CN113051519B - Ground settlement early warning monitoring system based on geophysics - Google Patents
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Abstract
The invention discloses a ground settlement early warning monitoring system based on geophysics, which belongs to the field of road engineering and is used for solving the problems that environmental factors and external factors of settlement deformation cannot be supervised in real time and the degree of the settlement deformation cannot be graded and matched with corresponding supervision early warning strength, and comprises a data analysis module, an environment monitoring module, a region grading module and an early warning grading module, wherein the environment monitoring module is used for analyzing and monitoring the environmental factors of the region where the ground is located, the region grading module grades the monitoring region according to a ring shadow value and an external shadow value, the data analysis module is used for analyzing ground data to obtain displacement average quantity, and the early warning grading module is used for early warning and grading the settlement region. Thereby matching the corresponding supervision early warning strength.
Description
Technical Field
The invention belongs to the field of road engineering, relates to a ground settlement monitoring technology, and particularly relates to a ground settlement early warning and monitoring system based on geophysical property.
Background
The ground subsidence refers to the phenomenon of ground subsidence, is the current main engineering geological problem, and generally presents two forms of regional subsidence and local subsidence, can cause building inclination, destroy the stability of foundation, coastal cities can cause seawater to flow backward, bring great influence to production and life, and cause a lot of reasons of ground subsidence, the motion of crust, sea level rise, etc. can cause regional subsidence, the main reason causing the local ground subsidence of cities is closely related to a large amount of mining underground water;
in the early warning and monitoring of ground settlement deformation, environmental factors and external factors of the settlement deformation are difficult to monitor in real time; meanwhile, the degree of the ground settlement deformation is not graded, and corresponding supervision early warning strength is matched according to the grade;
therefore, a ground settlement early warning and monitoring system based on the geophysics is provided.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a ground settlement early warning and monitoring system based on geophysical.
The technical problem to be solved by the invention is as follows:
(1) how to monitor the environmental factors and external factors of the settlement deformation in real time in the early warning and monitoring of the settlement deformation of the ground;
(2) and the problem of how to grade the settlement deformation degree of the ground and match the corresponding supervision early warning strength is solved.
The purpose of the invention can be realized by the following technical scheme:
a ground settlement early warning monitoring system based on geophysical comprises a data acquisition module, a data analysis module, a user terminal, an environment monitoring module, a region division module, an early warning classification module and a server;
the environment monitoring module is used for analyzing and monitoring the environmental factors of the area where the ground is located, and analyzing and monitoring to obtain the ring shadow value and the external shadow value of the monitored area; the environment monitoring module sends the ring shadow value and the outer shadow value of the monitoring area to the server, the server forwards the received ring shadow value and the outer shadow value of the monitoring area to the area dividing module, the area dividing module performs grading on the monitoring area according to the ring shadow value and the outer shadow value, and the grading step is as follows:
step SS 1: if HYu is not less than X1 and WYu is not less than X2, the monitoring area is judged to be a high-risk settlement area, and a high-risk early warning signal is generated;
step SS 2: if HYu is larger than X1 and WYu is larger than or equal to X2, the monitoring area is determined to be a medium-risk settlement area, and a medium-risk early warning signal is generated;
if HYu is more than or equal to X1 and WYu is less than X2, the monitoring area is determined to be a medium-risk settlement area, and a medium-risk early warning signal is generated;
step SS 3: if HYu is more than X1 and WYu is more than X2, the monitoring area is judged to be a low-risk settlement area, and a low-risk early warning signal is generated;
the region division module feeds back the high-risk early warning signal, the medium-risk early warning signal and the low-risk early warning signal to the early warning classification module and the server; the early warning classification module is used for setting early warning classification coefficients for a high-risk settlement area, a medium-risk settlement area and a low-risk settlement area respectively;
the server sends the high-risk early warning signal to a corresponding user terminal, a ground manager reaches a region where the ground corresponding to the high-risk early warning signal is located within a specified time, and the ground manager acquires ground settlement data of the current region through ground monitoring equipment and sends the ground settlement data to the server;
the server sends the ground settlement data to a data analysis module; the data analysis module is used for analyzing the ground data to obtain displacement average quantity; the data analysis module sends the displacement average quantity of the monitoring area corresponding to the high-risk early warning signal to a server, and the server forwards the displacement average quantity to the area division module;
the early warning classification module is used for carrying out early warning classification on the settlement area after receiving the emergency early warning signal, the high-risk early warning signal, the medium-risk early warning signal and the low-risk early warning signal; and the early warning grading module feeds back the adjusted number of the managers and the number of the early warning devices to the server.
Furthermore, the data acquisition module is used for acquiring ground environment data and sending the ground data to the server for storage; the ground data comprises ground width, ground crack number, ground crack length, floor layer number, floor thickness, surface displacement, deep displacement, settlement displacement, ground picture data, soil water content of a ground area, rainfall of the ground area, vegetation number of the ground area, pedestrian flow and traffic flow of the ground area, building number of the ground area and the number of times of previous settlement of the ground area;
the user terminals are used for registering and logging after personal information is input by ground management personnel and sending the personal information to the server for storage;
the personal information includes personnel name, cell phone number, time of employment, personal photograph and ground area of responsibility.
