CN-121994189-A - Colliery building foundation subsides monitoring early warning system based on intelligent sensor

CN121994189ACN 121994189 ACN121994189 ACN 121994189ACN-121994189-A

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent sensor-based coal mine building foundation settlement monitoring and early warning system, which comprises a processor and a memory, wherein the processor executes a computer program of the memory to realize the following steps of acquiring an initial threshold value of any monitoring index at any monitoring point of a target coal mine building at the current moment; acquiring target environment indexes with overrun environment data, carrying out self-adaptive adjustment on an initial threshold according to the influence of the data change of each target environment index on the monitoring data of any monitoring index and the data change condition of any monitoring index in a local time range of the current moment, obtaining the self-adaptive threshold of any monitoring index at any monitoring point at the current moment, carrying out monitoring and early warning on the foundation settlement risk of a target coal mine building at the future moment according to the self-adaptive threshold of each monitoring index at each monitoring point, and improving the early warning accuracy.

Inventors

GE HAIBIN
ZHANG HONGMENG
WANG JIANGANG
Li Tongshuai

Assignees

济宁市金桥煤矿

Dates

Publication Date: 20260508
Application Date: 20260330

Claims (10)

1. The coal mine building foundation settlement monitoring and early warning system based on the intelligent sensor comprises a memory, a processor and a computer program stored in the memory and running on the processor, and is characterized in that the computer program is implemented when executed by the processor as follows: Acquiring at least one monitoring index for monitoring the foundation settlement risk of a target coal mine building, and acquiring monitoring data of any monitoring index at each moment in a preset period of time up to the current moment at any monitoring point of the target coal mine building according to a preset sampling frequency and an initial threshold value of any monitoring index at the current moment; acquiring environment data of at least two preset environment indexes at each moment in the preset time period, marking a preset environment index with overrun environment data continuously at least at preset times in the preset time period as a target environment index, and if the number of the target environment indexes is not 0, acquiring an adjustment coefficient for adaptively adjusting the initial threshold value at the current moment according to the influence of the data change of each target environment index in the preset time period on the monitoring data of any monitoring index and the data change condition of any monitoring index in a local time range up to the current moment; And carrying out self-adaptive adjustment on the initial threshold according to the adjustment coefficient to obtain the self-adaptive threshold of any monitoring index at any monitoring point at the current moment, and carrying out monitoring and early warning on the foundation settlement risk of the target coal mine building at the future moment according to the self-adaptive threshold of each monitoring index at each monitoring point.
2. The intelligent sensor-based monitoring and early warning system for foundation settlement of coal mine building according to claim 1, wherein the marking of the preset environmental index, which is an overrun of environmental data at least at a preset number of times continuously within the preset period, as the target environmental index comprises: According to the monitoring data of any monitoring index at any monitoring point at each moment in a preset time period up to the current moment, at least one normal subperiod is obtained, the monitoring data of any monitoring index at each moment in the normal subperiod is smaller than the initial threshold, and the duration of the normal subperiod is greater than or equal to the preset shortest duration; obtaining the maximum value and the minimum value of each preset environmental index in all normal subintervals according to the environmental data of each preset environmental index at each time in each normal subinterval, and obtaining the safety range of each preset environmental index according to the maximum value and the minimum value corresponding to each preset environmental index; For any preset environmental index, if the environmental data of the any preset environmental index at each time in any subinterval in the preset time interval exceeds the safety range of the any preset environmental index, and the environmental data of other preset environmental indexes except the any preset environmental index at each time in any subinterval does not exceed the safety range of each other preset environmental index, wherein the any subinterval at least comprises a preset number of moments, marking the any subinterval as the data change time interval of the any preset environmental index; And if the number of the data change time periods of any preset environmental index is not 0, marking any preset environmental index as a target environmental index.
