A wireless distributed geological disaster data acquisition method and system

Abstract

The present invention discloses a wireless distributed geological disaster data acquisition method and system, which specifically relates to the field of data processing. The method analyzes and calculates the gradient entropy of each sensor node, identifies potential abnormal nodes, performs relative difference analysis around the abnormal nodes, comprehensively considers the overall changes between nodes, and provides more comprehensive information for determining the relative risk level of sensor failure. By recording the cumulative value of the spatial distance per unit time between each suspicious sensor node and other non-suspicious nodes, marking it as a spatial distance measurement value, and calculating the spatial position distance fluctuation value, the position change pattern of the sensor node is comprehensively evaluated, which helps to detect the change of the sensor in the spatial position early and identify potential position dynamic accuracy anomalies in advance. The risk level of abnormal position data acquisition is given to facilitate the allocation of corresponding maintenance priority levels, and more effectively realize the monitoring and risk assessment of geological disasters.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a wireless distributed geological disaster data acquisition method and system. Background Art

[0002] Traditional wireless distributed geological hazard risk data processing has some obvious shortcomings. One of the main problems is that it relies too much on the data measured by the position change of the sensor, while ignoring the in-depth analysis of the reliability of the sensor. In this method, the system focuses on the location information of the sensor node, while ignoring the stability and accuracy of the node itself. Since the sensor may be affected by many factors in the field environment, including weather, temperature, battery status, etc., its working state may be unstable. This may cause certain errors in the sensor's measurement results, thereby affecting the accuracy of the final risk assessment results.

[0003] Another problem is the lack of a mechanism for real-time monitoring and evaluation of sensor reliability. Traditional approaches often fail to consider building a system to dynamically detect and repair possible sensor faults or failures. During long-term operation, sensor nodes may fail for various reasons, such as battery exhaustion, communication module failure, or sensor failure. Without real-time monitoring of sensor status and fault detection mechanisms, the system may not be able to detect and handle these problems in a timely manner, resulting in inaccurate risk assessment results.

[0004] These shortcomings lead to significant discrepancies in the actual application of traditional wireless distributed geological hazard risk data processing methods.

[0005] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiment of the present invention provides a wireless distributed geological disaster data acquisition method and system, which quantifies the complexity of node data changes by analyzing and calculating the gradient entropy of each sensor node, helps to identify potential abnormal nodes, and improves the system's perception of geological disaster risks. Relative difference analysis is performed around abnormal nodes, and the overall changes between nodes are comprehensively considered, providing more comprehensive information for determining the relative risk level of sensor failures. The technical features and application effects are coordinated with each other to jointly build an efficient and reliable geological monitoring system. By recording the cumulative value of the spatial distance per unit time between each suspicious sensor node and other non-suspicious nodes, marking it as a spatial distance measurement value, and calculating the spatial position distance fluctuation value, the position change pattern of the sensor node is comprehensively evaluated to achieve comprehensive monitoring of the overall geological environment. The calculation of the sensitive spatial position dynamic difference index helps to detect the abnormality of the sensor in the spatial position as early as possible and identify potential faults in advance. Different risk signals are generated, and the risk level of abnormal position data acquisition is given, which is convenient for allocating corresponding maintenance priority levels, improving the stability and accuracy of the system, and more effectively realizing the monitoring and risk assessment of geological disasters to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: S1. Place sensors at grid node positions in the target monitoring area, collect acceleration data regularly and calculate gradient entropy, use information entropy to analyze the uncertainty of the gradient, judge the environmental stability, and mark suspicious sensor nodes;

[0008] S2. Obtaining the relative degree of abnormality between the suspicious sensor node and the adjacent sensor nodes according to the information entropy, further analyzing the relative degree of abnormality, and generating a corresponding first stable signal or a first risk signal;

[0009] S3. By calculating the spatial distance between the suspicious sensor node and other non-suspicious nodes, the spatial position dynamic difference index is obtained to measure the degree of spatial dynamic change of the suspicious sensor, and a second stable or second risk signal is generated accordingly;

[0010] S4. Classify and discuss the signals generated in steps S2 and S3, determine the monitoring accuracy of the sensor nodes, and give corresponding monitoring risk signals.

