A task scheduling system and method based on a large-scale power grid topology partition algorithm

Abstract

The application discloses a kind of task scheduling system and method based on large-scale power grid topology partition algorithm, it is related to power grid dispatching technical field, including identity recognition unit, dispatching command control system, receiving unit, execution unit, monitoring unit;Identity recognition unit is based on face recognition technology, the identity of dispatcher can be identified, improve the security of power grid task scheduling;Dispatching command control system can intelligently dispatch command to power grid, improve dispatching efficiency and resource utilization rate;Receiving unit is used to receive the operation instruction issued by dispatcher after successful login of dispatcher;Execution unit is used to issue execution instruction to dispatch server according to operation instruction when dispatcher logs in successfully, and dispatch server carries out operation scheduling task;Monitoring system is used for real-time monitoring to power grid.The application greatly improves the dispatching efficiency and resource utilization rate of power grid task scheduling system, and improves the security of power grid task scheduling system.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a task scheduling system and method based on a large-scale power grid topology partitioning algorithm. Background Technology

[0002] Power companies divide electricity consumption into residential and commercial systems. The power dispatching system refers to the appropriate restriction of commercial electricity consumption and its redirection to residential users when residential electricity consumption exceeds a certain limit. This is a modern monitoring, control, and management method that has emerged in recent years with continuous technological advancements.

[0003] With the advancement of smart distribution network construction, the requirements for optimal allocation of distribution network resources are constantly increasing, and higher demands are being placed on the scheduling and operation and optimization of the distribution network.

[0004] In large-scale power grid topology partitioning task scheduling, power grid scheduling typically employs a master server and a slave server working together. The master server calculates the received scheduling tasks and distributes them to various execution units, while the slave server acts as a backup server, waiting to be used in case the master server fails. However, when the power grid load is heavy, the master server becomes overloaded, while the slave server is on standby. This leads to low resource utilization in power grid scheduling and reduces the lifespan of the master server. Since the performance of the master server is usually higher than that of the slave server, in order to improve the resource allocation rationality of the power grid task scheduling system, we propose a task scheduling system based on a large-scale power grid topology partitioning algorithm. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] In view of the aforementioned existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a task scheduling system and method based on a large-scale power grid topology partitioning algorithm, which can solve the problems mentioned in the background art.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides a task scheduling system based on a large-scale power grid topology partitioning algorithm, which includes an identity recognition unit, a scheduling command and control system, a receiving unit, an execution unit, and a monitoring unit;

[0010] The identity recognition unit, based on facial recognition technology, identifies the dispatcher's identity, thereby improving the security of the power grid task scheduling system.

[0011] The dispatch and command control system is used for intelligent dispatch and command of the power grid.

[0012] The receiving unit is used to receive operation instructions issued by the dispatcher after the dispatcher successfully logs in;

[0013] The execution unit is used to send an execution instruction to the scheduling server according to the operation instructions after the scheduler successfully logs in, and the scheduling server then carries out the operation scheduling task.

[0014] The monitoring unit is used to monitor the power grid in real time, and the monitoring unit also monitors the execution status of the execution unit in real time.

[0015] As a preferred embodiment of the task scheduling system based on the large-scale power grid topology partitioning algorithm described in this invention, the identity recognition unit includes a control module, a face recognition shooting module, a face recognition comparison module, a face recognition database, a face recognition recording module, and an alarm module.

[0016] The control module activates the face recognition shooting module and sends a shooting command;

[0017] The facial recognition shooting module captures the dispatcher's face and automatically adjusts the light and zoom during the shooting process to prevent the photo from being overexposed.

[0018] The face recognition comparison module compares the captured photo with the photos stored in the face recognition database one by one, and issues a dispatch task to the dispatch and command system after finding the corresponding face data.

[0019] As a preferred embodiment of the task scheduling system based on the large-scale power grid topology partitioning algorithm described in this invention, the scheduling command and control system includes a scheduling server, a master server, and slave servers.

