Sensor data storage method based on local area network

Abstract

The invention discloses a sensor data storage method based on a local area network, relates to the technical field of data storage, and effectively improves the accuracy of a data transmission result. According to the method, an application scene is divided into a plurality of acquisition areas, a plurality of data origin centers are positioned in the acquisition areas, then, each sensor acquires a scene data set of the acquisition area where the sensor is located in each data acquisition period, and a local data unit is set for each acquisition area; comparing the same type of scene data in the scene data set in the acquisition areas at adjacent spatial positions, acquiring a calibration scene data set of each acquisition area in each data acquisition period according to a comparison result, and establishing a corresponding area data chain according to the calibration scene data set in each data acquisition period, and connecting each central area data chain and each secondary area data chain to obtain an origin center data area until the data origin center stops displacement.

Description

Technical Field

[0001] The invention relates to the technical field of data storage, and in particular to a sensor data storage method based on a local area network. Background Art

[0002] With the development of Internet of Things (IoT) technology, various sensors are widely used in environmental monitoring, smart home, industrial automation and other fields. These sensors can collect a large amount of data in real time and transmit it to the central system for analysis and processing.

[0003] Existing sensor data storage technology has the following defects:

[0004] Bandwidth limitation: Sensor data is often collected at a high frequency and the data volume is huge. During the transmission process over the Internet, bandwidth limitations may cause slow data transmission and affect real-time performance. When storing data on multiple nodes, ensuring the consistency and accuracy of the data is a complex task, especially when there are too many types of sensors.

[0005] Poor data visualization: When there are too many types of sensors, the types of data also increase, and existing technologies cannot effectively display the process of data changing over time.

[0006] Therefore, how to improve the efficiency of sensor data transmission and ensure the accuracy of data transmission results while realizing data visualization of sensor data is a defect of the existing technology. For this purpose, a sensor data storage method based on a local area network is provided. Summary of the invention

[0007] In order to solve the above technical problems, the object of the present invention is to provide a sensor data storage method based on a local area network.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] A sensor data storage method based on a local area network comprises the following steps:

[0010] Step S1, dividing the application scene into several collection areas, and setting a plurality of sensors for each collection area, locating a plurality of data origin centers in the collection area, and then as the data origin center moves in the collection area, each sensor collects the scene data set of its collection area in each data collection cycle;

[0011] Step S2, setting a local data unit for each acquisition area, comparing the same type of scene data in the scene data set for acquisition areas at adjacent spatial positions, and obtaining a calibration scene data set for each acquisition area in each data acquisition cycle according to the comparison result;

[0012] Step S3, establish corresponding regional data chains according to the calibration scene data sets under each data collection cycle, record the regional data chain corresponding to the collection area where the data origin center is located as the central regional data chain, and record the regional data chains corresponding to the remaining collection areas as secondary regional data chains, and connect each central regional data chain and the secondary regional data chain to obtain the origin center data area until the data origin center stops moving.

[0013] Furthermore, the process of dividing the application scenarios includes:

[0014] Divide the application scene into several collection areas of the same spatial volume, set a number for each collection area, and set several types of sensors for each collection area;

[0015] Multiple scene dynamic objects are located in the application scene, and each scene dynamic object is recorded as a data origin center. Before each data origin center moves in the application scene, the collection area where the data origin center is currently located is recorded as the central collection area, and the collection area that does not include the data origin center is recorded as the secondary collection area. The same data collection period and data collection range are set for each sensor in each collection area, and a unit moving speed is set for each data origin center.

[0016] Furthermore, the process of collecting the scene data set includes:

[0017] Then, when the data origin center moves, the sensors in each collection area collect various scene data of its collection area, and mark the scene data with the corresponding collection area number. Then, when the data collection cycle ends, the current data collection cycle is integrated to generate all scene data to generate a scene data set, and the scene data set is marked with the collection area number of the center position and the current label. The scene data includes video data and chart data.

[0018] Furthermore, a local data unit is bound to each acquisition area, and each local data unit is connected to communicate through a local area network. Whenever a acquisition area generates a scene data set, each local data unit shares data with the local data unit at an adjacent spatial position of the acquisition area, and then sends the scene data set generated in the current data acquisition cycle to the local data unit at the adjacent spatial position.

