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CN-122028151-A - Self-powered wireless sensor node energy efficiency control method and system for indoor dim light scene

CN122028151ACN 122028151 ACN122028151 ACN 122028151ACN-122028151-A

Abstract

The invention provides an energy efficiency control method and system of a self-powered wireless sensor node for an indoor dim light scene, and belongs to the technical field of self-powered wireless sensors of the Internet of things. The method comprises the steps of constructing an energy acquisition and energy perception calculation model, comprising an energy collection module, an energy perception calculation module, an energy prediction module and an energy management module, wherein the energy perception calculation module dynamically adjusts capacitance capacity by utilizing a temperature correction model in the process of calculating available energy according to super capacitor voltage, the energy prediction module predicts by adopting a hybrid entropy weight rolling self-adaptive energy prediction algorithm based on low energy density and high-frequency fluctuation characteristics of an indoor dim light scene, and balances indoor energy prediction precision and energy consumption in the energy acquisition process by distinguishing situations, dormancy control, quantifying energy fluctuation and dynamically adjusting prediction weight according to the energy fluctuation condition, and the energy management module performs energy efficiency control based on the model to calculate an energy prediction value of the next time slot and perform node working mode control.

Inventors

  • GE YONGQI
  • Jin Zhugui
  • Chang Shuaibing
  • Yin Guangpu
  • LIU RUI
  • GUO YIFAN

Assignees

  • 宁夏大学

Dates

Publication Date
20260512
Application Date
20260127

Claims (10)