[0020] The scheduling server establishes communication connections with the master server and slave servers;

[0021] The scheduling server is used to obtain scheduling instructions from the identity recognition unit;

[0022] Both the master server and the slave server are equipped with a first threshold and a second threshold.

[0023] When the scheduling server determines that the load exceeds the first threshold of the master server but does not exceed the first threshold of the slave server, it sends the scheduling task to the slave server.

[0024] When the scheduling server determines that the load exceeds the first threshold of the slave server but does not exceed the second threshold of the master server, it sends the scheduling task to the master server.

[0025] When the scheduling server determines that the load exceeds the second threshold of the master server but does not exceed the second threshold of the slave server, it sends the scheduling task to the slave server.

[0026] When the scheduling server determines that the load exceeds the second threshold of the master server but does not exceed the second threshold of the slave server, it sends the scheduling task to the slave server.

[0027] As a preferred embodiment of the task scheduling system based on the large-scale power grid topology partitioning algorithm of the present invention, wherein: the first threshold of the master server is greater than the first threshold of the slave server, and the second threshold of the master server is greater than the second threshold of the slave server;

[0028] The second threshold of the master server is greater than its first threshold, and the second threshold of the slave server is greater than its first threshold.

[0029] As a preferred embodiment of the task scheduling system based on the large-scale power grid topology partitioning algorithm described in this invention, the monitoring unit preprocesses the monitoring data, including removing duplicate values, processing missing values, processing outliers, data smoothing, and data merging.

[0030] As a preferred embodiment of the task scheduling system based on the large-scale power grid topology partitioning algorithm described in this invention, the data merging process includes:

[0031] Feature extraction is performed on each sub-data in the monitoring data, and the feature indicators are numerically processed to obtain indicator values; the feature indicators include data keywords, data size, data recording time, data type, and data source address;

[0032] Select any two sub-data points and calculate the probability of merging the two sub-data points;

[0033]

[0034] Among them, P i,j Let T be the probability that the i-th sub-data and the j-th sub-data can be merged. i,a Let T be the a-th index value of the i-th sub-data. j,a For the a-th index value of the j-th sub-data, max(T) a) represents the maximum value of the a-th indicator among several sub-data in the monitoring data. The first indicator value is the numerical processing result of the data keywords, the second indicator value is the numerical processing result of the data size, the third indicator value is the numerical processing result of the data recording time, the fourth indicator value is the numerical processing result of the data type, and the fifth indicator value is the numerical processing result of the data source address.

[0035] Two sub-data points with a probability greater than a preset probability threshold are merged to obtain the monitoring data after data merging.

[0036] As a preferred embodiment of the task scheduling system based on the large-scale power grid topology partitioning algorithm of the present invention, wherein: when the scheduling server determines that the load simultaneously exceeds the second threshold of the master server and the second threshold of the slave server, it calculates the total amount of load to be cut off according to the load situation;

[0037] The face comparison module compares the photo with the face data in the face recognition database one by one. If the photo of the dispatcher cannot be found in the face recognition database, the authentication fails. The face recognition recording module records and saves the photo just taken by the face recognition shooting module as evidence.

[0038] After the face recognition recording module records the photo, it sends the instruction to the alarm module, which then issues an alarm and sends an alert via the network.

[0039] Secondly, the present invention provides a task scheduling method based on a large-scale power grid topology partitioning algorithm, which includes: the identity recognition unit activates the face recognition shooting module through the control module to take a picture of the dispatcher's face and automatically adjust the light and zoom; then the face recognition comparison module compares the captured photo with the photo in the face recognition database; if the comparison is successful, a scheduling task is issued to the dispatch command system.

[0040] After the dispatcher's identity is verified and login is successful, the receiving unit receives the operation instructions issued by the dispatcher;

[0041] The execution unit sends an execution instruction to the scheduling server according to the operation instruction received by the receiving unit. The scheduling server executes the corresponding scheduling task, while the monitoring unit monitors the power grid and the execution status of the execution unit in real time.

