Task scheduling system and method based on large-scale power grid topology partitioning algorithm

Abstract

The invention discloses a task scheduling system and method based on a large-scale power grid topology partitioning algorithm, and relates to the technical field of power grid scheduling, and the system comprises an identity recognition unit, a scheduling command control system, a receiving unit, an execution unit, and a monitoring unit. The identity recognition unit can recognize the identity of a dispatcher based on a face recognition technology, so that the safety of power grid task scheduling is improved; the dispatching command control system can intelligently dispatch and command the power grid, so that the dispatching efficiency and the resource utilization rate are improved; the receiving unit is used for receiving an operation instruction sent by the dispatcher after the dispatcher logs in successfully; the execution unit is used for sending an execution instruction to the scheduling server according to the operation instruction after the dispatcher logs in successfully, and the scheduling server carries out an operation scheduling task; and the monitoring system is used for monitoring the power grid in real time. The scheduling efficiency and the resource utilization rate of the power grid task scheduling system are greatly improved, and the safety of the power grid task scheduling system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a task dispatching system and method based on a large-scale power grid topology partitioning algorithm. Background Art

[0002] The power company divides electricity consumption into residential power systems and commercial enterprise power systems. The power dispatching system refers to when the residential power consumption exceeds a certain limit, the power consumption of enterprises is appropriately restricted and transmitted to the residential power consumption. It is a modern monitoring, control and management method formed in recent years with the continuous development of science and technology.

[0003] With the advancement of smart distribution network construction, the requirements for optimal allocation of distribution network resources are constantly increasing, and higher requirements are put forward for the dispatching operation and dispatching optimization of distribution networks.

[0004] In the task scheduling of large-scale power grid topology partitioning, the power grid scheduling usually uses a master server and a slave server to cooperate. The master server calculates the received scheduling tasks and distributes them to each execution unit, while the slave server serves as a backup server waiting to be used when the master server fails. However, when the power grid is heavily loaded, the master server is overwhelmed and the slave server is on standby, which leads to low resource utilization of power grid scheduling and reduces the service life 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 rationality of resource allocation 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 summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above 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 technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[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 identifies the identity of the dispatcher based on face recognition technology, thereby improving the security of the power grid task dispatching system;

[0011] The dispatching command and control system is used to perform intelligent dispatching and command of the power grid;

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

[0013] The execution unit is used to send an execution instruction to the dispatch server according to the operation instruction after the dispatcher successfully logs in, and the dispatch server carries out the operation dispatch task;

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

[0015] As a preferred solution of the task scheduling system based on the large-scale power grid topology partitioning algorithm described in the present invention, wherein: 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 starts the face recognition shooting module and sends a shooting instruction;

[0017] The face recognition shooting module shoots the dispatcher's face, and automatically adjusts the light and zooms during the shooting process to prevent the photo from being exposed;

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

[0019] As a preferred solution of the task scheduling system based on the large-scale power grid topology partitioning algorithm described in the present invention, wherein: the scheduling command and control system includes a scheduling server, a master server and a slave server;

[0020] The scheduling server establishes a communication connection with the master server and the slave server;

[0021] The dispatch server is used to obtain a dispatch instruction from an identity recognition unit;

[0022] The master server and the slave server are both provided 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, the scheduling task is sent 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, the scheduling task is sent 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, the scheduling task is sent 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, the scheduling task is sent to the slave server.

[0027] As a preferred solution 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 solution of the task scheduling system based on the large-scale power grid topology partitioning algorithm described in the present invention, the monitoring unit preprocesses the monitoring data, and the preprocessing includes removing duplicate values, processing missing values, processing abnormal values, data smoothing and data merging.

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

[0031] Extract features from each sub-data in the monitoring data, and digitize the feature indicators to obtain the indicator values; the feature indicators include data keywords, data size, data recording time, data type and data source address;

[0032] Randomly select two sub-data and calculate the probability of merging the two sub-data;

[0033]

[0034] Among them, P i,j is the probability that the i-th sub-data and the j-th sub-data can be combined, T i,a is the ath index value of the ith sub-data, T j,a is the ath index value of the jth sub-data, max(T a) is the maximum value of the ath indicator value among several sub-data in the monitoring data, the first indicator value is the numerical processing result of the data keyword, 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] The two sub-data with probabilities greater than a preset probability threshold are merged to obtain monitoring data after data merging.

