Well lid transaction monitoring module and method based on big data

Abstract

The invention discloses a manhole cover transaction monitoring module and method based on big data, and relates to the technical field of manhole cover transaction monitoring, and the method comprises the steps: S10, judging whether the obtained real-time response data is abnormal response data or not; s20, the real-time moving-out degree of the well lid relative to the well mouth is predicted; s30, predicting the transaction index of the well lid at the next moment; and S40, the wireless well lid monitoring module selects whether to send an early warning signal to a well lid maintenance terminal in a wireless transmission mode according to the transaction index of the well lid at the next moment. According to the invention, whether the well lid has abnormal movement or not is judged according to the movement condition of the well lid under the action of external force, and the abnormal movement displacement of the well lid is predicted according to the abnormal movement response data of the well lid, so that compared with monitoring of the abnormal movement displacement of the well lid through a sensor, the accuracy is higher, the infinitesimal displacement of the well lid can be effectively identified, and the accuracy of the well lid is improved. And the monitoring precision of the system on the abnormal movement of the well lid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of manhole cover movement monitoring, and specifically to a manhole cover movement monitoring module and method based on big data. Background Art

[0002] Manhole covers are used to cover roads or deep wells at home to prevent people or objects from falling. Since manhole covers are installed on the road, problems such as the manhole cover being run over by vehicles or tilting under the action of other external pressures often occur.

[0003] In the prior art, an intelligent manhole cover sensor is used to monitor the displacement and tilt state of the manhole cover, and corresponding measures are taken according to the monitoring results. This intelligent manhole cover sensor can only achieve the effect of real-time monitoring, and cannot analyze the movement situation of the manhole cover at the next moment based on the movement state of the manhole cover, nor can it take early warning measures in advance, that is, it cannot effectively avoid the occurrence of safety accidents. At the same time, the prior art only compares the displacement and inclination angle of the manhole cover with the set threshold to realize the early warning of manhole cover movement, and the monitoring accuracy is low. Summary of the Invention

[0004] The purpose of the present invention is to provide a manhole cover movement monitoring module and method based on big data to solve the problems proposed in the prior art.

[0005] 1. To achieve the above purpose, the present invention provides the following technical solution: A manhole cover movement monitoring method based on big data, the method includes: S10: Fix the wireless manhole cover monitoring module at the center of the back of the manhole cover through a fixing device, obtain the real-time response data of the manhole cover by using the wireless manhole cover monitoring module, and determine whether the obtained real-time response data is abnormal response data; S20: When it is determined that the obtained real-time response data is abnormal response data, obtain the response data of the manhole cover in adjacent time periods by using the wireless manhole cover monitoring module, analyze the movement trajectory of the center of the manhole cover in adjacent time periods based on the obtained response data, and predict the real-time moving-out degree of the manhole cover compared to the wellhead according to the analysis result; S30: Predict the movement index of the manhole cover at the next moment according to the abnormality of the response data collected by the wireless manhole cover monitoring module after adjacent time periods, and the average moving-out rate of the manhole cover compared to the wellhead in adjacent time periods; S40: The wireless manhole cover monitoring module selects whether to send a warning signal to the manhole cover maintenance terminal through wireless transmission according to the movement index of the manhole cover at the next moment.

[0006] Further, the specific method for the S10 to determine whether the obtained real-time response data is abnormal response data is: Ⅰ. Based on the world coordinate system, obtain the initial position coordinates of the manhole cover, and construct a three-dimensional coordinate system with the initial position coordinates as the coordinate origin. The positive direction of the X-axis in the three-dimensional coordinate system points to the due east direction. Collect the real-time motion accelerations of the manhole cover on the X 0 -axis, Y 0 -axis, and Z 0 -axis through the triaxial acceleration sensor set in the wireless manhole cover monitoring module. Collect the real-time tilt angles of the manhole cover on the X-axis, Y-axis, and Z-axis respectively through the six-axis gyroscope set in the wireless manhole cover monitoring module. The response data refers to the motion accelerations of the manhole cover on the X 0 -axis, Y 0 -axis, and Z 0 -axis, as well as the tilt angles of the manhole cover on the X-axis, Y-axis, and Z-axis; Ⅱ. At time t, take the motion acceleration a xt *cosβ xt ≠0 of the manhole cover on the X-axis as the first screening condition, take the motion acceleration a yt *cosβ yt ≠0 of the manhole cover on the Y-axis as the second screening condition, take the motion acceleration a zt *cosβ zt ≠0 of the manhole cover on the Z-axis as the third screening condition. If any one of the three screening conditions is satisfied, then judge the response data: a xt *cosβ xt , a yt *cosβ yt , a zt *cosβ zt as abnormal response data. If none of the three screening conditions is satisfied, then judge the response data: a xt *cosβ xt , a yt *cosβ yt , a zt *cosβ zt is not abnormal response data. a xt , a yt , a zt respectively represent the motion accelerations of the manhole cover on the X 0 -axis, Y 0 -axis, and Z 0 -axis at time t, and β xt , β yt , β zt respectively represent the tilt angles of the manhole cover on the X-axis, Y-axis, and Z-axis at time t. When a moving object passes over a manhole cover that does not become loose, the manhole cover will not move. Therefore, according to the acceleration change of the manhole cover in each direction axis, it is beneficial to quickly and accurately analyze the abnormal movement of the manhole cover.

