Method for dynamic power consumption optimization of iot metering nodes

Abstract

The application relates to the technical field of Internet of Things communication control, and discloses a dynamic power consumption optimization method for an Internet of Things instrument node, which comprises the following steps: storing collected network communication characteristic parameters into a dual-port random access memory cache area; constructing a dynamic mapping model according to a first-order time sequence difference of a ratio of data transmission frequency to a network congestion state characteristic value in a sliding time window to obtain a load pulsation convergence characteristic value; calculating an actual deviation amount of a network heartbeat packet period actual value of a previous control period from a current time sequence reference; and using a discrete state machine control logic to correct the dynamic mapping model when the actual deviation amount exceeds 50 ms, and to adjust a communication wake-up period, a data packaging and reporting strategy and a network heartbeat packet period. The application offsets measurement time lag caused by channel mutation, smoothes control fluctuation to stabilize a sleep configuration state and eliminates transient pulse power consumption of a power supply coupling circuit.

Description

Technical Field

[0001] This invention relates to a dynamic power consumption optimization method for IoT instrument nodes, belonging to the field of IoT communication and control technology. Background Technology

[0002] Currently, collecting terminal instrument communication parameters and adjusting local status cycles on the main control side is a common practice to maintain network energy balance. The main control center collects terminal data transmission frequency and network congestion status measurement values ​​to adaptively set the wake-up and sleep alternation cycle of nodes, thereby mitigating the data interaction overhead generated during meter reading. Power line carrier channel transmission has the physical characteristic of being susceptible to interference from sudden changes in power grid load. When the channel is subjected to transient superposition of sudden noise pulses, the calculated network congestion characteristics undergo a nonlinear step. Due to the large scale of carrier network nodes, this status feedback is accompanied by inherent information measurement time delay, causing a timing mismatch between the main control side control commands and the actual physical channel environment where the terminal instruments are located.

[0003] This timing mismatch causes the terminal microprocessor to frequently switch between deep sleep and high-frequency wake-up modes. The transient impact of state switching causes a continuous transient pulse current to flow through the power coupling circuit of the smart meter, generating leakage current loss and parasitic power consumption. This leads to abnormal heating of power supply branch components and accelerates the physical aging of core power devices. Although hardware measures such as improving the physical specifications of power supply branch components can delay the aging of core power devices to some extent, existing anti-interference and modulation mechanisms also have mechanistic defects at the software control level. For example, Chinese invention patent application CN111404582A discloses a plug-and-play IoT power broadband carrier HPLC system, which eliminates interference by classifying components in the smart distribution network and establishing static noise models for each. Static modeling relies on fixed network topology and noise distribution. In real-world conditions involving nonlinear, dynamic, time-varying power line carrier channels and the superposition of sudden noise transients, the system cannot adaptively adjust to the nonlinear step transitions in network congestion caused by sudden changes in grid load. The lack of a time-series differential mechanism for measurement delays and dynamic feedback damping prevents the control loop from smoothing control fluctuations caused by transient distortions in the channel. Instrument nodes frequently switch states due to timing mismatches, making it difficult to suppress parasitic power consumption added by the power coupling circuit. To suppress state switching oscillations caused by feedback delays, conventional methods include using linear weighting to smooth network characteristic inputs or increasing the switching threshold to limit the frequency of reversals. However, linear weighting leads to a decrease in the sensitivity of the control loop, causing instrument nodes to remain in long-period sleep states, resulting in data reporting delays exceeding the 15-minute meter reading window. Simply increasing the threshold, on the other hand, prevents the energy consumption regulation strategy from switching to a safe and energy-saving state in time during sudden noise events.

[0004] Therefore, the technical problem to be solved by this invention is how to construct an adaptive timing differential and feedback closed-loop adjustment mechanism to smooth the state jump oscillation caused by feedback delay while meeting the 15-minute data reporting timeliness requirement, and to block the high-frequency transient pulse current of the power supply coupling circuit to suppress parasitic power consumption. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A dynamic power consumption optimization method for IoT instrument nodes, comprising the following steps:

