CN114882689A - Dam safety detection early warning method based on big data - Google Patents
Dam safety detection early warning method based on big data Download PDFInfo
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Abstract
The invention belongs to the technical field of safety early warning, and particularly relates to a dam safety detection early warning method based on big data, which comprises the following steps: s1, deploying hardware; s2, collecting data; and S3, calculating data. In step S3, the data processing system corrects the hazard score based on the historical temperature data and the length of the dam operation. The dam safety early warning evaluation method has the advantages that when dam safety early warning evaluation is carried out, multi-party data are considered, historical temperature data and operation time are introduced, so that a monitoring result is more accurate, safety of dam safety monitoring is improved, when safety of a dam is evaluated, dam danger scores are calculated and comprise displacement degree scores, stress degree scores and water quantity scores, danger score parameters are arranged in a data processing system, the data processing system compares the dam danger scores with the danger score parameters, safety scores are set by considering the multi-party data, the monitored data are visual to a home, and accuracy of the monitoring result is improved.
Description
Technical Field
The invention relates to the technical field of safety early warning, in particular to a dam safety detection early warning method based on big data.
Background
The dam safety monitoring is measurement and observation of a main body structure, a foundation, two-bank side slopes, related facilities and the surrounding environment of the hydraulic and hydroelectric engineering through instrument observation and inspection tour; the monitoring includes the instrument observation of fixed measuring points of the building according to a certain frequency, and also includes regular or irregular visual inspection and instrument exploration of large-scale objects on the outer surface and the inner part of the building.
Chinese patent publication No.: CN 111508216A. Disclosed is a method. An intelligent early warning method for dam safety monitoring data comprises the steps of early warning model establishment, threshold value drafting and mutual feed type early warning, wherein model sample data quality is improved through gross error identification and gross error processing, and different early warning models and indexes are established according to monitoring items, independent variable relevance, historical monitoring data quantity and historical monitoring data distribution: the method comprises the steps of establishing a stepwise regression model, a correlation vector machine model and a gray system model, wherein the established model can reflect the relation between independent variables and dependent variables more truly, the application range is wide, and real-time early warning is carried out on monitoring data according to measuring instruments, measuring point attributes, threshold values, early warning models and indexes. However, as the age of the dam increases, the emphasis on monitoring at different times may be of different importance.
Therefore, the conventional dam safety monitoring lacks acquisition of historical data, and monitoring results have deviation.
Disclosure of Invention
Therefore, the invention provides a dam safety detection early warning method based on big data, which is used for overcoming the problems that the dam safety monitoring in the prior art lacks acquisition of historical data and the monitoring result has deviation.
In order to achieve the above object, the present invention provides a dam safety detection early warning method based on big data, comprising,
s1, deploying hardware, including arranging a plurality of displacement sensors and a plurality of stress detection devices in the dam body for detecting the deformation displacement condition of the material in the dam body and the stress condition of each point; arranging a first temperature sensor group on the bottom surface of the dam body for detecting the temperature of water in the dam, arranging a second temperature sensor group in the dam body for detecting the temperature of each point of the dam body, arranging a water level detector on the dam body for detecting the water level in the dam, and arranging a data processing system for integrating and judging the detected data;
s2, data acquisition, each sensor, each detection device and each detector transmit detected data to the data processing system, the data processing system is connected with an external database, the external database can acquire weather information and upstream drainage information, and transmit an acquisition result to the data processing system;
s3, calculating data, evaluating risk, summarizing the acquired data by the data processing system, evaluating risk, and alarming by the data processing system when the risk is over;
in step S3, the data processing system corrects the hazard score based on the historical temperature data and the length of the dam operation.
Further, when the safety of the dam is evaluated, calculating a dam risk score F, wherein F is F1+ F2+ F3, wherein F1 is a displacement degree score, F2 is a stress degree score, and F3 is a water quantity score;
a danger scoring parameter Fc is arranged in the data processing system, the data processing system compares the dam danger scoring F with the danger scoring parameter Fc,
when F is less than or equal to Fc, the data processing system judges that the dam danger score is in a safety range;
when F > Fc, the data processing system determines that the dam hazard score is not within a safe range.
