:

[0176] S31. Calculate the field complexity score based on the number of fields in the business object using the following formula:

[0177]

[0178] in, Score the complexity based on the number of fields. The number of fields in the business object. The number of baseline fields.

[0179] Understandably, the more fields there are, the higher the complexity, and the longer the data parsing takes, leading to a decrease in batch processing efficiency. Therefore, when there are more fields, it is necessary to guide the reduction of the batch size for data synchronization processing.

[0180] For example, when the number of baseline fields =50. If a business object has 20 fields, then the field complexity score is 50. If a business object has 120 fields, then the complexity score for the number of fields is... .

[0181] S32. Based on the nesting level of the business object's data structure, calculate the nesting level complexity score using the following formula:

[0182]

[0183] in, Score the nested complexity. The data structure nesting hierarchy of business objects. This represents the maximum nesting level.

[0184] Understandably, the more nested levels of a data structure, the higher its complexity, and the longer the time required for structured parsing, leading to a decrease in batch processing efficiency. Therefore, when there are many nested levels of data structures, it is necessary to guide the reduction of batch size in synchronous data processing.

[0185] For example, when the maximum nesting level When the score is 5, if the business object is a simple object with only one level of nesting, then the nesting complexity score is 5. If the business object is a complex object with a 7-level nested structure, then the nesting level complexity score is 1. .

[0186] S33. Calculate the complexity score of associated objects based on the number of associated objects of the business object using the following formula:

[0187]

[0188] in, Score the complexity of associated objects. The number of associated objects of the business object. The number of associated objects is the baseline.

[0189] It is understandable that the more associated objects there are, the higher the complexity, and the longer the data parsing takes, leading to a decrease in batch processing efficiency. Therefore, when there are more associated objects, it is necessary to guide the reduction of the batch size for data synchronization processing.

[0190] For example, when the baseline number of associated objects... =10. If the business object (basic data) has 3 associated objects, then the complexity score of the associated objects is 10. If a business object (complex document) has 25 associated objects, then the complexity score of the associated objects is 1. .

[0191] S34. Based on the data types contained in the data to be synchronized, calculate the data type complexity score using the following formula:

[0192]

[0193] in, Rate the complexity of data types. Let i be the number of objects of the i-th data type. The unit weight for the i-th data type, This is the maximum total type weight, which can be a fixed preset value, such as 10.0.

[0194] Understandably, the more data types there are, the greater the parsing cost, requiring more verification and computation operations, which increases complexity and leads to a decrease in batch processing efficiency. Therefore, when there are more data types, it is necessary to guide the reduction of batch size in data synchronization processing.

[0195] For example, if data types include basic types, complex types, and collection types, with a weight of 0.1 for basic types, 0.5 for complex types, and 0.3 for collection types, then when a business object contains 5 basic types + 3 JSON objects + 2 collections, the data type complexity score is (0.1*5 + 0.5*3 + 0.3*2) / 10.0 = 0.26.

[0196] S35. Calculate the business rule complexity score based on the number of business rules for each business object using the following formula:

[0197]

[0198] in, Score the complexity of associated objects. The number of business rules for a business object. The number of baseline rules.

[0199] Understandably, the more business rules a business object has, the greater the parsing cost, the more verification and calculation operations are required, and the higher its complexity, leading to a decrease in batch processing efficiency. Therefore, when there are more business rules, it is necessary to guide the reduction of the batch size for data synchronization processing.

[0200] For example, if the base number of rules is 20, and the business object only needs simple synchronization, containing only 5 validation rules, then the business rule complexity score is: If the business object requires complex synchronization and contains 35 business rules, then... .

[0201] S36. Calculate the current data complexity score and the highest data complexity score using the following formula:

[0202]

[0203] in, Rate the current data complexity. The highest data complexity score represents the most complex scenario that the ERP system can handle (at this point, the score is 0.8 in the data type dimension, and 1 in the number of fields, nesting level, number of related objects, and number of business rules dimensions). , , , and These are the first, second, third, fourth, and fifth weighting coefficients of the complexity factor, respectively. .

[0204] S37. Divide the current data complexity score by the highest data complexity score to obtain the batch adjustment complexity factor.

