A method for industrial field signal monitoring in low-power ad hoc networks

Abstract

The invention proposes an industrial field signal monitoring method for a low-power self-organizing network, which is a star network composed of low-power self-organizing network nodes, and performs monitoring based on the star network. The method comprises the following steps: sensor nodes collect signals, and the sensor nodes transmit the collected signals to a gateway via wireless transmission; all sensor nodes are divided into three levels, namely, level 1 nodes, level 2 nodes and level 3 nodes, the monitoring range of level 1 nodes is 50 meters, the monitoring range of level 2 nodes is 25 meters, and the monitoring range of level 3 nodes is 1 meter; when there is a level 1 node within a certain monitoring range, signal monitoring is completed by the level 1 node; when there is no level 1 node within a certain monitoring range, the monitoring task of the signal in this range is completed by the level 2 node; when there is no level 1 or level 2 node within a certain monitoring range, the signal monitoring is completed by the level 3 node.

Description

Technical Field

[0001] The invention belongs to the field of field signal monitoring, and in particular relates to an industrial field signal monitoring method for a low-power self-organizing network. Background Art

[0002] At present, low-power self-organizing network technology plays a vital role in the field of industrial field signal monitoring. This technology aims to achieve real-time monitoring and data transmission of various industrial field signals by building an autonomous, low-power wireless network. However, the existing technology has exposed several significant shortcomings in the application process, which limit its performance and reliability in some complex industrial environments.

[0003] Some low-power self-organizing network technologies, such as self-organizing network solutions based on specific protocols, have the characteristics of low power consumption in theory, but in practical applications, their power consumption performance is often unsatisfactory. For example, some chips supported by self-organizing network protocols have high current consumption in the receiving state, which means shorter battery life and frequent maintenance requirements for industrial field monitoring equipment that needs to run for a long time. This not only increases operating costs, but may also affect the continuity and accuracy of monitoring.

[0004] Existing low-power self-organizing network technologies also have shortcomings in terms of communication distance and stability. For example, although Zigbee technology has strong self-organizing and self-recovery capabilities, its diffraction and wall penetration capabilities are weak in complex industrial environments, and it is easily affected by obstacles, resulting in signal attenuation and communication interruption. This not only reduces the real-time and reliability of monitoring data, but may also cause safety hazards.

[0005] Existing technologies also face challenges in terms of network capacity and scalability. Although some technologies such as Zigbee support up to tens of thousands of nodes, in actual applications, due to the diversity and complexity of industrial field equipment, the number of network nodes is often far below the theoretical upper limit. Moreover, as the scale of industrial sites expands and the number of devices increases, the shortcomings of existing technologies in terms of network scalability and flexibility become increasingly prominent.

[0006] Security is also an important issue that existing low-power ad hoc networking technologies need to pay attention to. Due to design limitations, some technologies are susceptible to interference and attacks during data transmission, resulting in data leakage or tampering. This not only damages the authenticity and integrity of monitoring data, but may also pose a threat to the safe operation of industrial sites.

[0007] The existing low-power self-organizing network technology has many shortcomings in terms of power consumption, communication distance, stability, network capacity and security. These shortcomings limit its application and development in the field of industrial field signal monitoring. Therefore, the development of more efficient, stable and secure low-power self-organizing network technology is of great significance to promote the progress of industrial field signal monitoring technology. Now there is an urgent need for an industrial field signal monitoring method for low-power self-organizing network. Summary of the invention

[0008] The present invention proposes an industrial field signal monitoring method for low-power self-organizing networks. The technical problem solved by the method is how to realize low-power, efficient and reliable signal monitoring in industrial fields, especially in a star network composed of low-power self-organizing network nodes, how to reasonably allocate monitoring tasks according to the hierarchical type and monitoring range of the nodes, and how to ensure the real-time and accuracy of monitoring data through network parameter optimization and maintenance.

