Data transmission method and device, DTU and cloud

Abstract

The invention relates to a data transmission method and device, a DTU and a cloud, and the method comprises the steps: obtaining original perception data collected in a preset time period; dividing the original sensing data according to a preset importance degree division rule to obtain high-priority data, medium-priority data and low-priority data; the high-priority data, the middle-priority data and the low-priority data are compressed according to different compression strategies respectively, a high-priority data packet, a middle-priority data packet and a low-priority data packet are obtained, and the compression ratios of the compression strategies corresponding to the high-priority data, the middle-priority data and the low-priority data are sequentially increased; and uploading the low-priority data packet, the medium-priority data packet and the high-priority data packet to the cloud. According to the embodiment of the invention, the method can prevent the perception data with low importance from wasting transmission resources, reserves more transmission resources for the perception data with high importance, and guarantees the timely, accurate, smooth and efficient transmission of the perception data with high importance.

Description

Technical Field

[0001] The present application relates to the field of industrial Internet of Things, and in particular to a data transmission method, device, DTU and cloud. Background Art

[0002] The DTU (Data Transfer Unit) module is a key communication component in the Industrial Internet of Things (IIoT) system, widely used in scenarios such as environmental monitoring, power equipment, and industrial control. It is used to upload data collected by edge devices to cloud platforms via cellular networks. Traditional DTU communication processes often follow a fixed "collect → compress → upload" process. Compression strategies are typically statically configured and lack the ability to determine data value. Consequently, all data is compressed and uploaded uniformly.

[0003] In real-world applications, data generated on industrial sites contains significant amounts of redundancy, duplication, and flat trends, while critical, high-priority data requires high real-time transmission and integrity. Traditional solutions fail to distinguish these differences, leading to the following issues: wasted network bandwidth and high traffic costs; delayed or compressed real-time, high-priority data; data congestion or upload failures due to network instability; and a lack of fault tolerance in single-link transmission. Summary of the Invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a data transmission method, device, DTU and cloud.

[0005] In a first aspect, the present application provides a data transmission method, applied to a data transmission unit (DTU), the method comprising:

[0006] Obtaining raw sensory data collected within a preset time period;

[0007] Dividing the original perception data according to a preset importance classification rule to obtain high priority data, medium priority data and low priority data;

[0008] Compressing the high-priority data, medium-priority data, and low-priority data respectively according to different compression strategies to obtain a high-priority data packet, a medium-priority data packet, and a low-priority data packet, wherein the compression ratios of the compression strategies corresponding to the high-priority data, the medium-priority data, and the low-priority data increase in sequence;

[0009] The low-priority data packet, the medium-priority data packet, and the high-priority data packet are uploaded to the cloud.

[0010] Optionally, the original perception data is divided according to a preset importance division rule to obtain high-priority data, including:

[0011] extracting numerical data from the original perception data;

[0012] Determining the rate of change and fluctuation range of the numerical data;

[0013] If any data value in the numerical data is outside the preset threshold range, the change rate is outside the preset change rate range, or the fluctuation amplitude is outside the preset fluctuation range, the numerical data is determined to be the high priority data.

[0014] Optionally, the original perception data is divided according to a preset importance division rule to obtain high-priority data, including:

[0015] extracting state data and event data from the original perception data;

[0016] The state-type data and the event-type data are determined as the high-priority data.

[0017] Optionally, the original perception data is divided according to a preset importance division rule to obtain medium priority data, including:

[0018] If the change rate of the numerical data in the original perception data is within a preset change rate range, all data values ​​in the numerical data are within a preset threshold range, or the fluctuation amplitude of the numerical data is within a preset fluctuation range, determining a change trend function and inflection point data of the numerical data;

[0019] The change trend function and the inflection point data are determined as the medium priority data.

