Information processing method and storage medium based on power system

Abstract

The present invention relates to the technical field of power system information processing, and specifically discloses an information processing method and storage medium based on a power system, the method comprising: analyzing the load rate of power data of each monitoring section, extracting a heavy-load section, analyzing the load balancing adjustment index of the heavy-load section data, determining data adjustment classification label information, if the heavy-load section data adjustment classification label information is determined to be a data migration label, performing a data transfer pre-operation simulation, performing adaptive adjustment on the heavy-load section data pre-transfer line, if the heavy-load section data adjustment classification label information is determined to be a thread pool resource adjustment label, analyzing the thread pool adjustment parameter to perform thread pool resource adjustment. The present invention collects power load data of each monitoring section, analyzes the load balancing adjustment index, realizes dynamic load balancing, improves system reliability, prevents failures and data loss caused by overload, and ensures the stability and reliability of power data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of power system information processing, and in particular to an information processing method and a storage medium based on a power system. Background Art

[0002] With the increasing complexity of the power grid architecture and the increasing requirements for power supply quality, the traditional fixed and lack of dynamic adjustment line monitoring and load control mode is difficult to accurately and timely reflect the actual status of line operation, resulting in the difficulty in timely detection of line load imbalance problems and delayed warning of potential fault risks. Therefore, through the power system information processing method, scientific analysis can be carried out based on the real-time operation data of the line, to achieve intelligent control and optimization of the line load, and improve the reliability and safety of the power system operation.

[0003] For example, the invention patent with announcement number CN109034405B announces a system for configuring power system data information based on big data. The distribution operation data server organizes the maintenance data information obtained during the maintenance process, pairs the obtained data information with the preset threshold data information corresponding to the data information, and compares the data information obtained during the maintenance process with the preset threshold to form maintenance comparison and judgment information, and sends the maintenance comparison and judgment information to the distribution operation terminal for distribution operation personnel to view.

[0004] For example, the invention patent with announcement number CN103325074B announces a method for real-time data processing in an electric power system, including: classifying the real-time data in the electric power system according to several preset subject types; generating data files corresponding to several subject types according to the classified real-time data, and storing the data files; receiving a data access request sent by a data access request end; and returning the data file of the corresponding subject type to the data access request end.

[0005] However, in the process of implementing the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] The current power information processing system has multiple data sources, complex structures, and high transmission real-time requirements in the complex and ever-changing power grid operation environment. The traditional relatively fixed information processing mode is difficult to effectively process in terms of line load dynamic analysis and intelligent control, which can easily lead to incomplete monitoring data, difficulty in timely detection of line load hazards, and difficulty in flexibly allocating resources in response to load changes, reducing the reliability and stability of power system operation. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an information processing method and a storage medium based on a power system, which can effectively solve the problems involved in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides an information processing method based on a power system, comprising:

[0009] S1, setting each monitoring point on the information transmission line of the power system, thereby obtaining each monitoring section, collecting the power load data of each monitoring section, and thereby analyzing the power data load rate of each monitoring section.

[0010] S2, extracting the heavy-load sections based on the power data load rate of each monitoring section, thereby collecting the resource occupancy data and power data growth prediction parameters of the heavy-load sections, and analyzing the load balancing adjustment index of the heavy-load section data.

[0011] S3, determine the data adjustment classification label information based on the heavy-load section data load balancing adjustment index. If the heavy-load section data adjustment classification label information is determined to be a data migration label, execute S4; if the heavy-load section data adjustment classification label information is determined to be a thread pool resource adjustment label, execute S5.

[0012] S4, extracting the data pre-transfer line information of the heavy-loaded road section, and performing data transfer pre-operation simulation, obtaining the data transfer pre-operation simulation result, and adaptively adjusting the data pre-transfer line of the heavy-loaded road section, thereby performing data load transfer on the heavy-loaded road section.

[0013] S5, combining the load balancing adjustment index of the heavy-load section data, analyzing the thread pool adjustment parameters, and adjusting the thread pool resources according to the thread pool adjustment parameters.

[0014] A second aspect of the present invention provides a computer-readable storage medium for storing a program, wherein the program, when executed by a processor, implements an information processing method for a power system.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0016] (1) The present invention provides an information processing method and storage medium based on the power system, collects power load data of each monitoring section, accurately locks the heavy-load section, provides a numerical basis for subsequent optimization, analyzes the load balancing adjustment index, and accurately determines the adjustment strategy to achieve dynamic load balancing, improve system reliability, prevent failures and data loss caused by overload, and ensure the stability and reliability of power data transmission. Simulate the data transfer pre-operation, and adaptively adjust the pre-transfer line according to the simulation results to ensure accurate and efficient data transfer, thereby improving the quality and efficiency of power data transmission.