  1. 1. The self-powered wireless sensor node energy efficiency control method for the indoor dim light scene is characterized by constructing an energy acquisition and energy perception calculation model, comprising an energy collection module, an energy perception calculation module, an energy prediction module and an energy management module, wherein the energy collection module is used for realizing energy efficient collection by adjusting an indoor solar panel through MPPT, the energy perception calculation module is used for calculating available energy according to super capacitor voltage, the energy prediction module is used for dynamically adjusting capacitance capacity in the calculation process, the energy prediction module is used for predicting by adopting a hybrid entropy weight rolling self-adaptive energy prediction algorithm HERA based on low energy density and high frequency fluctuation characteristics of the indoor dim light scene, the prediction weight is dynamically adjusted according to the situation, dormancy control, quantized energy fluctuation and energy fluctuation conditions, the prediction accuracy of the energy in a balancing room and the energy consumption in the energy acquisition process are balanced, and the energy management module is used for adjusting the working mode of the node through a prediction value of the next time slot available energy and the current super capacitor energy value; self-powered wireless sensor node energy efficiency control is performed based on a model: step 1, initializing a system, and completing calibration of all devices in the system and configuration of system parameters; step 2, data are collected at fixed time, wherein the data comprise real-time voltage and working temperature of the super capacitor, and a situation label is added according to the current situation type; Step 3, energy sensing calculation, namely inputting the working temperature into a temperature dynamic correction model to obtain the dynamic capacity of the super capacitor, and calculating the collected available energy by combining the voltage; Step 4, judging by the dormancy control mechanism, if the sum of the available energy collected by the S time slots is lower than a threshold value, triggering a dormancy gating strategy to output a simplified predicted value, otherwise, continuing to execute the step 5; step 5, matching the situation units, namely matching the corresponding situation units in a history reference library according to the available energy and the situation labels, and extracting a history reference energy value; Step 6, utilizing HERA algorithm to make dynamic energy prediction, quantizing energy fluctuation degree, calculating normalized shannon entropy of near S time slot energy values, further utilizing linear interpolation to obtain dynamic weight, according to historical reference energy value and near S time slot energy values Calculating an energy prediction value of the next time slot by combining the prediction error of the current time slot; Step 7, regulating the working mode, namely controlling the working mode of the sensor node through the super capacitor voltage of the current time slot and the predicted available energy value of the next time slot to realize the dynamic balance of energy supply and demand; Step 8, updating the historical reference energy value, namely calculating a new reference value according to the actual energy value of the current time slot and the historical reference energy value before updating; Step 9, after finishing the task of predicting the current day, updating the state, namely storing the available energy collected by all time slots of the current day into a corresponding history reference library, and when the number of days of data under the same situation label reaches And deleting the one-day historical data with highest similarity with the new data, and updating the historical data of the corresponding situation unit.
  2. 2. The method for controlling energy efficiency of a self-powered wireless sensor node for an indoor dim light scene according to claim 1, wherein the energy acquisition and energy perception calculation model is as follows: the task sequence is defined as Wherein Is the first Task of energy prediction of individual time slots Is defined as the demand vector of (1) , The situation type of the current day of the task; Is that The voltage of the time slot super capacitor; Is that The working temperature of the time slot super capacitor is divided into K large time slots equally K is E [1, K ], each big time slot Comprising S minislots ,i∈[1,S]; The HERA algorithm parameter set is defined as: , wherein, Is the first Historical reference energy values under the corresponding situation of the time slot; Is that Normalized shannon entropy of time slots represents quantization of energy fluctuation; Is that Dynamic weights of time slots; Is that An environmental correction factor for the time slot; Is that Prediction error of time slot; is an error correction coefficient; Is a sleep threshold; HERA dynamic prediction logic is that by Dynamically adjusting weight ratio of real-time data to historical reference Is combined with And (3) with Realizing environment adaptation and error correction, predicting result The expression is: ; ; Representing time slots The collected available energy.
  3. 3. The method for controlling energy efficiency of a self-powered wireless sensor node for an indoor dim light scene according to claim 2, wherein the step 1 system initialization comprises: calibrating a system, namely calibrating an ADC and a temperature sensor which are arranged in the MCU; parameter configuration, capacitance value of super capacitor at 25 DEG C Temperature coefficient HERA algorithm weight upper and lower limits 、 Reference update coefficient Sleep threshold Error feedback coefficient Time slot value , Total number of time slots K, total number of small time slots S, total number of situation types W, situation type Writing in the MCU; reference library initialization based on current Reading the same situation history data and constructing an energy matrix Composing a context library : ; ; Wherein, the Representing the first under the corresponding situation w Available energy collected in the kth large slot; calculating initial reference value of each time slot Expressed as: ; Wherein, the For the corresponding number of days in the first scenario, Is the first The number of days corresponding to the situation is x epsilon [1, N ], and k epsilon [1, K ]; and configuring a timer according to the preset acquisition frequency.
  4. 4. The method for controlling the energy efficiency of the self-powered wireless sensor node for an indoor dim light scene according to claim 3, wherein the step 3 energy sensing calculation comprises: Correcting super capacitor dynamic capacity by using temperature correction model The expression is: ; According to super capacitor Time slot stored energy The expression is: ; When the stored energy exceeds the maximum capacity of the super capacitor When at present The energy stored by the time slot super capacitor is defined as ; Combining sampling and sleep power consumption, computing Average power consumption of time slot The expression is: ; Wherein the method comprises the steps of At the sampling rate for the sensor The power consumption of the down-sampling, For the power consumption when the node is dormant, Is that Intra-slot sampling rate Duty cycle of (2); Calculation of Time slot energy consumption The expression is: ; Time slots Collected available energy The expression is: ; In the formula, Is that Total energy consumed by the sensor in the time slot; Available energy for time slot collection The expression is: 。
