CN115409647A

CN115409647A - Energy router service life prediction method and device based on artificial intelligence

Info

Publication number: CN115409647A
Application number: CN202211020499.3A
Authority: CN
Inventors: 黄峰; 刘磊; 陈英炜; 孙钦根; 甘俊杰
Original assignee: Kalif Electronics Co ltd
Current assignee: Kalif Electronics Co ltd
Priority date: 2022-08-24
Filing date: 2022-08-24
Publication date: 2022-11-29

Abstract

The application relates to a life prediction method and a life prediction device of an energy router based on artificial intelligence, which relate to the technical field of information processing, and the method comprises the steps of obtaining a standard degradation curve and abnormal data of the energy router; determining a reduced duration based on the anomaly data; generating a predicted degradation curve based on the standard degradation curve and the reduction duration; the service life is determined based on the predicted degradation curve. The method and the device have the effect of facilitating accurate prediction of the service life of the energy router by workers.

Description

Energy router service life prediction method and device based on artificial intelligence

Technical Field

The application relates to the technical field of information processing, in particular to an artificial intelligence-based energy router service life prediction method and device.

Background

The energy router, as a backbone device of the energy network, has various important functions such as energy access, transmission, conversion, routing and the like, and is the most important information and energy infrastructure in the energy network.

At present, an energy router integrates an information technology and a power electronic conversion technology, and efficient transmission and utilization of distributed energy are achieved, but the energy router may break down in the using process, and the service life of the energy router is affected by the faults, so that workers cannot accurately predict the service life of the energy router, the energy router is found when the energy router is not available, and normal operation of an energy network is affected.

Disclosure of Invention

In order to facilitate workers to accurately predict the service life of an energy router, the application provides an energy router service life prediction method and device based on artificial intelligence.

In a first aspect, the present application provides a method for predicting a lifetime of an energy router based on artificial intelligence, which adopts the following technical scheme:

an artificial intelligence-based energy router life prediction method comprises the following steps:

acquiring a standard degradation curve and abnormal data of the energy router;

determining a reduced duration based on the anomaly data;

generating a predicted degradation curve based on the standard degradation curve and the reduced duration;

determining a service life based on the predicted degradation curve.

By adopting the technical scheme, the standard degradation curve and the abnormal data of the energy router are obtained, and further the conventional degradation rule of the energy router of the type and the fault of the energy router in the using process are obtained. And determining the reduction duration based on the abnormal data, wherein the energy router can have a certain influence on the service life after the abnormality occurs. And generating a prediction degradation curve based on the standard degradation curve and the reduction duration, wherein the prediction degradation curve is more accurate to the prediction of degradation relative to the standard degradation curve, and the service life is determined based on the prediction degradation curve, so that the service life is closer to the specific use condition of the energy router, and the prediction is more accurate.

In another possible implementation manner, the determining a reduced duration based on the anomaly data includes:

acquiring maintenance information corresponding to the abnormal data, wherein the maintenance information comprises maintenance duration, debugging times and replacement information;

determining a fault level based on the repair information;

and determining the reduction duration corresponding to the fault level.

By adopting the technical scheme, the maintenance information corresponding to the abnormal data is obtained, and if the abnormal data occurs, the energy router fails, and related workers can maintain the failure, so that the maintenance information can be generated. And determining a fault grade based on the maintenance information, wherein different maintenance information corresponds to different fault grades, determining the reduction duration corresponding to the fault grade, and the damage of the fault with higher grade to the energy router is also larger, so that the reduction duration is higher, and the subsequent service life calculation is more accurate.

In another possible implementation manner, the determining a fault level based on the repair information includes:

judging whether the maintenance information contains replacement information or not;

if yes, determining the fault grade as a first grade;

if not, judging whether the maintenance time length is greater than a preset time length or not, and whether the debugging times are greater than preset times or not;

if the fault level is greater than the preset time and greater than the preset times, determining that the fault level is a second level;

and if the fault grade is not greater than the preset time length and/or the preset times, determining the fault grade as a third grade.

