Binary sensor network extensible distributed estimation method and system based on energy harvesting and dynamic event triggering protocol

Abstract

The invention provides a binary sensor network extensible distributed estimation method and system based on an energy harvesting and dynamic event triggering protocol, and belongs to the field of sensor network control. The problem that the service life of a sensor network node is short due to the fact that an existing sensor node is limited in data communication energy is solved. Comprising the following steps: establishing a linear dynamic model based on binary sensor network measurement; obtaining useful measurement information based on the difference between the true value and the estimated value of the binary sensor; a dynamic event triggering protocol and an energy harvester are introduced to schedule transmission of neighbor node information; constructing a distributed estimator for each binary sensor, and giving an expected finite time domain performance constraint; sufficient conditions are established for the presence of a desired distributed estimator. The communication cost is reduced, and the system reliability is improved; the communication burden is relieved, and the probability of incomplete information is reduced; the problem of insufficient energy during communication between the nodes is solved, and the stability of the system is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of sensor network control, and more particularly, to a scalable distributed estimation method and system for binary sensor networks based on energy harvesting and dynamic event-triggered protocols. Background Art

[0002] Sensor networks have a wide range of applications in various fields. Generally speaking, the purpose of a sensor network is to monitor key information in the monitored area through cooperation among a large number of wireless sensor nodes. However, this purpose is full of challenges because the cooperation among nodes is through a common wireless channel for data transmission, which may lead to data collisions. Especially due to limited transmission capabilities, sensor nodes can only perform local communication. Moreover, the capacity of non-replaceable batteries is also limited, resulting in energy constraints. Undoubtedly, these non-negligible factors hinder the potential applications of sensor networks. In addition, the data collected by sensor nodes has certain noise, so it is very important to filter out the noise influence and obtain key measurement information. For this reason, distributed filtering has emerged. As a generalization of traditional filtering methods, distributed filtering is based on a sensor network as the hardware foundation. Each sensor node can obtain the measurement information of adjacent nodes and has its own filter. Therefore, distributed filtering has good robustness and scalability.

[0003] With the rapid development of low-power digital and analog circuits, low-power radio frequency design, and sensor technology, the power consumption of sensor nodes has been reduced significantly compared with that in the early stage, and most of the energy is mainly consumed in data communication. The coverage area of sensor networks is relatively wide. Considering environmental factors, the energy consumption of data transmission is usually proportional to the cube of the distance. Once the distance changes slightly, the energy consumption will increase significantly. Since sensor nodes are usually powered by non-replaceable batteries, that is to say, the lifespan of sensor network nodes is closely related to the usage duration of the batteries. Therefore, studying the energy problem in the transmission process to meet the communication requirements of sensor networks is a crucial issue. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: To solve the problem that the existing sensor nodes have limited energy in data communication, resulting in a low lifespan of sensor network nodes.

[0005] The technical solution adopted by the present invention to solve the above technical problem: A scalable distributed estimation method for binary sensor networks based on energy harvesting and dynamic event-triggered protocols, comprising the following steps: S100. Establish a linear dynamics model based on the measurements of the binary sensor network; S200. Introduce a dynamic event-triggering protocol and an energy harvesting device in each distributed state estimation unit to obtain measurement information based on the difference between the true value and the estimated value of the binary sensor, so as to solve the problem of insufficient binary measurement information; S300. Introduce a dynamic event-triggering protocol and an energy harvester to schedule the transmission of neighbor node information; S400. Use the available information to construct a distributed estimator for each binary sensor and give the desired performance constraints for the estimation error system; S500. Establish sufficient conditions for the existence of the desired distributed estimator by means of local performance analysis methods.

[0006] Furthermore, in step S100, it includes The linear dynamic model of the distributed state estimation of the th sensor node is: (1) where is the state vector; is the input of node ; and are both external disturbance noises and satisfy , where represents the 2-norm, the known time-varying matrix and have compatible dimensions; is the initial state of the system; is the relationship between the sensor input and the output of the binary sensor; The measurement output of the binary sensor with a fixed threshold is as follows: (2) where is the measurement output of the binary sensor ; is a given constant representing the threshold of the binary sensor .

