A wireless sensing and monitoring system

Abstract

The present invention discloses a wireless sensing monitoring system, which includes: a controller, at least one router, and several sensors communicatively connected to the router; based on a wireless sensor network, by using the cooperation between the sensors, the router, and the controller, a sensing status monitoring network is built in an industrial factory environment; taking advantage of the fast and accurate positioning of the sensors and combining with an improved algorithm, when a fault event is detected by the sensors, it is not only possible to search and locate in real time and efficiently, saving time for technical maintenance personnel to troubleshoot the fault points, helping the maintenance personnel to perform fixed-point maintenance, and greatly improving the maintenance efficiency; and a non-linear iterative model is used for multiple iterations to optimize the positioning coordinates, reducing the sensor positioning error, shortening the positioning time-consuming, and improving the accuracy of sensor positioning.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring, and in particular to a wireless sensor monitoring system. Background Art

[0002] Currently, for the monitoring of production equipment in industrial engineering, it mainly relies on monitoring or manual supervision to issue alarms for abnormal movements of production equipment; the status of equipment on the production line cannot be monitored in real time to the controller computer through monitoring or manual supervision, resulting in low monitoring efficiency, low monitoring accuracy, and poor real-time performance of safety monitoring. Summary of the Invention

[0003] An embodiment of the present invention provides a wireless sensor monitoring system that can efficiently and accurately monitor the location where a fault occurs.

[0004] An embodiment of the present invention provides a wireless sensor monitoring system, which includes: a controller, at least one router, and several sensors communicatively connected to the router;

[0005] The controller is configured to:

[0006] Obtain the monitoring data collected by each sensor, and store the obtained monitoring data into different fuzzy subsets according to different monitoring types;

[0007] When the data value of a certain monitoring data is greater than the set threshold of the monitoring type corresponding to the fuzzy subset where the monitoring data is located, it is determined that a fault has occurred in the monitoring type corresponding to the monitoring data;

[0008] Obtain the positions of the sensors corresponding to all the monitoring data in the fuzzy subset where a fault occurs as beacon nodes, and obtain the hop counts of each beacon node;

[0009] According to the positions and hop counts of each beacon node, determine a system of distance relationship equations between the unknown node at the fault location and any beacon node, and solve to obtain the preliminary position of the unknown node;

[0010] Iteratively update the preliminary position according to a preset non-linear iterative model, and calculate the positioning accuracy of each iteration until the positioning accuracy reaches the preset accuracy, and output the latest iterative position as the precise positioning.

[0011] Preferably, the obtaining the monitoring data collected by each sensor and storing the obtained monitoring data into different fuzzy subsets according to different monitoring types specifically includes:

[0012] Obtain the monitoring data collected by each sensor through the router;

[0013] Calculate the membership function values of each monitoring data with respect to the fuzzy subsets of different monitoring types according to the membership function;

[0014] Store all the monitoring data into the corresponding fuzzy subsets respectively according to the membership function values of each monitoring data;

[0015] Among them, the membership function is The i-th monitoring data u i The clustering distance from the j-th fuzzy subset u i Is the i-th monitoring data, r ij = D imax - D imin D imax And D imin Are respectively the maximum clustering distance and the minimum clustering distance between the i-th monitoring data and all fuzzy subsets, c j Is the sample data preset for the monitoring type corresponding to the j-th fuzzy subset, μ Pj (μ i ) Is the membership function value of the i-th monitoring data ui with respect to the j-th fuzzy subset.

[0016] Furthermore, the storing all the monitoring data into the corresponding fuzzy subsets respectively according to the membership function values of each monitoring data specifically includes:

[0017] Calculate the membership function values μ i Of the monitoring data with respect to all the fuzzy subsets P1, P2......P n In turn, i is any monitoring data, i > 0, k = 1, 2, 3..., n; Pk (μ i )

[0018] Store the monitoring data μ i Into the fuzzy subset with the largest membership function value of the monitoring data μ i .