By adopting the technical scheme, whether the maintenance information contains the replacement information or not is judged, if yes, it is indicated that parts inside the energy router are possibly damaged, the maintenance degree is large, and therefore the fault grade is determined to be the first grade. If not, it is stated that the internal parts are not damaged, a problem may occur in the line, whether the maintenance time is longer than a preset time or not and whether the debugging frequency is longer than a preset frequency or not are judged, and the maintenance difficulty can be visually embodied by the maintenance time and the debugging frequency. If the maintenance duration is longer than the preset duration and the debugging times are greater than the preset times, it is indicated that the current maintenance difficulty is higher, the fault level is relatively higher if the difficulty is higher, and therefore the fault level is determined to be the second level. If the maintenance duration is not greater than the preset duration and/or the debugging frequency is not greater than the preset frequency, the maintenance difficulty is low, and therefore the fault level is determined to be a third level. And determining the fault level based on the maintenance information enables the fault level to be more accurate and to be more consistent with the service condition of the energy router.

In another possible implementation manner, the generating a predicted degradation curve based on the standard degradation curve and the reduction duration includes:

determining abnormal time corresponding to the abnormal data;

generating an attenuation coordinate point based on the abnormal time and the reduced duration;

fitting the predicted degradation curve based on the standard degradation curve and the attenuation coordinate points.

By adopting the technical scheme, the abnormal time corresponding to the abnormal data, namely the time when the fault occurs is determined. And generating an attenuation coordinate point based on the abnormal time and the shortened duration, wherein the attenuation coordinate point can intuitively reflect the influence of the abnormal data on the service life of the energy router at the abnormal time. And fitting the predicted degradation curve based on the standard degradation curve and the attenuation coordinate points, so that the predicted degradation curve prediction is closer to the actual situation.

In another possible implementation manner, the fitting the predicted degradation curve based on the standard degradation curve and the attenuation coordinate point includes:

determining a first standard point and a second standard point on the standard degradation curve based on the attenuation coordinate point, wherein the first standard point corresponds to a moment earlier than the abnormal moment, and the second standard point corresponds to a moment later than the abnormal moment;

determining fitting coordinate points based on the first standard points, the second standard points, and the attenuation coordinate points;

generating the predicted degradation curve based on the first standard points, the second standard points, and the fitted coordinate points.

By adopting the technical scheme, the fitting coordinate point is determined based on the first standard point, the second standard point and the attenuation coordinate point, so that the prediction degradation curve can be more accurately fitted without generating obvious mutation. And generating a prediction degradation curve based on the first standard point, the second standard point and the fitted coordinate point, wherein the prediction degradation curve combines the practical situation of the energy router, so that the service life of the energy router is more accurately predicted subsequently.

In another possible implementation manner, the determining a fitting coordinate point based on the first standard point, the second standard point, and the attenuation coordinate point includes:

determining a first vector based on the first criterion point and the attenuation coordinate point;

determining a second vector based on the first criterion point and the second criterion point;

determining an attenuation vector based on the first vector and the second vector, the attenuation vector being one-half of a sum of the first vector and the second vector;

determining fitting coordinate points based on the first criterion points and the attenuation vectors.

By adopting the technical scheme, the attenuation vector is determined based on the first vector and the second vector, and the fitting coordinate point is determined based on the first standard point and the attenuation vector, so that the fitting coordinate point is more reasonable, has smaller deviation with the attenuation coordinate point, and is combined with the actual use condition of the energy router.

In another possible implementation manner, the determining the service life based on the predicted degradation curve further includes:

acquiring initial time, wherein the initial time is the time when the energy router starts to use;

determining a replacement time based on the initial time and the service life;

and outputting the replacing time.

By adopting the technical scheme, the replacement time is determined and output so as to prompt relevant co-workers to replace the energy router in time before the energy router is unusable, and the possibility that the normal operation of the energy network is influenced due to untimely replacement is reduced.

In a second aspect, the present application provides an artificial intelligence-based energy router lifetime prediction apparatus, which adopts the following technical solution:

an artificial intelligence-based energy router life prediction apparatus, comprising:

the acquisition module is used for acquiring a standard degradation curve and abnormal data of the energy router;

a duration determination module to determine a reduced duration based on the anomaly data;

a generate curve module to generate a predicted degradation curve based on the standard degradation curve and the reduced duration;

a lifetime determination module to determine a lifetime based on the predicted degradation curve.

