Bridge monitoring system based on sensor

Abstract

The invention belongs to the field of bridge monitoring, and discloses a sensor-based bridge monitoring system, which comprises a plurality of wireless sensor nodes, the plurality of wireless sensor nodes generate member nodes and cluster head nodes based on a clustering algorithm; the cluster head node is used for calculating an updating period of a window distribution scheme based on an event monitoring result, a trend monitoring result and a time period characteristic value; calculating the time length of an uploading window of each member node and the number of temporary windows; generating a window allocation scheme based on the time length of the uploading window and the number of the temporary windows, and sending the window allocation scheme to the member node; the member nodes are used for uploading the collected monitoring data based on the uploading windows distributed to the member nodes; and when it is detected that the change of the monitoring data is greater than an adaptive threshold, sending the monitoring data to the cluster head node based on the temporary window. According to the invention, the timeliness of monitoring the emergencies is ensured, and the energy consumption of the member nodes is reduced.

Description

Technical Field

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

[0002] When using wireless sensor nodes to monitor the status of a bridge, the question of how to conserve the energy of the wireless sensor nodes usually arises. In the prior art, in order to conserve energy while maintaining the effectiveness of monitoring emergencies, a fixed upload cycle and a change threshold are usually set. In addition to uploading the collected data based on the fixed upload cycle, the wireless sensor nodes also upload data when the change in the monitored data (for example, the difference between the latest collected data and the previous collected data) is greater than the change threshold. In this way, on the one hand, the monitoring center can continuously receive the data collected by the wireless sensor nodes and continuously update the status of the wireless sensor nodes. On the other hand, it can also effectively cope with data changes caused by emergencies and monitor emergencies in a timely manner.

[0003] However, a fixed upload cycle has the disadvantage that during stable periods (such as late at night when there are no cars), the data changes very little, and sampling at a fixed period can easily lead to a lot of energy waste.

[0004] However, during sensitive periods (such as the passage of heavy vehicles or the detection of tiny crack expansion), fixed thresholds may not be able to capture key early subtle changes, resulting in the monitoring center failing to obtain relevant data in a timely manner to implement corresponding bridge control plans (such as suspending traffic). Summary of the Invention

[0005] The purpose of the present invention is to disclose a sensor-based bridge monitoring system to solve the technical problems pointed out in the background technology.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention provides a sensor-based bridge monitoring system, comprising:

[0008] Multiple wireless sensor nodes;

[0009] Multiple wireless sensor nodes generate member nodes and cluster head nodes based on clustering algorithm;

[0010] The cluster head node is used to:

[0011] Calculate the update cycle of the window allocation plan based on event monitoring results, trend monitoring results and time period characteristic values;

[0012] Calculate the upload window length and the number of temporary windows for each member node;

[0013] Generate a window allocation plan based on the time length of the upload window and the number of temporary windows and send it to the member nodes;

[0014] Member nodes are used to:

[0015] Upload the collected monitoring data based on the upload window allocated to it;

[0016] When it is detected that the change of the monitoring data is greater than the adaptive threshold, the monitoring data is sent to the cluster head node based on the temporary window.

[0017] Preferably, it further includes a relay communication device;

[0018] The cluster head node is used to send monitoring data to the relay communication device.

[0019] Preferably, it also includes a bridge monitoring device;

[0020] The relay communication device is used to send the monitoring data to the bridge monitoring device.

[0021] Preferably, the plurality of wireless sensor nodes generate member nodes and cluster head nodes based on a clustering algorithm, including:

[0022] Each wireless sensor node runs the clustering algorithm periodically;

[0023] Based on the clustering algorithm, it is determined whether the wireless sensor node is a member node. If not, the wireless sensor node is the cluster head node.

[0024] Preferably, the update period of the window allocation scheme is calculated based on the event monitoring results, the trend monitoring results and the time period characteristic values, including:

[0025] After the previous update cycle ends, it enters the calculation cycle, in which the next update cycle is calculated based on the event monitoring results, trend monitoring results and time period characteristic values;

[0026] After the calculation cycle ends, the next update cycle begins.