[0042] The dispatch server allocates dispatch tasks according to the power grid load and the preset thresholds of the master server and slave server. When the load exceeds the second threshold of both the master server and slave server, the total amount of load to be cut off is calculated. If the face recognition comparison fails, the face recognition recording module records the evidence, and the alarm module issues an alarm and connects to the network for alarm.

[0043] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a task scheduling system based on a large-scale power grid topology partitioning algorithm.

[0044] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements the steps of a task scheduling system based on a large-scale power grid topology partitioning algorithm.

[0045] Compared with the prior art, the beneficial effects of the present invention are that the task scheduling system based on the large-scale power grid topology partitioning algorithm of the present invention, through the establishment of an identity recognition unit, a scheduling command and control system, a receiving unit, an execution unit, and a monitoring unit, not only significantly improves the scheduling efficiency and resource utilization of the power grid task scheduling system, reduces the load on the master server, makes reasonable use of idle slave servers, and has a high degree of intelligence, but also improves the security of the power grid task scheduling system. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A system architecture diagram of a task scheduling system and method based on a large-scale power grid topology partitioning algorithm provided in one embodiment of the present invention;

[0048] Figure 2 An internal structure diagram of a computer device for a task scheduling system and method based on a large-scale power grid topology partitioning algorithm, provided as an embodiment of the present invention;

[0049] Figure 3 A flowchart illustrating a task scheduling system and method based on a large-scale power grid topology partitioning algorithm, provided as an embodiment of the present invention; Detailed Implementation

[0050] To make the above-mentioned objects, features, and advantages of the present invention more readily understood, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0051] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0053] Example 1, referring to Figures 1-3 This is the first embodiment of the present invention, which provides a task scheduling system based on a large-scale power grid topology partitioning algorithm, comprising:

[0054] This application provides a solution to the problems mentioned above. The following will describe in detail how to implement the task scheduling system based on the large-scale power grid topology partitioning algorithm with reference to several embodiments.

[0055] Figure 1 A system architecture diagram of a task scheduling system and method based on a large-scale power grid topology partitioning algorithm is shown, including:

[0056] It includes an identification unit, a dispatch and command control system, a receiving unit, an execution unit, and a monitoring unit;

[0057] The identity recognition unit, based on facial recognition technology, can identify the dispatcher's identity, thus improving the security of the power grid task scheduling system.

[0058] The dispatch and command control system can intelligently dispatch and command the power grid, improving dispatch efficiency and resource utilization.

[0059] The receiving unit is used to receive operation instructions issued by the dispatcher after the dispatcher has successfully logged in;

[0060] The execution unit is used to send execution instructions to the scheduling server according to the operation instructions after the dispatcher successfully logs in, and the scheduling server then carries out the operation scheduling task.

[0061] The monitoring system is used to monitor the power grid in real time, and it can monitor the execution status of the execution units in real time.

[0062] The identity recognition unit includes a control module, a face recognition shooting module, a face recognition comparison module, a face recognition database, a face recognition recording module, and an alarm module;

[0063] The control module activates the face recognition shooting module and sends a shooting command;

[0064] The facial recognition camera module captures the dispatcher's face and automatically adjusts the brightness and zoom during the shooting process to prevent the photo from being overexposed.

[0065] The face recognition comparison module compares the captured photo with the photos stored in the face recognition database one by one. Only after finding the corresponding face data in the database can the dispatch and command system issue a dispatch task.

[0066] The dispatch and command control system includes a dispatch server, a master server, and slave servers;

[0067] The scheduling server establishes communication connections with the master server and slave servers;

[0068] The scheduling server receives scheduling instructions from the identification unit;

[0069] Both the master server and the slave server have a first threshold and a second threshold.

[0070] When the scheduling server determines that the load exceeds the first threshold of the master server but does not exceed the first threshold of the slave server, it sends the scheduling task to the slave server.

[0071] When the scheduling server determines that the load exceeds the first threshold of the slave server but does not exceed the second threshold of the master server, it sends the scheduling task to the master server.

[0072] When the scheduling server determines that the load exceeds the second threshold of the master server but does not exceed the second threshold of the slave server, it sends the scheduling task to the slave server.

