CN112067997B - Diagnosis method and system for portable power source leasing equipment, electronic equipment and storage medium - Google Patents
Diagnosis method and system for portable power source leasing equipment, electronic equipment and storage medium Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 306
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000003860 storage Methods 0.000 title claims description 11
- 230000005856 abnormality Effects 0.000 claims abstract description 24
- 238000001514 detection method Methods 0.000 claims abstract description 24
- 238000013145 classification model Methods 0.000 claims abstract description 10
- 230000002159 abnormal effect Effects 0.000 claims description 33
- 238000004590 computer program Methods 0.000 claims description 15
- 238000004891 communication Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 8
- 238000007599 discharging Methods 0.000 claims description 6
- 230000001172 regenerating effect Effects 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 abstract description 47
- 208000028659 discharge Diseases 0.000 description 16
- 238000010586 diagram Methods 0.000 description 7
- 238000002405 diagnostic procedure Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
- G07F17/0042—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for hiring of objects
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
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Abstract
The application relates to a mobile power rental equipment diagnosis method, a system and electronic equipment, wherein the mobile power rental equipment diagnosis method is used for acquiring operation data of the mobile power rental equipment; classifying the operation data according to a classification model to obtain a data set; and carrying out abnormality detection on the data set according to a diagnosis strategy to obtain a diagnosis report. The problem that diagnosis results depend on field overhaul of operation and maintenance personnel or abnormality reported by merchants and cannot accurately describe fault phenomena and reasons is solved, operation and maintenance efficiency of equipment is improved, and operation and maintenance cost of the equipment is reduced.
Description
Technical Field
The application relates to the field of battery diagnosis, in particular to a mobile power rental device diagnosis method, a system, an electronic device and a storage medium.
Background
With the improvement of the dependence degree of the mobile phone and the deep penetration of the sharing economy, the service scale of the mobile power source for sharing the mobile phone is larger and larger, and the operation and maintenance cost of the mobile power source for sharing is higher and higher.
In the related technology, the shared mobile power supply operation and maintenance personnel can find the abnormality of the mobile power supply or the equipment through the inspection of each equipment placement point in daily life, or the merchant telephone of the placement point is returned to the operator, and the operator can only know the abnormality of the corresponding product if the operator inspects or the merchant reports; therefore, operation and maintenance personnel are required to confirm and maintain on site, the operation and maintenance efficiency of the shared mobile power supply is reduced, and the operation and maintenance cost is increased.
At present, aiming at the problems that the diagnosis result in the related technology depends on the field overhaul of operation and maintenance personnel or the report of abnormality by a merchant, and the phenomenon and the reason of the fault cannot be accurately described, no effective solution is proposed.
Disclosure of Invention
The application provides a mobile power supply diagnosis method, a mobile power supply diagnosis system and electronic equipment, which at least solve the problem that the diagnosis result depends on the field overhaul of operation and maintenance personnel or the report of abnormality of a merchant and cannot accurately describe the phenomenon and cause of the fault.
In a first aspect, the present application provides a method for diagnosing a portable power source rental device, the method comprising:
acquiring operation data of the portable power source leasing equipment;
classifying the operation data according to the classification model to obtain a data set;
and carrying out anomaly detection on the data set according to a diagnosis strategy to obtain a diagnosis report.
In some embodiments, the portable power source rental device comprises a portable power source charging cabinet and/or a portable power source, and the operational data comprises device data and business data of the portable power source charging cabinet and/or the portable power source.
In some of these embodiments, the classifying the operational data according to a classification model, the obtaining the data set includes:
classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions;
the data set is obtained from the diagnostic dimensions required for each diagnostic item, wherein each diagnostic item may include a plurality of the diagnostic dimensions.
In some of these embodiments, the anomaly detection of the data set according to a diagnostic strategy, the obtaining the diagnostic report comprises:
performing anomaly detection on the data set to obtain a diagnosis result of a diagnosis dimension;
and processing the diagnosis results of the diagnosis dimension to obtain diagnosis results of the diagnosis items, and regenerating a diagnosis report.
In some embodiments, processing the results of the anomaly detection to obtain diagnostic results for the diagnostic item includes:
multiplying the relative error of the diagnosis dimension by the weight factor of the diagnosis dimension to obtain the weight parameter of the diagnosis dimension;
weighting the diagnosis result of the diagnosis dimension and the weight parameter through data to obtain an abnormal duty ratio bitmap;
and obtaining the diagnosis result of the diagnosis item according to the abnormal duty ratio bitmap.
In a second aspect, the application provides a system for diagnosing a portable power source leasing device, comprising the portable power source leasing device and a cloud server;
the portable power source leasing equipment comprises a portable power source charging cabinet and/or a portable power source;
this portable power source cabinet that charges can collect the operation data of this portable power source leasing equipment and upload this cloud ware, and this portable power source cabinet that charges includes: the device comprises a battery compartment module, a communication module, a control module and a driving power supply module;
the mobile power supply is used for collecting the operation data of the mobile power supply and directly or indirectly uploading the operation data to the cloud server;
the cloud server is used for receiving operation data generated by the mobile power supply charging cabinet and the mobile power supply; classifying the operation data according to the classification model to obtain a data set; and carrying out anomaly detection on the data set according to a diagnosis strategy to obtain a diagnosis report.
