CN115034769A - Power information generation method and device, electronic equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a power information generation method, a power information generation device, an electronic device and a computer readable medium. One embodiment of the method comprises: determining a climate information sequence set; resampling each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtaining a resampled climate information sequence set; carrying out abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, and obtaining an adjusted climate information sequence set; filtering and denoising each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, and obtaining a processed climate information sequence set; generating a target power information generation model group; a power information set is generated. This embodiment may provide for efficiency of power information generation.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a power information generation method, a power information generation device, electronic equipment and a computer readable medium.
Background
The power information generation method is widely applied to the power industry. At present, when generating power information, the following methods are generally adopted: and acquiring data required by power information generation, and generating power information by using a pre-trained model.
However, the inventors have found that when the power information generation is performed in the above manner, there are often technical problems as follows:
firstly, the model trained in advance cannot be dynamically adjusted and optimized according to requirements, so that models meeting different requirements need to be trained, and even if the models meet different requirements, the models need to be retrained if the requirements are temporarily adjusted, so that the efficiency of generating the power information is reduced;
second, the power information generation model suitable for the customer cannot be customized according to the customer's needs, thereby resulting in a reduction in the accuracy of the power information generated from the model.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art in this country.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose a power information generation method, apparatus, electronic device and computer readable medium to solve the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a power information generation method, including: determining a climate information sequence set in response to determining that no power information generation model matching the preset model configuration information exists in the preset power information generation model list; resampling each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtaining a resampled climate information sequence set; performing abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, and obtaining an adjusted climate information sequence set; filtering and denoising each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, and obtaining a processed climate information sequence set; determining an initial power information generation model matched with the model configuration information based on the processed climate information sequence set, and performing model training on the initial power information generation model to generate a target power information generation model group; and generating a power information set based on the target power information generation model set, wherein each power information in the power information set comprises a generator power.
In a second aspect, some embodiments of the present disclosure provide an apparatus for generating power information, the apparatus comprising: a determination unit configured to determine a climate information sequence set in response to determining that there is no power information generation model matching the preset model configuration information in the preset power information generation model list; the resampling unit is configured to resample each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtain a resampled climate information sequence set; the adjustment processing unit is configured to perform abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, and obtain an adjusted climate information sequence set; a noise reduction processing unit configured to perform filtering and noise reduction processing on each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, so as to obtain a processed climate information sequence set; a determining and training unit configured to determine an initial power information generation model matching the model configuration information based on the processed climate information sequence set, and perform model training on the initial power information generation model to generate a target power information generation model group; and a generating unit configured to generate a power information set based on the target power information generation model set, wherein each power information in the power information set includes a generator power.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium on which a computer program is stored, wherein the program when executed by a processor implements the method described in any implementation of the first aspect.
In a fifth aspect, some embodiments of the present disclosure provide a computer program product comprising a computer program that, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantages: by the power information generation method of some embodiments of the present disclosure, the efficiency of generating power information generation may be improved. Specifically, the reason why the efficiency of generating the electric power information is reduced is that: the inability to dynamically adjust and optimize the pre-trained models as required results in the need to train models that meet different requirements, even though the models need to be retrained if the requirements are adjusted temporarily. Based on this, the power information generation method of some embodiments of the present disclosure first determines a climate information sequence set in response to determining that there is no power information generation model matching preset model configuration information in a preset power information generation model list. The climate information sequence set is introduced by considering that the pre-trained models (i.e., the individual power information generation models in the power information generation model list) do not comply with the demand for power information generation (i.e., there is no power information generation model matching the model configuration information). In this way, a power information generation model that can be used to train load demand can be generated. And then, resampling each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtaining a resampled climate information sequence set. And then, carrying out abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, and obtaining an adjusted climate information sequence set. And then, filtering and denoising each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, so as to obtain a processed climate information sequence set. In consideration of the situations of errors, errors and the like of the introduced climate information sequence, factors influencing model training in the climate information sequence are greatly removed through modes of resampling, abnormal point adjustment processing, filtering and noise reduction processing and the like. Then, based on the processed climate information sequence set, an initial power information generation model matched with the model configuration information is determined, and model training is carried out on the initial power information generation model to generate a target power information generation model group. And a large number of factors influencing the model training efficiency are removed, so that the model can be converged more quickly, and the model training efficiency is improved. Therefore, a model that meets the power information generation requirement can be obtained. Finally, a power information set is generated based on the target power information generation model set, wherein each power information in the power information set comprises a generator power. Therefore, the trained model can be used for generating the power information more efficiently. Therefore, the dynamic adjustment and optimization of the pre-trained model according to the requirements are realized. Further, the efficiency of power information generation can be improved.