Further, for the displacement degree score F1, F1 ═ F1c × Cw × T1, where F1c is the initial displacement degree score, Cw is the temperature-to-displacement degree score correction parameter, T1 is the dam working time length-to-displacement degree score correction parameter, where,
the displacement degree initial score F1c is determined by the displacement value of each of the displacement sensor detectors;
the temperature-to-displacement degree grading and correcting parameter Cw is determined by real-time temperature and historical temperature data;
the dam working length-to-displacement degree grading correction parameter T1 is determined by the dam working length.
Further, the displacement of the point detected by each of the displacement sensor detectors and the result of the detection are transmitted to the data processing system, the distance of movement detected by the first displacement sensor is L1, the distance of movement detected by the second displacement sensor is L2, the distance of movement detected by the third displacement sensor is L3, the distance of movement detected by the nth displacement sensor … … is Ln,
the data processing system calculates an initial score of degree of displacement F1c,
and Li is the moving distance detected by the ith displacement sensor, and Pi is the compensation parameter for calculating the initial score of the displacement degree by the moving distance detected by the ith displacement sensor.
Further, for the temperature-to-displacement degree scoring correction parameter Cw, Cw is equal to C1 × b1+ C2 × b2, where C1 is the water temperature-to-displacement degree scoring correction parameter, C2 is the dam body temperature-to-displacement degree scoring correction parameter, b1 is the weight value of the water temperature-to-correction parameter Cw, and b2 is the weight value of the dam body temperature-to-correction parameter Cw.
Further, the first temperature sensor group detects the water temperature in the dam in real time and transmits the detected data to the data processing system, and for any moment, the data processing system calculates the average value Wp of all the current temperatures detected by the first temperature sensor group and records the calculation result;
the data processing system integrates the water temperature average value data to generate a temperature curve W (f) (t) which is a time variation curve of the water temperature in the dam, the data transmission system calculates a grading and correcting parameter C1 of the water temperature to the displacement degree,
wherein Wt is a temperature value at any time on the temperature curve W ═ f (t) in the detection time period t, and c1 is the calculation of the historical water temperature value pair CwAnd the compensation parameter, wherein Ws is the average value of the current water temperature detected by the temperature sensor, and c2 is the calculated compensation parameter of the current water temperature to Cw.
Further, the second temperature sensor group detects the temperature in the dam body in real time and transmits the detected data to the data processing system, and for any moment, the data processing system calculates the average value Mp of all the current temperatures detected by the second temperature sensor group and records the calculation result;
the data processing system integrates the average value data of the temperature in the dam body to generate a dam body temperature curve M (g) (t), wherein M (g) (t) is a curve of the temperature in the dam body of the dam changing along with time, the data transmission system calculates a dam body temperature-to-displacement degree grading correction parameter C2,
wherein, Mt is a temperature value at any time on a temperature curve M ═ g (t) in the detection time period t, C3 is a calculation compensation parameter of the historical dam temperature value to C2, Ms is a current dam temperature average value detected by the temperature sensor, and C4 is a calculation compensation parameter of the current dam temperature to Cw.
Further, the dam working time length is graded on the displacement degree to correct the parameter T1,
wherein a is a basic value of the correction parameter T1, K is a calculated adjustment value of the dam working time length T to the correction parameter T1, and K is less than 0.8.
Furthermore, m pressure detection devices are arranged and are respectively marked as a first pressure detection device and a second pressure detection device;
the stress detection devices detect the stress condition of each point and transmit the detection result to the data processing system, the stress value detected by the first pressure detection device is Y1, the stress value detected by the second pressure detection device is Y2,. the stress value detected by the mth pressure detection device is Ym,
for the stress degree score F2,
and the Yi is a stress value detected by the ith pressure detection device, the Qi is a calculation compensation parameter of the Yi corresponding force score, and the R is an adjustment parameter of the stress degree score.
Further, the water level sensor detects a current water level value H1 and transmits a detection result to the data processing system, the data processing system acquires weather information and upstream drainage information, the data processing system estimates a highest water level H2 in a period T1 according to the current water level value H1, the weather information and the upstream drainage information, a standard water level value Hb is arranged in the data processing system, and the data processing system calculates a water level score F3, wherein F3 is S (H2-Hb) And + Y, wherein S is a water volume score calculation adjusting parameter, and Y is a water volume score basic value.
Compared with the prior art, the dam safety early warning method has the advantages that when dam safety early warning evaluation is carried out, multi-party data are considered, meanwhile, historical temperature data and operation time are introduced, so that the monitoring result is more accurate, and the safety of dam safety monitoring is improved.