In some embodiments, the cloud server is further configured to classify the operation data according to a classification model to obtain a data set, including:
classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions;
the data set is obtained from the diagnostic dimensions required for each diagnostic item, wherein each diagnostic item may include a plurality of the diagnostic dimensions.
In some embodiments, the cloud server is further configured to perform anomaly detection on the data set according to a diagnostic policy, to obtain a diagnostic report, including:
performing anomaly detection on the data set to obtain a diagnosis result of a diagnosis dimension;
and processing the diagnosis results of the diagnosis dimension to obtain diagnosis results of the diagnosis items, and regenerating a diagnosis report.
In some embodiments, the cloud server is further configured to process a result of the anomaly detection to obtain a diagnosis result of the diagnosis item, including:
multiplying the relative error of the diagnosis dimension by the weight factor of the diagnosis dimension to obtain the weight parameter of the diagnosis dimension;
weighting the diagnosis result of the diagnosis dimension and the weight parameter through data to obtain an abnormal duty ratio bitmap;
and obtaining the diagnosis result of the diagnosis item according to the abnormal duty ratio bitmap.
In a third aspect, the application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing any of the methods of the first to second aspects when executing the computer program.
In a fourth aspect, the present application provides a storage medium comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing any of the methods of the first to second aspects when executing the computer program.
Compared with the related art, the mobile power diagnosis method provided by the embodiment of the application has the advantages that the operation data of the mobile power leasing equipment are obtained; classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions; obtaining the data set according to the diagnostic dimension required for each diagnostic item, wherein each diagnostic item can comprise a plurality of the diagnostic dimensions; and carrying out anomaly detection on the data set according to a diagnosis strategy to obtain a diagnosis report. Each diagnosis item obtains a diagnosis report, and finally generates a comprehensive diagnosis result, and the diagnosis result is sent to the operation and maintenance terminal, so that the operation and maintenance personnel can clearly know the fault point of the equipment through the operation and maintenance terminal. The problem that the diagnosis result depends on field overhaul of operation and maintenance personnel or the report of abnormality of a merchant and cannot accurately describe the phenomenon and the cause of the fault is solved, the operation and maintenance efficiency of the equipment is improved, and the operation and maintenance cost of the equipment is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic diagram of a mobile power rental equipment diagnostic system, in accordance with one embodiment of the application;
FIG. 2 is a schematic diagram of a mobile power rental equipment diagnostic system, in accordance with another embodiment of the application;
FIG. 3 is a flow chart of a mobile power rental equipment diagnostic method, according to an embodiment of the present application;
FIG. 4 is a flow chart of a mobile power rental equipment diagnostic method, according to another embodiment of the present application;
FIG. 5 is a flow chart of a mobile power rental equipment diagnostic method, according to another embodiment of the present application;
FIG. 6 is a flow chart of a mobile power rental equipment diagnostic method, according to yet another embodiment of the present application;
fig. 7 is a schematic view of an internal structure of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described and illustrated with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application. All other embodiments, which can be made by a person of ordinary skill in the art based on the embodiments provided by the present application without making any inventive effort, are intended to fall within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," and similar referents in the context of the application are not to be construed as limiting the quantity, but rather as singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in connection with the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
The present embodiment provides a diagnosis system for portable power source rental equipment, which can be used for diagnosis of portable power source rental equipment, and fig. 1 is a schematic diagram of a diagnosis system for portable power source according to an embodiment of the present application, and as shown in fig. 1, the system includes an operation and maintenance terminal 11, a portable power source 12, a portable power source charging cabinet 13, and a cloud server 14.
The operation and maintenance terminal 11 is configured to receive a diagnostic report sent by the cloud server 14, where the operation and maintenance terminal 11 includes a smart phone, a tablet computer, and the like.
The mobile power supply 12 can store the operation data of the mobile power supply by itself and send the operation data to the database of the cloud server through the built-in communication module. The operational data includes device data and service data of the mobile power supply 12. The service data includes the order number, order duration, and order amount of the mobile power supply 12, and the device data includes a discharge voltage curve, a discharge current curve, a temperature curve, and the like.
The mobile power supply charging cabinet 13 comprises a plurality of battery compartment modules. Wherein, a mobile power supply 12 is arranged in the bin body of each battery bin module.
The mobile power supply charging cabinet 13 can collect operation data of the mobile power supply leasing equipment, and then the operation data is sent to the data storage module of the cloud server 14 through the networking module such as 4G, WIFI or 5G based on the HTTP interface, the internet of things protocol disclosed by TCP or the private internet of things protocol based on TCP. The operation data includes device data when the portable power source 12 is located in the portable power source charging cabinet 13 and device data of the portable power source charging cabinet 13. The above-mentioned device data of the mobile power supply 12 include a charging voltage curve, a charging current curve, a charging temperature curve, etc., and the device data of the mobile power supply charging cabinet 13 includes a bin state, a signal intensity, a bin temperature, a power consumption condition, etc.
Further, the equipment data of the mobile power supply charging cabinet also comprises driving power supply working operation time data, non-working operation time data, information working time data sent by the communication module, battery module working time data and working time data of other functional modules and components, the data are collected and sent to the cloud server, and the abnormal functional modules or abnormal components are analyzed through diagnostic logic preset by the cloud server and then sent to the operation and maintenance terminal so as to timely arrange operation and maintenance for overhaul. Or the equipment data of the mobile power supply can be the operation data of a battery in the mobile power supply, the operation data of a chip or the operation data of other electronic components, the data are collected and sent to the cloud server, and the abnormal function module or the abnormal components are analyzed through the diagnosis logic preset by the cloud server and then sent to the operation and maintenance terminal so as to arrange the operation and maintenance in time for overhauling.