[0073] When the scheduling server determines that the load exceeds the second threshold of the master server but does not exceed the second threshold of the slave server, it sends the scheduling task to the slave server.

[0074] The first threshold of the master server is greater than the first threshold of the slave server, the second threshold of the master server is greater than the second threshold of the slave server, the second threshold of the master server is greater than its first threshold, and the second threshold of the slave server is greater than its first threshold.

[0075] The monitoring unit preprocesses the monitoring data, including: removing duplicate values, handling missing values, handling outliers, data smoothing, and data merging.

[0076] The monitoring unit performs data merging processing on the monitoring data, including:

[0077] Feature extraction is performed on each sub-data in the monitoring data, and the feature indicators are numerically processed to obtain indicator values; the feature indicators include data keywords, data size, data recording time, data type, and data source address;

[0078] Choose any two sub-data points and calculate the probability that the two sub-data points can be combined;

[0079]

[0080] Among them, P i,j Let T be the probability that the i-th sub-data and the j-th sub-data can be merged; i,a T represents the a-th index value of the i-th sub-data; j,a Let be the a-th index value of the j-th sub-data; max(T) a ) represents the maximum value of the a-th indicator among several sub-data in the monitoring data; the first indicator value is the numerical processing result of the data keywords; the second indicator value is the numerical processing result of the data size; the third indicator value is the numerical processing result of the data recording time; the fourth indicator value is the numerical processing result of the data type; and the fifth indicator value is the numerical processing result of the data source address.

[0081] Two data points with a probability greater than a preset probability threshold are merged to obtain the merged monitoring data.

[0082] When the scheduling server determines that the load exceeds both the second threshold of the master server and the second threshold of the slave server at the same time, it calculates the total amount of load that needs to be cut off based on the load situation.

[0083] The face comparison module compares the photo with the face data in the face recognition database one by one. If the photo of the dispatcher cannot be found in the face recognition database, the authentication fails. The face recognition recording module records and saves the photo just taken by the face recognition shooting module as evidence.

[0084] After the face recognition recording module records the photo, it sends the instruction to the alarm module, which then issues an alarm and sends the alert via the network.

[0085] The dispatch server establishes communication connections with the master server and slave servers. The dispatch server receives dispatch instructions from the identification unit. Both the master server and the slave server have a first threshold and a second threshold. When the dispatch server determines that the load exceeds the first threshold of the master server but not the first threshold of the slave server, it sends the dispatch task to the slave server. When the dispatch server determines that the load exceeds the first threshold of the slave server but not the second threshold of the master server, it sends the dispatch task to the master server. When the dispatch server determines that the load exceeds the second threshold of the master server but not the second threshold of the slave server, it sends the dispatch task to the slave server. When the dispatch server determines that the load exceeds the second threshold of the master server but not the second threshold of the slave server, it sends the dispatch task to the slave server. This significantly improves the dispatch efficiency and resource utilization of the power grid task dispatch system and has a high degree of intelligence.

[0086] The control module activates the face recognition shooting module and sends a shooting command. The face recognition shooting module captures the dispatcher's face, automatically adjusting the light and zooming during the shooting process to prevent overexposure. The face recognition comparison module compares the captured photo with photos stored in the face recognition database one by one. Only after finding the corresponding face data in the database can the dispatching command system issue a dispatching task. If the face recognition comparison module cannot find the dispatcher's photo in the face recognition database, the authentication fails. The face recognition recording module records and saves the photo just captured by the face recognition shooting module as evidence. After recording the photo, the face recognition recording module sends the command to the alarm module. The alarm module issues an alarm and alarms through the network, improving the security of the power grid task dispatching system.

[0087] Furthermore, this embodiment also provides a task scheduling method based on a large-scale power grid topology partitioning algorithm, including: the identity recognition unit activates the face recognition shooting module through the control module to take a picture of the dispatcher's face and automatically adjust the light and zoom; then the face recognition comparison module compares the captured photo with the photos in the face recognition database; if the comparison is successful, a scheduling task is issued to the dispatch command system.

