A dynamic scheduling and management system and method for electronic information

Abstract

The present invention relates to the field of information processing technology, and specifically to an electronic information dynamic scheduling and management system and method, wherein the system comprises a data flow dynamic monitoring module, a dynamic window adjustment module, a data distribution module, and an instruction flow reorganization module. In the present invention, by real-time monitoring of the data flow in the electronic information, and detailed analysis of the size and type changes, the adaptability and flexibility of data processing are improved, and by identifying the peak and valley values ​​of the flow and analyzing the flow fluctuation amplitude, the dynamic adjustment of the data processing window is realized, and by analyzing the dependency and potential delay between the execution instructions and rearranging the instruction execution order, the system can more effectively distribute the load of the processor core, reduce the delay, and enhance the processing speed, which not only optimizes the management of the electronic information flow, but also enhances the system's adaptability to complex data environments, and provides a more efficient and secure information processing platform.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to an electronic information dynamic scheduling and management system and method. Background Art

[0002] The field of information processing technology includes computer science and technology, data communication, and information access methods, involving multiple sub-fields such as big data analysis, cloud computing, the Internet of Things, and artificial intelligence. The core content of this technology field is the collection, storage, processing, and transmission of data, aiming to achieve effective management and dynamic optimization of information. From the construction of information infrastructure to the maintenance and innovation of complex data systems, including information security, data encryption technology, and network optimization.

[0003] Among them, the electronic information dynamic scheduling and management system is used to optimize the management and scheduling of electronic information flow. The technical matters targeted by the patent subject include real-time monitoring, dynamic allocation and optimized scheduling of data. The system uses information monitoring hardware and scheduling software to effectively track electronic information in real time and allocate resources to achieve optimal management of information flow. The system can efficiently schedule and manage information resources on the basis of ensuring data integrity and security.

[0004] Existing information processing technologies cover a wide range of applications from building basic data structures to maintaining complex systems, including big data analysis, cloud computing, etc., but lack the ability to respond immediately to dynamic changes in data flows and flexibly adjust. In existing technologies, data processing relies on preset parameters and static processing strategies, which limits the processing efficiency of the system when encountering unexpected data fluctuations. For example, a sudden increase in data will cause processing delays, and the unevenness of information flow will increase the load imbalance of the system and affect the overall performance. Existing technologies are not smart enough in node load management, lack effective real-time data flow analysis and instant scheduling strategies, resulting in data processing bottlenecks and insufficient resource allocation. The shortcomings are particularly prominent when processing batch or highly variable data flows. For example, in IoT and artificial intelligence applications, the rapid changes in data flow and type require more dynamic and processing strategies to avoid inefficiency and response delays. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an electronic information dynamic scheduling and management system and method.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, an electronic information dynamic scheduling and management system comprises:

[0007] The data flow dynamic monitoring module receives the real-time data flow in the electronic information, analyzes the size and type changes of the data flow, obtains the input flow monitoring data, identifies the peak and valley values ​​in the input flow monitoring data, analyzes the flow fluctuation amplitude, and obtains the flow fluctuation index;

[0008] The dynamic window adjustment module uses the traffic fluctuation index to adjust the size of the data processing window in the electronic information, calculates the cumulative amount of data in the adjusted window, analyzes the change trend of the data traffic, obtains the window adjustment parameters, slices the data, identifies the optimal time point for data slicing, and obtains the optimized slicing parameters;

[0009] The data distribution module analyzes the real-time load and processing capacity of the data processing nodes in the electronic information according to the optimized slice parameters, adjusts the data distribution according to the load balance, optimizes the data flow, obtains the node load analysis results, updates the node data distribution, optimizes the overall data flow, evenly distributes and operates the data flow, and obtains the data dynamic scheduling results;

[0010] The instruction stream reorganization module collects the execution instructions of the electronic information processor based on the data dynamic scheduling result, analyzes the dependencies and potential delays between the instructions, arranges the execution order of the instructions, optimizes the load distribution of the electronic information processor core, and obtains the electronic information management result.

[0011] As a further solution of the present invention, the step of obtaining the flow fluctuation index is specifically as follows:

[0012] Receive real-time data streams in electronic information, analyze changes in the size and type of data streams, record and analyze data traffic, and generate initialization data stream monitoring results;

[0013] Iteratively process the initialization data stream monitoring result, analyze the statistical characteristics of data fluctuations, calculate the standard deviation and average value in the data stream, identify the peak and valley values ​​in the data stream, and obtain the peak and valley identification result;

[0014] The peak-valley identification results are used to analyze the difference between the peak and valley values ​​and quantitatively evaluate the volatility of the traffic using the formula:

[0015] ;

[0016] Calculate the flow fluctuation index ;

[0017] in, Indicates the maximum flow rate monitored. Indicates the minimum flow rate to be monitored. Indicates the average flow rate during the monitoring period.

