CN111405469A - Mine earthquake monitoring system based on mobile phone mobile sensing network and crowd-sourcing positioning method - Google Patents

Mine earthquake monitoring system based on mobile phone mobile sensing network and crowd-sourcing positioning method Download PDF

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CN111405469A
CN111405469A CN202010210821.3A CN202010210821A CN111405469A CN 111405469 A CN111405469 A CN 111405469A CN 202010210821 A CN202010210821 A CN 202010210821A CN 111405469 A CN111405469 A CN 111405469A
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罗浩
潘一山
于靖康
宋宝燕
张利
王俊陆
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Liaoning University
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Abstract

The mine earthquake monitoring system based on the mobile phone sensor network and the crowd intelligent positioning method comprise the following steps: 1) establishing a database and initializing data; 2) recording data of a three-axis acceleration sensor of the mobile phone, and monitoring vibration information; 3) distinguishing data characteristic values of the triaxial acceleration sensor; 4) when the vibration is determined, denoising the section of signal; 5) calculating the initial arrival time of the vibration signal after the noise reduction treatment; 6) the vibration signal is initially arrived, the mobile phone terminal is positioned by a GPS, and the mobile phone coding information is uploaded to a central machine through a cellular network or a wifi network; 7) and carrying out statistical analysis on the uploaded vibration data in the same time period, and when the number of the mobile phones which are determined to be vibrating in the same time period in the area range in the network exceeds a certain proportion, determining that a mine earthquake triggering event occurs, and calculating mine earthquake occurrence time and position information. By the method, the invention provides the mine earthquake monitoring system and the crowd positioning method which are wide in monitoring coverage range, high in positioning accuracy and low in cost.

Description

Mine earthquake monitoring system based on mobile phone mobile sensing network and crowd-sourcing positioning method
Technical Field
The invention belongs to the field of mining mine earthquake monitoring in coal mining, and particularly relates to a mine earthquake monitoring system based on a mobile phone mobile sensing network and a crowd-sourcing positioning method.
Background
Mine earthquake is mine earthquake induced by mining, underground surrounding rocks quickly release energy when mine earthquake occurs, underground roadways or mining working faces are often suddenly damaged, ground vibration, house damage and the like are often caused, casualties are caused in serious cases, mine earthquake accidents are frequent along with the increase of the mining depth of coal mines in China, and the safety production of the coal mines in China is seriously threatened.
At present, mine earthquake monitoring is mainly completed by a downhole professional micro-earthquake monitoring system, when mine earthquake occurs, a vibration wave detection station sends monitored vibration wave data records to a ground monitoring system, and the sending time, place and energy are obtained by extracting mine earthquake wave information and calculating. The existing mine earthquake monitoring equipment is expensive in equipment price, complex in mechanism, few in distribution points of each mine, small in network coverage area, and difficult to monitor and position timely and accurately, and achieves the purpose of effective early warning, and a large number of monitoring blind areas exist.
The mobile phone becomes a necessity in life of people, and the function of the smart mobile phone is not only provided with the functions of conversation and short message, but also has stronger intelligent perception capability. The smart phone senses the change information such as acceleration, direction and the like through the built-in acceleration sensor and the built-in magnetic sensor, so that the functions of screen display, navigation, games and the like are realized, and meanwhile, the vibration signal can be very easily detected. Compared with a professional underground micro-seismic sensor, the sensitivity of the mobile phone accelerometer is slightly insufficient, but the seismic source of a mine earthquake occurrence place is shallow, the damage energy is large, the ground seismic sense is strong, the sensitivity of the smart phone accelerometer is enough for the mine earthquake, meanwhile, a network system is established by using the smart phone, the mine earthquake monitoring data of the smart phone is published in a network mode and is connected with a special server, a cloud monitoring network is formed, and massive basic data monitored by the mobile phone are fully used for mine earthquake monitoring and positioning analysis.
At present, a mine earthquake monitoring system established by using a mobile sensing network of a smart phone is not available.
Disclosure of Invention
The invention aims to provide a mine earthquake monitoring system based on a mobile phone mobile sensing network and a crowd-sourcing positioning method, so as to overcome a series of problems of small monitoring coverage, inaccurate positioning, high cost and the like of the existing positioning system.