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The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
Fig. 1 is a flow diagram of some embodiments of a power information generation method according to the present disclosure;
fig. 2 is a schematic block diagram of some embodiments of a power information generation apparatus according to the present disclosure;
FIG. 3 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates a flow 100 of some embodiments of a power information generation method according to the present disclosure. The power information generation method comprises the following steps:
In some embodiments, the executive agent of the power information generation method may determine the climate information sequence set in response to determining that no power information generation model matching the preset model configuration information exists in the preset power information generation model list. Each power information generation model in the power information generation model list may be a model trained in advance. The model configuration information may be information uploaded by the user for selecting the power information generation model required by the user. Each climate information sequence in the set of climate information sequences may be an information sequence of a certain climate type, and each climate information in the climate information sequences may correspond to consecutive time points, and the time intervals between each time point are the same. For example, the time point corresponding to the first climate information in the climate information sequence is 1 point, the time point corresponding to the second climate information is 2 points, the time point corresponding to the third climate information is 3 points, and the like. The climate information may be information of a weather environment. The climate type may include, but is not limited to, at least one of: temperature type, humidity type, wind speed type, etc.
In some optional implementations of some embodiments, each climate information in each climate information sequence in the set of climate information sequences includes at least one of: temperature value, humidity value, wind speed value, etc. And the executing body determines the climate information sequence set, and may include the following steps:
the method comprises the steps of firstly, receiving a climate information sequence group. The climate information sequence group uploaded by the user through the user terminal can be received in a wired or wireless mode. Here, the individual climate information in each climate information sequence in the set of climate information sequences may also correspond to successive time points, and the time intervals between the individual time points may be the same. In addition, the amount of the received climate information data in the climate information series group may be insufficient. Thus, climate information may be obtained from the database.
And secondly, acquiring a climate information sequence matched with the climate information sequence group from a preset database to obtain a matched climate information sequence group. The matching may be that the continuous time points corresponding to each climate information in the climate information sequence and the continuous time points corresponding to each matching climate information in the matching climate information sequence are continuous. For example, three pieces of climate information are included in the climate information sequence, and the continuous time points corresponding to the climate information may be 1 point, 2 points, and 3 points. The matching climate information sequence comprises two pieces of matching climate information, and the continuous time points corresponding to the matching climate information can be 4 points and 5 points.
And thirdly, combining the matching climate information sequence group and the climate information sequence group to generate a climate information sequence set. The combination processing may be to add each piece of matching climate information in each matching climate information sequence in the matching climate information sequence group as climate information to the corresponding climate information sequence. Here, it may correspond to the climate information sequence and the matching climate information sequence characterizing the same climate type.
And 102, resampling each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtaining a resampled climate information sequence set.
In some embodiments, the execution subject may perform resampling processing on each piece of climate information in each climate information sequence in the set of climate information sequences to generate a resampled climate information sequence, resulting in a set of resampled climate information sequences.