Further, when the safety of the dam is evaluated, dam danger scores are calculated and consist of displacement degree scores, stress degree scores and water quantity scores, danger score parameters are arranged in the data processing system, the data processing system compares the dam danger scores with the danger score parameters, and the monitored data are visual to home by considering the multi-party data and setting the safety scores, so that the accuracy of monitoring results is improved.
Particularly, the displacement degree score is composed of a displacement degree initial score, a temperature-to-displacement degree score correction parameter and a dam working time-to-displacement degree score correction parameter, wherein the displacement degree initial score is determined by the displacement value of each displacement sensor detector; the temperature-to-displacement degree grading and correcting parameters are determined by real-time temperature and historical temperature data; the grading and correcting parameters of the dam working time to the displacement degree are determined by the dam working time, a plurality of displacement sensors are arranged in the dam body of the dam and used for detecting the displacement condition of each point in the dam, the displacement degree is adjusted through temperature when the displacement degree is calculated, the influence of temperature change on the position change of the displacement sensors is reduced, and meanwhile, the influence of self-deformation due to aging is reduced by setting time correcting parameters.
Especially, the displacement sensors are arranged in n number, multipoint monitoring is carried out, accurate displacement distance scoring is obtained, different compensation parameters are set for different places, and therefore the evaluation result is more accurate.
Particularly, for the temperature-to-displacement degree grading and correcting parameter, the temperature-to-displacement degree grading and correcting parameter and the dam body temperature-to-displacement degree grading and correcting parameter form the temperature-to-displacement degree grading and correcting parameter, and when the temperature-to-displacement degree grading and correcting parameter is calculated, the water temperature and the internal temperature of the dam body are considered, so that the calculation result is more accurate.
Particularly, the first temperature sensor group detects the water temperature in the dam in real time and transmits the detected data to the data processing system, and for any time, the data processing system calculates the average value of all the current temperatures detected by the first temperature sensor group and records the calculation result. The data processing system integrates the average water temperature data to generate a temperature curve, and historical water temperature and real-time water temperature are considered when calculating the water temperature to displacement degree grading correction parameters, so that the calculation result is more accurate.
Particularly, the second temperature sensor group detects the temperature in the dam body in real time and transmits the detected data to the data processing system, and for any moment, the data processing system calculates the average value of all the current temperatures detected by the second temperature sensor group and records the calculation result; and the data processing system integrates the average data of the temperature in the dam body to generate a dam body temperature curve. When calculating the dam temperature displacement degree grading correction parameter, the historical temperature and the real-time temperature are considered, so that the calculation result is more accurate.
Particularly, in the early stage of dam operation, the interior is unstable, the displacement can be greatly changed in a short time, after the displacement is stable, the displacement gradually approaches to a fixed variable related to time, and the calculated numerical value is more accurate by adjusting the working time of the dam to grade and correct parameters of the displacement degree.
Furthermore, the pressure detection devices are m in number, each stress detection device detects the stress condition of each point and transmits the detection result to the data processing system, the stress degree of the dam is different in different places, and the stress of each point is set with compensation parameters, so that the accurate stress condition of the dam body can be reasonably and integrally obtained, and the calculation result is more accurate.
Further, the water level sensor detects a current water level value H1 and transmits a detection result to the data processing system, the data processing system acquires weather information and upstream drainage information, the data processing system estimates a highest water level H2 in a period T1 according to the current water level value H1, the weather information and the upstream drainage information, a standard water level value Hb is arranged in the data processing system, and the data processing system calculates a water volume score F3 and a water volume score F3S (H2-Hb) And + Y, evaluating the water level condition in a certain time by acquiring the external condition in real time, wherein the higher the water level is, the larger the water quantity score value is, and the exponential increase is formed in the conversion process, so that the dangerous case is acquired more timely, and the evaluation accuracy is improved.
Drawings
Fig. 1 is a schematic flow chart of a dam safety detection early warning method based on big data in an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a dam safety detection and early warning method based on big data according to an embodiment of the present invention.
