CN115273411B - Geological disaster monitoring and early warning method and system, electronic equipment and storage medium - Google Patents
Geological disaster monitoring and early warning method and system, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a geological disaster monitoring and early warning method, a geological disaster monitoring and early warning system, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring data in a preset time period to obtain a monitoring data packet; judging whether the data in at least one monitoring data class in the multiple monitoring data classes is not less than a first preset threshold value; if yes, acquiring M groups of monitoring data groups again, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, and calculating the fitting linearity of the M groups of monitoring data groups and the N groups of historical monitoring data groups; judging whether the fitting linearity is not less than a second preset threshold value or not; if yes, the last group of monitoring data groups in the M groups of monitoring data groups are used as uploading data groups to upload a management platform to trigger an early warning signal, and after early warning, the monitoring equipment is triggered to enter a dormant state. Through the application, the purpose of reducing the occurrence of the geological disaster early warning and false alarm phenomena can be achieved.
Description
Technical Field
The invention belongs to the technical field of landslide geological disaster monitoring and early warning, and particularly relates to a geological disaster monitoring and early warning method, a geological disaster monitoring and early warning system, electronic equipment and a storage medium.
Background
Landslide accidents in geological disasters occur frequently, and means for preventing and monitoring landslide are mainly carried out by manually inspecting and installing corresponding monitoring and early warning equipment. The existing general automatic monitoring equipment used for landslide geological disasters can immediately report and collect abnormal signals and push the abnormal signals to a management platform for early warning once abnormal data occurs in a single data collection process. However, such collected data is susceptible to interference from external environmental factors during collection and influence of self factors of the collection device during collection, which results in a phenomenon of early warning and false alarm.
Therefore, how to discriminate, analyze and process the data acquired in the landslide geological disaster monitoring process is to improve the defect of early warning and false alarm to a certain extent in the existing landslide geological disaster monitoring.
Disclosure of Invention
In order to solve the technical problems, the invention provides a geological disaster monitoring and early warning method, a geological disaster monitoring and early warning system, an electronic device and a storage medium, wherein a first threshold value is judged according to the parameter type aiming at collected monitoring data, the monitoring data is collected again under the condition that the judgment of the first threshold value exists, then a preset mechanism comprising a bubble sorting method and a linear fitting method is triggered, the newly collected monitoring data is processed, and a second threshold value is judged according to the fitting linearity of the newly collected monitoring data and a historical monitoring data group, so that the aim of reducing the occurrence of an early warning false alarm phenomenon is fulfilled.
In a first aspect, the invention provides a geological disaster monitoring and early warning method, which comprises the following steps:
acquiring data in a preset time period to obtain a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes consisting of the same type of parameters;
judging whether the data in at least one monitoring data class in the multiple monitoring data classes is not less than a first preset threshold value;
if yes, acquiring M groups of monitoring data groups again, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, and calculating the fitting linearity of the M groups of monitoring data groups and N groups of historical monitoring data groups; the preset mechanism comprises a bubble sorting method and a linear fitting method;
judging whether the fitting linearity is not less than a second preset threshold value;
if yes, the last group of monitoring data group in the M groups of monitoring data groups is used as an uploading data group uploading management platform to trigger an early warning signal, and after early warning, monitoring equipment is triggered to enter a dormant state.
Preferably, the step of acquiring data within a preset time period to obtain the monitoring data packet specifically includes:
when the monitoring equipment acquires a wake-up instruction, initializing the monitoring equipment;
acquiring data once every preset time within a preset time period to acquire a monitoring data packet;
and classifying the categories based on the parameter categories in the monitoring data packet to obtain multiple monitoring data categories composed of the same parameter categories.
Preferably, the step of re-acquiring M groups of monitoring data sets, triggering data processing of a preset mechanism for the M groups of monitoring data sets, and calculating the fitting linearity with N groups of historical monitoring data sets specifically includes:
re-collecting M groups of monitoring data groups to obtain M types of monitoring data groups composed of the same type of parameters;
sorting the data in the M types of monitoring data classes based on a bubble sorting method, and selecting the monitoring data in the M types of monitoring data classes, which are sorted in the middle position, to combine the monitoring data into a target monitoring data group;
and performing linear fitting on the target monitoring data set and the N groups of historical monitoring data sets so as to calculate the fitting linearity between the target monitoring data set and the N groups of historical monitoring data sets.
Preferably, the N groups of historical monitoring data sets are monitoring data acquired by the M groups of monitoring data sets under the same acquisition environment.
