CN115792476B - Charging pile rectifying module abnormality early warning method, device, terminal and storage medium - Google Patents
Charging pile rectifying module abnormality early warning method, device, terminal and storage medium Download PDFInfo
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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- Y02T10/60—Other road transportation technologies with climate change mitigation effect
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
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Abstract
The invention relates to the technical field of monitoring of charging equipment, in particular to a charging pile rectifying module abnormality early warning method, a charging pile rectifying module abnormality early warning device, a charging pile rectifying module abnormality early warning terminal and a charging pile rectifying module abnormality early warning storage medium; then, according to the influence of a plurality of influence factors on the normal operation of the charging pile rectifying module, respectively carrying out data preprocessing on the plurality of monitoring data to obtain a plurality of factor data; and finally, inputting the plurality of factor data into an early warning model to obtain an early warning message. According to the embodiment of the invention, the working state of the rectifying module is judged according to the monitoring data through the artificial neural network, so that the abnormality of the rectifying module can be found in time according to the monitoring data, the processes of manual inspection and maintenance are reduced, and the maintenance efficiency is improved. According to the embodiment of the invention, the data accumulation data and the change rate data are extracted according to the monitoring data, the influences of the occult factors and the severe change factors on the working state of the rectification module are fully considered, and the abnormal early warning is more sensitive.
Description
Technical Field
The invention relates to the technical field of monitoring of charging equipment, in particular to a charging pile rectifying module abnormality early warning method, a charging pile rectifying module abnormality early warning device, a charging pile rectifying module abnormality early warning terminal and a charging pile rectifying module storage medium.
Background
Engineering vehicles using fuel oil as energy sources, in particular to 'oil tiger' for heavy trucks, mining trucks, loaders and the like, are coming into electric lasting change.
Green development is becoming a trend, and the engineering vehicle with high carbon emission is naturally also added with carbon-reducing wave. With the increasing complexity of the use environment of electric vehicles, not only living places, but also new energy automobiles, such as mining sites, iron and steel plants, and the like, are beginning to appear in more and more industrial sites. With the increasing complexity of the environment in which the vehicle is used, the charging pile must also be adapted to a complex industrial environment in which a great problem that the charging pile needs to face is the problem of dust pollution on site.
Different from living places, the dust components are more complex, such as metal-containing dust and coal dust in mines and iron and steel plants. Because the direct current fills electric pile power big, intensity of use is high, and its heat dissipation capacity is also great, and the cooling fan power is high, consequently necessarily leads to the accumulation of this kind of dust in the rectifier module inside.
The rectifier module inner space is narrow and small, the inside dust of clearance must open the module and just can operate, and ordinary inspection naked eyes hardly observe inside deposition, and it is also unrealistic to open the rectifier module and observe the dust deposition at every turn to patrol and examine. Excessive dust accumulation can have the following serious consequences: heat dissipation is affected, so that the service life of the module is reduced due to overheating, and even the module is burnt; when the humidity is high, the dust absorbs water and wets, so that the components are short-circuited; in special use places, such as iron and steel works, mines and other places, excessive metal dust can be sucked to cause direct short circuit, and serious consequences such as explosion and fire are generated.
Aiming at the problems, a method for realizing early warning of the charging pile rectifying module is provided.
Disclosure of Invention
The embodiment of the invention provides a charging pile rectifying module abnormality early warning method, a charging pile rectifying module abnormality early warning device, a charging pile rectifying module abnormality early warning terminal and a charging pile rectifying module abnormality early warning storage medium, which are used for solving the problem that in the prior art, inspection and maintenance are required to be carried out manually and regularly.
In a first aspect, an embodiment of the present invention provides a method for early warning of an abnormality of a rectifying module of a charging pile, including:
acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influence factors influencing the normal operation of the charging pile rectifying module;
according to the influence of a plurality of influence factors on the normal operation of the charging pile rectifying module, respectively carrying out data preprocessing on the plurality of monitoring data to obtain a plurality of factor data;
and inputting the plurality of factor data into an early warning model to obtain early warning information, wherein the early warning model builds an initial model based on an artificial neural network model, trains the initial model based on a plurality of histories, and records the histories of the working states of the charging pile rectifying modules and the histories of the monitoring data of the operation of the charging pile rectifying modules in the histories.
