CN116189389A - Landslide monitoring data processing method, landslide monitoring data processing system, computer and storage medium - Google Patents
Landslide monitoring data processing method, landslide monitoring data processing system, computer and storage medium Download PDFInfo
- Publication number
- CN116189389A CN116189389A CN202310121921.2A CN202310121921A CN116189389A CN 116189389 A CN116189389 A CN 116189389A CN 202310121921 A CN202310121921 A CN 202310121921A CN 116189389 A CN116189389 A CN 116189389A
- Authority
- CN
- China
- Prior art keywords
- landslide
- data
- monitoring
- parameters
- early warning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 131
- 238000003672 processing method Methods 0.000 title claims abstract description 26
- 238000012545 processing Methods 0.000 title claims abstract description 12
- 238000006073 displacement reaction Methods 0.000 claims description 36
- 238000012806 monitoring device Methods 0.000 claims description 17
- 230000005587 bubbling Effects 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 13
- 238000000034 method Methods 0.000 claims description 13
- 230000001133 acceleration Effects 0.000 claims description 9
- 238000012163 sequencing technique Methods 0.000 claims description 9
- 238000012098 association analyses Methods 0.000 claims 1
- 238000007405 data analysis Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 2
- 238000004891 communication Methods 0.000 description 10
- 238000001514 detection method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 241001465754 Metazoa Species 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000005059 dormancy Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geology (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention provides a landslide monitoring data processing method, a landslide monitoring data processing system, a computer and a storage medium, wherein the landslide monitoring data processing method comprises the steps of acquiring landslide data of a monitoring point in real time and judging whether the landslide data acquired at the current moment exceeds a threshold value or not; if yes, acquiring historical landslide data of the monitoring points, and performing linear fitting on the landslide data of the monitoring points and the historical landslide data to acquire fitting linearity; when the linearity is not less than the linearity threshold value, judging that landslide data of the monitoring point are reliable; acquiring multiple parameters of a monitoring point, and judging whether at least one of the multiple parameters has an ultra-limit value; and when at least one of the parameters has an exceeding limit value, early warning is carried out, judgment by only a single threshold value is prevented, and the false alarm probability is reduced. In addition, analysis and judgment are carried out by combining other associated parameters of the monitoring point positions, so that more accurate early warning data are obtained, and the probability of false alarm is further reduced.
Description
Technical Field
The invention belongs to the technical field of landslide monitoring, and particularly relates to a landslide monitoring data processing method and system.
Background
The Chinese operators are wide, the geological structure is complex, the safety of the landslide of the geological disaster is more and more concerned by related management departments and technical units, and the landslide of the mountain is the most extensive secondary geological disaster in a plurality of areas of China. The deformation monitoring of the landslide caused by geological disasters is an important index, and the means for preventing and monitoring the landslide is mainly carried out by a method of manually inspecting and installing corresponding automatic monitoring and early warning equipment, so that the traditional manual method and the automatic monitoring method have certain limitations, and are difficult to meet the current demands.
The automatic monitoring and early warning equipment predicts the trend of landslide through various technical methods and is a main means for preventing landslide. Through landslide monitoring, the evolution process of the landslide body can be known and mastered, the characteristic information of landslide disasters is captured in time, and scientific basis is provided for landslide prevention. However, when the automatic monitoring and early warning equipment is installed in the field, the automatic monitoring and early warning equipment is easily affected by factors such as external environment, wild animals and the like, so that false alarm of monitoring data is caused, and a certain disturbance problem is caused.
Disclosure of Invention
In order to solve the technical problems, the invention provides a landslide monitoring data processing method and system, which are used for solving the technical problems that in the prior art, when automatic monitoring and early warning equipment is installed in the field, the automatic monitoring and early warning equipment is easily influenced by factors such as external environment, wild animals and the like, so that the monitoring data is misalarmed, and a certain disturbance is caused.
In one aspect, the invention provides the following technical scheme, and a landslide monitoring data processing method comprises the following steps:
acquiring landslide data of a monitoring point in real time, and judging whether the landslide data acquired at the current moment exceeds a threshold value or not;
if yes, acquiring historical landslide data of the monitoring points, and performing linear fitting on the landslide data of the monitoring points and the historical landslide data to acquire fitting linearity;
when the linearity is not less than the linearity threshold value, judging that landslide data of the monitoring point are reliable;
acquiring multiple parameters of a monitoring point, and judging whether at least one of the multiple parameters has an ultra-limit value;
and when at least one of the parameters has an exceeding limit value, early warning is carried out.