[0007] Further, the S20 includes: S201: When it is determined that the response data obtained at time t is abnormal response data, the wireless manhole cover monitoring module is used to obtain the response data of the manhole cover within the time period [t, t + r], and a response data set M is obtained, M = {(a xt *cosβ xt , a yt *cosβ yt , a zt *cosβ zt ),…,( a x(t+r) *cosβ x(t+r) , a y(t+r) *cosβ y(t+r) , a z(t+r) *cosβ z(t+r) )}, within the time period [t, t + r], the relationship models f(T), g(T), and h(T) between the motion acceleration of the manhole cover on the X-axis, Y-axis, and Z-axis and time T are respectively constructed, where r represents the single average duration value of the abnormal state of the manhole cover; According to Determine the displacement function of the manhole cover center on the X-axis within the time period [t, t + r]; According to Determine the displacement function of the manhole cover center on the Y-axis within the time period [t, t + r]; According to Determine the displacement function of the manhole cover center on the Z-axis within the time period [t, t + r]; Among them, ∬ represents the double integral symbol, and dT represents the infinitesimal change of time T; S202: Number the acquisition times of each response data by the wireless manhole cover monitoring module within the time period [t, t + r] in chronological order, and the numbering result is: j = 1, 2,…, r / d; r / d represents the number of groups of response data collected by the wireless manhole cover monitoring module within the time period [t, t + r] and r / d is an integer, and d represents the acquisition interval time of the wireless manhole cover monitoring module for the response data; Represent the coordinates (F t+j*d , G t+j*d , H t+j*d ) in the constructed three-dimensional coordinate system, connect the representation points in the three-dimensional coordinate system with a smooth curve to obtain the abnormal movement trajectory of the manhole cover center within the time period [t, t + r]; by analyzing the abnormal movement trajectory of the manhole cover center, the specific abnormal movement situation of the manhole cover can be intuitively observed, which is beneficial to accurately predicting the degree of the manhole cover moving out compared to the wellhead. This process can avoid the influence of the manhole cover returning to its position during abnormal movement on the prediction result. Returning to the position means the manhole cover returns to the installation position; S203: Based on the movement trajectory of the manhole cover center within the time period [t, t + r], at times t + j*d and t + (j + 1)*d respectively, obtain the coordinates (F t+j*d , G t+j*d , H t+j*d ) and (F t+(j+1)*d , G t+(j+1)*d , H t+(j+1)*d ) of the manhole cover center in the three-dimensional coordinate system. Calculate the movement distance L t+(j+1)*d of the manhole cover center at time t + (j + 1)*d according to the three-dimensional distance formula. Determine the movement direction angle A t+(j+1)*d of the manhole cover center at time t + (j + 1)*d relative to the positive direction of the X-axis according to the calculation formula of the spatial vector direction angle. Predict the degree of removal of the manhole cover relative to the wellhead at time t + (j + 1)*d according to W t+(j+1)*d = L t+(j+1)*d cosA t+(j+1)*d / (2*R), where R represents the radius of the manhole cover.

[0008] Further, the S30 includes: S301: Calculate the average removal rate of the manhole cover relative to the wellhead within adjacent time periods according to , where W t+j*d represents the degree of removal of the manhole cover relative to the wellhead at time t + j*d; S302: Obtain the response data of the manhole cover at time t + r + d through the wireless manhole cover monitoring module; If the response data obtained at time t + r + d does not belong to abnormal response data, the prediction formula for the movement index of the manhole cover at the next moment is: ; If the response data obtained at time t + r + d belongs to abnormal response data, the prediction formula for the movement index of the manhole cover at the next moment is: ; where E represents the maximum allowable removal displacement of the manhole cover relative to the wellhead, K 1(t+r+2*d) represents the predicted movement index of the manhole cover at time t + r + 2*d when the response data collected by the wireless manhole cover monitoring module after adjacent time periods is non-abnormal response data, and K 2(t+r+2*d) represents the predicted movement index of the manhole cover at time t + r + 2*d when the response data collected by the wireless manhole cover monitoring module after adjacent time periods is abnormal response data.

[0009] In the process of predicting the real-time movement index of the manhole cover, there is no need to consider the influence of the inclination angle of the manhole cover on the movement index again. Because when the manhole cover is tilted (that is, the manhole cover has an inclination angle), the position of the center of the manhole cover has shifted. Therefore, only by predicting the degree of movement of the manhole cover relative to the wellhead can the real-time monitoring of the movement degree of the manhole cover be realized.