[0009] The technical solution of the present invention is implemented as follows: a method for industrial field signal monitoring for low-power self-organizing network, a star network composed of low-power self-organizing network nodes, monitoring based on the star network, the method comprising the following steps:

[0010] The sensor nodes collect signals and transmit the collected signals to the gateway via wireless. All sensor nodes are divided into three levels: level 1 nodes, level 2 nodes and level 3 nodes. The monitoring range of level 1 nodes is 50 meters, the monitoring range of level 2 nodes is 25 meters, and the monitoring range of level 3 nodes is 1 meter.

[0011] When there is a level 1 node in a certain monitoring range, the signal monitoring is completed by the level 1 node; when there is no level 1 node in a certain monitoring range, the monitoring task of the signal in this range is completed by the level 2 node; when there is no level 1 or level 2 node in a certain monitoring range, the signal monitoring is completed by the level 3 node;

[0012] During wireless transmission, the sensor node communicates with the gateway, sends the signal wirelessly to the base station of the gateway, and completes the communication within the set time; the gateway then sends it to the industrial monitoring platform; the industrial monitoring platform optimizes the network parameters and performs network maintenance on the sensor nodes in the gateway;

[0013] When optimizing network parameters, the wireless rates of level 1 nodes, level 2 nodes, and level 3 nodes are monitored respectively, and the average values ​​of the three wireless rates of the three levels are taken. Based on the average values, the wireless rates of the three levels are adjusted respectively.

[0014] Before monitoring, the monitoring gateway is used to determine whether there is network delay. If so, the network delay is eliminated. If not, the average wireless rate is fed back. The industrial field signal is monitored by monitoring the changes in the node rate range.

[0015] This method adopts a more sophisticated and flexible strategy in the division of node hierarchical types and monitoring scopes. In traditional technologies, sensor nodes are often assigned the same monitoring scope and responsibilities, which leads to excess monitoring resources in some areas and insufficient monitoring in other areas. This method effectively solves this problem by introducing level 1, level 2, and level 3 nodes and assigning tasks according to their monitoring scopes. This hierarchical design enables monitoring tasks to be more evenly distributed throughout the industrial site, thereby improving monitoring efficiency and accuracy.

[0016] In terms of network parameter optimization, this method adopts an adjustment strategy based on the average value of wireless rate. In traditional technologies, the adjustment of network parameters often relies on fixed rules or empirical values, which may result in suboptimal network performance. This method monitors the wireless rates of nodes of various types at different levels in real time and calculates the average value as the basis for adjustment, making the adjustment of network parameters more scientific and reasonable. This optimization strategy based on real-time data can better adapt to the complex and changing environmental conditions of industrial sites, thereby improving the stability and reliability of the network.

[0017] This method also emphasizes the importance of network maintenance. In traditional technologies, network maintenance is often ignored or used only as an auxiliary means. This method uses the industrial monitoring platform to regularly optimize and maintain the network parameters of the sensor nodes in the gateway, ensuring the long-term stable operation of the network. This comprehensive network management strategy not only improves the real-time and accuracy of monitoring data, but also extends the service life of the network.

[0018] This method is also innovative in wireless transmission. By setting a communication time limit, it ensures that the communication between the sensor node and the gateway can be completed within the specified time, thereby improving the efficiency of data transmission. At the same time, this method also monitors the gateway to determine whether there is network delay and takes corresponding elimination measures, further ensuring the real-time and reliability of data transmission.

[0019] As a preferred implementation, if the monitoring gateway detects that there is a network delay during the process of monitoring whether there is a network delay, the sampling interval of the sensor node in the gateway is reduced by 20% for detection. If there is still a delay in the detection after the reduction, the sampling interval is further reduced by 20% until the sampling interval is reduced to half of the set interval. If there is still a delay, the delay information is fed back, and after the feedback, the sampling interval is increased in the reverse direction until it returns to the set sampling interval, and the delay information is fed back again.

[0020] As a preferred implementation, when network delay elimination is performed, the central node in the gateway is started, and the gateway resends a request to the sensor node that sent the signal, and the node resends the data packet after receiving the request command.

