Engineering disease emergency monitoring method based on sensor group

Abstract

The invention discloses an engineering disease emergency monitoring method based on a sensor group, and relates to the technical field of engineering monitoring. The method comprises the following steps: constructing a sensing detection network in a target engineering area; the sensing detection network comprises a plurality of groups of nodes, and a plurality of sensors are arranged in each group of nodes; a relay node is arranged between every two adjacent groups of nodes for data transmission and storage; the sensing detection network is controlled according to the monitoring instruction, so that the sensors in each group of nodes execute actions according to the operation state in the monitoring instruction, and monitoring data in the operation state is obtained; uploading the monitoring data to a cloud server, comparing the monitoring data with a set limit value built in the cloud server, and triggering a corresponding control signal according to an engineering health state; the control signal comprises a no-action signal and an early warning signal. According to the invention, the monitoring timeliness of engineering diseases can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of engineering monitoring, and in particular to an engineering disease emergency monitoring method based on a sensor group. Background Art

[0002] In the field of modern engineering, as the scale of infrastructure continues to expand and become more complex, health monitoring and disease prevention of engineering structures have become an important part of ensuring public safety and extending the life of engineering projects. Traditional manual inspection methods are limited by low efficiency, difficulty in real-time monitoring, and large subjective judgment errors, and are no longer able to meet the needs of modern engineering management. Especially in the case of frequent natural disasters such as extreme climate and geological changes, engineering structures often face more severe safety challenges.

[0003] Therefore, an emergency monitoring method for engineering defects based on advanced technologies such as the Internet of Things, big data, and cloud computing has emerged. This method deploys a dense group of sensors in the target engineering area to build an efficient and intelligent sensor detection network, which realizes real-time, remote, and high-precision monitoring of the engineering structure status. The sensor group can capture and transmit a variety of physical parameters including stress, displacement, vibration, temperature, and humidity. These data are the key basis for evaluating the health of the project and predicting potential defects.

[0004] However, the monitoring range and data volume of a single sensor are limited, which makes it difficult to meet the needs of comprehensive monitoring of large-scale engineering structures. Summary of the invention

[0005] The purpose of the present invention is to provide a method for emergency monitoring of engineering defects based on a sensor group, which can improve the timeliness of monitoring of engineering defects.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for emergency monitoring of engineering diseases based on sensor groups, comprising:

[0008] Constructing a sensor detection network in the target engineering area; the sensor detection network includes multiple groups of nodes, each of which is provided with multiple sensors; and a relay node is also provided between each two adjacent groups of nodes for data transmission and storage;

[0009] Controlling the sensor detection network according to the monitoring instruction, so that the sensors in each group of nodes perform actions according to the operating state in the monitoring instruction, and obtaining monitoring data in the operating state;

[0010] The monitoring data is uploaded to a cloud server, and the monitoring data is compared with the set limit values ​​built into the cloud server, and a corresponding control signal is triggered according to the health status of the project; the control signal includes a no-action signal and a warning signal.

[0011] Optionally, in each of the group nodes, a sensor is fixed according to a preset position.

[0012] Optionally, the sensor includes: a displacement sensor, a stress sensor, a temperature sensor, a humidity sensor and a seepage sensor.

[0013] Optionally, the operating status in the monitoring instruction includes a sentinel mode and a sleep mode; the sentinel mode means being in a sentinel state all the time; the sleep mode means being in a sleep state within a set time period to save energy.

[0014] Optionally, controlling the sensor detection network according to the monitoring instruction so that the sensors in each group of nodes perform actions in the operating state in the monitoring instruction and obtain monitoring data in the operating state specifically includes:

[0015] When the monitoring instruction is output to operate some of the set sensors in sentinel mode, and some of the sensors were in sleep mode at the previous moment, the set some sensors receive the wake-up signal in the monitoring instruction, switch from sleep mode to sentinel mode, and perform continuous monitoring to obtain monitoring data in the running state.

[0016] Optionally, a filter is also provided in the cloud server for filtering the monitoring data.

[0017] Optionally, the monitoring data is uploaded to a cloud server, and the monitoring data is compared with a set limit value built into the cloud server, and a corresponding control signal is triggered according to the engineering health status, specifically including:

[0018] Uploading the monitoring data to a cloud server, and when the monitoring data is less than a set limit value built into the cloud server, determining that the engineering health state is good, triggering a no-action signal, and when the monitoring data is equal to or less than a set limit value built into the cloud server, determining that the engineering health state is poor, triggering an early warning signal;

[0019] When the warning signal is triggered, the warning signal is transmitted to the corresponding management terminal.

[0020] Optionally, the management end realizes real-time monitoring and management of engineering defects through a remote monitoring platform such as a Web or a mobile APP.

