CN115792476A - Charging pile rectification module abnormity early warning method and device, terminal and storage medium - Google Patents
Charging pile rectification module abnormity early warning method and device, terminal and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of charging equipment monitoring, in particular to a charging pile rectifier module abnormity early warning method, a charging pile rectifier module abnormity early warning device, a charging pile rectifier module abnormity early warning terminal and a charging pile rectifier module abnormity early warning storage medium; then respectively carrying out data preprocessing on the monitoring data according to the influence of a plurality of influence factors on the normal work of the charging pile rectification module to obtain a plurality of factor data; and finally, inputting the multiple factor data into an early warning model to obtain early warning information. According to the embodiment of the invention, the working state of the rectifier module is judged according to the monitoring data through the artificial neural network, the abnormity of the rectifier module can be found in time according to the monitoring data, the manual routing inspection and maintenance processes 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 influence of latent undersizing factors and severe change factors on the working state of the rectification module is fully considered, and the abnormity early warning is more sensitive.
Description
Technical Field
The invention relates to the technical field of charging equipment monitoring, in particular to a charging pile rectifier module abnormity early warning method, a charging pile rectifier module abnormity early warning device, a charging pile rectifier module abnormity early warning terminal and a storage medium.
Background
Engineering vehicles using fuel oil as energy, in particular 'oil tigers' of heavy trucks, mine trucks, loaders and the like are coming to be electrified for a long time.
Green development is becoming a trend, and engineering vehicles with high carbon emission are naturally added with carbon wave reduction. With the increasingly complex use environment of electric vehicles, the electric vehicles are not limited to living places, and new energy vehicles are beginning to appear on more and more industrial sites, such as mining sites, steel plants and the like. Along with the service environment of vehicle is more and more complicated, fill electric pile and must also adapt to complicated industrial environment, fill electric pile and need a big problem that faces under this environment and be the on-the-spot dust pollution problem.
Different from living places, the dust components are more complicated, such as metal-containing dust, coal dust and the like in mines and steel plants. Because direct current fills electric pile power big, use intensity is high, and its heat dissipation capacity is also great, and cooling fan power is high, consequently inevitably leads to the accumulation of this type of dust in rectifier module inside.
Rectifier module inner space is narrow and small, and the module could be operated just must be opened to the inside dust of clearance, and the inside deposition volume of hardly observing of the ordinary naked eye of patrolling and examining, patrols and examines at every turn and all open rectifier module and observe the dust deposition volume unrealistic yet. Excessive dust accumulation can have the following serious consequences: influence heat dissipation, thereby causing the module to be overheated, reducing the service life and even burning out; when the humidity is high, the dust absorbs water and is affected with damp, so that the short circuit of components is caused; in special use places, such as steel plants or mines, too much metal dust can be sucked to cause direct short circuit, and serious consequences such as explosion, fire and the like are generated.
Aiming at the problems, a method for realizing early warning of a charging pile rectifying module is provided.
Disclosure of Invention
The embodiment of the invention provides a charging pile rectification module abnormity early warning method, a charging pile rectification module abnormity early warning device, a charging pile rectification module abnormity early warning terminal and a charging pile rectification module abnormity early warning storage medium, which are used for solving the problem that manual regular inspection and maintenance are needed in the prior art.
In a first aspect, an embodiment of the present invention provides an abnormality early warning method for a charging pile rectifier module, including:
acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influence factors influencing the normal work of a charging pile rectification module;
according to the influence of a plurality of influence factors on the normal work of a charging pile rectification module, respectively carrying out data preprocessing on the plurality of monitoring data to obtain a plurality of factor data;
and inputting the factor data 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 historical records, and the historical working state of the charging pile rectifying module and the historical monitoring data of the operation of the charging pile rectifying module are recorded in the historical records.