[0017] (2) The present invention determines the data adjustment classification label information based on the data load balancing adjustment index of the heavy-load section. When the index reaches a specific threshold and is determined to be a data migration label, the data transfer process is started to solve the data load pressure from the root and avoid the risks of system failure, data loss and transmission interruption caused by local overload. If it is determined to be a thread pool resource adjustment label, the thread pool core and maximum number of threads are optimized to alleviate the load pressure, improve resource utilization, and reduce the additional risks caused by data transfer, such as delay fluctuations and consistency maintenance problems, to ensure the smooth operation of the system and flexibly adapt to dynamic changes in load, and to improve the reliability, stability and intelligent adaptability of the power system.

[0018] (3) The present invention can adaptively adjust the data pre-transfer line of the heavy-load section by obtaining the data transfer pre-operation simulation results, which can effectively avoid the drawbacks such as data loss, transmission interruption and resource waste caused by blind data transfer. By implementing adaptive adjustment according to the simulation results, it can ensure that the line bandwidth and the number of packet retransmissions are accurately allocated on demand. While maintaining high data quality, it ensures the continuity and accuracy of power data transmission, and improves system stability and reliability. At the same time, it optimizes resource allocation, avoids excessive investment or idleness of resources, improves resource utilization efficiency, and enhances the adaptability of the power system to complex working conditions and dynamic load changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0020] Figure 1 The figure is a schematic flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 As shown, the first aspect of the present invention provides an information processing method based on a power system, comprising:

[0023] S1, setting each monitoring point on the information transmission line of the power system, thereby obtaining each monitoring section, collecting the power load data of each monitoring section, and thereby analyzing the power data load rate of each monitoring section.

[0024] It should be noted that the information transmission line of the power system refers to the communication link used to transmit various types of data in the power system.

[0025] It should also be noted that the rules for setting each monitoring point are extracted from a database. In a specific embodiment, the rules can be evenly distributed according to the line length.

[0026] In a specific embodiment, for example, in the layout of the power system information transmission line in a certain city, the total length of the line is 500 kilometers. According to the setting rules extracted from the database, it is decided to build a monitoring network by setting a monitoring point every 50 kilometers on average. Based on this, this line is accurately divided into 10 monitoring sections.

[0027] S2, extracting the heavy-load sections based on the power data load rate of each monitoring section, thereby collecting the resource occupancy data and power data growth prediction parameters of the heavy-load sections, and analyzing the load balancing adjustment index of the heavy-load section data.

[0028] In this embodiment, the heavy-load sections are extracted based on the load rate of the power data of each monitoring section. The specific analysis process is as follows:

[0029] During the preset monitoring period, the power load data of each monitoring segment is collected, including the data flow of each monitoring segment, the data request response time, the number of concurrent connections and the length of the data processing task queue.

[0030] It should be noted that data traffic refers to the total amount of data transmitted on the information transmission line during the preset monitoring period, including the cumulative number of bytes of various data packets such as power equipment monitoring data.

[0031] The number of concurrent connections refers to the total number of valid data connections established on the information transmission line.

[0032] The length of the data processing task queue refers to the number of elements in the queue composed of data tasks waiting to be processed. It is used to characterize the workload status of the line to be processed. The longer the queue, the greater the system processing pressure and the higher the risk of processing delay.

[0033] It should also be noted that the power load data can be collected by a network analyzer.

[0034] Based on the power load data analysis and processing of each monitoring segment, the power data load rate of each monitoring segment is obtained, and the power data load rate of each monitoring segment is used to characterize the power data load degree of each monitoring segment.

[0035] In a specific embodiment, the specific method of obtaining the power data load rate of each monitoring segment is as follows:

[0036] ,

[0037] in, is the power data load rate of the ith monitoring segment, is the data flow of the ith monitoring segment, is the data request response time of the i-th monitoring segment, is the number of concurrent connections in the ith monitoring segment, is the length of the data processing task queue of the i-th monitoring segment, is the load factor corresponding to the unit data flow, is the load factor corresponding to the unit data request response time, is the load factor corresponding to a single concurrent connection, is the load factor corresponding to the unit data processing task queue length, i is the number of each monitoring segment, , m is the number of monitoring segments, and e is a natural constant.

[0038] It should be noted that the load factor corresponding to unit data flow, the load factor corresponding to unit data request response time, the load factor corresponding to a single concurrent connection and the load factor corresponding to unit data processing task queue length are pre-set values ​​in the database and can be directly extracted from the database when used. The extraction method is, for example, to construct a mapping set with the data flow, data request response time, number of concurrent connections and data processing task queue length and the load factor corresponding to unit data flow, unit data request response time, single concurrent connection and unit data processing task queue length. When in use, the real-time data flow, data request response time, number of concurrent connections and data processing task queue length are input into the mapping set to extract the corresponding load factors.

