CN112529733A - Power distribution network operation safety remote control method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a remote control method for operation safety of a power distribution network, which comprises the steps of acquiring a plurality of operation data of power distribution network equipment at intervals of the same time period in an intelligent lock opening stage of the power distribution network; constructing the operation data into a power distribution network time sequence parameter; inputting the time sequence parameters of the power distribution network into a preset neural network model for calculation to obtain prediction data; acquiring target index data corresponding to power distribution network equipment; the target index data and the prediction data are compared, the monitoring result is determined according to the comparison result, remote control of operation safety of the power distribution network is achieved, real-time monitoring is guaranteed, accuracy of the monitoring result is guaranteed due to the fact that prediction and monitoring are carried out on the basis of operation data in a period of time, monitoring efficiency of power distribution network equipment is greatly improved, and therefore unsafe behaviors of operation of the power distribution network can be timely processed according to the monitoring result, and safe operation of the power distribution network is guaranteed. In addition, a remote control device, computer equipment and a storage medium for operation safety of the power distribution network are further provided.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method, a device, equipment and a storage medium for remotely controlling operation safety of a power distribution network.
Background
At distribution network production operation management and control in-process, in order to guarantee that distribution network operation staff carries out the distribution operation safely, each local power supply bureau needs write the operation file when carrying out production operations such as salvageing, maintenance to make things convenient for distribution operation personnel to guide according to the operation file and carry out standard operation, with the compliance of guaranteeing distribution operation personnel's safety and distribution operation. After the job file is checked, whether to execute the job file strictly according to the content of the job file in the execution process is an important factor of safety operation. Therefore, the operation of the power distribution network needs to be monitored, and at present, the existing power distribution network monitoring method adopts on-site manual monitoring or video monitoring, and then the manual monitoring method is time-consuming and labor-consuming, is easy to make mistakes, and is not high in real-time performance; the video monitoring needs a large amount of equipment, so that the cost is delivered, the lag problem exists in the monitoring and analyzing process, the early warning is difficult to timely carry out, and the running safety of the power distribution network is reduced.
Disclosure of Invention
Therefore, in order to solve the above problems, a method, an apparatus, a computer device, and a storage medium for remotely controlling operation safety of a power distribution network are provided, so as to improve real-time performance and monitoring efficiency of remote monitoring of operation of the power distribution network and ensure safe operation of the power distribution network.
A power distribution network operation safety remote control method comprises the following steps:
acquiring a plurality of operation data of the power distribution network equipment every other same time period at the unlocking stage of the intelligent lock of the power distribution network;
constructing the operation data into a power distribution network time sequence parameter;
inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data;
acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence intervals of the time sequence parameters of the power distribution network;
and comparing the target index data with the prediction data, and determining a monitoring result according to a comparison result.
A remote management and control device for operation safety of a power distribution network, comprising:
the first acquisition module is used for acquiring a plurality of operation data of the power distribution network equipment every other same time period in the intelligent lock opening stage of the power distribution network;
the construction module is used for constructing the operation data into a power distribution network time sequence parameter;
the prediction module is used for inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data;
the second acquisition module is used for acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence interval of the time sequence parameters of the power distribution network;
and the determining module is used for comparing the target index data with the prediction data and determining a monitoring result according to a comparison result.
A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of:
acquiring a plurality of operation data of the power distribution network equipment every other same time period at the unlocking stage of the intelligent lock of the power distribution network;
constructing the operation data into a power distribution network time sequence parameter;
inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data;
acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence intervals of the time sequence parameters of the power distribution network;
and comparing the target index data with the prediction data, and determining a monitoring result according to a comparison result.
A computer-readable medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring a plurality of operation data of the power distribution network equipment every other same time period at the unlocking stage of the intelligent lock of the power distribution network;
constructing the operation data into a power distribution network time sequence parameter;
inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data;
acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence intervals of the time sequence parameters of the power distribution network;
and comparing the target index data with the prediction data, and determining a monitoring result according to a comparison result.