[0020] Optionally, the original perception data is divided according to a preset importance division rule to obtain low-priority data, including:

[0021] Determining whether there is redundant data and / or duplicate data in the numerical data of the original perception data;

[0022] If redundant data and / or duplicate data exist in the original perception data, the redundant data and / or duplicate data are determined as the low-priority data.

[0023] Optionally, uploading the low-priority data packet and the high-priority data packet to the cloud includes:

[0024] Uploading the low-priority data to the cloud using the first transmission link;

[0025] Uploading the medium-priority data packet to the cloud using a second transmission link, where the link score of the second transmission link is higher than the link score of the first transmission link;

[0026] The high-priority data packet is uploaded to the cloud using a third transmission link, where the link score of the third transmission link is higher than the link score of the second transmission link.

[0027] Optionally, the method further includes:

[0028] Obtain link quality, power consumption, and CPU resource utilization of multiple transmission links;

[0029] A link score for each transmission link is determined according to the link quality, power consumption, and CPU resource utilization.

[0030] In a second aspect, the present application provides a data transmission method, applied to the cloud, comprising:

[0031] receiving the low-priority data packet, the medium-priority data packet, and the high-priority data packet from the DTU;

[0032] The low priority data packet, the medium priority data packet and the high priority data packet are decompressed using different decompression strategies to obtain low priority data, medium priority data and high priority data.

[0033] Optionally, the method further includes:

[0034] Obtaining network occupancy status of the low-priority data packet, the medium-priority data packet, and the high-priority data packet;

[0035] Determining a decompression and restoration status according to the low-priority data, the medium-priority data, and the high-priority data;

[0036] Generate a compression optimization instruction according to the network occupancy status and the decompression and restoration status;

[0037] Send the compression optimization instruction to the DTU.

[0038] Optionally, the method further includes:

[0039] determining data classification accuracy based on the low-priority data, the medium-priority data, and the high-priority data;

[0040] Generating a grading optimization instruction for the preset importance classification rule according to the data grading accuracy;

[0041] Send the hierarchical optimization instruction to the DTU.

[0042] In a third aspect, the present application provides a data transmission device, applied to a data transmission unit DTU, the device comprising:

[0043] An acquisition module is used to acquire raw perception data collected within a preset time period;

[0044] A classification module is used to classify the original perception data according to a preset importance classification rule to obtain high priority data, medium priority data and low priority data;

[0045] a compression module, configured to compress the high-priority data, the medium-priority data, and the low-priority data according to different compression strategies to obtain a high-priority data packet, a medium-priority data packet, and a low-priority data packet, wherein the compression ratios of the compression strategies corresponding to the high-priority data, the medium-priority data, and the low-priority data increase in sequence;

[0046] The uploading module is used to upload the low-priority data packet, the medium-priority data packet and the high-priority data packet to the cloud.

[0047] In a fourth aspect, the present application provides a data transmission device for use in the cloud, the device comprising:

[0048] A receiving module, configured to receive the low-priority data packet, the medium-priority data packet, and the high-priority data packet from a DTU;

[0049] The decompression module is used to decompress the low priority data packet, the medium priority data packet and the high priority data packet using different decompression strategies to obtain low priority data, medium priority data and high priority data.

[0050] In a fifth aspect, the present application provides a DTU, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0051] Memory for storing computer programs;

[0052] The processor is configured to implement any of the data transmission methods described in the first aspect when executing a program stored in the memory.

[0053] In a sixth aspect, the present application provides a cloud comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0054] Memory for storing computer programs;

[0055] The processor is configured to implement any of the data transmission methods described in the second aspect when executing a program stored in the memory.

