CN115201616B - Charger operation online monitoring method based on big data - Google Patents
Charger operation online monitoring method based on big data Download PDFInfo
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Abstract
The invention belongs to the field of power equipment, relates to a data processing technology, and aims to solve the problems that the running state of the existing charger is monitored by adopting a single standard and the accuracy of a monitoring result is not high in the existing charger running online monitoring method, in particular to a charger running online monitoring method based on big data, which comprises the following steps: acquiring a charging mode of a charger and respectively monitoring and analyzing the running state of the charger in different charging modes, wherein the charging modes comprise a trickle charging mode and a quick charging mode; acquiring temperature data WD, humidity data SD and dust data HC when a charger is charged, and obtaining an external coefficient WB when a monitoring object is charged by carrying out numerical calculation on the temperature data WD, the humidity data SD and the dust data HC; the invention can match corresponding external standard ranges for the chargers working in different modes, thereby ensuring that the chargers working in different modes can work in the best state.
Description
Technical Field
The invention belongs to the field of power equipment, relates to a data processing technology, and particularly relates to a charger operation online monitoring method based on big data.
Background
The charger adopts a high-frequency power supply technology, an advanced intelligent dynamic adjustment charging technology and a constant-current/constant-voltage/small-constant-current intelligent three-stage charging mode, and has the characteristics of high charging efficiency, simplicity in operation, light weight, small size and the like;
the existing online monitoring method for the operation of the charger generally monitors the operation state of the charger by adopting a single standard, the charger comprises a plurality of charging modes when charging, the working characteristics of the charger in different charging modes are different, and the key factors needing to be monitored are different, so that the accuracy of the result of the operation monitoring of the charger by the existing online monitoring method for the operation of the charger is not high, and meanwhile, corresponding external parameters cannot be set for the charger before the charger works, so that the unstable operation state of the charger and the accelerated aging of the charger are caused;
in view of the above technical problems, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide a charger operation online monitoring method based on big data, which is used for solving the problem that the accuracy of the operation state monitoring result of the existing charger operation online monitoring method is low by adopting a single standard.
The technical problems to be solved by the invention are as follows: how to provide an on-line monitoring method for the running state of a charger, which can monitor the running state by adopting a corresponding monitoring mode according to different working characteristics of the charger.
The purpose of the invention can be realized by the following technical scheme:
a big data-based charger operation online monitoring method comprises the following steps:
the method comprises the following steps: acquiring a charging mode of a charger and respectively monitoring and analyzing the running state of the charger in different charging modes, wherein the charging modes comprise a trickle charging mode and a quick charging mode;
step two: acquiring temperature data WD, humidity data SD and dust data HC when the charger is charged, and carrying out numerical calculation on the temperature data WD, the humidity data SD and the dust data HC to obtain an external coefficient WB when a monitoring object is subjected to WB charging; the method comprises the steps of obtaining a first standard range and a second standard range of a charger working in different modes, obtaining an external standard range through the first standard range and the second standard range, and calling the corresponding external standard range from a storage module through a charging mode of the charger and sending the external standard range to a mobile phone terminal of a manager before the charger starts to charge;
step three: and when the charger operates abnormally, troubleshooting is carried out, and the abnormal reasons are marked as poor contact of the input end, grid faults or charger faults.
