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CN-122027551-A - Remote data transmission system based on Internet of things

CN122027551ACN 122027551 ACN122027551 ACN 122027551ACN-122027551-A

Abstract

The invention discloses a remote data transmission system based on the Internet of things, which relates to the technical field of Internet of things communication and comprises a summary data generation module, a disconnection module and a link recovery module, wherein the summary data generation module is used for collecting original sensing data for carrying out real-time feature analysis, identifying the change trend of the original sensing data compared with a historical data baseline, combining a preset redundancy judgment rule to generate structured feature summary data, the disconnection module is used for triggering a local caching mechanism of the disconnection network if detecting the link interruption of a data transmission gateway, dividing the structured feature summary data into a key priority queue and a common priority queue for storage according to a data sensitivity threshold, and the link recovery module is used for receiving a confirmation instruction returned by a cloud server after link recovery connection, preferentially sending the structured feature summary data in the key priority queue and calculating the difference value of the structured feature summary data in the key priority queue of a current period and a previous period. The invention improves the transmission efficiency after the link recovery.

Inventors

  • YANG TIANLE

Assignees

  • 徐州心晨科技有限公司

Dates

Publication Date
20260512
Application Date
20260304

Claims (10)

  1. 1. A remote data transmission system based on the internet of things, comprising: The device comprises a summary data generation module, a network disconnection module and a link recovery module; The link recovery module is electrically connected with the network disconnection module; The abstract data generation module is used for collecting original sensing data for real-time feature analysis, identifying the change trend of the original sensing data compared with the historical data baseline, and generating structural feature abstract data by combining with a preset redundancy judgment rule; The network disconnection module monitors network quality parameters of the current data transmission gateway link in real time, and if the data transmission gateway link is detected to be interrupted, triggers a network disconnection local caching mechanism, and divides the structured feature abstract data into a key priority queue and a common priority queue for storage according to a data sensitivity threshold; The link recovery module is used for receiving a confirmation instruction returned by the cloud server after the link recovery connection, preferentially sending the structured feature abstract data in the key priority queue, calculating the difference value of the structured feature abstract data in the key priority queue of the current period and the key priority queue of the previous period, substituting the difference value into the corresponding threshold interval, and generating a data frame adapting to the current link bandwidth for uploading.
  2. 2. The remote data transmission system based on the internet of things according to claim 1, wherein the summary data generation module specifically comprises: Based on an acceleration sensor, a temperature sensor and a current sensor, setting the length and sampling frequency of a fixed sliding time window, collecting original physical quantity data in the time window, aligning a time stamp, preprocessing the data, calculating a data statistics baseline in the time window, calculating the average value of data points in the current time window according to the historical data baseline in the last time window by utilizing an exponential weighted moving average formula aiming at each newly arrived physical quantity data point, and obtaining a dynamically updated data statistics baseline.
  3. 3. The remote data transmission system based on the internet of things according to claim 2, wherein the summary data generation module further comprises: comparing the original physical quantity data in the time window with a data statistics baseline updated dynamically, reading a preset compression threshold, and if a new data record is started, taking a newly arrived first data point as a first archiving point, and calculating an upper threshold line and a lower threshold line according to the preset compression threshold; the preset compression threshold is the allowable floating error of the original physical quantity data relative to the dynamic updated data statistical base line in the time window.
  4. 4. A remote data transmission system based on the internet of things according to claim 3, wherein the summary data generating module further comprises: Calculating the slope of the real-time data point relative to the first archiving point and the upper and lower threshold lines for each newly arrived real-time data point, continuously carrying out iterative updating to obtain a maximum upper threshold slope and a minimum lower threshold slope, taking a sector area constructed by the maximum upper threshold slope and the minimum lower threshold slope as a redundancy judgment basis, when the maximum upper threshold slope is greater than or equal to the minimum lower threshold slope by the new data point, reserving the previous data point as a characteristic data point, removing the rest data points covered by the sector area as redundancy data, arranging the reserved characteristic data points in a time stamp sequence, combining a dynamic updated data statistical baseline in a corresponding time window, and generating the structured characteristic abstract data comprising a data identifier, a time interval, a statistical baseline parameter and the characteristic data point.
  5. 5. The remote data transmission system based on the internet of things according to claim 4, wherein the network breaking module specifically comprises: Based on the monitoring of the link state of the data transmission gateway, sending heartbeat packets to a cloud server through a network at regular time, sending a confirmation signal to the data transmission gateway, confirming that the link state of the data transmission gateway is normal, monitoring network quality parameters of the current data transmission gateway link in real time, judging that the link of the data transmission gateway is interrupted if the network quality parameters are detected to be lower than a set threshold h, and immediately triggering a network disconnection local caching mechanism; The heartbeat packet comprises a data identifier and a time interval.
  6. 6. The internet of things-based remote data transmission system of claim 5, wherein the network breaking module further comprises: Based on triggering a network disconnection local caching mechanism, extracting data identification, a time interval, statistical baseline parameters and characteristic data points according to structured characteristic abstract data, setting a scoring rule, if the data identification is alarm information, dividing a judgment basis into 1, otherwise, dividing a judgment basis into 0, if the absolute value of a data change slope is greater than a preset absolute value threshold f of the data change slope, dividing a judgment slope contribution into 2, and accumulating to obtain a structured characteristic abstract data sensitivity score; Comparing the structured feature summary data sensitivity score with a data sensitivity threshold, if the structured feature summary data sensitivity score is greater than or equal to the data sensitivity threshold, classifying the data into a key priority queue, and if the structured feature summary data sensitivity score is less than the data sensitivity threshold, classifying the data into a common priority queue.
  7. 7. The internet of things-based remote data transmission system of claim 6, wherein the network breaking module further comprises: The method comprises the steps of realizing local caching by using a double-area storage architecture, storing a key priority queue in a high-speed nonvolatile memory, ensuring that data is not lost when power is off, and the read-write response speed is less than or equal to 10ms, storing a common priority queue in an expansion storage device, adopting a cyclic overwriting writing mechanism, and automatically deleting structured feature abstract data in the earliest common priority queue according to the time stamp sequence when the storage space utilization reaches a preset upper limit of 80%.
  8. 8. The remote data transmission system based on the internet of things according to claim 7, wherein the link recovery module specifically comprises: Based on the continuous monitoring of the link state of the data transmission gateway, once the link recovery is detected, the secure connection is established with the cloud server through the TCP handshake reestablishment protocol, the confirmation instruction returned by the cloud server is received, the data transmission gateway locates the last data packet ID successfully uploaded last time as a breakpoint start, and the unacknowledged structured feature abstract data in the key priority queue is preferentially sent.
  9. 9. The internet of things-based remote data transmission system of claim 8, wherein the link recovery module further comprises, within the link recovery module: And calculating the average value and the peak value of the structured feature abstract data of the key queue data of the current period by taking the fixed sliding time window length as one period to obtain the feature vector of the key queue data of the current period.
  10. 10. The internet of things-based remote data transmission system of claim 9, wherein the link recovery module further comprises, within the link recovery module: The characteristic vector of the key queue data of the current period is subjected to differential comparison with the characteristic vector of the key queue data of the previous period, euclidean distance between the characteristic vector of the key queue data of the current period and the characteristic vector of the key queue data of the previous period is calculated to be used as a differential value, the differential value is compared with a preset threshold value, if the differential value is lower than a first threshold value, the data is judged to be in a static period, a state maintaining instruction is generated to be used as new structured characteristic abstract data for uploading, and if the differential value is higher than a second threshold value, the data is judged to be in a dynamic period; based on the data in the dynamic period, the real-time bandwidth of the current link is acquired through the network speed measuring unit, the size and the sampling frequency of the data frame are dynamically adjusted, and the data frame adapting to the bandwidth of the current link is generated for uploading.