[0019] As a preferred method, the determining the distance relationship equations between the unknown node at the fault location and any beacon node according to the positions and hop counts of each beacon node, and solving to obtain the preliminary position of the unknown node specifically includes:

[0020] Sort all the hop counts of each beacon node to obtain the minimum hop count of all the beacon nodes;

[0021] Calculate the average hop distance of the unknown node where the fault location is located through the Dv-Hop positioning optimization algorithm;

[0022] Calculate the distance relation equations between the unknown node and any beacon node according to the average hop distance;

[0023] Solve the distance relation equations to obtain the preliminary position of the unknown node.

[0024] As an improvement of the above solution, the average hop distance is

[0025] where {x i , y i} and {x j , y j} are the position coordinates of beacon node i and beacon node j, and h i,j is the minimum hop count of beacon node i.

[0026] Preferably, the distance relation equations are specifically:

[0027] where (x, y) are the position coordinates of the unknown node, {x k , y k} are the position coordinates of beacon node k, the measured value d k of the distance between the unknown node and beacon node k is d k = HopSize * h j , HopSize is the average hop distance of the unknown node, h

[0028] is the minimum hop count between the unknown node and beacon node k, and n is the number of beacon nodes, k = 1, 2,..., n.

[0029] where (x′, y′) are the position coordinates of the unknown node after update, is the average value of the position coordinates of all unknown nodes obtained in the current iteration, the initial value of (x′, y′) is the preliminary position of the unknown node, (x, y) are the position coordinates of the unknown node in the current iteration, {x j , y j} are the position coordinates of beacon node j, and d j is the measured value of the distance between the unknown node and beacon node j.

[0030] Preferably, the positioning accuracy is |x k+1 - x k |;

[0031] where x k+1 is the abscissa of the unknown node in the current iteration, and x k is the abscissa of the unknown node in the previous iteration.

[0032] Preferably, the precise positioning is

[0033] where (x′, y′) are the position coordinates of the latest iterative position of the unknown node, k is the current iteration number, and the differential increment (x, y) are the position coordinates of the unknown node in the current iteration, {x j , y j} are the position coordinates of beacon node j, d j is the measured value of the distance between the unknown node and beacon node j, and n is the number of beacon nodes.

[0034] As a preferred embodiment, the sensor includes: a speed sensor, a displacement sensor, an angular velocity sensor, and an infrared sensor;

[0035] The monitoring types include: speed anomaly, displacement anomaly, angular velocity anomaly, and position anomaly.

[0036] A wireless sensor monitoring system provided by the present invention is based on a wireless sensor network, and uses the cooperation between sensors and routers and controllers to build a sensing state monitoring network in an industrial factory environment; taking advantage of the fast and accurate positioning of sensors and combining with an improved algorithm, when a fault event is detected by the sensor, it can not only locate in real time and efficiently, saving time for technical maintenance personnel to troubleshoot the fault point, helping the maintenance personnel to perform fixed-point maintenance, and greatly improving the maintenance efficiency; moreover, a non-linear iterative model is used to iteratively optimize the positioning coordinates multiple times, reducing the sensor positioning error, shortening the positioning time, and improving the accuracy of sensor positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic structural diagram of a wireless sensor monitoring system provided by the present invention;

[0038] Figure 2 is a schematic flowchart of the working principle of the controller provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] An embodiment of the present invention provides a wireless sensor monitoring system, which includes a controller, at least one router communicatively connected to the controller, and several sensors communicatively connected to the router;

[0041] Specifically, referring to Figure 1 shown in the figure, it is a schematic structural diagram of a sensor detection system provided by the present invention. The system includes a router 1, sensors 1-1, 1-2, 1-3, and 1-4;

[0042] The controller is communicatively connected to the router 1, and the router 1 is communicatively connected to the sensors 1-1, 1-2, 1-3, and 1-4;

[0043] It should be noted that in the accompanying drawings of this embodiment, the specific structure of the wireless sensor monitoring system is illustrated by taking one router as an example. In other embodiments, there are multiple routers in the system; each router is connected to several sensors.

[0044] It should be noted that in this embodiment, the host connected to the router is used as the controller. In other embodiments, a separate controller can be used to perform the corresponding functions.