[0006] Step S1: Obtain the network communication characteristic parameters of the IoT meter node. The network communication characteristic parameters include data transmission frequency, network congestion status characteristic value and information interaction priority. Store the network communication characteristic parameters in the dual-port random access memory cache. Step S2: Read the data transmission frequency and network congestion state characteristic value within the preset sliding time window in the dual-port random access memory buffer, calculate the first-order time difference of the ratio of data transmission frequency to network congestion state characteristic value to construct a dynamic image model between communication load and node energy consumption, obtain the load pulsation convergence characteristic value, and read the actual value of the network heartbeat packet cycle of the previous control cycle from the configuration register to calculate the actual deviation between the actual value of the network heartbeat packet cycle and the current timing reference. Step S3: Using the discrete state machine control logic based on conditional branching, the image feature components in the dynamic image model are corrected in real time when the actual deviation exceeds 50ms. By superimposing the bias operator to overwrite the power consumption state convergence benchmark value and compressing the adjustment step size feature value, the unidirectional linear progressive state of the control timing is changed. Based on the corrected dynamic image model, the communication wake-up period, data packaging and reporting strategy and network heartbeat packet period are adjusted to generate dynamic power consumption optimization parameters for IoT instrument nodes.

[0007] Preferably, in step S1, the network congestion state characteristic value is normalized to a scalar between 0 and 1, and the information interaction priority is graded and calibrated according to the urgency of the power grid meter reading data; in step S2, if the actual deviation does not exceed 50ms, the linear mapping coefficient of the dynamic mapping model is maintained, the current parameters are used for control, and the parameters are recalculated and set according to the real-time updated network communication characteristic parameters in each control cycle.

[0008] Preferably, in step S3, the characteristic value of the adjustment step size for controlling fluctuations is adjusted. The calculation formula is: ,in, To adjust the step size characteristic value; The data transmission frequency obtained for the current control cycle. The data transmission frequency obtained in the previous control cycle. The network congestion state feature value obtained in the current control cycle is a scalar normalized to between 0 and 1; The time-series smoothing coefficient is a pre-defined factor.

[0009] Preferred time-series smoothing coefficient The value range is set to 0.6 to 1.4 to match the burst high-frequency noise data of the power line carrier network, and the characteristic value of the network congestion state caused by channel mutation. Nonlinear step and time-series smoothing coefficient When the value is less than 0.6, the time series smoothing coefficient can be increased. Increase the smoothing effect of measurement time delay, set the calculated adjustment step size characteristic value, and control the overshoot fluctuation of node parameters.

[0010] Preferably, in step S3, the discrete state machine control logic based on conditional branching adopts hierarchical conditional branching. Step S3 includes the following sub-steps: Step S31, the system compares the actual deviation with 50ms to determine whether the power consumption state convergence benchmark is in the divergence range; Step S32, when the actual deviation exceeds 50ms and the power consumption state convergence benchmark is in the divergence range, the real-time correction of the mapped feature components is triggered and the data packaging and reporting strategy is switched.

[0011] Preferably, the adaptive algorithm dynamically generates adjustment instructions based on heuristic constraint step threshold logic. When the power consumption state convergence benchmark value is in the divergence range, the system triggers discrete gradient step adjustment, which increases the communication wake-up period of the IoT instrument node step by step in a fixed step of 100ms.

[0012] Preferably, while increasing the communication wake-up period of the IoT meter node step by step in fixed increments of 100ms, the system also switches the data packaging and reporting strategy of the IoT meter node to the maximum load delay aggregation mode to perform multi-data packet merging and reporting frequency limitation.

[0013] Preferably, by progressively increasing the communication wake-up cycle of IoT meter nodes and switching the data packaging and reporting strategy to the maximum load delay aggregation mode, the data interaction latency of IoT meter nodes is controlled within the 15-minute power grid meter reading time window, thus maintaining the stability of data transmission status.

[0014] Preferably, the method further includes the following steps: Step S4, continuously record the length of the sleep time of the IoT meter node in each control cycle to construct a historical sleep feature sequence, determine whether the trend index of the historical sleep feature sequence is continuously decreasing, and when the trend index is continuously decreasing and the network congestion state feature value does not undergo a nonlinear step, determine that the IoT meter node has a risk of static power consumption increase due to hardware configuration degradation and output an abnormal prompt command.

[0015] Preferably, after adjusting the communication wake-up period, data packet reporting strategy, and network heartbeat packet period, the system stores the adjusted actual value of the network heartbeat packet period back into the configuration register as the timing feedback quantity for the next control period, thus constructing a parameter feedback loop between two adjacent control periods.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. In the dynamic power consumption optimization of IoT instrument nodes, the data transmission frequency and network congestion status metric of each instrument node are collected through the network communication interface and stored in the dual-port random access memory buffer. The first-order timing difference operation is performed based on the ratio of timing data within a preset sliding time window, thereby calculating the load pulsation convergence characteristic value used to characterize the coupling relationship between node communication load and channel time delay. This characteristic value is imported into the adaptive algorithm as a logic state parameter, which can directly offset the measurement time delay caused by power line carrier channel mutation, eliminate the unidirectional pipeline distribution state in the traditional control architecture, smooth the control fluctuations caused by channel transient distortion, and stabilize the local sleep configuration state.

