CN113326897A - Temperature measurement plan generation method and device for overhead transmission line and electronic equipment - Google Patents
Temperature measurement plan generation method and device for overhead transmission line and electronic equipment Download PDFInfo
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Abstract
The application discloses a method and a device for generating a temperature measurement plan of an overhead transmission line and electronic equipment, wherein the method and the device are used for classifying lines of which the temperature measurement plan is to be generated according to load types; collecting historical data of a line of a target type to obtain sample data; performing model training based on sample data to obtain a load prediction model; generating a predicted line load value of future time by using a load prediction model; and generating a temperature measurement plan according to the predicted line load value. The temperature measurement plan is not made according to the manual experience of the operation and inspection personnel, so that the objectivity is high, and the effectiveness of temperature measurement work can be guaranteed when the temperature is measured according to the temperature measurement plan.
Description
Technical Field
The application relates to the technical field of electric power, in particular to a method and a device for generating a temperature measurement plan of an overhead power transmission new road.
Background
In overhead transmission line, when the wire presss from both sides tightly through gold utensil such as strain clamp, because technical factor such as crimping leads to the resistance of here bigger than other positions, therefore the too high emergence of circuit temperature mainly takes place in this position, can take place serious accidents such as disconnected strand even fracture when the temperature of here circuit is high to a certain extent.
Aiming at the problem of circuit heating, whether the circuit has an overheating condition is judged mainly by adopting an infrared temperature measurement mode at present. However, the line temperature is directly related to the line load, and if the highest value of the line temperature is to be measured, infrared temperature measurement needs to be performed at the peak of the line load, and the line load has greater fluctuation and difference along with the influence of factors such as load property, season, weather and the like. When infrared temperature measurement is carried out, if the line load is not in a peak period, the obtained measurement result cannot effectively analyze the operation condition of the equipment, so that hidden dangers cannot be found in time.
At present, the method commonly adopted in the industry is that a patrol worker carries an infrared thermal imager and carries out temperature measurement according to a temperature measurement plan planned in advance to the site. Because the line temperature is directly related to the line load, that is, the highest temperature of the line occurs in the load peak period, and the current temperature measurement plan is mainly made according to the operation experience, with the continuous development of the new energy industry in recent years, the target of 'carbon peak reaching and carbon neutralization' is provided, the occupation ratio of new energy in the power grid is higher and higher, and the change of the load is difficult to be correctly predicted and a correct and reasonable temperature measurement plan is made according to the manual experience, so that the effectiveness of the temperature measurement work cannot be ensured.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for generating a temperature measurement plan of an overhead transmission line, which are used to output a correct and reasonable temperature measurement plan so as to ensure validity of temperature measurement work.
In order to achieve the above object, the following solutions are proposed:
a temperature measurement plan generation method of an overhead transmission line is applied to electronic equipment and comprises the following steps:
classifying the lines of the temperature measurement plan to be generated according to the load types;
collecting historical data of the line of the target type to obtain sample data;
performing model training based on the sample data to obtain a load prediction model;
generating a predicted line load value at a future time by using the load prediction model;
and generating a temperature measurement plan according to the predicted line load value.
Optionally, the classifying the line to be generated with the temperature measurement plan according to the load type includes:
classifying the line where the traditional power consumer is located into a type of line;
classifying the line where the railway user and the special industrial area are located into two types of lines;
the line where the new energy is located is classified into three types of lines.
Optionally, the acquiring historical data of the line of the target type to obtain sample data includes:
extracting historical data and storing the historical data in a preset format, wherein the historical data comprises a plurality of elements, and the elements comprise time, historical highest load, average load and real-time load.
And carrying out normalization processing on the historical data by adopting a supervised learning method to obtain the sample data.
Optionally, the performing model training based on the sample data to obtain a load prediction model includes:
performing model training based on a shallow neural network and the sample data to obtain a load prediction model for the line of the same type;
performing model training based on a deeper neural network and the sample data to obtain a load prediction model for the two types of lines;
and performing model training based on a deep neural network and the sample data to obtain a load prediction model for the three routes.