In a possible implementation manner, the determination of the influence of the plurality of influence factors on the normal operation of the charging pile rectifier module is performed according to the plurality of history records, and the determination of the influence of the plurality of influence factors on the normal operation of the charging pile rectifier module includes:
sequencing the plurality of historical records according to the time sequence to obtain a historical record queue;
collecting historical monitoring data belonging to the same influence factor in the historical record queues according to the time sequence 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 to obtain a historical working state queue;
for each of the plurality of historical monitoring data queues, respectively performing the following steps:
generating an accumulation queue and a differential queue according to the historical monitoring data queue, wherein the accumulation queue represents the accumulation sum of a plurality of historical monitoring data, and the differential queue represents the fluctuation of the plurality of historical monitoring data;
and determining the influence of the influence factors on the normal work of the charging pile rectifying module according to the historical monitoring data queue, the accumulation queue and the difference queue.
In one possible implementation, generating the 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 formula (I), the compound is shown in the specification,
is the first of an accumulation queue
The number of the elements is one,
monitoring data queues for history
An element;
generating a differential queue according to the historical monitoring data queue, comprising:
generating the differential queue according to a historical monitoring data queue and a second formula, wherein the second formula is as follows:
in the formula (I), the compound is shown in the specification,
is the first of a differential queue
And (4) each element.
In a possible implementation manner, the determining, according to the historical monitoring data queue, the accumulation queue, and the differential queue, the influence of the influence factor on the normal operation of the charging pile rectifier module includes:
determining influence vectors representing influences on normal work of the charging pile rectifier module according to a third formula, a historical monitoring data queue, an accumulation queue and a difference queue, wherein the influence vectors comprise monitoring data influence coefficients, accumulation influence coefficients and difference influence coefficients, and the third formula is as follows:
in the formula (I), the compound is shown in the specification,
as influence vector
The number of the elements is one,
monitoring data queues, accumulation queues, or differential queues for history
The number of the elements is one,
queue for historical operating state
The number of the elements is one,
is the total number of elements in the historical working state queue.
In a possible implementation manner, the data preprocessing is performed on the plurality of monitoring data respectively according to the influence of a plurality of influence factors on the normal operation of the charging pile rectification module, so as to obtain a plurality of factor data, including:
the influential factor includes to filling the influence nature of electric pile rectifier module normal work: monitoring a data influence coefficient, an accumulated influence coefficient and a difference influence coefficient;
if the absolute value of the influence coefficient of the monitoring data is larger than the monitoring threshold, adding the monitoring data into the multiple 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 multiple factor data;
and if the absolute value of the difference influence coefficient is larger than the difference threshold, adding the last data of the difference queue obtained according to the second formula into the multiple factor data.
In one possible implementation, the initial model of the early warning model includes:
the system comprises an input layer, an intermediate layer and an output layer, wherein the intermediate layer receives a plurality of data input by the input layer and performs fitting transformation on the data, and the output layer receives the data output by the intermediate layer and outputs an early warning message;
the input layer includes a plurality of input nodes for inputting the multi-factor data, the intermediate layer includes a plurality of intermediate nodes constituting a multi-layer and fully connected network, the plurality of input nodes are connected with nodes next to the input layer among the plurality of intermediate nodes, and the output layer includes an output node connected with nodes next to the output layer among the plurality of intermediate nodes.
In one possible implementation manner, the training of the early warning model on the basis of a plurality of historical records includes:
according to the data preprocessing mode of the multiple factor data, preprocessing the historical monitoring data of each historical record in the multiple historical records to obtain multiple historical factor data sets, wherein the historical factor data sets comprise the multiple historical factor data;
and (3) testing the model: inputting the plurality of historical factor data sets into the initial model, and obtaining a plurality of output results of the initial model;
determining an output residual error of the initial model according to the output results and historical working states of the historical records;
and if the residual error is larger than the 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 an abnormality warning device for a charging pile rectification module, which is used to implement the abnormality warning method for the charging pile rectification module according to the first aspect or any one of the possible implementation manners of the first aspect, and the abnormality warning device for the charging pile rectification module includes:
the monitoring data acquisition module is used for acquiring a plurality of monitoring data, wherein the monitoring data is acquired based on influence factors influencing the normal work of the charging pile rectification module;
the data preprocessing module is used for respectively preprocessing the monitoring data according to the influence of a plurality of influence factors on the normal work of the charging pile rectifying module to obtain a plurality of factor data;
and the number of the first and second groups,
and the early warning output module is used for inputting the multiple 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 multiple historical records, and the historical working state of the charging pile rectifying module and historical monitoring data of the operation of the charging pile rectifying module are recorded in the historical records.
