Three-dimensional displacement sensor and real-time transmission method thereof

Abstract

The invention relates to the technical field of three-dimensional displacement sensors, and discloses a three-dimensional displacement sensor and a real-time transmission method thereof. The three-dimensional displacement sensor comprises a three-dimensional displacement measurement unit and an edge calculation module connected with the three-dimensional displacement measurement unit. The edge calculation module comprises a data preprocessing unit, a trend analysis and prediction unit, a dynamic displacement threshold adjustment unit, an intelligent decision and control unit and a data transmission unit. According to the invention, the edge calculation module is introduced to the sensor equipment end, and the collected data is efficiently processed and optimally transmitted, so that many problems in the data processing and transmission process of the traditional sensor are solved, and the overall performance and application flexibility of the system are improved; and meanwhile, a displacement early warning threshold value can be dynamically adjusted through a dynamic displacement threshold value adjusting unit according to the field environment condition, early warning can be achieved by conducting intelligent analysis on a displacement predicted value and the dynamic displacement early warning threshold value, and the safety of mining activities is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional displacement sensors, and in particular to a three-dimensional displacement sensor and a real-time transmission method thereof. Background Art

[0002] In mining activities, three-dimensional displacement sensors play many important roles, such as displacement monitoring, stability assessment, safety warning, etc. Traditional three-dimensional displacement sensors often simply transmit the collected raw data directly to the cloud server. Due to the lack of effective local processing capabilities, a large amount of unprocessed raw data not only occupies a large amount of transmission bandwidth, but also brings a heavy computing burden to the cloud server. At the same time, the working environment of the mine is complex, and geological factors, mining depth factors, etc. will cause the safety warning threshold of the three-dimensional displacement sensor to change. The existing three-dimensional displacement sensor cannot dynamically adjust the warning threshold according to the actual working environment of the mine, and there is still a safety hazard of untimely warning.

[0003] In order to solve the above problems, we propose a three-dimensional displacement sensor and its real-time transmission method. Summary of the invention

[0004] The purpose of the present invention is to provide a three-dimensional displacement sensor and a real-time transmission method thereof, which can dynamically adjust the warning threshold according to the actual working environment of the mine and improve the safety of mining activities.

[0005] To achieve the above object, the present invention provides the following technical solution: a real-time transmission method of a three-dimensional displacement sensor, comprising the following steps: The edge computing module obtains the displacement time series data of the monitoring point through data transmission with the three-dimensional displacement measurement unit; The edge computing module performs stationarity test and difference processing on the displacement time series data to determine the order of the ARIMA model; A displacement trend prediction model based on the ARIMA model is built in the edge computing module to predict the future displacement of the monitoring point and obtain the displacement prediction value; The edge computing module transmits data with the cloud server through the data transmission unit. The cloud server sends the geological comprehensive information, rainfall information, wind speed information, and mining depth information of each monitoring point to the edge computing module arranged at each monitoring point at regular intervals. Based on the new geological comprehensive information, rainfall information, wind speed information, and mining depth information, the displacement warning threshold is dynamically adjusted to obtain a dynamic displacement warning threshold after comprehensive adjustment; Compare the predicted displacement value with the dynamic displacement threshold, and trigger an early warning when the predicted value is equal to or exceeds the dynamic displacement threshold; The edge computing module sends the displacement data monitored in real time by the three-dimensional displacement measurement unit, the predicted value of the displacement trend prediction model, and early warning information to the cloud server through the data transmission unit.

[0006] Preferably, the method of performing stationarity test and difference processing on the displacement time series data to determine the order of the ARIMA model is as follows: Determine whether the displacement time series data has stationary characteristics; If the displacement time series data is not stationary, then difference processing is performed to make the data stationary; The appropriate order combination of the ARIMA model is determined by observing the autocorrelation function and the partial autocorrelation function.