Further, the analysis monitoring process of the environment monitoring module is specifically as follows:
step S1: acquiring field monitoring stations in a monitoring area, marking the field monitoring stations in the monitoring area as ui, and meanwhile, marking the number of the field monitoring stations in the monitoring area as Jui;
step S2: obtaining rainfall JYUI and soil moisture content TSui collected by a field monitoring station in a monitoring area, and calculating the mean value to obtain the rainfall average JYu and soil moisture average TSu of the monitoring area; acquiring the vegetation number acquired by field monitoring stations in a monitoring area, marking the vegetation number as Zbui, and adding the vegetation number acquired by each field monitoring station to obtain the total vegetation number ZBu of the monitoring area;
step S3: using formulas
Calculating to obtain an annular shadow value HJu of the monitored area;
step S4: acquiring traffic flow and pedestrian flow collected by a field monitoring station in a monitoring area, and calculating an average value to obtain CLu for average traffic flow and RLu for average pedestrian flow; acquiring picture data of each monitoring area, acquiring cracks and crack numbers in the monitoring areas according to the picture data, extracting profile maps of the cracks in the monitoring areas, calculating the lengths of a plurality of crack profile maps, and adding the lengths of the crack profile maps in the monitoring areas to obtain the crack length LFu of the monitoring areas; acquiring the number JZu of buildings in a monitored area and the number CJu of previous settlement;
step S5: using formulas
Calculating to obtain the outer shadow value of the monitored areaWYu。
Further, the analysis process of the data analysis module is specifically as follows:
step P1: acquiring an original elevation and a current elevation of the ground where a ground manager is located, and recording time points corresponding to the original elevation and the current elevation;
step P2: calculating to obtain the accumulated settlement amount of the ground by subtracting the current elevation from the original elevation of the ground, and obtaining the settlement time by subtracting the time of the current elevation from the time of the original elevation;
step P3: accumulating the settlement amount/settlement time to obtain the settlement rate of the ground where the ground management personnel are located;
step P4: substituting the sedimentation rate into the sedimentation early warning model, entering the next step if the sedimentation rate accords with the sedimentation early warning model, and if the sedimentation rate does not accord with the sedimentation early warning model, re-acquiring the ground sedimentation data by the ground management personnel;
step P5: and obtaining the surface displacement and the deep displacement of the ground where the ground management personnel are located, and calculating the mean value of the surface displacement and the deep displacement to obtain the displacement average.
Further, the early warning classification step of the early warning classification module is as follows:
the method comprises the following steps: obtaining corresponding early warning strength according to the original grade of the settlement area, wherein the early warning strength specifically comprises the number of managers and the number of early warning devices, and the original number of managers and the number of early warning devices are respectively recorded as R1Yu and S1 Bu;
step two: acquiring the number of managers in each settlement area in the previous month, and calculating the average value to obtain the number of managers JR1Yu in each day; acquiring the number of early warning devices in each settlement area in the previous month, and calculating the average value to obtain the number of early warning devices JS1Bu each day;
step three: acquiring the number of managers and the number of early warning devices corresponding to the current level of the subsidence area every day, and respectively marking as JR2Yu and JS2 Bu;
step four: calculating to obtain a preventive value YF1u corresponding to the original grade of the settlement region by using a formula YF1u = JR1Yu × c1+ JS1Bu × c 2; calculating a prevention value YF2u corresponding to the current level of the subsidence area by using a formula YF2u = JR2Yu × c1+ JS2Bu × c 2;
step five: if the preventive value YF1u is larger than or equal to the preventive value YF2u, the early warning strength of the settlement area does not need to be adjusted; if the preventive value YF1u is smaller than the preventive value YF2u, the early warning strength of the settlement area needs to be adjusted, and the next step is carried out;
step six: and calculating the difference between the preventive value YF1u and the preventive value YF2u, and adjusting the number of management personnel and the number of early warning devices according to corresponding weight coefficients.
Further, the early warning classification coefficient of the high-risk settlement area is larger than that of the medium-risk settlement area, and the early warning classification coefficient of the medium-risk settlement area is larger than that of the low-risk settlement area.
Further, after the area division module receives the displacement average amount, the area division module is used for upgrading the high-risk early warning signal of the high-risk settlement area, and the process specifically comprises the following steps:
step W1: acquiring an early warning classification coefficient alpha of a high-risk settlement area;
step W2: according to the traffic flow and the people flow of the high-risk settlement area, the through flow TLu of the monitoring area is calculated by combining a formula TLu = CLu × b1+ RLu × b 2; recording the displacement average of the monitoring area as WJu;
step W3: by the formula
Calculating to obtain a danger value WXu of the high-risk settlement area;
step W4: if the danger value of the high-risk settlement area is larger than a set threshold value, generating an emergency early warning signal;
and the region division module sends the emergency early warning signal to the early warning classification module.