Preferably, after the step of determining whether data in at least one of the monitoring data classes is not less than a first preset threshold, the method further includes:
and if the data in the multiple monitoring data classes is judged to be smaller than a first preset threshold value, the monitoring data packet is used as an uploading data group to be uploaded to a management platform, and meanwhile, the monitoring equipment is triggered to enter a dormant state.
Preferably, after the step of determining whether the fitting linearity is not less than a second preset threshold, the method further includes:
and if the fitting linearity is judged to be smaller than a second preset threshold value, taking the monitoring data group acquired before the preset time period as an uploading data group uploading management platform, and simultaneously triggering the monitoring equipment to enter a dormant state.
Preferably, the categories of the multiple monitoring data categories include a combination of at least two of a groundwater level parameter category, a relative displacement parameter category, a fracture parameter category, a pore water pressure category and a stress parameter category.
In a second aspect, the invention provides a geological disaster monitoring and early warning system, comprising:
the acquisition module is used for acquiring data within a preset time period to acquire a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes consisting of the same type of parameters;
the first judging module is used for judging whether the data in at least one monitoring data class in the multiple monitoring data classes is not smaller than a first preset threshold value;
the data processing module is used for re-collecting M groups of monitoring data sets if the data in at least one monitoring data set in the multiple monitoring data sets is judged to be not smaller than a first preset threshold value, triggering data processing of a preset mechanism aiming at the M groups of monitoring data sets, and calculating the fitting linearity of the N groups of historical monitoring data sets; the preset mechanism comprises a bubble sorting method and a linear fitting method;
the second judging module is used for judging whether the fitting linearity is not less than a second preset threshold value or not;
and the first monitoring module is used for taking the last group of monitoring data groups in the M groups of monitoring data groups as an uploading data group to upload a management platform to trigger an early warning signal if the fitting linearity is judged to be not less than a second preset threshold value, and triggering the monitoring equipment to enter a dormant state after early warning.
Preferably, the acquisition module comprises:
the device comprises an initialization unit, a receiving unit and a sending unit, wherein the initialization unit is used for carrying out initialization operation on monitoring equipment when the monitoring equipment acquires a wake-up instruction;
the first acquisition unit is used for acquiring data once every other preset time within a preset time period so as to acquire a monitoring data packet;
and the classification unit is used for classifying the classes based on the parameter classes in the monitoring data packet so as to obtain multiple monitoring data classes consisting of the same parameter classes.
Preferably, the data processing module includes:
the second acquisition unit is used for re-acquiring the M groups of monitoring data groups to acquire M monitoring data classes composed of the same type of parameters;
the selecting unit is used for sorting data in the M types of monitoring data based on a bubbling sorting method, and selecting the monitoring data with the sorting of each type of monitoring data in the M types of monitoring data in a middle position to form a target monitoring data set;
and the linear fitting unit is used for performing linear fitting on the target monitoring data set and the N groups of historical monitoring data sets so as to calculate the fitting linearity between the target monitoring data set and the N groups of historical monitoring data sets.
Preferably, the system further comprises:
and the second monitoring module is used for taking the monitoring data packet as an uploading data group to be uploaded to the management platform and simultaneously triggering the monitoring equipment to enter a dormant state if the fact that the data in the various monitoring data classes are smaller than a first preset threshold value is judged.
Preferably, the system further comprises:
and the third monitoring module is used for taking the monitoring data group acquired before the preset time period as an uploading data group uploading management platform and simultaneously triggering the monitoring equipment to enter a dormant state if the fitting linearity is judged to be smaller than a second preset threshold value.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the geological disaster monitoring and early warning method according to the first aspect is implemented.
In a fourth aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, where the program is executed by a processor, and the geological disaster monitoring and early warning method according to the first aspect is implemented.
