CN116415385A

CN116415385A - Fan joint debugging simulation method and device, server and computer storage medium

Info

Publication number: CN116415385A
Application number: CN202111635183.0A
Authority: CN
Inventors: 刘润林; 马羽龙
Original assignee: Xinjiang Goldwind Science and Technology Co Ltd
Current assignee: Xinjiang Goldwind Science and Technology Co Ltd
Priority date: 2021-12-29
Filing date: 2021-12-29
Publication date: 2023-07-11

Abstract

The application discloses a fan joint debugging simulation method, a device, a server and a computer storage medium. The fan joint debugging simulation method comprises the following steps: monitoring the use state of computing resources of all devices in a target system, wherein the target system comprises a fan main control subsystem, a fan edge computing subsystem and a fan hardware-in-the-loop subsystem; responding to the existence of target computing resources of equipment in a target system, and establishing a fan-end joint debugging simulation model based on the target computing resources, wherein the target computing resources are available computing resources; based on the fan end joint debugging simulation model, the operation of the fan is simulated, and a fan simulation result is obtained. According to the embodiment of the application, the additional configuration requirement of the fan simulation on the computing resources can be reduced, and the cost of the fan simulation is reduced.

Description

Fan joint debugging simulation method and device, server and computer storage medium

Technical Field

The application belongs to the technical field of wind power generation, and particularly relates to a fan joint debugging simulation method, a device, a server and a computer storage medium.

Background

With the increasing importance of clean energy, wind power generation has been widely used. A wind power generator (hereinafter referred to as a fan) is a key device for wind power generation, and in order to ensure the reliability of the operation of the fan, a related model of the fan is generally simulated. In the related art, additional configuration of computing resources for simulation is generally required, resulting in high cost of simulation.

Disclosure of Invention

The embodiment of the application provides a fan joint debugging simulation method, a device, a server and a computer storage medium, which are used for solving the problem of high cost of simulation of a fan by related technologies.

In a first aspect, an embodiment of the present application provides a fan joint debugging simulation method, including:

monitoring the use state of computing resources of all devices in a target system, wherein the target system comprises a fan main control subsystem, a fan edge computing subsystem and a fan hardware-in-the-loop subsystem;

responding to the existence of target computing resources of equipment in a target system, and establishing a fan-end joint debugging simulation model based on the target computing resources, wherein the target computing resources are available computing resources;

based on the fan end joint debugging simulation model, the operation of the fan is simulated, and a fan simulation result is obtained.

In a second aspect, an embodiment of the present application provides a fan joint debugging simulation device, where the device includes:

the monitoring module is used for monitoring the use state of the computing resources of each device in the target system, and the target system comprises a fan main control subsystem, a fan edge computing subsystem and a fan hardware-in-the-loop subsystem;

the first building module is used for building a fan end joint debugging simulation model based on target computing resources which are available computing resources in response to the existence of the target computing resources in the equipment in the target system;

The first simulation module is used for simulating the operation of the fan based on the fan end joint debugging simulation model to obtain a fan simulation result.

In a third aspect, an embodiment of the present application provides a server, including: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the fan joint debugging simulation method as shown in the first aspect.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium, where computer program instructions are stored, where the computer program instructions, when executed by a processor, implement a fan joint debugging simulation method as shown in the first aspect.

In a fifth aspect, embodiments of the present application provide a computer program product, where instructions in the computer program product, when executed by a processor of a server, cause the server to perform the fan joint debugging simulation method as shown in the first aspect.

According to the fan joint debugging simulation method, the use state of computing resources of all devices in a target system is monitored, and the target system comprises a fan main control subsystem, a fan edge computing subsystem and a fan hardware-in-the-loop subsystem; responding to the existence of target computing resources of equipment in a target system, and establishing a fan-end joint debugging simulation model based on the target computing resources, wherein the target computing resources are available computing resources; based on the fan end joint debugging simulation model, the operation of the fan is simulated, and a fan simulation result is obtained. According to the embodiment of the application, the fan end joint debugging simulation model can be established by utilizing the computing resources in the available state in the target system to simulate the operation of the fan, so that the additional configuration requirement of the fan simulation on the computing resources can be reduced, and the cost of the fan simulation is reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.

FIG. 1 is a schematic flow chart of a fan joint debugging simulation method provided in an embodiment of the present application;

FIG. 2 is a schematic diagram of the connection relationship between devices in a hub dispatch system and a target system;

FIG. 3 is a schematic diagram of constructing a simulation environment for fan simulation;

FIG. 4 is a workflow diagram of a fan end-wind farm collaborative joint debugging simulation model established in one specific application;

FIG. 5 is a schematic diagram of the simulation operation of a fan end-wind farm collaborative joint debugging simulation model;

FIG. 6 is a schematic diagram of a model configuration;

fig. 7 is a schematic structural diagram of a fan joint debugging simulation device provided in an embodiment of the present application;

fig. 8 is a schematic structural diagram of a server provided in the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application are described in detail below to make the objects, technical solutions and advantages of the present application more apparent, and to further describe the present application in conjunction with the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative of the application and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.

