A local area network-based sensor data storage method

Abstract

This invention discloses a sensor data storage method based on a local area network (LAN), relating to the field of data storage technology, which effectively improves the accuracy of data transmission results. The invention divides the application scenario into several acquisition areas, locates multiple data origin centers within each acquisition area, and then each sensor collects scene datasets for its respective acquisition area under various data acquisition cycles. Local data units are set for each acquisition area, and the scene data of the same type in adjacent spatial locations is compared. Based on the comparison results, calibration scene datasets for each acquisition area under various data acquisition cycles are obtained. Corresponding regional data chains are established based on the calibration scene datasets under each data acquisition cycle. The data chains of each central region and secondary regions are connected to obtain the origin center data region, until the data origin center stops shifting.

Description

Technical Field

[0001] This invention relates to the field of data storage technology, specifically a sensor data storage method based on a local area network. Background Technology

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

[0003] Existing sensor data storage technologies have the following drawbacks:

[0004] Bandwidth limitations: Sensor data is often acquired at high frequencies, resulting in massive data volumes. During transmission over the internet, bandwidth limitations can lead to slow data transmission, affecting real-time performance. Furthermore, ensuring data consistency and accuracy is a complex task when storing data across multiple nodes, especially when there are 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 show the process of data changing over time.

[0006] Therefore, improving the efficiency of sensor data transmission and ensuring the accuracy of data transmission results while realizing data visualization of sensor data are shortcomings of existing technologies. To address this, a sensor data storage method based on a local area network is proposed. Summary of the Invention

[0007] To address the aforementioned technical problems, the present invention aims to provide a sensor data storage method based on a local area network.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

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

[0010] Step S1: Divide the application scenario into several collection areas, and set up multiple sensors in each collection area. Locate multiple data origin centers in the collection area. Then, as the data origin centers move in the collection area, each sensor collects the scene dataset of its collection area under each data collection cycle.

[0011] Step S2: Set up local data units for each acquisition area, compare the acquisition areas in adjacent spatial locations with the same type of scene data in the scene dataset, and obtain the calibration scene dataset for each acquisition area under each data acquisition cycle based on the comparison results.

[0012] Step S3: Establish corresponding regional data chains based on the calibration scenario dataset under each data acquisition cycle. The regional data chain corresponding to the acquisition area where the data origin center is located is recorded as the central regional data chain, and the regional data chains corresponding to the other acquisition areas are recorded as secondary regional data chains. Connect each central regional data chain and secondary regional data chain to obtain the origin center data area until the data origin center stops shifting.

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

[0014] The application scenario is divided into several collection areas of the same size, and each collection area is assigned a number and several types of sensors are set for each collection area.

[0015] Within the application scenario, multiple dynamic objects are located, and each dynamic object is designated as a data origin center. Before each data origin center moves within the application scenario, the current acquisition area of ​​the data origin center is designated as the central acquisition area, and the acquisition area that does not include the data origin center is designated as the secondary acquisition area. The same data acquisition cycle and data acquisition range are set for each sensor within each acquisition area, and a unit movement speed is set for each data origin center.

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

[0017] Then, when the data origin center moves, the sensors in each acquisition area collect various scene data in their respective acquisition areas and label the scene data with the corresponding acquisition area number. Then, when the data acquisition cycle ends, the current data acquisition cycle is integrated to generate all scene data to generate a scene dataset, and the acquisition area number of the center position and the current label are labeled on the scene dataset. The scene data includes video data and chart data.

[0018] Furthermore, each acquisition area is bound to a local data unit, and the local data units are connected to communicate via a local area network. Whenever an acquisition area generates a scene dataset, each local data unit shares data with the local data units in the adjacent spatial location of its acquisition area, and then sends the scene dataset generated in the current data acquisition cycle to the local data units in the adjacent spatial location.

[0019] Furthermore, the process of comparing datasets from different scenarios includes:

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

[0021] The system obtains the pixel values ​​of each pixel in the image region from different scene datasets, and then obtains the similarity between the pixel values ​​of pixels at the same position in each image region. A similarity threshold is set. If the similarity between the pixel values ​​of two pixels is greater than or equal to the similarity threshold, the two pixels are considered similar; otherwise, they are considered dissimilar.