In some optional implementations of some embodiments, the performing main body performs resampling processing on each piece of climate information in each climate information sequence in the set of climate information sequences to generate a resampled climate information sequence, and may include the following steps:
and performing resampling processing on each climate information in each climate information sequence in the climate information sequence set based on a preset time interval to generate a resampled climate information sequence. The resampling process may be to adjust a time interval between time points corresponding to the resampling climate information in the resampling climate information sequence to the preset time interval. Here, if the time interval between the time points corresponding to the two pieces of climate information in the climate information sequence is greater than the time interval, resampling processing may be performed on every two pieces of adjacent climate information in the climate information sequence through a preset resampling algorithm, so as to generate the sampled climate information. And finally, the sampled climate information and climate information can be used as resampling climate information, and the resampling climate information sequence can be obtained by arranging according to the sequence of time points.
As an example, the sequence of climate information may be: [ point 1: temperature 30 degrees, 2 points: temperature 32 degrees ]. The preset time interval may be 0.5 hours. Then, the resampled climate information obtained by the resampling algorithm may be: [1 point and half: temperature 31 deg.c ]. Thus, the resulting resampled climate information sequence may be: [ point 1: temperature 30 degrees, 1 point and half: temperature 31 degrees, 2 points: temperature 32 degrees ]. Additionally, the resampling algorithm described above may include, but is not limited to, at least one of: nearest neighbor interpolation, cubic spline interpolation, etc.
In practice, the data density can be varied by resampling. Thus, data required by the user can be obtained. In addition, the data density may be increased by a difference method or may be decreased by averaging a plurality of data.
And 103, performing filtering and noise reduction processing on each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, so as to obtain a processed climate information sequence set.
In some embodiments, the executing body may perform filtering and denoising processing on each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, so as to obtain a processed climate information sequence set.
Here, noise reduction by filtering may be used to smooth the data.
In some optional implementation manners of some embodiments, the performing main body may perform filtering and denoising processing on each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, and may include the following steps:
and step one, carrying out abnormity detection on each resampled climate information in the resampled climate information sequence to obtain an abnormal climate information group. The abnormal climate information in the abnormal climate information group may be abnormal resampled climate information in the resampled climate information sequence. And carrying out anomaly detection on each resampled climate information in the resampled climate information sequence through a preset anomaly detection algorithm to obtain an abnormal climate information group. Secondly, the above-mentioned abnormality may refer to a geographical group or a geographical group of the resampled climate information sequence
As an example, the anomaly detection algorithm may include, but is not limited to, at least one of: mean square error, clustering, isolated forest, etc.
And secondly, generating adjusted climate information by using two adjacent resampled climate information corresponding to each abnormal climate information in the abnormal climate information group to obtain an adjusted climate information sequence. The corresponding two adjacent resampled climate information may be two adjacent resampled climate information of the resampled climate information corresponding to the abnormal climate information. For each exceptional climate information: first, the target climate information may be generated by the above resampling algorithm using two adjacent resampled climate information. Then, the target climate information may be replaced with the abnormal climate information as adjusted climate information in the adjusted climate information sequence. Finally, the replaced adjusted climate information sequence may be determined as a processed climate information sequence.
And 104, performing filtering and noise reduction processing on each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, and obtaining a processed climate information sequence set.
In some embodiments, the executing body may perform filtering and noise reduction processing on each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, so as to obtain a processed climate information sequence set. And filtering and denoising each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set through a preset filtering and denoising algorithm to generate a processed climate information sequence.
As an example, the filtering noise reduction algorithm may include, but is not limited to, at least one of: a sliding window filtering algorithm, an amplitude limiting filtering algorithm, a median average filtering algorithm, an amplitude limiting average filtering algorithm, and the like.
And 105, determining an initial power information generation model matched with the model configuration information based on the processed climate information sequence set, and performing model training on the initial power information generation model to generate a target power information generation model group.
In some embodiments, the execution subject may determine an initial power information generation model matching the model configuration information based on the post-processing climate information sequence set, and perform model training on the initial power information generation model to generate a target power information generation model group.
In some optional implementations of some embodiments, the model configuration information includes a network model type, model structure data, and a model training parameter set. And the executing body may determine an initial power information generation model matching the model configuration information based on the processed climate information sequence set, and perform model training on the initial power information generation model to generate a target power information generation model group, including:
firstly, selecting a network model corresponding to the network model type from a preset network model set as a target network model. The preset network model set may be a set composed of different types of network models. The network model type may be used to uniquely identify the network model. Network model types may include, but are not limited to, at least one of: feed-forward neural networks, recurrent neural networks, symmetrically connected neural networks, and the like.