[0056] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art:

[0057] The embodiment of the present application divides the original perception data according to a preset importance classification rule to obtain high-priority data, medium-priority data and low-priority data, and then compresses the high-priority data, medium-priority data and low-priority data according to the compression ratio, and then uploads the compressed low-priority data packet, medium-priority data packet and high-priority data packet to the cloud. By distinguishing perception data of different importance and differentially compressing and uploading the perception data of different importance according to different compression ratios, waste of transmission resources for perception data of low importance is avoided, and more transmission resources are reserved for perception data of high importance, thereby ensuring timely, accurate, smooth and efficient transmission of perception data of high importance. Moreover, by using a compression strategy with a low compression ratio to compress high-priority data, compression loss of perception data of high importance can be reduced or avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0060] Figure 1 A flowchart of a data transmission method provided in an embodiment of the present application;

[0061] Figure 2 A flowchart of another data transmission method provided in an embodiment of the present application;

[0062] Figure 3 A structural diagram of a data transmission device provided in an embodiment of the present application;

[0063] Figure 4 A structural diagram of another data transmission device provided in an embodiment of the present application;

[0064] Figure 5 This is a structural diagram of a DTU or cloud provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0066] In actual applications, the data generated by industrial sites contains a large amount of redundancy, duplication, and stable trends, while critical high-priority data requires high real-time and integrity transmission. Traditional solutions cannot distinguish these differences, resulting in the following problems: network bandwidth is wasted and traffic costs are high; real-time high-priority data is delayed or compressed and lost; data is blocked or upload fails when the network is unstable; and single-link transmission lacks fault tolerance. To this end, the embodiments of the present application provide a data transmission method, device, DTU, and cloud.

[0067] The embodiment of the present application provides a data transmission method, which is applied to a data transmission unit DTU. The DTU is set in an edge device or can be set outside the edge device. The DTU includes a data acquisition module, an edge intelligent analysis module, a compression module, a communication module (supporting multiple links) and an upload scheduling module. Figure 1 As shown, the method includes:

[0068] Step S101, obtaining raw sensing data collected within a preset time period;

[0069] In an embodiment of the present application, the data acquisition module can collect raw perception data continuously or at preset time intervals through interfaces such as serial ports and buses; the preset time period can be a time period before the current moment, such as: the past 0.5 seconds, the past 1 second, the past 3 seconds, the past 10 seconds, etc.; the raw perception data includes: numerical data (Numeric Data), state data (State Data) and event data (Event Data), etc.

[0070] Numerical data refers to continuous, quantifiable sensor data with temporal trends, which can be used for curve reconstruction and prediction. Examples include: temperature sensor: 23.6°C → 24.0°C → 24.2°C; current sampling value: 1.12A → 1.15A → 1.18A; humidity: 45.2% → 46.0% → 47.3%.

[0071] State data refers to the operating state or signal flag of a device with a limited number of state transitions. It is usually an enumeration or Boolean value and reflects the current operating mode or switch status of the system. Examples include: Air conditioning mode: Cooling → Standby → Heating; Fan switch status: ON → OFF → ON; Network connection status: Connected → Disconnected.

[0072] Event-based data refers to data changes that occur at specific moments and are not persistent. It is often used to record important triggers, alarms, or control operations and has a clear timestamp. Examples include: Alarm event: 2025-06-23 10:21:33, temperature exceeded the limit; User operation: 2025-06-23 08:12:01, remote power on; System reboot: 2025-06-22 23:59:59, abnormal reboot.

[0073] In this step, the original perception data whose collection moment is within the preset time period can be obtained.

[0074] Step S102, dividing the original perception data according to a preset importance classification rule to obtain high priority data, medium priority data and low priority data;

[0075] In an embodiment of the present application, the preset importance division rule may refer to a rule for dividing the original perception data into data slices of different priorities according to the importance of each data in the original perception data, so that data of different importance levels can be compressed and uploaded separately, avoiding wasting transmission resources for unimportant data and leaving more transmission resources for important data.

[0076] Among them, high-priority data can refer to data with the highest importance (such as: critical data such as anomalies, alarms, real-time control, etc.), such as: abnormal data in status data, event data and numerical data; medium-priority data refers to data with medium importance, such as: normal data in numerical data; low-priority data refers to data with lower importance (stable, repeated or ordinary monitoring data with stable trends), such as: redundant data and repeated data in numerical data, etc.