In order to achieve the purpose, the invention adopts the technical scheme that: the mine earthquake monitoring system based on the mobile phone sensor network and the crowd intelligent positioning method are characterized by comprising the following steps:
1) connecting the central machine with the mobile phone terminal through a cellular network, establishing a database, and initializing initial data of the mobile phone terminal and the central machine which participate in monitoring work;
2) recording data of a triaxial acceleration sensor through software, and continuously monitoring nearby vibration information;
3) processing the triaxial acceleration sensor data recorded in the step 2), and extracting a mine earthquake signal segment from the data;
3.1) distinguishing the vibration signal from the daily activities of the human body;
3.1.1) windowing the continuously vibrating signal, wherein the window size is selected from 0.2 to 2 seconds, and half of the window size slides forwards each time;
3.1.2) calculating the sum of the zero-crossing numbers of the x, y and z three components in each window for signal frequency discrimination; calculating the four-quadrant distance of the acceleration vector sum for signal amplitude discrimination; calculating the accumulated absolute speed of the acceleration to distinguish signal energy;
3.2) when the signal characteristic parameter meets the high-frequency low-amplitude low-energy characteristic, determining the signal as a vibration signal;
4) denoising the vibration signal by a wavelet transform method;
5) processing the obtained vibration signal by using a long-and-short time window method, and judging the initial arrival time of the vibration signal;
6) the vibration signal is initially arrived, the mobile phone terminal is positioned by a GPS, and the mobile phone coding information is uploaded to a central machine through a cellular network;
7) the central machine counts the uploaded vibration signal data in the same time period, when the number of mobile phones which are judged to be vibration signals is divided by the total number of mobile phone terminals around to reach a set value, the central machine is triggered by a network to judge that the mobile phones are a mineral earthquake event, and the central machine adopts a Geiger algorithm to obtain the results of the earthquake source position and the earthquake generating time according to the initial arrival time of the vibration signals and the GPS positioning data signals of the mobile phone terminals, wherein the Geiger algorithm adopts the following formula:
Figure BDA0002422751040000021
target function of absolute deviation
Figure BDA0002422751040000022
Wherein: smFor robust estimation of the microseismic occurrence instants t, i.e.
Figure BDA0002422751040000023
siCalculating theoretical origin time for the mobile intelligent terminal; t is the unknown origin time; x, y, z are and three-axis coordinates, xi，yi，zi，tiRespectively obtaining the three-axis coordinates of the ith mobile phone terminal and the initial arrival time of the obtained vibration signal;
8) and the central machine displays the determined earthquake point position and the earthquake origin time through a mine map and sends the earthquake point position and the earthquake origin time to the database.
In the step 3.3), the high-frequency low-amplitude low energy is specifically as follows: the frequency is characterized by the zero crossing number, when the zero crossing number reaches a set range, the frequency is considered to be high frequency, the amplitude is characterized by the quarter-bit distance of the acceleration vector sum, when the quarter-bit distance of the acceleration vector sum reaches the set range, the amplitude is considered to be low amplitude, the energy is characterized by the accumulated absolute velocity, and when the accumulated absolute velocity reaches the set range, the energy is considered to be low energy.