Compared with the prior art, the geological disaster monitoring and early warning method, the geological disaster monitoring and early warning system, the electronic equipment and the storage medium provided by the application acquire the monitoring data packets of various monitoring data types consisting of the same type of parameters by acquiring data within a preset time period; according to the judgment of whether the data in at least one monitoring data class in the multiple monitoring data classes is not smaller than a first preset threshold value, different program selections are made: if the data in at least one monitoring data class in the multiple monitoring data classes is judged to be not smaller than a first preset threshold value, acquiring M groups of monitoring data groups again, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, calculating the fitting linearity of the monitoring data groups and N groups of historical monitoring data groups, or if the data in the multiple monitoring data classes is judged to be smaller than the first preset threshold value, taking the monitoring data packet as an uploading data group to be uploaded to a management platform, and simultaneously triggering the monitoring equipment to enter a dormant state; and then according to whether the fitting linearity is judged to be not less than a second preset threshold value, making different program selections: and if the fitting linearity is judged to be not less than a second preset threshold value, the last group of monitoring data groups in the M groups of monitoring data groups is used as an uploading data group to upload a management platform to trigger an early warning signal, and the monitoring equipment is triggered to enter a dormant state after early warning, or if the fitting linearity is judged to be less than the second preset threshold value, the monitoring data groups collected before the preset time period are used as the uploading data group to upload the management platform, and the monitoring equipment is triggered to enter the dormant state. According to the method and the device, the first threshold value judgment is carried out according to the parameter types aiming at the collected monitoring data, the monitoring data are collected again under the condition that the first threshold value judgment is judged, then the preset mechanism comprising a bubble sorting method and a linear fitting method is triggered, the monitoring data are collected again, the second threshold value judgment is carried out aiming at the fitting linearity of the collected monitoring data and the historical monitoring data set, and therefore the purpose of reducing the occurrence of the early warning and false alarm phenomena is achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of a geological disaster monitoring and early warning method provided in embodiment 1 of the present invention;
fig. 2 is a flowchart of a specific step of step S101 provided in embodiment 1 of the present invention;
fig. 3 is a flowchart illustrating a specific step of step S103 according to embodiment 1 of the present invention;
fig. 4 is a structural block diagram of a geological disaster monitoring and early warning system corresponding to the method in embodiment 1, provided by embodiment 2 of the present invention;
fig. 5 is a flowchart of a geological disaster monitoring and early warning method provided in embodiment 3 of the present invention;
fig. 6 is a structural block diagram of a geological disaster monitoring and early warning system corresponding to the method in embodiment 3 provided in embodiment 4 of the present invention;
fig. 7 is a flowchart of a geological disaster monitoring and early warning method provided in embodiment 5 of the present invention;
fig. 8 is a structural block diagram of a geological disaster monitoring and early warning system corresponding to the method in embodiment 5 provided in embodiment 6 of the present invention;
fig. 9 is a schematic diagram of a hardware structure of an electronic device provided in embodiment 7 of the present invention.
Description of the reference numerals:
10-an acquisition module, 11-an initialization unit, 12-a first acquisition unit and 13-a classification unit;
20-a first judgment module;
30-a data processing module, 31-a second acquisition unit, 32-a selection unit and 33-a linear fitting unit;
40-a second judging module;
50-a first monitoring module;
60-a second monitoring module;
70-a third monitoring module;
80-bus, 81-processor, 82-memory, 83-communication interface.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The existing geological disaster monitoring and early warning arranges a large amount of professional monitoring equipment on a geological disaster site, and realizes self-transmission of data. The existing general automatic monitoring equipment used for landslide geological disasters can immediately report and collect abnormal signals and push the abnormal signals to a management platform for early warning once abnormal data occurs in a single data collection process. However, the collected data is easily interfered by external environment factors during collection, and the influence of the self factors of the collecting equipment during collection, so that the phenomenon of early warning and false alarm is caused. The present application is based on this proposal.
Example 1
Specifically, fig. 1 is a schematic flow chart of a geological disaster monitoring and early warning method provided in this embodiment.
As shown in fig. 1, the geological disaster monitoring and early warning method of the embodiment includes the following steps:
s101, acquiring data in a preset time period to obtain a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes composed of the same kind of parameters.
Specifically, the categories of the multiple monitoring data categories include a combination of at least two of a groundwater level parameter category, a relative displacement parameter category, a fracture parameter category, a pore water pressure category and a stress parameter category. In this embodiment, the multiple monitoring data classes include combinations of three types of parameters, i.e., a relative displacement class, a fracture class, and a pore water pressure class. The purpose of monitoring multiple types of monitoring data is to weaken the influence of external factors on monitored landslide related monitoring data, such as the influence of impact of external animals on monitoring equipment on the correctness of relative displacement data, or the influence of external natural environments in sunny days and rainy days on the correctness of crack parameter data and pore water pressure data; based on the influence of the external factors, the embodiment avoids the influence of the external factors on the related monitoring data as much as possible by detecting various monitoring data classes, so as to ensure the reliability of the monitoring data, which means that the accuracy of the monitoring and early warning of the monitored mountain is improved.