3. The intelligent sensor-based monitoring and early warning system for the settlement of the coal mine building foundation according to claim 2, wherein the obtaining the adjustment coefficient for adaptively adjusting the initial threshold value at the current time according to the influence of the data change of each target environmental index in the preset period on the monitoring data of any one of the monitoring indexes and the data change condition of any one of the monitoring indexes in the local time range up to the current time comprises: according to the influence of the data change of each target environment index in the preset period on the monitoring data of any monitoring index at any monitoring point, the influence weight of each target environment index on any monitoring index at any monitoring point is respectively obtained; Performing smoothing processing on the monitoring data of any monitoring index at any monitoring point at each moment in the preset period to obtain smoothing data of any monitoring index at any monitoring point at each moment in the preset period; Performing straight line fitting on smooth data of any monitoring index at any monitoring point in a local time range of the current moment, obtaining a fitted straight line, taking the slope of the fitted straight line as a local trend characteristic value of any monitoring index in the local time range of the current moment, obtaining a local trend characteristic value of any monitoring index in the local time range of the previous moment of the current moment, marking the local trend characteristic value as a historical local trend characteristic value, calculating the absolute value of the difference value between the local trend characteristic value of any monitoring index in the local time range of the current moment and the historical local trend characteristic value, and obtaining the local trend change degree of any monitoring index in the local time range of the current moment; Calculating difference coefficients of smooth data of any monitoring index at any monitoring point in all time points in a local time range of the current moment, obtaining local fluctuation degrees of any monitoring index in the local time range of the current moment, obtaining local fluctuation degrees of any monitoring index in a local time range of the previous moment of the current moment, recording the local fluctuation degrees as historical local fluctuation degrees, and calculating difference absolute values between the fluctuation degrees of any monitoring index in the local time range of the current moment and the historical local fluctuation degrees to obtain local fluctuation change degrees of any monitoring index in the local time range of the current moment; Normalizing the product between the local trend change degree and the local fluctuation change degree to obtain a local change characteristic value of any monitoring index at any monitoring point in a local time range up to the current moment; And acquiring an adjustment coefficient for adaptively adjusting the initial threshold value at the current moment according to the local change characteristic value and the influence weight corresponding to each target environment index.
4. The intelligent sensor-based monitoring and early warning system for the settlement of the coal mine building foundation according to claim 3, wherein the step of respectively acquiring the influence weight of each target environmental index on any monitoring index at any monitoring point according to the influence of the data change of each target environmental index in the preset period on the monitoring data of any monitoring index at any monitoring point comprises the following steps: Calculating standard deviations of the monitoring data of any monitoring index at any monitoring point at all times in each normal subperiod respectively, and calculating the average value of all standard deviations to obtain the allowable fluctuation degree of any monitoring index; For any data change period of any target environmental index, the environmental data of the any target environmental index at each time point in the any data change period is formed into an environmental data sequence, and the monitoring data of the any monitoring index at any monitoring point at each time point in the any data change period is formed into a monitoring data sequence; calculating standard deviation of all data in the monitoring data sequence to obtain fluctuation degree of any monitoring index in any data change period, and calculating absolute value of difference between the fluctuation degree and the allowable fluctuation degree to obtain abnormal fluctuation degree of any monitoring index in any data change period; Calculating the absolute value of a spearman correlation coefficient between the environment data sequence and the monitoring data sequence to obtain the correlation degree between the environment data sequence and the monitoring data sequence; Normalizing the product of the abnormal fluctuation degree and the correlation degree to obtain the response degree of any target environmental index to any monitoring index in any data change period; The response degree of any target environmental index to any monitoring index in each data change period of any target environmental index is obtained respectively, and the average value of all the response degrees is calculated to obtain the influence degree of any target environmental index to any monitoring index; Acquiring fusion weights of any target environmental index according to the intensity of influence of the any target environmental index on any monitoring index in each data change period of the any target environmental index; And calculating the product of the influence degree and the fusion weight to obtain the influence weight of any target environment index on any monitoring index at any monitoring point.