As a preferred embodiment of the present invention, in the step one, the specific process of monitoring and analyzing the operating state of the charger in the trickle charge mode includes: the method comprises the following steps of marking a charger as a monitoring object, dividing the charging duration of the monitoring object in a trickle charging mode into a plurality of monitoring periods, obtaining an electric quantity increasing value of a storage battery in the monitoring periods and marking the electric quantity increasing value as a charging value of the monitoring periods, establishing a charging set of the charging value of the monitoring periods, carrying out variance calculation on the charging set to obtain a charging coefficient CD of the monitoring object, obtaining the electric storage saturation of the storage battery when the monitoring object finishes charging and marking the electric storage saturation as a saturation value BH, obtaining a charging threshold value and a saturation threshold value through a storage module, and comparing the charging coefficient CD and the saturation value BH of the monitoring object with the charging threshold value and the saturation threshold value respectively: if the charging coefficient CD is smaller than the charging threshold and the saturation value BH is larger than the saturation threshold, judging that the running state of the monitoring object in the trickle charging mode is normal, sending the charging coefficient CD and the saturation value BH of the monitoring object to an online monitoring platform, and sending the charging coefficient CD and the saturation value BH to an external analysis module after the online monitoring platform receives the charging coefficient CD and the saturation value BH; otherwise, judging that the operating state of the monitoring object in the trickle charging mode is abnormal, sending a trickle abnormal signal to the online monitoring platform by the trickle monitoring unit, and sending the trickle abnormal signal to the troubleshooting module after the online monitoring platform receives the trickle abnormal signal.
As a preferred embodiment of the present invention, in the step one, a specific process of monitoring and analyzing the operating state of the charger in the fast charging mode includes: the method comprises the following steps of marking a charger as a monitoring object, marking the charging duration of the monitoring object in a quick charging mode as charging time, marking the electric quantity added value of a storage battery in the quick charging mode as charging amount, marking the ratio of the charging amount to the charging time as an efficiency coefficient, obtaining an efficiency threshold value through a storage module, and comparing the efficiency coefficient with the efficiency threshold value: if the efficiency coefficient is larger than or equal to the efficiency threshold value, judging that the running state of the monitored object in the quick charging mode is normal, sending the efficiency coefficient of the monitored object to an online monitoring platform, and sending the efficiency coefficient to an external analysis module after the online monitoring platform receives the efficiency coefficient; if the efficiency coefficient is smaller than the efficiency threshold value, the running state of the monitored object in the rapid charging mode is judged to be abnormal, the rapid monitoring unit sends a rapid abnormal signal to the online monitoring platform, and the online monitoring platform sends the rapid abnormal signal to the troubleshooting module after receiving the rapid abnormal signal.
In a preferred embodiment of the present invention, in the second step, a trickle charge coefficient JC is obtained by performing numerical calculation on a charge coefficient CD and a saturation value BH, a historical external coefficient of a trickle charge process performed by a charger is obtained by a storage module, an external range is formed by an external coefficient maximum value and an external coefficient minimum value, the external range is divided into a plurality of external sections, the trickle charge coefficients of the trickle charge process in the external sections are summed and averaged to obtain a trickle charge appearance value in the external section, the external section with the largest trickle charge appearance value is marked as an optimized section, the trickle charge process corresponding to the trickle charge coefficient maximum value in the optimized section is marked as a first standard process, and the trickle charge process with the smallest difference distance between the trickle charge coefficient and the trickle charge appearance value in the optimized section is marked as a second standard process.
As a preferred embodiment of the present invention, in the second step, the process of acquiring the first standard range and the second standard range of the fast charging mode includes: the method comprises the steps of obtaining historical external coefficients of a monitoring object in a quick charging process through a storage module, forming an external range by the maximum value of the external coefficients and the minimum value of the external coefficients, dividing the external range into a plurality of external intervals, summing efficiency coefficients of the quick charging process in the external intervals, averaging the efficiency performance values of the external intervals to obtain the efficiency performance values of the external intervals, marking the external interval with the maximum efficiency performance value as an optimized interval, marking the quick charging process corresponding to the maximum value of the efficiency coefficients in the optimized interval as a first standard process, and marking the quick charging process with the minimum difference between the efficiency coefficients and the efficiency performance values in the optimized interval as a second standard process.
As a preferred embodiment of the present invention, in the second step, the specific process of obtaining the external standard range through the first standard range and the second standard range includes: respectively marking the temperature data, the humidity data and the dust data in the first standard process as first temperature data, first humidity data and first dust data, and respectively marking the temperature data, the humidity data and the dust data in the second standard process as second temperature data, second humidity data and second dust data; forming a temperature standard range by the first temperature data and the second temperature data, forming a humidity standard range by the first humidity data and the second humidity data, and forming a dust standard range by the first dust data and the second dust data; and marking the temperature standard range, the humidity standard range and the dust standard range as external standard ranges and sending the external standard ranges to the storage module for storage.
As a preferred embodiment of the present invention, in step three, the specific process of performing troubleshooting when the charger is abnormally operated includes: dividing the charging time of a charger into a plurality of investigation periods, acquiring an average value of the voltage at the input end of a charging motor in the investigation periods and marking the average value as a voltage representative value of the investigation periods, summing the voltage representative values of all the investigation periods to obtain an average value to obtain a voltage coefficient, establishing a voltage set of the voltage representative values of all the investigation periods, and performing variance calculation on the voltage set to obtain a fluctuation coefficient; the voltage threshold value and the fluctuation threshold value are obtained through the storage module, the voltage coefficient and the fluctuation coefficient of the charger are compared with the voltage threshold value and the fluctuation threshold value respectively, and the abnormal reasons are marked as poor input end contact, grid faults or charger faults through the comparison result.
As a preferred embodiment of the present invention, in step three, the process of comparing the voltage coefficient and the fluctuation coefficient of the charger with the voltage threshold and the fluctuation threshold respectively includes: if the fluctuation coefficient is larger than or equal to the fluctuation threshold value, judging that the abnormality is caused by poor contact of the input end, and sending a contact fault signal to the online monitoring platform by the troubleshooting module; if the fluctuation coefficient is smaller than the fluctuation threshold and the voltage coefficient is smaller than the voltage threshold, judging that the abnormal reason is the power grid fault, and sending a power grid fault signal to the detection platform by the troubleshooting module; if the fluctuation coefficient is smaller than the fluctuation threshold and the voltage coefficient is larger than or equal to the voltage threshold, judging that the abnormal factor is the charger fault, and sending a charger fault signal to the online monitoring platform by the fault troubleshooting module.
The invention has the following beneficial effects:
1. the operating state of the charger in the charging process can be monitored and analyzed through the charging monitoring module, the operating states of the charger in the trickle charging mode and the quick charging mode are fed back through the trickle monitoring unit and the quick monitoring unit respectively, early warning can be timely carried out when the charger works abnormally in different modes, the operating state of the charger is monitored by adopting different parameters and different data processing modes according to the working characteristics in different modes, and the state monitoring accuracy of the charger is improved;
2. the external analysis module can match corresponding external standard ranges for the chargers working in different modes, after data processing is carried out on external parameters, the chargers working in different modes are matched with different external standard ranges, the external standard ranges are the external environment ranges which are obtained through the data processing and most suitable for the chargers to work, and therefore the chargers working in different modes can be guaranteed to work in the best state;
3. the fault troubleshooting module can be used for troubleshooting the abnormal reasons when the charger works abnormally, and troubleshooting the power grid fault, the contact fault and the charger fault one by one, so that the fault reasons can be quickly and directly maintained when fault processing is performed, and the fault processing efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in fig. 1, the big data-based online monitoring system for the operation of the charger comprises an online monitoring platform, wherein the online monitoring platform is in communication connection with a charging monitoring module, an external analysis module, a troubleshooting module and a storage module.
The charging monitoring module is used for monitoring and analyzing the running state of the charger in the charging process, respectively feeding back the running states of the charger in a trickle charging mode and a quick charging mode through the trickle monitoring unit and the quick monitoring unit, timely early warning can be carried out when the charger works abnormally in different modes, the running state of the charger is monitored by adopting different parameters and different data processing modes according to the working characteristics in different modes, and the state monitoring accuracy of the charger is improved; the charging monitoring module comprises a trickle monitoring unit and a quick monitoring unit.
The trickle monitoring unit is used for monitoring and analyzing the running state of the charger in the trickle charging mode: the method comprises the following steps of marking a charger as a monitoring object, dividing the charging time of the monitoring object in a trickle charging mode into a plurality of monitoring time periods, obtaining an electric quantity increasing value of a storage battery in the monitoring time periods and marking the electric quantity increasing value as a charging value of the monitoring time periods, establishing a charging set of the charging value of the monitoring time periods, carrying out variance calculation on the charging set to obtain a charging coefficient CD of the monitoring object, obtaining the storage saturation of the storage battery when the monitoring object finishes charging and marking the storage saturation as a saturation value BH, obtaining a charging threshold value and a saturation threshold value through a storage module, and comparing the charging coefficient CD and the saturation value BH of the monitoring object with the charging threshold value and the saturation threshold value respectively: if the charge coefficient CD is smaller than the charge threshold and the saturation value BH is larger than the saturation threshold, judging that the operation state of the monitored object in the trickle charge mode is normal, sending the charge coefficient CD and the saturation value BH of the monitored object to an online monitoring platform, and sending the charge coefficient CD and the saturation value BH to an external analysis module after the online monitoring platform receives the charge coefficient CD and the saturation value BH; otherwise, the operating state of the monitored object in the trickle charge mode is judged to be abnormal, the trickle monitoring unit sends a trickle abnormal signal to the online monitoring platform, the online monitoring platform sends the trickle abnormal signal to the troubleshooting module after receiving the trickle abnormal signal, the trickle charge mode is used for maintaining the full charge state of the storage battery or just offsetting the self discharge of the storage battery by using a smaller charge current (about 5 percent of the rated capacity value of the storage battery) and a lower charge voltage (about 115 percent of the rated capacity value of the storage battery) and effectively recovering the charge performance of the storage battery with deep discharge, and therefore the trickle monitoring unit is used for detecting and feeding back the charge saturation and the stability of the charge process.
The quick monitoring unit is used for monitoring and analyzing the running state of the charger in the quick charging mode: the method comprises the following steps of marking a charger as a monitoring object, marking the charging duration of the monitoring object in a quick charging mode as charging time, marking the electric quantity increasing value of a storage battery in the quick charging mode as charging quantity, marking the ratio of the charging quantity to the charging time as an efficiency coefficient, acquiring an efficiency threshold value through a storage module, and comparing the efficiency coefficient with the efficiency threshold value: if the efficiency coefficient is larger than or equal to the efficiency threshold value, judging that the running state of the monitored object in the rapid charging mode is normal, sending the efficiency coefficient of the monitored object to an online monitoring platform, and sending the efficiency coefficient to an external analysis module after the online monitoring platform receives the efficiency coefficient; if the efficiency coefficient is smaller than the efficiency threshold value, the running state of the monitored object in the quick charging mode is judged to be abnormal, the quick monitoring unit sends a quick abnormal signal to the online monitoring platform, the online monitoring platform receives the quick abnormal signal and then sends the quick abnormal signal to the troubleshooting module, the quick charging mode fills the storage battery with large current (30% of the storage battery capacity) and high voltage (125-130% of the rated voltage of the storage battery) within 3-4 hours, and therefore the quick monitoring unit is mainly used for monitoring and feeding back the charging efficiency of the charger.
The external analysis module is used for distributing corresponding external standard ranges for chargers working in different charging modes: acquiring temperature data WD, humidity data SD and dust data HC when a charger is charged, wherein the temperature data WD is the maximum value of air temperature in the trickle charging process of a monitoring object, the humidity data is the maximum value of air humidity in the trickle charging process of the monitoring object, the dust data HC is the maximum value of air dust concentration in the trickle charging process of the monitoring object, and an external coefficient WB when the monitoring object is charged is obtained through a formula WB = beta 1 × WD + beta 2 × SD + beta 3 × HC, wherein beta 1, beta 2 and beta 3 are proportional coefficients, and beta 1 > beta 2 > beta 3 > 1; when the external analysis module receives a charging coefficient CD and a saturation value BH, a trickle charging coefficient JC is obtained through a formula JC = (alpha 1 × BH)/(alpha 2 × CD), the trickle charging coefficient is a numerical value which reflects the integral state of the charging machine working in a trickle charging mode, and the larger the numerical value of the trickle charging coefficient is, the better the integral state of the charging machine in the trickle charging mode is; the trickle charging method comprises the following steps that alpha 1 and alpha 2 are both proportional coefficients, alpha 1 is larger than alpha 2 and larger than 1, historical external coefficients of a trickle charging process of a charger are obtained through a storage module, an external range is formed by the maximum value of the external coefficients and the minimum value of the external coefficients, the external range is divided into a plurality of external intervals, the trickle charging coefficients of the trickle charging process in the external intervals are summed and averaged to obtain trickle charge expression values of the external intervals, the external interval with the maximum trickle charge expression value is marked as an optimized interval, the trickle charging process corresponding to the maximum value of the trickle charging coefficient in the optimized interval is marked as a first standard process, and the trickle charging process with the minimum difference distance between the trickle charging coefficient and the trickle charge expression value in the optimized interval is marked as a second standard process; when the external analysis module receives the efficiency coefficients, acquiring historical external coefficients of a monitored object in a rapid charging process through the storage module, forming an external range by an external coefficient maximum value and an external coefficient minimum value, dividing the external range into a plurality of external intervals, summing the efficiency coefficients of the rapid charging process in the external intervals, averaging the efficiency performance values of the external intervals to obtain efficiency performance values, marking the external interval with the maximum efficiency performance value as an optimized interval, marking the rapid charging process corresponding to the efficiency coefficient maximum value in the optimized interval as a first standard process, marking the rapid charging process with the minimum difference between the efficiency coefficient and the efficiency performance value in the optimized interval as a second standard process, matching the corresponding external standard range for the chargers working in different modes, performing data processing on the external parameters, matching different external standard ranges for the chargers working in different modes, wherein the external standard ranges are external environment ranges which are obtained through data processing and are most suitable for the chargers working, and further guarantee that the chargers working in different modes can work in the best state;
respectively marking the temperature data, the humidity data and the dust data in the first standard process as first temperature data, first humidity data and first dust data, and respectively marking the temperature data, the humidity data and the dust data in the second standard process as second temperature data, second humidity data and second dust data; forming a temperature standard range by the first temperature data and the second temperature data, forming a humidity standard range by the first humidity data and the second humidity data, and forming a dust standard range by the first dust data and the second dust data; marking the temperature standard range, the humidity standard range and the dust standard range as external standard ranges and sending the external standard ranges to a storage module for storage; before the charger starts to charge, the corresponding external standard range is called from the storage module through the charging mode of the charger and is sent to the mobile phone terminal of the manager, and the manager receives the external standard range and then manages and controls the external parameters of the charger through the external standard range, so that the running state of the charger is guaranteed, and the aging of the charger is delayed.
The troubleshooting module is used for carrying out troubleshooting analysis on the charger after receiving the trickle abnormal signal or the quick abnormal signal: dividing the charging time of a charger into a plurality of investigation periods, acquiring an average value of the voltage at the input end of a charging motor in the investigation periods and marking the average value as a voltage representative value of the investigation periods, summing the voltage representative values of all the investigation periods to obtain an average value to obtain a voltage coefficient, establishing a voltage set of the voltage representative values of all the investigation periods, and performing variance calculation on the voltage set to obtain a fluctuation coefficient; the voltage threshold and the fluctuation threshold are obtained through the storage module, and the voltage coefficient and the fluctuation coefficient of the charger are respectively compared with the voltage threshold and the fluctuation threshold: if the fluctuation coefficient is larger than or equal to the fluctuation threshold value, judging that the abnormality is caused by poor contact of the input end, and sending a contact fault signal to the online monitoring platform by the troubleshooting module; if the fluctuation coefficient is smaller than the fluctuation threshold and the voltage coefficient is smaller than the voltage threshold, judging that the abnormal reason is the power grid fault, and sending a power grid fault signal to the detection platform by the troubleshooting module; if the fluctuation coefficient is smaller than the fluctuation threshold and the voltage coefficient is larger than or equal to the voltage threshold, judging that the abnormal factor is the charger fault, and sending a charger fault signal to the online monitoring platform by the fault troubleshooting module; when the charger works abnormally, the abnormal reasons are checked, and the power grid fault, the contact fault and the charger fault are checked one by one, so that the fault reason can be directly maintained quickly when the fault is processed, and the fault processing efficiency is improved.
Example two
As shown in fig. 2, a big data-based online monitoring method for the operation of a charger includes the following steps:
the method comprises the following steps: the method comprises the steps of acquiring a charging mode of a charger and respectively monitoring and analyzing the running state of the charger in different charging modes, wherein the charging mode comprises a trickle charging mode and a quick charging mode, and the running state of the charger is monitored by adopting different parameters and different data processing modes according to the working characteristics in different modes, so that the state monitoring accuracy of the charger is improved; the charging monitoring module comprises a trickle monitoring unit and a quick monitoring unit;
step two: acquiring temperature data WD, humidity data SD and dust data HC when the charger is charged, and carrying out numerical calculation on the temperature data WD, the humidity data SD and the dust data HC to obtain an external coefficient WB when a monitoring object is subjected to WB charging; the method comprises the steps of obtaining a first standard range and a second standard range of a charger working in different modes, obtaining an external standard range through the first standard range and the second standard range, calling the corresponding external standard range in a storage module through a charging mode of the charger before the charger starts to charge, and sending the external standard range to a mobile phone terminal of a manager, wherein the external standard range is an external environment range which is most suitable for the charger to work and is obtained after data processing, so that the charger in different modes can work in the best state;
step three: when the charger is abnormally operated, fault troubleshooting is carried out, and the abnormal reason is marked as poor contact of the input end, grid fault or charger fault, so that the fault reason can be directly maintained quickly when fault processing is carried out, and the fault processing efficiency is accelerated.
The online monitoring method for the operation of the charger based on the big data comprises the steps of acquiring a charging mode of the charger during working, and respectively monitoring and analyzing the operating state of the charger in different charging modes, wherein the charging modes comprise a trickle charging mode and a quick charging mode; acquiring temperature data WD, humidity data SD and dust data HC when a charger is in charging, and carrying out numerical calculation to obtain an external coefficient WB when a monitoring object is in WB charging; the method comprises the steps that a first standard range and a second standard range of a charger working in different modes are obtained, an external standard range is obtained through the first standard range and the second standard range, and before the charger starts to charge, the corresponding external standard range is called in a storage module through a charging mode of the charger and is sent to a mobile phone terminal of a manager; and when the charger operates abnormally, troubleshooting is carried out, and the abnormal reasons are marked as poor contact of the input end, power grid faults or charger faults.
The foregoing is merely illustrative and explanatory of the present invention and various modifications, additions or substitutions may be made to the specific embodiments described by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
The formulas are all obtained by acquiring a large amount of data and performing software simulation, and a formula close to a true value is selected, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula WB = β 1 × wd + β 2 × sd + β 3 × hc; collecting multiple groups of sample data and setting corresponding external coefficients for each group of sample data by a person skilled in the art; substituting the set external coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of beta 1, beta 2 and beta 3 which are respectively 6.87, 4.25 and 2.31;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the external coefficient preliminarily set by a person skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantized value is not affected, for example, the external coefficient is proportional to the value of the temperature data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (5)
1. A big data-based charger operation online monitoring method is characterized by comprising the following steps:
the method comprises the following steps: acquiring a charging mode of a charger and respectively monitoring and analyzing the running state of the charger in different charging modes, wherein the charging modes comprise a trickle charging mode and a quick charging mode;
step two: acquiring temperature data WD, humidity data SD and dust data HC when a charger is charged, and acquiring an external coefficient WB when a monitoring object is charged according to a formula WB = beta 1 × WD + beta 2 × SD + beta 3 × HC, wherein beta 1, beta 2 and beta 3 are proportional coefficients, and beta 1 is more than beta 2 and more than beta 3 is more than 1; the method comprises the steps of obtaining a first standard range and a second standard range of a charger working in different modes, obtaining an external standard range through the first standard range and the second standard range, and calling the corresponding external standard range from a storage module through a charging mode of the charger and sending the external standard range to a mobile phone terminal of a manager before the charger starts to charge;
step three: when the charger operates abnormally, troubleshooting is carried out, and the abnormal reasons are marked as poor contact of the input end, grid faults or charger faults;
in the second step, a trickle charge coefficient JC is obtained through a formula JC = (alpha 1 × BH)/(alpha 2 × CD), wherein alpha 1 and alpha 2 are both proportional coefficients, alpha 1 is larger than alpha 2 and is larger than 1, historical external coefficients of the charger in the trickle charge process are obtained through a storage module, an external range is formed by the maximum value of the external coefficients and the minimum value of the external coefficients, the external range is divided into a plurality of external intervals, the trickle charge coefficients of the trickle charge process in the external intervals are summed and averaged to obtain the trickle charge expression value of the external intervals, the external interval with the maximum value of the trickle charge expression value is marked as an optimized interval, the trickle charge process corresponding to the maximum value of the trickle charge coefficient in the optimized interval is marked as a first standard process, and the trickle charge process with the minimum distance between the trickle charge coefficient and the trickle charge expression value in the optimized interval is marked as a second standard process;
in step two, the obtaining process of the first standard range and the second standard range of the fast charging mode includes: acquiring historical external coefficients of a monitoring object in a quick charging process through a storage module, forming an external range by the maximum value of the external coefficients and the minimum value of the external coefficients, dividing the external range into a plurality of external intervals, summing efficiency coefficients of the quick charging process in the external intervals, averaging the efficiency coefficients to obtain efficiency performance values of the external intervals, marking the external interval with the maximum efficiency performance value as an optimized interval, marking the quick charging process corresponding to the maximum value of the efficiency coefficients in the optimized interval as a first standard process, and marking the quick charging process with the minimum difference between the efficiency coefficients and the efficiency performance values in the optimized interval as a second standard process;
in the second step, the specific process of obtaining the external standard range through the first standard range and the second standard range includes: respectively marking the temperature data, the humidity data and the dust data in the first standard process as first temperature data, first humidity data and first dust data, and respectively marking the temperature data, the humidity data and the dust data in the second standard process as second temperature data, second humidity data and second dust data; forming a temperature standard range by the first temperature data and the second temperature data, forming a humidity standard range by the first humidity data and the second humidity data, and forming a dust standard range by the first dust data and the second dust data; and marking the temperature standard range, the humidity standard range and the dust standard range as external standard ranges and sending the external standard ranges to the storage module for storage.
2. The big-data-based online monitoring method for the operation of the charger according to claim 1, wherein in the first step, the specific process of monitoring and analyzing the operating state of the charger in the trickle-charge mode comprises: the method comprises the following steps of marking a charger as a monitoring object, dividing the charging time of the monitoring object in a trickle charging mode into a plurality of monitoring time periods, obtaining an electric quantity increasing value of a storage battery in the monitoring time periods and marking the electric quantity increasing value as a charging value of the monitoring time periods, establishing a charging set of the charging value of the monitoring time periods, carrying out variance calculation on the charging set to obtain a charging coefficient CD of the monitoring object, obtaining the storage saturation of the storage battery when the monitoring object finishes charging and marking the storage saturation as a saturation value BH, obtaining a charging threshold value and a saturation threshold value through a storage module, and comparing the charging coefficient CD and the saturation value BH of the monitoring object with the charging threshold value and the saturation threshold value respectively: if the charge coefficient CD is smaller than the charge threshold and the saturation value BH is larger than the saturation threshold, judging that the operation state of the monitored object in the trickle charge mode is normal, sending the charge coefficient CD and the saturation value BH of the monitored object to an online monitoring platform, and sending the charge coefficient CD and the saturation value BH to an external analysis module after the online monitoring platform receives the charge coefficient CD and the saturation value BH; otherwise, the operating state of the monitored object in the trickle charge mode is judged to be abnormal, the trickle monitoring unit sends a trickle abnormal signal to the online monitoring platform, and the online monitoring platform sends the trickle abnormal signal to the troubleshooting module after receiving the trickle abnormal signal.