Further, as shown in fig. 2, the specific steps of step S101 include:
and S1011, when the monitoring equipment acquires the awakening instruction, performing initialization operation on the monitoring equipment.
Specifically, because the monitored object is a mountain, and the landslide monitoring and early warning is a long-time and progressive process for natural disasters of landslide, the monitored monitoring device of the mountain cannot be in a working state for a long time, and the dormant state is a normal state of the monitoring device, so that the monitoring device can be used for a long time under the condition that the reserved electric quantity is allowed. Therefore, in the using process of the monitoring equipment, the related monitoring data of the monitored mountain is collected by adopting an interval wake-up mode as much as possible. Of course, in order to ensure the reliability of the data collected once, an initialization operation is required after the monitoring device wakes up each time.
And S1012, performing data acquisition once every other preset time within a preset time period to obtain a monitoring data packet.
Specifically, the data analysis set in this embodiment selects three parameters, namely the relative displacement parameter, the fracture parameter and the pore water pressure parameter, so that after the monitoring device is awakened, the acquired monitoring data includes the selected three parameters at each time, the data of the primary acquisition amount of the monitoring device is a monitoring data packet including the relative displacement parameter, the fracture parameter and the pore water pressure parameter, and the acquired monitoring data packet is cached in advance to be processed.
And S1013, classifying the types based on the parameter types in the monitoring data packet to obtain multiple monitoring data types composed of the same parameter types.
Specifically, the classification method can perform class classification based on the unit of the parameter and the decimal point number; in this embodiment, the pore water pressure type can be distinguished from the relative displacement type and the fracture type by using the kPa as a parameter unit, and the relative displacement type and the fracture type can be distinguished by using the decimal point number of the parameter. And classifying the collected monitoring data packet into pore water pressure data, relative displacement data and crack parameters through the classification.
S102, judging whether the data in at least one monitoring data class in the multiple monitoring data classes is not smaller than a first preset threshold value.
Specifically, the monitoring data collected in this embodiment includes pore water pressure, relative displacement, and crack; and according to whether the data in at least one of the three types of monitoring data is larger than a first preset threshold value set for the monitoring data, making corresponding program selection which needs further judgment and does not need further judgment. For example, a threshold value of 1mm is set for the relative displacement class, a threshold value of 3mm is set for the fracture class, and a threshold value of 5000kPa is set for the pore water pressure class.
And S103, if yes, acquiring M groups of monitoring data groups again, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, and calculating the fitting linearity of the M groups of monitoring data groups and the N groups of historical monitoring data groups.
The preset mechanism comprises a bubble sorting method and a linear fitting method. Specifically, bubble sort refers to repeatedly walking through the series to be sorted, comparing two elements at a time, and swapping them if their order is wrong; the task of walking through the sequence is repeated until no more exchanges are required, meaning that the sequence has been sorted to completion. In the embodiment, the reliability of the acquired data is represented by calculating the fitting linearity through a linear fitting method.
Further, as shown in fig. 3, the specific steps of step S103 include:
and S1031, re-collecting the M groups of monitoring data groups to obtain M types of monitoring data groups composed of the same type of parameters.
Specifically, the re-collecting means that after it is determined that data in at least one type of monitoring data in the multiple types of monitoring data collected this time is not smaller than a first preset threshold, a monitoring data packet is re-collected after the data is collected this time, where the monitoring data packet includes M monitoring data groups, and the M types of monitoring data are obtained through classification processing by the method in step S1013.
S1032, sorting processing is carried out on the data in the M classes of monitoring data based on a bubble sorting method, and monitoring data which are sorted in the middle position of each class of monitoring data in the M classes of monitoring data are selected and combined into a target monitoring data set.
Specifically, in this embodiment, the monitoring data sorted in the middle position is selected from each type of monitoring data, and then the selected middle position data of each type of monitoring data is recombined to form a target monitoring data set containing three types of data, namely, pore water pressure data, relative displacement data and fracture data, so as to reduce the influence of external relevant factors and improve the accuracy of the collected data.
And S1033, performing linear fitting on the target monitoring data group and the N groups of historical monitoring data groups to calculate the fitting linearity between the target monitoring data group and the N groups of historical monitoring data groups.
And the N groups of historical monitoring data groups are monitoring data acquired by the M groups of monitoring data groups under the same condition of acquisition environment. Specifically, the target monitoring data set with smaller error is obtained by performing bubbling sequencing on the M groups of monitoring data sets which are re-collected, and then the target monitoring data set and the N groups of historical monitoring data sets are subjected to linear fitting processing, so that the reliability of the collected data is further improved.