According to the remote control method, device, computer equipment and storage medium for the operation safety of the power distribution network, a plurality of operation data of the power distribution network equipment are acquired every other same time period in the intelligent lock opening stage of the power distribution network; constructing the operation data into a power distribution network time sequence parameter; inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data; acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence intervals of the time sequence parameters of the power distribution network; and determining a monitoring result according to the target index data and the prediction data. The volume operation data of the power distribution network equipment is predicted through the neural network model, verification and comparison are carried out based on target index data, remote monitoring of the power distribution network is achieved, monitoring instantaneity is guaranteed, and power distribution network operation monitoring efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention 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 invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
FIG. 1 is a flow chart of a method for remotely controlling the operation safety of a power distribution network in one embodiment;
FIG. 2 is a flow diagram of a method for constructing timing parameters of a power distribution network according to an embodiment;
FIG. 3 is a flowchart of a target metric data acquisition method in one embodiment;
fig. 4 is a flowchart of a remote control method for operation safety of a power distribution network in another embodiment;
fig. 5 is a block diagram illustrating an embodiment of a remote control device for operation security of a power distribution network;
FIG. 6 is a block diagram of a computer device in one embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
As shown in fig. 1, in an embodiment, a remote management and control method for operation safety of a power distribution network is provided, where the remote management and control method for operation safety of a power distribution network can be applied to a terminal and a server, and this embodiment is exemplified by being applied to a server. The remote control method for the operation safety of the power distribution network specifically comprises the following steps:
102, acquiring a plurality of operation data of the power distribution network equipment every the same time period at the unlocking stage of the intelligent lock of the power distribution network.
The power distribution network equipment comprises a transformer, a relay or a line and the like, and the operation data refers to index data reflecting the operation state of the power distribution network equipment, such as voltage, current or load and the like. Specifically, when the smart lock of the power distribution network is unlocked, the real-time data returned by the power distribution network monitoring control system in the same time period is used as the operation data of the corresponding power distribution network device, where the same time period may be 5 minutes, 10 minutes, 15 minutes, or the like, and is not limited herein.
And 104, constructing the running data into a power distribution network time sequence parameter.
The time sequence parameter refers to data based on a time sequence, for example, 3 pieces of operation data 100V (volt), 101V and 103V at 3 moments 10:00, 10:05 and 10:10, and the time sequence parameter of the power distribution network is {00V, 101V, 103V }. Specifically, the power distribution network time sequence parameters are constructed directly according to the time sequence of each operation data. It can be understood that the time series data is a time series recorded according to a time sequence, which changes depending on time and can reflect the degree of change of the operation of the power distribution network equipment.
And 106, inputting the time sequence parameters of the power distribution network into a preset neural network model for calculation to obtain prediction data.
The preset neural network model is a pre-trained deep learning prediction model, such as a bidirectional long-time and short-time memory neural network (BLSTM), a Deep Belief Network (DBN), a recurrent neural network RNN, a gate cycle unit (GRU), and the like. Specifically, the power distribution network time sequence parameter is used as the input of a preset neural network model, and the power distribution network time sequence parameter is calculated to obtain the prediction data corresponding to the power distribution network equipment in a target time period, wherein the target time period can be a future time period, specifically, a time period after the last time period in the power distribution network time sequence parameter. Continuing with the example of 3 operating data of 100V (volts), 101V and 103V at 3 times 10:00, 10:05 and 10:10 in step 104, the predicted data are 3 operating data of 101V (volts), 102V and 104V at 3 times 18:00, 18:05 and 18: 10.
Preferably, in this embodiment, the neural network model is a bidirectional long-term and short-term memory neural network, so as to ensure that information at a certain time point can simultaneously consider information in front of and behind the certain time point, thereby being beneficial to ensuring accuracy of predicted data.
And 108, acquiring target index data corresponding to the power distribution network equipment, wherein the target index data is time sequence data consistent with the time sequence interval of the power distribution network time sequence parameters.
The target index data refers to operation data of power distribution network equipment in an ideal operation state. Specifically, the target index data may be extracted from a power distribution network operation file written by a power distribution network operator. It can be understood that the target index data is also time sequence data, and the corresponding time sequence interval is time sequence data consistent with the time sequence parameter of the power distribution network.
And 110, comparing the target index data with the prediction data, and determining a monitoring result according to a comparison result.
Specifically, the target index data and the prediction data are compared one by one according to corresponding time sequences to obtain a plurality of comparison results, the comparison results are comprehensively processed, such as the mean value, the variance and the like of the difference are calculated, the comprehensive processing result and a preset threshold value are used for determining a monitoring result, and the monitoring result comprises normal operation of the power distribution network equipment and abnormal operation of the power distribution network equipment. The monitoring result is determined by comparing the target index data with the prediction data, so that the remote control of the operation safety of the power distribution network is realized, the real-time performance of monitoring is ensured, the accuracy of the monitoring result is ensured due to the fact that the prediction and the monitoring are performed based on the operation data within a period of time, the monitoring efficiency of the power distribution network equipment is greatly improved, and further the unsafe behavior of the operation of the power distribution network is timely processed according to the monitoring result, and the safe operation of the power distribution network is guaranteed.