[0077] On the edge device side, the DTU can perform real-time analysis of raw sensory data by integrating a lightweight rule engine or pre-trained small models (such as decision trees and RNNs). The rule engine can determine anomalies, trend changes, or data stability based on set thresholds and rates of change. The model predicts trends and identifies mutations based on historical data sequences. The input is raw sensory data and historical window values, and the output is a data value label (anomaly / trend / stability) and a recommended compression strategy, enabling intelligent screening and differentiated processing.

[0078] In this step, numerical data, state data, event data, etc. in the original perception data can be divided into high priority data, medium priority data, and low priority data according to preset importance classification rules.

[0079] In one embodiment of the present application, numerical data can be extracted from the original perception data; the change rate and fluctuation amplitude of the numerical data are determined; if any data value in the numerical data is outside the preset threshold range, the change rate is outside the preset change rate range, or the fluctuation amplitude is outside the preset fluctuation range, the numerical data is determined to be the high-priority data.

[0080] In another embodiment of the present application, state-type data and event-type data may be extracted from the original perception data; and the state-type data and the event-type data may be determined as the high-priority data.

[0081] In another embodiment of the present application, if the change rate of the numerical data in the original perception data is within a preset change rate range, all data values ​​in the numerical data are within a preset threshold range, or the fluctuation amplitude of the numerical data is within a preset fluctuation range, the change trend function and inflection point data of the numerical data are determined; and the change trend function and the inflection point data are determined as the medium priority data.

[0082] In another embodiment of the present application, it can be determined whether there is redundant data and / or duplicate data in the numerical data in the original perception data; if there is redundant data and / or duplicate data in the original perception data, the redundant data and / or duplicate data are determined as the low-priority data.

[0083] Redundant data refers to data that exceeds actual needs and can be simplified or eliminated, with the core concept being "low necessity." Examples include excessive sampling frequency resulting in "excess data" (e.g., collecting temperature once every 1 second, when a 10-second measurement is sufficient to reflect the status); or derived data containing duplicate information (e.g., collecting both a "pressure value" and a "pressure level description," where the latter can be derived from the former).

[0084] Duplicate data refers to data with completely identical or highly similar content, with high consistency being the core. For example, the same sensor may acquire the same temperature value multiple times (e.g., 25.0°C for five consecutive times), or data from the same source may be acquired by different devices (e.g., two adjacent sensors acquire identical ambient humidity values).

[0085] Step S103, compressing the high priority data, medium priority data and low priority data according to different compression strategies to obtain a high priority data packet, a medium priority data packet and a low priority data packet;

[0086] In an embodiment of the present application, the compression ratios of the compression strategies corresponding to the high-priority data, medium-priority data and low-priority data increase successively. The compression strategy for high-priority data may refer to a strategy of no compression or low compression, such as using bitmap compression for state-type data, and the compression ratio is exemplarily between 2:1 and close to 1:1 to avoid data loss; the compression strategy for medium-priority data may refer to a strategy of medium compression, and the compression ratio is exemplarily between 10:1 and 2:1 to balance the degree of data loss and transmission resource consumption; the compression strategy for low-priority data may refer to a strategy of high compression, such as differential coding or Huffman, and the compression ratio is exemplarily between 100:1 and 10:1 to save transmission resources.

[0087] In this step, the compression strategies corresponding to different priority data (high priority data, medium priority data and low priority data) can be obtained respectively, and the corresponding priority data can be compressed according to the compression strategies to obtain high priority data packets, medium priority data packets and low priority data packets.

[0088] Step S104: Upload the low-priority data packet, the medium-priority data packet, and the high-priority data packet to the cloud.

[0089] In this step, low-priority data packets, medium-priority data packets, and high-priority data packets can be uploaded to the cloud through the same or different transmission links. High-priority data packets need to be uploaded immediately, while low-priority data packets can be delayed, cached, batched, or uploaded with high compression.