[0039] It should also be noted that the power data load rate of each monitoring section is obtained by analyzing and processing the power load data of each monitoring section, taking into account the mutual influence between these parameters. For example, when the data flow increases, the system needs to process and transmit more data. Because many data sources establish various connection transmissions, it is easy to increase the number of concurrent connections. The response time of data requests is extended accordingly, and massive data congests the processing channel and consumes system resources, resulting in a sharp increase in the time required to process a single request. The length of the data processing task queue increases, and new tasks accumulate and queue up for processing, exacerbating the system load and response delay. The increase in the number of concurrent connections means that more data requests are pouring in in parallel, which directly increases the data flow, prolongs the response time of data requests, and increases the length of the data processing task queue. The increase in the response time of data requests reflects the bottleneck of system processing capacity or resource shortage, slow data processing, backlog of unprocessed tasks, and increased length of the data processing task queue.

[0040] Extract the power data load threshold preset in the database.

[0041] It should be noted that the power data load threshold is a critical indicator pre-set in the database and used to characterize the power data load level of the monitoring section.

[0042] The monitoring section whose power data load rate is greater than or equal to the power data load threshold is recorded as a heavy-load section, thereby extracting the heavy-load section.

[0043] It should be understood that if the power data load rate is greater than or equal to the power data load threshold, it means that the data load level of the monitoring section is high, which may easily lead to data loss or processing errors. Therefore, it is recorded as a heavy-load section and subsequent analysis and processing are carried out.

[0044] It should be noted that if the power data load rate of the monitoring segment is less than the power data load threshold, no in-depth monitoring is performed and the segment can continue to transmit information.

[0045] In this embodiment, resource occupancy data and power data growth prediction parameters of heavy-load sections are collected, where:

[0046] The resource occupancy data is the resource occupancy data of the line corresponding to the heavy-load section, including the average delay jitter of the line, the total number of thread resources occupied, the peak value of process memory occupancy and the average data packet sending and receiving rate.

[0047] The reference resource occupancy data stored in the database is extracted, including the reference average delay jitter, the reference total number of thread resource occupancy, the reference process memory occupancy peak value, and the reference average data packet sending and receiving rate.

[0048] It should be noted that the average delay jitter refers to the average fluctuation of the data packet transmission delay within the preset monitoring period. The delay jitter can reflect the stability of the line. A large value indicates that the transmission delay fluctuates violently and the data transmission timing is poor.

[0049] The total number of thread resources occupied refers to the overall size of active threads in the line-related processing process. A large number means that the system has heavy parallel processing tasks and the processing efficiency may be reduced due to scheduling complexity.

[0050] The process memory usage peak refers to the maximum value of the memory occupied by the process during the monitoring period. A high peak value indicates that memory resources are tight.

[0051] It should also be noted that the average delay jitter and the average packet sending and receiving rate can be collected through a network analyzer, and the total number of thread resources occupied and the peak value of process memory usage can be collected through performance monitoring software, such as SolarWinds Server & Application Monitor (SAM).

[0052] The power data growth prediction parameters are power data growth prediction parameters for the area corresponding to the heavy-load section, including the historical average growth rate of power consumption in the area, the increase in equipment active power, the historical load data variance and the average operating time of power-consuming equipment.

[0053] The reference power data growth prediction parameters stored in the database are extracted, including the reference historical average growth rate of electricity consumption, the increase in active power of the reference equipment, the reference historical load data variance and the reference average operating time of the power consumption equipment.

[0054] It should be noted that the increase in equipment active power refers to the increase in equipment active power in the area during the monitoring period.

[0055] The variance of historical load data is obtained as follows: collect the hourly load data of the historical period corresponding to the heavy-load section, and record it as a series . First calculate the mean , and then according to the variance formula , where j is the number of the historical period hours, , n is the number of hours in the historical period, from which the variance of the historical load data is obtained.

[0056] In this embodiment, the load balancing adjustment index of the heavy-load section data is analyzed, and the specific analysis process is as follows:

[0057] The line data load characteristic value is obtained according to the resource occupancy data analysis and processing, and the line data load characteristic value is used to characterize the resource occupancy degree of the line corresponding to the heavy-load section.

[0058] In a specific embodiment, the line data load characteristic value is obtained in the following manner:

[0059] ,

[0060] in, is the line data load characteristic value, is the average delay jitter of the line, The total number of thread resources occupied by the line. is the peak memory usage of the line process. is the average data packet sending and receiving rate of the line, For reference, the average delay jitter is The total number of reference thread resources occupied. is the peak memory usage of the reference process, is the average sending and receiving rate of reference data packets, and e is a natural constant.

[0061] It is important to understand that the softplus function is a built-in function in Python. .

[0062] It should be noted that the characteristic value of line data load is obtained by analyzing and processing the resource occupancy data, taking into account the mutual influence between these parameters. For example, the average delay jitter increases, the timing of data packet transmission is destroyed, and in order to ensure accurate data reception and processing, the system needs more buffer resources, error correction mechanisms and retransmission operations, which occupy a large number of thread resources, resulting in an increase in the total number of thread resources occupied. The line transmission stability is poor, the data packet transmission is blocked, causing a large number of retransmissions and queuing, which reduces the average data packet sending and receiving rate. The total number of thread resources occupied increases, a large number of threads compete for limited CPU time slices, memory and other system resources, the processing efficiency is reduced, the data processing delay accumulates, and the average delay jitter increases. The peak value of process memory occupancy increases, memory resources are scarce, the system frequently reads and writes the hard disk virtual memory, the disk I / O operations increase dramatically, the data reading and writing speed is slowed down, the data packet transmission is blocked, and the average delay jitter increases. The average data packet sending and receiving rate increases, the line data flow increases, the processing tasks are heavier, the thread resource demand increases, and the total number of thread resources occupied increases. A large number of data packets need sufficient memory support for caching, processing and transmission, and the peak value of process memory occupancy increases with the pressure of data packet sending and receiving.

