CN116317037A - Intelligent management method and system for charging cabinet - Google Patents
Intelligent management method and system for charging cabinet Download PDFInfo
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- CN116317037A CN116317037A CN202310579319.3A CN202310579319A CN116317037A CN 116317037 A CN116317037 A CN 116317037A CN 202310579319 A CN202310579319 A CN 202310579319A CN 116317037 A CN116317037 A CN 116317037A
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- 238000007726 management method Methods 0.000 title claims abstract description 20
- 230000002457 bidirectional effect Effects 0.000 claims abstract description 40
- 238000012544 monitoring process Methods 0.000 claims abstract description 33
- 238000000034 method Methods 0.000 claims abstract description 16
- 230000008569 process Effects 0.000 claims abstract description 11
- 230000006835 compression Effects 0.000 claims description 8
- 238000007906 compression Methods 0.000 claims description 8
- 238000013527 convolutional neural network Methods 0.000 claims description 8
- 238000009499 grossing Methods 0.000 claims description 5
- 239000000284 extract Substances 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 abstract description 12
- 230000005611 electricity Effects 0.000 description 11
- 238000005457 optimization Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Manufacturing & Machinery (AREA)
- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- General Chemical & Material Sciences (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses an intelligent management method and system for a charging cabinet, comprising a charging module, wherein the input end of the charging module is electrically connected with an electric control lock module, the input end of the electric control lock module is electrically connected with a power supply, the input end of the electric control lock module is electrically connected with a main control board in a bidirectional manner, the input end of the main control board is electrically connected with a terminal processor in a bidirectional manner, the output end of the terminal processor is electrically connected with a monitoring module in a bidirectional manner, the output end of the monitoring module is electrically connected with the input end of the charging module in a bidirectional manner, the output end of the terminal processor is electrically connected with a wireless transceiver module in a bidirectional manner, and the output end of the wireless transceiver module is electrically connected with a cloud server in a bidirectional manner. The invention can monitor the charging running state of the equipment in the charging process of the existing charging cabinet, and can perform power-off treatment on the equipment when the equipment is abnormal in charging, so that economic loss caused by fire disaster caused by long-time abnormal equipment is prevented.
Description
Technical Field
The invention relates to the technical field of charging cabinets, in particular to an intelligent charging cabinet management method and system.
Background
The charging cabinet refers to equipment for intensively charging various digital electronic products in a plurality of types and a plurality of numbers, is commonly used for occasions such as schools, enterprises, training institutions and the like, and can intensively store and charge equipment such as flat plates, notebooks, VR glasses, mobile phones and the like.
The cabinet that charges is mainly through placing the equipment that needs to charge inside the cabinet body, but current cabinet that charges can't monitor the charge running state of equipment in the charging process, when the equipment appears charging unusual, the supervisor can't respond the rescue in time, and long-time unusual equipment causes the conflagration easily and leads to economic loss.
Therefore, the intelligent management method and system of the charging cabinet need to be designed and created.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide an intelligent management method and system for a charging cabinet, which have the advantage of intelligently monitoring abnormal states, and solve the problems that the charging running state of equipment cannot be monitored in the charging process of the existing charging cabinet, a supervisor cannot respond to rescue in time when the equipment is abnormal in charging, and the equipment which is abnormal for a long time easily causes fire disaster to cause economic loss.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent management system of a charging cabinet comprises a charging module;
the intelligent charging system comprises a charging module, an electric control lock module, a main control board, a terminal processor, a monitoring module, a wireless transceiver module, a cloud server, a prediction model generation module and a prediction model generation module, wherein the input end of the charging module is electrically connected with the electric control lock module, the input end of the electric control lock module is electrically connected with a power supply, the input end of the electric control lock module is electrically connected with the main control board in a bidirectional manner, the output end of the terminal processor is electrically connected with the monitoring module in a bidirectional manner, the output end of the monitoring module is electrically connected with the input end of the charging module in a bidirectional manner, the output end of the terminal processor is electrically connected with the wireless transceiver module in a bidirectional manner, the output end of the wireless transceiver module is electrically connected with the cloud server in a bidirectional manner, the output end of the cloud server is electrically connected with the classified storage hard disk in a bidirectional manner, and the output end of the prediction model generation module is electrically connected with the input end of the classified storage hard disk in a bidirectional manner.