[0019] Furthermore, the process of comparing different scene data sets includes:

[0020] When each collection area carries out the next data collection cycle, each local data unit records the scene data set of its collection area as the verification scene data set, and then compares the verification scene data set with the same state data with the same number in other local scene areas;

[0021] Obtain the pixel value of each pixel in the image area from different scene data sets, and then obtain the similarity between the pixel values ​​of pixels at the same position in each image area in turn, and set a similarity threshold. If the similarity of the pixel values ​​of two pixels is greater than or equal to the similarity threshold, the corresponding two pixels are similar, otherwise they are judged to be dissimilar;

[0022] The similarity relationships between pixels are merged, and then the pixels with the largest number of similarity relationships are selected as the calibration pixels at the corresponding positions. The calibration pixels corresponding to each scene area are stitched together in sequence, and then the calibration scene data of each acquisition area in the corresponding data acquisition period is obtained, and the data type is video data.

[0023] Furthermore, the process of comparing different scene data sets also includes:

[0024] For scene data of the type of chart data, each local data unit establishes a two-dimensional coordinate system, maps scene data from different scene data sets but corresponding to the same acquisition area onto the same two-dimensional coordinate system, divides each scene data into several data points, obtains the distance values ​​between data points corresponding to different scene data under the same horizontal coordinate, counts the sum of the distance values ​​between each data point and other data points, and then selects the data point with the smallest sum of distance values ​​as the calibration data point under the corresponding horizontal coordinate;

[0025] If there are multiple data points with the smallest sum of distance values ​​at the same time, a data point is randomly selected as the calibration data point under the corresponding horizontal axis;

[0026] Connect the calibration data points under each horizontal axis in sequence, and then obtain calibration scene data of each acquisition area in the corresponding data acquisition period, where the data type is chart data;

[0027] Integrate all calibration scene data, and then each local data unit obtains the calibration scene data set of the previous data collection cycle and marks the collection area number.

[0028] Furthermore, the process of establishing the origin center data area includes:

[0029] At the end of each data collection cycle, each local data unit generates a regional association node and marks the regional association node with the corresponding collection area number. At the same time, several data storage nodes are set according to the number of data types in the calibration scene data set, and each data storage node is connected to the corresponding regional association node to obtain a regional data chain;

[0030] During the next data collection cycle, each calibration scene data in the calibration scene data set is sequentially stored in each data storage node, and according to the calibration scene data generated in the previous data collection cycle, each local data unit associated with the secondary collection area in the previous data collection cycle sends its currently generated regional data link to the local data unit associated with the central collection area;

[0031] The local data unit associated with each central collection area records the regional data chain it generates as the central regional data chain, and the regional data chain corresponding to the secondary collection area is recorded as the secondary regional data chain;

[0032] First, the data chains of each central area are connected head to tail according to the distribution of the corresponding central collection area in the application scenario to obtain the origin central data area, wherein the origin central data area only contains the central area associated nodes outside and contains the data storage nodes inside;

[0033] Then, the secondary area associated nodes in each secondary collection area are connected with the central area associated nodes of the origin center data area in sequence according to the distribution in the application scenario, so as to obtain the scene data chain of the corresponding data origin center in the corresponding data collection cycle.

[0034] Furthermore, if the collection area where the data origin center is located changes during the previous data collection cycle, the scene data chain generated by the local data unit associated with the central collection area during the previous data collection cycle is sent to the local data unit associated with the central collection area in the previous data collection cycle, and then the central area associated nodes in the scene data chains of each data collection cycle are connected in sequence.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention divides the application scenario into several collection areas, locates multiple data origin centers in the collection area, and then each sensor collects the scene data set of its collection area in each data collection cycle, sets a local data unit for each collection area, compares the same type of scene data types in the scene data set of the collection areas at adjacent spatial positions, obtains the calibration scene data set of each collection area in each data collection cycle according to the comparison result, establishes the corresponding regional data chain according to the calibration scene data set in each data collection cycle, connects each central area data chain and the secondary area data chain to obtain the origin center data area, until the data origin center stops moving, thereby improving the sensor data transmission efficiency and ensuring the accuracy of the data transmission result, while realizing data visualization of the sensor data. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention.

