Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
Referring to fig. 1, a first aspect of the present invention provides an information processing method based on a power system, including:
s1, setting each monitoring point on an information transmission line of the power system, thus obtaining each monitoring section, collecting power load data of each monitoring section, and analyzing the power data load rate of each monitoring section.
The information transmission line of the power system refers to a communication link for transmitting various types of data in the power system.
It should be further noted that the rule for setting each monitoring point is extracted from the database, and in a specific embodiment, the rule may be equally distributed according to the line length.
In a specific embodiment, for example, in a layout of an information transmission line of an electric power system in a city, the total length of the line reaches 500 km, and according to a setting rule extracted from a database, it is decided to construct a monitoring network in such a way that one monitoring point is set every 50 km on average, based on which the line is precisely divided into 10 monitoring segments.
And S2, extracting a heavy load road section based on the power data load rate of each monitoring section, thereby acquiring the resource occupation data and the power data growth prediction parameters of the heavy load road section, and analyzing the data load balance adjustment index of the heavy load road section.
In this embodiment, the heavy load road section is extracted based on the power data load rate of each monitoring section, and the specific analysis process is as follows:
And in a preset monitoring period, collecting the power load data of each monitoring section, wherein the power load data comprises the data flow of each monitoring section, the data request response time length, the concurrent connection data and the data processing task queue length.
It should be noted that, the data traffic refers to the total amount of data transmitted on the information transmission line in a preset monitoring period, including the cumulative byte amount of various data packets such as the monitoring data of the power equipment.
The concurrent connection number refers to the total number of valid data connections established on the information transmission line.
The length of the data processing task queue refers to the number of elements of the queue formed by the data tasks waiting to be processed, the number is used for representing the condition of the work load waiting to be processed of a line, and the longer the queue is, the larger the processing pressure of the system is, and the higher the processing delay risk is.
It should be noted that, the electrical load data may be acquired by a network analyzer.
And analyzing and processing the electric power load data of each monitoring segment to obtain the electric power data load rate of each monitoring segment, wherein the electric power data load rate of each monitoring segment is used for representing the electric power data load degree of each monitoring segment.
In a specific embodiment, the specific acquisition mode of the power data load rate of each monitoring segment is as follows:
,
wherein,For the ith monitoring segment power data load rate,For the data traffic of the ith monitoring segment,The data request response time length for the ith monitoring segment,For the number of concurrent connections of the ith monitoring segment,The data processing task queue length for the ith monitored segment,Is the load factor corresponding to the unit data flow,Load factors corresponding to the unit data request response time length,For a single concurrent connection with a corresponding load factor,Load factors corresponding to the length of a unit data processing task queue, i is the number of each monitoring segment,M is the number of monitored segments and e is a natural constant.
The load factor corresponding to the unit data flow, the load factor corresponding to the unit data request response time length, the load factor corresponding to the single concurrent connection and the load factor corresponding to the unit data processing task queue length are preset values in the database, and can be directly extracted from the database when in use, for example, the load factor corresponding to the unit data flow, the load factor corresponding to the unit data request response time length, the load factor corresponding to the single concurrent connection and the load factor corresponding to the unit data processing task queue length are extracted by the data flow, the data request response time length, the concurrent connection data and the data processing task queue length, and when in use, the real-time data flow, the data request response time length and the data processing task queue length are input into the map set, so that the load factor corresponding to the data flow, the data request response time length and the concurrent connection data processing task queue length are extracted.
It should be noted that, the power load data rate of each monitoring segment is obtained through the analysis and processing of the power load data of each monitoring segment, so that when the data flow increases, for example, the system needs to process and transmit more data, and multiple connection transmission is established due to numerous data sources, so that the number of concurrent connections is easily increased. The response time of the data request is prolonged, the massive data is crowded in a processing channel, system resources are consumed, and the time for processing a single request is greatly increased. The length of the data processing task queue is increased, the tasks are stacked newly, and the processing is waited in a queuing way, so that the system load and response delay are increased. The concurrent connection number is increased, which means that more data requests are flushed in parallel, and the data flow is directly pushed up, so that the response time of the data requests is prolonged, and the length of the data processing task queue is increased. The response time of the data request is increased, the bottleneck of the processing capacity of the system or the shortage of resources are reflected, the data processing is slow, the unprocessed task is backlogged, and the length of the queue of the data processing task is increased.
And extracting a preset power data load threshold value in the database.
It should be noted that the power data load threshold is a critical index preset in the database and used for representing the power data load degree of the monitoring segment.