The positioning method in the step 7) is specifically realized as
(1) Dividing the mobile phone terminals receiving the vibration signals into n groups equally, wherein the number of the mobile phone terminals in each group is M, and the simulated origin time and the seismic source position information of each group are Ai，i＝(1，2，3，4，…，n)，AiThe value of (A) is taken as the following method,
Figure BDA0002422751040000031
(2) n parts of AiAccording to f (A)i) The values of (A) are arranged from small to large, fi) I.e. an objective function, wherein the smaller the objective function value is, the smaller the interpretation error is, i.e. the global optimal solution at the current gradient, X ═ { a ═ a1，A2，A3，…，An}. If the convergence condition is satisfied-that is,
f(Ai)＜，i＝(1，2，3，...，n)
Figure BDA0002422751040000032
wherein, if the values are empirical convergence condition values, stopping the operation and outputting the lowest point, AiNamely the position of the seismic source and the seismic origin time, otherwise, executing the step (3);
(3) if X is an empty set, returning to the step (2), otherwise, adding A in XmaxTaking out, if max is less than p, (p is more than zero and less than integer), executing step (5), otherwise, equally dividing the rest elements in the set into b equal parts, and numbering X from front to back1，X2，X3，XbFirst at X1To obtain a corresponding to the position of k of the random numberkAnd calculates a new column variable vector AnewThat is to say that the first and second electrodes,
Anew＝Amax+[Ak+Amax]/2
if f (A)new)＜f(Amax) Then, Amax＝Anew；f(Amax)＝f(Anew) Executing the step (4); otherwise at X1In the process, the search is repeated w timeskAnd comparing, if no better solution exists, jumping into X2Repeat X1Operation in (1) obtainskAnd so on until XbIn, if XbWhile in the middle stage, there is still no better solution AmaxMove from the direction of the random itself,
Anew＝wAmax+cr[rand(A)]
where c is a random variable, r is a step size factor, if f (A)new)＜＜f(Amax) Then A ismax＝Anew(ii) a Otherwise, AmaxThe random operation is repeated until the maximum number of iterations is tolerated. And (4) repeatedly executing the step (3).
(4) After the better value A has been obtainednewOn the basis of the method, e times of expansion operation is carried out to increase the randomness of the system so as to jump out local superiority and improve the calculation speed of the system,
Ati＝At+cr[rand(A)]，i＝(1，2，3，e)
wherein A istiIs AnewA is a unit column variable vector, if f (A) existsmin)＝min[f(Ati)]And f (A)min)＜f(Anew) Then f (A)max)＝f(Amin)；Amax＝Amin. Returning to the step (3);
(5)X＝{A1，A2，A3，...，Apwill be element A in the setiThe random movement is carried out by one step,
An＝wAi+cr[rand(A)]
if f (A)n)＜f(Ai) Then A isi＝An(ii) a Otherwise AiAnd (5) repeating the random operation until the maximum number of iterations is tolerated, and returning to the step (2).
The monitoring system applied to the mobile phone mobile sensing network-based mine earthquake monitoring system and the crowd sourcing positioning method comprises a mobile phone terminal, a base station tower and a central machine, wherein the mobile phone terminal and the central machine are in signal transmission through the base station tower; a three-axis acceleration sensor is arranged in the mobile phone terminal; the central machine is provided with a database and an information processing module.
The beneficial effects created by the invention are as follows: the method provides a monitoring and positioning method with large monitoring coverage, accurate positioning and low cost through the mine earthquake monitoring system based on the mobile phone sensing network and the crowd-sourcing positioning method.
Drawings
Fig. 1 is a diagram of a mine earthquake monitoring system architecture of a mobile phone sensor network.
Fig. 2 is a schematic diagram of mobile phone sensor network triggering in a mine earthquake generating process.
Fig. 3 is a flow chart of mobile phone sensor network mine earthquake monitoring and crowd sourcing.
Detailed Description
The mine earthquake monitoring system based on the mobile phone sensor network and the crowd intelligent positioning method comprise the following steps:
1) connecting the central machine with the mobile phone terminal through a cellular network, establishing a database, and initializing initial data of the mobile phone terminal and the central machine which participate in monitoring.
2) And recording data of the triaxial acceleration sensor through software, and continuously monitoring nearby vibration information. The software for recording the data of the triaxial acceleration sensor specifically comprises the following software: the acceleration of the three components of the acceleration sensor of the mobile phone in the current state can be acquired in real time and signal distinguishing processing is carried out. And uploading the current position and time of the mobile phone terminal when the vibration signal is generated, and providing a data basis for calculating the earthquake-initiating time and positioning the earthquake source position.
3) Processing the triaxial acceleration sensor data recorded in the step 2), and extracting mine earthquake signals from the data:
3.1) distinguishing the conventional human body daily activities in the waveform from the receiving end mine earthquake signals: the distinguishing principle is that the daily activities of the human body belong to low-frequency high-amplitude high-energy signals, and the mine earthquake signals are high-frequency low-amplitude low-energy signals;
3.1.1) windowing the continuous vibration signal, wherein the window size is selected from 0.2 to 2 seconds, and the window size is slid forward by half each time.
