Intelligent sensing device data acquisition and transmission scheduling control method and system

Abstract

The application provides a kind of intelligent sensing equipment data acquisition and transmission scheduling control method and system, it is related to Internet of Things data processing and communication control technical field, this method includes: real-time acquisition current acquisition data dynamic value index, generates scheduling execution queue, for each data transmission task dynamically allocates current state optimal transmission channel, and sets high priority preemption channel, when detecting that channel quality drops and node energy is lower than preset threshold, trigger transmission adaptive adjustment mechanism, edge gateway executes data transmission control according to scheduling execution queue and channel allocation result.This application solves the problem that multi-source sensing data acquisition and transmission lack cooperative scheduling in the prior art, cannot adapt to network load and channel change, thereby leading to data redundancy and critical data transmission delay. Realize sampling, scheduling and channel allocation cooperative closed-loop control, thereby improve data transmission efficiency and resource utilization, significantly improve system real-time performance and stability.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) data processing and communication control technology, and in particular to a method and system for data acquisition and transmission scheduling control of intelligent sensing devices. Background Technology

[0002] With the rapid development of IoT technology, edge computing technology, and intelligent sensing devices, intelligent sensing systems have been widely used in industrial production monitoring, environmental condition sensing, and equipment operation management. By deploying a large number of sensor nodes within the monitoring area, real-time acquisition and transmission of multi-source heterogeneous data can be achieved, providing fundamental support for subsequent data analysis and decision-making. However, in practical applications, as the scale of sensor nodes continues to expand and the data acquisition frequency increases, higher demands are placed on data transmission efficiency and system scheduling capabilities.

[0003] In existing technologies, intelligent sensor data acquisition and transmission systems typically employ fixed sampling frequencies or simple threshold triggering mechanisms for data uploading, and central nodes or edge gateways handle unified data scheduling and forwarding. However, these methods generally lack the ability to dynamically identify differences in data value and cannot adaptively adjust based on data change characteristics, node energy status, and network congestion. Furthermore, in multi-node concurrent transmission scenarios, the channel resource allocation method is relatively static, easily leading to channel contention conflicts, increased transmission delays, and data packet loss.

[0004] In summary, existing intelligent sensor data acquisition and transmission scheduling methods still have shortcomings in terms of dynamic sensing capabilities, resource adaptive scheduling capabilities, and multi-source data collaborative processing capabilities. They are unable to meet the requirements for efficient, stable, and low-redundancy data transmission in complex dynamic environments. Therefore, it is urgent to propose an intelligent sensor data acquisition and transmission scheduling control method that can realize data value perception, dynamic scheduling optimization, and closed-loop feedback control. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for data acquisition and transmission scheduling control of intelligent sensing devices, in order to solve the problems in the existing technology of lacking collaborative perception and dynamic scheduling capabilities for data value, node status and network operating environment during intelligent sensing data acquisition and transmission, resulting in fixed data transmission strategies, unreasonable channel resource allocation, transmission congestion and high data redundancy, thereby affecting the overall data transmission efficiency and system operation stability.

[0006] In view of the above problems, this application provides a method and system for data acquisition, transmission scheduling and control of intelligent sensing devices.

[0007] In a first aspect, this application provides a data acquisition and transmission scheduling control method for intelligent sensing devices. The method is implemented through an intelligent sensing device data acquisition and transmission scheduling control system. The method includes: each sensing node having a built-in multi-level value assessment module to obtain the dynamic value index of the currently acquired data in real time. The dynamic value index is obtained based on a comprehensive assessment of data change amplitude, node remaining energy, and event sensitivity. The dynamic value index is compared with a first preset threshold and a second preset threshold. When the dynamic value index is lower than the first preset threshold, the sensing node is controlled to reduce its sampling frequency. When the dynamic value index is higher than the second preset threshold, the sensing node is controlled to increase its sampling frequency and generate a data transmission request. An edge gateway receives data transmission requests from each sensing node. Each data transmission request includes data volume information, priority information, time constraint information, and a dynamic value index. The edge gateway performs comprehensive sorting processing on all data transmission requests based on request attributes and system operating status to generate a scheduling execution queue. The edge gateway constructs a real-time channel status table, which includes channel noise level, occupancy rate, and historical transmission reliability. The edge gateway periodically updates the real-time channel status table to reflect changes in the current network transmission environment. Based on the scheduling execution queue and the real-time channel status table, it dynamically allocates the optimal transmission channel for each data transmission task and sets high-priority preemption channels for immediate transmission control of urgent tasks. During data transmission, the edge gateway monitors channel status changes and node energy status in real time. When it detects a decrease in channel quality or a node energy level below a preset threshold, it triggers a transmission adaptive adjustment mechanism. After triggering the transmission adaptive adjustment mechanism, it performs compression processing on the data to be transmitted to reduce the transmission load. When there is spatial correlation between data collected by multiple sensor nodes, it performs upload control according to preset rules. The edge gateway executes data transmission control according to the scheduling execution queue and channel allocation results, and collects real-time transmission feedback information, including transmission success rate, transmission delay, and packet loss. The edge gateway dynamically updates the scheduling control parameters based on the transmission feedback information and distributes the updated sampling control parameters and scheduling control parameters to each sensor node and edge gateway execution unit to achieve a closed-loop optimized operation mechanism for sampling control, task scheduling control, and transmission path control.

[0008] Secondly, this application also provides a data acquisition and transmission scheduling control system for intelligent sensing devices, used to execute a data acquisition and transmission scheduling control method for intelligent sensing devices as described in the first aspect, wherein the system includes: a multi-level data value assessment module, used for each sensing node to build a multi-level value assessment module to obtain the dynamic value index of the currently acquired data in real time, wherein the dynamic value index is obtained by comprehensively assessing the data change amplitude, the remaining energy of the node, and the sensitivity of the event; a sampling control module, used to compare and judge according to the dynamic value index with a first preset threshold and a second preset threshold; wherein, when the dynamic value index is lower than the first preset threshold, The system controls the sensor nodes to reduce their sampling frequency; when the dynamic value index exceeds a second preset threshold, it controls the sensor nodes to increase their sampling frequency and generate data transmission requests; the scheduling and sorting module is used by the edge gateway to receive data transmission requests sent by each sensor node. Each data transmission request includes data volume information, priority information, time constraint information, and a dynamic value index. The edge gateway performs comprehensive sorting processing on all data transmission requests based on request attributes and system operating status to generate a scheduling execution queue; the channel state update module is used by the edge gateway to construct a real-time channel state table, which includes channel noise level, occupancy rate, and historical transmission reliability, and updates the channel state table accordingly. The system periodically updates the real-time channel status table to reflect changes in the current network transmission environment. The channel allocation and preemption module dynamically allocates the optimal transmission channel to each data transmission task based on the scheduling execution queue and the real-time channel status table, and sets high-priority preemption channels for immediate transmission control of urgent tasks. The status monitoring and adjustment module monitors channel status changes and node energy status in real time during data transmission. When a channel quality degradation or node energy falling below a preset threshold is detected, a transmission adaptive adjustment mechanism is triggered. The space compression upload module executes the data to be transmitted after the transmission adaptive adjustment mechanism is triggered. Compression processing reduces transmission load. When spatial correlation exists between data collected by multiple sensor nodes, upload control is performed according to preset rules. The transmission feedback acquisition module is used by the edge gateway to execute data transmission control according to the scheduling execution queue and channel allocation results, and to collect transmission process feedback information in real time, including transmission success rate, transmission delay, and packet loss. The closed-loop update module is used by the edge gateway to dynamically update the scheduling control parameters based on the transmission feedback information, and to send the updated sampling control parameters and scheduling control parameters to each sensor node and edge gateway execution unit, thereby realizing a closed-loop optimization operation mechanism for sampling control, task scheduling control, and transmission path control.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application provides a data acquisition and transmission scheduling control method and system for intelligent sensing devices. By introducing a dynamic value assessment mechanism at the sensing node side and adaptively adjusting sampling behavior based on node remaining energy, data change characteristics, and event sensitivity, the data acquisition process can dynamically adjust the sampling frequency according to the actual information value, thereby avoiding excessive acquisition of invalid data. At the edge gateway side, by constructing a multi-dimensional scheduling feature set and system operating state constraints, dynamic sorting and hierarchical scheduling of data transmission requests are achieved, allowing high-value data to obtain transmission resources first, improving the real-time performance and reliability of critical data. Simultaneously, by introducing a real-time channel state table, the channel noise level, occupancy rate, and historical transmission reliability are comprehensively characterized, enabling the channel allocation process to dynamically match the current network environment, thereby improving overall transmission efficiency. Furthermore, this application sets up a preemptive channel control mechanism to achieve resource replacement of low-priority tasks by high-priority tasks, thereby ensuring the immediate transmission capability of urgent data; by introducing an adaptive adjustment mechanism based on state change trends, continuous monitoring and hierarchical response control of channel quality and node energy status are performed, enabling the system to adopt differentiated adjustment strategies under different degradation levels, avoiding sudden changes in system performance. Furthermore, by employing spatial correlation analysis and a group upload control mechanism, highly consistent data is aggregated or uploaded representatively, effectively reducing redundant data transmission and network communication load. Simultaneously, this application introduces a closed-loop feedback adjustment mechanism, using multi-source feedback information such as transmission success rate, transmission delay, and packet loss rate for dynamic updates to scheduling parameters. This enables sampling control, scheduling control, and channel allocation control to form a unified and collaborative optimization process, thereby achieving adaptive optimization capabilities during long-term system operation. In summary, this application effectively improves data transmission efficiency and resource utilization, reduces communication load, and enhances overall system stability and real-time response capabilities. In conclusion, this application achieves full-process collaborative control of data acquisition, transmission scheduling, and feedback optimization, effectively reducing communication overhead and system energy consumption while improving data transmission efficiency, demonstrating strong engineering application value. The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to implement it according to the contents of the specification, and to make the above and other objects, features, and advantages of this application more apparent and understandable, specific embodiments of this application are described below. It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a data acquisition and transmission scheduling control method for an intelligent sensing device according to this application. Figure 2 This is a schematic diagram of the structure of a data acquisition, transmission, scheduling, and control system for an intelligent sensing device according to this application.