[0045] Referring to Figure 2 , it is a schematic flowchart of the working principle of the controller provided by the embodiment of the present invention; the controller is configured to execute steps S1 to S5:

[0046] S1, obtain the monitoring data collected by each sensor, and store the obtained monitoring data into different fuzzy subsets according to different monitoring types;

[0047] S2, when the data value of a certain monitoring data is greater than the set threshold of the monitoring type corresponding to the fuzzy subset where the monitoring data is located, determine that a fault has occurred in the monitoring type corresponding to the monitoring data;

[0048] S3, obtain the positions of the sensors corresponding to all the monitoring data in the fuzzy subset where the fault occurs as beacon nodes, and obtain the hop counts of each beacon node;

[0049] S4, according to the positions and hop counts of each beacon node, determine a system of distance relationship equations between the unknown node at the fault location and any beacon node, and solve to obtain the preliminary position of the unknown node;

[0050] S5, perform iterative update on the preliminary position according to a preset non-linear iterative model, and calculate the positioning accuracy of each iteration until the positioning accuracy reaches the preset accuracy, and output the latest iterative position as the precise positioning.

[0051] In the specific implementation of this embodiment, all routers are communicatively connected to the controller, and each router is connected to several different types of sensors.

[0052] With the controller as the cluster head for data distribution, routers as the child nodes of the cluster head, and each sensor as the child node of a router, the address interval expression between each pair of routers is as follows:

[0053]

[0054] Where C skip (d) is the address interval, C m is the maximum number of child nodes of the upper-level node, L m is the maximum depth of the monitoring system network topology, R m is the maximum number of routers connected to the child nodes;

[0055] The controller obtains the real-time monitoring data of all sensors through the routers, calculates the membership function values of each monitoring data relative to different fuzzy subsets according to the membership function. Each fuzzy subset corresponds to a type of sensor, that is, a type of fault. The monitoring data is divided into different monitoring types according to the membership function values of each monitoring data and stored in the corresponding fuzzy subsets;

[0056] Judging one by one whether the values of all monitoring data are greater than the preset thresholds of the monitoring types corresponding to the fuzzy subsets where they are located;

[0057] When the data value of a certain monitoring data is not greater than the set threshold of the monitoring type corresponding to the fuzzy subset where the monitoring data is located, it is determined that the monitoring data with the data value is normal;

[0058] When the data value of a certain monitoring data is greater than the set threshold of the monitoring type corresponding to the fuzzy subset where the monitoring data is located, it is determined that the monitoring type corresponding to the monitoring data has a fault;

[0059] Since the beacon nodes send information in the entire wireless sensor network, the information mainly includes: the location and hop count of the beacon nodes. The location of the sensors corresponding to all the monitoring data in the fuzzy subset where the data value with an obstacle occurs can be obtained through the routers as the beacon nodes;

[0060] According to the minimum hop count between the beacon nodes and the average hop distance between the nodes in the entire wireless sensor network, determine the distance relationship equations between the unknown node at the fault location and any beacon node, and solve to obtain the preliminary location of the unknown node;

[0061] Iteratively update the preliminary location according to the pre-designed non-linear iterative model. Substitute the initial positioning data into the model for iterative optimization. The continuous iterative update of the initial location can continuously improve the accuracy of unknown node positioning, and calculate the positioning accuracy of each iteration until the positioning accuracy reaches the preset accuracy, and output the latest iterative location as the precise positioning, that is, the location of the fault occurrence point.

[0062] Based on a wireless sensor network, by coordinating measurement sensors with routers and controllers, a monitoring network for the sensing status of equipment is built in the industrial plant environment to achieve remote monitoring of factory equipment.

[0063] In an embodiment of the present invention, by utilizing the agility and high precision of the sensor network, if abnormal equipment movement occurs in the production equipment within the factory, the sensors installed on the equipment can sensitively detect and identify it, and transmit the abnormal data to the controller in real time and efficiently, sending out an alarm signal to remind to shut down the production line to prevent further damage to the machine; then, by using the high precision of the sensors, through sensor node positioning, the location of the fault point in the production equipment can be quickly determined to help the staff carry out targeted repairs and save repair time.

