High formwork safety monitoring and early warning method and device, electronic equipment and readable medium

Abstract

The embodiment of the application provides a high formwork safety monitoring and early warning method, device, electronic equipment and readable medium, the method comprises the following steps: acquiring measured parameter data of high formwork and parameter index data of each safety mode, the safety mode comprises an intact mode and at least one damage mode, performing correlation processing on the measured parameter data and the parameter index data, determining an expected damage mode, comparing the parameter index data of the expected damage mode with the measured parameter data and outputting early warning information. The application considers that the sensitivity of each parameter under different safety modes is different, so the index system of each safety mode is established in advance according to theoretical analysis and numerical analysis, then the measured parameter data and the parameter index data are subjected to correlation processing, the expected damage mode is determined, the parameter index data of the expected damage mode is compared with the measured parameter data for early warning, the early warning index system is dynamically adjusted, and the accuracy of early warning is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high formwork safety monitoring, in particular to a high formwork safety monitoring and early warning method, a high formwork safety monitoring and early warning device, an electronic device and a computer readable medium. BACKGROUND

[0002] High formwork refers to formwork operation with a height greater than or equal to 5m, which is widely used in construction engineering and belongs to dangerous engineering. Since it is a temporary structure, it has low safety reserve capacity and is prone to cause mass casualties. In order to real-time monitor the stress performance of high formwork, the current high formwork monitoring system monitors parameters such as pressure and displacement through pressure sensors, inclination sensors and tension sensors, and directly performs early warning according to these parameter values. However, the construction environment is very complex, which is prone to false alarms and brings certain difficulties to the smooth implementation of the project. SUMMARY

[0003] In view of the above problems, the present application is proposed to provide a high formwork safety monitoring and early warning method and corresponding high formwork safety monitoring and early warning device, electronic device and computer readable medium which overcome the above problems or at least partially solve the above problems.

[0004] The present application discloses a high formwork safety monitoring and early warning method, which comprises:

[0005] Obtaining measured parameter data of high formwork and parameter index data of each safety mode; the safety mode includes an intact mode and at least one damage mode;

[0006] Correlating the measured parameter data and the parameter index data to determine an expected damage mode;

[0007] Comparing the parameter index data of the expected damage mode with the measured parameter data and outputting early warning information.

[0008] Optionally, the parameter index data is obtained in the following manner:

[0009] Respectively constructing a finite element model of each safety mode;

[0010] Setting a plurality of monitoring points for the finite element model of each safety mode;

[0011] For each finite element model, recording the parameter value of each parameter at each monitoring point under different loads;

[0012] Calculating the change between two parameter values of each monitoring point under adjacent loads;

[0013] The matrix formed by the variation is used as parameter index data of a safety mode corresponding to the finite element model.

[0014] Optionally, the step of performing correlation processing on the measured parameter data and the parameter index data to determine an expected failure mode comprises:

[0015] Pearson correlation analysis is performed on the measured parameter data and the parameter index data of each safety mode to obtain a correlation coefficient of the measured parameter data and the parameter index data of each safety mode.

[0016] The correlation coefficient is weighted to obtain a target correlation coefficient of the measured parameter data and the parameter index data of each safety mode.

[0017] The numerical value of the target correlation coefficient is compared.

[0018] The safety mode corresponding to the target correlation coefficient with the largest numerical value is determined as the expected failure mode.

[0019] Optionally, the step of weighting the correlation coefficient to obtain a target correlation coefficient of the measured parameter data and the parameter index data of each safety mode comprises:

[0020] A weight coefficient between each parameter of each safety mode is determined.

[0021] The weight coefficient and the correlation coefficient are weighted to obtain the target correlation coefficient of the measured parameter data and the parameter index data of each safety mode.

[0022] Optionally, the step of determining the weight coefficient between each parameter of each safety mode comprises:

[0023] Historical high-formwork safety accident events are analyzed, and the sensitivity level between each parameter of each safety mode is determined in combination with the finite element analysis result of each project.

[0024] The weight coefficient between each parameter of each safety mode is calculated using the analytic hierarchy process according to the sensitivity level between each parameter of each safety mode.

[0025] Optionally, the step of comparing the parameter index data of the expected failure mode with the measured parameter data and outputting a warning information comprises:

[0026] It is judged whether the measured parameter data is greater than the parameter index data of the expected failure mode.

[0027] If the measured parameter data is greater than the parameter index data of the expected failure mode, it is determined that there is parameter overrun and a warning information is outputted.

[0028] Optionally, the method further comprises:

[0029] If the number of the out-of-limit parameters recorded in the early warning information does not exceed the preset out-of-limit number, it is determined whether the correlation between the out-of-limit parameters and the corresponding parameters of the expected damage mode is weak, the correlation between the non-out-of-limit parameters and the corresponding parameters of the expected damage mode is strong, and the remote video auxiliary verification result is abnormal or not;

[0030] If the correlation between the out-of-limit parameters and the corresponding parameters of the expected damage mode is weak, the correlation between the non-out-of-limit parameters and the corresponding parameters of the expected damage mode is strong, and the remote video auxiliary verification result is abnormal, it is determined that the current early warning is a false early warning.

[0031] The application further discloses a high-formwork safety monitoring and early warning device.