[0007] Preferably, the formula of the displacement trend prediction model based on the ARIMA model is as follows: ; Among them, p is the autoregressive order, q is the moving average order, is the i-th monitoring point The predicted value at time, represents the number of future time steps, is the mean of the time series, is the autoregression coefficient, is the moving average coefficient, is the index variable, is the i-th monitoring point at The actual displacement at time, is a white noise sequence.

[0008] Preferably, the method for dynamically adjusting the displacement warning threshold based on the new comprehensive geological information, rainfall information, wind speed information, and mining depth information to obtain the comprehensively adjusted dynamic displacement warning threshold is as follows: Calculate the threshold adjustment coefficients of comprehensive geological information, rainfall information, wind speed information, and mining depth information respectively; The threshold adjustment coefficient of comprehensive geological information is: ; in, represents the comprehensive geological impact factor around the ith monitoring point, is the intensity coefficient affected by geological conditions; The threshold adjustment coefficient of rainfall information is: ; in, It is a weight coefficient determined based on experience and is used to adjust the degree of influence of different rainfall intervals on threshold adjustment. is the displacement exceeding threshold frequency during rainfall, is the frequency of displacement exceeding the threshold in normal weather; The threshold adjustment coefficient of wind speed information is: ; in, It is a coefficient determined according to the accuracy requirements and stability characteristics of the monitoring facilities and is used to adjust the degree of wind speed influence. To monitor the shaking amplitude of the facility, is the threshold of the allowable shaking amplitude; the threshold adjustment coefficient of the mining depth information is: ; in, It is the reference value of the depth threshold initially set. It is a depth value predetermined based on the geological conditions of the mine and engineering experience. Increase the amount for mining depth; The weights of the threshold adjustment coefficients of geological comprehensive information, rainfall information, wind speed information, and mining depth information are calculated by the hierarchical analysis method, respectively: ; Calculate the comprehensive threshold adjustment factor: ; Calculate the synthetically adjusted dynamic displacement threshold: ;in, is the initial displacement warning threshold.

[0009] Preferably, the method of calculating the weights of the threshold adjustment coefficients of the geological comprehensive information, rainfall information, wind speed information, and mining depth information respectively by using the hierarchical analysis method is as follows: The problem of determining the weight of the threshold adjustment coefficient of the mine displacement monitoring and early warning system is divided into the target layer, the criterion layer and the scheme layer. The main task of the target layer is to reasonably determine the weight of the threshold adjustment coefficient. The criterion layer includes the threshold adjustment coefficient corresponding to the geological comprehensive information, rainfall information, wind speed information, and mining depth information. The scheme layer is a specific weight allocation scheme. The judgment matrix is ​​constructed by comparing the relative importance of each factor in the criterion layer; The judgment matrix is ​​decomposed into eigenvalues ​​to obtain the maximum eigenvalue and its corresponding eigenvector. The eigenvector is normalized to obtain the weight vector of the threshold adjustment coefficient of each factor.

[0010] A three-dimensional displacement sensor, comprising: A three-dimensional displacement measurement unit, used to collect displacement time series data of monitoring points; An edge computing module is connected to the three-dimensional displacement measurement unit, and the edge computing module includes: A data preprocessing unit receives the displacement time series data from the three-dimensional displacement measurement unit and performs filtering processing on the data to remove noise interference; A trend analysis and prediction unit is used to predict the displacement value of the monitoring point and output the displacement prediction value; A dynamic displacement threshold adjustment unit, used to adjust the dynamic displacement warning threshold according to the comprehensive geological information, rainfall information, wind speed information, and mining depth information of the monitoring point sent by the cloud server to the edge computing module; The intelligent decision-making and control unit compares the displacement prediction value with the dynamic displacement warning threshold. When the displacement prediction value is equal to or greater than the dynamic displacement warning threshold, an alarm signal is triggered or the action of related equipment is controlled; The data transmission unit is connected to the edge computing module and is used to transmit the data processed by the edge computing module to the cloud server. The data transmission unit adopts an optimized transmission protocol to reduce protocol overhead, and combines a reliable transmission mechanism with a real-time transmission protocol to ensure stable and fast data transmission.