[0010] Further, the S40 includes: When K 1 ≥P or K 2 ≥P, it is considered that the early warning level of the manhole cover at the next moment is level one. At this time, the wireless manhole cover monitoring module sends the early warning signal and the initial position coordinates of the moving manhole cover to the manhole cover maintenance terminal through wireless transmission; When 0.5 < K 1 <P or 0.5*P < K 2 <P, it is considered that the early warning level of the manhole cover at the next moment is level two. At this time, the wireless manhole cover monitoring module sends the early warning signal to the manhole cover maintenance terminal through wireless transmission; When 0 < K 1 ≤0.5*P or 0 < K 2 ≤0.5*P, it is considered that the early warning level of the manhole cover at the next moment is level three. At this time, the wireless manhole cover monitoring module does not send an early warning signal, where P represents the movement index threshold of the manhole cover.

[0011] A manhole cover movement monitoring module based on big data, the module includes an abnormal response data analysis and judgment sub-module, a movement degree prediction sub-module, a movement index prediction sub-module and a movement supervision sub-module; The abnormal response data analysis and judgment sub-module is used to analyze and judge whether the acquired real-time response data is abnormal response data; The movement degree prediction sub-module is used to predict the real-time movement degree of the manhole cover relative to the wellhead; The movement index prediction sub-module is used to predict the movement index of the manhole cover at the next moment; The movement supervision sub-module is used to select whether to send the early warning signal to the manhole cover maintenance terminal through wireless transmission.

[0012] Further, the abnormal response data analysis and judgment sub-module includes a response data acquisition unit and an abnormal response data analysis and judgment unit; The response data acquisition unit acquires the real-time movement acceleration of the manhole cover on each direction axis and the real-time inclination angle of the manhole cover on each direction axis through the wireless manhole cover monitoring module; The abnormal response data analysis and judgment unit inputs the response data obtained by the response data acquisition unit into the set screening conditions for analysis, and determines whether the obtained response data is abnormal response data according to the satisfaction of the screening conditions.

[0013] Further, the removal degree prediction sub-module includes a displacement function determination unit, a movement track analysis unit, and a removal degree prediction unit; The displacement function determination unit respectively constructs a relationship model between the movement acceleration of the manhole cover on each direction axis and time according to the response data obtained by the manhole cover in adjacent time periods, performs double integral processing on each constructed relationship model, and obtains the displacement function of the center of the manhole cover on each direction axis; The movement track analysis unit determines the position coordinates of the center of the manhole cover in the three-dimensional coordinate system at each response data acquisition time point according to the displacement function of the center of the manhole cover on each direction axis transmitted by the displacement function determination unit, and uses a smooth curve to connect the determined position coordinates to obtain the movement track of the center of the manhole cover in adjacent time periods; The removal degree prediction unit determines the movement distance and movement direction angle of the center of the manhole cover at each response data acquisition time point based on the movement track of the center of the manhole cover in adjacent time periods, and constructs a mathematical model based on the determination results to predict the removal degree of the manhole cover compared to the wellhead at each response data acquisition time point.

[0014] Further, the movement index prediction sub-module includes an average removal rate calculation unit, a movement index prediction unit one, and a movement index prediction unit two; The average removal rate calculation unit calculates the real-time average removal rate of the manhole cover compared to the wellhead in adjacent time periods according to the prediction results of the removal degree prediction unit; When the movement index prediction unit one determines that the response data collected after the adjacent time period by the wireless manhole cover monitoring module does not belong to abnormal response data, it predicts the movement index of the manhole cover at the next moment according to the first prediction model constructed based on the average removal rate; When the movement index prediction unit two determines that the response data collected after the adjacent time period by the wireless manhole cover monitoring module belongs to abnormal response data, it predicts the movement index of the manhole cover at the next moment according to the second prediction model constructed based on the average removal rate.

[0015] Further, the movement supervision sub-module includes a warning level determination unit and a movement supervision unit; The warning level determination unit compares the predicted movement index of the manhole cover at the next moment with P and 0.5*P, and determines the warning level of the manhole cover based on the comparison results, where P represents the movement index threshold of the manhole cover; According to the determined warning level, the abnormal movement monitoring unit selects whether to send the warning signal and the initial position coordinates of the abnormal manhole cover to the manhole cover maintenance terminal through wireless transmission.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By judging whether the obtained real-time response data is abnormal response data, this process can determine whether the manhole cover is in an abnormal movement state, select whether to obtain the response data of the manhole cover in adjacent time periods, reduce the calculation process, construct a relationship model based on the obtained response data, analyze the abnormal movement trajectory of the manhole cover center in adjacent time periods, and through the analyzed abnormal movement trajectory, the specific abnormal movement situation of the manhole cover can be intuitively observed, which is beneficial to accurately predict the degree of the manhole cover moving out compared to the wellhead. This process can avoid the influence of the manhole cover's return position during abnormal movement on the prediction result.

[0017] 2. This application predicts the abnormal movement index of the manhole cover at the next moment based on the predicted degree of the manhole cover moving out compared to the wellhead, the average moving out rate of the manhole cover compared to the wellhead in adjacent time periods, and the abnormality of the response data collected by the wireless manhole cover monitoring module after adjacent time periods. During the prediction process, there is no need to consider the influence of the inclination angle of the manhole cover on the abnormal movement index again, which improves the prediction accuracy and prediction efficiency. According to the predicted abnormal movement index, a warning can be issued before a safety accident occurs, improving the monitoring effect of the system.