[0063] According to the power data growth prediction parameter analysis and processing, the regional power data pre-growth characteristic value is obtained, and the regional power data pre-growth characteristic value is used to characterize the expected growth state of the regional power data.

[0064] In a specific embodiment, the specific method of obtaining the regional power data pre-growth characteristic value is as follows:

[0065] ,

[0066] in, is the pre-growth characteristic value of regional power data, is the historical average growth rate of regional electricity consumption, is the increase in active power of the equipment in the area, is the variance of the historical load data of the region, is the average operating time of the electrical equipment in the area, For reference to the average growth rate of historical electricity consumption, is the active power increase of the reference equipment, is the reference historical load data variance, For reference, the average operating time of electrical equipment, Parameter weights for power data growth prediction.

[0067] It should be noted that the power data growth prediction parameter weights are pre-set in the database, and the value range is 0-1. When in use, the set values ​​can be directly extracted from the database. The extraction method is, for example, to construct a mapping set of power data growth prediction parameters and power data growth prediction parameter weights. When in use, the real-time power data growth prediction parameters are input into the mapping set to extract the power data growth prediction parameter weights.

[0068] It should also be noted that the regional power data pre-growth characteristic value is obtained by analyzing and processing the power data growth prediction parameters, taking into account the mutual influence between these parameters. For example, when the historical average growth rate of power consumption increases, it usually means that the power demand of the entire region is increasing, which is likely due to the addition of new power-consuming equipment or the increase in the frequency and power of existing equipment. In this case, the increase in the active power of the equipment will also increase. Conversely, when the increase in the active power of the equipment increases, the power of the new equipment is higher, which will directly lead to an increase in the total power consumption, thereby increasing the historical average growth rate of power consumption. The increase in the historical average growth rate of power consumption indicates the growth trend of power demand. More power fluctuations may occur during the growth process, which may increase the variance of historical load data. The increase in the historical average growth rate of power consumption is generally accompanied by an increase in the average operating time of power-consuming equipment. Because the increase in power demand may be due to the extension of equipment operation time. When the average operating time of power-consuming equipment increases, the total power consumption will increase, thereby driving the increase in the historical average growth rate of power consumption. When the increase in the active power of the equipment increases, if there is no restriction on the operating time of the equipment, the total power consumption will increase if the average operating time of the power-consuming equipment remains unchanged.

[0069] Based on the line data load characteristic value and the regional power data pre-growth characteristic value, the heavy-load section data load balancing adjustment index is obtained through analysis and processing, and the heavy-load section data load balancing adjustment index is used to characterize the degree of demand for heavy-load section data load balancing adjustment.

[0070] In a specific embodiment, the load balancing adjustment index of the heavy-load section data is obtained in the following specific method:

[0071] ,

[0072] in, Adjust the load balancing index for heavy-load road sections. is the line data load characteristic value, is the pre-growth characteristic value of regional power data, is the line data load characteristic value weight coefficient, is the weight coefficient of the regional power data pre-growth eigenvalue, is a natural constant.

[0073] It should be understood that the softplus function is a built-in function in Python. .

[0074] It should be noted that the line data load characteristic value weight coefficient and the regional power data pre-growth characteristic value weight coefficient are set in the database, and the value range is 0-1. When used, the pre-set values ​​can be directly extracted from the database. The extraction method is, for example, to construct a mapping set by combining the line data load characteristic value and the regional power data pre-growth characteristic value with the line data load characteristic value weight coefficient and the regional power data pre-growth characteristic value weight coefficient. When used, the real-time line data load characteristic value and the regional power data pre-growth characteristic value are input into the mapping set to extract the corresponding weight coefficient.

[0075] It should be noted that when the expected growth characteristic value of regional power data is negative, it indicates that future electricity demand may decrease, so the current line load condition is a key factor directly affecting whether the power system can operate normally. Therefore, the size of the line data load characteristic value at this time has a greater impact on the size of the load balancing adjustment index of the heavy-load section data.

[0076] S3, determine the data adjustment classification label information based on the heavy-load section data load balancing adjustment index. If the heavy-load section data adjustment classification label information is determined to be a data migration label, execute S4; if the heavy-load section data adjustment classification label information is determined to be a thread pool resource adjustment label, execute S5.

[0077] In this embodiment, the load balancing adjustment index of the heavy-load section data determines the data adjustment classification label information, and the specific analysis process is as follows:

[0078] Extract the preset data load balancing adjustment verification indicators in the database.