[0038] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0039] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0040] Example 1

[0041] like Figure 1 As shown, a sensor data storage method based on a local area network includes the following steps:

[0042] Step S1, dividing the collection area into several collection areas, and setting a plurality of sensors for each collection area, positioning a plurality of data origin centers in the collection area, and then as the data origin center moves in the collection area, each sensor collects a scene data set of its collection area in each data collection cycle;

[0043] Step S2, setting a local data unit for each acquisition area, comparing the same type of scene data in the scene data set for acquisition areas at adjacent spatial positions, and obtaining a calibration scene data set for each acquisition area in each data acquisition cycle according to the comparison result;

[0044] Step S3, establish corresponding regional data chains according to the calibration scene data sets under each data collection cycle, record the regional data chain corresponding to the collection area where the data origin center is located as the central regional data chain, and record the regional data chains corresponding to the remaining collection areas as secondary regional data chains, and connect each central regional data chain and the secondary regional data chain to obtain the origin center data area until the data origin center stops moving.

[0045] Example 2

[0046] This embodiment is a further limitation of Embodiment 1, and Step S1 is implemented by the following process:

[0047] Divide the application scene into several collection areas of equal spatial volume, and assign a number to each collection area. 1 、a2 、a 3 ,……,a n , and setting a number of sensors for each collection area, where n is a natural number greater than 0, and the types of sensors include cameras, temperature sensors, laser sensors, etc.;

[0048] A number of scene dynamic objects that perform fixed behavior actions are set in the application scene. For example, when the application scene is an intelligent warehousing scene, the scene dynamic object is a warehousing robot, and the fixed action is to move goods;

[0049] Locate multiple scene dynamic objects in the application scene, and record each scene dynamic object as the data origin center and set the number b 1 , b 2 , b 3 ,……,b m , m is a natural number greater than 0;

[0050] Before each data origin center moves in the application scenario, the collection area where the data origin center is currently located is recorded as the central collection area, and the collection area that does not include the data origin center is recorded as the secondary collection area. It should be noted that any data origin center can be in multiple collection areas at the same time node, that is, one data origin center is associated with multiple collection areas at the same time;

[0051] The same data collection cycle and data collection range are set for each sensor in each collection area, and a unit moving speed is set for each data origin center, wherein the length of the data collection cycle is equal to five to ten times the time taken by the data origin center to pass through any collection area at the unit moving speed, and the data collection range of the sensor in each collection area includes its corresponding collection area, for example, a sensor in one collection area can collect various data in its eight adjacent collection areas;

[0052] When the data origin center moves, the sensors in each collection area collect various scene data in the collection area where they are located, and mark the scene data with the corresponding collection area number. When the data collection cycle ends, the current data collection cycle is integrated to generate all scene data to generate a scene data set, and the scene data set is marked with the collection area number of the center position and the current annotation. The scene data includes video data and chart data.

[0053] It should be noted that as the data origin center continues to move within the application scenario, the collection area where the data origin center is located is also constantly changing, so the central collection area associated with the data origin center also changes accordingly, and when the position of the data origin center changes, the collection area originally marked as the central collection area will be synchronously changed to a secondary collection area.

[0054] Example 3

[0055] This embodiment is a further limitation of Embodiment 1, and Step S2 is implemented by the following process:

[0056] A local data unit is bound to each acquisition area respectively, and each local data unit is connected to each other through a local area network. Whenever a scene data set is generated in the acquisition area, each local data unit shares data with the local data unit in the adjacent spatial position of the acquisition area where it is located, and then sends the scene data set generated in the current data acquisition cycle to the local data unit in the adjacent spatial position;

[0057] When each collection area carries out the next data collection cycle, each local data unit records the scene data set of its collection area as the verification scene data set, and then compares the verification scene data set with the same state data with the same number in other local scene areas;

[0058] For scene data of the type of video data, scene data from different scene data but with the same number are split into a plurality of scene image data according to time, and each scene image data is divided into a plurality of image regions;

[0059] The image regions at the same position from different scene data sets but corresponding to the same scene image data are compared. The comparison process includes:

[0060] Obtain the pixel value of each pixel in the image area from different scene data sets, and then obtain the similarity between the pixel values ​​of pixels at the same position in each image area in turn, and set a similarity threshold. If the similarity of the pixel values ​​of two pixels is greater than or equal to the similarity threshold, the corresponding two pixels are similar, otherwise they are judged to be dissimilar;

[0061] Merge the similarity relationships between the pixels, and then select the pixel with the largest number of similarity relationships as the calibration pixel at the corresponding position. It should be noted that if it is judged that the pixels are not similar according to the comparison results, the pixels in the calibration scene data set are selected as the calibration pixels;

[0062] The calibration pixels corresponding to each scene area are sequentially spliced, so as to obtain calibration scene data of each acquisition area in the corresponding data acquisition period, where the data type is video data.

