CN112067997A - Diagnosis method and system for mobile power supply leasing equipment, electronic equipment and storage medium - Google Patents
Diagnosis method and system for mobile power supply leasing equipment, electronic equipment and storage medium Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 320
- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000002159 abnormal effect Effects 0.000 claims abstract description 38
- 238000001514 detection method Methods 0.000 claims abstract description 23
- 238000013145 classification model Methods 0.000 claims abstract description 12
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004891 communication Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 abstract description 51
- 208000028659 discharge Diseases 0.000 description 18
- 230000005856 abnormality Effects 0.000 description 12
- 238000010586 diagram Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 5
- 230000007488 abnormal function Effects 0.000 description 4
- 238000002405 diagnostic procedure Methods 0.000 description 4
- 238000007599 discharging Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
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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
Abstract
The application relates to a diagnosis method and a diagnosis system for a mobile power supply leasing device and an electronic device, wherein the diagnosis method for the mobile power supply leasing device obtains operation data of the mobile power supply leasing device; classifying the operating data according to a 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. The problem that the diagnosis result depends on field maintenance of operation and maintenance personnel or abnormal reporting of merchants and the fault phenomenon and the reason cannot be accurately described is solved, the operation and maintenance efficiency of the equipment is improved, and the 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 diagnosis method and system for a mobile power supply leasing device, an electronic device and a storage medium.
Background
With the improvement of the dependence degree of people on the mobile phone and the deep mind of sharing economy, the mobile power sharing service scale of the mobile phone is larger and larger, and the operation and maintenance cost of the mobile power sharing is higher and higher.
In the related technology, the operation and maintenance personnel sharing the mobile power supply can find that the mobile power supply or the equipment is abnormal through the routing inspection of each equipment placing point in daily life, or the telephone of a merchant of the placing point is returned to the operation personnel, and the operation personnel can know the corresponding product abnormality only through the routing inspection or the reporting of the merchant by the operation personnel; 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, no effective solution is provided for the problem that in the related technology, the diagnosis result depends on-site maintenance by operation and maintenance personnel or abnormal reporting by merchants, and the fault phenomenon and the cause cannot be accurately described.
Disclosure of Invention
The invention provides a mobile power supply diagnosis method, a mobile power supply diagnosis system and electronic equipment, which are used for at least solving the problem that the diagnosis result depends on-site maintenance of operation and maintenance personnel or abnormal reporting reported by a merchant, and the fault phenomenon and the cause cannot be accurately described.
In a first aspect, the present invention provides a mobile power supply leasing equipment diagnosis method, including:
acquiring operation data of the mobile power supply leasing equipment;
classifying the operating data according to a classification model to obtain a data set;
and carrying out anomaly detection on the data set according to the diagnosis strategy to obtain a diagnosis report.
In some embodiments, the mobile power supply rental equipment comprises a mobile power supply charging cabinet and/or a mobile power supply, and the operation data comprises equipment data and service data of the mobile power supply charging cabinet and/or the mobile power supply.
In some embodiments, the classifying the operational data according to a classification model, and obtaining the data set comprises:
classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions;
the data set is obtained according to the diagnostic dimension required for each diagnostic item, wherein each diagnostic item may include a plurality of the diagnostic dimensions.
In some embodiments, the performing anomaly detection on the data set according to the diagnosis policy and obtaining the diagnosis report includes:
carrying out anomaly detection on the data set to obtain a diagnosis result of a diagnosis dimension;
and processing the diagnosis result of the diagnosis dimension to obtain the diagnosis result of the diagnosis item, and then generating a diagnosis report.
In some embodiments, the processing the results of the anomaly detection to obtain the diagnostic result of the diagnostic item comprises:
multiplying the relative error of the diagnosis dimension by the weight factor of the diagnosis dimension to obtain a weight parameter of the diagnosis dimension;
weighting the diagnosis result of the diagnosis dimension and the weight parameter through data to obtain an abnormal proportion bitmap;
and obtaining the diagnosis result of the diagnosis item according to the abnormality proportion bitmap.
