CN112348699A - Power supply system power equipment life cycle management method and system - Google Patents
Power supply system power equipment life cycle management method and system Download PDFInfo
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Abstract
The invention provides a method and a system for managing the life cycle of power equipment of a power supply system, wherein the method comprises the following steps: collecting power equipment information in a power system; analyzing the collected power equipment information, and acquiring power equipment information with a life cycle deadline contained in the power equipment; predicting the life cycle of the power equipment with the life cycle deadline according to the actual working environment; determining the life loss probability of the detection time point according to the predicted life cycle of the actual working environment; establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point; and performing calculation processing according to the maintenance planning model, and obtaining the optimal maintenance time of the power equipment with the life cycle deadline. Therefore, the overhaul has purposiveness and high informatization level, and the condition that the service life expires is properly treated, so that the running risk of the power equipment is reduced.
Description
Technical Field
The invention relates to the technical field of power supply equipment management of a power system, in particular to a life cycle management method and system of power supply equipment of the power system.
Background
The power equipment of the power supply system is generally supervised and managed in a scheduled inspection mode, the inspection range is usually selected according to the sampling inspection proportion required by the relevant standard, and the supervision and management mode has many problems, the sampling inspection is blind, the informatization level is low, and continuous comparative analysis and accurate evaluation cannot be carried out; the accumulated checking frequency of each power device has obvious difference, the phenomena of over-checking, over-repairing, under-checking and missing-checking commonly exist, the condition of service life expiration is easy to occur, but the condition of failure of the power device is not properly processed, and the operation risk of the power device is invisibly increased.
Disclosure of Invention
Aiming at the blindness of spot inspection, the informatization level is low, and continuous comparative analysis and accurate evaluation cannot be carried out; the invention provides a method and a system for managing the life cycle of power equipment of a power supply system.
The technical scheme of the invention is as follows:
on one hand, the technical scheme of the invention provides a power supply system power equipment life cycle management method, which comprises the following steps:
collecting power equipment information in a power system;
analyzing the collected power equipment information, and acquiring power equipment information with a life cycle deadline contained in the power equipment;
predicting the life cycle of the power equipment with the life cycle deadline according to the actual working environment;
determining the life loss probability of the detection time point according to the predicted life cycle of the actual working environment;
establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point;
and performing calculation processing according to the maintenance planning model, and obtaining the optimal maintenance time of the power equipment with the life cycle deadline.
Establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point; and performing calculation processing according to the maintenance planning model, and obtaining the optimal maintenance time of the power equipment with the life cycle deadline. The calculated optimal maintenance time is timely output in the form of early warning information, and the electric power equipment is convenient for workers to overhaul and maintain.
Further, the step of predicting the life cycle of the power equipment with the life cycle deadline according to the actual working environment comprises the following steps:
s1: collecting running state data of the power equipment with a life cycle deadline, wherein the running state data comprises the running state data of each time point under different working environment conditions;
s2: establishing a neural network prediction model through the operation state data of the power equipment with the life cycle deadline;
s3: and predicting the life cycle of the power equipment with the life cycle deadline through the established neural network prediction model.
The life cycle of the power equipment is predicted by establishing a neural network prediction model, so that potential safety hazards caused by the fact that invalid spare parts with expired lives cannot be found are avoided, and the safe operation of the whole power grid can be guaranteed.
Further, the different working environment conditions include operating the power equipment with the life cycle life at ambient temperatures of-40 ℃, 20 ℃, 0 ℃, 25 ℃, 35 ℃ and 60 ℃, respectively.