And recording the monitoring section with the power data load rate being greater than or equal to the power data load threshold value as a heavy load section, thereby extracting the heavy load section.
It should be understood that if the force data load rate is greater than or equal to the power data load threshold, it indicates that the monitored segment has a higher data load level, and is prone to data loss or processing errors, so that the monitored segment is marked as a heavy load segment and is subjected to subsequent analysis and processing.
If the power data load rate of the monitoring segment is smaller than the power data load threshold, the monitoring segment does not need to be deeply monitored, and can continue to transmit information.
In this embodiment, resource occupation data and power data growth prediction parameters of a heavy load road section are collected, where:
The resource occupation data is the resource occupation data of the corresponding line of the heavy load road section, and comprises the average delay jitter amount of the line, the total number of occupied thread resources, the occupied peak value of the process memory and the average receiving and transmitting rate of the data packet.
And extracting reference resource occupation data stored in a database, wherein the reference resource occupation data comprises reference average delay jitter amount, total reference thread resource occupation amount, reference process memory occupation peak value and reference data packet average receiving and transmitting rate.
It should be noted that the average delay jitter amount refers to an average value of fluctuation degrees of data packet transmission delay in a preset monitoring period, the delay jitter amount can reflect line stability, and a large value indicates that the transmission time lag fluctuates severely, and the data transmission timing is poor.
The total number of occupied thread resources refers to the total scale of active threads in a line-related processing process, and the large number means that the parallel processing task of the system is heavy, and the processing efficiency may be reduced due to the complexity of scheduling.
The process memory occupation peak value refers to the maximum value of the process operation occupation memory in the monitoring period, and the high occupation peak value indicates that the memory resource is tense.
It should be further noted that, the average delay jitter and the average sending and receiving rate of the data packets may be acquired by a network analyzer, and the total number of occupied thread resources and the peak occupied process memory may be acquired by performance monitoring software, for example SolarWinds Server & Application Monitor (SAM, server and application monitor).
And the power data increase prediction parameters comprise the historical power consumption level average increase rate of the area, the equipment active power increase amount, the historical load data variance and the average running time of the electric equipment.
And extracting reference power data growth prediction parameters stored in a database, wherein the reference power data growth prediction parameters comprise a reference historical use level average growth rate, a reference device active power increase amount, a reference historical load data variance and a reference electric equipment average operation duration.
It should be noted that, the device active power increase refers to the device active power increase of the area during the monitoring period.
The historical load data variance is obtained by collecting time-by-time load data of historical time periods of corresponding areas of heavy load road sections and recording the time-by-time load data as a series. First calculate the averageThen according to the variance formulaWhere j is the number of history period hours,N is the number of hours of the historical period from which the historical load data variance is derived.
In this embodiment, the data load balance adjustment index of the heavy load road section is analyzed, and the specific analysis process is as follows:
And analyzing and processing according to the resource occupation data to obtain a line data load characteristic value, wherein the line data load characteristic value is used for representing the resource occupation degree of a corresponding line of the heavy load road section.
In a specific embodiment, the specific acquisition mode of the line data load characteristic value is as follows:
,
wherein,For the characteristic value of the line data load,As the average amount of delay jitter for the line,The total number of thread resources for a line is occupied,The process memory for the line occupies a peak,For the average transceiving rate of the data packets of the line,To refer to the average amount of delay jitter,To reference the total number of thread resource occupancy,To reference the process memory occupancy peaks,E is a natural constant for the average transceiving rate of the reference data packet.
It is to be understood that softplus functions are built-in functions in Python,。
It should be noted that, the line data load characteristic value is obtained by analyzing and processing the data according to the resource occupation, which considers the mutual influence among the parameters, for example, the average delay jitter is increased, the data packet transmission timing is damaged, and in order to ensure the accurate receiving and processing of the data, the system needs more buffer resources, error correction mechanism and retransmission operation, occupies a large amount of thread resources, so that the total number of occupied thread resources is increased. The line transmission stability is poor, the data packet transmission is blocked, and a large number of retransmissions and queuing are caused, so that the average transceiving rate of the data packet is reduced. The total number of occupied thread resources is increased, 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 is accumulated, and the average delay jitter amount is increased. The process memory occupies the peak value, memory resources are deficient, the system frequently reads and writes the hard disk virtual memory, the disk I/O operation is increased dramatically, the data reading and writing speed is dragged and delayed, the data packet transmission is blocked, and the average delay jitter is increased. The average receiving and transmitting rate of the data packets is improved, the line data flow is increased, the processing task is increased, the thread resource requirement is increased, and the total number of occupied thread resources is increased. And a large number of data packets need sufficient memory support for buffering, processing and transmission, and the process memory occupation peak value rises along with the data packet receiving and transmitting pressure.