Compared with the prior art, the invention has the beneficial effects that:
1. the environmental factors of the area where the ground is located are analyzed and monitored through an environmental monitoring module, the ring shadow value of the monitoring area is obtained through calculation according to the rainfall average, the soil water content average rate and the total vegetation number of the monitoring area, and the outer shadow value of the monitoring area is obtained through calculation according to the traffic flow average, the people flow average, the crack length, the building number and the previous settlement times of the monitoring area;
2. according to the invention, the ring shadow value and the external shadow value are sent to the server through the environment monitoring module, the area division module is used for carrying out grade division on the monitored area according to the ring shadow value and the external shadow value, and the ring shadow value and the external shadow value are respectively compared with the threshold value to judge the high-risk settlement area, the medium-risk settlement area and the low-risk settlement area, so that the grade division on the settlement deformation degree of the ground is facilitated;
3. the invention sends high-risk early warning signals to corresponding user terminals, ground managers reach a designated area within a designated time to collect ground settlement data, the ground data are analyzed through a data analysis module, settlement rates are obtained according to accumulated settlement amount and settlement time, the settlement rates are substituted into a settlement early warning model, the mean value of surface displacement and deep displacement is calculated to obtain displacement average amount, the displacement average amount is sent to a region dividing module to upgrade the high-risk early warning signals in the high-risk settlement area, the danger value of the high-risk settlement area is obtained according to the early warning classification coefficient and the flow rate of the high-risk settlement area, the danger value of the high-risk settlement area is compared with a set threshold value to generate emergency early warning signals, and the early warning classification module receives the emergency early warning signals, the high-risk early warning signals, the medium-risk early warning signals and the low-risk early warning signals and performs early warning classification on the settlement area through an early warning classification module, the ground settlement deformation degree is graded, so that corresponding supervision early warning strength is matched according to the grade.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a geophysical-based ground settlement early warning monitoring system includes a data acquisition module, a data analysis module, a task allocation module, a user terminal, an environment monitoring module, a layout optimization module, a region division module, an early warning classification module, and a server;
the data acquisition module is used for acquiring ground environment data and sending the ground data to the server for storage; the ground data comprises ground width, ground crack number, ground crack length, floor layer number, floor thickness, surface displacement, deep displacement, settlement displacement, ground picture data, soil water content of a region where the ground is located, rainfall of the region where the ground is located, vegetation number of the region where the ground is located, pedestrian flow and traffic flow of the region where the ground is located, building number of the region where the ground is located, the number of times of previous settlement of the region where the ground is located and the like; the data acquisition module is specifically a plurality of field monitoring stations arranged in the area where the ground is located;
the user terminals are used for registering and logging after personal information is input by ground management personnel and sending the personal information to the server for storage; the personal information comprises personnel names, mobile phone numbers, time of job entry, personal photos, responsible ground areas and the like;
in specific implementation, the region dividing module is configured to perform region division on a system monitoring range, divide the system monitoring range into a plurality of monitoring regions u, u =1, 2, … …, z, u represents a monitoring region, and z is a positive integer;
the environment monitoring module is used for analyzing and monitoring the environmental factors of the area where the ground is located, and the analyzing and monitoring process is as follows:
step S1: acquiring field monitoring stations in a monitoring area, marking the field monitoring stations in the monitoring area as ui, and meanwhile, marking the number of the field monitoring stations in the monitoring area as Jui, wherein i =1, 2, … …, x, i represents the number of the field monitoring stations, and x is a positive integer;
step S2: obtaining rainfall JYUI and soil moisture content TSui collected by a field monitoring station in a monitoring area, and calculating the mean value to obtain the rainfall average JYu and soil moisture average TSu of the monitoring area; acquiring the vegetation number acquired by field monitoring stations in a monitoring area, marking the vegetation number as Zbui, and adding the vegetation number acquired by each field monitoring station to obtain the total vegetation number ZBu of the monitoring area;
step S3: using formulas
Calculating to obtain an ambient shadow value HJu (short for environmental impact value) of the monitored area; in the formula, a1 and a2 are both fixed values of proportionality coefficients, and the values of a1 and a2 are both greater than zero;
step S4: acquiring traffic flow and pedestrian flow collected by a field monitoring station in a monitoring area, and calculating an average value to obtain CLu for average traffic flow and RLu for average pedestrian flow; acquiring picture data of each monitoring area through a satellite technology, acquiring cracks and the number of the cracks in the monitoring area according to the picture data, extracting a profile of each crack in the monitoring area, calculating the lengths of a plurality of crack profiles through opencv, and adding the lengths of the plurality of crack profiles in the monitoring area to obtain the crack length LFu of the monitoring area; acquiring the number JZu of buildings in a monitored area and the number CJu of previous settlement;