[0017] 2. The system limits the range of the timing damping factor to a specific physical boundary to cover the sudden high-frequency noise conditions of the power line carrier network. When there is a nonlinear step between the data transmission frequency obtained in the current timing and the network congestion state characteristic value, the control step size characteristic is calculated by the product ratio of the timing differential frequency and the congestion characteristic. This can avoid the transition oscillation of the node control logic due to insufficient damping, and also prevent the control step size from being excessively suppressed, resulting in delays in the packaging and reporting logic. This ensures that the data interaction delay is within the power grid meter reading time window, guaranteeing real-time data transmission and reliable network communication.

[0018] 3. The controller reads the actual value of the network heartbeat packet cycle output from the configuration register of the previous cycle as a timing feedback quantity and imports it into the adaptive algorithm of the current control cycle. In this way, the actual deviation between the actual value of the network heartbeat packet cycle and the current timing reference is calculated. The timing damping adjustment mechanism uses discrete state machine control logic based on conditional branching. When the actual deviation exceeds the preset damping threshold, the in-situ correction of the mapped feature component is automatically triggered. By superimposing the bias operator, the load pulsation convergence feature value reference of the current cycle is overwritten and the control step size is forcibly compressed to suppress the unidirectional progression of the control timing before and after, and to solve the local control pulsation caused by timing mismatch. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the feature calculation and strategy adjustment process for dynamic power consumption optimization of IoT instrument nodes in this invention. Figure 2 This is a multi-branch state diagram of the discrete state machine control logic of the present invention.

[0020] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0022] A method for dynamic power consumption optimization of IoT instrument nodes includes the following steps: Step S1: Obtain the network communication characteristic parameters of the IoT meter node. The network communication characteristic parameters include data transmission frequency, network congestion status characteristic value and information interaction priority. Store the network communication characteristic parameters in the dual-port random access memory cache. Step S2: Read the data transmission frequency and network congestion state characteristic value within the preset sliding time window in the dual-port random access memory buffer, calculate the first-order time difference of the ratio of data transmission frequency to network congestion state characteristic value to construct a dynamic image model between communication load and node energy consumption, obtain the load pulsation convergence characteristic value, and read the actual value of the network heartbeat packet cycle of the previous control cycle from the configuration register to calculate the actual deviation between the actual value of the network heartbeat packet cycle and the current timing reference. Step S3: Using the discrete state machine control logic based on conditional branching, the image feature components in the dynamic image model are corrected in real time when the actual deviation exceeds 50ms. By superimposing the bias operator to overwrite the power consumption state convergence benchmark value and compressing the adjustment step size feature value, the unidirectional linear progressive state of the control timing is changed. Based on the corrected dynamic image model, the communication wake-up period, data packaging and reporting strategy and network heartbeat packet period are adjusted to generate dynamic power consumption optimization parameters for IoT instrument nodes.

[0023] Preferably, in step S1, the network congestion state characteristic value is normalized to a scalar between 0 and 1, and the information interaction priority is graded and calibrated according to the urgency of the power grid meter reading data; in step S2, if the actual deviation does not exceed 50ms, the linear mapping coefficient of the dynamic mapping model is maintained, the current parameters are used for control, and the parameters are recalculated and set according to the real-time updated network communication characteristic parameters in each control cycle.

[0024] Preferably, in step S3, the characteristic value of the adjustment step size for controlling fluctuations is adjusted. The calculation formula is: ,in, To adjust the step size characteristic value; The data transmission frequency obtained for the current control cycle. The data transmission frequency obtained in the previous control cycle. The network congestion state feature value obtained in the current control cycle is a scalar normalized to between 0 and 1; The time-series smoothing coefficient is a pre-defined factor.

[0025] Preferred time-series smoothing coefficient The value range is set to 0.6 to 1.4 to match the burst high-frequency noise data of the power line carrier network, and the characteristic value of the network congestion state caused by channel mutation. Nonlinear step and time-series smoothing coefficient When the value is less than 0.6, the time series smoothing coefficient can be increased. Increase the smoothing effect of measurement time delay, set the calculated adjustment step size characteristic value, and control the overshoot fluctuation of node parameters.