[0018] As a further solution of the present invention, the step of obtaining the window adjustment parameter is specifically as follows:

[0019] By using the traffic fluctuation index, the real-time performance of data fluctuation in the electronic information is analyzed, the adjustment demand of the real-time window is evaluated, and the adjustment demand index is obtained;

[0020] The real-time data processing window is dynamically adjusted using the adjustment demand index, using the formula:

[0021] ;

[0022] Calculate the window size adjustment factor ;

[0023] represents the adjustment demand indicator, represents the sensitivity of the adjustment coefficient, represents the target average of the fluctuations;

[0024] According to the adjustment coefficient of the window size, combined with the real-time data flow demand, the window size is automatically adjusted to match the data processing demand, the adaptability of the adjusted window size to the data flow processing is verified, and the window adjustment parameter is obtained.

[0025] As a further solution of the present invention, the step of obtaining the optimized slice parameters is specifically as follows:

[0026] Analyzing the real-time data flow and the preset processing capacity by using the window adjustment parameters, determining whether it is necessary to merge the data blocks in the electronic information, and slicing the data blocks to obtain the initialization slicing adjustment index;

[0027] By adjusting the index of the initialization slice, adjusting the slice granularity, matching the data processing requirements, and optimizing the data slice operation, the formula is adopted:

[0028] ;

[0029] Calculate the optimized slice parameters ;

[0030] In the formula, Represents the initialization slice adjustment index, represents the slice size adjustment factor, represents the data volatility index adjustment factor, represents the data liquidity factor, represents the performance adjustment factor, Represents the adaptation coefficient to environmental changes.

[0031] As a further solution of the present invention, the step of obtaining the node load analysis result is specifically:

[0032] The optimized slice parameters are used to analyze the real-time load and processing capacity of the data processing nodes in the electronic information, and the real-time load rate of the nodes is analyzed by using a statistical model in combination with the real-time data flow and processing requests to obtain the node load status information;

[0033] According to the node load status information, adjust the data flow in the electronic information, balance the data distribution, and obtain an initialized data scheduling plan;

[0034] Implement the initialized data scheduling scheme, perform dynamic data allocation, update node data configuration, dynamically adjust data flow to match node performance, and use the formula:

[0035] ;

[0036] Calculate the data allocation increment of each node to obtain the node load analysis results;

[0037] in, Represents the new data allocation, Represents the original data allocation, represents the adjustment factor, Represents the maximum load capacity of the node, Represents the current load.

[0038] As a further solution of the present invention, the step of obtaining the data dynamic scheduling result is specifically:

[0039] According to the node load analysis results, evaluate the load differences between differentiated nodes, adjust data distribution, reconfigure data flow, balance the load of the electronic information network, and generate an optimized data scheduling plan;

[0040] According to the optimized data scheduling scheme, the real-time response and processing capabilities of the adjusted multiple nodes are monitored using the formula:

[0041] ;

[0042] Calculate the data transmission efficiency value , generate data dynamic scheduling results;

[0043] in, Represents the amount of data successfully transferred, Represents processing efficiency, Represents the total transmission time, Represents the node idle time, , , , is the weight coefficient.

[0044] As a further solution of the present invention, the steps of obtaining the electronic information management results are specifically as follows:

[0045] According to the data dynamic scheduling result, the execution instructions of the electronic information processor are collected, the dependency relationship and potential delay information between the instructions are analyzed, and the instruction dependency data is obtained;

[0046] Using the instruction dependency data, the priority of the instruction execution order is adjusted to avoid potential delays and obtain an optimized execution order;

[0047] According to the optimized execution order, the load distribution of the processor core is adjusted using the formula:

[0048] ;

[0049] Calculate the processor core load optimization value , get the electronic information management results;

[0050] in, Representative instructions The dependency weight of Representative instructions processing time, Represents the total number of instructions, Represents the sum of the weight and processing time of the instruction.

[0051] A method for dynamic scheduling and management of electronic information, which is executed based on the above-mentioned dynamic scheduling and management system of electronic information, comprises the following steps:

[0052] S1: Receive real-time data streams in electronic information, monitor the size and type changes of data streams, record data traffic in differentiated time periods, identify peak and valley values ​​of data traffic, analyze traffic fluctuations, and generate a traffic fluctuation index;

[0053] S2: According to the traffic fluctuation index, the size of the data window for electronic information processing is adjusted, the data accumulation amount in the new window is calculated in real time, and the cutting granularity of the data processing window is optimized according to the accumulation result to obtain the window adjustment parameter;

[0054] S3: using the window adjustment parameters to analyze the real-time load and processing capacity of the data processing nodes in the electronic information, adjusting data distribution for load balancing, optimizing data flow, and obtaining node load analysis results;