[0027] Preferably, calculating the time length of the upload window of each member node and the number of temporary windows includes:

[0028] The length of the upload window of each member node and the number of temporary windows are calculated during the calculation cycle.

[0029] Preferably, generating a window allocation scheme based on the time length of the upload window and the number of temporary windows and sending it to the member nodes includes:

[0030] In the calculation cycle, after the time length of the upload window and the number of temporary windows are calculated, a window allocation scheme is generated based on the time length of the upload window and the number of temporary windows and sent to the member nodes.

[0031] Preferably, the event monitoring result is the number of events of a preset type monitored in the last update cycle;

[0032] The trend monitoring result is the trend change characteristic value of the preset type of monitoring data monitored in the previous update cycle;

[0033] The time period characteristic value is the characteristic value corresponding to the time period at the end time of the previous time window.

[0034] Preferably, calculating the next update period based on the event monitoring results, the trend monitoring results and the time period characteristic value includes:

[0035] Get the standard update cycle;

[0036] Obtain the number of events of a preset type, the weighted values ​​of the trend change characteristic value, and the time period characteristic value;

[0037] Gets the next update period based on the standard update period and weighted value.

[0038] Preferably, calculating the time length of the upload window of each member node includes:

[0039] Get the distance feature values ​​of adjacent clusters;

[0040] Get the first fluctuation eigenvalue of the adjacent cluster;

[0041] Get the second data fluctuation characteristic value of the cluster to which it belongs;

[0042] The time length of the upload window is calculated based on the next update period, the distance characteristic value, the first fluctuation characteristic value, and the second data fluctuation characteristic value.

[0043] Beneficial effects:

[0044] In the process of monitoring the bridge, the member nodes of the present invention do not upload the monitoring data based on a fixed upload cycle and change threshold, but calculate the update cycle through the event monitoring results, trend monitoring results and time period characteristic values, and generate a window allocation scheme including an upload window and a temporary window based on the update cycle, so that the window allocation scheme can be generated based on dynamic data such as event monitoring results, trend monitoring results and time period characteristic values. In this way, when the member nodes upload the monitoring data based on the window allocation scheme, they can perform the upload action based on a more adaptable upload cycle. Moreover, the addition of the temporary window also reduces the probability of network congestion, while ensuring the timeliness of emergency monitoring and reducing the energy consumption of the member nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is a first schematic diagram of a sensor-based bridge monitoring system of the present invention.

[0047] Figure 2 This is a second schematic diagram of a sensor-based bridge monitoring system of the present invention.

[0048] Figure 3 This is a third schematic diagram of a sensor-based bridge monitoring system of the present invention. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0050] like Figure 1 In one embodiment shown, the present invention provides a sensor-based bridge monitoring system comprising:

[0051] Multiple wireless sensor nodes are dispersedly set at multiple locations on the bridge to achieve overall monitoring of the bridge;

[0052] Multiple wireless sensor nodes generate member nodes and cluster head nodes based on clustering algorithm;

[0053] The cluster head node is used to:

[0054] Calculate the update cycle of the window allocation plan based on event monitoring results, trend monitoring results and time period characteristic values;

[0055] Calculate the upload window length and the number of temporary windows for each member node;

[0056] Generate a window allocation plan based on the time length of the upload window and the number of temporary windows and send it to the member nodes;

[0057] Member nodes are used to:

[0058] Upload the collected monitoring data based on the upload window allocated to it;

[0059] When it is detected that the change of the monitoring data is greater than the adaptive threshold, the monitoring data is sent to the cluster head node based on the temporary window.

[0060] This approach allows window allocation to be generated dynamically based on event monitoring results, trend monitoring results, and time period characteristic values. This allows member nodes to upload monitoring data based on a more adaptable upload cycle. Furthermore, the inclusion of temporary windows reduces the probability of network congestion, ensuring timely monitoring of emergencies and reducing the capacity consumption of member nodes.

[0061] According to existing technologies, in addition to uploading data according to a fixed upload cycle, member nodes will also upload data when they detect that the change in monitoring data is greater than a threshold. If there happens to be another member node uploading data according to last week's cycle at this time, it is easy for both member nodes to perform communication avoidance actions during the upload, resulting in the upload not being completed at the end of the upload cycle, causing the member node that originally uploaded data according to last week's cycle to miss the upload opportunity.