According to the remote control method for the operation safety of the power distribution network, a plurality of operation data of power distribution network equipment are acquired every other same time period in the intelligent lock opening stage of the power distribution network; constructing the operation data into a power distribution network time sequence parameter; inputting the time sequence parameters of the power distribution network into a preset neural network model for calculation to obtain prediction data; acquiring target index data corresponding to power distribution network equipment; the target index data and the prediction data are compared, the monitoring result is determined according to the comparison result, remote control of operation safety of the power distribution network is achieved, real-time monitoring is guaranteed, accuracy of the monitoring result is guaranteed due to the fact that prediction and monitoring are carried out on the basis of operation data in a period of time, monitoring efficiency of power distribution network equipment is greatly improved, and therefore unsafe behaviors of operation of the power distribution network can be timely processed according to the monitoring result, and safe operation of the power distribution network is guaranteed.
In one embodiment, the preset neural network model is a bidirectional long-time memory neural network.
In this embodiment, a unidirectional long-short term memory neural network (LSTM) model introduces a "gate unit" structure, which can be used to control the flow or blockage of characteristic information, and further store the previous information for a long time, but the LSTM network structure is unidirectional, and although timing information can be processed, only information sequences in the previous direction and the next direction can be considered, and in an actual environment, information at a certain time point not only relates to the following information, but also possibly relates to the previous information, so that the bidirectional long-short term memory neural network which can consider bidirectional information is adopted, which is beneficial to ensuring the accuracy of predicted data.
As shown in FIG. 2, in one embodiment, the operational data is constructed as power distribution grid timing parameters, including:
104A, selecting operation data in each time period;
and step 104B, arranging the operation data according to the sequence in the time period to obtain the time sequence parameters of the power distribution network.
In this embodiment, the operation data is accumulated with time, the operation data is more and more, and in order to improve subsequent calculation efficiency, necessary compression processing needs to be performed on the operation data, specifically, the operation data in each time period is selected, and then the operation data is sequenced according to the time sequence of each operation data to form a power distribution network time sequence parameter, so that the relevance of the operation data is enhanced, and the accuracy of subsequent prediction is ensured.
As shown in fig. 3, in an embodiment, acquiring target index data corresponding to a power distribution network device includes:
and 108C, screening target index data from the keywords of the power distribution network according to the time sequence of the time sequence parameters of the power distribution network.
In this embodiment, the job file refers to text information that can reflect the distribution network planning operation information and is input to the server by the user through the terminal device. Performing semantic analysis (NLP) on the operation file to extract distribution network keywords, wherein the distribution network keywords refer to words which are firstly related to distribution network operation, such as voltage 101V, current value of a relay at 10:05 time being 150A (amperes), and the like. Semantic analysis, which may involve steps such as word segmentation, language model (N-Gram language model, neural network language model, etc.), weight calculation, and core word extraction, will not be described herein. For example, the filling content may be divided into a plurality of parts according to any one of punctuation marks, numbers, or verb parts of speech, and each part is subjected to semantic analysis to obtain a plurality of distribution network keywords included in the filling content. In one example, the job file includes "the current value of the relay at time 10:05 is 150A", and the following distribution network keywords "10: 05", "relay", and "current value is 150A" can be obtained by semantic analysis. And screening target index data from the power distribution network keywords according to the time sequence of the power distribution network time sequence parameters. Understandably, by extracting the keywords of the power distribution network, the simplified processing of the operation files is realized, the interference of invalid contents is avoided, and the efficiency of acquiring the target index data is improved.
As shown in fig. 4, in an embodiment, comparing the target index data with the prediction data, and determining the monitoring result according to the comparison result includes:
and step 110B, when the stability is within a preset range threshold, determining the predicted data as a monitoring result.
The stability is a measurement index used for measuring the deviation degree of the prediction data and the target index data, and the stability shows that the smaller the difference between the target index data and the prediction data is, the monitoring result is close to the normal operation of the power distribution network equipment. And when the stability is within the preset range threshold, the high accuracy of the predicted data can be reflected, and the predicted data can be directly used as a monitoring result. Understandably, by calculating the stability of the target index data and the predicted data, the further verification of the predicted data is realized, and the monitoring result can be determined based on the stability, so that the determination efficiency of the monitoring effect is improved.