And analyzing and processing according to the power data growth prediction parameters to obtain regional power data pre-growth characteristic values, wherein the regional power data pre-growth characteristic values are used for representing the predicted growth state of the regional power data.
In a specific embodiment, the specific acquisition mode of the regional power data pre-growth characteristic value is as follows:
,
wherein,The characteristic value is pre-incremented for the regional power data,The rate of increase in level for the historical use of the region,The amount of active power increase for the devices in the area,As the variance of the historical load data of the region,For the average operating time of the consumers of the area,To reference the historical level average growth rate,To reference the amount of active power increase of the device,In order to reference the historic load data variance,For reference to the average operating time of the consumer,The predicted parameter weights are incremented for the power data.
The power data growth prediction parameter weight is preset in the database, the value range is 0-1, the set value can be directly extracted from the database when in use, for example, the power data growth prediction parameter and the power data growth prediction parameter weight are constructed into a mapping set, and the real-time power data growth prediction parameter is input into the mapping set when in use, so that the power data growth prediction parameter weight is extracted.
It should be further noted that, the analysis and processing of the regional power data pre-growth characteristic value according to the power data growth prediction parameter considers the mutual influence of the parameters, for example, when the average growth rate of the historical power consumption level increases, it generally means that the power consumption requirement of the whole region increases, which is likely due to the increase of the frequency and power of the use of the electric equipment or the existing equipment. In this case, the device active power increase will also increase. Conversely, when the active power increase of the device increases, the power of the new device is higher, which directly results in an increase in the total power usage, thereby increasing the rate of increase in the historical usage level. The average rate of increase in historical electricity consumption level increases, indicating a trend in electricity demand growth, and more electricity consumption wave conditions may occur during the growth process, which may increase the variance of historical load data. The increase in the average rate of increase in the historical power consumption level is generally accompanied by an increase in the average operating time of the powered device. As the increase in electricity demand may be due to an increase in the operating time of the device. When the average running time of the electric equipment is increased, the total electric consumption is increased, so that the average increase rate of the historical electric consumption level is promoted to be increased. When the active power increment of the equipment is increased, if the running time of the equipment is not limited, the total power consumption can be increased under the condition that the average running time of the electric equipment is unchanged.
And analyzing and processing to obtain a heavy load road section data load balance adjustment index based on the line data load characteristic value and the regional power data pre-growth characteristic value, wherein the heavy load road section data load balance adjustment index is used for representing the heavy load road section data load balance adjustment demand degree.
In a specific embodiment, the method for obtaining the load balancing adjustment index of the data of the heavy load road section specifically includes the following steps:
,
wherein,The index is adjusted for load balancing of the data of the heavy load road section,For the characteristic value of the line data load,The characteristic value is pre-incremented for the regional power data,The characteristic value weight coefficient is loaded for the line data,The eigenvalue weight coefficients are pre-incremented for the regional power data,Is a natural constant.
It is to be understood that the softplus functions are built-in functions in Python,。
The line data load characteristic value weight coefficient and the regional power data pre-growth characteristic value weight coefficient are set in a database, the value range is 0-1, the preset value can be directly extracted from the database when the method is used, for example, the line data load characteristic value and the regional power data pre-growth characteristic value, the line data load characteristic value weight coefficient and the regional power data pre-growth characteristic value weight coefficient are used for constructing a mapping set, and the real-time line data load characteristic value and the regional power data pre-growth characteristic value are input into the mapping set when the method is used, so that the corresponding weight coefficient is extracted.
It should be noted that when the pre-growth characteristic value of the regional power data is negative, it is predicted that the future power demand may decrease, so the load condition of the current line is a key factor that directly affects whether the power system can normally operate, and therefore the magnitude of the line data load characteristic value affects the magnitude of the load balancing adjustment index of the heavy load section data to a higher degree.
S3, judging data adjustment classification label information based on the heavy load road section data load balance adjustment index, if the heavy load road section data adjustment classification label information is judged to be a data migration label, executing S4, and if the heavy load road section data adjustment classification label information is judged to be a thread pool resource adjustment label, executing S5.
