A geological disaster early warning system based on multi-source sensor monitoring technology

Abstract

The present application belongs to the field of geological disaster early warning, and relates to resource allocation analysis technology, and is used for solving the problem that the prior art cannot optimize resource allocation of key monitoring equipment according to geological characteristics of different regions, in particular to a geological disaster early warning system based on multi-source sensor monitoring technology, which comprises region segmentation module, feature analysis module, equipment configuration module and disaster monitoring module connected in sequence, and the feature analysis module, the equipment configuration module and the disaster monitoring module are all in communication connection with a database; the region segmentation module is used for region segmentation of a geological disaster early warning region; the geological disaster early warning region is segmented into a plurality of preprocessing regions by using rectangular segmentation mode, ring segmentation mode and random segmentation mode respectively; the present application can perform monitoring equipment configuration analysis on a monitoring region, and the optimized sensors are uniformly arranged in the monitoring region, giving consideration to regional resource inclination and uniformity of monitoring density within the region.

Description

Technical Field

[0001] The present invention belongs to the field of geological disaster early warning, relates to resource allocation analysis technology, and specifically is a geological disaster early warning system based on multi-source sensor monitoring technology. Background Art

[0002] The geological disaster early warning system is a comprehensive prevention and control system that uses integrated sensors, communication technology, data analysis models, and other means to conduct real-time monitoring and risk warning of disasters such as landslides, collapses, and mudslides, aiming to reduce casualties and economic losses caused by disasters.

[0003] The invention patent with announcement number CN115424426A discloses a method for improving the accuracy of regional geological disaster early warning and forecast. The early warning method corrects the regional geological disaster meteorological early warning results, and through "point" feedback to "surface", it makes up for the deficiency of large differences in the degree of disaster occurrence in different regions under the action of rainfall, which helps to improve the accuracy of regional geological disaster meteorological early warning and forecast, and better serve geological disaster prevention and mitigation; however, the early warning method cannot optimize the resource allocation of key monitoring equipment according to the geological characteristics of different regions, and the existing technology generally adopts a uniform configuration method for sensor deployment. The configuration number of monitoring equipment does not match the geological disaster risk level of the monitoring area, resulting in the accuracy of monitoring results and the timeliness of alarms cannot be guaranteed.

[0004] In response to the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide a geological disaster early warning system based on multi-source sensor monitoring technology to solve the problem that the existing technology cannot optimize the resource allocation of key monitoring equipment according to the geological characteristics of different regions;

[0006] The technical problem to be solved by the present invention is: how to provide a geological disaster early warning system based on multi-source sensor monitoring technology that can optimize the resource allocation of key monitoring equipment according to the geological characteristics of different regions.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A geological disaster early warning system based on multi-source sensor monitoring technology includes a region segmentation module, a feature analysis module, an equipment configuration module, and a disaster monitoring module connected in sequence, wherein the feature analysis module, the equipment configuration module, and the disaster monitoring module are all connected to a database for communication;

[0009] The region segmentation module is used to segment the geological disaster warning area into several pre-processing areas by using a rectangular segmentation mode, a ring segmentation mode and a random segmentation mode respectively;

[0010] The slope values ​​in the pre-processed area obtained by using the rectangular segmentation mode, the annular segmentation mode, and the random segmentation mode are processed respectively to obtain the rectangular priority coefficient, the annular priority coefficient, and the random priority coefficient; the pre-processed area obtained by the segmentation mode corresponding to the minimum value of the rectangular priority coefficient, the annular priority coefficient, and the random priority coefficient is marked as the monitoring area;

[0011] The feature analysis module is used to analyze the geological characteristics of the monitoring area: obtain the matching range of key parameters and key sensor types of various types of geological hazards through the database, obtain the values ​​of key parameters corresponding to the geological hazard types in the monitoring area and mark them as matching values, compare the matching values ​​with the corresponding matching ranges, and mark the key sensors based on the comparison results;

[0012] The device configuration module is used to analyze the monitoring equipment configuration of the monitoring area: sensors are evenly distributed to each monitoring area according to type, and the number of sensors of the same type allocated to each monitoring area is equal. The configuration optimization process is performed based on the marking results of the sensors in the monitoring area;

[0013] The disaster monitoring module is used to perform geological disaster monitoring and analysis in geological disaster warning areas.

[0014] Furthermore, the specific process of regional segmentation using the rectangular segmentation mode includes: dividing the geological disaster warning area into several rectangular pre-processing areas through several mutually parallel horizontal dividing lines and mutually parallel vertical dividing lines, any horizontal dividing line is perpendicular to any vertical dividing line, the spacing between any adjacent horizontal dividing lines is equal, and the spacing between any adjacent vertical dividing lines is equal.