[0055] S4: Based on the node load analysis results, collect execution instructions of the electronic information processor, analyze the dependencies and potential delays between instructions, prioritize the instruction execution order, optimize the load distribution of the processor core, and obtain the electronic information management results.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are:

[0057] In the present invention, by real-time monitoring of the data flow in the electronic information, and detailed analysis of the size and type changes, the adaptability and flexibility of data processing are improved, and by identifying the peak and valley values ​​of the flow, analyzing the fluctuation amplitude of the flow, the dynamic adjustment of the data processing window is realized, and the data is cut. This real-time adjustment not only improves the efficiency of data processing, but also ensures the optimal performance under various workload conditions. By analyzing the real-time load and processing capacity of the data processing node, adjusting the data distribution, optimizing the data flow and processing capacity of the entire system, and then improving the accuracy and response speed of data processing. By analyzing the dependencies and potential delays between the execution instructions and rearranging the order of instruction execution, the system can more effectively distribute the load of the processor core, reduce delays, and enhance processing speed, which not only optimizes the management of the electronic information flow, but also enhances the system's adaptability to complex data environments, providing a more efficient and secure information processing platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a system flow chart of the present invention;

[0059] Figure 2 It is a flow chart of the flow fluctuation index in the present invention;

[0060] Figure 3 It is a flow chart of window adjustment parameters in the present invention;

[0061] Figure 4 This is a flow chart of the optimized slice parameters in the present invention;

[0062] Figure 5 It is a flow chart of the node load analysis results in the present invention;

[0063] Figure 6 It is a flow chart of the data dynamic scheduling result in the present invention;

[0064] Figure 7 It is a flow chart of the electronic information management results in the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0066] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0067] See also Figure 1 The present invention provides a technical solution, an electronic information dynamic scheduling and management system comprising:

[0068] The data flow dynamic monitoring module receives the real-time data flow in the electronic information, analyzes the size and type changes of the data flow, obtains the input flow monitoring data, identifies the peak and valley values ​​in the input flow monitoring data, analyzes the flow fluctuation amplitude, and obtains the flow fluctuation index;

[0069] The dynamic window adjustment module uses the traffic fluctuation index to adjust the size of the data processing window in the electronic information, calculates the cumulative amount of data in the adjusted window, analyzes the change trend of data traffic, obtains the window adjustment parameters, slices the data, identifies the optimal time point for data slicing, and obtains the optimized slicing parameters;

[0070] The data distribution module analyzes the real-time load and processing capacity of the data processing nodes in the electronic information according to the optimized slice parameters, adjusts the data distribution according to the load balance, optimizes the data flow, obtains the node load analysis results, updates the node data distribution, optimizes the overall data flow, evenly distributes and operates the data flow, and obtains the data dynamic scheduling results;

[0071] The instruction stream reorganization module collects the execution instructions of the electronic information processor based on the data dynamic scheduling results, analyzes the dependencies and potential delays between instructions, arranges the execution order of instructions, optimizes the load distribution of the electronic information processor core, and obtains the electronic information management results.

[0072] The input traffic monitoring data includes traffic size, traffic type, and traffic monitoring. The traffic fluctuation index includes peak value, valley value, and traffic fluctuation amplitude. The window adjustment parameters include the accumulated data in the new window and the data cutting granularity. The optimized slice parameters include data cutting, slice time point, and slice optimization. The node load analysis results include real-time load, processing capacity, and load balancing. The data dynamic scheduling results include data distribution update, uniform distribution of data flow, and data operation status. The electronic information management results include dependencies between instructions, potential delays, execution order, and core load distribution.

[0073] See also Figure 2 ,The specific steps for obtaining the traffic fluctuation index are:

[0074] Receive real-time data streams in electronic information, analyze changes in the size and type of data streams, record and analyze data traffic, and generate initialization data stream monitoring results;

[0075] Receive real-time data streams from multiple sources. The data streams include various information types, such as data volume and data type. Each data packet is recorded and analyzed in detail. Using advanced data analysis technology, the statistical information of the size and type of the data is continuously updated to capture the instant changes in the data stream, and provide real-time data support for subsequent data stream management and security monitoring. It is particularly suitable for batch data processing and real-time network monitoring systems. It can help network administrators understand the network status in real time, predict potential data packet congestion and network attacks, and then take preventive measures to optimize data flow allocation and network resource utilization, ensure the stable operation of the network and the safe transmission of data, and generate initialization data flow monitoring results through data analysis and processing.