The mobile power supply charging cabinet 13 is internally provided with a UPS (uninterrupted Power supply), and under the condition that the mobile power supply charging cabinet 13 is powered off, the UPS can supply power to the mobile power supply charging cabinet 13 for a short time, and meanwhile, a communication module of the mobile power supply charging cabinet 13 sends operation data information to the cloud server 14.
The cloud server 14 is used for collecting and analyzing the operation data of the portable power source leasing equipment. The operation data includes service data of the mobile power supply 12, device data when the mobile power supply 12 is leased, device data of the mobile power supply 12 located in the mobile power supply charging cabinet 13, and device data of the mobile power supply charging cabinet 13. The device data when the mobile power supply 12 is leased is uploaded to the cloud server 14 by the mobile power supply 12 through its own communication module. And inputting the operation data into a classification model to classify the operation data, and obtaining data sets of different diagnosis dimensions according to the preset diagnosis dimensions. The required diagnostic dimension of each diagnostic item is different, and then a data set of the corresponding diagnostic dimension is acquired according to each diagnostic item. Wherein each diagnostic item may include a plurality of diagnostic dimensions therein. Data cleansing is performed on the abnormal data in the data set in the diagnosis item. Then, abnormality detection is performed on the data set by using a diagnostic strategy, a diagnostic result of a diagnostic dimension is diagnosed first, then the diagnostic result of the diagnostic dimension is processed to obtain a diagnostic result of a diagnostic item, and then a diagnostic report is produced and transmitted to the operation and maintenance terminal 11.
Through the system, in the related art, the diagnosis result depends on the problems that the operation and maintenance personnel overhauls on site or the business reports abnormality and the phenomenon and the cause of the fault cannot be accurately described, and the mobile power diagnosis system provided by the embodiment of the application comprehensively collects the operation data of the mobile power leasing equipment, diagnoses each diagnosis item from a plurality of diagnosis dimensions after data cleaning, generates a diagnosis report from each diagnosis item, generates a diagnosis result from a plurality of diagnosis reports, and the operation and maintenance personnel can clearly know the phenomenon and the cause of the fault through the diagnosis result, so that the working efficiency is greatly improved, and the operation and maintenance cost of enterprises is reduced.
The present embodiment provides a diagnosis system for portable power source rental equipment, which can be used for diagnosis of portable power source rental equipment, and fig. 2 is a schematic diagram of a diagnosis system for portable power source according to another embodiment of the present application, and as shown in fig. 2, the system includes an operation and maintenance terminal 21, a portable power source 22, a portable power source charging cabinet 23, and a cloud server 24.
The operation and maintenance terminal 21 is configured to receive a diagnostic report sent by the cloud server 24, where the operation and maintenance terminal 21 includes a smart phone, a tablet computer, and the like.
The mobile power supply 22 can collect the operation data of the mobile power supply 22 through the mobile power supply charging cabinet 23, and then send the operation data to the cloud server through the communication module of the mobile power supply charging cabinet 23.
The portable power source charging cabinet 23 includes: and a plurality of battery compartment modules, wherein a mobile power supply 22 is arranged in the compartment body of each battery compartment module.
The mobile power supply charging cabinet 23 can collect operation data of the mobile power supply, and then the operation data is sent to the data storage module of the cloud server 24 through a 4G or WIFI networking module based on an HTTP interface, an internet of things protocol disclosed by TCP, or a private internet of things protocol based on TCP, wherein the operation data includes service data of the mobile power supply 22, device data when the mobile power supply 22 is leased out, device data of the mobile power supply 22 in the mobile power supply charging cabinet 23, and device data of the mobile power supply charging cabinet 23. The device data when the mobile power supply 22 is leased is stored in the mobile power supply 22, and then when the mobile power supply 22 is located in the mobile power supply charging cabinet 23, the mobile power supply charging cabinet 23 collects the device data of the device data when the mobile power supply 22 is leased. The device data of the mobile power supply charging cabinet comprises a bin state, signal intensity, bin temperature, power consumption condition and the like.
The mobile power supply charging cabinet 23 is internally provided with a UPS (uninterrupted Power supply), and under the condition that the mobile power supply charging cabinet 23 is powered off, the UPS can supply power to the mobile power supply charging cabinet 23 for a short time, and meanwhile, the mobile power supply charging cabinet 23 sends service data information to the cloud server 24 through a communication module.
Further, or the user stores the data generated when the mobile power supply is leased, and when the mobile power supply returns, the mobile power supply charging cabinet 13 reads the data stored in the mobile power supply and sends operation data information to the cloud server through the communication module.
The cloud server 24 is configured to collect and analyze operation data of the portable power source rental device, where the operation data includes service data of the portable power source 22, device data when the portable power source 22 is rented, device data of the portable power source 22 in the portable power source charging cabinet 23, and device data of the portable power source charging cabinet 23. The device data when the mobile power supply 22 is leased is stored in the mobile power supply 22, and then when the mobile power supply 22 is located in the mobile power supply charging cabinet 23, the mobile power supply charging cabinet 23 collects the device data of the device data when the mobile power supply 22 is leased. . And inputting the operation data into a classification model to classify the operation data, and obtaining data sets of different diagnosis dimensions according to the preset diagnosis dimensions. The required diagnostic dimension of each diagnostic item is different, and then a data set of the corresponding diagnostic dimension is acquired according to each diagnostic item. Wherein each diagnostic item may include a plurality of diagnostic dimensions therein. Data cleansing is performed on the abnormal data in the data set in the diagnosis item. Then, abnormality detection is performed on the data set by using a diagnostic strategy, a diagnostic result of a diagnostic dimension is diagnosed first, then the diagnostic result of the diagnostic dimension is processed to obtain a diagnostic result of a diagnostic item, and then a diagnostic report is produced and transmitted to the operation and maintenance terminal 21.