In one possible implementation manner, the influence of the plurality of influence factors on the normal operation of the charging pile rectifying module is determined according to the plurality of history records, and the determining process of the influence of the plurality of influence factors on the normal operation of the charging pile rectifying module includes:
sequencing the plurality of history records according to the time sequence to obtain a history record queue;
collecting the historical monitoring data belonging to the same influencing factor in the historical record queues according to the time sequence order to obtain a plurality of historical monitoring data queues;
collecting the historical working states of the charging pile rectifying modules in the historical record queue according to the time sequence, and obtaining a historical working state queue;
for each of the plurality of history monitoring data queues, performing the steps of:
generating an accumulation queue and a differential queue according to the historical monitoring data queue, wherein the accumulation queue represents accumulation sums of a plurality of historical monitoring data, and the differential queue represents fluctuation of the plurality of historical monitoring data;
and determining the influence of the influence factors on the normal operation of the charging pile rectifying module according to the historical monitoring data queue, the accumulation queue and the differential queue.
In one possible implementation, generating an accumulation queue from the historical monitoring data queue includes:
generating the accumulation queue according to a historical monitoring data queue and a first formula, wherein the first formula is as follows:
in the method, in the process of the invention,
is the ∈th of the accumulation queue>
Element(s)>
Monitoring data queue for history>
An element;
generating a differential queue according to the historical monitoring data queue, including:
generating the differential queue according to a historical monitoring data queue and a second formula, wherein the second formula is as follows:
in the method, in the process of the invention,
is the->
The elements.
In one possible implementation manner, the determining, according to the historical monitoring data queue, the accumulation queue and the differential queue, an influence of an influence factor on the normal operation of the charging pile rectifying module includes:
according to a third formula, a historical monitoring data queue, an accumulation queue and a differential queue, determining an influence vector representing influence of influence factors on normal operation of the charging pile rectifying module, wherein the influence vector comprises a monitoring data influence coefficient, an accumulation influence coefficient and a differential influence coefficient, and the third formula is as follows:
in the method, in the process of the invention,
is the +.>
Element(s)>
A first +.>
Element(s)>
Is the +.>
Element(s)>
Is the total number of elements in the historical working state queue.
In one possible implementation manner, according to the influence of the plurality of influence factors on the normal operation of the charging pile rectifying module, the data preprocessing is performed on the plurality of monitoring data respectively to obtain a plurality of factor data, including:
the influence of the influence factors on the normal operation of the charging pile rectifying module comprises the following steps: monitoring a data influence coefficient, accumulating the influence coefficient and differentiating the influence coefficient;
if the absolute value of the influence coefficient of the monitoring data is larger than the monitoring threshold value, adding the monitoring data into the plurality of factor data;
if the absolute value of the accumulation influence coefficient is larger than the accumulation threshold value, adding the last data of the accumulation queue obtained according to the first formula into the plurality of factor data;
and if the absolute value of the differential influence coefficient is larger than the differential threshold value, adding the last data of the differential queue obtained according to the second formula to the plurality of factor data.
In one possible implementation, the initial model of the early warning model includes:
the system comprises an input layer, a middle layer and an output layer, wherein the middle layer receives a plurality of data input by the input layer and performs fitting transformation on the plurality of data, and the output layer receives the data output by the middle layer and outputs early warning information;
the input layer comprises a plurality of input nodes for inputting the plurality of factor data, the middle layer comprises a plurality of intermediate nodes forming a multi-layer and fully-connected network, the plurality of input nodes are connected with nodes, adjacent to the input layer, in the plurality of intermediate nodes, and the output layer comprises output nodes, and the output nodes are connected with nodes, adjacent to the output layer, in the plurality of intermediate nodes.