[0205] It is important to note that since the data complexity parameter determines the processing difficulty of a single batch of data, it has a reverse effect on the batch size, and the batch adjustment complexity factor... Since the value is positive, the weight coefficient corresponding to the batch adjustment complexity factor is negative when calculating the batch size of data synchronously.

[0206] In some embodiments of this application, the process of optimizing the reference batch size of the current processing cycle by adjusting the network factor, load factor, and complexity factor in batches, as mentioned in the above embodiments, to obtain the data synchronization processing batch size of the current processing cycle is described. Specifically, this process may include:

[0207] The batch size for data synchronization processing in the current processing cycle is calculated using the following formula:

[0208]

[0209] in, This is the reference batch size for the current processing cycle. To synchronize the batch size of data for the current processing cycle, , and These are the weighting coefficients for network factors, system load factors, and complexity factors, respectively. To adjust the network factor in batches, Adjust the load factor for the batch. Adjust the complexity factor for batches.

[0210] It is understandable that the network factor is adjusted in batches. It is positively correlated with the batch size of data synchronization processing, therefore A positive coefficient indicates that the batch load factor is adjusted. And batch adjustment complexity factor All of these are negatively correlated with the batch size of data synchronization processing, and are used to guide the reverse adjustment of the batch size of data synchronization processing. and All are negative coefficients. , and The absolute values ​​represent the batch size of the guidance data synchronization processing batch relative to the reference batch size. The adjustment range is based on this, and the specific value can be obtained through testing.

[0211] To allow for more flexible adjustment of data synchronization batch sizes, dynamic optimization can be further performed by considering system load and the complexity of business objects. In some embodiments of this application, after the ERP system completes data synchronization for the current processing cycle with the calculated data synchronization batch size, a reference batch size for the next processing cycle can be calculated based on the system load and business object complexity of the current processing cycle. Figure 2 The process may include:

[0212] Step S210: Determine whether the following conditions are met: the batch adjustment load factor is greater than the high threshold of the load factor or the batch adjustment complexity factor is greater than the high threshold of the complexity factor. If yes, proceed to step S220; otherwise, proceed to step S230.

[0213] Among them, the high threshold of the load factor represents the system safety threshold, and the high threshold of the complexity factor represents the complexity threshold of the complex scenarios that the system can handle.

[0214] Step S220: Multiply the data synchronization processing batch size by the reduction coefficient, and use the result as the reference batch size for the next processing cycle.

[0215] Understandably, when the system load exceeds the system security threshold (0.85) or the complexity of the business object exceeds the complexity threshold (0.9) of the complex scenarios that the system can handle, it is necessary to reduce the batch size. For example, the data synchronization processing batch size * 0.8 can be used as the reference batch size for the next processing cycle.

[0216] Step S230: Further determine whether the following conditions are met: the batch adjustment load factor is less than the low threshold of the load factor and the batch adjustment complexity factor is less than the low threshold of the complexity factor. If yes, proceed to step S240; otherwise, proceed to step S250.

[0217] Among them, the low threshold of the load factor represents the qualified threshold of system resource utilization, and the low threshold of the complexity factor represents the qualified threshold of system processing performance.

[0218] Specifically, if the system load does not reach the system safety threshold and the complexity of the business object does not reach the complexity threshold of the complex scenario that the system can handle, then it is further determined whether the ERP system resources are fully utilized. For example, if the system load batch adjustment load factor is less than the system resource utilization qualified threshold (0.5) and the batch adjustment complexity factor is less than the system processing performance qualified threshold (0.5), it means that the current data synchronization process is not fully utilizing the ERP system resources.

[0219] Step S240: Multiply the data synchronization processing batch size by the amplification coefficient, and use the result as the reference batch size for the next processing cycle.

[0220] It is understandable that when the current data synchronization process does not fully utilize ERP system resources, the batch size can be increased, for example, by multiplying the data synchronization batch size by 1.2 as the reference batch size for the next processing cycle.

[0221] Step S250: Use the data synchronization processing batch size as the reference batch size for the next processing cycle.

[0222] Specifically, if the conditions for judging the results of steps S210 and S230 are not met at the same time, there is no need to further adjust the batch size of data synchronization processing, and the batch size of data synchronization processing can be directly used as the reference batch size for the next processing cycle.