[0018] 3. By the movement situation of the manhole cover under external force, it is judged whether the manhole cover has an abnormal movement, and based on the abnormal movement response data of the manhole cover, the abnormal movement displacement of the manhole cover is predicted. Compared with monitoring the abnormal movement displacement of the manhole cover through sensors, the accuracy is higher (without considering the influence of the measurement accuracy of the sensors, and the measurement accuracy of the sensors will decrease after long-term use), and the abnormal response data can effectively identify the micro displacement of the manhole cover, improving the monitoring accuracy of the system for the abnormal movement of the manhole cover. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the working process of a manhole cover abnormal movement monitoring module and method based on big data according to the present invention; Figure 2 It is a schematic diagram of the working principle structure of a manhole cover abnormal movement monitoring module and method based on big data according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a manhole cover abnormal movement monitoring method based on big data. The method includes: S10: Fix the wireless manhole cover monitoring module at the center of the back of the manhole cover through a fixing device. The fixing device includes screws, strong magnets, etc. The back of the manhole cover refers to the lower surface after the manhole cover is installed. Use the wireless manhole cover monitoring module to obtain the real-time response data of the manhole cover, and judge whether the obtained real-time response data is abnormal response data. The specific method is as follows: Ⅰ. Based on the world coordinate system, obtain the initial position coordinates of the manhole cover. The initial position coordinates of the manhole cover refer to the coordinates corresponding to the center of the manhole cover when the manhole cover is installed. Construct a three-dimensional coordinate system with the initial position coordinates as the coordinate origin. The positive direction of the X-axis in the three-dimensional coordinate system points to the due east direction. Collect the real-time motion accelerations of the manhole cover on the X 0 axis, Y 0 axis, and Z 0 axis through the three-axis acceleration sensor set in the wireless manhole cover monitoring module. The X 0 axis, Y 0 axis, and Z 0 axis represent the three direction axes corresponding to the three-axis acceleration sensor following the state change of the manhole cover. The three-axis acceleration sensor is a sensor that measures the acceleration of an object in three directions (X, Y, and Z axes). Collect the real-time tilt angles of the manhole cover on the X-axis, Y-axis, and Z-axis through the six-axis gyroscope set in the wireless manhole cover monitoring module. The gyroscope is a device that detects the angular motion of the shell relative to the inertial space around one or two axes orthogonal to the self-rotation axis using the angular momentum of a high-speed rotating body. The six-axis gyroscope combines a three-axis gyroscope and a three-axis accelerometer and can detect the tilt angles on the X-axis, Y-axis, and Z-axis. The response data refers to the motion accelerations of the manhole cover on the X 0 axis, Y 0 axis, and Z 0 axis, as well as the tilt angles of the manhole cover on the X-axis, Y-axis, and Z-axis; Ⅱ. At time t, use a *cosβ xt ≠0 on the X-axis of the manhole cover as the first screening condition, and use a *cosβ xt ≠0 on the Y-axis of the manhole cover as the second screening condition, and use a on the Z-axis of the manhole cover yt *cosβ yt ≠0 as the third screening conditionzt *cosβ zt ≠0 is used as the third screening condition. If any of the three screening conditions is met, the response data is judged: a xt *cosβ xt 、a yt *cosβ yt 、a zt *cosβ zt For abnormal response data, if the three screening conditions are not met, the response data is judged: a xt *cosβ xt 、a yt *cosβ yt 、a zt *cosβ zt Not abnormal response data, a xt 、a yt 、a zt They represent the time when the manhole cover is at X 0 Axis, Y 0 Axis, Z 0 Acceleration of motion on the axis, β xt , β yt , β zt Respectively represent the inclination angles of the manhole cover on the X-axis, Y-axis, and Z-axis at time t; S20: When it is determined that the acquired real-time response data is abnormal response data, the response data of the manhole cover in the adjacent time period is acquired by using the wireless manhole cover monitoring module, and based on the acquired response data, the abnormal movement trajectory of the manhole cover center in the adjacent time period is analyzed, and according to the analysis result, the real-time displacement degree of the manhole cover compared with the wellhead is predicted; The S20 includes: S201: When the response data obtained at time t is determined to be abnormal response data, the wireless manhole cover monitoring module is used to obtain the response data of the manhole cover in the time period [t, t+r], where the adjacent time period refers to the time range from time point t to time point t+r, and the response data set M is obtained, M={(a xt *cosβ xt 、a yt *cosβ yt 、a zt *cosβ zt ),…,( a x(t+r) *cosβ x(t+r) 、a y(t+r) *cosβ y(t+r) 、a z(t+r) *cosβ z(t+r))}, within the time period [t, t + r], relationship models f(T), g(T), and h(T) between the motion acceleration of the manhole cover on the X-axis, Y-axis, and Z-axis and time T are respectively constructed, where r represents the single average duration value of the abnormal state of the manhole cover; According to Determine the displacement function of the manhole cover center on the X-axis within the time period [t, t + r]; According to Determine the displacement function of the manhole cover center on the Y-axis within the time period [t, t + r]; According to Determine the displacement function of the manhole cover center on the Z-axis within the time period [t, t + r]; Among them, ∬ represents the double integral symbol, and dT represents the infinitesimal change in time T; S202: Number the acquisition times of each response data by the wireless manhole cover monitoring module within the time period [t, t + r] in chronological order. The numbering result is: j = 1, 2, …, r / d; r / d represents the number of groups of response data collected by the wireless manhole cover monitoring module within the time period [t, t + r] and r / d is an integer, and d represents the acquisition interval time of the wireless manhole cover monitoring module for the response data; Represent the coordinates (F t+j*d , G t+j*d , H t+j*d ) in the constructed three-dimensional coordinate system, and connect the represented points in the three-dimensional coordinate system with a smooth curve to obtain the abnormal movement trajectory of the manhole cover center within the time period [t, t + r]; S203: Based on the abnormal movement trajectory of the manhole cover center within the time period [t, t + r], at times t + j*d and t + (j + 1)*d respectively, obtain the coordinates (F t+j*d , G t+j*d , H t+j*d ) and (F t+(j+1)*d , G t+(j+1)*d , H t+(j+1)*d ) of the manhole cover center in the three-dimensional coordinate system. Calculate the abnormal movement distance L t+(j+1)*d of the manhole cover center at time t + (j + 1)*d according to the three-dimensional distance formula, , determine the abnormal movement direction angle A t+(j+1)*d of the manhole cover center at time t + (j + 1)*d relative to the positive direction of the X-axis according to the space vector direction angle calculation formula. According to W t+(j+1)*d = L t+(j+1)*d cosA t+(j+1)*d / (2*R) to predict the degree of movement out of the wellhead of the manhole cover at time t + (j + 1)*d; Among them, , when calculating the abnormal direction angle, the unit vector (1, 0, 0) parallel to the positive direction of the X-axis is used, and R represents the radius of the manhole cover; S30: Predict the abnormal movement index of the manhole cover at the next moment according to the abnormal situation of the response data collected by the wireless manhole cover monitoring module after adjacent time periods, and the average moving-out rate of the manhole cover relative to the wellhead within adjacent time periods; S30 includes: S301: According to Calculate the average moving-out rate of the manhole cover relative to the wellhead within adjacent time periods. Among them, W t+j*d Represents the degree of movement out of the manhole cover relative to the wellhead at the moment of t + j * d, and Q (t,t+r) Represents the real-time average moving-out rate of the manhole cover relative to the wellhead within adjacent time periods; S302: Obtain the response data of the manhole cover at the moment of t + r + d through the wireless manhole cover monitoring module; If the response data obtained at the moment of t + r + d does not belong to abnormal response data, the prediction formula for the abnormal movement index of the manhole cover at the next moment is: ; If the response data obtained at the moment of t + r + d belongs to abnormal response data, the prediction formula for the abnormal movement index of the manhole cover at the next moment is: ; Among them, E represents the maximum allowable displacement of the manhole cover relative to the wellhead, and K 1(t+r+2*d) Represents the predicted abnormal movement index of the manhole cover at the moment of t + r + 2 * d when the response data collected by the wireless manhole cover monitoring module after adjacent time periods is non-abnormal response data, and K 2(t+r+2*d) Represents the predicted abnormal movement index of the manhole cover at the moment of t + r + 2 * d when the response data collected by the wireless manhole cover monitoring module after adjacent time periods is abnormal response data; S40: The wireless manhole cover monitoring module selects whether to send the warning signal to the manhole cover maintenance terminal through wireless transmission according to the abnormal movement index of the manhole cover at the next moment; S40 includes: When K 1 ≥P or K 2 ≥P, it is considered that the warning level of the manhole cover at the next moment is level one. At this time, the wireless manhole cover monitoring module sends the warning signal and the initial position coordinates of the abnormal manhole cover to the manhole cover maintenance terminal through wireless transmission; When 0.5 < K 1 < P or 0.5 * P < K 2 < P, it is considered that the warning level of the manhole cover at the next moment is level two. At this time, the wireless manhole cover monitoring module sends the warning signal to the manhole cover maintenance terminal through wireless transmission; When 0 < K 1 ≤ 0.5 * P or 0 < K 2 ≤ 0.5 * P, it is considered that the early warning level of the manhole cover at the next moment is level three. At this time, the wireless manhole cover monitoring module does not send out early warning signals, where P represents the threshold of the movement index of the manhole cover.