[0088] After the dispatcher's identity is verified and login is successful, the receiving unit receives the operation instructions issued by the dispatcher;

[0089] The execution unit sends an execution instruction to the scheduling server according to the operation instruction received by the receiving unit. The scheduling server executes the corresponding scheduling task, while the monitoring unit monitors the power grid and the execution status of the execution unit in real time.

[0090] The dispatch server allocates dispatch tasks according to the power grid load and the preset thresholds of the master server and slave server. When the load exceeds the second threshold of both the master server and slave server, the total amount of load to be cut off is calculated. If the face recognition comparison fails, the face recognition recording module records the evidence, and the alarm module issues an alarm and connects to the network for alarm.

[0091] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as follows. Figure 2As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a task scheduling system based on a large-scale power grid topology partitioning algorithm. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0092] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the following steps: an identity recognition unit, a dispatch and command control system, a receiving unit, an execution unit, and a monitoring unit.

[0093] The identity recognition unit, based on facial recognition technology, identifies the dispatcher's identity, thereby improving the security of the power grid task scheduling system.

[0094] The dispatch and command control system is used for intelligent dispatch and command of the power grid.

[0095] The receiving unit is used to receive operation instructions issued by the dispatcher after the dispatcher successfully logs in;

[0096] The execution unit is used to send an execution instruction to the scheduling server according to the operation instructions after the scheduler successfully logs in, and the scheduling server then carries out the operation scheduling task.

[0097] The monitoring unit is used to monitor the power grid in real time, and the monitoring unit also monitors the execution status of the execution unit in real time.

[0098] Example 2, refer to Figure 1 - Figure 2 This is the second embodiment of the present invention, which provides a task scheduling system based on a large-scale power grid topology partitioning algorithm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0099] To verify the effectiveness and superiority of the task scheduling system based on a large-scale power grid topology partitioning algorithm, this embodiment was tested in a simulated large-scale power network environment. This power network consists of multiple regions, each with power equipment connected via the power grid, forming a complex power system. To ensure the realism of the experiment, actual operating power data was used as the test basis.

[0100] First, the task scheduling system was deployed, including an identity recognition unit, a scheduling command and control system, a receiving unit, an execution unit, and a monitoring unit. The identity recognition unit is equipped with advanced facial recognition equipment, capable of accurately identifying the dispatcher's identity and improving system security. Before testing, the information of all dispatchers participating in the test was entered into the facial recognition database, ensuring the smooth progress of the experiment.

[0101] During testing, the identity recognition unit activates the facial recognition camera module via the control module to capture images of each dispatcher's face, automatically adjusting lighting and focus to ensure image quality. The facial recognition comparison module compares the captured image with images stored in the database. After confirming identity, the dispatch command system receives the dispatch task. Subsequently, after a dispatcher successfully logs in, the receiving unit receives the operation instructions issued by the dispatcher. The execution unit then sends execution instructions to the dispatch server based on these instructions. The dispatch server, according to the current state of the power grid and load conditions, appropriately allocates dispatch tasks to the master or slave servers.

[0102] To evaluate the system's performance under different load conditions, this embodiment sets up four different load test scenarios to simulate the operation of the power grid under low load, medium load, high load, and overload conditions. In each test scenario, by adjusting power demand and supply, the system observes how the dispatch server allocates tasks according to the load conditions, as well as the response speed and processing capacity of the master and slave servers.

[0103] In addition, the monitoring unit monitored the entire testing process in real time, collecting a large amount of data on the power grid's operating status, including but not limited to key parameters such as current, voltage, and power factor. This data underwent preprocessing, removing duplicate values, handling missing and outlier values, and performing data smoothing and merging to ensure accuracy and reliability.

[0104] Finally, when the system detects that the load simultaneously exceeds the second threshold of both the master and slave servers, the scheduling server calculates the total load that needs to be removed based on the actual situation, effectively avoiding the risk of overload. Throughout the test, if authentication fails, the face recognition recording module automatically records relevant evidence and triggers the alarm module to issue an alert, further improving system security.

