Event-driven self-powered data acquisition device and method for ultra-low power consumption scenarios

Abstract

The application discloses an event-driven self-powered data collection device and method for an extremely low-power consumption scene, relates to the technical field of power supply, and has the following steps: constructing an asynchronous event sampling module and an energy closed-loop regulation module, determining an asynchronous time sequence collection item corresponding to the asynchronous event sampling module and an energy closed-loop scheduling item corresponding to the energy closed-loop regulation module, and marking the asynchronous time sequence collection item and the energy closed-loop scheduling item as to-be-configured execution items; based on a preset collection scheduling combination rule, the to-be-configured execution items are combined and evaluated to obtain a target scheduling combination set, a link actual execution behavior is compared with a corresponding link standard behavior index to obtain a comparison result; according to the comparison result, the link collection state of each collection execution link is judged, and a corresponding link closed-loop power supply mode is selected based on the link collection state to supply power to the low-power consumption scene, so that the closed-loop collaborative optimization of collection and power supply is realized.

Description

Technical Field

[0001] This invention relates to the field of power supply technology, and more specifically, to an event-driven self-powered data acquisition device and method for ultra-low power consumption scenarios. Background Technology

[0002] In low-power applications, data acquisition is fundamental for environmental sensing, status monitoring, and health management. Its performance directly determines device battery life, data reliability, and scenario adaptability. Traditional low-power data acquisition technologies often employ continuous timed sampling mechanisms, starting sampling at fixed time intervals regardless of whether there is a valid acquisition need. This approach has significant drawbacks in low-power scenarios: continuous sampling during periods without events generates substantial redundant power consumption, rapidly depleting battery power and drastically shortening battery life. It is ill-suited for portable or unattended scenarios with limited battery capacity and inconvenient battery replacement. Furthermore, continuous sampling lacks event-driven flexibility, making it impossible to dynamically adjust sampling strategies. This can easily lead to problems such as sampling timing disorder, crosstalk between multiple channels, and unreasonable energy allocation, affecting the accuracy and reliability of data acquisition. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an event-driven self-powered data acquisition device and method for ultra-low power consumption scenarios.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] An event-driven, self-powered data acquisition method for ultra-low power consumption scenarios includes the following steps:

[0006] Construct an asynchronous event sampling module and an energy closed-loop control module, determine the asynchronous time-series acquisition items corresponding to the asynchronous event sampling module and the energy closed-loop scheduling items corresponding to the energy closed-loop control module, and mark the asynchronous time-series acquisition items and energy closed-loop scheduling items as items to be configured and executed;

[0007] Based on the preset collection and scheduling combination rules, the items to be configured and executed are combined and evaluated to obtain the target scheduling combination set. The items to be configured and executed in the target scheduling combination set are then used to form an event-driven self-powered collection process.

[0008] Based on the event-driven self-powered data acquisition process, the project to be configured for execution is obtained as the data acquisition execution stage, and standard behavior indicators are configured for each data acquisition execution stage.

[0009] Based on the collected feedback data of low-power scenarios, the actual execution behavior of each stage of the low-power scenario is determined, and the actual execution behavior of each stage is compared with the corresponding standard behavior indicators of each stage to obtain the comparison results.

[0010] Based on the comparison results, the acquisition status of each acquisition execution stage is determined, and the corresponding closed-loop power supply method is selected to power the low-power scenario based on the acquisition status of each stage.

[0011] Preferably, the asynchronous event sampling module includes an asynchronous triggering unit, a timing calibration unit, a multi-channel isolation unit, and a data temporary storage unit;

[0012] The asynchronous timing acquisition items include asynchronous trigger acquisition items, timing calibration items, multi-channel isolation items, and data temporary storage items corresponding to each unit.

[0013] Preferably, the project types corresponding to the projects to be configured for execution include asynchronous triggering type, timing calibration type, multi-channel isolation type, data temporary storage type, and energy closed loop type.

[0014] Preferably, the target scheduling combination set is obtained by combining and evaluating the execution items to be configured based on preset collection and scheduling combination rules, specifically including the following steps:

[0015] Based on the target steady-state power consumption threshold of the acquisition and scheduling combination rules, the items to be configured and executed are combined to obtain a steady-state power consumption combination set. The steady-state adaptation coefficient is obtained according to the total number of item types of the items to be configured and executed in the steady-state power consumption combination set.

[0016] Based on the target number of asynchronous sampling channels according to the acquisition scheduling combination rules, the projects to be configured and executed are combined to obtain a sampling channel combination set. The channel matching coefficient is obtained according to the total power consumption of the projects to be configured and executed in the sampling channel combination set. The candidate channel combination set is obtained according to the channel matching coefficient.

[0017] Set steady-state weights and channel weights;

[0018] The scheduling evaluation coefficients are obtained based on the steady-state weights, channel weights, steady-state adaptation coefficients, and channel matching coefficients corresponding to the candidate channel combination set. The target scheduling combination set is then determined from the candidate channel combination set based on the scheduling evaluation coefficients.

[0019] Preferably, the types of the data collection and execution stages include a first stage type and a second stage type;

[0020] The first stage types include asynchronous trigger stage type, timing calibration stage type, multi-channel isolation stage type, data temporary storage stage type, and energy closed-loop stage type;

[0021] The second type of stage includes deep sleep stage, asynchronous listening stage, timing verification stage, channel switching stage, and closed-loop power supply stage.

[0022] Preferably, standard behavioral indicators are configured for each data collection and execution stage, specifically including the following steps:

[0023] Set the weight value of each data collection and execution stage according to the type of the first stage;

[0024] Set standard behavioral indicators for each data collection and execution stage according to the type of the second stage;

[0025] The standard behavior indicators and weight values ​​of each stage are generated to collect the corresponding standard indicators for each stage of execution.

[0026] Preferably, the actual execution behavior of each acquisition and execution stage in the low-power scenario is determined based on the acquisition feedback data of the low-power scenario, specifically including the following steps:

[0027] The steady-state feedback behavior of each acquisition and execution stage is obtained based on the steady-state energy feedback data;

[0028] The asynchronous feedback behavior of each collection and execution stage is obtained based on the asynchronous event feedback data;

[0029] The actual execution behavior of each data acquisition and execution stage is determined by the steady-state feedback behavior and the asynchronous feedback behavior of each stage.