[0032] The device comprises:

[0033] The device comprises:

[0034] The device comprises:

[0035] Optionally, the device further comprises:

[0036] The device comprises:

[0037] The device comprises:

[0038] The device comprises:

[0039] The device comprises:

[0040] The device comprises:

[0041] Optionally, the device comprises:

[0042] a Pearson correlation analysis submodule configured to perform Pearson correlation analysis on the measured parameter data and parameter index data of each safety mode to obtain a correlation coefficient of the measured parameter data and the parameter index data of each safety mode;

[0043] a weighting submodule configured to perform weighting processing on the correlation coefficient to obtain a target correlation coefficient of the measured parameter data and the parameter index data of each safety mode;

[0044] a comparison submodule configured to compare the numerical value of the target correlation coefficient;

[0045] an expected damage mode determination submodule configured to determine that the safety mode corresponding to the target correlation coefficient with the largest numerical value is the expected damage mode.

[0046] Optionally, the weighting submodule comprises:

[0047] a weight coefficient determination unit configured to determine a weight coefficient between each parameter of each safety mode;

[0048] a weighting unit configured to perform weighting on the weight coefficient and the correlation coefficient to obtain the target correlation coefficient of the measured parameter data and the parameter index data of each safety mode.

[0049] Optionally, the weight coefficient determination unit comprises:

[0050] a sensitive level determination unit configured to analyze historical high-support-mode safety accident events and determine a sensitive level between each parameter of each safety mode in combination with finite element analysis results of each project;

[0051] a calculation unit configured to calculate the weight coefficient between each parameter of each safety mode by using an analytic hierarchy process according to the sensitive level between each parameter of each safety mode.

[0052] Optionally, the early warning module comprises:

[0053] a judgment submodule configured to determine whether the measured parameter data is greater than the parameter index data of the expected damage mode;

[0054] an early warning submodule configured to determine that there is parameter overrun and output early warning information if the measured parameter data is greater than the parameter index data of the expected damage mode.

[0055] Optionally, the device further comprises:

[0056] The judgment module is used for judging whether the correlation between the over-limit parameter and the corresponding parameter of the expected damage mode is weak, the correlation between the non-over-limit parameter and the corresponding parameter of the expected damage mode is strong, and the remote video auxiliary checking result is abnormal or not if the number of over-limit parameters recorded in the early warning information does not exceed the preset over-limit number.

[0057] The false alarm module is used for determining that the current early warning is a false early warning if the correlation between the over-limit parameter and the corresponding parameter of the expected damage mode is weak, the correlation between the non-over-limit parameter and the corresponding parameter of the expected damage mode is strong, and the remote video auxiliary checking result is abnormal.

[0058] The embodiment of the application further discloses an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus.

[0059] The memory is used for storing a computer program.

[0060] The processor is used for executing the program stored on the memory, and realizes the high formwork safety monitoring and early warning method as described in the embodiment of the application.

[0061] The embodiment of the application further discloses one or more computer readable media having instructions stored thereon, which, when executed by one or more processors, cause the processor to execute the high formwork safety monitoring and early warning method as described in the embodiment of the application.

[0062] The embodiment of the application has the following advantages:

[0063] The high formwork safety monitoring and early warning method of the embodiment of the application acquires measured parameter data of the high formwork and parameter index data of each safety mode, the safety mode includes an intact mode and at least one damage mode, performs correlation processing on the measured parameter data and the parameter index data, determines an expected damage mode, compares the parameter index data of the expected damage mode with the measured parameter data and outputs early warning information. The application considers that the sensitivity of each parameter under different safety modes is different, so the index system of each safety mode is established according to theoretical analysis and numerical analysis in advance, then the measured parameter data and the parameter index data are subjected to correlation processing, the expected damage mode is determined, the parameter index data of the expected damage mode is compared with the measured parameter data for early warning, and the early warning index system is dynamically adjusted, thereby improving the accuracy of early warning. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 It is a step flow chart of the high formwork safety monitoring and early warning method provided in the embodiment of the application.

[0065] Figure 2is a step flow chart of another high formwork safety monitoring and early warning method provided in the embodiments of the present application;

[0066] Figure 3 is a few destruction mode schematic diagram provided in the embodiments of the present application;

[0067] Figure 4 is a hierarchical analysis method structure diagram provided in the embodiments of the present application;

[0068] Figure 5 is a structural block diagram of a high formwork safety monitoring and early warning device provided in the embodiments of the present application;

[0069] Figure 6 is a block diagram of an electronic device provided in the embodiments of the present application;

[0070] Figure 7 is a schematic diagram of a computer readable medium provided in the embodiments of the present application. DETAILED DESCRIPTION

[0071] In order to make the above objectives, characteristics and advantages of the present application more apparent, comprehensible and easily understood, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0072] Referring to Figure 1 , a step flow chart of a high formwork safety monitoring and early warning method provided in the embodiments of the present application is shown, which can specifically include the following steps:

[0073] Step 101, obtaining measured parameter data of the high formwork and parameter index data of each safety mode; the safety mode includes an intact mode and at least one destruction mode;

[0074] In fact, for the high formwork, each monitoring parameter will change no matter which destruction mode it is in, but the sensitivity of each parameter under different destruction modes is different, so in order to improve the accuracy of high formwork safety monitoring and early warning, the parameter index data of the most likely destruction mode occurring at present needs to be used as the basis for early warning.

[0075] Therefore, the present application establishes an index system of each safety mode in advance according to theoretical analysis and numerical analysis, then performs correlation processing on the measured parameter data and the parameter index data, determines the expected destruction mode, compares the parameter index data of the expected destruction mode with the measured parameter data for early warning, dynamically adjusts the early warning index system, and improves the accuracy of early warning.

[0076] In the embodiments of the present application, in order to realize monitoring and early warning of high formwork safety, the measured parameter data of the high formwork and the parameter index data of each safety mode can be obtained in real time, and then the two can be processed for early warning of collapse.