[0026] Preferably, in step S3, the discrete state machine control logic based on conditional branching adopts hierarchical conditional branching. Step S3 includes the following sub-steps: Step S31, the system compares the actual deviation with 50ms to determine whether the power consumption state convergence benchmark is in the divergence range; Step S32, when the actual deviation exceeds 50ms and the power consumption state convergence benchmark is in the divergence range, the real-time correction of the mapped feature components is triggered and the data packaging and reporting strategy is switched.

[0027] Preferably, the adaptive algorithm dynamically generates adjustment instructions based on heuristic constraint step threshold logic. When the power consumption state convergence benchmark value is in the divergence range, the system triggers discrete gradient step adjustment, which increases the communication wake-up period of the IoT instrument node step by step in a fixed step of 100ms.

[0028] Preferably, while increasing the communication wake-up period of the IoT meter node step by step in fixed increments of 100ms, the system also switches the data packaging and reporting strategy of the IoT meter node to the maximum load delay aggregation mode to perform multi-data packet merging and reporting frequency limitation.

[0029] Preferably, by progressively increasing the communication wake-up cycle of IoT meter nodes and switching the data packaging and reporting strategy to the maximum load delay aggregation mode, the data interaction latency of IoT meter nodes is controlled within the 15-minute power grid meter reading time window, thus maintaining the stability of data transmission status.

[0030] Preferably, the method further includes the following steps: Step S4, continuously record the length of the sleep time of the IoT meter node in each control cycle to construct a historical sleep feature sequence, determine whether the trend index of the historical sleep feature sequence is continuously decreasing, and when the trend index is continuously decreasing and the network congestion state feature value does not undergo a nonlinear step, determine that the IoT meter node has a risk of static power consumption increase due to hardware configuration degradation and output an abnormal prompt command.

[0031] Preferably, after adjusting the communication wake-up period, data packet reporting strategy, and network heartbeat packet period, the system stores the adjusted actual value of the network heartbeat packet period back into the configuration register as the timing feedback quantity for the next control period, thus constructing a parameter feedback loop between two adjacent control periods.

[0032] Example 1: In an IoT environment used for power line carrier meter reading architecture, IoT meter nodes are deployed in complex channels with high-frequency noise interference. The system continuously faces the challenge of control fluctuations caused by measurement delays due to channel mutations. In this scenario, each IoT meter node reports network communication characteristic parameters to the controller in real time via the communication bus. The controller stores the acquired data transmission frequency, network congestion status characteristic values, and information interaction priority in a time-series structure in the dual-port random access memory buffer.

[0033] The controller reads the data transmission frequency and network congestion state characteristic values ​​within a preset sliding time window from the buffer. By comparing the data of the current period with that of the previous period, it performs a first-order time-series difference operation to calculate the load pulsation convergence characteristic value. This characteristic value serves as a dimensionless scalar that quantifies the coupling relationship between the current communication load and channel time delay, characterizing the real-time fluctuation of data interaction. The first-order time-series difference, calculated by the ratio of the data transmission frequency to the network congestion state characteristic value, constructs a dynamic image model. The expression is ,in, To collect data transmission frequency in real time, These are characteristic values ​​of network congestion status. For mapping feature components; This serves as the convergence reference value for power consumption states. For a first-order time-difference operator, the actual deviation exceeding 50ms corrects the mapped eigencomponents: according to Calculate the bias operator Superimposed on the baseline value Generate updated values. This is the actual value of the network heartbeat packet cycle, in units of ; Here, is the timing reference value, This is a linear correction coefficient used to convert timing deviations into energy consumption baseline offsets. While calculating this characteristic value, the controller reads the actual value of the network heartbeat packet cycle output from the previous control cycle from the local configuration register and performs deviation analysis with the current timing baseline. When the controller detects that the actual deviation exceeds 50ms, it triggers a conditional branch-based discrete state machine control logic. By superimposing a preset bias operator onto the image feature components in the dynamic image model, it corrects the load pulsation convergence characteristic value in situ, thereby forcibly compressing the control step size characteristic value used to smooth fluctuations. The formula for calculating this characteristic is as follows: ,in, To adjust the step size characteristic, The data transmission frequency obtained for the current control cycle. The data transmission frequency obtained in the previous control cycle. The network congestion state feature value is obtained for the current control cycle. This feature value is a scalar normalized to between 0 and 1. The initialization phase executes the preset timing damping factor. The pre-calibration procedure involves sampling the background noise at a frequency of 5kHz under no-service load conditions at the instrument node, obtaining the noise power spectral density distribution using discrete Fourier transform, and determining the noise power spectral density based on the ratio of the noise power spectral density to the standard channel model for a carrier environment without pulse interference. Calibration value, monitoring data transmission frequency Variance exceeding the stability threshold triggers online calibration, with a sliding time window discarding old data and recalibrating. Eliminate reference drift.