[0036] As a preferred solution of the task scheduling system based on the large-scale power grid topology partitioning algorithm of the present invention, when the scheduling server determines that the load exceeds the second threshold of the master server and the second threshold of the slave server at the same time, the total amount of load to be removed is calculated according to the load situation;

[0037] After the face comparison module compares the photo with the face data in the face recognition database one by one, if the dispatcher's photo cannot be found in the face recognition database, the authentication fails, and the face recognition recording module records the photo just taken by the face recognition shooting module to save as evidence;

[0038] After recording the photo, the face recognition recording module sends a command to the alarm module, and the alarm module sounds an alarm and sends an alarm through the network.

[0039] In a second aspect, the present invention provides a task scheduling method based on a large-scale power grid topology partitioning algorithm, which comprises: the identity recognition unit starts a face recognition shooting module through a control module, shoots the dispatcher's face and automatically adjusts the light and zoom, and then the face recognition comparison module compares the taken photo with the photo in the face recognition database, and if the comparison is successful, a scheduling task is issued to the dispatch command system;

[0040] The receiving unit receives the operation instruction issued by the dispatcher after the dispatcher's identity authentication is passed and the login is successful;

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

[0042] The dispatch server allocates dispatch tasks according to the preset thresholds of the master server and slave server based on the load conditions of the power grid. When the load exceeds the second threshold of the master server and the slave server at the same time, the total amount of load that needs to be removed is calculated. If the face recognition comparison fails, the face recognition recording module records the evidence, and the alarm module sounds an alarm and sends an alarm to the Internet.

[0043] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of a task scheduling system based on a large-scale power grid topology partitioning algorithm are implemented.

[0044] In a fourth aspect, 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, the steps of a task scheduling system based on a large-scale power grid topology partitioning algorithm are implemented.

[0045] Compared with the prior art, the beneficial effect of the present invention is that the task scheduling system of the present invention based on the large-scale power grid topology partitioning algorithm, through the identity recognition unit, scheduling command and control system, receiving unit, execution unit, and monitoring unit, not only greatly improves the scheduling efficiency and resource utilization of the power grid task scheduling system, reduces the load of the main server, and reasonably utilizes idle slave servers, has a high degree of intelligence, but also improves the security of the power grid task scheduling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

[0049] Figure 3 A flowchart of a task scheduling system and method based on a large-scale power grid topology partitioning algorithm provided by an embodiment of the present invention; DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

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

[0054] The present application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to implement the task scheduling system based on the large-scale power grid topology partitioning algorithm;

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

[0056] It includes identity recognition unit, dispatch command and control system, receiving unit, execution unit and monitoring unit;

[0057] The identity recognition unit is based on face recognition technology and can identify the identity of the dispatcher, thus improving the security of the power grid task dispatching system;

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

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

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

[0061] The monitoring system is used to monitor the power grid in real time, and the monitoring system can monitor the execution status of the execution unit 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 starts the face recognition shooting module and sends a shooting instruction;

[0064] The face recognition shooting module takes a picture of the dispatcher's face, and automatically adjusts the light and zooms during the shooting process to prevent the photo from being exposed;

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

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

[0067] Establishing communication connection between the dispatch server and the master server and slave server;

[0068] The dispatch server obtains the dispatch instruction from the identity recognition unit;

[0069] Both the master server and the slave server are provided with 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, the scheduling task is sent 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, the scheduling task is sent 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, the scheduling task is sent 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, the scheduling task is sent 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 pre-processes the monitoring data, including: removing duplicate values, processing missing values, processing abnormal values, data smoothing and data merging.

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

[0077] Extract features from each sub-data in the monitoring data, and digitize the feature indicators to obtain the indicator values; the feature indicators include data keywords, data size, data recording time, data type and data source address;

[0078] Randomly select two sub-data and calculate the probability that the two sub-data can be merged;

[0079]

[0080] Among them, P i,j is the probability that the i-th sub-data and the j-th sub-data can be combined; T i,a is the ath index value of the ith sub-data; T j,a is the ath index value of the jth sub-data; max(T a ) is the maximum value of the ath indicator value among several sub-data in the monitoring data; the first indicator value is the numerical processing result of the data keyword; 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; the fifth indicator value is the numerical processing result of the data source address;

[0081] The two sub-data with probabilities greater than the preset probability threshold are merged to obtain the monitoring data after data merging.