[0090] When uploading data packets of different priorities using different transmission links, the system can periodically collect status information on each available link, including bandwidth, latency, signal strength, power consumption, and cost, to allocate different transmission links to each priority. Based on a preset weighted scoring model, each link is comprehensively scored, and a link priority table is dynamically generated. Links with better performance and lower costs are then prioritized for uploading data.

[0091] Calculate link scores of multiple communication links. Specifically, obtain link quality, power consumption, and CPU resource utilization of multiple transmission links; and determine a link score for each transmission link based on the link quality, power consumption, and CPU resource utilization.

[0092] In one embodiment of the present application, the low-priority data can be uploaded to the cloud using a first transmission link; the medium-priority data packet can be uploaded to the cloud using a second transmission link, and the link score of the second transmission link is higher than the link score of the first transmission link; and the high-priority data packet can be uploaded to the cloud using a third transmission link, and the link score of the third transmission link is higher than the link score of the second transmission link.

[0093] DTU can dynamically determine the data upload path based on the current link status (such as the availability and quality of cellular, Wi-Fi, LoRa, etc.), battery power level, and the priority of the perceived data. High-priority data packets (such as abnormal alarms) are preferentially lightly compressed or uploaded originally through low-latency links (such as cellular); medium-priority data packets are appropriately compressed based on the link cost and sent through Wi-Fi or LoRa; low-priority data packets can be batch cached and uploaded later when the battery is low or the network is poor. By combining the device power, load, and link status for scheduling, the device life can be extended and power consumption reduced; and the transmission link is dynamically selected through the link score, so that when the main link is unavailable, the upload can still be completed through other links.

[0094] The implementation manner of the present application can allocate transmission links with different link scores to data packets of different high priorities according to the importance of the perceived data, thereby improving transmission efficiency and reliability.

[0095] The embodiment of the present application divides the original perception data according to a preset importance classification rule to obtain high-priority data, medium-priority data and low-priority data, and then compresses the high-priority data, medium-priority data and low-priority data according to the compression ratio, and then uploads the compressed low-priority data packet, medium-priority data packet and high-priority data packet to the cloud. By distinguishing perception data of different importance and differentially compressing and uploading the perception data of different importance according to different compression ratios, waste of transmission resources for perception data of low importance is avoided, and more transmission resources are reserved for perception data of high importance, thereby ensuring timely, accurate, smooth and efficient transmission of perception data of high importance. Moreover, by using a compression strategy with a low compression ratio to compress high-priority data, compression loss of perception data of high importance can be reduced or avoided.

[0096] In another embodiment of the present application, a data transmission method is provided, which is applied to the cloud. The cloud includes a data analysis module, an intelligent decoding module, a strategy generation and feedback module, and a model management module. Figure 2 As shown, the method includes:

[0097] Step S201, receiving the low priority data packet, the medium priority data packet and the high priority data packet from the DTU;

[0098] Step S202 , decompressing the low-priority data packet, the medium-priority data packet and the high-priority data packet using different decompression strategies to obtain low-priority data, medium-priority data and high-priority data.

[0099] After receiving low-, medium-, and high-priority data packets, the cloud first reads the metadata included in these packets. This metadata includes the compression algorithm identifier, data type, compression parameters, data start and end times, and model version. Based on this metadata, the intelligent decoding module automatically matches the corresponding decoder, restoration method, and precision control parameters, enabling intelligent decompression and reconstruction of different low-, medium-, and high-priority data packets.

[0100] After obtaining low-priority data, medium-priority data, and high-priority data, the data curve can be restored based on the medium-priority data. The data curve refers to the trend chart of the change of continuous numerical sensor data collected by the DTU device in the time dimension, such as time series data of temperature, voltage, current, humidity, pressure, etc., for further analysis and display.