[0030] Preferably, the comparison results are obtained by comparing the actual execution behavior of the process with the corresponding standard behavior indicators of the process, specifically including the following steps:

[0031] If the actual execution behavior of a step is consistent with the corresponding standard behavior indicators, then the step data collection status is judged to be normal.

[0032] If the actual execution behavior of a step is inconsistent with the corresponding standard behavior indicators, then the step's data collection status is judged to be abnormal.

[0033] Preferably, the standard behavior indicators of the process include steady-state power consumption standard indicators and asynchronous acquisition standard indicators.

[0034] An event-driven, self-powered data acquisition device for ultra-low power consumption scenarios includes:

[0035] Module construction: Construct an asynchronous event sampling module and an energy closed-loop control module, determine the asynchronous time-series acquisition project corresponding to the asynchronous event sampling module and the energy closed-loop scheduling project corresponding to the energy closed-loop control module, and mark the asynchronous time-series acquisition project and the energy closed-loop scheduling project as projects to be configured and executed;

[0036] Combination Module: Based on preset collection and scheduling combination rules, the module performs combination evaluation on the items to be configured and executed to obtain a target scheduling combination set, and then assembles an event-driven self-powered collection process based on the items to be configured and executed in the target scheduling combination set.

[0037] Processing module: Based on the event-driven self-powered data acquisition process, the project to be configured for execution obtains the data acquisition execution stage, and configures standard behavior indicators for each data acquisition execution stage;

[0038] Comparison module: Based on the collected feedback data of low power scenarios, determine the actual execution behavior of each stage of the low power scenario in each collection and execution stage, and compare the actual execution behavior of each stage with the corresponding standard behavior indicators of each stage to obtain the comparison results;

[0039] Judgment module: Based on the comparison results, it determines the acquisition status of each acquisition execution stage, and selects the corresponding closed-loop power supply mode to supply power to the low-power scenario based on the acquisition status of each stage.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] This invention uses preset acquisition scheduling combination rules to evaluate the configuration execution items, selects a target scheduling combination set, and builds an optimal event-driven self-powered acquisition process. This combination evaluation process uses steady-state power consumption and sampling channels as dual constraints, combined with weighted comprehensive evaluation, to select the optimal item combination that meets the low-power constraints and multi-channel acquisition requirements. This ensures both the functional integrity of the acquisition process and low-power optimization, avoiding power redundancy caused by invalid item combinations, and ensuring a high degree of adaptability of the acquisition process to low-power scenarios. By classifying the acquisition execution stages into two layers and configuring stage weight values ​​and standard behavior indicators, specific stage acquisition standard indicators are generated for each stage, providing a quantitative benchmark for stage status determination. Through dual-dimensional acquisition of steady-state energy feedback data and asynchronous event feedback data, the actual execution behavior of each acquisition execution stage is fully restored, achieving accurate perception of the stage's operating status throughout the entire time period. Steady-state energy feedback data includes the steady-state sleep state when no event is triggered, while asynchronous event feedback data includes the working state after an event is triggered. By fusing these two-dimensional data, a complete picture of the actual execution behavior of each stage is formed. This ensures effective control over the steady-state sleep state in low-power scenarios and performance control over the working state after an event is triggered. By comparing the actual execution behavior of each stage with its standard performance indicators, the acquisition status of each acquisition stage is determined. Based on the acquisition status, the corresponding closed-loop power supply method is selected, achieving closed-loop collaborative optimization of acquisition and power supply. Overall, this achieves closed-loop management of the entire data acquisition process in low-power scenarios. Attached Figure Description

[0042] Figure 1 A schematic diagram illustrating the steps of an event-driven self-powered data acquisition method for ultra-low power consumption scenarios, as provided in this embodiment of the invention.

[0043] Figure 2This invention provides a schematic diagram illustrating the steps involved in obtaining the target scheduling combination set in an event-driven self-powered data acquisition method for ultra-low power scenarios, as provided in this embodiment.

[0044] Figure 3 This invention provides a schematic diagram illustrating the steps involved in obtaining the standard indicators for data acquisition in an event-driven, self-powered data acquisition method for ultra-low power scenarios, as described in this embodiment.

[0045] Figure 4 This invention provides a schematic diagram illustrating the steps involved in obtaining the actual execution behavior of a process in an event-driven self-powered data acquisition method for ultra-low power consumption scenarios, as provided in this embodiment of the invention.

[0046] Figure 5 The diagram below shows a module schematic of an event-driven self-powered data acquisition device for ultra-low power consumption scenarios, as provided in this embodiment of the invention. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0050] Reference Figures 1-5 As shown.

[0051] The embodiments further illustrate the event-driven self-powered data acquisition device and method for ultra-low power consumption scenarios proposed in this invention.

[0052] An event-driven, self-powered data acquisition method for ultra-low power consumption scenarios includes the following steps:

[0053] Construct an asynchronous event sampling module and an energy closed-loop control module, determine the asynchronous time-series acquisition items corresponding to the asynchronous event sampling module and the energy closed-loop scheduling items corresponding to the energy closed-loop control module, and mark the asynchronous time-series acquisition items and energy closed-loop scheduling items as items to be configured and executed;

[0054] Based on the preset collection and scheduling combination rules, the items to be configured and executed are combined and evaluated to obtain the target scheduling combination set. The items to be configured and executed in the target scheduling combination set are then used to form an event-driven self-powered collection process.

[0055] Based on the event-driven self-powered data acquisition process, the project to be configured for execution is obtained as the data acquisition execution stage, and standard behavior indicators are configured for each data acquisition execution stage.

[0056] Based on the collected feedback data of low-power scenarios, the actual execution behavior of each stage of the low-power scenario is determined, and the actual execution behavior of each stage is compared with the corresponding standard behavior indicators of each stage to obtain the comparison results.

[0057] Based on the comparison results, the acquisition status of each acquisition execution stage is determined, and the corresponding closed-loop power supply method is selected to power the low-power scenario based on the acquisition status of each stage.

