Power load simulation training method and device and storage medium

Abstract

The invention discloses a power load simulation training method and device and a storage medium, and the method comprises the steps: building a virtual operation environment for electrical equipment in a power system according to a training task when the training task for a user is received; constructing simulation load data for the electrical equipment according to the training task; when the simulation load data is loaded in the virtual operation environment, controlling the electrical equipment to operate according to the operation of a user on the electrical equipment so as to collect operation data generated by the electrical equipment; and generating a training result for the user by using the operation data according to the training task. According to the embodiment, full-process simulation can be carried out on layout operation of the power system, targeted training is provided by self-adaption to the function level of a user, the capacity of the user for novel power load management is effectively improved, the user can easily deal with various complex conditions in daily operation and maintenance of the power system, the fault occurrence probability is reduced, and the user experience is improved. And normal and stable operation of a power system is ensured.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an electric load simulation training method, equipment and storage medium. Background Art

[0002] With the changes in energy structure and the development of the electricity market, new power systems are constantly renovating and expanding existing power equipment, as well as adding new power equipment to adapt to a more complex and dynamic operating environment.

[0003] During the renovation and construction of new power loads, failures often occur due to the technicians' low familiarity with the new business. Summary of the invention

[0004] In view of this, the present invention provides a power load simulation training method, device and storage medium, which are used to provide simulation training for new loads, thereby reducing the probability of failure.

[0005] A first aspect of the present invention provides a power load simulation training method, which is applied to a power load management terminal, and the method comprises:

[0006] When receiving a training task for a user, constructing a virtual operating environment for electrical equipment in the power system according to the training task;

[0007] Constructing simulated load data for the electrical equipment according to the training task;

[0008] When the simulated load data is loaded in the virtual operation environment, the operation of the electrical equipment is controlled according to the operation of the electrical equipment by the user, so as to collect the operation data generated by the electrical equipment;

[0009] The operating data is used to generate a training result for the user according to the training task.

[0010] A second aspect of the present invention provides a power load simulation training device, which is applied to a power load management terminal, and the device comprises:

[0011] A virtual operating environment building module, used for building a virtual operating environment for electrical equipment in the power system according to the training task when receiving a training task for the user;

[0012] A simulation load data construction module, used to construct simulation load data for the electrical equipment according to the training task;

[0013] An operation data acquisition module, used for controlling the operation of the electrical equipment according to the operation of the electrical equipment by the user when the simulated load data is loaded in the virtual operation environment, so as to collect the operation data generated by the electrical equipment;

[0014] A training result generating module is used to generate a training result for the user using the operation data according to the training task.

[0015] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power load simulation training method as described in the first aspect above.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the power load simulation training method as described in the first aspect above.

[0020] A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the power load simulation training method as described in the first aspect above is implemented.

[0021] In this embodiment, when a training task for a user is received, a virtual operating environment is constructed for the electrical equipment in the power system according to the training task; simulated load data is constructed for the electrical equipment according to the training task; when the simulated load data is loaded in the virtual operating environment, the operation of the electrical equipment is controlled according to the user's operation of the electrical equipment to collect the operating data generated by the electrical equipment; and the operating data is used to generate training results for the user according to the training task. This embodiment can simulate the entire process of the layout and operation of the power system, provide targeted training based on the functional level of the adaptive user, effectively improve the user's ability to face new types of power load management, facilitate users to calmly deal with various complex situations in the daily operation and maintenance of the power system, reduce the probability of failures, and ensure the normal and stable operation of the power system.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in 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.

[0024] Figure 1 It is a flow chart of a power load simulation training method provided in the first embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of an electric load simulation training device provided in Embodiment 2 of the present invention.

[0026] Figure 3 It is a structural schematic diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] See also Figure 1 , shows a flow chart of a power load simulation training method provided by the first embodiment of the present invention, the method can be executed by a power load simulation training device, the power load simulation training device can be implemented in the form of hardware and / or software, the power load simulation training device can be configured in an electronic device, especially applied to a power load management terminal. Figure 1As shown, the method includes:

[0031] Step 101: upon receiving a training task for a user, construct a virtual operating environment for electrical equipment in a power system according to the training task.