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 operable on the processor, and the processor executes the computer program to implement the steps of the method according to the first aspect or any possible implementation manner of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any one of the possible implementations of the first aspect.
Compared with the prior art, the implementation mode of the invention has the following beneficial effects:
the embodiment of the invention discloses an abnormity early warning method for a charging pile rectifier module, which comprises the steps of firstly obtaining a plurality of monitoring data, wherein the monitoring data are obtained based on influence factors influencing the normal work of the charging pile rectifier module; then, respectively carrying out data preprocessing on the monitoring data according to the influence of a plurality of influence factors on the normal work of the charging pile rectification module to obtain a plurality of factor data; and finally, inputting the factor data 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 historical records, and the historical working state of the charging pile rectifying module and the historical monitoring data of the operation of the charging pile rectifying module are recorded in the historical records. According to the embodiment of the invention, the working state of the rectifier module is judged according to the monitoring data through the artificial neural network, the abnormity of the rectifier 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 present invention, if the system detects that the dust accumulation exceeds a condition threshold, the system is forced to stop, and the device is returned to service after the dust is 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 influence of latent undersizing factors and severe change factors on the working state of the rectification module is fully considered, and the abnormity early warning is more sensitive.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart of an abnormality early warning method for a charging pile rectification module according to an embodiment of the present invention;
fig. 2 is a functional block diagram of an abnormality early warning device for a charging pile rectification module 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 particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the 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.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made with reference to the accompanying drawings.
The following is a detailed description of the embodiments of the present invention, which is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Fig. 1 is a flowchart of an abnormality early warning method for a charging pile rectification module according to an embodiment of the present invention.
As shown in fig. 1, which shows a flowchart of an implementation of an abnormal early warning method for a charging pile rectifier module according to an embodiment of the present invention, the detailed description is as follows:
in step 101, a plurality of monitoring data are obtained, wherein the monitoring data are obtained based on influence factors influencing normal work of a charging pile rectification module.
In step 102, according to the influence of the plurality of influence factors on the normal operation of the charging pile rectification module, respectively performing data preprocessing on the plurality of monitoring data to obtain a plurality of factor data.
In some embodiments, the influence of the plurality of influence factors on the normal operation of the charging pile rectifier module is determined according to the plurality of history records, and the determination of the influence of the plurality of influence factors on the normal operation of the charging pile rectifier module includes:
sequencing the plurality of historical records according to the sequence of time to obtain a historical record queue;
collecting historical monitoring data belonging to the same influence factor in the historical record queues according to the time sequence 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 to obtain a historical working state queue;
for each of the plurality of historical monitoring data queues, respectively performing the following steps:
generating an accumulation queue and a differential queue according to the historical monitoring data queue, wherein the accumulation queue represents the accumulation sum of a plurality of historical monitoring data, and the differential queue represents the fluctuation of the plurality of historical monitoring data;
and determining the influence of the influence factors on the normal work of the charging pile rectifying module according to the historical monitoring data queue, the accumulation queue and the difference queue.
In some embodiments, generating the accumulation queue from the historical 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 formula (I), the compound is shown in the specification,
is the first of an accumulation queue
The number of the elements is one,
monitoring data queues for history
An element;
generating a differential queue according to the historical monitoring data queue, comprising:
generating the differential queue according to a historical monitoring data queue and a second formula, wherein the second formula is as follows:
in the formula (I), the compound is shown in the specification,
as a differential queue
And (4) each element.
