CN115664991A - Data acquisition method for edge calculation and operation and maintenance system - Google Patents
Data acquisition method for edge calculation and operation and maintenance system Download PDFInfo
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Abstract
The invention discloses a data acquisition method and an operation and maintenance system for edge calculation, wherein the data acquisition method for edge calculation comprises the following steps; 1. collecting data through a sensor; 2. the collected and uploaded data are effectively received; 3. acquiring target data; 4. judging whether the target data conforms to a first type or a second type; the invention has the advantages that whether the network is abnormal or not can be monitored by the network monitoring device; the storage device can store the metering information when the network is abnormal; the device is sent out in a supplementing mode, the metering information stored when the network is abnormal can be stored in a classified mode again when the network is normal, collected metering information can be effectively stored after the network connection of the device and a cloud server is disconnected, the collector can send out the metering information collected but not sent successfully after the network link is recovered to be normal, the instant experience of a user is strengthened, and meanwhile the operation and maintenance cost of a management department is reduced.
Description
Technical Field
The invention relates to the technical field of data acquisition, in particular to a data acquisition method for edge calculation and an operation and maintenance system.
Background
With the gradual application of big data in industry and the continuous development of internet of things technology, the predictive maintenance of equipment also enters the era of intelligent interconnection and data driving. How to efficiently utilize data generated in industrial production to carry out online monitoring and fault diagnosis on equipment is a problem which needs to be solved in an industrial intelligent process. At present, the predictive maintenance of equipment in industry follows a cloud-centric internet architecture, i.e., data is collected by front-end equipment (sensors, cameras) and stored, processed and managed by remote services. However, with huge data volume, equipment amount, industrial production requirements, and the like, remote computing in the traditional sense may face various challenges in terms of bandwidth, time delay, connection quality, resource allocation, security, and the like. The number of devices, the abrupt increase of data volume, the time-dependent requirements of data and the diversity and complexity of industrial scenes put further demands on software and hardware, data transmission capability and intelligence level.
The existing method and system have the following defects:
(1) The storage and transmission pressure of data are still large, the data are transmitted by waking up the equipment regularly, a large amount of resources such as storage, transmission and battery energy are consumed in the process, if the equipment is always in a stable state, the information contained in the acquired data is too repeated, the time interval of acquisition is hour, redundant information is too much, and further the waste of too many resources is caused.
(2) In the data acquisition process, as the sensors acquire more data, if the data are indiscriminately transmitted to the cloud platform through the Internet, great pressure is caused on data transmission and storage.
(3) Although data transmission and simple management of meter files are basically realized in the conventional data acquisition, more effective local control management cannot be implemented for use scenes, control execution actions of all terminal acquisition points need to be sent by an acquisition system, issued commands are executed by acquisition equipment, normal connection of an uplink network needs to be ensured, and once delay is connected or data of the acquisition system is not updated timely, the actions need to be executed in time, and user experience delay is caused.
Improvements are needed to address the above problems.
Disclosure of Invention
The invention aims to provide a data acquisition method and an operation and maintenance system for edge computing, which are used for solving the problems that the storage and transmission pressure of data provided by the background technology is still higher, the data transmission is carried out by regularly waking up equipment, a large amount of resources such as storage, transmission, battery energy and the like are consumed in the process, if the equipment is always in a stable state, the information contained in the acquired data is too repeated, the time interval used as the acquisition time interval is small, redundant information is too much, and further excessive resource waste is caused.
In order to achieve the purpose, the invention provides the following technical scheme: the data acquisition method of the edge calculation comprises the following steps;
1. collecting data through a sensor;
2. the collected and uploaded data are effectively received;
3. acquiring target data;
4. judging whether the target data conforms to a first type or a second type;
5. monitoring the network when judging the data;
6. and if the type accords with the first type, sending the target data to the cloud platform, and if the type accords with the second type, storing the target data to a local storage.
Further, in the first to second steps, data is acquired by the sensor, the sensor acquires two or more of the first data segment, the second data segment and the third data segment at the same time, and when the same characteristic index is calculated according to the two or more data segments acquired at the same time, only the characteristic index value calculated by the data segment with the longest length is retained, and the characteristic index values calculated by other data segments are discarded.