In a second aspect, the application provides a system for diagnosing a mobile power supply leasing device, which comprises the mobile power supply leasing device and a cloud server;
the mobile power supply leasing equipment comprises a mobile power supply charging cabinet and/or a mobile power supply;
this portable power source charges cabinet, can collect the operational data of this portable power source leased equipment and upload to this cloud ware, this portable power source charges cabinet 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 the operation data generated by the mobile power supply charging cabinet and the mobile power supply; classifying the operating data according to a classification model to obtain a data set; and carrying out anomaly detection on the data set according to the 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, and 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 according to the diagnostic dimension 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 diagnosis policy, and obtain a diagnosis report, including:
carrying out anomaly detection on the data set to obtain a diagnosis result of a diagnosis dimension;
and processing the diagnosis result of the diagnosis dimension to obtain the diagnosis result of the diagnosis item, and then generating 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 a diagnosis item, including:
multiplying the relative error of the diagnosis dimension by the weight factor of the diagnosis dimension to obtain a weight parameter of the diagnosis dimension;
weighting the diagnosis result of the diagnosis dimension and the weight parameter through data to obtain an abnormal proportion bitmap;
and obtaining the diagnosis result of the diagnosis item according to the abnormality proportion bitmap.
In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one 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 the method of any one of the first to second aspects when executing the computer program.
Compared with the related art, the mobile power supply diagnosis method provided by the embodiment of the application obtains the operation data of the mobile power supply leasing equipment; classifying the operation data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions; obtaining the data set according to the diagnosis dimension required by each diagnosis item, wherein each diagnosis item can comprise a plurality of diagnosis dimensions; and carrying out anomaly detection on the data set according to the diagnosis strategy to obtain a diagnosis report. Each diagnosis item obtains a diagnosis report, a comprehensive diagnosis result is finally generated, the diagnosis result is sent to the operation and maintenance terminal, and 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 maintenance of operation and maintenance personnel or abnormal reporting of merchants and the fault phenomenon and the reason cannot be accurately described 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 application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of a mobile power rental equipment diagnostic system, according to one embodiment of the present application;
FIG. 2 is a schematic diagram of a mobile power rental equipment diagnostic system, according to another embodiment of the present application;
FIG. 3 is a flowchart of a mobile power supply rental equipment diagnostic method according to an embodiment of the 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 an internal structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase 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. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The embodiment provides a portable power supply rental equipment diagnosis system, which can be used for portable power supply rental equipment diagnosis, and fig. 1 is a schematic diagram of a portable power supply diagnosis system according to an embodiment of the application, and as shown in fig. 1, the system includes an operation and maintenance terminal 11, a portable power supply 12, a portable power supply charging cabinet 13, and a cloud server 14.
The operation and maintenance terminal 11 is used for receiving the diagnosis report sent by the cloud server 14, and 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 operation data includes device data and service data of the mobile power supply 12. The service data includes an order number, an order duration and an 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 charging cabinet 13 includes a plurality of battery compartment modules. Wherein, a portable power source 12 is placed in the storehouse body of each battery compartment module.
This portable power source charging cabinet 13 can collect portable power source lease equipment's operating data, again based on HTTP interface or based on the public internet of things protocol of TCP or based on the private internet of things protocol of TCP passes through networking modules such as 4G or WIFI or 5G and sends the data storage module of this cloud ware 14. The operation data includes device data of the portable power source 12 when it is located in the portable power source charging cabinet 13 and device data of the portable power source charging cabinet 13. The device data of the mobile power supply 12 includes a charging voltage curve, a charging current curve, a charging temperature curve, and the like, 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, and the like.
Furthermore, the equipment data of the mobile power supply charging cabinet further comprises working operation time data of a driving power supply, non-working operation time data, information working time data sent by the communication module, working time data of the battery module and working time data of other functional modules and components, the data are collected and sent to the cloud server, abnormal functional modules or abnormal components are analyzed through diagnosis logic preset by the cloud server and then sent to the operation and maintenance terminal, and operation and maintenance are arranged in time for maintenance. 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, the abnormal function module or the abnormal components are analyzed through the preset diagnosis logic of the cloud server, and then the abnormal function module or the abnormal components are sent to the operation and maintenance terminal, so that the operation and maintenance can be arranged in time for maintenance.