Further, the equipment data of the mobile power supply charging cabinet also comprises driving power supply working operation time data, non-working operation time data, information working time data sent by the communication module, battery module working time data and working time data of other functional modules and components, the data are collected and sent to the cloud server, and the abnormal functional modules or abnormal components are analyzed through diagnostic logic preset by the cloud server and then sent to the operation and maintenance terminal so as to timely arrange operation and maintenance for overhaul. Or the equipment data of the mobile power supply can be the operation data of a battery in the mobile power supply, the operation data of a chip or the operation data of other electronic components, the data are collected and sent to the cloud server, and the abnormal function module or the abnormal components are analyzed through the diagnosis logic preset by the cloud server and then sent to the operation and maintenance terminal so as to arrange the operation and maintenance in time for overhauling.
Through the system, in the related art, the diagnosis result depends on the problems that the operation and maintenance personnel overhauls on site or the business reports abnormality and the phenomenon and the cause of the fault cannot be accurately described, and the mobile power diagnosis system provided by the embodiment of the application comprehensively collects the operation data of the mobile power leasing equipment, diagnoses each diagnosis item from a plurality of diagnosis dimensions after data cleaning, generates a diagnosis report from each diagnosis item, generates a diagnosis result from a plurality of diagnosis reports, and the operation and maintenance personnel can clearly know the phenomenon and the cause of the fault through the diagnosis result, so that the working efficiency is greatly improved, and the operation and maintenance cost of enterprises is reduced.
The embodiment provides a diagnosis method for portable power source leasing equipment, which can be used for remote intelligent diagnosis of a portable power source, and fig. 3 is a flowchart of the diagnosis method for portable power source leasing equipment according to an embodiment of the application, and the method comprises the following steps:
step S301, obtaining operation data of the portable power source rental device. The portable power source rental device includes a portable power source and/or a portable power source charging cabinet, and the operational data includes business data and device data. When the mobile power supply is in the mobile charging cabinet, the mobile power supply charging cabinet collects equipment data of the mobile power supply through a serial port protocol, the equipment data comprise a mobile power supply temperature curve, a charging voltage curve, a charging current curve, a capacity curve and the like, after the mobile power supply cabinet collects the equipment data of the mobile power supply, the data are transmitted to an Internet of things system arranged in the mobile power supply cabinet, the Internet of things system transmits the equipment data to a cloud server, the cloud server transmits the data to a diagnosis system, and the diagnosis system acquires service data from a service system, wherein the service data comprise a departure batch, product test data, order data and the like of leasing equipment of the mobile power supply.
Step S302, classifying the operation data according to the classification model to obtain a data set. Analyzing the equipment data sent by the object system, wherein the analyzed data comprises the following steps: the battery compartment module comprises a battery compartment body state, signal intensity, temperature and the like in a compartment position, and electric quantity, temperature, abnormal codes, cycle times and the like of a mobile power supply. Service data is acquired from the service system. Classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions. And acquiring a data set according to the required diagnosis dimension of each diagnosis item, wherein each diagnosis item can comprise a plurality of diagnosis dimensions, and the diagnosis item represents a label of a fault problem of the portable power source leasing equipment, such as a virtual electricity diagnosis item, a line loss diagnosis item, an abnormal base diagnosis item and the like.
Step S303, carrying out abnormality detection on the data set according to a diagnosis strategy to obtain a diagnosis report. The abnormality detection includes first diagnosing the diagnosis dimension to obtain diagnosis results of different diagnosis dimensions, then obtaining diagnosis results of the diagnosis items according to the diagnosis results of the diagnosis dimension, and then generating a diagnosis report in which the cause of the fault is specified in detail.
As one implementation, when the diagnostic item is an abnormal base diagnostic item, a mobile power order dimension, a bin status dimension, and a mobile power status dimension need to be obtained. The mobile power supply charging dimension comprises an order number, an order amount and an order state, for example, five orders in 10 orders are all in a cancel state or the order amount is 0, and the diagnosis dimension is diagnosed as abnormal; the bin state dimension comprises the removal times and the removal time of the mobile power supply in the bin, and if the removal times are greater than the threshold value of the dimension, the diagnosis dimension is abnormal; the mobile power state dimension includes a signal strength of the mobile power, and if the signal strength is always lower than a threshold value of the diagnosis dimension, the diagnosis dimension is diagnosed as abnormal. The threshold values can be obtained in a diagnostic strategy.
Through the steps S301 to S303, in the related art, the diagnosis result depends on the on-site maintenance of operation and maintenance personnel or the report of abnormality by a merchant, and the problem that the phenomenon and the cause of the fault cannot be accurately described.