Optionally, the generating a temperature measurement plan according to the predicted line load value includes:
and comparing the predicted value of the line load with the historical highest load of the line, and generating the temperature measurement plan according to the comparison result.
A temperature measurement plan generation device of an overhead transmission line is applied to electronic equipment, and comprises:
the classification processing module is configured to classify the lines of the temperature measurement plan to be generated according to the load types;
the data acquisition module is configured to acquire historical data of the line of the target type to obtain sample data;
the model training module is configured to perform model training based on the sample data to obtain a load prediction model;
a prediction execution module configured to generate a line load prediction value for a future time using the load prediction model;
and the plan generating module is configured to generate a temperature measuring plan according to the predicted line load value.
Optionally, the classification processing module is configured to classify the line where the traditional power consumer is located into a type of line; classifying the line where the railway user and the special industrial area are located into two types of lines; the line where the new energy is located is classified into three types of lines.
Optionally, the data acquisition module includes:
the data extraction unit is configured to extract historical data and store the historical data in a preset format, wherein the historical data comprises a plurality of elements, and the elements comprise time, historical highest load, average load and real-time load.
And the normalization processing unit is configured to perform normalization processing on the historical data by adopting a supervised learning method to obtain the sample data.
Optionally, the model training module includes:
the first training unit is used for carrying out model training based on a shallow neural network and the sample data to obtain a load prediction model aiming at the line of the same type;
the second training unit is used for carrying out model training based on a deeper neural network and the sample data to obtain a load prediction model aiming at the two lines;
and the third training unit is used for carrying out model training based on a deep neural network and the sample data to obtain a load prediction model aiming at the three routes.
Optionally, the plan generating module is configured to compare the predicted line load value with the historical highest load of the line, and generate the temperature measurement plan according to the comparison result.
An electronic device comprising at least one processor and a memory coupled to the processor, wherein:
the memory is for storing a computer program or instructions;
the processor is configured to execute the computer program or the instructions to cause the electronic device to execute the thermometry plan generating method described above.
According to the technical scheme, the method and the device for generating the temperature measurement plan of the overhead transmission line and the electronic equipment are specifically used for classifying the lines of the temperature measurement plan to be generated according to the load types; collecting historical data of a line of a target type to obtain sample data; performing model training based on sample data to obtain a load prediction model; generating a predicted line load value of future time by using a load prediction model; and generating a temperature measurement plan according to the predicted line load value. The temperature measurement plan is not made according to the manual experience of the operation and inspection personnel, so that the objectivity is high, and the effectiveness of temperature measurement work can be guaranteed when the temperature is measured according to the temperature measurement plan.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for generating a temperature measurement plan of an overhead line according to an embodiment of the present disclosure;
fig. 2 is a block diagram of a temperature measurement plan generation apparatus for an overhead line according to an embodiment of the present disclosure;
fig. 3 is a block diagram of another temperature measurement plan generation apparatus for an overhead line according to an embodiment of the present disclosure;
fig. 4 is a block diagram of a temperature measurement plan generation apparatus for an overhead line according to another embodiment of the present disclosure;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
Fig. 1 is a flowchart of a method for generating a temperature measurement plan of an overhead line according to an embodiment of the present application.
As shown in fig. 1, the temperature measurement plan generating method provided in this embodiment is applied to an electronic device, such as a computer or a server, having an information processing capability or a data processing capability, and includes the following steps:
and S1, classifying the lines according to the load types.
The method aims at more than one line needing to generate the temperature measurement plan, and therefore, one important characteristic of the method is to classify all lines needing to generate the temperature measurement plan. Specifically, the method classifies the load type, the load rule and the nonlinearity degree of the line according to the load type, the load rule and the nonlinearity degree of the line.
For the line where the common user is located, because the electricity utilization behavior of the user has obvious regularity, the regularity is relatively easy to calculate, and the line is classified into a line type; for special user lines such as electric railways, high-speed railways, special industrial areas and the like, the load rules have certain complexity and are classified into two types of lines; for lines with new energy such as photovoltaic energy, wind power energy and the like, the load rule is relatively more complex, and the lines are classified into three types.
