Adaptive data compression apparatus, method and application for industrial wireless remote control

Abstract

The application relates to the technical field of industrial remote control, and discloses an adaptive data compression device, method and application for an industrial wireless remote controller, which divides original data streams to be sent into continuous slowly changing data, sparse event data and high-priority control data; for the continuous slowly changing data, dynamic change characteristics are monitored in real time, and an adaptive change threshold for differential compression judgment is adaptively adjusted according to the dynamic change characteristics, so that the adaptive change threshold can dynamically balance between ensuring control accuracy and improving compression efficiency in response to data states; cooperative differential compression encoding processing is performed on all kinds of data, and compression encoding results from all kinds of data and frame control information are assembled into a unified communication frame; the assembled communication frame is sent and scheduled, and the communication frame containing the high-priority control data is given the highest sending priority, so as to ensure low-delay transmission of key control and safety information.

Description

Technical Field

[0001] This invention relates to the field of industrial remote control technology, and in particular, to an adaptive data compression device, method, and application for an industrial wireless remote controller. Background Technology

[0002] Industrial wireless remote controllers are core control devices for remote operation of industrial equipment such as construction machinery, lifting equipment, and material handling machinery. A typical industrial wireless remote control system consists of a handheld transmitter and a receiver on the controlled equipment, which establish bidirectional data transmission through a wireless communication link. The data stream transmitted by this system exhibits significant heterogeneity; for example, it includes continuously changing analog signals from a joystick, sudden button input signals, and high-priority safety commands such as emergency stop and alarms.

[0003] In complex industrial environments, the reliability, real-time performance, and transmission distance of wireless communication are key performance indicators. To improve the anti-interference capability and coverage of communication links, the industry often adopts low-rate, high-sensitivity modulation techniques, such as LoRa (Long-Range Radio). The transmission rate of this type of technology is typically significantly lower than that of traditional high-speed modulation methods such as FSK (Frequency Shift Keying). For example, in the 433MHz band, its typical rate range is between 0.3kbps and 50kbps. While the lower transmission rate brings advantages in communication distance and reliability, it also directly leads to a sharp reduction in available channel bandwidth, creating a transmission constraint with limited bandwidth.

[0004] Under these constraints, existing technologies face challenges in achieving efficient and reliable data transmission. A common approach is to use fixed-period polling transmission, sending all or most of the data in each communication cycle. However, industrial remote control operations typically exhibit locality and sparsity, meaning that only a few control channels change state at any given time. This periodic full-data transmission method generates a large amount of redundant data, leading to low channel utilization and increased system power consumption.

[0005] To reduce data volume, some solutions employ compression techniques. For example, for continuously changing analog data, differential compression based on a fixed threshold is used, meaning data is only transmitted when the change exceeds a preset threshold. However, this method has inherent limitations: a fixed compression threshold cannot simultaneously meet the needs of different operating conditions, such as fine-grained operations (requiring high sensitivity and low thresholds) and fast-response operations (requiring high compression ratios and high thresholds). Improper threshold settings can lead to decreased control accuracy, poor compression performance, or increased bandwidth pressure.

[0006] Furthermore, existing solutions typically handle data streams in a relatively simplistic manner, failing to fully consider the fundamental differences in the changing characteristics and real-time requirements of different data types (such as continuous data, sudden events, and critical instructions). This may result in insufficient real-time guarantees for the transmission of high-priority critical information, or failure to achieve optimal compression efficiency when processing sparse event data.

[0007] Therefore, for industrial remote control systems using low-speed wireless communication, how to design a data processing method that can more intelligently adapt to data change characteristics, more efficiently compress heterogeneous data streams, and reliably ensure the real-time transmission of critical information under limited bandwidth conditions is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0008] This invention provides an adaptive data compression device, method, and application for industrial wireless remote controllers. By classifying heterogeneous data streams from industrial remote controllers based on business logic and real-time requirements, and introducing an adaptive threshold adjustment mechanism based on the dynamic characteristics of continuously changing data for the main data types, such as continuously changing data, collaborative differentiated compression encoding and priority-based transmission scheduling are performed on various types of data. This constructs an intelligent communication middleware integrating sensing, decision-making, execution, and scheduling, thereby solving the comprehensive technical problem that existing technologies cannot simultaneously achieve high control accuracy, high channel utilization, low system power consumption, and low-latency transmission of high-priority information under low-rate wireless channels due to fixed compression strategies and single processing modes.

[0009] According to one aspect of the present invention, an adaptive data compression method for an industrial wireless remote controller is provided, applied to a wireless remote control system including a transmitter and a receiver, wherein the wireless remote control system employs LoRa modulation technology, comprising the following steps: S100, based on the business logic and real-time requirements of the downlink control data and uplink status data of the wireless remote control system, the raw data stream to be transmitted is divided into continuously slowly varying data, sparse event data, and high-priority control data, forming the basis for differentiated compression and priority scheduling, so that subsequent processing can be optimized for the characteristics of different types of data; S200, for continuously slowly varying data, dynamic change characteristics are monitored in real time, and based on the dynamic... The adaptive change threshold used for differential compression judgment is adaptively adjusted according to the change characteristics, so that the adaptive change threshold can dynamically balance between ensuring control accuracy and improving compression efficiency in response to the data status; S300, based on the data classification results and the determined compression adaptive change threshold, collaborative differential compression coding processing is performed on various types of data, and the compression coding results from various types of data and frame control information are assembled into a unified communication frame; S400, according to the service priority corresponding to the data classification, the assembled communication frame is scheduled for transmission, and the highest transmission priority is assigned to the communication frame containing high-priority control data to ensure low-latency transmission of key control and security information.

[0010] Furthermore, the adaptive change threshold in step S200 Obtain it using the following formula:

[0011] ;

[0012] in, Based on the sensitivity threshold, For short-term variance, The absolute value of the instantaneous change. and These are configurable weighting coefficients. , and The value can be switched according to different operating modes set by the wireless remote control system.

[0013] Further, step S300 specifically involves: differential compression coding processing, performing corresponding compression coding operations according to data categories, and generating a unified channel effective bitmap; performing differential compression based on an adaptive change threshold for continuously varying data, comparing the difference between the current sampled value and the previous effective transmitted value with the threshold, and suppressing transmission if the absolute value of the difference is less than the threshold, otherwise encoding the difference; using event-driven coding for sparse event data, encoding is performed only when its state changes; using independent and robust coding for high-priority control data, including high-priority emergency stop data and high-priority alarm data; event-driven coding avoids redundant transmission of sparse event data without change, high-priority independent coding ensures the reliability of key information transmission, and combined with adaptive differential compression for continuously varying data, achieves overall optimal compression of heterogeneous data streams under limited bandwidth.

[0014] Furthermore, the differential compression coding process in step S300 specifically includes: S310, performing dynamic differential compression based on a compression adaptive change threshold for continuously varying data, and generating a channel valid bitmap indicating the validity of the current frame for each data channel; S320, using event-driven coding for sparse event data; S330, using a coding method that ensures transmission priority and reliability for high-priority control data; S340, assembling the differentially compressed data, channel valid bitmap, and frame control information into a unified communication frame. By assigning coding strategies to different types of data and combining them with a unified frame structure, efficient compression is achieved while maintaining the integrity and resolvability of data transmission.

[0015] Furthermore, the differential compression coding processing for continuously slowly changing data is as follows: if the absolute value of the current change value is less than the adaptive change threshold, the transmission of data in this channel is suppressed in the current communication cycle; if the absolute value of the current change value is greater than or equal to the adaptive change threshold, differential coding is performed on the change value, and variable-length coding can be used for further compression. The bitmap information is an N-bit bitmap, where N is the total number of continuously slowly changing data channels, and the state of the nth bit in the bitmap indicates whether the compressed data of the nth channel exists in the current communication frame.

[0016] Furthermore, the differential compression encoding processing for sparse event data specifically involves: using event-driven encoding for the compression encoding operation of sparse event data, encoding events into a compact format containing event type identifiers and event IDs only when a state change is detected, and supporting the sequential packaging of multiple independent events in the data field of the same communication frame.

[0017] Furthermore, the priority scheduling of high-priority alarm data is as follows: when high-priority alarm data is generated, the assembly process of non-high-priority data frames that are being assembled is interrupted, and a communication frame containing the alarm data is assembled and sent first. After the transmission is completed, the interrupted frame assembly process is resumed.

[0018] According to another aspect of the present invention, an adaptive data compression device for an industrial wireless remote controller is also provided, for implementing the aforementioned adaptive data compression method for an industrial wireless remote controller, comprising: a data classification module, connected to a data input interface, for sensing the characteristics of the input data stream based on the business logic and real-time requirements of the data in the industrial remote control scenario, and classifying the data stream into continuously varying, sparse event, and high-priority control data, so as to initiate subsequent differentiated processing procedures; an adaptive decision module, including a strategy configuration unit, connected to the data classification module, for adaptively determining a compression adaptive change threshold for continuously varying data based on the real-time dynamic characteristics of the continuously varying data; the decision logic of the adaptive decision module is configured to: output a low threshold to ensure resolution when high-precision control requirements are identified, and output a high threshold to improve compression when high-efficiency transmission requirements are identified. The system includes a compression ratio module; a cooperative coding execution module, connected to the data classification module and the adaptive decision-making module, which receives classification and decision information and performs compression coding on various types of data in parallel according to their data types and transmission requirements; for continuously slowly varying data, selective differential coding is performed based on the compression adaptive change threshold, and bitmap information indicating the effective data channel is generated; a frame assembly and priority scheduling module, connected to the cooperative coding execution module and the transmission interface, assembles various compressed coded data, bitmap information and frame control information from the cooperative coding execution module into a unified communication frame, and schedules transmission according to the service priority determined by data classification; the frame assembly and priority scheduling module is configured to perform priority scheduling on communication frames in the transmission queue according to the service priority determined by data classification, wherein communication frames containing high-priority control data are assigned the highest scheduling priority.

