Data collaboration processing method and system of OA platform

Abstract

The application relates to the technical field of data processing, and provides a data collaborative processing method and system of an OA platform. The method comprises the following steps: connecting the OA platform, reading a plurality of concurrent tasks to be processed in a preset time zone; analyzing the tasks and collecting multi-process collaborative users and operation characteristics to generate task execution characteristics; monitoring the state of an OA special network to generate network monitoring indexes; performing multi-task concurrent simulation based on the network indexes and in combination with the task characteristics to generate processing adaptation indexes; if the adaptation indexes are substandard, optimizing the multi-task processing time sequence with the time zone as a constraint to generate a serial optimization time zone. The application solves the technical problem that the existing OA platform cannot dynamically schedule task processing time zones according to network resources when facing multi-task concurrent processing, leading to the need for multiple attempts by users and low data processing efficiency, and achieves the technical effect of improving data processing efficiency and overall collaborative performance of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a data collaborative processing method and system of an OA platform. BACKGROUND

[0002] In modern enterprise management, the OA (Office Automation) platform has become a core tool for improving office efficiency and coordinating work processes. With the expansion of enterprise size and the increase in task complexity, task processing on the OA platform becomes increasingly heavy, involving multiple concurrent tasks, different user collaborative operations, and complex network environments. However, in existing OA platforms, task processing usually relies on user experience and time arrangement, especially when network resources are tight or congested, users often face problems such as prolonged task processing time and low efficiency.

[0003] Traditional OA platforms usually fail to dynamically optimize for network congestion and lack multi-dimensional collaborative scheduling mechanisms, resulting in users being able to find the best processing time only through repeated trials during task processing, thus wasting a lot of time and resources. This phenomenon not only affects overall data processing efficiency, but also can lead to excessive occupation of system resources and management bottlenecks. Therefore, how to intelligently schedule processing tasks according to network conditions, task characteristics, and user needs has become a key to improving the processing efficiency and user experience of OA platforms. SUMMARY

[0004] The present application provides a data collaborative processing method and system of an OA platform, aiming to solve the technical problem that existing OA platforms cannot dynamically schedule task processing time zones according to network resources when facing multiple concurrent tasks, resulting in users needing to try multiple times and low data processing efficiency when the network is congested, and to achieve the technical effect of dynamically optimizing and reminding the processing time zone of multiple task processes based on network congestion degree, improving data processing efficiency and overall collaborative performance of the system.

[0005] The first aspect of the present application provides a data collaborative processing method of an OA platform, the method comprising: connecting an OA platform, reading multiple concurrent tasks to be processed in a preset time zone; analyzing the multiple concurrent tasks to collect multi-process collaborative user and collaborative operation characteristics, generating multiple task execution characteristics; monitoring the network state of the dedicated network of the OA platform, generating network monitoring indicators; based on the network monitoring indicators as the basis for network simulation, performing multi-task concurrent simulation based on the multiple task execution characteristics, generating concurrent processing adaptation indicators; when the concurrent processing adaptation indicators are less than the preset adaptation indicators, scheduling and optimizing the processing time zone of the multiple concurrent tasks of the multi-process collaborative user based on the preset time zone as a time constraint, generating a serial optimization time zone of the multiple concurrent tasks of the multi-process collaborative user.

[0006] In another aspect of the present application, a data collaborative processing system of an OA platform is provided, which comprises: a concurrent task reading module: connecting to the OA platform, reading a plurality of concurrent tasks to be processed in a preset time zone; a feature collection module: analyzing the plurality of concurrent tasks to collect features of collaborative users and collaborative operations in multiple processes, and generating a plurality of task execution features; a network monitoring module: monitoring the network state of a special network of the OA platform to generate network monitoring indicators; a concurrent simulation module: taking the network monitoring indicators as the basis for network simulation, and based on the plurality of task execution features, performing multiple task concurrent simulation to generate concurrent processing adaptation indicators; a scheduling optimization module: when the concurrent processing adaptation indicators are less than a preset adaptation indicator, performing time zone scheduling optimization of the plurality of concurrent tasks in the multiple processes of the collaborative users based on the preset time zone, and generating a serial optimized time zone of the multiple processes of the collaborative users of the plurality of concurrent tasks.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] The above-mentioned data collaborative processing method of the OA platform first connects to the OA platform and reads a plurality of concurrent tasks to be processed in a preset time zone. Then, the tasks are analyzed, the execution features of each task are extracted, and the user collaboration and operation features in the task process are collected. Subsequently, the state of the special network of the OA platform is monitored to generate network monitoring indicators. Based on these network monitoring indicators, combined with the task execution features, concurrent simulation is performed to generate concurrent processing adaptation indicators. If the adaptation indicators are lower than the preset standard, scheduling optimization is performed according to the preset time zone and task characteristics, ensuring that the tasks are optimized in the time zone in a serial manner, thereby improving the task processing efficiency and avoiding the influence of network congestion.

[0009] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any creative labor.

[0011] Figure 1 A flowchart of the data collaborative processing method of the OA platform in an embodiment.