3.1.2) calculating the sum of the zero-crossing numbers of the x, y and z three components in each window for signal frequency discrimination; calculating the four-quadrant distance of the acceleration vector sum for signal amplitude discrimination; calculating the accumulated absolute speed of the acceleration to distinguish signal energy;
and 3.2) determining the monitoring signal as a vibration signal when the monitoring signal meets the characteristics of high frequency, low amplitude and low energy. The high-frequency low-amplitude low energy is specifically as follows: the frequency is characterized by the zero crossing number, when the zero crossing number reaches a set range, the frequency is considered to be high frequency, the amplitude is characterized by the quarter-bit distance of the acceleration vector sum, when the quarter-bit distance of the acceleration vector sum reaches the set range, the amplitude is considered to be low amplitude, the energy is characterized by the accumulated absolute velocity, and when the accumulated absolute velocity reaches the set range, the energy is considered to be low energy.
4) And denoising the vibration signal by a wavelet transform method.
5) And processing the obtained vibration signal by using a long-short time window method, and judging the initial arrival time of the vibration signal.
6) And (4) uploading the initial arrival time of the vibration signal, the GPS positioning of the mobile phone terminal and the mobile phone coding information to the central machine through the cellular network.
7) The central machine counts the uploaded vibration signal data in the same time period, when the number of mobile phones which are judged to be vibration signals is divided by the total number of mobile phone terminals around to reach a set value, the central machine is triggered by a network to judge that the mobile phones are a mineral earthquake event, and the central machine adopts a Geiger algorithm to obtain the results of the earthquake source position and the earthquake generating time according to the initial arrival time of the vibration signals and the GPS positioning data signals of the mobile phone terminals, wherein the Geiger algorithm adopts the following formula:
Figure BDA0002422751040000051
target function of absolute deviation
Figure BDA0002422751040000052
Wherein: smIs the moment of occurrence of microseismsRobust estimation of t, i.e.
Figure BDA0002422751040000053
siCalculating theoretical origin time for the mobile intelligent terminal; t is the unknown origin time; x, y, z are and three-axis coordinates, xi，yi，zi，tiRespectively is the triaxial coordinate of the ith mobile phone terminal and the initial arrival time of the obtained vibration signal.
8) And the central machine displays the determined earthquake point position and the earthquake origin time through a mine map and sends the earthquake point position and the earthquake origin time to the database.
The monitoring system applied to the mobile phone mobile sensing network-based mine earthquake monitoring system and the crowd sourcing positioning method comprises a mobile phone terminal, a base station tower and a central machine, wherein the mobile phone terminal and the central machine are in signal transmission through the base station tower; a three-axis acceleration sensor is arranged in the mobile phone terminal; the central machine is provided with a database and an information processing module.
Example 1:
step S1: the central machine is connected with the mobile phone terminal through a mobile cellular network, a database is established, and the specific architecture diagram of the mine location and the system time is initialized as shown in fig. 1.
Step S2: staff participating in the monitoring system in the mining area starts software for recording data of the three-axis acceleration sensor and a GPS (global positioning system), calibrates system time and monitors vibration signals in real time.
Step S3: and distinguishing the routine human body daily activities in the waveform from the receiving end mine earthquake signals.
The distinguishing principle is that the daily activities of the human body belong to low-frequency high-amplitude high-energy signals, and the mine earthquake signals are high-frequency low-amplitude low-energy signals.
Firstly, windowing is carried out on a continuous vibration signal, the size of a window is selected to be 0.25 second, half of the size of the window is slid forwards each time, and zero number, the quartile distance of acceleration vector sum and the accumulated absolute speed of acceleration are calculated in each time window.
The specific method comprises the following steps:
(1) calculating zero-crossing number for distinguishing frequency, and performing zero-returning process on the signal before calculating zero-crossing number, wherein the processing method comprises calculating average value of three components of each window in normal state
Figure BDA0002422751040000054
And obtaining the value after the zero setting
Figure BDA0002422751040000061
Wherein: ziFor acceleration of each component, ZjFor each ZiAnd
Figure BDA0002422751040000062
is a difference of (c), over ZjAnd (4) counting zero-crossing numbers once when positive and negative values are converted once, and summing the zero-crossing numbers of the three components in each time window to obtain the total zero-crossing number.