[0062] Therefore, the setting of the temporary window limits the time range for member nodes to upload monitoring data with changes greater than the adaptive threshold. Therefore, it can avoid the upload action caused by the emergency affecting the upload of other member nodes that originally uploaded data according to the upload window, thereby further ensuring the timeliness of the monitoring data upload.

[0063] Preferably, the plurality of wireless sensor nodes generate member nodes and cluster head nodes based on a clustering algorithm, including:

[0064] Each wireless sensor node runs the clustering algorithm periodically;

[0065] Based on the clustering algorithm, it is determined whether the wireless sensor node is a member node. If not, the wireless sensor node is the cluster head node.

[0066] Clustering algorithms include LEACH, HEED, DEEC, etc. For example, for LEACH, if the random number generated by the wireless sensor node is less than the corresponding threshold, the wireless sensor node is the cluster head node.

[0067] Specifically, the clustering algorithm may be run periodically, i.e., once every 24 hours.

[0068] In the present invention, the time interval between two adjacent runs of the clustering algorithm is much longer than the update period.

[0069] Preferably, the update period of the window allocation scheme is calculated based on the event monitoring results, the trend monitoring results and the time period characteristic values, including:

[0070] After the previous update cycle ends, it enters the calculation cycle, in which the next update cycle is calculated based on the event monitoring results, trend monitoring results and time period characteristic values;

[0071] After the calculation cycle ends, the next update cycle begins.

[0072] In the present invention, the calculation cycle can be a relatively short time period, such as 10 seconds. During the calculation cycle, when a member node has not yet received a new window allocation scheme, it still uploads data based on the window allocation scheme used in the previous update cycle; after receiving a new window allocation scheme, it uploads data based on the new window allocation scheme.

[0073] Preferably, the event monitoring result is the number of events of a preset type monitored in the last update cycle.

[0074] The events of the preset types of the present invention include:

[0075] Strong winds, abnormal vibrations, extreme temperatures, etc.

[0076] The wireless sensor node of the present invention has sensors such as a wind speed sensor, a vibration sensor and a temperature sensor.

[0077] If the wind speed sensor detects a wind speed greater than a set wind speed threshold (eg, 15 m / s), it indicates that strong wind is detected.

[0078] If the vibration sensor detects that the vibration acceleration is greater than the set amplitude threshold (for example, 30cm / s 2), it means abnormal vibration is detected.

[0079] If the temperature sensor detects a temperature greater than a set threshold (e.g., 60 degrees Celsius), it indicates that extreme temperatures have been detected. Different thresholds can be set for temperatures at different locations on the bridge. For example, the temperature threshold on the sun-facing side can be set lower than that on the sun-facing side.

[0080] The trend monitoring result is the trend change characteristic value of the preset type of monitoring data monitored in the previous update cycle.

[0081] In the present invention, the preset types of monitoring data include temperature, vibration acceleration, wind speed, etc.

[0082] The process of obtaining the trend change characteristic value includes:

[0083] The first step is to process the various types of monitoring data obtained in the previous update cycle as follows:

[0084] Normalize the monitoring data;

[0085] After deleting the maximum and minimum values ​​of the data obtained after normalization of the monitoring data, the variance of the data obtained after normalization is calculated;

[0086] In the second step, the maximum value of all variances is taken as the trend change characteristic value.

[0087] The time period characteristic value is the characteristic value corresponding to the time period at the end time of the previous time window.

[0088] In the present invention, the time period includes a stable period and a non-stationary period. The stable period is a period with fewer vehicles, such as 22:00 to 7:00 the next day, while the non-stationary period is a period with more vehicles, such as 8:00 to 21:00.

[0089] If the time period at the end of the previous time window is a stable period, the corresponding eigenvalue is 0.5; otherwise, the corresponding eigenvalue is 1.