Further, the step of establishing the neural network prediction model by the operation state data of the power equipment with the life cycle deadline comprises the following steps:
s21: the number of layers and the number of nodes of an input layer, a hidden layer and an output layer of the neural network are selected, and a function and a training algorithm are selected to initially construct a prediction model;
s22: training a neural network model by using the collected partial operation state data as training data and adopting a simulated annealing algorithm to optimize parameters of the neural network;
s23: taking the optimized optimal parameters as initial values of corresponding parameters of the neural network, and training again by combining an ant colony optimization algorithm to obtain output values of the neural network;
s24: judging the obtained output value of the neural network and a set accurate value, and stopping training the neural network model when the output value of the neural network reaches the set accurate value to obtain an optimized neural network prediction model;
s25: and predicting the optimized neural network prediction model by using the collected residual running state data as test data, and outputting a prediction result.
And determining the structure of the model by training the established neural network model memorability network model, inputting test data into the input end of the prediction model, and outputting a prediction result. In general, in order to save costs, the replacement of the electric power equipment is performed after the predicted life cycle value of the electric power equipment reaches a set time threshold.
Further, the inputs to the neural network are: ambient temperature, running power consumption, running voltage, running current, operating time, the output is: the life cycle of the electrical equipment.
Further, the step of predicting the life cycle of the device with life cycle deadline according to the actual working environment further comprises the following steps:
an ideal lifecycle for a device having a lifecycle deadline is obtained.
Further, the step of determining the life loss probability of the detection time point according to the predicted life cycle of the actual working environment comprises:
performing finite element calculation on the power system according to the running state data;
determining damage parameters according to the influence degree of each environmental factor on the service life of power equipment in the power system;
establishing a power equipment damage probability model by determining the probability distribution of damage parameters;
calculating the failure probability of the power equipment according to the damage probability model;
and determining the life loss probability of the detection time point according to the calculated failure probability and the predicted life cycle of the actual working environment.
And (4) calculating the life loss rate in an irregular way to be used as a parameter of a training sample, and training the prediction model.
On the other hand, the technical scheme of the invention provides a power supply system power equipment life cycle management system which comprises an acquisition module, an analysis module, a life cycle prediction module, a life loss probability calculation module, a maintenance planning model establishment module and a calculation processing module, wherein the analysis module is used for analyzing the life cycle of power equipment;
the acquisition module is used for acquiring the information of the power equipment in the power system;
the analysis module is used for analyzing the collected power equipment information and acquiring the power equipment information with the life cycle deadline contained in the power equipment;
the life cycle prediction module is used for predicting the life cycle of the power equipment with the life cycle deadline according to the actual working environment;
the life loss probability calculation module is used for determining the life loss probability of the detection time point according to the predicted life cycle of the actual working environment;
the maintenance planning model establishing module is used for establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point;
and the calculation processing module is used for performing calculation processing according to the maintenance planning model and obtaining the optimal maintenance time of the power equipment with the life cycle deadline.
Furthermore, the life cycle prediction module comprises a state data acquisition unit, a neural network prediction model establishing unit and a life cycle prediction unit;
the state data acquisition unit is used for acquiring running state data of the power equipment with a life cycle deadline, wherein the running state data comprises the running state data of each time point under different working environment conditions;
the neural network prediction model establishing unit is used for establishing a neural network prediction model through the operation state data of the power equipment with the life cycle deadline;
and the life cycle prediction unit is used for predicting the life cycle of the power equipment with the life cycle deadline through the established neural network prediction model.
Further, the neural network prediction model establishing unit comprises a model establishing sub-module, a first training sub-module, a second training sub-module, a judgment output sub-module and a prediction output sub-module;
the model construction submodule is used for primarily constructing a prediction model by selecting the number of layers and the number of nodes of an input layer, a hidden layer and an output layer of the neural network, and a function and a training algorithm;
the first training submodule is used for training the neural network model by using the collected partial operation state data as training data and adopting a simulated annealing algorithm to optimize parameters of the neural network;
the second training submodule is used for training again by combining the ant colony optimization algorithm by taking the optimized optimal parameters as initial values of corresponding parameters of the neural network to obtain output values of the neural network;
the judgment output submodule is used for judging the acquired output value of the neural network and a set accurate value, and stopping training the neural network model when the output value of the neural network reaches the set accurate value to obtain an optimized neural network prediction model;
and the prediction output sub-module is used for predicting the optimized neural network prediction model by taking the collected residual running state data as test data and outputting a prediction result.
