CN114257418A - Raspberry pie-based load prediction method and device and electronic equipment - Google Patents
Raspberry pie-based load prediction method and device and electronic equipment Download PDFInfo
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- H—ELECTRICITY
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- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/02—Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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Abstract
The disclosure provides a raspberry group-based load prediction method and device and electronic equipment, and relates to the technical field of energy and data transmission. The method comprises the following steps: acquiring extranet weather data sent by a first raspberry group based on a simplex communication channel between the first raspberry group and the extranet weather data; and storing the external network weather data into a database of an internal network so as to enable a preset internal network client to carry out load prediction based on the external network weather data. Therefore, the second raspberry group and the first raspberry group transmit the weather data of the external network based on the simplex communication channel, and the weather data of the external network is stored in the internal network database, so that the internal network client calls the weather data from the internal network database, the safety and the confidentiality of the weather data transmission of the external network are guaranteed, and the load prediction efficiency of the internal network client is improved.
Description
Technical Field
The disclosure relates to the technical field of energy and data transmission, in particular to a raspberry group-based load prediction method and device and electronic equipment.
Background
In the process of predicting the energy load, the load prediction is carried out by combining weather data, so that the result of the load prediction is more accurate. In the related art, weather data such as temperature and humidity can be acquired through a sensor, but weather data of a region with a long distance or a large range cannot be acquired. Although the weather data of each region can be acquired through the network at the same time, the security and confidentiality of the data cannot be guaranteed in the process of acquiring the weather data through the external network.
Disclosure of Invention
The present disclosure is directed to solving, at least to some extent, one of the technical problems in the related art.
The embodiment of the first aspect of the disclosure provides a load prediction method based on a raspberry pi, which includes:
acquiring extranet weather data sent by a first raspberry group based on a simplex communication channel between the first raspberry group and the extranet weather data;
and storing the external network weather data into a database of an internal network so as to enable a preset internal network client to carry out load prediction based on the external network weather data.
Optionally, the weather data of the extranet is weather data obtained from the extranet based on a web crawler technology by the first raspberry group.
Optionally, the obtaining, based on a simplex communication channel with the first raspberry pi, the extranet weather data sent by the first raspberry pi includes:
determining the time interval of sending the external network weather data by the first raspberry serving according to the time interval of obtaining the external network weather data by the first raspberry serving;
and receiving the extranet weather data based on the time interval of the first raspberry group sending the extranet weather data.
Optionally, the storing the weather data of the external network into a database of the internal network includes:
and storing the extranet weather data into a database of the intranet in a preset storage format.
Optionally, the method further includes:
starting a watchdog program, and monitoring the process of acquiring the external network weather data and the process of storing the external network weather data into the database;
and restarting the system in response to the abnormity of the process of acquiring the external network weather data or the abnormity of the process of storing the external network weather data into the database.
An embodiment of a second aspect of the present disclosure provides a load prediction apparatus based on a raspberry pi, including:
the first acquisition module is used for acquiring the extranet weather data sent by the first raspberry group based on a simplex communication channel between the first acquisition module and the first raspberry group;
and the data storage module is used for storing the external network weather data into a database of the internal network so as to enable a preset internal network client to carry out load prediction based on the external network weather data.
Optionally, the weather data of the extranet is weather data obtained from the extranet based on a web crawler technology by the first raspberry group.
Optionally, the first obtaining module is specifically configured to:
determining the time interval of sending the external network weather data by the first raspberry serving according to the time interval of obtaining the external network weather data by the first raspberry serving;
and receiving the extranet weather data based on the time interval of the first raspberry group sending the extranet weather data.
Optionally, the data storage module is specifically configured to:
and storing the extranet weather data into a database of the intranet in a preset storage format.
Optionally, the system further includes a monitoring module, specifically configured to:
starting a watchdog program, and monitoring the process of acquiring the external network weather data and the process of storing the external network weather data into the database;
and restarting the system in response to the abnormity of the process of acquiring the external network weather data or the abnormity of the process of storing the external network weather data into the database.