[0223] It is important to note that the batch size for data synchronization processing should be no less than the minimum batch size and no more than the maximum batch size (5 ≤ BatchSize ≤ 100). If the current processing cycle is the first processing cycle, the reference batch size can be a custom initial batch size between the minimum and maximum batch sizes.

[0224] The ERP system data synchronization processing batch optimization and adjustment method provided in this embodiment can maximize synchronization efficiency and achieve optimal utilization of system resources by dynamically adjusting the reference batch size after each processing cycle is completed, while ensuring system stability.

[0225] The following describes the optimization and adjustment device for implementing batch data synchronization processing of an ERP system provided in the embodiments of this application. The optimization and adjustment device for implementing batch data synchronization processing of an ERP system described below and the optimization and adjustment method for implementing batch data synchronization processing of an ERP system described above can be referred to in correspondence with each other.

[0226] See Figure 3 , Figure 3 This is a schematic diagram of a device for optimizing and adjusting batches of data synchronization processing in an ERP system, as disclosed in an embodiment of this application.

[0227] like Figure 3 As shown, the device may include:

[0228] Parameter monitoring module 11 is used to monitor the network performance parameters, system load parameters, and data complexity parameters of the ERP system in the current processing cycle;

[0229] The batch adjustment factor calculation module 12 is used to calculate the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor based on the network performance parameters, the system load parameters, and the data complexity parameters, respectively.

[0230] The batch size optimization module 13 is used to optimize the reference batch size of the current processing cycle through the batch adjustment network factor, the batch adjustment load factor and the batch adjustment complexity factor to obtain the data synchronization processing batch size of the current processing cycle, so that the ERP system can use the data synchronization processing batch size to perform data synchronization of the current processing cycle.

[0231] Optionally, the batch adjustment factor calculation module includes:

[0232] The batch adjustment network factor calculation module is used to calculate the current network status score and the optimal network status score based on the network performance parameters, and to calculate the batch adjustment network factor using the current network status score and the optimal network status score.

[0233] The batch adjustment load factor calculation module is used to calculate the current system load score and the maximum load score based on the system load parameters, and to calculate the batch adjustment load factor using the current system load score and the maximum load score.

[0234] The batch adjustment complexity factor calculation module is used to calculate the current data complexity score and the highest data complexity score based on the data complexity parameter, and to calculate the batch adjustment complexity factor using the current data complexity score and the highest data complexity score.

[0235] Optionally, the network performance parameters include network latency, bandwidth utilization, and packet loss rate;

[0236] The batch adjustment network factor calculation module includes:

[0237] The latency score calculation module is used to calculate the latency score based on the network latency value using the following formula:

[0238]

[0239] in, For the aforementioned delay score, The network latency value, The maximum acceptable network latency threshold;

[0240] The bandwidth score calculation module is used to subtract the bandwidth utilization rate from 1 to obtain the bandwidth score.

[0241] The packet loss score calculation module is used to calculate the packet loss score based on the packet loss rate using the following formula:

[0242]

[0243] in, Score the packet loss. The packet loss rate is... This is the maximum acceptable packet loss rate threshold.

[0244] The network condition score calculation module uses the following formula to calculate the current network condition score and the optimal network condition score:

[0245]

[0246] in, Rate the current network condition. Score the bandwidth. , and These are the first weight coefficient, the second weight coefficient, and the third weight coefficient of the network factor, respectively. , The optimal network condition score;

[0247] The batch adjustment network factor determination module is used to divide the current network condition score by the optimal network condition score to obtain the batch adjustment network factor.

[0248] Optionally, the system load parameters include CPU utilization, memory utilization, thread pool utilization, and processing queue length;

[0249] The batch adjustment load factor calculation module includes:

[0250] The processing queue utilization calculation module is used to divide the length of the processing queue by the maximum capacity of the processing queue to obtain the processing queue utilization rate.