[0011] Preferably, the three-dimensional displacement measurement unit is constructed using laser interferometry, capacitance or other principles capable of realizing three-dimensional displacement measurement.

[0012] Preferably, the data preprocessing unit in the edge computing module adopts Kalman filtering, mean filtering or other effective filtering algorithms to remove noise, and adopts dictionary coding, wavelet transform or other suitable data compression algorithms.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention introduces an edge computing module at the sensor device end to efficiently process and optimize the transmission of collected data, solve many problems in the traditional sensor data processing and transmission process, and improve the overall system performance and application flexibility; 2. The present invention introduces a trend analysis and prediction unit in the edge computing module, which can predict the future displacement value based on the displacement time series data of the monitoring point. At the same time, the dynamic displacement threshold adjustment unit can dynamically adjust the displacement warning threshold according to the on-site environmental conditions. By performing intelligent analysis on the predicted value of the trend analysis and prediction unit and the dynamic displacement warning threshold of the dynamic displacement threshold adjustment unit, early warning can be achieved, thereby increasing the safety of mining activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic flow chart of a real-time transmission method for a three-dimensional displacement sensor according to the present invention; Figure 2 It is a schematic diagram of a three-dimensional displacement sensor system of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solution of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] See also Figure 1-Figure 2 The present invention provides a technical solution: a real-time transmission method for a three-dimensional displacement sensor, comprising the following steps: The edge computing module obtains the displacement time series data of the monitoring point through data transmission with the three-dimensional displacement measurement unit; the displacement time series data refers to a data set of the displacement conditions of the monitoring point at different time points, which can be used to analyze the displacement movement laws of the monitoring point, predict future displacement conditions, etc.

[0017] The edge computing module performs stationarity test and difference processing on the displacement time series data to determine the order of the ARIMA model; the method is as follows: Determine whether the displacement time series data has stationary characteristics; If the displacement time series data is not stationary, then difference processing is performed to make the data stationary; The appropriate order combination of the ARIMA model is determined by observing the autocorrelation function and the partial autocorrelation function.

[0018] The autocorrelation function describes the correlation between a time series and its own lagged values. The partial autocorrelation function measures the correlation between a time series and its lagged values ​​after controlling for the influence of the intermediate lagged terms. For the ARIMA model, the values ​​of p and q are preliminarily determined by observing the autocorrelation function and the partial autocorrelation function graphs. If the autocorrelation function suddenly cuts off after a certain lag order (i.e., it approaches zero quickly), it may indicate the value of q; if the partial autocorrelation function suddenly cuts off after a certain lag order, it may indicate the value of p.

[0019] A displacement trend prediction model based on the ARIMA model is built in the edge computing module to predict the future displacement of the monitoring point and obtain the displacement prediction value; The formula of the displacement trend prediction model based on the ARIMA model is as follows: ; Among them, p is the autoregressive order, q is the moving average order, is the i-th monitoring point The predicted value at time, represents the number of future time steps, is the mean of the time series; is the autoregression coefficient, which is used to measure the influence of past displacement values ​​on the current predicted displacement values ​​in the autoregression part of the ARIMA model. For example, Represents the influence weight of the displacement of the previous time step on the current predicted displacement, Represents the weight of the displacement of the previous two time steps on the current predicted displacement, and so on. These coefficients are estimated from historical displacement data through model training (such as least squares method, etc.) to reflect the autocorrelation of displacement data.

[0020] is the moving average coefficient, which is used to measure the influence of past forecast errors (white noise) on the current forecast displacement value in the moving average part (MA part) of the ARIMA model. For example, Represents the weight of the impact of the prediction error of the previous time step on the current prediction displacement, represents the weight of the impact of the prediction error of the previous two time steps on the current predicted displacement, and so on. These coefficients are also estimated from historical data through model training.

