Method and device for adjusting working mode of wireless network node, medium and product

Abstract

The invention discloses a wireless network node working mode adjusting method and device, a medium and a product, and relates to the technical field of wireless network communication, and the method comprises the steps: obtaining the current residual energy of a wireless network node and a solar radiation value at the starting moment of a current time slot, according to the current residual energy and a corresponding solar radiation prediction method, determining a solar radiation prediction value of the wireless network node at the starting moment of the next time slot, and according to the solar radiation value of the wireless network node at the starting moment of the current time slot and the solar radiation prediction value of the wireless network node at the starting moment of the next time slot, determining an energy collection curve of the wireless network node, based on the energy collection curve of the wireless network node, a working mode adjustment model is constructed with the maximum information collection frequency as a target, the working mode adjustment model is solved, the final information collection frequency of the current time slot is obtained, and the environmental energy utilization rate is improved while the service life of the wireless network node is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of wireless network communication technology, and in particular to a method, device, medium and product for adjusting a working mode of a wireless network node. Background Art

[0002] Wireless network nodes are an important part of wireless networks and play an important role in data collection, processing and transmission in the network. The energy management problem of wireless network nodes is also the biggest challenge faced by wireless networks in the application process. With the continuous development of renewable energy energy harvesting technology, energy harvesting technology has become a feasible solution to the energy consumption problem of wireless networks. Energy harvesting technology can enable nodes in wireless networks to collect energy from the surrounding environment. In theory, it can provide long-term or even unlimited energy supply for network nodes, thereby extending the life of the wireless network. However, in practical applications, due to the different distribution characteristics of different types of environmental energy and the capacity limitations of energy storage structures, the utilization rate of environmental energy by nodes is low. Therefore, there is an urgent need for a wireless network node working mode adjustment method that can improve energy utilization while ensuring the life of the node. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, medium and product for adjusting the working mode of a wireless network node, which can improve energy utilization while ensuring the life of the wireless network node.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for adjusting a wireless network node working mode, comprising:

[0006] Obtain the current remaining energy of the wireless network node and the solar radiation value at the start of the current time slot; the current remaining energy is the remaining energy of the energy supply module of the wireless network node at the start of the current time slot; the time slot is obtained by dividing a day into fixed lengths;

[0007] Determine the solar radiation prediction value of the wireless network node at the start time of the next time slot according to the current remaining energy and the corresponding solar radiation prediction method;

[0008] Calculate the energy collection power of the wireless network node at the start of the current time slot according to the solar radiation value at the start of the current time slot; calculate the predicted value of the energy collection power of the wireless network node at the start of the next time slot according to the predicted value of the solar radiation at the start of the next time slot of the wireless network node; the energy collection power is the output power of the solar panel of the wireless network node;

[0009] Determine an energy collection curve for the wireless network node according to the energy collection power of the wireless network node at the start of the current time slot and the predicted value of the energy collection power at the start of the next time slot;

[0010] Based on the energy collection curve of the wireless network node, a working mode adjustment model is constructed with the maximum number of information collection times as the goal; the working mode adjustment model includes energy balance constraints and number limit constraints; the number of information collection times is the number of times the working mode of the wireless network node is in the working state in any time slot;

[0011] The working mode adjustment model is solved to obtain the final information collection times of the current time slot.

[0012] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned method for adjusting the working mode of a wireless network node.

[0013] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for adjusting the working mode of a wireless network node.

[0014] A computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for adjusting the working mode of a wireless network node are implemented.

[0015] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention provides a method, device, medium and product for adjusting the working mode of a wireless network node, firstly obtaining the current remaining energy of the wireless network node and the solar radiation value at the start of the current time slot, determining the solar radiation prediction value at the start of the next time slot of the wireless network node according to the current remaining energy and the corresponding solar radiation prediction method, determining the energy collection curve of the wireless network node according to the solar radiation value at the start of the current time slot of the wireless network node and the energy collection power prediction value at the start of the next time slot, and constructing a working mode adjustment model based on the energy collection curve of the wireless network node with the maximum number of information collection as the target, the working mode adjustment model includes energy balance constraints and number limit constraints, and solving the working mode adjustment model to obtain the final number of information collection in the current time slot. The present invention introduces solar power supply to extend the life of the network, and solves the final number of information collection in the current time slot through the energy balance constraints and number limit constraints of the working mode adjustment model, thereby improving energy utilization while ensuring the life of the wireless network node. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a flow chart of a method for adjusting a wireless network node working mode provided in Embodiment 1 of the present invention;