[0058] The asynchronous event sampling module includes an asynchronous triggering unit, a timing calibration unit, a multi-channel isolation unit, and a data temporary storage unit;

[0059] The asynchronous timing acquisition project includes asynchronous trigger acquisition projects, timing calibration projects, multi-channel isolation projects, and data temporary storage projects corresponding to each unit.

[0060] The asynchronous triggering unit, corresponding to the asynchronous triggering acquisition project, undertakes the triggering control function of acquisition actions in low-power scenarios. In low-power scenarios, traditional continuous sampling will generate a lot of redundant power consumption, while the asynchronous triggering acquisition project will only start the sampling action when the preset event conditions are met. For example, when the monitored parameters such as temperature and humidity in the environmental monitoring sensor node exceed the preset threshold, or when the external trigger signal shows a valid transition, the data acquisition process is triggered. At other times, the system maintains a deep sleep state, reducing invalid power consumption from the source.

[0061] The asynchronous triggering unit listens to event signals in low-power scenarios in real time to determine the validity of events. It generates a data acquisition start command only when a valid event occurs, driving subsequent units to perform data acquisition operations, thus realizing low-power operation logic of on-demand sampling.

[0062] The timing calibration unit's corresponding timing calibration project addresses timing disorder issues caused by asynchronous event triggering, ensuring the time synchronization and accuracy of multi-channel data acquisition. Due to the randomness of asynchronous trigger events, sampling actions from different channels may exhibit timing offsets, leading to inconsistent timestamps across multiple data sources and affecting the accuracy of subsequent data fusion and analysis. The timing calibration project uses a built-in high-precision time base to timestamp the acquisition start command generated by the asynchronous trigger unit, while simultaneously calibrating the sampling actions of each channel to align the sampling times of different channels to the global time base. Since asynchronous trigger times from different sensors exhibit millisecond-level deviations, the timing calibration project calculates the difference between each channel's sampling time and the global base time to generate a timing compensation amount. This compensation amount corrects the timestamps of the sampled data from each channel, ensuring consistency of multi-channel data in the time dimension. The timing compensation amount = global base time - channel sampling time. This compensation amount is then added to the sampling timestamps of each channel to achieve timing calibration.

[0063] The multi-channel isolation unit, corresponding to the multi-channel isolation project, is used to eliminate signal crosstalk during multi-channel data acquisition, ensuring the independence and accuracy of data acquisition from each channel. In low-power scenarios, multi-channel sensing nodes typically integrate multiple different types of sensors. Electromagnetic coupling and crosstalk can easily occur during transmission between different channels, leading to data distortion. The multi-channel isolation project achieves electrical and data isolation between channels through a dual mechanism of hardware and software isolation. At the hardware level, isolation devices disconnect the electrical connections between channels to prevent the conduction of electromagnetic interference. At the software level, independent channel data caching and verification mechanisms ensure that data from each channel does not interfere with each other during transmission and processing. The multi-channel isolation project blocks crosstalk between different signals through electrical isolation, while software verification eliminates abnormal data caused by crosstalk, ensuring the accuracy of physiological signal acquisition from each channel and avoiding diagnostic errors caused by crosstalk.

[0064] The data storage unit, corresponding to the data storage item, is used to temporarily store asynchronously acquired multi-channel data, providing a data buffer for subsequent data transmission and energy closed-loop control. In low-power scenarios, the data transmission unit typically operates in an intermittent mode to reduce transmission power consumption. Therefore, asynchronously acquired data cannot be transmitted in real time and needs to be temporarily stored through the data storage unit. The data storage item, through the configuration of independent cache space, classifies and stores the calibrated acquisition data of each channel, and controls the timing of data reading and transmission according to the scheduling instructions of the energy closed-loop control module. For example, in a wireless sensor network node, the data storage unit temporarily stores asynchronously acquired environmental monitoring data. When the energy closed-loop control module determines that the node has sufficient remaining energy and the communication link is in good condition, it initiates data transmission, sending the temporarily stored data in batches to the cloud server, avoiding power waste caused by frequent transmissions. The data storage unit also has data verification and redundancy backup functions, performing integrity verification on the temporarily stored data to prevent data loss and ensure the reliability of the acquired data.

[0065] The asynchronous timing acquisition projects correspond to the units of the asynchronous event sampling module, forming a collaborative mapping relationship between unit functions and acquisition projects. Among them, the asynchronous trigger acquisition project corresponds to the event triggering function of the asynchronous triggering unit, the timing calibration project corresponds to the timing calibration function of the timing calibration unit, the multi-channel isolation project corresponds to the channel isolation function of the multi-channel isolation unit, and the data temporary storage project corresponds to the data temporary storage function of the data temporary storage unit.

[0066] The project types corresponding to the projects to be configured and executed include asynchronous trigger type, timing calibration type, multi-channel isolation type, data temporary storage type, and energy closed loop type.

[0067] The asynchronous trigger type corresponds to the configurable execution item, which implements on-demand sampling control in low-power scenarios, reducing redundant power consumption from the source. This type of item uses asynchronous events as trigger conditions, initiating data acquisition only when a preset valid event occurs. The system remains in a low-power sleep state at other times, avoiding the wasted power consumption of traditional continuous sampling. The execution logic of the asynchronous trigger type item revolves around event listening and trigger determination. It continuously determines the validity of events by collecting event signals in real time, generating a collection start command only when the trigger condition is met, driving subsequent type items to execute sequentially.

[0068] The timing calibration type, corresponding to the configurable execution item, is used to resolve timing disorder issues caused by asynchronous triggering, ensuring the time synchronization and accuracy of multi-channel data acquisition. Because the occurrence time of asynchronous trigger events is random, sampling actions in different channels may experience timing offsets, leading to inconsistent timestamps across multiple data sources. The timing calibration type item uses a built-in global time base to timestamp the acquisition commands generated by asynchronous triggering, and simultaneously calibrates the timing of sampling actions in each channel, aligning the sampling times of different channels to the global time base. The timing compensation amount = global base time - channel sampling time. This timing compensation amount is then added to the sampling timestamps of each channel to achieve unified calibration of the multi-channel sampling timing.

