CN112583899B

CN112583899B - Internet of things data acquisition system, method and edge computing equipment

Info

Publication number: CN112583899B
Application number: CN202011390586.9A
Authority: CN
Inventors: 孙唐; 梁龙飞
Original assignee: Shanghai Yixin Industry Co ltd; Shanghai New Helium Brain Intelligence Technology Co ltd
Current assignee: Shanghai Yixin Industry Co ltd; Shanghai New Helium Brain Intelligence Technology Co ltd
Priority date: 2020-12-02
Filing date: 2020-12-02
Publication date: 2023-09-15
Anticipated expiration: 2040-12-02
Also published as: CN112583899A

Abstract

The application provides an Internet of things data acquisition system, an Internet of things data acquisition method and edge computing equipment. The edge computing device comprises a computing unit and a storage device, wherein the computing unit is coupled with at least one of a sensor, an Internet of things gateway and a cloud computing platform, the computing unit is further coupled with the storage device, and the cloud computing platform is coupled with the edge computing device through a network; the computing unit acquires first data, wherein the first data is data collected by a sensor; the computing unit compresses, counts and/or marks the first data to obtain second data; the computing unit writes the second data to the storage device.

Description

Internet of things data acquisition system, method and edge computing equipment

Technical Field

The application relates to a storage technology, in particular to an internet of things data acquisition system, an internet of things data acquisition method and edge computing equipment.

Background

The internet of things (IoT, internet of Things) has been widely used in various fields, such as industry, agriculture, environment, traffic, etc. The traditional physical network data acquisition method is that a sensor acquires data, and a physical network gateway pushes the data to a cloud server. The data collected by the sensor is huge, so that the data are pushed into the cloud server every moment, and the cloud server loads process massive data. Therefore, the traditional physical network data acquisition system not only requires the network bandwidth to be wide enough, but also requires the cloud server to have strong data processing capability.

Fig. 1 shows a schematic diagram of an internet of things system in the prior art. As shown in fig. 1, the internet of things system includes a cloud computing platform, a plurality of internet of things gateways, and a plurality of sensors. Each internet of things gateway is connected with at least one sensor for coupling the sensor to a network and further to a cloud computing platform. The sensor includes various types such as a temperature sensor, a camera, a position sensor, and the like.

The scale of the internet of things is massive. The number of sensors may be thousands or even more. The sensors are diverse in variety, from multiple suppliers, with multiple data formats and multiple data transmission protocols. The sensor also works for a long time, e.g. data is collected every minute. The cloud computing platform needs to obtain data collected by each sensor, process the data and utilize the data.

In order to accommodate a large amount of devices in the internet of things, a hierarchical network provided through the internet of things network is effective. The gateway of the Internet of things is coupled with a plurality of sensors at the edge end so as to reduce the equipment scale required to be managed by the cloud. To accommodate many types of devices, data transmissions between internet of things devices have been proposed, such as the Modbus protocol, MQTT (Message Queuing Telemetry Transport, message queue telemetry transport protocol), and the like.

Disclosure of Invention

With the further increase of the number and variety of devices accommodated by the internet of things, in particular, the widespread deployment of large-data-volume sensors such as cameras, the data scale of continuous production in the internet of things brings challenges to the deployment and operation of large-scale internet of things. Taking cameras as an example, the number of cameras deployed in a large city exceeds 100 ten thousand, 1MB of video data is generated per second according to each high-definition camera, and the video data generated in the whole city within 1 day is close to 10-8 GB. Storage and retrieval of data of such scale is not available with a typical cloud platform or requires extremely high costs.

Further, data produced in the internet of things needs to be applied to produce value. While the mass data generated by the mass sensor needs to be accessed in the application, usually only a very small part. For example, it is not reasonable to store and traverse all 10-8 GB of data for retrieval, just for the purpose of obtaining the time and location of the appearance of a face or license plate from video data acquired by a camera. For another example, temperature data of the area needs to be obtained from a temperature sensor. The temperature of the area is typically represented by a daily average temperature, while the temperature sensor provides data updated every second.

New internet of things devices are being rapidly generated, and the development of the existing internet of things data transmission protocol often cannot meet the requirements of the new devices.

It is desirable to improve the manner in which data is collected and applied in internet of things devices and in the internet of things to meet the above-mentioned needs.

In order to solve the technical problem, according to a first aspect of the embodiments of the present application, there is provided a first edge computing device according to the first aspect of the embodiments of the present application, including a computing unit and a storage device, the computing unit being coupled with at least one of a sensor, an internet of things gateway, and a cloud computing platform, the computing unit being further coupled with the storage device, the cloud computing platform being coupled with the edge computing device through a network; the computing unit acquires first data, wherein the first data is data collected by a sensor; the computing unit compresses, counts and/or marks the first data to obtain second data; the computing unit writes the second data to the storage device.

According to a first edge computing device of a first aspect of embodiments of the present application, there is provided a second edge computing device of the first aspect of embodiments of the present application, further comprising a network element coupled with at least one of a sensor, an internet of things gateway and a cloud computing platform, the network element coupled with the computing element and the storage device, the computing element coupled with at least one of a sensor, an internet of things gateway and a cloud computing platform through the network element; the computing unit obtains the first data through the network unit.

According to a first or second edge computing device of the first aspect of the embodiment of the present application, a third edge computing device of the first aspect of the embodiment of the present application is provided, where the edge computing device further includes a memory, and after the first data is acquired, the first data is stored in the memory.

According to one of the first to third edge computing devices of the first aspect of the embodiment of the present application, there is provided a fourth edge computing device of the first aspect of the embodiment of the present application, the computing unit sending a first request to the storage device indicating to acquire data; in response to the first request, the storage device decompresses and retrieves the second data to obtain the third data, wherein the third data is part of the first data; the storage device sends the third data to the computing unit; the computing unit pushes the third data to a cloud computing platform.