[0007] Furthermore, in step S200, it includes Introduce a dynamic event-triggering protocol for each estimator, and determine whether the data packet of the neighbor should be sent to node at the current time according to the dynamic event-triggering condition and the node energy level; among them, the event-triggering time sequence is determined by the following dynamic event-triggering protocol: (3) where belongs to the set of positive integers; (4) wherein, represents the estimated value of the triggering moment; is an internal dynamic variable, and its dynamic evolution equation is: (5) wherein, is a given positive scalar, and are two given positive scalars that satisfy the constraint ; for all , there exists ; The energy level represents the energy collected by the sensor node at time ; it is assumed that the amount of collected energy is finite and the random variable follows a binomial distribution, specifically: (6) wherein, represents the number of energy units collected by node at time ; represents the maximum number of energy units that node can collect at time ; represents the probability of node collecting each unit of energy at time ; is abbreviated as , and satisfies ; The energy level of sensor node at time is represented by wherein, represents the maximum number of energy units that can be stored in sensor node ; at time , if the energy level is non-zero, sensor node consumes one unit of energy to send its state estimate to node ; when the collected energy exceeds the storage capacity of the battery, the excess energy will be discarded; The dynamic change model of the energy level is: (7) wherein, (8) Among them, means that due to insufficient energy, neighbor nodes cannot send information to node .

[0008] Furthermore, in step S300, it includes Set the indicator variable , used to describe whether node receives information from neighbor node , defined as follows: (9) Among them, depends on the event-triggering protocol and energy constraint: (10) Among them, is the indicator variable, is a random variable subject to the Bernoulli distribution; describes the effect of the dynamic event-triggering protocol, defined as follows: (11) Obtain the distribution estimator in the following form: (12) Among them, is the uncertain parameter, is the indicator variable of the binary measurement; and both represent the distributed estimator gains to be calculated.

[0009] Furthermore, in step S400, it includes Define , and the dynamic equation of the estimation error is: (13) Define and , and obtain the following equation: (14) Among them, (15) The desired performance index of the dynamic system is: (16) Among them, are all given weighted matrices, are all given positive constants, representing the fixed upper bounds in the performance index; It represents the ability of the measurement system to suppress external interference.

[0010] Furthermore, in step S500, it includes (17) S520, select the following Lyapunov function: , where , , represents the positive definite matrix used to define the quadratic form energy function in the Lyapunov function; For a given and , calculate the conditional expectation of the following formula: (18) (19) where represents the Lyapunov function based on the dynamic event-triggering protocol; From formula (18) and formula (19), it can be obtained that: (20) Introduce a positive scalar , such that the following inequality holds:

[0011] Get:

[0012] Then: (21) where represents the supply rate function in vector dissipation theory, represents a parameter in the vector dissipation matrix; Then, based on vector dissipation theory and the given vector dissipation inequality, finally derive (22): (22) Therefore, by using Lyapunov stability theory and local performance analysis method, it is proved that by using the local performance analysis method, sufficient conditions are respectively constructed to make the estimation error dynamics satisfy the consistency constraint in the mean square sense.

[0013] A binary sensor network scalable distributed estimation system based on energy harvesting and dynamic event-triggering protocol, the system has program modules corresponding to the above steps, and executes the steps in the above binary sensor network scalable distributed estimation method based on energy harvesting and dynamic event-triggering protocol when running.

[0014] A computer-readable storage medium stores a computer program, and the computer program is configured to implement steps of a scalable distributed estimation method for a binary sensor network based on energy harvesting and a dynamic event triggering protocol when called by a processor.

[0015] A scalable distributed estimation method and system for a binary sensor network based on energy harvesting and a dynamic event triggering protocol according to the present invention adopts a fully distributed state estimator, which only uses information of itself and its neighbor units, reduces communication costs, and increases system reliability; the design of the estimator is based on a dynamic event triggering protocol, which can reduce the communication burden and reduce the probability of information incompleteness; in addition, the estimator design also considers an energy harvesting device, which can solve the problem of insufficient energy during communication between nodes and enhance the stability of the system. Description of the Drawings

