CN112419679B

CN112419679B - Building safety monitoring method and device, storage medium and electronic equipment

Info

Publication number: CN112419679B
Application number: CN202011161724.6A
Authority: CN
Inventors: 孙飞; 张逾峰; 陈丹维
Original assignee: Hangzhou Weigan Technology Co ltd
Current assignee: Hangzhou Weigan Technology Co ltd
Priority date: 2020-10-27
Filing date: 2020-10-27
Publication date: 2023-02-28
Anticipated expiration: 2040-10-27
Also published as: CN112419679A

Abstract

The application provides a building safety monitoring method and device, a storage medium and electronic equipment. According to the building safety monitoring method provided by the embodiment of the application, the current state measurement parameters of the target building and the environment prediction parameters of the local environment are obtained firstly, and then the safety state of the target building is determined according to the state measurement parameters, the environment prediction parameters and the preset safety assessment model, so that more comprehensive risk early warning can be realized on the building safety in advance, and the efficiency and the reliability of the building safety risk prevention and control work in the area are improved.

Description

Building safety monitoring method and device, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of building safety technologies, and in particular, to a building safety monitoring method, a building safety monitoring device, a storage medium, and an electronic device.

Background

With the development of domestic tourism industry, the large-scale development of urban leisure landscape, beautiful country construction and the like in recent years, a large number of garden buildings are constructed in various cities and countries in each city.

Generally, these buildings are important leisure and recreation places for local people and tourists, and not only are the beauty needed, but also safety guarantee is needed. However, garden buildings are different from ordinary buildings in terms of structure, materials, functions, use scenes and the like, and are affected by a plurality of safety factors including structural design, building materials, construction quality, surrounding environment, weather influence, geological change, worm damage, decay, man-made damage and the like, wherein adverse factors can cause damage and even collapse of garden buildings, particularly wooden buildings, and cause irreparable loss.

However, current security monitoring for such buildings relies on manual periodic inspection, which is typically only discovered when major damage occurs, and monitoring is inefficient and less reliable.

Disclosure of Invention

The embodiment of the application provides a building safety monitoring method and device, a storage medium and electronic equipment, and aims to solve the technical problems of low building monitoring efficiency and poor reliability of current manual regular inspection.

In a first aspect, the present application provides a building safety monitoring method, including:

acquiring current state measurement parameters of a target building and environment prediction parameters of a local environment;

and determining the safety state of the target building according to the state measurement parameters, the environment prediction parameters and a preset safety evaluation model.

In a possible design, the building safety monitoring method further includes:

acquiring a state measurement parameter set of a calibration building in a preset time period and an environment measurement parameter set of a local environment, wherein the calibration building corresponds to the target building;

and determining the preset safety evaluation model according to the time parameter of a preset time period, the state measurement parameter set and the environment measurement parameter set.

In one possible design, after the determining the safety state of the target building according to the state measurement parameter, the environment prediction parameter, and a preset safety evaluation model, the method further includes:

and determining recommended measure information according to the safety state and a preset early warning level rule, wherein the recommended measure information is used for indicating a recommended processing mode for the target building.

In one possible design, the determining the preset security assessment model according to the time parameter of the preset time period, the state measurement parameter set, and the environment measurement parameter set includes:

determining a first linear fitting measurement line of the target building according to a time parameter, an inclination parameter set and a wind load parameter set of a preset time period, wherein the state measurement parameter set comprises the inclination parameter set, the environment measurement parameter set comprises the wind load parameter set, and the preset safety assessment model is determined based on the first linear fitting measurement line.

In one possible design, the determining recommended action information according to the safety status and a preset early warning level rule includes:

determining an inclination angle early warning threshold value set according to the first linear fitting measurement line, wherein the inclination angle early warning threshold value set comprises a plurality of inclination angle early warning threshold values, and each inclination angle early warning threshold value corresponds to an early warning grade;

and determining the recommended measure information according to the current measured inclination of the target building and the inclination early warning threshold value set.

In one possible design, the target building is a timber structure building.