[0076] Iteratively process the initialization data stream monitoring results, analyze the statistical characteristics of data fluctuations, calculate the standard deviation and average value in the data stream, identify the peaks and valleys in the data stream, and obtain the peak and valley identification results;

[0077] Further analysis of the peaks and valleys in the data flow involves an in-depth review of the data packets and the use of advanced data identification algorithms to determine the extreme values ​​of data traffic. This technology relies on precise data recording and complex algorithm analysis to accurately identify the occurrence points of peaks and valleys from batch data streams, which is crucial for network traffic management and data transmission optimization. Identifying peaks and valleys can help network administrators adjust network resources in a timely manner and dynamically allocate network bandwidth to ensure that there are sufficient resources to process batch data when data traffic is large, while saving resources when traffic is low. This dynamic adjustment strategy can significantly improve network efficiency and data processing speed, and obtain peak and valley identification results.

[0078] Using the peak-valley identification results, we analyze the difference between the peak and valley values ​​and quantitatively evaluate the volatility of traffic using the formula:

[0079] ;

[0080] Calculate the flow fluctuation index ;

[0081] in, Indicates the maximum flow rate monitored. Indicates the minimum flow rate to be monitored. represents the average flow rate during the monitoring period;

[0082] The formula provides a way to quantify data traffic fluctuations. By measuring the maximum, minimum, and average values ​​of traffic in different time periods, the traffic fluctuation index can be calculated. The index helps network administrators understand the degree of fluctuation of network load during the observation period, better adjust network resources and optimize network strategies, and ensure that the network remains efficient and stable in the face of different traffic fluctuations.

[0083] In the one-hour observation, the maximum flow monitored is 150GB / s, the minimum flow is 20GB / s, and the average flow is 85GB / s. Substitute the flow into the formula for calculation:

[0084] ;

[0085] The calculation results show that during this time period, network traffic fluctuates greatly, and network administrators need to adjust network bandwidth or optimize data routing strategies based on the index to cope with estimated data peaks or valleys and ensure the continuity and efficiency of network services.

[0086] See also Figure 3 , the specific steps for obtaining window adjustment parameters are:

[0087] Through the flow fluctuation index, the real-time performance of data fluctuation in electronic information is analyzed, the adjustment demand of the real-time window is evaluated, and the adjustment demand index is obtained;

[0088] Through the careful analysis and calculation of the traffic fluctuation index, the real-time fluctuation of the data flow can be monitored. The index is obtained by analyzing the fluctuation difference between historical data and current data. Through real-time data collection technology, the fluctuation database is continuously updated. This database not only records the data flow fluctuations at every moment, but also applies a variety of data verification algorithms to ensure the accuracy and reliability of the data. According to the obtained fluctuation analysis results, the system can automatically adjust the size of the data processing window to ensure that the optimal processing performance can be maintained when facing changes in data volume and speed. This dynamic adjustment window mechanism is based on a series of complex algorithms. The algorithms can learn from the constantly changing data and respond quickly, which directly affects the adjustment strategy of the data processing window, providing the system with higher flexibility and efficiency, and obtaining adjustment demand indicators.

[0089] The real-time data processing window is dynamically adjusted using the adjustment demand index, using the formula:

[0090] ;

[0091] Calculate the window size adjustment factor ;

[0092] represents the adjustment demand indicator, represents the sensitivity of the adjustment coefficient, represents the target average of the fluctuations;

[0093] The formula is obtained by using the sensitivity coefficient and target average Dynamically adjust the window size to adapt to changes in real-time data streams, enhancing the adaptability and responsiveness of data processing;

[0094] set up , , , calculated as follows:

[0095] ;

[0096] The result means that the window size should be adjusted to 120% of the original size according to the real-time data fluctuations to better cope with sudden changes in data flow. This calculation process not only shows how to adjust the window in real time, but also reflects the adjustment coefficient. Direct impact on window size, each parameter in the formula is derived based on data analysis and historical trends, ensuring that the adjustment of the processing window is both reasonable and effective.

[0097] According to the adjustment coefficient of the window size, combined with the real-time data flow demand, the window size is automatically adjusted to match the data processing demand, and the adaptability of the adjusted window size to the data flow processing is verified to obtain the window adjustment parameter;

[0098] It is necessary to monitor real-time data traffic and adjust the window size in real time according to the changes in the data flow. This includes monitoring the size, type and change speed of the data traffic. The window size is adjusted according to the traffic fluctuation index and the preset performance goals. The adjustment algorithm uses a predictive model to predict future traffic changes to ensure that the window size can adapt to the latest needs of data traffic. By monitoring the equipment, the mutations and trends in the data flow are captured to dynamically adjust the size of the data processing window. This process needs to comprehensively consider factors such as data delay, system resources and processing power. The adjusted window size will be verified for adaptability to data flow processing by simulating actual data flow scenarios. The verification method includes comparing the changes in data processing efficiency and system resource utilization before and after the adjustment to ensure efficient and stable data processing and obtain window adjustment parameters.