In this embodiment, the adjustment classification label information is adjusted based on the heavy load road section data load balance adjustment index determination data, and the specific analysis process is as follows:
and extracting a preset data load balancing adjustment verification index in the database.
The data adjustment classification tag information comprises a data migration tag and a thread pool resource adjustment tag.
And if the data load balance adjustment index of the heavy load road section is greater than or equal to the data load balance adjustment verification index, judging the data adjustment classification label information of the heavy load road section as a data migration label.
It should be understood that if the data load balance adjustment index of the heavy load road section is greater than or equal to the data load balance adjustment verification index, the load condition of the current heavy load road section is severe, and the load condition cannot be effectively relieved only by self-adjustment, so that the pressure of the heavy load road section needs to be improved from the root by data transfer, the consequences of data loss and transmission interruption caused by excessive load are avoided, and the reliability of continuous data transmission is ensured.
And if the data load balance adjustment index of the heavy load road section is smaller than the data load balance adjustment verification index, judging the data adjustment classification label information of the heavy load road section as a thread pool resource adjustment label.
If the data load balance adjustment index of the heavy load road section is smaller than the data load balance adjustment verification index, the fact that the road section is in a heavy load state is indicated, but the data migration is not needed to be carried out, at the moment, the load balance condition can be improved by adjusting the thread pool resources, the load pressure is relieved on the premise that large-scale data migration is not carried out, the resource utilization rate is improved, the heavy load road section gradually tends to be load balanced, and the problems of delay, data consistency maintenance and the like in the data transmission process are reduced.
In this embodiment, the data adjustment classification tag information is determined based on the data load balancing adjustment index of the heavy load road section, and when the index reaches a specific threshold value and is determined as a data migration tag, a data transfer flow is started, so that the data load pressure is solved from the root, and the system fault, the data loss and the transmission interruption risk caused by local overload are avoided. If the thread pool resource regulation label is judged, the thread pool core and the maximum thread number are optimized, the load pressure is relieved, the resource utilization rate is improved, the extra risks caused by data transfer, such as delay fluctuation and consistency maintenance difficulty, are reduced, the smooth operation of the system is ensured, the dynamic load change is flexibly adapted, and the operation reliability, stability and intelligent strain capacity of the power system are improved.
And S4, extracting the information of the heavy load road section data pre-transfer line, performing data transfer pre-operation simulation, and obtaining a data transfer pre-operation simulation result to perform self-adaptive adjustment on the heavy load road section data pre-transfer line, thereby performing heavy load road section data load transfer.
In this embodiment, the heavy load road section data pre-transfer line information is extracted, and data transfer pre-operation simulation is performed, and the specific analysis process is as follows:
And extracting the information of the heavy load road section data pre-transfer line, wherein the information comprises the heavy load road section data pre-transfer line, the network bandwidth of the pre-transfer line and the retransmission times of the pre-transfer line packets.
And extracting the pre-transfer proportion of the data preset in the database.
And carrying out heavy load road section data transfer on the heavy load road section data pre-transfer line according to the data pre-transfer proportion, thereby carrying out data transfer pre-operation simulation.
In one embodiment, for example, in a power data management system, a pre-transfer proportion of data preset in the database is extracted, for example, set to 20%. After a certain heavy load road section is monitored, the system conducts data transfer pre-operation simulation on a data pre-transfer line of the heavy load road section according to the proportion. Assuming that the current data flow of the heavy load road section is 500MB/s, screening out 100MB/s data according to a 20% data pre-transfer proportion, preparing to transfer the data to a pre-transfer line, gradually transmitting the 100MB/s data to the pre-transfer line, and continuously monitoring load change conditions of the original heavy load road section and the pre-transfer line in the process, wherein the load change conditions comprise indexes such as average delay jitter quantity of the line, average receiving and transmitting rate of a data packet and the like, so as to judge the data transfer quality.
In this embodiment, the obtained data transfer pre-operation simulation result performs adaptive adjustment on the heavy load road section data pre-transfer line, and the specific analysis process is as follows:
And collecting data transfer quality parameters, wherein the data transfer quality parameters comprise data integrity rate, data value accuracy deviation coefficient, data readability success rate and data delay time length.
The data integrity rate is obtained by dividing the total number of bytes of data after transfer by the total number of bytes of data before transfer.
The specific acquisition mode of the data value accuracy deviation coefficient is as follows, the data value set before transfer is setThe transferred data value setThen the data value accuracy deviation coefficient。
Where s is the number of each data value,T is the number of data values.