[0090] Since there are fewer vehicles passing through the non-stationary section, the present invention sets a smaller eigenvalue so that when the update cycle is subsequently calculated, the value of the update cycle is expanded, the frequency of change of the update cycle is reduced, and the purpose of expanding the upload interval of the member node is achieved, and the effect of saving energy is achieved.

[0091] Preferably, calculating the next update period based on the event monitoring results, the trend monitoring results and the time period characteristic value includes:

[0092] Get the standard update cycle:

[0093] In the present invention, the standard update period can be set to 10 minutes.

[0094] Get the number of events of the preset type, the weighted values ​​of the trend change characteristic value, and the time period characteristic value:

[0095] The number of events of the preset type is expressed as N, the characteristic value of the trend change is expressed as σ, and the characteristic value of the time period is expressed as T;

[0096] The weighted value is calculated using the following formula:

[0097]

[0098] wv represents the weighted value, Ns represents the maximum number of events of the preset type monitored in all update cycles, α1, α2, and α3 represent the weights of N, σ, and T respectively (for example, 0.5, 0.3, and 0.2 respectively), σs is the set variance comparison value (for example, 0.25), Express Perform normalization processing;

[0099] Get the next update period based on the standard update period and weighted value, including:

[0100] Use the following formula to calculate the next update period:

[0101] NT=ST×(1-wv)

[0102] NT indicates the next update cycle, and ST indicates the standard update cycle.

[0103] Since the monitoring data is normalized, its maximum variance is 0.25. By setting a comparison value, the change of the monitoring data in the last update cycle can be determined.

[0104] The weighted value of the present invention calculates the update cycle from several different perspectives, including the number of events of preset types, trend monitoring results, and time period characteristic values. Therefore, the larger the value of N, the larger the value of σ, and the larger the value of T, the greater the current environmental changes and state changes of the bridge. At this time, the larger the weighted value, the larger the weighted value of the present invention, and the smaller the next update cycle, so that the window allocation scheme can be updated at a higher frequency to adapt to changes in the bridge environment and changes in the state of the bridge.

[0105] Preferably, calculating the time length of the upload window of each member node and the number of temporary windows includes:

[0106] The length of the upload window of each member node and the number of temporary windows are calculated during the calculation cycle.

[0107] The present invention puts the time length of the upload window and the number of temporary windows into the calculation cycle for calculation, and can effectively utilize various statistical data obtained in the previous update cycle so that the time length of the upload window and the number of temporary windows can change more promptly with the status of the bridge and changes in the environment.

[0108] Preferably, calculating the time length of the upload window of each member node includes:

[0109] The first step is to obtain the distance feature values ​​of adjacent clusters:

[0110] The distance eigenvalue is calculated using the following formula:

[0111]

[0112] dist ave is the distance characteristic value of cluster b; (x b ,y b ) is the center coordinate of cluster b; x b and y b are the average values ​​of the X-axis coordinates and Y-axis coordinates of the member nodes of cluster b; Ub is the set of clusters adjacent to cluster b; NUb is the total number of clusters in Ub;

[0113] (x c ,y c ) is the center coordinate of cluster c; x c and y c are the average values ​​of the X-axis coordinates and Y-axis coordinates of the member nodes of cluster c;

[0114] Since cluster head nodes communicate with each other, the cluster head node of the adjacent cluster attaches the numbers of each member node in its cluster when communicating with the cluster head node of the current cluster for the first time; and the positions of each wireless sensor node can be stored in the database of the cluster head node in advance, so the cluster head node of the current cluster can calculate the center coordinates of the adjacent cluster based on the numbers of the member nodes of the adjacent cluster.

[0115] In this invention, adjacent clusters refer to clusters containing other cluster head nodes within the communication range of the cluster head node of the current cluster. For example, for cluster b, if the cluster head nodes of clusters c and cluster d are both within the communication range of the cluster head node of cluster b, then clusters c and cluster d are adjacent clusters of cluster b.