And secondly, determining the model structure of the target network model corresponding to the model structure data to generate an initial power information generation model. Wherein the model structure data includes: the network layer type group, the number of network layers corresponding to each network layer type in the network layer type group, and the number of nodes corresponding to each network layer. Here, the group of network layer types may be a collection of different network layer types. Network layer types may include, but are not limited to, at least one of the following: convolutional layers, pooling layers, active layers, fully-connected layers, and the like. The number of network layers may be the number of network layers. The number of nodes corresponding to the network layer may be the number of nodes of the hidden layer. In practice, model configuration information preset by a user is introduced to include a network model type, model structure data and a model training parameter group. The model required by the user can be flexibly configured, so that the deployment of the model can be quickly realized. A model suitable for the scene may thus be generated. This can improve the efficiency of power information generation.
And thirdly, performing model training on the initial power information generation model based on the model training parameter group and the processed climate information sequence set to generate a target power information generation model group. Wherein each model training parameter in the set of model training parameters may include: model training times, activation functions, model training data volume, learning rate, and the like. Each model training parameter in the set of model training parameters may be used to perform model training on the initial power information generation model to generate a target power information generation model. Here, the initial power information generation model may be trained by different model training parameters to obtain a target power information generation model group. The model training data volume may be a number of processed climate information in a sequence of processed climate information participating in model training.
The above steps and their related contents are regarded as an invention of the embodiment of the present disclosure, and further solve the technical problem mentioned in the background art that "the model trained in advance cannot be dynamically adjusted and optimized according to the requirement, which results in the need to train models meeting different requirements, even if the requirement is temporarily adjusted, the model needs to be retrained again, which results in the reduction of the efficiency of generating the power information". First, consider the situation where the temporary adjustment needs, leading to pre-trained failure to meet the requirements. Thus, model configuration information and the network model type, model structure data, model training parameter set, etc. included therein are introduced. Thereby, it is made possible to temporarily generate the initial power information generation model according to the demand of the user. In particular, the model structure data may further include: the network layer type group, the number of network layers corresponding to each network layer type in the network layer type group and the number of nodes corresponding to each network layer ensure that the initial power information generation model is generated quickly. Then, the model training parameters that can be introduced include: under the constraint of information such as model training times, activation functions, model training data volume, learning rate and the like, the convergence efficiency of the model can be further improved. Thus, a target power information generation model required by the user can be efficiently generated. Further, it can be used to improve the efficiency of generating the power information.
In some optional implementations of some embodiments, the performing main body performing model training on the initial power information generation model based on the set of model training parameters and the set of processed climate information sequences to generate a target power information generation model set may include:
firstly, each processed climate information in each processed climate information sequence in the processed climate information sequence set is normalized to generate a normalized climate information sequence, and a normalized climate information sequence set is obtained. The normalization processing can be carried out on each processed climate information in each processed climate information sequence through the following steps: first, the processed climate information including the largest data value may be selected from the respective processed climate information as the largest processed climate information and the processed climate information including the smallest data value may be selected as the smallest processed climate information. And then, carrying out normalization processing on data values included in each processed climate information through a normalization algorithm by utilizing the maximum processed climate information and the minimum processed climate information to obtain a normalized data value sequence serving as a normalized climate information sequence.
As an example, the data in the processed climate information may be a temperature value, a wind speed value, or a humidity value, etc. Different processed climate information sequences may correspond to different types of data.