Compared with the prior art, the beneficial effects of this application are: and judging whether the landslide real-time data exceeds a threshold value, then taking a group of historical data for linear fitting, calculating and obtaining a correlation coefficient value, and when the correlation coefficient value is larger than a preset threshold value, judging that the correlation is high, and carrying out early warning when the data reliability is high, so that the judgment is prevented by only using a single threshold value, and the false alarm probability is reduced. In addition, analysis and judgment are carried out by combining other associated parameters of the monitoring point positions, so that more accurate early warning data are obtained, and the probability of false alarm is further reduced.
Further, the step of performing linear fitting on landslide data of the monitoring point and the historical landslide data comprises the following steps:
acquiring preset quantity group landslide data after the current moment, and performing bubbling sequencing on the preset quantity group landslide data;
and linearly fitting the intermediate value of the bubbling ordered data with the historical data.
Further, the plurality of parameters includes: crack value, rain value, acceleration and dip angle data.
Further, when at least one of the plurality of parameters has an exceeding limit value, the pre-warning specifically includes:
when one of the parameters exceeds a limit value, blue early warning is carried out;
when two parameters in the plurality of parameters exceed the limit value, yellow early warning is carried out;
when three parameters in the plurality of parameters exceed the limit value, orange early warning is carried out;
and when four parameters in the plurality of parameters exceed the limit value, red early warning is carried out.
Further, the acquiring landslide data of the monitoring point in real time includes:
and the displacement data of the monitoring points are monitored in real time through a laser displacement sensor carried by the displacement monitoring device, and the displacement data are transmitted to the cloud.
Further, after the step of performing the early warning when the plurality of parameters find that there is at least one of the parameters has an overrun value, the method further includes:
and transmitting an early warning signal generated by the early warning to the mobile user side.
Further, before the step of acquiring landslide data of the monitoring point in real time and judging whether the landslide data acquired at the current moment exceeds a threshold value, the method further comprises:
and acquiring landslide data of the monitoring points every preset time.
In a second aspect, the present invention provides a landslide monitoring data processing system, including:
the first judging module is used for acquiring landslide data of the monitoring point in real time and judging whether the landslide data acquired at the current moment exceeds a threshold value or not;
the fitting module is used for acquiring historical landslide data of the monitoring points if yes, and linearly fitting the landslide data of the monitoring points with the historical landslide data to acquire fitting linearity;
the judging module is used for judging that landslide data of the monitoring point are reliable when the linearity is not smaller than the linearity threshold value;
the second judging module is used for acquiring various parameters of the monitoring point and judging whether at least one parameter in the various parameters has an exceeding limit value;
and the early warning module is used for carrying out early warning when at least one parameter in the plurality of parameters exceeds the limit value.
Further, the fitting module includes:
the bubbling unit is used for acquiring preset quantity group landslide data after the current moment and conducting bubbling sequencing on the preset quantity group landslide data;
and the linear unit is used for performing linear fitting on the intermediate value of the bubbling ordered data and the historical data.
Further, the system further comprises:
parameter module: the parameters for the plurality of parameters include: crack value, rain value, acceleration and dip angle data.
Further, the early warning module includes:
the dividing unit is used for judging blue early warning when one parameter in the plurality of parameters exceeds a limit value;
when two parameters in the plurality of parameters exceed the limit value, judging that the yellow warning is performed;
when three parameters in the plurality of parameters exceed the limit value, judging that the color is orange, and giving an early warning;
and when four parameters in the multiple parameters exceed the limit value, judging that the red warning is performed.
Further, the first judging module includes:
and the displacement unit is used for monitoring displacement data of the monitoring points in real time through a laser displacement sensor carried by the displacement monitoring device and transmitting the displacement data to the cloud.
Further, the system further comprises:
and the transmission module is used for transmitting the early warning signal generated by the early warning to the mobile user side.
Further, the system further comprises:
and the interval module is used for acquiring landslide data of the monitoring points every preset time.