Preferably, the monitoring module comprises an electric quantity monitoring module, a temperature monitoring module and an internal resistance monitoring module.
As a preferred embodiment of the present invention, an input end of the wireless transceiver module is electrically connected with a data buffer module in a bidirectional manner, and an output end of the data buffer module is electrically connected with an input end of the cloud transmitter in a bidirectional manner.
As a preferred embodiment of the present invention, an input end of the cloud server is electrically connected to a compression classification module in a bidirectional manner, and an output end of the compression classification module is electrically connected to an input end of the classified storage hard disk in a bidirectional manner.
Preferably, the prediction model generation module is composed of a data fitting unit and an exponential smoothing model.
As preferable in the invention, the output end of the prediction model generation module is electrically connected with a convolutional neural network in a bidirectional way, and the output end of the convolutional neural network is electrically connected with the output end of the classified storage hard disk in a bidirectional way.
As a preferred embodiment of the present invention, the output end of the cloud server is electrically connected with a report output unit in two directions, the output end of the report output unit is electrically connected with a feedback module, and the output end of the feedback module is electrically connected with the input end of the wireless transceiver module.
An intelligent management method for a charging cabinet comprises the following steps:
s1: the charging cabinet utilizes the power source energy through the charging module and charges the equipment;
s2: the monitoring module can monitor the electric quantity, the temperature and the internal resistance of the equipment in the equipment charging process and send data to the cloud server through the terminal processor and the wireless receiving and transmitting module, the cloud transmitter guides the data into the prediction model generating module, the prediction model generating module extracts long-short-term data stored in the classified storage hard disk at the same time, different weights are given to historical data in different periods, and then non-derived variable values are predicted;
s3: when the predicted value exceeds the preset value, the cloud server sends an alarm signal to the terminal processor through the feedback module, and the terminal processor controls the electric control lock module to be closed by using the main control board, so that the charging module is powered off, and the equipment is prevented from being damaged due to continuous abnormal charging.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can monitor the charging running state of the equipment in the charging process of the existing charging cabinet, and can perform power-off treatment on the equipment when the equipment is abnormal in charging, so that economic loss caused by fire disaster caused by long-time abnormal equipment is prevented.
2. According to the invention, the electric quantity monitoring module, the temperature monitoring module and the internal resistance monitoring module are arranged, so that the data acquisition range can be increased, and the monitoring effect can be further improved.
3. According to the cloud server data buffer method and device, the data buffer module is arranged, so that data can be buffered, and the operation pressure of the cloud server is reduced.
4. The invention can reduce the occupied space of the data and is convenient for searching the data by arranging the compression classification module.
5. According to the invention, the data fitting unit and the exponential smoothing model are arranged, so that the model connection accuracy can be improved, and the establishment error is avoided.
6. According to the invention, the convolutional neural network is arranged, so that the intelligent degree of the charging cabinet can be improved, and the self-learning effect is improved.
7. According to the invention, the report output unit and the feedback module are arranged, so that the charging cabinet can respond conveniently, and the safety of the charging cabinet is improved.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the intelligent management system of the charging cabinet provided by the invention comprises a charging module;
the input of module electricity that charges is connected with automatically controlled lock module, automatically controlled lock module's input electricity is connected with the power, automatically controlled lock module's input two-way electricity is connected with the main control board, the input of main control board two-way electricity is connected with terminal processor, terminal processor's output two-way electricity is connected with the control module, control module's output and the input of module that charges two-way electricity are connected, terminal processor's output two-way electricity is connected with wireless transceiver module, wireless transceiver module's output two-way electricity is connected with the high in the clouds server, high in the clouds server's output two-way electricity is connected with the classified storage hard disk, the output of high in the clouds server two-way electricity is connected with prediction model generation module, prediction model generation module's output and classified storage hard disk's input two-way electricity are connected.