And secondly, mapping each normalized climate information in each normalized climate information sequence in the normalized climate information sequence set to generate a mapped climate information sequence, and obtaining a mapped climate information sequence set. Among these, the mapping process may be to adjust the data name corresponding to each normalized climate information sequence (e.g., to adjust "temperature" to "target temperature") for input of the corresponding model. First, a mapping relationship table may be obtained from a database. The mapping table may include data names (e.g., temperatures) and target data names (e.g., target temperatures) corresponding to the data to be adjusted. The data name corresponding to each normalized climate information sequence may then be modified. And finally, the modified data name can be used as the name of the corresponding normalized climate information sequence to obtain a mapping climate information sequence.
Thirdly, for each model training parameter in the above model training parameter group, executing the following model training steps to generate a target power information generation model in the target power information generation model group:
the first substep, select a group of mapping climate information from the mapping climate information sequence set as training sample. The training sample may include a sample label, and the sample label may include a target power information value. Here, a set of mapping climate information corresponding to the same time point may be selected from the set of mapping climate information sequences as a training sample. The sample label may be one of the selected set of mapped climate information. Second, the target power information value may be a data value included in the mapped climate information representing the sample tag.
As an example, a set of mapper climate information sequences may include four mapper climate information sequences, and the corresponding data names may be: "temperature", "humidity", "wind speed" and "generator power". The mapping climate information sequence with the data name of "generator power" is a column of data of the sample label.
And a second substep, inputting the training sample to the initial power information generation model to obtain a power information result. Wherein the power information result may be a model generated generator power.
And a third substep of performing inverse normalization processing on the power information result based on a preset extreme value data set to obtain a power information value. The extremum data group can include maximum extremum data and minimum extremum data. The maximum extreme data and the minimum extreme data may be data values included in a post-processing climate information sequence named "power of generator", maximum post-processing climate information and minimum post-processing climate information selected when normalization processing is performed, respectively.
And a fourth substep of adjusting parameters in the initial power information generation model in response to determining that the difference between the power information value and the target power information value included in the sample tag does not satisfy a preset training condition, and performing the model training step again. The preset training condition may be that a difference between the power information value and a target power information value included in the sample tag is smaller than a preset threshold.
And a fifth substep of determining the initial power information generation model as the target power information generation model in response to determining that the difference between the power information value and the target power information value included in the sample tag satisfies the preset training condition.
And 106, generating a model group based on the target power information, and generating a power information group.
In some embodiments, the execution agent may generate the power information set based on the target power information generation model set. Wherein, each power information in the power information group may include generator power. First, a climate information set of the climate at the power information generation time point may be acquired at a preset power information generation time point. The power information generation time point may be a time preset by a user at which the model is generated using the target power information. Each historical climate information in the historical climate information set may be information of a different climate type. The historical climate information may include, but is not limited to, at least one of: temperature value, wind speed value or humidity value, etc. Then, each climate information in the historical climate information set may be input to each target power information generation model in the target power information generation model set to generate power information, resulting in a power information set.
Optionally, the executing main body may further perform the following steps:
and generating a power information group based on the matched power information generation model in response to determining that the power information generation model matched with the model configuration information exists in the preset power information generation model list. The climate information in the historical climate information set may be input into each of the matched power information generation models to generate power information, so as to obtain a power information set.
Optionally, the executing body may further execute the following steps:
in the first step, power information satisfying a preset screening condition is selected from the power information in the power information group as target power information. The preset screening condition may be that the generator power included in the power information group is in a preset power interval.
And a second step of storing a target power information generation model corresponding to the target power information. The target power information generation model corresponding to the target power information may be stored in the power information generation model list for subsequent use.
The above steps and their related contents are an inventive point of the embodiments of the present disclosure, and solve the second technical problem mentioned in the background art that "the power information generation model suitable for the user cannot be customized according to the user's requirement, thereby causing the accuracy of the power information generated according to the model to be reduced". Firstly, through normalization processing, the processed climate information sequence set can be processed into a normalized climate information sequence set which meets the requirements of users. Then, it is considered that data sources of different users are different, and there is a case where the data cannot be input to the model for training. Therefore, the mapping relation table is introduced and can be used for adjusting the data name, so that the data input model is convenient to train. Then, under the constraint of information such as the number of times of model training, activation function, model training data volume, learning rate, etc. set by the user himself, the convergence efficiency of the model can be further improved. And then, through the inverse normalization processing, whether the model is trained or not can be conveniently determined according to the power information value. Finally, the power information meeting the preset screening condition is selected from the power information in the power information group, so that the method can be used for selecting the target power information generation model with high model accuracy from the target power information generation model group. Thus, the accuracy of the power information generated using the model can be further improved.