[0116] The second step is to obtain the first fluctuation eigenvalue of the adjacent cluster:

[0117] Calculate the distance between each cluster in Ub and the center coordinates of cluster b respectively;

[0118] The cluster corresponding to the minimum distance is taken as the target cluster;

[0119] Calculate the variance of various types of monitoring data sent by the cluster head node of the target cluster and received by the cluster head node of cluster b in the previous update cycle:

[0120] The monitoring data received here from other cluster head nodes will be deleted after the calculation is completed to save storage space.

[0121] Normalize each type of monitoring data separately to obtain a data set;

[0122] Delete the maximum and minimum values ​​in each data set respectively to obtain the updated data set;

[0123] Calculate the variance of each element in the updated data set separately;

[0124] The maximum value of all variances is taken as the first fluctuation eigenvalue;

[0125] Obtaining the first fluctuation characteristic value based on the maximum value of the variance can more effectively represent the fluctuation of data in adjacent clusters.

[0126] The third step is to obtain the second data fluctuation characteristic value of the cluster to which it belongs;

[0127] In the present invention, the calculation result of the update period can be used to take the trend change characteristic value as the second data fluctuation characteristic value of the cluster to which it belongs;

[0128] Step 4: Calculate the duration of the upload window based on the next update cycle, the distance characteristic value, the first fluctuation characteristic value, and the second data fluctuation characteristic value:

[0129] Use the following formula to calculate the upload window length:

[0130]

[0131] winlenth represents the length of the upload window, Ψ(·) represents the normalization of the data in the brackets, and dist max represents the maximum value of the distance between the center coordinates of the cluster in Ub and the center coordinates of cluster b, σ1 represents the first fluctuation eigenvalue, σ2 represents the second fluctuation eigenvalue, lenth represents the maximum time length of the upload window (for example, 1 second, this value can be adaptively adjusted based on the size of the cluster, and the more member nodes, the smaller the value); β1, β2, β3 and β4 are the weights of the next update cycle, distance eigenvalue, first fluctuation eigenvalue and second fluctuation eigenvalue, respectively (for example, they can be 0.4, 0.3, 0.15 and 0.15, respectively).

[0132] The time length of the upload window of the present invention is considered from four different directions when calculating. Therefore, the larger the value of the next update cycle, the smaller the distance characteristic value, the larger the first fluctuation characteristic value, and the smaller the second fluctuation characteristic value, the longer the time length of the time window. In this way, the changes in the monitoring data of the adjacent cluster and the distance situation can be introduced into the calculation process of the upload window of the current cluster, which can effectively reduce the impact of the upload process of the member nodes of the current cluster on the communication of the adjacent cluster. That is, when the distance characteristic value is smaller, the degree of overlap between the communication range of the member nodes of the adjacent cluster and the communication range of the member nodes of the current cluster is lower, and the larger and smaller the first fluctuation characteristic value is, the stronger the upload demand of the adjacent cluster is. At this time, the longer the upload window is set, the more likely it is that after the member nodes of the current cluster complete the upload, the upload window has not ended yet, and the member nodes of the adjacent cluster can have a higher probability of using this period of time to transmit data. When the next update cycle is larger and the second fluctuation characteristic value is smaller, it means that the data upload demand of the current cluster is weaker. At this time, the present invention realizes adaptive control of the data upload cycle of the member nodes by increasing the time length of the upload window, which can effectively reduce the energy consumption rate of the member nodes when the upload demand is weak; and when the upload demand is strong, the upload cycle can be shortened with a shorter time length, which can ensure the timely transmission of monitoring data with large changes caused by emergencies.

[0133] Preferably, the calculation process of the number of temporary windows includes:

[0134] The calculation formula for the number of temporary windows NW is:

[0135]

[0136] NWS represents the maximum value of the number of temporary windows (eg, 10).

[0137] The number of temporary windows of the present invention can change with the change of the update cycle, so that the monitoring data with large changes caused by emergencies can be transmitted in time.

[0138] In the present invention, the number of temporary windows is smaller than the number of member nodes.

[0139] Preferably, generating a window allocation scheme based on the time length of the upload window and the number of temporary windows and sending it to the member nodes includes:

[0140] In the calculation cycle, after the time length of the upload window and the number of temporary windows are calculated, a window allocation scheme is generated based on the time length of the upload window and the number of temporary windows and sent to the member nodes.