Description

Remote data transmission system based on Internet of things Technical Field The invention relates to the technical field of communication of the Internet of things, in particular to a remote data transmission system based on the Internet of things. Background The conventional remote data transmission system of the Internet of things generally adopts a timed reporting or continuous online transmission mode, has obvious defects in practical application, is used for directly transmitting massive original sensing data, has extremely high consumption on bandwidth and storage resources, is easy to cause key data loss or cache overflow under the condition of network disconnection, is usually only used for simply carrying out breakpoint continuous transmission or full-quantity retransmission due to lack of an intelligent transmission strategy after a link is recovered, cannot carry out self-adaptive adjustment according to dynamic change of data content and real-time bandwidth of the link, and is used for leading key characteristic data to be unable to be reported preferentially when the bandwidth is tense or still transmitting a large amount of redundant information when the data is in a static period, so that network resource waste and key information delay are caused, and the overall reliability and transmission efficiency of the system are needed to be improved. Disclosure of Invention In order to solve the technical problems, the technical scheme solves the problems by providing a remote data transmission system based on the Internet of things. In order to achieve the above purpose, the invention adopts the following technical scheme: a remote data transmission system based on the internet of things, comprising: The device comprises a summary data generation module, a network disconnection module and a link recovery module; The link recovery module is electrically connected with the network disconnection module; The abstract data generation module is used for collecting original sensing data for real-time feature analysis, identifying the change trend of the original sensing data compared with the historical data baseline, and generating structural feature abstract data by combining with a preset redundancy judgment rule; The network disconnection module monitors network quality parameters of the current data transmission gateway link in real time, and if the data transmission gateway link is detected to be interrupted, triggers a network disconnection local caching mechanism, and divides the structured feature abstract data into a key priority queue and a common priority queue for storage according to a data sensitivity threshold; The link recovery module is used for receiving a confirmation instruction returned by the cloud server after the link recovery connection, preferentially sending the structured feature abstract data in the key priority queue, calculating the difference value of the structured feature abstract data in the key priority queue of the current period and the key priority queue of the previous period, substituting the difference value into the corresponding threshold interval, and generating a data frame adapting to the current link bandwidth for uploading. Preferably, the summary data generating module specifically includes: Based on an acceleration sensor, a temperature sensor and a current sensor, setting the length and sampling frequency of a fixed sliding time window, collecting original physical quantity data in the time window, aligning a time stamp, preprocessing the data, calculating a data statistics baseline in the time window, calculating the average value of data points in the current time window according to the historical data baseline in the last time window by utilizing an exponential weighted moving average formula aiming at each newly arrived physical quantity data point, and obtaining a dynamically updated data statistics baseline. Preferably, the summary data generating module further includes: comparing the original physical quantity data in the time window with a data statistics baseline updated dynamically, reading a preset compression threshold, and if a new data record is started, taking a newly arrived first data point as a first archiving point, and calculating an upper threshold line and a lower threshold line according to the preset compression threshold; the preset compression threshold is the allowable floating error of the original physical quantity data relative to the dynamic updated data statistical base line in the time window. Preferably, the summary data generating module further includes: Calculating the slope of the real-time data point relative to the first archiving point and the upper and lower threshold lines for each newly arrived real-time data point, continuously carrying out iterative updating to obtain a maximum upper threshold slope and a minimum lower threshold slope, taking a sector area constructed by the maximum upper threshold slope a