[0251] The load score calculation module is used to calculate the current system load score and the maximum load score using the following formula:

[0252]

[0253] in, Score the current system load. The CPU utilization rate, The memory usage rate, The thread pool utilization rate, The processing queue utilization rate, , , and These are the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient of the load factor, respectively. , The maximum load fraction;

[0254] The batch adjustment load factor determination module is used to divide the current system load score by the maximum load score to obtain the batch adjustment load factor.

[0255] Optionally, the data complexity parameters include: the number of fields of the business object in the data to be synchronized, the nesting level of the data structure of the business object, the number of associated objects of the business object, the data type contained in the data to be synchronized, and the number of business rules of the business object;

[0256] The batch adjustment complexity factor calculation module includes:

[0257] The field quantity complexity score calculation module is used to calculate the field quantity complexity score based on the number of fields of the business object using the following formula:

[0258]

[0259] in, Score the complexity of the number of fields. The number of fields in the business object. Number of baseline fields;

[0260] Based on the nesting level of the data structure of the business object, the nesting level complexity score is calculated using the following formula:

[0261]

[0262] in, Score the complexity of the nested levels. The data structure nesting hierarchy of the business object. This represents the maximum nesting level.

[0263] The associated object complexity score calculation module is used to calculate the associated object complexity score based on the number of associated objects of the business object using the following formula:

[0264]

[0265] in, Score the complexity of the associated objects. The number of associated objects of the business object. The number of associated objects is the baseline.

[0266] The data type complexity score calculation module is used to calculate the data type complexity score based on the data type contained in the data to be synchronized using the following formula:

[0267]

[0268] in, Score the complexity of the data type. Let i be the number of objects of the i-th data type. The unit weight for the i-th data type, This represents the sum of the weights of the largest type.

[0269] The business rule complexity score calculation module is used to calculate the business rule complexity score based on the number of business rules of the business object using the following formula:

[0270]

[0271] in, Score the complexity of the associated objects. The number of business rules for the business object. The number of baseline rules;

[0272] The complexity score calculation module is used to calculate the current data complexity score and the highest data complexity score using the following formula:

[0273]

[0274] in, Score the complexity of the current data. The highest data complexity score, , , , and These are the first, second, third, fourth, and fifth weighting coefficients of the complexity factor, respectively. ;

[0275] The batch adjustment complexity factor determination module is used to divide the current data complexity score by the highest data complexity score to obtain the batch adjustment complexity factor.

[0276] Optionally, the batch size optimization module includes:

[0277] The batch size calculation module is used to calculate the data synchronization batch size for the current processing cycle using the following formula:

[0278]

[0279] in, This is the reference batch size for the current processing cycle. To synchronize the batch size of data for the current processing cycle, , and These are the weighting coefficients for network factors, system load factors, and complexity factors, respectively. Adjust the network factor for the batch. Adjust the load factor for the batch. Adjust the complexity factor for the batch.

[0280] Optionally, the device may also include:

[0281] The next reference batch quantity first determination module is used to multiply the data synchronization processing batch quantity by the reduction coefficient if the batch adjustment load factor is greater than the load factor high threshold or the batch adjustment complexity factor is greater than the complexity factor high threshold, and the result is used as the reference batch quantity for the next processing cycle.

[0282] The next reference batch size second determination module is used to multiply the data synchronization processing batch size by the amplification coefficient if the batch adjustment load factor is less than the load factor low threshold and the batch adjustment complexity factor is less than the complexity factor low threshold, and the result is used as the reference batch size for the next processing cycle.

[0283] The third module for determining the next reference batch size is used to determine the data synchronization processing batch size as the reference batch size for the next processing cycle if the batch adjustment load factor is not greater than the high threshold of the load factor, and the batch adjustment complexity factor is not greater than the high threshold of the complexity factor, and the first condition is met.

[0284] The ERP system data synchronization processing batch optimization and adjustment device provided in this application embodiment can be applied to ERP system data synchronization processing batch optimization and adjustment devices, such as terminals: mobile phones, computers, etc. Optionally, Figure 4 This diagram shows the hardware structure of the equipment for optimizing batch data synchronization processing in the ERP system. (Refer to...) Figure 4The hardware structure of the ERP system data synchronization processing batch optimization adjustment equipment may include: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4.