[0077] The parameters can include vertical displacement, rod inclination, jacking rod pressure, and overall displacement. The safety modes of the high formwork can include a complete mode and at least one failure mode. The complete mode can be a case where the high formwork does not have defects and generally does not collapse in this case. The failure mode can include ground subsidence, jacking rod instability, fastener failure, and overall overturning. Different monitoring parameters are proposed for different failure modes, such as using a laser or a wire displacement meter to monitor the vertical displacement of the formwork to prevent ground subsidence failure mode; using a pressure sensor to monitor the jacking rod pressure to prevent jacking rod instability failure mode due to excessive pressure; using an inclination sensor to monitor the rod inclination to prevent component failure mode and auxiliary judgment of the formwork vertical displacement; and using a displacement meter to monitor the overall horizontal displacement to prevent overall overturning failure and auxiliary judgment of the rod inclination.

[0078] For obtaining the measured parameter data, sensors can be arranged at positions with large stress or deformation, and in accordance with a pre-set pouring mode, each group of dynamic monitoring data is connected in real time at each pouring process step. Taking the vertical displacement as an example, the matrix S -d＝ [S 1m-d S 2m-d ... S sm-d ] and the rod inclination, jacking rod pressure, and overall displacement matrices are respectively: -d＝ [Q 1m-d Q 2m-d ... Q qm-d ], Y -d＝ [Y 1m-d Y 2m-d ... Y ym-d ], and W -d =[W 1m-d W 2m-d ... W wm-d ].

[0079] Step 102, performing correlation processing on the measured parameter data and the parameter index data to determine an expected failure mode.

[0080] After obtaining the measured parameter data and the parameter index data of each safety mode, the measured parameter data can be correlated with the parameter index data of each safety mode, so as to determine whether the high formwork is in a safe mode or is likely to collapse based on the current measured parameter data, and which failure mode is more likely to occur.

[0081] Step 103, comparing the parameter index data of the expected failure mode with the measured parameter data and outputting a warning information.

[0082] After the expected damage mode is determined, the measured parameter data can be compared with the parameter index data of the expected damage mode, specifically, the parameter matrix of each parameter of the measured parameter data can be compared with the parameter matrix of the corresponding parameter in the parameter index data of the expected damage mode, and the warning information is generated and output according to the comparison result. The warning information can record the expected damage mode, the comparison result of each parameter, the warning conclusion obtained by comprehensively comparing all parameters, and the like.

[0083] In the embodiment of the application, the measured parameter data of the high formwork and the parameter index data of each safety mode are acquired, the safety mode includes the intact mode and at least one damage mode, the correlation of the measured parameter data and the parameter index data is processed, the expected damage mode is determined, the parameter index data of the expected damage mode is compared with the measured parameter data, and the warning information is output. The application considers that the sensitivity of each parameter under different safety modes is different, so the index system of each safety mode is established according to the theoretical analysis and numerical analysis in advance, the correlation of the measured parameter data and the parameter index data is processed, the expected damage mode is determined, the parameter index data of the expected damage mode is compared with the measured parameter data for warning, and the warning index system is dynamically adjusted, so that the accuracy of the warning is improved.

[0084] Reference Figure 2 The step flowchart of another high formwork safety monitoring and warning method provided in the embodiment of the application is shown, and specifically can include the following steps:

[0085] Step 201, acquiring the measured parameter data of the high formwork and the parameter index data of each safety mode; the safety mode includes the intact mode and at least one damage mode;

[0086] In one embodiment of the application, the acquisition method of the parameter index data includes:

[0087] S11, respectively constructing the finite element model of each safety mode;

[0088] S12, respectively setting a plurality of monitoring points for the finite element model of each safety mode;

[0089] S13, recording the parameter value of each parameter at each monitoring point under different loads for each finite element model;

[0090] S14, calculating the change amount between two parameter values of each monitoring point under adjacent loads;

[0091] S15, using the matrix composed of the change amount as the parameter index data of the safety mode corresponding to the finite element model.

[0092] The form of high formwork varies in different projects, and different finite element analysis models need to be established for each project, and the early warning index system under the safety mode and different failure modes needs to be established.

[0093] According to the design load, material properties, site pouring method and other information, a complete mode finite element model is established, defects are not considered, the load distribution is loaded for several times, and the vertical displacement (S1, S2,..., S s ), bar inclination (Q1, Q2,..., Q q ), overall displacement (W1, W2,..., W w ), and top rod pressure (Y1, Y2,..., Y y ) parameter values are obtained at different analysis stages, and sensors are arranged at positions with larger stress or deformation (generally not less than 3 for each parameter).

[0094] On the basis of the above, the analysis result values of each parameter and each measuring point at different loading steps are obtained. S, Q, W and Y represent vertical displacement, bar inclination, overall displacement and top rod pressure, respectively, and s, q, w and y represent vertical displacement, bar inclination, overall displacement and top rod pressure sensor monitoring points, respectively. For example, taking vertical displacement S 1-0 as an example (-0 indicates the complete mode, i.e. the stress mode without considering defects, and generally no collapse accident occurs in this mode), the data matrix S 1-0＝ [S 11-0 S 12-0 ...S 1n-0 ] of n loading steps, S 1n-0 represents the change of vertical displacement of monitoring point 1 at loading step n without considering defects (to reflect the data change relationship, the change between this loading and the last loading is selected, and the following is the same), and the vertical displacement s×n matrix S -0 is as follows:

[0095]

[0096] The bar inclination, top rod pressure and overall displacement matrices Q -0 , Y -0 , W -0 can be obtained in the same way, as shown below:

[0097]

[0098] In this way, the early warning index system under the complete mode is established, and the parameter index data of the high formwork complete mode is obtained.