In one possible implementation, the early warning model is obtained by training the initial model based on a plurality of histories, including:
preprocessing the history monitoring data of each history record in the plurality of history records according to a data preprocessing mode of the plurality of factor data to obtain a plurality of history factor data sets, wherein the history factor data sets comprise a plurality of history factor data;
model testing: inputting the plurality of historical factor data sets into the initial model to obtain a plurality of output results of the initial model;
determining an output residual error of the initial model according to the plurality of output results and the historical working states of the plurality of historical records;
and if the residual error is larger than a residual error threshold value, adjusting a plurality of parameters of the initial model, and returning to the model testing step.
In a second aspect, an embodiment of the present invention provides a charging pile rectifying module abnormality pre-warning device, configured to implement the charging pile rectifying module abnormality pre-warning method according to the first aspect or any one of possible implementation manners of the first aspect, where the charging pile rectifying module abnormality pre-warning device includes:
the monitoring data acquisition module is used for acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influence factors which influence the normal operation of the charging pile rectifying module;
the data preprocessing module is used for respectively preprocessing the plurality of monitoring data according to the influence of a plurality of influence factors on the normal operation of the charging pile rectifying module to obtain a plurality of factor data;
the method comprises the steps of,
and the early warning output module is used for inputting the plurality of factor data into an early warning model to obtain early warning information, wherein the early warning model constructs an initial model based on the artificial neural network model, the initial model is trained based on a plurality of historical records, and the historical records are recorded with the historical working state of the charging pile rectifying module and the historical monitoring data of the operation of the charging pile rectifying module.
In a third aspect, an embodiment of the present invention provides a terminal, including a memory and a processor, where the memory stores a computer program executable on the processor, and where the processor implements the steps of the method according to the first aspect or any one of the possible implementations of the first aspect when the processor executes the computer program.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
the embodiment of the invention discloses a charging pile rectifying module abnormality early warning method, which comprises the steps of firstly, acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influencing factors which influence the normal operation of the charging pile rectifying module; then, according to the influence of a plurality of influence factors on the normal operation of the charging pile rectifying module, respectively carrying out data preprocessing on the plurality of monitoring data to obtain a plurality of factor data; and finally, inputting the plurality of factor data into an early warning model to obtain early warning information, wherein the early warning model builds an initial model based on an artificial neural network model, trains the initial model based on a plurality of histories, and records the histories of the working states of the charging pile rectifying modules and the histories of the monitoring data of the operation of the charging pile rectifying modules in the histories. According to the embodiment of the invention, the working state of the rectifying module is judged according to the monitoring data through the artificial neural network, so that the abnormality of the rectifying module can be found in time according to the monitoring data, the processes of manual inspection and maintenance are reduced, and the maintenance efficiency is improved. For example, according to an embodiment of the invention, if the system detects that dust accumulation exceeds a condition threshold, i.e. forced shut down, the device is returned to service after the dust has been cleaned. According to the embodiment of the invention, the data accumulation data and the change rate data are extracted according to the monitoring data, the influences of the occult factors and the severe change factors on the working state of the rectification module are fully considered, and the abnormal early warning is more sensitive.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for early warning of abnormality of a rectifying module of a charging pile according to an embodiment of the present invention;
fig. 2 is a functional block diagram of an abnormality early warning device for a rectifying module of a charging pile according to an embodiment of the present invention;
fig. 3 is a functional block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made with reference to the accompanying drawings.
The following describes in detail the embodiments of the present invention, and the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation procedure are given, but the protection scope of the present invention is not limited to the following embodiments.
Fig. 1 is a flowchart of a method for early warning of abnormality of a rectifying module of a charging pile according to an embodiment of the present invention.
As shown in fig. 1, a flowchart for implementing the method for early warning of abnormality of a rectifying module of a charging pile according to an embodiment of the present invention is shown, and is described in detail as follows:
in step 101, a plurality of monitoring data are acquired, wherein the monitoring data are acquired based on influencing factors that influence the normal operation of the charging pile rectifying module.