[0012] Explanation of reference numerals in the attached figures: The system includes a multi-level data value assessment module 11, a sampling control module 12, a scheduling and sorting module 13, a channel state update module 14, a channel allocation and preemption module 15, a state monitoring and adjustment module 16, a spatial compression and uploading module 17, a transmission feedback acquisition module 18, and a closed-loop update module 19. Detailed Implementation

[0013] This application provides a data acquisition and transmission scheduling control method and system for intelligent sensing devices. It solves the problems in the prior art where the lack of a collaborative perception and dynamic scheduling mechanism for data value, node energy status, and network operating environment makes it impossible to adaptively adjust sampling strategies and transmission resources according to the characteristics of multi-source data and the system operating status, resulting in fixed data transmission strategies, low channel resource utilization, and high data redundancy. It realizes data value-driven sampling control, dynamic scheduling optimization under multiple constraints, and closed-loop feedback adjustment of the transmission process, thereby improving the system's adaptability and operational stability in complex dynamic environments, and significantly improving data transmission efficiency and resource utilization.

[0014] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0015] Example 1 Please see the appendix Figure 1This application provides a data acquisition and transmission scheduling control method for intelligent sensing devices. The method is applied to a data acquisition and transmission scheduling control system for intelligent sensing devices, and specifically includes the following steps: S1: Each sensing node has a built-in multi-level value assessment module to obtain the dynamic value index of the currently collected data in real time. The dynamic value index is obtained by comprehensively assessing the data change range, the node's remaining energy, and the sensitivity of events.

[0016] Furthermore, step S1 of this application also includes: S11. Obtain the real-time change sequence of the currently collected data and construct a dynamic reference interval based on preset historical benchmark data; determine the data deviation degree by the deviation position and fluctuation amplitude of the currently collected data in the dynamic reference interval. D The calculation formula is: ;in, x(t) For current data, This is a historical benchmark value. S12: Obtain the current remaining energy E of the sensor node and assess the energy state based on the energy consumption rate per unit time; determine the energy consumption weighting coefficient according to the relationship between the degree of energy decay and the operating load level. W The calculation formula is: ;in, As initial energy, E For current energy, L This represents the current load strength of the node. or S13, based on the data deviation. (This is the load enhancement factor; S13, based on the data deviation.) D Energy consumption weighting coefficient W and event sensitivity factors S The three types of factors are hierarchically integrated, and a dynamic value index is obtained by comprehensively evaluating the balance between information value and resource consumption. V The calculation formula is: .

[0017] Specifically, the value of the collected data is assessed at the sensor node level to achieve pre-optimization control of data sampling and subsequent transmission scheduling. Specifically, each sensor node has a pre-installed multi-level value assessment module for real-time analysis of the currently collected data to obtain a corresponding dynamic value index. The dynamic value index characterizes the priority of current data processing under the combined effects of multiple factors such as information change degree, resource consumption constraints, and event importance, thus providing a basis for subsequent sampling frequency adjustment and data transmission scheduling. In step S11, the sensor node first acquires the real-time change sequence of the currently collected data within a preset time window and reads the corresponding historical benchmark data from its local storage unit. The historical benchmark data can be the long-term statistical mean or moving average of the sensor node under normal and stable operating conditions. Based on the historical benchmark data, a dynamic reference interval is constructed. The range of the dynamic reference interval is determined by the historical benchmark value and the historical fluctuation amplitude, where the historical fluctuation amplitude... This is obtained by calculating the standard deviation or statistically analyzing the maximum fluctuation range of historical data series. Subsequently, based on the currently collected data... x(t) Historical benchmark The degree of deviation between them, combined with historical fluctuations, to determine the data bias. D The calculation is performed, and the specific calculation method is as follows: Where x(t) is the current data, This is a historical benchmark value. This represents the historical fluctuation range. By using the above method, the degree of anomaly or intensity of change in the current data can be normalized based on the characteristics of historical fluctuations, thereby avoiding misjudgments caused by large fluctuations in the data itself. In step S12, the sensing node acquires the current remaining energy. E The remaining energy is calculated based on a preset energy monitoring mechanism to determine the energy consumption rate per unit time. E The initial energy can be read in real time through the node power management module. This represents the node's rated energy capacity when fully charged. Simultaneously, it obtains the current node's operating load intensity. L The load intensity can be calculated by normalizing parameters such as node CPU utilization, number of data processing tasks, or communication transmission frequency. Based on this, the energy consumption weighting coefficient is adjusted by combining the degree of energy attenuation with the load intensity. W The calculation is performed as follows: ;in, As initial energy, E For current energy, L This represents the current load strength of the node. orThis is the load enhancement factor; through the above calculation method, the node's energy consumption state can be coupled with the current operating load, thereby more accurately reflecting the resource constraints of the node when performing data acquisition and transmission tasks. In step S13, the data deviation obtained in step S11 is... D The energy consumption weighting coefficient obtained in step S12 W and event sensitivity factors S The final dynamic value index is obtained through comprehensive integration processing. V Among them, event sensitivity factors S Used to characterize the importance level of the event to which the current data belongs. S This can be obtained through a preset event sensitivity level table, for example, by mapping based on data type (abnormal alarm, critical status monitoring, or routine data collection), with a value range of 0 to 1. Dynamic Value Index V The calculation method is as follows: , The energy consumption suppression coefficient κ is adaptively determined based on the node's operating status and system load. First, the sensor node or edge gateway acquires the node's energy consumption rate per unit time and the current task load intensity in real time. The energy consumption rate can be calculated from the difference in remaining energy between two consecutive sampling periods, and the task load intensity can be characterized by the current sampling frequency, data generation rate, or the number of queued tasks. Second, an energy consumption pressure index is constructed based on the energy consumption rate and load intensity to characterize the resource stress of the current node during the execution of data acquisition and transmission tasks. When the energy consumption rate is high and the load intensity is high, it indicates that the node is in a high energy consumption pressure state. Based on this, the energy consumption suppression coefficient κ is adaptively mapped and determined according to the energy consumption pressure index. Specifically, this can be done as follows: Based on the energy consumption pressure index, the energy consumption suppression coefficient κ is... Adaptive mapping can be performed, specifically in the following manner: ;in, The basic suppression coefficient is a preset constant. Energy consumption pressure indicator; For adjustment coefficients, ， To represent the allowable variation in the suppression coefficient under the condition of satisfying performance constraints, This represents the corresponding change range of energy consumption pressure indicators; used to control the impact of energy consumption pressure on... The extent of the impact. Energy consumption per unit time; The initial energy of the node; The current load intensity (such as task queue length or normalized value of sampling frequency); As the load impact coefficient, during system operation, sensor nodes or edge gateways continuously record operational data under different load states. Load states can be characterized by task queue length, data generation rate, or sampling frequency, while simultaneously recording node energy consumption within the corresponding time period. By comparing and analyzing data from multiple time periods, the trend of node energy consumption changes when the load increases or decreases can be obtained, thus determining the strength of the load change's impact on energy consumption. When the load change causes a significant change in energy consumption, it indicates a large impact on energy consumption, and the load impact coefficient is set to a higher level; when the load change has a small impact on energy consumption, the load impact coefficient is set to a lower level. Furthermore, to improve stability, the statistical results from multiple time periods can be comprehensively processed, for example, by taking the average degree of change or selecting a typical operating range as a reference, thereby determining a stable load impact coefficient value. Through the above calculation method, a balance control between "data change value" and "resource consumption constraints" is achieved: when data changes are significant and the event sensitivity is high, the dynamic value index... V Increase; when node energy consumption pressure is high, through the suppression term Value is reduced to avoid frequent processing of low-priority data under resource constraints. Through the coordinated processing of steps S11 to S13, this application achieves multi-dimensional value assessment of the collected data, enabling the dynamic value index to not only reflect the changing characteristics of the data itself, but also to comprehensively consider the node operating status and event importance. This provides a reliable basis for subsequent sampling frequency control and data transmission scheduling, ensuring that the system has good adaptability and resource utilization efficiency in complex operating environments.

[0018] S2: The dynamic value index is compared with the first preset threshold and the second preset threshold to determine the value index. When the dynamic value index is lower than the first preset threshold, the sensor node is controlled to reduce the sampling frequency. When the dynamic value index is higher than the second preset threshold, the sensor node is controlled to increase the sampling frequency and generate a data transmission request.

[0019] Specifically, step S2 is used to adaptively control the data sampling and data uploading behavior of the sensor node based on the dynamic value index obtained in step S1. Specifically, a first preset threshold is pre-set during the system initialization phase. With the second preset threshold And satisfy Less than The first preset threshold is used to define the low-value data range, and the second preset threshold is used to define the high-value data range. These thresholds can be determined statistically based on historical operational data, for example, set based on the quantile intervals of the historical dynamic value index, and can be configured according to different application scenarios. During operation, the sensing node acquires the current dynamic value index in each sampling period. V and respectively with and When comparing, V Less than or equal to When the current data is determined to be in a low-value state, the sampling frequency is reduced by extending the sampling period or decreasing the number of samples per unit time; when V In and In between, maintain the current sampling frequency; when V Greater than or equal to When the current data is determined to be in a high-value state, the sampling frequency is increased by shortening the sampling period, and a data transmission request generation mechanism is triggered simultaneously. The data transmission request includes data volume information, priority information, time constraint information, and a dynamic value index, and is sent to the edge gateway for subsequent scheduling processing. In one embodiment, data that meets the high-value condition for multiple consecutive sampling periods can be batch-packaged to reduce the number of transmission requests. Furthermore, to avoid frequent fluctuations in the sampling frequency around a threshold, a minimum adjustment time interval or a minimum change threshold can be set. Frequency adjustment is only performed when the dynamic value index changes beyond a preset range, thereby improving system stability. Through the above methods, dynamic adjustment of the sampling frequency based on data value and upload trigger control are achieved.

[0020] S3: The edge gateway receives data transmission requests from each sensor node. Each data transmission request includes data volume information, priority information, time constraint information, and dynamic value index. Based on the request attributes and system operating status, the edge gateway performs comprehensive sorting and processing on all data transmission requests and generates a scheduling execution queue.

[0021] Furthermore, step S3 of this application also includes: S31. The edge gateway parses the received data transmission requests and encapsulates each request into a standard scheduling unit. The standard scheduling unit includes data volume information, priority information, time constraint information, and a dynamic value index. S32. A multi-dimensional scheduling feature set is constructed based on the standard scheduling units to characterize the scheduling demand intensity of tasks in the system. S33. The current system operating status of the edge gateway is obtained, including computing load, cache occupancy rate, and communication congestion level. A scheduling constraint set is generated based on the system operating status. S34. The scheduling weight of each data transmission request is dynamically adjusted according to the scheduling feature set and the scheduling constraint set. S35. The data transmission requests are processed hierarchically based on the adjusted scheduling weights. A scheduling execution queue, including at least a high-priority queue and a normal-priority queue, is constructed. The scheduling execution queue is dynamically updated according to changes in the system operating status.