[0069] The multi-channel isolation type corresponds to a configurable execution item used to eliminate signal crosstalk during multi-channel data acquisition, ensuring the independence and accuracy of data acquisition from each channel. In low-power scenarios, multi-channel sensing devices typically integrate multiple different types of sensors. Electromagnetic coupling and crosstalk can easily occur during transmission between different channels, leading to data distortion and affecting data reliability. The multi-channel isolation type project achieves electrical and data isolation between channels through a dual mechanism of hardware and software isolation. At the hardware level, isolation devices disconnect the electrical connections between channels, blocking the conduction path of electromagnetic interference. At the software level, independent channel data caching and verification mechanisms ensure that data from each channel does not interfere with each other during transmission and processing.

[0070] The data temporary storage type corresponds to the configurable execution item, used for temporarily storing asynchronously acquired multi-channel data. In low-power scenarios, data transmission units typically operate in an intermittent mode to reduce transmission power consumption. Asynchronously acquired data cannot be transmitted in real time and needs to be temporarily stored through the data temporary storage type item. This type of item configures an independent cache space to classify and store the acquired data from each channel calibration. At the same time, according to the scheduling instructions of energy closed-loop control, it controls the timing of data reading and transmission, realizing batch data transmission and avoiding power waste caused by frequent transmission.

[0071] The energy closed-loop type of project, corresponding to the configurable execution project, is the core control unit for achieving self-powered data acquisition. It performs closed-loop control of energy allocation and power supply status in low-power scenarios, achieving efficient energy utilization and dynamic adaptation of the acquisition process. This type of project monitors the power consumption of each acquisition execution stage in real time by collecting energy status data and acquisition execution status data within the scenario. It dynamically adjusts the power supply strategy based on the operating status of each acquisition stage, ensuring that each stage minimizes power consumption while meeting acquisition requirements. The operational logic of the energy closed-loop type project revolves around energy monitoring, status determination, and power supply adjustment. By continuously collecting energy feedback data and combining it with the operating status of each acquisition stage, it generates corresponding power supply control commands, achieving coordinated optimization of acquisition and power supply. This is the core guarantee for achieving self-powered data acquisition.

[0072] Based on preset collection and scheduling combination rules, the project to be configured for execution is combined and evaluated to obtain the target scheduling combination set, which specifically includes the following steps:

[0073] Based on the target steady-state power consumption threshold of the acquisition and scheduling combination rules, the items to be configured and executed are combined to obtain a steady-state power consumption combination set. The steady-state adaptation coefficient is obtained according to the total number of item types of the items to be configured and executed in the steady-state power consumption combination set.

[0074] Based on the target number of asynchronous sampling channels according to the acquisition scheduling combination rules, the projects to be configured and executed are combined to obtain a sampling channel combination set. The channel matching coefficient is obtained according to the total power consumption of the projects to be configured and executed in the sampling channel combination set. The candidate channel combination set is obtained according to the channel matching coefficient.

[0075] Set steady-state weights and channel weights;

[0076] The scheduling evaluation coefficients are obtained based on the steady-state weights, channel weights, steady-state adaptation coefficients, and channel matching coefficients corresponding to the candidate channel combination set. The target scheduling combination set is then determined from the candidate channel combination set based on the scheduling evaluation coefficients.

[0077] Based on the target steady-state power consumption threshold of the acquisition scheduling combination rules, all items to be configured for execution are combined in multiple dimensions to generate multiple sets of different steady-state power consumption combinations. The target steady-state power consumption threshold is a power consumption upper limit preset according to the endurance requirements of low-power scenarios and the hardware carrying capacity. It is used to constrain the overall steady-state power consumption level of the items within the combination set, ensuring that the power consumption of the acquisition process remains within the preset range in the sleep state without event triggering, and avoiding redundant power consumption waste. The steady-state adaptation coefficient is calculated based on the total number of item types of the items to be configured for execution in each steady-state power consumption combination set. The steady-state adaptation coefficient = the total number of effective item types in the combination / the total number of preset complete item types. The preset total number of complete item types includes 5 types: asynchronous trigger type, timing calibration type, multi-channel isolation type, data temporary storage type, and energy closed loop type. If a steady-state power consumption combination set contains all 5 item types, the steady-state adaptation coefficient is 1. If it contains only 3 item types, the steady-state adaptation coefficient is 0.6. This coefficient is used to characterize the coverage of the combination set for the complete acquisition function. The higher the coefficient, the stronger the functional integrity of the combination and the better it can meet the full-process acquisition requirements.

[0078] Based on the target asynchronous sampling channel number according to the acquisition scheduling combination rules, the projects to be configured and executed are combined in multiple dimensions to generate multiple different sampling channel combination sets. The target asynchronous sampling channel number is a pre-set upper limit of the number of channels based on the acquisition requirements of low-power scenarios. It is used to constrain the number of sampling channels that the projects within the combination set can support, ensuring that the combined acquisition process can meet the multi-channel acquisition requirements of the scenario. The channel matching coefficient is calculated based on the total power consumption of the projects to be configured and executed in each sampling channel combination set. The channel matching coefficient = rated power consumption corresponding to the target asynchronous sampling channel number / total power consumption of projects within the combination set. This coefficient is used to characterize the degree of matching between the power consumption level of the combination set and the target channel requirements. The closer the coefficient is to 1, the more the power consumption of the combination set is adapted to the target channel requirements. It will not exceed the scenario constraints due to excessive power consumption, nor will it result in insufficient channel numbers due to excessive power consumption. The candidate channel combination set is obtained by filtering based on the channel matching coefficient. The filtering rule is to retain combinations with channel matching coefficients within a preset reasonable range and remove combinations with matching coefficients that are too low or too high. For example, the preset reasonable range is 0.8 to 1.2. Only combinations with channel matching coefficients within this range are retained as candidate channel combinations, ensuring that the candidate combination set meets both the channel quantity requirement and power consumption constraint. For example, in a multi-channel industrial sensing acquisition scenario, the target number of asynchronous sampling channels is set to 8 channels, corresponding to a rated power consumption of 2 milliwatts. The total power consumption of a certain sampling channel combination set is 1.9 milliwatts, then the channel matching coefficient = 2 / 1.9 ≈ 1.05, which is within the preset reasonable range and is included in the candidate channel combination set. The total power consumption of another combination set is 3 milliwatts, and the channel matching coefficient = 2 / 3 ≈ 0.67, which is below the lower limit of the preset range and is removed from the candidate range.