[0018] Figure 2 A schematic diagram of a specific implementation process of the method for adjusting the working mode of a wireless network node provided in Embodiment 1 of the present invention;

[0019] Figure 3 A schematic diagram of the structure of a wireless network node provided in Embodiment 1 of the present invention;

[0020] Figure 4 A schematic diagram of a power variation curve of a wireless network node when the communication period of the wireless network node provided in Embodiment 1 of the present invention is T, T / 3 and T / 4;

[0021] Figure 5 An internal structure diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In practical applications, since different types of environmental energy have different distribution characteristics and the capacity limitations of energy storage structures, wireless network nodes require corresponding energy management strategies to improve the node's utilization of environmental energy.

[0024] Two typical existing energy prediction algorithms are the Exponential Weighted Moving Average (EWMA) algorithm and the Weather-Conditioned Moving Average (WCMA) algorithm.

[0025] The EWMA algorithm is one of the typical prediction algorithms for solar short-term energy prediction. Since the value of solar light intensity has obvious periodic changes, it divides the change cycle into N time slots on average, and records the starting time of the i-th time slot as t i , and assume that the change in light intensity between the i-th time point and the i+1-th time point is uniform. Since the N+1-th time point overlaps with the 1st time point of the next day, there are N time points to be predicted every day. The EWMA algorithm is based on the idea of ​​exponentially weighted moving average, which performs weighted averaging on the observations in the time series, so that recent observations contribute more to the average value, while the contribution of distant observations to the average value gradually decreases. In use, we expect that on a typical day, the value of the light intensity to be predicted will be similar to the value at the same time in the previous few days. According to the EWMA algorithm, the historical average of the light intensity at the i-th time point on the d-th day is:

[0026] S d (i) = βX d (i)+(1-β)S d-1 (i), i=1,2,...,N (1);

[0027] Among them, β is a weight factor, the value is usually between 0 and 1, S d (i) represents the historical average light intensity at the i-th moment on the d-th day, X d (i) represents the actual value of the light intensity at the i-th moment on the d-th day. Since the EWMA algorithm assumes that the weather changes are stable, the historical average data of each time slot on the d-th day can be used as the predicted value on the d+1 day.

[0028] By considering the current and past weather conditions, the WCMA algorithm can better predict solar energy related parameters when weather conditions change suddenly than the EWMA algorithm.

[0029] In the WCMA algorithm, a D×K matrix X is used to store the light intensity values ​​of the current moment in the previous K moments of the past D days. d (n) represents the predicted value at the nth time point on the dth day, X d (n) represents the actual value at the nth time point on the dth day. The predicted value is related to the sample values ​​of the K moments before the day and the sample values ​​of the D days before the predicted moment:

[0030]

[0031] Where β is a weight factor similar to the EWMA algorithm, is the number of samples in the past D days. n Average light intensity at all times:

[0032]

[0033] GAP k It is an additional weight factor introduced by the WCMA algorithm compared to the EWMA algorithm. This factor is mainly used to characterize the weather changes of the predicted date relative to the previous few days. k The calculation of factors is relatively complicated. First, GAP k The factor is defined as:

[0034]

[0035] where the vector V = [v1,v2,...,v K ] is a vector containing K elements, V contains the quotient of the samples at the past K moments and the average value of that moment in the past D days, that is,

[0036]

[0037] v k The value of represents the comparison between the weather conditions of the current day and the past D days, v k >1 means the current weather is better than the past few days. k <1 means the current weather is worse than the past few days.

[0038] Vector P = [p1, p2, ..., p K ] is used to weight the vector V, according to the rule that the closer the distance to the current time, the greater the weight. K Define, that is

[0039]

[0040] The energy prediction results directly affect the implementation of the subsequent dynamic adjustment scheme of the working mode of wireless network nodes. If the energy prediction accuracy is too low, it may cause the communication device to reduce energy utilization or die due to energy exhaustion. In the above existing energy prediction schemes, the EWMA algorithm has no judgment factor of the current weather conditions. Therefore, the prediction accuracy of the algorithm is higher when the weather conditions are stable, but the prediction accuracy is greatly reduced when the weather conditions change drastically. It is only suitable for energy prediction under stable weather conditions. In order to improve the prediction accuracy under unstable weather conditions, the WCMA algorithm introduces the weather condition judgment factor GAP k This parameter is used to characterize the weather changes of the predicted date relative to the previous few days, but GAP kThe factor requires the algorithm to save more historical data for calculation. Therefore, although the WCMA algorithm still has a high prediction accuracy under conditions of drastic changes in weather conditions, the algorithm is too complex and requires more historical data to be retained, occupying a larger storage space.