5. The intelligent sensor-based monitoring and early warning system for the settlement of a coal mine building foundation according to claim 4, wherein the acquiring the fusion weight of any target environmental index according to the intensity of the influence of the any target environmental index on the any monitoring index in each data change period of the any target environmental index comprises: For any data change period of any target environmental index, obtaining a maximum value and a minimum value of the any target environmental index in the any data change period, calculating a difference absolute value between the maximum value and the minimum value, marking the absolute value as an environmental difference value, calculating a difference absolute value of monitoring data of the any monitoring index at any monitoring point at the moment corresponding to the maximum value and the moment corresponding to the minimum value, marking the absolute value as a monitoring difference value, taking the environmental difference value as a denominator, taking the monitoring difference value as a numerator, and obtaining a sensitivity coefficient of the any target environmental index to the any monitoring index in the any data change period; Calculating standard deviation of all data of any target environmental index in any data change period, and calculating the product between the standard deviation and the sensitivity coefficient to obtain the intensity of influence of any target environmental index on any monitoring index in any data change period; The intensity of the influence of any one target environmental index on any one monitoring index in each data change period of any one target environmental index is obtained respectively, and the average value of all intensity is calculated to obtain the contribution degree of any one target environmental index on any one monitoring index; And respectively acquiring the contribution degree of each target environment index to any monitoring index, and calculating the duty ratio of the contribution degree of any target environment index to any monitoring index in the accumulation sum of all the contribution degrees to obtain the fusion weight of any target environment index.
6. The intelligent sensor-based coal mine building foundation settlement monitoring and early warning system according to claim 3, wherein the obtaining the adjustment coefficient for adaptively adjusting the initial threshold value at the current time according to the local variation characteristic value and the influence weight corresponding to each target environment index comprises: accumulating the influence weights corresponding to all the target environment indexes to obtain comprehensive environment weights, and calculating the product between the comprehensive environment weights and the local change characteristic values to obtain a first factor; Acquiring a global change characteristic value of any monitoring index in the preset time period up to the current moment according to the slope of a fitting straight line of the smooth data of any monitoring index in the preset time period up to the current moment and the difference coefficient of the smooth data of all the moments; Calculating the absolute value of the difference between the global change characteristic value and the local change characteristic value, normalizing the absolute value of the difference to obtain a data change characteristic difference, and subtracting the data change characteristic difference from a constant 1 to obtain a second factor; and carrying out weighted summation on the first factor and the second factor to obtain an adjustment coefficient for carrying out self-adaptive adjustment on the initial threshold value at the current moment.
7. The intelligent sensor-based coal mine building foundation settlement monitoring and early warning system according to claim 6, wherein if the number of target environmental indexes is 0, the obtaining of the adjustment coefficient for adaptively adjusting the initial threshold at the current moment comprises: And setting the credibility of the local change characteristic value to be 1, and obtaining an adjustment coefficient for adaptively adjusting the initial threshold value at the current moment according to the credibility, the global change characteristic value and the local change characteristic value.
8. The intelligent sensor-based coal mine building foundation settlement monitoring and early warning system according to claim 1, wherein the adaptive adjustment of the initial threshold according to the adjustment coefficient is performed to obtain an adaptive threshold of the any monitoring index at the any monitoring point at the current time, and the system comprises: Acquiring an adjustment coefficient for adaptively adjusting the initial threshold value at the previous moment of the current moment, recording the adjustment coefficient as a historical adjustment coefficient, calculating the sum of a constant 1 and the adjustment coefficient to obtain a sum result, and calculating the product of the sum result and the initial threshold value to obtain an adaptive threshold value of any monitoring index at any monitoring point at the current moment if the adjustment coefficient corresponding to the current moment is larger than the historical adjustment coefficient; If the adjustment coefficient corresponding to the current moment is equal to the historical adjustment coefficient, the initial threshold is used as the self-adaptive threshold of any monitoring index at any monitoring point at the current moment; And if the adjustment coefficient corresponding to the current moment is smaller than the historical adjustment coefficient, subtracting the adjustment coefficient from a constant 1 to obtain a target difference value, and calculating the product between the target difference value and the initial threshold value to obtain the self-adaptive threshold value of any monitoring index at any monitoring point at the current moment.
9. The intelligent sensor-based monitoring and early warning system for foundation settlement of coal mine building according to claim 1, wherein the monitoring and early warning of the foundation settlement risk of the target coal mine building at future time according to the adaptive threshold value of each monitoring index at each monitoring point comprises: If the monitoring data of any monitoring index at any monitoring point is larger than or equal to the self-adaptive threshold value of any monitoring index at any monitoring point, abnormal early warning is carried out on the foundation settlement risk of the target coal mine building at the future time.
10. The intelligent sensor-based monitoring and early warning system for foundation settlement of coal mine building according to claim 1, wherein the monitoring indexes comprise settlement amount, settlement rate and inclination angle.