[0015] Furthermore, the specific process of regional segmentation using the annular segmentation mode includes: drawing a circle with the center point of the geological disaster warning area as the center, marking the obtained circular area and several annular areas as preprocessing areas, and the area value of the circular area is equal to the area value of all annular areas.

[0016] Furthermore, the specific process of adopting the random segmentation mode includes: randomly segmenting the geological disaster warning area into a number of closed pre-processing areas, and the area value of each pre-processing area is equal.

[0017] Furthermore, the process of obtaining the rectangular priority coefficient includes: marking the difference between the maximum slope value and the minimum slope value in the preprocessing area obtained by the rectangular segmentation mode as the rectangular smoothness value of the preprocessing area, summing and averaging the rectangular smoothness values ​​of all preprocessing areas obtained by the rectangular segmentation mode to obtain the rectangular priority coefficient; the data processing process of the annular priority coefficient and the random priority coefficient is the same as the data processing process of the rectangular priority coefficient.

[0018] Furthermore, the specific process of marking key sensors includes: determining whether the matching value of the monitoring area is within the matching range of the corresponding key parameter: if so, the key sensor corresponding to the geological disaster type is marked as the tendency equipment of the monitoring area; if not, the key sensor corresponding to the geological disaster type is marked as the conventional equipment of the monitoring area.

[0019] Furthermore, the specific process of configuration optimization processing includes: if the sensor is marked as a conventional device in the monitoring area, L1 corresponding sensors are extracted from the monitoring area as configuration optimization objects, and all configuration optimization objects corresponding to the same sensor type constitute a configuration optimization set; if the sensor is marked as a tendency device in the monitoring area, the corresponding monitoring area is marked as a tendency area, and all sensors in the configuration optimization set are evenly distributed to each tendency area; the allocated sensors are evenly deployed in the monitoring area for data collection and monitoring.

[0020] Furthermore, the specific process of the disaster monitoring module conducting geological disaster monitoring and analysis in the geological disaster warning area includes: collecting data through sensors in the monitoring area and marking them as monitoring values, retrieving the monitoring threshold corresponding to the sensor through the database, and comparing the monitoring value with the monitoring threshold: if the monitoring values ​​of all sensors in the monitoring area are less than the corresponding monitoring threshold, it is determined that the monitoring area does not have a geological disaster risk; otherwise, it is determined that the monitoring area has a geological disaster risk, generating a risk warning signal and sending the risk warning signal to the mobile phone terminal of the manager.

[0021] The present invention has the following beneficial effects:

[0022] 1. The regional segmentation module can be used to segment the geological disaster warning area. Multiple segmentation modes can be used to perform independent segmentation. Then, the slope difference of the pre-processed area obtained by a single segmentation mode is analyzed, and the monitoring area is marked to ensure the slope consistency of the monitoring area, providing data support for the feature analysis process.

[0023] 2. The feature analysis module can analyze the geological characteristics of the monitoring area, and differentiate the association between sensors and monitoring areas according to the matching range of key parameters of geological hazard types and key sensor types. Then, the sensor configuration of the monitoring area can be optimized and analyzed based on the differentiated labeling results, so that monitoring areas with higher risk levels can be allocated more corresponding types of sensor equipment, thereby improving the accuracy of early warning results for different types of geological hazards;

[0024] 3. The device configuration module can be used to analyze the configuration of monitoring equipment in the monitoring area. Based on the uniform configuration, the configuration optimization analysis is performed according to the correlation marking results between the sensors and the monitoring area. The optimized sensors are then evenly deployed within the monitoring area, taking into account the regional resource inclination and the uniformity of monitoring density within the area.

[0025] 4. The disaster monitoring module can be used to conduct geological disaster monitoring and analysis in geological disaster warning areas, compare the monitoring values ​​collected by the sensors with the corresponding monitoring thresholds, and directly feedback the geological disaster risk level in the monitoring area based on the comparison results. The timeliness of geological disaster alarms can be improved in the form of edge detection, reducing the probability of geological disasters and the impact after they occur. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0027] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0028] Figure 2 This is a flow chart of the method of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] Example 1: Figure 1As shown, a geological disaster early warning system based on multi-source sensor monitoring technology includes a region segmentation module, a feature analysis module, an equipment configuration module and a disaster monitoring module connected in sequence. The feature analysis module, the equipment configuration module and the disaster monitoring module are all connected to the database for communication.