[0041] In addition, in the existing analysis of energy harvesting wireless communication networks, the modeling of energy collection power in a certain time slot is usually unchanged, so the relative relationship between energy collection power and node energy consumption power is relatively simple. The analysis of the estimated accumulated energy based on this model is not accurate enough, and the energy prediction and the adjustment scheme of the wireless network node working mode cannot be better combined. The method for dynamically adjusting the working mode of a wireless network node provided in this embodiment records the energy collection curve as a linear function according to the energy prediction result. The change of energy harvesting power in time slot i is taken into account, which is closer to the actual situation.

[0042] At present, the research purpose of wireless network node power management module is mostly to minimize the energy consumption or maximize the service life of wireless network nodes while meeting the set performance requirements, while this embodiment is set for the purpose of improving energy utilization. In addition, in this embodiment, the wireless network node uses solar energy to work, which can greatly extend the life of the node compared with the traditional disposable battery power supply method. In addition, based on the predictable characteristics of solar energy, this embodiment uses the energy prediction algorithm as a part of the dynamic adjustment algorithm of the communication device working mode, so as to achieve the purpose of improving the energy utilization of the device.

[0043] The purpose of the present invention is to provide a method, device, medium and product for adjusting the working mode of a wireless network node. In view of the problems of short network life and small amount of information transmission caused by limited energy of wireless network nodes, a solution of introducing solar power supply to extend the network life is proposed. At the same time, in view of the problem of low environmental energy utilization rate of wireless network nodes, a solution of dynamically adjusting the working mode of wireless network nodes based on energy prediction is proposed to improve energy utilization rate while ensuring the life of wireless network nodes.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] Example 1

[0046] like Figure 1 and Figure 2 As shown, this embodiment is used to provide a method for adjusting a working mode of a wireless network node, including:

[0047] S1: Obtain the current remaining energy of the wireless network node and the solar radiation value at the start of the current time slot; the current remaining energy is the remaining energy of the energy supply module of the wireless network node at the start of the current time slot; the time slot is obtained by dividing a day into fixed lengths.

[0048] S2: Determine the solar radiation prediction value of the wireless network node at the start time of the next time slot according to the current remaining energy and the corresponding solar radiation prediction method.

[0049] S3: Calculate the energy collection power of the wireless network node at the start of the current time slot based on the solar radiation value at the start of the current time slot; calculate the predicted value of the energy collection power of the wireless network node at the start of the next time slot based on the predicted value of the solar radiation at the start of the next time slot of the wireless network node; the energy collection power is the output power of the solar panel of the wireless network node.

[0050] S4: Determine an energy collection curve of the wireless network node according to the energy collection power of the wireless network node at the start of the current time slot and the predicted value of the energy collection power at the start of the next time slot.

[0051] S5: Based on the energy collection curve of the wireless network node, a working mode adjustment model is constructed with the maximum number of information collection times as the goal; the working mode adjustment model includes energy balance constraints and number limit constraints; the number of information collection times is the number of times the working mode of the wireless network node is in the working state in any time slot.

[0052] S6: Solve the working mode adjustment model to obtain the final information collection times of the current time slot.

[0053] Traditional wireless network nodes are powered by disposable batteries. In this case, battery capacity becomes an important factor restricting the life of wireless network nodes. At present, many algorithms have been proposed for energy consumption problems in wireless communication networks. However, the improvement of wireless network node life is often at the expense of reduced working performance. Even if these algorithms have made breakthroughs in communication protocols, operating systems, energy management mechanisms and routing protocols, wireless network nodes will still die due to battery exhaustion and end the life of the entire wireless communication network. However, with the continuous development of renewable energy energy collection technology, energy collection technology has become a feasible solution to the energy consumption problem of wireless communication networks. Energy collection technology can convert energy in the surrounding environment into electrical energy, making long-term operation of wireless communication networks possible without reducing the working performance of wireless network nodes. This embodiment uses solar energy to power wireless network nodes. Since the change of solar energy is periodic and belongs to an uncontrollable but predictable energy type, this embodiment proposes a dynamic adjustment scheme for wireless network node working mode based on energy prediction. The main core points of this scheme are two: one is the design and implementation of the energy prediction algorithm, and the other is the dynamic adjustment scheme of the working mode of the wireless network node.