In order to solve the problems in the prior art, the embodiment of the application provides a fan joint debugging simulation method, device and equipment and a computer storage medium. The fan joint debugging simulation method provided by the embodiment of the application is first described below.

Fig. 1 shows a flow chart of a fan joint debugging simulation method according to an embodiment of the present application. As shown in fig. 1, the method includes:

Step 101, monitoring the use state of computing resources of all devices in a target system, wherein the target system comprises a fan main control subsystem, a fan edge computing subsystem and a fan hardware-in-the-loop subsystem.

Step 102, in response to the existence of a target computing resource in the equipment in the target system, a fan-end joint debugging simulation model is established based on the target computing resource, wherein the target computing resource is in an available state.

And 103, simulating the operation of the fan based on the fan end joint debugging simulation model to obtain a fan simulation result.

The fan joint debugging simulation method provided by the embodiment of the application can be applied to electronic equipment, servers and the like, and is not particularly limited herein.

Taking an execution main body of the fan joint debugging simulation method as an example of a server, the server can establish communication connection with each device in the target system and can dispatch the computing resources of each device in the target system. Accordingly, in the following embodiments, a server that can perform the above-described method may also be referred to as a hub dispatch system.

As shown in fig. 2, fig. 2 is a schematic diagram of a connection relationship between devices in a hub scheduling system and a target system. As can be seen in conjunction with fig. 2, the target system may include a fan master subsystem, a fan edge calculation subsystem, and a fan hardware-in-the-loop subsystem.

One or more devices may be included in each subsystem. For convenience of description, the devices in the fan master control subsystem may be referred to as fan master control devices, the devices in the fan edge computing subsystem may be referred to as fan edge computing devices, and the devices of the fan hardware in the ring subsystem may be referred to as fan hardware in ring devices. And the devices in the respective subsystems may be collectively referred to as devices in the target system.

In this embodiment, each device in the target system may be capable of performing communication connection with the hub scheduling system, where the communication connection may be remote communication or local communication. In other words, the devices in each subsystem may be local devices, off-site devices, or both, as opposed to a hub scheduling system, and are not specifically limited herein.

The computing resources of each device in the target system may refer to central processing unit (Central Processing Unit, CPU) resources, memory resources, hard disk resources, network resources, or the like of each device.

It is to be readily appreciated that the fan master subsystem may be a generic term for each fan master device connected to the hub dispatch system, and thus, the use of the fan master subsystem may be the same as or similar to the use of the fan master device. Similarly, other subsystems may also be identical or similar in purpose to the devices under the subsystems. The purpose of each subsystem will be explained mainly from the viewpoint of the apparatus.

Each device in the target system may be preconfigured.

For example, in a wind turbine in an operating or testing state, a fan master device and a fan edge computing device are typically included, where the fan master device may be used to perform off-grid control or optimization control on a fan set.

Edge computing devices are typically located near the side of the object or data source and provide near-end data analysis services for an open platform that integrates network, computing, storage, and application core capabilities. The fan edge computing equipment can be used for acquiring the running state of the fan and data generated by running, and judging and recording the running state of the fan through the diagnosis and monitoring model.

For another example, the fan hardware in-loop device may be a hardware platform capable of running related fan simulation software, where the hardware platform may be an independent computer, may also be a control module in the wind generator, or may also be a cloud server, where no illustration is made here.

In step 101, the hub dispatch system may monitor the usage status of computing resources of each device in the target system.

Taking the example of a central dispatching system monitoring the computing resources of each fan master control device in the fan master control subsystem. The central dispatching system can monitor whether each fan main control device is in standby, and if one fan main control device is in standby, the computing resources of the fan main control device are all in an available state.

Or, the central dispatching system can monitor whether the left computing resources such as CPU resources in each fan main control device exist or not, and the left computing resources can be considered to be in an available state.

As for monitoring of the usage status of the computing resources of the devices in the other subsystems, reference may be made to the related description of the fan master device, and the description will not be repeated here.

In step 102, the hub dispatch system may establish a fan end joint debugging simulation model based on the target computing resources in response to the presence of the target computing resources by the devices in the target system.

Referring to FIG. 2, in one example, when there is a fan master device, a fan edge computing device, and a fan hardware in loop device in standby, it is illustrated that there are computing resources in the available state in all three types of devices. These computing resources in the available state together constitute the target computing resource described above.

The central dispatching system can integrate the computing resources in the available state, for example, the main control equipment of the fan, the edge computing equipment of the fan and the hardware-in-the-loop equipment of the fan to be in communication connection, so as to form a simulation environment for fan simulation. The central dispatching system can further utilize computing resources in the simulation environment to establish a fan-end joint debugging simulation model.