[0032] In this embodiment, users can be trained by means of power simulation experiments. One of the main purposes of power load simulation experiments is to reveal the inherent laws and influencing factors of load changes, and to provide scientific basis and decision support for power system dispatching, demand side management, and new energy access. Through power load simulation experiments, future load change trends can be predicted, power dispatch strategies can be optimized, and the stability and economy of the power system can be improved.

[0033] In scenarios such as users (such as technical engineers in power systems) practicing, appropriate training tasks can be generated for the user based on the user's skill information. When the training tasks generated for the user are received, simulation technology is used to build a virtual operating environment for the electrical equipment in the power system based on the training tasks.

[0034] In one embodiment of the present invention, step 101 may include the following steps:

[0035] Step 1011: Read the user's training goal in the training task.

[0036] In practical applications, multiple knowledge points can be set according to the new services of the new power system, and each knowledge point can be divided into training objectives at different levels. For example, the wiring and configuration of the new smart meter, the troubleshooting and repair of faults (such as communication failures, inaccurate metering, etc.),

[0037] Complex plan, etc.

[0038] When conducting simulation training on power load for users, appropriate training goals can be set for users according to their functions by using methods such as step-by-step progression, and the training goals can be written into the training tasks.

[0039] If you start a training task built for a user, you can read the training goals set for that user from the fields specified in the training task.

[0040] Generally speaking, the granularity of segmenting training objectives is small. Therefore, in the same training task, multiple training objectives can be set for users to improve the efficiency of training.

[0041] Step 1012: Identify various electrical equipment involved in the training target.

[0042] In this embodiment, a variety of key power equipment used to achieve each training goal may be entered in advance for each training goal.

[0043] For example, for smart meters, such as single-phase smart meters, three-phase smart meters, etc., in addition to smart meters, key power equipment such as concentrators and communication modules may also be involved.

[0044] If a training task constructed for a user is started, the various key power equipment involved can be queried according to the identifier (such as ID) of the training target of the training task.

[0045] Step 1013: Query multiple power grid models configured for the power system.

[0046] In this embodiment, some classic microgrids in the power system can be virtualized, and a highly simulated power grid model can be constructed in conjunction with a GIS (geographic information) system.

[0047] In actual applications, users do not provide maintenance, dispatching and other operations for a single power equipment, but for the entire power grid. Various power equipment will have different failures due to different factors in the operating environment (including the power system environment, natural environment, etc.).

[0048] Therefore, multiple power grid models configured for the power system can be queried, and a highly simulated virtual operating environment can be built for users based on the power grid models.

[0049] Step 1014: Calculate the fitness of each power grid model in the dimension covering multiple power equipment.

[0050] The same training task may involve multiple training objectives. Different training objectives involve different power equipment, which may have different distributions in different power grid models. Therefore, the fitness of each power grid model can be calculated in the dimension covering multiple power equipment. The higher the fitness, the more suitable the power grid model is for training the current user.

[0051] In a specific implementation, the first device weights of various power devices individually and the second device weights of the combination of at least two power devices are read in the training target. The combination of at least two power devices indicates that the at least two power devices are associated and affect each other during operation.

[0052] In each power grid model, a first number of each type of power equipment appearing alone and a second number of at least two types of power equipment appearing in combination (ie, co-occurrence) are counted respectively.

[0053] For each power grid model, the product of each first device weight and the first quantity is added to the product of each second device weight and the second quantity to serve as the fitness of the power grid model.

[0054] Step 1015: Select the power grid model with the highest fitness to build a virtual operating environment.

[0055] In this embodiment, the fitness of each power grid model can be compared, and the power grid model with the highest fitness can be selected as the power grid model most suitable for the training task, and a virtual operating environment can be constructed on this basis.

[0056] Step 102: construct simulated load data for the electrical equipment according to the training task.

[0057] In this embodiment, simulated load data may be constructed for the electrical equipment in the selected power grid model according to the requirements of the training task.

[0058] In general, the real historical load data of the microgrid can be directly selected as the simulation load data.