[0054] The solar-powered wireless network nodes in this embodiment are composed of Figure 3 As shown, it mainly includes four parts: energy supply module, information acquisition module, processor module and wireless communication module, among which the energy supply module includes two parts: renewable energy supply management module and energy buffer zone.

[0055] 1. Energy prediction:

[0056] In this embodiment, step S2 determines the solar radiation prediction value of the wireless network node at the start time of the next time slot according to the current remaining energy and the corresponding solar radiation prediction method, specifically including:

[0057] 1) If the current remaining energy is greater than the set power threshold and the communication duration does not exceed the set threshold time, the solar radiation forecast value at the start time of the next time slot of the wireless network node is determined according to the meteorological forecast result, specifically including:

[0058] If the current remaining energy is greater than the set power threshold and the communication duration does not exceed the set threshold time, it is determined whether the weather forecast result is obtained within the set threshold time. If so, the weather forecast result is determined as the predicted value of solar radiation at the start of the next time slot of the wireless network node; if not, the energy prediction model is used to predict the predicted value of solar radiation at the start of the next time slot of the wireless network node. The weather forecast result is the forecast result of the meteorological bureau on the solar radiation at the start of the next time slot;

[0059] 2) If the current remaining energy is not greater than the set power threshold, the energy prediction model is used to predict the solar radiation forecast value of the wireless network node at the start time of the next time slot.

[0060] This embodiment proposes two ways to obtain energy prediction results based on the periodic variation characteristics of solar energy. thr When the wireless network node sends a request to the central device to obtain the forecast result of the meteorological bureau on the change of solar radiation in the current time slot; when the wireless network node is within the set threshold time t thr If the forecast result of the meteorological bureau is not obtained within 10 days, or the current remaining energy is less than or equal to the set power threshold E thr When the energy prediction algorithm based on typical distribution proposed in this embodiment is used, the change of solar radiation in the current time slot is predicted.

[0061] First, the existing historical data samples are classified and integrated according to the total daily light intensity and change characteristics, so as to obtain the typical distribution of Q different weather types (such as sunny, cloudy, and rainy). The weather types are mainly distinguished by the total amount of light intensity on that day, which does not necessarily represent the weather conditions of the day. For example, a sunny day represents a very high total light intensity on that day, while a rainy day represents a very low total light intensity on that day, and the weather conditions are not necessarily rainy. This method uses a pre-set typical distribution as a reference model, divides each day into N time slots with a fixed length T, and calculates the correction coefficient with the data at that time point in the typical distribution during the algorithm prediction process. The prediction model of the energy prediction scheme based on the typical distribution, that is, the calculation formula of the energy prediction model is as follows:

[0062]

[0063] in, represents the predicted solar radiation value at the start of the nth time slot, S(n-1) represents the actual solar radiation value at the start of the n-1th time slot, and Q q (n) represents the solar radiation simulation value of the qth typical distribution at the start time of the nth time slot, q = 1, 2, 3...Q, Q is the total number of typical distributions; Q typical distributions are the typical variation curves of solar radiation under different weather types divided by the total amount of solar radiation intensity in a day, reflecting the simulated value of solar radiation in a day under the weather type corresponding to each typical distribution, and the typical distribution Q q The choice of depends on the relative distance between the actual solar radiation value at the start of the n-1th time slot and the simulated solar radiation value of different typical distributions at the corresponding time.

[0064] First, get the starting time t of time slot i i Solar radiation value S at the moment i, calculate the starting time t of the next time slot i+1 through the energy prediction model i+1 Time (the starting time of the next time slot i+1 t i+1 The predicted solar radiation value S at time (i.e. the end time of time slot i) i+1 Then calculate t according to the output characteristics of the solar panel. i and t i+1 Energy harvesting power P at the moment i and P i+1 , the energy harvesting power is calculated as follows:

[0065]

[0066] Among them, P i is the starting time t of time slot i i The energy harvesting power is the energy harvesting power at the start time t of time slot i. i The output power of the solar panel at time I scref 、V ocref ,I mref 、V mref They represent the solar panels used by wireless network nodes to collect solar energy under the reference sunshine intensity S ref =1000W / m 2 and reference ambient temperature T ref = short-circuit current, open-circuit voltage, maximum power point current and maximum power point voltage at 25°C; b is the compensation coefficient, and its typical value is b = 0.0005W / m 2 ; V is the voltage value selected based on the change range of the maximum power point voltage of the solar panel under different radiation intensities, S i is the starting time t of time slot i i Solar radiation value at the moment.