And S104, judging whether the fitting linearity is not less than a second preset threshold value.
Specifically, according to the comparison between the fitting linearity and a second preset threshold, making corresponding program judgment; such as: and when the linearity of the data fitting is not less than a second preset threshold, the relevance is judged to be high, the reliability of the data is high, and the acquired value can be used for judgment and early warning. Or when the linearity of the data fitting is smaller than a second preset threshold value, namely the relevance is judged to be low, and the reliability of the data is low, no early warning is carried out. In different embodiments, the number of the M groups, the number of the N groups of historical monitoring data groups, and the specific size of the second preset threshold that are newly collected are selected according to actual situations.
And S105, if yes, using the last group of monitoring data group in the M groups of monitoring data groups as an uploading data group uploading management platform to trigger an early warning signal, and triggering the monitoring equipment to enter a dormant state after early warning.
Specifically, when the linearity of data fitting is not less than a second preset threshold, namely the relevance is determined to be high, the data reliability is high, the newly acquired value can be used as the determination, and because the difference between the newly acquired M groups of monitoring data sets is small, the last group of the M groups of monitoring data sets can be selected as an uploaded data set to be uploaded, and early warning is triggered to prompt monitoring personnel to perform corresponding treatment.
In summary, the data acquisition is performed within the preset time period to obtain the monitoring data packets of multiple monitoring data types including the same type of parameters; judging that the data in at least one monitoring data class in the multiple monitoring data classes is not smaller than a first preset threshold value, re-collecting M groups of monitoring data groups, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, and calculating the fitting linearity of the N groups of historical monitoring data groups; and then judging that the fitting linearity is not less than a second preset threshold value, taking the last group of monitoring data groups in the M groups of monitoring data groups as an uploading data group to upload a management platform to trigger an early warning signal, and triggering the monitoring equipment to enter a dormant state after early warning so as to achieve the purpose of reducing the occurrence of early warning false alarms.
Example 2
This embodiment provides a block diagram of a system corresponding to the method described in embodiment 1. Fig. 4 is a block diagram of a geological disaster monitoring and early warning system according to an embodiment of the present disclosure, and as shown in fig. 4, the system includes:
the acquisition module 10 is used for acquiring data within a preset time period to acquire a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes consisting of the same type of parameters;
the first judging module 20 is configured to judge whether data in at least one monitoring data class of the multiple monitoring data classes is not smaller than a first preset threshold;
the data processing module 30 is configured to, if it is determined that data in at least one type of monitoring data in the multiple types of monitoring data is not smaller than a first preset threshold, reacquire M sets of monitoring data sets, trigger data processing of a preset mechanism for the M sets of monitoring data sets, and calculate fitting linearity with N sets of historical monitoring data sets; the preset mechanism comprises a bubble sorting method and a linear fitting method;
a second determining module 40, configured to determine whether the fitting linearity is not less than a second preset threshold;
and the first monitoring module 50 is configured to, if it is determined that the fitting linearity is not less than a second preset threshold, use the last monitoring data group of the M monitoring data groups as an upload data group to the management platform to trigger an early warning signal, and trigger the monitoring device to enter a sleep state after early warning.
Further, the acquisition module 10 includes:
an initialization unit 11, configured to perform an initialization operation on a monitoring device when the monitoring device obtains a wake-up instruction;
the first acquisition unit 12 is configured to perform data acquisition once every preset time interval in a preset time period to obtain a monitoring data packet;
and the classification unit 13 is configured to perform class classification based on the parameter classes in the monitoring data packet to obtain multiple monitoring data classes composed of the same parameter class.
Further, the data processing module 30 includes:
the second acquisition unit 31 is configured to reacquire the M groups of monitoring data sets to obtain M types of monitoring data sets composed of similar parameters;
the selecting unit 32 is configured to perform sorting processing on the data in the M types of monitoring data based on a bubble sorting method, and select and combine monitoring data in the M types of monitoring data, which are sorted in a middle position, into a target monitoring data set;
and a linear fitting unit 33, configured to perform linear fitting on the target monitoring data set and the N sets of historical monitoring data sets, so as to calculate a fitting linearity between the target monitoring data set and the N sets of historical monitoring data sets.
It should be noted that the above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules may be located in different processors in any combination.
Example 3
Specifically, fig. 5 is a schematic flow chart of the geological disaster monitoring and early warning method provided in this embodiment.