[0285] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0286] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0287] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0288] The memory stores a program, which the processor can call. The program is used for:

[0289] Monitor the network performance parameters, system load parameters, and data complexity parameters of the ERP system during the current processing cycle;

[0290] Based on the network performance parameters, the system load parameters, and the data complexity parameters, the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor are calculated respectively.

[0291] The reference batch size for the current processing cycle is optimized by the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor to obtain the data synchronization processing batch size for the current processing cycle, so that the ERP system can use the data synchronization processing batch size to perform data synchronization for the current processing cycle.

[0292] Optionally, the refined and extended functions of the program can be found in the description above.

[0293] This application embodiment also provides a storage medium that can store a program suitable for execution by a processor, the program being used for:

[0294] Monitor the network performance parameters, system load parameters, and data complexity parameters of the ERP system during the current processing cycle;

[0295] Based on the network performance parameters, the system load parameters, and the data complexity parameters, the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor are calculated respectively.

[0296] The reference batch size for the current processing cycle is optimized by the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor to obtain the data synchronization processing batch size for the current processing cycle, so that the ERP system can use the data synchronization processing batch size to perform data synchronization for the current processing cycle.

[0297] Optionally, the refined and extended functions of the program can be found in the description above.

[0298] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0299] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0300] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing and adjusting batch data synchronization processing in an ERP system, characterized in that, include: Monitor the network performance parameters, system load parameters, and data complexity parameters of the ERP system during the current processing cycle; Based on the network performance parameters, the system load parameters, and the data complexity parameters, the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor are calculated respectively. The reference batch size for the current processing cycle is optimized by the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor to obtain the data synchronization processing batch size for the current processing cycle, so that the ERP system can use the data synchronization processing batch size to perform data synchronization for the current processing cycle.

2. The method according to claim 1, characterized in that, The calculation of the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor based on the network performance parameters, the system load parameters, and the data complexity parameters respectively includes: Calculate the current network condition score and the optimal network condition score based on the network performance parameters, and calculate the batch adjustment network factor using the current network condition score and the optimal network condition score; Calculate the current system load score and the maximum load score based on the system load parameters, and calculate the batch adjustment load factor using the current system load score and the maximum load score; The current data complexity score and the highest data complexity score are calculated based on the data complexity parameter, and the batch adjustment complexity factor is calculated using the current data complexity score and the highest data complexity score.

3. The method according to claim 2, characterized in that, The network performance parameters include network latency, bandwidth utilization, and packet loss rate; Calculate the current network condition score and the optimal network condition score based on the network performance parameters, and calculate the batch adjustment network factor using the current network condition score and the optimal network condition score, including: Based on the network latency value, the latency score is calculated using the following formula: in, For the aforementioned delay score, The network latency value, The maximum acceptable network latency threshold; Subtract the bandwidth utilization rate from 1 to obtain the bandwidth score; Based on the packet loss rate, the packet loss score is calculated using the following formula: in, Score the packet loss. The packet loss rate is... This is the maximum acceptable packet loss rate threshold. The following formula is used to calculate the current network condition score and the optimal network condition score: in, Rate the current network condition. Score the bandwidth. , and These are the first weight coefficient, the second weight coefficient, and the third weight coefficient of the network factor, respectively. , The optimal network condition score; Divide the current network condition score by the optimal network condition score to obtain the batch adjustment network factor.

4. The method according to claim 2, characterized in that, The system load parameters include CPU utilization, memory utilization, thread pool utilization, and processing queue length. Calculate the current system load score and maximum load score based on the system load parameters, and calculate the batch adjustment load factor using the current system load score and the maximum load score, including: Divide the length of the processing queue by the maximum capacity of the processing queue to obtain the processing queue utilization rate. Calculate the current system load score and the maximum load score using the following formula: in, Score the current system load. The CPU utilization rate, The memory usage rate, The thread pool utilization rate, The processing queue utilization rate, , , and These are the first weighting coefficient, the second weighting coefficient, the third weighting coefficient, and the fourth weighting coefficient of the load factor, respectively. , The maximum load fraction; Divide the current system load score by the maximum load score to obtain the batch adjustment load factor.