By adopting the technical scheme, the acquisition module acquires the standard degradation curve and the abnormal data of the energy router, so that the conventional degradation rule of the energy router of the type and the fault of the energy router in the use process are known. The time length determining module determines the shortened time length based on the abnormal data, and the energy router can have certain influence on the service life after the abnormality occurs. The generation curve module generates a prediction degradation curve based on the standard degradation curve and the reduction duration, the prediction degradation curve is more accurate to the prediction of degradation relative to the standard degradation curve, and the service life determining module determines the service life based on the prediction degradation curve, so that the service life is closer to the specific use condition of the energy router, and the prediction is more accurate.

In another possible implementation manner, the duration determining module, when determining the reduced duration based on the abnormal data, is specifically configured to:

determining a fault level based on the repair information;

and determining the reduction duration corresponding to the fault level.

In another possible implementation manner, when the duration determining module determines the fault level based on the maintenance information, the duration determining module is specifically configured to:

if yes, determining the fault grade as a first grade;

if not, judging whether the maintenance time length is greater than a preset time length or not, and whether the debugging frequency is greater than a preset frequency or not;

and if the fault level is not greater than the preset time length and/or the preset times, determining that the fault level is a third level.

In another possible implementation, the curve generation module, when generating the predicted degradation curve based on the standard degradation curve and the reduced duration, is specifically configured to:

determining abnormal time corresponding to the abnormal data;

In another possible implementation manner, the curve generation module, when fitting the predicted degradation curve based on the standard degradation curve and the attenuation coordinate points, is specifically configured to:

generating the predicted degradation curve based on the first standard point, the second standard point, and the fitted coordinate point.

In another possible implementation manner, when determining the fitting coordinate point based on the first standard point, the second standard point, and the attenuation coordinate point, the curve generation module is specifically configured to:

In another possible implementation manner, the apparatus further includes:

the acquisition time module is used for acquiring initial time, wherein the initial time is the time when the energy router starts to be used;

a replacement determining module for determining a replacement time based on the initial time and the service life;

and the output module is used for outputting the replacement time.

In a third aspect, the present application provides an electronic device, which adopts the following technical solutions:

an electronic device, comprising:

one or more processors;

a memory;

one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, the one or more application programs configured to: an artificial intelligence based energy router lifetime prediction method according to any one of the possible implementations of the first aspect is performed.

In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:

a computer-readable storage medium, comprising: there is stored a computer program that can be loaded by a processor and that executes a method for artificial intelligence based energy router lifetime prediction as illustrated in any one of the possible implementations of the first aspect.

In summary, the present application includes at least one of the following beneficial technical effects:

1. and acquiring a standard degradation curve and abnormal data of the energy router, and further acquiring a conventional degradation rule of the energy router of the type and faults of the energy router in the using process. And determining the reduction duration based on the abnormal data, wherein the energy router can have a certain influence on the service life after the abnormality occurs. Generating a prediction degradation curve based on the standard degradation curve and the reduction duration, wherein the prediction degradation curve is more accurate to the prediction of degradation relative to the standard degradation curve, and the service life is determined based on the prediction degradation curve, so that the service life is closer to the specific use condition of the energy router, and the prediction is more accurate;

2. and acquiring maintenance information corresponding to the abnormal data, wherein the abnormal data indicates that the energy router has a fault, and related workers can maintain the fault, so that the maintenance information can be generated. And determining a fault grade based on the maintenance information, wherein different maintenance information corresponds to different fault grades, determining the reduction duration corresponding to the fault grade, and the damage of the fault with higher grade to the energy router is also larger, so that the reduction duration is higher, and the subsequent service life calculation is more accurate.

Drawings

Fig. 1 is a schematic flowchart of an artificial intelligence-based energy router life prediction method according to an embodiment of the present application.

Fig. 2 is a schematic flowchart of an artificial intelligence-based energy router life prediction apparatus according to an embodiment of the present application.

Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The present application is described in further detail below with reference to figures 1-3.

A person skilled in the art, after reading the present specification, may make modifications to the present embodiments as necessary without inventive contribution, but only within the scope of the claims of the present application are protected by patent laws.

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 some embodiments of the present application, but 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 application.

In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship, unless otherwise specified.

The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.