[0099] In actual formwork erection, problems such as substandard fasteners, defective top rods, and installation errors may occur, leading to various failure modes including ground settlement, top rod instability, fastener failure, and overall overturning. To simulate different collapse failure modes, a comprehensive early warning indicator system for each mode is established, and the following analyses are conducted:

[0100] (1) Ground subsidence failure mode

[0101] To simulate ground settlement failure mode, a weak foundation defect was introduced into the formwork foundation. The stress mechanism under ground settlement failure mode was analyzed, and the S21 analysis process was repeated to obtain the analysis results values ​​of each parameter at each measuring point under different loading steps. For example, the vertical displacement S 1-1 For example (-1 represents the ground subsidence failure mode), the data matrix S of n loading steps 1-1＝ [S 11-1 S 12-1 ...S 1n-1 ], S 1n-1 Let S represent the vertical displacement of settlement monitoring point 1 in loading step n under the ground settlement failure mode. Then, the vertical displacement s×n matrix S -1 for:

[0102]

[0103] The remaining parameters, including member inclination, top rod pressure, and overall displacement matrix Q, can be obtained in this way. -1 Y -1 W -1 As shown below:

[0104]

[0105] (2) Failure mode of push rod instability

[0106] To simulate the instability failure mode of the mandrel, weak stiffness and strength defects are introduced into the mandrel through methods such as cross-sectional weakening and weak connections. Following the steps described above, the vertical displacement, member inclination, mandrel pressure, and overall displacement matrix Q can be obtained. -2 Y -2 W -2 S -2 As shown below:

[0107]

[0108]

[0109] (3) Fastener failure mode

[0110] To simulate the instability failure mode of the fastener, a defective connection is set in the fastener. Following the steps described above, the vertical displacement, member inclination, top rod pressure, and overall displacement matrix Q can be obtained.-3 , Y -3 , W -3 , S -3 , as follows:

[0111]

[0112]

[0113] (4) Overall tilting failure mode

[0114] To simulate the overall tilting failure mode, set up a tilting defect connection mode on the vertical rod, and according to the above steps, the vertical displacement, rod tilting, top rod pressure and overall displacement matrix Q -4 , Y -4 , W -4 , S -4 , as follows:

[0115]

[0116]

[0117] Step 202, perform Pearson correlation analysis on the measured parameter data and the parameter index data of each safety mode respectively, to obtain the correlation coefficient of the measured parameter data and the parameter index data of each safety mode;

[0118] Step 203, performing weighted processing on the correlation coefficient to obtain the target correlation coefficient of the measured parameter data and the parameter index data of each safety mode;

[0119] In an embodiment of the present application, the step of performing weighted processing on the correlation coefficient to obtain the target correlation coefficient of the measured parameter data and the parameter index data of each safety mode comprises:

[0120] S21, determining the weight coefficient between each parameter of each safety mode;

[0121] S22, weighting the weight coefficient and the correlation coefficient to obtain the target correlation coefficient of the measured parameter data and the parameter index data of each safety mode.

[0122] In an embodiment of the present application, the step of determining the weight coefficient between each parameter of each safety mode comprises:

[0123] S31, analyzing historical high formwork safety accident events, and combining the finite element analysis results of each project to determine the sensitivity level between each parameter of each safety mode;

[0124] S32, according to the sensitive level between the parameters of each security mode, the weight coefficients between the parameters of each security mode are calculated by using the analytic hierarchy process.

[0125] Referring to Figure 3 , several schematic diagrams of the damage modes provided in the embodiments of the present application are shown. In order to find the sensitive parameters in each damage mode, from the perspective of conceptual structural mechanics, the sensitive parameters in different damage modes can also be obtained through a large number of finite element analyses, starting from the mechanical mechanism in different high formwork damage modes, determining the sensitive parameters in different damage modes, and grading and marking the parameters, dividing them into important parameters, secondary parameters, and general parameters, as shown in Table 1. If a parameter changes most significantly for a certain damage mode, it is called an important parameter; if a parameter does not change significantly for a certain damage mode, it is called a general parameter; and the rest are secondary parameters. For example, in the overall overturning mode, the overall displacement is the most sensitive, at this time the rod inclination also changes, while the top rod pressure and ground settlement are not significant. As can be seen from the table, the rod inclination is basically an important or secondary parameter for all damage modes, so it is the most important, followed by the top rod pressure (top rod instability, fastener failure mode appears more), and then the overall displacement and ground settlement.

[0126] Table 1 Analysis of sensitive parameters in different collapse failure modes

[0127]

[0128] Referring to Figure 4 , a structure diagram of an analytic hierarchy process provided in an embodiment of the present application is shown. According to the analytic hierarchy process, the importance of various damage modes and the weight coefficients of the parameters are analyzed, including scale definition, construction of judgment matrix, and weight coefficient analysis. Based on the previous research literature, the scale definition can be as shown in Table 2 as follows:

[0129] Table 2 Scale definition

[0130]

[0131] The judgment matrix is constructed. First, the judgment matrix of various damage modes and the target layer parameters is constructed. According to the template collapse accident statistical data, the top rod instability and fastener failure modes are the most common, the overall overturning is the second, and the ground settlement is the least. At the same time, as can be seen from Table 1, it is preliminarily judged that the rod inclination is basically an important or secondary parameter for all damage modes, so it is the most important, followed by the top rod pressure, and then the overall displacement and ground settlement, which can be adjusted according to subsequent theoretical analysis and accident experience. Here, the important parameter / secondary parameter = 3 or 5, the important parameter / general parameter = 7 or 9, and the secondary parameter / general parameter = 3 or 5, which can be adjusted according to the theoretical analysis and accident experience.