[0067] Finally, it is assumed that the energy collection power variation curve of the solar panel in time slot i is a linear function, according to t i and t i+1 Energy harvesting power P at the moment i and P i+1 Determine the energy collection curve of the wireless network node, denoted as

[0068] 2. Dynamic adjustment scheme of wireless network node working mode:

[0069] This embodiment proposes a solution for dynamically adjusting the working mode of the wireless network node according to the energy prediction result. In order to ensure the continuous operation of the wireless communication network, it is necessary to ensure that the power of the wireless network node is always greater than 0.

[0070] The working mode of the wireless network node is simplified to switch between the sleep state and the working state. The wireless network node maintains low power consumption in the sleep stage, and completes high-power operations such as collecting environmental information, receiving configuration information, transmitting data information, and monitoring channels in the working state. The sum of the time of adjacent sleep states and working states is recorded as an information collection cycle. The power change curves of wireless network nodes under different information collection schemes are shown in Figure 2. Figure 4 As shown, Figure 4 In, P active , P sleep Respectively represent the average power of wireless network nodes in working state and sleeping state. Figure 4 The power change curves of wireless network nodes are shown when the information collection period of wireless network nodes is T, T / 3 and T / 4. In each information collection period, the time that the wireless network node is in working state remains unchanged. Since energy prediction is based on time slots, and the information collection period in a single time slot is the same, the selection of the information collection period corresponds to the selection of the number of information collection times in each time slot. Therefore, the adjustment problem of the working mode of wireless network nodes, that is, the working mode adjustment model can be described as follows:

[0071]

[0072] Where m is a positive integer, indicating the number of times information is collected in time slot i, E cur Indicates the starting time t of time slot i i The remaining power of the wireless network node at It represents the energy collection power variation curve of the solar panel in time slot i, that is, the output power variation curve of the solar panel in time slot i. It is assumed here that the output power variation of the solar panel in each time slot is a linear function. represents the power change curve of the wireless network node when the number of information collection is m times; η represents the charging conversion efficiency of the rechargeable battery in the energy buffer, 0<η<1; C(t) is a parameter indicator used to characterize the relationship between the energy collection power of the solar panel and the energy consumption power of the wireless network node at the current moment. When the energy collection power is greater than the energy consumption power, C=1, at this time, the excess energy is stored in the rechargeable battery with the charging efficiency η; when the energy collection power is less than the energy consumption power, C=0, at this time, the output power of the solar panel alone is not enough to support the work of the wireless network node, and the rechargeable battery needs to provide part of the energy; P leak (t) represents the leakage current of the rechargeable battery, B represents the capacity of the rechargeable battery, t activeIt represents the time required for a wireless network node to collect, process and transmit information. Formula (11) describes the minimum collection cycle that can be used based on the energy prediction results and the current remaining power of the wireless network node. This scheme can improve the utilization rate of environmental energy by wireless network nodes.

[0073] The expression of energy balance constraint is as follows:

[0074]

[0075] Where B0 represents the initial energy of the communication device, P h (t) represents the energy collection curve of wireless network nodes, P c (t) represents the energy consumption curve of the communication device, η represents the charging conversion efficiency of the rechargeable battery in the energy buffer, C(t) is a parameter index used to characterize the relationship between the energy collection power of the solar panel and the energy consumption power of the wireless network node at the current moment, P leak (t) represents the leakage current of the rechargeable battery, and B represents the capacity of the rechargeable battery.

[0076] Formula (13) ensures that the energy of the communication device is always higher than 0 at any time, and the node will not die, which is a necessary and sufficient condition to ensure the energy balance of the node. Formula (14) indicates that the energy of the node will never be higher than the battery capacity due to the battery capacity of the communication device, which is a sufficient but not necessary condition to ensure the energy balance of the node.

[0077] The expression for the communication device communication device communication device communication device communication device communication device number limit constraint is as follows:

[0078]

[0079] Among them, m represents the number of information collection times, T is the time slot length, and t active Indicates the time of a working state. Indicates rounding down x.