[0105] Table 1: System performance test data under different load conditions

[0106]

[0107] Table 2: Results of Data Preprocessing and Merging

[0108]

[0109] Analysis of the data in the two tables above clearly demonstrates the superiority and innovation of this invention in practical applications.

[0110] As shown in Table 1, the response times of the master and slave servers gradually increase with increasing load, but remain within a reasonable range. Even under the highest load (2000MW), the response times of the master and slave servers are 5.0 seconds and 5.5 seconds respectively, far below the industry standard of 10 seconds. This demonstrates that the scheduling system of this invention can still operate efficiently under high load conditions.

[0111] The task completion rate remained above 93% in all test scenarios, especially under low and medium load conditions, where the completion rate approached 100%. This indicates that the system can maintain a high task completion rate under different load conditions, ensuring the stable operation of the power grid.

[0112] Table 1 shows that data processing efficiency gradually decreases with increasing load, but remains at a high level across all test scenarios. For example, at the lowest load (500MW), the data processing efficiency reaches 1000 records / second, while at the highest load (2000MW), it is 400 records / second. This demonstrates that the system can still efficiently process large amounts of data under high load conditions, ensuring the system's real-time performance and accuracy.

[0113] In all test scenarios, the number of security incidents was 0, which indicates that the system of the present invention performs excellently in terms of authentication and exception handling, effectively preventing unauthorized access and operation, and improving the security of the system.

[0114] Table 2 shows that the quality of the original data was significantly improved through data preprocessing and merging. For example, in Scenario 4, the original data volume was 40,000 records; after preprocessing, the final merged data volume was 36,000 records, reducing redundant data by approximately 10%. This not only improved the accuracy and reliability of the data but also reduced the system's processing burden and improved overall performance.

[0115] When the load simultaneously exceeds the second threshold of both the master and slave servers, the system can calculate the total load that needs to be cut off based on the actual situation, effectively avoiding the risk of overload. This function is particularly important in practical applications, enabling timely measures to be taken when the power grid faces extreme loads, ensuring the safe and stable operation of the power grid.

[0116] In summary, the present invention demonstrates excellent performance and advantages in practical applications, particularly in response time, task completion rate, data processing efficiency, security, and load management under high load conditions, all of which outperform existing scheduling systems. The tests conducted in this embodiment fully demonstrate the innovation and practicality of the present invention, providing reliable technical support for intelligent scheduling of large-scale power grids.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0118] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0123] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A task scheduling system based on a large-scale power grid topology partitioning algorithm, characterized in that: It includes an identification unit, a dispatch and command control system, a receiving unit, an execution unit, and a monitoring unit; The identity recognition unit includes a control module, a face recognition shooting module, a face recognition comparison module, a face recognition database, a face recognition recording module, and an alarm module; The control module activates the face recognition shooting module and sends a shooting command; The facial recognition shooting module captures the dispatcher's face and automatically adjusts the light and zoom during the shooting process to prevent the photo from being overexposed. The face recognition comparison module compares the captured photo with the photos stored in the face recognition database one by one, and issues a dispatch task to the dispatch and command system after finding the corresponding face data. The identity recognition unit uses facial recognition technology to identify the dispatcher's identity, thereby improving the security of the power grid task scheduling system. The dispatch and command control system is used for intelligent dispatch and command of the power grid. The receiving unit is used to receive operation instructions issued by the dispatcher after the dispatcher successfully logs in; The execution unit is used to send an execution instruction to the scheduling server according to the operation instructions after the scheduler successfully logs in, and the scheduling server carries out the operation scheduling task. The monitoring unit is used to monitor the power grid in real time, and the monitoring unit also monitors the execution status of the execution unit in real time. The monitoring unit preprocesses the monitoring data, including removing duplicate values, handling missing values, handling outliers, data smoothing, and data merging. The data merging process includes extracting features from each sub-data in the monitoring data and quantifying the feature indicators to obtain indicator values. The characteristic indicators include data keywords, data size, data recording time, data type, and data source address; Select any two sub-data points and calculate the probability of merging the two sub-data points; Among them, P i,j Let T be the probability that the i-th sub-data and the j-th sub-data can be merged. i,a Let T be the a-th index value of the i-th sub-data. j,a For the a-th index value of the j-th sub-data, max(T) a ) represents the maximum value of the a-th indicator among several sub-data in the monitoring data. The first indicator value is the numerical processing result of the data keywords, the second indicator value is the numerical processing result of the data size, the third indicator value is the numerical processing result of the data recording time, the fourth indicator value is the numerical processing result of the data type, and the fifth indicator value is the numerical processing result of the data source address. Two sub-data points with a probability greater than a preset probability threshold are merged to obtain the monitoring data after data merging.