[0132] The judgment matrix of the expected damage mode and each parameter is as shown in Table 3.

[0133] Table 3 judgment matrix of expected failure mode and each parameter

[0134] Expected failure mode Rod tilt Rod pressure Vertical displacement Overall displacement Rod tilt 1 5 7 9 Rod pressure 1 / 5 1 3 5 Vertical displacement 1 / 7 1 / 3 1 3 Overall displacement 1 / 9 1 / 5 1 / 3 1

[0135] The above judgment matrix is a positive reciprocal matrix, which has the following characteristics: the diagonal is 1, and the product of the symmetric elements is equal to 1. The weight is calculated by the algorithm average method. First, normalize each column to obtain:

[0136] Column normalized judgment matrix

[0137] Expected failure mode Rod tilt Rod pressure Vertical displacement Overall displacement Rod tilt 0.688 0.765 0.618 0.500 Rod pressure 0.138 0.153 0.265 0.278 Vertical displacement 0.098 0.051 0.088 0.167 Overall displacement 0.076 0.031 0.029 0.056

[0138] Further, the weight w of each parameter is obtained, and S, Q, W, and Y represent vertical displacement, rod inclination, overall displacement, and top rod pressure, respectively. The average of each row is obtained as

[0139] w S-0 = 0.688 / (0.688+0.765+0.618+0.500) = 0.643,

[0140] and w Q-0 = 0.208, w W-0 = 0.101, w Y-0 = 0.048.

[0141] Then the weight matrix W0 of each parameter and each failure mode is

[0142]

[0143] The judgment matrix of rod inclination and each failure mode is as follows in Table 4.

[0144] Table 4 judgment matrix of rod inclination and each failure mode

[0145] Rod tilt Ground settlement Rod instability Fastener failure Overall overturning Ground settlement 1 1 / 3 1 / 7 1 Rod instability 3 1 1 / 3 1 Fastener failure 7 1 1 3 Overall overturning 1 1 1 / 3 1

[0146] Column normalized judgment matrix

[0147] Rod tilt Ground settlement Rod instability Fastener failure Overall overturning Ground settlement 0.083 0.100 0.079 0.167 Rod instability 0.250 0.300 0.184 0.167 Fastener failure 0.583 0.300 0.553 0.500 Overall overturning 0.083 0.300 0.184 0.167

[0148] Further, we can obtain

[0149] w Q-1 = 0.107, w Q-2 = 0.225, w Q-3 = 0.484, w Q-4 = 0.184.

[0150] Where -1, -2, -3, and -4 represent ground subsidence, top rod instability, fastener failure, and overall overturning, respectively.

[0151] The judgment matrix of the ram pressure and each failure mode is shown in Table 5.

[0152] Table 5 Judgment matrix of the ram pressure and each failure mode

[0153] Rod tilt Ground settlement Rod instability Fastener failure Overall overturning Ground settlement 1 1 / 5 1 3 Rod instability 5 1 7 9 Fastener failure 1 1 / 7 1 3 Overall overturning 1 / 3 1 / 9 1 / 3 1

[0154] Column normalized judgment matrix

[0155] Rod pressure Ground settlement Rod instability Fastener failure Overall overturning Ground settlement 0.136 0.138 0.107 0.188 Rod instability 0.682 0.688 0.750 0.563 Fastener failure 0.136 0.098 0.107 0.188 Overall overturning 0.045 0.076 0.036 0.063

[0156] Further, it can be obtained that

[0157] w Y-1 = 0.142, w Y-2 = 0.671, w Y-3 = 0.132, w Y-4 = 0.055.

[0158] The judgment matrix of the vertical displacement and each failure mode is shown in Table 6.

[0159] Table 6 Judgment matrix of the vertical displacement and each failure mode

[0160] Rod pressure Ground settlement Rod instability Fastener failure Overall overturning Ground settlement 1 3 3 7 Rod instability 1 / 3 1 1 3 Fastener failure 1 / 3 1 1 3 Overall overturning 1 / 7 1 / 3 1 / 3 1

[0161] Column normalized judgment matrix

[0162] Rod pressure Ground settlement Rod instability Fastener failure Overall overturning Ground settlement 0.553 0.563 0.563 0.500 Rod instability 0.184 0.188 0.188 0.214 Fastener failure 0.184 0.188 0.188 0.214 Overall overturning 0.079 0.062 0.063 0.071

[0163] Further, it can be obtained that

[0164] w S-1 = 0.544, w S-2 = 0.193, w S-3 = 0.193, w S-4 = 0.069.

[0165] The judgment matrix of the overall displacement and each failure mode is shown in Table 7.

[0166] Table 7 Judgment matrix of the overall displacement and each failure mode

[0167] Vertical displacement Ground settlement Rod instability Fastener failure Overall overturning Ground settlement 1 1 1 1 / 9 Rod instability 1 1 1 1 / 9 Fastener failure 1 1 1 1 / 9 Overall overturning 9 9 9 1

[0168] Column normalized judgment matrix

[0169] Vertical displacement Ground settlement Rod instability Fastener failure Overall overturning Ground settlement 0.083 0.083 0.083 0.083 Rod instability 0.083 0.083 0.083 0.083 Fastener failure 0.083 0.083 0.083 0.083 Overall overturning 0.083 0.083 0.083 0.083

[0170] Further, it can be obtained that

[0171] w W-1 = 0.083, w W-2 = 0.083, w W-3 = 0.083, w W-4= 0.750.