[0064] In another embodiment provided by the present invention, the step S1 specifically includes:

[0065] Obtain the monitoring data collected by each sensor through the router;

[0066] Calculate the membership function values of each monitoring data with respect to the fuzzy subsets of different monitoring types according to the membership function;

[0067] Store all the monitoring data into the corresponding fuzzy subsets respectively according to the membership function values of each monitoring data;

[0068] Among them, the membership function is The i-th monitoring data u i The clustering distance from the j-th fuzzy subset u i Is the i-th monitoring data, r ij = D imax - D imin D imax And D imin Are respectively the maximum clustering distance and the minimum clustering distance of the i-th monitoring data with all fuzzy subsets, c j Is the sample data preset for the monitoring type corresponding to the j-th fuzzy subset, μ Pj (μ i ) Is the membership function value of the i-th monitoring data ui with the j-th fuzzy subset.

[0069] In the specific implementation of this embodiment, for fuzzy recognition of the monitoring data, the membership function is used to calculate the membership function value of the monitoring data μ i Relative to P j Of the membership function value

[0070] Among them, r ij = Dimax -D imin , D imax and D imin are respectively the maximum clustering distance and the minimum clustering distance between the i-th monitoring data and all fuzzy subsets. The i-th monitoring data u i and the clustering distance from the j-th fuzzy subset u i is the i-th monitoring data, and c j is the sample data preset for the monitoring type corresponding to the j-th fuzzy subset. μ Pj (μ i ) is the membership function value of the i-th monitoring data ui and the j-th fuzzy subset. c j is the normal sample data preset for the fuzzy subset of each monitoring type, that is, no fault occurs.

[0071] In another embodiment provided by the present invention, storing all monitoring data into the corresponding fuzzy subsets respectively according to the membership function values of each monitoring data specifically includes:

[0072] Calculate successively the membership function values μ i of the monitoring data μ n relative to all fuzzy subsets P1, P2......P Pk (μ i ), where i is any monitoring data, i > 0, and k = 1, 2, 3..., n;

[0073] Store the monitoring data μ i into the fuzzy subset with the largest membership function value of the monitoring data μ i .

[0074] When specifically implementing this embodiment, by substituting the monitoring data into the membership function, calculate successively the membership function values μ i of the monitoring data μ n relative to all fuzzy subsets P1, P2......P Pk (μ i ), where i is any monitoring data, i > 0, and k = 1, 2, 3..., n;

[0075] When the monitoring data μ i satisfies: μ P1 (μ i ) = max(μ P1 (μ i ), μ P2 (μ i ),..., μ Pn (μ i ))), it is determined that the monitoring data μ iSubordinate to the fuzzy subset P1, all the monitoring data are divided and stored into different fuzzy subsets.

[0076] Compare the calculated membership function values. The larger the obtained value, the higher the probability that the monitoring data represents abnormal device movement. Through this method, the data can be classified into different data categories. Moreover, by using different sensors to detect different types of movement data of the device, the integration and classification of the data are achieved.

[0077] Through the membership function, the integration and classification of the monitoring data are realized. The monitoring data of various sensors after classification are compared with the corresponding set alarm thresholds. If the data exceeds the set threshold, it is determined that there is a problem with the monitoring parameter corresponding to the monitoring data, and an alarm instruction is sent to the coordinator to control the alarm to sound an alarm, realizing the alarm function.

[0078] In another embodiment provided by the present invention, the step S4 specifically includes:

[0079] Sort all the hop counts of each beacon node to obtain the minimum hop count of all beacon nodes;

[0080] Calculate the average hop distance of the unknown node where the fault location is located through the Dv-Hop positioning optimization algorithm;

[0081] Calculate the distance relationship equations between the unknown node and any beacon node according to the average hop distance;

[0082] Solve the distance relationship equations to obtain the preliminary position of the unknown node.

[0083] In the specific implementation of this embodiment, since multiple beacon nodes send information simultaneously, the controller will receive a lot of hop count information. The controller needs to sort all the hop count information, select and save the minimum hop count information to obtain the minimum hop count with all beacon nodes;

[0084] Through the Dv-Hop positioning optimization algorithm, the average hop distance between the beacon nodes of the entire wireless sensor network can be obtained according to the minimum hop count of the beacon nodes;

[0085] According to the minimum hop count between nodes and the average hop distance between nodes of the entire wireless sensor network, the actual distance values between the unknown node and each beacon node can be obtained;

[0086] Establish the position coordinates of the unknown node and the position coordinates of each beacon node to calculate the distance between the unknown node and each beacon node, and establish the distance relationship equations;

[0087] Solve the distance relationship equations to obtain the preliminary position of the unknown node.