In one possible design, the determining the preset security assessment model according to the time parameter of the preset time period, the state measurement parameter set, and the environment prediction parameter set includes:

and determining a second linear fitting measurement line of the target building according to a time parameter, a deflection parameter set and a snow load parameter set of a preset time period, wherein the state measurement parameter set comprises the deflection parameter set, the environment measurement parameter set comprises the snow load parameter set, and the preset safety assessment model is determined based on the second linear fitting measurement line.

In one possible design, the determining recommended action information according to the safety state and a preset early warning level rule includes:

determining a deflection early warning threshold value set according to the second linear fitting measuring line, wherein the deflection early warning threshold value set comprises a plurality of deflection early warning threshold values, and each deflection early warning threshold value corresponds to an early warning grade;

and determining the recommended measure information according to the current measured deflection of the target building and the deflection early warning threshold value set.

determining a third linear fitting measurement line of the target building according to a time parameter, a water level parameter set and a rainfall parameter set of a preset time period, wherein the state measurement parameter set comprises the water level parameter set, the environment measurement parameter set comprises the rainfall parameter, and the preset safety assessment model is determined based on the third linear fitting measurement line.

determining a water level early warning threshold value set according to the third linear fitting measurement line, wherein the water level early warning threshold value set comprises a plurality of water level early warning threshold values, and each water level early warning threshold value corresponds to an early warning grade;

and determining the recommended measure information according to the current rainfall of the target building and the water level early warning threshold value set.

In a second aspect, the present application also provides a building safety monitoring device, comprising:

the measurement acquisition module is used for acquiring current state measurement parameters of the target building and environment prediction parameters of the local environment;

and the safety evaluation module is used for determining the safety state of the target building according to the state measurement parameter, the environment prediction parameter and a preset safety evaluation model.

In one possible design, the building safety monitoring device further includes:

the calibration acquisition module is used for acquiring a state measurement parameter set of a calibration building in a preset time period and an environment measurement parameter set of a local environment, wherein the calibration building corresponds to the target building;

and the model determining module is used for determining the preset safety evaluation model according to the time parameter of the preset time interval, the state measurement parameter set and the environment measurement parameter set.

In one possible design, the building safety monitoring device further includes:

and the safety prompting module is used for determining recommended measure information according to the safety state and a preset early warning level rule after the safety state of the target building is determined according to the state measurement parameter, the environment prediction parameter and a preset safety evaluation model, wherein the recommended measure information is used for indicating a recommended processing mode for the target building.

In one possible design, the model determination module is specifically configured to:

In one possible design, the safety prompt module is specifically configured to:

In one possible design, the target building is a timber structure building.

In one possible design, the safety prompt module is specifically configured to:

and determining the recommended measure information according to the current rainfall capacity of the target building and the water level early warning threshold value set.

In a third aspect, an embodiment of the present application further provides an electronic device, including:

a processor; and the number of the first and second groups,

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform any one of the building safety monitoring methods of the first aspect via execution of the executable instructions.

In a fourth aspect, an embodiment of the present application further provides a storage medium, where a computer program is stored, and when the program is executed by a processor, the method for monitoring building safety in the first aspect is implemented.

According to the building safety monitoring method, the building safety monitoring device, the storage medium and the electronic equipment, the current state measurement parameters of the target building and the environment prediction parameters of the local environment are obtained firstly, and then the safety state of the target building is determined according to the state measurement parameters, the environment prediction parameters and the preset safety assessment model, so that more comprehensive risk early warning can be realized on the building safety in advance, and the efficiency and the reliability of the building safety risk prevention and control work in the area are improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

Fig. 1 is a diagram illustrating an application scenario of a building security monitoring method according to an example embodiment;

FIG. 2 is a schematic structural diagram of a fixed security monitoring terminal device in the embodiment shown in FIG. 1;

FIG. 3 is a schematic flow chart of a building safety monitoring method according to a first embodiment of the present application;

FIG. 4 is a schematic flow chart diagram of a building security monitoring method according to a second embodiment of the present application;

FIG. 5 is a schematic flow chart diagram of a building safety monitoring method according to a third embodiment of the present application;

FIG. 6 is a graph illustrating the load versus relative displacement of a shear wall according to a third embodiment;

FIG. 7 is a graph illustrating wind load and inclination angle prediction of the wooden structure according to the third embodiment;