[0172] Then each parameter and the weight matrix W of each failure mode are,

[0173]

[0174] Take the complete mode as an example (0), the parameter index data S -0＝ [Q 1m-0 Q 2m-0 ... Q sm-0 ] of each parameter, and other Q -0 = [Q 1m-0 Q 2m-0 ... Q qm-0 ], Y -0＝ [Y 1m-0 Y 2m-0 ... Y ym-0 ], and W -0＝ [W 1m-0 W 2m-0 ... W wm-0 ] are obtained. Through Pearson correlation analysis, the correlation coefficients of the two matrices corresponding to the parameters are obtained, such as the correlation coefficient I -d of S -0 and S sd0 (between 1 and -1, the closer to 1 or -1 indicates the more significant correlation), and similarly, I qd0 , I yd0 , I wd0 are obtained. In the same way, the correlation coefficients I sd1 , I qd1 , I yd1 , I wd1 of the four failure modes of ground subsidence (1), top rod instability (2), fastener failure (3), and overall overturning (4) are obtained, I sd2 , I qd2 , I yd2 , I wd2 , I sd3 , I qd3 , I yd3 , I wd3 , I sd4 , I qd4 , I yd4 , I wd4 .

[0175] On the basis of obtaining the above-mentioned mode correlation coefficients, the correlation coefficients are weighted. The weight coefficients of each parameter of the five modes of complete mode (0), ground subsidence (1), top rod instability (2), fastener failure (3), and overall overturning (4) are called. The target correlation coefficient I d0 is calculated by the complete mode:

[0176]

[0177] Ground subsidence (-1), top rod instability (-2), fastener failure (-3), overall overturning (-4) four defect modes are considered to calculate the target correlation coefficient I di :

[0178]

[0179] Wherein is the i-th column element in .

[0180] Step 204, compare the numerical value of the target correlation coefficient;

[0181] Step 205, determine the safety mode corresponding to the target correlation coefficient with the largest numerical value as the expected failure mode.

[0182] After obtaining the target correlation coefficient, the numerical value of the target correlation coefficient can be compared, and the safety mode corresponding to the target correlation coefficient with the largest numerical value is determined as the expected failure mode.

[0183] Step 206, compare the parameter index data of the expected failure mode with the measured parameter data and output a warning information.

[0184] In an embodiment of the present application, the step of comparing the parameter index data of the expected failure mode with the measured parameter data and outputting a warning information comprises:

[0185] S41, judge whether the measured parameter data is greater than the parameter index data of the expected failure mode;

[0186] S42, if the measured parameter data is greater than the parameter index data of the expected failure mode, it is determined that there is parameter overrun and a warning information is output.

[0187] In an embodiment of the present application, the method further comprises:

[0188] S51, if the number of overrun parameters recorded in the warning information does not exceed the preset overrun number, judge whether the correlation of the overrun parameter and the corresponding parameter of the expected failure mode is weak, the correlation of the non-overrun parameter and the corresponding parameter of the expected failure mode is strong, and the remote video auxiliary verification result is abnormal or not;

[0189] S52, if the correlation of the overrun parameter and the corresponding parameter of the expected failure mode is weak, the correlation of the non-overrun parameter and the corresponding parameter of the expected failure mode is strong, and the remote video auxiliary verification result is abnormal, it is determined that this warning is a false alarm.

[0190] After the expected damage mode is determined, the parameter index data of the expected damage mode can be called, and the parameter values of each parameter in the measured data are compared with the parameter values of the corresponding parameters in the parameter index data of the expected damage mode to determine which parameter values in the measured parameter data are greater than the parameter values of the corresponding parameters in the parameter index data of the expected damage mode. If the parameter values of all parameters in the measured parameter data are not greater than the parameter values of the corresponding parameters in the parameter index data of the expected damage mode, it can be concluded that the high formwork currently has no defects and no collapse risk, and no warning can be given. If the parameter values of all parameters in the measured parameter data are greater than the parameter values of the corresponding parameters in the parameter index data of the expected damage mode, it can be concluded that the high formwork currently has serious defects and is extremely likely to collapse in the expected damage mode, and a warning can be given to enable the staff to take preventive measures in advance. If the parameter values of some parameters in the measured parameter data are greater than the parameter values of the corresponding parameters in the parameter index data of the expected damage mode, and the parameter values of the other parameters are not out of limit, further analysis and testing means can be adopted to determine whether the high formwork really has defects, such as analyzing the correlation of the out-of-limit parameters and the parameters of the expected damage mode, the correlation of the parameters not out of limit and the parameters of the expected damage mode, and determining the possibility of collapse by the strength of the correlation. A number of cameras can also be installed on the high formwork to monitor the actual situation on site in real time, and remote visual auxiliary verification can be achieved.

[0191] Specifically, as to whether the out-of-limit of some parameters will cause collapse, the following method can be used for judgment: first, it can be determined whether the number of out-of-limit parameters exceeds the preset number of out-of-limit, if yes, it can be determined that collapse is extremely likely to occur; if not, it is determined whether the correlation of the out-of-limit parameters and the corresponding parameters of the expected damage mode is weak, the correlation of the parameters not out of limit and the corresponding parameters of the expected damage mode is strong, and the remote video auxiliary verification result is abnormal, if the correlation of the out-of-limit parameters and the corresponding parameters of the expected damage mode is weak, the correlation of the parameters not out of limit and the corresponding parameters of the expected damage mode is strong, and the remote video auxiliary verification result is abnormal, it can be determined that the current warning is a false alarm.