The present embodiment provides a diagnosis method for portable power source rental equipment, which can be used for remote intelligent diagnosis of portable power source, and fig. 4 is a flowchart of a diagnosis method for portable power source rental equipment according to another embodiment of the present application, and the method includes, as shown in fig. 4:
step S401, operation data of the portable power source leasing equipment is obtained. The portable power source rental device includes a portable power source and/or a portable power source charging cabinet, and the operational data includes business data and device data. When the mobile power supply is in the mobile charging cabinet, the mobile power supply charging cabinet collects equipment data of the mobile power supply through a serial port protocol, the equipment data comprise a mobile power supply temperature curve, a charging voltage curve, a charging current curve, a capacity curve and the like, after the mobile power supply cabinet collects the equipment data of the mobile power supply, the data are transmitted to an Internet of things system arranged in the mobile power supply cabinet, the Internet of things system transmits the equipment data to a cloud server, the cloud server transmits the data to a diagnosis system, and the diagnosis system acquires service data from a service system, wherein the service data comprise a departure batch, product test data, order data and the like of leasing equipment of the mobile power supply.
Step S402, classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions. The diagnosis dimension comprises a mobile power supply order diagnosis dimension, a mobile power supply charging cabinet state diagnosis dimension, a mobile power supply charging diagnosis dimension, a mobile power supply discharging diagnosis dimension and the like. The mobile power supply order diagnosis dimension comprises an order state, an order duration, an order amount, a lease voltage, a lease charging cabinet number, a lease bin, a lease time, a return voltage, a return charging cabinet number, a return time and the like. The mobile power supply charging cabinet state diagnosis dimension comprises bin state, signal intensity, single-day power consumption, equipment temperature, equipment online time length, equipment online record, equipment offline record and the like. The mobile power supply charge diagnosis dimension comprises a charge duration, a charge voltage, a charge duration, a starting voltage, a termination voltage and the like. The dimension of the mobile power supply discharge diagnosis comprises a discharge current, a discharge voltage, a discharge duration, a discharge capacity and the like.
Step S403, acquiring a data set according to a diagnosis dimension of each diagnosis item, wherein each diagnosis item may include a plurality of diagnosis dimensions. The diagnosis items comprise an abnormal base diagnosis item, a high-risk mobile power supply diagnosis item, a virtual power mobile power supply diagnosis item, a line loss mobile power supply diagnosis item and an over-discharge mobile power supply diagnosis item. The abnormal base diagnosis items comprise a mobile power supply order diagnosis dimension, a mobile power supply charging cabinet state diagnosis dimension and a mobile power supply state diagnosis dimension; the high-risk mobile power supply diagnosis items comprise a mobile power supply charging current diagnosis dimension and a mobile power supply voltage diagnosis dimension; the virtual power mobile power diagnosis items comprise a mobile power capacity diagnosis dimension and a mobile power order diagnosis dimension; the line loss mobile power diagnosis items comprise a mobile power discharging diagnosis dimension and a mobile power order diagnosis dimension; the overdischarge mobile power supply diagnostic item includes a mobile power supply discharge diagnostic dimension.
And step S404, carrying out abnormality detection on the data set according to a diagnosis strategy to obtain a diagnosis report. The abnormality detection includes first diagnosing the diagnosis dimension to obtain diagnosis results of different diagnosis dimensions, then obtaining diagnosis results of the diagnosis items according to the diagnosis results of the diagnosis dimension, and then generating a diagnosis report in which the cause of the fault is specified in detail.
Through the steps S401 to S404, in the related art, the diagnosis result depends on the on-site maintenance of operation and maintenance personnel or the report of abnormality by a merchant, and the problem of failure phenomenon and cause cannot be accurately described.
The present embodiment provides a diagnosis method for portable power source rental equipment, which can be used for remote intelligent diagnosis of portable power source, and fig. 5 is a flowchart of a diagnosis method for portable power source rental equipment according to still another embodiment of the present application, and the method includes, as shown in fig. 5:
step S501, obtaining operation data of the portable power source rental device. The portable power source rental device includes a portable power source and/or a portable power source charging cabinet, and the operational data includes business data and device data. When the mobile power supply is in the mobile charging cabinet, the mobile power supply charging cabinet collects equipment data of the mobile power supply through a serial port protocol, the equipment data comprise a mobile power supply temperature curve, a charging voltage curve, a charging current curve, a capacity curve and the like, after the mobile power supply cabinet collects the equipment data of the mobile power supply, the data are transmitted to an Internet of things system arranged in the mobile power supply cabinet, the Internet of things system transmits the equipment data to a cloud server, the cloud server transmits the data to a diagnosis system, and the diagnosis system acquires service data from a service system, wherein the service data comprise a departure batch, product test data, order data and the like of leasing equipment of the mobile power supply.
Step S502, classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions. The diagnosis dimension comprises a mobile power supply order diagnosis dimension, a mobile power supply charging cabinet state diagnosis dimension, a mobile power supply charging diagnosis dimension, a mobile power supply discharging diagnosis dimension and the like. The mobile power supply order diagnosis dimension comprises an order state, an order duration, an order amount, a lease voltage, a lease charging cabinet number, a lease bin, a lease time, a return voltage, a return charging cabinet number, a return time and the like. The mobile power supply charging cabinet state diagnosis dimension comprises bin state, signal intensity, single-day power consumption, equipment temperature, equipment online time length, equipment online record, equipment offline record and the like. The mobile power supply charge diagnosis dimension comprises a charge duration, a charge voltage, a charge duration, a starting voltage, a termination voltage and the like. The dimension of the mobile power supply discharge diagnosis comprises a discharge current, a discharge voltage, a discharge duration, a discharge capacity and the like.
In step S503, a data set is acquired according to a diagnostic dimension of each diagnostic item, where each diagnostic item may include a plurality of diagnostic dimensions. The diagnosis items comprise an abnormal base diagnosis item, a high-risk mobile power supply diagnosis item, a virtual power mobile power supply diagnosis item, a line loss mobile power supply diagnosis item and an over-discharge mobile power supply diagnosis item. The abnormal base diagnosis items comprise a mobile power supply order diagnosis dimension, a mobile power supply charging cabinet state diagnosis dimension and a mobile power supply state diagnosis dimension; the high-risk mobile power supply diagnosis items comprise a mobile power supply charging current diagnosis dimension and a mobile power supply voltage diagnosis dimension; the virtual power mobile power diagnosis items comprise a mobile power capacity diagnosis dimension and a mobile power order diagnosis dimension; the line loss mobile power diagnosis items comprise a mobile power discharging diagnosis dimension and a mobile power order diagnosis dimension; the overdischarge mobile power supply diagnostic item includes a mobile power supply discharge diagnostic dimension.
Step S504, the relative error of the diagnosis dimension is multiplied by the weight factor of the diagnosis dimension to obtain the weight parameter of the diagnosis dimension. The weight parameter of the diagnostic dimension is equal to the relative error of the diagnostic dimension multiplied by the weight factor of the diagnostic dimension. Wherein the weight factor is given by the diagnostic strategy.
Step S505, the diagnosis result and the weight parameter of the diagnosis dimension are weighted by data to obtain an abnormal duty ratio bitmap. Obtaining an abnormal duty ratio bitmap of the fault through data weighting processing according to the diagnosis result of the diagnosis dimension and the weight parameter, wherein the data weighting is expressed by the following formula 1:
m is the weight parameter of the diagnostic dimension, p is the weight occupied by the diagnostic dimension
And step S506, obtaining a diagnosis result of the diagnosis item according to the abnormal duty ratio bitmap. A single diagnostic report corresponds to one diagnostic item, and a plurality of diagnostic reports constitute a diagnostic result.
As an alternative embodiment, when performing the diagnosis of the exception removal diagnostic item, the diagnostic item includes a mobile power source exception dimension, a base exception diagnostic dimension, a mobile power source order exception diagnostic dimension. The weight parameter is the dimension of abnormality diagnosis of the mobile power supply: dimension of mobile power source anomaly diagnosis: mobile power supply order anomaly diagnosis dimension = 2:3:5. And obtaining an abnormal duty ratio bitmap of the fault through data weighting processing according to the diagnosis result of the diagnosis dimension and the weight parameter, and converting the abnormal duty ratio bitmap=1/2:1/3:1/5 into an integer of approximately equal to 5:3:2. And then the fault information is displayed in detail according to the abnormal duty ratio bitmap of the abnormal removal fault and the data set.
Through the steps S501 to S406, in the related art, the diagnosis result depends on the on-site maintenance of operation and maintenance personnel or the report of abnormality by a merchant, and the problem of failure phenomenon and cause cannot be accurately described.
The present embodiment provides a diagnosis method for portable power source rental equipment, which can be used for remote intelligent diagnosis of portable power source, and fig. 6 is a flowchart of a diagnosis method for portable power source rental equipment according to still another embodiment of the present application, and the method includes, as shown in fig. 6:
when the mobile power supply is located on the base (namely in the mobile power supply charging cabinet), the internet of things system built in the mobile power supply charging cabinet can collect equipment data of the mobile power supply and the mobile power supply charging cabinet. And then the internet of things system uploads the equipment data to a database of the diagnosis system. The diagnosis system acquires the service data of the mobile power supply from the service system at the same time. And dividing the service data and the equipment data into data sets with different diagnosis dimensions according to the preset diagnosis dimensions in the diagnosis strategy. And acquiring a corresponding data set according to the diagnosis dimension required to be considered for the diagnosis of each diagnosis item, and acquiring a diagnosis report after diagnosing the data set according to a diagnosis strategy, wherein a diagnosis result is generated by a plurality of diagnosis reports.
In the related art, the diagnosis result depends on the problem that the operation and maintenance personnel overhauls on site or the merchant reports abnormality and cannot accurately describe the phenomenon and the cause of the fault, and the mobile power diagnosis system provided by the embodiment of the application comprehensively collects the operation data of the mobile power leasing equipment, diagnoses each diagnosis item from a plurality of diagnosis dimensions after data cleaning, generates a diagnosis report for each diagnosis item, generates a diagnosis result for a plurality of diagnosis reports, and the operation and maintenance personnel can clearly know the phenomenon and the cause of the fault through the diagnosis result, thereby greatly improving the working efficiency and reducing the operation and maintenance cost of enterprises.
In one embodiment, fig. 7 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 7, an electronic device, which may be a server, is provided, and an internal structure diagram thereof may be as shown in fig. 7. The electronic device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the electronic device is for storing data. The network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of mobile power diagnosis.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the electronic device to which the present inventive arrangements are applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, an electronic device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps in the mobile power diagnosis method provided in the foregoing embodiments.