[0022] Furthermore, step S34 of this application also includes: S34-1. Based on the scheduling feature set and scheduling constraint set, parse each data transmission request, and determine the corresponding basic scheduling weight based on the priority information, dynamic value index, and time constraint information of each data transmission request. This is used to characterize the initial scheduling priority of each data transmission request under conditions where system operating state constraints are not considered; S34-2, Obtain the current system operating load information of the edge gateway, and adjust the basic scheduling weights based on the system operating load information. The first correction process is performed, in which the system operating load... L The first adjusted scheduling weight is determined by the normalized result of CPU utilization and scheduling queue length, and is used to characterize the resource consumption of the edge gateway. The calculation formula is: ;in, This is the load impact factor. Based on historical scheduling success rate Confirmed, among which The average success rate of tasks within the historical scheduling period; S34-3, obtain the degree of communication congestion. C And based on the degree of communication congestion C For the first revised scheduling weight A second correction process is performed, in which the degree of communication congestion is considered. C The scheduling weight is determined by a combination of current channel occupancy and packet loss rate, and is used to characterize the congestion state of the network transmission environment. (Second modified scheduling weight) Represented as: ;in, This is the communication compensation coefficient. Determined based on the historical proportion of low-latency tasks; S34-4, Acquiring event sensitivity. And based on the sensitivity of the event For the second revised scheduling weight A third correction process is performed to obtain the final scheduling weight. ,in: ;in, c γ is the event enhancement coefficient, which is determined based on the performance improvement ratio brought about by the advance scheduling of historical emergency tasks and is used to reflect the system's enhanced response capability to high-priority events.

[0023] Specifically, this system is used to uniformly process and schedule data transmission requests from various sensor nodes at the edge gateway side, enabling orderly transmission of multi-source data under conditions of limited communication and computing resources. Specifically, the edge gateway first receives data transmission requests from each sensor node. Each data transmission request includes at least fields such as data volume information, priority information, time constraint information, and a dynamic value index. Subsequently, the edge gateway parses the received data transmission requests and encapsulates each request into a standard scheduling unit. This standard scheduling unit uses a unified data structure to store the aforementioned fields for subsequent unified scheduling and computation. Based on this, the edge gateway constructs a multi-dimensional scheduling feature set based on the standard scheduling units. This scheduling feature set includes at least time constraint features reflecting task urgency, priority features reflecting task importance, load features reflecting data scale, and dynamic value index features reflecting data value. By normalizing each feature, data of different dimensions can participate in unified scheduling computation. Simultaneously, the edge gateway acquires the current system operating status in real time, including processor computing load, cache utilization, and communication link congestion. Computational load can be obtained through CPU utilization or task queue length; cache utilization can be obtained through memory usage ratio; and communication congestion can be calculated through data queue length per unit time or channel occupancy ratio. Based on this operating status, a set of scheduling constraints is constructed to limit the scheduling range that the system can handle under the current state. Furthermore, the edge gateway dynamically adjusts the scheduling weights of each data transmission request according to the scheduling feature set and the scheduling constraint set. For example, it reduces the weight of large-volume tasks when the system load is high, and increases the weight of high-priority and latency-sensitive tasks when communication congestion is severe, thus adapting the scheduling results to changes in the current system state. Further, based on the adjusted scheduling weights, all data transmission requests are processed hierarchically, constructing a scheduling execution queue that includes at least a high-priority queue and a normal-priority queue. The high-priority queue stores data transmission tasks with high dynamic value indices or strict time constraints, while the normal-priority queue stores regular tasks and can be further subdivided into multiple sub-queues as needed. During the scheduling process, the edge gateway extracts tasks from each queue sequentially according to a preset queue scheduling strategy. Simultaneously, it dynamically updates the scheduling queues based on real-time changes in the system's operating status. For example, when a decrease in system load or an improvement in channel conditions is detected, some delayed tasks are re-promoted to higher-priority queues to improve overall system throughput. Through these steps, adaptive sorting and scheduling control of multi-source data transmission requests in complex operating environments is achieved, providing an orderly and efficient task execution foundation for subsequent channel allocation and data transmission.Furthermore, the scheduling weights of data transmission requests are calculated and dynamically adjusted at the edge gateway to ensure that the scheduling results take into account both the importance of the task itself and the current resource status of the system. Specifically, in step S34-1, the edge gateway first parses the encapsulated standard scheduling unit to extract its priority information, dynamic value index, and time constraint information. To ensure the comparability of data across different dimensions, the priority information can be mapped to numerical priority coefficients according to a preset priority level table. P (For example, mapping high, medium, and low priorities to 1, 0.6, and 0.3 respectively), Dynamic Value Index V The result of step S1 is used directly, while the time constraint information is based on the remaining time between the task deadline and the current time. Δt Quantification processing, for example, through Δt With the preset maximum allowable delay The ratio is normalized to obtain the timeliness coefficient. T ,and The above features are then weighted and fused to obtain the basic scheduling weight. , is used to characterize the initial scheduling priority of each data transmission request under conditions where system operating state constraints are not considered, where It can be determined by linear combination, for example = P+ V+ T , in 、 、 For the preset weighting coefficients, satisfy + + = 1, and each coefficient can be preset according to business needs or configured based on historical experience. In step S34-2, the edge gateway further obtains the current system operating load information and adjusts the basic scheduling weights based on this information. Perform the first corrective action. Specifically, the system operating load... L The resource utilization of an edge gateway is characterized by a combination of CPU utilization and scheduling queue length. CPU utilization is obtained in real-time by the processor monitoring module and normalized to a value between 0 and 1. The scheduling queue length is normalized to the ratio of the number of currently waiting tasks to the preset maximum queue capacity, also mapped to a value between 0 and 1. The two are then merged according to a preset ratio, for example...L CPU Q Where CPU represents the normalized CPU utilization rate. Q This is the normalized queue length. This is the weighting coefficient, with a value ranging from 0 to 1. This is determined after obtaining the system operating load. L Then, the basic scheduling weights are adjusted. Make corrections, the first corrected scheduling weight The calculation formula is: ;in, This is the load impact factor. Based on historical scheduling success rate Confirmed, among which The edge gateway calculates the historical average scheduling success rate based on the average success rate of tasks over historical scheduling periods. It statistically analyzes the execution results of each data transmission task across multiple historical scheduling periods, recording the number of successfully completed tasks and the total number of tasks in each period. Furthermore, based on the historical average scheduling success rate The correspondence between the system's operating status and its impact on the load coefficient. A hierarchical determination is made. When A high level indicates that the system is running stably under the current load conditions and that scheduling resources are relatively sufficient. At this point, [the system can proceed]. Set to a smaller value to reduce the suppressive effect on the basic scheduling weight; when When the level is moderate, it indicates that the system is experiencing some degree of resource contention or scheduling pressure. At this point, [the system will...]. Set to a medium value; when A low level indicates a decrease in system scheduling success rate, suggesting significant load congestion or resource shortage. In this case, [the system will...]. Setting a larger value enhances the suppression effect on scheduling weights, thereby reducing the system resource consumption of low-priority tasks. In specific implementations, multiple success rate ranges can be pre-defined as judgment criteria. For example, high success rate ranges, medium success rate ranges, and low success rate ranges can be determined based on historical running data, each corresponding to different levels. The range of values; at the same time, to avoid scheduling instability caused by sudden changes in coefficients, adjustments can be made between adjacent scheduling cycles. Limit the magnitude of changes, for example, by setting a maximum adjustment step size or using a gradual incrementing or decrementing update method. Furthermore, the edge gateway can be continuously updated during system operation. And recalculate periodically. This enables it to dynamically reflect changes in system load, thereby achieving adaptive adjustment and control of scheduling weights. It also allows for the acquisition of communication congestion levels. C And based on the degree of communication congestion C For the first revised scheduling weight A second correction process is performed, in which the degree of communication congestion is considered. C The scheduling weight is determined by a combination of current channel occupancy and packet loss rate, and is used to characterize the congestion state of the network transmission environment. (Second modified scheduling weight) Represented as: ;in, This is the communication compensation coefficient. Determined based on the historical percentage of low-latency tasks; Sensitivity of events is assessed. S Sensitivity of the event S This level of importance, used to characterize the current data acquisition or transmission task to the system, is determined based on a comprehensive analysis of data content characteristics, historical event response rules, and real-time system status. Specifically, when an edge gateway or sensor node generates a data transmission request, it first identifies event characteristics of the currently acquired data. These event characteristics include at least one or more of the following: whether the data change exceeds the historical normal fluctuation range; whether it triggers a preset event threshold (e.g., a mutation threshold or anomaly threshold); whether the data corresponds to a defined key monitoring type (e.g., anomaly detection point, key area node, or high-risk indicator); and whether the duration of the data change exceeds a continuous time window threshold. Based on this, the above event characteristics are mapped to a basic event level value. For example, events can be categorized as: normal events ( Lower), focus on events ( Medium), key events ( (Higher), emergency ( (Highest). Furthermore, to enhance the adaptability of event sensitivity to system operating states, a dynamic correction factor is introduced for adjustment. This dynamic correction factor is determined based on the current system load state. When the system is under high load or congestion, the distinguishability of event sensitivity is increased; when the system is idle, the distinguishability is decreased to avoid excessive triggering of scheduling resource contention. The final event sensitivity is... This is a combination of a basic event level value and a system state correction factor, reflecting both the importance of the data itself and the current system's capacity to handle the event. It is also based on the event sensitivity. For the second revised scheduling weight A third correction process is performed to obtain the final scheduling weight. ,in: ;in, c The event amplification factor. cThe performance improvement rate is determined based on the historical performance gains from advance scheduling of emergency tasks, reflecting the system's enhanced response capability to high-priority events; among which, the communication compensation coefficient... Statistical results characterizing the system's ability to guarantee low-latency tasks under historical communication conditions are used. Specifically, the edge gateway statistically analyzes the task sets completed within multiple historical scheduling periods and calculates the proportion of tasks that meet low-latency constraints, denoted as Rlow, which is the proportion of low-latency tasks (number of low-latency tasks / total number of tasks). The value of Rlow is then used to... Hierarchical mapping is performed to determine: when Rlow is higher, it indicates that the system communication conditions are better. Take the lower value; when Rlow is at a medium level, then Take the median value; when Rlow is low, it indicates insufficient low-latency guarantee capability, so increase it. To enhance communication compensation. These are parameters that are updated on a rolling basis according to historical scheduling cycles to reflect long-term communication performance trends. Among them, the event enhancement coefficient... c This is used to characterize the system's enhanced response capability to high-priority or emergency events, and is determined based on the historical emergency task scheduling results. Specifically, the edge gateway statistically analyzes the execution results of historical emergency tasks and calculates the proportion of emergency tasks completed ahead of schedule, denoted as [missing information]. , =Statistical value of the early completion rate of emergency tasks, based on Size pair c Perform mapping: when A higher value indicates that the system has a strong ability to respond to emergency tasks. c Take a higher value to enhance the effect of event-driven scheduling; when At a lower level, c A lower value is chosen to avoid excessive priority skew that could negatively impact overall system stability. When the system load is low, the scheduling weights are less affected, thus improving the overall system throughput. Through these steps, interpretable and implementable dynamic adjustments to the scheduling weights are achieved without relying on complex models.