And S2, collecting historical data of the line of the target type to obtain sample data.
The target type is a type of a line to be planned and generated, that is, historical data acquisition is performed on a specific type of line, so as to obtain sample data for training a corresponding model, where the sample data includes a training set and a test set.
Specifically, firstly, historical data received by a load curve is extracted and stored in a csv form, and the element names of the historical data are time, historical highest load, average load corresponding to time and real-time load;
then, normalization processing is carried out on the manufactured data set by adopting a supervised learning method, so that normalized sample data is obtained. In the specific implementation, firstly, a series _ to _ super () function is defined, and then, the multivariate time series data frame is converted into a data frame suitable for supervised learning.
And S3, performing model training based on the sample data to obtain a load prediction model.
On the basis of obtaining the sample data, the sample data is distributed into a training set and a testing set according to a certain proportion, the proportion of the training set to the testing set can be 8:2, and then the training set is used for training the neural network, so that a load prediction model is obtained.
When the model training is specifically implemented, aiming at a line of the same type, a shallow neural network can be used for calculation, and for example, a 3-layer LSTM network structure +1 full-connection layer +1 output layer can be set; aiming at the second line, a deeper neural network is used for calculation, and for example, 8 layers of LSTM network structures +1 full connection layers +1 output layers can be set; for three types of lines, deep neural networks are used for calculation, for example, 16 layers of LSTM network structures +1 full connection layers +1 output layers can be set.
During specific implementation, the algorithm depth is set according to the complexity of the load rule in a targeted manner, a deep algorithm is set for a line with high nonlinear degree, the accuracy of a prediction result is guaranteed, and a shallow algorithm is set for a line with low nonlinear degree, so that the cost is reduced, and the speed is increased.
Setting training parameters: the input shape is 1 time step, 128 hidden nodes are set in each LSTM structure, the iteration number (epoch) is 50, the batch size (batch _ size) is 80, the probability of randomly discarding neurons (drop) is 0.9, the learning rate (learning-rate) is 0.01, and the loss function uses the Mean Absolute Error (MAE) to prevent overfitting of the network structure. Training is performed in the established LSTM neural network model using the training set.
During specific training, the test set is put into a trained LSTM neural network model for regression analysis, and then a Tenscoreboard is utilized to check the loss and convergence of the LSTM neural network. The model is finally evaluated using Mean Absolute Error (MAE).
And S4, generating a predicted line load value of the future time by using the load prediction model.
Inputting the future time to be predicted into a corresponding load prediction model for a specific line to be subjected to temperature measurement detection in the district, for example, inputting the future time into a load prediction model obtained based on shallow neural network training for processing for a line in which the future time is commonly used, and obtaining a line load prediction value I of the future timepre。
And S5, generating a temperature measurement plan according to the line conformity predicted value.
After the predicted value of the line load of the specific line at the future time is obtained, a corresponding temperature measurement plan is generated based on the predicted value of the line conformity and the preset rule, and the operation and inspection personnel can measure the temperature of the corresponding line according to the temperature measurement plan.
Obtaining a predicted value I of the line loadpreOn the basis of the load, the load is equal to the historical highest load ImaxAnd comparing, and issuing a targeted infrared temperature measurement instruction according to a comparison result, wherein the specific steps are as follows:
predicted value I of line loadpreIts same history highest load ImaxLess than 0.9, no special measures are needed;
line load predicted value I is more than 0.9preIts same history highest load ImaxLess than or equal to 1, and infrared temperature measurement is needed within 3 working days;
predicted value I of line loadpreIts same history highest load ImaxIf the temperature is more than 1, the infrared temperature measurement work needs to be carried out immediately.
In addition, a temperature measurement plan can be made according to the real-time load, for example:
monitoring the load in real time: monitoring the real-time load I, in parallelCorresponding to the time-averaged load IaveAnd carrying out comparison calculation, and issuing an infrared temperature measurement instruction according to a calculation result, wherein the method specifically comprises the following steps:
when the real-time load I/the corresponding time average load Iave< 1.2 and real-time load I < historical maximum load ImaxAnd infrared temperature measurement is not needed.