FIG. 8 is a schematic diagram showing another relationship between wind load and inclination angle prediction of the wooden structure in the third embodiment;

fig. 9 is a schematic flow chart of a building safety monitoring method according to a fourth embodiment of the present application;

fig. 10 is a schematic view of a terminal mounting manner in the fourth embodiment;

fig. 11 is a schematic flow chart of a building safety monitoring method according to a fifth embodiment of the present application;

fig. 12 is a schematic view of a terminal mounting manner in the fifth embodiment;

fig. 13 is a schematic flow chart of a building safety monitoring device according to a sixth embodiment of the present application;

fig. 14 is a schematic flow chart of a building safety monitoring apparatus according to a seventh embodiment of the present application;

fig. 15 is a schematic structural diagram of an electronic device according to an eighth embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the above-described drawings (if any) are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The application aims to provide a building safety monitoring method, a building safety monitoring device, a storage medium and electronic equipment so as to perform a safety evaluation method under the action of environmental factors. The method and the system can not only sense the current state of the building through modules such as the attitude sensor and send an alarm when danger is detected, but also sense and learn the state change of the building under the action of environmental factors through software programs and extract key information as a basis, so that the state change of the building when the environmental factors change in the future is predicted, and early warning is sent to possible risks.

Building risks comprise dumping, collapsing, washing, submerging and the like, building states are mainly obtained by fixed safety monitoring terminal devices installed at key parts of a building, built-in and external sensor modules of the terminals can be flexibly assembled according to needs, and data are transmitted to a data comprehensive analysis center through the Internet of things technology to be used for learning, analyzing and predicting; the environmental information comprises data which can be acquired through a public network, such as meteorological information and forecasts, meteorological disaster early warning, forest fire risk levels, hydrological information and predictions, and the like, and is acquired through an environmental element sensor which is specially deployed in a target monitoring region.

This application is carrying out on-line monitoring to building safety simultaneously, has added the consideration that environmental factor produced the influence to building safety, therefore can realize more comprehensive risk early warning in advance to building safety, and then improves the efficiency of building safety risk prevention and control work in the region.

Fig. 1 is a diagram of an application scenario of a building security monitoring method according to an example embodiment, and fig. 2 is a schematic structural diagram of a fixed security monitoring terminal device in the embodiment shown in fig. 1. As shown in fig. 1-2, the building safety monitoring method provided by this embodiment can be applied to a building safety monitoring system. The building safety monitoring system can flexibly assemble required sensor modules as required, and communicates and controls the sensor modules through the I/O ports.

In particular, the attitude sensor may be used to monitor acceleration/tilt changes, and may be external or internal. And for the accuracy of the sensor, for example, tilt angle monitoring accuracy of 0.05 degrees (in the static case) and 0.1 degrees (in the dynamic case) and acceleration monitoring accuracy of 0.01mg are possible. And the attitude sensor keeps a dormant state when being static, is awakened when a timer is triggered or the displacement with set strength is detected, reads data such as inclination angle/acceleration and the like, and reports the data through the Internet of things (NB-IOT).

In addition, the device can also comprise a temperature sensor and a humidity sensor, and can be externally arranged or internally arranged. The device also can comprise a smoke alarm module, an acousto-optic alarm module, an infrared human body detection module and the like, and can be externally arranged or internally arranged. And the laser sensor can be also included, and can be externally arranged or internally arranged. And for a laser sensor, the accuracy may be, for example, 1mm.

A water level sensor may also be provided, but its accuracy may be 1cm or less.

Furthermore, an air speed sensor, a wind direction sensor, a rainfall sensor, a snow sensor and the like can be arranged, a data line can be connected into the fixed safety monitoring terminal, and data can be independently transmitted to the data comprehensive analysis center.

It should be noted that the above-mentioned sensors are provided for exemplary purposes only, and in the building safety monitoring system disclosed in the present application, various sensors may be flexibly assembled as required, that is, the sensors may be adaptively configured according to the target detection project of the building, or may cooperate with various sensors to detect the target detection project of the building.

And for the mobile information terminal, the data can be checked from the data and comprehensive analysis center in real time, and the mobile information terminal can be used for reporting building safety related information including photos, videos, data and the like during patrol and can also be used for inputting related installation information such as installation positions, photos and the like during installation of fixed safety terminals.