[0034] In this scenario, the time-series damping factor The value is set to 1.0 to cover the sudden high-frequency noise conditions of power line carrier networks, and the characteristic value of network congestion caused by channel mutation. When a nonlinear step occurs, the discrete state machine control logic is based on... The calculation results dynamically generate optimized control commands for the communication wake-up cycle, data packet reporting strategy, and network heartbeat packet cycle of IoT instrument nodes. The controller adjusts the optimized parameters for these parameters and introduces a spatiotemporal mapping factor. Communication wake-up cycle adjustment amount according to It is confirmed that, among them, To adjust the step size characteristic value, Inputting the microcontroller's hardware timer register changes the comparison match value to constrain the underlying physical layer wake-up duration, through a spatiotemporal mapping factor. The conversion transforms the load pulsation characteristics into a hard limit on the operating frequency of the power coupling circuit, blocking the generation of transient pulse current. When the load pulsation convergence characteristic value is in the divergence range, the controller drives discrete gradient step adjustment, increases the communication wake-up period by a fixed step size of 100ms, and switches the data packet reporting strategy to the maximum load delay aggregation mode. At the same time, the network heartbeat packet period is locked at a safe baseline value, blocking the frequent transitions between deep sleep and high-frequency wake-up states of the meter node, eliminating the transient pulse power consumption generated in the power coupling circuit of the smart meter, and ensuring that the data interaction delay is maintained within the 15-minute grid meter reading time window.

[0035] Example 2: The current test platform is built in a typical power line carrier meter reading environment. The system is configured with a data acquisition controller, which has an analog signal acquisition channel with a sampling accuracy of 10 bits and a sampling rate of 5kHz. This channel is used to monitor the current pulses and communication link status of IoT meter nodes in real time. The test environment is injected with broadband Gaussian white noise with a signal-to-noise ratio of 15dB and power frequency harmonic interference at a frequency of 50Hz to simulate the electromagnetic noise conditions in an industrial field. The test sets the data processing cycle to 500ms. During the test, the communication wake-up cycle and heartbeat packet strategy of multiple IoT meter nodes are gradient-adjusted to verify the disturbance rejection stability of the dynamic image model. In the test of the sample group of this invention, a timing damping factor is set. The value was 0.8. The control group used a control method based on traditional static thresholds, that is, without adding feedback based on load pulsation convergence characteristic values, directly applied the network congestion state characteristic values. Implement hard threshold decisions.

[0036] When the data interaction load suddenly rises to the saturation critical point, the acquired raw data sequence exhibits high-frequency jitter characteristics. Data from the control group, which was not processed by this invention, shows that the instrument node experienced four consecutive forced transitions between sleep and wake-up states due to channel measurement lag, resulting in a high-frequency transient pulse current with an average peak value of 150mA generated in the power coupling circuit. The measured parasitic power consumption increased by approximately 22.5%. In the same channel environment, the controller in this invention utilizes the acquired data... and Numerical values ​​are calculated by performing first-order time-difference operations to obtain load pulsation convergence characteristic values ​​when channel environment changes abruptly. When a nonlinear step occurs and the actual execution deviation reaches 65ms, the controller initiates the dynamic image model correction logic and calculates the adjustment step size characteristic based on the formula. The communication wake-up cycle is smoothly increased. The test results show that the sleep state of the instrument node remains stable and no forced jump occurs. The peak value of the high-frequency transient pulse current generated by the power coupling circuit is suppressed to within 35mA, and the resulting additional parasitic power consumption is reduced to below 4.8%.

[0037] In the boundary validation of key parameters, an out-of-range control group is set up. When set to 0.4, test data shows that the controller's countermeasure against time delay is insufficient, leading to frequent oscillations in the load pulsation convergence characteristic value during channel abrupt changes, ultimately causing an unexpected reversal of the sleep configuration state. When set to 1.6, the calculated adjustment step size characteristic is too sluggish, increasing the system response measurement lag time to over 200ms. Although this smooths out power consumption fluctuations, it causes the data packet transmission success rate within the grid meter reading window to drop below 85%. These data indicate that the calculation of load ripple convergence characteristic values ​​depends on accurate... and Real-time input, when the time-series damping factor When the value is in the range of 0.6 to 1.4, the system can effectively offset communication latency, ensuring the timeliness of meter reading data while effectively optimizing the dynamic power consumption of nodes.