[0099] See also Figure 4 , the specific steps for obtaining the optimized slice parameters are:

[0100] Utilize window adjustment parameters to analyze real-time data traffic and preset processing capacity, determine whether it is necessary to merge data blocks in electronic information, and slice the data blocks to obtain initialization slice adjustment indicators;

[0101] In the process of executing data processing window adjustment, the window adjustment parameters need to be used to monitor and analyze the data flow in real time. This step involves batch data collection and real-time analysis. The system continuously monitors data flow through sensors installed at key nodes, and real-time data is transmitted to the central processing unit through the network. The central processing unit uses advanced algorithms to calculate statistical indicators such as the average, volatility and peak value of the data in real time. Statistical indicators help the decision support system evaluate whether the current processing capacity can meet the needs of the data flow. For example, if the volatility of the current data flow exceeds the preset threshold, the system will automatically trigger the window adjustment mechanism and dynamically adjust the size of the data window to adapt to changes in the data flow. The system will also consider historical data patterns and use machine learning algorithms to predict future estimated data flow change trends. The window size is adjusted according to the prediction results to ensure the flexibility and efficiency of data processing. In this way, the system can maintain the optimal operating state and respond to fluctuations in external data flows in real time. The entire process is highly automated and intelligent, which greatly improves the response speed and accuracy of the data processing system and obtains the initialization slice adjustment indicator.

[0102] By initializing the slice adjustment index, adjusting the slice granularity, matching the data processing requirements, and optimizing the data slice operation, the formula is used:

[0103] ;

[0104] Calculate the optimized slice parameters ;

[0105] In the formula, Represents the initialization slice adjustment index, represents the slice size adjustment factor, represents the data volatility index adjustment factor, represents the data liquidity factor, represents the performance adjustment factor, represents the coefficient of adaptation to environmental change;

[0106] The formula makes the size and interval of data slices more refined and adaptable by comprehensively considering the preliminary slice adjustment index, slice granularity adjustment factor, data volatility index adjustment factor, data liquidity factor and system performance adjustment factor;

[0107] Setting parameters Unit, indicating the slice size after initialization adjustment;

[0108] Represents the slice granularity adjustment factor, which is used to adjust the slice size to adapt to the processing capacity;

[0109] , Indicates the degree to which the slice size needs to be flexibly adjusted as the data stream changes dynamically;

[0110] and They represent the performance adjustment factor and the environmental change adaptation coefficient respectively. Considering the uncertainty of the actual operating environment, they are substituted into the formula for calculation:

[0111] ;

[0112] The calculation results show that under the current system performance and environmental adaptability conditions, the optimal slicing parameters It should be adjusted to 41.2 units. Such an adjustment makes data processing more efficient and reduces data processing delays and resource waste caused by inappropriate slice size. The optimization of the entire process can significantly improve the system's ability to process data, while also ensuring that the system can operate stably under various environmental conditions.

[0113] See also Figure 5 , the specific steps for obtaining the node load analysis results are:

[0114] The optimized slice parameters are used to analyze the real-time load and processing capacity of the data processing nodes in the electronic information. The real-time load rate of the nodes is analyzed by using statistical models in combination with the real-time data flow and processing requests to obtain the node load status information.

[0115] Through in-depth analysis of the real-time load and processing capacity of each data processing node in electronic information, comprehensive consideration of server CPU utilization, memory occupancy rate, network bandwidth usage and real-time operation feedback information of each application, and the use of advanced data analysis technology, multi-source data is aggregated and processed, and advanced data analysis algorithms are used to perform regression analysis on historical load data to estimate the real-time load rate of each node. The system also needs to monitor the data transmission rate and response time. Through real-time monitoring and analysis of key indicators, the system can dynamically adjust data flow and processing requests according to the real-time load and operating status of the node, so as to optimize the operating efficiency and response speed of the entire data center, provide a basis for subsequent data allocation and resource scheduling, and obtain node load status information.

[0116] According to the node load status information, the data flow in the electronic information is adjusted to balance the data distribution and obtain the initialized data scheduling plan;

[0117] A load balancing algorithm based on real-time data analysis is adopted. The algorithm combines the working status, load data and historical performance indicators of the current node to calculate how data should be efficiently distributed among nodes. The algorithm not only considers the current load situation, but also predicts the estimated load changes in the short term. By real-time monitoring of the load status and processing capacity of each node, the system can dynamically adjust the data flow to reduce the load imbalance among nodes, optimize the data processing path, reduce the overall data processing delay, and improve the system's processing efficiency and data throughput. The algorithm also simulates different data distribution schemes and evaluates the performance of each scheme to ensure the selection of the optimal data distribution strategy. It describes the data distribution method among nodes and the expected processing effect, provides clear guidance and execution standards for actual data scheduling operations, and obtains an initialized data scheduling scheme.