In one embodiment, the stability is a difference of a mean of the target index data and the prediction data, a variance of the target index data and the prediction data, or a covariance of the target index data and the prediction data.
In this embodiment, the calculated result may be determined as the stability by a difference value of the target index data and the mean of the prediction data, a variance of the target index data and the prediction data, or a covariance of the target index data and the prediction data.
In one embodiment, after calculating the stability of the target index data and the prediction data, the method further includes: and when the stability is not within the preset range threshold, controlling the intelligent lock of the power distribution network to lock.
In the embodiment, when the stability is not within the preset range threshold, the difference between the target index data and the predicted data is large, so that the abnormal operation monitoring result of the power distribution network equipment is determined, the intelligent lock of the power distribution network is controlled to be locked, and the safety of the power distribution network is guaranteed.
As shown in fig. 5, in one embodiment, a remote management and control device for operation safety of a power distribution network is provided, the device includes:
a first obtaining module 502, configured to obtain multiple pieces of operation data of power distribution network equipment every other same time period in an intelligent lock unlocking stage of the power distribution network;
a construction module 504, configured to construct the operation data into a power distribution network timing parameter;
the prediction module 506 is configured to input the power distribution network timing parameters into a preset neural network model for calculation to obtain prediction data;
a second obtaining module 508, configured to obtain target index data corresponding to the power distribution network device, where the target index data is timing sequence data that is consistent with a timing sequence interval of the power distribution network timing sequence parameter;
a determining module 510, configured to compare the target index data with the prediction data, and determine a monitoring result according to a comparison result.
In one embodiment, the building block comprises:
the selection unit is used for selecting the operation data in each time period;
and the construction unit is used for arranging the operation data according to the sequence in the time period to obtain the time sequence parameters of the power distribution network.
In one embodiment, the second obtaining module includes:
the acquisition unit is used for acquiring an operation file written by a power distribution network operator;
the analysis unit is used for performing semantic analysis on the operation files and extracting keywords of the power distribution network;
and the screening unit is used for screening the target index data from the power distribution network keywords according to the time sequence of the power distribution network time sequence parameters.
In one embodiment, the determining module includes:
a calculation unit configured to calculate a degree of stability of the target index data and the prediction data;
and the comparison unit is used for determining the predicted data as the monitoring result when the stability is within a preset range threshold.
In one embodiment, the remote management and control device for the operation safety of the power distribution network comprises:
and the locking module is used for controlling the intelligent lock of the power distribution network to lock when the stability is not within the preset range threshold.
FIG. 6 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be a server including, but not limited to, a high performance computer and a cluster of high performance computers. As shown in fig. 6, the computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and also stores a computer program, and when the computer program is executed by the processor, the processor can realize the remote control method for the operation safety of the power distribution network. The internal memory may also store a computer program, and when the computer program is executed by the processor, the computer program may enable the processor to execute a remote control method for operation security of the power distribution network. Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the remote management and control method for operation safety of the power distribution network provided by the present application may be implemented in the form of a computer program, and the computer program may be run on a computer device as shown in fig. 6. And each program template forming the remote control device for the operation safety of the power distribution network can be stored in a memory of the computer equipment. For example, the first obtaining module 502, the constructing module 504, the predicting module 506, the second obtaining module 508, and the determining module 510.
A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring a plurality of operation data of the power distribution network equipment every other same time period at the unlocking stage of the intelligent lock of the power distribution network; constructing the operation data into a power distribution network time sequence parameter; inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data; acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence intervals of the time sequence parameters of the power distribution network; and comparing the target index data with the prediction data, and determining a monitoring result according to a comparison result.
In one embodiment, the preset neural network model is a bidirectional long-time memory neural network.
In one embodiment, constructing the operation data as a power distribution network timing parameter includes: selecting operation data in each time period; and arranging the operation data according to the sequence in the time period to obtain the time sequence parameters of the power distribution network.
In one embodiment, the obtaining target index data corresponding to the power distribution network device includes: acquiring an operation file written by power distribution network operators; performing semantic analysis on the operation file, and extracting keywords of the power distribution network; and screening the target index data from the power distribution network keywords according to the time sequence of the power distribution network time sequence parameters.
In one embodiment, the comparing the target index data with the prediction data and determining a monitoring result according to a comparison result includes: calculating the stability of the target index data and the prediction data; and when the stability is within a preset range threshold, determining the predicted data as the monitoring result.
In one embodiment, the stability is a difference of a mean of the target indicator data and the prediction data, a variance of the target indicator data and the prediction data, or a covariance of the target indicator data and the prediction data.