[0021] is the index variable, in the autoregressive part of the formula , Used to traverse the past time steps corresponding to the autoregressive order p. For example, when p=3, will be 1, 2, and 3 respectively. This means that the model is predicting When , the effect of the displacement deviation from the mean in the past 1, 2, and 3 time steps on the current prediction value is considered. Specifically, Multiply Indicates the contribution of the displacement deviation from the mean in the previous time step to the current predicted displacement, Multiply It represents the contribution of the displacement deviation from the mean in the previous two time steps to the current predicted displacement, and so on.

[0022] The moving average part of the formula , Used to traverse the past time steps corresponding to the moving average order q. For example, when q=2, will be 1 and 2 respectively. This means that the model is predicting When , the impact of the prediction errors (white noise) of the past 1 and 2 time steps on the current prediction value is considered. Specifically, Multiply represents the contribution of the prediction error of the previous time step to the current predicted displacement, Multiply Represents the contribution of the prediction errors of the previous two time steps to the current predicted displacement.

[0023] is the i-th monitoring point at The actual displacement at time, minus the mean After that, we can get the deviation of displacement at that moment relative to the average level. This deviation is related to the autoregressive coefficient Multiplied together, they are used to calculate the contribution of past displacement deviations to future predicted displacements.

[0024] is a white noise series. White noise is a random process that represents the random fluctuations in the time series that cannot be explained by the autoregressive and moving average parts of the model. In the ARIMA model, the forecast error (i.e., the difference between the actual displacement and the predicted displacement) is assumed to follow a white noise distribution. The values ​​of these white noise series are obtained by fitting and estimating historical data during the model training process to take into account the random uncertainty factors in the displacement data.

[0025] The edge computing module transmits data with the cloud server through the data transmission unit. The cloud server sends information on factors that may affect the displacement monitoring value to the edge computing module arranged at each monitoring point at regular intervals, such as 1 hour, including comprehensive geological information, rainfall information, wind speed information, mining depth information, etc. of each monitoring point.

[0026] Based on the new comprehensive geological information, rainfall information, wind speed information, mining depth information and other factors that may affect the displacement monitoring value, the displacement warning threshold is dynamically adjusted to obtain a dynamic displacement warning threshold after comprehensive adjustment. In this embodiment, the comprehensive geological information, rainfall information, wind speed information and mining depth information are taken as examples, and the method is as follows: Calculate the threshold adjustment coefficients of comprehensive geological information, rainfall information, wind speed information, and mining depth information respectively; The threshold adjustment coefficient of comprehensive geological information is: ,in, It represents the comprehensive geological impact factor around the i-th monitoring point. Its value range is 0-1 and is determined based on geological factors such as stratum lithology, fault distribution, and the degree of development of joints and fissures. It is the strength coefficient affected by geological conditions and is also determined based on experience or tests. When the geological conditions around the monitoring point are complex (such as soft rock, many faults, and developed joints and fissures), The value is larger, It is also larger, which increases the adjusted displacement warning threshold. This is because in areas with poor geological conditions, the rock mass itself is more deformable, so the displacement threshold needs to be appropriately relaxed.