Of course, in practical application, the central dispatching system may also directly monitor whether computing resources in each device in the target system remain, and when the remaining computing resources are enough to build the fan-end joint debugging simulation model, the target computing resources can be considered to exist.

In step 103, the central dispatching system may simulate the operation of the fan based on the fan end joint debugging simulation model, to obtain a fan simulation result.

In this embodiment, the fan-end joint debugging simulation model may be used to simulate a fan and simulate the operation process of the fan.

The fan simulation result may include a running state of the fan, or include a reliability test result of the fan, which is not particularly limited herein, and may be set according to actual simulation requirements.

Optionally, the fan main control subsystem includes P typhoon main control devices, the fan edge computing subsystem includes Q typhoon edge computing devices, the fan hardware-in-loop subsystem includes R typhoon hardware-in-loop devices, P, Q and R are positive integers.

After the use state of the computing resources of each device in the target system is monitored, determining that the target computing resources exist in the target system under the condition that at least one fan main control device, at least one fan edge computing device and at least one fan hardware are in an available state.

As shown in fig. 3, fig. 3 shows a schematic diagram of constructing a simulation environment for fan simulation based on the states of the respective devices in the monitor target system.

Based on fig. 3, in the fan master subsystem, there may be multiple fan master devices, some of which may be in "in use" and some of which may be in "waiting for hit". The central dispatching system can acquire the states of the fan main control equipment, and when one fan main control equipment is in a standby state, the fan main control equipment can be considered to be in an available state, wherein the computing resources can be applied to fan simulation.

Similarly, the hub dispatch system may monitor the status of devices in other subsystems to determine devices that are in a possible state.

Of course, in practical applications, the number of devices in each subsystem may depend on the device composition of the network topology where the central scheduling system is located, and the network topology may be constructed according to actual needs, which is not described in detail herein.

In this embodiment, the hub scheduling system may determine that the target computing resource exists in the target system when it monitors that at least one fan master device, at least one fan edge computing device, and at least one fan hardware are in an available state in the ring device.

In other words, in this embodiment, the hub scheduling system may be regarded as monitoring the usage status of the computing resources in units of devices. To some extent, it is considered that when a device is in an available state, the computing resources therein may also be in an available state, and vice versa.

In the case of the target computing resource, the central dispatching system can establish a fan-end joint debugging simulation model based on the target computing resource and simulate the operation of the fan, and the specific process is not repeated here.

In this embodiment, the hub scheduling system monitors the usage status of the computing resource in units of devices, and the monitoring process is easy to implement. The fan-end joint debugging simulation model is established by using the computing resources in the equipment in the available state, and the situation that the normal operation of the equipment is influenced due to the occupation of the computing resources of the equipment in use can be avoided.

In some possible implementations, some fan master devices may have sufficient computing resources to undertake edge computing or hardware-in-loop tasks. When the central dispatching system detects that the fan main control equipment is in a usable state, a fan end joint debugging simulation model can be established based on the computing resources of the fan main control equipment.

In other possible embodiments, when the cloud server where the central dispatching system is located has enough computing resources, the fan-end joint debugging simulation model can be further built by combining the computing resources of the cloud server.

Optionally, after monitoring the usage state of the computing resource of each device in the target system, the method further includes:

utilizing the residual computing resources of each device in the target system to form a Docker container;

acquiring computing resource configuration corresponding to a Docker container;

in response to the computing resources of the devices in the target system being in an available state satisfying the computing resource configuration, determining that the target computing resources are present in the target system.

This embodiment can be regarded as a variation of the previous embodiment. Briefly, in the above embodiment, when a device is in an "in-use" state, all computing resources in the device may be considered unavailable. In this embodiment, when a device is in a "in-use" state, if there are remaining computing resources (i.e., computing resources are not completely used), it may be considered that there are still computing resources in the device that are in an available state.

The following describes the implementation procedure of the present embodiment in detail.

In this embodiment, the hub dispatch system may utilize the remaining computing resources of each device in the target system to form a Docker container.

In some examples, the Docker container may be specifically built on a fan master device with remaining computing resources, a fan edge computing device, or fan hardware on a ring device.

In composing the Docker container, the computing resource configuration of the Docker container may be set. The configuration of the computing resource may be default or user-set, and is not specifically limited herein.

From a quantitative perspective, the computing resource configuration of the Docker container may indicate the amount of computing resources that need to be allocated from each subsystem.

For example, the computing resource may be a memory resource, and the computing resource configuration of the Docker container may indicate that 800M memory resources need to be allocated from the device of the fan master subsystem, 500M memory resources need to be allocated from the device of the fan edge computing subsystem, and so on.

Of course, the foregoing is illustrative for facilitating understanding of the computing resource configuration corresponding to the Docker container, and the computing resource configuration may also indicate the configuration requirements of other types of computing resources in practical applications, which are not illustrated herein.

When the computing resources of the equipment in the target system in the available state meet the computing resource configuration, the central dispatching system can determine that the target computing resources exist in the target system, and establish a fan-end joint debugging simulation model based on the target computing resources.