The invention provides a dam safety detection early warning method based on big data, which comprises the following steps,
s1, deploying hardware, including arranging a plurality of displacement sensors and a plurality of stress detection devices in the dam body for detecting the deformation displacement condition of the material in the dam body and the stress condition of each point; arranging a first temperature sensor group on the bottom surface of the dam body for detecting the temperature of water in the dam, arranging a second temperature sensor group in the dam body for detecting the temperature of each point of the dam body, arranging a water level detector on the dam body for detecting the water level in the dam, and arranging a data processing system for integrating and judging the detected data;
s2, data acquisition, each sensor, each detection device and each detector transmit detected data to the data processing system, the data processing system is connected with an external database, the external database can acquire weather information and upstream drainage information, and transmit an acquisition result to the data processing system;
s3, calculating data, evaluating risk, summarizing the acquired data by the data processing system, evaluating risk, and alarming by the data processing system when the risk is over;
in step S3, the data processing system corrects the hazard score based on the historical temperature data and the length of the dam operation.
Historical temperature data and running time are quoted in the detection process, so that the monitoring result is more accurate, and the safety of dam safety monitoring is improved.
Further, when the safety of the dam is evaluated, calculating a dam risk score F, wherein F is F1+ F2+ F3, wherein F1 is a displacement degree score, F2 is a stress degree score, and F3 is a water quantity score;
a danger scoring parameter Fc is arranged in the data processing system, the data processing system compares the dam danger score F with the danger scoring parameter Fc,
when F is less than or equal to Fc, the data processing system judges that the dam danger score is in a safety range;
when F > Fc, the data processing system determines that the dam hazard score is not within a safe range.
The monitored data is visual to the home by setting the safety score, and the accuracy of the monitoring result is improved.
Further, for the displacement degree score F1, F1 ═ F1c × Cw × T1, where F1c is the initial displacement degree score, Cw is the temperature-to-displacement degree score correction parameter, T1 is the dam working time length-to-displacement degree score correction parameter, where,
the displacement degree initial score F1c is determined by the displacement value of each of the displacement sensor detectors;
the temperature-to-displacement degree grading correction parameter Cw is determined by real-time temperature and historical temperature data;
the dam working length-to-displacement degree grading correction parameter T1 is determined by the dam working length.
The dam body of dam sets up a plurality of displacement sensor for detect the displacement condition of each point in the dam, when calculating the displacement degree, adjust the displacement degree through the temperature, reduce the influence of temperature transformation to displacement sensor position transformation, simultaneously, through setting up time correction parameter, reduce because the influence of ageing self-deformation.
Further, n displacement sensors are arranged and are respectively marked as a first displacement sensor, a second displacement sensor and an … … nth displacement sensor; displacement of the detection point of each displacement sensor detector and transmitting the detection result to the data processing system, wherein the movement distance detected by the first displacement sensor is L1, the movement distance detected by the second displacement sensor is L2, the movement distance detected by the third displacement sensor is L3, the movement distance detected by the n-th displacement sensor of … … is Ln,
the data processing system calculates an initial score of degree of displacement F1c,
and Li is the moving distance detected by the ith displacement sensor, and Pi is the compensation parameter for calculating the initial score of the displacement degree by the moving distance detected by the ith displacement sensor.
And multi-point monitoring is carried out, accurate displacement distance scores are obtained, and different compensation parameters are set for different places, so that the evaluation result is more accurate.
Further, for the temperature-to-displacement degree scoring correction parameter Cw, Cw is equal to C1 × b1+ C2 × b2, where C1 is the water temperature-to-displacement degree scoring correction parameter, C2 is the dam body temperature-to-displacement degree scoring correction parameter, b1 is the weight value of the water temperature-to-correction parameter Cw, and b2 is the weight value of the dam body temperature-to-correction parameter Cw.
When calculating the temperature-to-displacement degree grading correction parameter, the water temperature and the internal temperature of the dam body are considered, so that the calculation result is more accurate.
Further, the first temperature sensor group detects the water temperature in the dam in real time and transmits the detected data to the data processing system, and for any moment, the data processing system calculates the average value Wp of all the current temperatures detected by the first temperature sensor group and records the calculation result;
the data processing system integrates the water temperature average value data to generate a temperature curve W (f) (t) which is a time variation curve of the water temperature in the dam, the data transmission system calculates a grading and correcting parameter C1 of the water temperature to the displacement degree,
whereinWt is a temperature value at any time on the temperature curve W ═ f (t) in the detection time period t, c1 is a calculation compensation parameter of the historical water temperature value pair Cw, Ws is an average value of the current water temperatures detected by the temperature sensors, and c2 is a calculation compensation parameter of the current water temperature pair Cw.
When the water temperature-to-displacement degree grading correction parameter is calculated, the historical water temperature and the real-time water temperature are considered, so that the calculation result is more accurate.