[0019] Furthermore, the strategy configuration unit includes a storage subunit and a processing subunit; the storage subunit is used to store configurable compression strategy parameters; the processing subunit is connected to the storage subunit and is used to calculate the compression adaptive change threshold based on the real-time dynamic characteristics of continuously slowly changing data and the compression strategy parameters.

[0020] Furthermore, the compression strategy parameters include at least a basic sensitivity threshold. Variance weighting coefficient and instantaneous weighting coefficient The processing subunit is configured based on the formula.

[0021] ;

[0022] in, For short-term variance, Calculate the compression adaptive change threshold based on the absolute value of the instantaneous change. .

[0023] Furthermore, the collaborative coding execution module includes: a first coding submodule for performing selective differential coding on continuously slowly varying data; a second coding submodule for using event-driven coding on sparse event data; and a third coding submodule for using coding with forward error correction or retransmission mechanisms on high-priority control data.

[0024] Furthermore, the transmission queue managed by the frame assembly and priority scheduling module is a priority queue, with the highest scheduling priority being the ability to interrupt the currently being transmitted non-high-priority communication frame.

[0025] According to another aspect of the present invention, an industrial wireless remote controller is also provided, comprising a transmitter and a receiver, wherein the transmitter and / or receiver integrates an adaptive data compression device as described above for an industrial wireless remote controller.

[0026] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the adaptive data compression method for industrial wireless remote controllers as described above.

[0027] The present invention has the following beneficial effects:

[0028] 1. Significantly improve wireless channel utilization and reduce average power consumption: Step S100, data classification, is a prerequisite for efficiency improvement. It separates sparse event data and slowly changing continuous data from the original stream, making it possible to apply efficient compression strategies in a targeted manner. Step S200, adaptive threshold adjustment, intelligently adjusts the compression decision strategy for the continuously changing data, which accounts for the largest proportion of data volume, by sensing its dynamic characteristics (such as the degree of fluctuation) in real time. When the data changes slowly, the system automatically adopts a compression mode with a higher compression ratio (achieved by increasing the judgment threshold), thereby suppressing the transmission of more small-change data. When the data changes rapidly or is in a critical control range, a more refined (or lower compression ratio) compression mode is adopted (achieved by lowering the judgment threshold) to ensure the integrity and accuracy of control information. This dynamic adjustment, compared with the single fixed threshold in the existing technology, can be closer to the optimal compression point of the current state at any time, thereby minimizing the wireless transmission of redundant data throughout the entire working cycle. Step S300, differential coding and assembly, is the execution layer. It employs event-driven coding for sparse event-type data, ensuring that data is not transmitted unless there is change. This eliminates the overhead of empty data caused by periodic polling. The unified frame assembly structure, especially in conjunction with the channel effective bitmap, further eliminates protocol overhead. Therefore, the average amount of data transmitted on the overall wireless link is significantly reduced. When using low-rate modulation such as LoRa, the reduction in data volume shortens the air time required for each transmission, not only increasing the available capacity of the limited channel (improved channel utilization) but also significantly reducing the overall system power consumption and extending device battery life due to the reduced cumulative operating time of the RF module.

[0029] 2. While achieving efficient compression, intelligent protection and optimization of control accuracy and real-time performance are ensured: The dynamic threshold mechanism in step S200 introduces a real-time feedback control system that takes data characteristics as input. Its adaptive adjustment logic sets "ensuring control accuracy" as a high-priority control requirement. When the system detects that the data is in a fine-tuning range or a stable state (where high accuracy is required), it automatically lowers the threshold to ensure that subtle changes are transmitted, thereby proactively ensuring control resolution when needed. This is not at the expense of efficiency, but rather by increasing the threshold to compensate when high accuracy is not required. Therefore, at the system level, the optimal balance between compression efficiency and control accuracy in the time dimension is achieved dynamically and intelligently. The classification in step S100 assigns a business label to high-priority control data, and the priority scheduling in step S400 ensures that this type of data enjoys absolute priority at the physical transmission level. Its transmission delay does not increase due to the presence of a large amount of ordinary data in the channel. A reliable low-latency channel is constructed at the communication protocol level, specifically serving critical safety information such as emergency stops and alarms. Therefore, the adversarial relationship between compression and performance is changed. It is not simple compression, but a kind of "perception-decision" intelligent data processing. With a significant reduction in the overall data volume (average power consumption) of the system, the key control accuracy indicators and emergency information real-time indicators not only do not deteriorate, but are instead guaranteed and optimized in a targeted and predictable manner.

[0030] 3. Enhanced system adaptability and overall reliability in complex industrial environments: Higher channel utilization means an increased probability of successfully completing a valid communication under the same interference conditions, improving system robustness; the dynamic adaptive mechanism enables the system to automatically adapt to different operator habits and equipment operating modes (such as precision assembly and rapid movement) without manual intervention for reconfiguration, reducing the barrier to entry and the risk of misoperation; deterministic low-latency transmission of high-priority information greatly improves the system's functional safety level; exhibiting stronger environmental adaptability, operational friendliness, and safety reliability, it can meet more stringent industrial application requirements.

[0031] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0032] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0033] Figure 1 This is a flowchart of an adaptive data compression method for an industrial wireless remote controller according to a preferred embodiment of the present invention;

[0034] Figure 2 This is a flowchart of a preferred embodiment of the present invention for continuously varying data compression;

[0035] Figure 3 This is a schematic diagram of the priority preemption mechanism in a preferred embodiment of the present invention;

[0036] Figure 4 This is a schematic diagram of the structure of an industrial wireless remote controller according to a preferred embodiment of the present invention. Detailed Implementation

[0037] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered below.

[0038] like Figure 1As shown, the adaptive data compression method for industrial wireless remote controllers in this embodiment is applied to a wireless remote control system including a transmitter and a receiver, and the wireless remote control system adopts LoRa modulation technology with a transmission rate of less than 50kbps. The method includes the following steps: S100: Based on the business logic and real-time requirements of the downlink control data and uplink status data of the wireless remote control system, the raw data stream to be transmitted is divided into continuously changing data, sparse event data, and high-priority control data, forming the basis for differentiated compression and priority scheduling, so that subsequent processing can be optimized for the characteristics of different types of data; S200: For continuously changing data, the dynamic change characteristics are monitored in real time, and based on... The adaptive change threshold used for differential compression judgment is adaptively adjusted by the dynamic change characteristics, so that the adaptive change threshold can dynamically balance between ensuring control accuracy and improving compression efficiency in response to data status; S300, based on the data classification results and the determined compression adaptive change threshold, collaborative differential compression coding processing is performed on various types of data, and the compression coding results from various types of data and necessary frame control information are assembled into a unified communication frame; S400, according to the service priority corresponding to the data classification, the assembled communication frame is scheduled for transmission, and the highest transmission priority is assigned to the communication frame containing high-priority control data to ensure low-latency transmission of key control and safety information. The adaptive data compression method for industrial wireless remote controllers of the present invention, step S100 data classification, is a prerequisite for efficiency improvement, separating sparse event data with sparsity and slowly changing continuous slowly changing data from the original stream, providing the possibility for targeted application of efficient compression strategies. Step S200, adaptive threshold adjustment, intelligently adjusts the compression decision strategy for continuously changing data, which constitutes the largest proportion of the data volume, by sensing its dynamic characteristics (such as the degree of fluctuation) in real time. When the data changes slowly, the system automatically adopts a compression mode with a higher compression ratio (achieved by increasing the judgment threshold), thereby suppressing the transmission of more small-change data. When the data changes rapidly or is in a critical control range, a more refined (or lower compression ratio) compression mode is adopted (achieved by lowering the judgment threshold) to ensure the integrity and accuracy of control information. This dynamic adjustment, compared with the single fixed threshold in existing technologies, can be closer to the optimal compression point of the current state at any time, thereby minimizing the wireless transmission of redundant data throughout the entire working cycle. Step S300, differentiated encoding and assembly, is the execution layer. It uses event-driven encoding for sparse event-type data, realizing no transmission without change, eliminating the empty data overhead caused by periodic polling, and the unified frame assembly structure, especially in conjunction with the channel effective bitmap, further eliminates protocol overhead.Therefore, the average amount of data transmitted on the overall wireless link is inevitably reduced significantly. When using low-rate modulation such as LoRa, the reduction in data volume shortens the air time required for each transmission, which not only improves the available capacity of the limited channel (increased channel utilization) but also significantly reduces the overall power consumption of the system due to the reduced cumulative working time of the RF module, thus extending the device's battery life. Traditional fixed threshold schemes are a static compromise between efficiency and accuracy; however, the dynamic threshold mechanism in step S200 of this invention introduces a real-time feedback control system with data characteristics as input. Its adaptive adjustment logic sets "ensuring control accuracy" as a high-priority control requirement. When the system detects that the data is in a fine-tuning range or a stable state (where high accuracy is required), it automatically lowers the threshold to ensure that subtle changes are transmitted, thereby actively ensuring control resolution when needed. This is not at the expense of efficiency, but rather compensates by raising the threshold during periods when high accuracy is not required. Therefore, at the system level, the optimal balance between compression efficiency and control accuracy in the time dimension is achieved dynamically and intelligently. The classification in step S100 assigns a business label to high-priority control data, and the priority scheduling in step S400 ensures that this type of data enjoys absolute priority at the physical transmission level. Its transmission delay does not increase due to the presence of a large amount of ordinary data in the channel. A reliable low-latency channel is constructed at the communication protocol level, specifically serving critical safety information such as emergency stops and alarms. Therefore, the adversarial relationship between compression and performance is changed. It is not simple compression, but a kind of "perception-decision" intelligent data processing. With a significant reduction in the overall data volume (average power consumption) of the system, the key control accuracy indicators and emergency information real-time indicators not only do not deteriorate, but are instead guaranteed and optimized in a targeted and predictable manner. Higher channel utilization means an increased probability of successfully completing a valid communication under the same interference, improving system robustness; the dynamic adaptive mechanism enables the system to automatically adapt to different operator habits and equipment operating modes (such as fine assembly and rapid movement) without manual intervention and reconfiguration, reducing the barrier to entry and the risk of misoperation; deterministic low-latency transmission of high-priority information greatly improves the functional safety level of the system; it exhibits stronger environmental adaptability, user-friendliness, and safety reliability, and can meet more stringent industrial application requirements.This invention presents an adaptive data compression method for industrial wireless remote controllers, constructing an intelligent communication processing system deeply integrated with business perception and real-time decision-making capabilities. It is not a simple application of general compression algorithms, but a systematic solution addressing the three core challenges of "data heterogeneity," "strict bandwidth," and "performance contradictions" in industrial wireless remote control scenarios. Through a closed-loop process of "classification perception, dynamic decision-making, differentiated execution, and priority scheduling," it achieves synergistic optimization and improvement of multiple key performance indicators such as compression efficiency, control accuracy, real-time security, and system power consumption. This effectively overcomes the various performance trade-offs and inherent defects caused by fixed strategies in existing technologies, thereby significantly enhancing the overall performance and reliability of industrial wireless remote control systems in complex and constrained environments.