According to one of the first to third edge computing devices of the first aspect of the embodiments of the present application, there is provided a fifth edge computing device of the first aspect of the embodiments of the present application, the computing unit sending a second request to the storage device indicating to acquire data; responding to the second request, decompressing the second data by the storage device, and storing the first data into a memory; in response to the first data being stored in the memory, the computing unit retrieves the first data to obtain the third data, wherein the third data is part of the first data; the computing unit pushes the third data to a cloud computing platform.

According to one of the first to fifth edge computing devices of the first aspect of the embodiments of the present application, there is provided a sixth edge computing device of the first aspect of the embodiments of the present application, the second data being a compressed stored form of the first data; alternatively, the second data includes a compressed stored form of the first data and statistical information of the first data.

According to a sixth edge computing device of the first aspect of the embodiment of the present application, there is provided a seventh edge computing device of the first aspect of the embodiment of the present application, the first data and the third data are both time-series data, and the second data is key-value data.

According to a second edge computing device of the first aspect of the embodiment of the present application, there is provided an eighth edge computing device of the first aspect of the embodiment of the present application, the network element sending a first request to the storage device indicating to obtain data; in response to the first request, the storage device decompresses and retrieves the second data to obtain the third data, wherein the third data is part of the first data; the storage device sends the third data to the network element; and the network unit pushes the third data to the cloud computing platform.

According to one of the first to eighth edge computing devices of the first aspect of the embodiment of the present application, there is provided a ninth edge computing device of the first aspect of the embodiment of the present application, before the computing unit acquires the first data, the computing unit acquires configuration information, the configuration information indicating at least one of a data acquisition mode, a data push mode, and a data processing mode, so that the computing unit processes the first data according to the configuration information.

According to a fifth edge computing device of the first aspect of the embodiment of the present application, a tenth edge computing device of the first aspect of the embodiment of the present application is provided, and in response to the second request, the computing unit retrieves the first data according to the configuration information, and obtains the third data; the configuration information indicates a data index, and the third data is part of first data corresponding to the data.

According to a ninth edge computing device of the first aspect of the embodiment of the present application, there is provided an eleventh edge computing device of the first aspect of the embodiment of the present application, the configuration information being stored in the edge computing device in advance; alternatively, the configuration information is obtained by the edge computing device from the cloud computing platform.

According to a third aspect of the present application, there is provided a first data acquisition system according to the third aspect of the present application, comprising at least one sensor, at least one edge computing device, each edge computing device being coupled with at least one sensor, and a cloud computing platform coupled with the at least one edge computing device through a network; the at least one sensor collects first data; the at least one sensor sending the first data to the at least one edge computing device; each edge computing device processes the first data according to configuration information to obtain second data, the size of a storage space occupied by the second data is smaller than that occupied by the first data, formats of the first data and the second data are different, and the configuration information indicates at least one of a data acquisition mode, a data pushing mode and a data processing mode; the edge computing device writes the second data to a storage medium, the storage medium being a storage medium in the edge computing device or the storage medium being coupled with the edge computing device.

According to a first data acquisition system of a third aspect of the present application, there is provided a second data acquisition system of the third aspect of the present application, the data acquisition system further comprising at least one internet of things gateway, each internet of things gateway being coupled with at least one sensor, each edge computing device being coupled with at least one internet of things gateway; the at least one sensor sends the first data to the at least one edge computing device through the at least one internet of things gateway.

According to a first or second data acquisition system of a third aspect of the present application, there is provided a third data acquisition system according to the third aspect of the present application, the edge computing device acquires third data from the storage medium according to the configuration information, the third data being part of the first data; and the edge computing equipment pushes the third data to the cloud computing platform according to the configuration information.

According to one of the first to third data acquisition systems of the third aspect of the present application, there is provided a fourth data acquisition system according to the third aspect of the present application, the edge computing device being an edge computing device as described in any one of the first aspects above.

According to a fourth aspect of the present application, there is provided a first internet of things data acquisition method according to the fourth aspect of the present application, applied to a data acquisition system as set forth in any one of the third aspects, the method comprising: acquiring first data according to configuration information, wherein the first data are acquired by a sensor, and the configuration information indicates at least one of a data acquisition mode, a data pushing mode and a data processing mode; processing the first data according to the configuration information to obtain second data, wherein the formats of the first data and the second data are different; and storing the second data in a local storage device.

According to a fourth aspect of the present application, there is provided a second internet of things data acquisition method according to the fourth aspect of the present application, wherein the configuration information is acquired before the first data is acquired.

According to a third Internet of things data acquisition method, third data are acquired from the local storage device according to the configuration information, and the third data are part of the first data; pushing the third data.

According to a third internet of things data collection system of a fourth aspect of the present application, there is provided a fourth internet of things data collection method according to the fourth aspect of the present application, wherein the obtaining third data from the local storage device includes: acquiring a data index according to the configuration information, wherein the data index points to the position of the part of second data corresponding to the third data in the local storage device; and acquiring the part of second data, and processing the part of second data to restore the part of second data into part of first data corresponding to the part of second data, wherein the part of first data is the third data.

According to a fifth aspect of the present application, there is provided a first device registration method according to the fifth aspect of the present application, applied to the data acquisition system as set forth in any one of the third aspects, the method comprising: a second device obtains a self-registration request of a first device, wherein the first device comprises a sensor, an Internet of things gateway and an edge computing device, the second device is registered device in the data acquisition system, and the second device comprises the Internet of things gateway, the edge computing device and a cloud computing platform; and the second equipment authenticates the first equipment according to the information carried by the self-registration request.

According to a first device registration method of a fifth aspect of the present application, there is provided a second device registration method according to the fifth aspect of the present application, the method further comprising: in response to authentication completion, the second device sends authentication completion information to the first device to indicate that the first device registration is complete.