[0022] A manhole cover movement monitoring module based on big data, the module includes an abnormal response data analysis and judgment sub-module, a removal degree prediction sub-module, a movement index prediction sub-module, and a movement supervision sub-module; The abnormal response data analysis and judgment sub-module is used to analyze and judge whether the acquired real-time response data is abnormal response data; The abnormal response data analysis and judgment sub-module includes a response data acquisition unit and an abnormal response data analysis and judgment unit; The response data acquisition unit acquires the real-time motion acceleration of the manhole cover on each direction axis and the real-time tilt angle of the manhole cover on each direction axis through the wireless manhole cover monitoring module; The abnormal response data analysis and judgment unit inputs the response data acquired by the response data acquisition unit into the set screening conditions for analysis, and judges whether the acquired response data is abnormal response data according to the satisfaction of the screening conditions; The removal degree prediction sub-module is used to predict the real-time removal degree of the manhole cover compared to the wellhead; The removal degree prediction sub-module includes a displacement function determination unit, a movement trajectory analysis unit, and a removal degree prediction unit; The displacement function determination unit respectively constructs a relationship model between the motion acceleration and time of the manhole cover on each direction axis according to the response data acquired by the manhole cover in adjacent time periods, performs double integral processing on the constructed relationship models, and obtains the displacement function of the manhole cover center on each direction axis; The movement trajectory analysis unit determines the position coordinates of the manhole cover center in the three-dimensional coordinate system at each response data acquisition time point according to the displacement function of the manhole cover center on each direction axis transmitted by the displacement function determination unit, and connects the determined position coordinates with a smooth curve to obtain the movement trajectory of the manhole cover center in adjacent time periods; The removal degree prediction unit determines the movement distance and movement direction angle of the manhole cover center at each response data acquisition time point based on the movement trajectory of the manhole cover center in adjacent time periods, and constructs a mathematical model based on the determination results to predict the removal degree of the manhole cover compared to the wellhead at each response data acquisition time point; The movement index prediction sub-module is used to predict the movement index of the manhole cover at the next moment; The movement index prediction sub-module includes an average removal rate calculation unit, a movement index prediction unit one, and a movement index prediction unit two; The average removal rate calculation unit calculates the real-time average removal rate of the manhole cover relative to the wellhead in adjacent time periods according to the prediction result of the removal degree prediction unit; When the abnormal index prediction unit 1 determines that the response data collected by the wireless manhole cover monitoring module after an adjacent time period does not belong to abnormal response data, it predicts the abnormal index of the manhole cover at the next moment according to the first prediction model constructed based on the average removal rate; When the abnormal index prediction unit 2 determines that the response data collected by the wireless manhole cover monitoring module after an adjacent time period belongs to abnormal response data, it predicts the abnormal index of the manhole cover at the next moment according to the second prediction model constructed based on the average removal rate; The abnormal monitoring sub-module is used to select whether to send the warning signal to the manhole cover maintenance terminal by wireless transmission; The abnormal monitoring sub-module includes a warning level determination unit and an abnormal monitoring unit; The warning level determination unit compares the predicted abnormal index of the manhole cover at the next moment with P and 0.5*P, and determines the warning level of the manhole cover based on the comparison result, where P represents the abnormal index threshold of the manhole cover; The abnormal monitoring unit selects whether to send the warning signal and the initial position coordinates of the abnormal manhole cover to the manhole cover maintenance terminal by wireless transmission according to the determined warning level.