In one embodiment, after the calculating the stability of the target index data and the prediction data, the method further includes: and when the stability is not within the preset range threshold value, controlling the intelligent lock of the power distribution network to lock.
A computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of: acquiring a plurality of operation data of the power distribution network equipment every other same time period at the unlocking stage of the intelligent lock of the power distribution network; constructing the operation data into a power distribution network time sequence parameter; inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data; acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence intervals of the time sequence parameters of the power distribution network; and comparing the target index data with the prediction data, and determining a monitoring result according to a comparison result.
In one embodiment, the preset neural network model is a bidirectional long-time memory neural network.
In one embodiment, constructing the operation data as a power distribution network timing parameter includes: selecting operation data in each time period; and arranging the operation data according to the sequence in the time period to obtain the time sequence parameters of the power distribution network.
In one embodiment, the obtaining target index data corresponding to the power distribution network device includes: acquiring an operation file written by power distribution network operators; performing semantic analysis on the operation file, and extracting keywords of the power distribution network; and screening the target index data from the power distribution network keywords according to the time sequence of the power distribution network time sequence parameters.
In one embodiment, the comparing the target index data with the prediction data and determining a monitoring result according to a comparison result includes: calculating the stability of the target index data and the prediction data; and when the stability is within a preset range threshold, determining the predicted data as the monitoring result.
In one embodiment, the stability is a difference of a mean of the target indicator data and the prediction data, a variance of the target indicator data and the prediction data, or a covariance of the target indicator data and the prediction data.
In one embodiment, after the calculating the stability of the target index data and the prediction data, the method further includes: and when the stability is not within the preset range threshold value, controlling the intelligent lock of the power distribution network to lock.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A remote control method for operation safety of a power distribution network is characterized by comprising the following steps:
acquiring a plurality of operation data of the power distribution network equipment every other same time period at the unlocking stage of the intelligent lock of the power distribution network;
constructing the operation data into a power distribution network time sequence parameter;
inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data;
acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence intervals of the time sequence parameters of the power distribution network;
and comparing the target index data with the prediction data, and determining a monitoring result according to a comparison result.
2. The remote management and control method for operation safety of the power distribution network according to claim 1, wherein the preset neural network model is a bidirectional long-time and short-time memory neural network.
3. The method for remotely controlling the operation safety of the power distribution network according to claim 1, wherein the constructing the operation data into the timing parameters of the power distribution network comprises:
selecting operation data in each time period;
and arranging the operation data according to the sequence in the time period to obtain the time sequence parameters of the power distribution network.
4. The remote management and control method for the operation safety of the power distribution network according to claim 1, wherein the obtaining of the target index data corresponding to the power distribution network equipment comprises:
acquiring an operation file written by power distribution network operators;
performing semantic analysis on the operation file, and extracting keywords of the power distribution network;
and screening the target index data from the power distribution network keywords according to the time sequence of the power distribution network time sequence parameters.
5. The remote control method for the operation safety of the power distribution network according to claim 1, wherein the comparing the target index data with the prediction data and determining a monitoring result according to the comparison result comprises:
calculating the stability of the target index data and the prediction data;
and when the stability is within a preset range threshold, determining the predicted data as the monitoring result.
6. The method according to claim 5, wherein the stability is a difference between a mean of target index data and the prediction data, a variance of the target index data and the prediction data, or a covariance of the target index data and the prediction data.
7. The method according to claim 5, wherein after the calculating the stability of the target index data and the prediction data, the method further comprises:
and when the stability is not within the preset range threshold value, controlling the intelligent lock of the power distribution network to lock.
8. The utility model provides a long-range management and control device of distribution network operation safety which characterized in that, the device includes:
the first acquisition module is used for acquiring a plurality of operation data of the power distribution network equipment every other same time period in the intelligent lock opening stage of the power distribution network;
the construction module is used for constructing the operation data into a power distribution network time sequence parameter;
the prediction module is used for inputting the power distribution network time sequence parameters into a preset neural network model for calculation to obtain prediction data;
the second acquisition module is used for acquiring target index data corresponding to the power distribution network equipment, wherein the target index data are time sequence data consistent with the time sequence interval of the time sequence parameters of the power distribution network;
and the determining module is used for comparing the target index data with the prediction data and determining a monitoring result according to a comparison result.
9. Computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor, when executing said computer program, carries out the steps of a method for remote security management and control of power distribution network operations according to any one of claims 1 to 7.
10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for remote management and control of operation security of a power distribution network according to any one of claims 1 to 7.
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