In a third aspect, the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the landslide monitoring data processing method as described above when executing the computer program.
In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, the computer program implementing a landslide monitoring data processing method as described above when executed by a processor.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a landslide monitoring data processing method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a linear fitting result according to a first embodiment of the present invention;
FIG. 3 is a flowchart of a landslide monitoring data processing method according to a second embodiment of the present invention;
FIG. 4 is a schematic diagram of a linear fitting result in a second embodiment of the present invention;
FIG. 5 is a block diagram of a landslide monitoring data processing system according to a third embodiment of the present invention;
fig. 6 is a schematic hardware structure of a computer device according to a fourth embodiment of the present invention.
Embodiments of the present invention will be further described below with reference to the accompanying drawings.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended to illustrate embodiments of the invention and should not be construed as limiting the invention.
In the description of the embodiments of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate description of the embodiments of the present invention and simplify description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless explicitly defined otherwise.
In the embodiments of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured" and the like are to be construed broadly and include, for example, either permanently connected, removably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the embodiments of the present invention will be understood by those of ordinary skill in the art according to specific circumstances.
Example 1
Referring to fig. 1, a landslide monitoring data processing method according to a first embodiment of the present invention is shown, and specifically includes steps S101 to S105:
s101, landslide data of a monitoring point are obtained in real time, and whether the landslide data obtained at the current moment exceeds a threshold value is judged;
in the specific implementation, the displacement data of the monitoring point is monitored in real time through a laser displacement sensor mounted on the displacement monitoring device, the displacement data are transmitted to the cloud end, whether the displacement data generated at the moment exceeds a preset threshold value is judged, if yes, the next step S102 is carried out, and if no, landslide data of the monitoring point are continuously obtained;
the displacement monitoring device is used for acquiring measurement data of different times at monitoring points in a monitoring area; in the monitoring data acquired by the displacement monitoring device, the condition that the data exceeds a preset threshold value (safety threshold value) can be generated, the existing displacement monitoring device directly informs the early warning equipment to perform early warning, but the data exceeding the safety threshold value is not necessarily accurate at the moment and can be possibly caused by external reasons, such as the influence of factors such as an external environment and wild animals, so that the error early warning is generated at the moment, after the data exceeds the preset safety threshold value, the next data processing is performed, the early warning caused by the influence of the external factors is prevented, and the reliability is improved;
the monitoring points are distributed according to the topography and monitoring precision of the monitoring area, the monitoring points are generally distributed in frequent landslide areas, the mountain, the road and village in the area are distributed according to a certain distance, and of course, the higher the density among the monitoring points is, the more the prediction precision of the landslide is improved.
S102, if yes, acquiring historical landslide data of the monitoring points, and performing linear fitting on the landslide data of the monitoring points and the historical landslide data to acquire fitting linearity;
in the specific implementation, when monitored landslide data obtained by the displacement monitoring device exceeds a preset threshold value, M groups of landslide data and N groups of landslide data are taken, the landslide data of the landslide are recalculated, namely M groups of (M, such as 20 groups, 30 groups and 40 groups) data are collected, an intermediate value is taken through bubbling sequencing (bubbling sequencing is used for removing certain burr/abnormal data) as a calculation result of the moment, the calculated result is the landslide data, then N groups of historical landslide data are taken and the calculated landslide data are subjected to linear fitting to obtain linearity, then whether the linearity is smaller than the linear threshold value is judged, the linear fitting is carried out on a plurality of groups of (N, such as 20 groups, 30 groups and 40 groups) data in excel, and a (correlation coefficient) R2 value is obtained, the value is approximately close to 1, the description data relevance is good, the description trend is about detail, and the measured value is credible;
the landslide data of M groups are corresponding to the landslide data exceeding a preset threshold value at the time and are future landslide data, and the landslide data of N groups are historical data relative to the landslide data exceeding the preset threshold value at the time and are generated landslide data.