[0016] Figure 1 is a flowchart of a scalable distributed estimation method for a binary sensor network based on energy harvesting and a dynamic event triggering protocol in an embodiment of the present invention; Figure 2 is a distribution diagram of the energy levels of nodes in an embodiment of the present invention, where (a), (b), (c), (d), (e) and (f) are distribution diagrams of the energy levels of six sensor nodes respectively; Figure 3 is a comparison diagram of binary measurement information extraction times in an embodiment of the present invention; Figure 4 is a comparison diagram of measurement errors and binary measurement errors of sensor nodes in an embodiment of the present invention, where (a), (b), (c), (d), (e) and (f) are comparison diagrams of measurement errors and binary measurement errors of six sensor nodes respectively; Figure 5 is a comparison diagram of the actual values and estimated values of elements estimated by sensor nodes in an embodiment of the present invention, where (a) is a comparison diagram of the actual value and estimated value of element 1, and (b) is a comparison diagram of the actual value and estimated value of element 2; Figure 6 is an estimation error diagram of six nodes in an embodiment of the present invention; Figure 7 is a consistency error diagram of six nodes in an embodiment of the present invention; Figure 8 is a dynamic event triggering time diagram of six nodes in an embodiment of the present invention. Detailed Embodiments

[0017] To make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of specific embodiments of the present invention is made with reference to the accompanying drawings.

[0018] Specific implementation method 1: Combined with Figure 1 As shown in the figure, the present invention provides a scalable distributed estimation method for a binary sensor network based on energy harvesting and dynamic event-triggered protocol, including the following steps: S100. Establish a linear dynamics model based on the measurements of the binary sensor network, including The linear dynamics model for the distributed state estimation of the (1) Wherein, is the state vector; is the input of node ; and are both external interference noises and satisfy , wherein represents the 2-norm, which is the square root of the sum of the squares of the elements of the vector and is commonly used to measure the magnitude of the vector; the known time-varying matrices and have compatible dimensions; is the initial state of the system; is the relationship between the sensor input and the binary sensor output; The measurement output of the binary sensor with a fixed threshold is as follows: (2) Wherein, is the measurement output of the binary sensor ; the binary sensor represents the th sensor node; represents a given constant of the threshold of the binary sensor ; S200. Introduce a dynamic event-triggered protocol and an energy harvesting device in each distributed state estimation unit, and obtain useful measurement information based on the difference between the true value and the estimated value of the binary sensor to solve the problem of insufficient binary measurement information; The present invention uses a dynamic event-triggered protocol and the energy level to determine whether the data packet of the neighbor should be sent to node at the current time node ; specifically, introduce a dynamic event-triggered protocol for each estimator, and determine whether the data packet of the neighbor should be sent to node at the current time according to the dynamic event-triggered condition and the node energy level; wherein, the event-triggered time sequence is determined by the following dynamic event-triggered protocol: (3) where belongs to the set of positive integers; (4) where represents the estimated value of the trigger time; is an internal dynamic variable, and its dynamic evolution equation is: (5) where is a given positive scalar, and are two given positive scalars that satisfy the constraint ; thus, it can be known that for all , there exists ; The energy level represents the energy collected by the sensor node at time ; assuming that the amount of energy collected is finite and the random variable follows a binomial distribution, specifically: (6) where represents the number of energy units collected by node at time ; represents the maximum number of energy units that node can collect at time ; represents the probability that node collects each energy unit at time ; is abbreviated as and satisfies ; in addition, for different nodes and times , is independent of the other previously mentioned random variables; Next, the energy level of the sensor node at time is represented by , represents the energy level collected by node at time represents the energy level of node at time , and the relationship between the two is expressed by (7), where Represents the sensor node The maximum number of energy units that can be stored; at time , if the energy level is non-zero, the sensor node will consume one unit of energy to send its state estimate to the node ; in addition, when the collected energy exceeds the storage capacity of the battery, the excess energy will be discarded; according to the above formula, the dynamic change model of the energy level is: (7) where (8) where means that due to insufficient energy, the neighbor node cannot send information to the node ; S300. Introduce a dynamic event-triggered protocol and an energy harvester to schedule the transmission of neighbor node information; Using the dynamic event-triggered protocol and the energy harvesting formula, further construct the final distributed filter; let the indicator variable , used to describe whether the node receives information from the neighbor node , is defined as follows: (9) where depends on the event-triggered protocol and the energy constraint; based on this consideration, the following model is created: (10) where is the indicator variable, is a random variable subject to the Bernoulli distribution; describes the effect of the dynamic event-triggered protocol and is defined as follows: (11) According to the previous analysis, a distributed estimator in the following form can be obtained: (12) where is the uncertain parameter, and both represent the distributed estimator gains to be calculated; S400. Obtain useful information based on the difference between the true value and the estimated value of the binary sensor input to solve the problem of insufficient estimated information. Information from neighboring nodes is transmitted only when its energy level is positive and satisfies the triggering protocol; utilize the available information (including the estimated value from the node itself , the estimated value from neighboring nodes , the indicator variable of the binary measurement ), construct a distributed estimator for each binary sensor, and give the desired performance constraints for the estimation error system; Define , and the dynamic equation of the estimation error is: (13) Define and Furthermore, the following equation can be obtained: (14) where (15) In the following invention, the desired performance index of the system dynamic system is given: (16) where are all given weighting matrices, are all given positive constants, representing the fixed upper bounds in the performance index; represents the ability to measure the suppression of external disturbances by the system; S500. By means of the local performance analysis method, establish the sufficient conditions for the existence of the desired distributed estimator, specifically including, S510. Process step S200 to obtain each distributed estimator as follows: (17) S520. Select the following Lyapunov function: , where , , represents the positive definite matrix used to define the quadratic energy function in the Lyapunov function; calculate the derivative of to verify whether the estimation errors of all distributed state estimations satisfy the consistency constraint in the mean square sense; For the given and , calculate the conditional expectation of the following formula: (18) (19) Among them, represents the Lyapunov function based on the dynamic event-triggering protocol; From formulas (18) and (19), it can be obtained that: (20) We introduce a positive scalar such that the following inequality holds:

[0019] Therefore, it can be further obtained that:

[0020] This means that: (21) Among them, represents the supply rate function in the vector dissipation theory, represents a parameter in the vector dissipation matrix; Then, based on the vector dissipation theory and the given vector dissipation inequality, (22) is finally derived: (22) Therefore, by using the Lyapunov stability theory and the local performance analysis method, it is proved that the design of the present invention constructs sufficient conditions respectively by using the local performance analysis method so that the estimation error dynamics satisfy the H∞ consistency performance in the mean square sense.

[0021] Specific implementation plan two: A binary sensor network scalable distributed estimation system based on energy harvesting and dynamic event-triggering protocol according to the present invention, the system has program modules corresponding to the above steps, and executes the steps in the above-mentioned binary sensor network scalable distributed estimation method based on energy harvesting and dynamic event-triggering protocol when running.

[0022] Other combinations and connection relationships in this implementation plan are the same as those in the specific implementation plan one.

[0023] Specific implementation plan three: A computer-readable storage medium according to the present invention, the computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the binary sensor network scalable distributed estimation method based on energy harvesting and dynamic event-triggering protocol when called by a processor.

[0024] Other combinations and connection relationships in this implementation plan are the same as those in the specific implementation plan one.

[0025] Simulation experiment To verify the effectiveness of the scalable distributed state estimation method for binary sensor networks with energy harvesting and dynamic event-triggered protocols, the present invention constructs a test system in the MATLAB / YALMIP system toolbox. The present invention can be specifically applied in the field of target tracking to estimate the position and velocity of a moving target (such as an aircraft).

[0026] First, model the moving target: (23) Among them,

[0027] Assume that the target moves in a two-dimensional plane, and its state vector is: (24) Among them, represents the motion state of the moving target at time , and respectively represent the positions of the moving target on the axis and the axis at time , and respectively represent the velocities of the moving target on the axis and the axis at time , represents the state of the moving target at time , represents the time step, is an external disturbance that satisfies .

[0028] Next, model the sensor: The sensor used in this experiment is a binary sensor, and the binary measurement values used are distances, obtaining the observation model of sensor : (25) Among them, represents the distance between each sensor node and the moving target, is the position of the th sensor, and are the positions of sensor on the axis and the axis respectively, represents the fixed threshold of the binary measurement, is the measurement noise that satisfies .

[0029] Next, the Taylor expansion is used to linearize the non - linear function in (25); for the distance measurement value, a Taylor expansion is carried out around the point and only the first - order term is retained, resulting in: (26) where Through this transformation, the measurement equation is presented in a linear form, facilitating subsequent processing and analysis.