[0063] Furthermore, for scene data of the type of chart data, each local data unit establishes a two-dimensional coordinate system, maps scene data from different scene data sets but corresponding to the same acquisition area onto the same two-dimensional coordinate system, divides each scene data into a number of data points, obtains distance values ​​between data points corresponding to different scene data under the same horizontal coordinate, counts the sum of distance values ​​between each data point and other data points, and then selects the data point with the smallest sum of distance values ​​as the calibration data point under the corresponding horizontal coordinate;

[0064] If there are multiple data points with the smallest sum of distance values ​​at the same time, a data point is randomly selected as the calibration data point under the corresponding horizontal axis;

[0065] Connect the calibration data points under each horizontal axis in sequence, and then obtain calibration scene data of each acquisition area in the corresponding data acquisition period, where the data type is chart data;

[0066] Integrate all calibration scene data, and then each local data unit obtains the calibration scene data set under the previous data collection cycle, and marks the collection area number and the current annotation.

[0067] Example 4

[0068] This embodiment is a further limitation of Embodiment 1, and step S3 is implemented by the following process:

[0069] At the end of each data collection cycle, each local data unit generates a regional association node and marks the regional association node with the corresponding collection area number. At the same time, several data storage nodes are set according to the number of data types in the calibration scene data set, and each data storage node is connected to the corresponding regional association node to obtain a regional data chain;

[0070] During the next data collection cycle, each calibration scene data in the calibration scene data set is sequentially stored in each data storage node, and according to the calibration scene data generated in the previous data collection cycle, each local data unit associated with the secondary collection area in the previous data collection cycle sends its currently generated regional data link to the local data unit associated with the central collection area;

[0071] The local data unit associated with each central collection area records the regional data chain it generates as the central regional data chain, and the regional data chain corresponding to the secondary collection area is recorded as the secondary regional data chain;

[0072] First, the data chains of each central area are connected head to tail according to the distribution of the corresponding central collection area in the application scenario to obtain the origin central data area, wherein the origin central data area only contains the central area associated nodes outside and contains the data storage nodes inside;

[0073] Then, the secondary area associated nodes in each secondary collection area are connected with the central area associated nodes of the origin center data area in sequence according to the distribution in the application scenario, so as to obtain the scene data chain of the corresponding data origin center in the corresponding data collection cycle;

[0074] If the collection area where the data origin center is located changes during the previous data collection cycle, the scene data chain generated by the local data unit associated with the central collection area during the previous data collection cycle is sent to the local data unit associated with the central collection area marked in the previous data collection cycle, and then the central area associated nodes in the scene data chains of each data collection cycle are connected in sequence;

[0075] The process of connecting the scene data links in each data collection cycle is repeated until the data origin center stops moving.

[0076] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A sensor data storage method based on a local area network, characterized in that: The following steps are involved: Step S1, dividing the application scene into several collection areas, and setting a plurality of sensors for each collection area, locating a plurality of data origin centers in the collection area, and then as the data origin center moves in the collection area, each sensor collects the scene data set of its collection area in each data collection cycle; Step S2, setting a local data unit for each acquisition area, comparing the same type of scene data in the scene data set for acquisition areas at adjacent spatial positions, and obtaining a calibration scene data set for each acquisition area in each data acquisition cycle according to the comparison result; Step S3, establish corresponding regional data chains according to the calibration scene data sets under each data collection cycle, record the regional data chain corresponding to the collection area where the data origin center is located as the central regional data chain, and record the regional data chains corresponding to the remaining collection areas as secondary regional data chains, and connect each central regional data chain and the secondary regional data chain to obtain the origin center data area until the data origin center stops moving.

2. A sensor data storage method based on a local area network according to claim 1, characterized in that: The process of dividing the application scenarios includes: Divide the application scene into several collection areas of the same spatial volume, set a number for each collection area, and set several types of sensors for each collection area; Multiple scene dynamic objects are located in the application scene, and each scene dynamic object is recorded as a data origin center. Before each data origin center moves in the application scene, the collection area where the data origin center is currently located is recorded as the central collection area, and the collection area that does not include the data origin center is recorded as the secondary collection area. The same data collection period and data collection range are set for each sensor in each collection area, and a unit moving speed is set for each data origin center.