The embodiment of the application provides an energy router service life prediction method based on artificial intelligence, which is executed by electronic equipment, wherein the electronic equipment can be a server or terminal equipment, the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud computing service. The terminal device may be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but is not limited thereto, the terminal device and the server may be directly or indirectly connected through a wired or wireless communication manner, and the embodiment of the present application is not limited thereto, as shown in fig. 1, the method includes step S101, step S102, step S103 and step S104, wherein,

and step S101, acquiring a standard degradation curve and abnormal data of the energy router.

For the embodiment of the application, the electronic device may obtain a standard degradation curve from a database or a cloud server, where the standard degradation curves corresponding to different types of energy routers are different, where an abscissa of the standard degradation curve is a date and a time, and a ordinate of the standard degradation curve is a usage duration of the energy router. The electronic device can acquire abnormal data from a sensor installed inside the energy router, and the electronic device can also acquire abnormal data from a voltmeter or an ammeter connected with the energy router. Energy router has standard operating data when steady operation, if electronic equipment detects that current operating data and standard operating data have the deviation, and the time of taking place the deviation is greater than the preset duration, then electronic equipment can confirm current operating data and be abnormal data, wherein, electronic equipment can obtain the preset duration from the database, also can artifically set for the preset duration, for example: the preset time is 30 seconds.

Step S102, determining the reduction duration based on the abnormal data.

For the embodiment of the application, the energy router can have certain influence on the service life after being abnormal, and the electronic equipment can acquire the shortened duration from the database and also acquire the shortened data from the cloud server based on the different shortened durations corresponding to different abnormal data.

In step S103, a predicted degradation curve is generated based on the standard degradation curve and the reduction period.

For the embodiment of the application, on the basis of a standard degradation curve, the ordinate of the standard degradation curve is adjusted based on the electronic equipment with reduced time length, and then a predicted degradation curve is generated, and the predicted degradation curve is more accurate in prediction of degradation relative to the standard degradation curve, wherein the abscissa of the predicted degradation curve is the date and time, and the ordinate is the service time of the energy router.

And step S104, determining the service life based on the predicted degradation curve.

For the embodiment of the application, the electronic device can know the service life of the energy router at different dates and times based on the abscissa of the prediction degradation curve, and the electronic device can obtain the date and time when the energy router needs to be replaced by determining the value corresponding to the abscissa when the ordinate of the prediction degradation curve is reduced to zero, and determine the service life based on the prediction degradation curve, so that the service life is closer to the specific service condition of the energy router, and the prediction is more accurate.

In a possible implementation manner of the embodiment of the present application, the determining the reduced duration in step S102 based on the abnormal data specifically includes step S1021 (not shown), step S1022 (not shown), and step S1023 (not shown), wherein,

step S1021, the maintenance information corresponding to the abnormal data is obtained.

The maintenance information comprises maintenance duration, debugging times and replacement information.

For the embodiment of the application, if abnormal data occurs in the energy router, the energy router is indicated to have a fault, and related workers can maintain the fault, so that maintenance information can be generated, the workers upload the maintenance information to the database or the cloud server after maintenance, and the electronic equipment can acquire the maintenance information from the database or the cloud server. Wherein, the replacement information in the maintenance information comprises information of replacing internal parts of the energy router.

In step S1022, a failure level is determined based on the maintenance information.

For the embodiment of the application, different maintenance information corresponds to different fault grades, and the fault with higher grade has larger damage to the energy router.

In step S1023, the reduced duration corresponding to the failure level is determined.

For the embodiment of the application, different fault levels correspond to different reduction durations, the corresponding relation between the fault levels and the reduction durations can be stored in a database or uploaded to a cloud server, and the reduction time with a higher fault level is longer. For example:

the electronic equipment acquires the reduced time length corresponding to the first level in the fault levels from the database, wherein the reduced time length is 10 days.

In a possible implementation manner of the embodiment of the present application, the determining the fault level based on the maintenance information in step S1022 specifically includes step S1022a (not shown in the figure), step S1022b (not shown in the figure), step S1022c (not shown in the figure), step S1022d (not shown in the figure), and step S1022e (not shown in the figure), wherein,

in step S1022a, it is determined whether the maintenance information includes replacement information.