[0030] In summary, by comprehensively considering various factors and corresponding processing methods, based on equations (23), (25) and (26), the state - space equation of the discrete linear time - varying system is given, while considering that multiple sensors perform distance observations on the target. The state - space system equation is as follows: (27) where represents the binary measurement output, .

[0031] The present invention discusses the system model (27) and estimates the system state through the binary measurement .

[0032] Numerical simulation: The topological structure of the binary sensor network is described by the directed graph where the binary sensor nodes are represented by the set and the edge set . The adjacency matrix is as follows:

[0033] Considering that six sensor nodes simultaneously monitor a moving target, the following parameters are given for numerical simulation:

[0034] In the simulation test, assume that the positions of the sensors are where , , . Calculate and according to (5.5).

[0035] Note that the estimated values at time 0 are known, so there is no need to calculate and .

[0036] Figure 2Among them, (a), (b), (c), (d), (e), and (f) are the energy levels of sensor nodes 1-6. Figure 3 is the binary measurement innovation extraction time. Figure 4 Among them, (a), (b), (c), (d), (e), and (f) are the comparison between the measurement errors of nodes 1-6 and the binary measurement errors. Figure 5 Among them, (a) and (b) are respectively the comparison between the actual motion curves of moving targets 1 and 2 and the motion curves estimated by the estimator designed in the present invention. Figure 6 is the estimator error graph of the node. Figure 7 is the consensus error graph. Figure 8 is the dynamic event trigger time.

[0037] The simulation results are as Figure 2-8 shown. Among them, Figure 5 Among (a) and (b), they respectively depict the actual motion trajectory of the target and the estimated motion trajectory of the estimator designed in the present invention. Among them, the red color represents the actual motion trajectory, and other colors represent the estimated trajectories of the estimator. From Figure 5 Among (a) in Figure 5 and (b) in Figure 3 it can be seen that the estimator designed in the present invention can well track the motion state of the target in real time. Figure 4 depicts the innovation extraction time of the binary sensor of the present invention. Figure 6 and Figure 7 depict the estimator error curve and the consensus error curve of the target tracking algorithm. Figure 2 Among (a)-(f), they represent the transformation of the internal energy of each sensor node with energy harvesting in the present invention. The maximum energy storage designed here is 3. Through Figure 6 and Figure 7 it can be seen that at the initial moment, the error of the actual motion state of the target is not 0. As time goes by, at time 30, both the estimator error and the consensus error approach 0, and the estimator error and the consensus error of each binary sensor have reached the expected performance standards.

[0038] The present invention is a distributed state estimation scheme using integral measurement in a sensor network based on energy harvesting technology and dynamic event-triggered mechanism. By selecting integral measurement as the input of the boundary model, a new binary measurement model is established for each node. A random variable obeying the binomial distribution is used to describe the energy level, and an indicator variable is introduced to characterize the influence of the dynamic event-triggered protocol. The dynamic event-triggered protocol and the energy harvesting method work together on each node to decide whether to transmit information from its neighboring nodes. Based on the available information, a distributed consensus estimator is constructed for each sensor. The present invention can be used to monitor a part of the area in a harsh environment, and since it is equipped with an energy harvesting device, the problem of limited energy of the sensor is solved.

[0039] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art of the present invention can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the protection scope of the present invention.

Claims

1. A scalable distributed estimation method for binary sensor networks based on energy harvesting and dynamic event triggering protocol, characterized in that: The following steps are involved: S100, establishing a linear dynamics model based on binary sensor network measurement; S200, introducing a dynamic event triggering protocol and an energy harvesting device in each distributed state estimation unit, for obtaining measurement information based on the difference between the real value and the estimated value of the binary sensor, so as to solve the problem of insufficient binary measurement information; S300, introducing a dynamic event triggering protocol and energy harvester scheduling transmission of neighbor node information; S400, constructing a distributed estimator for each binary sensor using available information, and providing a desired performance constraint for the estimation error system; S500. Establish sufficient conditions for the existence of an expected distributed estimator by means of a local performance analysis method.

2. The scalable distributed estimation method for binary sensor networks based on energy harvesting and dynamic event triggering protocol according to claim 1 is characterized in that: In step S100, it includes: No. The linear dynamics model of distributed state estimation of sensor nodes is: （1） in, is the state vector; For Node Input; and They are all external interference noises and meet the ,in, represents the 2-norm, given the time-varying matrix and have compatible dimensions; is the initial state of the system; is the relationship between the mapping of sensor input to binary sensor output; Binary sensor with fixed threshold The measurement output is as follows: （2） in, It is a binary sensor The measurement output of It indicates a binary sensor The threshold value is a given constant.