An embodiment of a third aspect of the present disclosure provides an electronic device, including: the load prediction method based on the raspberry pi comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor executes the program to realize the load prediction method based on the raspberry pi as set forth in the embodiment of the first aspect of the disclosure.
A fourth aspect of the present disclosure provides a storage medium, where instructions, when executed by a processor of an electronic device, enable the electronic device to perform a raspberry pi based load prediction method as set forth in the first aspect of the present disclosure.
A fifth aspect of the present disclosure provides a computer program product, which when executed by an instruction processor performs the method for raspberry pi-based load prediction as set forth in the first aspect of the present disclosure.
The raspberry pi-based load prediction method, the raspberry pi-based load prediction device and the electronic equipment have the following beneficial effects:
in the embodiment of the disclosure, the second raspberry group obtains the extranet weather data sent by the first raspberry group based on the simplex communication channel with the first raspberry group, and then stores the extranet weather data into the database of the intranet, so that the preset intranet client performs load prediction based on the extranet weather data. Therefore, the second raspberry group and the first raspberry group transmit the weather data of the external network based on the simplex communication channel, and the weather data of the external network is stored in the internal network database, so that the internal network client calls the weather data from the internal network database, the safety and the confidentiality of the weather data transmission of the external network are guaranteed, and the load prediction efficiency of the internal network client is improved.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.
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The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart illustrating a raspberry pi-based load prediction method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart illustrating a raspberry pi-based load prediction method according to another embodiment of the present disclosure;
FIG. 2a is an interaction diagram of a raspberry pi-based load prediction method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a raspberry pi-based load prediction apparatus according to an embodiment of the present disclosure;
FIG. 4 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the present disclosure, and should not be construed as limiting the present disclosure.
The raspberry pi-based load prediction method, apparatus, and electronic device according to the embodiments of the present disclosure are described below with reference to the drawings.
Fig. 1 is a schematic flowchart of a raspberry pi-based load prediction method according to an embodiment of the present disclosure.
The embodiment of the present disclosure is exemplified by the case that the raspberry group based load prediction method is configured in a raspberry group based load prediction apparatus, and the raspberry group based load prediction apparatus can be applied to any electronic device, so that the electronic device can realize a raspberry group based load prediction function.
As shown in fig. 1, the raspberry pi-based load prediction method may include the following steps:
and 101, acquiring the extranet weather data sent by the first raspberry group based on the simplex communication channel between the first raspberry group and the first raspberry group.
It can be understood that, in order to ensure the confidentiality and security of the transmission of the external network weather data between the first Raspberry Pi (PRI) and the second Raspberry Pi (Raspberry Pi, PRI), in the present disclosure, a simplex mode in serial port communication is adopted between the first Raspberry Pi and the second Raspberry Pi (Raspberry Pi, PRI) for the transmission of the external network weather data, that is, the first Raspberry Pi is used as a fixed sending end, and the second Raspberry Pi is used as a fixed receiving end. The first raspberry group can acquire weather data from the external network firstly, and then sends the weather data to the second raspberry group, and the second raspberry group can receive the weather data sent by the first raspberry group based on a simplex communication channel between the first raspberry group and the second raspberry group.
The simplex communication channel can be a transmission channel between the first raspberry pi and the second raspberry pi, namely the first raspberry pi can only transmit data to the second raspberry pi, and the second raspberry pi can not transmit data to the first raspberry pi, so that the safety of the external network weather data in the transmission process can be ensured.
Optionally, the weather data of the extranet is obtained from the extranet based on a web crawler technology for the first raspberry group. The weather data may include city, time, temperature, humidity, wind direction, wind power, and the like, which is not limited in this disclosure.
The web crawler is a program or script that automatically captures web information according to a certain rule. Since the weather data is common in the internet, the weather data of the extranet can be acquired by using a web crawler technology.
It should be noted that the web crawler in the embodiment of the present disclosure complies with the Robots protocol, which is a popular ethical specification in the internet world, and is used to inform the web crawler of page information that can be crawled.