[0059] However, considering that the real historical load data of the microgrid is mostly the load data of normal operation, the probability of fault occurrence is low, which has little effect on user training. Moreover, the scope of the fault is small, which is easy for users to memorize as a question bank, reducing the effectiveness of training.

[0060] Therefore, interference can be added on the basis of the real historical load data of the microgrid to construct diversified simulated load data, increase the scope of faults and their probability of occurrence, and enhance the role of training.

[0061] In a specific implementation, the training level can be read in the training task; the training level is configured with the failure rates of various types of failures, and the training level is positively correlated with the failure rates of various types of failures, that is, the lower the training level, the lower the user requirements, and the lower the failure rates of various types of failures; conversely, the higher the training level, the higher the user requirements, and the higher the failure rates of various types of failures.

[0062] The microgrid where the grid model with the highest fitness is actually located is queried in the power system, so as to query the first historical load data generated when the microgrid is operating normally.

[0063] Load the load reconstruction network, where the load reconstruction network is usually a Seq2Seq (sequence to sequence) model, which can be trained using the load data generated during historical normal operation of each microgrid and the load data generated during historical failures, so that the load reconstruction network has the ability to reconstruct the load data during normal operation and failures.

[0064] The first label indicating normal operation and the first historical load data are input into the load reconstruction network to generate the first predicted load data. Then, the first predicted load data is the normal operation of the microgrid.

[0065] Load data during operation.

[0066] The second label representing the fault (including the fault type) and the first historical load data are input into the load reconstruction network to generate second predicted load data. Then, the second predicted load data is the load data when the microgrid fails.

[0067] Generally speaking, the fault tolerance of load data during normal operation of the microgrid is high and can accommodate some random disturbances. Therefore, the first predicted load data is not screened. When a microgrid fails, there will be certain mechanisms and the fault tolerance to random disturbances is low. Therefore, the second predicted load data can be screened.

[0068] At this time, the second historical load data generated when the microgrid fails is queried, and the correlation coefficient between the second predicted load data and the second historical load data is calculated using algorithms such as Pearson.

[0069] If the correlation coefficient is greater than or equal to a preset correlation threshold, it means that the second predicted load data is relatively close to the second historical load data, and the second predicted load data is retained.

[0070] If the correlation coefficient is less than a preset correlation threshold, it means that the second predicted load data is significantly different from the second historical load data, and the second predicted load data is then filtered out.

[0071] When the number of the first predicted load data and the number of the second predicted load data are both sufficient, the first predicted load data and the second predicted load data are selected by random method or the like, and the first predicted load data and the second predicted load data that meet the failure rate are combined into simulation load data.

[0072] Step 103 : When the simulated load data is loaded in the virtual operation environment, the operation of the electrical equipment is controlled according to the user's operation on the electrical equipment to collect the operation data generated by the electrical equipment.

[0073] The simulated load data is loaded in the virtual operating environment to simulate the scenario of the microgrid running with load. At this time, some normal scheduling operations may occur, and some faults may also occur. Then, the user operates the simulated electrical equipment in the virtual operating environment according to the scenario occurring in the virtual operating environment, and controls the operation of the simulated electrical equipment in the virtual environment according to the operation, thereby collecting the operating data generated by the simulated electrical equipment.

[0074] In the specific implementation, the power load management terminal provides hardware operations to better interact with the virtual operating environment and provide users with a more realistic training scenario.

[0075] In some cases, the power load management terminal transmits control instructions to the branch device in the virtual operating environment, receives status information such as the power quantity and circuit breaker position collected by the branch device through the 485 interface, monitors the control loop breakage, and the branch device executes after receiving the control command, collects the remote control signal of the circuit breaker and the real-time power energy data of the branch, and transmits the relevant operating data to the power load management terminal through the 485 interface.

[0076] In some cases, a circuit board module specifically for simulating faults is installed on the power load management terminal. The fault circuit board module is provided with circuit boards for simulating various types of node faults, and is used to simulate various fault scenarios in conjunction with virtual operation scenarios.

[0077] In some cases, a virtual load power supply module is installed on the power load management terminal, and a digital signal processor is used to generate a standard sinusoidal wave signal, which is split into a three-phase AC signal. The three-phase AC signal is sent to a power amplifier for amplification, and the amplified three-phase AC signal is provided to the load with the amplitude and power required, so as to simulate various normal operating scenarios in conjunction with the virtual operating scenarios.