[0039] In this embodiment, the adaptive change threshold in step S200 Obtain it using the following formula:

[0040] ;

[0041] in, Based on the sensitivity threshold, For short-term variance, The absolute value of the instantaneous change. and These are configurable weighting coefficients. , and The value can be switched according to different operating modes set by the wireless remote control system. The formula decomposes the determining factors of the threshold into three core parts, including the basic sensitivity threshold. A baseline is provided, representing the system's basic sensitivity to data changes; short-term variance. Weighted terms ( This reflects the macroeconomic volatility or stability trend of the data over a period of time. It is relevant when operations are in a state of rapid and significant change. Enlargement, leading to The corresponding improvement is equivalent to the system automatically recognizing that it is currently in a "high-intensity, high-efficiency" mode, thus proactively relaxing the compression criteria, allowing larger changes to be ignored (not transmitted), and prioritizing high compression ratio and channel efficiency; the absolute value of instantaneous changes Weighted terms ( It captures the drastic nature of instantaneous changes in data; when sudden, large-scale instantaneous changes occur, Enlargement can also lead to The improvement can be understood as an intelligent response of the system to sudden changes, avoiding frequent triggering of high-precision transmission due to a single large value jump, and maintaining a high overall compression efficiency even when sudden changes occur. This invention does not use a fixed threshold, but rather uses a formula to quantitatively and in real-time analyze the time-domain characteristics (long-term fluctuation trends) of the data itself. With instantaneous impact strength This is transformed into the basis for adjusting the compression strategy, allowing the compression criterion to be flexibly adjusted according to the state of the data stream. It tends towards high precision when the data is stable and high efficiency when the data changes drastically, thus dynamically and automatically seeking the optimal balance between precision and efficiency in the time domain. This overcomes the inherent limitation of fixed thresholds, which cannot adapt to different operating modes. (The formula contains...) , and Designed as configurable weighting coefficients, their values ​​can be switched according to different operating modes set by the system; by adjusting... and It can precisely control the macroeconomic volatility of data (long-term volatility trend). ) and instantaneous change (instantaneous impact intensity) The weights of the influence of these two factors on the final threshold; for example, if more emphasis is placed on long-term stability, the weights can be increased. If more attention is paid to tolerance for sudden changes, then it can be increased. This provides a mathematical foundation for implementing differentiated compression strategies. Mode switching allows for different parameter sets to be preset for "fine mode," "standard mode," and "efficient mode," etc. In "fine mode," smaller... , and This makes the system more sensitive overall. In "high-efficiency mode," larger parameters can be used, making the system more "tolerant." The adaptive strategy is elevated from a single algorithm level to a system behavior level that can be driven by upper-layer applications or user intentions. This gives the adaptive compression method extremely strong flexibility and customizability. It is no longer a "black box" algorithm, but an open, parameterized strategy framework. The system can shape the compression behavior by configuring different parameter groups according to different characteristics of the controlled equipment (such as the different accuracy requirements of cranes and excavators), different working scenarios (such as indoor precise positioning and outdoor rapid movement), and even different user operating habits. This greatly enhances the adaptability and optimization space for different industrial remote control application scenarios, upgrading it from a fixed solution to a configurable strategy platform.

[0042] like Figure 2As shown, in this embodiment, step S300 specifically involves: differential compression coding processing, performing corresponding compression coding operations according to data categories, and generating a unified channel effective bitmap; performing differential compression based on an adaptive change threshold for continuously varying data, comparing the difference between the current sampled value and the previous effective transmitted value with the threshold, and suppressing transmission if the absolute value of the difference is less than the threshold, otherwise encoding the difference; using event-driven coding for sparse event data, encoding is performed only when its state changes; using independent and robust coding for high-priority control data, including high-priority emergency stop data and high-priority alarm data; event-driven coding avoids redundant transmission of sparse event data without change, high-priority independent coding ensures the reliability of key information transmission, and combined with adaptive differential compression for continuously varying data, achieves overall optimal compression of heterogeneous data streams under limited bandwidth. By applying distinctly different but highly compatible compression coding strategies to the inherent characteristics of different data types, the "invalid" or "inefficient" transmission overhead in various types of data is eliminated at the source. For continuously varying data, conditional differential compression based on an adaptive threshold is employed. The difference between the current value and the previous transmitted value is compared to a dynamic threshold. When the data change is subtle (below the threshold), it is determined that the change is insignificant under the current control precision requirements, thus suppressing transmission and avoiding a large number of data packets representing minute fluctuations with minimal contribution to control decisions occupying the channel. Only when the change is significant (exceeding the threshold) is the encoded difference transmitted. Compared to traditional periodic full transmission or fixed threshold compression, this minimizes the throughput of this type of data by transmitting only the effective change when necessary. For sparse event data, event-driven encoding is used, transforming data transmission from a "time-triggered" mode to a "state change-triggered" mode. Since events such as button presses are inherently sparse and bursty, when there are no state changes... Maintaining silence completely eliminates the transmission of "event-free" empty data or duplicate data packets, which constitute the vast majority of data in fixed-period polling. For high-priority control data, independent and robust encoding is employed, providing an independent and enhanced protection channel for critical data at the encoding level. This is achieved through more robust forward error correction, retransmission, or dedicated short frame structures, ensuring reliable decoding even under poor channel conditions and reducing retransmission probability and latency due to bit errors. The synergistic application of these three strategies precisely reduces "redundant variation," "empty query overhead," and "retransmission overhead due to unreliability." The combined effect of these three factors systematically minimizes the total amount of necessary information transmitted on the wireless channel from both the source of data generation and the reliability of transmission, thereby improving the effective utilization of limited bandwidth. While improving efficiency, a hierarchical reliability assurance system is constructed. Instead of indiscriminately reducing the reliability of all data in pursuit of overall efficiency, differentiated reliability design is implemented based on the service importance of the data.For continuously changing data, its reliability is ensured by a mechanism that guarantees transmission once the change exceeds a threshold, guaranteeing that no important changes are missed. For sparse event data, its reliability is guaranteed by the accuracy of event-driven state change detection, ensuring that every valid operation is captured and transmitted. For high-priority control data (emergency stop, alarm), independent robust coding is used to give it anti-interference and error correction capabilities far superior to other data categories. This independence avoids it competing with ordinary data for coding resources or being affected by errors in ordinary data. Step S300 establishes a hierarchical data transmission reliability pyramid. Under the condition of limited overall channel resources, by using efficient compression on general data to save resources, and allocating the saved resources (such as coding redundancy, transmission opportunities) to the most critical high-priority data, the organic unity of "global efficiency optimization" and "local reliability enhancement" is achieved, ensuring that while the overall compression rate is improved, the transmission reliability of critical instructions (emergency stop, alarm) related to life and property safety is verifiable and guaranteed. After differential coding is completed, a unified channel validity bitmap is generated. This bitmap is the key map for the receiver to correctly and quickly parse the compressed frame. It clearly indicates which channels in the current frame contain valid compressed data and which channels are suppressed because they have not reached the threshold or have no change, with minimal overhead (1 bit per channel). There is no need to reserve placeholders or send zero values ​​for each suppressed channel in the frame. Only one bit is needed to represent its status, which greatly reduces the overhead of the protocol control header. By parsing the bitmap, the receiver can directly locate and decode valid data without sequential scanning or complex judgment of the data in the frame, which reduces processing latency and computational burden. Even if the data of some channels is suppressed for several consecutive cycles, the transmitter and receiver can still keep in sync to understand the current valid value of the channel through the indication of the bitmap, avoiding the problem of control state loss of synchronization caused by data suppression.