[0141] The time length of the temporary window of the present invention is set to Z times the time length of the upload window. For example, the preset value Z can be 3. In this way, when the probability of an emergency event occurring is higher, a larger temporary window can be provided for the member node to upload monitoring data.

[0142] Store the member node number into sequence K1;

[0143] In sequence K1, NW numbers representing temporary windows are scattered and inserted to obtain sequence K2;

[0144] Sequence K3 is obtained by sequentially inserting the numbers of the time windows in which the cluster head node forwards monitoring data and the time windows in which the cluster head node receives monitoring data sent by other cluster head nodes after sequence K2;

[0145] The start time of the next update cycle, sequence K3, and the lengths of the temporary window, upload window, time window for cluster head node to forward monitoring data, and time window for cluster head node to receive monitoring data sent by other cluster head nodes are used as the window allocation scheme.

[0146] The time window length of the cluster head node forwarding monitoring data may be 1 second. The time window length of the cluster head node receiving monitoring data sent by other cluster head nodes may be 3 seconds.

[0147] The start time of the next update cycle can be obtained by adding the end time of the previous update cycle to the length of the calculation cycle.

[0148] Furthermore, the process of inserting the number representing the temporary window includes:

[0149] Calculate insertion interval NK1 represents the total number of numbers in K1;

[0150] In K1, every Insert a number representing a temporary window into the number of the member node.

[0151] Preferably, uploading the collected monitoring data based on the upload window allocated to itself includes:

[0152] When the time enters the time period corresponding to the upload window allocated to itself, the wireless sensor node sends the collected monitoring data to be uploaded to the cluster head node.

[0153] Since the member node already knows the start time of the next update cycle, the member node can calculate the time period corresponding to each upload window allocated in the next update cycle and the time period corresponding to each temporary window based on the position of its own number in K3.

[0154] The time windows corresponding to the elements in K3 of the present invention take effect in turn, that is, after taking effect from the beginning to the end, they take effect again starting from the time window corresponding to the first element.

[0155] Preferably, when it is detected that the change of the monitoring data is greater than the adaptive threshold, the monitoring data is sent to the cluster head node based on the temporary window, including:

[0156] In the first step, the member node subtracts the last acquired monitoring data from the latest acquired monitoring data to obtain a change value;

[0157] The second step is to determine whether the result of dividing the change value by the previous monitoring data is greater than the adaptive threshold. If so, proceed to the third step; if not, proceed to the fourth step;

[0158] The third step is to determine whether the start time of the upload window closest to the current time is less than the start time of the temporary window closest to the current time. If not, proceed to the fourth step; if so, when entering the temporary window closest to the current time, the monitoring data is sent to the cluster head node;

[0159] The fourth step is to send the latest monitoring data to the cluster head node when entering the upload window closest to the current time allocated to itself.

[0160] In the present invention, if it is detected that the time interval between the most recent upload window and the current time is less than the time interval of the most recent temporary window, the data will be uploaded directly until the most recent upload window, thereby leaving more opportunities for other member nodes to upload monitoring data with large changes caused by emergencies (such as the passage of heavy-loaded convoys, typhoons, etc.).

[0161] In the present invention, the adaptive threshold is calculated based on the next update cycle:

[0162]

[0163] athr d is the adaptive threshold of monitoring data of type d, thr d is the basic threshold for monitoring data of type d (for example, 0.2).

[0164] The adaptive threshold of the present invention can closely follow the changes of the next update cycle, so that the smaller the next update cycle, the smaller the threshold, thereby improving the sensitivity of the detection system and realizing timely transmission of monitoring data. Conversely, the threshold is increased to reduce the probability of member nodes using temporary windows, avoiding the average time interval of member nodes uploading monitoring data being too short, thereby saving energy for member nodes.

[0165] Preferably, if Figure 2 , further comprising a relay communication device;

[0166] The cluster head node is used to send monitoring data to the relay communication device.

[0167] If the cluster head node of the present invention detects that the relay communication device is within its communication range, it will directly send the monitoring data to the relay communication device. For cluster head nodes that cannot directly communicate with the relay communication device, the monitoring data can be sent to another cluster head node, and the monitoring data can be forwarded to the relay communication device by forwarding between cluster head nodes.