[0038] Example 3: This example combines Figures 1 to 2 The method for optimizing the dynamic power consumption of IoT instrument nodes is explained, such as... Figure 1As shown, the network communication characteristic parameters obtained include frequency and congestion value. The basic input source for this step is set as data transmission frequency and congestion state. The system stores the above-obtained parameters into a dual-port memory as a characteristic parameter cache. Based on this cache, a dynamic mapping model is constructed to calculate the first-order timing difference and outputs the calculated load pulsation convergence characteristic value. Then, the system introduces the heartbeat packet period of the previous cycle as a comparison parameter to calculate the actual deviation and compare it with the current timing reference. Then, it moves downstream and enters the judgment node to verify whether the deviation exceeds the threshold of 50ms. When the logic judgment result is yes, the system receives and executes the core operation of overwriting the power consumption state reference by correcting the mapping characteristic components according to the externally injected discrete state machine control logic. After the overwriting is completed, it moves to the end stage, that is, adjusting the communication and data strategy to generate power consumption optimization parameters.

[0039] like Figure 2 As shown, the underlying operating mechanism of the discrete state machine control logic in the aforementioned process is revealed. Its internal control state transition network consists of a linear following state, a discrete gradient step adjustment state, and an anomaly alert state. During the system's timing operation, if the front-end input indicates that the actual deviation does not exceed 50ms, the control flow is directly directed to the linear following state. When the input indicates that the actual deviation exceeds 50ms, the control node jumps unidirectionally from the linear following state to the discrete gradient step adjustment state. Under this specific adjustment branch, the system maintains a closed-loop self-iterative cycle by increasing the wake-up period by a fixed step size. If the feature value is identified as being in the convergence range during this cycle adjustment, the control is redirected from the discrete gradient step adjustment state back to the linear following state. Furthermore, within any parallel lifecycle of the system in the linear following state or the discrete gradient step adjustment state, once the independent trigger condition of a continuous decrease in the historical dormant feature sequence is met, both control links of the system will unconditionally and forcibly jump and converge to the anomaly alert state.

[0040] Example 4: In IoT applications for high-frequency load management of power grid meter clusters, IoT meter nodes normally operate in power line carrier communication channels. When multiple meter nodes send data transmission requests concurrently within similar time windows, the physical layer channel generates non-implicit negative event records, causing long-term cyclical oscillations in the network congestion control logic. The controller of the IoT meter node has an implicit negative event record injection processing module. This module operates at the front end of the communication control logic and monitors the instantaneous response characteristics of the communication link in real time at a preset sampling frequency. When the controller detects that the actual acknowledgment response delay of the data packet transmission exceeds the preset transmission control threshold, it is identified as an implicit negative event record. This module couples the timing identifier in the implicit negative event record with the load pulsation convergence characteristic value for calculation.

[0041] The controller uses a preset discrete mapping operator to translate implicit negative event records into regulating current signals. By injecting an offset correction amount into the communication wake-up cycle reference value in the system storage register, it achieves targeted correction of implicit negative events. The calculation formula is as follows: ,in, This is the offset correction amount. The actual acknowledgment response delay for the currently detected data packets. The preset transmission control threshold, The instantaneous noise level of the current communication channel is a dimensionless coefficient characterizing the intensity of interference in the channel environment. After the controller reads the implicit negative event record, it calculates the noise level using the formula above. This value is then added to the communication wake-up cycle of the IoT meter node. By converting negative events into quantifiable communication latency adjustments, the module utilizes existing control logic to achieve adaptive avoidance of network congestion. In a stress test of 50 consecutive control cycles, the module reduced the network fluctuation recovery time caused by implicit negative events from the original 300ms to less than 40ms, improving the meter reading success rate under complex channel conditions. Moreover, its control behavior is built into the existing software framework, avoiding additional data packet overhead.

[0042] Example 5: In an IoT environment with a power line carrier meter reading architecture, considering the high data transmission frequency and significant channel noise interference of IoT meter nodes in complex network environments, an offline calibration and field environment adaptive calibration procedure is established to address the uncertainty of the coupling relationship between the communication load of IoT meter nodes and channel time delay. Before field deployment, a channel simulation environment based on a specific noise power spectral density distribution is constructed to perform stress tests on various typical communication load states of IoT meter nodes. By collecting jitter data of data transmission frequency under different noise levels, a noise alignment operator characterizing the environmental features is obtained through fitting. This operator is used to compensate for the calculation deviation of the convergence characteristic value of load pulsation caused by transient noise, and establishes the engineering application benchmark for communication regulation mechanism.