In one embodiment, for example, a set of data values prior to transferThe transferred data value setThen the data value accuracy deviation coefficient0.13%.
The data readability success rate refers to the total number of bytes successfully read by the system computer before transfer divided by the total number of bytes successfully read by the computer after transfer.
It should be noted that the data transfer quality parameters can be acquired by a network analyzer.
And extracting reference data transfer quality parameters stored in a database, wherein the reference data transfer quality parameters comprise reference data integrity rate, reference data value accuracy deviation coefficient, reference data readability success rate and reference data delay time.
And analyzing and processing based on the data transfer quality parameters to obtain a data pre-transfer quality characteristic index of the heavy load road section, wherein the data pre-transfer quality characteristic index of the heavy load road section is used for representing the transfer quality of the heavy load road section in the data pre-transfer process and is used for being used as a numerical basis for judging the data transfer pre-operation simulation result.
In a specific embodiment, the specific acquisition method of the pre-transfer quality characteristic index of the heavy load road section data is as follows:
,
wherein,Pre-transferring the quality characteristic index for the heavy load road section data,In order for the data to be of a full rate,As a coefficient of deviation of the accuracy of the data values,For the success rate of the readability of the data,For the duration of the data delay,For the purpose of reference to the data integrity rate,For the reference data value accuracy deviation factor,For the reference data readability success rate,For reference data delay duration, e is a natural constant.
It should be noted that, the pre-transfer quality characteristic index of the heavy load road section data is obtained through analysis and processing according to the data transfer quality parameters, so as to consider the mutual influence among the parameters, for example, the data integrity rate is increased, which indicates that the data integrity is good before and after transmission, the loss damage is less, the accuracy deviation coefficient of the data value is usually reduced, the original value accuracy is maintained in the whole data transmission, the deviation is reduced, the high integrity rate is beneficial to the system to analyze and read, and the success rate of the data readability is improved. Meanwhile, the operations of data retransmission, error correction and the like of the complete and efficient transmission link are reduced, and the data delay time is shortened. The accuracy deviation coefficient of the data value is increased, which means that the data has large fluctuation distortion in transmission or processing, the data integrity rate is threatened, and the error data can be partially lost or cannot be matched with the complete frame, so that the integrity rate is slipped down. The deviation data is easy to cause system analysis ambiguity and error, and the success rate of data readability is reduced. The success rate of data readability is improved, the meaning that the system is easy to accurately analyze data and restore information connotation is reflected, the complete receiving and utilization of the data are facilitated, and the stability of the data integrity rate is ensured. The accurate analysis reduces the data value deviation generated by misunderstanding and reduces the data value accuracy deviation coefficient. The efficient read mechanism shortens the data processing cycle, compresses the data delay time. The data delay time length is increased, and data retention is caused by network congestion, equipment performance bottleneck or long transmission distance. The long time delay increases the risk of data loss and damage, and the data integrity rate is damaged. The transmission delay accumulates uncertainty, and the accuracy deviation coefficient of the data value is improved due to environmental change and equipment state fluctuation. The delay prevents the system from timely acquiring the processed data, so that the data is difficult to read, and the success rate of data readability is reduced.
And extracting self-adaptive adjustment parameters corresponding to the preset data pre-transfer quality characteristic index intervals in the database, mapping and extracting the self-adaptive adjustment parameters corresponding to the intervals where the heavy load road section data pre-transfer quality characteristic indexes are located, and recording the self-adaptive adjustment parameters of the heavy load road section data pre-transfer line.
The self-adaptive adjusting parameters of the data pre-transfer line of the heavy load road section refer to the network bandwidth adjusting value of the pre-transfer line and the packet retransmission times adjusting value of the pre-transfer line.
It is to be understood that the larger the pre-transfer quality characteristic index of the heavy load road section data is, the better the data transfer quality is, the smaller the pre-transfer line network bandwidth regulating value and the pre-transfer line packet retransmission times regulating value which are mapped and extracted are, the self-adaptive regulating parameters of the heavy load road section data pre-transfer line are mapped and extracted through the pre-transfer quality characteristic index of the heavy load road section data, the self-adaptive regulating parameters can be reasonably determined, the stable transmission rate is maintained, the accurate transmission and transfer of the power data are ensured, and meanwhile, the unnecessary network resource consumption is reduced.