[0078] In this embodiment, a master station may not be configured in the microgrid. In order to ensure the stable and accurate operation of the virtual operating environment, a simulation master station can be constructed in the virtual operating environment. The simulation master station is constructed using hardware and / or software. The simulation master station can establish a local area network with the power load management terminal using wireless (including carrier) or wired methods.

[0079] The operation data is transmitted to the simulation master station to verify the operation data and realize the control cycle.

[0080] Generally, a symmetric key function is used to encrypt and package the running data. When the simulation master station receives the running data, it unpacks and decrypts the running data, restores it to a readable format and stores it in the MySQL database to ensure the security and integrity of the running data and prevent it from being tampered with or eavesdropped during transmission.

[0081] Step 104: Generate training results for users using the operating data according to the training tasks.

[0082] When the conditions such as reaching the training time specified in the training task are met, the training task is completed. At this time, the operating data collected during the training can be used to generate training results for users according to the requirements of the training task for users to browse and to rate users.

[0083] In one embodiment of the present invention, step 104 may include the following steps:

[0084] Step 1041: Read the training indicators set in multiple dimensions from the training task.

[0085] In this embodiment, training indicators set in multiple dimensions can be read in the training task. For example, for smart meters, the training indicators include the accuracy of wiring, the accuracy of configuration parameters, the accuracy of troubleshooting, the troubleshooting time, and so on.

[0086] Generally speaking, training objectives and training indicators are matched.

[0087] Step 1042: Calculate operation indicators in multiple dimensions using the operation data.

[0088] In this embodiment, parameters related to each dimension may be parsed from the operating data, and the parameters may be substituted into the functions configured for each dimension (such as weighted sum, etc.) to calculate the operating index.

[0089] Step 1043: compare the training indicators with the operation indicators according to the differences between the training indicators and the operation indicators.

[0090] The training score is calculated for each user.

[0091] In this embodiment, for the same dimension, the training indicator and the operating indicator under the dimension can be compared to obtain the difference between the two, so as to calculate the training score for the user using the difference between the training indicator and the operating indicator under multiple dimensions, and quantify the effect of user training.

[0092] Exemplarily, for the same dimension, the ratio between the operating indicator and the training indicator is calculated to obtain the indicator ratio, and the indicator ratio is multiplied by the preset indicator weight for the dimension to obtain the candidate score, that is, the candidate score is the product of the indicator ratio and the preset indicator weight.

[0093] For the same dimension, the maximum value between the candidate score and the preset upper limit value for the dimension is taken as the indicator score.

[0094] Add up the candidate scores under multiple dimensions to get the user's training score.

[0095] Step 1044: Write the training score into the training result generated for the user.

[0096] In this embodiment, the performance information of the user in this training can be written into the training template, especially the training score can be written into the training template, so as to generate the training result.

[0097] In this embodiment, when a training task for a user is received, a virtual operating environment is constructed for the electrical equipment in the power system according to the training task; simulated load data is constructed for the electrical equipment according to the training task; when the simulated load data is loaded in the virtual operating environment, the operation of the electrical equipment is controlled according to the user's operation of the electrical equipment to collect the operating data generated by the electrical equipment; and the operating data is used to generate training results for the user according to the training task. This embodiment can simulate the entire process of the layout and operation of the power system, provide targeted training based on the functional level of the adaptive user, effectively improve the user's ability to face new types of power load management, facilitate users to calmly deal with various complex situations in the daily operation and maintenance of the power system, reduce the probability of failures, and ensure the normal and stable operation of the power system.

[0098] Embodiment 2

[0099] See also Figure 2 , showing a schematic diagram of the structure of a power load simulation training device provided by the second embodiment of the present invention. Applied to power load management terminals, such as Figure 2 As shown, the device comprises:

[0100] A virtual operating environment building module 201 is used to build a virtual operating environment for electrical equipment in the power system according to the training task when receiving a training task for the user;

[0101] A simulation load data construction module 202 is used to construct simulation load data for the electrical equipment according to the training task;

[0102] An operation data collection module 203 is used to control the operation of the electrical device according to the user's operation on the electrical device when the simulated load data is loaded in the virtual operation environment, so as to collect the operation data generated by the electrical device;

[0103] The training result generating module 204 is configured to generate a training result for the user using the operation data according to the training task.