In step 102, according to the influence of a plurality of influencing factors on the normal operation of the charging pile rectifying module, respectively performing data preprocessing on the plurality of monitoring data to obtain a plurality of factor data.
In some embodiments, the determining, according to the plurality of histories, the influence of the plurality of influencing factors on the normal operation of the charging pile rectifying module includes:
sequencing the plurality of history records according to the time sequence to obtain a history record queue;
collecting the historical monitoring data belonging to the same influencing factor in the historical record queues according to the time sequence order to obtain a plurality of historical monitoring data queues;
collecting the historical working states of the charging pile rectifying modules in the historical record queue according to the time sequence, and obtaining a historical working state queue;
for each of the plurality of history monitoring data queues, performing the steps of:
generating an accumulation queue and a differential queue according to the historical monitoring data queue, wherein the accumulation queue represents accumulation sums of a plurality of historical monitoring data, and the differential queue represents fluctuation of the plurality of historical monitoring data;
and determining the influence of the influence factors on the normal operation of the charging pile rectifying module according to the historical monitoring data queue, the accumulation queue and the differential queue.
In some embodiments, generating an accumulation queue from the historical monitoring data queue includes:
generating the accumulation queue according to a historical monitoring data queue and a first formula, wherein the first formula is as follows:
in the method, in the process of the invention,
is the ∈th of the accumulation queue>
Element(s)>
Monitoring data queue for history>
An element;
generating a differential queue according to the historical monitoring data queue, including:
generating the differential queue according to a historical monitoring data queue and a second formula, wherein the second formula is as follows:
in the method, in the process of the invention,
is the->
The elements.
In some embodiments, determining, according to the historical monitoring data queue, the accumulation queue and the differential queue, an influence of an influence factor on normal operation of the charging pile rectifying module includes:
according to a third formula, a historical monitoring data queue, an accumulation queue and a differential queue, determining an influence vector representing influence of influence factors on normal operation of the charging pile rectifying module, wherein the influence vector comprises a monitoring data influence coefficient, an accumulation influence coefficient and a differential influence coefficient, and the third formula is as follows:
in the method, in the process of the invention,
is the +.>
Element(s)>
A first +.>
Element(s)>
Is the +.>
Element(s)>
Is the total number of elements in the historical working state queue.
In some embodiments, according to the influence of the plurality of influence factors on the normal operation of the charging pile rectifying module, the data preprocessing is performed on the plurality of monitoring data respectively to obtain a plurality of factor data, including:
the influence of the influence factors on the normal operation of the charging pile rectifying module comprises the following steps: monitoring a data influence coefficient, accumulating the influence coefficient and differentiating the influence coefficient;
if the absolute value of the influence coefficient of the monitoring data is larger than the monitoring threshold value, adding the monitoring data into the plurality of factor data;
if the absolute value of the accumulation influence coefficient is larger than the accumulation threshold value, adding the last data of the accumulation queue obtained according to the first formula into the plurality of factor data;
and if the absolute value of the differential influence coefficient is larger than the differential threshold value, adding the last data of the differential queue obtained according to the second formula to the plurality of factor data.
For example, for the charging pile rectifying module, the influencing factors include various factors such as the well known ambient temperature, humidity and airflow velocity, in addition, the rectifying module is not only related to the current factor data, but also related to some long-term data, for example, the short-time dust content for dust may not have a significant influence on the rectifying module, and the long-term dust content for the rectifying module may not have a negligible influence, for example, some scenes with a larger dust content may have long-term dust accumulated on the rectifying module, which results in difficult heat dissipation of the rectifying module. The rectifier module may also be subject to a rate of change of some factor, for example, when the ambient humidity is high, if the rectifier module experiences a relatively rapid temperature drop, it may cause moisture to condense on the rectifier module, causing the rectifier module to generate a short circuit fault.