According to a first or second device registration method of a fifth aspect of the present application, there is provided a third device registration method of the fifth aspect of the present application, wherein after the first device acquires the authentication completion information or push enabling information sent by the second device, the first device pushes data to the second device.

According to a first device registration method of a fifth aspect of the present application, there is provided a fourth device registration method according to the fifth aspect of the present application, when the first device is a sensor, the second device is an internet of things gateway or an edge computing device; when the first device is an internet of things gateway, the second device is an edge computing device; and when the first device is an edge computing device, the second device is a cloud computing platform.

According to a fourth device registration method of a fifth aspect of the present application, there is provided the fifth device registration method according to the fifth aspect of the present application, when the first device is an edge computing device and the second device is a cloud computing platform, the method further includes: in response to authentication completion, the second device sends configuration information to the first device, the configuration information indicating at least one of a data acquisition mode, a data push mode, and a data processing mode.

According to a fourth device registration method of a fifth aspect of the present application, there is provided a sixth device registration method according to the fifth aspect of the present application, when the first device is an edge computing device and the second device is a cloud computing platform, the method further comprising: the first device sends a message for confirming configuration information to the second device, wherein the configuration information indicates at least one of a data acquisition mode, a data pushing mode and a data processing mode; the second device confirms whether the configuration information is wrong.

According to a sixth device registration method of a fifth aspect of the present application, there is provided a seventh device registration method of the fifth aspect of the present application, the second device confirms that the configuration information is error-free, and transmits push enable information to the first device; the second device confirms that the configuration information is wrong and sends the correct configuration information to the first device.

According to the embodiment of the application, the edge computing equipment is used for replacing the cloud computing platform to perform partial data processing, so that the technical problem that equipment of the Internet of things cannot meet the demands of people on the Internet of things in the prior art is solved, and the technical effects of reducing the deployment and operation difficulty of the Internet of things and improving the poor adaptation of the data acquisition and application modes of the equipment of the Internet of things and the Internet of things are achieved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.

FIG. 1 is a schematic diagram of a conventional Internet of things data acquisition system in the prior art;

fig. 2 is a schematic diagram of a physical network data acquisition system according to an embodiment of the present application;

FIG. 3 is a block diagram of an edge computing device provided by an embodiment of the present application;

fig. 4 is a timing chart of an internet of things data acquisition system according to an embodiment of the present application;

fig. 5 is a schematic flow chart of an internet of things data acquisition method according to an embodiment of the present application;

fig. 6 is a schematic flow chart of another method for acquiring data of the internet of things according to an embodiment of the present application;

fig. 7 is a schematic diagram of an edge computing device pushing data to a cloud computing platform according to an embodiment of the present application;

FIG. 8 is a schematic diagram of an edge computing device compressing data according to an embodiment of the present application;

fig. 9 is a schematic diagram of an edge computing device according to an embodiment of the present application for reading data and pushing data.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Although the examples referred to in the present application are described for illustrative purposes only and not to be limiting of the application, modifications, additions and/or deletions to the embodiments may be made without departing from the scope of the application.

Fig. 2 is a schematic diagram of a data acquisition system 200 according to an embodiment of the present application. As shown in fig. 2, the data acquisition system 200 includes a cloud computing platform 210, an edge computing device 220, an edge computing device 221, an edge computing device 222, an internet of things gateway 230, an internet of things gateway 231, an internet of things gateway 232, and sensors 240, 241, 242, … …, 248. Cloud computing platform 210 is coupled with edge computing devices 220-222, edge computing device 220 is coupled with internet of things gateways 230-232, internet of things gateway 230 is coupled with sensors 240-242, internet of things gateway 231 is coupled with sensors 243-245, and internet of things gateway 232 is coupled with sensors 246-248. Although not shown in fig. 2, edge computing devices 221 and 222 are also optionally coupled to an internet of things gateway. Still alternatively, the edge computing devices 221 and 222 are directly coupled to one or more sensors without passing through the internet of things gateway. The sensor in the embodiment of the application comprises information sensing equipment such as a temperature sensor, a radio frequency identification (RFID, radio Frequency Identification) device, an infrared sensor, a humidity sensor, a global positioning system, a laser scanner, a video and audio recorder and the like.

Compared with the internet of things system in the prior art, the data acquisition system provided by the embodiment of the application deploys a plurality of edge computing devices. The edge computing device manages data collection from the sensors and stores raw data collected by the sensors to reduce storage capacity and storage bandwidth requirements of the data collection system on the cloud computing platform. The edge computing device also aggregates, counts, or processes the collected data to reduce data transmission in the network when the cloud computing platform applies the sensor data. The edge computing device also pushes data to the cloud computing platform.

The following describes the operation of the data acquisition system 200, taking the sensor 240, the internet of things gateway 230, the edge computing device 220, and the cloud computing platform 210 as examples. The sensors 240 collect data according to the internet of things protocol. Optionally, the internet of things protocol includes MQTT, DDS, AMQP, XMPP, JMS, REST, coAP and the like. Further optionally, the data collected by the sensor 240 includes status data, positioning data, personalization data, behavioral reference data, user feedback data, and the like. The sensor 240 collects and transmits data at predetermined time intervals or continuously, so that the data obtained from the sensor 240 is referred to as time-series data. Table 1 shows the characteristics of time series data, the data source of which is, for example (sensor a), comprising a plurality of time value data in chronological order. Over time, new time value data is continuously present, while old time value data is generally not needed to be rewritten. The time series data may be retrieved over time. In table 1, for entry 1, the data collected at time 1 was 12, the data collected at time 2 was 17, and the data collected at time 3 was 10.

TABLE 1

Index	Time value 1	Time value2	Time value 3	…
					Sensor A	12	17	10	…

When accessing time series data, an index (e.g., sensor a) and a time interval are typically specified to obtain a corresponding one or more values. In yet another example, it is desirable to obtain statistics (e.g., averages) of time series data.