  5. 5. the method for controlling energy efficiency of a self-powered wireless sensor node for an indoor dim light scene as set forth in claim 4, wherein said step4 sleep control mechanism comprises: 。
  6. 6. the method for controlling energy efficiency of a self-powered wireless sensor node for an indoor dim light scene according to claim 5, wherein said step 5 of contextual unit matching comprises: Based on the current day situation label Matching corresponding situation units E w in the history reference library, and extracting corresponding situation units E w Values.
  7. 7. The method for controlling energy efficiency of a self-powered wireless sensor node for an indoor dim light scene according to claim 6, wherein said step 6 of performing dynamic energy prediction by using a HERA algorithm comprises: wave motion quantization calculation, taking the first S in time slot The available energy collected by the time slot is used for calculating normalized shannon entropy The expression is: ; Wherein the method comprises the steps of Is that Probability distribution of the time slot energy sample values, Is that At S number The number of occurrences in the available energy value collected by the time slot, ; Dynamic weight calculation based on Determination of dynamic weights by linear interpolation Based on Determining an environmental correction factor from actual energy values for approximately H similar time periods : ; ; ; ; Wherein, the Is a ratio vector of the actual value to the reference value, In order to increment the time-weight, Is the number of similar time periods; error feedback correction by combining the current time slot prediction error Substituting the predicted value into a prediction formula to calculate the predicted value of the next time slot Error of The expression is: ; Wherein, the Is the first The predicted value of the time slot is used, Is the first The true value of the slot.
  8. 8. The method for controlling energy efficiency of self-powered wireless sensor nodes for indoor dim light scene as claimed in claim 7, wherein in step 7, the wireless sensor nodes are operated in a mode of operation Determining a working mode to be adopted in the next time slot according to the super capacitor voltage and the predicted value, and executing the working mode, wherein the specific working mode determining mode is as follows: Super capacitor voltage below death voltage threshold When the wireless sensor node is in a cold start state, the wireless sensor node is forced to enter a dead halt locking working mode level= -1; The super capacitor voltage rises back to the threshold of the starting voltage When the system is unlocked, the system is allowed to enter a low-power-consumption working mode level=0, and energy prediction is carried out; In a low power consumption mode of operation, according to Determining that the working mode of the wireless sensor node at the next moment is a mode L, wherein L is [0, Q ]: Computing a system target energy value E target : ; In the formula, Representing a system target energy value when the energy is sufficient, Representing a system target energy value at the time of the energy deficiency, Is a set system target energy threshold; calculating the deviation Gap between the predicted value of the next time slot and the target energy required for maintaining the system to operate: ; Determining L according to Gap: ,Gap>0; ,Gap≤0; In the formula, Is the energy of the super capacitor of the current time slot, and round () represents rounding function; is the maximum energy storage value of the super capacitor, Is death energy value, coefficient mk, F when energy is collected, E when no energy is collected, F > E, 1 Data acquisition is performed, and the sampling frequency is highest when l=q.
  9. 9. The method for controlling energy efficiency of a self-powered wireless sensor node for an indoor dim light scene according to claim 8, wherein said step 8 of updating the historical reference energy value comprises: Updating historical reference values for a contextual unit using an exponential moving average algorithm The formula is: ; Wherein, the To update the pre-update historical baseline energy value, A new reference value after the current real-time energy value is fused; the step 9 of updating the state after completing the current day prediction task includes: after all time slots of the same day are predicted, the real energy value of each time slot of the same day is calculated Storing the historical reference value of each situation unit into the situation unit corresponding to the situation label, and recalculating the historical reference value of each situation unit to finish updating the historical reference library; Judging whether the storage days reach the corresponding fixed storage days N or not under the situation units with the situation labels corresponding to the current day, if so, deleting the available energy data collected on the day most similar to the current day data, and storing the available energy data of the K time slots collected on the current day into the corresponding situation units, wherein the integral energy scale difference between the to-be-judged days and the historical days under the same situation is quantified through the daily average energy deviation rate: ; ; Wherein, the For the available energy collected for the day of the day to be determined, For available energy collected on the daily average of historical days in the same situation, , The available energy values collected for the big time slots of the day to be determined and the history day in the same situation respectively, Selecting min from the historical days with the same daily average energy deviation rate as the situation of the day to be judged ) As the most similar day.
  10. 10. A self-powered wireless sensor node energy efficiency control system for an indoor dim light scene, characterized in that the system is used for executing the self-powered wireless sensor node energy efficiency control method for an indoor dim light scene according to any one of claims 1-9, and the system comprises: The construction unit is used for an energy collection module, an energy perception calculation module, an energy prediction module and an energy management module; the energy prediction module predicts the energy by adopting a hybrid entropy weight rolling self-adaptive energy prediction algorithm HERA based on low energy density and high frequency fluctuation characteristics of an indoor dim light scene, and balances the indoor energy prediction precision and energy consumption in the energy acquisition process by distinguishing situations, dormancy control, quantized energy fluctuation and dynamically adjusting the prediction weight according to the energy fluctuation condition; and the calculation unit is used for controlling the energy efficiency of the self-powered wireless sensor node based on the model, calculating the energy prediction value of the next time slot and controlling the working mode of the node.