[0104] In one embodiment of the present invention, the virtual operating environment construction module 201 includes:

[0105] A training target reading module, used for reading the training target of the user in the training task;

[0106] An electric power equipment identification module, used to identify various electric power equipment involved in the training target;

[0107] A power grid model query module, used to query multiple power grid models configured for the power system;

[0108] A fitness calculation module, used for calculating the fitness of each of the power grid models in a dimension covering a plurality of the power equipment;

[0109] The power grid model construction module is used to select the power grid model with the highest fitness to construct a virtual operating environment.

[0110] In one embodiment of the present invention, the fitness calculation module includes:

[0111] A weight reading module, used for reading in the training target the first device weights of various power devices individually and the second device weights of the combination of at least two of the power devices;

[0112] A quantity statistics module, used for respectively counting the first quantity of each of the power equipment appearing alone and the second quantity of at least two of the power equipment appearing in combination in each of the power grid models;

[0113] A weighted summation module is used to add, for each of the power grid models, the product of the weight of each of the first devices and the first quantity, and the product of the weight of each of the second devices and the second quantity, as the fitness of the power grid model.

[0114] In one embodiment of the present invention, the simulation load data construction module 202 includes:

[0115] A training level reading module, used to read the training level in the training task; the training level is configured with failure rates of various types of failures;

[0116] A microgrid query module, used for querying the microgrid in which the grid model with the highest fitness actually resides in the power system;

[0117] A first historical load data query module, used to query first historical load data generated when the microgrid is operating normally;

[0118] A load reconstruction network loading module is used to load the load reconstruction network;

[0119] A first predicted load data generating module, configured to input a first label indicating normality and the first historical load data into the load reconstruction network to generate first predicted load data;

[0120] The first predicted load data generating module is used to associate a second tag indicating a fault with the

[0121] The first historical load data is input into the load reconstruction network to generate second predicted load data;

[0122] The simulation load data composition module is used to compose the first predicted load data and the second predicted load data that meet the failure rate into simulation load data.

[0123] In one embodiment of the present invention, the simulation load data construction module 202 further includes:

[0124] A second historical load data query module, used to query second historical load data generated when a fault occurs in the microgrid;

[0125] A correlation coefficient calculation module, used for calculating the correlation coefficient between the second predicted load data and the second historical load data;

[0126] a second predicted load data retaining module, configured to retain the second predicted load data if the correlation coefficient is greater than or equal to a preset correlation threshold;

[0127] The second predicted load data filtering module is used to filter out the second predicted load data if the correlation coefficient is less than a preset correlation threshold.

[0128] In one embodiment of the present invention, the training result generating module 204 includes:

[0129] A training indicator reading module, used to read the training indicators set in multiple dimensions from the training task;

[0130] An operation index calculation module, used to calculate the operation index in multiple dimensions using the operation data;

[0131] A training score calculation module, used for calculating a training score for the user according to the difference between the plurality of training indicators and the plurality of operation indicators;

[0132] The training score writing module is used to write the training score into the training result generated for the user.

[0133] In one embodiment of the present invention, the training score calculation module includes:

[0134] An indicator ratio calculation module, used for calculating the ratio between the operation indicator and the training indicator for the same dimension to obtain the indicator ratio;

[0135] An indicator score calculation module is used to take the maximum value between the candidate score and the preset upper limit value for the same dimension as the indicator score; the candidate score is the product of the indicator ratio and the preset indicator weight;

[0136] The training score generating module is used to add the candidate scores under multiple dimensions to obtain the training score of the user.

[0137] In one embodiment of the present invention, it also includes:

[0138] A simulation master station construction module, used to construct a simulation master station in the virtual operating environment;

[0139] The operation data verification module is used to transmit the operation data to the simulation master station to verify the operation data.