[0022] The similarity relationships between pixels are merged, and the pixel with the most similar relationships is selected as the calibration pixel at the corresponding position. The calibration pixels of each scene area are then stitched together in sequence to obtain the calibration scene data of each acquisition area, which is video data under the corresponding data acquisition period.

[0023] Furthermore, the process of comparing datasets from different scenarios also includes:

[0024] For scenario data of the type of chart data, a two-dimensional coordinate system is established for each local data unit. Scenario data from different scenario datasets but corresponding to the same collection area are mapped onto the same two-dimensional coordinate system. Each scenario data is divided into several data points. The distance values ​​between data points corresponding to different scenario data under the same horizontal axis are obtained. The sum of the distance values ​​between each data point and other data points is calculated. Then, the data point with the smallest total distance value is selected as the calibration data point under the corresponding horizontal axis.

[0025] If multiple data points have the smallest sum of distance values ​​at the same time, then one of the data points is randomly selected as the calibration data point under the corresponding x-axis.

[0026] By connecting the calibration data points under each horizontal axis in sequence, the calibration scenario data of each collection area, which is chart data, is obtained in the corresponding data collection period.

[0027] All calibration scenario data are integrated, and then each local data unit obtains the calibration scenario dataset from the previous data acquisition cycle and labels the acquisition area number.

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

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

[0030] During the next data acquisition cycle, each calibration scenario data in the calibration scenario dataset is stored sequentially in each data storage node. Based on the calibration scenario data generated in the previous data acquisition cycle, each local data unit associated with the secondary acquisition area in the previous data acquisition cycle sends its currently generated regional data chain to the local data unit associated with the central acquisition area.

[0031] Each central data collection area is associated with a local data unit that generates a regional data chain, which is called the central regional data chain. The regional data chain corresponding to the secondary data collection area is called the secondary regional data chain.

[0032] First, the data chains of each central region are connected end to end according to the distribution of the corresponding central collection areas in the application scenario to obtain the origin center data region. The origin center data region contains only the central region associated nodes outside and the data storage nodes inside.

[0033] Then, the secondary region association nodes in each secondary collection area are sequentially connected to the central region association nodes of the origin center data area according to the distribution in the application scenario, thereby obtaining the scene data chain of the corresponding data origin center under the corresponding data collection cycle.

[0034] Furthermore, if the data origin center's location changes during the previous data acquisition cycle, the scene data chain generated by the local data unit associated with the central acquisition area in the data acquisition cycle two years prior will be sent to the local data unit marked as associated with the central acquisition area in the previous data acquisition cycle. Then, the central area-associated nodes in the scene data chains of each data acquisition cycle will be sequentially connected.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] This invention divides the application scenario into several acquisition areas, locates multiple data origin centers within each acquisition area, and then each sensor acquires scene datasets for its respective acquisition area under various data acquisition cycles. Local data units are set for each acquisition area, and the scene data of the same type in adjacent spatial locations are compared. Based on the comparison results, calibration scene datasets for each acquisition area under various data acquisition cycles are obtained. Corresponding regional data chains are established based on the calibration scene datasets under various data acquisition cycles. The data chains of each central region and the secondary region data chains are connected to obtain the origin center data region until the data origin center stops shifting. This achieves improved sensor data transmission efficiency and ensures the accuracy of data transmission results, while also enabling data visualization of sensor data. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention.

[0038] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this 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: Divide the collection area into several collection areas and set up multiple sensors in each collection area. Locate multiple data origin centers in the collection area. Then, as the data origin centers move in the collection area, each sensor collects the scene dataset of its collection area under each data collection cycle.

[0043] Step S2: Set up local data units for each acquisition area, compare the acquisition areas in adjacent spatial locations with the same type of scene data in the scene dataset, and obtain the calibration scene dataset for each acquisition area under each data acquisition cycle based on the comparison results.