[0027] The threshold adjustment coefficient of rainfall information is: ,in, It is a weight coefficient determined based on experience and is used to adjust the degree of influence of different rainfall intervals on threshold adjustment. is the displacement exceeding threshold frequency during rainfall, is the frequency of displacement exceeding the threshold in normal weather; The threshold adjustment coefficient of wind speed information is: ,in, It is a coefficient determined according to the accuracy requirements and stability characteristics of the monitoring facilities and is used to adjust the degree of wind speed influence. To monitor the shaking amplitude of the facility, is the allowed shaking amplitude threshold; The threshold adjustment factor for mining depth information is: ,in, It is the reference value of the depth threshold initially set. It is a depth value predetermined based on the geological conditions of the mine and engineering experience. Increase the amount for mining depth; The weights of the threshold adjustment coefficients of geological comprehensive information, rainfall information, wind speed information, and mining depth information are calculated by the hierarchical analysis method, respectively: ; The calculation method is as follows: The problem of determining the weight of the threshold adjustment coefficient of the mine displacement monitoring and early warning system is divided into the target layer, the criterion layer and the scheme layer. The main task of the target layer is to reasonably determine the weight of the threshold adjustment coefficient. The criterion layer includes the threshold adjustment coefficient corresponding to the geological comprehensive information, rainfall information, wind speed information, and mining depth information. The scheme layer is a specific weight allocation scheme. The judgment matrix is ​​constructed by comparing the relative importance of each factor in the criterion layer; for example, for the comparison of the weights of the threshold adjustment coefficients corresponding to the mining depth and geological conditions, mining engineering experts, geological experts, etc. are invited to make judgments based on their experience and professional knowledge. If the experts believe that the importance of mining depth to the displacement threshold adjustment is twice that of geological conditions, then the value of the intersection of the row corresponding to the mining depth and the column corresponding to the geological conditions in the judgment matrix is ​​2, otherwise the value of the intersection of the row corresponding to the geological conditions and the column corresponding to the mining depth is 1 / 2.

[0028] Perform eigenvalue decomposition on the judgment matrix, find the maximum eigenvalue and its corresponding eigenvector, and normalize the eigenvector to obtain the weight vector of the threshold adjustment coefficient of each factor. For example, assuming that the eigenvector obtained after calculation is [0.4, 0.1, 0.1, 0.4], then the threshold adjustment coefficient weight of the geological comprehensive information is 0.4, the threshold adjustment coefficient of the rainfall information is 0.1, the threshold adjustment coefficient of the wind speed information is 0.1, the threshold adjustment coefficient of the mining depth information is 0.4, and so on.

[0029] Calculate the comprehensive threshold adjustment factor: ; Calculate the synthetically adjusted dynamic displacement threshold: ;in, is the initial displacement warning threshold.

[0030] Compare the predicted displacement value with the dynamic displacement threshold, and trigger an early warning when the predicted value is equal to or exceeds the dynamic displacement threshold; The edge computing module sends the displacement data monitored in real time by the three-dimensional displacement measurement unit, the predicted value of the displacement trend prediction model, and early warning information to the cloud server through the data transmission unit.

[0031] A three-dimensional displacement sensor, such as Figure 2 As shown, including: A three-dimensional displacement measurement unit, used to collect displacement time series data of monitoring points; An edge computing module is connected to the three-dimensional displacement measurement unit, and the edge computing module includes: A data preprocessing unit receives the displacement time series data from the three-dimensional displacement measurement unit and performs filtering processing on the data to remove noise interference; A trend analysis and prediction unit is used to predict the displacement value of the monitoring point and output the displacement prediction value; The dynamic displacement threshold adjustment unit is used to adjust the dynamic displacement warning threshold according to the comprehensive geological information, rainfall information, wind speed information, and mining depth information of the monitoring point sent by the cloud server to the edge computing module.

[0032] The intelligent decision-making and control unit compares the displacement prediction value with the dynamic displacement warning threshold. When the displacement prediction value is equal to or greater than the dynamic displacement warning threshold, an alarm signal is triggered or the action of related equipment is controlled; The data transmission unit is connected to the edge computing module and is used to transmit the data processed by the edge computing module to the cloud server. The data transmission unit adopts an optimized transmission protocol to reduce protocol overhead, and combines a reliable transmission mechanism with a real-time transmission protocol to ensure stable and fast data transmission.

[0033] The three-dimensional displacement measurement unit is constructed using laser interference, capacitance or other principles capable of realizing three-dimensional displacement measurement.