The mobile power supply charging cabinet 13 is internally provided with a UPS power supply, and under the condition that the mobile power supply charging cabinet 13 is powered off, the UPS power supply 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 mobile power supply leasing equipment. The operation data includes service data of the mobile power supply 12, device data when the mobile power supply 12 is rented, device data of the mobile power supply 12 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 rented out 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 preset diagnosis dimensions. And acquiring a data set of the corresponding diagnosis dimension according to each diagnosis item, wherein the diagnosis dimension required by each diagnosis item is different. Wherein each diagnostic item may include a plurality of diagnostic dimensions. And performing data cleaning on abnormal data in the data set in the diagnosis item. Then, the data set is subjected to anomaly detection by using a diagnosis strategy, a diagnosis result of a diagnosis dimension is diagnosed first, then the diagnosis result of the diagnosis dimension is processed to obtain a diagnosis result of a diagnosis item, and then a diagnosis report is produced and sent to the operation and maintenance terminal 11.
Through the system, in the related technology, the diagnosis result depends on the field maintenance of operation and maintenance personnel or the reporting abnormality of a merchant, and the problem of the fault phenomenon and the reason cannot be accurately described.
The embodiment provides a portable power supply rental equipment diagnosis system, which can be used for portable power supply rental equipment diagnosis, and fig. 2 is a schematic diagram of a portable power supply diagnosis system 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 supply 22, a portable power supply charging cabinet 23, and a cloud server 24.
The operation and maintenance terminal 21 is configured to receive a diagnosis report sent by the cloud server 24, and the operation and maintenance terminal 21 includes a smart phone, a tablet computer, and the like.
This portable power source 22 can gather portable power source 22's operating data through portable power source charging cabinet 23 after, and then through portable power source charging cabinet 23's communication module with operation data transmission to cloud ware.
This portable power source charges cabinet 23 includes: a plurality of battery compartment modules, a portable power source 22 is placed in the storehouse body of each battery compartment module.
This portable power source charges cabinet 23 can collect this portable power source's operating data, again based on HTTP interface or based on the public internet of things agreement of TCP or based on the private internet of things agreement of TCP sends the data storage module to this cloud server 24 through networking modules such as 4G or WIFI, and wherein, this operating data includes portable power source 22's service data, portable power source 22 when rented out equipment data, portable power source 22 is located portable power source and charges the equipment data of cabinet 23 and portable power source. The device data when the mobile power source 22 is rented is stored in the mobile power source 22, and then when the mobile power source 22 is located in the mobile power source charging cabinet 23, the mobile power source charging cabinet 23 collects the device data when the mobile power source 22 is rented. The equipment data of the mobile power supply comprises a discharging voltage curve (when rented), a discharging current curve (when rented), a charging voltage curve, a charging temperature curve and the like, and the equipment data of the mobile power supply charging cabinet comprises a bin position state, signal intensity, bin position temperature, power consumption conditions and the like.
The mobile power supply charging cabinet 23 is internally provided with a UPS power supply, and under the condition that the mobile power supply charging cabinet 23 is powered off, the UPS power supply 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 the communication module.
Further, or the user rents the portable power source to generate data, the portable power source stores the data, and when the portable power source returns, the portable power source charging cabinet 13 reads the data stored in the portable power source and sends operation data information to the cloud server through the communication module.