[0012] Figure 2This is a data collaboration processing system architecture diagram of an OA platform in one embodiment.

[0013] Figure labeling: 11 concurrent task reading module, 12 feature acquisition module, 13 network monitoring module, 14 concurrent simulation module, 15 scheduling optimization module. Detailed Implementation

[0014] This application provides a data collaborative processing method and system for an OA platform, which solves the technical problem that existing OA platforms cannot dynamically schedule task processing time zones based on network resources when facing concurrent multi-task processing, resulting in users having to try multiple times and low data processing efficiency when the network is congested. The method and system achieve the technical effect of dynamically optimizing and reminding users of the processing time zones of multi-task processes based on the degree of network congestion, thereby improving data processing efficiency and the overall collaborative performance of the system.

[0015] 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 a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides a data collaborative processing method for an OA platform, the method comprising:

[0018] Connect to the OA platform and read multiple concurrent tasks to be processed within the preset time zone.

[0019] In this embodiment, a data communication channel is established by connecting with the enterprise's OA platform through a reserved API interface. Subsequently, based on a preset time zone range, such as the next 2 hours, the next 6 hours, or the current work cycle, the system automatically identifies and retrieves a list of tasks that need to be processed within that time zone. These tasks may include different types such as cross-departmental approvals, document collaboration, and data reporting. Furthermore, these tasks are typically concurrent, meaning they need to be processed by different users or processes within the same time period. This process ensures that the system can accurately collect and process task information, providing a data foundation for subsequent task analysis and scheduling, and ensuring that scheduling optimization is based on actual business needs to be addressed.

[0020] The multiple concurrent tasks are parsed to collect the multi-process collaborative user and collaborative operation features, and multiple task execution features are generated.

[0021] In one embodiment, the multiple concurrent tasks read are parsed in detail, the processing chain of each task is analyzed, multiple processing processes involved are identified, for example, the processing chain 1: application -> department approval -> financial audit -> archiving, each task includes different operation steps and collaborative processing of multiple users, therefore, the responsible person corresponding to each processing link also needs to be determined, and the responsible person is combined with the operation features (such as required resources, processing order, execution time, etc.) in each processing link, thereby generating the execution features of each concurrent task, helping to further understand the required operations and resource allocation of each concurrent task in the execution process, and providing data support for subsequent simulation and scheduling.

[0022] Further, the application provides that the multiple concurrent tasks are parsed to collect the multi-process collaborative user and collaborative operation features, and multiple task execution features are generated, including:

[0023] Multiple task generation information of the multiple concurrent tasks is read; task processing flow and processing user of each flow are parsed based on the multiple task generation information, and multi-process collaborative users of the multiple concurrent tasks are generated; operation resource demand analysis of any processing flow of any task in the multiple concurrent tasks is performed based on the task processing flow, and collaborative operation features of the multiple concurrent tasks are generated; and the multiple task execution features are generated based on the multi-process collaborative users and the collaborative operation features of the multiple concurrent tasks.

[0024] Preferably, the task generation information of multiple concurrent tasks is first read from the OA platform, which includes basic data of each task, such as task creation time, task type (reimbursement / approval, etc.), initiating department, execution requirements, etc., which provides basic data for subsequent task analysis and scheduling. Subsequently, according to the read task generation information, the processing flow of each task is analyzed, which includes each processing step involved in the task (e.g., approval process, document processing, etc.), and according to the analyzed processing flow of each task, the user roles and responsibilities corresponding to each processing flow are determined in combination with the organizational structure and role permissions, so as to determine the executor of each flow, i.e., the collaborative user of task processing. In this way, the multi-flow collaborative users of multiple concurrent tasks can be generated, that is, it is determined which users participate in different processing stages of the task. After analyzing the task processing flow and collaborative users, the resource requirements of each task in the execution process are analyzed in combination with historical data, such as different interfaces (such as forms, approval buttons, etc. UI components), computing resources (such as CPU / memory consumption), network bandwidth, storage space, etc. required by different flows, and the operation resources required by each flow in the execution process are extracted, including hardware, software or other supporting tools, which helps to evaluate resource bottlenecks and challenges that may be faced in the task execution process. Then, the extracted operation resources are classified and summarized to generate collaborative operation features for each concurrent task, which describe the coordination requirements and resource allocation features of the task in the execution process. These collaborative operation features can help the system better understand the relationship between tasks and the complexity of collaborative operation. Finally, the multi-flow collaborative users and collaborative operation features are stored correspondingly, that is, the multi-flow collaborative users and collaborative operation features belonging to the same concurrent task are stored in a set to generate task execution features for each concurrent task. These execution features summarize the overall execution process of the task, the collaboration mode of the participating users, the use of resources, etc., providing detailed data support for subsequent task scheduling optimization and concurrent simulation.