[0011] In a preferred embodiment, S1 includes the following contents:

[0012] Determine the target monitoring area and place sensor nodes according to the grid node positions. The sensor nodes regularly collect acceleration data of the surrounding environment. If the acceleration exceeds the corresponding threshold, an alarm signal is issued. If the acceleration does not exceed the threshold, the collected data is gradient calculated, that is, the rate of change between adjacent data points is calculated to obtain the gradient entropy.

[0013] In a preferred embodiment, a differential operation is performed on the data collected by the sensor to obtain the rate of change between adjacent data points;

[0014] Count the values ​​of the gradient and get the probability distribution of the gradient;

[0015] The entropy of the gradient is calculated using the information entropy formula;

[0016] The sensor nodes whose statistical gradient entropy is greater than or equal to the critical threshold are marked as suspicious sensor nodes.

[0017] In a preferred embodiment, S2 includes the following contents:

[0018] For a suspicious sensor node, the gradient entropy of its adjacent sensor nodes is obtained to obtain a set of gradient entropy values;

[0019] The relative degree of variation is calculated using the obtained gradient entropy of adjacent sensor nodes:

[0020] Among them, RV i is the relative mutation degree of node i, N is the number of adjacent sensor nodes, H i and H j are the gradient entropy of node i and its adjacent node j respectively.

[0021] In a preferred embodiment, the relative mutation degree value is compared with the mutation threshold. If the relative mutation degree value is less than the mutation threshold, a first stable signal is generated; otherwise, if the relative mutation degree value is greater than or equal to the mutation threshold, a first risk signal is generated.

[0022] In a preferred embodiment, S3 includes the following contents:

[0023] If no alarm signal is obtained, the accumulated spatial distance per unit time between each suspicious sensor node and other non-suspicious sensor nodes is recorded and marked as the spatial distance measurement value. The spatial position distance fluctuation value is calculated based on the spatial distance measurement value. The acquisition process is as follows:

[0024] For each suspicious sensor node, record the spatial distance per unit time between it and other non-suspicious sensor nodes;

[0025] The spatial distance measurement value is the cumulative value of the spatial distance to other non-suspicious nodes per unit time;

[0026] The accumulated spatial distances between suspicious sensor nodes and all non-suspicious sensor nodes are counted to construct a spatial data set, and the dynamic difference index of spatial position is obtained by calculating the standard deviation.

[0027] In a preferred embodiment, the spatial position dynamic difference index is compared with the fluctuation threshold. If the spatial position dynamic difference index is less than the fluctuation threshold, a second stable signal is generated; conversely, if the spatial position dynamic difference index is greater than or equal to the fluctuation threshold, a second risk signal is generated.

[0028] In a preferred embodiment, if the first risk signal and the second stable signal are generated simultaneously, a medium risk signal is generated; and a high risk signal is generated.

[0029] A wireless distributed geological disaster data acquisition system, including a suspicious node identification module, an abnormal degree analysis module, a dynamic difference measurement module and a risk monitoring output module;

[0030] The suspicious node identification module places sensors at grid node positions in the target monitoring area, collects acceleration data regularly and calculates gradient entropy, uses information entropy to analyze the uncertainty of gradients, determines environmental stability, marks suspicious sensor nodes, and sends information entropy and suspicious sensors to the anomaly degree analysis module;

[0031] The abnormality degree analysis module obtains the relative abnormality degree of the suspicious sensor node and the adjacent sensor node according to the information entropy, further analyzes the relative abnormality degree, generates the corresponding first stable signal or first risk signal, and sends the signal to the risk monitoring output module;

[0032] The dynamic difference measurement module obtains the spatial position dynamic difference index by calculating the spatial distance between the suspicious sensor node and other non-suspicious nodes, measures the spatial dynamic change degree of the suspicious sensor, and generates a second stable or second risk signal accordingly, and sends the signal to the risk monitoring output module;

[0033] The risk monitoring output module classifies and discusses the signals output by the anomaly degree analysis module and the dynamic difference measurement module, determines the monitoring accuracy of the sensor node, and gives the corresponding monitoring risk signal.

[0034] The technical effects and advantages of a wireless distributed geological disaster data acquisition method and system of the present invention are as follows:

[0035] 1. First, the dense layout enables the sensor nodes to cover every small area of ​​the geological area, improves the spatial resolution of monitoring, and can more accurately capture the microscopic changes in the geological environment. Secondly, the calculation method of analyzing and calculating the gradient entropy of each sensor node can quantify the complexity of the changes in node data, help identify potential abnormal nodes, and improve the system's perception of geological disaster risks. Finally, a relative difference analysis is conducted around abnormal nodes, further comprehensively considering the overall changes between nodes, and providing more comprehensive information for determining the relative risk level of sensor failures. Therefore, it is helpful to improve the fault detection accuracy, timeliness and reliability of the system in geological disaster monitoring, help to more comprehensively understand the dynamic changes of the entire geological area, and provide effective support for timely preventive measures. Such technical features and application effects work together to build an efficient and reliable geological monitoring system.

[0036] 2. By recording the cumulative spatial distance per unit time between each suspicious sensor node and other non-suspicious sensor nodes, marking it as a spatial distance measurement value, and calculating the spatial position distance fluctuation value, the position change pattern of the sensor node is comprehensively evaluated to achieve comprehensive monitoring of the overall geological environment. This includes the calculation of sensitive spatial position dynamic difference indexes, which helps to detect abnormalities in the spatial position of sensors early and identify potential faults in advance. And for the different risk signals generated, the risk level of abnormal location data acquisition is given to facilitate the allocation of corresponding maintenance priority levels, which in turn helps to improve the stability and accuracy of the system, thereby more effectively realizing the monitoring and risk assessment of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a wireless distributed geological disaster data collection method and system of the present invention;

[0038] Figure 2 The present invention is a structural schematic diagram of a wireless distributed geological disaster data acquisition method and system. DETAILED DESCRIPTION

[0039] 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.

[0040] Example 1

[0041] Figure 1 The present invention provides a wireless distributed geological disaster data collection method, which is characterized by:

[0042] S1. Place sensors at grid node positions in the target monitoring area, collect acceleration data regularly and calculate gradient entropy, use information entropy to analyze the uncertainty of gradient, judge environmental stability, and mark suspicious sensor nodes;

[0043] S2. Obtaining the relative degree of abnormality between the suspicious sensor node and the adjacent sensor nodes according to the information entropy, further analyzing the relative degree of abnormality, and generating a corresponding first stable signal or a first risk signal;

[0044] S3. By calculating the spatial distance between the suspicious sensor node and other non-suspicious nodes, the spatial position dynamic difference index is obtained to measure the degree of spatial dynamic change of the suspicious sensor, and a second stable or second risk signal is generated accordingly;

[0045] S4. Classify and discuss the signals generated in steps S2 and S3, determine the monitoring accuracy of the sensor nodes, and give corresponding monitoring risk signals.

[0046] Analyzing the gradient entropy of each sensor node in the grid provides a deep insight into the complexity and regularity of the data collected by the node. The calculation of gradient entropy can quantify the complexity of data changes and reflect the degree of uncertainty of environmental data. For each node, through the analysis of gradient entropy, the system can reveal the changing pattern of the data it monitors and determine whether the data is relatively regular or relatively chaotic. This provides important clues for subsequent geological environmental changes.

[0047] The subsequent relative difference analysis calculations appear reasonable and powerful on this basis. By comparing the relative differences in gradient entropy between nodes, the system can gain a deeper understanding of the overall data changes of each node relative to the surrounding nodes. This analysis method not only takes into account the independent characteristics of each node, but also examines the mutual influence between nodes through the calculation of relative differences. Therefore, the analysis of gradient entropy provides a strong foundation for the subsequent relative difference analysis, enabling the system to more comprehensively and accurately evaluate the dynamic data characteristics of the entire geological area, which helps to discover potential geological disaster risks more timely and accurately.

[0048] S1 includes the following:

[0049] Determine the target monitoring area and place sensor nodes according to the grid node positions. The distance between sensor nodes can be set according to actual requirements, for example, 2 meters or 3 meters.

[0050] The sensor nodes periodically collect acceleration data of the surrounding environment;

[0051] If the acceleration exceeds the corresponding threshold, an alarm signal is issued, indicating that the geological hazard has occurred and intensified. If the acceleration does not exceed the threshold, the collected data is gradient calculated, that is, the rate of change between adjacent data points is calculated to obtain the gradient entropy. This can be achieved through differential operations, such as the difference between the current data point and the previous data point.

[0052] 1. Perform differential operation on the data collected by the sensor to obtain the rate of change between adjacent data points: Gradient(i) = Data(i) - Data(i-1);

[0053] 2. Count the values ​​of the gradient and get the probability distribution of the gradient:

[0054] in:

[0055] P(Gradient=x i ) represents the probability of the event with gradient xi;

[0056] Count(Gradient=x i ) indicates that the gradient is the number of times xi appears in the data;

[0057] TotalDataPoints represents the total number of data points, that is, the total amount of data collected by the sensor;

[0058] x i The specific value of the gradient refers to the rate of change between adjacent data points, which is calculated by differential operation;

[0059] 3. Use the information entropy formula to calculate the entropy of the gradient: H(Gradient)=-∑ i P(Gradient=x i )·log2(P(Gradient=x i ));

[0060] in:

[0061] H (Gradient) represents the entropy of gradient data;

[0062] P(Gradient=x i ) indicates that the gradient value is x i probability.

[0063] The gradient entropy value is used to reflect and represent the complexity and uncertainty of the changes in the data collected by the sensor. The larger the gradient entropy, the more complex and uncertain the changes in the data gradient are; the smaller the gradient entropy, the relatively simple and predictable the changes in the data gradient are.

[0064] When the gradient entropy is large: the data gradient changes irregularly and has large fluctuations, indicating that the environmental changes are more complex. This may be normal in some cases, but when the gradient entropy increases abnormally, it may mean that the data collected by the sensor is affected by abnormalities and there may be problems.

[0065] When the gradient entropy is small, the data gradient changes relatively smoothly and regularly, indicating that the environment is relatively stable. This is normal in some cases, but in geological disaster monitoring, a small gradient entropy may also indicate that the data change pattern is relatively simple, and the changes in the geological environment may not be captured in time.

[0066] Gradient entropy is not limited to indicating that there are hidden dangers in the sensor itself, but is also a comprehensive measure of environmental changes. In geological disaster monitoring, the increase in gradient entropy may indeed reflect the instability of the geological environment, but it does not necessarily mean that disasters occur frequently. Abnormal changes in gradient entropy may be due to environmental changes, or due to problems with the sensor itself, or a combination of the two.

[0067] Therefore, it is necessary to further analyze the situation where the node information entropy is large. It is necessary to analyze whether it is caused by the data collection failure of the sensor node itself, or it is really caused by the change of the monitored address environment. Therefore, it is necessary to coordinate the overall situation and conduct an overall analysis;

[0068] Compare the gradient entropy with the critical threshold. If the gradient entropy is less than the critical threshold, it indicates that the data changes of the sensor node are relatively simple and regular, the uncertainty of the gradient information is low, and the environment monitored by the sensor node is in a relatively stable state, the changes are relatively predictable, and there is no obvious complex fluctuation.

[0069] On the contrary, if the gradient entropy is greater than or equal to the critical threshold, it indicates that the data changes of the sensor node are relatively complex and irregular, the uncertainty of the gradient information is high, and the environment monitored by the sensor node has undergone complex and unpredictable changes, and there may be geological activities or other abnormal conditions.

[0070] The sensor nodes whose statistical gradient entropy is greater than or equal to the critical threshold are marked as suspicious sensor nodes.

[0071] S2 includes the following:

[0072] For a suspicious sensor node, the gradient entropy of its adjacent sensor nodes is obtained to obtain a set of gradient entropy values;

[0073] The relative degree of variation is calculated using the obtained gradient entropy of adjacent sensor nodes:

[0074] Among them, RV iis the relative mutation degree of node i, N is the number of adjacent sensor nodes, H i and H j are the gradient entropy of node i and its adjacent node j respectively;

[0075] The relative variation degree is used to measure the overall change degree of the sensor node in the gradient entropy relative to other surrounding nodes. When the relative variation degree is large, it means that the gradient entropy of the sensor node is significantly different from that of the surrounding nodes, reflecting the instability of the node itself, monitoring drift, and causing local variation. On the contrary, when the relative variation degree is small, it means that the gradient entropy of the sensor node is relatively consistent with that of the surrounding nodes, which may indicate that the environmental changes are relatively uniform or predictable, and the possibility of sensor node failure is small. Therefore, by comparing the relative variation degree, the system can have a more comprehensive understanding of the overall changes of the sensor nodes, which helps to timely detect potential geological disasters or sensor anomalies and improve the reliability of the monitoring system.

[0076] The relative variation degree value is compared with the variation threshold. If the relative variation degree value is less than the variation threshold, it means that the gradient entropy change of the sensor node relative to other surrounding nodes is relatively uniform or predictable, and has not reached the abnormal threshold set by the system. The possibility of sensor node failure is small, and the first stable signal is generated; on the contrary, if the relative variation degree value is greater than or equal to the variation threshold, it implies that the gradient entropy of the sensor node is significantly different from that of the surrounding nodes, indicating that the sensor node itself is very likely to fail, which needs to attract the attention of the system and may trigger further monitoring, analysis or alarm, generating the first risk signal.

[0077] When monitoring geological disasters in an entire area, it is reasonable to adopt a grid layout with sensor nodes placed at fixed intervals. For example, placing a sensor node every 2 meters can form a dense sensor network, which helps to monitor changes in the geological environment comprehensively and efficiently. By analyzing and calculating the gradient entropy to determine the sensor nodes that may be abnormal, the system can quickly find potential problem areas in the grid. Further analyzing the relative difference between the suspicious sensor node and the surrounding sensor nodes can more accurately evaluate the overall changes of the abnormal nodes. This method can not only improve the sensitivity of geological disaster monitoring, but also help to detect sensor failures or abnormalities in a timely manner, thereby improving the reliability and practicality of the entire monitoring system. Through such a layout and analysis method, the system can more comprehensively understand the dynamic changes of the entire geological area, which helps to take timely and effective measures to deal with potential geological disaster risks.

[0078] First, the dense layout enables the sensor nodes to cover every small area of ​​the geological area, improves the spatial resolution of monitoring, and can more accurately capture the microscopic changes in the geological environment. Secondly, the calculation method of analyzing and calculating the gradient entropy of each sensor node can quantify the complexity of the changes in node data, help identify potential abnormal nodes, and improve the system's perception of geological disaster risks. Finally, relative difference analysis is carried out around abnormal nodes, further comprehensively considering the overall changes between nodes, and providing more comprehensive information for determining the relative risk level of sensor failure. Therefore, it is helpful to improve the fault detection accuracy, timeliness and reliability of the system in geological disaster monitoring, help to more comprehensively understand the dynamic changes of the entire geological area, and provide effective support for timely preventive measures. Such technical features and application effects work together to build an efficient and reliable geological monitoring system.

[0079] By recording the cumulative spatial distance between suspicious sensor nodes and other non-suspicious sensor nodes per unit time, the sensor node's built-in GPS module is used to achieve this (the position monitoring sensitivity of the acceleration sensor is better than the spatial position perception accuracy of the GPS module), and marking it as a spatial distance measurement value, and then calculating the spatial position fluctuation value based on these values, it is reasonable and meaningful in analyzing the hidden dangers and failures of sensor nodes. The rationality of this method is reflected in the following aspects:

[0080] First, the absence of an alarm signal does not mean that there are no hidden dangers or faults in the system. It may be due to the fact that the data of the faulty sensor is not accurately captured or other reasons. By recording the cumulative spatial distance between the suspicious sensor node and other normal nodes to form a spatial distance measurement value, the position changes between sensor nodes can be more comprehensively captured, even if no obvious alarm signal is generated.

[0081] Secondly, by calculating the spatial position fluctuation value, the degree of position fluctuation of the sensor node can be quantified to help identify possible hidden dangers or failures. Large spatial position fluctuation values ​​may mean that some sensor nodes have unstable changes in spatial position, which may be caused by equipment failure, environmental changes or other problems. Such analysis helps to detect potential problems in advance and take maintenance or replacement measures in time to ensure the reliability and stability of the monitoring system.

[0082] In summary, through the analysis of spatial distance measurement values ​​and spatial position fluctuation values, even if no alarm signal is obtained, the system can still fully understand the positional relationship between sensor nodes, thereby discovering potential hidden dangers and faults earlier and improving the reliability and sensitivity of the monitoring system.

[0083] S3 includes the following:

[0084] If no alarm signal is obtained, the accumulated spatial distance per unit time between each suspicious sensor node and other non-suspicious sensor nodes is recorded and marked as the spatial distance measurement value. The spatial position distance fluctuation value is calculated based on the spatial distance measurement value. The acquisition process is as follows:

[0085] For each suspicious sensor node, record the spatial distance per unit time between it and other non-suspicious sensor nodes;

[0086] The spatial distance measurement value is the cumulative value of the spatial distance to other non-suspicious nodes per unit time: D o =∑ o≠ v d ov ;

[0087] Among them, D o It represents the accumulated value of the spatial distance between the suspicious sensor node o and the non-suspicious sensor node v in unit time;

[0088] The accumulated spatial distances between suspicious sensor nodes and all non-suspicious sensor nodes are counted to construct a spatial data set, and the dynamic difference index of spatial position is obtained by calculating the standard deviation.

[0089] The spatial position dynamic difference index is used to indicate the degree of dynamic change of the spatial position of the sensor node. The calculation of this index covers the spatial distance between each suspicious sensor node and other non-suspicious nodes. By measuring the fluctuation of these distance values, it reflects the instability and dynamic nature of the node position. When the spatial position dynamic difference index is large, it means that there are significant fluctuations and irregularities in the spatial position between the suspicious sensor node and other non-suspicious nodes, which may imply potential problems or abnormalities in the spatial position data collection. On the contrary, a small index indicates that the spatial position of the node is relatively stable and the spatial position data collection changes are more orderly. Therefore, the size of the spatial position dynamic difference index is of great significance for identifying the position change pattern of the sensor node and possible hidden dangers and failures.

[0090] Compare the spatial position dynamic difference index with the fluctuation threshold. If the spatial position dynamic difference index is less than the fluctuation threshold, it means that the dynamic change of the sensor node in the spatial position is relatively small and is in a relatively stable state. This may mean that the spatial position relationship between the nodes changes in a relatively orderly manner, without obvious signs of abnormality or failure, and a second stable signal is generated. On the contrary, if the spatial position dynamic difference index is greater than or equal to the fluctuation threshold, it implies that the sensor node has a large dynamic change in the spatial position, which exceeds the normal fluctuation range preset by the system. This indicates a potential problem that needs to attract the attention of the system and may require further investigation and maintenance, generating a second risk signal.

[0091] S4 includes the following:

[0092] If the first risk signal and the second stable signal are generated at the same time, it means that the gradient entropy of the sensor node changes greatly and there is a large irregularity, that is, the acceleration sensor has potential risks in data acquisition, but the dynamic change in the spatial position is relatively small, that is, the data acquisition status of the spatial position is normal. Since the acquisition position capture accuracy of the acceleration sensor is greater than the acquisition accuracy of the GPS module in the spatial position, in this case, only a rough analysis of the geological changes at the location of the sensor node can be made, the monitoring accuracy is poor, and a medium-risk signal is generated.

[0093] If the first risk signal and the second risk signal are generated at the same time, it means that the sensor node has a large change in gradient entropy, there are potential risks in the acceleration sensor data acquisition, and the dynamic change in the spatial position is large, that is, the data acquisition state of the spatial position is also abnormal. This implies that the sensor node has problems in both aspects, that is, there are problems in both high-precision and low-precision geological change acquisition. Urgent monitoring, analysis or alarm measures need to be taken immediately to generate a high-risk signal.

[0094] By recording the cumulative spatial distance per unit time between each suspicious sensor node and other non-suspicious sensor nodes, marking it as a spatial distance measurement value, and calculating the spatial position distance fluctuation value, the position change pattern of the sensor node is comprehensively evaluated to achieve comprehensive monitoring of the overall geological environment. This includes the calculation of sensitive spatial position dynamic difference indexes, which helps to detect abnormalities in the spatial position of sensors early and identify potential faults in advance. And for the different risk signals generated, the risk level of abnormal location data acquisition is given to facilitate the allocation of corresponding maintenance priority levels, which in turn helps to improve the stability and accuracy of the system, thereby more effectively realizing the monitoring and risk assessment of geological disasters.

[0095] Example 2

[0096] Figure 2 The invention provides a wireless distributed geological disaster data acquisition system, including a suspicious node identification module, an abnormal degree analysis module, a dynamic difference measurement module and a risk monitoring output module;

[0097] The suspicious node identification module places sensors at grid node positions in the target monitoring area, collects acceleration data regularly and calculates gradient entropy, uses information entropy to analyze the uncertainty of gradients, determines environmental stability, marks suspicious sensor nodes, and sends information entropy and suspicious sensors to the anomaly degree analysis module;

[0098] The abnormality degree analysis module obtains the relative abnormality degree of the suspicious sensor node and the adjacent sensor node according to the information entropy, further analyzes the relative abnormality degree, generates the corresponding first stable signal or first risk signal, and sends the signal to the risk monitoring output module;

[0099] The dynamic difference measurement module obtains the spatial position dynamic difference index by calculating the spatial distance between the suspicious sensor node and other non-suspicious nodes, measures the spatial dynamic change degree of the suspicious sensor, and generates a second stable or second risk signal accordingly, and sends the signal to the risk monitoring output module;

[0100] The risk monitoring output module classifies and discusses the signals output by the anomaly degree analysis module and the dynamic difference measurement module, determines the monitoring accuracy of the sensor node, and gives the corresponding monitoring risk signal.

[0101] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0102] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0103] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed systems and devices can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0107] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0109] Finally: 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 principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A wireless distributed geological disaster data collection method, characterized in that: S1. Place sensors at grid node positions in the target monitoring area, collect acceleration data regularly and calculate gradient entropy, use information entropy to analyze the uncertainty of gradient, judge environmental stability, and mark suspicious sensor nodes; S2. Obtaining the relative degree of abnormality between the suspicious sensor node and the adjacent sensor nodes according to the information entropy, further analyzing the relative degree of abnormality, and generating a corresponding first stable signal or a first risk signal; S3. By calculating the spatial distance between the suspicious sensor node and other non-suspicious nodes, the spatial position dynamic difference index is obtained to measure the degree of spatial dynamic change of the suspicious sensor, and a second stable or second risk signal is generated accordingly; S4. Classify and discuss the signals generated in step S2 and step S3, determine the monitoring accuracy of the sensor node, and give the corresponding monitoring risk signal; S2 Includes the following: For a suspicious sensor node, the gradient entropy of its adjacent sensor nodes is obtained to obtain a set of gradient entropy values; The relative degree of variation is calculated using the obtained gradient entropy of adjacent sensor nodes: Among them, RV i is the relative mutation degree of node i, N is the number of adjacent sensor nodes, H i and H j are the gradient entropy of node i and its adjacent node j respectively; The relative mutation degree value is compared with the mutation threshold value. If the relative mutation degree value is less than the mutation threshold value, a first stable signal is generated; otherwise, if the relative mutation degree value is greater than or equal to the mutation threshold value, a first risk signal is generated; S3 includes the following: If no alarm signal is obtained, the accumulated spatial distance per unit time between each suspicious sensor node and other non-suspicious sensor nodes is recorded and marked as the spatial distance measurement value. The spatial position distance fluctuation value is calculated based on the spatial distance measurement value. The acquisition process is as follows: For each suspicious sensor node, record the spatial distance per unit time between it and other non-suspicious sensor nodes; The spatial distance measurement value is the cumulative value of the spatial distance to other non-suspicious nodes per unit time; The spatial distance accumulation values ​​between suspicious sensor nodes and all non-suspicious sensor nodes are counted to construct a spatial data set, and the spatial position dynamic difference index is obtained by calculating the standard deviation. Compare the spatial position dynamic difference index with the fluctuation threshold, and if the spatial position dynamic difference index is less than the fluctuation threshold, generate a second stable signal; conversely, if the spatial position dynamic difference index is greater than or equal to the fluctuation threshold, generate a second risk signal; If the first risk signal and the second stable signal are generated at the same time, a medium risk signal is generated; if the first risk signal and the second risk signal are generated at the same time, a high risk signal is generated.

2. A wireless distributed geological disaster data acquisition method according to claim 1, characterized in that: S1 includes the following: Determine the target monitoring area and place sensor nodes according to the grid node positions. The sensor nodes regularly collect acceleration data of the surrounding environment. If the acceleration exceeds the corresponding threshold, an alarm signal is issued. If the acceleration does not exceed the threshold, the collected data is gradient calculated, that is, the rate of change between adjacent data points is calculated to obtain the gradient entropy.

3. A wireless distributed geological disaster data acquisition method according to claim 2, characterized in that: Perform differential calculation on the data collected by the sensor to obtain the rate of change between adjacent data points; Count the values ​​of the gradient and get the probability distribution of the gradient; The gradient entropy is calculated using the information entropy formula; The sensor nodes whose statistical gradient entropy is greater than or equal to the critical threshold are marked as suspicious sensor nodes.

4. A wireless distributed geological disaster data acquisition system, used to implement the acquisition method described in any one of claims 1 to 3, comprising a suspicious node identification module, an abnormality degree analysis module, a dynamic difference measurement module and a risk monitoring output module; The suspicious node identification module places sensors at grid node positions in the target monitoring area, collects acceleration data regularly and calculates gradient entropy, uses information entropy to analyze the uncertainty of the gradient, determines the environmental stability, marks suspicious sensor nodes, and sends information entropy and suspicious sensors to the anomaly degree analysis module; The abnormality degree analysis module obtains the relative abnormality degree of the suspicious sensor node and the adjacent sensor node according to the information entropy, further analyzes the relative abnormality degree, generates the corresponding first stable signal or first risk signal, and sends the signal to the risk monitoring output module; The dynamic difference measurement module obtains the spatial position dynamic difference index by calculating the spatial distance between the suspicious sensor node and other non-suspicious nodes, measures the spatial dynamic change degree of the suspicious sensor, and generates a second stable or second risk signal accordingly, and sends the signal to the risk monitoring output module; The risk monitoring output module classifies and discusses the signals output by the anomaly degree analysis module and the dynamic difference measurement module, determines the monitoring accuracy of the sensor node, and gives the corresponding monitoring risk signal.

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