In some embodiments, the determining, according to the historical monitoring data queue, the accumulation queue and the differential queue, the influence of the influence factor on the normal operation of the charging pile rectification module includes:
determining influence vectors representing influences on normal work of the charging pile rectifier module according to a third formula, a historical monitoring data queue, an accumulation queue and a difference queue, wherein the influence vectors comprise monitoring data influence coefficients, accumulation influence coefficients and difference influence coefficients, and the third formula is as follows:
in the formula (I), the compound is shown in the specification,
as the influence vector
The number of the elements is one,
monitoring data queues, accumulation queues, or differential queues for history
The number of the elements is one,
queue for historical operating state
The number of the elements is one,
is the total number of elements in the historical working state queue.
In some embodiments, the performing data preprocessing on the plurality of monitoring data according to the influence of the plurality of influencing factors on the normal operation of the charging pile rectification module to obtain a plurality of factor data includes:
the influential factor includes to filling the influence nature of electric pile rectifier module normal work: monitoring a data influence coefficient, an accumulated influence coefficient and a difference influence coefficient;
if the absolute value of the influence coefficient of the monitoring data is larger than the monitoring threshold, adding the monitoring data into the multiple 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 multiple factor data;
and if the absolute value of the difference influence coefficient is greater than the difference threshold, adding the last data of the difference queue obtained according to the second formula into the multiple factor data.
For example, for the charging pile rectifier module, the influence factors include various factors, such as ambient temperature, humidity, and airflow rate, which are well known, and furthermore, the rectifier module is not only related to the current factor data, but also related to some long-term data, for example, for dust, the short-term dust content may not have a significant influence on the rectifier module, and for long-term dust content, the influence on the rectifier module is not negligible, for example, in some scenes with a large dust content, long-term dust may be accumulated on the rectifier module, which causes difficulty in heat dissipation of the rectifier module. The rectifier module may also be affected by the change rate of some factors, for example, when the ambient humidity is high, if the rectifier module is cooled down more sharply, moisture may be condensed on the rectifier module, which may cause a short-circuit fault of the rectifier module.
Therefore, in terms of determining the hard factor of the rectifier module, the influence of the monitoring data on the working state of the rectifier module should be analyzed from the aspects of both the accumulation amount and the variation amount according to the monitoring data.
According to the embodiment of the invention, the influence of monitoring data, the accumulated amount of the monitoring data and the variation of the monitoring data on the working state of the rectifier module is mined from historical monitoring data.
In some embodiments, the accumulation queue is constructed based on historical monitoring data and a first formula, the first formula being:
in the formula (I), the compound is shown in the specification,
is the first of an accumulation queue
The number of the elements is one,
monitoring data queues for history
And (4) each element.
Constructing a differential queue according to historical monitoring data and a second formula, wherein each element in the differential queue represents the variation of the monitoring data along with time, and the second formula is as follows:
in the formula (I), the compound is shown in the specification,
is the first of a differential queue
And (4) each element.
An accumulation queue and a difference queue are constructed according to the first formula, and three queues of each factor are formed by arranging historical monitoring data according to a time sequence to form a historical monitoring data queue.
The influence of the three queues on the working state of the rectification module is judged according to a third formula, wherein the third formula is as follows:
in the formula (I), the compound is shown in the specification,
as influence vector
The number of the elements is one,
monitoring data queues, accumulation queues, or differential queues for history
The number of the elements is one,
queue for historical operating state
The number of the elements is one,
is the total number of elements in the historical work state queue.
The three elements of the influence vector obtained by calculation according to the formula represent the influence of the monitored data, the monitored data accumulation amount and the monitored data difference amount on the working state of the rectifier module respectively, wherein the numerical value of the calculated elements may be a positive value or a negative value, when the absolute value is larger, the correlation with the working state of the rectifier module is stronger, when the absolute value is larger, the correlation with the working state of the rectifier module is positive, and when the absolute value is negative, the correlation with the working state of the rectifier module is negative.
For example, the operating state of the rectifier module is represented by the conversion efficiency, and when the conversion efficiency is low, the rectifier module has large heat productivity and is accelerated in aging, and the heat productivity 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 is, the lower the conversion efficiency is, if the value obtained by the calculation of the third formula should be a larger negative value.
In step 103, the factor data are input 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, the initial model is trained based on a plurality of historical records, and historical working states of a charging pile rectifying module and historical monitoring data of the operation of the charging pile rectifying module are recorded in the historical records.
In some embodiments, the initial model of the early warning model comprises:
the system comprises an input layer, an intermediate layer and an output layer, wherein the intermediate layer receives a plurality of data input by the input layer and performs fitting transformation on the data, and the output layer receives the data output by the intermediate layer and outputs an early warning message;
the input layer includes a plurality of input nodes for inputting the multi-factor data, the intermediate layer includes a plurality of intermediate nodes constituting a multi-layer and fully connected network, the plurality of input nodes are connected with nodes next to the input layer among the plurality of intermediate nodes, and the output layer includes an output node connected with nodes next to the output layer among the plurality of intermediate nodes.
In some embodiments, the early warning model is obtained by training the initial model based on a plurality of historical records, and includes:
according to the data preprocessing mode of the multiple factor data, preprocessing the historical monitoring data of each historical record in the multiple historical records to obtain multiple historical factor data sets, wherein the historical factor data sets comprise the multiple historical factor data;
and (3) testing the model: inputting the plurality of historical factor data sets into the initial model, and obtaining a plurality of output results of the initial model;
determining an output residual error of the initial model according to the output results and historical working states of the historical records;
and if the residual error is larger than the residual error threshold value, adjusting a plurality of parameters of the initial model, and returning to the model testing step.
Illustratively, in practice, the operating state of the rectifier module is a result of the combined action of a plurality of factors, and is an operating state determined by the combination of the plurality of factors.
Therefore, a plurality of factors can be used as input, the working state of the rectifier module can be used as a result, and the relation between the factors and the working state can be fitted through some mathematical methods.
The embodiment of the invention adopts manual network stretching for fitting. The artificial neural network comprises an input layer and is used for inputting input factor data processed by the first formula and the second formula, the input factor data are subjected to main fitting work through a plurality of intermediate nodes of the intermediate layer, the 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 all connected to form an all-connected network. And the fully-connected network is connected with the output node, outputs the fitting result of the intermediate layer to the output node, and outputs the fitting result after the fitting result of the output node to the intermediate layer is processed.
The artificial neural network is trained through historical records, and parameters of all nodes are adjusted, so that the artificial neural network has fitting capacity.
A training method is that historical records are sorted into factor accumulated quantity and factor difference quantity according to a first formula and a second formula, factor data are input into an artificial neural network model to obtain a fitting result of the model, the fitting result represents the working state of a sorting module, when the fitting result and the working state of a rectifying module have large deviation, the fitting effect is poor, and parameters of the model are adjusted according to the deviation of fitting. The adjustment mode can adopt a back propagation algorithm, and parameters of each node are adjusted in sequence through the output layer, the middle layer and the input layer. And after parameter adjustment is finished, factor data input operation is carried out until the fitting deviation is smaller than a threshold value.
According to the embodiment of the early warning method for the abnormity of the charging pile rectifying module, a plurality of monitoring data are obtained firstly, wherein the monitoring data are obtained based on influence factors influencing the normal work of the charging pile rectifying module; then, respectively carrying out data preprocessing on the plurality of monitoring data according to the influence of a plurality of influencing factors on the normal work of the charging pile rectification module to obtain a plurality of factor data; and finally, inputting the multiple factor data 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 multiple historical records, and the historical working state of a charging pile rectifying module and historical monitoring data of the operation of the charging pile rectifying module are recorded in the historical records. According to the embodiment of the invention, the working state of the rectifier module is judged according to the monitoring data through the artificial neural network, the abnormity of the rectifier module can be found in time according to the monitoring data, the manual routing inspection and maintenance processes are reduced, and the maintenance efficiency is improved. For example, according to an embodiment of the present invention, if the system detects that the dust accumulation exceeds a condition threshold, the system is forced to stop, and the device is returned to service after the dust is 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 influence of the latent understandings and the violent change factors on the working state of the rectification module is fully considered, and the abnormity early warning is more sensitive.