specifically, the following are: opencv is a BSD license (open source) based distributed cross-platform computer vision and machine learning software library that can run on Linux, Windows, Android, and Mac OS operating systems. The method is light and efficient, is composed of a series of C functions and a small number of C + + classes, provides interfaces of languages such as Python, Ruby, MATLAB and the like, realizes a plurality of general algorithms in the aspects of image processing and computer vision, and is not specifically described herein for the existing mature technology;
step S5: using formulas
Is calculated toThe external influence value WYu (short for external influence value) of the monitored area is specifically expressed as follows:
in the formula, a3 and a4 proportionality coefficients are fixed numerical values, and the values of a3 and a4 are both larger than zero;
the environment monitoring module sends the ring shadow value and the outer shadow value of the monitoring area to the server, the server forwards the received ring shadow value and the outer shadow value of the monitoring area to the area division module, the area division module is further used for carrying out grading on the monitoring area according to the ring shadow value and the outer shadow value, and the grading step is as follows:
step SS 1: if HYu is not less than X1 and WYu is not less than X2, the monitoring area is judged to be a high-risk settlement area, and a high-risk early warning signal is generated;
step SS 2: if HYu is larger than X1 and WYu is larger than or equal to X2, the monitoring area is determined to be a medium-risk settlement area, and a medium-risk early warning signal is generated;
if HYu is more than or equal to X1 and WYu is less than X2, the monitoring area is determined to be a medium-risk settlement area, and a medium-risk early warning signal is generated;
step SS 3: if HYu is more than X1 and WYu is more than X2, the monitoring area is judged to be a low-risk settlement area, and a low-risk early warning signal is generated; in the formula (I); x1 corresponds to HYu, X2 corresponds to WYu, X1 is the ring shadow threshold, X2 is the appearance threshold, and X1 and X2 are both fixed values;
in specific implementation, the ring shadow threshold and the outer shadow threshold can be scientifically set according to factors such as previous settlement times, building number, vehicle amount, annual rainfall amount, soil moisture content, surface displacement amount, deep displacement amount and settlement displacement amount of a monitored area after prediction and drilling of actual conditions;
the region division module feeds back the high-risk early warning signal, the medium-risk early warning signal and the low-risk early warning signal to the early warning classification module and the server;
the early warning classification module sets early warning classification coefficients for a high-risk settlement area, a medium-risk settlement area and a low-risk settlement area respectively, and the early warning classification coefficients are marked as alpha; the early warning grading coefficient of the high-risk settlement area is greater than that of the medium-risk settlement area, and the early warning grading coefficient of the medium-risk settlement area is greater than that of the low-risk settlement area;
in specific implementation, the early warning classification coefficient of the high-risk settlement area can be 2.2, the early warning classification coefficient of the medium-risk settlement area can be 1.5, and the early warning classification coefficient of the low-risk settlement area can be 1.1;
the method comprises the steps that a server sends high-risk early warning signals to corresponding user terminals, ground managers reach areas where the ground corresponding to the high-risk early warning signals are located within specified time, the ground managers collect ground settlement data of current areas through ground monitoring equipment and send the ground settlement data to the server;
in a specific implementation, the ground monitoring device may be one or more of a resistance displacement sensor, a flowmeter, a differential transformer sensor, a level gauge, and a total station, but is not limited thereto;
specifically, the following are: the ground settlement data specifically comprises actual ground width, ground crack number and crack length, floor layer number and thickness, surface displacement, deep displacement, settlement displacement, soil moisture content, vegetation number, actual pedestrian flow, actual vehicle flow and the like;
the server sends the ground settlement data to a data analysis module; the data analysis module is used for analyzing the ground data, and the analysis process specifically comprises the following steps:
step P1: acquiring an original elevation and a current elevation of the ground where a ground manager is located, and recording time points corresponding to the original elevation and the current elevation;
step P2: calculating to obtain the accumulated settlement amount of the ground by subtracting the current elevation from the original elevation of the ground, and obtaining the settlement time by subtracting the time of the current elevation from the time of the original elevation;
step P3: accumulating the settlement amount/settlement time to obtain the settlement rate of the ground where the ground management personnel are located;
step P4: substituting the sedimentation rate into the sedimentation early warning model, entering the next step if the sedimentation rate accords with the sedimentation early warning model, and if the sedimentation rate does not accord with the sedimentation early warning model, re-acquiring the ground sedimentation data by the ground management personnel;
step P5: acquiring the surface displacement and the deep displacement of the ground where a ground manager is located, and calculating the mean value of the surface displacement and the deep displacement to obtain the displacement average;
specifically, the following are: the settlement early warning model is constructed by a hyperbolic curve method, the hyperbolic curve method is mainly used for monitoring the settlement obtained on the ground and drawing a deformation process curve graph of the settlement on the ground and time t, in the method, the settlement s and the time t are supposed to change according to the rule that the settlement average speed is gradually decreased in a hyperbolic curve, and the expressions are two types:
in the formula: s0Time T0The sedimentation amount is mm;
stcumulative settling volume, mm, from time T0 to time T;
s∞-monitoring the final sedimentation amount at the last T instant from the beginning; mm;
t0from the start of monitoring (or reference time) to T0Monitoring time of the moment, d;
t-monitoring time from start of monitoring (or reference time) to time T, d;
alpha and beta are parameters to be solved;
formula (1) can be rewritten as:
(3) is provided with