[0088] In another embodiment provided by the present invention, the average hop distance is

[0089] where {x i , y i} and {x j , y j} are the position coordinates of beacon node i and beacon node j, and h i，j is the minimum hop count of beacon node i.

[0090] When this embodiment is specifically implemented, assume that the positions of beacon nodes i and j are: {x i , y i} and {x j , y j}. According to the minimum hop count h ij of beacon node i, the average hop distance between beacon nodes in the entire wireless sensor network can be obtained. The calculation formula is:

[0091] where {x i , y i} and {x j , y j} are the position coordinates of beacon node i and beacon node j, and h i，j is the minimum hop count of beacon node i.

[0092] In another embodiment provided by the present invention, the distance relationship equation set is specifically:

[0093] where (x, y) are the position coordinates of the unknown node, {x k , y k} are the position coordinates of beacon node k, the measured value d k of the distance between the unknown node and beacon node k k = HopSize * h j , HopSize is the average hop distance of the unknown node, h

[0094] When this embodiment is specifically implemented, assume that the position of the sensor node to be located is (x, y). The measured value d k of the distance between the unknown node and beacon node k k = HopSize * h j , HopSize is the average hop distance of the unknown node, and h

[0095] The distance relationship equations between the unknown nodes of the faulty sensor and n beacon nodes can be established by using the multi - lateral method, that is: {x k , y k} is the position coordinate of beacon node k, k = 1, 2,..., n;

[0096] Then, by solving the distance relationship equations, the initial position of the unknown sensor node can be obtained.

[0097] Through the distance relationship equations, the initial position of the faulty sensor can be solved, and the positioning efficiency is high.

[0098] In another embodiment provided by the present invention, the non - linear iterative model is specifically:

[0099]

[0100] Among them, (x′, y′) is the position coordinate of the unknown node after update, (x′, y′) is the average value of the position coordinates of all unknown nodes obtained in the current iteration, and the initial value of (x′, y′) is the initial position of the unknown node, (x, y) is the position coordinate of the unknown node in the current iteration, {x j , y j} is the position coordinate of beacon node j, and d j is the measured value of the distance between the unknown node and beacon node j.

[0101] When specifically implementing this embodiment, a linear transformation is performed on the distance relationship equations to obtain AX = B; where,

[0102] Affected by environmental factors and the influence of the measurement device itself, there is a certain deviation between the measured value of the distance and the actual distance between the unknown node and the beacon node. However, this interference is unavoidable in practical applications, and a random error vector ε needs to be introduced. Then, after the linear transformation of the distance relationship equations, AX + ε = B is obtained.

[0103] E i is the ranging error between the unknown node and beacon node i, and the solution error function of the unknown node position (x, y) is:

[0104] The higher the positioning accuracy of the unknown node, the smaller the value of f(x, y) should be. That is, the obtained objective function is

[0105] Through the objective function, the goal of minimizing the positioning error of sensors using traditional ranging - algorithm - independent methods is transformed into a non - linear optimization problem. The positioning results of sensor nodes using traditional ranging - algorithm - independent methods are used as the initial values of the iterative model. Finally, through continuous iteration, the positioning results of unknown nodes using traditional ranging - algorithm - independent methods are corrected, thereby obtaining the optimal positions of unknown nodes and improving the positioning accuracy of unknown nodes.

[0106] The above - mentioned objective function transforms the sensor positioning error problem into a non - linear optimization problem. Assume the non - linear equation is The initial approximation is x0. According to the iterative function A sequence can be established, specifically:

[0107]

[0108] Therefore, a multi - iteration model with a fast convergence speed, high iteration efficiency, and small iteration error is established. Its iterative function is specifically:

[0109]

[0110] The specific iterative method can be described as:

[0111]

[0112] The working steps of the multi - iteration model can be specifically as follows:

[0113] Step 1: Set the initial values: x0 and the error vector ε, and the iteration count k = 0;

[0114] Step 2: Calculate

[0115] Step 3: If the condition |x k+1 -x k |<ε is satisfied, then stop the iteration; otherwise, increment the iteration count k by 1 and return to Step 2.