3. The scalable distributed estimation method for binary sensor networks based on energy harvesting and dynamic event triggering protocol according to claim 1 is characterized in that: In step S200, it includes: A dynamic event triggering protocol is introduced for each estimator to determine whether the neighbor's data packet should be sent to the node at the current time according to the dynamic event triggering condition and the node energy level. ; Among them, the event trigger sequence Determined by the following dynamic event triggering protocol: （3） in, belongs to the set of positive integers; （4） in, Representative Node Trigger moment An estimated value of is an internal dynamic variable, and its dynamic evolution equation is: （5） in, is a given positive scalar, and are two given positive scalars that satisfy the constraints ; for all ,exist ; Energy Level Represents a sensor node At the moment Collected energy; Assume that the amount of collected energy is finite and the random variable satisfies the binomial distribution, specifically: （6） in, Representation Node At the moment The amount of energy collected; Representation Node At the moment The maximum amount of energy collected; Representation Node At the moment The probability of collecting each energy; The abbreviation is ,and satisfy ; Sensor Node At the moment The energy level of It indicates that, Represents a sensor node The maximum number of energy units that can be stored in , if the energy level If non-zero, the sensor node Consumes one unit of energy to send It sends an estimate of its state; when the energy harvested exceeds the battery’s storage capacity, the excess energy is discarded; energy level The dynamic change model is: （7） in, （8） in, This means that due to insufficient energy, the neighboring nodes Cannot send node Send a message.

4. The scalable distributed estimation method for binary sensor networks based on energy harvesting and dynamic event triggering protocol according to claim 1 is characterized in that: In step S300, it includes: set up Indicator variables , used to describe the node Whether to obtain from neighboring nodes Receive information, defined as follows: （9） in, Depending on the event triggering protocol and energy constraints: （10） in, is an indicator variable, is a random variable that follows a Bernoulli distribution; Describes the effects of the dynamic event triggering protocol, which is defined as follows: （11） The distribution estimator is obtained as follows: （12） in, is an uncertain parameter, It is an indicator variable with binary measurement; and Both represent the distributed estimator gains that need to be calculated.

5. The scalable distributed estimation method for binary sensor networks based on energy harvesting and dynamic event triggering protocol according to claim 1 is characterized in that: In step S400, it includes: definition , the dynamic equation of the estimation error is: （13） definition and , we get the following equation: （14） in, （15） The expected performance indicators of dynamic systems are: （16） in, are all given weight matrices, All are given positive constants, indicating fixed upper bounds in the performance indicators; It is used to measure the system's ability to suppress external interference.

6. The scalable distributed estimation method for binary sensor networks based on energy harvesting and dynamic event triggering protocol according to claim 1 is characterized in that: In step S500, it includes: S510, process step S300 to obtain each distributed estimator as follows: （17） S520, select the following Lyapunov function: ,in, , , represents the positive definite matrix used to define the quadratic energy function in the Lyapunov function; For a given and , calculate the conditional expectation of the following formula: （18） （19） in, represents the Lyapunov function based on the dynamic event-triggered protocol; From formula (18) and formula (19), we can get: （20） Introducing a positive scalar , so that the following inequality holds: ； get: ； but: （21） in, represents the supply rate function in vector dissipation theory, Represents a parameter in the vector dissipation matrix; According to the vector dissipation theory and the given vector dissipation inequality, (22) is finally derived: （22） Therefore, Lyapunov stability theory and local performance analysis method are used to prove that sufficient conditions are constructed by using local performance analysis method to make the estimated error dynamically satisfy the H∞ consistency constraint in the mean square sense.

7. A scalable distributed estimation system for binary sensor networks based on energy harvesting and dynamic event triggering protocol, characterized by: The system has a program module corresponding to the steps of any one of claims 1 to 5, and executes the steps of the above-mentioned binary sensor network scalable distributed estimation method based on energy harvesting and dynamic event triggering protocol during operation.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the scalable distributed estimation method for binary sensor networks based on energy harvesting and dynamic event triggering protocol according to any one of claims 1 to 5 when called by a processor.

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