[0079] Set steady-state weights and channel weights. If the core objective of the scenario is low power consumption and battery life, the steady-state weight can be set to 0.7 and the channel weight to 0.3 to strengthen the weight of steady-state power consumption adaptability. If the core objective of the scenario is multi-channel acquisition performance, the steady-state weight can be set to 0.3 and the channel weight to 0.7 to strengthen the weight of sampling channel matching. The sum of the two weights is 1.

[0080] Based on the steady-state weights, channel weights, steady-state adaptation coefficients, and channel matching coefficients corresponding to the candidate channel combination sets, a scheduling evaluation coefficient is calculated. The scheduling evaluation coefficient = steady-state weights × steady-state adaptation coefficient + channel weights × channel matching coefficient. This coefficient comprehensively considers the steady-state power consumption adaptability and sampling channel matching of the combination set; a higher coefficient indicates that the combination set is more suitable for the acquisition requirements of low-power scenarios. Based on the scheduling evaluation coefficient, a target scheduling combination set is determined from the candidate channel combination set. Typically, the combination set with the highest scheduling evaluation coefficient is selected as the target scheduling combination set. This ensures that the final project combination simultaneously meets the steady-state power consumption constraints of the sampling channel requirements and the functional integrity requirements, providing an optimal project combination foundation for subsequently building an event-driven self-powered acquisition process.

[0081] The types of stages in the data collection and execution process include the first stage type and the second stage type;

[0082] The first stage types include asynchronous trigger stage type, timing calibration stage type, multi-channel isolation stage type, data temporary storage stage type, and energy closed-loop stage type;

[0083] The second type of stage includes deep sleep stage, asynchronous listening stage, timing verification stage, channel switching stage, and closed-loop power supply stage.

[0084] And configure standard behavioral indicators for each data collection and execution stage, specifically including the following steps:

[0085] Set the weight value of each data collection and execution stage according to the type of the first stage;

[0086] Set standard behavioral indicators for each data collection and execution stage according to the type of the second stage;

[0087] The standard behavior indicators and weight values ​​of each stage are generated to collect the corresponding standard indicators for each stage of the execution process.

[0088] All data acquisition and execution stages are divided into two types: the first stage and the second stage. These two types each have different functional roles, together forming a complete data acquisition and execution system. The first stage type comprises the functional stages of the acquisition process, specifically including asynchronous triggering, timing calibration, multi-channel isolation, data buffering, and energy closed-loop control. This type of stage is the core functional carrier for event-driven data acquisition, directly corresponding to the core functions of the project to be configured and executed, and is the fundamental support for the normal operation of the acquisition process. The asynchronous triggering stage corresponds to the asynchronous triggering function, undertaking the event triggering control of acquisition actions; the timing calibration stage corresponds to the timing calibration function, ensuring the timing synchronization of multi-channel data; the multi-channel isolation stage corresponds to the multi-channel isolation function, eliminating signal crosstalk; the data buffering stage corresponds to the data buffering function, realizing data buffering and storage; and the energy closed-loop control stage corresponds to the energy closed-loop regulation function, realizing dynamic power supply management. The auxiliary operation components include deep sleep, asynchronous listening, timing verification, channel switching, and closed-loop power supply components. These components provide auxiliary support for ensuring low-power and stable operation of the acquisition process, enabling state management, performance verification, and dynamic adaptation. Deep sleep is used for low-power sleep control when no events are triggered, reducing overall system power consumption. Asynchronous listening continuously monitors for asynchronous trigger events to ensure timely triggering. Timing verification performs secondary verification of timing calibration results to ensure timing accuracy. Channel switching dynamically switches sampling channels according to acquisition needs, adapting to multiple acquisition scenarios. Closed-loop power supply dynamically adjusts the power supply strategy based on component status, achieving closed-loop energy control.

[0089] Based on the type of the first stage, set the corresponding stage weight value for each data acquisition and execution stage. The stage weight value is used to characterize the importance of each core functional stage in the entire data acquisition process. The higher the weight value, the greater the impact of the stage on the data acquisition process, and the higher its priority in subsequent status determination and power supply control. The setting of stage weight values ​​needs to be combined with the actual needs of low-power scenarios and the functional positioning of the stages. Generally, the more critical the core function, the higher the weight value. For example, in scenarios where low power consumption and battery life are the core requirements, the weight value of the energy closed-loop stage type can be set to 0.3, the weight value of the asynchronous trigger stage type can be set to 0.25, the weight value of the timing calibration stage type can be set to 0.15, the weight value of the multi-channel isolation stage type can be set to 0.15, the weight value of the data temporary storage stage type can be set to 0.15, and the sum of the weight values ​​of all first stage types is 1.

[0090] Based on the second stage type, standard behavioral indicators are set for each acquisition and execution stage. These standard behavioral indicators are benchmark parameters used to determine whether the actual operating state of the stage is normal. Each second stage type corresponds to a specific standard behavioral indicator used to quantify the ideal operating state of the stage. For example, the standard behavioral indicator for the deep sleep stage type can be set as the steady-state power consumption threshold in sleep mode, for example, 0.1 milliwatts, to determine whether the stage is in a normal low-power sleep state; the standard behavioral indicator for the asynchronous listening stage type can be set as the event listening response time threshold, for example, 1 millisecond, to determine the timeliness of event listening; the standard behavioral indicator for the timing verification stage type can be set as the timing deviation threshold, for example, 0.5 milliseconds, to determine the accuracy of timing calibration; the standard behavioral indicator for the channel switching stage type can be set as the channel switching time threshold, for example, 2 milliseconds, to determine the efficiency of channel switching; and the standard behavioral indicator for the closed-loop power supply stage type can be set as the power supply response time threshold, for example, 1.5 milliseconds, to determine the timeliness of power supply control. For example, in a low-power environment monitoring sensor node, the standard behavior index for a deep sleep type of link is set to 0.08 milliwatts. When the actual power consumption of the link exceeds this index, the link is judged to be operating abnormally. The standard behavior index for an asynchronous listening type of link is set to 0.8 milliseconds. When the actual response time exceeds this index, the link is judged to be operating abnormally. The standard behavior index enables accurate quantitative judgment of the link status.