S103, when the linearity is not smaller than a linearity threshold value, judging that landslide data of the monitoring point are reliable;
when the obtained linearity is greater than or equal to the preset linearity threshold, the relevance is judged to be high, the data reliability is high, and the preset linearity threshold is set according to actual conditions;
in the present embodiment, as shown in fig. 2, when the linearity (R2 value) of the obtained correlation coefficient is approximately 1, it is indicated that the data correlation is good, it is indicated that the trend is approximately detailed, and the measured value is approximately reliable. The linearity threshold is a judging threshold, and is set according to practical situations, for example, an R2 value takes 0.9 as a judging threshold, and is higher than 0.9 and is considered to be high in reliability, and lower than 0.9 and is low in reliability;
s104, acquiring various parameters of the monitoring points, and judging whether at least one parameter in the various parameters has an ultra-limit value;
in specific implementation, the monitoring device acquires various parameters in the monitoring area, wherein the various parameters include, but are not limited to, crack value, rainfall value, acceleration and inclination angle data, and then judges whether one or more than one parameter has an overrun value preset corresponding to the parameters according to the acquired various parameters.
S105, when at least one of the parameters has an exceeding value, early warning is carried out;
in the implementation, if one or more of crack value, rainfall value, acceleration and inclination angle data in a monitoring area obtained by the monitoring device have data overrun, early warning is carried out.
In summary, when landslide data monitored at a time exceeds a set threshold value, taking N groups of data of historical data to perform linear fitting, calculating linearity, and when the linearity is larger than the linear threshold value, judging that the relativity is high, the data reliability is high, then further judging by acquiring multiple parameters of monitoring points, thereby improving the early warning accuracy, further reducing the false alarm probability, and solving the problem in the prior art that the false alarm probability is increased only by judging by a single threshold value.
Example two
Referring to fig. 3, a landslide monitoring data processing method according to a second embodiment of the present invention is shown, and the method specifically includes steps S301 to S310:
s301, monitoring displacement data of monitoring points in real time through a laser displacement sensor mounted on a displacement monitoring device, transmitting the displacement data to a cloud end, and judging whether the landslide real-time data exceeds a threshold value or not;
during implementation, a laser displacement sensor mounted on the displacement monitoring device monitors displacement data of monitoring points in real time, and wirelessly communicates the displacement data to a cloud through a ZigBee network, and then whether the landslide real-time data exceeds a threshold value is judged.
S302, if not, repeating the step of acquiring landslide data of the monitoring point in real time;
in the specific implementation, if no data exceeds a preset threshold (safety threshold) in the monitoring data acquired by the displacement monitoring device, the data is stored, and then the step of acquiring landslide data of the landslide in real time is repeatedly executed (step S301).
S303, if so, acquiring historical landslide data of the monitoring points and preset quantity group landslide data after the current moment, performing bubbling sequencing on the preset quantity group landslide data, and performing linear fitting on intermediate values of the bubbling sequenced data and the historical data to acquire fitting linearity;
in specific implementation, collecting M groups and N groups (M and N are 20 groups, 30 groups and 40 groups, etc.), taking an intermediate value through bubbling sequencing (bubbling sequencing is used for removing certain burrs/abnormal data) of the M groups of data as a measuring result of the time, storing the measuring result as landslide data, and then linearly fitting the landslide data of the measuring result with the historical landslide data of the N groups to obtain linearity.
And S304, when the linearity is smaller than the linearity threshold value, judging that landslide data of the monitoring point is unreliable, and repeatedly executing the step of acquiring the landslide data of the monitoring point in real time.
As shown in fig. 4, in the implementation, when the linearity (correlation coefficient value) of the correlation coefficient is smaller than the linearity threshold, it is determined that the data of the landslide data is unreliable, and the step of acquiring the landslide data of the monitoring point in real time (step S301) is repeatedly performed. If the correlation coefficient value R3 is smaller, the correlation confidence before and after the data is indicated, which may be a random jump value, and the reliability is low.
And S305, when the linearity is not smaller than the linearity threshold value, judging that landslide data of the monitoring point is reliable.
S306, acquiring various parameters of the monitoring point positions, and judging whether at least one parameter in the various parameters has an overrun value.