Referring to fig. 1, the monitoring module is composed of an electric quantity monitoring module, a temperature monitoring module and an internal resistance monitoring module.
As a technical optimization scheme of the invention, the data acquisition range can be increased and the monitoring effect can be further improved by arranging the electric quantity monitoring module, the temperature monitoring module and the internal resistance monitoring module.
Referring to fig. 1, an input end of the wireless transceiver module is electrically connected with a data buffer module in a bidirectional manner, and an output end of the data buffer module is electrically connected with an input end of the cloud transmitter in a bidirectional manner.
By means of the technical optimization scheme, the data can be buffered by the data buffer module, and the operation pressure of the cloud server is reduced.
Referring to fig. 1, an input end of the cloud server is electrically connected with a compression classification module in a bidirectional manner, and an output end of the compression classification module is electrically connected with an input end of the classified storage hard disk in a bidirectional manner.
As a technical optimization scheme of the invention, the compression classification module is arranged, so that the occupied space of data can be reduced, and the data can be searched conveniently.
Referring to fig. 1, the prediction model generation module is composed of a data fitting unit and an exponential smoothing model.
As a technical optimization scheme of the invention, the model connection accuracy can be improved and the establishment error is avoided by setting the data fitting unit and the exponential smoothing model.
Referring to fig. 1, the output end of the prediction model generation module is electrically connected with a convolutional neural network in a bidirectional manner, and the output end of the convolutional neural network is electrically connected with the output end of the classified storage hard disk in a bidirectional manner.
By means of the technical optimization scheme, the intelligent degree of the charging cabinet can be improved, and the self-learning effect can be improved through the convolutional neural network.
Referring to fig. 1, an output end of the cloud server is electrically connected with a report output unit in a bidirectional manner, an output end of the report output unit is electrically connected with a feedback module, and an output end of the feedback module is electrically connected with an input end of the wireless transceiver module.
By means of the technical optimization scheme, the charging cabinet can respond conveniently through the report output unit and the feedback module, and the safety of the charging cabinet is improved.
Referring to fig. 1, an intelligent management method of a charging cabinet includes the following steps:
s1: the charging cabinet utilizes the power source energy through the charging module and charges the equipment;
s2: the monitoring module can monitor the electric quantity, the temperature and the internal resistance of the equipment in the equipment charging process and send data to the cloud server through the terminal processor and the wireless receiving and transmitting module, the cloud transmitter guides the data into the prediction model generating module, the prediction model generating module extracts long-short-term data stored in the classified storage hard disk at the same time, different weights are given to historical data in different periods, and then non-derived variable values are predicted;
s3: when the predicted value exceeds the preset value, the cloud server sends an alarm signal to the terminal processor through the feedback module, and the terminal processor controls the electric control lock module to be closed by using the main control board, so that the charging module is powered off, and the equipment is prevented from being damaged due to continuous abnormal charging.
The working principle and the using flow of the invention are as follows: when the device is used, the charging cabinet utilizes power energy through the charging module and charges the device, the monitoring module can monitor the electric quantity, the temperature and the internal resistance of the device in the charging process of the device and send data to the cloud server through the terminal processor and the wireless transceiver module, the cloud transmitter guides the data into the prediction model generating module, the prediction model generating module extracts long-short-term data stored in the classified storage hard disk at the same time and gives different weights to historical data in different periods, then the cloud server predicts that the data are not from variable values, when the predicted values exceed preset values, the cloud server sends alarm signals to the terminal processor through the feedback module, and the terminal processor controls the electric control lock module to be closed by using the main control board, so that the charging module is powered off, and the device is prevented from being damaged due to continuous abnormal charging.