Therefore, in determining the hardness factor of the rectifying module, the influence of the monitoring data on the working state of the rectifying module should be analyzed from the two aspects of the accumulation amount and the variation amount according to the monitoring data.
According to the embodiment of the invention, the monitoring data, the accumulation amount of the monitoring data and the influence of the variation amount of the monitoring data on the working state of the rectifying module are mined from the historical monitoring data.
In some embodiments, the accumulation queue is constructed from the historical monitoring data and a first formula, the first formula being:
in the method, in the process of the invention,
is the ∈th of the accumulation queue>
Element(s)>
Monitoring data queue for history>
The elements.
Constructing a differential queue according to the historical monitoring data and a second formula, wherein each element in the differential queue represents the change amount of the monitoring data along with time, and the second formula is as follows:
in the method, in the process of the invention,
is the->
The elements.
According to the first formula, an accumulation queue and a differential queue are constructed, and together with a history monitoring data queue formed by arranging history monitoring data according to time sequence, three queues of each factor are formed.
The three queues judge the influence on the working state of the rectifying module according to a third formula, wherein the third formula is as follows:
in the method, in the process of the invention,
is the +.>
Element(s)>
A first +.>
Element(s)>
Is the +.>
Element(s)>
Is the total number of elements in the historical working state queue.
The three elements of the influence vector obtained by calculation according to the formula respectively represent the influence of the monitoring data, the accumulation amount of the monitoring data and the difference amount of the monitoring data on the working state of the rectifying module, wherein the calculated element can be positive or negative in value, and the element is indicated to have strong correlation with the working state of the rectifying module when the absolute value is large, and is indicated to have positive correlation with the working state of the rectifying module when the element is positive, and is indicated to have negative correlation with the working state of the rectifying module when the element is negative.
For example, the working state of the rectifying module is characterized by conversion efficiency, and when the conversion efficiency is low, the rectifying module generates large heat, so that aging is aggravated and the phenomenon should be avoided as much as possible. In an application scenario, the conversion efficiency is inversely related to the ambient temperature, and the higher the ambient temperature, the lower the conversion efficiency, if the value obtained by the calculation of the third formula should be a larger negative value.
In step 103, the plurality of factor data are input into an early warning model to obtain early warning information, wherein the early warning model constructs an initial model based on an artificial neural network model, the initial model is trained based on a plurality of histories, and the histories record the historical working state of the charging pile rectifying module and the historical monitoring data of the operation of the charging pile rectifying module.
In some embodiments, the initial model of the early warning model comprises:
the system comprises an input layer, a middle layer and an output layer, wherein the middle layer receives a plurality of data input by the input layer and performs fitting transformation on the plurality of data, and the output layer receives the data output by the middle layer and outputs early warning information;
the input layer comprises a plurality of input nodes for inputting the plurality of factor data, the middle layer comprises a plurality of intermediate nodes forming a multi-layer and fully-connected network, the plurality of input nodes are connected with nodes, adjacent to the input layer, in the plurality of intermediate nodes, and the output layer comprises output nodes, and the output nodes are connected with nodes, adjacent to the output layer, in the plurality of intermediate nodes.
In some embodiments, the early warning model is obtained by training the initial model based on a plurality of histories, including:
preprocessing the history monitoring data of each history record in the plurality of history records according to a data preprocessing mode of the plurality of factor data to obtain a plurality of history factor data sets, wherein the history factor data sets comprise a plurality of history factor data;
model testing: inputting the plurality of historical factor data sets into the initial model to obtain a plurality of output results of the initial model;
determining an output residual error of the initial model according to the plurality of output results and the historical working states of the plurality of historical records;
and if the residual error is larger than a residual error threshold value, adjusting a plurality of parameters of the initial model, and returning to the model testing step.
In practice, the operating state of the rectifier module is, for example, a result of the combined action of a plurality of factors, which together determine an operating state.