It should be understood that the sequence numbers of the steps in the above embodiments do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following are apparatus embodiments of the invention, and 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 charging pile rectification module according to an embodiment of the present invention, and referring to fig. 2, the abnormality warning device for a charging pile rectification module 2 includes: monitoring data acquisition module 201, data preprocessing module 202 and early warning output module 203, wherein:
the monitoring data acquisition module 201 is used for acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influence factors influencing the normal work of the charging pile rectification module;
the data preprocessing module 202 is configured to perform data preprocessing on the multiple monitoring data respectively according to influence of multiple influencing factors on normal operation of the charging pile rectification module, so as to obtain multiple factor data;
and the early warning output module 203 is used for inputting the multiple factor data 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 multiple historical records, and the historical working state of the charging pile rectifying module and the historical monitoring data of the operation of the charging pile rectifying module are recorded in the historical 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. When the processor 300 executes the computer program 302, the aforementioned method for warning abnormality of each charging pile rectifier module and the steps in the embodiments thereof, such as the steps 101 to 103 shown in fig. 1, are implemented.
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 implement the present invention.
The terminal 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal 3 may include, but is not limited to, a processor 300, a memory 301. It will be appreciated by a person skilled in the art that fig. 3 is only an example of a terminal 3 and does not constitute a limitation of the terminal 3, and that it may comprise more or less components than those shown, or some components may be combined, or different components, e.g. the terminal 3 may further comprise input and output devices, network access devices, buses, etc.
The Processor 300 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. 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 memory Card (Flash Card) and the like 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-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit, and the integrated unit may be implemented in a form of hardware, or may be implemented in a form of 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 processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment is focused on, and for parts that are not described or illustrated in detail in a certain embodiment, reference may be made to the description of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may 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 technical 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 above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the above embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and used by a processor to implement the steps of the method and apparatus embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may be modified or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. The early warning method for the abnormity of the charging pile rectifying module is characterized by comprising the following steps of:
acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influence factors influencing the normal work of a charging pile rectifying module;
according to the influence of a plurality of influence factors on the normal work of a charging pile rectification module, respectively carrying out data preprocessing on the plurality of monitoring data to obtain a plurality of factor data;
and inputting the multiple 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 multiple historical records, and the historical working state of a charging pile rectifying module and historical monitoring data of the operation of the charging pile rectifying module are recorded in the historical records.
2. The charging pile rectification module abnormality early warning method according to claim 1, wherein the influence of the plurality of influence factors on the normal operation of the charging pile rectification module is determined according to the plurality of history records; the determining process of the influence of the plurality of influence factors on the normal work of the charging pile rectifying module comprises the following steps:
sequencing the plurality of historical records according to the sequence of time to obtain a historical record queue;
collecting historical monitoring data belonging to the same influence factor in the historical record queues according to the time sequence to obtain a plurality of historical monitoring data queues;
collecting historical working states of charging pile rectifying modules in the historical record queue according to a time sequence to obtain a historical working state queue;
for each of the plurality of historical monitoring data queues, respectively performing the following steps:
generating an accumulation queue and a differential queue according to the historical monitoring data queue, wherein the accumulation queue represents the accumulation sum of a plurality of historical monitoring data, and the differential queue represents the fluctuation of the plurality of historical monitoring data;
and determining the influence of the influence factors on the normal work of the charging pile rectifying module according to the historical monitoring data queue, the accumulation queue and the difference queue.
3. The charging pile rectification module abnormity early warning method according to claim 2, wherein an accumulation queue is generated according to a historical monitoring data queue, and the method comprises the following steps:
generating the accumulation queue according to a historical monitoring data queue and a first formula, wherein the first formula is as follows:
in the formula (I), the compound is shown in the specification,
is the first of an accumulation queue
The number of the elements is one,
monitoring data queues for history
An element;
generating a differential queue according to the historical monitoring data queue, comprising:
generating the differential queue according to a historical monitoring data queue and a second formula, wherein the second formula is as follows:
in the formula (I), the compound is shown in the specification,
as a differential queue
And (4) each element.