[0091] The standard behavior indicators and weight values ​​of each stage are integrated to generate the standard acquisition indicators for the data collection and execution stage. These standard indicators serve as a comprehensive benchmark for judging the importance of each stage and its ideal operating state. The standard acquisition indicator is calculated as: Standard Acquisition Indicator = Stage Weight Value × Stage Standard Behavior Indicator. This indicator reflects both the importance of the stage and clarifies its ideal operating parameters, providing a unified comprehensive benchmark for comparing and judging the actual execution behavior of subsequent stages. For example, if a data collection and execution stage belongs to the asynchronous trigger stage type (Type 1) with a corresponding stage weight value of 0.3, and also belongs to the asynchronous listening stage type (Type 2) with a corresponding stage standard behavior indicator of 0.8 milliseconds, then the standard acquisition indicator for this stage is 0.3 × 0.8 = 0.24 milliseconds. This indicator serves as the comprehensive benchmark for judging the actual operating state of the stage. If the comprehensive indicator corresponding to the actual execution behavior of the stage exceeds this benchmark, the stage is considered to be operating abnormally. For example, if the weight value of the closed-loop energy link type is 0.25 and the standard behavior index of the closed-loop power supply link type is 1.5 milliseconds, then the link acquisition standard index = 0.25 × 1.5 = 0.375 milliseconds, which is used to determine whether the operation status of the closed-loop power supply link is normal.

[0092] Based on the feedback data collected in low-power scenarios, the actual execution behavior of each stage of the data collection and execution process in low-power scenarios is determined, specifically including the following steps:

[0093] The steady-state feedback behavior of each acquisition and execution stage is obtained based on the steady-state energy feedback data;

[0094] The asynchronous feedback behavior of each collection and execution stage is obtained based on the asynchronous event feedback data;

[0095] The actual execution behavior of each data acquisition and execution stage is determined by the steady-state feedback behavior and the asynchronous feedback behavior of each stage.

[0096] Steady-state energy feedback data is used to obtain the steady-state feedback behavior of each acquisition and execution stage. Steady-state energy feedback data refers to the energy operation parameters and status data of each acquisition and execution stage in a steady-state sleep state without asynchronous event triggers. It characterizes the low-power operation of each stage during non-working periods and is the core basis for evaluating the low-power performance of each stage. In low-power scenarios, the stage is in a steady-state sleep state without event triggers for most of the time. The operation behavior of the stages in this state directly determines the overall battery life of the system. Therefore, it is necessary to comprehensively collect the steady-state operation parameters of each stage through steady-state energy feedback data. Specifically, steady-state energy feedback data includes parameters such as steady-state power consumption, sleep state duration, and wake-up response latency of each stage. These parameters are collected in real time by the system's built-in energy monitoring unit to extract the corresponding steady-state feedback behavior of each acquisition and execution stage. For example, in the low-power environment monitoring sensor node, the steady-state energy feedback data acquisition shows that the actual steady-state power consumption of the deep sleep stage is 0.09 mW, the steady-state power consumption of the asynchronous listening stage is 0.05 mW, and the steady-state power consumption of the energy closed-loop stage is 0.03 mW. These parameters together constitute the steady-state feedback behavior of each stage, which is used to compare with the standard behavior indicators of the stages to determine whether the steady-state operation of the stages is normal. If the actual steady-state power consumption of the deep sleep stage exceeds the preset standard behavior indicator of the stage, the steady-state operation of the stage is determined to be abnormal, and power consumption optimization is required through closed-loop power supply control.

[0097] The asynchronous event feedback data is used to obtain the asynchronous feedback behavior of each acquisition execution stage. Asynchronous event feedback data refers to the event response parameters and operational data of each acquisition execution stage in the working state after an asynchronous event is triggered. It characterizes the event processing performance of each stage during the working period and is the core basis for evaluating the reliability of the stage's acquisition function. When the asynchronous event triggers the acquisition process, each acquisition execution stage sequentially enters the working state, generating corresponding event response data. This data is collected in real time by the system's built-in event monitoring unit, extracting the asynchronous feedback behavior of each acquisition execution stage. The asynchronous event feedback data includes the event response time, sampling timing deviation, channel switching time, and power supply adjustment delay of each stage, comprehensively covering the operational performance of each stage in the working state. For example, when a temperature anomaly triggers the data acquisition process, the asynchronous event feedback data can collect the following parameters: the event response time of the asynchronous triggering stage is 0.9 milliseconds; the timing deviation of the timing calibration stage is 0.4 milliseconds; the signal crosstalk suppression rate of the multi-channel isolation stage is 99.8%; the switching time of the channel switching stage is 1.8 milliseconds; and the power supply adjustment delay of the closed-loop power supply stage is 1.2 milliseconds. These parameters together constitute the asynchronous feedback behavior of each stage, which is used to compare with the standard behavior indicators of the stage to determine whether the stage is operating normally. If the actual timing deviation of the timing calibration stage exceeds the preset standard behavior indicator, the stage is determined to be operating abnormally and needs to be corrected through secondary timing calibration.

[0098] The actual execution behavior of each data acquisition and execution stage is constructed based on its steady-state feedback behavior and asynchronous feedback behavior. This actual execution behavior provides a complete representation of the stage's operational status throughout all time periods, encompassing both the steady-state low-power operation without event triggers and the operational performance status after an event trigger, achieving full-cycle coverage of the stage's operational status. When constructing the actual execution behavior, the steady-state feedback behavior is used as the state benchmark for non-working periods, and the asynchronous feedback behavior is used as the state benchmark for working periods. A complete actual execution behavior is formed through data fusion: Actual Execution Behavior = Steady-State Feedback Behavior + Asynchronous Feedback Behavior, ensuring that the actual behavior data fully covers the stage's operational status throughout all time periods. For example, the steady-state feedback behavior of a step-triggered stage is 0.04 milliwatts of steady-state power consumption, and the asynchronous feedback behavior is 0.8 milliseconds of event response time. After fusion, the actual execution behavior of this stage includes both steady-state power consumption and event response time, fully restoring the stage's true operation in both sleep and working states. For example, the steady-state feedback behavior of the energy closed-loop link is a steady-state power consumption of 0.03 milliwatts, and the asynchronous feedback behavior of the link is a power supply adjustment delay of 1.2 milliseconds. After being integrated, they form the complete actual execution behavior of the link, providing comprehensive actual data support for subsequent state determination.