The above embodiments of the present disclosure have the following beneficial effects: by the power information generation method of some embodiments of the present disclosure, the efficiency of generating power information generation may be improved. Specifically, the reason why the efficiency of generating the electric power information is reduced is that: the inability to dynamically adjust and optimize the pre-trained models as required results in the need to train models that meet different requirements, even though the models need to be retrained if the requirements are adjusted temporarily. Based on this, the power information generation method of some embodiments of the present disclosure first determines a climate information sequence set in response to determining that there is no power information generation model matching preset model configuration information in a preset power information generation model list. The climate information sequence set is introduced by considering that the pre-trained models (i.e., each power information generation model in the power information generation model list) do not comply with the demand for power information generation (i.e., there is no power information generation model matching the model configuration information). In this way, a power information generation model that can be used to train load demand can be generated. And then, resampling each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtaining a resampled climate information sequence set. And then, carrying out abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, and obtaining an adjusted climate information sequence set. And then, filtering and denoising each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, so as to obtain a processed climate information sequence set. In consideration of the situations of errors, errors and the like of the introduced climate information sequence, factors influencing model training in the climate information sequence are greatly removed through modes of resampling, abnormal point adjustment processing, filtering and noise reduction processing and the like. Then, based on the processed climate information sequence set, an initial power information generation model matched with the model configuration information is determined, and model training is carried out on the initial power information generation model to generate a target power information generation model group. And because factors influencing the model training efficiency in the training data are removed, the model can be converged more quickly. And the efficiency of model training is improved. Therefore, a model that meets the power information generation requirement can be obtained. Finally, a power information set is generated based on the target power information generation model set, wherein each power information in the power information set comprises a generator power. Therefore, the trained model can be used for generating the power information more efficiently. Therefore, the dynamic adjustment and optimization of the pre-trained model according to the requirements are realized. Further, the efficiency of power information generation can be improved.
With further reference to fig. 2, as an implementation of the methods illustrated in the above figures, the present disclosure provides some embodiments of a power information generation apparatus, which correspond to those illustrated in fig. 1, and which may be particularly applicable in various electronic devices.
As shown in fig. 2, the power information generation apparatus 200 of some embodiments includes: a determination unit 201, a resampling unit 202, an adjustment processing unit 203, a noise reduction processing unit 204, a determination and training unit 205, and a generation unit 206. Wherein the determining unit 201 is configured to determine the climate information sequence set in response to determining that no power information generation model matching the preset model configuration information exists in the preset power information generation model list; a resampling unit 202, configured to perform resampling processing on each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, so as to obtain a resampled climate information sequence set; an adjusting processing unit 203, configured to perform abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, so as to obtain an adjusted climate information sequence set; a noise reduction processing unit 204 configured to perform filtering and noise reduction processing on each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, so as to obtain a processed climate information sequence set; a determining and training unit 205 configured to determine an initial power information generation model matching the model configuration information based on the processed climate information sequence set, and perform model training on the initial power information generation model to generate a target power information generation model group; a generating unit 206 configured to generate a power information set based on the target power information generation model set, wherein each power information in the power information set includes a generator power.
It will be understood that the units described in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 200 and the units included therein, and are not described herein again.