[0043] In this embodiment, the differential compression coding process in step S300 specifically includes: S310, performing dynamic differential compression based on a compression adaptive change threshold for continuously varying data, and generating a channel valid bitmap indicating the validity of the current frame for each data channel; S320, using event-driven coding for sparse event data; S330, using an encoding method that ensures transmission priority and reliability for high-priority control data; S340, assembling the differentially compressed data, channel valid bitmap, and frame control information into a unified communication frame. By assigning appropriate coding strategies to different types of data and combining them with a unified frame structure, efficient compression is achieved while maintaining the integrity and resolvability of data transmission. The data determined in step S200... The direct result of the synergistic effect with the specific compression action performed in step S310; The calculation formula makes the threshold a dynamic variable, whose value varies with the short-term volatility of the data (short-term variance). The instantaneous change magnitude (absolute value of instantaneous change |△|) is adaptively adjusted; when the data change is gradual ( When the instantaneous fluctuation is small (|△| small), Approaching The system is in a high-sensitivity, high-fidelity mode; at this time, the differential compression decision condition in step S310 is that if the absolute value of the difference is less than the threshold, no data is sent, ensuring that subtle changes are transmitted, thus guaranteeing high control accuracy during the fine-tuning stage of operation; when the data changes rapidly or experiences a large jump ( When |△| increases, As the data dynamically increases, the system switches to a high compression rate mode; at this time, the compression decision conditions are relaxed, suppressing the transmission of more variable data, prioritizing channel efficiency during periods of drastic operation, and avoiding data flooding; through real-time feedback of data characteristics ( Dynamically adjust the compression decision threshold (adaptive changing threshold) using the method described in the image (△). This approach intelligently and dynamically rebalances the traditionally conflicting performance metrics of accuracy and efficiency, overcoming the inherent limitations of fixed-threshold schemes that cannot accommodate the needs of different operational scenarios. Step S330 specifies that high-priority control data (such as emergency stops and alarms) should be encoded using a method that ensures transmission priority and reliability. This is not a single encoding action, but rather a manifestation of identifying and applying differentiated processing based on the importance of data services at the encoding level. This independent and reinforced encoding implies stronger error correction capabilities (such as forward error correction codes) or higher transmission redundancy (such as repeated transmission). This measure, combined with subsequent transmission scheduling (step S400, assigning the highest transmission priority), constructs a privileged channel for high-priority data at both the encoding and link layers of the communication protocol stack. Under the same physical transmission environment and limited overall bandwidth, the channel resources and processing time saved by efficiently compressing ordinary data are reallocated to enhance the transmission robustness of high-priority data and reduce its queuing latency. This ensures that the transmission of critical commands such as emergency stops and alarms achieves a predictable success rate and timeliness far exceeding that of ordinary data, directly improving the functional safety level of the system. In step S310, while generating compressed data, a channel valid bitmap is generated. This bitmap is assembled into a unified communication frame in step S340. Each bit of the bitmap serves as a valid flag bit for the corresponding data channel, using 1 bit of information to explicitly indicate whether the channel has a new valid differential value in the current frame. Compared to allocating a fixed field to each channel (regardless of whether there is data) or using complex variable-length indicators, the 1-bit / channel bitmap is the most concise and definitive encoding method for indicating "presence or absence" information, greatly reducing frame header overhead. By parsing the bitmap, the receiving end can directly locate the precise position of valid data within the frame for decoding without traversal or guessing, significantly reducing decoding complexity and processing latency. This mechanism provides unambiguous synchronization information for selective transmission at the transmitting end and correct reconstruction at the receiving end. Even if some channels have no data updates for several consecutive frames, both parties can always maintain a consistent understanding of the "latest valid value" of the channel through the bitmap, avoiding control state asynchrony problems that may be caused by data suppression.Steps S310 - S340, which承接步骤S100的分类、步骤S200的决策,并启接S400的调度,的核心执行与封装环节;S310依据S200的动态阈值执行压缩;S320、S330依据S100的分类结果执行差异化编码;S340则将所有结果连同位图统一封装;使得数据从进入处理流程开始,其业务分类、动态特征决策就决定了其编码方式与封装形式,形成了一个高度协同的流水线,确保了前序分类与决策意图被准确、高效地执行,为后续的优先级调度提供了结构清晰、语义明确的通信帧,通过各步骤的紧密耦合,最小化了处理延迟与资源开销,实现了从原始数据到无线报文的全流程优化。is the core execution and encapsulation link that承接 the classification in step S100, the decision - making in step S200, and initiates the scheduling in S400; S310 performs compression according to the dynamic threshold in S200; S320 and S330 perform differential encoding according to the classification result in S100; S340 encapsulates all results together with the bitmap; so that from the moment the data enters the processing flow, its service classification and dynamic feature decision - making determine its encoding method and encapsulation form, forming a highly collaborative pipeline, ensuring that the previous classification and decision - making intentions are accurately and efficiently executed, providing a communication frame with clear structure and explicit semantics for subsequent priority scheduling, minimizing processing delay and resource overhead through the tight coupling of each step, and achieving the full - process optimization from raw data to wireless messages.

[0044] In this embodiment, the differential compression encoding process for continuously slowly varying data is specifically as follows: If the absolute value of the current change value is less than the adaptive change threshold, the transmission of the data in this channel in this communication cycle is suppressed; If the absolute value of the current change value is greater than or equal to the adaptive change threshold, differential encoding is performed on this change value, and optionally variable - length encoding is used for further compression; The bitmap information is an N - bit bitmap, where N is the total number of continuously slowly varying data channels, and the state of the nth bit in the bitmap indicates whether the compressed data of the nth channel exists in the current communication frame. An intelligent decision - making and selective transmission mechanism based on dynamic thresholds is introduced, and its decision - making logic is driven by the threshold defined by the formula; When |current change value| < T_dynamic, it is determined that the current data change is not significant (that is, the impact on control accuracy is within an acceptable range), thus suppressing the transmission, eliminating the transmission of minor fluctuation data with low contribution to control decisions, which is the core source of compression; When |current change value| ≥ It should be noted that the text in has some garbled or incorrect expressions in the original Chinese which may affect the accurate understanding. The translation is done as accurately as possible based on the existing text.When a significant change is detected, differential encoding is applied. This differential encoding eliminates absolute redundancy in the data values, and subsequent optional variable-length encoding (such as Huffman coding) can further utilize the statistical distribution characteristics of the changed values ​​to perform secondary compression on the differentially encoded data, achieving efficient bit-level representation. Through a three-level processing flow of "decision filtering → difference extraction → secondary compression," it is ensured that every bit transmitted on the channel carries "necessary and significant change information." Compared to traditional periodic full transmission or fixed differential threshold schemes, this invention systematically and adaptively filters redundant information, thereby achieving a theoretical minimum approach to the throughput of continuously slowly varying data at the source. This significantly improves the effective data carrying capacity of low-rate wireless channels (such as LoRa), providing a foundation for transmitting more information or reducing power consumption. The channel effective bitmap mechanism achieves unambiguous synchronization in a highly dynamic compression state with constant and extremely low overhead. Generating an N-bit channel effective bitmap is a necessary and efficient complement to the selective transmission mechanism at the protocol level. Each bit in the bitmap is a direct, lossless mapping of the transmitter's "transmit / suppress" decision. By parsing the bitmap, the receiver can precisely determine which channels in the current frame contain new data and which channels should retain their previous valid values. This achieves synchronization between the transmitter and receiver even when data transmission is periodically suppressed, avoiding control drift or loss of synchronization. This bitmap is constant and extremely small (N bits). Furthermore, the overhead, linearly related to the number of channels, replaces the redundant scheme of allocating a fixed field to each channel (regardless of whether there is data), allowing the length of the communication frame to closely match the changes in the actual effective data volume, achieving dynamic optimization of the frame structure. By generating and transmitting the effective bitmap of the channels, it provides the receiver with accurate and unambiguous indication information of the distribution of effective data within the frame, thus reliably solving the problem of receiver data positioning and state synchronization caused by the selective transmission mechanism, ensuring the reliability of communication and the continuity of control. Frame control information (or validity indication information) for the intra-frame data layout is completed in an optimal manner (1 bit / channel), keeping the ratio of protocol control overhead to effective data volume at an extremely low level, further improving the overall channel utilization. A quantitative control interface is provided to guarantee compression strength and accuracy, enhancing the system's configurability and scenario adaptability; the compression behavior is ultimately determined by an adaptively changing threshold. Control, and this threshold is controlled by configurable parameters ( , and The operation mode switching is associated with the upper-layer application; by adjusting... The system's basic sensitivity can be set directly; by adjusting... and This allows for separate and quantitative control over the influence of macroscopic volatility and transient mutability of data on compressed decision-making; for example, increasing... This will cause the system to more "aggressively" raise the threshold to improve efficiency when data fluctuations intensify; increase This makes the system more tolerant of single large jumps in performance; by pre-setting different parameter groups for "fine mode," "standard mode," and "high-speed mode," one-click switching of compression strategies is achieved; transforming the compression scheme from a fixed algorithm into an open, parameterized strategy platform. Compression behavior can be intuitively shaped by configuring these parameters according to different equipment dynamic characteristics, control precision requirements, and working scenarios, thereby making targeted and predictable trade-offs between compression efficiency and control fidelity, greatly enhancing adaptability and optimizability for different industrial remote control applications (such as high-precision hoisting and rapid excavation).