For the embodiment of the application, the electronic equipment can perform semantic recognition on the maintenance information through a natural language technology, and further know whether the maintenance information contains the replacement information; the electronic device can also extract keywords from the maintenance information through a natural language technology, check whether verbs such as 'change' or similar word senses exist, and further know whether the maintenance information contains the change information.

In step S1022b, if yes, the failure level is determined to be the first level.

For the embodiment of the application, if the electronic device determines that the maintenance information includes the replacement information, it indicates that not only is a common circuit damaged or a signal problem in the energy router, but also internal parts are damaged, which may be burned or artificially broken, and the like, and the damage to the energy router is large, which directly affects the service life of the energy router, and the impact is large, so that the electronic device determines that the fault level is the first level.

Step S1022c, if not, determine whether the maintenance duration is greater than the preset duration, and whether the debugging frequency is greater than the preset frequency.

In the embodiment of the present application, if the electronic device determines that the replacement information is not included, it indicates that the abnormality of the energy router may be a circuit problem, a signal failure, or the like, not due to damage of internal components. The electronic equipment judges whether the maintenance time length is greater than a preset time length, the longer the maintenance time length is, the more complex the current fault is, wherein the preset time length can be obtained from a database or manually input, the preset time length is the average time length when a worker maintains common faults,

for example: and if the preset time is 30 minutes, the electronic equipment judges whether the maintenance time is more than 30 minutes.

The electronic equipment judges whether the debugging times are greater than preset times, the more debugging times indicate that the current fault is more complex, wherein the preset times can be obtained from a database or manually input, the preset times are average times when workers maintain common faults,

for example: assuming that the preset number of times is 10 times, the electronic device determines whether the debugging number of times is greater than 10 times.

In step S1022d, if the time length is greater than the preset time length and the number of times is greater than the preset number, it is determined that the fault level is the second level.

For the embodiment of the application, if the electronic device determines that the maintenance time is longer than the preset time and the debugging frequency is greater than the preset frequency, the current maintenance time is longer, a more complex fault is likely to be processed, the debugging needs to be repeated in the fault processing process, each step of processing the fault is important, and the next step can be carried out after the debugging is passed; or the fault needs to be handled and then debugged repeatedly to achieve the best effect. The electronics determine such a failure that is difficult and complex to repair as a second level, which is lower than the first level, which is biased toward difficult repair, which is biased toward greater damage to the energy router, but both of which reduce the useful life of the energy router.

In step S1022e, if the time length is not greater than the preset time length and/or the time length is not greater than the preset number, the fault level is determined to be the third level.

For the embodiment of the application, if the electronic device determines that the maintenance duration is not greater than the preset duration, it indicates that the maintenance is simpler for a worker, and perhaps only the line is disconnected, so that the service life of the energy router is slightly affected. If the electronic device determines that the debugging times are not greater than the preset times, it indicates that the worker determines that the energy router is repaired through only a few simple debugging times, which may be caused by poor signals and has little influence on the service life of the energy router. Accordingly, the electronic device determines the failure level as the third level. And determining the fault level based on the maintenance information enables the fault level to be more accurate and to be more consistent with the service condition of the energy router.

In a possible implementation manner of the embodiment of the present application, the step S103 of fitting and predicting the degradation curve based on the standard degradation curve and the attenuation coordinate points specifically includes a step S1031 (not shown in the figure), a step S1032 (not shown in the figure), and a step S1033 (not shown in the figure), wherein,

and step S1031, determining an abnormal time corresponding to the abnormal data.

For the embodiment of the application, the electronic device determines the time corresponding to the acquired abnormal data, that is, the time when the fault occurs, and determines the time as the abnormal time, and the electronic device may acquire the time based on a built-in clock chip, and may also acquire the time from the internet.

In step S1032, an attenuation coordinate point is generated based on the abnormality time and the reduction period.

For the embodiment of the application, the abscissa in the predicted degradation curve is the date time, the ordinate is the usage duration, wherein the abnormal time corresponds to the abscissa, the reduced duration is related to the ordinate of the curve, assuming that the reduced duration is 10 days, the abnormal time is 2022/7/18, the electronic device knows that the original usage duration at 2022/7/18 is 100 days based on the standard degradation curve, and the electronic device determines that the new usage duration at 2022/7/18 is: 100-10=90 days, therefore, the electronic device determines the attenuation coordinate point based on the abnormal time 2022/7/18 and the new usage duration of 90 days, and the attenuation coordinate point can intuitively reflect the influence of the abnormal data on the service life of the energy router at the abnormal time.