Real-time load I/corresponding time-averaged load IaveNot less than 1.2 or the real-time load I not less than the historical maximum load ImaxThe infrared temperature measurement work needs to be carried out immediately.
According to the technical scheme, the embodiment provides the method for generating the temperature measurement plan of the overhead transmission line, which is applied to electronic equipment, and specifically classifies lines to be generated with the temperature measurement plan according to load types; collecting historical data of a line of a target type to obtain sample data; performing model training based on sample data to obtain a load prediction model; generating a predicted line load value of future time by using a load prediction model; and generating a temperature measurement plan according to the predicted line load value. The temperature measurement plan is not made according to the manual experience of the operation and inspection personnel, so that the objectivity is high, and the effectiveness of temperature measurement work can be guaranteed when the temperature is measured according to the temperature measurement plan.
Example two
Fig. 2 is a block diagram of a temperature measurement plan generation device for an overhead line according to an embodiment of the present application.
As shown in fig. 2, the thermometry plan generating apparatus provided in this embodiment is applied to an electronic device with information processing capability or data processing capability, such as a computer or a server, and may be specifically regarded as a functional module or itself of the electronic device, where the thermometry plan generating apparatus includes a classification processing module 10, a data acquisition module 20, a model training module 30, a prediction execution module 40, and a plan generating module 50.
And the classification processing module is used for classifying the lines according to the load types.
The method aims at more than one line needing to generate the temperature measurement plan, and therefore, one important characteristic of the method is to classify all lines needing to generate the temperature measurement plan. Specifically, the method classifies the load type, the load rule and the nonlinearity degree of the line according to the load type, the load rule and the nonlinearity degree of the line.
For the line where the common user is located, because the electricity utilization behavior of the user has obvious regularity, the regularity is relatively easy to calculate, and the line is classified into a line type; for special user lines such as electric railways, high-speed railways, special industrial areas and the like, the load rules have certain complexity and are classified into two types of lines; for lines with new energy such as photovoltaic energy, wind power energy and the like, the load rule is relatively more complex, and the lines are classified into three types.
The data acquisition module is used for acquiring historical data of the line of the target type to obtain sample data.
The target type is a type of a line to be planned and generated, that is, historical data acquisition is performed on a specific type of line, so as to obtain sample data for training a corresponding model, where the sample data includes a training set and a test set.
In particular, the module comprises a data extraction unit 21 and a normalization processing unit 22, as shown in fig. 3. The data extraction unit is used for extracting historical data received by the load curve, storing the historical data in a csv form, and respectively listing elements in the historical data in the csv form as time, historical highest load, average load corresponding to time and real-time load;
the normalization processing unit is used for carrying out normalization processing on the manufactured data set by adopting a supervised learning method so as to obtain normalized sample data. In the specific implementation, firstly, a series _ to _ super () function is defined, and then, the multivariate time series data frame is converted into a data frame suitable for supervised learning.
The model training module is used for carrying out model training based on the sample data to obtain a load prediction model.
On the basis of obtaining the sample data, the sample data is distributed into a training set and a testing set according to a certain proportion, the proportion of the training set to the testing set can be 8:2, and then the training set is used for training the neural network, so that a load prediction model is obtained.
The module comprises a first training unit 31, a second training unit 32 and a third training unit, as shown in fig. 4. When the model training is specifically implemented, the first training unit is used for calculating by using a shallow neural network for a class of lines, and if the structure of a 3-layer LSTM network +1 full-connection layer +1 output layer can be set; the second training unit is used for calculating by using a deeper neural network for the second line, and for example, 8 layers of LSTM network structures +1 full connection layer +1 output layer can be set; the third training unit is used for calculating by using a deep neural network for three types of lines, such as 16 layers of LSTM network structures +1 full connection layers +1 output layers.
During specific implementation, the algorithm depth is set according to the complexity of the load rule in a targeted manner, a deep algorithm is set for a line with high nonlinear degree, the accuracy of a prediction result is guaranteed, and a shallow algorithm is set for a line with low nonlinear degree, so that the cost is reduced, and the speed is increased.