[0021] As a preferred implementation, when performing network maintenance, four network parameters of three levels of nodes are taken respectively according to node density, network topology, network coverage and node reliability, and the parameter values ​​of these four networks are substituted into the prediction formula; then the average packet size transmitted by the sensor node to the gateway is calculated according to the prediction formula and compared with the threshold, the wireless rate of wireless transmission of the network is adjusted, and the node density, network topology, network coverage and node reliability are updated.

[0022] As a preferred implementation, after collecting signals, the sensor node compresses the collected signals and uses wavelet transform to achieve adaptive signal compression; noise is eliminated from the compressed data through a threshold function; data eigenvalues ​​are calculated to divide the data into a data characteristic part and a data non-characteristic part; and the compressed signal is transmitted to the gateway via wireless.

[0023] As a preferred implementation, the process of adaptively compressing a signal using the wavelet transform method includes: setting an initial threshold value according to the amplitude range of the acquired signal, performing wavelet decomposition on the signal to obtain coefficients at different levels; adaptively selecting a threshold value to perform threshold function processing on the signal, so that coefficients above the threshold value take the threshold value, and coefficients below the threshold value are reset to zero, and then performing wavelet reconstruction on the decomposed coefficients to obtain a compressed signal.

[0024] After adopting the above technical solution, the beneficial effect of the present invention is that through hierarchical node design and task allocation strategy, the method realizes the optimal configuration of monitoring resources. This not only improves the monitoring efficiency, but also reduces energy consumption. In low-power self-organizing networks, the energy consumption of nodes is a key issue. By reasonably allocating monitoring tasks, each node can play the maximum utility within its capability, thereby effectively extending the service life of the network.

[0025] The network parameter optimization strategy based on the average value of wireless rate has significantly improved the network performance. By monitoring and adjusting the wireless rate in real time, this method can ensure that the network maintains optimal performance under different environmental conditions. This not only improves the rate and stability of data transmission, but also reduces the risk of data loss and errors. This is crucial for industrial sites, as any data loss or error can lead to serious consequences.

[0026] The method also achieves comprehensive network maintenance and management through the industrial monitoring platform. Regular network parameter optimization and maintenance not only ensures the long-term stable operation of the network, but also improves the accuracy and reliability of monitoring data. This is of great significance for the monitoring and management of industrial sites, because accurate monitoring data is the basis for making effective decisions.

[0027] The innovation of this method in wireless transmission also brings significant beneficial effects. By setting communication time limits and monitoring network delays, this method ensures the real-time and reliability of data transmission. This is crucial for industrial sites that require real-time monitoring and control. Real-time data transmission enables operators to understand the situation on site in a timely manner and make corresponding adjustments, thereby avoiding potential safety risks and production losses. This method achieves low-power, efficient and reliable industrial site signal monitoring through improvements and innovations in multiple aspects such as hierarchical node design, network parameter optimization strategy based on wireless rate average value, comprehensive network maintenance and management, and innovations in wireless transmission. This is of great significance in improving the monitoring efficiency of industrial sites, reducing energy consumption, and ensuring production safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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 or the description of the prior art 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.

[0029] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] Example:

[0032] like Figure 1 As shown, an industrial field signal monitoring method for low-power self-organizing network, a star network composed of low-power self-organizing network nodes, monitoring based on the star network, the method comprises the following steps:

[0033] The sensor nodes collect signals and transmit the collected signals to the gateway via wireless. All sensor nodes are divided into three levels: level 1 nodes, level 2 nodes and level 3 nodes. The monitoring range of level 1 nodes is 50 meters, the monitoring range of level 2 nodes is 25 meters, and the monitoring range of level 3 nodes is 1 meter.