(2) Calculating the quartering distance of the acceleration vector sum for distinguishing the amplitude, i.e. calculating the acceleration vector sum in each time window
Figure BDA0002422751040000063
Wherein: x, y and z are three-component accelerations respectively, and then are arranged from large to small to form Vj＝sort(Vi) I, j ═ 1, 2, 3, …, n), VjDivided equally into 4 parts, wherein the third quantile is R3The first quantile is R1Finally, the four-bit distance R ═ R is obtained3-R1。
(3) Calculating the cumulative absolute acceleration of the accelerations for distinguishing the energies, calculating the acceleration integral of n time windows
Figure BDA0002422751040000064
In the method, if the zero crossing number is 60 times per second, the quadrant distance of the acceleration velocity vector sum reaches 0.0072m/s2And the accumulated absolute speed reaches 32.85mm/s, so that the mine earthquake vibration signal can be determined.
Step S4: and reserving the vibration signal, performing noise reduction processing by using a wavelet transform technology, and directly throwing out other signals.
Step S5: calculating the initial arrival time of the vibration signal by a large and small time window method after processing, wherein the specific calculation method comprises the steps of windowing the vibration signal twice, the sizes of time windows are 0.08 second and 0.17 second respectively, and if the zero crossing number, the four-quadrant distance of the acceleration vector sum and the ratio of the accumulated absolute speed of the acceleration in the two time windows reach the same value
Figure BDA0002422751040000068
Then it is recorded as the first arrival of the P-wave.
Step S6: the personnel acquiring the vibration signals need to send the initial arrival time of the vibration signals acquired by the mobile phone, the GPS positioning data signals of the mobile phone terminal and the mobile phone number to the central machine through the mobile cellular network, the central machine counts the uploaded vibration signal data in the same time period, when the number of the mobile phones of the vibration signals is judged to reach 50% of the total number of the surrounding mobile phone terminals, the network triggers, the mining earthquake event is judged, and the distribution schematic diagram of the mobile phone terminals is shown in FIG. 2.
Step S7: after the vibration event is determined, the central machine calculates the vibration generating time and the position of the vibration point, and the specific implementation method is as follows, the Geiger algorithm:
Figure BDA0002422751040000065
target function of absolute deviation
Figure BDA0002422751040000066
Wherein: smFor robust estimation of the microseismic occurrence instants t, i.e.
Figure BDA0002422751040000067
siCalculating theoretical origin time for the mobile intelligent terminal; t is the unknown origin time; x, y, z are and three-axis coordinates, xi，yi，zi，tiThree-axis coordinates and acquired vibration of the ith mobile phone terminal respectivelyThe initial arrival time of the signal.
(1) Dividing the mobile phone terminals receiving the vibration signals into 100 groups equally, wherein the number of the mobile phone terminals in each group is M, and the simulated origin time and the seismic source position information of each group are Ai，i＝(1，2，3，4，…，100)，AiMethod for obtaining (A)
Figure BDA0002422751040000071
(2) 100 parts of AiAccording to f (A)i) The values of (A) are arranged from small to large, fi) I.e. an objective function, wherein the smaller the objective function value is, the smaller the interpretation error is, i.e. the global optimal solution at the current gradient, X ═ { a ═ a1，A2，A3，...，A100}. If the convergence condition is satisfied-that is,
f(Ai)＜，i＝(1，2，3，…，100)
Figure BDA0002422751040000072
wherein, if the values are empirical convergence condition values, stopping the operation and outputting the lowest point, AiI.e. the source location and origin time, otherwise step (3) is performed.