Description

Self-powered wireless sensor node energy efficiency control method and system for indoor dim light scene Technical Field The invention relates to the technical field of self-powered wireless sensors of the Internet of things, in particular to an energy efficiency control method and system of a self-powered wireless sensor node for an indoor dim light scene. Background With the wide application of the internet of things in the fields of intelligent buildings, intelligent home and the like, the deployment scale of indoor self-powered wireless sensors increases exponentially. Because of the significant "weak energy" and "high dynamic" characteristics of the indoor environment, this requires that the indoor self-powered wireless sensor must meet stringent ultra-low power consumption operating requirements. Therefore, in order to reduce the complexity of the circuit and reduce the loss in the energy transmission process to the greatest extent, the indoor micro-energy system currently mainstream adopts a simplified direct working mode of energy collection, energy storage and energy consumption. In the mode, compared with the traditional chemical battery, the super capacitor becomes an energy storage element more suitable for an indoor self-powered system by virtue of the advantages of high charge and discharge efficiency, high power density, long cycle life and the like. However, when the mode is used for controlling the energy efficiency of the self-powered wireless sensor node, the problems of insufficient energy sensing precision, poor prediction logic adaptability and poor energy efficiency control capability exist in the prior art, namely, on one hand, the self-powered wireless sensor node fails to control the energy efficiency due to the fact that the actual capacity of the super capacitor is influenced by the working temperature, and the existing scheme lacks targeted dynamic compensation, so that the actual energy cannot be accurately reversely pushed through voltage, and on the other hand, the existing energy efficiency control method still uses an indirect control thought of 'theoretical output energy + efficiency conversion + energy efficiency control' for outdoor strong light, and the mode introduces too many intermediate conversion links, so that the calculation energy consumption is increased, the complex loss from the theoretical output to the actual storage process is difficult to be accurately quantized, the energy efficiency control failure of the self-powered wireless sensor node is caused by the initial low power consumption of an indoor micro energy system 'direct working mode', and the energy overflow or cold start of the sensor node cannot meet the requirements of the indoor micro-energy self-powered wireless sensor on high-precision and low-power long-term working. Disclosure of Invention The invention provides an energy efficiency control method and system of a self-powered wireless sensor node for an indoor dim light scene, which overcome the defects of insufficient precision, poor suitability of the energy efficiency control method to the indoor dim light environment and the like caused by an energy perception calculation model in the existing energy collection prediction technology, realize accurate measurement of the indoor dim available energy by constructing an energy perception calculation model, and adapt to energy fluctuation in the dim light environment by combining a hybrid entropy weight rolling self-adaptive energy prediction algorithm HERA, thereby reducing loss in the energy perception calculation and prediction process, remarkably improving the prediction precision, and finally adjusting the working mode of the node according to a prediction value, thereby providing reliable support for long-term stable operation of the self-powered wireless sensor. The technical scheme adopted by the implementation of the invention for solving the technical problems is as follows: The first aspect of the invention provides a self-powered wireless sensor node energy efficiency control method for an indoor dim light scene, which comprises the following steps: the energy collection module is used for realizing energy efficient collection through an MPPT (maximum power point tracking) adjusting indoor solar panel, the energy perception calculation module is used for calculating available energy according to super capacitor voltage, a temperature correction model is utilized in the calculation process, the capacity of a capacitor is dynamically adjusted, the energy prediction module is used for predicting based on low energy density and high-frequency fluctuation characteristics of an indoor dim light scene, the model adopts a hybrid entropy weight rolling self-adaptive energy prediction algorithm HERA, and the working mode of a node is adjusted by distinguishing situations, dormancy control, quantifying energy fluctuation and dynamically adjusting prediction weight according to energy fluctua