For the data comprehensive analysis center, the data comprehensive analysis center can be based on a cloud computing server and a storage server. Wherein the safety assessment model determination and the safety state assessment for each building may be implemented at this center by a software program. And the system can also acquire internet data including environmental information such as weather and the like, and provide a Web server for the monitoring center and the mobile information terminal to inquire and manage the data.

It should be noted that the above security evaluation model may be based on a logic algorithm, or may be based on a neural network model. If the logic algorithm is based on, the logic algorithm can be determined according to physical characteristics of the building and the target detection item. If the method is based on the neural network model, relevant data including building structure types, state parameters of various building structures and main components, various environmental data and strain data of the building under the action of environmental factors can be collected, so that training materials of the neural network model are established, then, based on the training material data, the strain relation model of various buildings under various state conditions under the influence of various environmental factors is learned through the neural network, and therefore, the change of the same type of building under the action of the same environmental factor can be predicted by the relation model.

Fig. 3 is a schematic flow chart of a building safety monitoring method according to a first embodiment of the present application. As shown in fig. 3, the building safety monitoring method provided in this embodiment includes:

step 101, obtaining a current state measurement parameter of a target building and an environment prediction parameter of a local environment.

In particular, it may be a fixed safety monitoring terminal installed on the main structure of a building, and generally includes one or more columns, beams, roofs, and the like. The terminal can acquire the current state measurement parameters of the target building and the environment prediction parameters of the local environment by a built-in or external sensor according to requirements.

Alternatively, environmental data and forecasts acquired over public networks are also possible. And for the target building and environment information reported by the mobile information terminal, the target building and environment information comprises but is not limited to a whole building and local risk point photo, a video, a three-dimensional image, measurement data, a building surrounding environment and a risk point.

And step 102, determining the safety state of the target building according to the state measurement parameters, the environment prediction parameters and the preset safety evaluation model.

After the current state measurement parameters of the target building and the environment prediction parameters of the local environment are obtained, the safety state of the target building can be determined according to the state measurement parameters, the environment prediction parameters and a preset safety assessment model, wherein the safety state can be divided into a plurality of safety level states, and therefore different measures can be taken according to different safety level states.

In the embodiment, the current state measurement parameters of the target building and the environment prediction parameters of the local environment are obtained first, and then the safety state of the target building is determined according to the state measurement parameters, the environment prediction parameters and the preset safety assessment model, so that more comprehensive risk early warning can be realized on the building safety in advance, and the efficiency and the reliability of the building safety risk prevention and control work in the area are improved.

Fig. 4 is a schematic flow chart of a building safety monitoring method according to a second embodiment of the present application. As shown in fig. 4, the building safety monitoring method provided in this embodiment includes:

step 201, obtaining a state measurement parameter set of a calibration building in a preset time period and an environment measurement parameter set of a local environment.

Specifically, a state measurement parameter set of the calibration building in a preset time period and an environment measurement parameter set of the local environment may be obtained, where the calibration building corresponds to the target building. Specifically, the measurement state measurement parameters and the environment measurement parameters corresponding to each time node may be first formed, then a data set is formed, and finally, the formed data set is used for fitting, so as to determine the relationship between the measurement state measurement parameters and the environment measurement parameters.

Step 202, determining the preset security assessment model according to the time parameter, the state measurement parameter set and the environment measurement parameter set of the preset time period.

After a state measurement parameter set of a calibrated building in a preset time period and an environment measurement parameter set of a local environment are obtained, a preset safety assessment model can be determined according to a time parameter, the state measurement parameter set and the environment measurement parameter set of the preset time period, wherein the preset safety assessment model can be generated based on a relation between a measurement state measurement parameter and the environment measurement parameter.

And step 203, acquiring current state measurement parameters of the target building and environment prediction parameters of the local environment.

Specifically, the safety monitoring terminal may be a fixed safety monitoring terminal installed on a main structure of a building, and generally includes one or more columns, beams, roofs, and the like. The terminal can obtain the current state measurement parameters of the target building and the environmental prediction parameters of the local environment by a built-in or external sensor according to the requirement.