[0031] The regional segmentation module is used to segment the geological disaster warning area: the rectangular segmentation mode, the circular segmentation mode and the random segmentation mode are used to segment the geological disaster warning area into several pre-processing areas;

[0032] The specific process of regional segmentation using the rectangular segmentation mode includes: dividing the geological disaster warning area into a number of rectangular pre-processing areas by a number of mutually parallel horizontal segmentation lines and mutually parallel vertical segmentation lines, any horizontal segmentation line is perpendicular to any vertical segmentation line, the spacing between any adjacent horizontal segmentation lines is equal, and the spacing between any adjacent vertical segmentation lines is equal;

[0033] The specific process of regional segmentation using the annular segmentation mode includes: drawing a circle with the center point of the geological disaster warning area as the center, marking the obtained circular area and several annular areas as pre-processing areas, and the area value of the circular area is equal to the area value of all annular areas;

[0034] The specific process of using the random segmentation model includes: randomly segmenting the geological disaster warning area into several closed pre-processing areas, and the area value of each pre-processing area is equal;

[0035] The difference between the maximum slope value and the minimum slope value in the preprocessing area obtained by the rectangular segmentation mode is marked as the rectangular smooth value of the preprocessing area, and the rectangular smooth values ​​of all preprocessing areas obtained by the rectangular segmentation mode are summed and averaged to obtain the rectangular priority coefficient; the difference between the maximum slope value and the minimum slope value in the preprocessing area obtained by the annular segmentation mode is marked as the annular smooth value of the preprocessing area, and the annular smooth values ​​of all preprocessing areas obtained by the annular segmentation mode are summed and averaged to obtain the annular priority coefficient; the difference between the maximum slope value and the minimum slope value in the preprocessing area obtained by the random segmentation mode is marked as the random smooth value of the preprocessing area, and the random smooth values ​​of all preprocessing areas obtained by the random segmentation mode are summed and averaged to obtain the random priority coefficient;

[0036] The pre-processed area obtained by the segmentation mode corresponding to the minimum value among the rectangular priority coefficient, the annular priority coefficient and the random priority coefficient is marked as the monitoring area; the geological disaster warning area is regionally segmented and independently segmented using multiple segmentation modes, and then the degree of slope difference of the pre-processed area obtained by a single segmentation mode is analyzed, and then the monitoring area is marked to ensure the slope consistency of the monitoring area and provide data support for the feature analysis process.

[0037] The feature analysis module is used to analyze the geological characteristics of the monitoring area: it obtains the matching range of key parameters and key sensor types of various types of geological hazards through the database, obtains the values ​​of key parameters corresponding to the geological hazard types in the monitoring area and marks them as matching values, and determines whether the matching values ​​of the monitoring area are within the matching range of the corresponding key parameters:

[0038] If so, the key sensor corresponding to the geological hazard type is marked as a tendency device in the monitoring area;

[0039] If not, the key sensors corresponding to the geological hazard type are marked as conventional equipment in the monitoring area;

[0040] For example, when the geological disaster type is "collapse", the corresponding key parameter is slope, and the matching range is (55 o , 75 o ) The key sensor is the laser displacement monitor, that is, the matching value (maximum slope) of the monitoring area is within the matching range (55 o , 75 o ), the laser displacement monitor is marked as a tilt device in the monitoring area; the matching value (maximum slope) of the monitoring area is within the matching range (55 o , 75 o ), the laser displacement monitor is marked as a conventional device in the monitoring area; when the geological disaster type is "landslide", the corresponding key parameter is slope, and the matching range is (30 o , 55 o ), the key sensor is the surface settlement displacement monitor, that is, the matching value (maximum slope) of the monitoring area is within the matching range (30 o , 55 o ), the surface settlement displacement monitor is marked as a tilt device in the monitoring area; the matching value (maximum slope) of the monitoring area is within the matching range (30 o , 55 o ), the surface settlement displacement monitor is marked as a regular device in the monitoring area.

[0041] The geological characteristics of the monitoring area are analyzed, and the association between sensors and monitoring areas is differentially marked according to the matching range of key parameters of geological hazard types and key sensor types. Then, the sensor configuration of the monitoring area is optimized and analyzed based on the differentiated marking results, so that monitoring areas with higher risk levels can be allocated more corresponding types of sensor equipment, thereby improving the accuracy of early warning results for different types of geological hazards.