The cloud server 24 is configured to receive and analyze operation data of the mobile power supply rental equipment, where the operation data includes service data of the mobile power supply 22, equipment data of the mobile power supply 22 when rented, equipment data of the mobile power supply 22 in the mobile power supply charging cabinet 23, and equipment data of the mobile power supply charging cabinet 23. The device data when the mobile power source 22 is rented is stored in the mobile power source 22, and then when the mobile power source 22 is located in the mobile power source charging cabinet 23, the mobile power source charging cabinet 23 collects the device data when the mobile power source 22 is rented. . And inputting the operation data into a classification model to classify the operation data, and obtaining data sets of different diagnosis dimensions according to preset diagnosis dimensions. And acquiring a data set of the corresponding diagnosis dimension according to each diagnosis item, wherein the diagnosis dimension required by each diagnosis item is different. Wherein each diagnostic item may include a plurality of diagnostic dimensions. And performing data cleaning on abnormal data in the data set in the diagnosis item. Then, the data set is subjected to anomaly detection by using a diagnosis strategy, a diagnosis result of a diagnosis dimension is diagnosed first, then the diagnosis result of the diagnosis dimension is processed to obtain a diagnosis result of a diagnosis item, and then a diagnosis report is produced and sent to the operation and maintenance terminal 21.
Furthermore, the equipment data of the mobile power supply charging cabinet further comprises working operation time data of a driving power supply, non-working operation time data, information working time data sent by the communication module, working time data of the battery module and working time data of other functional modules and components, the data are collected and sent to the cloud server, abnormal functional modules or abnormal components are analyzed through diagnosis logic preset by the cloud server and then sent to the operation and maintenance terminal, and operation and maintenance are arranged in time for maintenance. 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, the abnormal function module or the abnormal components are analyzed through the preset diagnosis logic of the cloud server, and then the abnormal function module or the abnormal components are sent to the operation and maintenance terminal, so that the operation and maintenance can be arranged in time for maintenance.
Through the system, in the related technology, the diagnosis result depends on the field maintenance of operation and maintenance personnel or the reporting abnormality of a merchant, and the problem of the fault phenomenon and the reason cannot be accurately described.
The embodiment provides a mobile power supply leasing equipment diagnosis method, which can be used for remote intelligent diagnosis of a mobile power supply, and fig. 3 is a flowchart of the mobile power supply leasing equipment diagnosis method according to the embodiment of the application, and as shown in fig. 3, the method includes:
step S301, obtaining the operation data of the mobile power supply leasing equipment. The mobile power supply leasing equipment comprises a mobile power supply and/or a mobile power supply charging cabinet, and the operation data comprises service data and equipment data. When the mobile power supply is in the mobile charging cabinet, the mobile charging cabinet acquires 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 acquires the equipment data of the mobile power supply, the data are transmitted to an internet of things system built in the mobile power supply cabinet, the internet of things system sends the equipment data to a cloud server, the cloud server sends the data to a diagnosis system, and the diagnosis system acquires service data from a service system, wherein the service data comprise a field batch, product test data, order data and the like of mobile power supply leasing equipment.
Step S302, the operation data is classified according to a classification model, and a data set is obtained. Analyzing equipment data sent by an Internet of things system, wherein the analyzed data comprises: the battery compartment module comprises a battery compartment module, a compartment position, signal strength, temperature and the like, and a mobile power supply, wherein the battery compartment module comprises a compartment body state, a compartment position, signal strength, temperature and the like, and the mobile power supply comprises electric quantity, temperature, abnormal codes, cycle times and the like. Service data is obtained from a service system. And 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 diagnosis dimension required by 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 mobile power supply leasing equipment, such as a virtual power diagnosis item, a line loss diagnosis item, an abnormal base diagnosis item and the like.
Step S303, carrying out abnormity detection on the data set according to the diagnosis strategy to obtain a diagnosis report. The abnormality detection comprises the steps of firstly diagnosing diagnosis dimensions, obtaining diagnosis results of different diagnosis dimensions, then obtaining diagnosis results of diagnosis items according to the diagnosis results of the diagnosis dimensions, and then generating a diagnosis report, wherein fault reasons are specified in the diagnosis report.
As an implementable manner, when the diagnosis item is an abnormal base diagnosis item, the mobile power supply order dimension, the bin state dimension, and the mobile power supply state dimension need to be acquired. The mobile power supply charging dimension comprises an order number, an order amount and an order state, for example, if five orders in 10 orders are in a cancel state or the order amount is 0, the diagnosis dimension is diagnosed to be abnormal; the bin state dimension comprises the taking times and the taking time of the mobile power supply in the bin, and if the taking times are larger than the threshold value of the dimension, the diagnosis dimension is diagnosed to be abnormal; the mobile power state dimension comprises the signal strength of the mobile power, and if the signal strength is always lower than the threshold value of the diagnosis dimension, the diagnosis dimension is diagnosed to be abnormal. The upper appeal threshold can be obtained in the diagnostic strategy.