Sensor a also includes attributes such as the location of sensor a, the data transfer protocol used, etc. Such data is characterized by KV (key-value) data.

In order to effectively store and retrieve data collected by the sensor, the edge computing device according to the embodiment of the application further comprises a time sequence database and a KV storage device, so as to respectively accommodate the time sequence data collected by the sensor and KV data related to the sensor. Alternatively, the time series data is also regarded as key "index" (see also table 1), and the time value series is worth KV data, so that it can also be recorded in KV storage devices.

In the data acquisition system 200, edge computing devices (220, 221, and 222) store data acquired by various sensors, according to an embodiment of the present application. Whereby a plurality of edge computing devices constitute a distributed storage system. The data collected by the sensor does not need to be sent to the cloud computing platform 210, and the cloud computing platform 210 does not need to store the original data collected by the sensor, so that the data transmission amount in the data collection system 200 is reduced, and the requirements on the storage space and data access of the cloud computing platform are also reduced. The edge computing devices (220, 221, and 222) also provide data retrieval services for the data acquisition system to replace or assist in the utilization of the sensor data by the cloud computing platform 210. For example, if the cloud computing platform 210 needs to use the data of the sensor 244 in a specified period of time, a data query request for the specified period of time of the sensor 244 is broadcasted to, for example, the whole network, and the edge device 220 responds to receiving the query request and knows that the data of the sensor 244 is stored in itself, so that the data of the specified period of time of the sensor 244 is obtained from the own time sequence database and/or KV storage device and provided to the cloud computing platform 210.

For example, the sensor 240 pushes data to the internet of things gateway 230 1 time every 10 minutes, or the sensor 240 pushes data to the internet of things gateway 230 while collecting data. After the internet of things gateway 230 receives the data pushed by sensor 240, it continues to push the data to edge computing device 220 according to the internet of things protocol. Optionally, the manner in which the internet of things gateway 230 pushes data is similar to the manner in which the sensor 240 pushes data, and reference may be made to the manner in which the sensor 240 pushes data, which is not described herein. The data pushed by the gateway 230 of the internet of things of the edge computing device 220 is processed and stored. Optionally, edge computing device 220 performs processing such as compressing, merging, counting, labeling, indexing, defining the arrangement of the data, and the like. The edge computing device 220 stores the processed data in a local storage device. Further optionally, the edge computing device 220 periodically pushes the portion of the data in the local storage device to the cloud computing platform 210, or, in response to an indication by the cloud computing platform 210, the edge computing device 220 pushes the portion of the data in the local storage device to the cloud computing platform 210 according to the indication. Alternatively, the plurality of sensors of the data acquisition system 200 in this embodiment are the same or different kinds of sensors.

In one embodiment, the data acquisition system is not provided with an internet of things gateway, and the sensors push data directly to the edge computing device. For example, the data acquisition system 200 does not provide the internet of things gateways 230-232 and the sensors 240-248 directly push the collected data to the edge computing device 220. The manner in which the sensors 240-248 push data can be referred to in the above embodiments is not described herein.

In another embodiment, a portion of the sensors in the data acquisition system push data to the internet of things gateway and another portion of the sensors push data to the edge computing device. For example, data acquisition system 200 does not have Internet of things gateway 232, sensors 240-242 push data to Internet of things gateway 230, sensors 243-245 push data to Internet of things gateway 231, and sensors 246-248 push data to edge computing device 220.

For example, the data acquisition system 200 is applied to an automated control system, and the data acquisition system 200 needs to acquire 50 or 60 kinds of data, such as gas amount, power generation amount, average power generation efficiency, and the like. The edge computing device in the data collection system 200 in the present embodiment calculates, for example, an average power generation efficiency (average power generation efficiency is a ratio of the power generation amount accumulated for the past 1 hour to the input energy) from the collected data every hour. The edge computing device simply pushes the results of the computation representing the average power generation efficiency to the cloud computing platform 210. Therefore, a cloud computing platform is not required to acquire all acquired data such as generated energy/input energy. Optionally, one or more of the plurality of edge computing devices (220, 221, and 223) provide the cloud computing platform 210 with the power generation and input energy of the past 1 hour provided by the sensors it is responsible for collecting every hour, so that the cloud computing platform 210 obtains the power generation and input energy provided by multiple or all sensors of the data collection system 200 every hour, and thus obtains the average power generation efficiency of the data collection system 200 as a whole.

The edge computing equipment is used for carrying out storage and some computation of the acquired data, so that the data volume uploaded to the cloud computing platform is reduced, and the requirements on the storage and computing capacity of the cloud computing platform are reduced, thereby achieving the technical effects of business throughput and overall performance improvement of the data acquisition system.

Fig. 3 is a block diagram of an edge computing device according to an embodiment of the present application.

Referring to fig. 3, an edge computing device 300 includes a solid state disk 310 and a computing unit 330. The solid state disk 310 is coupled to a computing unit 330, and the computing unit 330 is coupled to sensors and cloud computing platforms external to the edge computing device 300.

Optionally, the edge computing device 300 further comprises a network element 320. The solid state disk 310 is coupled to the computing unit 330 and the network unit 320, the computing unit 330 is coupled to the network unit 320, and the network unit 320 is further coupled to sensors external to the edge computing device 300, an internet of things gateway (the internet of things gateway is not shown in fig. 3), and a cloud computing platform.

The operation of the edge computing device 300 will be described below taking the example in which the edge computing device 220 in the data acquisition system 200 is the edge computing device 300 shown in fig. 3, and the data collected by the sensors as image data.

In one embodiment, the computing unit 330 receives image data pushed by the sensor 247 or the internet of things gateway 232. The computing unit 330 stores the image data into the memory of the edge computing device 300. The computing unit 330 also processes the image data in the memory according to the configuration information. The calculation unit 330 stores the processed image data in the solid state disk 310.

In yet another embodiment, the computing unit 330 receives image data pushed by the sensor 247 or the internet of things gateway 232. The computing unit 330 stores the image data into the memory of the edge computing device 300. The computing unit 330 compresses and/or marks the first data (i.e., the image data) in the memory to obtain the second data. The computing unit 330 stores the second data to the solid state disk 310.

In yet another embodiment, the network element 320 receives image data pushed by the sensor 247 or the internet of things gateway 232. The network element 320 stores the image data in the memory of the edge computing device 300. The network unit 320 sends a first message to the computing unit 330 to instruct the computing unit 330 to process the data in the memory. In response to acquiring the first message, the computing unit 330 processes the image data in the memory according to the configuration information. The calculation unit 330 stores the processed image data in the solid state disk 310. ( Using configuration information is a separate embodiment; without using configuration information, as an embodiment) (to introduce configuration information again )

In another embodiment, the network element 320 receives image data pushed by the sensor 247 or the internet of things gateway 232. The network element 320 stores the image data in the memory of the edge computing device 300. The network unit 320 sends a first message to the computing unit 330 to instruct the computing unit 330 to process the data in the memory. In response to retrieving the first message, the computing unit 330 compresses and/or marks the first data in the memory to retrieve the second data. The computing unit 330 stores the second data to the solid state disk 310.

Optionally, the computing unit 330 in this embodiment includes a configuration acquisition module, a sensor data acquisition configuration module, and a command processing module. The configuration acquisition module acquires configuration information sent by the cloud computing platform or configuration information locally stored by the edge computing device. And the command processing module processes the data reading command sent by the cloud computing platform.

The sensor data acquisition configuration module is used for operating one or more of the acquisition module, the aggregation module, the storage module and the pushing module to acquire sensor data according to the configuration information provided by the configuration acquisition module and pushing the sensor data to the cloud computing platform. The configuration information indicates, for example, the address, model number, transmission protocol, format of the collected data, storage name and/or location of the collected data, kind of aggregation operation on the collected data, pushing manner of the collected data, and the like.

The acquisition module acquires data to be processed sent by the network unit 320, the sensor or the gateway of the internet of things. The aggregation module generates encoded time series data (see also table 3) according to the acquired data, and marks and/or compresses the data acquired by the acquisition module to acquire statistical information corresponding to the data and/or compressed data. The storage module stores the data processed by the aggregation module into the solid state disk 310 in the form of compressed time series data and/or KV data, and reads the specified data from the solid state disk 310. The pushing module pushes the data read from the solid state disk 310 to the network unit 320 or the cloud computing platform. The command processing module acquires a data reading command sent by the cloud computing platform, and indicates one or more of the acquisition module, the aggregation module, the storage module and the pushing module according to the data reading command so as to process the data reading command.

In this embodiment, the configuration information is obtained by the computing unit 330 from the cloud computing platform 210. Alternatively, after the edge computing device 300 registers with the data collection system, the cloud computing platform 210 sends the configuration information to the edge computing device 300, where the edge computing device 300 stores the configuration information locally (e.g., in the memory or the solid state disk 310), and the computing unit 330 obtains the configuration information locally. Alternatively, after the edge computing device 300 acquires the configuration information, the computing unit 330 is configured so that the subsequent data processing manners thereof are all performed according to the configuration information. The configuration information comprises data acquisition configuration information, and indicates names, types, enabling states, addresses and lengths of data to be acquired and the like of all sensors in the data acquisition system. The configuration information also indicates the internet of things protocol (e.g., modbusTCP) used to collect data from each sensor, the type of data collected, the binning rules (added data table and corresponding index) in the time series database, and the triggering conditions (e.g., interval time) for collecting data. The configuration information also indicates the type and manner of aggregating the collected data, such as the compression algorithm, the ordering manner and the encoding manner employed, calculating the average value, calculating the cumulative value, and recording the names of keys or labels used for aggregating the data. Optionally, the configuration information indicates that multiple data are collected from a single sensor and/or that data are collected in multiple ways.

For example, according to the configuration information sensor data acquisition configuration unit, the acquisition module is instructed to acquire each frame of image data in the video stream from the sensor, the aggregation module is instructed to extract a face from the image data, a tag is generated for the image data containing the face, the storage module is instructed to record each frame of image data in the time sequence database, and the image data with the tag is stored in the solid state disk 310 in KV form. Optionally, the label also indicates clustering information for the face.

Still alternatively, the computing unit 330 requests update/acquisition of configuration information from the cloud computing platform 210 at intervals of a preset duration (e.g., 1 hour, 1 day, 3 days, etc.). As another example, in response to receiving a command provided by cloud computing platform 210, computing unit 330 requests acquisition of configuration information from cloud computing platform 210.

Optionally, the configuration information further includes data push configuration information. The data push information indicates a requirement of the edge computing device to push data to the cloud computing platform, e.g., the data push configuration includes a data index of the data to be pushed or an identification pointing to the data index. As another example, the data push configuration includes algorithms (e.g., averaging, accumulating, etc.) that process the data. Further optionally, the configuration information is transmitted or stored in the data acquisition system in the form of a configuration table.

In one implementation, the computing unit 330 receives a data push request of the cloud computing platform 210, the data push request carrying a data index or an identification indicating a data index. Chinese patent applications 201910874351.8, 201910944900.4, 201910247686.7, 201810271465.9, 201810207416.9 and 201810332295.0 provide methods of querying or searching data in a distributed storage system. By way of example, the data push requests provided by the cloud computing platform include the query requests or requests to search for data in the previous applications described above. Specifically, one data push request (denoted as R1) is intended to acquire image data of a specified time and place, and the other data push request (denoted as R2) is intended to acquire image data of a face having a specified type.