[0045] In this embodiment, the differential compression encoding processing of sparse event data is specifically as follows: the compression encoding operation of sparse event data adopts event-driven encoding, and the event is encoded into a compact format containing event type identifier and event ID only when a state change is detected, and multiple independent events are sequentially packaged in the data field of the same communication frame. The data transmission mode is transformed from traditional time-driven (periodic polling) to event-driven (state change triggered), with encoding operations only initiated when a state change is detected. During the vast majority of the time when no events occur, the system remains silent on event-type data channels, avoiding the periodic transmission of empty data packets or "heartbeat frames" with no actual content for querying the status. When an event occurs, the event information is encoded into a compact format containing only the event type identifier and event ID. This is equivalent to transmitting only the two core semantic information of the event: its "identity" and "action," stripping away all fixed-format protocol padding fields and redundant bytes unrelated to the specific event. Because events such as buttons and switches in industrial remote control inherently have low duty cycles and burstiness, this combination of "event-driven and compact encoding" compresses wireless transmission activities for such data in the time dimension to be completely synchronized with the event frequency, and in the data dimension, simplifies them to retain only the necessary information. Channel resources are only occupied when an event actually occurs, and the occupation is minimized, thus achieving near-optimal channel utilization for such sparse services. It supports "sequentially packaging multiple independent events in the data field of the same communication frame." Multiple independent events are packaged into the same physical layer frame for transmission, sharing the same physical layer and link layer protocol overhead, such as the frame header and trailer. Compared to encapsulating each event into a complete frame for transmission, this aggregation method significantly reduces the average protocol overhead per event. When multiple events occur in a short period of time, this mechanism allows them to be quickly collected and sent together at the next available transmission opportunity, avoiding additional queuing delays that may be caused by waiting for individual encapsulation and serial transmission. Especially for continuous and rapid operations (such as continuous key presses), this mechanism can effectively reduce the overall latency of event transmission. This mechanism optimizes event transmission in two ways: first, by amortizing and distributing the cost of transmission per event, it reduces the transmission cost of a single event and improves the macroscopic efficiency of bandwidth; second, by batch transmission, it reduces the waiting time of events in the transmission buffer and improves the microscopic real-time performance of events. This allows the system to maintain both high efficiency and good response speed when processing bursts of events.The system employs a standardized and compact encoding format that includes an event type identifier and an event ID. The event type identifier defines the category of the event (e.g., button press, emergency stop activation, mode switching), while the event ID uniquely identifies the specific event source within that category (e.g., button 1, joystick 2). This (type, ID) tuple structure provides an unambiguous semantic description for each event. This format is extremely concise while ensuring semantic completeness. New event types can be easily added to the protocol without changing the overall frame structure, exhibiting good backward compatibility and scalability. It provides an efficient and standardized language for event information exchange between the transmitter and receiver. The receiver can quickly and accurately parse the precise meaning of the event based on this format and drive the corresponding control actions. It facilitates the expansion of system functions (e.g., adding new button or switch types) and reduces the complexity of protocol upgrades and the difficulty of interoperability between devices.

[0046] like Figure 3As shown, in this embodiment, the priority scheduling of high-priority alarm data is as follows: when high-priority alarm data is generated, the assembly process of non-high-priority data frames that are being assembled is interrupted, and a communication frame containing the alarm data is assembled and sent first. After the sending is completed, the interrupted frame assembly process is resumed. The system employs a preemptive or interruptible scheduling strategy. When high-priority alarm data is generated, the system immediately interrupts the ongoing assembly process of lower-priority frames and prioritizes the assembly and transmission of the alarm data. This gives alarm data absolute priority in both processing logic (CPU / processor time) and transmission queue (channel access rights), eliminating the need to wait for non-critical frames to complete assembly and transmission, thus eliminating the non-deterministic delays caused by queuing. Alarm frames assembled with priority immediately enter the transmission process after formation, minimizing the processing delay between the occurrence of the alarm event and the start of wireless signal transmission. For critical information concerning equipment and personal safety, such as emergency stops and serious fault alarms, this mechanism ensures that the end-to-end delay from generation to the start of wireless transmission is theoretically the shortest and most predictable. This deterministic low-latency guarantee is a core requirement of industrial safety control systems, directly determining whether the system can take timely braking or protective measures when danger occurs, thereby elevating the system's functional safety level from "best effort" to "guaranteed." Instead of simply discarding interrupted ordinary data, this mechanism resumes the assembly process of interrupted non-high-priority data frames after the interruption alarm handling is completed. This mechanism saves the current ordinary frame assembly context (such as assembled data and pointer positions) when an interruption occurs and accurately restores it after the alarm frame is sent, ensuring the integrity and correctness of ordinary data transmission and preventing data loss or corruption due to interruption. It embodies a dynamic resource allocation strategy, where system resources (processing time, channels) are fairly allocated to all types of data under normal conditions to maintain high throughput. In the event of an emergency, resources are instantly concentrated on the most critical alarm data. After the emergency task is completed, normal service resumes immediately. This design achieves a balance between "absolute priority for critical tasks" and "considering overall system efficiency." It avoids wasting channel resources when there are no alarms by continuously reserving fixed bandwidth (such as dedicated time slots) for high-priority data, and it avoids blocking all subsequent ordinary frames with an alarm frame due to a purely static priority queue (the recovery mechanism ensures that interrupted frames can continue to complete). Thus, without significantly sacrificing the overall average throughput of the system, it provides the highest level of real-time guarantee for the highest priority data, adapting to demand.This scheduling strategy defines explicit behavioral rules for the system when facing internal resource contention (processing and transmission). High-priority alarms unconditionally interrupt low-priority tasks, ensuring that the system's response time to alarm data has an analyzable and calculable upper bound under worst-case conditions (such as busy channels and high processing load). For industrial control systems, this mechanism can be used to quantitatively analyze and verify the maximum delay from alarm triggering to signal transmission under the most unfavorable scenario, thus providing a solid design basis for Safety Integrity Level (SIL) assessment. This ensures that the system's real-time performance is not only excellent under normal conditions but also has deterministic guarantees under stress or abnormal operating conditions, enhancing the reliability and robustness of the entire control system.

[0047] The adaptive data compression device for industrial wireless remote controllers in this embodiment implements the aforementioned adaptive data compression method for industrial wireless remote controllers. It includes: a data classification module connected to a data input interface, used to sense the characteristics of the input data stream based on the business logic and real-time requirements of the data in the industrial remote control scenario, and classify the data stream into continuously varying, sparse event, and high-priority control data to facilitate subsequent differentiated processing; an adaptive decision module, including a strategy configuration unit connected to the data classification module, used to adaptively determine the compression adaptive change threshold for continuously varying data based on the real-time dynamic characteristics of the continuously varying data; the decision logic of the adaptive decision module is configured to: output a low threshold to ensure resolution when high-precision control requirements are identified, and output a high threshold to improve the compression ratio when high-efficiency transmission requirements are identified; and a collaborative encoding... The code execution module, connected to the data classification module and the adaptive decision-making module respectively, receives classification and decision information and performs compression coding in parallel on various types of data according to their data types and transmission requirements. For continuously slowly varying data, selective differential coding is performed based on the compression adaptive change threshold, and bitmap information indicating the effective data channel is generated. The frame assembly and priority scheduling module, connected to the cooperative coding execution module and the transmission interface, assembles various compressed coded data, bitmap information and frame control information from the cooperative coding execution module into a unified communication frame, and schedules the transmission according to the service priority determined by the data classification. The frame assembly and priority scheduling module is configured to perform priority scheduling on the communication frames in the transmission queue according to the service priority determined by the data classification, wherein communication frames containing high-priority control data are assigned the highest scheduling priority.This invention relates to an adaptive data compression device for industrial wireless remote controllers, which constitutes a dedicated data processing pipeline based on modular division of labor and pipeline collaboration. The "data classification module," "adaptive decision-making module," "cooperative coding execution module," and "frame assembly and priority scheduling module" are sequentially connected, realizing task-level pipelined processing. The cooperative coding execution module performs parallel encoding of various types of data, further utilizing data-level parallelism. This hardware-software collaborative architecture allows for highly overlapping classification, decision-making, encoding, assembly, and scheduling processes after the raw data enters, significantly reducing the time required from data input to wireless... The total processing latency for frame readiness is reduced; complex algorithms such as adaptive decision-making and differential coding are embedded in a dedicated module, which, compared to pure software implementation, reduces the occupation of general-purpose processors and the uncertainty caused by operating system scheduling, making data processing time shorter and more predictable, and enhancing the system's real-time response capability; it can complete the entire process of real-time, adaptive compression and scheduling of input data streams with hardware-level speed and determinism, which is particularly suitable for the strict requirements of low latency and high determinism in industrial wireless remote control scenarios, ensuring the timeliness of control commands and status feedback, and guaranteeing the reliable implementation of the technical effects of the aforementioned method embodiments from a physical level. The modules are clearly divided to correspond to the core steps of the method, and key modules are internally configurable. Each module is connected through a defined interface, and the functional boundaries are clear. For example, the adaptive decision module focuses on threshold calculation and contains an independent strategy configuration unit. This design reduces the coupling between modules, so that upgrading the algorithm or adjusting the parameters of a single module (such as optimizing the decision logic) does not require modifying other modules, thus improving the maintainability and upgradeability of the system. The existence of the strategy configuration unit means that the core strategy that determines the compression behavior (represented by parameters or microcode) can be stored, updated, and switched independently of the processing logic. This allows the system to adapt to different device models, application scenarios, or optimization goals by updating the configuration of this unit without changing the hardware circuit. This transforms the device from a fixed-function "black box" into an open and customizable intelligent compression platform. It can not only efficiently execute predetermined adaptive compression tasks, but also has the flexibility to adapt to future changes in demand through configuration. This reduces the cost of product serialization development and allows for performance optimization in the field based on actual working conditions, greatly enhancing the product's market competitiveness and life cycle.Starting with the data classification module, the device labels data at the hardware level with service type tags (continuous, sparse, high priority). These tags are passed as "metadata" in subsequent modules. The collaborative coding execution module performs differentiated coding based on the type tags, especially applying robust coding to high-priority data. The frame assembly and priority scheduling module ultimately implements hardware-level priority scheduling in the transmission queue based on these tags, forming a full-chain priority awareness and guarantee channel from input to output. The frame assembly and priority scheduling module is configured to assign the highest scheduling priority to high-priority frames. Implemented in the hardware queue manager, it can provide more timely and less susceptible to interference from other tasks than software queue management. The device inherently supports and enhances differentiated quality of service at the hardware architecture level. High-priority control data (such as emergency stop and alarm) in this device obtains hardware-guaranteed privileges in terms of coding resources and transmission opportunities from the moment it is identified. This provides a deterministic, low-latency, and highly reliable transmission path from the application layer to the physical layer for critical safety commands, which is an important hardware foundation for building a high security integrity level (SIL) industrial control system. This invention relates to an adaptive data compression device for industrial wireless remote controllers. It not only efficiently and reliably executes adaptive data compression and scheduling methods, but also comprehensively improves system performance and quality through its hardware-based pipeline architecture, modular and configurable design, and a consistent priority-aware hardware path. Hardware acceleration ensures low latency and high determinism throughout the processing flow, meeting the real-time requirements of industrial control. A clear modular and configurable strategy unit design endows the system with excellent flexibility, maintainability, and scenario adaptability. At the hardware level, an end-to-end quality of service assurance system from classification to scheduling is constructed, providing chip-level reliability support for critical secure data transmission. The device transforms the adaptive compression algorithm into a reliable industrial product with market competitiveness, effectively solving the technical challenge of achieving complex, real-time, and reliable data processing in resource-constrained embedded environments.