In one embodiment, a storage medium is provided having a computer program stored thereon, which when executed by a processor, implements the steps of the above embodiments in providing a mobile power diagnostic method.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiment may be arbitrarily combined, and all possible combinations of the technical features in the above embodiment are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (4)
1. A method of diagnosing a portable power rental device, the method comprising:
acquiring operation data of the portable power source leasing equipment; the mobile power supply leasing equipment comprises a mobile power supply charging cabinet and a mobile power supply, and the operation data comprise equipment data and service data of the mobile power supply charging cabinet and the mobile power supply;
classifying the operation data according to a classification model to obtain a data set; classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions; obtaining the data set according to the required diagnostic dimension of each diagnostic item, wherein each diagnostic item can comprise a plurality of diagnostic dimensions; the diagnosis dimension comprises a mobile power supply order diagnosis dimension, a mobile power supply charging cabinet state diagnosis dimension, a mobile power supply charging diagnosis dimension and a mobile power supply discharging diagnosis dimension;
performing anomaly detection on the data set according to a diagnosis strategy to obtain a diagnosis report; the abnormality detection comprises the steps of firstly diagnosing a diagnosis dimension to obtain diagnosis results of different diagnosis dimensions, and then obtaining diagnosis results of a diagnosis item according to the diagnosis results of the diagnosis dimension;
wherein, the performing anomaly detection on the data set according to the diagnosis strategy, and obtaining the diagnosis report includes:
performing anomaly detection on the data set to obtain a diagnosis result of a diagnosis dimension;
processing the diagnosis results of the diagnosis dimension to obtain diagnosis results of the diagnosis items, and regenerating a diagnosis report;
wherein, processing according to the result of the abnormality detection, obtaining the diagnosis result of the diagnosis item includes:
multiplying the relative error of the diagnosis dimension by the weight factor of the diagnosis dimension to obtain the weight parameter of the diagnosis dimension;
the diagnosis results of the diagnosis dimension and the weight parameters are weighted through data to obtain an abnormal duty ratio bitmap;
and obtaining the diagnosis result of the diagnosis item according to the abnormal duty ratio bitmap.
2. The system for diagnosing the portable power source leasing equipment is characterized by comprising the portable power source leasing equipment and a cloud server;
the portable power source leasing equipment comprises a portable power source charging cabinet and a portable power source;
the mobile power supply charging cabinet can collect the operation data of the mobile power supply leasing equipment and upload the operation data to the cloud server, and the mobile power supply charging cabinet comprises: the device comprises a battery compartment module, a communication module, a control module and a driving power supply module;
the mobile power supply is used for collecting the operation data of the mobile power supply and directly or indirectly uploading the operation data to the cloud server;
the cloud server is used for receiving operation data generated by the mobile power supply charging cabinet and the mobile power supply;
classifying the operation data according to a classification model to obtain a data set; classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions; obtaining the data set according to the required diagnostic dimension of each diagnostic item, wherein each diagnostic item can comprise a plurality of diagnostic dimensions; performing abnormality detection on the data set according to a diagnosis strategy to obtain a diagnosis report, wherein the abnormality detection comprises the steps of firstly diagnosing a diagnosis dimension to obtain diagnosis results of different diagnosis dimensions, and then obtaining diagnosis results of a diagnosis item according to the diagnosis results of the diagnosis dimension;
the cloud server is further used for carrying out anomaly detection on the data set to obtain a diagnosis result of a diagnosis dimension; processing the diagnosis results of the diagnosis dimension to obtain diagnosis results of the diagnosis items, and regenerating a diagnosis report;
the cloud server is further configured to multiply the relative error of the diagnostic dimension by a weight factor of the diagnostic dimension to obtain a weight parameter of the diagnostic dimension; the diagnosis results of the diagnosis dimension and the weight parameters are weighted through data to obtain an abnormal duty ratio bitmap; obtaining the diagnosis result of the diagnosis item according to the abnormal duty ratio bitmap;
the operation data comprise equipment data and service data of a mobile power supply charging cabinet and a mobile power supply; the diagnostic dimensions include a mobile power order diagnostic dimension, a mobile power charging cabinet status diagnostic dimension, a mobile power charging diagnostic dimension, and a mobile power discharging diagnostic dimension.
3. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of claim 1 when executing the computer program.
4. A storage medium comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of claim 1 when the computer program is executed by the processor.