[0101] In the embodiment of the present application, the cloud can automatically decompress low-priority data packets, medium-priority data packets, and high-priority data packets respectively after receiving them to restore the compressed data.

[0102] In another embodiment of the present application, the method further includes:

[0103] Step S301, obtaining network occupancy status of the low-priority data packet, the medium-priority data packet, and the high-priority data packet;

[0104] In the embodiment of the present application, the network occupancy can be determined based on the data packet volume and the frequency of network occupancy.

[0105] Step S302, determining a decompression and restoration status according to the low-priority data, the medium-priority data, and the high-priority data;

[0106] In the embodiment of the present application, the decompression and restoration conditions can be determined based on the distortion of the data curve.

[0107] Step S303: generating a compression optimization instruction according to the network occupancy status and the decompression and restoration status;

[0108] If it is determined based on the network usage that the packets of various priorities uploaded by the DTU are large in size and frequently occupy the network, a compression optimization instruction can be generated to instruct to increase the compression ratio and enable a more efficient algorithm (such as changing from Huffman to LZ77);

[0109] If it is determined based on the decompression and restoration situation that the decompressed data curve is significantly distorted, a compression optimization instruction may be generated to instruct to reduce the compression strength, increase the accuracy, or use a lossless compression method.

[0110] Step S304: Send the compression optimization instruction to the DTU.

[0111] In an embodiment of the present application, the cloud can dynamically generate compression optimization instructions based on the network occupancy and decompression and restoration status of the received low-priority data packets, medium-priority data packets, and high-priority data packets to instruct the DTU to adjust the compression strategy and achieve adaptive optimization of the compression strategy.

[0112] In another embodiment of the present application, the method further includes:

[0113] Step S401, determining data classification accuracy based on the low-priority data, the medium-priority data, and the high-priority data;

[0114] Step S402, generating a grading optimization instruction for the preset importance classification rule according to the data grading accuracy;

[0115] Step S403: Send the hierarchical optimization instruction to the DTU.

[0116] After obtaining low-priority data, medium-priority data and high-priority data, the anomaly recognition accuracy of low-priority data, medium-priority data and high-priority data, the normal data misjudged as anomalies, trend prediction errors and other factors can be detected to determine the data classification accuracy. If the abnormal data received by the cloud is too little or missed, a hierarchical optimization instruction is generated to indicate the update of model weights, the lowering of detection thresholds, and the switching of model types (such as SVM→RNN); if the anomaly rate of the perception data is determined to be inflated and affects the network and storage according to the data classification accuracy, a hierarchical optimization instruction is generated to indicate the increase of the judgment threshold, the adjustment of the feature window size, and the streamlining of the model structure; if the reconstructed data curve is determined to have a large deviation from the actual one according to the data classification accuracy, a hierarchical optimization instruction is generated to indicate the use of a more robust model (such as LSTM), the adjustment of the training set source, and the shortening of the prediction cycle.

[0117] In an embodiment of the present application, the cloud can dynamically generate a grading optimization instruction based on the data grading accuracy of the low-priority data, the medium-priority data, and the high-priority data to instruct the DTU to adjust the preset importance classification rules to achieve adaptive optimization of the preset importance classification rules.

[0118] In another embodiment of the present application, the DTU may also send notification information to the cloud indicating that the model load is high and the model is running slowly, such as notification information including an increase in CPU load or latency; the cloud instructs the DTU to replace the current model with a lighter model or reduce the sampling frequency of collecting raw perception data within a preset time period.

[0119] In another embodiment of the present application, if the cloud finds that the perception data is frequently compressed but the compression ratio is limited, it indicates that there may be redundant transmission. At this time, a deduplication instruction can be sent to the DTU to adjust the preset importance division rules, or change the perception data to a "cache + merge upload" mechanism.