Further, the second temperature sensor group detects the temperature in the dam body in real time and transmits the detected data to the data processing system, and for any time, the data processing system calculates the average value Mp of all current temperatures detected by the second temperature sensor group and records the calculation result;
the data processing system integrates the average value data of the temperature in the dam body to generate a dam body temperature curve M (g) (t), wherein M (g) (t) is a curve of the temperature in the dam body changing along with time, the data transmission system calculates a dam body temperature to displacement degree grading correction parameter C2,
wherein, Mt is a temperature value at any time on a temperature curve M ═ g (t) in the detection time period t, C3 is a calculation compensation parameter of the historical dam temperature value to C2, Ms is a current dam temperature average value detected by the temperature sensor, and C4 is a calculation compensation parameter of the current dam temperature to Cw.
When calculating the dam temperature displacement degree grading correction parameter, the historical temperature and the real-time temperature are considered, so that the calculation result is more accurate.
Further, the dam working time length is graded on the displacement degree to correct the parameter T1,
wherein a is a basic value of the correction parameter T1, K is a calculated adjustment value of the dam working time length T to the correction parameter T1, and K is less than 0.8.
In this example, K is 0.3.
In the early stage of dam operation, the interior is unstable, the displacement can change greatly in a short time, the displacement gradually approaches to a fixed variable related to time after the displacement is stable, and the calculated numerical value is more accurate by adjusting the grading correction parameter of the displacement degree during dam operation.
Furthermore, m pressure detection devices are arranged and are respectively marked as a first pressure detection device and a second pressure detection device;
the stress detection devices detect the stress condition of each point and transmit the detection result to the data processing system, the stress value detected by the first pressure detection device is Y1, the stress value detected by the second pressure detection device is Y2,. the stress value detected by the mth pressure detection device is Ym,
for the stress degree score F2,
and the Yi is a stress value detected by the ith pressure detection device, the Qi is a calculation compensation parameter of the Yi corresponding force score, and the R is an adjustment parameter of the stress degree score.
The stress degree of the dam is different in different places, and the accurate stress condition of the dam body can be reasonably and integrally obtained by setting compensation parameters for stress of each point, so that the calculation result is more accurate.
Further, the water level sensor detects a current water level value H1 and transmits a detection result to the data processing system, the data processing system acquires weather information and upstream drainage information, the data processing system estimates a highest water level H2 in a period T1 according to the current water level value H1, the weather information and the upstream drainage information, a standard water level value Hb is arranged in the data processing system, and the data processing system calculates a water volume score F3 and a water volume score F3 ═ S (H2-Hb) And + Y, wherein S is a water volume score calculation adjusting parameter, and Y is a water volume score basic value.
Through acquiring external conditions in real time, the water level condition in a certain time is evaluated, the higher the water level is, the larger the water quantity score value is, and the transformation process becomes exponential growth, so that the dangerous case is acquired more timely, and the evaluation accuracy is improved.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is apparent to those skilled in the art that the scope of the present invention is not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can be within the protection scope of the invention.
Claims (10)
1. A dam safety detection early warning method based on big data is characterized by comprising the following steps,
s1, deploying hardware, including arranging a plurality of displacement sensors and a plurality of stress detection devices in the dam body for detecting the deformation displacement condition of the material in the dam body and the stress condition of each point; arranging a first temperature sensor group on the bottom surface of the dam body for detecting the temperature of water in the dam, arranging a second temperature sensor group in the dam body for detecting the temperature of each point of the dam body, arranging a water level detector on the dam body for detecting the water level in the dam, and arranging a data processing system for integrating and judging the detected data;
s2, data acquisition, each sensor, each detection device and each detector transmit detected data to the data processing system, the data processing system is connected with an external database, the external database can acquire weather information and upstream drainage information, and transmit an acquisition result to the data processing system;
s3, calculating data, evaluating risk, summarizing the acquired data by the data processing system, evaluating risk, and alarming by the data processing system when the risk is over;
in step S3, the data processing system corrects the hazard score based on the historical temperature data and the length of the dam operation.
2. The big-data-based dam safety detection early warning method according to claim 1, wherein when the safety of a dam is evaluated, a dam risk score F is calculated, wherein F is F1+ F2+ F3, wherein F1 is a displacement degree score, F2 is a stress degree score, and F3 is a water volume score;
a danger scoring parameter Fc is arranged in the data processing system, the data processing system compares the dam danger score F with the danger scoring parameter Fc,
when F is less than or equal to Fc, the data processing system judges that the dam danger score is in a safety range;
and when F is larger than Fc, the data processing system judges that the dam danger score is not in a safety range, and the data processing system gives an alarm.