In this embodiment, the distribution of computing resources can be realized through the Docker container, and when the device in the target system is in a use state, the remaining computing resources can still be utilized to build the fan-end joint debugging simulation model, so that the use efficiency of computing resources in the target system is effectively improved.

In some examples, the number of the Docker containers may be one or more, and each Docker container may build a fan-end joint debugging simulation model by allocating computing resources. Correspondingly, in practical application, the computing resources in one device can be distributed to one or more fan-end joint debugging simulation models, so that the use efficiency of the computing resources is effectively improved.

Optionally, the fan-end joint debugging simulation model comprises a simulation fan and a fan edge calculation model, wherein the simulation fan is established based on calculation resources in a fan main control subsystem and calculation resources of fan hardware in a ring subsystem, and the fan edge calculation model is established based on calculation resources in a fan edge calculation subsystem;

Based on a fan end joint debugging simulation model, the operation of the fan is simulated, and the step of obtaining the fan simulation result specifically comprises the following steps:

generating fan operation state data by using the simulated fan;

and analyzing the running state data of the fan by using a fan edge calculation model to obtain a fan simulation result.

In combination with an example, the hub scheduling system may compose a simulated fan and simulate the operation of the unit based on the computing resources in the fan main control subsystem and the computing resources of the fan hardware in the ring subsystem, and generate fan operation status data.

In addition, the hub scheduling system can also establish a fan edge calculation model based on calculation resources in the fan edge calculation subsystem. The fan edge calculation model can acquire fan operation state data, judge and record the operation state of the simulated fan based on the fan operation state data, and test the reliability of the simulated fan.

In other examples, a fan edge calculation model may be used to make a health diagnosis for a simulated fan. For example, after the fan edge calculation model obtains the fan running state data, the fan large part abnormality monitoring diagnosis, the unit running state monitoring diagnosis test and the like can be carried out through data processing modes such as data cleaning, feature extraction, data statistics and the like, so that the functions of unit running early warning, large part early warning, unit health degree insight and the like can be realized.

The fan simulation result may include a status monitoring result, an early warning result, a health diagnosis result, or the like of the fan, and may be set according to actual needs.

Therefore, the fan end joint debugging simulation model established in the embodiment comprises a simulation fan and a fan edge calculation model, fan operation state data can be generated by using the simulation fan, and fan operation state data are analyzed by using the fan edge calculation model, so that multiple types of fan simulation results can be obtained, and the fan simulation effect is improved.

Optionally, after the fan end joint debugging simulation model is established based on the target computing resource, the method further comprises:

responding to the establishment of M fan end joint debugging simulation models, and establishing a wind power plant joint debugging simulation model, wherein M is an integer greater than 1;

establishing a fan end-wind power plant collaborative joint debugging simulation model according to the M fan end joint debugging simulation models and the wind power plant joint debugging simulation model;

based on a fan end-wind power plant collaborative joint debugging simulation model, the operation of the wind power plant is simulated, and a wind power plant simulation result is obtained.

It is easy to understand that the fan-end joint debugging simulation model can be a simulation model established for a single fan. In order to realize simulation of the wind power plant, in this embodiment, a plurality of fan-end joint debugging simulation models may be established, and each fan-end joint debugging simulation model may correspond to one simulated fan.

In this embodiment, under the condition that M fan-end joint debugging simulation models are established, a wind farm joint debugging simulation model may be further established. In some examples, the wind farm joint debugging simulation model may be used to simulate related control systems in a wind farm, such as a farm group control system, or a power generation boost system, or the like.

The wind farm joint debugging simulation model can be built based on computing resources in a target system. Alternatively, the wind farm joint debugging simulation model may also be directly built in the central dispatching system, which may not be limited herein.

According to the M fan end joint debugging simulation models and the wind power plant joint debugging simulation model, a fan end-wind power plant collaborative joint debugging simulation model can be established.

By combining some examples, running state data can be generated in each fan-end joint debugging simulation model, and the running state data can be directly or processed and then sent to the wind power plant joint debugging simulation model. And the wind farm joint debugging simulation model can also send control instructions to each fan end joint debugging simulation model, so that the simulated fans in the fan end joint debugging simulation model realize corresponding actions and the like according to the control instructions.

Based on the above examples, in the fan end-wind power plant collaborative joint debugging simulation model, data interaction between the fan end joint debugging simulation model and the wind power plant joint debugging simulation model can correspond to data interaction between the fan and the wind power plant control system. In other words, based on the fan end-wind power plant collaborative joint debugging simulation model, the operation of the wind power plant can be simulated.

In the above example, the wind farm control system may be a power generation amount lifting system, in which a relevant power generation amount lifting model exists, and accordingly, a wind farm simulation result obtained by simulation may be used to indicate an operation effect of the power generation amount lifting model.