Further, the different working environment conditions include operating the power equipment with the life cycle life at ambient temperatures of-40 ℃, 20 ℃, 0 ℃, 25 ℃, 35 ℃ and 60 ℃, respectively.
Further, the inputs to the neural network are: ambient temperature, running power consumption, running voltage, running current, operating time, the output is: the life cycle of the electrical equipment.
Further, the system also comprises an ideal life cycle acquisition module;
and the ideal life cycle acquisition module is used for acquiring the ideal life cycle of the equipment with the life cycle deadline. The ideal life cycle of each device is extracted from the device workbook information and stored in a database.
Furthermore, the life loss probability calculation module comprises a calculation unit, a damage parameter determination unit, a damage probability model establishment unit and a failure probability calculation unit;
the computing unit is used for carrying out finite element computation on the power system according to the running state data;
the damage parameter determining unit is used for determining damage parameters according to the influence degree of each environmental factor on the service life of the power equipment in the power system;
the damage probability model establishing unit is used for establishing a power equipment damage probability model by determining the probability distribution of damage parameters;
the failure probability calculation unit is used for calculating the failure probability of the power equipment according to the damage probability model;
and the calculating unit is also used for determining the life loss probability of the detection time point according to the calculated failure probability and the predicted life cycle of the actual working environment.
According to the technical scheme, the invention has the following advantages: establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point; and performing calculation processing according to the maintenance planning model, and obtaining the optimal maintenance time of the power equipment with the life cycle deadline. The calculated optimal maintenance time is timely output in the form of early warning information, and the electric power equipment is convenient for workers to overhaul and maintain. Therefore, the overhaul has purposiveness and high informatization level, and the condition that the service life expires is properly treated, so that the running risk of the power equipment is reduced.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a schematic block diagram of a system of one embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all 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. The following explains key terms appearing in the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for managing a lifecycle of power equipment in a power supply system, including the following steps:
s1: collecting power equipment information in a power system; in this step, can set up electric power system's operational environment and carry out the simulation of actual operation condition, gather the power equipment information of simulation process, specifically include: : s11: accessing the power equipment to a simulated operation environment; s12: the method comprises the steps of setting parameters of the power equipment, then operating the power equipment, and collecting information of the power equipment in the operation process.
S2: analyzing the collected power equipment information, and acquiring power equipment information with a life cycle deadline contained in the power equipment;
s3: predicting the life cycle of the power equipment with the life cycle deadline according to the actual working environment;
s4: determining the life loss probability of the detection time point according to the predicted life cycle of the actual working environment;
s5: establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point;
s6: and performing calculation processing according to the maintenance planning model, and obtaining the optimal maintenance time of the power equipment with the life cycle deadline.
In some embodiments, the step of predicting the life cycle of the power device having a life cycle deadline according to the actual working environment comprises:
s1: collecting running state data of the power equipment with a life cycle deadline, wherein the running state data comprises the running state data of each time point under different working environment conditions;
s2: establishing a neural network prediction model through the operation state data of the power equipment with the life cycle deadline;
s3: and predicting the life cycle of the power equipment with the life cycle deadline through the established neural network prediction model.
In the step, original state inspection data of each component of the power equipment, historical inspection data after the equipment is used and fault state information data are obtained; continuously comparing and analyzing the acquired data, and fitting the variation trend of each key index in the operation of the power equipment; and the life cycle of the power equipment with the life cycle deadline is predicted in time by combining with accurate evaluation of the inspection data.
In some embodiments, the different operating environmental conditions include operating the life cycle capable power equipment at ambient temperatures of-40 ℃, 20 ℃, 0 ℃, 25 ℃, 35 ℃ and 60 ℃, respectively.