[0024] S4: The edge gateway constructs a real-time channel state table, which includes channel noise level, occupancy rate and historical transmission reliability, and periodically updates the real-time channel state table to reflect changes in the current network transmission environment.

[0025] Specifically, a real-time channel status table is constructed and maintained at the edge gateway to continuously perceive the current communication environment, thereby providing a basis for subsequent channel allocation and data transmission control. Specifically, the edge gateway establishes corresponding status record entries for each available communication channel. The real-time channel status table includes at least parameters such as channel noise level, channel occupancy rate, and historical transmission reliability. The channel noise level can be obtained by statistically analyzing the bit error rate and signal interference intensity within a unit time window, and then normalizing the results to obtain the corresponding noise index. The channel occupancy rate can be obtained by statistically analyzing the ratio between the duration of channel occupancy within a preset time window and the total time window length. Historical transmission reliability characterizes the stability of the current channel during historical data transmission. In specific implementation, historical transmission reliability... R It can be calculated using the following formula: ;in, This indicates the number of successfully received data packets, which is obtained by counting the acknowledgment messages returned by the receiving end. This represents the total number of data packets sent within the current statistical period, which is obtained by statistically analyzing the sending records of the edge gateway. This indicates the number of data packets that were retransmitted, which is obtained by detecting the number of times the same data packet was repeatedly sent within a preset time window. The number of data packets that timed out is obtained by counting the number of data packets that did not receive an acknowledgment within a preset waiting time. Specifically, a data packet is considered successfully transmitted when it receives an acknowledgment from the receiver within the preset waiting time after transmission; a data packet is considered to have failed transmission when it times out without acknowledgment, fails verification, or exceeds a preset limit for retransmissions, and is included in the historical transmission reliability statistics process. In specific implementations, the edge gateway periodically collects and updates the aforementioned channel state parameters according to a preset time period. For example, it triggers a state refresh operation at fixed time intervals and performs normalization processing on the currently collected data, while also performing weighted updates based on historical state data to reduce the impact of instantaneous fluctuations on the channel state judgment results. Furthermore, to improve the stability of the real-time channel state table, a minimum update granularity or change threshold can be set for channel noise indicators, channel occupancy indicators, and historical transmission reliability indicators. The state table update operation is only performed when the change in the corresponding indicator exceeds the preset threshold range; for minor changes within the preset threshold range, the current historical state record remains unchanged. The above method allows the real-time channel state table to continuously reflect changes in the current network transmission environment.

[0026] S5: Based on the scheduling execution queue and the real-time channel status table, dynamically allocate the transmission channel with the best current status to each data transmission task, and set a high-priority preemption channel for immediate transmission control of urgent tasks.

[0027] Furthermore, step S5 of this application also includes: S51. Perform a comprehensive evaluation of each communication channel in the real-time channel status table to determine the channel quality level, which characterizes the transmission reliability and resource occupancy status of the current channel; S52. Based on the priority level of tasks in the scheduling execution queue and the channel quality level, match and allocate each data transmission task to generate an initial channel allocation result; S53. Perform conflict detection on the initial channel allocation result to identify the contention and occupancy relationship of multiple data transmission tasks on the same channel; S54. When a channel conflict is detected between a high-priority task and a low-priority task, trigger a preemptive control mechanism to release or delay the execution of the low-priority task from the current channel and allocate channel resources to the high-priority task; S55. When the channel status or task priority changes, dynamically reallocate the allocated channel resources and reinstate the delayed task to the scheduling execution queue for subsequent execution control.

[0028] Specifically, step S5 is used to dynamically allocate channels for each data transmission task on the edge gateway side based on the scheduling execution queue and the real-time channel state table, and to ensure the priority transmission of urgent tasks by introducing a preemption mechanism. Specifically, the edge gateway first processes the real-time channel state table constructed in step S4, and comprehensively evaluates the noise level, occupancy rate, and historical transmission reliability of each communication channel to obtain the corresponding channel quality level. For ease of implementation, different indicators can be normalized and then weighted and fused according to preset weights. The edge gateway first parses the real-time channel state table constructed in step S4 to extract the noise level corresponding to each communication channel. N Channel occupancy rate O and historical transmission reliability R The above indicators were then normalized to eliminate dimensional differences between them. Among these, noise level... N Used to characterize the interference intensity and occupancy rate in a channel communication environment. O Used to characterize the current congestion level of channel resources and historical transmission reliability. R This is used to characterize the stability and successful transmission capability of the channel during historical communication processes. Based on this, a comprehensive channel quality value is constructed. Q The calculation method is as follows: ,in, N and O Larger values ​​indicate higher levels of channel interference and more severe resource consumption, respectively. Therefore, by... and It is then characterized in reverse to make it positively correlated with channel quality. R It directly participates in the comprehensive calculation as a positive enhancement term, thereby...Q It can simultaneously reflect the level of environmental interference, resource consumption, and historical communication reliability of the channel. Furthermore, the edge gateway adjusts the overall channel quality value based on preset thresholds. Q A hierarchical mapping process is performed to divide the channel into different quality levels, with the following division rule: ;in, For high-quality thresholds, The low quality threshold, and Greater than Through the above method, the edge gateway can fuse multi-dimensional channel state information into a unified channel quality level output, thereby providing a standardized basis for channel selection and scheduling control of subsequent data transmission tasks, and improving the system's adaptability and transmission stability in dynamic network environments. For example, the lower the noise level, the lower the occupancy rate, and the higher the historical transmission success rate, the higher the corresponding channel quality level, and the channel can be divided into high-quality, medium-quality, and low-quality levels. Based on this, combined with the scheduling execution queue generated in step S3, according to the priority level of the task and its time constraints, high-priority or delay-sensitive tasks are preferentially allocated to high-quality channels, and ordinary tasks are allocated to medium- and low-quality channels, thus forming the initial channel allocation result. Subsequently, the edge gateway performs conflict detection on this initial allocation result, specifically by scanning the task occupancy of each channel, identifying situations where multiple data transmission tasks are allocated to the same channel within the same time window and there is bandwidth or time slot contention, and marking them as channel conflicts. When a high-priority task and a low-priority task are detected competing for the same channel, a preemptive control mechanism is triggered. The low-priority task is released from the current channel or delayed to a later time window, and the channel resource is reallocated to the high-priority task to ensure priority transmission of critical data. The preemptive control mechanism refers to a dynamic scheduling control process where, when multiple tasks of different priorities are simultaneously competing for channel resources, the edge gateway releases or delays resources for low-priority tasks based on task priority relationships, and reallocates the corresponding channel resources to high-priority tasks. To avoid system instability caused by frequent preemption, a minimum time interval or a maximum number of preemption attempts can be set for the preemption operation. Simultaneously, during system operation, when a change in channel state is detected (e.g., increased noise level or occupancy) or a task priority is adjusted, the edge gateway dynamically reallocates the allocated channel resources, migrating tasks no longer suitable for the current channel state to more appropriate channels, and reinstating delayed or released tasks into the scheduling execution queue for subsequent scheduling processing. Through these steps, adaptive resource allocation and priority guarantee control for multi-task, multi-channel environments are achieved, effectively improving channel utilization and the real-time performance and reliability of critical data transmission.

[0029] S6: During data transmission, the edge gateway monitors channel status changes and node energy status in real time. When it detects a decline in channel quality or a node energy level below a preset threshold, it triggers a transmission adaptive adjustment mechanism.

[0030] Furthermore, step S6 of this application also includes: S61. Real-time acquisition of channel state parameters and node energy state parameters, wherein the channel state parameters include at least channel quality indicators and occupancy change rate; S62. Trend analysis of channel state parameters and node energy state parameters over multiple consecutive time sampling periods to determine the state change trend and its intensity; S63. Based on the state change trend, classify the system operating state into stable state, degraded state, and critical state; wherein, stable state corresponds to a gradual state change trend; degraded state corresponds to a continuous downward trend; and critical state corresponds to a rapid downward trend; S64. Trigger different levels of [unspecified] based on different operating states. Adaptive adjustment mechanism; when in a degraded state, a mild adjustment mechanism is triggered; when in a critical state, a forced adjustment mechanism is triggered; when in a stable state, no adjustment mechanism is triggered; S65, when the adaptive adjustment mechanism is triggered, it is applied to the channel control parameters or node energy control parameters according to the trigger level to achieve targeted adjustment; S66, the continuously triggered adaptive adjustment behavior is suppressed and controlled. When the adjustment frequency exceeds the preset upper limit, new adjustment triggers are restricted to avoid system oscillation; S67, the deviation between the state change trend and the actual trigger result is corrected and analyzed to dynamically adjust the subsequent trigger control strategy.

[0031] Furthermore, step S67 of this application also includes: S67-1. Establish a mapping relationship between the trend of state changes and the actual trigger execution results to characterize the actual response effect of trigger control; S67-2. Compare and analyze the expected trigger state and the actual trigger result to identify trigger lag deviation and trigger over-deviation; S67-3. According to the magnitude of the trigger deviation, classify the system trigger lag degree into first-level lag, second-level lag, and third-level lag; S67-4. Perform hierarchical adaptive correction of subsequent trigger thresholds and trigger levels according to the lag level; Specifically, when it is a first-level lag, the trigger threshold is adjusted in a single step according to a preset step size; when it is a second-level lag, the trigger threshold and trigger level are adjusted simultaneously with multiple parameters; when it is a third-level lag, the trigger threshold is reset within a preset range, and the trigger level is switched to a high-priority control mode.