(3) If X is an empty set, returning to the step (2), otherwise, adding A in XmaxTaking out, if max is less than 7, executing step (5), otherwise, equally dividing the rest elements in the set into 3 equal parts, and numbering X from front to back1，X2，X3First at X1To obtain a corresponding to the position of k of the random numberkAnd calculates a new column variable vector AnewThat is to say that the first and second electrodes,
Anew＝Amax+[Ak+Amax]/2
if f (A)new)＜f(Amax) Then, Amax＝Anew；f(Amax)＝f(Anew) Executing the step (4); otherwise at X1In the search, A is repeatedly searched for 2 timeskAnd comparing, if notIf there is a better solution, then jump into X2Repeat X1Operation in (1) obtainskAnd so on until X3If X is3If there is no better solution in the middlemaxMove from the direction of the random itself,
Anew＝wAmax+cr[rand(A)]
wherein c is [1, 2, 3., 10 ]]R is a step factor, if f (A)new)＜f(Amax) Then A ismax＝Anew(ii) a Otherwise, AmaxAnd (4) repeating the random operation until the maximum number of iterations is tolerated, and repeatedly executing the step (3).
(4) After the better value A has been obtainednewOn the basis of the method, 3 times of expansion operation is carried out to increase the randomness of the system so as to jump out local superiority and improve the calculation speed of the system,
Ati＝Anew+cr[rand(A)]，i＝(1，2，3)
wherein A istiIs AnewA is a unit column variable vector, if f (A) existsmin)＝min[f(Ati)]And f (A)min)＜f(Anew) Then f (A)max)＝f(Amin)；Amax＝Amin. And (4) returning to the step (3).
(5)X＝{A1，A2，A3，...，A10Will be element A in the setiThe random movement is carried out by one step,
An＝wAi+cr[rand(A)]
if f (A)n)＜f(Ai) Then A isi＝An(ii) a Otherwise AiAnd (5) repeating the random operation until the maximum number of iterations is tolerated, and returning to the step (2).
Step S8: and the central machine displays the determined earthquake point position and the earthquake origin time through a mine map and sends the earthquake point position and the earthquake origin time to the database.

Claims (4)

1. The mine earthquake monitoring system based on the mobile phone sensor network and the crowd intelligent positioning method are characterized by comprising the following steps:
1) connecting the central machine with the mobile phone terminal through a cellular network, establishing a database, and initializing initial data of the mobile phone terminal and the central machine which participate in monitoring work;
2) recording data of a three-axis acceleration sensor of the mobile phone through software, and continuously monitoring nearby vibration signals;
3) processing the data of the mobile phone triaxial acceleration sensor recorded in the step 2), and extracting a mine earthquake signal segment from the data;
3.1) distinguishing the vibration signal from the daily activities of the human body;
3.1.1) windowing the continuously vibrating signal, wherein the window size is selected from 0.2 to 2 seconds, and half of the window size slides forwards each time;
3.1.2) calculating the sum of the zero-crossing numbers of the x, y and z three components in each window for signal frequency discrimination; calculating the four-quadrant distance of the acceleration vector sum for signal amplitude discrimination; calculating the accumulated absolute speed of the acceleration to distinguish signal energy;
3.2) when the signal characteristic parameter meets the high-frequency low-amplitude low-energy characteristic, determining the signal as a vibration signal;
4) denoising the vibration signal by a wavelet transform method;
5) processing the obtained vibration signal by using a long-and-short time window method, and judging the initial arrival time of the vibration signal;
6) the vibration signal is initially arrived, a mobile phone terminal GPS positioning data signal and mobile phone coding information are uploaded to a central machine through a cellular network or a wifi network;
7) the central machine counts the uploaded vibration signal data in the same time period, when the number of mobile phones which are judged to be vibration signals is divided by the total number of mobile phone terminals around to reach a set value, the central machine is triggered by a network to judge that the mobile phones are a mineral earthquake event, and the central machine adopts a Geiger algorithm to obtain the results of the earthquake source position and the earthquake generating time according to the initial arrival time of the vibration signals and the GPS positioning data signals of the mobile phone terminals, wherein the Geiger algorithm adopts the following formula:
Figure FDA0002422751030000011
target function of absolute deviation
Figure FDA0002422751030000012
Wherein: smFor robust estimation of the microseismic occurrence instants t, i.e.
Figure FDA0002422751030000013
siCalculating theoretical origin time for the mobile intelligent terminal; t is the unknown origin time; x, y, z are and three-axis coordinates, xi，yi，zi，tiRespectively obtaining the three-axis coordinates of the ith mobile phone terminal and the initial arrival time of the obtained vibration signal;
8) and the central machine displays the determined earthquake point position and the earthquake origin time through a mine map and sends the earthquake point position and the earthquake origin time to the database.