Setting training parameters: the input shape is 1 time step, 128 hidden nodes are set in each LSTM structure, the iteration number (epoch) is 50, the batch size (batch _ size) is 80, the probability of randomly discarding neurons (drop) is 0.9, the learning rate (learning-rate) is 0.01, and the loss function uses the Mean Absolute Error (MAE) to prevent overfitting of the network structure. Training is performed in the established LSTM neural network model using the training set.
During specific training, the test set is put into a trained LSTM neural network model for regression analysis, and then a Tenscoreboard is utilized to check the loss and convergence of the LSTM neural network. The model is finally evaluated using Mean Absolute Error (MAE).
The prediction execution module is used for generating a line load prediction value of the future time by using the load prediction model.
Inputting the future time to be predicted into a corresponding load prediction model for a specific line to be subjected to temperature measurement detection in the district, for example, inputting the future time into a load prediction model obtained based on shallow neural network training for processing for a line in which the future time is commonly used, and obtaining a line load prediction value I of the future timepre。
And the plan generating module is used for generating a temperature measuring plan according to the line conformity predicted value.
After the predicted value of the line load of the specific line at the future time is obtained, a corresponding temperature measurement plan is generated based on the predicted value of the line conformity and the preset rule, and the operation and inspection personnel can measure the temperature of the corresponding line according to the temperature measurement plan.
Obtaining a predicted value I of the line loadpreOn the basis of the load, the load is equal to the historical highest load ImaxAnd comparing, and issuing a targeted infrared temperature measurement instruction according to a comparison result, wherein the specific steps are as follows:
predicted value I of line loadpreIts same history highest load ImaxLess than 0.9, no special measures are needed;
line load predicted value I is more than 0.9preIts same history highest load ImaxLess than or equal to 1, and infrared temperature measurement is needed within 3 working days;
predicted value I of line loadpreIts same history highest load ImaxIf the temperature is more than 1, the infrared temperature measurement work needs to be carried out immediately.
In addition, a temperature measurement plan can be made according to the real-time load, for example:
monitoring the load in real time: monitoring the real-time load I and corresponding time-averaged load IaveAnd carrying out comparison calculation, and issuing an infrared temperature measurement instruction according to a calculation result, wherein the method specifically comprises the following steps:
when the real-time load I/the corresponding time average load Iave< 1.2 and real-time load I < historical maximum load ImaxAnd infrared temperature measurement is not needed.
Real-time load I/corresponding time-averaged load IaveNot less than 1.2 or the real-time load I not less than the historical maximum load ImaxThe infrared temperature measurement work needs to be carried out immediately.
As can be seen from the above technical solutions, the present embodiment provides a temperature measurement plan generation device for an overhead transmission line, which is applied to an electronic device, and is specifically configured to classify lines to be generated with a temperature measurement plan according to load types; collecting historical data of a line of a target type to obtain sample data; performing model training based on sample data to obtain a load prediction model; generating a predicted line load value of future time by using a load prediction model; and generating a temperature measurement plan according to the predicted line load value. The temperature measurement plan is not made according to the manual experience of the operation and inspection personnel, so that the objectivity is high, and the effectiveness of temperature measurement work can be guaranteed when the temperature is measured according to the temperature measurement plan.
EXAMPLE III
Fig. 5 is a block diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 5, the electronic device provided in this embodiment is a computer or server with information processing capability or data processing capability, and includes at least a processor 101 and a memory 102, which are connected via a data line 103. The memory is used for storing a computer program or instructions, and the processor is used for executing the corresponding computer program or instructions, so that the electronic device can realize the temperature measurement plan generation method of the embodiment.