[0032] Specifically, step S6 is used to continuously monitor the system's operating status during data transmission and trigger an adaptive adjustment mechanism when adverse changes are detected to ensure the stability of the transmission process and the efficiency of resource utilization. Specifically, the edge gateway collects channel state parameters and node energy state parameters in real time during data transmission. The channel state parameters include at least the channel quality indicators obtained in step S4 (which can be comprehensively characterized by noise level, occupancy rate, and historical reliability) and the rate of change of channel occupancy rate per unit time. The node energy state parameters are the current remaining energy of each participating sensor node and its changes. To ensure the comparability of the parameters, the above parameters can be normalized and mapped to a unified numerical range. Based on this, the edge gateway performs trend analysis on the parameters collected over multiple consecutive time sampling periods. Specifically, a differential statistical analysis method based on a sliding time window can be used to process the channel state parameters and node energy state parameters. That is, within a preset window length, the data of adjacent sampling periods are differentially calculated, and the rate of change per unit time (slope) is combined to quantify the parameter change trend, thereby identifying the direction and intensity of state changes, and thus determining whether the channel quality and node energy are in a stable state, a declining trend, or a rapidly deteriorating trend. Based on the analysis results, the system's operating state is divided into stable, degraded, and critical states. A stable state is defined as a parameter change below a preset threshold; a degraded state is defined as a parameter continuously decreasing at a rate within a first preset range; and a critical state is defined as a parameter rapidly decreasing within a continuous period at a rate exceeding a second preset threshold. After state determination, the edge gateway triggers different levels of adaptive adjustment mechanisms based on the operating state. In a degraded state, a mild adjustment mechanism is triggered, such as appropriately reducing the transmission frequency of some low-priority tasks or adjusting channel allocation. In a critical state, a forced adjustment mechanism is triggered, such as prioritizing the transmission of high-priority tasks, suspending some non-critical tasks, or reallocating channel resources. In a stable state, no adjustment mechanism is triggered to maintain the current operating strategy. Furthermore, when the adjustment mechanism is triggered, channel control parameters or node energy control parameters are adjusted specifically according to the trigger level. This can be achieved using a rule-based mapping parameter adjustment method, i.e., pre-establishing a mapping relationship between different trigger levels and corresponding control parameter adjustment strategies, and combining this with a sliding correction mechanism to incrementally adjust the current parameters. Channel control parameters can be adjusted by modifying the channel occupancy time slot length, transmission bandwidth allocation ratio, or retransmission interval. Node energy control parameters can be adjusted by modifying the sampling frequency, sleep period, or data reporting period, thus enabling differentiated control strategies under different triggering conditions. For example, channel allocation strategies can be adjusted first when channel quality deteriorates, and the data transmission frequency of relevant nodes can be reduced when node energy is insufficient.Meanwhile, to avoid system oscillations caused by frequent adjustments, this application suppresses and controls the continuously triggered adaptive adjustment behavior. Specifically, this can be achieved using a time-window-based frequency limiting control method or a hysteresis control method. That is, the number of adjustment triggers is counted within a preset time window. When the trigger frequency exceeds a preset upper threshold, new adjustment trigger requests are paused or delayed. Simultaneously, upper and lower threshold hysteresis intervals are set to provide a buffer between entering and exiting the adjustment state, thereby avoiding frequent switching caused by short-term fluctuations in channel status or node energy. For example, a maximum number of triggers or a minimum trigger interval can be set per unit time; when a preset upper limit is reached, new adjustment triggers are suspended. Furthermore, the edge gateway analyzes the deviation between the state change trend and the actual adjustment results, such as comparing channel quality or energy changes before and after adjustment. When the expected improvement effect is not achieved, the subsequent adjustment trigger thresholds and adjustment intensity are corrected, thereby achieving gradual optimization of the adjustment strategy. Through the above steps, dynamic monitoring and hierarchical adjustment control of the transmission process are realized, enabling the system to maintain stable operation and improve resource utilization efficiency in complex and changing environments. Furthermore, feedback correction is applied to the triggering effect of the adaptive adjustment mechanism to improve the accuracy and timeliness of subsequent adjustment control. Specifically, in step S67-1, the edge gateway establishes a mapping relationship between the state change trend and the actual trigger execution result based on historical operation records. The state change trend can be characterized by the parameter change direction and rate of change obtained in step S62. In specific implementation, a statistical mapping method based on historical time series alignment can be adopted. That is, within a preset time window, the state change trend data and the trigger execution result at the corresponding moment are matched cycle by cycle, and the correspondence between the two is established by a sliding window cumulative statistical method, thereby forming a mapping relationship library for characterizing the trigger execution effect under different trend states. For example, the decline slope of channel quality indicators or node remaining energy in a continuous time window. The actual trigger execution result can be quantified by the degree of improvement of corresponding parameters before and after trigger adjustment, such as the improvement of channel quality or the slowdown of energy decline rate after adjustment. The above mapping relationship can be constructed by recording the triple data of "state before trigger - trigger action - result after trigger" in the time series and storing it in the local cache of the edge gateway in chronological order for subsequent comparative analysis. In step S67-2, based on the mapping relationship, the expected triggering state and the actual triggering result are compared and analyzed. The expected triggering state can be determined according to the current trend analysis results and the predetermined adjustment target. For example, in a degraded state, it is expected that the channel quality decline trend will be suppressed, and in a critical state, it is expected that it will quickly recover to the stable range. The actual triggering result is the actual change within a preset evaluation time window after the adjustment is executed. By calculating the difference between the expected change amplitude and the actual change amplitude, trigger lag bias (i.e., the adjustment effect lags behind the state change) and trigger over-bias (i.e., the adjustment intensity exceeds the actual demand) are identified.In step S67-3, the system trigger lag degree is classified according to the trigger deviation obtained in step S67-2. Specifically, the system trigger lag degree is divided into Level 1 lag, Level 2 lag, and Level 3 lag, with each lag level corresponding to a different deviation range. In practice, deviation threshold ranges corresponding to each level can be preset based on historical operation statistics. For example, a deviation absolute value lower than a first preset threshold is judged as Level 1 lag, a deviation absolute value between the first and second preset thresholds is judged as Level 2 lag, and a deviation absolute value higher than the second preset threshold is judged as Level 3 lag, thereby achieving graded identification of the system trigger lag degree. In step S67-4, the subsequent trigger threshold and trigger level are adaptively corrected according to the determined lag level. Specifically, a parameter adjustment method based on graded rule mapping can be used, that is, a mapping relationship between different lag levels and corresponding parameter adjustment strategies is pre-established, and the current control parameters are incrementally updated in combination with historical operation data. Specifically, for a level 1 lag, the trigger threshold is adjusted in a single step according to a preset step size, for example, by updating the current trigger threshold in a small range using a threshold correction method based on a moving average. For a level 2 lag, multiple parameters are simultaneously adjusted in conjunction with the trigger threshold, for example, the trigger timing is advanced while the adjustment level is increased synchronously. For a level 3 lag, a preset interval reset of the trigger threshold is performed using parameter backtracking and interval reset methods, and the trigger level is switched to a high-priority control mode to improve the system's response speed to abnormal state changes. The corrected parameters are applied to subsequent adjustment processes and are continuously updated dynamically in subsequent operating cycles, thus forming a closed-loop adaptive optimization mechanism based on actual execution results. Through this step, the risk of system oscillation caused by adjustment lag or over-adjustment can be effectively reduced, improving the stability and response accuracy of the overall scheduling and control process.

[0033] S7: After triggering the transmission adaptive adjustment mechanism, the data to be transmitted is compressed to reduce the transmission load. When there is spatial correlation between the data collected by multiple sensor nodes, the upload control is performed according to the preset rules.

[0034] Furthermore, step S7 of this application also includes: S71. Perform spatial correlation analysis on data collected by multiple sensor nodes within the same time window to identify the degree of spatial correlation between data; S72. Divide multiple sensor nodes into several spatially correlated groups according to the degree of spatial correlation, and perform aggregation control processing on data within the same group; S73. Within the same spatially correlated group, select sensor nodes that meet preset data validity conditions as upload nodes, and perform local caching control on data from other nodes; S74. Adaptively switch the transmission strategy according to the current channel load status and the strength of data spatial correlation; wherein, when the data spatial correlation is higher than the first correlation threshold, and the signal strength is lower than the threshold, the transmission strategy is adjusted accordingly. When the channel load is higher than the first load threshold, switch to compressed transmission mode; when the data spatial correlation is lower than the second correlation threshold and the channel load is lower than the second load threshold, switch to full transmission mode; when the data spatial correlation or channel load is between the two thresholds, maintain the current transmission mode or adopt partial compressed transmission mode; S75, in compressed transmission mode, perform redundancy suppression processing on the data to be transmitted to reduce duplicate information transmission and reduce the communication load of the edge gateway; S76, dynamically adjust the current transmission mode according to the transmission success rate and network load changes, and update the transmission control strategy for the next scheduling cycle.