[0044] Step S3: Establish corresponding regional data chains based on the calibration scenario dataset under each data acquisition cycle. The regional data chain corresponding to the acquisition area where the data origin center is located is recorded as the central regional data chain, and the regional data chains corresponding to the other acquisition areas are recorded as secondary regional data chains. Connect each central regional data chain and secondary regional data chain to obtain the origin center data area until the data origin center stops shifting.

[0045] Example 2

[0046] This embodiment further defines embodiment 1, and step S1 is implemented through the following process:

[0047] The application scenario is divided into several collection areas of equal size, and each collection area is assigned the numbers a1, a2, a3, ..., an And several types of sensors are set up in 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] Within the application scenario, several dynamic objects are set up to perform fixed actions. For example, when the application scenario is a smart warehouse scenario, the dynamic objects are warehouse robots, and their fixed actions are moving goods.

[0049] Locate multiple dynamic objects within the application scenario, and designate each dynamic object as a data origin center, assigning them the numbers b1, b2, b3, ..., b m m is a natural number greater than 0;

[0050] Before each data origin center moves within the application scenario, the current collection area where the data origin center is located is recorded as the central collection area, and the collection area that does not contain 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] Set the same data acquisition cycle and data acquisition range for each sensor in each acquisition area, and set the unit moving speed for each data origin center. The length of the data acquisition cycle is equal to five to ten times the time it takes for the data origin center to pass through any acquisition area at the unit moving speed. The data acquisition range of each sensor in each acquisition area includes its corresponding acquisition area. For example, a sensor in one acquisition area can collect data from its eight adjacent acquisition areas.

[0052] Then, when the data origin center moves, the sensors in each acquisition area collect various scene data in their respective acquisition areas and label the scene data with the corresponding acquisition area number. Then, when the data acquisition cycle ends, the current data acquisition cycle is integrated to generate all scene data to generate a scene dataset, and the acquisition area number of the center position and the current label are labeled on the scene dataset. The scene data includes video data and chart data.

[0053] It should be noted that as the data origin center moves continuously within the application scenario, the collection area where the data origin center is located also changes. Therefore, the central collection area associated with the data origin center also changes accordingly. Furthermore, when the location of the data origin center changes, the collection area that was originally marked as the central collection area will be marked as a secondary collection area.

[0054] Example 3

[0055] This embodiment further defines embodiment 1, and step S2 is implemented through the following process:

[0056] Each acquisition area is bound to a local data unit, and the local data units are connected to communicate through the local area network. Whenever an acquisition area generates a scene dataset, each local data unit shares data with the local data units in the adjacent spatial location of its acquisition area, and then sends the scene dataset generated in the current data acquisition cycle to the local data units in the adjacent spatial location.

[0057] When the next data collection cycle is carried out in each collection area, each local data unit records the scene dataset of its collection area as the verification scene dataset, and then compares the verification scene dataset with data of the same number and the same state 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 several scene image data according to time, and each scene image data is divided into several image regions;

[0059] The comparison process involves comparing image regions from different scene datasets that correspond to the same scene image data at the same location.

[0060] The system obtains the pixel values ​​of each pixel in the image region from different scene datasets, and then obtains the similarity between the pixel values ​​of pixels at the same position in each image region. A similarity threshold is set. If the similarity between the pixel values ​​of two pixels is greater than or equal to the similarity threshold, the two pixels are considered similar; otherwise, they are considered dissimilar.

[0061] The similarity relationships between each pixel are merged, and then the pixel with the most similar relationships is selected as the calibration pixel at the corresponding position. It should be noted that if the comparison results indicate that the pixels are not similar to each other, then the pixel in the calibration scene dataset is selected as the calibration pixel.

[0062] The calibration pixels corresponding to each scene area are stitched together sequentially to obtain the calibration scene data of each acquisition area, which is video data under the corresponding data acquisition period.

[0063] Furthermore, for scenario data of the type of chart data, a two-dimensional coordinate system is established for each local data unit. Scenario data from different scenario datasets but corresponding to the same collection area are mapped onto the same two-dimensional coordinate system. Each scenario data is divided into several data points. The distance values ​​between data points corresponding to different scenario data under the same horizontal axis are obtained. The sum of the distance values ​​between each data point and other data points is calculated. Then, the data point with the smallest sum of distance values ​​is selected as the calibration data point under the corresponding horizontal axis.