[0168] In the present invention, the relay communication device is a device provided on the bridge and has a long-distance communication capability.

[0169] Preferably, if Figure 3 , also including bridge monitoring devices;

[0170] The relay communication device is used to send the monitoring data to the bridge monitoring device.

[0171] The bridge monitoring device of the present invention may be a device installed in the operation and maintenance management department of the bridge. The bridge monitoring device can save and analyze monitoring data (for example, calculate data change trends, etc.).

[0172] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A sensor-based bridge monitoring system, characterized in that: including multiple wireless sensor nodes; Multiple wireless sensor nodes generate member nodes and cluster head nodes based on clustering algorithm; The cluster head node is used to: Calculate the update cycle of the window allocation plan based on event monitoring results, trend monitoring results and time period characteristic values; Calculate the upload window length and the number of temporary windows for each member node; Generate a window allocation plan based on the time length of the upload window and the number of temporary windows and send it to the member nodes; Member nodes are used to: Upload the collected monitoring data based on the upload window allocated to it; When it is detected that the change of the monitoring data is greater than the adaptive threshold, the monitoring data is sent to the cluster head node based on the temporary window.

2. A sensor-based bridge monitoring system according to claim 1, characterized in that: Also included is a relay communication device; The cluster head node is used to send monitoring data to the relay communication device.

3. A sensor-based bridge monitoring system according to claim 1, characterized in that: Also included are bridge monitoring devices; The relay communication device is used to send the monitoring data to the bridge monitoring device.

4. A sensor-based bridge monitoring system according to claim 1, characterized in that: Multiple wireless sensor nodes generate member nodes and cluster head nodes based on the clustering algorithm, including: Each wireless sensor node runs the clustering algorithm periodically; Based on the clustering algorithm, it is determined whether the wireless sensor node is a member node. If not, the wireless sensor node is the cluster head node.

5. The sensor-based bridge monitoring system according to claim 1, characterized in that: The update cycle of the window allocation scheme is calculated based on the event monitoring results, trend monitoring results, and time period characteristic values, including: After the previous update cycle ends, it enters the calculation cycle, in which the next update cycle is calculated based on the event monitoring results, trend monitoring results and time period characteristic values; After the calculation cycle ends, the next update cycle begins.

6. A sensor-based bridge monitoring system according to claim 5, characterized in that: Calculate the upload window length and the number of temporary windows for each member node, including: The length of the upload window of each member node and the number of temporary windows are calculated during the calculation cycle.

7. The sensor-based bridge monitoring system according to claim 5, characterized in that: A window allocation plan is generated based on the upload window length and the number of temporary windows and sent to the member nodes, including: In the calculation cycle, after the time length of the upload window and the number of temporary windows are calculated, a window allocation scheme is generated based on the time length of the upload window and the number of temporary windows and sent to the member nodes.

8. The sensor-based bridge monitoring system according to claim 5, characterized in that: The event monitoring result is the number of events of the preset type monitored in the last update cycle; The trend monitoring result is the trend change characteristic value of the preset type of monitoring data monitored in the previous update cycle; The time period characteristic value is the characteristic value corresponding to the time period at the end time of the previous time window.

9. The sensor-based bridge monitoring system according to claim 5, characterized in that: Calculate the next update cycle based on event monitoring results, trend monitoring results, and time period characteristic values, including: Get the standard update cycle; Obtain the number of events of a preset type, the weighted values ​​of the trend change characteristic value, and the time period characteristic value; Gets the next update period based on the standard update period and weighted value.

10. The sensor-based bridge monitoring system according to claim 5, characterized in that: Calculate the upload window length for each member node, including: Get the distance feature values ​​of adjacent clusters; Get the first fluctuation eigenvalue of the adjacent cluster; Get the second data fluctuation characteristic value of the cluster to which it belongs; The time length of the upload window is calculated based on the next update period, the distance characteristic value, the first fluctuation characteristic value, and the second data fluctuation characteristic value.

Images