2. The mobile phone sensor network-based mine earthquake monitoring system and the crowd-sourcing positioning method according to claim 1, wherein in step 3.2), the high-frequency low-amplitude low energy is specifically: the frequency is characterized by the zero crossing number, when the zero crossing number reaches a set range, the frequency is considered to be high frequency, the amplitude is characterized by the quarter-bit distance of the acceleration vector sum, when the quarter-bit distance of the acceleration vector sum reaches the set range, the amplitude is considered to be low amplitude, the energy is characterized by the accumulated absolute velocity, and when the accumulated absolute velocity reaches the set range, the energy is considered to be low energy.
3. The mobile phone sensor network-based mine earthquake monitoring system and the crowd sourcing method according to claim 1, wherein in step 7), the mobile phone sensor network-based mine earthquake monitoring system and the crowd sourcing method are implemented,
(1) dividing the mobile phone terminals receiving the vibration signals into n groups equally, wherein the number of the mobile phone terminals in each group is M, and the simulated origin time and the seismic source position information of each group are Ai，i＝(1，2，3，4，...，n),AiValue ofThe method comprises the following steps of,
Figure FDA0002422751030000021
(2) n parts of AiAccording to f (A)i) The values of (A) are arranged from small to large, fi) I.e. an objective function, wherein the smaller the objective function value is, the smaller the interpretation error is, i.e. the global optimal solution at the current gradient, X ═ { a ═ a1，A2，A3，...，An}. If the convergence condition is satisfied-that is,
f(Ai)＜，i＝(1，2，3，...，n)
Figure FDA0002422751030000022
wherein, if the values are empirical convergence condition values, stopping the operation and outputting the lowest point, AiNamely the position of the seismic source and the seismic origin time, otherwise, executing the step (3);
(3) if X is an empty set, returning to the step (2), otherwise, adding A in XmaxTaken out if max<p (p is more than zero and less than integer), executing step (5), otherwise, dividing the rest elements in the set into b equal parts, and numbering X from front to back1，X2，X3，XbFirst at X1To obtain a corresponding to the position of k of the random numberkAnd calculates a new column variable vector AnewThat is to say that the first and second electrodes,
Anew＝Amax+[Ak+Amax]/2
if f (A)new)＜(Amax) Then, Amax＝Anew；f(Amax)＝f(Anew) Executing the step (4); otherwise at X1In the process, the search is repeated w timeskAnd comparing, if no better solution exists, jumping into X2Repeat X1Operation in (1) obtainskAnd so on until XbIn, if XbWhile in the middle stage, there is still no better solution AmaxMove from the direction of the random itself,
Anew＝wAmax+cr[rand(A)]
where c is a random variable, r is a step size factor, if f (A)new)＜f(Amax) Then A ismax＝Anew(ii) a Otherwise, AmaxThe random operation is repeated until the maximum number of iterations is tolerated. Repeatedly executing the step (3);
(4) after the better value A has been obtainednewOn the basis of the method, e times of expansion operation is carried out to increase the randomness of the system so as to jump out local superiority and improve the calculation speed of the system,
Ati＝At+cr[rand(A)]，i＝(1，2，3e)
wherein A istuIs AnewA is a unit column variable vector, if f (A) existsmin)＝min[f(Ati)]And f (A)min)＜f(Anew) Then f (A)max)＝f(Amin)；Amax＝Amin. And (4) returning to the step (3).
(5)X＝{A1，A2，A3，...，ApWill be element A in the setiThe random movement is carried out by one step,
An＝wAi+cr[rand(A)]
if f (A)n)＜f(Ai) Then A isi＝An(ii) a Otherwise AiAnd (5) repeating the random operation until the maximum number of iterations is tolerated, and returning to the step (2).
4. The mine earthquake monitoring system based on the mobile phone sensor network and the monitoring system applied by the crowd sourcing positioning method according to any one of claims 1 to 3, wherein: the system comprises a mobile phone terminal, a base station tower and a central machine, wherein the mobile phone terminal and the central machine are in signal transmission through the base station tower; a three-axis acceleration sensor is arranged in the mobile phone terminal; the central machine is provided with a database and an information processing module.
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