[0025] Further, the present application provides operation resource demand analysis when any processing flow of any task in the plurality of concurrent tasks is executed based on the task processing flow, and generates collaborative operation features of the plurality of concurrent tasks, including:

[0026] extracting any processing flow of any task based on the task processing flow; collecting historical processing records according to the flow type of the any processing flow; extracting resource file calling features and processing component calling features based on the historical processing records to construct collaborative operation features of the plurality of concurrent tasks.

[0027] Optionally, in the process of analyzing multiple concurrent tasks, first, a specific non-task processing flow is selected from the non-task processing flows for detailed extraction, these processing flows are usually specific steps required for task completion, such as file review, approval, confirmation, etc., and are extracted randomly as analysis objects. Subsequently, according to the flow type (such as approval, review, etc.) of the processing flow, the historical processing records of the flow are collected from the operation log, these historical records include the execution of the flow in similar tasks in the past, the time used, the users involved, the processing results, etc. Data can provide past execution patterns and efficiency data for the processing flow, helping to understand the specific performance of the flow. Then, based on the historical processing records, the resource file calling characteristics and the processing component calling characteristics will be further analyzed, the resource file calling characteristics refer to the type of resource file called by a certain processing flow, the size of the file, the access frequency, the bandwidth occupation situation and other information in the historical records; The processing component calling characteristics involve the specific processing components (such as approval tools, data query tools, etc.) used in the flow, as well as the usage frequency and efficiency of these components. By extracting these characteristics, the actual demand for resources and tools during task execution can be obtained. Finally, by integrating the resource calling situation and processing component usage situation of each processing flow according to concurrent tasks, the collaborative operation characteristics of each concurrent task are constructed, these collaborative operation characteristics will reflect the resource competition, collaboration relationship and dependence on operation components between tasks when multiple tasks are executed concurrently. These characteristics help the system understand how to optimize task execution, allocate resources and avoid bottlenecks in the context of multiple task concurrency, providing support for subsequent scheduling optimization process, improving the execution efficiency and collaborative effect of concurrent tasks.

[0028] The network state of the special network of the OA platform is monitored to generate network monitoring indicators.

[0029] In one embodiment, in order to understand the load and available resources in the current network environment, network state monitoring is performed on the OA platform's private network. In this process, the deployment area of the OA platform's private network is first determined, and traffic monitoring devices such as traffic mirrors are deployed on key network nodes (such as servers, switches, routers, etc.) in the area to collect network traffic data in actual operation, including packet transmission rate, bandwidth occupation, packet loss rate, delay, etc. Subsequently, based on the collected network traffic data, real-time analysis and processing are performed to determine whether the network is in a congested state and the severity of the congestion. According to these analysis results, a set of network monitoring indicators are generated to quantify the current network state, such as network response delay, data throughput capacity, available bandwidth, congestion probability value, etc. These network monitoring indicators provide basic data for subsequent network simulation, enabling the system to more accurately assess the network's carrying capacity when tasks are executed concurrently, thereby supporting task scheduling optimization and rational allocation of network resources.

[0030] Further, the present application provides network state monitoring of the OA platform's private network and generating network monitoring indicators, including:

[0031] Determining the deployment area of the private network; configuring traffic mirrors on multiple network nodes in the deployment area to perform network traffic monitoring and generate traffic monitoring results; predicting the network congestion level based on the traffic monitoring results to generate the network monitoring indicators.

[0032] Preferably, when performing network state monitoring, the deployment area information of the OA platform's private network is first identified, including servers, core switches, data centers, and terminal devices involved in the network. The purpose of this stage is to define the network range that needs to be monitored to ensure that network state monitoring covers all key nodes related to task processing. Subsequently, traffic mirrors (such as monitoring modules based on port mirroring, SPAN, etc.) are configured on these key nodes, which are used to replicate and analyze network data streams in real time. Through these mirrors, network bandwidth occupation, packet transmission rate and packet loss rate, transmission delay, etc. can be captured, which will collectively form the traffic monitoring results to provide raw data support for subsequent analysis. Then, the collected traffic monitoring results are input into a long short-term memory network (LSTM) to predict the network congestion level, generating network monitoring indicators including network response delay, data throughput capacity, available bandwidth, etc. These network monitoring indicators will serve as the basis for building a digital simulation network to further deduce the processing capacity and adaptability of the network under concurrent tasks, providing accurate references for scheduling optimization, thereby ensuring rational allocation of network resources and smooth transmission during concurrent task execution.

[0033] For the long short-term memory network, the historical traffic monitoring results and historical network monitoring indicators are divided into a training set and a validation set, and an initial network structure is constructed using LSTM, including an input layer, an LSTM layer, a Dropout layer, a fully connected layer, and an output layer. Subsequently, the weights of each layer of the network are initialized using a random initialization method, and the training set is input into the initialized network for forward propagation, passing through the input layer, the LSTM layer, the Dropout layer, the fully connected layer, and the output layer layer by layer to calculate the prediction results including network response time, data throughput, and available bandwidth. Then, the mean square error loss function is used to calculate the error value between the network output and the historical network monitoring indicators, and the gradient of the loss to each layer parameter is calculated layer by layer through the back propagation algorithm. Then, the Adam optimizer is used to update the weights of the network parameters, and gradually converges to minimize the loss value. The above process continues to iterate and train until the set training rounds or network convergence are reached. After training is completed, the network performance is tested using the validation set to evaluate its accuracy and stability in the network indicator prediction task. If the network prediction results meet the preset threshold conditions, the current LSTM network is output for generating network monitoring indicators. Otherwise, the network structure or hyperparameters (such as learning rate, time step, number of hidden layer units, etc.) are adjusted to further improve the performance and generalization ability of the network.

[0034] Based on the network monitoring indicators, multi-task concurrent simulation is performed based on the plurality of task execution characteristics to generate a concurrent processing adaptation indicator.

[0035] In one embodiment, a digital network simulation model is constructed, which is based on network monitoring indicators to simulate the real running state of the OA platform dedicated network, such as current available bandwidth, delay level, and throughput capacity, etc. Then, the execution characteristics of each task are input into the simulation model, including the processing flow, resource demand, collaborative user distribution, and processing order of the task, etc. During the simulation process, the concurrent execution of multiple concurrent tasks in the same time zone is simulated, and the simultaneous occupation and mutual influence of tasks on network resources are analyzed. Through the simulation, a quantitative result, i.e., a concurrent processing adaptation indicator, can be obtained, which is used to measure the adaptability of the OA platform to concurrent processing of these tasks under the current network environment. If the indicator is high, it means that the network resources can better support the concurrent running of the current tasks, and the network adaptability is high. If the indicator is low, it means that there is a network bottleneck or task conflict, which needs further scheduling optimization. The goal of this process is to predict the impact of network congestion on task processing efficiency in advance and provide decision basis for subsequent time zone scheduling optimization to avoid waste of network resources.

[0036] Further, the application takes the network monitoring index as the basis for network simulation, performs multi-task concurrent simulation based on the multiple task execution characteristics, generates concurrent processing adaptation indexes, including:

[0037] The network architecture of the special-purpose network is digitally twin-simulated to generate a twin network architecture model; the network monitoring index is used to equivalently configure the twin network architecture model to generate a digital simulation network; the digital simulation network is used to perform multi-task concurrent simulation based on the multiple task execution characteristics, and the concurrent processing adaptation indexes are calculated according to the simulation results, wherein the concurrent processing adaptation indexes represent the network adaptability of the special-purpose network to the multiple concurrent tasks, and only one task processing flow participates in the multi-task concurrent simulation process.

[0038] Preferably, the dedicated network architecture of the OA platform is digitally twin simulated, that is, based on the existing network physical topology (such as core switch, firewall, server cluster) and logical configuration (such as VLAN division, routing strategy), a highly simulated virtual network model, namely the twin network architecture model, is constructed through physical layer mapping and logical layer mapping, which provides a structural basis for subsequent simulation, wherein the physical layer mapping refers to copying the device model, port quantity and connection relationship, and the logical layer mapping refers to synchronizing the IP address planning, ACL rule (such as allowing only the finance VLAN to access the budget interface). Subsequently, the previously obtained network monitoring indicators are applied to the twin network architecture model for equivalent configuration, that is, the available bandwidth, network delay, data throughput capacity and other indicators in the network monitoring indicators are mapped to each network node and path of the twin model, thereby generating a set of digital simulation network with the current network state characteristics. This configuration ensures that the simulation process is consistent with the actual running environment, has timeliness and practical significance. In the simulation process, the long short-term memory network dynamically adjusts the parameters to simulate the network state changes. Then, in the digital simulation network, multiple task concurrent simulations are performed based on the extracted multiple task execution characteristics, and each concurrent task only involves one process in the simulation process, because in the actual OA task processing flow, each task is usually completed by a user at a node before being passed to the next user, so even if there are multiple concurrent tasks, each concurrent task only involves one process at a certain time. In the simulation process, the concurrent occupation of network resources, network response delay and the number of successfully transmitted business data packets directly related to each task process at the same time node are recorded. After the simulation is completed, the business data successful transmission ratio is obtained by using the ratio of the number of successfully transmitted task data packets to the total demand data packet number, and the business data delay rate is obtained by using the difference between the network response delay and the benchmark time consumption divided by the benchmark time consumption. Then, 1 is subtracted from the business data delay rate, and the difference and the business data successful transmission ratio are weighted to obtain the concurrent processing adaptation index of the current dedicated network, which is used to represent the network adaptability of the dedicated network in the current state to process these concurrent tasks, the higher the value, the better the network can handle the current concurrent load; the lower the value, the more network resources are needed, which may need to be optimized and adjusted through subsequent task scheduling. Through this process, the task concurrent running effect in the current network environment can be accurately simulated to provide a scientific basis for determining whether task time zone scheduling is needed.

[0039] Further, the present application provides a digital simulation network based on the multiple task execution characteristics for multiple task concurrent simulation, further comprising:

[0040] The multiple concurrent tasks are respectively subjected to sequential constraint analysis of the task processing flow, and multiple sequential constraint analysis results are obtained; the multiple concurrent tasks are respectively subjected to multi-flow concurrent condition statistics based on the multiple sequential constraint analysis results, and multiple flow concurrent features are generated; and the multiple task concurrent simulation is optimized by using the multiple flow concurrent features.