Alternatively, environmental data and forecasts may also be obtained over public networks. And for the target building and environment information reported by the mobile information terminal, the target building and environment information comprises but is not limited to a whole building and local risk point photo, a video, a three-dimensional image, measurement data, a building surrounding environment and a risk point.

And step 204, determining the safety state of the target building according to the state measurement parameters, the environment prediction parameters and the preset safety evaluation model.

Fig. 5 is a schematic flow chart of a building safety monitoring method according to a third embodiment of the present application. As shown in fig. 5, the building safety monitoring method provided in this embodiment includes:

step 301, obtaining a tilt angle parameter set and a wind load parameter set of a calibrated building in a preset time period.

Step 302, a first linear fitting measurement line of the target building is determined according to the time parameter, the inclination angle parameter set and the wind load parameter set of the preset time period, and the preset safety assessment model is determined based on the first linear fitting measurement line.

Step 303, determining an inclination angle early warning threshold value set according to the first linear fitting measurement line, where the inclination angle early warning threshold value set includes a plurality of inclination angle early warning threshold values, and each inclination angle early warning threshold value corresponds to an early warning level.

And step 304, determining recommended measure information according to the current measured inclination angle of the target building and the inclination angle early warning threshold value set.

The monitoring method provided by the implementation can be based on the fact that the building structure can show the process from a linear elastic stage to an elastic-plastic stage to structural damage and collapse in the process of increasing and decreasing the transverse acting force.

Fig. 6 is a graph illustrating the relationship between the shear wall load and the relative displacement in the third embodiment. As shown in FIG. 6, the abscissa is the relative displacement component, γ _e To limit elastic deformation, gamma _y To yield deformation, gamma _u Ultimate deformation (gamma) _u Equivalent to building collapse) from the origin of coordinates to gamma _e Is a linear elastic phase of shear wall, gamma _e To gamma _y For the elastoplastic phase, γ _y This later means that the building structure, and in particular the joints, are severely damaged.

In addition, when the building structure is always in an elastic state, the structure analysis can be carried out by using an elastic theory, otherwise, the structure analysis is preferably carried out by using an elastic-plastic theory. The shear wall load-relative displacement curve of fig. 6 can be used to predict the corresponding building structure relative displacement value γ when the horizontal load is P; when the horizontal load is wind load, the size of the horizontal load borne by the building and the relative displacement possibly generated can be predicted according to information such as future wind speed and wind direction in weather forecast, and therefore the horizontal load can be used as an early warning basis.

However, the shear wall load-relative displacement curve of fig. 6 is only a static load curve in which wind load and dynamic load caused by fluctuating wind are actually present, and is difficult to obtain. In particular, an approximate curve can be obtained by calculation or software simulation, but is difficult to implement when there are a large number of buildings with different structures and materials. When more input and output exist in the aspects of material selection, construction process, use condition and the like and simulation conditions, the actual curve and the calculated or simulated curve have certain difference and cannot be completely used as a prediction basis. In addition, a real curve can be obtained through an actual building test but the method is destructive and unrealistic.

In this embodiment, the predicted building type can be reduced to a wood structure building, such as a wood structure pavilion, the wind load direction has no shear wall rigid support, and the building is relatively sensitive to wind load, and the case of casualties caused by overall collapse is also common.

The range of the elastic zone of the wooden building line is relatively wide, the wind pressure (namely the load per unit area) borne by the wooden building line is in direct proportion to the square of the wind speed, and the square v of the wind speed observed at the front section of the elastic zone can be utilized ² And the relation data between the structure inclination angle theta, namely the relation data of horizontal load-displacement of the building in the wind direction, can obtain an approximate elastic oblique line of the building line by performing linear fitting on the relation data set in the interval, the oblique line is taken as a measuring line, the load-inclination angle change relation of the rear section of the elastic zone can be predicted, and meanwhile, the oblique line can also be used as a judgment basis to dynamically search the maximum elastic inclination angle of the building through an approximation algorithm and used as the elastic limit inclination angle theta _t . Linear measurement line fitting procedure and elastic limit inclination angle theta _t Is based on the newly obtained informationAnd observing the learning process of the data repeatedly. Optionally, the linear fitting data set can also obtain more reliable theta _t Then expanding to the interval of the dip angle [ 0, theta ] _t Data set of (1).