[0082] When the scheduling server determines that the load exceeds 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 to be removed according to the load situation.

[0083] After the face comparison module compares the photo with the face data in the face recognition database one by one, if the dispatcher's photo cannot be found in the face recognition database, the authentication fails. The face recognition recording module records the photo just taken by the face recognition shooting module to save evidence.

[0084] After recording the photo, the face recognition recording module sends the instruction to the alarm module, and the alarm module sounds an alarm and sends an alarm through the network.

[0085] A communication connection is established between the dispatching server and the main server and the slave server. The dispatching server obtains a dispatching instruction from the identity identification unit. The main server and the slave server are both provided with a first threshold and a second threshold. When the dispatching server determines that the load exceeds the first threshold of the main server but does not exceed the first threshold of the slave server, the dispatching server sends the dispatching task to the slave server. When the dispatching server determines that the load exceeds the first threshold of the slave server but does not exceed the second threshold of the main server, the dispatching task is sent to the main server. When the dispatching server determines that the load exceeds the second threshold of the main server but does not exceed the second threshold of the slave server, the dispatching task is sent to the slave server. When the dispatching server determines that the load exceeds the second threshold of the main server but does not exceed the second threshold of the slave server, the dispatching task is sent to the slave server, which greatly improves the dispatching efficiency and resource utilization of the power grid task dispatching system, and has a high degree of intelligence.

[0086] The control module starts the face recognition shooting module and sends a shooting instruction. The face recognition shooting module shoots the dispatcher's face, and automatically adjusts the light and zooms during the shooting process to prevent the photo from being exposed. The face recognition comparison module compares the taken photos with the photos stored in the face recognition database one by one. Only after the corresponding face data is found in the database can the dispatch command system issue a dispatch task. After the face comparison module compares the photos with the face data in the face recognition database one by one, if the dispatcher's photo cannot be found in the face recognition database, the authentication fails. The face recognition recording module records the photos just taken by the face recognition shooting module to save evidence. After recording the photos, the face recognition recording module sends instructions to the alarm module. The alarm module sounds an alarm and sends an alarm through the Internet, thereby improving the security of the power grid task dispatching system.

[0087] Furthermore, the present embodiment also provides a task scheduling method based on a large-scale power grid topology partitioning algorithm, comprising: the identity recognition unit starts a face recognition shooting module through a control module, shoots the dispatcher's face and automatically adjusts the light and zoom, and then the face recognition comparison module compares the photographed photo with the photo in the face recognition database, and if the comparison is successful, a scheduling task is issued to the dispatch command system;

[0088] The receiving unit receives the operation instruction issued by the dispatcher after the dispatcher's identity authentication is passed and the login is successful;

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

[0090] The dispatch server allocates dispatch tasks according to the preset thresholds of the master server and slave server based on the load conditions of the power grid. When the load exceeds the second threshold of the master server and the slave server at the same time, the total amount of load that needs to be removed is calculated. If the face recognition comparison fails, the face recognition recording module records the evidence, and the alarm module sounds an alarm and sends an alarm to the Internet.

[0091] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a task scheduling system based on a large-scale power grid topology partitioning algorithm is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

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

[0093] The identity recognition unit identifies the identity of the dispatcher based on face recognition technology, thereby improving the security of the power grid task dispatching system;

[0094] The dispatching command and control system is used to perform intelligent dispatching and command of the power grid;

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

[0096] The execution unit is used to send an execution instruction to the dispatch server according to the operation instruction after the dispatcher successfully logs in, and the dispatch server carries out the operation dispatch task;

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

[0098] Example 2, reference Figure 1 - Figure 2 , which is the second embodiment of the present invention, 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] In order to verify the effectiveness and superiority of the task scheduling system based on the large-scale power grid topology partitioning algorithm, this embodiment was tested in a simulated large-scale power network environment. The power network consists of multiple areas, and the power equipment in each area is connected through the power grid to form a complex power system. In order to ensure the authenticity of the experiment, the actual power data in operation was used as the test basis.