[0034] The data preprocessing unit in the edge computing module uses Kalman filtering, mean filtering or other effective filtering algorithms to remove noise, and uses dictionary coding, wavelet transform or other suitable data compression algorithms. The implementation of the Kalman filtering algorithm is as follows: First, define the state equation and measurement equation of the system, and determine the relevant parameters in the Kalman filtering algorithm according to the sampling period of the sensor and the characteristics of the measurement noise and system noise, such as the state transfer matrix, measurement matrix, process noise covariance matrix and measurement noise covariance matrix. In the code, the prediction and update steps are continuously performed through the loop structure to perform real-time filtering on the collected raw displacement data. The implementation of the data compression algorithm based on dictionary coding first constructs a dictionary data structure. During the data processing process, the continuously appearing data sequence is matched with the entries in the dictionary. If the match is successful, the index in the dictionary is used instead of the original data sequence for transmission, thereby realizing data compression. During decompression, the original data sequence is restored according to the dictionary index.

[0035] The present invention introduces an edge computing module on the sensor device side to enhance the data processing capability of the device side and reduce the computing burden of the cloud server. At the same time, the safety warning threshold of the present application can be dynamically adjusted with changes in the environment, thereby improving the safety of mining activities.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time transmission method for a three-dimensional displacement sensor, characterized in that: The following steps are involved: The edge computing module obtains the displacement time series data of the monitoring point through data transmission with the three-dimensional displacement measurement unit; The edge computing module performs stationarity test and difference processing on the displacement time series data to determine the order of the ARIMA model; A displacement trend prediction model based on the ARIMA model is built in the edge computing module to predict the future displacement of the monitoring point and obtain the displacement prediction value; The edge computing module transmits data with the cloud server through the data transmission unit. The cloud server sends the geological comprehensive information, rainfall information, wind speed information, and mining depth information of each monitoring point to the edge computing module arranged at each monitoring point at regular intervals. Based on the new geological comprehensive information, rainfall information, wind speed information, and mining depth information, the displacement warning threshold is dynamically adjusted to obtain a dynamic displacement warning threshold after comprehensive adjustment; Compare the predicted displacement value with the dynamic displacement threshold, and trigger an early warning when the predicted value is equal to or exceeds the dynamic displacement threshold; The edge computing module sends the displacement data monitored in real time by the three-dimensional displacement measurement unit, the predicted value of the displacement trend prediction model, and early warning information to the cloud server through the data transmission unit.

2. A three-dimensional displacement sensor real-time transmission method according to claim 1, characterized in that: The method of performing stationarity test and difference processing on the displacement time series data to determine the order of the ARIMA model is as follows: Determine whether the displacement time series data has stationary characteristics; If the displacement time series data is not stationary, then difference processing is performed to make the data stationary; The appropriate order combination of the ARIMA model is determined by observing the autocorrelation function and the partial autocorrelation function.

3. A three-dimensional displacement sensor real-time transmission method according to claim 2, characterized in that: The formula of the displacement trend prediction model based on the ARIMA model is as follows: ； Among them, p is the autoregressive order, q is the moving average order, is the i-th monitoring point The predicted value at time, represents the number of future time steps, is the mean of the time series, is the autoregression coefficient, is the moving average coefficient, is the index variable, is the i-th monitoring point at The actual displacement at time, is a white noise sequence.