As shown in fig. 5, the geological disaster monitoring and early warning method of the embodiment includes the following steps:
s201, acquiring data in a preset time period to obtain a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes composed of similar parameters.
Specifically, the specific steps of step S201 include:
when the monitoring equipment acquires a wake-up instruction, initializing the monitoring equipment;
acquiring data once every preset time within a preset time period to acquire a monitoring data packet;
and classifying the classes based on the parameter classes in the monitoring data packet to obtain multiple monitoring data classes consisting of the same parameter classes.
S202, judging whether the data in at least one monitoring data class in the multiple monitoring data classes is not smaller than a first preset threshold value;
and S203, if not, taking the monitoring data packet as an uploading data group to be uploaded to a management platform, and simultaneously triggering the monitoring equipment to enter a dormant state.
In summary, data acquisition is performed within a preset time period to obtain a monitoring data packet comprising a plurality of monitoring data types composed of similar parameters; and if the data in the multiple monitoring data classes is judged to be smaller than the first preset threshold value, the monitoring data packet is used as an uploading data group to be uploaded to a management platform, and meanwhile, the monitoring equipment is triggered to enter a dormant state, so that the purpose of reducing the occurrence of early warning and false alarm is achieved.
Example 4
This embodiment provides a block diagram of a system corresponding to the method described in embodiment 3. Fig. 6 is a block diagram of a geological disaster monitoring and early warning system according to an embodiment of the present disclosure, and as shown in fig. 6, the system includes:
the acquisition module 10 is used for acquiring data within a preset time period to acquire a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes consisting of the same type of parameters;
the first judging module 20 is configured to judge whether data in at least one monitoring data class of the multiple monitoring data classes is not smaller than a first preset threshold;
and the second monitoring module 60 is configured to, if it is determined that data in the multiple monitoring data classes is smaller than a first preset threshold, upload the monitoring data packet as an upload data group to the management platform, and simultaneously trigger the monitoring device to enter a dormant state.
Further, the acquisition module 10 includes:
the initialization unit 11 is configured to perform initialization operation on the monitoring device when the monitoring device obtains a wake-up instruction;
the first acquisition unit 12 is configured to perform data acquisition once every preset duration within a preset time period to acquire a monitoring data packet;
and the classification unit 13 is configured to perform class classification based on the parameter classes in the monitoring data packet to obtain multiple monitoring data classes composed of the same parameter class.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
Example 5
Specifically, fig. 7 is a schematic flow chart of the geological disaster monitoring and early warning method provided in this embodiment.
As shown in fig. 7, the geological disaster monitoring and early warning method of the embodiment includes the following steps:
s301, acquiring data in a preset time period to obtain a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes composed of the same kind of parameters.
Specifically, the specific steps of step S301 include:
when the monitoring equipment acquires a wake-up instruction, initializing the monitoring equipment;
acquiring data once every preset time within a preset time period to acquire a monitoring data packet;
and classifying the classes based on the parameter classes in the monitoring data packet to obtain multiple monitoring data classes consisting of the same parameter classes.
S302, judging whether the data in at least one monitoring data class in the multiple monitoring data classes is not smaller than a first preset threshold value.
S303, if yes, acquiring M groups of monitoring data groups again, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, and calculating the fitting linearity with N groups of historical monitoring data groups; the preset mechanism comprises a bubble sorting method and a linear fitting method.
Specifically, the specific steps of step S303 include:
re-collecting M groups of monitoring data groups to obtain M types of monitoring data groups composed of the same type of parameters;
sorting the data in the M types of monitoring data classes based on a bubble sorting method, and selecting the monitoring data in the M types of monitoring data classes, which are sorted in the middle position, to combine the monitoring data into a target monitoring data group;
and performing linear fitting on the target monitoring data set and the N groups of historical monitoring data sets to calculate the fitting linearity between the target monitoring data set and the N groups of historical monitoring data sets.
S304, judging whether the fitting linearity is not less than a second preset threshold value;
and S305, if not, taking the monitoring data group acquired before the preset time period as an uploading data group uploading management platform, and simultaneously triggering the monitoring equipment to enter a dormant state.
In summary, data acquisition is performed within a preset time period to obtain a monitoring data packet comprising a plurality of monitoring data types composed of similar parameters; judging that the data in at least one monitoring data class in the multiple monitoring data classes is not smaller than a first preset threshold value, re-collecting M groups of monitoring data groups, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, and calculating the fitting linearity of the N groups of historical monitoring data groups; and then judging that the fitting linearity is smaller than a second preset threshold value, taking the monitoring data set acquired before the preset time period as an uploading data set uploading management platform, and simultaneously triggering the monitoring equipment to enter a dormant state so as to achieve the purpose of reducing the occurrence of early warning and false alarm.