In other examples, the wind farm joint debugging simulation model may receive data from each of the fan end joint debugging simulation models and send control instructions to each of the fan end joint debugging simulation models based on the data, and the wind farm simulation result may include a control process of the fan end joint debugging simulation models on the fan end joint debugging simulation models, and the like.

Of course, the above is some examples of simulation results of the wind farm, and in practical application, the specific content of the simulation results of the wind farm may be set according to needs.

In the embodiment, the central dispatching system responds to the establishment of M fan-end joint debugging simulation models, establishes a wind power plant joint debugging simulation model, and can establish a fan-end-wind power plant collaborative joint debugging simulation model based on the M fan-end joint debugging simulation models and the wind power plant joint debugging simulation model so as to simulate the operation of the wind power plant, enrich the simulation function and improve the fan joint debugging simulation effect.

As shown in fig. 4, fig. 4 is a workflow of building a fan end-wind farm collaborative joint debugging simulation model in a specific application example, and specifically includes steps 401 to 408:

step 401, obtaining a device condition in a target system.

In connection with the above example, the target system may include a fan master control subsystem, a fan edge computing subsystem, and a fan hardware-in-the-loop subsystem, where corresponding devices may exist, such as a fan master control device, a fan edge computing device, and a fan hardware-in-the-loop device, respectively. These devices may be local devices, off-site devices, or both.

Step 402, accessing a device in a target system to a hub dispatch system.

The central dispatching system can be used for establishing a fan end-wind power plant collaborative joint debugging simulation model, so that the central dispatching system can be called a wind power plant 'side-field' collaborative simulation central dispatching system.

The central dispatching system is connected with local and remote equipment, so that a user can carry out simulation test without being in a specific environment, and the convenience of the simulation test is improved.

Step 403, monitoring the operation condition of the device in the target system.

Step 404, determining whether the fan master control device, the fan edge computing device and the fan hardware in-loop device are in standby state at the same time, if yes, executing step 405, otherwise, returning to execute step 403.

In step 405, the fan master control device, the fan edge computing device, and the fan hardware in the standby state form a fan edge joint debugging simulation environment in the ring device.

Step 406, the fan edge joint debugging simulation environment realizes the operation of the simulation fan.

The computing resources of each device in the fan edge joint debugging simulation environment can be used for establishing a fan end joint debugging simulation model, and the fan end joint debugging simulation model comprises a simulation fan and fan edge computing model, so that the operation of the fan can be simulated.

Step 407, a plurality of fan edge end joint debugging simulation environments form a simulated wind field, and the simulated wind field is accessed into a field group control and power generation lifting simulation system of the wind field.

The fan edge joint debugging simulation environment can be used for establishing a fan end joint debugging simulation model to simulate the operation of a fan. The edge ends of the fans are connected with each other to regulate the simulation environment, so that a plurality of simulated fans in the simulated wind field can be formed.

The wind farm group control and power generation lifting simulation system can correspond to the wind farm joint debugging simulation model, and a plurality of fan end joint debugging simulation models can be connected into the wind farm joint debugging simulation model to obtain a fan end-wind farm collaborative joint debugging simulation model.

Step 408, testing a fan health diagnosis model through fan edge computing equipment, testing a wind power plant power generation lifting model, a plant-level fan control or fan performance evaluation and the like through a fan end-wind power plant collaborative joint debugging simulation model.

In the embodiment, a plurality of fan-end joint debugging simulation models and a wind-field joint debugging simulation model can form a simulated wind field, and intelligent simulation of the fans can be performed in a simulated environment. The fan-end joint debugging simulation model and the wind farm joint debugging simulation model are combined, and multi-dimensional fan and wind farm tests such as fan state monitoring, fan large part monitoring, fan and wind farm generating capacity assessment and the like can be performed. The user can obtain the model test report at the cloud end, quickly know the reliability of the model, and provide a basis for iteration of the model. According to the embodiment of the application, the manpower cost, the equipment management cost and the project resource investment can be effectively reduced, and meanwhile, the intelligent testing requirements of the wind power plant and the unit are met.

In some examples, the above-mentioned fan edge calculation model may be used to perform a health diagnosis on a simulated fan, and the health diagnosis function may be specifically implemented by the fan health diagnosis model.

In order to facilitate understanding of the test function of the fan end-wind power plant collaborative joint debugging simulation model, the following description is made for data interaction between each model in the fan end-wind power plant collaborative joint debugging simulation model.

Optionally, based on a fan end-wind power plant collaborative joint debugging simulation model, simulating the operation of the wind power plant to obtain a wind power plant simulation result, and specifically comprising the following steps of:

obtaining M fan simulation results generated by the M fan-end joint debugging simulation models;

determining target generated energy according to M fan simulation results;

and determining a fan control instruction according to the target power generation amount, and sending the fan control instruction to a corresponding fan end joint debugging simulation model.

As shown in FIG. 5, FIG. 5 is a schematic diagram of the simulation operation of the wind turbine end-wind farm collaborative joint debugging simulation model. In combination with the description of the above embodiments, the simulated fans in the fan-end joint debugging simulation model may generate fan operation state data, and these fan operation state data may be directly sent to the wind farm joint debugging simulation model.

The wind power plant joint debugging simulation model can specifically receive the fan running state data of each fan end joint debugging simulation model through the power generation amount lifting model, and send fan control instructions to the relevant fan end joint debugging simulation model based on the power generation amount lifting model.

In some examples, after the field-side generating capacity lifting model obtains the running state data of the fan, the whole-field and single-machine generating capacity conditions in a period of time can be evaluated through data processing modes such as data cleaning, feature extraction and data statistics, the whole-field generating capacity condition and the single-machine generating capacity condition are combined, the target generating capacity of each simulated fan is determined, a control command is sent to a corresponding fan-side joint debugging simulation model, and the simulated fans in the fan-side joint debugging simulation model realize corresponding actions (such as yaw, start-stop, power limiting and the like) according to the control command.

After the simulated fan acts, the generated corresponding running state data can be changed, the changed running state data can be sent to the field-end generating capacity lifting model, and the field-end generating capacity lifting model evaluates the lifting effect of the field-end generating capacity lifting model on the generating capacity of the unit and the generating capacity of the wind farm according to the data.

In practical applications, the model used for the field-side power generation amount lifting model and the power generation amount lifting effect evaluation may be the same model, and according to the functions that can be achieved by the model, the same model may be referred to as a field-side power generation amount lifting and evaluating model.

As for the wind power plant simulation result, the simulation result can be a model test report and the like output by a field-end power generation capacity lifting and evaluation model.

In the embodiment, through the cooperative work of the M fan-end joint debugging simulation models and the wind power plant joint debugging simulation model, the relevant control model in the wind power plant joint debugging simulation model can be tested, and the application range of simulation is expanded.

Of course, as shown in fig. 5, in practical application, the control model in the wind farm joint debugging simulation model is not limited to the above model for lifting and evaluating the generated energy of the farm end, but also can be related models for running early warning of a farm-level unit, early warning of a large part or health degree insight of the unit, etc., and these models can be based on the data analysis structure of each wind farm joint debugging simulation model for the running state data of the wind farm, and further monitor and evaluate the running state of the wind farm, etc.

Corresponding core algorithms can be arranged in the fan health diagnosis model, the field-end power generation lifting and evaluating model and other models, and the core algorithms can be existing algorithms, algorithms needing to be evaluated and the like, and are not particularly limited.

Based on the above embodiments, in some application scenarios, the hub scheduling system can perform device management and scheduling, saving the cost of manual device construction, and without manually configuring and debugging devices, the hub scheduling system can construct a fan simulation platform for edge calculation at a far end according to user requirements, thereby improving the working efficiency of fan simulation experiments.

In some embodiments, the fan health diagnosis model, the field-side power generation lifting and evaluation model and other models can generate model test reports, and the test reports can be used as simulation results and sent to be uploaded to a central dispatching system for storage or reference by a user.

In some embodiments, the fan edge joint debugging simulation environment may also provide data compressor storage functionality.

For example, the edge computing device is responsible for collecting raw high frequency data (typically 50 Hz) generated during operation of the analog fan, and also converting the raw high frequency data into low frequency data (e.g., 1 s/frame, 7 s/frame). The edge computing device and/or other devices in the fan edge joint debugging simulation environment may be responsible for storing and compressing the original high frequency and low frequency data in a database.

Optionally, a fan end joint debugging simulation model is established based on target computing resources, and specifically comprises the following steps:

in response to receiving a configuration input from a user, determining a target model configuration parameter from preset N model configuration parameters, wherein N is a positive integer;

and establishing a fan-end joint debugging simulation model based on the target computing resources and the target model configuration parameters.

In combination with some scenes, when performing fan-end joint simulation, a user may need to have corresponding configuration requirements for main control software, wind conditions, fan simulation, edge-end models and the like.

As shown in fig. 6, fig. 6 is a schematic diagram of a model configuration. As can be seen in connection with fig. 6, in some examples, the master software requirements may be manifested as matching the appropriate master software for different models, different sites. The wind condition needs may include light wind conditions, heavy wind conditions, extreme wind conditions, and the like. The fan model requirements may include fan power rating, blade model, tower height, tower model, and the like. The edge model requirements may include a centralized state monitoring model, a crew large component monitoring model, a life prediction model, and the like.

As shown above, in practical application, a fan end-wind farm collaborative joint debugging simulation model needs to be further established, and when model configuration is performed, field end model requirements, such as a power lifting model or a whole field power generation amount evaluation model, need to be further considered.

In some examples, the central dispatching system can be provided with a fan edge end health diagnosis model library and a field group control end generating capacity lifting model library of a wind farm, and a user can select a corresponding library and put down single-machine edge equipment and field end equipment in an edge joint debugging simulation environment, so that the simulation requirements of a target unit, the model requirements of the edge end and the field end can be rapidly realized, and further the overall simulation test of the user is met.

In these examples, the user can automatically issue configuration to the edge computing environment according to the requirements under the condition of receiving the test requirements provided by the user without solving the construction details of the simulation fan, and issue the model to the edge device and the field device, so that the user only needs to concentrate on model simulation, and the working efficiency of the fan simulation experiment is greatly improved.

In some embodiments, the N model configuration parameters may be pre-existing on the hub scheduling system or other device, and the user may select the target model configuration parameter directly from among the model configuration parameters. The central dispatching system can establish a fan-end joint debugging simulation model based on the target computing resources and the target model configuration parameters, so that user operation is effectively simplified, and the efficiency of constructing the simulation model is improved.

Optionally, the target system further comprises a sensor subsystem.

In this embodiment, the hub dispatching system may not only establish communication connection with each device in the ring subsystem by the fan main control subsystem, the fan edge computing subsystem, and the fan hardware, but also connect with sensors (such as laser radar, blade video monitoring, etc.) related to the intelligent fan.

The sensor of the intelligent fan can belong to a sensor subsystem. The hub dispatch system may monitor the usage status of the computing resources of the various sensors in the sensor subsystem.

Similar to the processing mode of the equipment in the other three subsystems, the central dispatching system can take the sensor as a unit, monitor whether the sensor is in a working state or a standby state, and classify the sensor in the standby state into a fan edge joint debugging simulation environment.

Alternatively, the hub scheduling system may also allocate computing resources in the sensor to each Docker container based on Docker container technology to obtain a hardware platform for building a fan-end joint debugging simulation model.

In this embodiment, the target system further includes a sensor subsystem, so, the central dispatching system may also incorporate the sensor into the fan edge joint debugging simulation environment, so that the established fan end joint debugging simulation model is more similar to the actual fan end structure, and accuracy and reliability of the fan simulation test result are improved.

As shown in fig. 7, the embodiment of the present application further provides a fan joint debugging simulation device, including:

the monitoring module 701 is configured to monitor a usage state of computing resources of each device in a target system, where the target system includes a fan main control subsystem, a fan edge computing subsystem, and a fan hardware-in-loop subsystem;

The first establishing module 702 is configured to establish, in response to the presence of a target computing resource in a device in the target system, a fan-end joint debugging simulation model based on the target computing resource, where the target computing resource is a computing resource in an available state.

The first simulation module 703 is configured to simulate the operation of the fan based on the fan end joint debugging simulation model, so as to obtain a fan simulation result.

Optionally, the fan main control subsystem includes P fan main control devices, the fan edge computing subsystem includes Q fan edge computing devices, the fan hardware-in-the-loop subsystem includes R fan hardware-in-the-loop devices, P, Q and R are positive integers;

correspondingly, the fan joint debugging simulation device can further comprise:

the first determining module is used for determining that target computing resources exist in the target system under the condition that at least one fan main control device, at least one fan edge computing device and at least one fan hardware are in an available state.

Optionally, the fan joint debugging simulation device may further include:

the building module is used for forming a Docker container by utilizing the residual computing resources of each device in the target system;

the acquisition module is used for acquiring the computing resource configuration corresponding to the Docker container;

And the second determining module is used for determining that the target computing resource exists in the target system in response to the condition that the computing resource in the available state of the equipment in the target system meets the computing resource configuration.

accordingly, the first simulation module 703 may include:

the generating unit is used for generating fan running state data by using the simulated fan;

and the simulation unit is used for analyzing the fan running state data by using the fan edge calculation model to obtain a fan simulation result.

Optionally, the fan joint debugging simulation device may further include:

the second building module is used for building a wind power plant joint debugging simulation model in response to the building of M fan end joint debugging simulation models, wherein M is an integer greater than 1;

the third building module is used for building a fan end-wind power plant collaborative joint debugging simulation model according to the M fan end joint debugging simulation models and the wind power plant joint debugging simulation model;

The second simulation module is used for simulating the operation of the wind power plant based on the fan end-wind power plant collaborative joint debugging simulation model to obtain a wind power plant simulation result.

Optionally, the second simulation module may include:

the acquisition unit is used for acquiring M fan simulation results generated by the M fan-end joint debugging simulation models;

the first determining unit is used for determining target power generation amount according to M fan simulation results;

and the sending unit is used for determining a fan control instruction according to the target power generation amount and sending the fan control instruction to the corresponding fan end joint debugging simulation model.

Optionally, the first establishing module specifically includes:

the second determining unit is used for determining a target model configuration parameter from N preset model configuration parameters in response to receiving configuration input of a user, wherein N is a positive integer;

the building unit is used for building the fan end joint debugging simulation model based on the target computing resources and the target model configuration parameters.

Optionally, the target system further comprises a sensor subsystem.

It should be noted that, the fan joint debugging simulation device is a device corresponding to the fan joint debugging simulation method, and all implementation manners in the method embodiment are applicable to the device embodiment, so that the same technical effect can be achieved.

Fig. 8 shows a schematic hardware structure of a server according to an embodiment of the present application.

A processor 801 and a memory 802 storing computer program instructions may be included on a server.

In particular, the processor 801 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.

Memory 802 may include mass storage for data or instructions. By way of example, and not limitation, memory 802 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the above. Memory 802 may include removable or non-removable (or fixed) media, where appropriate. Memory 802 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 802 is a non-volatile solid-state memory.

In particular embodiments, memory 802 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.

The processor 801 implements any of the fan joint debugging simulation methods of the above embodiments by reading and executing computer program instructions stored in the memory 802.

In one example, the server may also include a communication interface 803 and a bus 810. As shown in fig. 8, the processor 801, the memory 802, and the communication interface 803 are connected to each other via a bus 810 and perform communication with each other.

The communication interface 803 is mainly used to implement communication between each module, apparatus, unit and/or device in the embodiments of the present application.

Bus 810 includes hardware, software, or both, coupling components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 810 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

In addition, in combination with the fan joint debugging simulation method in the above embodiment, the embodiment of the application may provide a computer storage medium for implementation. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement any of the fan joint debugging simulation methods of the above embodiments.

It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.

The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.

Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. 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 may be provided to a processor of a general purpose computer, special purpose computer, 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, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, which are intended to be included in the scope of the present application.

Claims

1. The fan joint debugging simulation method is characterized by comprising the following steps of:

the method comprises the steps of monitoring the use state of computing resources of all devices in a target system, wherein the target system comprises a fan main control subsystem, a fan edge computing subsystem and a fan hardware-in-the-loop subsystem;

responding to the existence of target computing resources of equipment in the target system, and establishing a fan-end joint debugging simulation model based on the target computing resources, wherein the target computing resources are available computing resources;

and simulating the operation of the fan based on the fan-end joint debugging simulation model to obtain a fan simulation result.

2. The method of claim 1, wherein the fan master control subsystem comprises a P-station fan master control device, the fan edge computing subsystem comprises a Q-station fan edge computing device, the fan hardware-in-loop subsystem comprises a R-station fan hardware-in-loop device, P, Q, and R are positive integers;

after the use state of the computing resources of each device in the monitoring target system, the method further comprises:

and determining that the target computing resource exists in the target system under the condition that at least one fan main control device, at least one fan edge computing device and at least one fan hardware are in an available state.

3. The method of claim 1, wherein after monitoring the usage status of the computing resources of each device in the target system, the method further comprises:

acquiring computing resource configuration corresponding to the Docker container;

and determining that the target computing resource exists in the target system in response to the condition that the computing resource in the available state of the equipment in the target system meets the computing resource configuration.

4. The method of claim 1, wherein the fan-end joint debugging simulation model comprises a simulated fan and fan edge calculation model, the simulated fan is established based on computing resources in the fan main control subsystem and computing resources in the ring subsystem of the fan hardware, and the fan edge calculation model is established based on computing resources in the fan edge calculation subsystem;

the step of simulating the operation of the fan based on the fan end joint debugging simulation model to obtain a fan simulation result specifically comprises the following steps:

generating fan operation state data by using the simulated fan;

and analyzing the fan running state data by using the fan edge calculation model to obtain the fan simulation result.

5. The method of claim 1, wherein after establishing a fan-end joint debugging simulation model based on the target computing resource, the method further comprises:

responding to the establishment of M fan-end joint debugging simulation models, and establishing a wind power plant joint debugging simulation model, wherein M is an integer greater than 1;

And simulating the operation of the wind power plant based on the fan end-wind power plant collaborative joint debugging simulation model to obtain a wind power plant simulation result.

6. The method of claim 5, wherein simulating operation of a wind farm based on the fan end-wind farm collaborative joint debugging simulation model to obtain wind farm simulation results comprises:

determining target generated energy according to the M fan simulation results;

7. The method of claim 1, wherein the establishing a fan-end joint debugging simulation model based on the target computing resource specifically comprises:

8. The method of claim 1, wherein the target system further comprises a sensor subsystem.

9. A fan joint debugging simulation device, characterized in that the device comprises:

the system comprises a monitoring module, a control module and a control module, wherein the monitoring module is used for monitoring the use state of computing resources of all devices in a target system, and the target system comprises a fan main control subsystem, a fan edge computing subsystem and a fan hardware-in-loop subsystem;

the first establishing module is used for responding to the existence of target computing resources of equipment in the target system, establishing a fan-end joint debugging simulation model based on the target computing resources, wherein the target computing resources are available computing resources;

10. A server, the server comprising: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements a fan joint debugging simulation method as defined in any one of claims 1-8.

11. A computer readable storage medium, wherein computer program instructions are stored on the computer readable storage medium, which when executed by a processor, implement the fan joint debugging simulation method according to any one of claims 1-8.

12. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of a server, cause the server to perform the fan joint debugging simulation method of any of claims 1-8.