[0064] If multiple data points have the smallest sum of distance values ​​at the same time, then one of the data points is randomly selected as the calibration data point under the corresponding x-axis.

[0065] By connecting the calibration data points under each horizontal axis in sequence, the calibration scenario data of each collection area, which is chart data, is obtained in the corresponding data collection period.

[0066] All calibration scenario data are integrated, and then each local data unit obtains the calibration scenario dataset from the previous data acquisition cycle, and marks the acquisition area number and the current labeled data.

[0067] Example 4

[0068] This embodiment further defines embodiment 1, and step S3 is implemented through the following process:

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

[0070] During the next data acquisition cycle, each calibration scenario data in the calibration scenario dataset is stored sequentially in each data storage node. Based on the calibration scenario data generated in the previous data acquisition cycle, each local data unit associated with the secondary acquisition area in the previous data acquisition cycle sends its currently generated regional data chain to the local data unit associated with the central acquisition area.

[0071] Each central data collection area is associated with a local data unit that generates a regional data chain, which is called the central regional data chain. The regional data chain corresponding to the secondary data collection area is called the secondary regional data chain.

[0072] First, the data chains of each central region are connected end to end according to the distribution of the corresponding central collection areas in the application scenario to obtain the origin center data region. The origin center data region contains only the central region associated nodes outside and the data storage nodes inside.

[0073] Then, the secondary region association nodes in each secondary collection area are sequentially connected to the central region association nodes of the origin center data area according to the distribution in the application scenario, thereby obtaining the scene data chain of the corresponding data origin center under the corresponding data collection cycle.

[0074] If the data origin center's location changes during the previous data collection cycle, the scene data chain generated by the local data unit associated with the central collection area in the previous data collection cycle will be sent to the local data unit marked as associated with the central collection area in the previous data collection cycle. Then, the central area associated nodes in the scene data chains of each data collection cycle will be connected sequentially.

[0075] Repeat the process of connecting the scenario data links in each of the above data collection cycles until the data origin center stops shifting.

[0076] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A sensor data storage method based on a local area network, characterized in that, Includes the following steps: Step S1: Divide the application scenario into several acquisition areas and set up multiple sensors in each acquisition area. Locate multiple data origin centers in the acquisition area. As the data origin centers move in the acquisition area, each sensor collects the scene dataset of its acquisition area under each data acquisition cycle. In this process, locate multiple scene dynamic objects in the application scenario and record each scene dynamic object as a data origin center. Before each data origin center moves in the application scenario, record the acquisition area where the data origin center is currently located as the central acquisition area and the acquisition area that does not contain the data origin center as the secondary acquisition area. Set the same data acquisition cycle and data acquisition range for each sensor in each acquisition area and set the unit movement speed for each data origin center. Step S2: Set up local data units for each acquisition area, compare the scene data of the same type in the scene dataset of the acquisition areas in adjacent spatial locations, and obtain the calibration scene dataset of each acquisition area in each data acquisition cycle based on the comparison results. Here, each acquisition area is bound to a local data unit, and the local data units are connected to each other through the local area network. Whenever the acquisition area generates a scene dataset, each local data unit shares data with the local data units in the adjacent spatial locations of its acquisition area, and then sends the scene dataset generated in the current data acquisition cycle to the local data units in the adjacent spatial locations. Step S3: Establish corresponding regional data chains based on the calibration scenario datasets under each data acquisition cycle. The regional data chain corresponding to the acquisition area where the data origin center is located is designated as the central regional data chain, and the regional data chains corresponding to the other acquisition areas are designated as secondary regional data chains. Connect the central regional data chains and secondary regional data chains to obtain the origin center data region, until the data origin center stops shifting. The process of establishing the origin center data region includes: At the end of each data acquisition cycle, each local data unit simultaneously generates a regional association node and labels the regional association node with the corresponding acquisition area number. At the same time, several data storage nodes are set according to the number of data types in the calibration scenario dataset, and each data storage node is connected with the corresponding regional association node to obtain a regional data chain. During the next data acquisition cycle, each calibration scenario data in the calibration scenario dataset is stored sequentially in each data storage node. Based on the calibration scenario data generated in the previous data acquisition cycle, each local data unit associated with the secondary acquisition area in the previous data acquisition cycle sends its currently generated regional data chain to the local data unit associated with the central acquisition area. Each central data collection area is associated with a local data unit that generates a regional data chain, which is called the central regional data chain. The regional data chain corresponding to the secondary data collection area is called the secondary regional data chain. First, the data chains of each central region are connected end to end according to the distribution of the corresponding central collection areas in the application scenario to obtain the origin center data region. The origin center data region contains only the central region associated nodes outside and the data storage nodes inside. Then, the secondary region association nodes in each secondary collection area are sequentially connected to the central region association nodes of the origin center data area according to the distribution in the application scenario, thereby obtaining the scene data chain of the corresponding data origin center under the corresponding data collection cycle.