Through the steps S301 to S303, in the related art, the diagnosis result depends on the field maintenance of the operation and maintenance personnel or the reporting abnormality of the merchant, and the problem of the fault phenomenon and the cause cannot be accurately described.
The embodiment provides a mobile power supply leasing equipment diagnosis method, which can be used for remote intelligent diagnosis of a mobile power supply, and fig. 4 is a flowchart of a mobile power supply leasing equipment diagnosis method according to another embodiment of the application, and as shown in fig. 4, the method includes:
step S401, obtaining the operation data of the mobile power supply leasing equipment. The mobile power supply leasing equipment comprises a mobile power supply and/or a mobile power supply charging cabinet, and the operation data comprises service data and equipment data. When the mobile power supply is in the mobile charging cabinet, the mobile charging cabinet acquires 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 acquires the equipment data of the mobile power supply, the data are transmitted to an internet of things system built in the mobile power supply cabinet, the internet of things system sends the equipment data to a cloud server, the cloud server sends the data to a diagnosis system, and the diagnosis system acquires service data from a service system, wherein the service data comprise a field batch, product test data, order data and the like of mobile power supply leasing equipment.
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 renting voltage, a renting charging cabinet number, a renting position, a renting time, a returning voltage, a returning charging cabinet number, a returning time and the like. The diagnosis dimension of the state of the charging cabinet of the mobile power supply comprises the bin state, the signal intensity, the single-day power consumption, the equipment temperature, the online equipment time, the online equipment record, the offline equipment record and the like. The mobile power supply charging diagnosis dimension comprises a charging time length, a charging voltage, a charging time length, a starting voltage, an ending voltage and the like. The mobile power supply discharge diagnosis dimension comprises discharge current, discharge voltage, discharge time, discharge capacity and the like.
Step S403, acquiring a data set according to the diagnosis dimension of each diagnosis item, where 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 supply mobile power supply diagnosis item, a line loss mobile power supply diagnosis item and an overdischarge 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 item comprises a mobile power supply charging current diagnosis dimension and a mobile power supply voltage diagnosis dimension; the virtual mobile power supply diagnosis item comprises a mobile power supply capacity diagnosis dimension and a mobile power supply order diagnosis dimension; the line loss mobile power supply diagnosis item comprises a mobile power supply discharge diagnosis dimension and a mobile power supply order diagnosis dimension; the over-discharge mobile power supply diagnostic item comprises a mobile power supply discharge diagnostic dimension.
And S404, carrying out abnormity detection on the data set according to the diagnosis strategy to obtain a diagnosis report. The abnormality detection comprises the steps of firstly diagnosing diagnosis dimensions, obtaining diagnosis results of different diagnosis dimensions, then obtaining diagnosis results of diagnosis items according to the diagnosis results of the diagnosis dimensions, and then generating a diagnosis report, wherein fault reasons are specified in the diagnosis report.
Through the steps S401 to S404, in the related art, the diagnosis result depends on the field maintenance of the operation and maintenance personnel or the reporting abnormality of the merchant, and the problem of the fault phenomenon and the reason cannot be accurately described.
The embodiment provides a mobile power supply leasing equipment diagnosis method, which can be used for remote intelligent diagnosis of a mobile power supply, and fig. 5 is a flowchart of a mobile power supply leasing equipment diagnosis method according to another embodiment of the application, and as shown in fig. 5, the method includes:
step S501, obtaining operation data of the mobile power supply leasing equipment. The mobile power supply leasing equipment comprises a mobile power supply and/or a mobile power supply charging cabinet, and the operation data comprises service data and equipment data. When the mobile power supply is in the mobile charging cabinet, the mobile charging cabinet acquires 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 acquires the equipment data of the mobile power supply, the data are transmitted to an internet of things system built in the mobile power supply cabinet, the internet of things system sends the equipment data to a cloud server, the cloud server sends the data to a diagnosis system, and the diagnosis system acquires service data from a service system, wherein the service data comprise a field batch, product test data, order data and the like of mobile power supply leasing equipment.