The temperature measurement plan generation method specifically comprises the steps of classifying lines of a temperature measurement plan to be generated according to load types; collecting historical data of a line of a target type to obtain sample data; performing model training based on sample data to obtain a load prediction model; generating a predicted line load value of future time by using a load prediction model; and generating a temperature measurement plan according to the predicted line load value. The temperature measurement plan is not made according to the manual experience of the operation and inspection personnel, so that the objectivity is high, and the effectiveness of temperature measurement work can be guaranteed when the temperature is measured according to the temperature measurement plan.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The technical solutions provided by the present invention are described in detail above, and the principle and the implementation of the present invention are explained in this document by applying specific examples, and the descriptions of the above examples are only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. A temperature measurement plan generation method of an overhead transmission line is applied to electronic equipment and is characterized by comprising the following steps:
classifying the lines of the temperature measurement plan to be generated according to the load types;
collecting historical data of the line of the target type to obtain sample data;
performing model training based on the sample data to obtain a load prediction model;
generating a predicted line load value at a future time by using the load prediction model;
and generating a temperature measurement plan according to the predicted line load value.
2. The method for generating the temperature measurement plan according to claim 1, wherein the step of classifying the line on which the temperature measurement plan is to be generated according to the load type includes the steps of:
classifying the line where the traditional power consumer is located into a type of line;
classifying the line where the railway user and the special industrial area are located into two types of lines;
the line where the new energy is located is classified into three types of lines.
3. The method for generating a temperature measurement plan according to claim 1, wherein the step of collecting historical data of the line of the target type to obtain sample data comprises the steps of:
extracting historical data and storing the historical data in a preset format, wherein the historical data comprises a plurality of elements, and the elements comprise time, historical highest load, average load and real-time load;
and carrying out normalization processing on the historical data by adopting a supervised learning method to obtain the sample data.
4. The method for generating a temperature measurement plan according to claim 2, wherein the performing model training based on the sample data to obtain a load prediction model comprises:
performing model training based on a shallow neural network and the sample data to obtain a load prediction model for the line of the same type;
performing model training based on a deeper neural network and the sample data to obtain a load prediction model for the two types of lines;
and performing model training based on a deep neural network and the sample data to obtain a load prediction model for the three routes.
5. The method for generating a temperature measurement plan according to claim 1, wherein the generating a temperature measurement plan according to the predicted line load value includes:
and comparing the predicted value of the line load with the historical highest load of the line, and generating the temperature measurement plan according to the comparison result.
6. The utility model provides an overhead transmission line's temperature measurement plan generation device, is applied to electronic equipment, its characterized in that, temperature measurement plan generation device includes:
the classification processing module is configured to classify the lines of the temperature measurement plan to be generated according to the load types;
the data acquisition module is configured to acquire historical data of the line of the target type to obtain sample data;
the model training module is configured to perform model training based on the sample data to obtain a load prediction model;
a prediction execution module configured to generate a line load prediction value for a future time using the load prediction model;
and the plan generating module is configured to generate a temperature measuring plan according to the predicted line load value.
7. The thermometry plan generating apparatus of claim 6, wherein the classification processing module is configured to classify a line on which a legacy power consumer is located as a class of line; classifying the line where the railway user and the special industrial area are located into two types of lines; the line where the new energy is located is classified into three types of lines.
8. The thermometry plan generating apparatus of claim 6, wherein the data acquisition module comprises:
the data extraction unit is configured to extract historical data and store the historical data in a preset format, wherein the historical data comprises a plurality of elements, and the elements comprise time, historical highest load, average load and real-time load;
and the normalization processing unit is configured to perform normalization processing on the historical data by adopting a supervised learning method to obtain the sample data.
9. The thermometry plan generating apparatus of claim 7, wherein the model training module comprises:
the first training unit is used for carrying out model training based on a shallow neural network and the sample data to obtain a load prediction model aiming at the line of the same type;
the second training unit is used for carrying out model training based on a deeper neural network and the sample data to obtain a load prediction model aiming at the two lines;
and the third training unit is used for carrying out model training based on a deep neural network and the sample data to obtain a load prediction model aiming at the three routes.
10. The thermometry plan generating apparatus of claim 6, wherein the plan generating module is configured to compare the line load prediction value with a historical top load of the line, and generate the thermometry plan based on the comparison.
11. An electronic device comprising at least one processor and a memory coupled to the processor, wherein:
the memory is for storing a computer program or instructions;
the processor is used for executing the computer program or the instructions to enable the electronic equipment to execute the temperature measurement plan generating method according to any one of claims 1-5.
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