Description

Colliery building foundation subsides monitoring early warning system based on intelligent sensor Technical Field The invention relates to the technical field of data processing, in particular to a coal mine building foundation settlement monitoring and early warning system based on an intelligent sensor. Background The settlement of the foundation of the coal mine building is one of the major potential safety hazards faced in the coal mining process. Coal mining activities, particularly underground mining, can result in cavities being formed beneath the foundation, disrupting the equilibrium state of the original geologic structure. With the continuous advance of exploitation, the foundation gradually loses the support below, and uneven settlement is further caused. The uneven settlement can cause serious damage to the ground construction of the coal mine, such as wellhead facilities (a derrick, a lifting machine room, a wellhead room and the like) can incline and deform due to foundation settlement to influence normal lifting and ventilation of the mine, equipment foundations of a coal preparation plant can crack due to uneven settlement to cause unstable operation of equipment and even damage, and foundation settlement of a coal storage plant can cause ground collapse to cause coal slipping and accumulation confusion to influence storage and transportation safety. Under the traditional mode, the foundation settlement risk is pre-warned by setting a fixed threshold value of core monitoring indexes such as settlement rate, accumulated settlement amount and the like, and the pre-warning is triggered when the data exceeds the threshold value. However, the geological conditions of the coal mine are complex, and the sedimentation rate can dynamically change along with the exploitation depth, soil layer properties and environmental factors (such as rainfall and temperature). The inability of the fixed threshold to adapt to such changes may lead to false positives or false negatives, for example, early warning may be frequently triggered by the fixed threshold during periods of naturally accelerated settling rates (e.g., softening of the soil in rainy seasons), resulting in "early warning fatigue", and risk may not be captured in time during abnormal periods of sudden acceleration of settling rates (e.g., slump precursors) if the threshold is set too high. Therefore, how to adaptively set various thresholds of monitoring indexes for monitoring the settlement risk of the foundation of the coal mine building so as to improve the accuracy of early warning on the settlement risk of the foundation of the coal mine building becomes a problem to be solved. Disclosure of Invention In view of the above, the embodiment of the invention provides a coal mine building foundation settlement monitoring and early warning system based on an intelligent sensor, which aims to solve the problem of how to adaptively set various thresholds of monitoring indexes for monitoring the coal mine building foundation settlement risk so as to improve the accuracy of early warning the coal mine building foundation settlement risk. The embodiment of the invention provides an intelligent sensor-based coal mine building foundation settlement monitoring and early warning system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and is characterized in that the processor realizes the following steps when executing the computer program: Acquiring at least one monitoring index for monitoring the foundation settlement risk of a target coal mine building, and acquiring monitoring data of any monitoring index at each moment in a preset period of time up to the current moment at any monitoring point of the target coal mine building according to a preset sampling frequency and an initial threshold value of any monitoring index at the current moment; acquiring environment data of at least two preset environment indexes at each moment in the preset time period, marking a preset environment index with overrun environment data continuously at least at preset times in the preset time period as a target environment index, and if the number of the target environment indexes is not 0, acquiring an adjustment coefficient for adaptively adjusting the initial threshold value at the current moment according to the influence of the data change of each target environment index in the preset time period on the monitoring data of any monitoring index and the data change condition of any monitoring index in a local time range up to the current moment; And carrying out self-adaptive adjustment on the initial threshold according to the adjustment coefficient to obtain the self-adaptive threshold of any monitoring index at any monitoring point at the current moment, and carrying out monitoring and early warning on the foundation settlement risk of the target coal mine building at the future moment according to the