[0023] Embodiment 1: Let the average removal rate Q of the manhole cover relative to the wellhead in adjacent time periods (12:00:00,12:00:01) = 0.02, t = 12:00:00, r = 1 min, d = 10 s, then r / d = 6. Let W 12:00:10 = 0.01, W 12:00:20 = 0.015, W 12:00:30 = 0.01, W 12:00:40 = 0.005, W 12:00:50 = 0.006, W 12:01:00 = 0.004, R = 30 cm, E = 6 cm. It is determined that the response data obtained by the wireless manhole cover monitoring module at 12:01:10 belongs to abnormal response data. Then the abnormal index of the manhole cover at 12:01:20 is: ; Then the abnormal index of the manhole cover at 12:01:20 is 0.9; Let the abnormal index threshold of the manhole cover be 0.8. Since 0.9 > 0.8, it is considered that the warning level of the manhole cover at 12:01:20 is level one. At this time, the wireless manhole cover monitoring module sends the warning signal and the initial position coordinates of the abnormal manhole cover to the manhole cover maintenance terminal by wireless transmission.

[0024] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, in all respects, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A method for monitoring abnormal movement of manhole covers based on big data, characterized in that: The method comprises: S10: fixing the wireless manhole cover monitoring module at the center of the back of the manhole cover by a fixing device, acquiring the real-time response data of the manhole cover by using the wireless manhole cover monitoring module, and determining whether the acquired real-time response data is abnormal response data; S20: When it is determined that the acquired real-time response data is abnormal response data, the response data of the manhole cover in the adjacent time period is acquired by using the wireless manhole cover monitoring module, and based on the acquired response data, the abnormal movement trajectory of the manhole cover center in the adjacent time period is analyzed, and according to the analysis result, the real-time displacement degree of the manhole cover compared with the wellhead is predicted; S30: predicting the abnormal movement index of the manhole cover at the next moment according to the abnormal situation of the response data collected by the wireless manhole cover monitoring module after the adjacent time period and the average removal rate of the manhole cover compared with the wellhead in the adjacent time period; S40: The wireless manhole cover monitoring module selects whether to send a warning signal to the manhole cover maintenance terminal via wireless transmission according to the abnormal movement index of the manhole cover at the next moment.