S307, if at least one parameter in the plurality of parameters has an exceeding value, outputting an early warning signal;
in the specific implementation, if one or more of crack value, rainfall value, acceleration and inclination angle data in a monitoring area obtained by the monitoring device have data overrun, early warning is carried out;
when one of the parameters exceeds the limit value, blue early warning is judged, and the occurrence probability is very low within twenty-four hours;
when two parameters in the multiple parameters exceed the limit value, judging that the yellow warning is performed, and the occurrence possibility is low within twenty-four hours;
when three parameters in the multiple parameters exceed the limit value, judging that the color is orange, and giving early warning, wherein the occurrence possibility is high within twenty-four hours;
when four parameters in the multiple parameters exceed the limit value, the red early warning is judged, and the occurrence probability is high within twenty-four hours.
S308, when the parameter is not in a plurality of parameters and the parameter is in an overrun value, repeatedly executing the step of acquiring landslide data of the monitoring point in real time;
in the implementation, if the monitored parameter crack value, the rainfall value, the acceleration and the inclination angle data acquired by the monitoring device are all not out of limit, the data are unreliable, no early warning is carried out, and then the step of acquiring landslide real-time data of the landslide in real time is repeatedly executed (step S301).
S309, transmitting the early warning signal generated by the early warning to a mobile user terminal;
in a specific implementation, when the early warning is generated, the early warning signal and the early warning level are transmitted to a mobile user side, such as a smart phone, through wireless communication. The system is transmitted to maintenance personnel to ensure the timeliness of disaster and accident handling, and even can be transmitted to mobile phones of nearby residents to achieve the effect of early warning and prompting for the residents;
the maintenance personnel can be informed in time in a mobile phone APP or short message mode.
S310, executing a step of acquiring landslide data of a landslide in real time every preset time;
in the implementation, the detection equipment is driven to acquire detection data every a preset time, the preset time can be set according to the field condition, after the detection equipment acquires the monitoring data of the landslide, the detection equipment stops working, and after the next acquisition time is up, the equipment works again after a period of time (preset time) is needed;
the monitoring device has the functions of equipment initialization and equipment dormancy, equipment initialization is needed before each time of equipment formal acquisition, basic equipment working configuration such as ADC sampling, communication networking, platform connection and the like are needed, the equipment is started in normal working, after the monitoring equipment acquires the monitoring data of the landslide, the equipment needs dormancy to stop the work of the monitoring equipment, and after the next acquisition time is up, the equipment is restarted.
The second embodiment of the invention has the advantages that the early warning grade is defined by grading the early warning, and the early warning signal is timely sent to the mobile user side, so that the timeliness of disaster accident handling is ensured.
Example III
As shown in fig. 5, in a third embodiment of the present invention, there is provided a landslide monitoring data processing system based on multi-parameter correlation analysis, the system comprising:
the first judging module 10 is used for acquiring landslide data of a monitoring point in real time and judging whether the landslide data acquired at the current moment exceeds a threshold value or not;
the fitting module 20 is configured to obtain historical landslide data of the monitoring point if yes, and perform linear fitting on the landslide data of the monitoring point and the historical landslide data to obtain fitted linearity;
a determining module 30, configured to determine that landslide data of the monitoring point is reliable when the linearity is not less than a linearity threshold;
the second judging module 40 is configured to obtain multiple parameters of the monitoring point, and judge whether at least one parameter of the multiple parameters has an exceeding value;
the early warning module 50 is configured to perform early warning when at least one of the plurality of parameters exceeds a limit value.
In some alternative embodiments, the fitting module comprises:
the bubbling unit is used for acquiring preset quantity group landslide data after the current moment and conducting bubbling sequencing on the preset quantity group landslide data;
and the linear unit is used for performing linear fitting on the intermediate value of the bubbling ordered data and the historical data.
In some alternative embodiments, the system further comprises:
parameter module: the parameters for the plurality of parameters include: crack value, rain value, acceleration and dip angle data.
In some alternative embodiments, the early warning module includes:
the dividing unit is used for judging blue early warning when one parameter in the plurality of parameters exceeds a limit value;
when two parameters in the plurality of parameters exceed the limit value, judging that the yellow warning is performed;
when three parameters in the plurality of parameters exceed the limit value, judging that the color is orange, and giving an early warning;
and when four parameters in the multiple parameters exceed the limit value, judging that the red warning is performed.
In some optional embodiments, the first determining module includes:
and the displacement unit is used for monitoring displacement data of the monitoring points in real time through a laser displacement sensor carried by the displacement monitoring device and transmitting the displacement data to the cloud.