For the request R1, the computing unit 330 acquires the address of the time-series data image data to be read in the solid state disk 310 according to the data index or the identification of the data index. The computing unit 330 sends a read data access request to the solid state disk 310 according to the address, and after the solid state disk 310 receives the read data access request, reads out the encoded time-series data stored in the address, and restores it to the image data before processing (i.e., to the image data collected by the sensor 247). Further, the computing unit also acquires image data corresponding to a specified place (a sensor corresponding to the place) and a specified time from the read data, and the computing unit 330 pushes the acquired image data to the cloud computing platform 210. Thus, for request R1, only the required image data of the specified time and place is transmitted to the cloud computing platform, without transmitting data of other places or other times.

For request R2, the computing unit 330 generates a key (corresponding to the face type to be accessed) of the data to be read from the data index or the identification of the data index. The value corresponding to the key is obtained from the solid state disk 310 through the storage module. The acquired value is a face image having a face type to be accessed. Alternatively, the acquired values are thumbnail images of face images and their indexes in the time-series database, and the original image data in the time-series database may be further obtained. The pushing module pushes the obtained value to the cloud computing platform 210. In this embodiment, only face data of a face type to be accessed is pushed to the cloud computing platform, so that the amount of image data to be pushed to the cloud computing platform corresponding to the data index is greatly reduced. For example, 100 tens of thousands of image data are stored in the solid state disk 310, and the 100 tens of thousands of data are divided into 1000 classes by clustering calculation, each class having 1000 image data. Compared with the traditional method that 100 ten thousand image data are pushed to the cloud computing platform, the embodiment only needs to push the image data of class 1 (namely 1000) corresponding to the data index, and the effect of greatly reducing the data quantity to be pushed to the cloud computing platform is achieved.

In yet another embodiment, the network element 320 receives a data push request of the cloud computing platform 210, the data push request carrying a data index or an identification indicating a data index. The network unit 320 obtains the address of the image data to be read in the solid state disk 310 from the memory according to the data index or the identifier of the data index. The network unit 320 sends a read data access request to the solid state disk 310 according to the address, and after the solid state disk 310 receives the read data access request, reads out the processed image data stored in the address, and restores the processed image data to the image data before processing. The network element 320 pushes the pre-processed image data to the cloud computing platform 210.

Optionally, the processed image data stored in the solid state disk 310 in this embodiment is stored in a Key-Value (KV) storage manner.

TABLE 2

Index	Time value 1	Time value 2	Time value 3	…
					Item 1	12	17	10	…
Item 2	17	11	21	…
					Item 3	22	29	14	…
…	…	…	…	…

Table 2 illustratively gives raw data collected from sensors stored in the memory of the edge computing device. The data in table 2 is collected by a plurality of timing sensors. The index includes different entries, each corresponding to a different sensor. For example, entry 1 corresponds to sensor 240, entry 2 corresponds to sensor 241, and entry 3 corresponds to sensor 247, then Table 2 gives that at the time of time value 1, the data collected by sensors 240, 241, and 247 are 12, 17, and 22, respectively; at a time of time value 2, the data collected by sensors 240, 241 and 247 are 17, 11 and 29, respectively; at the time of time value 3, the data collected by sensors 240, 241 and 247 are 10, 21 and 14, respectively. Optionally, the data to be processed is stored in the edge computing device in the form of a time series database. The time series database is implemented by the providing computing unit 330 running software, the data of which is also stored in, for example, the solid state disk 310.

TABLE 3 Table 3

Key	Value
		Item 1	Encode{12,17，…}
Item 2	Encode{…，…，…}
		…	…

Table 3 gives data stored in the form of Key-Value. In this embodiment, the data in the solid state disk 310 is stored in the storage form as described in table 3.

For example, the edge computing device 300 receives the time series data in the format shown in table 2, and the aggregation module or the time series database performs format conversion on the received time series data to obtain key-value (KV) data and store the key-value (KV) data in the solid state disk 310. Optionally, the format transformations include compression, merging, indexing, defining an arrangement structure, and the like.

Optionally, the aggregation module performs clustering, statistics, and other processing on the received time series data as shown in table 2, and optionally generates a label. The storage module records the generated information in the solid state disk 310 in KV form.

For another example, data specifying features is required to be pushed to the cloud computing platform according to the configuration information. The solid state disk 310 acquires data conforming to the specified feature from all data stored in the solid state disk according to the configuration information. For example, image data collected by a sensor is divided into an image including a person and an image not including a person. If the designated feature indicated by the configuration information includes a person and the shooting time is 7 to 7 points and 10 minutes, the computing unit 330 reads all the image data including the person and the shooting time is 7 to 7 points and 10 minutes from the solid state disk 310 and pushes the image data to the cloud computing platform 210 according to the requirement of the configuration information.

Optionally, before outputting the data with the specified characteristics, the solid state disk 310 further performs decompression, definition and arrangement structure processing on the data, so that the output data with the specified characteristics has the same format as the data collected by the sensor.

Further alternatively, the edge computing device 300 does not provide the computing unit 330, and after the edge computing device 300 receives the pushed data, the edge computing device stores the pushed data into the memory, and instructs the solid state disk 310 to store the data. In response to the data storage instruction, the solid state disk 310 performs format conversion, compression, merging, configuration structure definition and other processes on the pushed data, and stores the processed data.

Fig. 4 is a timing chart of device registration in the data acquisition system according to an embodiment of the present application.

In this embodiment, the sensor registers with the internet of things gateway or the edge computing device, the internet of things gateway registers with the edge computing device, and the edge computing device registers with the cloud computing platform. The registration of the device is required both when the data acquisition system is built and when the data acquisition system is added. The flow of registering a sensor with an internet of things gateway, the flow of registering a sensor with an edge computing device, the flow of registering an internet of things gateway with an edge computing device, and the flow of registering an edge computing device with a cloud computing platform are given in fig. 4.