[0120] In another embodiment of the present application, a data transmission device is provided, which is applied to a data transmission unit DTU. Figure 3 As shown, the device includes:

[0121] An acquisition module 11 is configured to acquire raw sensing data collected within a preset time period;

[0122] A classification module 12 is configured to classify the original perception data according to a preset importance classification rule to obtain high priority data, medium priority data, and low priority data;

[0123] a compression module 13, configured to compress the high-priority data, the medium-priority data, and the low-priority data according to different compression strategies to obtain a high-priority data packet, a medium-priority data packet, and a low-priority data packet, wherein the compression ratios of the compression strategies corresponding to the high-priority data, the medium-priority data, and the low-priority data increase in sequence;

[0124] The uploading module 14 is configured to upload the low-priority data packet, the medium-priority data packet, and the high-priority data packet to the cloud.

[0125] In another embodiment of the present application, a data transmission device is provided, which is applied to the cloud. Figure 4 As shown, the device includes:

[0126] The receiving module 21 is configured to receive the low-priority data packet, the medium-priority data packet, and the high-priority data packet from the DTU;

[0127] The decompression module 22 is used to decompress the low priority data packet, the medium priority data packet and the high priority data packet using different decompression strategies to obtain low priority data, medium priority data and high priority data.

[0128] In another embodiment of the present application, a DTU is provided, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0129] Memory for storing computer programs;

[0130] The processor is configured to implement any of the aforementioned data transmission methods applied to the DTU when executing the program stored in the memory.

[0131] In another embodiment of the present application, a cloud is provided, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0132] Memory for storing computer programs;

[0133] The processor is configured to implement any of the aforementioned data transmission methods applied to the cloud when executing a program stored in the memory.

[0134] In the DTU provided by an embodiment of the present invention, the processor can execute a program stored in the memory to divide the original sensory data according to a preset importance classification rule to obtain high-priority data, medium-priority data, and low-priority data. The high-priority data, medium-priority data, and low-priority data are then compressed according to a compression ratio, and the compressed low-priority data packets, medium-priority data packets, and high-priority data packets are uploaded to the cloud. By distinguishing sensory data of different importance and differentially compressing and uploading the sensory data of different importance according to different compression ratios, waste of transmission resources for low-importance sensory data is avoided, and more transmission resources are reserved for high-importance sensory data, ensuring timely, accurate, smooth, and efficient transmission of high-importance sensory data. Moreover, by using a low-compression ratio compression strategy for high-priority data, compression loss of high-importance sensory data can be reduced or avoided.

[0135] In the cloud provided by the embodiment of the present invention, the processor can automatically decompress low-priority data packets, medium-priority data packets and high-priority data packets respectively after receiving them by executing the program stored in the memory, thereby realizing the restoration of compressed data.

[0136] The communication bus 1140 mentioned in the DTU or cloud can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0137] The communication interface 1120 is used for communication between the DTU or the cloud and other devices.

[0138] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0139] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0140] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0141] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A data transmission method, characterized in that: Applied to a data transmission unit (DTU), the method includes: Obtaining raw sensory data collected within a preset time period; Dividing the original perception data according to a preset importance classification rule to obtain high priority data, medium priority data and low priority data; Compressing the high-priority data, medium-priority data, and low-priority data respectively according to different compression strategies to obtain a high-priority data packet, a medium-priority data packet, and a low-priority data packet, wherein the compression ratios of the compression strategies corresponding to the high-priority data, the medium-priority data, and the low-priority data increase in sequence; The low-priority data packet, the medium-priority data packet, and the high-priority data packet are uploaded to the cloud.

2. The data transmission method according to claim 1, wherein: The original perception data is divided according to a preset importance division rule to obtain high-priority data, including: extracting numerical data from the original perception data; Determining the rate of change and fluctuation range of the numerical data; If any data value in the numerical data is outside a preset threshold range, the change rate is outside a preset change rate range, or the fluctuation amplitude is outside a preset fluctuation range, the numerical data is determined to be the high priority data.