[0116] In another embodiment, step S5 specifically includes:

[0117] Take the positioning results (x, y) of sensor nodes using traditional ranging - algorithm - independent methods as the initial values and perform operations through the designed multi - iteration model. The specific formula is as follows:

[0118]

[0119] Update and iterate the positions of unknown sensor nodes. The specific formula is as follows:

[0120]

[0121] is the average of the position coordinates of all unknown nodes obtained in the current iteration, (x′, y′) is the updated position coordinate of the unknown node, and the initial value of (x′, y′) is the initially determined position of the unknown node, {x j , y j} is the position coordinate of beacon node j, and d j is the measured value of the distance between the unknown node and beacon node j.

[0122] Calculate the differential increment for each iteration

[0123] where (x, y) is the position coordinate of the unknown node in the current iteration, {x j , y j} is the position coordinate of beacon node j, d j is the measured value of the distance between the unknown node and beacon node j, and n is the number of beacon nodes;

[0124] Iteratively update the initially determined position according to the preset non - linear iterative model, and calculate the positioning accuracy |x k+1 - x k |, where x k+1 is the abscissa of the unknown node in the current iteration, and x k is the abscissa of the unknown node in the previous iteration;

[0125] When the positioning accuracy |x k+1 - x k | < ε, it indicates that the positioning accuracy reaches the preset accuracy, ε is the preset accuracy, and output the final positioning result of the sensor k is the current iteration number.

[0126] Adopt a multi - iteration model to optimize the design of the positioning coordinates, reduce the sensor positioning error, shorten the positioning time, and improve the accuracy of sensor positioning.

[0127] In another embodiment provided by the present invention, the sensor includes: a speed sensor, a displacement sensor, an angular velocity sensor, and an infrared sensor;

[0128] The monitoring types include: speed anomaly, displacement anomaly, angular velocity anomaly, and position anomaly.

[0129] The measurement - type sensors mainly select speed sensors, displacement sensors, angular velocity sensors, infrared sensors, etc., and cooperate with monitoring probes to form a sensing state monitoring network to conduct safety monitoring on the movement of instrument equipment in industrial factories. The connection between each sensor and the controller is associated through a router.

[0130] The placement of sensor types varies according to different equipment and instruments. Speed and displacement sensors are mainly placed on moving devices in industrial production. For example, on the running tracks of intelligent vehicles, stackers or other mobile devices, to detect whether the moving speed of the equipment is reasonable and whether the moving distance is accurate.

[0131] Angular velocity sensors are mainly placed on intelligent devices such as robotic arms for picking and processing, to detect whether the angle they turn through is appropriate.

[0132] Infrared sensors can be used to detect the position of goods placed on the equipment, such as the processing platform where a robotic arm picks up goods, or the shelves where a vehicle transports goods, to detect whether there is a deviation between the actual position of the goods and the expected position. And because the information transmission of the sensor is real-time and rapid, when a deviation in the movement of the equipment is detected, the staff can immediately call up the monitoring probe to judge and conduct real-time tracking and monitoring of the abnormal movement of the equipment, thus constructing a reasonable and effective sensing state monitoring network.

[0133] It should be noted that in this embodiment, other sensors can also be used for the sensor type. For example, a temperature sensor can be used to detect the occurrence of temperature anomalies.

[0134] By monitoring different types of faults with different sensors, a multi-iteration model is used to optimize the design of the positioning coordinates, reducing the sensor positioning error.

[0135] The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A wireless sensing and monitoring system, characterized in that, The system includes: a controller, at least one router, and several sensors communicatively connected to the router; The controller is configured to: Obtain the monitoring data collected by each sensor, and store the obtained monitoring data into different fuzzy subsets according to different monitoring types; When the data value of a certain monitoring data is greater than the set threshold of the monitoring type corresponding to the fuzzy subset where the monitoring data is located, it is determined that a fault has occurred in the monitoring type corresponding to the monitoring data; Obtain the positions of the sensors corresponding to all the monitoring data in the faulty fuzzy subset as beacon nodes, and obtain the hop counts of each beacon node; According to the positions and hop counts of each beacon node, determine a system of distance relationship equations between the unknown node at the fault location and any beacon node, and solve to obtain the preliminary position of the unknown node; Iteratively update the preliminary position according to a preset non-linear iterative model, and calculate the positioning accuracy of each iteration until the positioning accuracy reaches the preset accuracy, and output the latest iterative position as the precise positioning; The specific non - linear iterative model is as follows: Among them, (x′, y′) is the position coordinate of the updated unknown node, which is the average value of the position coordinates of all unknown nodes obtained in the current iteration. The initial value of (x′, y′) is the initially determined position of the unknown node. (x, y) is the position coordinate of the unknown node in the current iteration, {x j , y j} is the position coordinate of beacon node j, and d j is the measured value of the distance between the unknown node and beacon node j.