Step S502, the operation data are classified according to preset diagnosis dimensions, and data sets of different diagnosis dimensions are obtained. 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 renting voltage, a renting charging cabinet number, a renting position, a renting time, a returning voltage, a returning charging cabinet number, a returning time and the like. The diagnosis dimension of the state of the charging cabinet of the mobile power supply comprises the bin state, the signal intensity, the single-day power consumption, the equipment temperature, the online equipment time, the online equipment record, the offline equipment record and the like. The mobile power supply charging diagnosis dimension comprises a charging time length, a charging voltage, a charging time length, a starting voltage, an ending voltage and the like. The mobile power supply discharge diagnosis dimension comprises discharge current, discharge voltage, discharge time, discharge capacity and the like.
Step S503, acquiring a data set according to the 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 supply mobile power supply diagnosis item, a line loss mobile power supply diagnosis item and an overdischarge 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 item comprises a mobile power supply charging current diagnosis dimension and a mobile power supply voltage diagnosis dimension; the virtual mobile power supply diagnosis item comprises a mobile power supply capacity diagnosis dimension and a mobile power supply order diagnosis dimension; the line loss mobile power supply diagnosis item comprises a mobile power supply discharge diagnosis dimension and a mobile power supply order diagnosis dimension; the over-discharge mobile power supply diagnostic item comprises 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 a diagnostic dimension is equal to the relative error of the diagnostic dimension multiplied by the weight factor of the diagnostic dimension. Wherein the weighting factors are given by the diagnostic strategy.
And step S505, weighting the diagnosis result of the diagnosis dimension and the weight parameter through data to obtain an abnormal proportion bitmap. Obtaining an abnormal proportion 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 a weight parameter of the diagnostic dimension, p is the weight taken by the diagnostic dimension
And step S506, obtaining a diagnosis result of the diagnosis item according to the abnormal proportion bitmap. A single diagnosis report corresponds to one diagnosis item, and a plurality of diagnosis reports constitute a diagnosis result.
As an optional embodiment, when performing the exception takeaway diagnosis, the diagnosis item includes a mobile power supply exception dimension, a base exception diagnosis dimension, and a mobile power supply order exception diagnosis dimension. The weight parameter is the dimension of abnormity diagnosis of the mobile power supply: mobile power supply abnormality diagnosis dimension: the mobile power supply order abnormity diagnosis dimension is 2:3: 5. And obtaining an abnormal proportion bitmap of the fault through data weighting processing according to the diagnosis result of the diagnosis dimension and the weight parameter, wherein the abnormal proportion bitmap is 1/2:1/3:1/5 and is converted into an integer which is approximately equal to 5:3: 2. And displaying the fault information in detail according to the abnormal ratio bitmap and the data set of the abnormal taking fault.
Through the steps S501 to S406, in the related art, the diagnosis result depends on the field maintenance of the operation and maintenance personnel or the reporting abnormality of the merchant, and the problem of the fault phenomenon and the cause cannot be accurately described.
The embodiment provides a mobile power supply leasing equipment diagnosis method, which can be used for remote intelligent diagnosis of a mobile power supply, and fig. 6 is a flowchart of a mobile power supply leasing equipment diagnosis method according to still another embodiment of the application, and as shown in fig. 6, the method includes:
when the mobile power supply is positioned 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 the 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. And the diagnosis system simultaneously acquires the service data of the mobile power supply from the service system. And dividing the service data and the equipment data into data sets with different diagnosis dimensions according to preset diagnosis dimensions in the diagnosis strategy. And acquiring a corresponding data set according to the diagnosis dimension required to be considered in the diagnosis of each diagnosis item, and then diagnosing the data set according to a diagnosis strategy to obtain diagnosis reports, wherein a plurality of diagnosis reports generate a diagnosis result.