It is understood that the first raspberry pie and the second raspberry pie in this disclosure may be ARM Cortex-a series processors with 64-bit 4-cores. The system can be provided with an onboard network port, WiFI and Bluetooth, and the internal storage hard disk is an SD card. The raspberry pie can support the development under a linux system and a Microsoft (Windows) system, not only has the basic functions of a computer, but also has low price, and provides an ideal development platform. In addition, the raspberry pie is small in size and convenient to deploy and install. Therefore, the cost of load prediction can be reduced by obtaining the weather data of the external network through the raspberry group.
It should be noted that the embodiments of the present disclosure do not limit the types of the first raspberry pie and the second raspberry pie.
And 102, storing the extranet weather data into a database of the intranet so as to enable a preset intranet client to carry out load prediction based on the extranet weather data.
It can be understood that after the second raspberry group obtains the weather data of the external network, the weather data of the external network can be stored in the database of the internal network, and then any client of the internal network can directly call the weather data from the database of the internal network and analyze the weather data to complete load prediction. Therefore, the intranet client can directly call the weather data from the database of the intranet to predict the load, and the problems of low safety and poor confidentiality caused by directly acquiring the weather data from the extranet are solved.
In the embodiment of the disclosure, the second raspberry group obtains the extranet weather data sent by the first raspberry group based on the simplex communication channel with the first raspberry group, and then stores the extranet weather data into the database of the intranet, so that the preset intranet client performs load prediction based on the extranet weather data. Therefore, the second raspberry group and the first raspberry group transmit the weather data of the external network based on the simplex communication channel, and the weather data of the external network is stored in the internal network database, so that the internal network client calls the weather data from the internal network database, the safety and the confidentiality of the weather data transmission of the external network are guaranteed, and the load prediction efficiency of the internal network client is improved.
Fig. 2 is a flowchart illustrating a raspberry pi-based load prediction method according to another embodiment of the disclosure. As shown in fig. 2, the raspberry pi-based load prediction method may include the following steps:
Optionally, the web crawler program for acquiring the external network weather data may be a Python language or a Java language, which is not limited in this disclosure.
For example, if the first raspberry group obtains the weather data of the external network through a web crawler program in Python language, the first raspberry group may determine a web page corresponding to the weather data to be crawled according to a web page link included in the web crawler program; determining the running time of a web crawler program according to a time (time) program, and acquiring the time of returning weather data; controlling a serial port switch of the first raspberry pie and reading, inputting and outputting of weather data according to a serial port operation (serial) program; and sending a data acquisition request to the extranet server according to a request program to acquire the weather data returned by the extranet server.
The web crawler program may further include a request header "heads" and a program for setting an output data format.
The request head can deal with anti-crawler measures taken by a plurality of websites, so that the request head can be modified before the web crawler program applies for accessing the source code, and the web crawler program is not easy to identify.
The program for setting the output data format may be encoding or utf-8, which indicates that the output extranet weather data is in a chinese format, so that the obtained extranet weather data can be prevented from being scrambled. The present disclosure is not limited.
Optionally, the web crawler in the embodiment of the present disclosure may adopt a try-except structure, so as to improve the fault tolerance, and prevent abnormal situations such as a Uniform Resource Locator (URL) being not connected or a network being disconnected from causing the web crawler to be unable to run normally, so that the code is more robust.
It should be noted that the web crawler in the embodiment of the present disclosure complies with the Robots protocol, which is a popular ethical specification in the internet world, and is used to inform the web crawler of page information that can be crawled.
It can be understood that the first raspberry group can obtain weather data from the extranet periodically according to a time program contained in the web crawler program, and then send the obtained extranet weather data to the second raspberry group periodically, so that the second raspberry group can determine a time interval for the first raspberry group to send the extranet weather data according to a time interval for the first raspberry group to obtain the extranet weather data.
It can be understood that, after the time interval of the first raspberry party for sending the extranet weather data is determined according to the time interval of the first raspberry party for obtaining the extranet weather data, the second raspberry party can accurately and safely receive the extranet weather data based on the time interval of the first raspberry party for sending the extranet weather data and the simplex communication channel between the second raspberry party and the first raspberry party.
And 203, storing the extranet weather data into a database of the intranet in a preset storage format so that a preset intranet client performs load prediction based on the extranet weather data.
It can be understood that the second raspberry group can arrange the weather data of the extranet after acquiring the weather data of the extranet, and store the weather data of the extranet into the database of the intranet in a preset storage format, so that the weather data can be conveniently searched and read by the intranet client from the intranet database, and the efficiency of acquiring the weather data of the extranet by the intranet client is improved.
Optionally, the second raspberry group may process the external network weather data by using programming languages such as Python and Java, so as to arrange the external network weather data into a preset storage format. For example, the preset storage format may be [ city, time, temperature, moderate ], which is not limited in this disclosure.
For example, if the second raspberry group uses Python language to process the weather data of the external network and store the weather data into the database of the internal network. The second raspberry pi may invoke a json program to sort the weather data into a predetermined storage format. The MySQL module can be called to transfer the external network weather data to the MySQL database in a preset storage format.
And step 204, starting a watchdog program, and monitoring the process of acquiring the extranet weather data and the process of storing the extranet weather data into the database.
It can be understood that a watchdog program is deployed in the second raspberry group, so that the watchdog program can be started, the process of acquiring the external network weather data and the process of storing the external network weather data into the database are monitored, and the second raspberry group is restarted under the condition that the process of acquiring the external network weather data is abnormal or the process of storing the external network weather data into the database is abnormal, so that the second raspberry group is prevented from having faults.
Optionally, a watchdog program is also deployed in the first raspberry group, so that the watchdog program can be started to monitor the process of acquiring the weather data from the external network, and the first raspberry group is restarted under the condition that the weather data is abnormal and the first raspberry group is prevented from being out of order.
As shown in fig. 2a, a first raspberry group first obtains weather data from an external network, then sends the obtained weather data of the external network to the first raspberry group through a simplex communication channel, the first raspberry group stores the received weather data of the external network into a database of an internal network, and finally, a client of the internal network can call the weather data of the external network from the database of the internal network through a second raspberry group to perform load prediction.
In the embodiment of the disclosure, the second raspberry group receives the external network weather data based on the time interval of sending the external network weather data by the first raspberry group and the simplex communication channel between the second raspberry group and the first raspberry group, and then stores the external network weather data in the database of the internal network in a preset storage format, so that a preset internal network client performs load prediction based on the external network weather data. The watchdog program can be started to monitor the process of acquiring the weather data of the external network and the process of storing the weather data of the external network into the database, and the watchdog program is restarted under the condition that the process of acquiring the weather data of the external network is abnormal or the process of storing the weather data of the external network into the database is abnormal. Therefore, the second raspberry group and the first raspberry group are subjected to outer network weather data transmission based on the simplex communication channel, and the outer network weather data are stored in the inner network database according to the preset storage format, so that the accuracy, the safety and the confidentiality of the outer network weather data transmission are guaranteed, the efficiency of acquiring the weather data from the inner network database by the inner network client is improved, and the efficiency of predicting the load of the inner network client is further improved.
In order to implement the above embodiments, the present disclosure further provides a load prediction apparatus based on a raspberry pi.
Fig. 3 is a schematic structural diagram of a raspberry pi-based load prediction apparatus according to an embodiment of the present disclosure.
As shown in fig. 3, the raspberry pi based load prediction apparatus 300 may include: a first acquisition module 310 and a data storage module 320.
A first obtaining module 310, configured to obtain, based on a simplex communication channel with the first raspberry pi, extranet weather data sent by the first raspberry pi;
and the data storage module 320 is configured to store the extranet weather data into a database of the intranet, so that a preset intranet client performs load prediction based on the extranet weather data.
Optionally, the weather data of the extranet is obtained from the extranet based on a web crawler technology for the first raspberry group.
Optionally, the first obtaining module 310 is specifically configured to:
determining a time interval for sending the external network weather data by the first raspberry group according to the time interval for obtaining the external network weather data by the first raspberry group;
and receiving the external network weather data based on the time interval of sending the external network weather data by the first raspberry.
Optionally, the data storage module 320 is specifically configured to:
and storing the weather data of the external network into a database of the internal network in a preset storage format.
Optionally, the monitoring module is specifically configured to:
starting a watchdog program, and monitoring the process of acquiring the external network weather data and the process of storing the external network weather data into a database;
and restarting the system in response to the abnormal process of acquiring the external network weather data or the abnormal process of storing the external network weather data into the database.
The functions and specific implementation principles of the modules in the embodiments of the present disclosure may refer to the embodiments of the methods, and are not described herein again.
In the embodiment of the disclosure, the second raspberry group obtains the extranet weather data sent by the first raspberry group based on the simplex communication channel with the first raspberry group, and then stores the extranet weather data into the database of the intranet, so that the preset intranet client performs load prediction based on the extranet weather data. Therefore, the second raspberry group and the first raspberry group transmit the weather data of the external network based on the simplex communication channel, and the weather data of the external network is stored in the internal network database, so that the internal network client calls the weather data from the internal network database, the safety and the confidentiality of the weather data transmission of the external network are guaranteed, and the load prediction efficiency of the internal network client is improved.
In order to implement the above embodiments, the present disclosure also provides an electronic device, including: the load prediction method based on the raspberry pi is provided by the foregoing embodiments of the present disclosure.
In order to implement the foregoing embodiments, the present disclosure also provides a storage medium, where instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the raspberry pi based load prediction method provided by the foregoing embodiments of the present disclosure.
In order to implement the foregoing embodiments, the present disclosure further provides a computer program product, which when executed by an instruction processor in the computer program product, performs the raspberry pi-based load prediction method as set forth in the foregoing embodiments of the present disclosure.
FIG. 4 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure. The electronic device 12 shown in fig. 4 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.
As shown in FIG. 4, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described in this disclosure.
The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.
In the embodiment of the disclosure, the second raspberry group obtains the extranet weather data sent by the first raspberry group based on the simplex communication channel with the first raspberry group, and then stores the extranet weather data into the database of the intranet, so that the preset intranet client performs load prediction based on the extranet weather data. Therefore, the second raspberry group and the first raspberry group transmit the weather data of the external network based on the simplex communication channel, and the weather data of the external network is stored in the internal network database, so that the internal network client calls the weather data from the internal network database, the safety and the confidentiality of the weather data transmission of the external network are guaranteed, and the load prediction efficiency of the internal network client is improved.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure, and that changes, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.
Claims (12)
1. A load prediction method based on a raspberry pi is characterized by comprising the following steps:
acquiring extranet weather data sent by a first raspberry group based on a simplex communication channel between the first raspberry group and the extranet weather data;
and storing the external network weather data into a database of an internal network so as to enable a preset internal network client to carry out load prediction based on the external network weather data.
2. The method of claim 1, wherein the extranet weather data is weather data obtained from an extranet based on web crawler technology for the first raspberry group.
3. The method of claim 2, wherein obtaining the extranet weather data sent by the first raspberry pi based on the simplex communication channel with the first raspberry pi comprises:
determining the time interval of sending the external network weather data by the first raspberry serving according to the time interval of obtaining the external network weather data by the first raspberry serving;
and receiving the extranet weather data based on the time interval of the first raspberry group sending the extranet weather data.
4. The method according to claim 1, wherein storing the weather data of the extranet into a database of the intranet comprises:
and storing the extranet weather data into a database of the intranet in a preset storage format.
5. The method of any of claims 1-4, further comprising:
starting a watchdog program, and monitoring the process of acquiring the external network weather data and the process of storing the external network weather data into the database;
and restarting the system in response to the abnormity of the process of acquiring the external network weather data or the abnormity of the process of storing the external network weather data into the database.
6. A raspberry pi-based load prediction device, comprising:
the first acquisition module is used for acquiring the extranet weather data sent by the first raspberry group based on a simplex communication channel between the first acquisition module and the first raspberry group;
and the data storage module is used for storing the external network weather data into a database of the internal network so as to enable a preset internal network client to carry out load prediction based on the external network weather data.
7. The apparatus of claim 6, wherein the extranet weather data is weather data obtained from an extranet based on web crawler technology for the first raspberry pi.
8. The apparatus of claim 7, wherein the first obtaining module is specifically configured to:
determining the time interval of sending the external network weather data by the first raspberry serving according to the time interval of obtaining the external network weather data by the first raspberry serving;
and receiving the extranet weather data based on the time interval of the first raspberry group sending the extranet weather data.
9. The apparatus of claim 6, wherein the data storage module is specifically configured to:
and storing the extranet weather data into a database of the intranet in a preset storage format.
10. The apparatus according to any one of claims 6 to 9, further comprising a monitoring module, specifically configured to:
starting a watchdog program, and monitoring the process of acquiring the external network weather data and the process of storing the external network weather data into the database;
and restarting the system in response to the abnormity of the process of acquiring the external network weather data or the abnormity of the process of storing the external network weather data into the database.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any of claims 1-5 when executing the program.
12. A storage medium in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform the method of any of claims 1-5.
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Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101888284A (en) * | 2010-07-08 | 2010-11-17 | 中国科学院高能物理研究所 | Method and device used for one-way transmission of data |
| CN101902448A (en) * | 2009-05-27 | 2010-12-01 | 厦门敏讯信息技术股份有限公司 | Method and system for implementing data transmission through serial ports |
| CN103237036A (en) * | 2013-05-08 | 2013-08-07 | 天津工业大学 | Device for realizing physical partition of internal and external networks |
| CN104092673A (en) * | 2014-06-27 | 2014-10-08 | 中国人民解放军第二军医大学 | System and method for achieving safe one-way data transmission between networks |
| CN104349174A (en) * | 2013-08-05 | 2015-02-11 | 浙江大华系统工程有限公司 | Audio/video data transmission system, audio/video data storage method, audio/video data generation method, audio/video data storage device, audio/video data generation device, audio/video data storage equipment and audio/video data generation equipment |
| CN104363165A (en) * | 2014-11-14 | 2015-02-18 | 华东电网有限公司 | Information interactive system under internal and external network isolation environment and data integrating method |
| KR101499097B1 (en) * | 2014-12-15 | 2015-03-05 | (주)에이원정보기술 | Data Gateway System with Compatibility MDWS-600S Only Network |
| CN205584313U (en) * | 2016-05-04 | 2016-09-14 | 中国人民解放军63626部队 | One -way net data transmission platform of striding based on raspberry group |
| CN107784097A (en) * | 2017-10-23 | 2018-03-09 | 浪潮软件股份有限公司 | A kind of database table data transmission method, system and client, service end |
| CN107800713A (en) * | 2017-11-10 | 2018-03-13 | 北京明朝万达科技股份有限公司 | The secure exchange method and system of data between a kind of net |
| CN108540499A (en) * | 2018-06-26 | 2018-09-14 | 中国华电集团科学技术研究总院有限公司 | Electric power networks data transmission system with duplex channel and data transmission method |
| CN108600274A (en) * | 2018-05-17 | 2018-09-28 | 淄博职业学院 | Safe communication system and its application method between a kind of realization computer inner-external network |