Then, equation (3) is simplified as: y isi=α+βxi （4）；
When assuming n sets of monitoring data, a system of equations with α, β as unknowns can be obtained, which can be expressed as:
wherein the content of the first and second substances,
；
solving the values of alpha and beta after linear regression by using a least square method, and carrying out left multiplication on two sides of the formula (5) respectively to obtain after simplification:
so as to obtain:
a, beta and t obtained0、s∞In the formula (2), the estimated accumulated sedimentation amount s (t) at any time t can be obtained, and a corresponding predicted sedimentation curve is drawn, wherein the predicted sedimentation curve is a sedimentation early warning model, and the final sedimentation amount is as follows:
s∞=s0+1/β （8）；
the data analysis module sends the displacement average quantity of the monitoring area corresponding to the high-risk early warning signal to the server, and the server forwards the displacement average quantity to the area division module; after the area division module receives the displacement average amount, the area division module is further used for upgrading the high-risk early warning signal of the high-risk settlement area, and the process is as follows specifically:
step W1: acquiring an early warning classification coefficient alpha of a high-risk settlement area;
step W2: according to the traffic flow and the people flow of the high-risk settlement area, the through flow TLu of the monitoring area is calculated by combining a formula TLu = CLu × b1+ RLu × b 2; recording the displacement average of the monitoring area as WJu;
step W3: by the formula
Calculating to obtain a danger value WXu of the high-risk settlement area; in the formula, b1, b2, b3 and b4 are all proportional coefficient fixed numerical values, and the values of b1, b2, b3 and b4 are all larger than zero;
step W4: if the danger value of the high-risk settlement area is larger than a set threshold value, generating an emergency early warning signal;
the region division module sends the emergency early warning signal to the early warning classification module;
the early warning grading module is used for carrying out early warning grading on the settlement area after receiving the emergency early warning signal, the high-risk early warning signal, the medium-risk early warning signal and the low-risk early warning signal, and the early warning grading steps are as follows:
the method comprises the following steps: obtaining corresponding early warning strength according to the original grade of the settlement area, wherein the early warning strength specifically comprises the number of managers and the number of early warning devices, and the original number of managers and the number of early warning devices are respectively recorded as R1Yu and S1 Bu;
step two: acquiring the number of managers in each settlement area in the previous month, and calculating the average value to obtain the number of managers JR1Yu in each day; acquiring the number of early warning devices in each settlement area in the previous month, and calculating the average value to obtain the number of early warning devices JS1Bu each day;
step three: acquiring the number of managers and the number of early warning devices corresponding to the current level of the subsidence area every day, and respectively marking as JR2Yu and JS2 Bu;
step four: calculating to obtain a preventive value YF1u corresponding to the original grade of the settlement region by using a formula YF1u = JR1Yu × c1+ JS1Bu × c 2; calculating a prevention value YF2u corresponding to the current level of the subsidence area by using a formula YF2u = JR2Yu × c1+ JS2Bu × c 2; in the formula, c1 and c2 are weight coefficients, and the values of c1 and c2 are both greater than zero;
step five: if the preventive value YF1u is larger than or equal to the preventive value YF2u, the early warning strength of the settlement area does not need to be adjusted; if the preventive value YF1u is smaller than the preventive value YF2u, the early warning strength of the settlement area needs to be adjusted, and the next step is carried out;
step six: calculating a difference value between the preventive value YF1u and the preventive value YF2u, and adjusting the number of management personnel and the number of early warning devices according to corresponding weight coefficients;
the early warning grading module feeds back the adjusted number of the managers and the number of the early warning devices to the server;
the system also comprises a task allocation module, wherein the task allocation module is used for intelligently allocating the monitoring tasks of the high-risk settlement area, and the allocation process specifically comprises the following steps:
q1: acquiring a user terminal connected with a server, and recording d, d =1, 2, … …, v and v as positive integers by a ground manager corresponding to the user terminal;
q2: the high-risk settlement area is subjected to point setting, a coordinate system is established by taking the high-risk settlement area as an original point, and the distance JLd between the ground management personnel and the original point is calculated through a distance formula;
q3: if the distance JLd is smaller than a set distance threshold, recording the ground manager as a pending person selection;
q4: calculating the employment duration JCd of the to-be-determined person according to the current time of the system and the first registration login time of the user terminal and the time difference;
q5: acquiring total monitoring amount JZd to be selected by a person, total monitoring duration JZTD and current amount to be monitored DJd; obtaining a monitoring average time JTd by using the total monitoring time/total monitoring amount;
q6: and calculating to obtain a monitoring value JCd of the undetermined person selection by combining a formula, wherein the formula is more than the following:
(ii) a In the formula, h1, h2 and h3 are correction factors, values of h1, h2 and h3 are all larger than 1, gamma is a calculated compensation value, and gamma = 0.01248511;
q7: sequencing the monitoring values, selecting the undetermined person corresponding to the maximum value as a monitoring person of a monitoring task of the high-risk settlement area, and increasing the total monitoring amount of the ground management person once;
the task allocation module sends the monitoring tasks of the high-risk settlement area to the corresponding user terminal, ground management personnel at the user terminal click to confirm after receiving the tasks and reach the high-risk settlement area within the specified time, and if the tasks fail to arrive on time, the total monitoring amount is deducted and the monitoring personnel are selected again.