Therefore, a plurality of factors can be taken as input, the working state of the rectifying module is taken as a result, and the relation between the factors and the working state is fitted through some mathematical methods.
The embodiment of the invention adopts manual stretching into the network for fitting. The artificial neural network comprises an input layer, wherein the input layer is used for inputting input factor data processed by the first formula and the second formula, the input data passes through a plurality of intermediate nodes of an intermediate layer to finish main fitting work, each intermediate layer comprises a plurality of layers, each layer is provided with a plurality of intermediate nodes, and the intermediate nodes of two adjacent layers are fully connected to form a fully connected network. And the fully-connected network is connected with the output node, outputs the fitting result of the middle layer to the output node, and outputs the fitting result after the fitting result of the middle layer is processed by the output node.
The artificial neural network is trained through the historical record, and parameters of all nodes are adjusted, so that the artificial neural network has fitting capacity.
A training method includes the steps of sorting out factor accumulation amount and factor difference amount according to a first formula and a second formula from a historical record, inputting the factor data into an artificial neural network model, obtaining fitting results of the model, representing working states of a sorting module by the fitting results, and when the results deviate from the working states of a rectifying module greatly, indicating that the fitting effect is poor, and adjusting parameters of the model according to fitting deviation. The adjustment mode can adopt a back propagation algorithm, and parameters of each node are sequentially adjusted through the output layer, the middle layer and the input layer. And after parameter adjustment is completed, factor data input operation is performed until the fitting deviation is smaller than a threshold value.
According to the embodiment of the abnormal early warning method of the charging pile rectifying module, a plurality of monitoring data are firstly obtained, wherein the monitoring data are obtained based on influence factors which influence the normal operation of the charging pile rectifying module; then, according to the influence of a plurality of influence factors on the normal operation of the charging pile rectifying module, respectively carrying out data preprocessing on the plurality of monitoring data to obtain a plurality of factor data; and finally, inputting the plurality of factor data into an early warning model to obtain early warning information, wherein the early warning model builds an initial model based on an artificial neural network model, trains the initial model based on a plurality of histories, and records the histories of the working states of the charging pile rectifying modules and the histories of the monitoring data of the operation of the charging pile rectifying modules in the histories. According to the embodiment of the invention, the working state of the rectifying module is judged according to the monitoring data through the artificial neural network, so that the abnormality of the rectifying module can be found in time according to the monitoring data, the processes of manual inspection and maintenance are reduced, and the maintenance efficiency is improved. For example, according to an embodiment of the invention, if the system detects that dust accumulation exceeds a condition threshold, i.e. forced shut down, the device is returned to service after the dust has been cleaned. According to the embodiment of the invention, the data accumulation data and the change rate data are extracted according to the monitoring data, the influences of the occult factors and the severe change factors on the working state of the rectification module are fully considered, and the abnormal early warning is more sensitive.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 2 is a functional block diagram of an abnormality warning device for a rectifying module of a charging pile according to an embodiment of the present invention, and referring to fig. 2, the abnormality warning device 2 for a rectifying module of a charging pile includes: a monitoring data acquisition module 201, a data preprocessing module 202 and an early warning output module 203, wherein:
the monitoring data acquisition module 201 is configured to acquire a plurality of monitoring data, where the monitoring data is acquired based on influence factors that affect normal operation of the charging pile rectifying module;
the data preprocessing module 202 is configured to perform data preprocessing on the plurality of monitoring data according to influence of a plurality of influence factors on normal operation of the charging pile rectifying module, so as to obtain a plurality of factor data;
and the early warning output module 203 is configured to input the plurality of factor data into an early warning model to obtain an early warning message, wherein the early warning model constructs an initial model based on an artificial neural network model, trains the initial model based on a plurality of history records, and records the history working state of the charging pile rectifying module and the history monitoring data of the operation of the charging pile rectifying module in the history records.