Step S1033, fits the prediction degradation curve based on the standard degradation curve and the attenuation coordinate points.

For the embodiment of the application, the electronic device adjusts the standard degradation curve based on the attenuation coordinate point, so as to generate the predicted degradation curve, and the predicted degradation curve is closer to the actual use condition of the energy router.

In a possible implementation manner of the embodiment of the present application, the step S1034 of fitting the predicted degradation curve based on the standard degradation curve and the attenuation coordinate points specifically includes a step S10341 (not shown), a step S10342 (not shown), and a step S10343 (not shown), where,

in step S10341, a first criterion point and a second criterion point are determined on the criterion degradation curve based on the attenuation coordinate points.

The time corresponding to the first standard point is earlier than the abnormal time, and the time corresponding to the second standard point is later than the abnormal time.

For the embodiment of the application, the standard degradation curve includes the service life corresponding to each date and time, the electronic device determines the first standard point and the second standard point based on the abnormal time, and if the abnormal time is 2022/7/18, the electronic device determines that the first standard point is 2022/7/17 and the second standard point is 2022/7/19, and the electronic device may also determine a plurality of first standard points and a plurality of second standard points, which is more accurate in subsequent calculation.

In step S10342, fitting coordinate points are determined based on the first standard points, the second standard points, and the attenuation coordinate points.

For the embodiment of the application, the electronic equipment determines the fitting coordinate points, so that the fitting prediction of the degradation curve is more accurate, and no obvious mutation is generated.

In step S10343, a predicted degradation curve is generated based on the first standard points, the second standard points, and the fitted coordinate points.

For the embodiment of the application, the electronic equipment can generate the prediction degradation curve through a least square method, the electronic equipment can also perform curve simulation by adopting Matlab, and the prediction degradation curve combines with the practical situation of the energy router, so that the service life of the energy router can be predicted more accurately in the follow-up process.

In a possible implementation manner of the embodiment of the present application, the determining of the fitting coordinate points based on the first standard points, the second standard points, and the attenuation coordinate points in step S10342 specifically includes step S10342a (not shown in the figure), step S10342b (not shown in the figure), step S10342c (not shown in the figure), and step S10342d (not shown in the figure), wherein,

in step S10342a, a first vector is determined based on the first criterion point and the attenuation coordinate point.

For the embodiment of the application, the electronic device performs a difference between the attenuation coordinate point and the first standard point to obtain a first vector, for example:

assuming that the attenuation coordinate point A is (x 1, y 1) and the first standard point B is (x 2, y 2), the electronic device calculates a first vector AB as (x 2-x1, y2-y 1).

In step S10342b, a second vector is determined based on the first criterion point and the second criterion point.

For the embodiment of the present application, the electronic device obtains the second vector by subtracting the first standard point from the second standard point, taking the first standard point B in step S10342a as an example:

assuming that the first criterion point C is (x 3, y 3), the electronic device calculates a second vector AC as (x 3-x2, y3-y 2).

In step S10342c, an attenuation vector is determined based on the first vector and the second vector.

Wherein the attenuation vector is one-half of the sum of the first vector and the second vector.

For the embodiment of the application, the electronic device calculates the vector sum of the first vector and the second vector, and then calculates one half of the vector sum to obtain the attenuation vector. Taking the first vector and the second vector of step S10342a and step S10342b as an example:

the electronic device calculates the vector sum of vector AB and vector AC as: (x 3-x1, y3-y 1). The electronic device calculates one-half of the vector sum to obtain an attenuation vector as: (1/2 (x 3-x 1), 1/2 (y 3-y 1)). And determining an attenuation vector based on the first vector and the second vector, and determining a fitting coordinate point based on the first standard point and the attenuation vector, so that the fitting coordinate point is more reasonable and has smaller deviation with the attenuation coordinate point, and the actual use condition of the energy router is also combined.

In a possible implementation manner of the embodiment of the present application, the method further includes step S105 (not shown in the figure), step S106 (not shown in the figure), and step S107 (not shown in the figure), the step S105 may be executed after step S104, and step S106 and step S107 are executed sequentially after step S105, wherein,

in step S105, an initial time is acquired.