[0192] In the embodiment of the present application, measured parameter data of a high formwork and parameter index data of each safety mode are acquired, the safety mode includes a complete mode and at least one damage mode, the measured parameter data and the parameter index data are processed in correlation, an expected damage mode is determined, the parameter index data of the expected damage mode is compared with the measured parameter data, and early warning information is output. The present application considers that the sensitivities of parameters in different safety modes are different, so an index system of each safety mode is established in advance according to theoretical analysis and numerical analysis, the measured parameter data and the parameter index data are processed in correlation, the expected damage mode is determined, the parameter index data of the expected damage mode is compared with the measured parameter data for early warning, the early warning index system is dynamically adjusted, and the accuracy of early warning is improved.

[0193] It should be noted that, for the method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present application are not limited to the action sequence described, because according to the embodiments of the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions involved are not necessarily necessary for the embodiments of the present application.

[0194] Referring to Overall displacement , a structure block diagram of a high formwork safety monitoring and early warning device provided in the embodiment of the present application is shown, which can specifically include the following modules:

[0195] The acquisition module 501 is configured to acquire measured parameter data of a high formwork and parameter index data of each safety mode; the safety mode includes a complete mode and at least one damage mode;

[0196] The correlation processing module 502 is configured to process the measured parameter data and the parameter index data in correlation, and determine an expected damage mode.

[0197] The early warning module 503 is configured to compare the parameter index data of the expected damage mode with the measured parameter data, and output early warning information.

[0198] Optionally, the device further includes:

[0199] The construction module is configured to construct a finite element model of each safety mode respectively;

[0200] The monitoring point setting module is configured to set a plurality of monitoring points for the finite element model of each safety mode respectively;

[0201] The recording module is configured to record, for each finite element model, a parameter value of each parameter at each monitoring point under different loads;

[0202] A calculating module is configured to calculate a variation between two parameter values of each monitoring point under adjacent loads of different sizes;

[0203] A matrix forming module is configured to form a matrix using the variations as parameter index data of a safety mode corresponding to the finite element model.

[0204] Optionally, the correlation processing module comprises:

[0205] A Pearson correlation analysis submodule is configured to perform Pearson correlation analysis on the measured parameter data and the parameter index data of each safety mode respectively to obtain a correlation coefficient between the measured parameter data and the parameter index data of each safety mode.

[0206] A weighting submodule is configured to perform weighting processing on the correlation coefficient to obtain a target correlation coefficient between the measured parameter data and the parameter index data of each safety mode.

[0207] A comparison submodule is configured to compare numerical values of the target correlation coefficients.

[0208] An expected failure mode determination submodule is configured to determine that a safety mode corresponding to a target correlation coefficient with the largest numerical value is an expected failure mode.

[0209] Optionally, the weighting submodule comprises:

[0210] A weight coefficient determination unit is configured to determine weight coefficients between parameters of each safety mode.

[0211] A weighting unit is configured to perform weighting using the weight coefficients and the correlation coefficients to obtain the target correlation coefficient between the measured parameter data and the parameter index data of each safety mode.

[0212] Optionally, the weight coefficient determination unit comprises:

[0213] A sensitive level determination unit is configured to analyze historical high-formwork safety accident events, and determine sensitive levels between parameters of each safety mode in combination with finite element analysis results of each project.

[0214] A calculation unit is configured to calculate weight coefficients between parameters of each safety mode using an analytic hierarchy process according to the sensitive levels between the parameters of each safety mode.

[0215] Optionally, the early warning module comprises:

[0216] A judgment submodule is configured to judge whether the measured parameter data is greater than the parameter index data of the expected failure mode.

[0217] The early warning sub-module is configured to determine that there is a parameter overrun and output early warning information if the measured parameter data is greater than the parameter index data of the expected damage mode.

[0218] Optionally, the device further comprises:

[0219] The judgment module is configured to determine whether the correlation between the overrun parameter and the corresponding parameter of the expected damage mode is weak, the correlation between the non-overrun parameter and the corresponding parameter of the expected damage mode is strong, and the remote video auxiliary verification result is abnormal or not if the number of the overrun parameters recorded in the early warning information does not exceed the preset number of overruns.

[0220] The false alarm module is configured to determine that the current early warning is a false alarm if the correlation between the overrun parameter and the corresponding parameter of the expected damage mode is weak, the correlation between the non-overrun parameter and the corresponding parameter of the expected damage mode is strong, and the remote video auxiliary verification result is abnormal.

[0221] For the device embodiment, it is basically similar to the method embodiment, so the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0222] In addition, the embodiment of the present application also provides an electronic device, such as Ground settlement As shown in the figure, the electronic device comprises a processor 601, a communication interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communication interface 602 and the memory 603 complete mutual communication through the communication bus 604,

[0223] The memory 603 is used to store a computer program.

[0224] The processor 601 is used to execute the program stored in the memory 603, and realize the high formwork safety monitoring and early warning method as described in the above embodiment.

[0225] The communication bus mentioned above can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0226] The communication interface is used for communication between the terminal and other devices.

[0227] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0228] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0229] like Rod instability Fastener failure Overall overturning Ground settlement Rod instability Fastener failure Overall overturning Overall displacement Ground settlement Rod instability Fastener failure Overall overturning Ground settlement Rod instability Fastener failure Overall overturning Figure 5 Figure 6 Figure 7 As shown, in another embodiment of the present invention, a computer-readable storage medium 701 is also provided, which stores instructions that, when run on a computer, cause the computer to execute the high formwork safety monitoring and early warning method described in the above embodiment.