3. The data transmission method according to claim 1, wherein: The original perception data is divided according to a preset importance division rule to obtain high-priority data, including: extracting state data and event data from the original perception data; The state-type data and the event-type data are determined as the high-priority data.

4. The data transmission method according to claim 1, wherein: The original perception data is divided according to a preset importance division rule to obtain medium priority data, including: If the change rate of the numerical data in the original perception data is within a preset change rate range, all data values ​​in the numerical data are within a preset threshold range, or the fluctuation amplitude of the numerical data is within a preset fluctuation range, determining a change trend function and inflection point data of the numerical data; The change trend function and the inflection point data are determined as the medium priority data.

5. The data transmission method according to claim 1, wherein: The original perception data is divided according to a preset importance division rule to obtain low-priority data, including: Determining whether there is redundant data and / or duplicate data in the numerical data of the original perception data; If redundant data and / or duplicate data exist in the original perception data, the redundant data and / or duplicate data are determined as the low-priority data.

6. The data transmission method according to claim 1, wherein: Uploading the low-priority data packet and the high-priority data packet to the cloud includes: Uploading the low-priority data to the cloud using the first transmission link; Uploading the medium-priority data packet to the cloud using a second transmission link, where the link score of the second transmission link is higher than the link score of the first transmission link; The high-priority data packet is uploaded to the cloud using a third transmission link, where the link score of the third transmission link is higher than the link score of the second transmission link.

7. The data transmission method according to claim 1, wherein: The method further comprises: Obtain link quality, power consumption, and CPU resource utilization of multiple transmission links; A link score for each transmission link is determined according to the link quality, power consumption, and CPU resource utilization.

8. A data transmission method, characterized in that: Applied to the cloud, the method includes: Receive low-priority data packets, medium-priority data packets, and high-priority data packets from DTU; The low priority data packet, the medium priority data packet and the high priority data packet are decompressed using different decompression strategies to obtain low priority data, medium priority data and high priority data.

9. The data transmission method according to claim 8, characterized in that: The method further comprises: Obtaining network occupancy status of the low-priority data packet, the medium-priority data packet, and the high-priority data packet; Determining a decompression and restoration status according to the low-priority data, the medium-priority data, and the high-priority data; Generate a compression optimization instruction according to the network occupancy status and the decompression and restoration status; Send the compression optimization instruction to the DTU.

10. The data transmission method according to claim 8, characterized in that: The method further comprises: determining data classification accuracy based on the low-priority data, the medium-priority data, and the high-priority data; Generating a grading optimization instruction for a preset importance classification rule according to the data grading accuracy; Send the hierarchical optimization instruction to the DTU.

11. A data transmission device, characterized in that: Applied to a data transmission unit (DTU), the device includes: An acquisition module is used to acquire raw perception data collected within a preset time period; A classification module is used to classify the original perception data according to a preset importance classification rule to obtain high priority data, medium priority data and low priority data; a compression module, configured to compress the high-priority data, the medium-priority data, and the low-priority data according to different compression strategies to obtain a high-priority data packet, a medium-priority data packet, and a low-priority data packet, wherein the compression ratios of the compression strategies corresponding to the high-priority data, the medium-priority data, and the low-priority data increase in sequence; The uploading module is used to upload the low-priority data packet, the medium-priority data packet and the high-priority data packet to the cloud.

12. A data transmission device, characterized in that: Applied to the cloud, the device includes: A receiving module is used to receive low-priority data packets, medium-priority data packets and high-priority data packets from the DTU; The decompression module is used to decompress the low priority data packet, the medium priority data packet and the high priority data packet using different decompression strategies to obtain low priority data, medium priority data and high priority data.

13. A DTU, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor is configured to implement the data transmission method according to any one of claims 1 to 7 when executing a program stored in a memory.

14. A cloud, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is configured to implement the data transmission method according to any one of claims 8 to 10 when executing a program stored in the memory.

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