Example 6
This embodiment provides a block diagram of a system corresponding to the method described in embodiment 5. Fig. 8 is a block diagram of a geological disaster monitoring and early warning system according to an embodiment of the present application, and as shown in fig. 8, the system includes:
the acquisition module 10 is used for acquiring data within a preset time period to acquire a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes consisting of the same type of parameters;
the first judging module 20 is configured to judge whether data in at least one monitoring data class of the multiple monitoring data classes is not smaller than a first preset threshold;
the data processing module 30 is configured to, if it is determined that data in at least one type of monitoring data in the multiple types of monitoring data is not smaller than a first preset threshold, reacquire M groups of monitoring data sets, trigger data processing of a preset mechanism for the M groups of monitoring data sets, and calculate a fitting linearity with the N groups of historical monitoring data sets; the preset mechanism comprises a bubble sorting method and a linear fitting method;
a second determining module 40, configured to determine whether the fitting linearity is not less than a second preset threshold;
and the third monitoring module 70 is configured to, if it is determined that the fitting linearity is smaller than a second preset threshold, use the monitoring data set acquired before the preset time period as an upload data set upload management platform, and simultaneously trigger the monitoring device to enter a sleep state.
Further, the acquisition module 10 includes:
the initialization unit 11 is configured to perform initialization operation on the monitoring device when the monitoring device obtains a wake-up instruction;
the first acquisition unit 12 is configured to perform data acquisition once every preset duration within a preset time period to acquire a monitoring data packet;
and the classification unit 13 is configured to perform class classification based on the parameter classes in the monitoring data packet to obtain multiple monitoring data classes composed of the same parameter class.
Further, the data processing module 30 includes:
the second acquisition unit 31 is configured to re-acquire the M groups of monitoring data sets to obtain M types of monitoring data sets composed of similar parameters;
the selecting unit 32 is used for sorting data in the M types of monitoring data based on a bubbling sorting method, and selecting the monitoring data which are sorted in the middle position in each type of monitoring data in the M types of monitoring data to be combined into a target monitoring data set;
and a linear fitting unit 33, configured to perform linear fitting on the target monitoring data set and the N groups of historical monitoring data sets, so as to calculate a fitting linearity between the target monitoring data set and the N groups of historical monitoring data sets.
It should be noted that the above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the above modules may be located in the same processor; or the modules may be located in different processors in any combination.
Example 7
The geological disaster monitoring and early warning method described in conjunction with fig. 1, 5 and 7 can be implemented by electronic devices. Fig. 9 is a schematic diagram of a hardware structure of the electronic device according to the embodiment.
The electronic device may comprise a processor 81 and a memory 82 in which computer program instructions are stored.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 82 may be used to store or cache various data files for processing and/or communication use, as well as possible computer program instructions executed by the processor 81.
The processor 81 reads and executes the computer program instructions stored in the memory 82 to implement the geological disaster monitoring and early warning method of the embodiment 1.
In some of these embodiments, the electronic device may also include a communication interface 83 and a bus 80. As shown in fig. 9, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between various modules, apparatuses, units and/or devices in the embodiments of the present application. The communication interface 83 may also enable communication with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The electronic device may execute the geological disaster monitoring and early warning method according to embodiment 1, embodiment 3, and embodiment 5 of the present application based on the acquisition of the geological disaster monitoring and early warning system.