The utility model provides a ground subsides early warning monitoring system based on geophysical, during operation, gathers ground environmental data through data acquisition module to save ground data transmission in the server, then carry out the analysis monitoring to the environmental factor in ground place region through environment monitoring module, according to the rainfall average JYu, soil moisture average rate TSu and vegetation total ZBu in monitoring area, utilize the formula
Calculating to obtain an environment shadow value HJu of the monitored area, then obtaining the average traffic flow CLu, the average people flow RLu, the length of a crack LFu, the number of buildings JZu and the number of previous settlement times CJu of the monitored area, and utilizing a formula
Calculating to obtain an outer shadow value WYu of the monitored area;
the method comprises the steps that an environment monitoring module sends a ring shadow value and an outer shadow value of a monitoring area to a server, the server forwards the received ring shadow value and the received outer shadow value of the monitoring area to an area dividing module, the area dividing module is used for carrying out grade division on the monitoring area according to the ring shadow value and the outer shadow value, if HYu is larger than or equal to X1 and WYu is larger than or equal to X2, the monitoring area is judged to be a high-risk settlement area, a high-risk early warning signal is generated, if HYu is smaller than X1 and WYu is larger than or equal to X2, if HYu is larger than or equal to X1 and WYu is smaller than X2, the monitoring area is judged to be a medium-risk settlement area, a medium-risk early warning signal is generated, and if HYu is smaller than X1 and WYu is smaller than X2, the monitoring area is judged to be a low-risk settlement area, and a low-risk early warning signal is generated;
the area division module feeds back high-risk early warning signals, medium-risk early warning signals and low-risk early warning signals to the early warning classification module and the server, the server sends the high-risk early warning signals to corresponding user terminals, ground managers reach areas where the ground corresponding to the high-risk early warning signals is located within a specified time, the ground managers collect ground settlement data of the current areas through ground monitoring equipment and send the ground settlement data to the server, the server sends the ground settlement data to the data analysis module, the ground data are analyzed through the data analysis module to obtain original elevations and current elevations of the ground where the ground managers are located, time points corresponding to the original elevations and the current elevations are recorded, the accumulated settlement amount of the ground is obtained by calculating the current elevations from the original elevations of the ground, and the time of the current elevations is subtracted from the time of the original elevations to obtain settlement time, accumulating sedimentation amount/sedimentation time to obtain the sedimentation rate of the ground where the ground management personnel are located, substituting the sedimentation rate into the sedimentation early warning model, further obtaining the surface displacement and the deep displacement of the ground where the ground management personnel are located if the sedimentation rate accords with the sedimentation early warning model, calculating the mean value of the surface displacement and the deep displacement to obtain the displacement average amount, sending the displacement average amount of the monitoring area corresponding to the high-risk early warning signal to a server by a data analysis module, and forwarding the displacement average amount to a region division module by the server;
after the displacement average quantity is received by the region dividing module, the high-risk early warning signal of the high-risk settlement region is upgraded to obtain the early warning classification coefficient alpha of the high-risk settlement region, the through flow TLu of the monitoring region is obtained by combining the formula TLu = CLu × b1+ RLu × b2 according to the traffic flow and the pedestrian flow of the high-risk settlement region, and then the formula is used for calculating
Calculating to obtain a danger value WXu of the high-risk settlement area, if the danger value of the high-risk settlement area is greater than a set threshold, generating an emergency early warning signal, and sending the emergency early warning signal to an early warning classification module by the area division module;
after the early warning classification module receives the emergency early warning signal, the high-risk early warning signal, the medium-risk early warning signal and the low-risk early warning signal, early warning classification is carried out on the settlement area through the early warning classification module, corresponding early warning strength is obtained according to the original grade of the settlement area, the number of managers JR1Yu and the number of early warning devices JS1Bu in each day in the settlement area are obtained, a preventive value YF1u corresponding to the original grade of the settlement area is obtained by calculation of a formula YF1u = JR1Yu × c1+ JS1Bu × c2, then the number of managers JR2Yu and the number of early warning devices JS2Bu corresponding to the current grade of the settlement area are obtained by calculation of a formula YF2u = JYFR 2 × c1+ JS2Bu × c2, if the preventive value YF1 is greater than or equal to the preventive value YF2u, the strength of the settlement area does not need to be adjusted, if the preventive value YF1 is smaller than the early warning strength of the settlement area u, and the early warning strength of the settlement area needs to be adjusted u, calculating a difference value between the preventive value YF1u and the preventive value YF2u, adjusting the number of management personnel and the number of early warning devices according to corresponding weight coefficients, and feeding back the adjusted number of management personnel and the adjusted number of early warning devices to a server by an early warning grading module;
meanwhile, monitoring tasks of the high-risk settlement area are intelligently distributed through the task distribution module, the high-risk settlement area is set in a point mode, a coordinate system is established by taking the high-risk settlement area as an original point, the distance JLd between the ground manager and the original point is obtained through calculation of a distance formula, if the distance JLd is smaller than a set distance threshold value, the ground manager is marked as to-be-selected, and according to the working duration JCd, the total monitoring amount JZd, the current to-be-monitored amounts DJd and JTd selected by the to-be-selected people, the formula is combined
Calculating to obtain monitoring values JCd to be selected by people, sorting the monitoring values, selecting the people to be selected corresponding to the maximum value as the monitoring personnel of the monitoring tasks of the high-risk settlement area, increasing the total monitoring amount of the ground management personnel once, sending the monitoring tasks of the high-risk settlement area to the corresponding user terminal by the task allocation module, clicking to confirm after the ground management personnel at the user terminal receives the tasks, reaching the high-risk settlement area within the specified time, deducting the total monitoring amount if the high-risk settlement area is not reached on time, and re-selecting the monitoring personnel.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A ground settlement early warning monitoring system based on geophysical is characterized by comprising a data acquisition module, a data analysis module, a user terminal, an environment monitoring module, a region division module, an early warning classification module and a server;
the environment monitoring module is used for analyzing and monitoring the environmental factors of the area where the ground is located, and analyzing and monitoring to obtain the ring shadow value and the external shadow value of the monitored area;
the analysis and monitoring process of the environment monitoring module is as follows:
step S1: acquiring field monitoring stations in a monitoring area, marking the field monitoring stations in the monitoring area as ui, and meanwhile, marking the number of the field monitoring stations in the monitoring area as Jui;
step S2: obtaining rainfall JYUI and soil moisture content TSui collected by a field monitoring station in a monitoring area, and calculating the mean value to obtain the rainfall average JYu and soil moisture average TSu of the monitoring area; acquiring the vegetation number acquired by field monitoring stations in a monitoring area, marking the vegetation number as Zbui, and adding the vegetation number acquired by each field monitoring station to obtain the total vegetation number ZBu of the monitoring area;
step S3: using formulas
Calculating to obtain an annular shadow value HJu of the monitored area; in the formula, a1 and a2 are both fixed values of proportionality coefficients, and the values of a1 and a2 are both greater than zero;
step S4: acquiring traffic flow and pedestrian flow collected by a field monitoring station in a monitoring area, and calculating an average value to obtain CLu for average traffic flow and RLu for average pedestrian flow; acquiring picture data of each monitoring area, acquiring cracks and crack numbers in the monitoring areas according to the picture data, extracting profile maps of the cracks in the monitoring areas, calculating the lengths of a plurality of crack profile maps, and adding the lengths of the crack profile maps in the monitoring areas to obtain the crack length LFu of the monitoring areas; acquiring the number JZu of buildings in a monitored area and the number CJu of previous settlement;
step S5: using formulas
Calculating to obtain an outer shadow value WYu of the monitored area; in the formula, the proportionality coefficients a3 and a4 are fixed values, and the values of a3 and a4 are both greater than zero;
the environment monitoring module sends the ring shadow value and the outer shadow value of the monitoring area to the server, the server forwards the received ring shadow value and the outer shadow value of the monitoring area to the area dividing module, the area dividing module performs grading on the monitoring area according to the ring shadow value and the outer shadow value, and the grading step is as follows:
step SS 1: if HYu is not less than X1 and WYu is not less than X2, the monitoring area is judged to be a high-risk settlement area, and a high-risk early warning signal is generated;
step SS 2: if HYu is larger than X1 and WYu is larger than or equal to X2, the monitoring area is determined to be a medium-risk settlement area, and a medium-risk early warning signal is generated;
if HYu is more than or equal to X1 and WYu is less than X2, the monitoring area is determined to be a medium-risk settlement area, and a medium-risk early warning signal is generated;
step SS 3: if HYu is more than X1 and WYu is more than X2, the monitoring area is judged to be a low-risk settlement area, and a low-risk early warning signal is generated; wherein X1 corresponds to HYu, X2 corresponds to WYu, X1 is the ring shadow threshold, X2 is the appearance shadow threshold, and X1 and X2 are both fixed values;
the region division module feeds back the high-risk early warning signal, the medium-risk early warning signal and the low-risk early warning signal to the early warning classification module and the server; the early warning classification module is used for setting early warning classification coefficients for a high-risk settlement area, a medium-risk settlement area and a low-risk settlement area respectively;
the server sends the high-risk early warning signal to a corresponding user terminal, a ground manager reaches a region where the ground corresponding to the high-risk early warning signal is located within a specified time, and the ground manager acquires ground settlement data of the current region through ground monitoring equipment and sends the ground settlement data to the server;
the server sends the ground settlement data to a data analysis module; the data analysis module is used for analyzing the ground data to obtain displacement average quantity; the data analysis module sends the displacement average quantity of the monitoring area corresponding to the high-risk early warning signal to a server, and the server forwards the displacement average quantity to the area division module;
the early warning classification module is used for carrying out early warning classification on the settlement area after receiving the emergency early warning signal, the high-risk early warning signal, the medium-risk early warning signal and the low-risk early warning signal; and the early warning grading module feeds back the adjusted number of the managers and the number of the early warning devices to the server.