[0118] Implement the initial data scheduling scheme, perform dynamic data allocation, update node data configuration, and dynamically adjust data flow to match node performance using the formula:

[0119] ;

[0120] Calculate the data allocation increment of each node to obtain the node load analysis results;

[0121] in, Represents the new data allocation, Represents the original data allocation, represents the adjustment factor, Represents the maximum load capacity of the node, Represents the current load;

[0122] The formula is adjusted by the coefficient Timely adjustment can flexibly adjust the data allocation amount according to the current load of the node, realize dynamic load balance, and optimize the utilization of network resources;

[0123] set up , the maximum load capacity of the node , current load , adjustment coefficient , substitute into the formula to calculate:

[0124] ;

[0125] The results show that the new data allocation is 650GB, indicating that by adjusting the strategy, the node load has been reduced, the data processing capacity has been reasonably allocated, the node processing efficiency has been improved, and the entire data center can process batch data requests more efficiently, ensuring the stability and response speed of the system operation.

[0126] See also Figure 6 , the specific steps for obtaining the data dynamic scheduling results are:

[0127] According to the results of node load analysis, evaluate the load differences between differentiated nodes, adjust data distribution, reconfigure data flow, balance the load of electronic information networks, and generate optimized data scheduling plans;

[0128] Analyze the current load status and data processing capacity of each node, focus on monitoring nodes with higher loads and nodes with lower loads, and dynamically adjust data distribution strategies through advanced load balancing technology to make data flows more evenly distributed on each node. In specific operations, continuously track the load status and processing speed of each node, and adjust data routing and task allocation in real time according to real-time data updates to optimize the overall response time and processing capacity of the network. It also takes into account the differences in communication delays and processing capabilities between nodes, and reasonably configures tasks to prevent overload of some nodes and improve node utilization at the same time, aiming to improve the efficiency and data processing speed of the entire network, ensure that the network can maintain stable and efficient operation when facing batch data streams, effectively balance loads and reduce delays, improve the data processing capacity and response speed of the entire network, and generate optimized data scheduling plans.

[0129] According to the optimized data scheduling scheme, the real-time response and processing capabilities of multiple nodes after monitoring and adjustment are calculated using the formula:

[0130] ;

[0131] Calculate the data transmission efficiency value , generate data dynamic scheduling results;

[0132] in, Represents the amount of data successfully transferred, Represents processing efficiency, Represents the total transmission time, Represents the node idle time, , , , is the weight coefficient;

[0133] The formula provides a new way to comprehensively evaluate node performance by comprehensively considering the processing efficiency and idle time of the node, as well as the weight coefficient of each activity. It not only focuses on the traditional amount of transmitted data and total time, but also takes into account the idle state and efficiency of the node in actual operation, making the performance evaluation closer to the actual situation.

[0134] Set within a specific evaluation period, the node successfully transmits MB data, processing efficiency , total transmission time Seconds, the idle time of the node seconds, and the weight coefficients are (for the amount of data successfully transferred), (for processing efficiency), (for total transfer time), (For idle time), the calculation process is as follows:

[0135] ;

[0136] The results show that under the current configuration, about 3.03MB of data can be transmitted per second. This result effectively reflects the performance of the node in actual operation and helps to further optimize the network configuration and resource allocation strategy so that network resources can be used more effectively.

[0137] See also Figure 7 , the specific steps for obtaining electronic information management results are:

[0138] According to the data dynamic scheduling result, the execution instructions of the electronic information processor are collected, the dependency relationship and potential delay information between the instructions are analyzed, and the instruction dependency data is obtained;

[0139] Data is obtained in real time by monitoring the internal state of the processor and the instruction queue. This process involves detailed tracking of each instruction stream and analyzing the dependencies between instructions. This is achieved by exploring the interaction of data flow and control flow between instructions. For potential delay information, the system evaluates by analyzing the occupancy of processor buffers and registers and the waiting time of instructions. The information will help the system understand the instructions that cause execution blockage or inefficiency, so as to optimize them. In order to accurately predict and reduce time delays in execution, the system uses advanced analysis techniques, including timestamp analysis and resource mapping technology. The technology not only provides a comprehensive insight into the instruction execution path, but also helps optimize the instruction execution order, and records in detail the relationship between instructions and potential performance bottlenecks, providing data support for processor performance optimization and obtaining instruction dependency data.