[0041] Optionally, the flow in the multiple concurrent tasks can be linear, that is, one step must wait for the completion of the previous step to start, for example, approval, confirmation and other links, such flow will use the simulation mode described above, and there can be multiple user parallel processing in the multiple concurrent tasks, for example, the task can send processing requests to multiple users at the same time, and the processing results of each user will be integrated in the subsequent steps. At this time, the sequential constraint analysis of the task processing flow is performed on the multiple concurrent tasks, the dependency relationship between the processing flows in the multiple concurrent tasks is understood, it is determined which flows must be executed in sequence and which steps can be executed in parallel, thereby identifying the parallel nodes and serial nodes existing in the multiple concurrent tasks, and the nodes are summarized according to the parallel tasks to form multiple sequential constraint analysis results. Subsequently, the sequential constraint analysis results with parallel nodes are extracted from the multiple sequential constraint analysis results, and the number of parallel nodes in the sequential constraint analysis results is counted, thereby understanding the parallel processing flow condition existing in the concurrent tasks. Then, by identifying the processing flows that can be executed in parallel in the concurrent tasks, multiple flow concurrent features are constructed, each flow concurrent feature includes the number of parallel execution flows, processing flow identification and concurrent execution time. Finally, the network resources required by each processing flow are obtained according to the multiple flow concurrent features, and the same simulation process described above is performed according to the network resources, thereby providing a scientific basis for judging whether task time zone scheduling is needed.

[0042] When the concurrent processing adaptation index is less than a preset adaptation index, the multiple concurrent tasks are subjected to multi-flow collaborative user processing time zone scheduling optimization with the preset time zone as the time constraint, and a serial optimized time zone of the multiple concurrent tasks is generated.

[0043] In one embodiment, after obtaining the concurrent processing adaptation index, the current concurrent processing adaptation index is compared with the preset adaptation index. If the adaptation index is low, it indicates that the current network and resource configuration does not meet the execution requirements of concurrent tasks, and scheduling optimization is needed. At this time, the preset time zone is taken as the time constraint, and the execution of the tasks is reasonably arranged in each divided time zone to avoid excessive tasks being processed concurrently in the same period, causing resource conflicts or task delays. Subsequently, based on the rearranged multiple concurrent tasks, the concurrent processing adaptation index of the rearranged multiple concurrent tasks is calculated and compared with the preset adaptation index. This process will continue until the adjustment result that meets the preset adaptation index is obtained. Finally, the optimal one is obtained from these adjustment results, and the serial optimization time zone of each task is generated. These optimization time zones will make the originally concurrent tasks be sequentially allocated to specific time zones, ensuring that each task is executed in sequence in its corresponding time zone, avoiding overlapping resource competition, and improving overall execution efficiency.

[0044] Further, the application provides that when the concurrent processing adaptation index is less than the preset adaptation index, the multiple concurrent tasks are processed in the time zone scheduling optimization of the multi-process collaborative user with the preset time zone as the time constraint, and the serial optimization time zone of the multiple concurrent tasks is generated, including:

[0045] With the preset time zone as the time constraint, the multi-process sequence constraint analysis result as the process sequence constraint, based on the processing time central value of each process in each task, the time zone division of the processing process of the multiple concurrent tasks is performed, and multiple time zone scheduling results are generated, wherein any time zone scheduling result includes several divided time zones, and each divided time zone corresponds to the processing process of the multiple tasks; the multiple time zone scheduling results are simulated by a digital simulation network to determine M time zone scheduling results whose updated concurrent processing adaptation index is greater than or equal to the preset adaptation index, wherein M is an integer greater than or equal to 1; the time tolerance comparison of each processing process is performed on the M time zone scheduling results to generate the serial optimization time zone with the optimal tolerance.

[0046] Optionally, when the concurrent processing adaptation index is less than the preset adaptation index, first, the preset time zone and the sequential constraint analysis result of the plurality of tasks are received, the preset time zone is used as a time constraint to determine the time window available to the user, and the sequential constraint analysis result is used to determine the execution order of the processing steps of each task, that is, to understand which steps of the tasks must be executed in sequence and which steps can be executed in parallel. Through these constraints, the dependency relationship between each task flow can be determined. Subsequently, for each processing flow in each task, the processing time central value, that is, the average time required by each processing flow, is obtained, and these time values are the time reference when the tasks are sequentially executed. Then, according to the preset time zone and the processing flow time of each task, combined with the sequential constraints of the tasks, the plurality of concurrent tasks are allocated to different time nodes in the preset time zone for processing, each time node can contain the processing flow of multiple tasks, and the execution time of these tasks will not conflict with other tasks. Through multiple permutations and combinations, a plurality of time zone scheduling results can be obtained, each time zone scheduling result includes a plurality of split time zones (i.e., a plurality of time nodes), and each split time zone corresponds to the processing flow of one or more concurrent tasks. Then, the aforementioned simulation of each time zone scheduling result is performed using a digital simulation network, the concurrent processing adaptation index of each time zone scheduling result is calculated, and M time zone scheduling results with concurrent processing adaptation indexes greater than or equal to the preset adaptation index are selected, wherein M is an integer greater than or equal to 1, representing a plurality of time zone scheduling results that meet the standard. Finally, a time tolerance comparison of these standard-compliant time zone scheduling results is performed, and the tolerance refers to the maximum delay that the task flow can tolerate under different time constraints. According to the time tolerance comparison, the time zone scheduling result with the optimal tolerance is selected as the serial optimization time zone, which is an optimized time zone scheduling scheme that ensures the sequential execution of concurrent tasks and avoids resource conflicts and processing delays. Through this serial optimization, the tasks can be completed in sequence within each time zone, while minimizing time conflicts and network load during task execution, thereby improving overall efficiency.