[0048] In this embodiment, the strategy configuration unit includes a storage subunit and a processing subunit; the storage subunit is used to store configurable compression strategy parameters; the processing subunit is connected to the storage subunit and is used to calculate the compression adaptive change threshold based on the real-time dynamic characteristics of continuously slowly changing data and the compression strategy parameters. The strategy configuration function is clearly divided into two sub-units with distinct responsibilities: a storage sub-unit and a processing sub-unit. The storage sub-unit is specifically responsible for maintaining the static definition of the compression strategy, i.e., storing configurable parameters, decoupling the algorithm logic from the strategy parameters, and using non-volatile or easily accessible storage. The processing sub-unit is specifically responsible for performing dynamic calculations of the strategy, receiving real-time data features and parameters from the storage sub-unit, and running specific algorithms to generate output. When it is necessary to optimize or change the compression strategy, it may only be necessary to update the parameter set or microcode in the storage sub-unit without modifying the hardware logic or firmware of the processing sub-unit, which greatly reduces the complexity and risk of system upgrades. The system can preset multiple sets of different parameters for "fine mode," "high-efficiency mode," etc., and store them in the storage sub-unit. When switching according to the operating conditions, the processing sub-unit only needs to read another set of parameters to change its behavior, realizing "one-click switching" or dynamic adaptive calling of compression strategies, enabling the device to quickly adapt to different application requirements. By isolating the computational function as a processing subunit, the calculation of the adaptive threshold is handled by a dedicated logic unit. This unit is physically or logically isolated from other tasks in the system (such as data classification and encoding) in terms of hardware resources (such as the arithmetic unit and bus access) or processing time slices. The processing subunit can complete the threshold calculation in a defined and usually shorter cycle without being disturbed by high-load tasks in other parts of the system, ensuring that the adaptive decision-making process does not become a bottleneck in the entire data processing pipeline. Dedicated processing units are usually more efficient and consume less power than general-purpose processors when executing the same algorithm. This hardware acceleration feature is particularly important for battery-powered industrial remote controls, improving performance while optimizing energy consumption. The relationship between the storage subunit and the processing subunit defines a clear and stable internal interface. The transmission path of policy parameters from the storage unit to the processing unit is hardware-guaranteed, avoiding the problem of parameters being accidentally modified or accessed in software memory. This makes the entire adaptive decision-making process highly deterministic and reproducible. As long as the same real-time dynamic characteristics and parameters in the storage subunit are input, the processing subunit will definitely output the same threshold. This determinism is the foundation of industrial control systems, enabling compression behavior to be precisely configured, rigorously tested, and safely certified. This provides a good design basis for meeting the requirements of functional safety standards for avoiding systemic failures.

[0049] In this embodiment, the compression strategy parameters include at least a basic sensitivity threshold. Variance weighting coefficient and instantaneous weighting coefficient The processing subunit is configured based on the formula.

[0050] ;

[0051] in, For short-term variance, Calculate the compression adaptive change threshold based on the absolute value of the instantaneous change. This formula will determine the final threshold for compression behavior, constructed as a weighted sum of three terms with explicit physical / statistical meaning; the base term (base sensitivity threshold) This provides a configurable baseline sensitivity that determines the system's basic behavior in the absence of significant fluctuations; the macroscopic fluctuation feedback term ( This introduces the short-term statistical properties of the data (short-term variance). (Short-term variance) as a feedback variable This quantifies the dispersion or volatility of data over a period of time. When operations are frequent or the amplitude changes significantly (high variance), this value increases, leading to… Improvement: The system recognizes the current macroscopic state as "highly dynamic and prone to change," thus automatically relaxing the decision conditions and tending to suppress more changes to prioritize overall channel efficiency; instantaneous impact feedback term ( This introduces the instantaneous change amplitude of the data. As another feedback variable, It captures the intensity of abrupt changes between adjacent sampling points. When a large, instantaneous jump occurs, this value increases, which also leads to... The system is improved to respond to sudden, large-scale changes, avoiding excessively frequent high-precision transmissions triggered by a single jump. While maintaining a certain level of awareness of sudden changes, it prevents the channel from being overwhelmed by sudden noise or data floods caused by instantaneous large movements. This threshold is no longer a fixed value, but a real-time value that follows the macroscopic trend of the data (short-term variance). ) and micro-impact (absolute value of instantaneous change) The dynamic variables of the compression system enable the system's sensitivity or tolerance to intelligently match the current state of the data: maintaining high sensitivity when the data is stable to capture subtle changes (ensuring accuracy), and automatically reducing sensitivity when the data fluctuates drastically to filter redundant changes (improving efficiency), thus overcoming the inherent defect that fixed thresholds cannot simultaneously adapt to scenarios of fine operation and rapid response.

[0052] In this embodiment, the cooperative coding execution module includes: a first coding submodule for performing selective differential coding on continuously varying data; a second coding submodule for using event-driven coding on sparse event data; and a third coding submodule for using coding with forward error correction or retransmission mechanisms on high-priority control data. Instead of using a single general-purpose encoder, three parallel dedicated coding paths are designed based on the essential characteristics and transmission requirements of the three types of data: continuously varying data, sparse event data, and high-priority control data. The first coding submodule (for continuously varying data) performs threshold-based selective differential coding, a mechanism directly related to the dynamic threshold calculation formula (adaptive changing threshold). The calculation formula is linked to the encoding behavior, which is driven by the dynamic characteristics of the data itself. , )pass The system employs real-time, intelligent control to preserve details (high fidelity) when data is stable and suppress redundancy (high efficiency) when data changes drastically. The second encoding submodule (for sparse event-based data) uses event-driven encoding, abandoning periodic polling. Encoding and transmission are triggered only when a state change event occurs, perfectly matching the sparse characteristics of this type of data—primarily static with minor mutations—eliminating any transmission overhead during periods without state changes. The third encoding submodule (for high-priority control data) uses encoding with forward error correction (FEC) or retransmission mechanisms. It proactively introduces redundancy (check bits or repeated frames) at the encoding level to combat noise, interference, and packet loss in the wireless channel, adding a layer of protection to critical data above the physical layer and significantly improving the probability of correct reception under adverse channel conditions. This parallel processing architecture achieves "one type of data, one optimal encoding strategy." Each path is deeply optimized for the data type it serves. It optimizes the "fidelity-efficiency" balance for continuous data, "static zero overhead" for event data, and "transmission reliability" for critical data. The collaborative work of these three paths makes the overall encoding scheme no longer a crude compromise between different types of data requirements, but provides encoding efficiency close to its theoretical optimal for each type of data, thereby achieving Pareto front approximation of the overall bandwidth utilization at the system level. The third coding submodule employs robust coding (FEC / repetition), which is an additional application layer or link layer reliability enhancement measure applied on top of general channel coding (such as the modulation of LoRa itself). Forward error correction (FEC) adds check information during coding, enabling the receiver to automatically detect and correct a certain number of bit errors without requesting retransmission, reducing end-to-end delay and additional retransmission overhead caused by bit errors. Repeated transmission increases the probability of at least one copy being correctly received by sending multiple copies of the same data at different times or frequencies using time / frequency diversity gain. This design establishes a logical channel with higher fault tolerance for high-priority control data (such as emergency stop and alarm). Even under the same physical channel conditions, this type of data enjoys a higher probability of successful transmission than ordinary data. It asymmetrically allocates the overall system reliability resources (coding redundancy and transmission opportunities), prioritizing the most critical services, thus building a critical defense for system security at the coding stage.The parallel operation of the three sub-modules means that the encoding and processing of continuous data, event data, and high-priority data are independent and simultaneous in hardware or logic. The robust encoding of high-priority data may be computationally more complex, event encoding may be bursty, and continuous data encoding is continuous. The parallel architecture ensures that an increase or burst in the load of processing one type of data will not block or significantly delay the encoding process of other types of data. For example, the complex encoding of a sudden emergency alarm will not affect the smooth compression of continuous joystick data. High-priority data requires extremely low latency, continuous data requires stable throughput, and event data requires timely response. Parallel processing allows each type of data to obtain deterministic processing resources in its dedicated encoding path, thereby making it easier to meet their different real-time constraints and enhancing the overall response performance and determinism of the system.