Fig. 3 is a functional block diagram of a terminal according to an embodiment of the present invention. As shown in fig. 3, the terminal 3 of this embodiment includes: a processor 300 and a memory 301, said memory 301 having stored therein a computer program 302 executable on said processor 300. The processor 300 executes the computer program 302 to implement the foregoing steps in the foregoing method and embodiment for warning of abnormality of each charging pile rectifying module, for example, steps 101 to 103 shown in fig. 1.
Illustratively, the computer program 302 may be partitioned into one or more modules/units that are stored in the memory 301 and executed by the processor 300 to accomplish the present invention.
The terminal 3 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal 3 may include, but is not limited to, a processor 300, a memory 301. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the terminal 3 and does not constitute a limitation of the terminal 3, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal 3 may further include input-output devices, network access devices, buses, etc.
The processor 300 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application SpecificIntegrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 301 may be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 301 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the terminal 3. Further, the memory 301 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 301 is used for storing the computer program 302 and other programs and data required by the terminal 3. The memory 301 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, and will not be described herein again.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and the details or descriptions of other embodiments may be referred to for those parts of an embodiment that are not described in detail or are described in detail.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on this understanding, the present invention may also be implemented by implementing all or part of the procedures in the methods of the above embodiments, or by instructing the relevant hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may be implemented by implementing the steps of the embodiments of the methods and apparatuses described above when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limited thereto; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and they should be included in the protection scope of the present invention.
Claims (9)
1. The utility model provides a charging pile rectification module abnormality early warning method which is characterized in that the method comprises the following steps:
acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influence factors influencing the normal operation of the charging pile rectifying module;
according to the influence of a plurality of influence factors on the normal operation of the charging pile rectifying module, respectively carrying out data preprocessing on the plurality of monitoring data to obtain a plurality of factor data;
inputting the plurality of factor data into an early warning model to obtain early warning information, wherein the early warning model builds an initial model based on an artificial neural network model, the initial model is trained based on a plurality of histories, and the histories record the historical working state of the charging pile rectifying module and the historical monitoring data of the operation of the charging pile rectifying module;
the influence of the influence factors on the normal operation of the charging pile rectifying module is determined according to the histories; the determining process of the influence of the plurality of influence factors on the normal operation of the charging pile rectifying module comprises the following steps:
sequencing the plurality of history records according to the time sequence to obtain a history record queue;
collecting the historical monitoring data belonging to the same influencing factor in the historical record queues according to the time sequence order to obtain a plurality of historical monitoring data queues;
collecting the historical working states of the charging pile rectifying modules in the historical record queue according to the time sequence, and obtaining a historical working state queue;
for each of the plurality of history monitoring data queues, performing the steps of:
generating an accumulation queue and a differential queue according to the historical monitoring data queue, wherein the accumulation queue represents accumulation sums of a plurality of historical monitoring data, and the differential queue represents fluctuation of the plurality of historical monitoring data;
and determining the influence of the influence factors on the normal operation of the charging pile rectifying module according to the historical monitoring data queue, the accumulation queue and the differential queue.
2. The method for warning of an abnormality of a rectification module of a charging pile according to claim 1, wherein generating an accumulation queue from a history monitoring data queue comprises:
generating the accumulation queue according to a historical monitoring data queue and a first formula, wherein the first formula is as follows:
in the method, in the process of the invention,
is the ∈th of the accumulation queue>
Element(s)>
Monitoring data queue for history>
An element;
generating a differential queue according to the historical monitoring data queue, including:
generating the differential queue according to a historical monitoring data queue and a second formula, wherein the second formula is as follows:
in the method, in the process of the invention,
is the->
The elements.
3. The method for warning of abnormality of a rectifying module of a charging pile according to claim 1, wherein determining the influence of the influence factors on the normal operation of the rectifying module of the charging pile according to the historical monitoring data queue, the accumulation queue and the differential queue comprises:
according to a third formula, a historical monitoring data queue, an accumulation queue and a differential queue, determining an influence vector representing influence of influence factors on normal operation of the charging pile rectifying module, wherein the influence vector comprises a monitoring data influence coefficient, an accumulation influence coefficient and a differential influence coefficient, and the third formula is as follows:
in the method, in the process of the invention,
is the +.>
Element(s)>
A first +.>
Element(s)>
Is the +.>
Element(s)>
Is the total number of elements in the historical working state queue.