2. The geophysical-based ground subsidence early warning and monitoring system of claim 1 wherein the data acquisition module is configured to acquire ground environment data and send the ground environment data to a server for storage; the ground data comprises ground width, ground crack number, ground crack length, floor layer number, floor thickness, surface displacement, deep displacement, settlement displacement, ground picture data, soil water content of a ground area, rainfall of the ground area, vegetation number of the ground area, pedestrian flow and traffic flow of the ground area, building number of the ground area and the number of times of previous settlement of the ground area;
the user terminals are used for registering and logging after personal information is input by ground management personnel and sending the personal information to the server for storage;
the personal information includes personnel name, cell phone number, time of employment, personal photograph and ground area of responsibility.
3. The geophysical-based ground subsidence early warning and monitoring system of claim 1, wherein the analysis process of the data analysis module is as follows:
step P1: acquiring an original elevation and a current elevation of the ground where a ground manager is located, and recording time points corresponding to the original elevation and the current elevation;
step P2: calculating to obtain the accumulated settlement amount of the ground by subtracting the current elevation from the original elevation of the ground, and obtaining the settlement time by subtracting the time of the current elevation from the time of the original elevation;
step P3: accumulating the settlement amount/settlement time to obtain the settlement rate of the ground where the ground management personnel are located;
step P4: substituting the sedimentation rate into the sedimentation early warning model, entering the next step if the sedimentation rate accords with the sedimentation early warning model, and if the sedimentation rate does not accord with the sedimentation early warning model, re-acquiring the ground sedimentation data by the ground management personnel;
step P5: and obtaining the surface displacement and the deep displacement of the ground where the ground management personnel are located, and calculating the mean value of the surface displacement and the deep displacement to obtain the displacement average.
4. The geophysical-based ground subsidence early warning and monitoring system as claimed in claim 1, wherein the early warning grading module has the following steps:
the method comprises the following steps: obtaining corresponding early warning strength according to the original grade of the settlement area, wherein the early warning strength specifically comprises the number of managers and the number of early warning devices, and the original number of managers and the number of early warning devices are respectively recorded as R1Yu and S1 Bu;
step two: acquiring the number of managers in each settlement area in the previous month, and calculating the average value to obtain the number of managers JR1Yu in each day; acquiring the number of early warning devices in each settlement area in the previous month, and calculating the average value to obtain the number of early warning devices JS1Bu each day;
step three: acquiring the number of managers and the number of early warning devices corresponding to the current level of the subsidence area every day, and respectively marking as JR2Yu and JS2 Bu;
step four: calculating to obtain a preventive value YF1u corresponding to the original grade of the settlement region by using a formula YF1u = JR1Yu × c1+ JS1Bu × c 2; calculating a prevention value YF2u corresponding to the current level of the subsidence area by using a formula YF2u = JR2Yu × c1+ JS2Bu × c 2; in the formula, c1 and c2 are weight coefficients, and the values of c1 and c2 are both greater than zero;
step five: if the preventive value YF1u is larger than or equal to the preventive value YF2u, the early warning strength of the settlement area does not need to be adjusted; if the preventive value YF1u is smaller than the preventive value YF2u, the early warning strength of the settlement area needs to be adjusted, and the next step is carried out;
step six: and calculating the difference between the preventive value YF1u and the preventive value YF2u, and adjusting the number of management personnel and the number of early warning devices according to corresponding weight coefficients.
5. The geophysical-based ground settlement early warning and monitoring system as claimed in claim 1, wherein the early warning classification coefficient of the high-risk settlement area is greater than the early warning classification coefficient of the medium-risk settlement area, and the early warning classification coefficient of the medium-risk settlement area is greater than the early warning classification coefficient of the low-risk settlement area.
6. The geophysical-based ground settlement early warning and monitoring system as claimed in claim 5, wherein the area division module is configured to update the high-risk early warning signal in the high-risk settlement area after receiving the displacement average amount, and the process specifically comprises:
step W1: acquiring an early warning classification coefficient alpha of a high-risk settlement area;
step W2: according to the traffic flow and the people flow of the high-risk settlement area, the through flow TLu of the monitoring area is calculated by combining a formula TLu = CLu × b1+ RLu × b 2; recording the displacement average of the monitoring area as WJu;
step W3: by the formula
Calculating to obtain a danger value WXu of the high-risk settlement area; in the formula, b1, b2, b3 and b4 are all proportionality coefficient fixed numerical values, and the values of b1, b2, b3 and b4 are all larger than zero；
Step W4: if the danger value of the high-risk settlement area is larger than a set threshold value, generating an emergency early warning signal;
and the region division module sends the emergency early warning signal to the early warning classification module.
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