[0042] The equipment configuration module is used to perform monitoring equipment configuration analysis on the monitoring area: sensors are evenly distributed to each monitoring area according to their type, and the number of sensors of the same type allocated to each monitoring area is equal; if a sensor is marked as a conventional device in the monitoring area, L1 corresponding sensors are extracted from the monitoring area as configuration optimization objects, where L1 is a numerical constant and the specific value of L1 is set by the management personnel. All configuration optimization objects corresponding to the same sensor type constitute a configuration optimization set; if a sensor is marked as a tendency device in the monitoring area, the corresponding monitoring area is marked as a tendency area, and all sensors in the configuration optimization set are evenly distributed to each tendency area; the allocated sensors are evenly deployed in the monitoring area for data collection and monitoring; monitoring equipment configuration analysis is performed on the monitoring area, and configuration optimization analysis is performed based on the correlation marking results between the sensors and the monitoring area on the basis of uniform configuration, and then the optimized sensors are evenly deployed within the monitoring area, taking into account both regional resource inclination and uniformity of monitoring density within the area.

[0043] The disaster monitoring module is used to monitor and analyze geological disasters in geological disaster warning areas: data is collected through sensors in the monitoring area and marked as monitoring values, the monitoring threshold corresponding to the sensor is retrieved through the database, and the monitoring value is compared with the monitoring threshold: if the monitoring values ​​of all sensors in the monitoring area are less than the corresponding monitoring threshold, the monitoring area is judged to have no geological disaster risk; otherwise, the monitoring area is judged to have geological disaster risk, a risk warning signal is generated and sent to the mobile phone terminal of the manager; geological disaster monitoring and analysis is carried out in the geological disaster warning area, the monitoring values ​​collected by the sensor are compared with the corresponding monitoring threshold and the geological disaster risk level of the monitoring area is directly fed back based on the comparison results, so as to improve the timeliness of geological disaster alarms in the form of edge detection and reduce the probability of occurrence of geological disasters and the impact after occurrence.

[0044] Example 2: Figure 2 As shown, a geological disaster early warning method based on multi-source sensor monitoring technology includes the following steps:

[0045] Step 1: Segment the geological hazard warning area into several pre-processing areas using the rectangular segmentation mode, the circular segmentation mode, and the random segmentation mode respectively. Then, the rectangular priority coefficient, the circular priority coefficient, and the random priority coefficient are compared and the monitoring area is marked.

[0046] Step 2: Analyze the geological characteristics of the monitoring area: Obtain the matching range of key parameters and key sensor types for each type of geological disaster through the database, compare the matching values ​​of key parameters corresponding to the geological disaster type in the monitoring area with the matching range, and mark the key sensors as inclined devices or conventional devices based on the comparison results;

[0047] Step 3: Analyze the monitoring equipment configuration for the monitoring area: Evenly distribute sensors to each monitoring area according to their type, and optimize the number of sensors allocated in the monitoring area based on the marking results of conventional and preferred devices;

[0048] Step 4: Conduct geological disaster monitoring and analysis in the geological disaster warning area: collect data through sensors in the monitoring area and mark it as monitoring values, compare the monitoring values ​​with the corresponding monitoring thresholds, and determine whether the monitoring area has geological disaster risks based on the comparison results.

[0049] A geological disaster early warning system based on multi-source sensor monitoring technology, when in operation, adopts rectangular segmentation mode, annular segmentation mode and random segmentation mode to divide the geological disaster early warning area into several pre-processing areas, compares the rectangular priority coefficient, the annular priority coefficient and the random priority coefficient and marks the monitoring area; obtains the matching range of key parameters of various types of geological disasters and key sensor types through a database, compares the matching value of the key parameter corresponding to the geological disaster type in the monitoring area with the matching range, and marks the key sensor as a tendency device or a conventional device based on the comparison result; evenly distributes sensors to each monitoring area according to type, and optimizes the distribution number of sensors in the monitoring area based on the marking results of conventional devices and tendency devices; collects data from sensors in the monitoring area and marks it as monitoring values, compares the monitoring values ​​with corresponding monitoring thresholds, and determines whether the monitoring area has geological disaster risks based on the comparison results.