2. The wireless sensing and monitoring system according to claim 1, characterized in that, The obtaining the monitoring data collected by each sensor, and storing the obtained monitoring data into different fuzzy subsets according to different monitoring types specifically includes: Obtain the monitoring data collected by each sensor through the router; Calculate the membership function values of each monitoring data with respect to the fuzzy subsets of different monitoring types according to the membership function; Store all the monitoring data into the corresponding fuzzy subsets according to the membership function values of each monitoring data; Among them, the membership function is The i-th monitoring data u i And the clustering distance from the j-th fuzzy subset u i Is the i-th monitoring data, r ij = D imax -D imin where D imax and D imin Are respectively the maximum clustering distance and the minimum clustering distance between the i-th monitoring data and all fuzzy subsets, and c j Is the sample data preset for the monitoring type corresponding to the j-th fuzzy subset. μ Pj (μ i ) is the membership function value of the i-th monitoring data u i And the j-th fuzzy subset.

3. The wireless sensing and monitoring system according to claim 2, characterized in that, The storing all the monitoring data into the corresponding fuzzy subsets according to the membership function values of each monitoring data specifically includes: Calculate the monitoring data μ successively i For all fuzzy subsets P1, P2 … P n The membership function values μ Pk (μ i ), where i is any monitoring data, i > 0, k = 1, 2, 3 …, n; Store the monitoring data μ i into the fuzzy subset with the largest membership function value of the monitoring data μ i .

4. The wireless sensing and monitoring system according to claim 1, characterized in that, The determining a system of distance relationship equations between the unknown node at the fault location and any beacon node according to the positions and hop counts of each beacon node, and solving to obtain the preliminary position of the unknown node specifically includes: Sort all the hop counts of each beacon node to obtain the minimum hop count of all the beacon nodes; Calculate the average hop distance of the unknown node at the fault location through the Dv-Hop positioning optimization algorithm; Calculate the system of distance relationship equations between the unknown node and any beacon node according to the average hop distance; Solve the system of distance relationship equations to obtain the preliminary position of the unknown node.

5. The wireless sensing and monitoring system according to claim 4, characterized in that, The average jump distance is Among them, {x i , y i} and {x j , y j} are the position coordinates of beacon node i and beacon node j, and h i,j is the minimum hop count of beacon node i.

6. The wireless sensing and monitoring system according to claim 4, characterized in that, The specific distance relation equations are as follows: where (x, y) are the position coordinates of the unknown node, {x k , y k} are the position coordinates of beacon node k, and the measured value d k = HopSize * h k , where HopSize is the average hop distance of the unknown node, h j is the minimum number of hops between the unknown node and beacon node k, n is the number of beacon nodes, and k = 1, 2,..., n.

7. The wireless sensing and monitoring system according to claim 1, characterized in that, The positioning accuracy is | xk+1 -x k |; where x k+1 is the abscissa of the unknown node in the current iteration, and x k is the abscissa of the unknown node in the previous iteration.

8. The wireless sensing and monitoring system according to claim 1, characterized in that, The precise positioning is where \((x', y')\) are the position coordinates of the latest iteration position of the unknown node, \(k\) is the current iteration number, and the differential increment (x, y) are the position coordinates of the unknown node in the current iteration, \(\{x j , y j \}\) are the position coordinates of beacon node \(j\), \(d j is the measured value of the distance between the unknown node and beacon node \(j\), and \(n\) is the number of beacon nodes.

9. The wireless sensing and monitoring system according to claim 1, characterized in that, The sensors include: speed sensors, displacement sensors, angular velocity sensors, and infrared sensors; The monitoring types include: speed anomaly, displacement anomaly, angular velocity anomaly, and position anomaly.

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