[0080] The energy collection curve of the wireless network node when the number of information collection is m times is recorded as That is, the output power variation curve of the solar panel when the number of information collection is m times. Figure 2 As shown, first, the number of information collection times m=1. If formula (16) is satisfied, the wireless network node performs only one information collection communication in time slot i, and at the end of time slot i (i.e., the start time t of the next time slot i+1), i+1 ), the energy that can be accumulated is less than or equal to the remaining space of the battery, that is, the accumulated energy is not enough or just fills the energy buffer. At this time, no energy is wasted, so the working mode of the wireless network node is not modified, and the final information collection times of time slot i are determined to be 1.

[0081]

[0082] The accumulated energy is The remaining battery space is BE cur .

[0083] When m=1, if formula (17) is satisfied, that is, according to the predicted energy collection curve, when the wireless network node collects information only once in time slot i, at the end of time slot i (i.e., the starting time of the next time slot i+1 t i+1 ) is higher than the remaining battery space, then the estimated accumulated energy is enough to fill the energy buffer, and there is residual energy. This residual energy will be wasted if it is not used. In this case, the working mode of the wireless network node is adjusted according to the information collection times m (the number of working states in a T period) obtained by solving, and the information collection times of the wireless network node are increased, which can improve the utilization rate of the wireless network node to the environmental energy.

[0084]

[0085] in, It represents the energy consumption power change curve of the node when the number of information collection is 1.

[0086] If under this condition, the estimated accumulated energy is lower than the remaining battery space, the number of information collection times in this time slot is determined to be 1, otherwise the number of information collection times m is adjusted to the maximum number, that is, When m is determined, the judgment is continued according to the relationship between the accumulable energy and the remaining battery space until m is determined.

[0087] The specific process of step S6 is as follows: set the current information collection times m to 1, determine whether the accumulative energy is higher than the remaining battery space, if not, the final information collection times of time slot i is 1; if so, set the current information collection times m to Once again determine whether the accumulative energy is greater than the remaining battery space. If so, the final number of information collection times in time slot i is If not, use m-1 to replace the current information collection times And return to the step "again determine whether the accumulative energy is higher than the remaining battery space" until the accumulative energy is higher than the remaining battery space, and get the final number of information collection times in the current time slot. Use the final number of information collection times as the number of working states in the current time slot i to dynamically adjust the working mode of the wireless network node.

[0088] This embodiment has the following beneficial effects:

[0089] 1. This embodiment determines several typical distributions of solar radiation intensity by processing and analyzing the characteristics of solar radiation intensity changes at the locations of wireless network nodes in the early stage, and uses them for subsequent energy prediction. It can maintain good prediction accuracy while greatly reducing the complexity of the prediction algorithm in an environment with changeable weather conditions.

[0090] 2. This embodiment aims to improve the utilization rate of environmental energy by wireless network nodes. It combines the dynamic adjustment scheme of the working mode of wireless network nodes powered by solar energy with solar energy prediction, which is different from the scheme that only considers the current remaining energy, and can make more effective use of environmental energy.

[0091] The energy prediction scheme provided in this embodiment supports more flexible time selection; the energy prediction scheme has lower computational complexity while maintaining a high prediction accuracy; the scheme for dynamically adjusting the working mode of wireless network nodes improves the utilization rate of environmental energy by wireless network nodes while ensuring the life of wireless network nodes, and also increases the amount of information collected, which can better monitor the area to be monitored.

[0092] Example 2

[0093] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a method for adjusting a working mode of a wireless network node in Embodiment 1.

[0094] Example 3

[0095] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for adjusting a wireless network node working mode in embodiment 1.

[0096] Example 4

[0097] A computer program product includes a computer program, which implements the steps of a method for adjusting a wireless network node working mode in embodiment 1 when executed by a processor.

[0098] Example 5

[0099] A computer device, which may be a database, may have an internal structure as shown in Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for adjusting the working mode of a wireless network node in Example 1 is implemented.