[0100] First, the task scheduling system was deployed, including the identity recognition unit, the scheduling command and control system, the receiving unit, the execution unit, and the monitoring unit. The identity recognition unit is equipped with advanced face recognition equipment, which can accurately identify the identity of the dispatcher and improve the security of the system. Before the test, the information of all dispatchers participating in the test has been entered into the face recognition database to ensure the smooth progress of the experiment.

[0101] During the test, the identity recognition unit starts the face recognition shooting module through the control module, takes pictures of each dispatcher's face, and automatically adjusts the light and focus to ensure image quality. The face recognition comparison module compares the taken photos with the photos stored in the database. After confirming the identity, the dispatch command system receives the dispatch task. Subsequently, after the dispatcher successfully logs in, the receiving unit receives the operation instructions issued by the dispatcher. The execution unit sends execution instructions to the dispatch server according to the operation instructions. The dispatch server reasonably allocates the dispatch task to the master server or slave server based on the current state and load of the power grid.

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

[0103] In addition, the monitoring unit monitors the entire test process in real time and collects a large amount of data on the operation status of the power grid, including but not limited to key parameters such as current, voltage, and power factor. These data are pre-processed to remove duplicate values, handle missing values ​​and abnormal values, and perform data smoothing and data merging to ensure the accuracy and reliability of the data.

[0104] Finally, when the system detects that the load exceeds the second threshold of both the master server and the slave server, the dispatch server calculates the total amount of load that needs to be removed based on the actual situation, effectively avoiding the risk of overload. During the entire test process, if the identity authentication fails, the face recognition recording module will automatically record the relevant evidence and trigger the alarm module to sound an alarm, further improving the security of the system.

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

[0106]

[0107] Table 2: Data preprocessing and merging results

[0108]

[0109] By analyzing the data in the above two tables, it can be clearly seen that the superiority and innovation of the present invention in practical application.

[0110] As can be seen from Table 1, as the load increases, the response time of the master server and the slave server gradually increases, but still remains within a reasonable range. Even at the highest load (2000MW), the response time of the master server and the slave server is 5.0 seconds and 5.5 seconds respectively, which is much lower than the industry standard response time of 10 seconds. This shows that the dispatching system of the present invention can still operate efficiently under high load conditions.

[0111] The dispatch task completion rate remained above 93% in all test scenarios, especially under low and medium load conditions, where the completion rate was close to 100%. This shows 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 as the load increases, the data processing efficiency gradually decreases, but in all test scenarios, the data processing efficiency remains at a high level. For example, under the lowest load (500MW), the data processing efficiency reaches 1000 pieces / second, while under the highest load (2000MW), the data processing efficiency is 400 pieces / second. This shows that the system can still efficiently process a large amount of data under high load conditions, ensuring the real-time and accuracy of the system.

[0113] In all test scenarios, the number of security events was 0, which shows that the system of the present invention performs well in identity authentication and exception handling, effectively prevents unauthorized access and operation, and improves the security of the system.

[0114] Table 2 shows that the quality of the original data has been significantly improved through data preprocessing and merging. For example, in scenario 4, the original data volume is 40,000. After preprocessing, the final merged data volume is 36,000, reducing about 10% of redundant data. This not only improves the accuracy and reliability of the data, but also reduces the system processing burden and improves the overall performance.

[0115] When the load exceeds the second threshold of the master server and the slave server at the same time, the system can calculate the total amount of load that needs to be removed according to the actual situation, effectively avoiding the risk of overload. This function is particularly important in practical applications, and can take timely measures to ensure the safe and stable operation of the power grid when the power grid faces extreme loads.

[0116] In summary, the content of the present invention has shown excellent performance and advantages in practical applications, especially in terms of response time, task completion rate, data processing efficiency, security and load management under high load conditions, which are superior to existing dispatching systems. The test of this embodiment fully proves the innovation and practicality of the content of the present invention, and provides reliable technical support for the intelligent dispatching 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 rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0118] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt 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.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0122] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0123] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A task scheduling system based on a large-scale power grid topology partitioning algorithm, characterized in that: It includes identity recognition unit, dispatch command and control system, receiving unit, execution unit and monitoring unit; The identity recognition unit identifies the identity of the dispatcher based on face recognition technology, thereby improving the security of the power grid task dispatching system; The dispatching command and control system is used to perform intelligent dispatching and command of the power grid; The receiving unit is used to receive the operation instruction issued by the dispatcher after the dispatcher successfully logs in; The execution unit is used to send an execution instruction to the dispatch server according to the operation instruction after the dispatcher successfully logs in, and the dispatch server carries out the operation dispatch task; The monitoring unit is used to monitor the power grid in real time, and the monitoring unit monitors the execution status of the execution unit in real time.