[0021] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0022] The present invention discloses an engineering disease emergency monitoring method based on a sensor group, the method comprising constructing a sensor detection network in a target engineering area; the sensor detection network comprises multiple groups of nodes, each of which is provided with multiple sensors; a relay node is also provided between each two adjacent groups of nodes for data transmission and storage; the sensor detection network is controlled according to a monitoring instruction, so that the sensors in each group of nodes perform actions in the operating state in the monitoring instruction, and obtain monitoring data in the operating state; the monitoring data is uploaded to a cloud server, and the monitoring data is compared with the set limit value built into the cloud server, and a corresponding control signal is triggered according to the engineering health state; the control signal comprises a no action signal and an early warning signal. The present invention can improve the timeliness of monitoring engineering diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0024] Figure 1 It is a schematic diagram of the flow of the engineering disease emergency monitoring method based on the sensor group of the present invention;

[0025] Figure 2 Schematic diagram of the application structure of the sensor detection network in this embodiment. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The purpose of the present invention is to provide a method for emergency monitoring of engineering defects based on a sensor group, which can improve the timeliness of monitoring of engineering defects.

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] like Figure 1-Figure 2 As shown, the present invention provides an engineering disease emergency monitoring method based on a sensor group, comprising:

[0030] Step 100: construct a sensor detection network in the target engineering area; the sensor detection network includes multiple groups of nodes, each of which is equipped with multiple sensors; and a relay node is also provided between each two adjacent groups of nodes for data transmission and storage.

[0031] Step 200: Control the sensor detection network according to the monitoring instruction, so that the sensors in each group of nodes perform actions in the operating state in the monitoring instruction, and obtain monitoring data in the operating state. The operating state in the monitoring instruction includes sentinel mode and sleep mode; the sentinel mode means that it is always in a sentinel state; the sleep mode means that it is in a sleep state within a set time period to save energy.

[0032] Step 300: Upload the monitoring data to the cloud server, and compare the monitoring data with the set limit values ​​built into the cloud server, and trigger a corresponding control signal according to the health status of the project; the control signal includes a no-action signal and a warning signal.

[0033] As a specific implementation, in each of the group nodes, a sensor is fixed according to a preset position, and the sensor includes: a displacement sensor, a stress sensor, a temperature sensor, a humidity sensor, and a seepage sensor.

[0034] As a specific implementation of step 200, it specifically includes: when the monitoring instruction is output to operate the set part of sensors in sentinel mode, and some sensors at the previous moment are in sleep mode, the set part of sensors receive the wake-up signal in the monitoring instruction, switch from sleep mode to sentinel mode, and perform continuous monitoring to obtain monitoring data in the running state.

[0035] As a specific implementation of step 300, it specifically includes: uploading the monitoring data to the cloud server, and when the monitoring data is less than the set limit value built into the cloud server, judging the engineering health status as good, triggering a no-action signal, and when the monitoring data is equal to or less than the set limit value built into the cloud server, judging the engineering health status as poor, triggering an early warning signal; when the early warning signal is triggered, transmitting the early warning signal to the corresponding management terminal. Among them, the cloud server is also provided with a filter for filtering the monitoring data.

[0036] As a specific implementation method, the management end realizes real-time monitoring and management of engineering defects through a remote monitoring platform such as the Web or a mobile APP.

[0037] According to the above technical solution, an embodiment as shown below is provided.

[0038] Step 100: Build a sensor network

[0039] Deploy nodes: Deploy multiple groups of nodes at key parts of the bridge (such as main beams, piers, supports, etc.), each group of nodes contains multiple sensors of different types. The nodes are connected by wire or wireless means to ensure the reliability of data transmission.

[0040] Install sensors: Displacement sensors, stress sensors, temperature sensors, humidity sensors and seepage sensors are installed in each group of nodes according to the preset positions. These sensors are used to monitor the displacement changes, stress distribution, temperature changes, humidity environment and potential seepage conditions of the bridge.

[0041] Set up relay nodes: Set up relay nodes between every two adjacent groups of nodes to be responsible for data transmission and storage, ensuring that monitoring data can be efficiently and stably transmitted to the cloud server.

[0042] Step 200: Control the sensor network and collect data

[0043] Send monitoring instructions: Send monitoring instructions through the remote monitoring platform (such as a web interface or mobile app) to specify that some sensors operate in sentinel mode and other sensors remain in sleep mode to save energy.

[0044] Mode conversion and data collection: When the designated sensor receives the wake-up signal, it switches from sleep mode to sentinel mode, starts to continuously monitor various parameters of the bridge, and uploads the monitoring data to the cloud server in real time. At the same time, other undesignated sensors continue to remain in sleep mode.

[0045] Step 300: Data analysis and early warning

[0046] Data upload and filtering: The monitoring data is transmitted to the cloud server through the relay node. The built-in filter of the cloud server filters the monitoring data to remove noise and outliers to ensure the accuracy and reliability of the data.

[0047] Comparative analysis: Compare and analyze the filtered monitoring data with the set limits built into the cloud server. For example, if the monitoring data of the displacement sensor exceeds the preset displacement threshold, or the monitoring data of the stress sensor is close to the strength limit of the material, it is considered that there may be problems with the health of the bridge.

[0048] Trigger control signal:

[0049] When all monitoring data are less than the set limit, the bridge is judged to be in good health, the cloud server triggers a no-action signal, and the remote monitoring platform displays a normal status.

[0050] When any monitoring data is equal to or exceeds the set limit, the bridge health status is judged to be poor, and the cloud server immediately triggers a warning signal and sends a warning notification to the management end through remote monitoring platforms such as the Web or mobile APP.

[0051] Management response: After receiving the early warning signal, the management immediately activated the emergency plan and organized professionals to go to the site for further inspection and processing to ensure the safe operation of the bridge.

[0052] This embodiment has the following beneficial effects: This embodiment uses multiple groups of nodes to work together and sets up relay nodes for data transmission and storage, which not only expands the monitoring range, but also improves the reliability and stability of data transmission. The introduction of relay nodes effectively solves the problems of data transmission distance limitation and signal attenuation, and ensures the continuity and integrity of monitoring data.

[0053] In addition, uploading monitoring data to the cloud server in real time and comparing and analyzing it with the preset limit values ​​is a key step in achieving intelligent early warning and emergency response. The cloud server's powerful data processing capabilities can quickly complete the analysis and judgment of massive monitoring data. Once the monitoring data exceeds the set limit, the corresponding control signal is triggered, including a no-action signal (indicating that the current state is normal) and a warning signal (indicating the existence of potential disease risks). This data-based automated decision-making process greatly improves the accuracy and timeliness of disease identification and provides valuable decision-making support for engineering management departments.

[0054] In summary, the engineering disease emergency monitoring method based on sensor groups provides strong technical support for the health management and safety assurance of modern engineering structures by building an intelligent sensor detection network, realizing real-time monitoring and data analysis, as well as automated early warning and emergency response.

[0055] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0056] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for emergency monitoring of engineering diseases based on sensor groups, characterized in that: include: Constructing a sensor detection network in the target engineering area; the sensor detection network includes multiple groups of nodes, each of which is provided with multiple sensors; and a relay node is also provided between each two adjacent groups of nodes for data transmission and storage; Controlling the sensor detection network according to the monitoring instruction, so that the sensors in each group of nodes perform actions according to the operating state in the monitoring instruction, and obtaining monitoring data in the operating state; The monitoring data is uploaded to a cloud server, and the monitoring data is compared with the set limit values ​​built into the cloud server, and a corresponding control signal is triggered according to the health status of the project; the control signal includes a no-action signal and a warning signal.

2. The engineering disease emergency monitoring method based on sensor groups according to claim 1 is characterized in that: In each of the groups of nodes, the sensor is fixed according to a preset position.

3. The engineering disease emergency monitoring method based on sensor groups according to claim 1 is characterized in that: The sensors include: a displacement sensor, a stress sensor, a temperature sensor, a humidity sensor and a seepage sensor.

4. The engineering disease emergency monitoring method based on sensor groups according to claim 1 is characterized in that: The operating states in the monitoring instructions include sentinel mode and sleep mode; the sentinel mode means being in a sentinel state all the time; the sleep mode means being in a sleep state within a set time period to save energy.

5. The engineering disease emergency monitoring method based on sensor groups according to claim 1 is characterized in that: The controlling of the sensor detection network according to the monitoring instruction so that the sensors in each group of nodes perform actions in the operating state in the monitoring instruction and obtain the monitoring data in the operating state specifically includes: When the monitoring instruction is output to operate some of the set sensors in sentinel mode, and some of the sensors were in sleep mode at the previous moment, the set some sensors receive the wake-up signal in the monitoring instruction, switch from sleep mode to sentinel mode, and perform continuous monitoring to obtain monitoring data in the running state.

6. The engineering disease emergency monitoring method based on sensor groups according to claim 1 is characterized in that: The cloud server is also provided with a filter for filtering the monitoring data.

7. The engineering disease emergency monitoring method based on sensor groups according to claim 1 is characterized in that: Uploading the monitoring data to the cloud server, comparing the monitoring data with the set limit values ​​built into the cloud server, and triggering corresponding control signals according to the engineering health status, specifically including: Uploading the monitoring data to a cloud server, and when the monitoring data is less than a set limit value built into the cloud server, determining that the engineering health state is good, triggering a no-action signal, and when the monitoring data is equal to or less than a set limit value built into the cloud server, determining that the engineering health state is poor, triggering an early warning signal; When the warning signal is triggered, the warning signal is transmitted to the corresponding management terminal.

8. The engineering disease emergency monitoring method based on sensor groups according to claim 7 is characterized in that: The management end realizes real-time monitoring and management of engineering defects through remote monitoring platforms such as Web or mobile APP.

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