2. The task scheduling system based on a large-scale power grid topology partitioning algorithm as described in claim 1, characterized in that: The dispatch and command control system includes a dispatch server, a master server, and slave servers; The scheduling server establishes communication connections with the master server and slave servers; The scheduling server is used to obtain scheduling instructions from the identity recognition unit; The dispatch server assigns dispatch tasks to the master server and slave servers based on the current state of the power grid and the load conditions. Both the master server and the slave server are equipped with a first threshold and a second threshold for power grid load. When the scheduling server determines that the load exceeds the first threshold of the master server but does not exceed the first threshold of the slave server, it sends the scheduling task to the slave server. When the scheduling server determines that the load exceeds the first threshold of the slave server but does not exceed the second threshold of the master server, it sends the scheduling task to the master server. When the scheduling server determines that the load exceeds the second threshold of the master server but does not exceed the second threshold of the slave server, it sends the scheduling task to the slave server. When the scheduling server determines that the load exceeds the second threshold of the master server but does not exceed the second threshold of the slave server, it sends the scheduling task to the slave server.

3. The task scheduling system based on a large-scale power grid topology partitioning algorithm as described in claim 2, characterized in that: The first threshold of the master server is greater than the first threshold of the slave server, and the second threshold of the master server is greater than the second threshold of the slave server; The second threshold of the master server is greater than its first threshold, and the second threshold of the slave server is greater than its first threshold.

4. The task scheduling system based on a large-scale power grid topology partitioning algorithm as described in claim 3, characterized in that: When the scheduling server determines that the load exceeds both the second threshold of the master server and the second threshold of the slave server at the same time, it calculates the total amount of load that needs to be cut off based on the load situation. The face recognition comparison module compares the photo with the face data in the face recognition database one by one. If the photo of the dispatcher cannot be found in the face recognition database, the authentication fails. The face recognition recording module records and saves the photo just taken by the face recognition shooting module as evidence. After the face recognition recording module records the photo, it sends the instruction to the alarm module, which then issues an alarm and sends the alert via network connection.

5. A task scheduling method based on a large-scale power grid topology partitioning algorithm, based on the task scheduling system based on the large-scale power grid topology partitioning algorithm according to any one of claims 1 to 4, characterized in that: The identity recognition unit activates the face recognition shooting module through the control module to take a picture of the dispatcher's face and automatically adjust the light and zoom. Then, the face recognition comparison module compares the captured photo with the photos in the face recognition database. If the comparison is successful, a dispatching task is issued to the dispatching and command system. After the dispatcher's identity is verified and login is successful, the receiving unit receives the operation instructions issued by the dispatcher; The execution unit sends an execution instruction to the scheduling server according to the operation instruction received by the receiving unit. The scheduling server executes the corresponding scheduling task, while the monitoring unit monitors the power grid and the execution status of the execution unit in real time. The dispatch server allocates dispatch tasks according to the power grid load conditions and the preset thresholds of the master server and slave server. When the load exceeds the second threshold of both the master server and slave server at the same time, it calculates the total amount of load that needs to be cut off. If the facial recognition comparison fails, the facial recognition recording module records the evidence, and the alarm module issues an alarm and connects to the network to trigger an alarm.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the task scheduling system based on the large-scale power grid topology partitioning algorithm according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the task scheduling system based on the large-scale power grid topology partitioning algorithm as described in any one of claims 1 to 4.

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