| CN109698826A (en) * | 2018-12-06 | 2019-04-30 | 贵州电网有限责任公司 | In a kind of electrical power services device between exchange system port security isolation implementation method |
| CN109783501A (en) * | 2018-12-27 | 2019-05-21 | 北京奇安信科技有限公司 | A kind of database synchronization method based on one-way optical gate, system, equipment and medium |
| CN111163116A (en) * | 2020-04-08 | 2020-05-15 | 成都新动力软件有限公司 | Equipment for extracting large-flow network data parameters |
| CN111786956A (en) * | 2020-06-05 | 2020-10-16 | 四川省东宇信息技术有限责任公司 | System and method for safely accessing intranet network based on message middleware communication |
| CN111988325A (en) * | 2020-08-25 | 2020-11-24 | 中国南方电网有限责任公司 | Transaction information processing system, method, apparatus, computer device and storage medium |
| CN113114626A (en) * | 2021-03-17 | 2021-07-13 | 宁波万德高科智能科技有限公司 | Security gateway system based on edge calculation and construction method thereof |
-
2021
- 2021-11-26 CN CN202111423575.0A patent/CN114257418A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101902448A (en) * | 2009-05-27 | 2010-12-01 | 厦门敏讯信息技术股份有限公司 | Method and system for implementing data transmission through serial ports |
| CN101888284A (en) * | 2010-07-08 | 2010-11-17 | 中国科学院高能物理研究所 | Method and device used for one-way transmission of data |
| CN103237036A (en) * | 2013-05-08 | 2013-08-07 | 天津工业大学 | Device for realizing physical partition of internal and external networks |
| CN104349174A (en) * | 2013-08-05 | 2015-02-11 | 浙江大华系统工程有限公司 | Audio/video data transmission system, audio/video data storage method, audio/video data generation method, audio/video data storage device, audio/video data generation device, audio/video data storage equipment and audio/video data generation equipment |
| CN104092673A (en) * | 2014-06-27 | 2014-10-08 | 中国人民解放军第二军医大学 | System and method for achieving safe one-way data transmission between networks |
| CN104363165A (en) * | 2014-11-14 | 2015-02-18 | 华东电网有限公司 | Information interactive system under internal and external network isolation environment and data integrating method |
| KR101499097B1 (en) * | 2014-12-15 | 2015-03-05 | (주)에이원정보기술 | Data Gateway System with Compatibility MDWS-600S Only Network |
| CN205584313U (en) * | 2016-05-04 | 2016-09-14 | 中国人民解放军63626部队 | One -way net data transmission platform of striding based on raspberry group |
| CN107784097A (en) * | 2017-10-23 | 2018-03-09 | 浪潮软件股份有限公司 | A kind of database table data transmission method, system and client, service end |
| CN107800713A (en) * | 2017-11-10 | 2018-03-13 | 北京明朝万达科技股份有限公司 | The secure exchange method and system of data between a kind of net |
| CN108600274A (en) * | 2018-05-17 | 2018-09-28 | 淄博职业学院 | Safe communication system and its application method between a kind of realization computer inner-external network |
| CN108540499A (en) * | 2018-06-26 | 2018-09-14 | 中国华电集团科学技术研究总院有限公司 | Electric power networks data transmission system with duplex channel and data transmission method |
| CN109698826A (en) * | 2018-12-06 | 2019-04-30 | 贵州电网有限责任公司 | In a kind of electrical power services device between exchange system port security isolation implementation method |
| CN109783501A (en) * | 2018-12-27 | 2019-05-21 | 北京奇安信科技有限公司 | A kind of database synchronization method based on one-way optical gate, system, equipment and medium |
| CN111163116A (en) * | 2020-04-08 | 2020-05-15 | 成都新动力软件有限公司 | Equipment for extracting large-flow network data parameters |
| CN111786956A (en) * | 2020-06-05 | 2020-10-16 | 四川省东宇信息技术有限责任公司 | System and method for safely accessing intranet network based on message middleware communication |
| CN111988325A (en) * | 2020-08-25 | 2020-11-24 | 中国南方电网有限责任公司 | Transaction information processing system, method, apparatus, computer device and storage medium |
| CN113114626A (en) * | 2021-03-17 | 2021-07-13 | 宁波万德高科智能科技有限公司 | Security gateway system based on edge calculation and construction method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 董得志等: "爬虫技术在分布式能源站负荷预测中的应用", 科学技术创新 * |
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