Referring now to FIG. 3, a block diagram of an electronic device 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, or the like; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device 300 to communicate wirelessly or by wire with other devices to exchange data. While fig. 3 illustrates an electronic device 300 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 3 may represent one device or may represent multiple devices, as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. The computer program, when executed by the processing apparatus 301, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with 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"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining a climate information sequence set in response to determining that no power information generation model matching the preset model configuration information exists in the preset power information generation model list; resampling each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtaining a resampled climate information sequence set; performing abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, and obtaining an adjusted climate information sequence set; filtering and denoising each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, and obtaining a processed climate information sequence set; determining an initial power information generation model matched with the model configuration information based on the processed climate information sequence set, and performing model training on the initial power information generation model to generate a target power information generation model group; and generating a power information set based on the target power information generation model set, wherein each power information in the power information set comprises a generator power.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may 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 the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted 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-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a determination unit, a resampling unit, an adjustment processing unit, a noise reduction processing unit, a determination and training unit, and a generation unit. Where the names of the units do not in some cases constitute a limitation of the unit itself, the determination unit may also be described as a "unit determining a set of sequences of climate information", for example.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A power information generation method, comprising:
determining a climate information sequence set in response to determining that no power information generation model matching the preset model configuration information exists in the preset power information generation model list;
resampling each climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtaining a resampled climate information sequence set;
performing abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, and obtaining an adjusted climate information sequence set;
filtering and denoising each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, and obtaining a processed climate information sequence set;
determining an initial power information generation model matched with the model configuration information based on the processed climate information sequence set, and performing model training on the initial power information generation model to generate a target power information generation model group;
generating a set of power information based on the set of target power information generation models, wherein each power information in the set of power information includes a generator power.
2. The method of claim 1, wherein the method further comprises:
in response to determining that there is a power information generation model in the list of power information generation models that matches the model configuration information, generating a set of power information based on the matched power information generation model.
3. The method of claim 1, wherein the method further comprises:
selecting power information meeting preset screening conditions from each piece of power information in the power information group as target power information;
and storing a target power information generation model corresponding to the target power information.
4. The method of claim 1, wherein the determining a set of climate information sequences comprises:
receiving a climate information sequence group;
acquiring a climate information sequence matched with the climate information sequence group from a preset database to obtain a matched climate information sequence group;
and combining the matching climate information sequence group and the climate information sequence group to generate a climate information sequence set.
5. The method of claim 1, wherein the resampling respective climate information in each of the set of sequences of climate information to generate a resampled sequence of climate information comprises:
and performing resampling processing on each piece of climate information in each climate information sequence in the climate information sequence set based on a preset time interval to generate a resampled climate information sequence.
6. The method of claim 1, wherein the performing anomaly adjustment processing on the respective resampled climate information in each of the set of resampled climate information sequences to generate an adjusted climate information sequence comprises:
performing anomaly detection on each resampled climate information in the resampled climate information sequence to obtain an abnormal climate information group, wherein the abnormal climate information in the abnormal climate information group is the abnormal resampled climate information in the resampled climate information sequence;
and generating adjusted climate information by using two adjacent resampled climate information corresponding to each abnormal climate information in the abnormal climate information group to obtain an adjusted climate information sequence.
7. A power information generation apparatus comprising:
a determination unit configured to determine a climate information sequence set in response to determining that there is no power information generation model matching the preset model configuration information in the preset power information generation model list;
the resampling unit is configured to resample each piece of climate information in each climate information sequence in the climate information sequence set to generate a resampled climate information sequence, and obtain a resampled climate information sequence set;
the adjustment processing unit is configured to perform abnormal point adjustment processing on each resampled climate information in each resampled climate information sequence in the resampled climate information sequence set to generate an adjusted climate information sequence, and obtain an adjusted climate information sequence set;
the noise reduction processing unit is configured to perform filtering noise reduction processing on each adjusted climate information in each adjusted climate information sequence in the adjusted climate information sequence set to generate a processed climate information sequence, so as to obtain a processed climate information sequence set;
a determining and training unit configured to determine an initial power information generation model matching the model configuration information based on the processed climate information sequence set, and perform model training on the initial power information generation model to generate a target power information generation model group;
a generating unit configured to generate a set of power information based on the set of target power information generation models, wherein each power information of the set of power information includes a generator power.
8. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-6.
9. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-6.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
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