Fig. 7 is a schematic diagram illustrating a curve relationship between the wind load and the inclination angle prediction of the wooden structure in the third embodiment, and the curve relationship between the wind load and the inclination angle prediction of the wooden structure can be obtained according to fig. 7.

From the perspective of early warning and safety control, a wooden building is firstly in a safety displacement (inclination angle) range required by relevant national regulations; secondly, the building is positioned in the elastic area range of the wooden building; the method is characterized in that the method comprises the following steps that (1) a large wind which is possible to make a wooden building exceed the range of an elastic zone is dangerous to the wooden building, and particularly, the method is forbidden to be used when the building is predicted to possibly exceed the elastic zone by considering the uncertainty of pulsating wind and the uncertainty of influence on the rigidity of the wooden building; the actual effect of predicting the inclination angle which possibly exceeds the elastic zone on the safety control of the wooden building is relatively small, and the accurate prediction difficulty is very large.

Wherein, the following table shows the relationship between the simulated overall dip angle early warning threshold value and the grade of the wooden building.

According to the wind load calculation specification in the GB50009-2012 specification, the wind load W of the building in a certain direction _f Proportional to the square of the average wind speed v (constant height/body shape factor, etc.), the specific process is as follows:

relation between wind pressure and wind speed:

ρ is the air density

Standard value of wind load on certain surface of building:

w _k ＝β _z μ _s μ _z w ₀

β _z is the wind vibration coefficient at the height z;

μ _s is the wind load body type coefficient;

μ _z is the wind pressure height variation coefficient;

wind pressure on certain surface of building:

wherein Si is the area of the ith windward side of the building, mu _si The wind load shape coefficient of the windward side.

If n faces are combined, the wind loads on all the faces of the wooden pavilion building are linearly superposed:

so that the square v of the wind load and wind speed of the building as a whole ² In direct proportion.

And when the building structure is always in an elastic state, the structure analysis can be carried out by using an elastic theory, otherwise, the structure analysis is carried out by using an elastic-plastic theory. In the linear elastic region of the integral structure of the wooden pavilion type building, the horizontal load (such as wind load) is in direct proportion to the horizontal displacement gamma of a certain high point on the integral structure.

γ＝W _f ×H

Where H is assumed to be the structural elastic constant.

Due to W _f Proportional to the square of the wind speed, so the horizontal displacement γ is also proportional to the square of the wind speed.

If the building column base is fixed with the foundation and the dip angle monitoring point T is arranged at a certain height h, the horizontal displacement gamma of the monitoring point can be approximate to

γ＝tanθ×h

Wherein, theta is the inclination angle between the monitoring point T and the Z axis.

If the column base is not fixed to the foundation, the relative displacement between the column base and the foundation is small under the condition that the wind load is not particularly large, so that the following can be approximately considered:

γ＝tanθ×h

for safety, an acceleration sensor can be mounted on the column base to monitor the displacement of the column base.

The allowed inclination angle of the building is small, and in an angle range less than 5 degrees, the radian value theta is almost the same as tan theta, and no difference exists in 3 bits after a decimal point, so that approximately:

γ≈θ×h

therefore, the inclination angle arc value theta of the inclination angle monitoring point T is in direct proportion to the horizontal displacement gamma, and the horizontal displacement gamma is in direct proportion to the square of the wind speed. In the linear elastic zone of the wooden pavilion type building (also in the displacement range allowed by the regulations), the inclination angle theta of the monitoring point T and the square of the wind speed, namely v ² In direct proportion.

Continuing with reference to the above table, for θ _L For the determination of (1), reference may be made to the rating standard of "integral tilt" in "ancient building timber structure maintenance and reinforcement technical Specification" GB 50165-2020 "Table 6.4.5 integral firmness assessment of the structure".