The initial time is the time when the energy router starts to be used.

For the embodiment of the application, when the energy router starts to be used, the staff can store the starting time of the energy router as the initial time in the database or upload the starting time to the cloud server, wherein the initial time is the abscissa value of the initial coordinate point of the standard degradation curve and the predicted degradation curve.

Step S106, the replacement time is determined based on the initial time and the service life.

For the embodiment of the application, the electronic device can obtain the replacement time of the energy router by determining the value corresponding to the abscissa when the ordinate of the predicted degradation curve is reduced to zero.

In step S107, the replacement time is output.

To this application embodiment, electronic equipment can send the terminal equipment of staff constantly to changing, and electronic equipment also can control the display screen and show in real time constantly changing to the relevant staff of working together of suggestion is in time changed before energy router can not use, reduces because change influence the possibility of energy network normal operating not in time.

The above embodiment introduces a method for predicting the life of an energy router based on artificial intelligence from the perspective of a method flow, and the following embodiment introduces a device for predicting the life of an energy router based on artificial intelligence from the perspective of a virtual module or a virtual unit, which is described in detail in the following embodiment.

The embodiment of the present application provides an artificial intelligence based energy router life prediction apparatus 20, as shown in fig. 2, the artificial intelligence based energy router life prediction apparatus 20 may specifically include:

an obtaining module 201, configured to obtain a standard degradation curve and abnormal data of an energy router;

a determine duration module 202 for determining a reduced duration based on the anomaly data;

a curve generation module 203 for generating a predicted degradation curve based on the standard degradation curve and the reduction duration;

a determine lifetime module 204 for determining a service life based on the predicted degradation curve.

By adopting the above technical solution, the obtaining module 201 obtains the standard degradation curve and the abnormal data of the energy router, and further obtains the conventional degradation rule of the energy router of the model, and the fault of the energy router in the using process. The duration determining module 202 determines the reduced duration based on the abnormal data, and the energy router may have a certain influence on the service life of the energy router after the abnormality occurs. The curve generation module 203 generates a predicted degradation curve based on the standard degradation curve and the reduction duration, the predicted degradation curve is more accurate in prediction of degradation relative to the standard degradation curve, and the life determination module 204 determines the service life based on the predicted degradation curve, so that the service life is closer to the specific use condition of the energy router, and the prediction is more accurate.

In a possible implementation manner of the embodiment of the present application, the duration determining module 202, when determining to reduce the duration based on the abnormal data, is specifically configured to:

determining a fault level based on the maintenance information;

and determining the reduction duration corresponding to the fault level.

In a possible implementation manner of the embodiment of the present application, when the duration determining module 202 determines the fault level based on the maintenance information, the duration determining module is specifically configured to:

judging whether the maintenance information contains replacement information;

if yes, determining the fault grade as a first grade;

if not, judging whether the maintenance time length is greater than the preset time length or not, and whether the debugging frequency is greater than the preset frequency or not;

if the fault grade is greater than the preset time and the preset times, determining the fault grade as a second grade;

and if the fault rate is not greater than the preset time length and/or the preset times, determining the fault rate as a third rate.

In a possible implementation manner of the embodiment of the present application, when the curve generation module 203 generates the predicted degradation curve based on the standard degradation curve and the reduction duration, it is specifically configured to:

determining abnormal time corresponding to the abnormal data;

and fitting and predicting the degradation curve based on the standard degradation curve and the attenuation coordinate points.

In a possible implementation manner of the embodiment of the present application, the curve generation module 203 is specifically configured to, when fitting the prediction degradation curve based on the standard degradation curve and the attenuation coordinate point:

determining a first standard point and a second standard point on the standard degradation curve based on the attenuation coordinate point, wherein the time corresponding to the first standard point is earlier than the abnormal time, and the time corresponding to the second standard point is later than the abnormal time;

determining a fitting coordinate point based on the first standard point, the second standard point and the attenuation coordinate point;

and generating a predicted degradation curve based on the first standard point, the second standard point and the fitted coordinate point.