In some embodiments, the step of building a neural network predictive model from the operational state data of the electrical devices for which there is a life cycle deadline includes:
s21: the number of layers and the number of nodes of an input layer, a hidden layer and an output layer of the neural network are selected, and a function and a training algorithm are selected to initially construct a prediction model;
s22: training a neural network model by using the collected partial operation state data as training data and adopting a simulated annealing algorithm to optimize parameters of the neural network;
s23: taking the optimized optimal parameters as initial values of corresponding parameters of the neural network, and training again by combining an ant colony optimization algorithm to obtain output values of the neural network;
s24: judging the obtained output value of the neural network and a set accurate value, and stopping training the neural network model when the output value of the neural network reaches the set accurate value to obtain an optimized neural network prediction model;
s25: and predicting the optimized neural network prediction model by using the collected residual running state data as test data, and outputting a prediction result. In this embodiment, the prediction model is optimized by using a simulated annealing algorithm.
Establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point; and performing calculation processing according to the maintenance planning model, and obtaining the optimal maintenance time of the power equipment with the life cycle deadline. The calculated optimal maintenance time is timely output in the form of early warning information, and the electric power equipment is convenient for workers to overhaul and maintain.
In some embodiments, the inputs to the neural network are: ambient temperature, running power consumption, running voltage, running current, operating time, the output is: the life cycle of the electrical equipment.
In some embodiments, the step of predicting the life cycle of the device having a life cycle deadline according to the actual working environment further comprises:
an ideal lifecycle for a device having a lifecycle deadline is obtained.
In some embodiments, the step of determining the probability of life loss at the detection time point based on the predicted life cycle of the actual operating environment comprises:
performing finite element calculation on the power system according to the running state data;
determining damage parameters according to the influence degree of each environmental factor on the service life of power equipment in the power system;
establishing a power equipment damage probability model by determining the probability distribution of damage parameters;
calculating the failure probability of the power equipment according to the damage probability model;
and determining the life loss probability of the detection time point according to the calculated failure probability and the predicted life cycle of the actual working environment. And the part with potential safety hazard in the power system is found to automatically alarm, and workers are timely informed to overhaul the equipment.
Therefore, the overhaul has purposiveness and high informatization level, and the condition that the service life expires is properly treated, so that the running risk of the power equipment is reduced.
As shown in fig. 2, an embodiment of the present invention provides a power equipment life cycle management system for a power supply system, including an acquisition module, an analysis module, a life cycle prediction module, a life loss probability calculation module, a maintenance planning model establishment module, and a calculation processing module;
the acquisition module is used for acquiring the information of the power equipment in the power system;
the analysis module is used for analyzing the collected power equipment information and acquiring the power equipment information with the life cycle deadline contained in the power equipment;
the life cycle prediction module is used for predicting the life cycle of the power equipment with the life cycle deadline according to the actual working environment;
the life loss probability calculation module is used for determining the life loss probability of the detection time point according to the predicted life cycle of the actual working environment;
the maintenance planning model establishing module is used for establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point;
and the calculation processing module is used for performing calculation processing according to the maintenance planning model and obtaining the optimal maintenance time of the power equipment with the life cycle deadline.
In some embodiments, the life cycle prediction module comprises a state data acquisition unit, a neural network prediction model establishing unit and a life cycle prediction unit;
the state data acquisition unit is used for acquiring running state data of the power equipment with a life cycle deadline, wherein the running state data comprises the running state data of each time point under different working environment conditions;
the neural network prediction model establishing unit is used for establishing a neural network prediction model through the operation state data of the power equipment with the life cycle deadline;
and the life cycle prediction unit is used for predicting the life cycle of the power equipment with the life cycle deadline through the established neural network prediction model.