4. The method for early warning of abnormality of a rectification module of a charging pile according to claim 2, wherein the performing data preprocessing on the plurality of monitoring data according to influence of a plurality of influence factors on normal operation of the rectification module of the charging pile to obtain a plurality of factor data includes:
the influence of the influence factors on the normal operation of the charging pile rectifying module comprises the following steps: monitoring a data influence coefficient, accumulating the influence coefficient and differentiating the influence coefficient;
if the absolute value of the influence coefficient of the monitoring data is larger than the monitoring threshold value, adding the monitoring data into the plurality of factor data;
if the absolute value of the accumulation influence coefficient is larger than the accumulation threshold value, adding the last data of the accumulation queue obtained according to the first formula into the plurality of factor data;
and if the absolute value of the differential influence coefficient is larger than the differential threshold value, adding the last data of the differential queue obtained according to the second formula to the plurality of factor data.
5. The method for warning of abnormality of a rectification module of a charging pile according to any one of claims 1 to 4, wherein an initial model of the warning model includes:
the system comprises an input layer, a middle layer and an output layer, wherein the middle layer receives a plurality of data input by the input layer and performs fitting transformation on the plurality of data, and the output layer receives the data output by the middle layer and outputs early warning information;
the input layer comprises a plurality of input nodes for inputting the plurality of factor data, the middle layer comprises a plurality of intermediate nodes forming a multi-layer and fully-connected network, the plurality of input nodes are connected with nodes, adjacent to the input layer, in the plurality of intermediate nodes, and the output layer comprises output nodes, and the output nodes are connected with nodes, adjacent to the output layer, in the plurality of intermediate nodes.
6. The method for warning of an abnormality of a rectification module of a charging pile according to claim 5, wherein the warning model is obtained by training the initial model based on a plurality of histories, comprising:
preprocessing the history monitoring data of each history record in the plurality of history records according to a data preprocessing mode of the plurality of factor data to obtain a plurality of history factor data sets, wherein the history factor data sets comprise a plurality of history factor data;
model testing: inputting the plurality of historical factor data sets into the initial model to obtain a plurality of output results of the initial model;
determining an output residual error of the initial model according to the plurality of output results and the historical working states of the plurality of historical records;
and if the residual error is larger than a residual error threshold value, adjusting a plurality of parameters of the initial model, and returning to the model testing step.
7. The charging pile rectifying module abnormality pre-warning device is characterized by being used for realizing the charging pile rectifying module abnormality pre-warning method according to any one of claims 1-6, and the charging pile rectifying module abnormality pre-warning device comprises:
the monitoring data acquisition module is used for acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influence factors which influence the normal operation of the charging pile rectifying module;
the data preprocessing module is used for respectively preprocessing the plurality of monitoring data according to the influence of a plurality of influence factors on the normal operation of the charging pile rectifying module to obtain a plurality of factor data;
the method comprises the steps of,
and the early warning output module is used for inputting the plurality of factor data into an early warning model to obtain early warning information, wherein the early warning model constructs an initial model based on the artificial neural network model, the initial model is trained based on a plurality of historical records, and the historical records are recorded with the historical working state of the charging pile rectifying module and the historical monitoring data of the operation of the charging pile rectifying module.
8. A terminal comprising a memory and a processor, the memory having stored therein a computer program executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method according to any of the preceding claims 1 to 6.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 6.
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| CN110232142A (en) * | 2019-06-03 | 2019-09-13 | 国家电网有限公司 | Charging pile fault detection method, system and terminal device |
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| CN115240382A (en) * | 2022-07-29 | 2022-10-25 | 绿能慧充数字技术有限公司 | Early warning system and method for charging pile |
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