In a possible implementation manner of the embodiment of the present application, when the curve generation module 203 determines the fitting coordinate point based on the first standard point, the second standard point, and the attenuation coordinate point, it is specifically configured to:

determining an attenuation vector based on the first vector and the second vector, the attenuation vector being one-half of the sum of the first vector and the second vector;

a fitting coordinate point is determined based on the first criterion point and the attenuation vector.

In a possible implementation manner of the embodiment of the present application, the apparatus 20 further includes:

the acquisition time module is used for acquiring initial time, and the initial time is the time when the energy router starts to use;

and the output module is used for outputting the replacing time.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In an embodiment of the present application, an electronic device is provided, and as shown in fig. 3, an electronic device 30 shown in fig. 3 includes: a processor 301 and a memory 303. Wherein processor 301 is coupled to memory 303, such as via bus 302. Optionally, the electronic device 30 may also include a transceiver 304. It should be noted that the transceiver 304 is not limited to one in practical applications, and the structure of the electronic device 30 is not limited to the embodiment of the present application.

The Processor 301 may be a CPU (Central Processing Unit), a general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other Programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 301 may also be a combination implementing a computing function. E.g., a combination comprising one or more microprocessors, a combination of a DSP and a microprocessor, etc.

Bus 302 may include a path that transfers information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 302 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 3, but this does not mean only one bus or one type of bus.

The Memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic Disc storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired application code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.

The memory 303 is used for storing application program codes for executing the scheme of the application, and the processor 301 controls the execution. The processor 301 is configured to execute application program code stored in the memory 303 to implement the aspects illustrated in the foregoing method embodiments.

Wherein, the electronic device includes but is not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. But also a server, etc. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

The present application provides a computer-readable storage medium, on which a computer program is stored, which, when running on a computer, enables the computer to execute the corresponding content in the foregoing method embodiments. Compared with the related art, the electronic equipment in the embodiment of the application acquires the standard degradation curve and the abnormal data of the energy router, so that the conventional degradation rule of the energy router of the type and the fault of the energy router in the use process are known. The electronic equipment determines the reduction duration based on the abnormal data, and the service life of the energy router can be influenced to a certain extent after the energy router is abnormal. The electronic equipment generates a prediction degradation curve based on the standard degradation curve and the reduction duration, the prediction degradation curve is more accurate in prediction of degradation relative to the standard degradation curve, and the electronic equipment determines the service life based on the prediction degradation curve, so that the service life is closer to the specific use condition of the energy router, and the prediction is more accurate.

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

The foregoing is only a partial embodiment of the present application, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations should also be regarded as the protection scope of the present application.

Claims

1. An artificial intelligence-based energy router life prediction method is characterized by comprising the following steps:

acquiring a standard degradation curve and abnormal data of the energy router;

determining a reduced duration based on the anomaly data;

determining a service life based on the predicted degradation curve.

2. The artificial intelligence-based energy router life prediction method of claim 1, wherein said determining a reduced duration based on said anomaly data comprises:

determining a fault level based on the repair information;

and determining the reduction duration corresponding to the fault level.

3. The artificial intelligence based energy router life prediction method of claim 2, wherein said determining a fault level based on said repair information comprises:

if yes, determining the fault grade as a first grade;

if the fault grade is greater than the preset time length and the preset times, determining that the fault grade is a second grade;

4. The artificial intelligence-based energy router life prediction method of claim 1, wherein generating a predicted degradation curve based on the standard degradation curve and the reduced duration comprises:

determining abnormal time corresponding to the abnormal data;

5. The artificial intelligence-based energy router life prediction method of claim 4, wherein said fitting the predicted degradation curve based on the standard degradation curve and the decay coordinate points comprises:

6. The artificial intelligence-based energy router life prediction method of claim 5, wherein the determining fitting coordinate points based on the first criterion point, the second criterion point, and the decay coordinate points comprises:

7. The artificial intelligence based energy router life prediction method of claim 1, wherein said determining a service life based on said predicted degradation curve further comprises:

determining a replacement time based on the initial time and the service life;

and outputting the replacing time.

8. An artificial intelligence-based energy router life prediction apparatus, comprising:

a reduced duration module to determine a reduced duration based on the anomaly data;

9. An electronic device, comprising:

one or more processors;

a memory;

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to: performing an artificial intelligence based energy router lifetime prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for artificial intelligence based energy router lifetime prediction according to any one of claims 1 to 7.