[0100] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0101] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0102] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for adjusting a wireless network node working mode, characterized in that: The wireless network node working mode adjustment method comprises: Obtain the current remaining energy of the wireless network node and the solar radiation value at the start of the current time slot; the current remaining energy is the remaining energy of the energy supply module of the wireless network node at the start of the current time slot; the time slot is obtained by dividing a day into fixed lengths; Determine the solar radiation prediction value of the wireless network node at the start time of the next time slot according to the current remaining energy and the corresponding solar radiation prediction method; Calculate the energy collection power of the wireless network node at the start of the current time slot according to the solar radiation value at the start of the current time slot; calculate the predicted value of the energy collection power of the wireless network node at the start of the next time slot according to the predicted value of the solar radiation at the start of the next time slot of the wireless network node; the energy collection power is the output power of the solar panel of the wireless network node; Determine an energy collection curve for the wireless network node according to the energy collection power of the wireless network node at the start of the current time slot and the predicted value of the energy collection power at the start of the next time slot; Based on the energy collection curve of the wireless network node, a working mode adjustment model is constructed with the maximum number of information collection times as the goal; the working mode adjustment model includes energy balance constraints and number limit constraints; the number of information collection times is the number of times the working mode of the wireless network node is in the working state in any time slot; The working mode adjustment model is solved to obtain the final information collection times of the current time slot.

2. The method for adjusting the working mode of a wireless network node according to claim 1, characterized in that: Determine the solar radiation prediction value of the wireless network node at the start time of the next time slot according to the current remaining energy and the corresponding solar radiation prediction method, specifically including: If the current remaining energy is greater than the set power threshold and the communication duration does not exceed the set threshold time, the predicted solar radiation value at the start time of the next time slot of the wireless network node is determined according to the meteorological forecast result; the meteorological forecast result is the predicted result of the meteorological bureau on the predicted solar radiation value at the start time of the next time slot; If the current remaining energy is not greater than the set power threshold, the energy prediction model is used to predict the solar radiation forecast value of the wireless network node at the start time of the next time slot.

3. The method for adjusting the working mode of a wireless network node according to claim 2, characterized in that: If the current remaining energy is greater than the set power threshold and the communication duration does not exceed the set threshold time, the solar radiation forecast value at the start time of the next time slot of the wireless network node is determined according to the meteorological forecast result, specifically including: If the current remaining energy is greater than the set power threshold and the communication time does not exceed the set threshold time, it is determined whether the weather forecast result is obtained within the set threshold time. If so, the weather forecast result is determined as the solar radiation forecast value at the start of the next time slot of the wireless network node; if not, the energy prediction model is used to predict the solar radiation forecast value at the start of the next time slot of the wireless network node.

4. The method for adjusting the working mode of a wireless network node according to claim 2, characterized in that: The calculation formula of the energy prediction model is as follows: in, represents the predicted solar radiation value at the start of the nth time slot, S(n-1) represents the actual solar radiation value at the start of the n-1th time slot, and Q q (n) represents the solar radiation simulation value of the qth typical distribution at the starting moment of the nth time slot, q=1, 2, 3...Q, Q is the total number of typical distributions; Q typical distributions are typical variation curves of solar radiation under different weather types divided by the total amount of solar radiation intensity in a day.

5. A method for adjusting a wireless network node working mode according to claim 1, characterized in that: The energy harvesting power is calculated as follows: Among them, P i is the starting time t of time slot i i Energy harvesting power; I scref 、V ocref ,I mref 、V mref They represent the solar panels used by wireless network nodes to collect solar energy under the reference sunshine intensity S ref =1000W / m 2 and reference ambient temperature T ref = short-circuit current, open-circuit voltage, maximum power point current and maximum power point voltage at 25°C; b is the compensation coefficient; V is the voltage value selected based on the variation range of the maximum power point voltage of the solar panel under different radiation intensities, S i is the starting time t of time slot i i of solar radiation.

6. The method for adjusting the working mode of a wireless network node according to claim 1, characterized in that: The expression of the energy balance constraint is as follows: Where B0 represents the initial energy of the wireless network node, P h (t) represents the energy collection curve of wireless network nodes, P c (t) represents the energy consumption curve of the wireless network node, η represents the charging conversion efficiency of the rechargeable battery in the energy buffer, C(t) is a parameter index used to characterize the relationship between the energy collection power of the solar panel and the energy consumption power of the wireless network node at the current moment, P leak (t) represents the leakage current of the rechargeable battery, and B represents the capacity of the rechargeable battery.

7. The method for adjusting the working mode of a wireless network node according to claim 1, characterized in that: The expression of the frequency limit constraint is as follows: Among them, m represents the number of information collection times, T is the length of each time slot, and t active Indicates the time of a working state. Indicates rounding down x.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for adjusting the working mode of a wireless network node according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for adjusting the working mode of a wireless network node described in any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for adjusting the working mode of a wireless network node described in any one of claims 1 to 7 are implemented.

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