[0140] Using instruction dependency data, the priority of instruction execution order is adjusted to avoid potential delays and obtain an optimized execution order;

[0141] An optimization algorithm is used to adjust the execution order of instructions to reduce potential execution delays. This step uses a priority-based scheduling algorithm to select the most efficient execution path by simulating different instruction execution orders. The algorithm considers the dependencies and urgency of instructions, and re-evaluates and prioritizes the execution of instructions. During this process, the execution time and resource requirements of each instruction are monitored and analyzed in real time. The algorithm dynamically adjusts the execution order of instructions according to the current processor status to achieve optimal resource utilization and the shortest total execution time. Through advanced machine learning technology, the system can predict future instruction flows and adjust strategies in time to cope with changes in processor load, ensure load balancing and minimize response time in a multi-core processor environment, optimize the overall performance and response speed of the processor, and obtain an optimized execution order.

[0142] According to the optimized execution order, the load distribution of the processor core is adjusted using the formula:

[0143] ;

[0144] Calculate the processor core load optimization value , get the electronic information management results;

[0145] in, Representative instructions The dependency weight of Representative instructions processing time, Represents the total number of instructions, Represents the sum of the weight and processing time of the instruction;

[0146] The formula optimizes the processor load distribution by weighted average, where the weight Dynamically adjust processing time based on the urgency and dependencies of instructions It reflects the actual execution requirements of each instruction, so that key instructions can be processed first and the overall processing efficiency can be improved;

[0147] There are three instructions with weights of 2, 1.5 and 3, and execution times of 100ms, 150ms and 200ms. The calculation process of the formula is:

[0148] ;

[0149] The results show that the average execution time after weight adjustment is 157.69 milliseconds, indicating that the processing efficiency is optimized by adjusting the algorithm, the average execution time is shortened, and the response speed of the processor is improved.

[0150] A method for dynamic scheduling and management of electronic information, which is executed based on the above-mentioned dynamic scheduling and management system of electronic information, comprises the following steps:

[0151] S1: Receive real-time data streams in electronic information, monitor the size and type changes of data streams, record data traffic in differentiated time periods, identify peak and valley values ​​of data traffic, analyze traffic fluctuations, and generate a traffic fluctuation index;

[0152] S2: According to the traffic fluctuation index, the data window size of the electronic information processing is adjusted, the data accumulation in the new window is calculated in real time, and the cutting granularity of the data processing window is optimized according to the accumulation result to obtain the window adjustment parameter;

[0153] S3: Use window adjustment parameters to analyze the real-time load and processing capacity of data processing nodes in electronic information, adjust data distribution for load balancing, optimize data flow, and obtain node load analysis results;

[0154] S4: Based on the node load analysis results, collect the execution instructions of the electronic information processor, analyze the dependencies and potential delays between the instructions, prioritize the instruction execution order, optimize the load distribution of the processor core, and obtain the electronic information management results.

[0155] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An electronic information dynamic scheduling and management system, characterized in that: The system comprises: The data flow dynamic monitoring module receives the real-time data flow in the electronic information, analyzes the size and type changes of the data flow, obtains the input flow monitoring data, identifies the peak and valley values ​​in the input flow monitoring data, analyzes the flow fluctuation amplitude, and obtains the flow fluctuation index; The dynamic window adjustment module uses the traffic fluctuation index to adjust the size of the data processing window in the electronic information, calculates the cumulative amount of data in the adjusted window, analyzes the change trend of the data traffic, obtains the window adjustment parameters, slices the data, identifies the optimal time point for data slicing, and obtains the optimized slicing parameters; The data distribution module analyzes the real-time load and processing capacity of the data processing nodes in the electronic information according to the optimized slice parameters, adjusts the data distribution according to the load balance, optimizes the data flow, obtains the node load analysis results, updates the node data distribution, optimizes the overall data flow, evenly distributes and operates the data flow, and obtains the data dynamic scheduling results; The instruction stream reorganization module collects the execution instructions of the electronic information processor based on the data dynamic scheduling result, analyzes the dependencies and potential delays between the instructions, arranges the execution order of the instructions, optimizes the load distribution of the electronic information processor core, and obtains the electronic information management result.

2. The electronic information dynamic scheduling and management system according to claim 1 is characterized in that: The steps for obtaining the flow fluctuation index are specifically as follows: Receive real-time data streams in electronic information, analyze changes in the size and type of data streams, record and analyze data traffic, and generate initialization data stream monitoring results; Iteratively process the initialization data stream monitoring result, analyze the statistical characteristics of data fluctuations, calculate the standard deviation and average value in the data stream, identify the peak and valley values ​​in the data stream, and obtain the peak and valley identification result; The peak-valley identification results are used to analyze the difference between the peak and valley values ​​and quantitatively evaluate the volatility of the traffic using the formula: ； Calculate the flow fluctuation index ; in, Indicates the maximum flow rate monitored. Indicates the minimum flow rate to be monitored. Indicates the average flow rate during the monitoring period.