[0079] The data adjustment classification label information includes a data migration label and a thread pool resource adjustment label.

[0080] If the data load balancing adjustment index of the heavy-load section is greater than or equal to the data load balancing adjustment verification index, the heavy-load section data adjustment classification label information is determined as a data migration label.

[0081] It should be understood that if the data load balancing adjustment index of the heavily loaded section is greater than or equal to the data load balancing adjustment verification index, it means that the current load condition of the heavily loaded section is relatively severe and cannot be effectively alleviated by self-adjustment alone. Data transfer is required to improve the pressure of the heavily loaded section from the root, avoid data loss and transmission interruption due to excessive load, and ensure the reliability of continued data transmission.

[0082] If the data load balancing adjustment index of the heavy-load section is less than the data load balancing adjustment verification index, the heavy-load section data adjustment classification label information is determined as the thread pool resource adjustment label.

[0083] If the data load balancing adjustment index of the heavily loaded section is less than the data load balancing adjustment verification index, it means that although the section is in a heavily loaded state, it has not yet reached the urgency of data migration. At this time, the load balancing situation can be improved by adjusting the thread pool resources. Without performing large-scale data migration, the load pressure can be alleviated, resource utilization can be improved, and the heavily loaded section can gradually tend to load balance, reducing the additional risks that may be caused by data migration, such as delays in data transmission and data consistency maintenance.

[0084] In this embodiment, the data adjustment classification label information is determined based on the data load balancing adjustment index of the heavy-load section. When the index reaches a specific threshold and is determined to be a data migration label, the data transfer process is started to solve the data load pressure from the root and avoid the risk of system failure, data loss and transmission interruption caused by local overload. If it is determined to be a thread pool resource adjustment label, the thread pool core and maximum number of threads are optimized to alleviate load pressure, improve resource utilization, and reduce additional risks caused by data transfer, such as delay fluctuations and consistency maintenance problems, to ensure the smooth operation of the system and flexibly adapt to dynamic changes in load, and to improve the reliability, stability and intelligent adaptability of the power system.

[0085] S4, extracting the data pre-transfer line information of the heavy-loaded road section, and performing data transfer pre-operation simulation, obtaining the data transfer pre-operation simulation result, and adaptively adjusting the data pre-transfer line of the heavy-loaded road section, thereby performing data load transfer on the heavy-loaded road section.

[0086] In this embodiment, the pre-transfer line information of the heavy-load section data is extracted, and the data transfer pre-operation simulation is performed. The specific analysis process is as follows:

[0087] Extract the heavy-load section data pre-transfer line information, including the heavy-load section data pre-transfer line, the pre-transfer line network bandwidth and the number of pre-transfer line packet retransmissions.

[0088] Extract the preset data pre-transfer ratio in the database.

[0089] The data transfer is performed on the heavy-load section data pre-transfer line according to the data pre-transfer ratio, thereby performing a data transfer pre-operation simulation.

[0090] In a specific embodiment, for example, in a certain power data management system, a preset data pre-transfer ratio in the database is extracted, such as set to 20%. After the system detects a certain heavy-load section, it conducts a data transfer pre-operation simulation on the data pre-transfer line of the heavy-load section according to this ratio. Assuming that the current data flow of the heavy-load section is 500MB / s, according to the data pre-transfer ratio of 20%, 100MB / s of data will be screened out and prepared to be transferred to the pre-transfer line, and 100MB / s of data will be gradually transmitted to the pre-transfer line. In this process, the load changes of the original heavy-load section and the pre-transfer line are continuously monitored, including indicators such as the average delay jitter of the line and the average data packet sending and receiving rate, so as to judge the quality of data transfer.

[0091] In this embodiment, the data transfer pre-operation simulation result is obtained to adaptively adjust the data pre-transfer line of the heavy-load section. The specific analysis process is as follows:

[0092] The data transfer quality parameters are collected, wherein the data transfer quality parameters include data integrity rate, data value accuracy deviation coefficient, data readability success rate and data delay duration.

[0093] It should be noted that the data integrity rate is obtained by dividing the total number of data bytes after the transfer by the total number of data bytes before the transfer.

[0094] The specific method of obtaining the data value accuracy deviation coefficient is as follows: Suppose the data value set before the transfer , the set of data values ​​after transfer , then the data value accuracy deviation coefficient .

[0095] Among them, s is the number of each data value, , t is the number of data values.

[0096] In one specific embodiment, for example, the data value set before the transfer , the set of data values ​​after transfer , then the data value accuracy deviation coefficient It is 0.13%.

[0097] The data readability success rate refers to the total number of bytes successfully read by the system computer before the transfer divided by the total number of bytes successfully read by the computer after the transfer.

[0098] It should also be noted that the data transfer quality parameters can be collected by a network analyzer.

[0099] The reference data transfer quality parameters stored in the extraction database include reference data completeness rate, reference data value accuracy deviation coefficient, reference data readability success rate and reference data delay duration.