[0140] The electric load simulation training device provided in the embodiment of the present invention can execute the electric load simulation training method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the electric load simulation training method.

[0141] Embodiment 3

[0142] See also Figure 3 , shows a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the invention described and / or required herein.

[0143] Implementation of the present invention.

[0144] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0145] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0146] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the power load simulation training method.

[0147] In some embodiments, the power load simulation training method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power load simulation training method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the power load simulation training method in any other appropriate manner (e.g., by means of firmware).

[0148] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0149] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0150] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0151] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0152] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0153] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0154] Embodiment 4

[0155] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the power load simulation training method provided by any embodiment of the present invention is implemented.

[0156] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0157] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0158] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A power load simulation training method, characterized in that: Applied to a power load management terminal, the method comprises: When receiving a training task for a user, constructing a virtual operating environment for electrical equipment in the power system according to the training task; Constructing simulated load data for the electrical equipment according to the training task; When the simulated load data is loaded in the virtual operation environment, the operation of the electrical equipment is controlled according to the operation of the electrical equipment by the user, so as to collect the operation data generated by the electrical equipment; The operating data is used to generate a training result for the user according to the training task.

2. The method according to claim 1, characterized in that The step of constructing a virtual operating environment for power equipment in the power system according to the training task includes: Reading the training goal of the user in the training task; Identify the various electrical equipment involved in the stated training objectives; query multiple grid models configured for the power system; Calculating the fitness of each of the power grid models in dimensions covering a variety of the power equipment; The power grid model with the highest fitness is selected to construct a virtual operating environment.

3. The method according to claim 2, characterized in that The calculating the fitness of each of the power grid models in the dimension covering a plurality of the power equipment includes: Reading in the training target a first device weight of each of the power devices individually and a second device weight of a combination of at least two of the power devices; In each of the power grid models, respectively counting a first number of each type of power equipment appearing alone and a second number of at least two types of power equipment appearing in combination; For each of the power grid models, the product of each of the first device weights and the first quantity is added to the product of each of the second device weights and the second quantity as the fitness of the power grid model.

4. The method according to claim 2, characterized in that: The constructing simulated load data for the power equipment according to the training task includes: reading a training level in the training task; the training level is configured with failure rates of various types of failures; Querying the microgrid in which the grid model with the highest fitness actually resides in the power system; Querying first historical load data generated when the microgrid is operating normally; Loading reconfiguration network; Inputting a first label indicating normality and the first historical load data into the load reconstruction network to generate first predicted load data; Inputting a second tag representing a fault and the first historical load data into the load reconstruction network to generate second predicted load data; The first predicted load data and the second predicted load data that satisfy the failure rate are combined into simulation load data.

5. The method according to claim 4, characterized in that The step of constructing simulated load data for the power equipment in the virtual operating environment according to the training task also includes: Querying second historical load data generated when a fault occurs in the microgrid; calculating a correlation coefficient between the second predicted load data and the second historical load data; If the correlation coefficient is greater than or equal to a preset correlation threshold, retaining the second predicted load data; If the correlation coefficient is less than a preset correlation threshold, the second predicted load data is filtered out.

6. The method according to any one of claims 1 to 5, characterized in that Generating a training result for the user using the operation data according to the training task includes: Reading training indicators set under multiple dimensions from the training task; Calculating operating indicators in multiple dimensions using the operating data; Calculating a training score for the user according to the difference between the plurality of training indicators and the plurality of operating indicators; The training score is written into the training result generated for the user.

7. The method according to claim 6, characterized in that The calculating the training score for the user according to the difference between the plurality of the training indicators and the plurality of the operation indicators comprises: For the same dimension, the ratio between the operation index and the training index is calculated to obtain an index ratio; For the same dimension, the maximum value between the candidate score and the preset upper limit value is taken as the index score; the candidate score is the product of the index ratio and the preset index weight; The candidate scores under multiple dimensions are added together to obtain the training score of the user.

8. The method according to any one of claims 1 to 5, characterized in that Also includes: Constructing a simulation master station in the virtual operating environment; The operation data is transmitted to the simulation master station to verify the operation data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power load simulation training method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the power load simulation training method according to any one of claims 1 to 8 is implemented.

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