3. A sensor data storage method based on a local area network according to claim 2, characterized in that: The process of collecting the scene data set includes: When the data origin center moves, the sensors in each collection area collect various scene data in its collection area and mark the scene data with the corresponding collection area number. Then, when the data collection cycle ends, all scene data generated in the current data collection cycle are integrated to generate a scene data set, and the scene data set is marked with the collection area number of the center position. The scene data includes video data and chart data.

4. A sensor data storage method based on a local area network according to claim 3, characterized in that: A local data unit is bound to each acquisition area respectively, and each local data unit is connected to communicate through a local area network. Whenever a collection area generates a scene data set, each local data unit shares data with the local data unit at the adjacent spatial position of the collection area where it is located, and then sends the scene data set generated in the current data collection cycle to the local data unit at the adjacent spatial position.

5. A sensor data storage method based on a local area network according to claim 4, characterized in that: The process of comparing different scene datasets includes: When each collection area carries out the next data collection cycle, each local data unit records the scene data set of its collection area as the verification scene data set, and then compares the verification scene data set with the same state data with the same number in other local scene areas; For scene data of the type of video data, the pixel value of each pixel in the image area from each different scene data set is obtained, and then the similarity between the pixel values ​​of pixels at the same position in each image area is obtained in turn, and a similarity threshold is set. If the similarity of the pixel values ​​of two pixels is greater than or equal to the similarity threshold, the corresponding two pixels are similar, otherwise they are judged to be dissimilar; The similarity relationships between pixels are merged, and then the pixels with the largest number of similarity relationships are selected as the calibration pixels at the corresponding positions. The calibration pixels corresponding to each scene area are stitched together in sequence, and then the calibration scene data of each acquisition area in the corresponding data acquisition period is obtained, and the data type is video data.

6. A sensor data storage method based on a local area network according to claim 4, characterized in that: The process of comparing different scene data sets also includes: For scene data of the type of chart data, each local data unit establishes a two-dimensional coordinate system, maps scene data from different scene data sets but corresponding to the same acquisition area onto the same two-dimensional coordinate system, divides each scene data into several data points, obtains the distance values ​​between data points corresponding to different scene data under the same horizontal coordinate, counts the sum of the distance values ​​between each data point and other data points, and then selects the data point with the smallest sum of distance values ​​as the calibration data point under the corresponding horizontal coordinate; If there are multiple data points with the smallest sum of distance values ​​at the same time, a data point is randomly selected as the calibration data point under the corresponding horizontal axis, and the calibration data points under each horizontal axis are connected in sequence, so as to obtain calibration scene data of each acquisition area in the corresponding data acquisition period, where the data type is chart data; Integrate all calibration scene data, and then each local data unit obtains the calibration scene data set of the previous data collection cycle and marks the collection area number.

7. A sensor data storage method based on a local area network according to claim 6, characterized in that: The process of establishing the origin center data area includes: At the end of each data collection cycle, each local data unit generates a regional association node and marks the regional association node with the corresponding collection area number. At the same time, several data storage nodes are set according to the number of data types in the calibration scene data set, and each data storage node is connected to the corresponding regional association node to obtain a regional data chain; During the next data collection cycle, each calibration scene data in the calibration scene data set is sequentially stored in each data storage node, and according to the calibration scene data generated in the previous data collection cycle, each local data unit associated with the secondary collection area in the previous data collection cycle sends its currently generated regional data link to the local data unit associated with the central collection area; The local data unit associated with each central collection area records the regional data chain it generates as the central regional data chain, and the regional data chain corresponding to the secondary collection area is recorded as the secondary regional data chain; First, the data chains of each central area are connected head to tail according to the distribution of the corresponding central collection area in the application scenario to obtain the origin central data area, wherein the origin central data area only contains the central area associated nodes outside and contains the data storage nodes inside; Then, the secondary area associated nodes in each secondary collection area are connected with the central area associated nodes of the origin center data area in sequence according to the distribution in the application scenario, so as to obtain the scene data chain of the corresponding data origin center in the corresponding data collection cycle.

8. A sensor data storage method based on a local area network according to claim 7, characterized in that: If the collection area where the data origin center is located changes during the previous data collection cycle, the scene data chain generated by the local data unit associated with the central collection area during the previous data collection cycle will be sent to the local data unit associated with the central collection area marked in the previous data collection cycle, and then the central area associated nodes in the scene data chains of each data collection cycle will be connected in sequence.

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