In the related technology, the diagnosis result depends on the field overhaul of operation and maintenance personnel or the reporting abnormality of a merchant, and the problem of failure phenomenon and reason cannot be accurately described.
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, there is provided an electronic device, which may be a server, and an internal structure diagram of which 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 equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the electronic device is used for storing data. The network interface of the electronic device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a method of mobile power supply diagnostics.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is a block diagram of only a portion of the architecture associated with the subject application, and does not constitute a limitation on the electronic devices to which the subject application may be applied, and that a particular electronic device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps in the mobile power supply diagnosis method provided in the foregoing embodiments are implemented.
In one embodiment, a storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps in the mobile power supply diagnosis method provided by the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile 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), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (11)
1. A mobile power supply leasing equipment diagnosis method is characterized by comprising the following steps:
acquiring operation data of the mobile power supply leasing equipment;
classifying the operating data according to a 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.
2. The method of claim 1, wherein the mobile power rental equipment comprises a mobile power charging cabinet and/or a mobile power supply, and the operation data comprises equipment data and service data of the mobile power charging cabinet and/or the mobile power supply.
3. The method of claim 1, wherein the classifying the operational data according to a classification model, and obtaining a data set comprises:
classifying the operating data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions;
the data set is obtained according to the diagnostic dimension required for each diagnostic item, wherein each diagnostic item may include a plurality of the diagnostic dimensions.
4. The method of claim 1, wherein the detecting anomalies in the data set according to the diagnostic policy and obtaining the diagnostic report comprises:
carrying out anomaly detection on the data set to obtain a diagnosis result of a diagnosis dimension;
and processing the diagnosis result of the diagnosis dimension to obtain the diagnosis result of the diagnosis item, and then generating a diagnosis report.
5. The method of claim 4, wherein the results of the anomaly detection are processed to obtain diagnostic results for diagnostic items comprising:
multiplying the relative error of the diagnosis dimension by a weight factor of the diagnosis dimension to obtain a weight parameter of the diagnosis dimension;
weighting the diagnosis result of the diagnosis dimension and the weight parameter through data to obtain an abnormal proportion bitmap;
and obtaining the diagnosis result of the diagnosis item according to the abnormal proportion bitmap.
6. A system for diagnosing mobile power supply leasing equipment is characterized by comprising the mobile power supply leasing equipment and a cloud server;
the mobile power supply leasing equipment comprises a mobile power supply charging cabinet and/or a mobile power supply;
the portable power source cabinet that charges can collect portable power source leased equipment's operational data and upload to the cloud ware, the 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 the mobile power supply charging cabinet and the operation data generated by the mobile power supply; classifying the operating data according to a 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.
7. The system of claim 6, wherein the cloud server is further configured to classify the operational data according to a classification model to obtain a data set, comprising:
classifying the operating data according to preset diagnosis dimensions to obtain data sets of different diagnosis dimensions;
the data set is obtained according to the diagnostic dimension required for each diagnostic item, wherein each diagnostic item may include a plurality of the diagnostic dimensions.
8. The system of claim 6, wherein the cloud server is further configured to perform anomaly detection on the data set according to a diagnosis policy, and obtain a diagnosis report, including:
carrying out anomaly detection on the data set to obtain a diagnosis result of a diagnosis dimension;
and processing the diagnosis result of the diagnosis dimension to obtain the diagnosis result of the diagnosis item, and then generating a diagnosis report.
9. The system of claim 8, wherein the cloud server is further configured to process the result of the anomaly detection to obtain a diagnosis result of a diagnosis item, including:
multiplying the relative error of the diagnosis dimension by a weight factor of the diagnosis dimension to obtain a weight parameter of the diagnosis dimension;
weighting the diagnosis result of the diagnosis dimension and the weight parameter through data to obtain an abnormal proportion bitmap;
and obtaining the diagnosis result of the diagnosis item according to the abnormal proportion bitmap.
10. 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 steps of the method according to any of claims 1 to 5 are implemented by the processor when executing the computer program.
11. A storage medium 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 according to any one of claims 1 to 5 when executing the computer program.
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