Specifically, the registering process of the sensor to the gateway of the internet of things comprises the following steps: the sensor pushes self-registration information to the gateway of the Internet of things; the gateway of the internet of things receives the self-registration information; the gateway of the internet of things defaults that the sensor has been registered successfully, or the gateway of the internet of things sends a message to the sensor that the authentication connection is successful. For example, the internet of things gateway and the sensor are configured such that the internet of things gateway defaults that the sensor has successfully registered, that is, the sensor only needs to push self-registration information to the internet of things gateway and also defaults that the sensor has completed registering itself, so that after the sensor pushes self-registration information to the internet of things gateway, the sensor can start to push data to the internet of things gateway at any time. For another example, the gateway of the internet of things and the sensor are configured such that the sensor receives the message indicating that the authentication connection is successful and completes the registration of the device, and then the sensor can push data to the gateway of the internet of things after the sensor receives the message indicating that the authentication connection is successful.

The flow of sensor registration with edge computing device is similar to the flow of sensor registration with internet of things gateway, except that the object of sensor push self-registration information is the edge computing device. The flow of registering the gateway of the internet of things to the edge computing device is similar to the flow of registering the sensor to the gateway of the internet of things, and the difference is that the gateway of the internet of things pushes self-registration information to the edge computing device.

The process of registering the edge computing device with the cloud computing platform comprises the following steps: the edge computing device pushes self-registration information to the cloud computing platform; the cloud computing platform receives the self-registration information; optionally, the cloud computing platform sends a message representing that the authentication connection is successful to the edge computing device; optionally, the cloud computing platform sends configuration information or a configuration table to the edge computing device; further, the edge computing device confirms the configuration information to the cloud computing platform, e.g., the edge computing device sends a confirmation message carrying the configuration information to the cloud computing platform. And the cloud computing platform optionally sends a push enabling indication to the edge computing device when confirming that the configuration information of the edge computing device side is correct. And if the cloud computing platform confirms that the configuration information of the edge computing device side is wrong, sending an instruction for correcting the configuration information to the edge computing device. The edge computing device may begin pushing data to the cloud computing platform without receiving an indication of the corrected configuration information.

In this embodiment, after registering to the data acquisition system, the sensor located at the extreme end of the data acquisition system starts to acquire data, and pushes the data to an upper level; after receiving the data pushed by the lower stage, the gateway and/or the edge computing device of the Internet of things push the data to the upper stage, so that the data collected by the sensor flows in the data collection system. For example, in fig. 4, in addition to pushing collected data, other data (such as self-registration information) may be circulated between the upper and lower devices. And after registering with the data acquisition system, each device can complete data pushing according to the configuration information.

According to the embodiment of the application, the cloud computing platform acquires the data of the data acquisition system through the edge computing equipment. The cloud computing platform provides the configuration table to the edge computing device, and the configuration table carries configuration information according to the embodiment of the application. The edge computing device collects data from the sensors according to the configuration information and pushes the data to the cloud computing platform. Optionally, the cloud computing platform issues the data push request by providing a configuration table to the edge computing device.

Fig. 5 is a schematic diagram of an internet of things data acquisition method according to an embodiment of the present application. Taking the example that the edge computing device 300 executes the data collection method of the internet of things as shown in fig. 5, the execution process of the method is described.

In step S501, the edge computing device 300 acquires data acquisition configuration information. For example, the computing unit 330 acquires configuration information indicating data acquisition from the cloud computing platform. In step S502, the edge computing device 300 collects data from the sensor according to the data collection manner indicated by the configuration information.

In step S503, the edge computing device 300 compresses the collected data according to the data processing manner indicated by the configuration information, obtains compressed data, and calculates the aggregate information of the collected data.

In step S504, the edge computing device 300 writes the compressed data and the aggregation information to the local storage device. After the computing unit 330 completes step S503, the compressed data and the aggregation information are written into the solid state disk 310.

In step S505, the edge computing device 300 pushes the decompressed data to the cloud computing platform according to the configuration information. In this embodiment, step S505 is an optional step, that is, the computing unit 330 performs step S505 when the configuration information indicates that the data is pushed to the Yun Jisuan platform after the data is processed; if the configuration information does not indicate that the data is also pushed to the cloud computing platform, the computing unit 330 stops after executing step S504.

Fig. 6 is a schematic diagram of another data pushing method of the internet of things according to an embodiment of the present application.

Step S601, obtaining data push configuration information.

Step S602, obtaining a data index to be pushed according to the configuration information.

In step S603, data is acquired from the storage device using the data index.

Step S604, pushing the acquired data to the cloud computing platform.

Also taking edge computing device 300 as an example, the implementation of the method is described. For example, the edge computing device 300 obtains data push configuration information from the cloud computing platform, the data push configuration information indicating a data index and a push occasion for data to be pushed. The computing unit 330 obtains the data index according to the data pushing configuration information, and reads the data to be pushed from the solid state disk 310 according to the data index. And pushing the data to be pushed to the cloud computing platform. Step S601 therein is an optional step, and in the case where the edge computing device 300 has already learned the data push configuration information, step S601 may be selectively performed.

Fig. 7 is a schematic diagram of an edge computing device pushing data to a cloud computing platform according to an embodiment of the present application.

The computing unit of the edge computing device first obtains a data index according to the configuration information. For example, the configuration information indicates that the data index of the data to be pushed is "entry 1". The computing unit accesses the solid state disk in a KV manner, for example, according to the data index. In the solid state disk, the corresponding key (k=item 1) stores compressed time-series data (denoted as Ecode {12,17, … }) of the corresponding item 1. In response to receiving the key (k=item 1), the solid state disk reads out the corresponding value (Ecode {12,17, … }), and also decompresses the value (Ecode {12,17, … }), moving the key (k=item 1) and the decompressed value ({ 12,17, … }) into the memory of the edge computing device. The computing unit pushes the key in memory (k=item 1) and the decompressed value ({ 12,17, … }) to the cloud computing platform.