In some embodiments, the neural network prediction model establishing unit includes a model constructing sub-module, a first training sub-module, a second training sub-module, a judgment output sub-module, and a prediction output sub-module;
the model construction submodule is used for primarily constructing a prediction model by selecting the number of layers and the number of nodes of an input layer, a hidden layer and an output layer of the neural network, and a function and a training algorithm;
the first training submodule is used for training the neural network model by using the collected partial operation state data as training data and adopting a simulated annealing algorithm to optimize parameters of the neural network;
the second training submodule is used for training again by combining the ant colony optimization algorithm by taking the optimized optimal parameters as initial values of corresponding parameters of the neural network to obtain output values of the neural network;
the judgment output submodule is used for judging the acquired output value of the neural network and a set accurate value, and stopping training the neural network model when the output value of the neural network reaches the set accurate value to obtain an optimized neural network prediction model;
and the prediction output sub-module is used for predicting the optimized neural network prediction model by taking the collected residual running state data as test data and outputting a prediction result.
In some embodiments, the different operating environmental conditions include operating the life cycle capable power equipment at ambient temperatures of-40 ℃, 20 ℃, 0 ℃, 25 ℃, 35 ℃ and 60 ℃, respectively.
In some embodiments, the inputs to the neural network are: ambient temperature, running power consumption, running voltage, running current, operating time, the output is: the life cycle of the electrical equipment.
In some embodiments, the system further comprises an ideal lifecycle acquisition module;
and the ideal life cycle acquisition module is used for acquiring the ideal life cycle of the equipment with the life cycle deadline. The ideal life cycle of each device is extracted from the device workbook information and stored in a database.
In some embodiments, the life loss probability calculation module comprises a calculation unit, a damage parameter determination unit, a damage probability model establishment unit and a failure probability calculation unit;
the computing unit is used for carrying out finite element computation on the power system according to the running state data;
the damage parameter determining unit is used for determining damage parameters according to the influence degree of each environmental factor on the service life of the power equipment in the power system;
the damage probability model establishing unit is used for establishing a power equipment damage probability model by determining the probability distribution of damage parameters;
the failure probability calculation unit is used for calculating the failure probability of the power equipment according to the damage probability model;
and the calculating unit is also used for determining the life loss probability of the detection time point according to the calculated failure probability and the predicted life cycle of the actual working environment.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A power supply system power equipment life cycle management method is characterized by comprising the following steps:
collecting power equipment information in a power system;
analyzing the collected power equipment information, and acquiring power equipment information with a life cycle deadline contained in the power equipment;
predicting the life cycle of the power equipment with the life cycle deadline according to the actual working environment;
determining the life loss probability of the detection time point according to the predicted life cycle of the actual working environment;
establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point;
and performing calculation processing according to the maintenance planning model, and obtaining the optimal maintenance time of the power equipment with the life cycle deadline.
2. The power supply system power device life cycle management method according to claim 1, wherein the step of predicting the life cycle of the power device having a life cycle deadline according to the actual working environment comprises:
s1: collecting running state data of the power equipment with a life cycle deadline, wherein the running state data comprises the running state data of each time point under different working environment conditions;
s2: establishing a neural network prediction model through the operation state data of the power equipment with the life cycle deadline;
s3: and predicting the life cycle of the power equipment with the life cycle deadline through the established neural network prediction model.
3. The power supply system power device lifecycle management method of claim 2, wherein the different operating environmental conditions comprise operating the power device with a lifecycle deadline at an ambient temperature of-40 ℃, -20 ℃, 0 ℃, 25 ℃, 35 ℃ and 60 ℃, respectively.
4. The power supply system power device lifecycle management method of claim 3, wherein the step of building a neural network predictive model from the operational state data of the power device for which there is a lifecycle deadline comprises:
s21: the number of layers and the number of nodes of an input layer, a hidden layer and an output layer of the neural network are selected, and a function and a training algorithm are selected to initially construct a prediction model;
s22: training a neural network model by using the collected partial operation state data as training data and adopting a simulated annealing algorithm to optimize parameters of the neural network;
s23: taking the optimized optimal parameters as initial values of corresponding parameters of the neural network, and training again by combining an ant colony optimization algorithm to obtain output values of the neural network;
s24: judging the obtained output value of the neural network and a set accurate value, and stopping training the neural network model when the output value of the neural network reaches the set accurate value to obtain an optimized neural network prediction model;
s25: and predicting the optimized neural network prediction model by using the collected residual running state data as test data, and outputting a prediction result.