In some alternative embodiments, the system further comprises:
and the transmission module is used for transmitting the early warning signal generated by the early warning to the mobile user side.
In some alternative embodiments, the system further comprises:
and the interval module is used for acquiring landslide data of the monitoring points every preset time.
Example IV
As shown in fig. 6, in a fourth embodiment of the present invention, the present invention provides a computer including a memory 202, a processor 201, and a computer program stored in the memory 202 and executable on the processor 201, where the processor 201 implements the landslide monitoring data processing method as described above when executing the computer program.
In particular, the processor 201 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
The processor 201 implements the landslide monitoring data processing method described above by reading and executing computer program instructions stored in the memory 202.
In some of these embodiments, the computer may also include a communication interface 203 and a bus 200. The processor 201, the memory 202, and the communication interface 203 are connected to each other through the bus 200 and perform communication with each other.
The communication interface 203 is configured to enable communication between modules, apparatuses, units, and/or devices in embodiments of the present application. Communication interface 203 may also enable communication with other components such as: and the external equipment, the image/data acquisition equipment, the database, the external storage, the image/data processing workstation and the like are used for data communication.
The computer can execute the landslide monitoring data processing method based on the acquired landslide monitoring data processing system, so that landslide early warning is realized.
Example five
In a fifth embodiment of the present invention, in combination with the above-mentioned landslide monitoring data processing method, the embodiment of the present invention provides a technical solution, a storage medium, on which a computer program is stored, where the computer program implements the above-mentioned landslide monitoring data processing method when executed by a processor.
Those of skill in the art will appreciate that the logic and/or steps represented in the flow diagrams or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A landslide monitoring data processing method, characterized by comprising:
acquiring landslide data of a monitoring point in real time, and judging whether the landslide data acquired at the current moment exceeds a threshold value or not;
if yes, acquiring historical landslide data of the monitoring points, and performing linear fitting on the landslide data of the monitoring points and the historical landslide data to acquire fitting linearity;
when the linearity is not less than the linearity threshold value, judging that landslide data of the monitoring point are reliable;
acquiring multiple parameters of a monitoring point, and judging whether at least one of the multiple parameters has an ultra-limit value;
and when at least one of the parameters has an exceeding limit value, early warning is carried out.
2. The landslide monitoring data processing method of claim 1 wherein the step of linearly fitting the landslide data at the monitoring point to the historical landslide data comprises:
acquiring preset quantity group landslide data after the current moment, and performing bubbling sequencing on the preset quantity group landslide data;
and linearly fitting the intermediate value of the bubbling ordered data with the historical data.
3. The landslide monitoring data processing method of claim 1 wherein the plurality of parameters comprises: crack value, rain value, acceleration and dip angle data.
4. The landslide monitoring data processing method of claim 1, wherein the performing the early warning specifically includes:
when one of the parameters exceeds a limit value, blue early warning is carried out;
when two parameters in the plurality of parameters exceed the limit value, yellow early warning is carried out;
when three parameters in the plurality of parameters exceed the limit value, orange early warning is carried out;
and when four parameters in the plurality of parameters exceed the limit value, red early warning is carried out.
5. The landslide monitoring data processing method of claim 1, wherein the acquiring landslide data of a monitoring point in real time comprises:
and the displacement data of the monitoring points are monitored in real time through a laser displacement sensor carried by the displacement monitoring device, and the displacement data are transmitted to the cloud.
6. A landslide monitoring data processing method according to claim 1, wherein after the step of performing an early warning when there is an overrun in the plurality of parameters for which there is at least one of the parameters, the method further comprises:
and transmitting an early warning signal generated by the early warning to the mobile user side.
7. The landslide monitoring data processing method based on multi-parameter association analysis of claim 1, wherein before the step of acquiring landslide data of a monitoring point in real time and judging whether the landslide data acquired at the current time exceeds a threshold value, the method further comprises:
and acquiring landslide data of the monitoring points every preset time.