2. The method for monitoring abnormal movement of manhole covers based on big data according to claim 1, characterized in that: The specific method of S10 for determining whether the acquired real-time response data is abnormal response data is: Ⅰ. Based on the world coordinate system, the initial position coordinates of the manhole cover are obtained, and a three-dimensional coordinate system is constructed with the initial position coordinates as the coordinate origin. The positive direction of the X-axis in the three-dimensional coordinate system points to the east direction. The real-time motion acceleration of the manhole cover on the X0 axis, Y0 axis, and Z0 axis is collected through the three-axis acceleration sensor set in the wireless manhole cover monitoring module. The real-time inclination angle of the manhole cover on the X axis, Y axis, and Z axis is collected through the six-axis gyroscope set in the wireless manhole cover monitoring module. The response data refers to the motion acceleration of the manhole cover on the X0 axis, Y0 axis, and Z0 axis, as well as the inclination angle of the manhole cover on the X axis, Y axis, and Z axis; II. At time t, the acceleration of the manhole cover on the X axis is a xt *cosβ xt ≠0 is used as the first screening condition, and the acceleration of the manhole cover on the Y axis is a yt *cosβ yt ≠0 is used as the second screening condition, and the acceleration of the manhole cover on the Z axis is a zt *cosβ zt ≠0 is used as the third screening condition. If any of the three screening conditions is met, the response data is judged: a xt *cosβ xt 、a yt *cosβ yt 、a zt *cosβ zt For abnormal response data, if the three screening conditions are not met, the response data is judged: a xt *cosβ xt 、a yt *cosβ yt 、a zt *cosβ zt Not abnormal response data, a xt 、a yt 、a zt They represent the acceleration of the manhole cover on the X0 axis, Y0 axis, and Z0 axis at time t, respectively. xt , β yt , β zt They represent the inclination angles of the manhole cover on the X-axis, Y-axis, and Z-axis at time t respectively.

3. The method for monitoring abnormal movement of manhole covers based on big data according to claim 2 is characterized in that: The S20 includes: S201: When the response data obtained at time t is determined to be abnormal response data, the wireless manhole cover monitoring module is used to obtain the response data of the manhole cover in the time period [t, t+r] to obtain a response data set M, M={(a xt *cosβ xt 、a yt *cosβ yt 、a zt *cosβ zt ),…,( a x(t+r) *cosβ x(t+r) 、a y(t+r) *cosβ y(t+r) 、a z(t+r) *cosβ z(t+r) )}, in the time period [t, t+r], the relationship models f(T), g(T), h(T) between the acceleration of the manhole cover on the X-axis, Y-axis, and Z-axis and the time T are constructed respectively, where r represents the single average duration value of the abnormal state of the manhole cover; according to Determine the displacement function of the center of the manhole cover on the X-axis in the time period [t, t+r]; according to Determine the displacement function of the center of the manhole cover on the Y axis in the time period [t, t+r]; according to Determine the displacement function of the center of the manhole cover on the Z axis in the time period [t, t+r]; Among them, ∬ represents the double integral symbol, and dT represents the small change in time T; S202: numbering the collection time of each response data by the wireless manhole cover monitoring module in the time period [t, t+r] in chronological order, and the numbering result is: j=1, 2, ..., r / d; r / d represents the number of response data groups collected by the wireless manhole cover monitoring module in the time period [t, t+r] and r / d is an integer, and d represents the collection interval time of the response data by the wireless manhole cover monitoring module; The coordinates (F t+j*d ,G t+j*d ,H t+j*d ) is represented in the constructed three-dimensional coordinate system, and each represented point in the three-dimensional coordinate system is connected by a smooth curve to obtain the abnormal movement trajectory of the center of the manhole cover in the time period [t, t+r]; S203: Based on the abnormal movement trajectory of the center of the manhole cover in the time period [t, t+r], the coordinates of the center of the manhole cover in the three-dimensional coordinate system (F t+j*d ,G t+j*d ,H t+j*d )、(F t+(j+1)*d ,G t+(j+1)*d ,H t+(j+1)*d ) is obtained, and the abnormal distance L of the manhole cover center at time t+(j+1)*d is calculated according to the three-dimensional distance formula. t+(j+1)*d Calculate the direction angle A of the center of the manhole cover relative to the positive direction of the X axis at time t+(j+1)*d according to the spatial vector direction angle calculation formula t+(j+1)*d Determine according to W t+(j+1)*d =L t+(j+1)*d cosA t+(j+1)*d / (2*R) predicts the extent to which the manhole cover is moved out of the wellhead at time t+(j+1)*d, where R represents the radius of the manhole cover.

4. The method for monitoring abnormal movement of manhole covers based on big data according to claim 3 is characterized in that: The S30 includes: S301: According to The average removal rate of the manhole cover compared to the wellhead in adjacent time periods is calculated, where W t+j*d It indicates the degree of displacement of the manhole cover compared to the wellhead at time t+j*d; S302: Acquiring the response data of the manhole cover at time t+r+d through the wireless manhole cover monitoring module; If the response data obtained at time t+r+d does not belong to abnormal response data, the prediction formula of the abnormal index of the manhole cover at the next moment is: ； If the response data obtained at time t+r+d is abnormal response data, the prediction formula of the abnormal index of the manhole cover at the next moment is: ； Where E represents the maximum displacement of the manhole cover relative to the wellhead, K 1(t+r+2*d) It indicates the predicted abnormal index of the manhole cover at time t+r+2*d when the response data collected by the wireless manhole cover monitoring module after the adjacent time period is non-abnormal response data, K 2(t+r+2*d) It indicates the predicted abnormal movement index of the manhole cover at time t+r+2*d when the response data collected by the wireless manhole cover monitoring module after the adjacent time period is abnormal response data.