[0047] Further, the application provides a time tolerance comparison of each processing flow of the M time zone scheduling results, which includes:

[0048] determining whether M is 1; if yes, directly generating the serial optimization time zone corresponding to the time zone scheduling result; if no, generating a time tolerance comparison instruction to control the time tolerance comparison of each processing flow.

[0049] Optionally, when M is 1, it means that only one time zone scheduling result meets the expected network adaptability and task scheduling requirements, at this time, the time zone scheduling result will be directly used as the serial optimization time zone. When M is greater than 1, it means that there are multiple time zone scheduling results that meet the conditions, at this time, a time tolerance comparison instruction is generated, that is, an instruction for comparing the time tolerances between different time zone scheduling results. In the comparison process, for each time zone scheduling result, the actual execution time of each task is obtained from the simulation result, and the expected time is calculated by difference to obtain the delay of each task. Subsequently, within the delay allowed range, the scheduling result with the minimum maximum delay is extracted from the M time zone scheduling results as the time zone scheduling result with the maximum allowed delay. Finally, the serial optimization time zone is generated according to the time zone scheduling result with the maximum allowed delay, ensuring the flexibility and optimal execution of tasks in terms of resources and time.

[0050] Further, the application provides that after generating the serial optimization time zone of the multiple concurrent tasks of the multi-process collaborative user, the following steps are included:

[0051] The serial optimization time zone is parsed to determine the optimization processing time zone of any associated user in the multiple concurrent tasks; and the optimization processing time zone is sent to the corresponding user for reminding.

[0052] Preferably, the generated serial optimization time zone is parsed to obtain the optimization processing time zone corresponding to each associated user in the multiple concurrent tasks, that is, the time period during which each associated user needs to process the related tasks. By sending these optimization processing time zones to the corresponding users, the users are reminded in a timely manner to process the related tasks within the optimal time period, ensuring that the tasks can be completed on time and efficiently.

[0053] In summary, the embodiments of the application have at least the following technical effects:

[0054] This embodiment first connects to an OA platform and reads multiple concurrent tasks to be processed within a preset time zone. Then, it analyzes these concurrent tasks to collect multi-process collaborative user and collaborative operation characteristics, generating multiple task execution characteristics. Next, it monitors the network status of the OA platform's dedicated network, generating network monitoring indicators. Then, using these network monitoring indicators as the basis for network simulation, it performs multi-task concurrent simulation based on the multiple task execution characteristics, generating concurrent processing adaptation indicators. Finally, when the concurrent processing adaptation indicators are less than a preset adaptation indicator, it optimizes the processing time zone scheduling of the multiple concurrent tasks for multi-process collaborative users, using the preset time zone as a time constraint, generating a serial optimization time zone for the multi-process collaborative users of the multiple concurrent tasks. These technical effects collectively solve the technical problem that existing OA platforms cannot dynamically schedule task processing time zones based on network resources when facing multi-task concurrent processing, leading to users needing to try multiple times and low data processing efficiency during network congestion. This achieves dynamic optimization and reminders of the processing time zone for multi-task processes based on network congestion levels, improving data processing efficiency and overall system collaborative performance.

[0055] Example 2, based on the same inventive concept as the data collaborative processing method of the OA platform in the foregoing examples, such as... Figure 2 As shown, this application provides a data collaborative processing system for an OA platform. The system includes: a concurrent task reading module 11, which connects to the OA platform and reads multiple concurrent tasks to be processed within a preset time zone; a feature acquisition module 12, which parses the multiple concurrent tasks to collect features of multi-process collaborative users and collaborative operations, generating multiple task execution features; a network monitoring module 13, which monitors the network status of the dedicated network of the OA platform and generates network monitoring indicators; a concurrent simulation module 14, which uses the network monitoring indicators as the basis for network simulation and performs multi-task concurrent simulation based on the multiple task execution features to generate concurrent processing adaptation indicators; and a scheduling optimization module 15, which, when the concurrent processing adaptation indicators are less than a preset adaptation indicator, uses the preset time zone as a time constraint to optimize the processing time zone of the multiple concurrent tasks for multi-process collaborative users, generating a serial optimized time zone for the multi-process collaborative users of the multiple concurrent tasks.