[0034] When there is a level 1 node in a certain monitoring range, the signal monitoring is completed by the level 1 node; when there is no level 1 node in a certain monitoring range, the monitoring task of the signal in this range is completed by the level 2 node; when there is no level 1 or level 2 node in a certain monitoring range, the signal monitoring is completed by the level 3 node;

[0035] During wireless transmission, the sensor node communicates with the gateway, sends the signal wirelessly to the base station of the gateway, and completes the communication within the set time; the gateway then sends it to the industrial monitoring platform; the industrial monitoring platform optimizes the network parameters and performs network maintenance on the sensor nodes in the gateway;

[0036] When optimizing network parameters, the wireless rates of level 1 nodes, level 2 nodes, and level 3 nodes are monitored respectively, and the average values ​​of the three wireless rates of the three levels are taken. Based on the average values, the wireless rates of the three levels are adjusted respectively.

[0037] Before monitoring, the monitoring gateway is used to determine whether there is network delay. If so, the network delay is eliminated. If not, the average wireless rate is fed back. The industrial field signal is monitored by monitoring the changes in the node rate range.

[0038] The working principle of this low-power self-organizing network industrial field signal monitoring method is based on a star network structure, and effective coverage and signal collection of the monitoring range are achieved through sensor nodes of different levels (level 1, level 2, and level 3). These nodes perform signal monitoring tasks in specific areas according to their different monitoring ranges (50 meters, 25 meters, and 1 meter), and the priority is from high to low, namely level 1, level 2, and level 3. When there are higher-level nodes in a monitoring area, the lower-level nodes will not participate in the signal monitoring of the area, thereby optimizing resource allocation and reducing energy consumption.

[0039] In a specific work scenario, suppose that in a large factory workshop, the operating status of the equipment needs to be monitored. First, deploy multiple sensor nodes, and reasonably distribute level 1, level 2, and level 3 nodes according to the workshop layout and monitoring needs. When a key device (such as a large motor) is running, the level 1 nodes around it (assuming a monitoring range of 50 meters) will first detect the signal and immediately transmit the data to the gateway wirelessly. If the device is outside the coverage range of the level 1 node but still within the level 2 node (25-meter range), the level 2 node will assume the monitoring task. If the device is extremely special or hidden and is only accessible to the level 3 node (1-meter range), the level 3 node will perform monitoring.

[0040] During the data transmission process, the sensor node maintains efficient communication with the gateway to ensure that the signal is transmitted within the set time and avoid communication delays. After receiving the data, the gateway further forwards it to the industrial monitoring platform, which not only receives the data, but is also responsible for network parameter optimization and maintenance. For example, according to the average wireless rate of nodes at each level monitored, the wireless rate of each node is dynamically adjusted to optimize network performance and energy efficiency. At the same time, the system will pre-detect whether there is network delay in the gateway, and take measures to eliminate it once the delay is found, to ensure the real-time and accuracy of data transmission. Finally, by monitoring the changes in the node rate range, accurate monitoring of industrial field signals can be achieved, providing strong support for equipment maintenance, fault warning, etc.

[0041] If the monitoring gateway detects that there is a network delay during the process of monitoring whether there is a network delay, the sampling interval of the sensor node in the gateway will be reduced by 20% for detection. If there is still a delay in the detection after the reduction, the sampling interval will continue to be reduced by 20% until the sampling interval is reduced to half of the set interval. If there is still a delay, the delay information will be fed back, and the sampling interval will be increased in the reverse direction after the feedback until it returns to the set sampling interval, and the delay information will be fed back again.

[0042] When the monitoring gateway detects that there is a delay in the network, it does not simply adopt a fixed strategy or alarm processing, but first reduces the sampling interval of the sensor node by 20% for detection. This is a dynamic adjustment strategy that aims to try to alleviate or detect the specific cause of network delay by increasing the data sampling frequency. If the delay still exists after adjustment, continue to reduce the sampling interval by 20% until the sampling interval is reduced to half of the set interval. This series of gradual adjustments not only reflects the hierarchical response to the delay problem, but also avoids the resource consumption that may be caused by blind adjustment. If the delay problem persists, the system will feedback the delay information, and after the feedback, increase the sampling interval in the opposite direction until it returns to the set sampling interval, and then feedback the delay information again. This "try-feedback-adjustment" cycle mechanism ensures that the system can actively respond to network delays while maintaining reasonable resource utilization efficiency.