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| CN113223244A (en) * | 2021-05-06 | 2021-08-06 | 广东品胜电子股份有限公司 | Shared charging equipment and mobile power supply warehousing method thereof |
| CN116125298B (en) * | 2022-11-28 | 2024-08-16 | 伏瓦科技(苏州)有限公司 | Battery fault detection method and device |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104037885A (en) * | 2014-06-27 | 2014-09-10 | 深圳市乐得瑞科技有限公司 | Portable power source, working condition monitoring system of portable power source and monitoring management method of portable power source |
| CN104620463A (en) * | 2015-02-12 | 2015-05-13 | 深圳来电科技有限公司 | Mobile power supply rental terminal |
| CN105044497A (en) * | 2015-06-30 | 2015-11-11 | 株洲南车时代电气股份有限公司 | Method for intelligently analyzing fault of traction converter |
| CN107276531A (en) * | 2017-06-08 | 2017-10-20 | 南昌航空大学 | A kind of online failure sorted diagnostic system of photovoltaic module and method |
| CN107437301A (en) * | 2017-03-14 | 2017-12-05 | 上海无衍电子科技有限公司 | Portable power source leases control system and lease control method |
| CN107589384A (en) * | 2017-08-29 | 2018-01-16 | 国网湖南省电力公司 | A kind of state evaluating method for DC power system |
| CN206946609U (en) * | 2017-05-17 | 2018-01-30 | 深圳来电科技有限公司 | A kind of portable power source is self-service to lease terminal |
| CN109508790A (en) * | 2018-10-17 | 2019-03-22 | 平安国际融资租赁有限公司 | Fault data processing method, device and computer equipment based on leased equipment |
| CN109738822A (en) * | 2018-12-20 | 2019-05-10 | 北京汉能光伏投资有限公司 | Fault detection method of mobile power supply and mobile energy networking system |
| CN110069810A (en) * | 2019-03-11 | 2019-07-30 | 北京百度网讯科技有限公司 | Battery failures prediction technique, device, equipment and readable storage medium storing program for executing |
| CN110146817A (en) * | 2019-05-13 | 2019-08-20 | 上海博强微电子有限公司 | The diagnostic method of lithium battery failure |
| CN110224470A (en) * | 2019-07-02 | 2019-09-10 | 深圳市云充吧科技有限公司 | A kind of mobile power source with detection device |
| CN110619321A (en) * | 2019-10-12 | 2019-12-27 | 国电南瑞南京控制系统有限公司 | Charging pile technical condition diagnosis method and system based on deep neural network |
| CN111025153A (en) * | 2018-10-09 | 2020-04-17 | 上海汽车集团股份有限公司 | Electric vehicle battery fault diagnosis method and device |
| CN111060838A (en) * | 2019-12-31 | 2020-04-24 | 中国人民解放军陆军军医大学第二附属医院 | Medical electronic equipment switching power supply fault diagnosis method based on multi-dimensional feature fusion |
| CN111368919A (en) * | 2020-02-25 | 2020-07-03 | 重庆大学 | Feature data construction method, data set construction method, data classification method, EMC fault diagnosis method and system |
| CN111474492A (en) * | 2020-03-31 | 2020-07-31 | 海南掌上能量传媒有限公司 | Mobile power supply detection method |
| CN111504676A (en) * | 2020-04-23 | 2020-08-07 | 中国石油大学(北京) | Equipment fault diagnosis method, device and system based on multi-source monitoring data fusion |
-
2020
- 2020-08-14 CN CN202010817606.XA patent/CN112067997B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104037885A (en) * | 2014-06-27 | 2014-09-10 | 深圳市乐得瑞科技有限公司 | Portable power source, working condition monitoring system of portable power source and monitoring management method of portable power source |
| CN104620463A (en) * | 2015-02-12 | 2015-05-13 | 深圳来电科技有限公司 | Mobile power supply rental terminal |
| CN105044497A (en) * | 2015-06-30 | 2015-11-11 | 株洲南车时代电气股份有限公司 | Method for intelligently analyzing fault of traction converter |
| CN107437301A (en) * | 2017-03-14 | 2017-12-05 | 上海无衍电子科技有限公司 | Portable power source leases control system and lease control method |
| CN206946609U (en) * | 2017-05-17 | 2018-01-30 | 深圳来电科技有限公司 | A kind of portable power source is self-service to lease terminal |
| CN107276531A (en) * | 2017-06-08 | 2017-10-20 | 南昌航空大学 | A kind of online failure sorted diagnostic system of photovoltaic module and method |
| CN107589384A (en) * | 2017-08-29 | 2018-01-16 | 国网湖南省电力公司 | A kind of state evaluating method for DC power system |
| CN111025153A (en) * | 2018-10-09 | 2020-04-17 | 上海汽车集团股份有限公司 | Electric vehicle battery fault diagnosis method and device |
| CN109508790A (en) * | 2018-10-17 | 2019-03-22 | 平安国际融资租赁有限公司 | Fault data processing method, device and computer equipment based on leased equipment |
| CN109738822A (en) * | 2018-12-20 | 2019-05-10 | 北京汉能光伏投资有限公司 | Fault detection method of mobile power supply and mobile energy networking system |
| CN110069810A (en) * | 2019-03-11 | 2019-07-30 | 北京百度网讯科技有限公司 | Battery failures prediction technique, device, equipment and readable storage medium storing program for executing |
| CN110146817A (en) * | 2019-05-13 | 2019-08-20 | 上海博强微电子有限公司 | The diagnostic method of lithium battery failure |
| CN110224470A (en) * | 2019-07-02 | 2019-09-10 | 深圳市云充吧科技有限公司 | A kind of mobile power source with detection device |
| CN110619321A (en) * | 2019-10-12 | 2019-12-27 | 国电南瑞南京控制系统有限公司 | Charging pile technical condition diagnosis method and system based on deep neural network |
| CN111060838A (en) * | 2019-12-31 | 2020-04-24 | 中国人民解放军陆军军医大学第二附属医院 | Medical electronic equipment switching power supply fault diagnosis method based on multi-dimensional feature fusion |
| CN111368919A (en) * | 2020-02-25 | 2020-07-03 | 重庆大学 | Feature data construction method, data set construction method, data classification method, EMC fault diagnosis method and system |
| CN111474492A (en) * | 2020-03-31 | 2020-07-31 | 海南掌上能量传媒有限公司 | Mobile power supply detection method |
| CN111504676A (en) * | 2020-04-23 | 2020-08-07 | 中国石油大学(北京) | Equipment fault diagnosis method, device and system based on multi-source monitoring data fusion |
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