[0035] Specifically, after triggering the transmission adaptive adjustment mechanism, the data to be transmitted is further optimized to reduce communication load and improve transmission efficiency, which is particularly suitable for multi-sensor node collaborative acquisition scenarios. Specifically, in step S71, the edge gateway or local coordination unit performs spatial correlation analysis on the data collected by multiple sensor nodes within the same time window. This spatial correlation analysis is based on the spatial location relationship of the sensor nodes and the consistency of data changes. In a specific implementation, a similarity analysis method based on spatial adjacency can be used. This involves constructing a set of adjacent nodes based on the physical spatial coordinates of the sensor nodes or a preset topological connection relationship, and performing point-by-point difference calculations or similarity calculations on the data collected by adjacent or neighboring nodes within a preset time window. For example, the degree of data difference can be quantitatively evaluated based on Euclidean distance or absolute difference. Simultaneously, a sliding time window is used to smooth and statistically analyze multi-period data, thereby identifying the consistency level or redundancy of data changes between different nodes, and thus determining the strength of the correlation between data in spatial distribution. In step S72, based on the identified spatial correlation, multiple sensor nodes are divided into several spatially related groups. Nodes within the same group have high similarity in spatial location or data change characteristics, and the data within the same group is aggregated and controlled. Specifically, a grouping method based on a similarity threshold or a spatial partitioning method based on cluster analysis can be used. That is, a similarity index is calculated based on the spatial distance between sensor nodes and the consistency of data changes, and nodes that meet the conditions are grouped into the same group according to a preset similarity threshold. Alternatively, spatially related nodes can be automatically grouped using density clustering or hierarchical clustering. After grouping, the data within the same group undergoes regular aggregation processing. For example, highly similar data or data with consistent change trends are redundantly identified and merged, retaining only representative data or dominant node data, thereby reducing the amount of duplicate data uploaded and lowering the communication load. In step S73, within each spatially related group, a subset of sensor nodes are selected as upload nodes based on preset data validity conditions. These conditions may include nodes with significant data fluctuations, high data integrity, or those located in critical monitoring positions. Nodes that do not meet these conditions undergo local caching control, temporarily storing the collected data in local storage units to avoid redundant uploads and wasting communication resources. Specifically, a node selection method based on rule-based thresholds can be used. This involves evaluating each node's data fluctuation range, data integrity rate, and preset spatial importance weight, and selecting nodes that meet the conditions as representative upload nodes based on preset thresholds. Simultaneously, data from unselected nodes is managed through local caching, using circular buffering or time-series caching to temporarily store data, supporting subsequent on-demand uploads or retrospective analysis, thereby reducing redundant communication and lowering network load.In step S74, the transmission strategy is adaptively switched based on the current channel load status and the strength of spatial correlation. The channel load status can be characterized by the current channel occupancy rate, data transmission queue length, or the number of tasks queued per unit time. The strength of spatial correlation can be characterized by the consistency of data changes within the same spatially correlated group. Specifically, the edge gateway continuously monitors the occupancy of each communication channel according to a preset sliding time window to obtain the current channel load status. The channel occupancy rate C can be calculated using the following formula: ;in, This represents the cumulative duration of channel occupancy within the current sliding time window, obtained by summing the durations between the start and end times of channel transmission. This represents the current sliding statistical time window length, which is a fixed statistical period preset by the system. Simultaneously, the queuing status of tasks in the current scheduling queue is statistically analyzed to obtain the queue length Q, where Q represents the number of data transmission tasks currently waiting to be executed, obtained by counting the number of data tasks that have not yet been completed at the current moment. Based on this, the current channel load status can be quantitatively determined by combining the channel occupancy rate C and the queue length Q. For example, when the channel occupancy rate C exceeds a first preset threshold and the queue length Q exceeds a second preset threshold, the current channel is determined to be in a high-load state. For obtaining the spatial correlation strength, the edge gateway first aligns the data collected by multiple sensor nodes within the same spatial correlation group within the same time window and calculates the data change difference between each node. Let the... i Each node at time... t The collected data is Then the data difference between any two nodes can be expressed as: ;in, Indicates the first i The data values ​​collected by each sensor node within the current time window, Indicates the first j Data values ​​collected by each sensor node within the same time window, - Represents a node i With nodes j The degree of difference in data changes between them. The edge gateway statistically analyzes the data differences between all nodes within the same spatially related group to obtain the average difference value: ;in, This indicates the number of node pairs participating in the statistics within the current spatial correlation group, - This indicates the average difference in spatially correlated data. When the average difference is below a preset difference threshold, the current spatial correlation is considered high; when the average difference is above the preset difference threshold, the current spatial correlation is considered low. This method allows for the quantitative acquisition of the current channel load state and spatial correlation strength. Based on this, a state switching mechanism based on rule thresholds is used to dynamically control the transmission strategy. When the data spatial correlation is higher than the first correlation threshold and the channel load state is higher than the first load threshold, the system switches to compressed transmission mode, reducing the amount of communication data through redundancy suppression, aggregated uploading, or incremental data transmission to reduce network transmission load. When the data spatial correlation is lower than the second correlation threshold and the channel load state is lower than the second load threshold, the system switches to full transmission mode to ensure data integrity and the transmission of information details. When the data spatial correlation or channel load state is between the corresponding thresholds, the system maintains the current transmission mode or switches to partial compressed transmission mode, i.e., only compressing some duplicate data, thus avoiding frequent mode switching that causes system transmission state oscillations. This method enables the transmission strategy to dynamically and adaptively adjust according to changes in network load state and spatial correlation. In step S75, under compressed transmission mode, redundancy suppression processing is performed on the data to be transmitted. For example, duplicate or highly similar data records are deleted, and only differentiated information or incremental change information is retained, thereby further reducing the amount of transmitted data and alleviating the communication load of the edge gateway. Specifically, a differential coding-based data compression method can be used, which compares the currently collected data with historical benchmark data or data from the previous sampling period item by item, generating an incremental data representation by calculating the data difference or incremental change. Simultaneously, a deduplication mechanism based on a similarity threshold is used to merge or filter data records with similarity exceeding a preset threshold, retaining only data records with significant change characteristics or key change node data, thereby effectively suppressing redundant data and compressing the amount of transmitted data. In step S76, the current transmission mode is dynamically adjusted based on the success rate and network load changes during actual transmission. Specifically, a statistical feedback control method based on a sliding time window can be used to continuously collect and average the transmission success rate and network load within a certain time period to eliminate the impact of instantaneous fluctuations, and combine this with a preset threshold to determine the transmission performance status. When a decrease in transmission success rate is detected in compressed transmission mode, the compression ratio is appropriately reduced through a rule-based adaptive adjustment strategy to improve data transmission reliability. When the network load drops below a preset threshold, the system switches to full transmission mode to restore data integrity. Furthermore, the above adjustment results are used as feedback parameters to update the transmission control strategy for the next scheduling cycle, thereby achieving continuous optimization and adaptive adjustment of the transmission strategy through a closed-loop feedback control mechanism.

[0036] S8: The edge gateway executes data transmission control according to the scheduling execution queue and channel allocation results, and collects feedback information on the transmission process in real time, including transmission success rate, transmission delay and packet loss.

[0037] Specifically, after channel allocation and scheduling are completed, the edge gateway performs unified execution control of the data transmission process and continuously monitors and collects feedback on the operational status of the transmission process to ensure its controllability and traceability. Specifically, based on the scheduling execution queue generated in step S3 and the channel allocation results determined in step S5, the edge gateway schedules each data transmission task sequentially according to a predetermined priority order to perform data sending and receiving control. High-priority tasks occupy allocated channel resources first, while ordinary tasks are executed sequentially according to the queue order. During execution, the edge gateway monitors the sending status of each data transmission task in real time, such as recording the data packet sending time, number of sending attempts, and sending completion status, and simultaneously monitors the acknowledgment feedback information from the corresponding receiving end to determine whether the data has successfully reached the target node. Based on this, the transmission success rate is calculated, which characterizes the proportion of data successfully transmitted per unit time or per task. Simultaneously, the edge gateway records and calculates the time interval between the data sending end and the receiving end to form a transmission delay index, used to characterize the timeliness of data transmission in the network. Furthermore, the edge gateway also monitors data loss during transmission. For example, by comparing the number of sent data packets with the number of received acknowledgment data packets, it determines the number of lost packets and the packet loss rate, thereby reflecting the current channel transmission reliability. Throughout the entire transmission process, the aforementioned transmission success rate, transmission delay, and packet loss are continuously collected and updated in real time as key feedback information, providing a basis for the dynamic adjustment of scheduling control parameters in subsequent step S9, thus achieving closed-loop monitoring and operational status feedback control of the data transmission process.

[0038] S9: The edge gateway dynamically updates the scheduling control parameters based on the transmission feedback information, and sends the updated sampling control parameters and scheduling control parameters to each sensor node and the edge gateway execution unit to realize a closed-loop optimized operation mechanism for sampling control, task scheduling control and transmission path control.

[0039] Furthermore, step S9 of this application also includes: S91. Acquire and aggregate multi-source feedback information during system operation, including at least: data transmission success rate, transmission delay, packet loss, channel occupancy status, and node energy consumption. S92. Classify the system operation status according to the multi-source feedback information, dividing it into normal operation, load stress, and abnormal scheduling status. S93. Under different operation states, perform graded correction processing on scheduling weight parameters, task priority parameters, and channel resource allocation parameters. Specifically, when in normal operation, the current scheduling parameter configuration remains unchanged; when in load stress, the scheduling weight parameters corresponding to high-priority tasks are incrementally adjusted according to a preset correction ratio, and the scheduling weight parameters for low-priority tasks are adjusted accordingly. The corresponding channel resource occupancy parameters are allocated with restrictions. When in an abnormal scheduling state, the task sorting relationship and channel resource allocation relationship in the current scheduling queue are reconstructed to regenerate the task scheduling order and the corresponding channel allocation results. S94. Based on the scheduling parameter correction results, the sampling frequency control strategy of the sensor nodes is adjusted in a coordinated manner to achieve collaborative optimization control between the sampling side and the scheduling side. S95. When the system enters a state of high load or abnormal scheduling, the channel resource reallocation control mechanism is triggered to readjust the current channel occupancy relationship. S96. The corrected sampling control parameters and scheduling control parameters are synchronously sent to each sensor node and edge gateway execution unit to achieve closed-loop control operation of the entire system. 。

[0040] Specifically, this is used to dynamically update the system scheduling and control strategy based on feedback information during the transmission execution process, and to achieve collaborative closed-loop optimization control of the sampling side, scheduling side, and transmission side through parameter distribution. Specifically, in step S91, the edge gateway uniformly collects and aggregates multi-source feedback information during system operation. This multi-source feedback information includes at least the data transmission success rate, transmission delay, and packet loss obtained in step S8, as well as the current occupancy status of each communication channel and the energy consumption of each sensor node. The channel occupancy status can be characterized by the channel usage ratio or queue length per unit time, and the node energy consumption can be characterized by remaining energy and energy change per unit time. The above data types are then time-aligned and normalized to form a unified system state assessment data basis. In step S92, the overall system operating state is graded based on the multi-source feedback information. Specifically, the transmission success rate, transmission delay, packet loss rate, channel occupancy rate, and node energy consumption rate are compared and analyzed with preset threshold intervals, where each indicator corresponds to a pre-set normal interval, warning interval, and abnormal interval. Specifically, when the transmission success rate, transmission latency, packet loss rate, channel occupancy rate, and node energy consumption rate are all within their respective normal ranges, the system is determined to be in normal operation. When any indicator enters the warning range but does not reach the abnormal range, and other indicators are still within the normal or warning range, the system is determined to be in a state of high load. When any key indicator (including transmission success rate below the lower threshold, packet loss rate above the upper threshold, or key node energy below the minimum threshold) enters the abnormal range, the system is determined to be in an abnormal scheduling state. In step S93, the scheduling weight parameters, task priority parameters, and channel resource allocation parameters are graded and corrected according to different operating states. In specific implementation, the edge gateway first reads the corresponding parameter correction rules based on the operating state determination result obtained in step S92. Various correction rules can be pre-stored in the scheduling strategy table and adjusted according to different parameter adjustment methods corresponding to different operating states.When the system is operating normally, the current scheduling parameters remain unchanged, meaning data transmission control continues to use the current scheduling queue sorting results and channel resource allocation relationships. When the system is under heavy load, the scheduling weight parameters corresponding to high-priority tasks are incrementally adjusted according to a preset correction ratio. Specifically, a fixed-step incremental method or a proportional incremental method can be used to correct the weight values ​​corresponding to high-priority tasks, for example, by increasing the preset ratio value on the original scheduling weight. At the same time, the channel resource occupancy parameters corresponding to low-priority tasks are restricted, for example, limiting the number of channel slots or data transmission bandwidth that low-priority tasks can occupy within a unit time window, in order to reduce the communication resource consumption of low-priority tasks. When the system is in an abnormal scheduling state, the task sorting relationship and channel resource allocation relationship in the current scheduling queue are reconstructed. Specifically, a reordering algorithm can be used to regenerate the scheduling order of tasks to be executed according to the latest task priority, time constraints, and current channel status, and re-establish the allocation relationship between tasks and communication channels to generate a new task scheduling order and corresponding channel allocation results. The adjusted parameter results will be synchronously updated to the current scheduling control flow and applied to the data transmission control process of the next scheduling cycle. Based on the scheduling parameter correction results in step S93, a linkage correction control is performed on the current sampling frequency of the sensor nodes. Specifically, the edge gateway generates a corresponding sampling frequency adjustment command based on the current scheduling queue congestion status, channel resource occupancy status, and task priority distribution, and sends the sampling frequency adjustment command to the corresponding sensor nodes. The sensor nodes, based on the current sampling frequency determined in step S2, correct their current sampling period according to the received sampling frequency adjustment command. When the system is under heavy load, the current sampling period of sensor nodes corresponding to low-priority tasks is extended according to a preset adjustment step size to reduce the data acquisition frequency per unit time; when the system returns to normal operation, it gradually recovers to the original sampling frequency corresponding to step S2 according to a preset recovery step size. In a specific implementation, a mapping relationship between different operating states and corresponding sampling period adjustment strategies can be pre-established, and the corresponding sampling adjustment rules can be automatically invoked according to the current scheduling state, thereby achieving coordinated linkage control between the sampling side and the scheduling side. In step S95, when the system enters a state of heavy load or abnormal scheduling, the edge gateway triggers a channel resource reallocation control mechanism to dynamically adjust the current channel occupancy relationship.In specific implementation, the edge gateway re-determines and classifies the tasks occupying each communication channel based on the task priority information and channel status information in the current scheduling queue. When a low-priority task is detected occupying a high-load or critical communication channel, the channel resources occupied by the low-priority task are released and reallocated to a high-priority task. At the same time, the correspondence between the task to be executed and the available communication channel is re-bound, so that the high-priority task is preferentially matched with the communication link with better current channel quality, thereby realizing the rescheduling and optimized allocation of channel resources. In step S96, the sampling control parameters and scheduling control parameters obtained in steps S93 to S95 are synchronously sent to each sensor node and the edge gateway execution unit through the communication interface. The communication interface is used to realize bidirectional data transmission and control command transmission between the edge gateway and each sensor node. In practice, the edge gateway encapsulates the updated control parameters into control command data packets and sends them in a targeted manner according to the node identification information. Upon receiving the corresponding control command, each sensor node updates its local sampling period parameters, data upload strategy parameters, and task execution order parameters, ensuring that it executes sampling control, task scheduling control, and data transmission path selection control according to the latest control parameters. Through this method, the system achieves a unified parameter control state with the edge gateway at each execution node, realizing synchronous updates and consistent execution of control parameters.