It should be noted that, the number of retransmission times of the pre-transfer line packet refers to the accumulated number of retransmission times required for the data packet due to transmission error, loss or incorrect reception in the power system data pre-transfer process. When data is transmitted from a source end to a destination end along a pre-transfer line, various interference factors may be encountered, such as network signal attenuation, line noise, bandwidth congestion, etc., which may cause problems of data bit errors, partial loss or non-passing verification of a receiving end in the data packet during transmission. At this time, in order to ensure the integrity and accuracy of the data, the system will automatically start the retransmission mechanism to retransmit the data packet, and the accumulated frequency of the retransmission is the retransmission frequency of the pre-transferred line packet.
And carrying out self-adaptive adjustment on the heavy load road section data pre-transfer line according to the self-adaptive adjustment parameters of the heavy load road section data pre-transfer line.
In a specific embodiment, through data transfer pre-operation simulation, the calculated pre-transfer quality characteristic index of the heavy load road section data is about 0.3, and according to a preset corresponding relation, the extracted self-adaptive adjustment parameter of the heavy load road section data pre-transfer line is that the adjustment value of the network bandwidth of the pre-transfer line is 3Mbps (i.e. 3Mbps bandwidth is increased), the adjustment value of the retransmission times of the packet of the pre-transfer line is 2 (i.e. 2 retransmissions are increased), the initial network bandwidth is 100Mbps, the adjusted network bandwidth is 103Mbps, the retransmission times of the initial packet is 10, and the retransmission times of the packet after adjustment is 12.
In this embodiment, the data transfer pre-operation simulation result is obtained to perform adaptive adjustment on the heavy load road section data pre-transfer line, so that the defects of data loss, transmission interruption, resource waste and the like caused by blind data transfer can be effectively avoided, and accurate allocation of line bandwidth and packet retransmission times as required is ensured by performing adaptive adjustment according to the simulation result. While maintaining high data quality, the consistency and the accuracy of power data transmission are ensured, and the stability and the reliability of the system are improved. Meanwhile, resource allocation is optimized, excessive input or idling of resources is avoided, resource utilization efficiency is improved, and adaptability of the electric power system to complex working conditions and dynamic load changes is enhanced.
S5, analyzing thread pool adjusting parameters by combining the heavy load road section data load balance adjusting index, and adjusting thread pool resources according to the thread pool adjusting parameters.
In this embodiment, the thread pool resource adjustment is performed according to the thread pool adjustment parameter, and the specific analysis process is as follows:
And extracting upgrade adjustment parameters corresponding to the intervals of the data load balance adjustment indexes stored in the database, mapping and extracting the upgrade adjustment parameters corresponding to the intervals of the data load balance adjustment indexes of the heavy load road section, and recording the upgrade adjustment parameters as thread pool adjustment parameters.
The thread pool adjustment parameter includes a core thread number adjustment value and a maximum thread number adjustment value.
And adjusting the thread pool resources based on the thread pool adjusting parameters.
It is to be understood that the larger the data load balance adjustment index is, the larger the data load adjustment requirement is, the larger the core thread number adjustment value and the maximum thread number adjustment value extracted by mapping are, the high-efficiency adjustment of the thread pool resources can be realized by extracting the thread pool adjustment parameters through the heavy load road section data load balance adjustment index mapping, the thread pool resources are accurately distributed, the system stability and the response speed are improved, and the smooth power data flow is ensured.
In a specific embodiment, for example, it is assumed that a period of time during which a heavy load road section is monitored has a data load balancing adjustment index of 8, which belongs to a high load adjustment requirement interval. According to a preset mapping relation, extracting core thread number adjusting values to be increased by 20 (50 primary core thread numbers and 70 after adjustment), and maximum thread number adjusting values to be increased by 50 (150 primary maximum thread numbers and 200 after adjustment).
A second aspect of the present invention provides a computer-readable storage medium storing a program which, when executed by a processor, implements a method of information processing of an electric power system.
According to the information processing method and the storage medium based on the power system, the power load data of each monitoring section are collected, the heavy load section is accurately locked, a numerical basis is provided for subsequent optimization, and the load balance adjustment index is analyzed, so that the adjustment strategy is accurately judged, the dynamic load balance is realized, the reliability of the system is improved, faults and data loss caused by overload are prevented, and the stability and the reliability of power data transmission are ensured. And simulating data transfer pre-operation, adaptively adjusting a pre-transfer circuit according to a simulation result, ensuring accurate and efficient data transfer, and improving the transmission quality and efficiency of power data.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art of describing particular embodiments without departing from the structures of the invention or exceeding the scope of the invention as defined by the claims.