In addition, with the geological disaster monitoring and early warning methods in the above embodiments 1, 3 and 5, the embodiments of the present application can provide a storage medium to implement. The storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement the geological disaster monitoring and early warning methods of embodiments 1, 3 and 5 above.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. A geological disaster monitoring and early warning method is characterized by comprising the following steps:
acquiring data in a preset time period to obtain a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes consisting of similar parameters;
judging whether the data in at least one monitoring data class in the multiple monitoring data classes is not smaller than a first preset threshold value;
if yes, acquiring M groups of monitoring data groups again, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, and calculating the fitting linearity of the M groups of monitoring data groups and the N groups of historical monitoring data groups; the preset mechanism comprises a bubble sorting method and a linear fitting method;
judging whether the fitting linearity is not less than a second preset threshold value or not;
if yes, the last group of monitoring data group in the M groups of monitoring data groups is used as an uploading data group uploading management platform to trigger an early warning signal, and after early warning, monitoring equipment is triggered to enter a dormant state;
the step of re-acquiring M groups of monitoring data sets, triggering data processing of a preset mechanism for the M groups of monitoring data sets, and calculating the fitting linearity with N groups of historical monitoring data sets specifically includes:
the M groups of monitoring data sets are collected again to obtain M types of monitoring data formed by the same type of parameters;
sorting the data in the M types of monitoring data classes based on a bubble sorting method, and selecting the monitoring data in the M types of monitoring data classes, which are sorted in the middle position, to combine the monitoring data into a target monitoring data group;
and performing linear fitting on the target monitoring data set and the N groups of historical monitoring data sets to calculate the fitting linearity between the target monitoring data set and the N groups of historical monitoring data sets.
2. The geological disaster monitoring and early warning method as claimed in claim 1, wherein said step of acquiring data within a predetermined period of time to obtain monitoring data packets comprises:
when the monitoring equipment acquires a wake-up instruction, initializing the monitoring equipment;
acquiring data once every other preset time within a preset time period to acquire a monitoring data packet;
and classifying the categories based on the parameter categories in the monitoring data packet to obtain multiple monitoring data categories composed of the same parameter categories.
3. The geological disaster monitoring and early warning method as claimed in claim 1, wherein said N groups of historical monitoring data sets are monitoring data collected by said M groups of monitoring data sets under the same environmental conditions.
4. The geological disaster monitoring and early warning method as claimed in claim 1, wherein after the step of determining whether the data in at least one of the plurality of monitoring data classes is not less than a first preset threshold, the method further comprises:
and if the data in the multiple monitoring data classes are judged to be smaller than a first preset threshold value, the monitoring data packet is used as an uploading data group to be uploaded to a management platform, and meanwhile, the monitoring equipment is triggered to enter a dormant state.
5. The geological disaster monitoring and early warning method as claimed in claim 1, wherein after the step of determining whether the fitting linearity is not less than a second preset threshold, the method further comprises:
and if the fitting linearity is judged to be smaller than a second preset threshold value, taking the monitoring data group collected before the preset time period as an uploading data group uploading management platform, and simultaneously triggering the monitoring equipment to enter a dormant state.
6. The geological disaster monitoring and early warning method as claimed in any one of claims 1 to 5, wherein the categories of the multiple monitoring data categories include a combination of at least two of a groundwater level parameter category, a relative displacement parameter category, a fracture parameter category, a pore water pressure category and a stress parameter category.
7. A geological disaster monitoring and early warning system is characterized by comprising:
the acquisition module is used for acquiring data within a preset time period to acquire a monitoring data packet; the monitoring data packet comprises a plurality of monitoring data classes consisting of the same type of parameters;
the first judging module is used for judging whether the data in at least one monitoring data class in the multiple monitoring data classes is not less than a first preset threshold value;
the data processing module is used for acquiring M groups of monitoring data groups again if the data in at least one monitoring data group in the multiple monitoring data groups is judged to be not less than a first preset threshold value, triggering data processing of a preset mechanism aiming at the M groups of monitoring data groups, and calculating the fitting linearity of the N groups of historical monitoring data groups; the preset mechanism comprises a bubble sorting method and a linear fitting method;
the second judging module is used for judging whether the fitting linearity is not less than a second preset threshold value;
the first monitoring module is used for taking the last group of monitoring data groups in the M groups of monitoring data groups as an uploading data group to upload a management platform to trigger an early warning signal if the fitting linearity is judged to be not less than a second preset threshold value, and triggering the monitoring equipment to enter a dormant state after early warning;
wherein the data processing module comprises:
the second acquisition unit is used for re-acquiring the M groups of monitoring data groups to acquire M monitoring data classes composed of the same type of parameters;
the selecting unit is used for sorting data in the M types of monitoring data based on a bubbling sorting method, and selecting the monitoring data with the sorting of each type of monitoring data in the M types of monitoring data in a middle position to form a target monitoring data set;
and the linear fitting unit is used for performing linear fitting on the target monitoring data set and the N groups of historical monitoring data sets so as to calculate the fitting linearity between the target monitoring data set and the N groups of historical monitoring data sets.
8. An apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements a geological disaster monitoring and warning method as claimed in any one of claims 1 to 6.
9. A storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a geological disaster monitoring and warning method as claimed in any one of claims 1 to 6.
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