4. A three-dimensional displacement sensor real-time transmission method according to claim 1, characterized in that: The method for dynamically adjusting the displacement warning threshold based on the new geological comprehensive information, rainfall information, wind speed information, and mining depth information to obtain the comprehensively adjusted dynamic displacement warning threshold is as follows: Calculate the threshold adjustment coefficients of comprehensive geological information, rainfall information, wind speed information, and mining depth information respectively; The threshold adjustment coefficient of comprehensive geological information is: ; in, represents the comprehensive geological impact factor around the ith monitoring point, is the geological condition influence strength coefficient; The threshold adjustment coefficient of rainfall information is: ; in, It is a weight coefficient determined based on experience and is used to adjust the degree of influence of different rainfall intervals on threshold adjustment. is the displacement exceeding threshold frequency during rainfall, is the frequency of displacement exceeding the threshold in normal weather; The threshold adjustment coefficient of wind speed information is: ; in, It is a coefficient determined according to the accuracy requirements and stability characteristics of the monitoring facilities and is used to adjust the degree of wind speed influence. To monitor the shaking amplitude of the facility, is the threshold of the allowable shaking amplitude; the threshold adjustment coefficient of the mining depth information is: ; in, It is the reference value of the depth threshold initially set. It is a depth value predetermined based on the geological conditions of the mine and engineering experience. Increase the amount for mining depth; The weights of the threshold adjustment coefficients of geological comprehensive information, rainfall information, wind speed information, and mining depth information are calculated by the hierarchical analysis method, respectively: ; Calculate the comprehensive threshold adjustment factor: ; Calculate the synthetically adjusted dynamic displacement threshold: ;in, is the initial displacement warning threshold.

5. A three-dimensional displacement sensor real-time transmission method according to claim 4, characterized in that: The method of calculating the weights of the threshold adjustment coefficients of geological comprehensive information, rainfall information, wind speed information, and mining depth information respectively by using the hierarchical analysis method is as follows: The problem of determining the weight of the threshold adjustment coefficient of the mine displacement monitoring and early warning system is divided into the target layer, the criterion layer and the scheme layer. The main task of the target layer is to reasonably determine the weight of the threshold adjustment coefficient. The criterion layer includes the threshold adjustment coefficient corresponding to the geological comprehensive information, rainfall information, wind speed information, and mining depth information. The scheme layer is a specific weight allocation scheme. The judgment matrix is ​​constructed by comparing the relative importance of each factor in the criterion layer; The judgment matrix is ​​decomposed into eigenvalues ​​to obtain the maximum eigenvalue and its corresponding eigenvector. The eigenvector is normalized to obtain the weight vector of the threshold adjustment coefficient of each factor.

6. A three-dimensional displacement sensor, used to implement the three-dimensional displacement sensor real-time transmission method according to claim 1, characterized in that: include: A three-dimensional displacement measurement unit, used to collect displacement time series data of monitoring points; An edge computing module is connected to the three-dimensional displacement measurement unit, and the edge computing module includes: A data preprocessing unit receives the displacement time series data from the three-dimensional displacement measurement unit and performs filtering processing on the data to remove noise interference; A trend analysis and prediction unit is used to predict the displacement value of the monitoring point and output the displacement prediction value; A dynamic displacement threshold adjustment unit, used to adjust the dynamic displacement warning threshold according to the comprehensive geological information, rainfall information, wind speed information, and mining depth information of the monitoring point sent by the cloud server to the edge computing module; The intelligent decision-making and control unit compares the displacement prediction value with the dynamic displacement warning threshold. When the displacement prediction value is equal to or greater than the dynamic displacement warning threshold, an alarm signal is triggered or the action of related equipment is controlled; The data transmission unit is connected to the edge computing module and is used to transmit the data processed by the edge computing module to the cloud server. The data transmission unit adopts an optimized transmission protocol to reduce protocol overhead, and combines a reliable transmission mechanism with a real-time transmission protocol to ensure stable and fast data transmission.

7. A three-dimensional displacement sensor according to claim 6, characterized in that: The three-dimensional displacement measurement unit is constructed by using laser interference, capacitance or other principles capable of realizing three-dimensional displacement measurement.

8. A three-dimensional displacement sensor according to claim 6, characterized in that: The data preprocessing unit in the edge computing module uses Kalman filtering, mean filtering or other effective filtering algorithms to remove noise, and uses dictionary coding, wavelet transform or other suitable data compression algorithms.

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