[0050] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0051] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0052] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A geological disaster early warning system based on multi-source sensor monitoring technology, characterized in that: It includes a region segmentation module, a feature analysis module, a device configuration module and a disaster monitoring module connected in sequence, wherein the feature analysis module, the device configuration module and the disaster monitoring module are all connected to the database in communication; The region segmentation module is used to segment the geological disaster warning area into several pre-processing areas by using a rectangular segmentation mode, a ring segmentation mode and a random segmentation mode respectively; The slope values ​​in the pre-processed area obtained by using the rectangular segmentation mode, the annular segmentation mode, and the random segmentation mode are processed respectively to obtain the rectangular priority coefficient, the annular priority coefficient, and the random priority coefficient; the pre-processed area obtained by the segmentation mode corresponding to the minimum value of the rectangular priority coefficient, the annular priority coefficient, and the random priority coefficient is marked as the monitoring area; The feature analysis module is used to analyze the geological characteristics of the monitoring area: obtain the matching range of key parameters and key sensor types of various types of geological hazards through the database, obtain the values ​​of key parameters corresponding to the geological hazard types in the monitoring area and mark them as matching values, compare the matching values ​​with the corresponding matching ranges, and mark the key sensors based on the comparison results; The device configuration module is used to analyze the monitoring equipment configuration of the monitoring area: sensors are evenly distributed to each monitoring area according to type, and the number of sensors of the same type allocated to each monitoring area is equal. The configuration optimization process is performed based on the marking results of the sensors in the monitoring area; The disaster monitoring module is used to perform geological disaster monitoring and analysis in geological disaster warning areas.

2. A geological disaster early warning system based on multi-source sensor monitoring technology according to claim 1, characterized in that: The specific process of regional segmentation using the rectangular segmentation mode includes: dividing the geological disaster warning area into several rectangular pre-processing areas through several mutually parallel horizontal dividing lines and mutually parallel vertical dividing lines, any horizontal dividing line is perpendicular to any vertical dividing line, the spacing between any adjacent horizontal dividing lines is equal, and the spacing between any adjacent vertical dividing lines is equal.

3. A geological disaster early warning system based on multi-source sensor monitoring technology according to claim 2, characterized in that: The specific process of regional segmentation using the annular segmentation mode includes: drawing a circle with the center point of the geological disaster warning area as the center, marking the resulting circular area and several annular areas as preprocessing areas, and the area value of the circular area is equal to the area value of all annular areas.

4. A geological disaster early warning system based on multi-source sensor monitoring technology according to claim 3, characterized in that: The specific process of using the random segmentation model includes: randomly dividing the geological disaster warning area into several closed pre-processing areas, and the area value of each pre-processing area is equal.

5. The geological disaster early warning system based on multi-source sensor monitoring technology according to claim 4 is characterized in that: The process of obtaining the rectangular priority coefficient includes: marking the difference between the maximum slope value and the minimum slope value in the preprocessing area obtained by the rectangular segmentation mode as the rectangular smoothing value of the preprocessing area, summing and averaging the rectangular smoothing values ​​of all preprocessing areas obtained by the rectangular segmentation mode to obtain the rectangular priority coefficient; the data processing process of the annular priority coefficient and the random priority coefficient is the same as the data processing process of the rectangular priority coefficient.

6. A geological disaster early warning system based on multi-source sensor monitoring technology according to claim 5, characterized in that: The specific process of marking key sensors includes: determining whether the matching value of the monitoring area is within the matching range of the corresponding key parameter: if so, the key sensor corresponding to the geological disaster type is marked as the tendency equipment of the monitoring area; if not, the key sensor corresponding to the geological disaster type is marked as the conventional equipment of the monitoring area.

7. The geological disaster early warning system based on multi-source sensor monitoring technology according to claim 6 is characterized in that: The specific process of configuration optimization processing includes: if the sensor is marked as a conventional device in the monitoring area, L1 corresponding sensors are extracted from the monitoring area as configuration optimization objects, and all configuration optimization objects corresponding to the same sensor type constitute a configuration optimization set; if the sensor is marked as a tendency device in the monitoring area, the corresponding monitoring area is marked as a tendency area, and all sensors in the configuration optimization set are evenly distributed to each tendency area; the allocated sensors are evenly deployed in the monitoring area for data collection and monitoring.

8. The geological disaster early warning system based on multi-source sensor monitoring technology according to claim 7 is characterized in that: The specific process of the disaster monitoring module conducting geological disaster monitoring and analysis in the geological disaster warning area includes: collecting data through sensors in the monitoring area and marking them as monitoring values, retrieving the monitoring threshold corresponding to the sensor through the database, and comparing the monitoring value with the monitoring threshold: if the monitoring values ​​of all sensors in the monitoring area are less than the corresponding monitoring threshold, it is determined that the monitoring area does not have a geological disaster risk; otherwise, it is determined that the monitoring area has a geological disaster risk, generating a risk warning signal and sending the risk warning signal to the mobile phone terminal of the manager.

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