[0100] Based on the data transfer quality parameter analysis and processing, the heavy-load section data pre-transfer quality characteristic index is obtained. The heavy-load section data pre-transfer quality characteristic index is used to characterize the transfer quality of the heavy-load section during the data pre-transfer process and is used as a numerical basis for judging the data transfer pre-operation simulation results.

[0101] In a specific embodiment, a specific method for obtaining the heavy-load road section data pre-transfer quality characteristic index is as follows:

[0102] ,

[0103] in, It is the quality characteristic index of the pre-transfer of the heavy-load section data. is the data integrity rate, is the data value accuracy deviation coefficient, is the data readability success rate, is the data delay time, is the reference data completeness rate, is the accuracy deviation coefficient of the reference data value, For the reference data readability success rate, is the reference data delay time, and e is a natural constant.

[0104] It should be noted that the quality characteristic indicators of data pre-transfer in heavy-load sections are obtained by analyzing and processing the data transfer quality parameters, taking into account the mutual influence between these parameters. For example, the increase in data integrity rate indicates that the data integrity before and after transmission is good, the loss and damage are less, the data value accuracy deviation coefficient is usually reduced, and the complete data transmission maintains the accuracy of the original value, reduces the deviation, and the high integrity rate is conducive to system parsing and reading, and improves the success rate of data readability. At the same time, the complete and efficient transmission link reduces data retransmission, error correction and other operations, shortening the data delay time. The increase in the data value accuracy deviation coefficient means that the data value fluctuations and distortions are large during transmission or processing, and the data integrity rate may be threatened. The erroneous data may cause partial loss or failure to match the complete framework, and the integrity rate declines. Deviation data is easy to cause system parsing ambiguity and errors, reducing the success rate of data readability. The improvement of the data readability success rate reflects that the system can easily accurately parse the data and restore the information connotation, which is conducive to the complete reception and utilization of the data and ensures the stability of the data integrity rate. Accurate parsing reduces the data value deviation caused by misunderstanding and reduces the data value accuracy deviation coefficient. The efficient reading mechanism shortens the data processing cycle and compresses the data delay time. Data delay increases, and data is retained due to network congestion, equipment performance bottlenecks, or long transmission distances. Long delays increase the risk of data loss and damage, and damage data integrity. Slow transmission accumulates uncertainty, and the coefficient of deviation in data value accuracy increases due to environmental changes and fluctuations in equipment status. Delays prevent the system from obtaining and processing data in a timely manner, making data interpretation difficult and reducing the success rate of data readability.

[0105] The adaptive adjustment parameters corresponding to each data pre-transfer quality characteristic index interval preset in the database are extracted, and the adaptive adjustment parameters corresponding to the interval where the heavy-load section data pre-transfer quality characteristic index is located are mapped and extracted, and recorded as the adaptive adjustment parameters of the heavy-load section data pre-transfer line.

[0106] The adaptive adjustment parameters of the data pre-transfer line in the heavy-load section refer to the network bandwidth adjustment value of the pre-transfer line and the number of packet retransmissions adjustment value of the pre-transfer line.

[0107] It should be understood that the larger the quality characteristic index of data pre-transfer in heavy-loaded sections, the better the data transfer quality, and the smaller the pre-transfer line network bandwidth adjustment value and the pre-transfer line packet retransmission number adjustment value extracted by its mapping. By mapping the quality characteristic index of data pre-transfer in heavy-loaded sections and extracting the adaptive adjustment parameters of the data pre-transfer line in heavy-loaded sections, it is possible to reasonably determine the adaptive adjustment parameters, maintain a stable transmission rate, ensure the accurate transmission and transfer of power data, and at the same time, reduce unnecessary network resource consumption.

[0108] It should be noted that the number of retransmissions of the pre-transfer line packet refers to the cumulative number of times that the data packet needs to be retransmitted due to transmission errors, loss, or incorrect reception during the pre-transfer process of the power system data. When data is transmitted from the source to the destination along the pre-transfer line, it may encounter various interference factors, such as network signal attenuation, line noise, bandwidth congestion, etc., which may cause data bit errors, partial loss, or failure of verification at the receiving end during the transmission of the data packet. At this time, in order to ensure the integrity and accuracy of the data, the system will automatically start the retransmission mechanism and send the data packet again, and the cumulative frequency of this retransmission is the number of retransmissions of the pre-transfer line packet.

[0109] The heavy-load section data pre-transfer line is adaptively adjusted according to the adaptive adjustment parameters of the heavy-load section data pre-transfer line.

[0110] In a specific embodiment, after data transfer pre-operation simulation, the calculated quality characteristic index of heavy-load section data pre-transfer is approximately 0.3. According to the pre-set corresponding relationship, the adaptive adjustment parameters of the heavy-load section data pre-transfer line are extracted: the pre-transfer line network bandwidth adjustment value is 3Mbps (i.e., increasing the bandwidth by 3Mbps), the pre-transfer line packet retransmission number adjustment value is 2 (i.e., increasing 2 retransmissions), the initial network bandwidth is 100Mbps, then the adjusted network bandwidth is 103Mbps, the initial packet retransmission number is 10 times, then the adjusted packet retransmission number is 12 times.