[0230] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the high formwork safety monitoring and early warning method described in the above embodiments.

[0231] In the embodiments described above, all or some of the steps can be implemented by software, hardware, firmware or any combination thereof. When implemented in software, all or some of the steps can be implemented in the form of one or more computer programs which are stored in a computer readable storage medium. The computer readable storage medium can be located in a computing device which is in operation. These computer programs (which may

[0232] It is to be noted that, in the present document, the terms such as first and second, etc., are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between such entities or operations. Moreover, the terms "comprising", "including", or any other variant are intended to cover a non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise a list of elements are not required to include only those elements in the list, but can include other elements not expressly listed, or also include elements inherent in such processes, methods, articles, or apparatuses. Without more limitations, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0233] Each of the embodiments in the present document is described in a related manner, and the same or similar parts among the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the description of the method embodiments.

[0234] The above merely provides the preferred embodiments of the application, and not intended to limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall within the protection scope of the application.

Claims

1. A high formwork safety monitoring and early warning method, characterized in that, The method comprises: acquiring measured parameter data of a high formwork and parameter index data of each safety mode; the safety mode comprises an intact mode and at least one damage mode; performing correlation processing on the measured parameter data and the parameter index data to determine an expected damage mode; comparing the parameter index data of the expected damage mode with the measured parameter data and outputting early warning information; the step of performing correlation processing on the measured parameter data and the parameter index data to determine an expected damage mode comprises: performing Pearson correlation analysis on the measured parameter data and the parameter index data of each safety mode respectively to obtain correlation coefficients of the measured parameter data and the parameter index data of each safety mode; analyzing historical high formwork safety accident events and combining finite element analysis results of each project to determine sensitivity levels between parameters of each safety mode; calculating weight coefficients between parameters of each safety mode by using the analytic hierarchy process according to the sensitivity levels between parameters of each safety mode; performing weighting on the weight coefficients and the correlation coefficients to obtain target correlation coefficients of the measured parameter data and the parameter index data of each safety mode; comparing numerical values of the target correlation coefficients; determining that a safety mode corresponding to a target correlation coefficient with the largest numerical value is an expected damage mode.

2. The method of claim 1, wherein, The acquisition method of the parameter index data comprises: respectively constructing finite element models of each safety mode; respectively setting a plurality of monitoring points for the finite element models of each safety mode; recording parameter values of each parameter at each monitoring point under different loads for each finite element model; calculating variation amounts between two parameter values of each monitoring point under adjacent loads; using a matrix composed of the variation amounts as parameter index data of a safety mode corresponding to the finite element model.

3. The method of claim 1, wherein, The step of comparing the parameter index data of the expected damage mode with the measured parameter data and outputting early warning information comprises: determining whether the measured parameter data is greater than the parameter index data of the expected damage mode; if the measured parameter data is greater than the parameter index data of the expected damage mode, determining that there is parameter overrun and outputting early warning information.

4. The method of claim 3, wherein, The method further comprises: if a number of overrun parameters recorded in the early warning information does not exceed a preset number of overruns, determining whether a correlation between the overrun parameters and corresponding parameters of the expected damage mode is weak, a correlation between non-overrun parameters and corresponding parameters of the expected damage mode is strong, and a remote video auxiliary verification result is abnormal or not; if the correlation between the overrun parameters and the corresponding parameters of the expected damage mode is weak, the correlation between the non-overrun parameters and the corresponding parameters of the expected damage mode is strong, and the remote video auxiliary verification result is abnormal, determining that the present early warning is a false early warning.

5. A high formwork safety monitoring and early warning device, characterized in that, The device comprises: an acquisition module configured to acquire measured parameter data of a high formwork and parameter index data of each safety mode; the safety mode comprises an intact mode and at least one damage mode; a correlation processing module configured to perform correlation processing on the measured parameter data and the parameter index data to determine an expected damage mode; An early warning module is configured to compare the parameter index data of the expected damage mode with the measured parameter data and output early warning information; The correlation processing module comprises: A Pearson correlation analysis submodule is configured to perform Pearson correlation analysis on the measured parameter data and the parameter index data of each safety mode to obtain a correlation coefficient of the measured parameter data and the parameter index data of each safety mode; A weighting submodule is configured to perform weighting processing on the correlation coefficient to obtain a target correlation coefficient of the measured parameter data and the parameter index data of each safety mode; A comparison submodule is configured to compare the numerical values of the target correlation coefficients; An expected damage mode determination submodule is configured to determine that the safety mode corresponding to the target correlation coefficient with the largest numerical value is the expected damage mode; The weighting submodule comprises: A weight coefficient determination unit is configured to determine the weight coefficients between the parameters of each safety mode; A weighting unit is configured to perform weighting on the weight coefficients and the correlation coefficients to obtain the target correlation coefficient of the measured parameter data and the parameter index data of each safety mode; The weight coefficient determination unit comprises: A sensitive level determination unit is configured to analyze historical high-formwork safety accident events, combine finite element analysis results of each project, and determine the sensitive levels between the parameters of each safety mode; A calculation unit is configured to calculate the weight coefficients between the parameters of each safety mode by using the analytic hierarchy process according to the sensitive levels between the parameters of each safety mode.

6. An electronic device, comprising: The system comprises a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; The memory is configured to store a computer program; The processor is configured to execute the program stored on the memory to implement the high-formwork safety monitoring and early warning method according to any one of claims 1-4.

7. One or more computer-readable media having instructions stored thereon that, when executed by one or more processors, cause the processors to perform the high-formwork safety monitoring and early warning method according to any one of claims 1-4.

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