[0043] After the IoT instrument node enters a long-cycle operation state, the controller periodically monitors the actual transmission frequency of the communication link and the reference transmission frequency in an interference-free environment, and performs an offset correction procedure to eliminate the performance degradation caused by system parameter drift. The controller reads the real-time measurement value of the transmission frequency from the current memory. Reference transmission frequency in an interference-free environment Based on sliding time window Calculate the offset correction amount for the transmission frequency. The calculation formula is as follows: ,in, This is the offset correction amount for the transmission frequency. For the current time The frequency of real-time data transmission is collected. This is the reference transmission frequency of the system when there is no noise interference. The duration of the sliding time window is determined by the controller based on... Feedforward compensation is applied to the load pulsation convergence characteristic value of subsequent cycles to ensure that the dynamic mapping of the communication load can still be synchronized with the actual physical link state under the condition of continuous high-frequency noise interference in the power line carrier channel. This eliminates the communication wake-up cycle setting error caused by the control logic reference deviation, ensures that the data packaging strategy of the meter node matches the real-time requirements of the power meter reading system, and realizes the long-term consistency of communication reliability and energy-saving control logic of IoT meter nodes in harsh communication environments.

[0044] Example 6: In a large-scale cluster deployment scenario of IoT instrument nodes, to address the system configuration instability caused by the dynamic evolution of communication link quality, the controller executes a standardized pre-deployment calibration procedure to establish the benchmark values ​​for each image feature component in the dynamic image model. This procedure considers the electromagnetic noise environment, node communication load distribution characteristics, and transient pulse response characteristics of the power coupling circuit as input parameters for system configuration. Through offline calibration and on-site online adaptive correction, it completes closed-loop optimization of the communication scheduling strategy. The controller executes the offline feature calibration process in the offline state of the system. The system injects a series of linearly increasing white noise signal sequences onto the communication bus and synchronously monitors the data transmission frequency fluctuations of IoT instrument nodes under different noise loads. This constructs a matrix corresponding to the communication load and node energy consumption. This matrix is ​​stored in the controller's non-volatile memory to characterize the energy consumption distribution density in different transmission frequency ranges. The system uses this distribution density as the statistical weight distribution benchmark for calculating the convergence characteristic value of load pulsation, ensuring that the algorithm logic can automatically complete the adaptive correction of the transient response characteristics of the power circuit of a specific node by loading this benchmark matrix when facing instrument nodes of different hardware batches.

[0045] During the field operation phase, the controller fine-tunes the mapped feature components based on the channel bit error rate statistics of the actual operating environment, and reads the real-time measurement value of the transmission frequency from the current memory. and compare it with the reference transmission frequency in the interference-free state. Perform a comparison and calculate the transmission frequency offset correction amount. The calculation logic for this correction amount is determined by the following formula: ,in, This is the offset correction amount for the transmission frequency. For the current time The frequency of real-time data transmission is collected. This is the reference transmission frequency of the system under interference-free conditions. The controller will determine the duration of the data sliding time window. As a feedforward compensation operator for the dynamic image model, the calculation parameters of the communication wake-up cycle are corrected to offset the frequent state transitions of nodes caused by channel time delay fluctuations. When the offset correction triggers the network congestion adjustment logic, the controller drives discrete gradient step adjustment to dynamically adjust the communication wake-up cycle in 20ms units. Before the communication success rate drops to the critical point of 90%, the load delay aggregation strategy is forcibly executed to stabilize the transient pulse current of the power coupling circuit. This calibration and correction process limits the measurement residual caused by sudden noise to the decision threshold of the control logic by performing continuous sliding integral and deviation feedback on the communication link state data, ensuring that the IoT instrument node can maintain a stable and reliable dynamic power consumption control state under complex electromagnetic interference.

[0046] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for dynamic power consumption optimization of IoT instrument nodes, characterized in that, Includes the following steps: Step S1: Obtain the network communication characteristic parameters of the IoT meter node. The network communication characteristic parameters include data transmission frequency, network congestion status characteristic value and information interaction priority. Store the network communication characteristic parameters in the dual-port random access memory cache. Step S2: Read the data transmission frequency and network congestion state characteristic value within the preset sliding time window in the dual-port random access memory buffer, calculate the first-order time difference of the ratio of data transmission frequency to network congestion state characteristic value to construct a dynamic image model between communication load and node energy consumption, obtain the load pulsation convergence characteristic value, and read the actual value of the network heartbeat packet cycle of the previous control cycle from the configuration register to calculate the actual deviation between the actual value of the network heartbeat packet cycle and the current timing reference. Step S3: Using the discrete state machine control logic based on conditional branching, the image feature components in the dynamic image model are corrected in real time when the actual deviation exceeds 50ms. By superimposing the bias operator to overwrite the power consumption state convergence benchmark value and compressing the adjustment step size feature value, the unidirectional linear progressive state of the control timing is changed. Based on the corrected dynamic image model, the communication wake-up period, data packaging and reporting strategy and network heartbeat packet period are adjusted to generate dynamic power consumption optimization parameters for IoT instrument nodes.