[0111] In this embodiment, by obtaining the data transfer pre-operation simulation results and adaptively adjusting the data pre-transfer line of the heavy-load section, it is possible to effectively avoid the drawbacks of data loss, transmission interruption and resource waste caused by blind data transfer. By implementing adaptive adjustment according to the simulation results, it is ensured that the line bandwidth and the number of packet retransmissions are accurately allocated as needed. While maintaining high data quality, the consistency and accuracy of power data transmission are guaranteed, and the system stability and reliability are improved. At the same time, resource allocation is optimized, excessive investment or idleness of resources is avoided, resource utilization efficiency is improved, and the adaptability of the power system to complex working conditions and dynamic load changes is enhanced.

[0112] S5, combining the load balancing adjustment index of the heavy-load section data, analyzing the thread pool adjustment parameters, and adjusting the thread pool resources according to the thread pool adjustment parameters.

[0113] In this embodiment, thread pool resources are adjusted according to thread pool adjustment parameters, and the specific analysis process is as follows:

[0114] The upgraded adjustment parameters corresponding to the load balancing adjustment index intervals of each data stored in the database are extracted, and the upgraded adjustment parameters corresponding to the intervals of the load balancing adjustment index of the heavy-load section data are mapped and extracted, and recorded as thread pool adjustment parameters.

[0115] The thread pool adjustment parameters include a core thread number adjustment value and a maximum thread number adjustment value.

[0116] Thread pool resources are adjusted based on thread pool adjustment parameters.

[0117] It should be understood that the larger the data load balancing adjustment index is, the greater the data load adjustment demand is, and the larger the adjustment value of the core thread number and the maximum thread number adjustment value extracted by the mapping will be. By mapping and extracting the thread pool adjustment parameters through the data load balancing adjustment index on heavy-loaded sections, efficient adjustment of thread pool resources can be achieved, thread pool resource allocation can be accurately controlled, system stability and response speed can be improved, and smooth flow of power data can be ensured.

[0118] In a specific embodiment, for example, assuming that the load balancing adjustment index of the heavy-load road section data monitored in a certain period is 8, which belongs to the high-load adjustment demand interval. According to the preset mapping relationship, the core thread number adjustment value is extracted as an increase of 20 (the original core thread number is 50, and the adjusted number is 70), and the maximum thread number adjustment value is an increase of 50 (the original maximum thread number is 150, and the adjusted number is 200).

[0119] A second aspect of the present invention provides a computer-readable storage medium for storing a program, wherein the program, when executed by a processor, implements an information processing method for a power system.

[0120] The present invention provides an information processing method and storage medium based on the power system, collects power load data of each monitoring section, accurately locks the heavy-load section, provides a numerical basis for subsequent optimization, analyzes the load balancing adjustment index, and thus accurately determines the adjustment strategy, realizes dynamic load balancing, improves system reliability, prevents failures and data loss caused by overload, and ensures the stability and reliability of power data transmission. Simulates data transfer pre-operation, and adaptively adjusts the pre-transfer line according to the simulation results to ensure accurate and efficient data transfer, and improves the quality and efficiency of power data transmission.

[0121] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An information processing method based on a power system, characterized in that: include: S1, setting each monitoring point on the information transmission line of the power system, thereby obtaining each monitoring section, collecting power load data of each monitoring section, and thereby analyzing the power data load rate of each monitoring section; S2, extracting the heavy-load sections based on the power data load rate of each monitoring section, thereby collecting the resource occupancy data and power data growth prediction parameters of the heavy-load sections, and analyzing the load balancing adjustment index of the heavy-load section data; S3, determining data adjustment classification label information based on the heavy-load section data load balancing adjustment index, if the heavy-load section data adjustment classification label information is determined to be a data migration label, executing S4, if the heavy-load section data adjustment classification label information is determined to be a thread pool resource adjustment label, executing S5; S4, extracting the data pre-transfer line information of the heavy-load section, and performing a data transfer pre-operation simulation, obtaining the data transfer pre-operation simulation result, and adaptively adjusting the data pre-transfer line of the heavy-load section, thereby performing data load transfer of the heavy-load section; S5, analyzing the thread pool adjustment parameters in combination with the load balancing adjustment index of the heavy-load section data, and adjusting the thread pool resources according to the thread pool adjustment parameters; The extraction of heavy-load section data pre-transfer line information and the data transfer pre-operation simulation are performed. The specific analysis process is as follows: Extracting the pre-transfer line information of the heavy-load section data, including the pre-transfer line of the heavy-load section data, the network bandwidth of the pre-transfer line and the number of retransmission times of the pre-transfer line packets; Extract the data pre-transfer ratio preset in the database; According to the data pre-transfer ratio, data transfer is performed on the data pre-transfer line of the heavy-load section, thereby performing a data transfer pre-operation simulation; The data transfer pre-operation simulation result is obtained to adaptively adjust the data pre-transfer line of the heavy-load section. The specific analysis process is as follows: Collecting data transfer quality parameters, wherein the data transfer quality parameters include data integrity rate, data value accuracy deviation coefficient, data readability success rate and data delay duration; Based on the data transfer quality parameter analysis and processing, a heavy-load section data pre-transfer quality characteristic index is obtained, wherein the heavy-load section data pre-transfer quality characteristic index is used to characterize the transfer quality of the heavy-load section during the data pre-transfer process and is used as a numerical basis for judging the data transfer pre-operation simulation result; Extract the adaptive adjustment parameters corresponding to each data pre-transfer quality characteristic index interval preset in the database, and map and extract the adaptive adjustment parameters corresponding to the interval where the heavy-load section data pre-transfer quality characteristic index is located, and record them as the adaptive adjustment parameters of the heavy-load section data pre-transfer line; The heavy-load section data pre-transfer line is adaptively adjusted according to the adaptive adjustment parameters of the heavy-load section data pre-transfer line.