[0043] When network delay elimination is performed, the central node in the gateway is started, and the gateway resends a request to the sensor node that sent the signal. After receiving the request command, the node resends the data packet.

[0044] In terms of network delay elimination, this preferred implementation is started by the central node in the gateway, and a request is resent to the sensor node that sends the signal to achieve the retransmission of the data packet. This is different from the simple timeout retransmission or discarding of data packets in traditional technologies. It emphasizes the active intervention and correction capabilities at the network level. Through the intervention of the central node, not only the reliability of data packet transmission is ensured, but also the source of delay can be analyzed and located to a certain extent, providing data support for subsequent network optimization. This mechanism improves the network's response speed and recovery ability to sudden delays, and enhances the stability and reliability of the network.

[0045] When performing network maintenance, four network parameters of the three levels of nodes are taken according to the node density, network topology, network coverage and node reliability, and the parameter values ​​of these four networks are substituted into the prediction formula; then the average packet size transmitted by the sensor node to the gateway is calculated according to the prediction formula, and compared with the threshold, the wireless rate of wireless transmission is adjusted for the network, and the node density, network topology, network coverage and node reliability are updated. By comprehensively considering multiple dimensions such as node density, network topology, network coverage and node reliability, the average packet size transmitted by the sensor node to the gateway is calculated by the prediction formula, and compared with the threshold, so as to dynamically adjust the wireless rate. Compared with the previous method of adjusting the network based on a single or a small number of factors, this is more comprehensive and scientific. It not only takes into account the physical layout and node performance of the network, but also introduces a prediction mechanism, which can predictively optimize the network configuration and improve the utilization efficiency of network resources. In addition, by continuously updating parameters such as node density and network topology, the dynamic and adaptive nature of network maintenance is achieved, providing a strong guarantee for the long-term and stable operation of the network.

[0046] After collecting signals, the sensor node compresses the collected signals and uses wavelet transform method to realize adaptive signal compression; noise is eliminated from the compressed data through a threshold function; data characteristic values ​​are calculated to divide the data into a data characteristic part and a data non-characteristic part; and the compressed signal is transmitted to the gateway via wireless.

[0047] The use of wavelet transform to achieve adaptive signal compression is a significant improvement over existing technologies. Wavelet transform can perform multi-scale decomposition based on the local characteristics of different signals, thereby compressing the signal more efficiently. By setting an initial threshold, performing wavelet decomposition on the signal, and then performing adaptive threshold processing and threshold function processing on the decomposed coefficients, effective signal compression and noise elimination are achieved. Compared with traditional compression methods, the wavelet transform method not only has a high compression rate, but also can better retain the key features of the signal, improving the efficiency and accuracy of data transmission. In addition, dividing the compressed signal into a data feature part and a non-feature part helps to further save bandwidth during transmission and optimize network resource allocation.

[0048] The process of adaptively compressing a signal using the wavelet transform method includes: setting an initial threshold value according to the amplitude range of the collected signal, performing wavelet decomposition on the signal to obtain coefficients at different levels; adaptively selecting a threshold value to perform threshold function processing on the signal, so that coefficients above the threshold value take the threshold value, and coefficients below the threshold value are reset to zero, and then performing wavelet reconstruction on the decomposed coefficients to obtain a compressed signal.

[0049] In the specific implementation of the wavelet transform method to compress signals, this preferred implementation method sets an initial threshold, performs wavelet decomposition on the signal, then adaptively selects a threshold to perform threshold function processing, and finally performs wavelet reconstruction to obtain a compressed signal. This process embodies the accurate capture and efficient compression of signal features. Compared with the existing fixed threshold or simple filtering method, adaptive threshold processing can better adapt to the dynamic changes of different signals and improve the flexibility and effect of compression. At the same time, the wavelet reconstruction process ensures that the compressed signal retains key features while minimizing distortion, providing a high-quality data basis for subsequent signal analysis and processing.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for industrial field signal monitoring for low-power self-organizing network, a star network composed of low-power self-organizing network nodes, monitoring based on the star network, characterized in that: The method comprises the following steps: The sensor nodes collect signals and transmit the collected signals to the gateway via wireless. All sensor nodes are divided into three levels: level 1 nodes, level 2 nodes and level 3 nodes. The monitoring range of level 1 nodes is 50 meters, the monitoring range of level 2 nodes is 25 meters, and the monitoring range of level 3 nodes is 1 meter. When there is a level 1 node in a certain monitoring range, the signal monitoring is completed by the level 1 node; when there is no level 1 node in a certain monitoring range, the monitoring task of the signal in this range is completed by the level 2 node; when there is no level 1 or level 2 node in a certain monitoring range, the signal monitoring is completed by the level 3 node; During wireless transmission, the sensor node communicates with the gateway, sends the signal wirelessly to the base station of the gateway, and completes the communication within the set time; the gateway then sends it to the industrial monitoring platform; the industrial monitoring platform optimizes the network parameters and performs network maintenance on the sensor nodes in the gateway; When optimizing network parameters, the wireless rates of level 1 nodes, level 2 nodes, and level 3 nodes are monitored respectively, and the average values ​​of the three wireless rates of the three levels are taken. Based on the average values, the wireless rates of the three levels are adjusted respectively. Before monitoring, the monitoring gateway is used to determine whether there is network delay. If so, the network delay is eliminated. If not, the average wireless rate is fed back. The industrial field signal is monitored by monitoring the changes in the node rate interval. If the monitoring gateway detects that there is a network delay during the process of monitoring whether there is a network delay, the sampling interval of the sensor node in the gateway will be reduced by 20% for detection. If there is still a delay in the detection after the reduction, the sampling interval will continue to be reduced by 20% until the sampling interval is reduced to half of the set interval. If there is still a delay, the delay information will be fed back, and the sampling interval will be increased in the reverse direction after the feedback until it returns to the set sampling interval, and the delay information will be fed back again.

2. The industrial field signal monitoring method for low-power self-organizing network according to claim 1, characterized in that: When network delay elimination is performed, the central node in the gateway is started, and the gateway resends a request to the sensor node that sent the signal. After receiving the request command, the node resends the data packet.

3. The industrial field signal monitoring method for low-power self-organizing network according to claim 1, characterized in that: When performing network maintenance, four network parameters of the three levels of nodes are taken respectively according to the node density, network topology, network coverage and node reliability, and the parameter values ​​of these four networks are substituted into the prediction formula; then the average packet size transmitted by the sensor node to the gateway is calculated according to the prediction formula and compared with the threshold, the wireless transmission rate of the network is adjusted, and the node density, network topology, network coverage and node reliability are updated.

4. The industrial field signal monitoring method for low-power self-organizing network according to claim 1, characterized in that: After collecting the signal, the sensor node compresses the collected signal and uses the wavelet transform method to realize adaptive signal compression; the compressed data is subjected to noise elimination through the threshold function; Calculate the data characteristic value and divide the data into the data characteristic part and the data non-characteristic part; transmit the compressed signal to the gateway via wireless.

5. The industrial field signal monitoring method for low-power self-organizing network according to claim 4, characterized in that: The process of adaptively compressing a signal using the wavelet transform method includes: setting an initial threshold value according to the amplitude range of the collected signal, performing wavelet decomposition on the signal to obtain coefficients at different levels; adaptively selecting a threshold value to perform threshold function processing on the signal, so that coefficients above the threshold value take the threshold value, and coefficients below the threshold value are reset to zero, and then performing wavelet reconstruction on the decomposed coefficients to obtain a compressed signal.

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