[0099] By acquiring data in both steady-state and asynchronous dimensions, the system can perceive the operational status of each acquisition and execution stage at all times. This ensures effective control over the steady-state sleep state in low-power scenarios and performance control over the working state after event triggering. This effectively improves the reliability and energy utilization efficiency of data acquisition in low-power scenarios and ensures that the acquisition process can meet the dual requirements of low power consumption and high performance at all times.

[0100] The comparison results are obtained by comparing the actual execution behavior of each step with the corresponding standard behavior indicators of that step. The specific steps include:

[0101] If the actual execution behavior of a step is consistent with the corresponding standard behavior indicators, then the step data collection status is judged to be normal.

[0102] If the actual execution behavior of a step is inconsistent with the corresponding standard behavior indicators, then the step's data collection status is judged to be abnormal.

[0103] The standard behavior indicators for each stage include steady-state power consumption standard indicators and asynchronous acquisition standard indicators.

[0104] The standard behavioral indicators for each component include steady-state power consumption standards and asynchronous data acquisition standards. These correspond to the ideal operating benchmarks for each component under different operating states, together forming a complete standard judgment system for each component. The steady-state power consumption standard is a power consumption benchmark set for the component in a steady-state sleep state without asynchronous event triggering. It is used to evaluate the component's low-power operating performance, ensuring that the system maintains a preset low power consumption level during sleep periods and guaranteeing device endurance. The asynchronous data acquisition standard is a performance benchmark set for the component's operating state after an asynchronous event is triggered. It is used to evaluate the component's event response and data acquisition performance, ensuring that the system meets acquisition requirements and timing requirements during operating periods.

[0105] The actual execution behavior of each data acquisition stage is compared with the corresponding standard behavior indicators. The actual execution behavior is composed of steady-state feedback behavior and asynchronous feedback behavior. The steady-state feedback behavior corresponds to the steady-state power consumption standard indicator, and the asynchronous feedback behavior corresponds to the asynchronous data acquisition standard indicator, ensuring a complete match between the comparison dimensions and the indicator dimensions. If the actual execution behavior of a stage is consistent with the corresponding standard behavior indicator, the stage's data acquisition status is considered normal. This means the actual behavior parameters are within the reasonable deviation range allowed by the standard behavior indicators. Actual parameter deviation = actual behavior parameter - standard behavior indicator. When the actual parameter deviation is within a preset allowable range, it is considered consistent. For example, the steady-state power consumption standard for the deep sleep stage is 0.1 milliwatts, with a preset allowable deviation range of -0.02 milliwatts to +0.02 milliwatts. If the actual steady-state power consumption of the stage is 0.09 milliwatts, the actual parameter deviation is 0.09 - 0.1 = -0.01 milliwatts, which is within the allowable range and is therefore considered consistent. Consequently, the stage's acquisition status is determined to be normal. Similarly, the asynchronous acquisition standard for the asynchronous listening stage is an event response time of 1 millisecond, with a preset allowable deviation range of -0.2 milliseconds to +0.2 milliseconds. If the actual response time is 0.9 milliseconds, the actual parameter deviation is 0.9 - 1 = -0.1 milliseconds, which is within the allowable range and is therefore considered consistent. The stage's acquisition status is then determined to be normal.

[0106] If the actual execution behavior of a step is inconsistent with the corresponding standard behavior indicator, the step's data acquisition status is judged to be abnormal. The criterion for inconsistency is that the actual behavior parameter exceeds the reasonable deviation range allowed by the standard behavior indicator, i.e., the actual parameter deviation exceeds the preset allowable range. In this case, the step is judged to be operating outside the ideal state and has an abnormal problem. For example, the actual steady-state power consumption of the deep sleep step is 0.15 milliwatts, and the actual parameter deviation = 0.15 - 0.1 = 0.05 milliwatts, exceeding the upper limit of the preset allowable range by 0.02 milliwatts, thus it is judged to be inconsistent, and the data acquisition status of this step is judged to be abnormal. Similarly, the asynchronous data acquisition standard indicator for the timing calibration step is a timing deviation of 0.5 milliseconds, and the preset allowable deviation range is -0.1 milliseconds to +0.1 milliseconds. If the actual timing deviation is 0.7 milliseconds, the actual parameter deviation = 0.7 - 0.5 = 0.2 milliseconds, exceeding the upper limit of the allowable range, thus it is judged to be inconsistent, and the step's data acquisition status is abnormal.

[0107] Event-driven, self-powered data acquisition devices for ultra-low power consumption scenarios include:

[0108] Module construction: Construct an asynchronous event sampling module and an energy closed-loop control module, determine the asynchronous time-series acquisition project corresponding to the asynchronous event sampling module and the energy closed-loop scheduling project corresponding to the energy closed-loop control module, and mark the asynchronous time-series acquisition project and the energy closed-loop scheduling project as projects to be configured and executed;

[0109] Combination Module: Based on preset collection and scheduling combination rules, the module performs combination evaluation on the items to be configured and executed to obtain a target scheduling combination set, and then assembles an event-driven self-powered collection process based on the items to be configured and executed in the target scheduling combination set.

[0110] Processing module: Based on the event-driven self-powered data acquisition process, the project to be configured for execution obtains the data acquisition execution stage, and configures standard behavior indicators for each data acquisition execution stage;

[0111] Comparison module: Based on the collection feedback data of low power scenario, determine the actual execution behavior of each collection execution stage in the low power scenario, and compare the actual execution behavior of each stage with the corresponding standard behavior indicators of each stage to obtain the comparison results;

[0112] Judgment module: Based on the comparison results, it determines the acquisition status of each acquisition execution stage, and selects the corresponding closed-loop power supply mode to supply power to the low-power scenario based on the acquisition status of each stage.