[0041] Example 2 Based on the aforementioned intelligent sensing device data acquisition and transmission scheduling control method, and using the same inventive concept, this application also provides an intelligent sensing device data acquisition and transmission scheduling control system. Please refer to the appendix. Figure 2 The system includes: The multi-level data value assessment module 11 is used to build a multi-level value assessment module in each sensing node to obtain the dynamic value index of the currently collected data in real time. The dynamic value index is obtained by comprehensively assessing the data change range, the remaining energy of the node and the sensitivity of the event. The sampling control module 12 is used to compare and judge the dynamic value index with the first preset threshold and the second preset threshold; in, When the dynamic value index is lower than the first preset threshold, control the sensing node to reduce the sampling frequency; When the dynamic value index is higher than the second preset threshold, the sensor node is controlled to increase the sampling frequency and generate a data transmission request. The scheduling and sorting module 13 is used by the edge gateway to receive data transmission requests sent by each sensor node. Each data transmission request includes data volume information, priority information, time constraint information and dynamic value index. The edge gateway performs comprehensive sorting processing on all data transmission requests based on request attributes and system operating status to generate a scheduling execution queue. The channel state update module 14 is used by the edge gateway to build a real-time channel state table, which includes channel noise level, occupancy rate and historical transmission reliability, and to periodically update the real-time channel state table to reflect changes in the current network transmission environment. The channel allocation and preemption module 15 is used to dynamically allocate the transmission channel with the best current state to each data transmission task according to the scheduling execution queue and the real-time channel status table, and set a high-priority preemption channel for immediate transmission control of emergency tasks. The status monitoring and adjustment module 16 is used to monitor channel status changes and node energy status in real time during data transmission. When a channel quality degradation or node energy is detected to be lower than a preset threshold, the transmission adaptive adjustment mechanism is triggered. The spatial compression upload module 17 is used to perform compression processing on the data to be transmitted after the transmission adaptive adjustment mechanism is triggered, so as to reduce the transmission load. When there is spatial correlation between the data collected by multiple sensor nodes, the upload control is performed according to the preset rules. The transmission feedback acquisition module 18 is used by the edge gateway to perform data transmission control according to the scheduling execution queue and channel allocation results, and to collect transmission process feedback information in real time, including transmission success rate, transmission delay and packet loss. The closed-loop update module 19 is used by the edge gateway to dynamically update the scheduling control parameters based on the transmission feedback information, and to send the updated sampling control parameters and scheduling control parameters to each sensor node and the edge gateway execution unit, so as to realize the closed-loop optimization operation mechanism of sampling control, task scheduling control and transmission path control.

[0042] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The intelligent sensing device data acquisition and transmission scheduling control method and specific examples in Embodiment 1 are also applicable to the intelligent sensing device data acquisition and transmission scheduling control system of this embodiment. Through the foregoing detailed description of the intelligent sensing device data acquisition and transmission scheduling control method, those skilled in the art can clearly understand the intelligent sensing device data acquisition and transmission scheduling control system of this embodiment; therefore, for the sake of brevity, it will not be described in detail here. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0043] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0044] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for data acquisition and transmission scheduling control of intelligent sensing devices, characterized in that, The method includes: S1. Each sensing node has a built-in multi-level value assessment module to obtain the dynamic value index of the currently collected data in real time. The dynamic value index is obtained by comprehensively assessing the data change range, the remaining energy of the node and the sensitivity of the event. S2. Make a judgment by comparing the dynamic value index with the first preset threshold and the second preset threshold; in, When the dynamic value index is lower than the first preset threshold, control the sensing node to reduce the sampling frequency; When the dynamic value index is higher than the second preset threshold, the sensor node is controlled to increase the sampling frequency and generate a data transmission request. S3. The edge gateway receives data transmission requests sent by each sensor node. Each data transmission request includes data volume information, priority information, time constraint information, and dynamic value index. Based on the request attributes and system operating status, the edge gateway performs comprehensive sorting processing on all data transmission requests and generates a scheduling execution queue. S4. The edge gateway constructs a real-time channel status table, which includes channel noise level, occupancy rate and historical transmission reliability, and periodically updates the real-time channel status table to reflect changes in the current network transmission environment. S5. Based on the scheduling execution queue and the real-time channel status table, dynamically allocate the transmission channel with the best current status to each data transmission task, and set a high-priority preemption channel for immediate transmission control of emergency tasks. S6. During data transmission, the edge gateway monitors channel status changes and node energy status in real time. When it detects a decline in channel quality and a node energy level below a preset threshold, it triggers a transmission adaptive adjustment mechanism. S7. After triggering the transmission adaptive adjustment mechanism, the data to be transmitted is compressed to reduce the transmission load. When there is spatial correlation between the data collected by multiple sensor nodes, the upload control is performed according to the preset rules. S8. The edge gateway executes data transmission control according to the scheduling execution queue and channel allocation results, and collects feedback information of the transmission process in real time, including transmission success rate, transmission delay and packet loss. S9. The edge gateway dynamically updates the scheduling control parameters based on the transmission feedback information, and sends the updated sampling control parameters and scheduling control parameters to each sensor node and the edge gateway execution unit to realize a closed-loop optimization operation mechanism for sampling control, task scheduling control and transmission path control.

2. The data acquisition and transmission scheduling control method for an intelligent sensing device according to claim 1, characterized in that, Each sensing node has a built-in multi-level value assessment module that acquires the dynamic value index of the currently collected data in real time. The dynamic value index is obtained by comprehensively assessing the data change amplitude, the node's remaining energy, and the sensitivity to events, including: S11. Obtain the real-time change sequence of the currently collected data, and construct a dynamic reference interval based on preset historical benchmark data. Determine the data deviation degree by measuring the deviation position and fluctuation amplitude of the currently collected data within the dynamic reference interval. D The calculation formula is: ； in, x(t) For current data, This is a historical benchmark value. This represents the historical fluctuation range; S12. Obtain the current remaining energy of the sensor node. E The energy state is assessed based on the energy consumption rate per unit time; and an energy consumption weighting coefficient is determined according to the relationship between the degree of energy decay and the operating load level. W The calculation formula is: ； in, As initial energy, E For current energy, L This represents the current load strength of the node. η This is the load enhancement factor; S13, Based on the data deviation D Energy consumption weighting coefficient W and event sensitivity factors S The three types of factors are hierarchically integrated, and a dynamic value index is obtained by comprehensively evaluating the balance between information value and resource consumption. V The calculation formula is: ； in, This is the energy consumption suppression coefficient.

3. The data acquisition and transmission scheduling control method for an intelligent sensing device according to claim 1, characterized in that, The edge gateway receives data transmission requests from each sensor node. Each data transmission request includes data volume information, priority information, time constraint information, and a dynamic value index. Based on the request attributes and system operating status, the edge gateway performs comprehensive sorting processing on all data transmission requests and generates a scheduling execution queue, including: S31. The edge gateway parses the received data transmission requests and encapsulates each request into a standard scheduling unit. The standard scheduling unit includes data volume information, priority information, time constraint information, and dynamic value index. S32. Construct a multi-dimensional scheduling feature set based on standard scheduling units to characterize the scheduling demand intensity of tasks in the system; S33. Obtain the current system operating status of the edge gateway, including computing load, cache utilization rate and communication congestion level, and generate a set of scheduling constraints based on the system operating status; S34. Based on the multidimensional scheduling feature set and the scheduling constraint set, dynamically adjust the scheduling weight of each data transmission request; S35. Based on the modified scheduling weights, perform hierarchical processing of data transmission requests, construct a scheduling execution queue that includes at least a high-priority queue and a normal-priority queue, and dynamically update the scheduling execution queue according to changes in the system's operating status.