Further, when all the characteristic indexes are normal, the second data segment and the third data segment are stored, the first data segment and all the characteristic indexes are sent to the sensor data acquisition station at regular time, when at least one characteristic index is abnormal, the data segment corresponding to the abnormal moment is obtained, the abnormal levels of the data segment and the measuring point and the first data segment and all the characteristic indexes are sent to the sensor data acquisition station in real time, and the acquired normal data are uploaded.
Further, in the third to fourth steps, the target data is compared with the predicted data, if an error between the target data and the predicted data is larger than a predetermined error, the target data is determined to be in accordance with the first type, and if the error is smaller than the predetermined error, the target data is determined to be in accordance with the second type.
Further, in the fifth step, the method is used for monitoring whether the network is abnormal; the storage device is used for storing the metering information when the network is abnormal; the device is sent in a supplementing mode, the metering information stored in the storage device when the network is abnormal is sent to the classification storage device, classified storage is conducted, the device is sent in a supplementing mode, the metering information stored in the storage device when the network is abnormal can be stored in a classification mode again when the network is normal, after the device is disconnected with the network link of the cloud server, the collected metering information can be effectively stored, the collected metering information which is not successfully sent can be sent by the collector after the network link is recovered to be normal, the instant experience of a user is enhanced, and meanwhile the operation and maintenance cost of a management department is reduced.
Further, in the sixth step, the first type data is stored in the cloud platform, and the second type data is stored in the local storage.
A data acquisition operation and maintenance system for edge calculation;
further, the sensor collects data, and the sensor collects running state data of corresponding measuring points at regular time according to a first interval time, a second interval time and a third interval time to obtain a first data segment, a second data segment and a third data segment, wherein the first interval time is greater than the second interval time;
the sensor respectively calculates a plurality of characteristic indexes according to the first data segment, the second data segment and the third data segment, and sends the first data segment and each characteristic index to the sensor data acquisition station according to a preset feedback mechanism; the acquisition station collects the first data segments and the characteristic indexes of the measurement points, uploads the collected normal data, and acquires the running state data of the measurement points at regular time by adopting different intervals to obtain first data segments, second data segments and third data segments with different lengths. The first interval time > the second interval time > the third interval time, and correspondingly, the three interval times respectively correspond to low-density, medium-density and high-density data acquisition mechanisms;
an edge computing device connected to the sensor and configured to obtain the target data from the sensor, the edge computing device having a local memory;
a cloud platform communicatively coupled with the edge computing device.
Further, the edge computing device is further configured to determine whether the target data conforms to a first type or a second type, where if the target data conforms to the first type, the target data is sent to the cloud platform, and if the target data conforms to the second type, the target data is stored in the local storage;
and comparing the target data with the predicted data, if the error between the target data and the predicted data is larger than a preset error, judging that the target data conforms to a first type, if the error is smaller than the preset error, judging that the target data conforms to a second type, storing the first type data to a cloud platform, and storing the second type data to a local storage.
Further, the network monitoring device is used for monitoring whether the network is abnormal or not; the storage device is used for storing the metering information when the network is abnormal; and the reissue device is used for sending the metering information stored when the uplink network is abnormal to the classification storage when the network is normal, and performing classification storage again.
Compared with the prior art, the invention has the beneficial effects that: whether the network is abnormal or not can be monitored through the network monitoring device; the storage device can store the metering information when the network is abnormal; the replenishment device can be used for reclassifying and storing the metering information stored when the network is abnormal when the network is normal, can effectively store the collected metering information after the network link of the replenishment device is disconnected with the network link of the cloud server, and can replenish and send the collected metering information which is not successfully sent after the network link is recovered to be normal, so that the instant experience of a user is enhanced, and the operation and maintenance cost of a management department is reduced; judging whether the target data conforms to the first type or the second type, if so, sending the target data to the cloud platform, if so, storing the target data to a local memory, and filtering through edge computing equipment to reduce the pressure on the cloud platform caused by transmission and storage of a large amount of data so that the cloud platform can be better used for processing key data; different acquisition mechanisms are arranged, under the condition of limited hardware resources of the sensor device, the running state data of low-density redundant information can be obtained at regular time, a large amount of valuable characteristic index data of medium density can be obtained, high-density key characteristic index data can also be obtained, the configuration between data and resources is optimized to a great extent, and the waste caused by redundant information is reduced.
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FIG. 1 is a schematic flow diagram of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: the data acquisition method for edge calculation comprises the following steps;
1. collecting data through a sensor;
2. the collected and uploaded data are effectively received;
3. acquiring target data;
4. judging whether the target data conforms to a first type or a second type;
5. monitoring the network when judging the data;
6. and if the type accords with the first type, the target data is sent to the cloud platform, and if the type accords with the second type, the target data is stored in the local storage.
And in the first step, data are collected through a sensor, the sensor collects more than two of the first data fragment, the second data fragment and the third data fragment at the same time, and when the same characteristic index is respectively calculated according to the more than two data fragments collected at the same time, only the characteristic index value calculated by the data fragment with the longest length is reserved, and the characteristic index values calculated by other data fragments are discarded.
When all the characteristic indexes are normal, the second data segment and the third data segment are stored, the first data segment and all the characteristic indexes are sent to the sensor data acquisition station at regular time, when at least one characteristic index is abnormal, the data segment corresponding to the abnormal moment is obtained, the abnormal grade of the data segment and the measuring point, the first data segment and all the characteristic indexes are sent to the sensor data acquisition station in real time, and the acquired normal data are uploaded.
And step three to step four, comparing the target data with the predicted data, if the error between the target data and the predicted data is larger than a preset error, judging that the target data conforms to the first type, if the error is smaller than the preset error, judging that the target data conforms to the second type, judging whether the target data conforms to the first type or the second type, if the target data conforms to the first type, sending the target data to the cloud platform, if the target data conforms to the second type, storing the target data to a local storage, and filtering through edge computing equipment, so that the pressure on the cloud platform caused by transmission and storage of a large amount of data can be reduced, and the cloud platform can be better used for processing key data.
Step five, whether the network is abnormal or not is monitored; the storage device is used for storing the metering information when the network is abnormal; and the replenishment device is used for sending the metering information stored in the storage device when the network is abnormal to the classification storage device when the network is normal, and performing classification storage.
And step six, storing the first type data to the cloud platform, and storing the second type data to the local storage.
A data acquisition operation and maintenance system for edge calculation;
the method comprises the steps that a sensor collects data, the sensor collects running state data of corresponding measuring points at regular time according to a first interval time, a second interval time and a third interval time respectively to obtain a first data segment, a second data segment and a third data segment, wherein the first interval time is greater than the second interval time and greater than the third interval time;
the sensor respectively calculates a plurality of characteristic indexes according to the first data segment, the second data segment and the third data segment, and sends the first data segment and each characteristic index to the sensor data acquisition station according to a preset return mechanism; the acquisition station collects the first data segments and the characteristic indexes of the measurement points and uploads the acquired normal data;
an edge computing device coupled to the sensor and configured to obtain target data from the sensor, the edge computing device having a local memory;
a cloud platform communicatively connected with the edge computing device.
The edge computing equipment is also used for judging whether the target data accords with a first type or a second type, if so, the target data is sent to the cloud platform, and if so, the target data is stored in the local memory;
and comparing the target data with the predicted data, if the error between the target data and the predicted data is larger than a preset error, judging that the target data conforms to a first type, if the error is smaller than the preset error, judging that the target data conforms to a second type, storing the first type of data to the cloud platform, and storing the second type of data to the local storage.
The network monitoring device is used for monitoring whether the network is abnormal or not; the storage device is used for storing the metering information when the network is abnormal; and the supplementary sending device is used for sending the metering information stored when the uplink network is abnormal to the classification storage when the network is normal, and performing classification storage again.
The working principle is as follows: as shown in fig. 1, firstly, a sensor data acquisition station is set, data is acquired by a sensor, the sensor acquires more than two of a first data segment, a second data segment and a third data segment at the same time, and when the same characteristic index is respectively calculated according to the more than two data segments acquired at the same time, only the characteristic index value calculated by the data segment with the longest length is retained, the characteristic index value calculated by other data segments is discarded, when each characteristic index is normal, the second data segment and the third data segment are stored, the first data segment and each characteristic index are sent to the sensor data acquisition station at regular time, when at least one characteristic index is abnormal, the data segment corresponding to the abnormal time is obtained, and the abnormal levels of the data segment and the measuring point, the first data segment and each characteristic index are sent to the sensor data acquisition station in real time, uploading the collected normal data, acquiring target data, judging whether the target data accords with a first type or a second type, if so, sending the target data to a cloud platform, if so, storing the target data to a local storage, judging whether the target data accords with the first type or the second type, if so, sending the target data to the cloud platform, if so, storing the target data to the local storage, and reducing the pressure on the cloud platform caused by the transmission and storage of a large amount of data through the filtration of edge computing equipment, so that the cloud platform can be better used for processing key data, storing the metering information when the network is abnormal, and classifying and storing the metering information stored when the network is abnormal again when the network is normal, the method and the device can effectively store the collected metering information after the network link of the cloud server is lost, and the collector can reissue the collected metering information which is not successfully sent after the network link is recovered to be normal.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing embodiments, or equivalents may be substituted for elements thereof.