[0113] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An event-driven, self-powered data acquisition method for ultra-low power consumption scenarios, characterized in that, The method includes the following steps: Construct an asynchronous event sampling module and an energy closed-loop control module, determine the asynchronous time-series acquisition items corresponding to the asynchronous event sampling module and the energy closed-loop scheduling items corresponding to the energy closed-loop control module, and mark the asynchronous time-series acquisition items and energy closed-loop scheduling items as items to be configured and executed; Based on the preset collection and scheduling combination rules, the items to be configured and executed are combined and evaluated to obtain the target scheduling combination set. The items to be configured and executed in the target scheduling combination set are then used to form an event-driven self-powered collection process. Based on the event-driven self-powered data acquisition process, the project to be configured for execution is obtained as the data acquisition execution stage, and standard behavior indicators are configured for each data acquisition execution stage. Based on the collected feedback data of low-power scenarios, the actual execution behavior of each stage of the low-power scenario is determined, and the actual execution behavior of each stage is compared with the corresponding standard behavior indicators of each stage to obtain the comparison results. Based on the comparison results, the acquisition status of each acquisition execution stage is determined, and the corresponding closed-loop power supply method is selected to power the low-power scenario based on the acquisition status of each stage.

2. The event-driven self-powered data acquisition method for ultra-low power scenarios according to claim 1, characterized in that, The asynchronous event sampling module includes an asynchronous triggering unit, a timing calibration unit, a multi-channel isolation unit, and a data temporary storage unit; The asynchronous timing acquisition items include asynchronous trigger acquisition items, timing calibration items, multi-channel isolation items, and data temporary storage items corresponding to each unit.

3. The event-driven self-powered data acquisition method for ultra-low power scenarios according to claim 2, characterized in that, The project types corresponding to the projects to be configured for execution include asynchronous triggering type, timing calibration type, multi-channel isolation type, data temporary storage type, and energy closed loop type.

4. The event-driven self-powered data acquisition method for ultra-low power consumption scenarios according to claim 3, characterized in that, Based on preset collection and scheduling combination rules, the project to be configured for execution is combined and evaluated to obtain the target scheduling combination set, which specifically includes the following steps: Based on the target steady-state power consumption threshold of the acquisition and scheduling combination rules, the items to be configured and executed are combined to obtain a steady-state power consumption combination set. The steady-state adaptation coefficient is obtained according to the total number of item types of the items to be configured and executed in the steady-state power consumption combination set. Based on the target number of asynchronous sampling channels according to the acquisition scheduling combination rules, the projects to be configured and executed are combined to obtain a sampling channel combination set. The channel matching coefficient is obtained according to the total power consumption of the projects to be configured and executed in the sampling channel combination set. The candidate channel combination set is obtained according to the channel matching coefficient. Set steady-state weights and channel weights; The scheduling evaluation coefficients are obtained based on the steady-state weights, channel weights, steady-state adaptation coefficients, and channel matching coefficients corresponding to the candidate channel combination set. The target scheduling combination set is then determined from the candidate channel combination set based on the scheduling evaluation coefficients.

5. The event-driven self-powered data acquisition method for ultra-low power scenarios according to claim 4, characterized in that, The types of stages in the data collection and execution process include the first stage type and the second stage type; The first stage types include asynchronous trigger stage type, timing calibration stage type, multi-channel isolation stage type, data temporary storage stage type, and energy closed-loop stage type; The second type of stage includes deep sleep stage, asynchronous listening stage, timing verification stage, channel switching stage, and closed-loop power supply stage.

6. The event-driven self-powered data acquisition method for ultra-low power scenarios according to claim 5, characterized in that, And configure standard behavioral indicators for each data collection and execution stage, specifically including the following steps: Set the weight value of each data collection and execution stage according to the type of the first stage; Set standard behavioral indicators for each data collection and execution stage according to the type of the second stage; The standard behavior indicators and weight values ​​of each stage are generated to collect the corresponding standard indicators for each stage of the execution process.

7. The event-driven self-powered data acquisition method for ultra-low power scenarios according to claim 6, characterized in that, Based on the feedback data collected in low-power scenarios, the actual execution behavior of each stage of the data collection and execution process in low-power scenarios is determined, specifically including the following steps: The steady-state feedback behavior of each acquisition and execution stage is obtained based on the steady-state energy feedback data; The asynchronous feedback behavior of each collection and execution stage is obtained based on the asynchronous event feedback data; The actual execution behavior of each data acquisition and execution stage is determined by the steady-state feedback behavior and the asynchronous feedback behavior of each stage.

8. The event-driven self-powered data acquisition method for ultra-low power scenarios according to claim 7, characterized in that, The comparison results are obtained by comparing the actual execution behavior of each step with the corresponding standard behavior indicators of that step. The specific steps include: If the actual execution behavior of a step is consistent with the corresponding standard behavior indicators, then the step data collection status is judged to be normal. If the actual execution behavior of a step is inconsistent with the corresponding standard behavior indicators, then the step's data collection status is judged to be abnormal.

9. The event-driven self-powered data acquisition method for ultra-low power scenarios according to claim 8, characterized in that, The standard behavioral indicators for this process include steady-state power consumption standard indicators and asynchronous acquisition standard indicators.

10. An event-driven self-powered data acquisition device for ultra-low power scenarios, applied to the event-driven self-powered data acquisition method for ultra-low power scenarios as described in any one of claims 1 to 9, characterized in that, include: Module construction: Construct an asynchronous event sampling module and an energy closed-loop control module, determine the asynchronous time-series acquisition project corresponding to the asynchronous event sampling module and the energy closed-loop scheduling project corresponding to the energy closed-loop control module, and mark the asynchronous time-series acquisition project and the energy closed-loop scheduling project as projects to be configured and executed; Combination Module: Based on preset collection and scheduling combination rules, the module performs combination evaluation on the items to be configured and executed to obtain a target scheduling combination set, and then assembles an event-driven self-powered collection process based on the items to be configured and executed in the target scheduling combination set. Processing module: Based on the event-driven self-powered data acquisition process, the project to be configured for execution obtains the data acquisition execution stage, and configures standard behavior indicators for each data acquisition execution stage; Comparison module: Based on the collection feedback data of low power scenario, determine the actual execution behavior of each collection execution stage in the low power scenario, and compare the actual execution behavior of each stage with the corresponding standard behavior indicators of each stage to obtain the comparison results; Judgment module: Based on the comparison results, it determines the acquisition status of each acquisition execution stage, and selects the corresponding closed-loop power supply mode to supply power to the low-power scenario based on the acquisition status of each stage.

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