4. The charging pile rectifying module abnormality early warning method according to claim 2, wherein the 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 difference queue comprises:
determining influence vectors representing influences on normal work of the charging pile rectifier module according to a third formula, a historical monitoring data queue, an accumulation queue and a difference queue, wherein the influence vectors comprise monitoring data influence coefficients, accumulation influence coefficients and difference influence coefficients, and the third formula is as follows:
in the formula (I), the compound is shown in the specification,
as influence vector
The number of the elements is one,
monitoring data queues, accumulation queues, or differential queues for history
The number of the elements is one,
queue for historical operating state
The number of the elements is one,
is the total number of elements in the historical working state queue.
5. The charging pile rectifying module abnormity early warning method according to claim 3, wherein the step of respectively performing data preprocessing on the monitoring data according to the influence of a plurality of influence factors on the normal work of the charging pile rectifying module to obtain a plurality of factor data comprises the following steps:
the influence of the influence factors on the normal work of the charging pile rectifying module comprises the following steps: monitoring a data influence coefficient, an accumulated influence coefficient and a difference influence coefficient;
if the absolute value of the influence coefficient of the monitoring data is larger than the monitoring threshold, adding the monitoring data into the multiple 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 multiple factor data;
and if the absolute value of the difference influence coefficient is larger than the difference threshold, adding the last data of the difference queue obtained according to the second formula into the multiple factor data.
6. The charging pile rectification module abnormity early warning method according to any one of claims 1 to 5, wherein an initial model of the early warning model comprises:
the system comprises an input layer, an intermediate layer and an output layer, wherein the intermediate layer receives a plurality of data input by the input layer and performs fitting transformation on the data, and the output layer receives the data output by the intermediate layer and outputs an early warning message;
the input layer includes a plurality of input nodes for inputting the plurality of factor data, the intermediate layer includes a plurality of intermediate nodes constituting a multi-layer and fully-connected network, the plurality of input nodes are connected with nodes next to the input layer among the plurality of intermediate nodes, and the output layer includes an output node connected with nodes next to the output layer among the plurality of intermediate nodes.
7. The charging pile rectification module abnormity early warning method according to claim 6, wherein the early warning model is obtained by training the initial model based on a plurality of historical records, and comprises the following steps:
according to the data preprocessing mode of the multiple factor data, preprocessing historical monitoring data of each historical record in the multiple historical records to obtain multiple historical factor data sets, wherein the historical factor data sets comprise multiple historical factor data;
and (3) testing the model: inputting the plurality of historical factor data sets into the initial model, and obtaining a plurality of output results of the initial model;
determining an output residual error of the initial model according to the output results and historical working states of the historical records;
and if the residual error is larger than the residual error threshold value, adjusting a plurality of parameters of the initial model, and returning to the model testing step.
8. A charging pile rectifying module abnormality early warning device for realizing the charging pile rectifying module abnormality early warning method according to any one of claims 1 to 7, the charging pile rectifying module abnormality early warning device comprising:
the monitoring data acquisition module is used for acquiring a plurality of monitoring data, wherein the monitoring data are acquired based on influence factors influencing the normal work of the charging pile rectification module;
the data preprocessing module is used for respectively preprocessing the monitoring data according to the influence of a plurality of influence factors on the normal work of the charging pile rectifying module to obtain a plurality of factor data;
and the number of the first and second groups,
and the early warning output module is used for inputting the multiple factor data 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 and obtained based on multiple historical records, and the historical working state of the charging pile rectifying module and the historical monitoring data of the operation of the charging pile rectifying module are recorded in the historical records.
9. A terminal comprising a memory and a processor, the memory having stored therein a computer program operable on the processor, wherein the processor when executing the computer program performs the steps of the method as claimed in any of claims 1 to 7 above.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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