3. The big data-based dam safety detection early warning method according to claim 2, wherein for a displacement degree score of F1, F1 is F1c x Cw x T1, wherein F1c is an initial displacement degree score, Cw is a temperature-to-displacement degree score correction parameter, and T1 is a dam working duration-to-displacement degree score correction parameter,
the displacement degree initial score F1c is determined by the displacement value of each of the displacement sensor detectors;
the temperature-to-displacement degree grading correction parameter Cw is determined by real-time temperature and historical temperature data;
the dam working length-to-displacement degree grading correction parameter T1 is determined by the dam working length.
4. The dam safety detection early warning method based on big data as claimed in claim 3, wherein n displacement sensors are provided, and are respectively marked as a first displacement sensor, a second displacement sensor and an … … nth displacement sensor;
displacement of the detection point of each displacement sensor detector and transmitting the detection result to the data processing system, wherein the movement distance detected by the first displacement sensor is L1, the movement distance detected by the second displacement sensor is L2, the movement distance detected by the third displacement sensor is L3, the movement distance detected by the n-th displacement sensor of … … is Ln,
the data processing system calculates an initial score of degree of displacement F1c,
and Li is the moving distance detected by the ith displacement sensor, and Pi is the compensation parameter for calculating the initial score of the displacement degree by the moving distance detected by the ith displacement sensor.
5. The big-data-based dam safety detection early warning method according to claim 3, wherein for the temperature-to-displacement degree score correction parameter Cw, Cw is C1 x b1+ C2 x b2, wherein C1 is the water temperature-to-displacement degree score correction parameter, C2 is the dam body temperature-to-displacement degree score correction parameter, b1 is the water temperature-to-correction parameter Cw weighted value, and b2 is the dam body temperature-to-correction parameter Cw weighted value.
6. The dam safety detection early warning method based on big data as claimed in claim 5, wherein the first temperature sensor group detects the water temperature in the dam in real time and transmits the detected data to the data processing system, and for any time, the data processing system calculates the average value Wp of all the current temperatures detected by the first temperature sensor group and records the calculation result;
the data processing system integrates the average data of the water temperature to generate a temperature curve W (f) (t), wherein W (f) (t) is a curve of the water temperature in the dam changing along with time, the data transmission system calculates a grading and correcting parameter C1 of the water temperature to the displacement degree,
wherein Wt is a temperature value at any time on the temperature curve W ═ f (t) in the detection time period t, c1 is a calculation compensation parameter of the historical water temperature value to Cw, Ws is an average value of the current water temperatures detected by the temperature sensors, and c2 is a calculation compensation parameter of the current water temperature to Cw.
7. The dam safety detection early warning method based on big data as claimed in claim 6, wherein the second temperature sensor group detects the temperature in the dam body in real time and transmits the detected data to the data processing system, and for any time, the data processing system calculates the average value Mp of all the current temperatures detected by the second temperature sensor group and records the calculation result;
the data processing system integrates the average value data of the temperature in the dam body to generate a dam body temperature curve M (g) (t), wherein M (g) (t) is a curve of the temperature in the dam body changing along with time, the data transmission system calculates a dam body temperature to displacement degree grading correction parameter C2,
wherein, Mt is a temperature value at any time on a temperature curve M ═ g (t) in the detection time period t, C3 is a calculation compensation parameter of the historical dam temperature value to C2, Ms is a current dam temperature average value detected by the temperature sensor, and C4 is a calculation compensation parameter of the current dam temperature to Cw.
8. The dam safety detection early warning method based on big data as claimed in claim 3, wherein the correction parameter T1 is scored for the dam working time length and the displacement degree,
wherein a is a basic value of the correction parameter T1, K is a calculated adjustment value of the dam working time length T to the correction parameter T1, and K is less than 0.8.
9. The dam safety detection early warning method based on big data according to claim 2, characterized in that m pressure detection devices are provided, and are respectively marked as a first pressure detection device and a second pressure detection device; the stress detection devices detect the stress condition of each point and transmit the detection result to the data processing system, the stress value detected by the first pressure detection device is Y1, the stress value detected by the second pressure detection device is Y2,. the stress value detected by the mth pressure detection device is Ym,
for the stress degree score F2,
and the Yi is a stress value detected by the ith pressure detection device, the Qi is a calculation compensation parameter of the Yi corresponding force score, and the R is an adjustment parameter of the stress degree score.