[0053] like Figure 3As shown, in this embodiment, the transmission queue managed by the frame assembly and priority scheduling module is a priority queue, with the highest scheduling priority being the interruptible non-high-priority communication frame currently being transmitted. By adopting a priority scheduling strategy that allows interruptible non-high-priority communication frames currently being transmitted, preemptive scheduling of transmission channel resources is achieved. When a high-priority frame is ready, it does not need to wait for the currently transmitting low-priority frame to complete; instead, its transmission process can be forcibly interrupted, and the channel can be immediately preempted for transmission, eliminating the non-deterministic queuing delay caused by long data frame transmission or queue waiting. The priority queue manages the queuing order of frames to be transmitted, ensuring that high-priority frames are always at the forefront of scheduling; while the interruptible transmission mechanism further allows high-priority frames to cross the "currently transmitting" time boundary, achieving immediate response. The combination of these two mechanisms constitutes a non-blocking path from the "ready state" to the "air interface." This ensures that the worst-case delay from the occurrence of a high-priority event (such as an emergency stop trigger) to the start of transmission of its corresponding wireless signal is deterministic and extremely compressed. This upper limit of delay depends only on fixed overhead such as interrupt handling and frame assembly, and is independent of the length or number of low-priority frames being transmitted in the channel at that time. This deterministic, extremely low delay is a prerequisite and key guarantee for industrial safety control systems to respond to dangerous events in a timely manner, improving the system's safety response capability from "statistically low average delay" to "bounded and extremely short worst-case delay". This mechanism is an event-based dynamic resource allocation strategy, rather than static reservation. When there are no urgent events, channel resources are fully used for transmitting ordinary data (continuously varying, sparse events), maximizing system throughput with no idle resources. When an emergency occurs, the system immediately and dynamically reallocates all channel resources to the highest priority service, ensuring it is transmitted with the shortest possible delay. After transmission is complete, channel control is immediately returned to the interrupted or subsequent ordinary frames, avoiding the waste of channel resources caused by statically reserving fixed time slices or bandwidth for high-priority services in the absence of emergencies. This achieves the optimal dynamic balance of "fully serving ordinary services during normal times and instantly allocating resources to critical services during emergencies." While ensuring the highest level of real-time security, it maximizes the average utilization of the channel and the overall system throughput under normal conditions. By establishing "priority queues" and "interruptible transmission" as fixed behavioral specifications for the module, the highest-priority, unambiguous arbitration rules are provided for potential resource contention scenarios (in this case, channel access rights) in the system. This ensures that the system's behavior remains fully predictable and analyzable even under the most unfavorable load scenarios (e.g., a sudden stop when the channel is continuously busy transmitting long frames). Based on this mechanism, the maximum upper bound of the time from event triggering to the start of wireless transmission can be accurately calculated. This determinism and analyzability of behavior form the basis for conducting system-level functional safety assessments (such as calculating response time and performing failure mode and effects analysis, FMEA), greatly enhancing the system's reliability and trustworthiness under boundary and stress conditions.

[0054] like Figure 4 As shown, the industrial wireless remote controller of this embodiment includes a transmitter and a receiver, and the transmitter and / or receiver integrates the adaptive data compression device for industrial wireless remote controllers as described above. More specifically, Figure 4 This invention illustrates the system architecture of an industrial wireless remote controller according to a preferred embodiment, comprising a transmitter and a receiver connected via a bidirectional wireless link. At the transmitter, a human-machine interface unit (joystick, button, screen) generates raw control and status data. This data is processed by an application processor to form a raw data stream to be transmitted. This data stream is then efficiently compressed by the compression device of this invention, outputting compressed, high-efficiency data frames, which are ultimately transmitted via a wireless communication module (e.g., 433MHz). At the receiver, a wireless communication module receives the compressed data frames and sends them to the compression device of this invention (an adaptive data compression device for industrial wireless remote controllers) for decompression. The decompressed raw data is then transmitted to the device controller to drive the controlled device interface (CAN / IO / relay). Simultaneously, the receiver collects device status and alarm information through the same interface to form raw data to be transmitted back. This data is again compressed by the compression device of this invention into compressed, high-efficiency data frames, which are then transmitted back to the transmitter via the wireless communication module. This constitutes a complete, bidirectional, and deeply integrated wireless remote control data processing closed loop that incorporates the adaptive data compression and decompression functions of the adaptive data compression device for industrial wireless remote controllers of this invention.

[0055] The computer-readable storage medium of this embodiment stores a computer program that, when executed by a processor, implements the adaptive data compression method for industrial wireless remote controllers as described above.

[0056] The beneficial effects of the adaptive data compression device, method, and application of the present invention for industrial wireless remote controllers are as follows:

[0057] 1. High compression efficiency and strong adaptability: Through dynamic threshold algorithm, the system can automatically adapt to different operating scenarios. While ensuring control accuracy, the compression rate of joystick data in static or micro-motion scenarios can exceed 80%, which greatly improves the utilization rate of wireless channel.

[0058] 2. Reliable data transmission: The "event-driven + periodic full-state synchronization" mechanism ensures the reliable transmission of sparse event data; an independent priority channel is set up for alarm data to ensure the timely delivery of system security information.

[0059] 3. Low system power consumption: Significantly reduces the amount of invalid data transmitted wirelessly and the transmitter's working time, directly reducing the overall system power consumption and extending the battery life of portable devices.

[0060] 4. Flexible and configurable: All adaptive parameters can be adjusted online through the transmitter display or configuration tool, enabling the same hardware platform to flexibly adapt to the needs of different control scenarios, realizing the platformization and flexibility of the product.

[0061] Example 1:

[0062] like Figure 4 As shown, the adaptive data compression device of the present invention is deployed symmetrically in the microcontrollers (such as the STM32 series) of the transmitter and receiver of the industrial wireless remote controller in the form of software modules, located between the application layer and the data link layer of its communication protocol stack.

[0063] like Figure 1 As shown, the overall process of this method begins with step S100: data classification. The system classifies the input data in real time into: continuously changing data (such as joystick analog signals), sparse events (such as button events), high-priority emergency stop data (such as emergency stop switch signals), and high-priority alarm data (such as device fault codes) based on the data source or predefined rules.

[0064] The next step is the core process S200: adaptive policy decision-making. Taking continuously changing data as an example... Figure 2 As shown, the specific process of feature analysis and strategy decision-making for this type of data is as follows: the system calculates the short-term variance of this type of data in real time. ) and instantaneous absolute value of change ( Next, based on the pre-stored strategy configuration table, the parameters for the current operation mode are read. , , and using the formula

[0065] ;

[0066] The adaptive threshold is dynamically calculated. When the system is in "high-precision mode", The value will be automatically lowered to make the system more sensitive to minor changes.

[0067] Step S300 involves differential compression encoding. For continuously varying data, the absolute value of the current change value is compared. With dynamic threshold .like < If the difference is large enough, the transmission of data from that channel in the current communication cycle is suppressed; otherwise, differential encoding is performed on the difference, and variable-length encoding compression can be further used. Simultaneously, a channel valid bitmap is generated, where each bit indicates whether the corresponding channel's data exists in the current transmission frame. For sparse event-type data, it is encoded in a compact format of [event type (1 bit)][event ID (7 bits)], and multiple events can be sequentially packaged within a single frame.

[0068] Step S400 involves assembling compressed data frames and prioritizing their transmission. See Table 1 for details.

[0069] Table 1 - Compressed Data Frames

[0070]

[0071] illustrate:

[0072] The “frame type” is defined as follows:

[0073] 0x01: High-priority emergency stop frame / alarm frame, the highest priority information such as alarm and emergency stop. It has the highest priority and can be preempted. Once generated, it must be sent immediately and can interrupt the assembly of other frames.