[0056] Furthermore, the feature acquisition module 12 is also used to perform the following method:

[0057] read a plurality of task generation information of the plurality of concurrent tasks; perform task processing flow and processing user resolution of each flow based on the plurality of task generation information, generate a plurality of concurrent task flow collaborative users; based on the task processing flow, analyze the operation resource demand of any processing flow of any task in the plurality of concurrent tasks when performing task execution, generate the collaborative operation characteristics of the plurality of concurrent tasks; generate the plurality of task execution characteristics based on the plurality of concurrent task flow collaborative users and the collaborative operation characteristics.

[0058] Further, the feature acquisition module 12 is also used to execute the following method:

[0059] Based on the task processing flow, extract any processing flow of any task; collect historical processing records based on the flow type of the any processing flow; extract resource file calling characteristics and processing component calling characteristics based on the historical processing records, and construct the collaborative operation characteristics of the plurality of concurrent tasks.

[0060] Further, the network monitoring module 13 is also used to execute the following method:

[0061] Determine the deployment area of the special network; configure a traffic mirror in a plurality of network nodes in the deployment area to perform network traffic monitoring and generate a traffic monitoring result; predict the network congestion degree based on the traffic monitoring result to generate the network monitoring index.

[0062] Further, the concurrent simulation module 14 is also used to execute the following method:

[0063] Carry out digital twin simulation on the network architecture of the special network to generate a twin network architecture model; configure the twin network architecture model based on the network monitoring index to generate a digital simulation network; based on the plurality of task execution characteristics, perform multi-task concurrent simulation on the digital simulation network, and calculate the concurrent processing adaptation index according to the simulation result, wherein the concurrent processing adaptation index represents the network adaptation degree of the special network to the plurality of concurrent tasks, and in the multi-task concurrent simulation process, only one task processing flow of each task is involved.

[0064] Further, the concurrent simulation module 14 is also used to execute the following method:

[0065] Respectively analyze the sequence constraint of the task processing flow of the plurality of concurrent tasks to obtain a plurality of sequence constraint analysis results; based on the plurality of sequence constraint analysis results, respectively perform multi-flow concurrent condition statistics on the plurality of concurrent tasks to generate a plurality of flow concurrent characteristics; and optimize the multi-task concurrent simulation based on the plurality of flow concurrent characteristics.

[0066] Further, the scheduling optimization module 15 is further configured to execute the following method:

[0067] The preset time zone is taken as a time constraint, and a plurality of sequential constraint analysis results are taken as process sequential constraints. Time zone division of processing processes of the plurality of concurrent tasks is performed based on a processing time central value of each process in each task, and a plurality of time zone scheduling results are generated, wherein any time zone scheduling result includes a plurality of split time zones, and each split time zone corresponds to a processing process of the plurality of tasks. The plurality of time zone scheduling results are simulated by a digital simulation network, and M time zone scheduling results in which an updated concurrent processing adaptation index is greater than or equal to a preset adaptation index are determined, wherein M is an integer greater than or equal to 1. Time tolerance comparison of each processing process is performed on the M time zone scheduling results, and the serial optimization time zone is generated based on a time zone scheduling result with optimal tolerance.

[0068] Further, the scheduling optimization module 15 is further configured to execute the following method:

[0069] It is determined whether M is 1. If yes, the serial optimization time zone is directly generated based on the corresponding time zone scheduling result. If no, a time tolerance comparison instruction is generated to control the time tolerance comparison of each processing process.

[0070] Further, the scheduling optimization module 15 is further configured to execute the following method:

[0071] The serial optimization time zone is analyzed to determine an optimization processing time zone of any associated user in the plurality of concurrent tasks. The optimization processing time zone is sent to the corresponding user for prompting.

[0072] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or can be advantageous.

[0073] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0074] The present application and the drawings are only exemplary descriptions of the present application, and are considered to cover any and all modifications, changes, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalents, the present application intends to include these modifications and changes.