2. The task scheduling system based on large-scale power grid topology partitioning algorithm according to claim 1, characterized in that: 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 starts the face recognition shooting module and sends a shooting instruction; The face recognition shooting module shoots the dispatcher's face, and automatically adjusts the light and zooms during the shooting process to prevent the photo from being exposed; The face recognition comparison module compares the taken photos with the photos stored in the face recognition database one by one, and issues a dispatch task to the dispatch command system after searching for the corresponding face data.

3. The task scheduling system based on large-scale power grid topology partitioning algorithm according to claim 2, characterized in that: The dispatch command and control system includes a dispatch server, a master server and a slave server; The scheduling server establishes a communication connection with the master server and the slave server; The dispatch server is used to obtain a dispatch instruction from an identity recognition unit; The master server and the slave server are both provided with a first threshold and a second threshold; 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, the scheduling task is sent 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, the scheduling task is sent 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, the scheduling task is sent 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, the scheduling task is sent to the slave server.

4. The task scheduling system based on large-scale power grid topology partitioning algorithm according to claim 3, 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.

5. The task scheduling system based on large-scale power grid topology partitioning algorithm according to claim 4, characterized in that: The monitoring unit preprocesses the monitoring data, and the preprocessing includes removing duplicate values, processing missing values, processing abnormal values, data smoothing and data merging.

6. The task scheduling system based on large-scale power grid topology partitioning algorithm according to claim 5, characterized in that: The data merging process includes: Extract features from each sub-data in the monitoring data, and digitize the feature indicators to obtain the indicator values; the feature indicators include data keywords, data size, data recording time, data type and data source address; Randomly select two sub-data and calculate the probability of merging the two sub-data; Among them, P i,j is the probability that the i-th sub-data and the j-th sub-data can be combined, T i,a is the ath index value of the ith sub-data, T j,a is the ath index value of the jth sub-data, max(T a ) is the maximum value of the ath indicator value among several sub-data in the monitoring data, the first indicator value is the numerical processing result of the data keyword, 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; The two sub-data with probabilities greater than a preset probability threshold are merged to obtain monitoring data after data merging.

7. The task scheduling system based on large-scale power grid topology partitioning algorithm according to claim 6, characterized in that: When the scheduling server determines that the load exceeds the second threshold of the master server and the second threshold of the slave server at the same time, the total amount of load to be removed is calculated according to the load situation; After the face comparison module compares the photo with the face data in the face recognition database one by one, if the dispatcher's photo cannot be found in the face recognition database, the authentication fails, and the face recognition recording module records the photo just taken by the face recognition shooting module to save as evidence; After recording the photo, the face recognition recording module sends a command to the alarm module, and the alarm module sounds an alarm and sends an alarm through the network.

8. A task scheduling method based on a large-scale power grid topology partitioning algorithm, based on the task scheduling system based on a large-scale power grid topology partitioning algorithm according to any one of claims 1 to 7, characterized in that: The identification unit starts the face recognition shooting module through the control module, shoots the dispatcher's face and automatically adjusts the light and zoom, and then the face recognition comparison module compares the taken photo with the photo in the face recognition database. If the comparison is successful, the dispatch command system issues a dispatch task; The receiving unit receives the operation instruction issued by the dispatcher after the dispatcher's identity authentication is passed and the login is successful; The execution unit sends an execution instruction to the dispatch server according to the operation instruction received by the receiving unit, and the dispatch server executes the corresponding dispatch task. At the same time, the monitoring unit monitors the execution status of the power grid and the execution unit in real time; The dispatch server allocates dispatch tasks according to the preset thresholds of the master server and the slave server based on the load of the power grid. When the load exceeds the second threshold of the master server and the slave server at the same time, the total amount of load that needs to be removed is calculated; If the face recognition comparison fails, the face recognition recording module records the evidence, and the alarm module sounds an alarm and connects to the Internet.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, 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 7 are implemented.

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

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