5. The method for monitoring abnormal movement of manhole covers based on big data according to claim 4 is characterized in that: The S40 includes: When K1≥P or K2≥P, the warning level of the manhole cover at the next moment is considered to be level 1. At this time, the wireless manhole cover monitoring module sends the warning signal and the initial position coordinates of the abnormal manhole cover to the manhole cover maintenance terminal through wireless transmission; When 0.5<K1<P or 0.5*P<K2<P, it is considered that the warning level of the manhole cover at the next moment is level 2, and the wireless manhole cover monitoring module sends the warning signal to the manhole cover maintenance terminal through wireless transmission; When 0<K1≤0.5*P or 0<K2≤0.5*P, it is considered that the warning level of the manhole cover at the next moment is level three, and the wireless manhole cover monitoring module does not issue a warning signal at this time, where P represents the abnormal index threshold of the manhole cover.

6. A manhole cover abnormal movement monitoring module based on big data applied to the manhole cover abnormal movement monitoring method based on big data according to any one of claims 1 to 5, characterized in that: The module includes an abnormal response data analysis and judgment submodule, a removal degree prediction submodule, an abnormal movement index prediction submodule and an abnormal movement supervision submodule; The abnormal response data analysis and judgment submodule is used to analyze and judge whether the acquired real-time response data is abnormal response data; The displacement degree prediction submodule is used to predict the real-time displacement degree of the manhole cover compared to the wellhead; The abnormal index prediction submodule is used to predict the abnormal index of the manhole cover at the next moment; The abnormal movement monitoring submodule is used to select whether to send the early warning signal to the manhole cover maintenance terminal via wireless transmission.

7. The big data-based manhole cover movement monitoring module according to claim 6, characterized in that: The abnormal response data analysis and judgment submodule includes a response data acquisition unit and an abnormal response data analysis and judgment unit; The response data acquisition unit acquires the real-time motion acceleration of the manhole cover on each direction axis and the real-time tilt angle of the manhole cover on each direction axis through the wireless manhole cover monitoring module; The abnormal response data analysis and judgment unit inputs the response data acquired by the response data acquisition unit into the set screening conditions for analysis, and judges whether the acquired response data is abnormal response data according to whether the screening conditions are satisfied.

8. The big data-based manhole cover movement monitoring module according to claim 7, characterized in that: The displacement degree prediction submodule includes a displacement function determination unit, an abnormal movement trajectory analysis unit and a displacement degree prediction unit; The displacement function determination unit constructs relationship models between the motion acceleration and time of the manhole cover on each direction axis according to the response data obtained by the manhole cover in adjacent time periods, and performs double integral processing on each constructed relationship model to obtain the displacement function of the center of the manhole cover on each direction axis; The abnormal movement trajectory analysis unit determines the position coordinates of the manhole cover center in the three-dimensional coordinate system at each response data collection time point according to the displacement function of the manhole cover center on each direction axis transmitted by the displacement function determination unit, and connects the determined position coordinates using a smooth curve to obtain the abnormal movement trajectory of the manhole cover center in adjacent time periods; The displacement degree prediction unit determines the displacement distance and displacement direction angle of the manhole cover center at each response data collection time point based on the displacement trajectory of the manhole cover center in adjacent time periods, and constructs a mathematical model based on the determination result to predict the displacement degree of the manhole cover compared to the wellhead at each response data collection time point.

9. The big data-based manhole cover movement monitoring module according to claim 8, characterized in that: The abnormal index prediction submodule includes an average removal rate calculation unit, an abnormal index prediction unit 1 and an abnormal index prediction unit 2; The average removal rate calculation unit calculates the real-time average removal rate of the manhole cover compared to the wellhead in adjacent time periods according to the prediction result of the removal degree prediction unit; The abnormal index prediction unit 1 predicts the abnormal index of the manhole cover at the next moment according to the first prediction model constructed based on the average removal rate when determining that the response data collected by the wireless manhole cover monitoring module after the adjacent time period does not belong to abnormal response data; When the abnormal index prediction unit 2 determines that the response data collected by the wireless manhole cover monitoring module after the adjacent time period is abnormal response data, it predicts the abnormal index of the manhole cover at the next moment according to the second prediction model constructed based on the average removal rate.

10. The big data-based manhole cover movement monitoring module according to claim 9, characterized in that: The abnormality supervision submodule includes an early warning level determination unit and an abnormality supervision unit; The warning level determination unit compares the predicted abnormal index of the manhole cover at the next moment with P and 0.5*P, and determines the warning level of the manhole cover based on the comparison result, wherein P represents the abnormal index threshold of the manhole cover; The abnormal movement supervision unit selects whether to send the warning signal and the initial position coordinates of the abnormal manhole cover to the manhole cover maintenance terminal through wireless transmission according to the determined warning level.

Images