8. A landslide monitoring data processing system comprising:
the first judging module is used for acquiring landslide data of the monitoring point in real time and judging whether the landslide data acquired at the current moment exceeds a threshold value or not;
the fitting module is used for acquiring historical landslide data of the monitoring points if yes, and linearly fitting the landslide data of the monitoring points with the historical landslide data to acquire fitting linearity;
the judging module is used for judging that landslide data of the monitoring point are reliable when the linearity is not smaller than the linearity threshold value;
the second judging module is used for acquiring various parameters of the monitoring point and judging whether at least one parameter in the various parameters has an exceeding limit value;
and the early warning module is used for carrying out early warning when at least one parameter in the plurality of parameters exceeds the limit value.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the landslide monitoring data processing method of any one of claims 1 to 7 when the computer program is executed by the processor.
10. A storage medium having stored thereon a computer program which when executed by a processor implements the dam safety warning method based on monitoring data analysis according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310121921.2A CN116189389A (en) | 2023-02-16 | 2023-02-16 | Landslide monitoring data processing method, landslide monitoring data processing system, computer and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310121921.2A CN116189389A (en) | 2023-02-16 | 2023-02-16 | Landslide monitoring data processing method, landslide monitoring data processing system, computer and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116189389A true CN116189389A (en) | 2023-05-30 |
Family
ID=86441864
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310121921.2A Pending CN116189389A (en) | 2023-02-16 | 2023-02-16 | Landslide monitoring data processing method, landslide monitoring data processing system, computer and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116189389A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117493832A (en) * | 2023-12-29 | 2024-02-02 | 江西飞尚科技有限公司 | Landslide hazard curve identification method, landslide hazard curve identification system, storage medium and computer |
-
2023
- 2023-02-16 CN CN202310121921.2A patent/CN116189389A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117493832A (en) * | 2023-12-29 | 2024-02-02 | 江西飞尚科技有限公司 | Landslide hazard curve identification method, landslide hazard curve identification system, storage medium and computer |
| CN117493832B (en) * | 2023-12-29 | 2024-04-09 | 江西飞尚科技有限公司 | Landslide hazard curve identification method, landslide hazard curve identification system, storage medium and computer |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108335377B (en) | GIS technology-based automatic check method for road inspection vehicle service | |
| CN112068065B (en) | Voltage transformer state early warning method and device and storage medium | |
| CN114148216B (en) | Method, system, equipment and storage medium for detecting battery self-discharge rate abnormality | |
| CN113415165B (en) | Fault diagnosis method and device, electronic equipment and storage medium | |
| CN109996278A (en) | Road network method for evaluating quality, device, equipment and medium | |
| CN116189389A (en) | Landslide monitoring data processing method, landslide monitoring data processing system, computer and storage medium | |
| CN112597263B (en) | Pipe network detection data abnormity judgment method and system | |
| CN110149654B (en) | Method and device for determining faults of base station antenna feeder system | |
| CN113310647A (en) | Method and device for detecting leakage of battery pack, electronic equipment and storage medium | |
| CN116432802A (en) | Early warning method, system, computer and storage medium for geological disasters in flood season in rainy season | |
| CN109684774A (en) | A kind of beam bridge safety monitoring and assessment device | |
| CN110726850A (en) | Railway crosswind early warning system based on wind direction decomposition and crosswind strength calculation method | |
| CN115184808B (en) | Battery thermal runaway risk detection method, device, equipment and computer storage medium | |
| CN114390438B (en) | Traffic equipment positioning method and device | |
| CN114994460A (en) | Cable insulation performance prediction device and method | |
| CN114296105A (en) | Method, device, equipment and storage medium for determining positioning fault reason | |
| CN108872780A (en) | Charge livewire work detection, system and the terminal device reconnoitred | |
| CN111695735A (en) | Railway bow net real-time early warning method, system and device based on flow calculation | |
| CN115293255B (en) | Expressway traffic accident risk model construction and risk discrimination method | |
| CN112082519A (en) | Method and device for checking position of ground transponder in rail transit | |
| CN115273411B (en) | Geological disaster monitoring and early warning method and system, electronic equipment and storage medium | |
| CN113362630B (en) | Traffic signal equipment fault analysis processing method, system and computer storage medium | |
| CN114241734B (en) | Immersion early warning method and device, electronic equipment and storage medium | |
| CN111090133B (en) | Rainfall radar data quality control method | |
| CN103472192A (en) | Intelligent positioning method of gas sensor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |