CN106713440B - Data transmission method and equipment - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/56—Provisioning of proxy services
- H04L67/565—Conversion or adaptation of application format or content
- H04L67/5651—Reducing the amount or size of exchanged application data
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Abstract
The application provides a data transmission method and equipment, wherein a group with similarity to sensor data to be uploaded within a preset range is found from pre-generated classifications, and the identification of the group and the distinguishing data of the sensor data to be uploaded and the group are uploaded. Compared with the prior art of uploading all sensor data to be uploaded, the data volume of uploading can be reduced, and the flow is saved. Further, the network load can be reduced, and in the case of transmitting data using a mobile network, since the amount of data transmitted is reduced, the use cost of the network can be reduced.
Description
Technical Field
The present application relates to the field of electronic information, and in particular, to a data transmission method and device.
Background
with the development of network technology and the application of various intelligent devices in enterprises, the technology of the internet of things brings great benefits to the development of the enterprises. Through the networking of various intelligent devices, enterprises can manage and monitor various key devices very conveniently, discover equipment abnormity and maintain the equipment rapidly in time, and economic loss is reduced. Meanwhile, enterprises can also utilize the collected real-time data of various intelligent devices, and various modes which can improve the utilization rate of the devices and reduce the operation and maintenance costs of the devices are analyzed by combining advanced algorithms such as artificial intelligence and the like, so that the enterprise benefits are improved.
In an internet of things system, various intelligent devices need to read attribute (including static attribute and dynamic attribute) data, the static attribute data is generally directly stored in a tag, and the dynamic attribute data needs to be detected by a sensor in real time. The equipment needs to periodically transmit attribute data (also called sensor data) detected in real time to the information processing center, and under the condition that the number of the sensors of the equipment is large and/or the acquisition frequency is high, the amount of the data needing to be uploaded is large, so that the flow consumption is overlarge.
Disclosure of Invention
The application provides a data transmission method and equipment, and aims to solve the problem that flow consumption is overlarge due to the fact that the data volume of sensor data needing to be uploaded by the equipment is large.
In order to achieve the above object, the present application provides the following technical solutions:
a method of data transmission, comprising:
Acquiring data through a sensor, wherein the acquired data is sensor data to be uploaded;
Searching a group with similarity to the sensor data to be uploaded within a preset range from a pre-generated classification, wherein the pre-generated classification comprises a plurality of classes, each class comprises a plurality of groups, each group comprises a plurality of sensor data uploaded at the same time point, and the sensor data acquired by each sensor of the same class have the same change period;
Uploading an identifier of a group with similarity to the sensor data to be uploaded within a preset range and distinguishing data of the sensor data to be uploaded and the group.
Optionally, the method for generating the pre-generated classification includes:
forming a matrix by historical data acquired by the sensors, wherein any one behavior of the matrix is data of a plurality of historical sensors uploaded at the same time point, and any one column of the matrix is data of the same sensor;
obtaining a transposed matrix of the matrix;
and carrying out hierarchical clustering on the transposed matrix by using a clustering algorithm, and dividing similar columns in the transposed matrix into a class.
Optionally, in a case that the similarity between the data to be uploaded and the sensor data to be uploaded is the same as the sensor data in the group within the preset range, the difference data is null.
Optionally, the identifier of the group whose similarity to the sensor data to be uploaded is within a preset range includes:
And the group identification and the class identification of the group with the similarity to the sensor data to be uploaded within a preset range.
Optionally, the difference data between the sensor data to be uploaded and the group includes:
The sensor data to be uploaded comprises values different from those in a similar group and identifiers of sensors for collecting the values, wherein the similar group is a group with similarity of the sensor data to be uploaded within a preset range.
an apparatus, comprising:
The acquisition control module is used for acquiring data through the sensor, and the acquired data is the sensor data to be uploaded;
the searching module is used for searching a group with the similarity to the sensor data to be uploaded within a preset range from a pre-generated classification, wherein the pre-generated classification comprises a plurality of classes, each class comprises a plurality of groups, each group comprises a plurality of sensor data uploaded at the same time point, and the sensor data acquired by each sensor of the same class has the same change rule;
And the data transmission module is used for uploading the identifier of a group with the similarity to the sensor data to be uploaded within a preset range and the distinguishing data of the sensor data to be uploaded and the group.
Optionally, the method further includes:
The classification module is used for forming a matrix by historical data acquired by the sensors, wherein any one row of the matrix is data of a plurality of historical sensors uploaded at the same time point, and any one column of the matrix is data of the same sensor; obtaining a transposed matrix of the matrix; and hierarchical clustering is carried out on the transposed matrix by using a clustering algorithm, and similar columns in the transposed matrix are divided into a class.
Optionally, the data transmission module is configured to upload an identifier of a group whose similarity with the sensor data to be uploaded is within a preset range and difference data between the group and the sensor data to be uploaded includes:
The data transmission module is specifically configured to, when the similarity between the data to be uploaded and the sensor data to be uploaded is the same as that of the sensor data in the group within the preset range, determine that the difference data is null.
Optionally, the identifier of the group, which is used for uploading the similarity with the sensor data to be uploaded within a preset range, of the data transmission module includes:
The data transmission module is specifically used for uploading a group identifier and a class identifier of a group with similarity to the sensor data to be uploaded within a preset range.
optionally, the data transmission module is configured to upload the sensor data to be uploaded and the group of difference data, and includes:
The data transmission module is specifically used for uploading a value different from that in a similar group in the sensor data to be uploaded and acquiring an identifier of a sensor of the value, wherein the similar group is a group with similarity of the sensor data to be uploaded within a preset range.
according to the data transmission method and the data transmission equipment, each group in each class in the pre-generated classes comprises a plurality of sensor data uploaded at the same time point, and the sensor data acquired by the same sensor belonging to different groups in the same class have the same change period, so that the group with the similarity within the preset range can be found from the classes of the pre-generated classes, only the difference data of the data to be uploaded compared with the searched group can be uploaded, all the data to be uploaded does not need to be uploaded, and the data volume to be uploaded can be reduced.
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 description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a data transmission method disclosed in an embodiment of the present application;
Fig. 2 is a flowchart of another data transmission method disclosed in the embodiment of the present application;
fig. 3 is a schematic structural diagram of an apparatus disclosed in an embodiment of the present application.
Detailed Description
The embodiment of the application can be applied to equipment in the Internet of things. The equipment in the Internet of things acquires sensor data through the sensor, uploads the sensor data to the information processing center, and the information processing center processes the sensor data.
During the research, the applicant finds that the data collected by the sensors in the same subsystem on one device have the same change period. Taking the data of the oil extraction machine as an example, in one stroke of the oil extraction machine, the data acquired by sensors such as a rope hanger dynamometer, an oil extraction rod tension sensor and a walking beam vibration sensor in the same subsystem of the oil extraction machine have the same change period. According to the characteristics, the data transmission method is provided, and the purpose is to reduce the data volume sent by the equipment.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
fig. 1 is a data transmission method disclosed in an embodiment of the present application, which is applied to a device in the internet of things, and includes the following steps:
S101: data are collected through the sensor, and the collected data are used as sensor data to be uploaded.
S102: and searching a group with the similarity to the sensor data to be uploaded within a preset range from the pre-generated classification.
the pre-generated classes comprise a plurality of classes, each class comprises a plurality of groups, and each group comprises a plurality of sensor data uploaded at the same time point. The sensor data collected by each sensor of the same class has the same variation period.
The above structure of the pre-generated classification is beneficial to determining the similarity between the subsequent data to be uploaded and the historical uploading data.
in the embodiment of the present application, the similarity between two sets of sensor data means that, in the two sets of sensor data, the same number of sensor data is: collected by the same sensor and have the same numerical value. For example, at 11:00, the sensor data collected by the sensor1, the sensor2 and the sensor 3 are 1,2 and 3, respectively, at 11:10, the sensor data collected by the sensor1, the sensor2 and the sensor 3 are 1,2 and 5, respectively, then the same sensor data in the sensor data collected at 11:00 and the sensor data collected at 11:10 are 1 (both collected by the sensor1 and having a value of 1) and 2 (both collected by the sensor2 and having a value of 2), respectively, and then the similarity between the two sets of sensor data is 2. The preset range may be determined according to actual conditions, and optionally, the upper limit of the preset range is the maximum value of the sensor data, and the lower limit is 1.
S103: and uploading identification of a group with similarity to the sensor data to be uploaded within a preset range and distinguishing data of the sensor data to be uploaded and the group.
Specifically, the group identifier includes a class identifier and a group identifier of the group. It should be noted that if there is no difference between the group whose similarity between the sensor data to be uploaded and the sensor data to be uploaded is within the preset range, the difference data is null, and only the group identifier and the class identifier of the group whose similarity between the sensor data to be uploaded and the sensor data to be uploaded is within the preset range may be uploaded.
It should be noted that if there is no group in the preset classification whose similarity to the sensor data to be uploaded is within the preset range, all the sensor data to be uploaded is uploaded.
as can be seen from the process shown in fig. 1, the device finds a group having a similarity to the data to be uploaded within a preset range from the pre-generated classifications, and uploads the identification of the group and the distinguishing data of the data to be uploaded from the group. Compared with the prior art of uploading all data to be uploaded, the method can reduce the data volume of uploading, thereby saving the flow. Further, the network load can be reduced, and in the case of transmitting data using a mobile network, since the amount of data transmitted is reduced, the use cost of the network can be reduced.
It should be noted that, because the data collected by the sensors belonging to the same subsystem in one device have the same change period, the sensor data in the same one of the pre-generated classifications described in this embodiment is the sensor data collected by the sensors in the same subsystem. Based on the corresponding relation, the purpose of saving data flow is achieved.
The method shown in fig. 1 is explained in more detail below:
Fig. 2 is a diagram of another data transmission method disclosed in the embodiment of the present application, which is applied to a device in the internet of things, and includes the following steps:
S201: and classifying the uploaded historical data, wherein each class comprises a plurality of groups, and each group comprises data acquired by a plurality of sensors uploaded at the same time point, namely a plurality of sensor data. The sensor data collected by each sensor in any one of the classes has the same period of variation.
for example, table 1 shows historical data collected by a sensor, where each row corresponds to a time point, and the data of each row is uploaded data at the time point. Sensor1 … … Sensor9 represents a Sensor for which the following values are data collected.
TABLE 1
Taking the clustering algorithm as an example, the data in table 1 are classified: first, the matrix formed by the data in Table 1DmnAny row of the sensor data is data of a plurality of historical sensors uploaded at the same time point, and any column is data of the same sensor. Then D is putmntransposing to obtainFinally using clustering algorithm to pair DTHierarchical clustering was performed to obtain the classification results shown in table 2. The algorithm of hierarchical clustering is the prior art and is not described herein.
the results of the classification in table 1 are shown in table 2, and it can be seen that the first class includes values of Sensor1 … … Sensor5, each group being values of Sensor1 … … Sensor5 uploaded at different time points. The sensor data collected by the same sensor belonging to different groups in the first category has the same variation period. Taking the Sensor data collected by the sensors 1 and 2 as an example, it can be seen that the variation period of the Sensor1 is four groups, and the variation period of the Sensor2 is also four groups.
TABLE 2
The second category includes values for Sensor6 … … Sensor9, each set being the value of Sensor6 … … Sensor9 uploaded at a different point in time. The Sensor data collected by the same Sensor belonging to the second category has the same variation period, for example, the variation periods of Sensor6 and Sensor9 are three groups.
Identification is made for each class and each group to distinguish between different classes and different groups. For example, in table 2, "I" and "II" are used to distinguish different classes, and the natural number "1" … … "4" is used to distinguish different groups.
S202: and collecting sensor data as sensor data to be uploaded.
For example, the sensor data collected is shown in table 3.
TABLE 3
| Sensor1 | Sensor2 | Sensor3 | Sensor4 | Sensor5 | Sensor6 | Sensor7 | Sensor8 | Sensor9 |
| 1 | 2 | 4 | 8 | 19 | 1 | 2 | 3 | 4 |
S203: from the classification obtained in S201, a group most similar to the sensor data to be uploaded is searched for.
the group that is most similar to the sensor data to be uploaded is the group that has the most identical sensor data compared to the sensor data to be uploaded. As for the above example, in the classification shown in table 2, according to the similarity calculation method in S102, the numbers of the sensor data having the same number as that of the sensor data to be uploaded in the group numbered from 1 to 8 in the class I are respectively: 4. 0, 4, 0. It can be seen that the groups numbered 1 and 5 in class I are most similar to Sensor1 … … Sensor5 in the received Sensor data. Similarly, the groups numbered 1, 4, and 7 in class II are most similar to Sensor6 … … Sensor9 in the received Sensor data.
S204: and uploading the class identification and the group identification of the group which is most similar to the sensor data to be uploaded in the classification, and the distinguishing data of the sensor data to be uploaded and the most similar group.
Specifically, the difference data includes a different value from the data in the most similar group in the sensor data to be uploaded, and the identifier of the sensor corresponding to the value, that is, the identifier of the sensor of the value.
In the above example, the difference between the received Sensor data and the most similar group is the data of Sensor5, and the uploaded content is: "I" "1" "Sensor 519" "II" "1".
It should be noted that, in this embodiment, there are a plurality of groups that are most similar to the sensor data to be uploaded, and in order to further save traffic, the class identifier and the group identifier of any one of the most similar groups may be uploaded.
In S204, after receiving the uploaded data, a receiving end of the uploaded data, for example, an information processing center, finds a group most similar to the sensor data to be uploaded from the preset classifications according to the group identifier and the class identifier of the group most similar to the sensor data to be uploaded, and modifies corresponding data in the group into difference data according to difference data between the sensor data to be uploaded and the group most similar to the sensor data to be uploaded.
In the above example, if the data received by the information processing center is "I", "1", "Sensor 519", "II", "1", group 1 in class I is found from table 2, and the value 16 of Sensor5 in group 1 is modified to the value 19.
As can be seen from the method shown in fig. 2, before uploading data, the device compares the similarity between the data to be uploaded and each group in the pre-generated classifications, finds out the group most similar to the data to be uploaded among the groups in the pre-generated classifications, and uploads the difference between the two groups and the identifier of the group most similar to the data to be uploaded without uploading all the data, so that the amount of data to be transmitted can be reduced. Further, in the case of using a mobile network as a transmission channel, the use cost of the network can be reduced.
fig. 3 is an apparatus disclosed in an embodiment of the present application, which may be disposed in an internet of things, and acquire transmitter data through a sensor, and upload the sensor data to an information processing center.
the apparatus comprises: the device comprises an acquisition control module, a searching module and a data transmission module. Optionally, a classification module may also be included.
The classification module is used for forming a matrix by historical data acquired by the sensors, any one row of the matrix is data of a plurality of historical sensors uploaded at the same time point, and any one column of the matrix is data of the same sensor; obtaining a transposed matrix of the matrix; and hierarchical clustering is carried out on the transposed matrix by using a clustering algorithm, and similar columns in the transposed matrix are divided into a class.
the acquisition control module is used for acquiring data through the sensor, and the acquired data is the sensor data to be uploaded.
The searching module is used for searching a group with the similarity of the sensor data to be uploaded within a preset range from a pre-generated classification, the pre-generated classification comprises a plurality of classes, each class comprises a plurality of groups, each group comprises a plurality of sensor data uploaded at the same time point, and the sensor data acquired by each sensor of the same class have the same change period.
And the data transmission module is used for uploading the identifier of a group with the similarity to the sensor data to be uploaded within a preset range and the distinguishing data of the sensor data to be uploaded and the group. Specifically, the group identifier includes a class identifier and a group identifier. The distinguishing data is a value different from that in the similar group in the sensor data to be uploaded and the identification of the sensor acquiring the value, and the similar group is a group with the similarity of the sensor data to be uploaded within a preset range. And in the case that the similarity between the data to be uploaded and the sensor data to be uploaded is the same as the sensor data in the group within the preset range, the distinguishing data is null.