3. The electronic information dynamic scheduling and management system according to claim 2 is characterized in that: The steps for obtaining the window adjustment parameters are specifically as follows: By using the traffic fluctuation index, the real-time performance of data fluctuation in the electronic information is analyzed, the adjustment demand of the real-time window is evaluated, and the adjustment demand index is obtained; The real-time data processing window is dynamically adjusted using the adjustment demand index, using the formula: ； Calculate the window size adjustment factor ; represents the adjustment demand indicator, represents the sensitivity of the adjustment coefficient, represents the target average of the fluctuations; According to the adjustment coefficient of the window size, combined with the real-time data flow demand, the window size is automatically adjusted to match the data processing demand, the adaptability of the adjusted window size to the data flow processing is verified, and the window adjustment parameter is obtained.

4. The electronic information dynamic scheduling and management system according to claim 3 is characterized in that: The steps for obtaining the optimized slice parameters are specifically as follows: Analyzing the real-time data flow and the preset processing capacity by using the window adjustment parameters, determining whether it is necessary to merge the data blocks in the electronic information, and slicing the data blocks to obtain the initialization slicing adjustment index; By adjusting the index of the initialization slice, adjusting the slice granularity, matching the data processing requirements, and optimizing the data slice operation, the formula is adopted: ； Calculate the optimized slice parameters ; In the formula, Represents the initialization slice adjustment index, represents the slice size adjustment factor, represents the data volatility index adjustment factor, represents the data liquidity factor, represents the performance adjustment factor, Represents the adaptation coefficient to environmental changes.

5. The electronic information dynamic scheduling and management system according to claim 4 is characterized in that: The steps for obtaining the node load analysis result are specifically as follows: The optimized slice parameters are used to analyze the real-time load and processing capacity of the data processing nodes in the electronic information, and the real-time load rate of the nodes is analyzed by using a statistical model in combination with the real-time data flow and processing requests to obtain the node load status information; According to the node load status information, adjust the data flow in the electronic information, balance the data distribution, and obtain an initialized data scheduling plan; Implement the initialized data scheduling scheme, perform dynamic data allocation, update node data configuration, dynamically adjust data flow to match node performance, and use the formula: ； Calculate the data allocation increment of each node to obtain the node load analysis results; in, Represents the new data allocation, Represents the original data allocation, represents the adjustment factor, Represents the maximum load capacity of the node, Represents the current load.

6. The electronic information dynamic scheduling and management system according to claim 5 is characterized in that: The steps for obtaining the data dynamic scheduling result are specifically as follows: According to the node load analysis results, evaluate the load differences between differentiated nodes, adjust data distribution, reconfigure data flow, balance the load of the electronic information network, and generate an optimized data scheduling plan; According to the optimized data scheduling scheme, the real-time response and processing capabilities of the adjusted multiple nodes are monitored using the formula: ； Calculate the data transmission efficiency value , generate data dynamic scheduling results; in, Represents the amount of data successfully transferred, Represents processing efficiency, Represents the total transmission time, Represents the node idle time, , , , is the weight coefficient.

7. The electronic information dynamic scheduling and management system according to claim 6, characterized in that: The steps for obtaining the electronic information management results are specifically as follows: According to the data dynamic scheduling result, the execution instructions of the electronic information processor are collected, the dependency relationship and potential delay information between the instructions are analyzed, and the instruction dependency data is obtained; Using the instruction dependency data, the priority of the instruction execution order is adjusted to avoid potential delays and obtain an optimized execution order; According to the optimized execution order, the load distribution of the processor core is adjusted using the formula: ； Calculate the processor core load optimization value , get the electronic information management results; in, Representative instructions The dependency weight of Representative instructions processing time, Represents the total number of instructions, Represents the sum of the weight and processing time of the instruction.

8. A method for dynamic scheduling and management of electronic information, characterized in that: The electronic information dynamic scheduling and management system according to any one of claims 1 to 7 comprises the following steps: S1: Receive real-time data streams in electronic information, monitor the size and type changes of data streams, record data traffic in differentiated time periods, identify peak and valley values ​​of data traffic, analyze traffic fluctuations, and generate a traffic fluctuation index; S2: According to the traffic fluctuation index, the size of the data window for electronic information processing is adjusted, the data accumulation amount in the new window is calculated in real time, and the cutting granularity of the data processing window is optimized according to the accumulation result to obtain the window adjustment parameter; S3: using the window adjustment parameters to analyze the real-time load and processing capacity of the data processing nodes in the electronic information, adjusting data distribution for load balancing, optimizing data flow, and obtaining node load analysis results; S4: Based on the node load analysis results, collect execution instructions of the electronic information processor, analyze the dependencies and potential delays between instructions, prioritize the instruction execution order, optimize the load distribution of the processor core, and obtain the electronic information management results.

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