5. The power supply system power device lifecycle management method of claim 3, wherein the inputs to the neural network are: ambient temperature, running power consumption, running voltage, running current, operating time, the output is: the life cycle of the electrical equipment.
6. The power supply system power device lifecycle management method of claim 1, wherein the step of predicting a lifecycle of the device having a lifecycle deadline based on an actual operating environment further comprises:
an ideal lifecycle for a device having a lifecycle deadline is obtained.
7. The power supply system power equipment lifecycle management method of claim 1, wherein the step of determining the lifetime loss probability at the detection time point according to the predicted lifecycle of the actual working environment comprises:
performing finite element calculation on the power system according to the running state data;
determining damage parameters according to the influence degree of each environmental factor on the service life of power equipment in the power system;
establishing a power equipment damage probability model by determining the probability distribution of damage parameters;
calculating the failure probability of the power equipment according to the damage probability model;
and determining the life loss probability of the detection time point according to the calculated failure probability and the predicted life cycle of the actual working environment.
8. A power supply system power equipment life cycle management system is characterized by comprising an acquisition module, an analysis module, a life cycle prediction module, a life loss probability calculation module, a maintenance planning model establishment module and a calculation processing module;
the acquisition module is used for acquiring the information of the power equipment in the power system;
the analysis module is used for analyzing the collected power equipment information and acquiring the power equipment information with the life cycle deadline contained in the power equipment;
the life cycle prediction module is used for predicting the life cycle of the power equipment with the life cycle deadline according to the actual working environment;
the life loss probability calculation module is used for determining the life loss probability of the detection time point according to the predicted life cycle of the actual working environment;
the maintenance planning model establishing module is used for establishing a maintenance planning model based on reliability analysis according to the ideal life cycle, the life loss probability and the detection time point;
and the calculation processing module is used for performing calculation processing according to the maintenance planning model and obtaining the optimal maintenance time of the power equipment with the life cycle deadline.
9. The power supply system power equipment life cycle management system of claim 8, wherein the life cycle prediction module comprises a state data acquisition unit, a neural network prediction model building unit, and a life cycle prediction unit;
the state data acquisition unit is used for acquiring running state data of the power equipment with a life cycle deadline, wherein the running state data comprises the running state data of each time point under different working environment conditions;
the neural network prediction model establishing unit is used for establishing a neural network prediction model through the operation state data of the power equipment with the life cycle deadline;
and the life cycle prediction unit is used for predicting the life cycle of the power equipment with the life cycle deadline through the established neural network prediction model.
10. The power supply system power equipment life cycle management system of claim 9, wherein the neural network prediction model establishing unit comprises a model construction sub-module, a first training sub-module, a second training sub-module, a judgment output sub-module, and a prediction output sub-module;
the model construction submodule is used for primarily constructing a prediction model by selecting the number of layers and the number of nodes of an input layer, a hidden layer and an output layer of the neural network, and a function and a training algorithm;
the first training submodule is used for training the neural network model by using the collected partial operation state data as training data and adopting a simulated annealing algorithm to optimize parameters of the neural network;
the second training submodule is used for training again by combining the ant colony optimization algorithm by taking the optimized optimal parameters as initial values of corresponding parameters of the neural network to obtain output values of the neural network;
the judgment output submodule is used for judging the acquired output value of the neural network and a set accurate value, and stopping training the neural network model when the output value of the neural network reaches the set accurate value to obtain an optimized neural network prediction model;
and the prediction output sub-module is used for predicting the optimized neural network prediction model by taking the collected residual running state data as test data and outputting a prediction result.
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