2. The 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: The application scenario is divided into several collection areas of the same size, and each collection area is assigned a number and several types of sensors are set in each collection area.

3. The sensor data storage method based on a local area network according to claim 2, characterized in that, The process of collecting the scene dataset includes: When the data origin center moves, sensors in each acquisition area collect various scene data in their respective acquisition areas and label the scene data with the corresponding acquisition area number. Then, when the data acquisition cycle ends, the current data acquisition cycle is integrated to generate all scene data to generate a scene dataset, and the acquisition area number of the center position is labeled on the scene dataset. The scene data includes video data and chart data.

4. The sensor data storage method based on a local area network according to claim 3, characterized in that, The process of comparing datasets from different scenarios includes: When the next data collection cycle is carried out in each collection area, each local data unit records the scene dataset of its collection area as the verification scene dataset, and then compares the verification scene dataset with data of the same number and the same state in other local scene areas. For scene data of the type of video data, the pixel values ​​of each pixel in the image region from different scene datasets are obtained, and then the similarity between the pixel values ​​of pixels at the same position in each image region is obtained in turn. A similarity threshold is set. If the similarity between the pixel values ​​of two pixels is greater than or equal to the similarity threshold, then the corresponding two pixels are similar; otherwise, they are judged to be dissimilar. The similarity relationships between pixels are merged, and the pixel with the most similar relationships is selected as the calibration pixel at the corresponding position. The calibration pixels of each scene area are then stitched together in sequence to obtain the calibration scene data of each acquisition area, which is video data under the corresponding data acquisition period.

5. A sensor data storage method based on a local area network according to claim 4, characterized in that, The process of comparing datasets from different scenarios also includes: For scenario data of the type of chart data, a two-dimensional coordinate system is established for each local data unit. Scenario data from different scenario datasets but corresponding to the same collection area are mapped onto the same two-dimensional coordinate system. Each scenario data is divided into several data points. The distance values ​​between data points corresponding to different scenario data under the same horizontal axis are obtained. The sum of the distance values ​​between each data point and other data points is calculated. Then, the data point with the smallest total distance value is selected as the calibration data point under the corresponding horizontal axis. If multiple data points have the smallest sum of distance values ​​at the same time, one data point is randomly selected as the calibration data point under the corresponding horizontal axis. The calibration data points under each horizontal axis are connected in sequence to obtain the calibration scenario data of each collection area under the corresponding data collection period, where the data type is chart data. All calibration scenario data are integrated, and then each local data unit obtains the calibration scenario dataset from the previous data acquisition cycle and labels the acquisition area number.

6. The sensor data storage method based on a local area network according to claim 5, characterized in that, If the data origin center's location changes during the previous data collection cycle, the scene data chain generated by the local data unit associated with the central collection area in the previous data collection cycle will be sent to the local data unit marked as associated with the central collection area in the previous data collection cycle. Then, the central area associated nodes in the scene data chains of each data collection cycle will be connected sequentially.

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