Claims

1. A data collaborative processing method for an OA platform, characterized in that, include: Connect to the OA platform and read multiple concurrent tasks to be processed within a preset time zone; The multiple concurrent tasks are analyzed to collect multi-process collaborative user and collaborative operation features, generating multiple task execution features; The dedicated network of the OA platform is monitored for network status, and network monitoring indicators are generated. Based on the network monitoring indicators, multi-task concurrent simulation is performed based on the execution characteristics of the multiple tasks to generate concurrent processing adaptation indicators, including: The network architecture of the dedicated network is simulated digitally to generate a twin network architecture model; The twin network architecture model is configured with the network monitoring indicators to generate a digital simulation network. Using the digital simulation network, multi-task concurrent simulation is performed based on the execution characteristics of the multiple tasks, and the concurrent processing adaptation index is calculated based on the simulation results. The concurrent processing adaptation index characterizes the network fitness of the dedicated network in processing the multiple concurrent tasks. In the multi-task concurrent simulation process, each task has only one task processing flow involved. When the concurrent processing adaptation index is less than the preset adaptation index, using the preset time zone as a time constraint, the processing time zone scheduling optimization for the multiple concurrent tasks is performed on the multi-process collaborative users, generating the serial optimized time zone for the multi-process collaborative users of the multiple concurrent tasks, including: Using the preset time zone as the time constraint and the parsing results of multiple sequence constraints as the process sequence constraint, the processing time of the multiple concurrent tasks is divided into time zones based on the processing time set value of each process in each task, generating multiple time zone scheduling results. Each time zone scheduling result includes several segmented time zones, and each segmented time zone corresponds to the processing flow of the multiple tasks. The scheduling results of the multiple time zones are simulated using a digital simulation network to determine M time zone scheduling results whose updated concurrent processing adaptation index is greater than or equal to the preset adaptation index, where M is an integer greater than or equal to 1. The time tolerance of each processing step is compared among the M time zone scheduling results, and the time zone scheduling result with the best tolerance is used to generate the serially optimized time zone.

2. The data collaborative processing method of the OA platform as described in claim 1, characterized in that, The multiple concurrent tasks are analyzed to collect multi-process collaborative user and collaborative operation features, generating multiple task execution features, including: Read the task generation information of the multiple concurrent tasks; Based on the multiple task generation information, the task processing flow and the processing user of each flow are parsed to generate multi-flow collaborative users for the multiple concurrent tasks. Based on the task processing flow, extract any processing flow of any task among the multiple concurrent tasks, perform operational resource requirement analysis during task execution, and generate collaborative operation characteristics of the multiple concurrent tasks. The multiple task execution features are generated using the multi-process collaborative user and collaborative operation features of multiple concurrent tasks.

3. The data collaborative processing method of the OA platform as described in claim 2, characterized in that, Based on the task processing flow, extract any processing flow of any task among the multiple concurrent tasks, perform operational resource requirement analysis during task execution, and generate collaborative operation characteristics of the multiple concurrent tasks, including: Extract any processing flow for any task based on the aforementioned task processing flow; Historical processing records are collected according to the process type of any of the aforementioned processing flows; The resource file call characteristics and processing component call characteristics are extracted from the historical processing records to construct the collaborative operation characteristics of the multiple concurrent tasks.

4. The data collaborative processing method of the OA platform as described in claim 1, characterized in that, Using the digital simulation network, multi-task concurrent simulation based on the multiple task execution characteristics further includes: The sequential constraint parsing of the task processing flow is performed on the multiple concurrent tasks respectively, and multiple sequential constraint parsing results are obtained; Based on the results of the multiple sequence constraint parsing, the concurrent status of the multiple concurrent tasks is statistically analyzed for each process, generating multiple process concurrency features; Optimize multi-task concurrent simulation based on the aforementioned multiple process concurrency characteristics.

5. The data collaborative processing method for an OA platform as described in claim 1, characterized in that, Before comparing the time tolerance of each processing step in the scheduling results of the M time zones, the process includes: Determine if M is 1; If so, the serially optimized time zone is directly generated based on the corresponding time zone scheduling result; If not, generate a time tolerance comparison instruction to control the time tolerance comparison of each processing flow.

6. The data collaborative processing method of the OA platform as described in claim 1, characterized in that, The dedicated network of the OA platform is monitored for network status, and network monitoring indicators are generated, including: Determine the deployment area of ​​the dedicated network; Traffic mirrors are configured on multiple network nodes within the deployment area to monitor network traffic and generate traffic monitoring results. The network congestion level is predicted based on the traffic monitoring results, and the network monitoring index is generated.

7. The data collaborative processing method of the OA platform as described in claim 1, characterized in that, After generating the serially optimized time zones for multi-process collaborative users of the multiple concurrent tasks, the process also includes: The serial optimization time zone is parsed to determine the optimization processing time zone for any associated user among the multiple concurrent tasks; The optimized time zone will be sent to the corresponding user as a reminder.

8. A data collaborative processing system for an OA platform, characterized in that, The system is used to execute the data collaborative processing method of the OA platform according to any one of claims 1-7, including: Concurrent task reading module: Connects to the OA platform and reads multiple concurrent tasks to be processed within a preset time zone; Feature acquisition module: parses the multiple concurrent tasks to collect features of multi-process collaborative users and collaborative operations, and generates multiple task execution features; Network monitoring module: Monitors the network status of the dedicated network of the OA platform and generates network monitoring indicators; Concurrent simulation module: Based on the network monitoring indicators, it performs multi-task concurrent simulation based on the execution characteristics of the multiple tasks, and generates concurrent processing adaptation indicators. Scheduling optimization module: When the concurrent processing adaptation index is less than the preset adaptation index, the module optimizes the processing time zone of the multiple concurrent tasks for multi-process collaborative users, using the preset time zone as the time constraint, and generates the serial optimized time zone for the multi-process collaborative users of the multiple concurrent tasks.

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