[0074] 0x02: Regular mixed data frame, compressed downlink control data (joystick, button, etc.) or uplink status data, sent periodically or triggered by events, with normal priority;

[0075] 0x03: Full-state synchronization frame, a complete state snapshot (uncompressed or lightly compressed) of all button and joystick channels, used for packet loss prevention synchronization, sent periodically (e.g., every 20 regular frames) or on demand, with normal priority;

[0076] 0x04: Acknowledgment frame, indicating whether the reception was successful or failed (ACK / NACK). It replies immediately upon receiving a valid frame and has a high priority.

[0077] The complete structure of a frame is displayed in tabular form, including the definitions, lengths, and descriptions of fields such as preamble, frame header, frame type, channel valid bitmap, compressed data block, event count, event data area, and checksum.

[0078] The structure of a communication frame includes fields such as frame header, frame type, channel valid bitmap, compressed data block, event count, event data area, and checksum. The frame type field defines frames of different priorities; for example, 0x01 is a preemptible, high-priority emergency stop / alarm frame. Figure 3 As shown, when such a high-priority frame is generated, the frame assembly and scheduling module will immediately interrupt the low-priority regular frame that is currently being assembled, prioritize assembling and sending the high-priority frame, and resume the interrupted frame assembly process after the high-priority frame is sent, thereby ensuring the lowest transmission delay.

[0079] In a remote control application for cranes, configuration =5, =0.1, =0.05. When the operator performs precise alignment of the spreader, the lever movement is subtle. Approaching 5, the system ensures precise control. When rapidly moving the spreader, the joystick is pushed significantly. The compression ratio is automatically increased to, for example, 15, effectively filtering out intermediate values ​​and improving the compression rate. Once a system alarm is detected, a high-priority alarm frame is immediately generated and the channel is preempted, delivering the alarm information to the transmitter within milliseconds, significantly improving system security.

[0080] Matters not covered in this invention are common knowledge.

[0081] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive data compression method for industrial wireless remote controllers, applied to a wireless remote control system including a transmitter and a receiver, wherein the wireless remote control system employs LoRa modulation technology, characterized in that... Includes the following steps: S100. Based on the business logic and real-time requirements of downlink control data and uplink status data of the wireless remote control system, the raw data stream to be sent is divided into continuously slowly changing data, sparse event data and high-priority control data, which forms the basis for differentiated compression and priority scheduling, so that subsequent processing can be optimized for the characteristics of different types of data. S200: For continuously slowly changing data, the dynamic change characteristics are monitored in real time, and the adaptive change threshold used for differential compression judgment is adaptively adjusted according to the dynamic change characteristics, so that the adaptive change threshold can dynamically balance between ensuring control accuracy and improving compression efficiency in response to the data status. S300: Based on the data classification results and the determined compression adaptive change threshold, perform collaborative differentiated compression coding processing on various types of data, and assemble the compression coding results from various types of data and frame control information into a unified communication frame. S400: Based on the service priority corresponding to the data classification, the assembled communication frames are scheduled for transmission. For communication frames containing high-priority control data, the highest transmission priority is assigned to ensure low-latency transmission of critical control and security information.

2. The adaptive data compression method for industrial wireless remote controllers according to claim 1, characterized in that, Adaptive change threshold in step S200 Obtain it using the following formula: in, Based on the sensitivity threshold, For short-term variance, The absolute value of the instantaneous change. and These are configurable weighting coefficients. , and The value can be switched according to different operating modes set by the wireless remote control system.

3. The adaptive data compression method for industrial wireless remote controllers according to claim 1, characterized in that, Step S300 specifically includes: Differentiated compression encoding processing performs corresponding compression encoding operations based on data category and generates a unified channel effective bitmap; For continuously slowly varying data, differential compression based on an adaptive threshold is performed. The difference between the current sampled value and the previous valid transmitted value is compared with the threshold. If the absolute value of the difference is less than the threshold, transmission is suppressed; otherwise, the difference is encoded. For sparse event-type data, event-driven encoding is used, and encoding is performed only when the state changes; For high-priority control data, including high-priority emergency stop data and high-priority alarm data, independent and robust coding is used; By using event-driven methods, redundant transmission of sparse, unchanging event data is avoided. High-priority independent encoding ensures the reliability of critical information transmission. Combined with adaptive differential compression of continuously slowly varying data, this achieves optimal overall compression of heterogeneous data streams under limited bandwidth.

4. The adaptive data compression method for industrial wireless remote controllers according to claim 3, characterized in that, The differential compression encoding process in step S300 specifically includes: S310. For continuously slowly varying data, perform dynamic differential compression based on the compression adaptive change threshold, and generate a channel valid bitmap indicating the validity of the current frame for each data channel. S320. For sparse event-type data, event-driven coding is adopted; S330. For high-priority control data, an encoding method is adopted to ensure transmission priority and reliability. S340: The various types of data, channel effective bitmaps, and frame control information after differential compression are assembled into a unified communication frame. By assigning encoding strategies to different types of data and combining them with a unified frame structure, efficient compression is achieved while maintaining the integrity and parseability of data transmission.

5. The adaptive data compression method for industrial wireless remote controllers according to claim 4, characterized in that, Differential compression encoding processing for continuously varying data is as follows: If the absolute value of the current change is less than the adaptive change threshold, then the transmission of data in this channel is suppressed in this communication cycle; If the absolute value of the current change value is greater than or equal to the adaptive change threshold, then the change value is differentially encoded, and optionally variable-length encoding is used for further compression; The bitmap information is an N-bit bitmap, where N is the total number of continuously slowly varying data channels. The state of the nth bit in the bitmap indicates whether the compressed data of the nth channel exists in the current communication frame.

6. The adaptive data compression method for industrial wireless remote controllers according to claim 4, characterized in that, Differential compression encoding processing for sparse event-type data, specifically: The compression encoding operation for sparse event-type data adopts event-driven encoding, which encodes events into a compact format containing event type identifier and event ID only when a state change is detected, and supports sequentially packing multiple independent events in the data field of the same communication frame.

7. The adaptive data compression method for industrial wireless remote controllers according to claim 4, characterized in that, Priority scheduling for high-priority alarm data is as follows: when high-priority alarm data is generated, the assembly process of non-high-priority data frames that are being assembled is interrupted, and a communication frame containing the alarm data is assembled and sent first. After the transmission is completed, the interrupted frame assembly process is resumed.

8. An adaptive data compression device for industrial wireless remote controllers, characterized in that, The adaptive data compression method for an industrial wireless remote controller according to any one of claims 1 to 7 includes: The data classification module, connected to the data input interface, is used to sense the characteristics of the input data stream based on the business logic and real-time requirements of the data in industrial remote control scenarios, and classify the data stream into continuously slowly changing data, sparse event data, and high-priority control data, so as to initiate subsequent differentiated processing procedures. The adaptive decision module, which includes a strategy configuration unit and is connected to the data classification module, is used to adaptively determine the compression adaptive change threshold based on the real-time dynamic characteristics of continuously slowly changing data. The decision logic of the adaptive decision module is configured to output a low threshold to ensure resolution when a high-precision control requirement is identified, and output a high threshold to improve the compression ratio when a high-efficiency transmission requirement is identified. The collaborative coding execution module is connected to the data classification module and the adaptive decision-making module, respectively. It is used to receive classification and decision information and perform compression coding on various types of data in parallel according to the data type and transmission requirements. For continuously slowly changing data, selective differential coding is performed according to the compression adaptive change threshold, and bitmap information indicating the effective data channel is generated. The frame assembly and priority scheduling module, connected to the cooperative coding execution module and the transmission interface, is used to assemble various compressed encoded data, bitmap information and frame control information from the cooperative coding execution module into a unified communication frame, and schedule the transmission according to the service priority determined by the data classification. The frame assembly and priority scheduling module is configured to perform priority scheduling on the communication frames in the transmission queue according to the service priority determined by the data classification, wherein the communication frames containing high-priority control data are assigned the highest scheduling priority.

9. The adaptive data compression device for industrial wireless remote controllers according to claim 8, characterized in that, The policy configuration unit includes: Storage subunit and processing subunit; Storage subunit, used to store configurable compression strategy parameters; The processing subunit, connected to the storage subunit, is used to calculate the compression adaptive change threshold based on the real-time dynamic characteristics of the continuously slowly changing data and the compression strategy parameters.

10. The adaptive data compression device for an industrial wireless remote controller according to claim 9, characterized in that, Compression strategy parameters should include at least the basic sensitivity threshold. Variance weighting coefficient and instantaneous weighting coefficient ; The processing subunit is configured based on the formula in, For short-term variance, Calculate the compression adaptive change threshold based on the absolute value of the instantaneous change. .

11. The adaptive data compression device for an industrial wireless remote controller according to claim 8, characterized in that, The collaborative coding execution module includes: The first encoding submodule is used to perform selective differential encoding on continuously slowly varying data. The second encoding submodule is used to perform event-driven encoding on sparse event-type data. The third encoding submodule is used to encode high-priority control data using a forward error correction or retransmission mechanism.

12. The adaptive data compression device for an industrial wireless remote controller according to claim 8, characterized in that, The frame assembly and priority scheduling module manages a priority queue for sending, with the highest scheduling priority being the ability to interrupt the currently being sent non-high-priority communication frame.

13. An industrial wireless remote control, characterized in that, It includes a transmitter and a receiver, wherein the transmitter and / or receiver integrate an adaptive data compression device for an industrial wireless remote controller as described in any one of claims 8 to 12.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the adaptive data compression method for an industrial wireless remote controller as described in any one of claims 1 to 7.

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