According to the embodiment of the application, the solid state disk also decompresses the read compressed data (e.g. Ecode {12,17, … }) in response to the access request of the read data, so that the computing unit of the edge computing device can directly use the decompressed data. Still alternatively, the computing unit compresses the data before writing the data into the solid state disk, and the compressed data is recorded in the solid state disk in a KV form, so as to save the storage space of the solid state disk.

Fig. 8 is a schematic diagram of an edge computing device compressing data according to an embodiment of the present application.

Fig. 8 illustrates a storage form of data collected by the sensor in the edge computing device. The data collected by the sensor is stored in the memory in the form of time series data, each entry comprising a plurality of values, for example, value 1, value 2, value 3 and value 4. And the computing unit of the edge computing device compresses the data items in the memory to obtain compressed data, and then the computing unit stores the compressed data in the solid state disk in a KV storage mode.

As shown in fig. 9, the computing unit of the edge computing device first reads data from the solid state disk according to the data index (key K). After receiving the data reading request from the computing unit, the solid state disk not only reads out the compressed data stored in the solid state disk, but also decompresses the compressed data. And outputting the decompressed data to the internal memory of the edge computing equipment by the solid state disk. In fig. 9, the time series data shown in the memory and including the 4 sensor data entries is the decompressed data corresponding to the same key K output by the solid state disk. The calculation unit searches 4 sensor data entries stored in the memory according to the configuration information or the data index to obtain, for example, the 2 nd value (denoted as "sensor data entry 1, value 2") of the sensor data entry 1. The computing unit pushes the data obtained after the coupling ("sensor data entry 1, value 2") to the cloud computing platform.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An edge computing device comprising a computing unit and a storage device, the computing unit coupled with at least one of a sensor, an internet of things gateway, and a cloud computing platform, the computing unit further coupled with the storage device, the cloud computing platform coupled with the edge computing device over a network;

the computing unit acquires first data, wherein the first data is data collected by a sensor; wherein the first data is image data;

the computing unit compresses, counts and/or marks the first data to obtain second data;

the computing unit writes the second data to the storage device;

the computing unit acquires configuration information, extracts a human face from the image data according to the configuration information, generates a label from the image data containing the human face, records each frame of image data in a time sequence database, and stores the image data with the label in a storage device in a KV mode;

And generating a key corresponding to the face type to be accessed according to the data index of the data to be pushed or the identification pointing to the data index indicated by the configuration information, and acquiring the face image of the face type to be accessed corresponding to the key from the storage device through the key.

2. The edge computing device of claim 1, further comprising a network element coupled with at least one of a sensor, an internet of things gateway, and a cloud computing platform, the network element coupled with the computing element and the storage device, the computing element coupled with at least one of a sensor, an internet of things gateway, and a cloud computing platform through the network element;

the computing unit obtains the first data through the network unit.

3. The edge computing device of claim 1 or 2,

the computing unit sends a first request indicating to acquire data to the storage device;

in response to the first request, the storage device decompresses and retrieves the second data to obtain third data, wherein the third data is part of the first data;

The storage device sends the third data to the computing unit;

the computing unit pushes the third data to a cloud computing platform.

4. The edge computing device of claim 3, wherein the computing unit is to send a second request to the storage device indicating to obtain data;

responding to the second request, decompressing the second data by the storage device, and storing the first data into a memory;

in response to the first data being stored in the memory, the computing unit retrieves the first data to obtain the third data, wherein the third data is part of the first data;

the computing unit pushes the third data to a cloud computing platform.

5. The edge computing device of claim 4, wherein the second data is a compressed stored form of the first data; alternatively, the second data includes a compressed stored form of the first data and statistical information of the first data.

6. The edge computing device of claim 3,

the network element sends a first request indicating to acquire data to the storage device;

In response to the first request, the storage device decompresses and retrieves the second data to obtain the third data, wherein the third data is part of the first data;

the storage device sends the third data to the network element;

and the network unit pushes the third data to the cloud computing platform.

7. The edge computing device of claim 4, wherein, in response to the second request, the computing unit retrieves the first data from the configuration information to obtain the third data;

the configuration information indicates a data index, and the third data is part of first data corresponding to the data.

8. A data acquisition system comprising at least one sensor, at least one edge computing device, each edge computing device coupled to the at least one sensor, and a cloud computing platform coupled to the at least one edge computing device via a network;

the at least one sensor collects first data;

the at least one sensor sending the first data to the at least one edge computing device;

Each edge computing device processes the first data according to configuration information to obtain second data, the size of a storage space occupied by the second data is smaller than that occupied by the first data, formats of the first data and the second data are different, and the configuration information indicates at least one of a data acquisition mode, a data pushing mode and a data processing mode;

the edge computing device writes the second data to a storage medium, the storage medium being a storage medium in the edge computing device or the storage medium being coupled with the edge computing device;

the edge computing device is an edge computing device as claimed in any one of claims 1-7.

9. The data acquisition method of the internet of things, which is applied to the data acquisition system of claim 8, comprises the following steps:

acquiring first data according to configuration information, wherein the first data are acquired by a sensor, and the configuration information indicates at least one of a data acquisition mode, a data pushing mode and a data processing mode;

processing the first data according to the configuration information to obtain second data, wherein the formats of the first data and the second data are different;

And storing the second data in a local storage device.

10. A device registration method applied to the data acquisition system of claim 8, the method comprising:

a second device obtains a self-registration request of a first device, wherein the first device comprises a sensor, an Internet of things gateway and an edge computing device, the second device is registered device in the data acquisition system, and the second device comprises the Internet of things gateway, the edge computing device and a cloud computing platform;

and the second equipment authenticates the first equipment according to the information carried by the self-registration request.