The apparatus shown in fig. 3 can upload only the data different from the historical sensor data group without uploading the entire sensor data to be uploaded, thereby being capable of reducing the transmission amount of data.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
the previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. a method of data transmission, comprising:
acquiring data through a sensor, wherein the acquired data is sensor data to be uploaded;
Searching a group with similarity to the sensor data to be uploaded within a preset range from a pre-generated classification, wherein the pre-generated classification comprises a plurality of classes, each class comprises a plurality of groups, each group comprises a plurality of sensor data uploaded at the same time point, and the sensor data acquired by each sensor of the same class have the same change period;
Uploading an identifier of a group with similarity to the sensor data to be uploaded within a preset range and distinguishing data of the sensor data to be uploaded and the group.
2. The method of claim 1, wherein the pre-generated classification is generated by a method comprising:
Forming a matrix by historical data acquired by the sensors, wherein any one behavior of the matrix is data of a plurality of historical sensors uploaded at the same time point, and any one column of the matrix is data of the same sensor;
obtaining a transposed matrix of the matrix;
And carrying out hierarchical clustering on the transposed matrix by using a clustering algorithm, and dividing similar columns in the transposed matrix into a class.
3. The method according to claim 1 or2, characterized in that the distinguishing data is null in the case where the data to be uploaded is identical to the sensor data in the group whose similarity to the sensor data to be uploaded is within a preset range.
4. The method of claim 1 or2, wherein the identification of the group having a similarity to the sensor data to be uploaded within a preset range comprises:
and the group identification and the class identification of the group with the similarity to the sensor data to be uploaded within a preset range.
5. The method of claim 1 or2, wherein the sensor data to be uploaded and the set of distinguishing data comprises:
The sensor data to be uploaded comprises values different from those in a similar group and identifiers of sensors for collecting the values, wherein the similar group is a group with similarity of the sensor data to be uploaded within a preset range.
6. An apparatus for data transmission, comprising:
the acquisition control module is used for acquiring data through the sensor, and the acquired data is the sensor data to be uploaded;
The searching module is used for searching a group with the similarity to the sensor data to be uploaded within a preset range from a pre-generated classification, wherein the pre-generated classification comprises a plurality of classes, each class comprises a plurality of groups, each group comprises a plurality of sensor data uploaded at the same time point, and the sensor data acquired by each sensor of the same class has the same change rule;
And the data transmission module is used for uploading the identifier of a group with the similarity to the sensor data to be uploaded within a preset range and the distinguishing data of the sensor data to be uploaded and the group.
7. the apparatus of claim 6, further comprising:
The classification module is used for forming a matrix by historical data acquired by the sensors, wherein any one row of the matrix is data of a plurality of historical sensors uploaded at the same time point, and any one column of the matrix is data of the same sensor; obtaining a transposed matrix of the matrix; and hierarchical clustering is carried out on the transposed matrix by using a clustering algorithm, and similar columns in the transposed matrix are divided into a class.
8. the device according to claim 6 or 7, wherein the data transmission module is configured to upload the identifier of the group whose similarity with the sensor data to be uploaded is within a preset range and the difference data between the group and the sensor data to be uploaded comprises:
The data transmission module is specifically configured to, when the similarity between the data to be uploaded and the sensor data to be uploaded is the same as that of the sensor data in the group within the preset range, determine that the difference data is null.
9. the device of claim 6 or 7, wherein the data transmission module is configured to upload the identification of the group having similarity to the sensor data to be uploaded within a preset range, and the identification comprises:
the data transmission module is specifically used for uploading a group identifier and a class identifier of a group with similarity to the sensor data to be uploaded within a preset range.
10. The apparatus of claim 6 or 7, wherein the data transmission module is configured to upload the sensor data to be uploaded and the set of distinguishing data comprises:
the data transmission module is specifically used for uploading a value different from that in a similar group in the sensor data to be uploaded and acquiring an identifier of a sensor of the value, wherein the similar group is a group with similarity of the sensor data to be uploaded within a preset range.
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