2. The dynamic power consumption optimization method for an IoT instrument node according to claim 1, characterized in that, In step S1, the network congestion state characteristic value is normalized to a scalar between 0 and 1, and the information interaction priority is graded and calibrated according to the urgency of the power grid meter reading data; in step S2, if the actual deviation does not exceed 50ms, the linear mapping coefficient of the dynamic mapping model is maintained, the current parameters are used for control, and the parameters are recalculated and set according to the real-time updated network communication characteristic parameters in each control cycle.

3. The dynamic power consumption optimization method for an IoT instrument node according to claim 1, characterized in that, In step S3, the characteristic value of the adjustment step size for controlling fluctuations is adjusted. The calculation formula is: ,in, To adjust the step size characteristic value; The data transmission frequency obtained for the current control cycle. The data transmission frequency obtained in the previous control cycle. The network congestion state feature value obtained in the current control cycle is a scalar normalized to between 0 and 1; The time-series smoothing coefficient is a pre-defined factor.

4. The dynamic power consumption optimization method for an IoT instrument node according to claim 3, characterized in that, Time series smoothing coefficient The value range is set to 0.6 to 1.4 to match the burst high-frequency noise data of the power line carrier network, and the characteristic value of the network congestion state caused by channel mutation. Nonlinear step and time-series smoothing coefficient When the value is less than 0.6, the time series smoothing coefficient can be increased. Increase the smoothing effect of measurement time delay, set the calculated adjustment step size characteristic value, and control the overshoot fluctuation of node parameters.

5. The dynamic power consumption optimization method for an IoT instrument node according to claim 1, characterized in that, In step S3, the discrete state machine control logic based on conditional branching adopts hierarchical conditional branching. Step S3 includes the following sub-steps: Step S31, the system compares the actual deviation with 50ms to determine whether the power consumption state convergence benchmark is in the divergence range; Step S32, when the actual deviation exceeds 50ms and the power consumption state convergence benchmark is in the divergence range, the real-time correction of the mapping feature components is triggered and the data packaging and reporting strategy is switched.

6. The dynamic power consumption optimization method for an IoT instrument node according to claim 5, characterized in that, The adaptive algorithm dynamically generates adjustment instructions based on heuristic constraint step threshold logic. When the power consumption state convergence benchmark value is in the divergence range, the system triggers discrete gradient step adjustment, which increases the communication wake-up period of the IoT instrument node step by step in a fixed step of 100ms.

7. The dynamic power consumption optimization method for an IoT instrument node according to claim 6, characterized in that, While increasing the communication wake-up period of IoT meter nodes in fixed steps of 100ms, the system also switches the data packaging and reporting strategy of IoT meter nodes to the maximum load delay aggregation mode to merge multiple data packets and limit the reporting frequency.

8. The dynamic power consumption optimization method for an IoT instrument node according to claim 7, characterized in that, By progressively increasing the communication wake-up cycle of IoT meter nodes and switching the data packaging and reporting strategy to the maximum load delay aggregation mode, the data interaction latency of IoT meter nodes is controlled within the 15-minute power grid meter reading time window, thus maintaining the stability of data transmission status.

9. The dynamic power consumption optimization method for an IoT instrument node according to claim 1, characterized in that, It also includes the following steps: Step S4: Continuously record the sleep time length of the IoT meter node in each control cycle to construct a historical sleep feature sequence. Determine whether the historical sleep feature sequence is continuously decreasing based on the changing trend index. When the changing trend index is continuously decreasing and the network congestion state feature value does not undergo a nonlinear step, determine that the IoT meter node has a risk of static power consumption increase due to hardware configuration degradation and output an abnormal prompt command.

10. The dynamic power consumption optimization method for an IoT instrument node according to claim 1, characterized in that, After adjusting the communication wake-up period, data packet reporting strategy, and network heartbeat packet period, the system stores the adjusted actual value of the network heartbeat packet period back into the configuration register as the timing feedback quantity for the next control period, thus constructing a parameter feedback loop between two adjacent control periods.

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