5. The method according to claim 2, characterized in that, The data complexity parameters include: the number of fields of the business object in the data to be synchronized, the nesting level of the data structure of the business object, the number of associated objects of the business object, the data type contained in the data to be synchronized, and the number of business rules of the business object; Calculate the current data complexity score and the highest data complexity score based on the data complexity parameter, and calculate the batch adjustment complexity factor using the current data complexity score and the highest data complexity score, including: Based on the number of fields in the business object, the field number complexity score is calculated using the following formula: in, Score the complexity of the number of fields. The number of fields in the business object. Number of baseline fields; Based on the nesting level of the data structure of the business object, the nesting level complexity score is calculated using the following formula: in, Score the complexity of the nested levels. The data structure nesting hierarchy of the business object. This represents the maximum nesting level. Based on the number of associated objects of the business object, the complexity score of the associated objects is calculated using the following formula: in, Score the complexity of the associated objects. The number of associated objects of the business object. The number of associated objects is the baseline. Based on the data types contained in the data to be synchronized, the data type complexity score is calculated using the following formula: in, Score the complexity of the data type. Let i be the number of objects of the i-th data type. The unit weight for the i-th data type, This represents the sum of the weights of the largest type. Based on the number of business rules for the business object, the business rule complexity score is calculated using the following formula: in, Score the complexity of the associated objects. The number of business rules for the business object. The number of baseline rules; Calculate the current data complexity score and the highest data complexity score using the following formula: in, Score the complexity of the current data. The highest data complexity score, , , , and These are the first, second, third, fourth, and fifth weighting coefficients of the complexity factor, respectively. ; Divide the current data complexity score by the highest data complexity score to obtain the batch adjustment complexity factor.

6. The method according to claim 1, characterized in that, The reference batch size for the current processing cycle is optimized using the batch adjustment network factor, the batch adjustment load factor, and the batch adjustment complexity factor to obtain the data synchronization processing batch size for the current processing cycle, including: The batch size for data synchronization processing in the current processing cycle is calculated using the following formula: in, This is the reference batch size for the current processing cycle. To synchronize the batch size of data for the current processing cycle, , and These are the weighting coefficients for network factors, system load factors, and complexity factors, respectively. Adjust the network factor for the batch. Adjust the load factor for the batch. Adjust the complexity factor for the batch.

7. The method according to any one of claims 1-6, characterized in that, Also includes: If the batch adjustment load factor is greater than the high threshold of the load factor or the batch adjustment complexity factor is greater than the high threshold of the complexity factor, then the batch size of the data synchronization processing is multiplied by the reduction coefficient, and the result is used as the reference batch size for the next processing cycle. If the batch adjustment load factor is less than the low threshold of the load factor and the batch adjustment complexity factor is less than the low threshold of the complexity factor, then the batch size of the data synchronization processing is multiplied by the amplification coefficient, and the result is used as the reference batch size for the next processing cycle. If the batch adjustment load factor is not greater than the high threshold of the load factor and the batch adjustment complexity factor is not greater than the high threshold of the complexity factor, and the first condition is met, then the data synchronization processing batch size is used as the reference batch size for the next processing cycle, wherein the first condition is that the batch adjustment load factor is not less than the low threshold of the load factor or the batch adjustment complexity factor is not less than the low threshold of the complexity factor.

8. An optimization and adjustment device for batch data synchronization processing in an ERP system, characterized in that, include: The parameter monitoring module is used to monitor the network performance parameters, system load parameters, and data complexity parameters of the ERP system in the current processing cycle. The batch adjustment factor calculation module is used to calculate the batch adjustment network factor, batch adjustment load factor, and batch adjustment complexity factor based on the network performance parameters, the system load parameters, and the data complexity parameters, respectively. The batch size optimization module is used to optimize the reference batch size of the current processing cycle through the batch adjustment network factor, the batch adjustment load factor and the batch adjustment complexity factor to obtain the data synchronization processing batch size of the current processing cycle, so that the ERP system can use the data synchronization processing batch size to perform data synchronization for the current processing cycle.

9. An optimization and adjustment device for batch data synchronization processing in an ERP system, characterized in that, Including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the optimization and adjustment method for batch data synchronization processing in the ERP system as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the optimization and adjustment method for batch data synchronization processing in the ERP system as described in any one of claims 1-7.

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