10. The dam safety detection early warning method based on big data as claimed in claim 2, wherein the water level sensor detects a current water level value H1 and transmits the detection result to the data processing system, the data processing system obtains weather information and upstream drainage information, the data processing system estimates a highest water level H2 in a period T1 according to the current water level value H1, the weather information and the upstream drainage information, a standard water level value Hb is arranged in the data processing system, and the data processing system calculates water volume scores F3 and F3 as S (H2-Hb) And + wherein S is a water volume score calculation adjustment parameter, and Y is a water volume score basic value.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116878592A (en) * | 2023-09-08 | 2023-10-13 | 安徽水安建设集团股份有限公司 | Water conservancy project main dam upstream surface slope protection safety detection system and detection method |
| CN117194527A (en) * | 2023-11-07 | 2023-12-08 | 安能三局(成都)工程质量检测有限公司 | Hydropower station dam abnormal data early warning method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008076117A (en) * | 2006-09-19 | 2008-04-03 | Kokusai Kogyo Co Ltd | Evaluation method of outer deformation of dam |
| CN104482991A (en) * | 2014-12-10 | 2015-04-01 | 青岛理工大学 | Parameter measuring and early warning method for determining dangerous dam body safe reservoir water level |
| CN104678954A (en) * | 2015-01-23 | 2015-06-03 | 中国长江三峡集团公司 | Dam safety intelligent monitoring and pre-warning system based on full life circle and method thereof |
| CN204613689U (en) * | 2015-05-29 | 2015-09-02 | 中水银星科技（北京）有限公司 | Monitoring System for Dam Safety |
| CN112287608A (en) * | 2020-11-19 | 2021-01-29 | 中国水利水电科学研究院 | Dam safety prediction system and method based on fuzzy comprehensive evaluation |
| CN112432665A (en) * | 2020-11-14 | 2021-03-02 | 合肥集知云信息科技有限公司 | Hydraulic engineering dam body safety real-time monitoring and early warning management system based on big data analysis |
| CN113155186A (en) * | 2021-04-06 | 2021-07-23 | 贵州加仕达水利机械有限公司 | Dam safety monitoring management equipment and system thereof |
-
2022
- 2022-05-09 CN CN202210498301.6A patent/CN114882689B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008076117A (en) * | 2006-09-19 | 2008-04-03 | Kokusai Kogyo Co Ltd | Evaluation method of outer deformation of dam |
| CN104482991A (en) * | 2014-12-10 | 2015-04-01 | 青岛理工大学 | Parameter measuring and early warning method for determining dangerous dam body safe reservoir water level |
| CN104678954A (en) * | 2015-01-23 | 2015-06-03 | 中国长江三峡集团公司 | Dam safety intelligent monitoring and pre-warning system based on full life circle and method thereof |
| CN204613689U (en) * | 2015-05-29 | 2015-09-02 | 中水银星科技（北京）有限公司 | Monitoring System for Dam Safety |
| CN112432665A (en) * | 2020-11-14 | 2021-03-02 | 合肥集知云信息科技有限公司 | Hydraulic engineering dam body safety real-time monitoring and early warning management system based on big data analysis |
| CN112287608A (en) * | 2020-11-19 | 2021-01-29 | 中国水利水电科学研究院 | Dam safety prediction system and method based on fuzzy comprehensive evaluation |
| CN113155186A (en) * | 2021-04-06 | 2021-07-23 | 贵州加仕达水利机械有限公司 | Dam safety monitoring management equipment and system thereof |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116878592A (en) * | 2023-09-08 | 2023-10-13 | 安徽水安建设集团股份有限公司 | Water conservancy project main dam upstream surface slope protection safety detection system and detection method |
| CN116878592B (en) * | 2023-09-08 | 2024-02-27 | 安徽水安建设集团股份有限公司 | Water conservancy project main dam upstream surface slope protection safety detection system and detection method |
| CN117194527A (en) * | 2023-11-07 | 2023-12-08 | 安能三局(成都)工程质量检测有限公司 | Hydropower station dam abnormal data early warning method |
| CN117194527B (en) * | 2023-11-07 | 2024-01-26 | 安能三局(成都)工程质量检测有限公司 | Hydropower station dam abnormal data early warning method |
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