2. The information processing method based on the power system according to claim 1 is characterized in that: The specific analysis process of extracting heavy-load sections based on the load rate of power data of each monitoring section is as follows: During the preset monitoring period, the power load data of each monitoring segment is collected, including the data flow, data request response time, number of concurrent connections and data processing task queue length of each monitoring segment; Based on the power load data of each monitoring segment, the power data load rate of each monitoring segment is obtained by analyzing and processing the power load data of each monitoring segment, and the power data load rate of each monitoring segment is used to characterize the power data load degree of each monitoring segment; Extracting the power data load threshold preset in the database; The monitoring section whose power data load rate is greater than or equal to the power data load threshold is recorded as a heavy-load section, thereby extracting the heavy-load section.

3. The information processing method based on the power system according to claim 1 is characterized in that: The resource occupancy data and power data growth prediction parameters of the heavy-load road section are collected, wherein: The resource occupancy data is the resource occupancy data of the line corresponding to the heavy-load section, including the average delay jitter of the line, the total number of thread resource occupancy, the peak value of process memory occupancy and the average data packet sending and receiving rate; The power data growth prediction parameters are power data growth prediction parameters for the area corresponding to the heavy-load section, including the historical average growth rate of power consumption in the area, the increase in equipment active power, the historical load data variance and the average operating time of power-consuming equipment.

4. The information processing method based on the power system according to claim 3 is characterized in that: The specific analysis process of analyzing the load balancing adjustment index of the heavy-load section data is as follows: Obtaining a line data load characteristic value according to the resource occupancy data analysis and processing, wherein the line data load characteristic value is used to characterize the resource occupancy degree of the line corresponding to the heavy-load section; According to the power data growth prediction parameter analysis and processing, a regional power data pre-growth characteristic value is obtained, wherein the regional power data pre-growth characteristic value is used to characterize the expected growth state of the regional power data; Based on the line data load characteristic value and the regional power data pre-growth characteristic value, the heavy-load section data load balancing adjustment index is obtained through analysis and processing, and the heavy-load section data load balancing adjustment index is used to characterize the degree of demand for heavy-load section data load balancing adjustment.

5. The information processing method based on the power system according to claim 1 is characterized in that: The load balancing adjustment index based on the heavy-load section data determines the data adjustment classification label information, and the specific analysis process is as follows: Extract the preset data load balancing adjustment verification indicators in the database; The data adjustment classification label information includes a data migration label and a thread pool resource adjustment label; If the data load balancing adjustment index of the heavy-load section is greater than or equal to the data load balancing adjustment verification index, the heavy-load section data adjustment classification label information is determined as a data migration label; If the data load balancing adjustment index of the heavy-load section is less than the data load balancing adjustment verification index, the heavy-load section data adjustment classification label information is determined as the thread pool resource adjustment label.

6. The information processing method based on the power system according to claim 1 is characterized in that: The thread pool resource adjustment is performed according to the thread pool adjustment parameters, and the specific analysis process is as follows: Extract the upgrade adjustment parameters corresponding to each data load balancing adjustment index interval, and map and extract the upgrade adjustment parameters corresponding to the interval where the data load balancing adjustment index of the heavy-load section is located, and record them as thread pool adjustment parameters; The thread pool adjustment parameters include a core thread number adjustment value and a maximum thread number adjustment value; Thread pool resources are adjusted based on thread pool adjustment parameters.

7. The information processing method based on the power system according to claim 4 is characterized in that: The specific method for obtaining the load balancing adjustment index of the heavy-load section data is as follows: ， in, Adjust the load balancing index for heavy-load road sections. is the line data load characteristic value, is the pre-growth characteristic value of regional power data, is the line data load characteristic value weight coefficient, is the weight coefficient of the regional power data pre-growth eigenvalue, is a natural constant.

8. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the information processing method based on the power system as described in any one of claims 1 to 7 is implemented.

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