To sum up: according to the intelligent management method and system for the charging cabinet, the charging running state of equipment can be monitored in the charging process of the existing charging cabinet, when the equipment is abnormal in charging, the equipment can be powered off, and the economic loss caused by fire disaster caused by equipment abnormal for a long time is prevented.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An intelligent management system of a charging cabinet comprises a charging module;
the method is characterized in that: the intelligent charging system comprises a charging module, an electric control lock module, a main control board, a terminal processor, a monitoring module, a wireless transceiver module, a cloud server, a prediction model generation module and a prediction model generation module, wherein the input end of the charging module is electrically connected with the electric control lock module, the input end of the electric control lock module is electrically connected with a power supply, the input end of the electric control lock module is electrically connected with the main control board in a bidirectional manner, the output end of the terminal processor is electrically connected with the monitoring module in a bidirectional manner, the output end of the monitoring module is electrically connected with the input end of the charging module in a bidirectional manner, the output end of the terminal processor is electrically connected with the wireless transceiver module in a bidirectional manner, the output end of the wireless transceiver module is electrically connected with the cloud server in a bidirectional manner, the output end of the cloud server is electrically connected with the classified storage hard disk in a bidirectional manner, and the output end of the prediction model generation module is electrically connected with the input end of the classified storage hard disk in a bidirectional manner.
2. The intelligent management system for a charging cabinet according to claim 1, wherein: the monitoring module consists of an electric quantity monitoring module, a temperature monitoring module and an internal resistance monitoring module.
3. The intelligent management system for a charging cabinet according to claim 1, wherein: the input end of the wireless transceiver module is electrically connected with a data cache module in a bidirectional mode, and the output end of the data cache module is electrically connected with the input end of the cloud transmitter in a bidirectional mode.
4. The intelligent management system for a charging cabinet according to claim 1, wherein: the input end of the cloud server is electrically connected with a compression classification module in a bidirectional mode, and the output end of the compression classification module is electrically connected with the input end of the classified storage hard disk in a bidirectional mode.
5. The intelligent management system for a charging cabinet according to claim 1, wherein: the prediction model generation module consists of a data fitting unit and an exponential smoothing model.
6. The intelligent management system for a charging cabinet according to claim 1, wherein: the output end of the prediction model generation module is electrically connected with a convolutional neural network in a bidirectional mode, and the output end of the convolutional neural network is electrically connected with the output end of the classified storage hard disk in a bidirectional mode.
7. The intelligent management system for a charging cabinet according to claim 1, wherein: the output end of the cloud server is electrically connected with a report output unit in a bidirectional mode, the output end of the report output unit is electrically connected with a feedback module, and the output end of the feedback module is electrically connected with the input end of the wireless transceiver module.
8. An intelligent management method for a charging cabinet is characterized by comprising the following steps of: the intelligent management system for the charging cabinet according to claim 1, comprising the following steps:
s1: the charging cabinet utilizes the power source energy through the charging module and charges the equipment;
s2: the method comprises the steps that in the charging process of equipment, a monitoring module monitors electric quantity, temperature and internal resistance of the equipment and sends data to a cloud server through a terminal processor and a wireless receiving and transmitting module, a cloud transmitter guides the data into a prediction model generation module, the prediction model generation module extracts long-short-term data stored in a classified storage hard disk at the same time, different weights are given to historical data in different periods, and then non-self-variable values are predicted;
s3: when the predicted value exceeds the preset value, the cloud server sends an alarm signal to the terminal processor through the feedback module, and the terminal processor controls the electric control lock module to be closed by using the main control board, so that the charging module is powered off.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310579319.3A CN116317037B (en) | 2023-05-23 | 2023-05-23 | Intelligent management method and system for charging cabinet |
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| CN202310579319.3A CN116317037B (en) | 2023-05-23 | 2023-05-23 | Intelligent management method and system for charging cabinet |
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| CN116317037A true CN116317037A (en) | 2023-06-23 |
| CN116317037B CN116317037B (en) | 2023-08-29 |
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| CN116317037B (en) | 2023-08-29 |
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