3. The big data-based online monitoring method for the running of the charger according to claim 1, wherein in the first step, a specific process of monitoring and analyzing the running state of the charger in the fast charging mode comprises the following steps: the method comprises the following steps of marking a charger as a monitoring object, marking the charging duration of the monitoring object in a quick charging mode as charging time, marking the electric quantity increasing value of a storage battery in the quick charging mode as charging quantity, marking the ratio of the charging quantity to the charging time as an efficiency coefficient, acquiring an efficiency threshold value through a storage module, and comparing the efficiency coefficient with the efficiency threshold value: if the efficiency coefficient is larger than or equal to the efficiency threshold value, judging that the running state of the monitored object in the quick charging mode is normal, sending the efficiency coefficient of the monitored object to an online monitoring platform, and sending the efficiency coefficient to an external analysis module after the online monitoring platform receives the efficiency coefficient; if the efficiency coefficient is smaller than the efficiency threshold value, the operation state of the monitored object in the quick charging mode is judged to be abnormal, the quick monitoring unit sends a quick abnormal signal to the online monitoring platform, and the online monitoring platform sends the quick abnormal signal to the troubleshooting module after receiving the quick abnormal signal.
4. The big data-based online monitoring method for the operation of the charger according to claim 1, wherein in step three, the specific process of troubleshooting when the charger is abnormally operated comprises: dividing the charging time of a charger into a plurality of investigation periods, acquiring the average value of the voltage at the input end of a charging motor in the investigation periods and marking the average value as the voltage representation value of the investigation periods, summing the voltage representation values of all the investigation periods to obtain an average value to obtain a voltage coefficient, establishing a voltage set of the voltage representation values of all the investigation periods, and carrying out variance calculation on the voltage set to obtain a fluctuation coefficient; the voltage threshold value and the fluctuation threshold value are obtained through the storage module, the voltage coefficient and the fluctuation coefficient of the charger are compared with the voltage threshold value and the fluctuation threshold value respectively, and the abnormal reasons are marked as poor input end contact, grid faults or charger faults through the comparison result.
5. The big-data-based online monitoring method for the operation of the charger according to claim 4, wherein in step three, the process of comparing the voltage coefficient and the fluctuation coefficient of the charger with the voltage threshold and the fluctuation threshold respectively comprises: if the fluctuation coefficient is larger than or equal to the fluctuation threshold value, judging that the abnormality is caused by poor contact of the input end, and sending a contact fault signal to the online monitoring platform by the troubleshooting module; if the fluctuation coefficient is smaller than the fluctuation threshold and the voltage coefficient is smaller than the voltage threshold, judging that the abnormal factor is the power grid fault, and sending a power grid fault signal to the detection platform by the troubleshooting module; if the fluctuation coefficient is smaller than the fluctuation threshold and the voltage coefficient is larger than or equal to the voltage threshold, judging that the abnormal factor is the charger fault, and sending a charger fault signal to the online monitoring platform by the fault troubleshooting module.
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| CN116317030B (en) * | 2023-05-17 | 2023-07-28 | 长通智能(深圳)有限公司 | Wireless device integrating wireless charging and data transmission functions |
| CN117507904A (en) * | 2023-10-20 | 2024-02-06 | 珠海康晋电气股份有限公司 | Charging pile automatic detection operation and maintenance system based on Internet of things |
| CN117458010B (en) * | 2023-12-20 | 2024-04-02 | 超耐斯(深圳)新能源集团有限公司 | Lithium battery energy storage monitoring system based on data analysis |
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