Claims (9)
1. The data acquisition method of the edge calculation is characterized by comprising the following steps;
1. collecting data through a sensor;
2. the collected and uploaded data are effectively received;
3. acquiring target data;
4. judging whether the target data conforms to a first type or a second type;
5. monitoring the network when judging the data;
6. and if the type accords with the first type, sending the target data to the cloud platform, and if the type accords with the second type, storing the target data to a local storage.
2. The edge-computed data acquisition method of claim 1, wherein: and in the first step, data is collected through a sensor, the sensor collects more than two of the first data segment, the second data segment and the third data segment at the same time, and when the same characteristic index value is respectively calculated according to the more than two data segments collected at the same time, only the characteristic index value calculated by the data segment with the longest length is reserved, and the characteristic index values calculated by other data segments are discarded.
3. The edge-computed data acquisition method of claim 2, wherein: when each characteristic index is normal, the second data segment and the third data segment are stored, the first data segment and each characteristic index are sent to the sensor data acquisition station at regular time, when at least one characteristic index is abnormal, the data segment corresponding to the abnormal moment is obtained, the abnormal grade of the data segment and the measuring point, the first data segment and each characteristic index are sent to the sensor data acquisition station in real time, and the acquired normal data are uploaded.
4. The edge-computed data acquisition method of claim 1, wherein: and in the third to fourth steps, the target data is compared with the predicted data, if the error between the target data and the predicted data is larger than a preset error, the target data is judged to be in accordance with the first type, and if the error is smaller than the preset error, the target data is judged to be in accordance with the second type.
5. The edge-computed data acquisition method of claim 1, wherein: the fifth step is used for monitoring whether the network is abnormal or not; the storage device is used for storing the metering information when the network is abnormal; and the replenishment device is used for sending the metering information stored in the storage device when the network is abnormal to the classification storage device when the network is normal, and performing classification storage.
6. The edge-computed data acquisition method of claim 1, wherein: and in the sixth step, the first type data is stored in the cloud platform, and the second type data is stored in the local storage.
7. An edge-computed data acquisition operation and maintenance system comprising the data acquisition method of any one of claims 1 to 6, characterized in that; the sensor collects data, and the sensor collects the running state data of corresponding measuring points at regular time according to a first interval time, a second interval time and a third interval time respectively to obtain a first data segment, a second data segment and a third data segment, wherein the first interval time is greater than the second interval time and the third interval time;
the sensor respectively calculates a plurality of characteristic indexes according to the first data segment, the second data segment and the third data segment, and sends the first data segment and each characteristic index to the sensor data acquisition station according to a preset feedback mechanism; the acquisition station collects the first data segments and the characteristic indexes of the measurement points and uploads the collected normal data;
an edge computing device coupled to the sensor and configured to obtain the target data from the sensor, the edge computing device having a local memory;
a cloud platform communicatively coupled with the edge computing device.
8. The edge-computed data acquisition operation and maintenance system according to claim 7, wherein: the edge computing equipment is further used for judging whether the target data conforms to a first type or a second type, if so, sending the target data to the cloud platform, and if so, storing the target data to the local storage;
and comparing the target data with the predicted data, if the error between the target data and the predicted data is larger than a preset error, judging that the target data conforms to a first type, if the error is smaller than the preset error, judging that the target data conforms to a second type, storing the first type data to a cloud platform, and storing the second type data to a local storage.
9. The edge-computed data acquisition, operation and maintenance system according to claim 8, wherein: the network monitoring device is used for monitoring whether the network is abnormal or not; the storage device is used for storing the metering information when the network is abnormal; and the reissue device is used for sending the metering information stored when the uplink network is abnormal to the classification storage when the network is normal, and performing classification storage again.
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