4. The data acquisition and transmission scheduling control method for an intelligent sensing device according to claim 3, characterized in that, Based on the multidimensional scheduling feature set and the scheduling constraint set, the scheduling weight of each data transmission request is dynamically adjusted, including: S34-1. Based on the multi-dimensional scheduling feature set and scheduling constraint set, analyze each data transmission request, and determine the corresponding basic scheduling weight based on the priority information, dynamic value index, and time constraint information of each data transmission request. This is used to characterize the initial scheduling priority of each data transmission request under conditions where system operating state constraints are not considered; S34-2. Obtain the current system load information of the edge gateway, and adjust the basic scheduling weights based on the system load information. The first correction process is performed, in which the system operating load... L The first adjusted scheduling weight is determined by the normalized result of CPU utilization and scheduling queue length, and is used to characterize the resource consumption of the edge gateway. The calculation formula is: ； in, This is the load impact factor. Based on historical scheduling success rate Confirmed, among which This represents the average success rate of tasks within the historical scheduling period. S34-3, Obtaining Communication Congestion Level C And based on the degree of communication congestion C For the first revised scheduling weight A second correction process is performed, in which the degree of communication congestion is considered. C The scheduling weight is determined by a combination of current channel occupancy and packet loss rate, and is used to characterize the congestion state of the network transmission environment. (Second modified scheduling weight) Represented as: ； in, This is the communication compensation coefficient. Determined based on the historical proportion of low-latency tasks; S34-4. Obtaining Event Sensitivity And based on the sensitivity of the event For the second revised scheduling weight A third correction process is performed to obtain the final scheduling weight. ,in: ； in, The event amplification factor. The percentage of performance improvement resulting from advance scheduling of historical emergency tasks is determined and used to reflect the system's enhanced response capability to high-priority events.

5. The data acquisition and transmission scheduling control method for an intelligent sensing device according to claim 1, characterized in that, Based on the scheduling execution queue and real-time channel status table, the optimal transmission channel in the current state is dynamically allocated to each data transmission task, and a high-priority preemption channel is set for immediate transmission control of urgent tasks, including: S51. Perform a comprehensive evaluation of each communication channel in the real-time channel status table to determine the channel quality level. The channel quality level is used to characterize the transmission reliability and resource occupancy status of the current channel. S52. Based on the priority level of tasks in the scheduling execution queue and the channel quality level, match and allocate each data transmission task to generate the initial channel allocation result. S53. Perform conflict detection on the initial channel allocation results to identify the contention and occupancy relationship of multiple data transmission tasks on the same channel; S54. When a channel conflict is detected between a high-priority task and a low-priority task, a preemptive control mechanism is triggered to release the low-priority task from the current channel or delay its execution, and allocate the channel resources to the high-priority task. S55. When the channel state or task priority changes, the allocated channel resources are dynamically reallocated, and the delayed tasks are reinstated into the scheduling execution queue for subsequent execution control.

6. The data acquisition and transmission scheduling control method for an intelligent sensing device according to claim 1, characterized in that, During data transmission, the edge gateway monitors channel status changes and node energy status in real time. When it detects a decline in channel quality or a node energy level below a preset threshold, it triggers a transmission adaptive adjustment mechanism, including: S61. Real-time acquisition of channel status parameters and node energy status parameters, wherein the channel status parameters include at least channel quality indicators and occupancy change rate; S62. Perform trend analysis on the channel state parameters and node energy state parameters over multiple consecutive time sampling periods to determine the state change trend and the intensity of the trend change. S63. Based on the trend of state changes, the system operating state is divided into stable state, degenerate state and critical state; in, A steady state corresponds to a state change trend that is gradual. The degenerate state corresponds to a continuous downward trend in the state; The critical state corresponds to a rapid downward trend in the state; S64. Trigger different levels of adaptive adjustment mechanisms according to different operating states; When in a degenerate state, a mild adjustment mechanism is triggered; When the system is in a critical state, a forced adjustment mechanism is triggered. When in a stable state, the adjustment mechanism is not triggered; S65. When the adaptive adjustment mechanism is triggered, it applies to the channel control parameters or node energy control parameters according to the trigger level to achieve targeted adjustment. S66. Suppress and control the continuously triggered adaptive adjustment behavior. When the adjustment frequency exceeds the preset upper limit, limit the triggering of new adjustments to avoid system oscillation. S67. Correction analysis is performed on the deviation between the state change trend and the actual triggering result, which is used to dynamically adjust the subsequent triggering control strategy.

7. The data acquisition and transmission scheduling control method for an intelligent sensing device according to claim 6, characterized in that, A correction analysis is performed on the deviation between the state change trend and the actual triggering result to dynamically adjust subsequent triggering control strategies, including: S67-1. Establish a corresponding mapping relationship between the trend of state change and the actual trigger execution result, which is used to characterize the actual response effect of trigger control; S67-2. Compare and analyze the expected triggering state with the actual triggering result to identify triggering lag deviation and triggering over-deviation. S67-3. Based on the magnitude of the trigger deviation, the system trigger lag is classified into first-level lag, second-level lag, and third-level lag. S67-4. Perform hierarchical adaptive correction of subsequent triggering thresholds and triggering levels based on lag levels; in, When it is a first-level lag, the trigger threshold is adjusted in a single level according to the preset step size; When the delay is level 2, the trigger threshold and trigger level are adjusted in conjunction with multiple parameters. When the delay is level three, the trigger threshold is reset to a preset range, and the trigger level is switched to high priority control mode.

8. The data acquisition and transmission scheduling control method for an intelligent sensing device according to claim 1, characterized in that, After triggering the adaptive transmission adjustment mechanism, compression processing is performed on the data to be transmitted to reduce the transmission load. When there is spatial correlation between the data collected by multiple sensor nodes, upload control is performed according to preset rules, including: S71. Perform spatial correlation analysis on data collected by multiple sensing nodes within the same time window to identify the degree of spatial correlation between data. S72. Based on the degree of spatial correlation, divide multiple sensing nodes into several spatially correlated groups, and perform aggregation control processing on the data within the same group. S73. Within the same spatial related group, select the sensor node that meets the preset data validity conditions as the upload node, and perform local caching control on the data of the remaining nodes; S74. Adaptively switch the transmission strategy based on the current channel load status and the spatial correlation strength of the data. in, When the spatial correlation of data is higher than the first correlation threshold and the channel load is higher than the first load threshold, switch to compressed transmission mode. When the spatial correlation of data is lower than the second correlation threshold and the channel load is lower than the second load threshold, switch to full transmission mode; When the spatial correlation of data or the channel load state is between the two thresholds, maintain the current transmission mode or adopt a partially compressed transmission mode. S75. In compressed transmission mode, redundancy suppression processing is performed on the data to be transmitted to reduce the transmission of duplicate information and reduce the communication load of the edge gateway. S76. Based on the transmission success rate and network load changes, dynamically adjust the current transmission mode and update the transmission control strategy for the next scheduling cycle.

9. The data acquisition and transmission scheduling control method for an intelligent sensing device according to claim 1, characterized in that, The edge gateway dynamically updates the scheduling control parameters based on the transmission feedback information, and sends the updated sampling control parameters and scheduling control parameters to each sensor node and the edge gateway execution unit, including: S91. Acquire and aggregate multi-source feedback information during system operation, wherein the multi-source feedback information includes at least: data transmission success rate, transmission delay, packet loss, channel occupancy status, and node energy consumption. S92. The system operating status is classified and determined according to multi-source feedback information, and divided into normal operating status, load stress status and abnormal scheduling status. S93. Under different operating states, the scheduling weight parameters, task priority parameters, and channel resource allocation parameters are subject to hierarchical correction. Specifically, when in normal operation, the current scheduling parameter configuration remains unchanged. When under heavy load, the scheduling weight parameters corresponding to high-priority tasks are incrementally adjusted according to a preset correction ratio, and the channel resource occupancy parameters corresponding to low-priority tasks are restricted. When in an abnormal scheduling state, the task sorting relationship and channel resource allocation relationship in the current scheduling queue are reconstructed to regenerate the task scheduling order and the corresponding channel allocation results. S94. Based on the scheduling parameter correction results, the sampling frequency control strategy of the sensing node is adjusted in a coordinated manner to achieve collaborative optimization control between the sampling side and the scheduling side. S95. When the system enters a state of high load or abnormal scheduling, the channel resource reallocation control mechanism is triggered to readjust the current channel occupancy relationship. S96. The corrected sampling control parameters and scheduling control parameters are synchronously sent to each sensor node and edge gateway execution unit to achieve closed-loop control operation of the entire system. 。 10. A data acquisition, transmission, and scheduling control system for intelligent sensing devices, characterized in that, The system is used to implement the data acquisition and transmission scheduling control method for an intelligent sensing device according to any one of claims 1 to 9, wherein the system comprises: A multi-level data value assessment module is used to build a multi-level value assessment module in each sensing node to obtain the dynamic value index of the currently collected data in real time. The dynamic value index is obtained by comprehensively assessing the data change range, the remaining energy of the node and the sensitivity of the event. The sampling control module is used to compare and judge the dynamic value index with the first preset threshold and the second preset threshold. in, When the dynamic value index is lower than the first preset threshold, control the sensing node to reduce the sampling frequency; When the dynamic value index is higher than the second preset threshold, the sensor node is controlled to increase the sampling frequency and generate a data transmission request. The scheduling and sorting module is used by the edge gateway to receive data transmission requests sent by each sensor node. Each data transmission request includes data volume information, priority information, time constraint information and dynamic value index. The edge gateway performs comprehensive sorting processing on all data transmission requests based on request attributes and system operating status to generate a scheduling execution queue. The channel state update module is used by the edge gateway to build a real-time channel state table, which includes channel noise level, occupancy rate and historical transmission reliability. The module also periodically updates the real-time channel state table to reflect changes in the current network transmission environment. The channel allocation and preemption module is used to dynamically allocate the optimal transmission channel in the current state to each data transmission task according to the scheduling execution queue and the real-time channel status table, and set a high-priority preemption channel for immediate transmission control of emergency tasks. The status monitoring and adjustment module is used to monitor channel status changes and node energy status in real time during data transmission. When a decline in channel quality or a node energy level below a preset threshold is detected, the transmission adaptive adjustment mechanism is triggered. The spatial compression upload module is used to compress the data to be transmitted after the transmission adaptive adjustment mechanism is triggered, so as to reduce the transmission load. When there is spatial correlation between the data collected by multiple sensor nodes, the upload is controlled according to the preset rules. The transmission feedback acquisition module is used by the edge gateway to execute data transmission control according to the scheduling execution queue and channel allocation results, and to collect transmission process feedback information in real time, including transmission success rate, transmission delay and packet loss. The closed-loop update module is used by the edge gateway to dynamically update the scheduling control parameters based on the transmission feedback information, and to send the updated sampling control parameters and scheduling control parameters to each sensor node and the edge gateway execution unit. This enables a closed-loop optimized operation mechanism for sampling control, task scheduling control and transmission path control.

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