Taking the inclination of the top point of the structure as 1/200 of the first alarm threshold theta of the pavilion building _L ：

θ _L ≈arctan(1/200)≈0.005

For theta _D And (4) determining. Referring to the evaluation limit of damage points in the integrity evaluation standard of wooden structures of ancient buildings GB-50165-1992 and other experimental researches on the damage degree of the wooden structures by transverse loads, for wooden pavilion buildings with good health conditions, 120mm is taken in the plane direction of the frames as the maximum displacement threshold of the structures damaged by damage, and if wind load and inclination angle change in the direction vertical to the planes of the frames need to be concerned, 60mm is taken in the direction as the maximum displacement threshold. If the main structure of the building has damage, the proper theta needs to be determined through identification _D 。

If the maximum height of the structure is H, the maximum allowable inclination arc value theta of the pavilion building can be calculated _D According to different directions, can be specifically calculated as

θ _D ＝arctan(120/H)

Or

θ _D ＝arctan(60/H)

θ _D May be used as a second high level alarm in addition to collapse.

And for theta _t Is determined. For general wooden buildings, theta _D Ratio theta _L Often much larger. Let θ _t As an approximation of the elastic limit of a building block, θ _t At (theta) _L ，θ _D ) In the meantime. As an initial value, theta can be set conservatively according to the actual condition of the building _t ＝2·θ _L The building is in good condition and can be set

Or other suitable values. When the monitoring and early warning system starts to operate, namely, the maximum inclination angle conforming to the linear elastic relation is continuously found by analyzing the inclination angle data obtained by the sensor and the corresponding wind speed to update the theta _t 。

For the determination of the preset safety evaluation model, in the actual monitoring, two monitoring terminals (inclination angle sensors) can be respectively arranged on a certain column head of the target timber structure and a beam connected with the column head (at the same height and adjacent to the column head), and if the column head is not in hard connection with the foundation, one monitoring terminal (acceleration sensor) can be arranged on the column head. The sensors detect the inclination of the building and transmit the data to the server by wireless transmission. Due to the dynamic change of the attitude of the building and the dynamic change of wind, in the prior art, data in two aspects are difficult to capture and match in two dimensions of time and space, so that the inclination angle and the maximum value of the wind speed in a certain observation period are adopted for matching.

If there is an on-site wind speed sensor, the observation time period can be set to Δ T (e.g. 30 seconds), the server program receives the sensor data, records the maximum inclination angle camber value θ of the building, and records the maximum wind speed v in the same period, using v ² The observed data, paired with the maximum tilt value θ, is recorded as (v, θ). May also be (v, θ, t), t may be set to the observation period at that timeThe intermediate position time, the data combination can also be added with other parameters related to the observation period, such as the ambient temperature, the humidity, and the like.

If there is no wind speed sensor on site, then, depending on the common weather report, the observation time period Δ T (e.g., 1 hour) is defined by the minimum period of the weather report during which the maximum pitch camber value θ recorded is used, together with the square v of the maximum wind speed in the weather report ² And (6) pairing.

After a period of time, if the paired data combination is found, the inclination angle arc value theta of more than 3 data groups is

Then all θ ≦ θ may be filtered out by the software program _L Is combined and a linear fitting operation is performed if the coefficient of determination of the fitting result is R ² Greater than, for example, 0.95, the linear relationship can be used as a basis for prediction, the linear relationship:

θ＝av ² + b. (formula 1)

Wherein the parameters a, b are obtained by software fitting; in the ideal case, the value of b is close to 0; otherwise it indicates that the building has a natural or plastic tilt angle.

Predicting that theta will be greater than theta by the linear relationship _t A serious warning should be issued and building disablement is advised.

If the camber value theta of the dip angle in the data combination is larger than theta _L And is less than theta _D And if the corresponding wind speed v is also larger, the building is indicated to be over-out of the safe displacement range under the condition of larger wind load. In this case, first, when the safe displacement range is exceeded, the system should send an alarm directly to the management unit; thirdly, whether the inclination angle conforms to the linear elastic relationship can be judged by the formula 1, and specifically, v in the data combination can be determined ² The substituted equation 1 is used to calculate θ'. Comparing θ' to the actual value θ, and if close (e.g., within ± 3%), indicating that the tilt is still within the elastic zone; if the difference is large, for example, θ - θ 'is greater than θ' x 3%, it indicates that the building is likely to enter the elasto-plastic area at that time. If the above-mentioned judgement finds that theta is still in the elastic relation range, it can be judged that theta is greater than theta _t If greater than, θ may be assigned to θ _t Therefore, theta continuously approaching the real elasticity limit value can be obtained according to the actual data _t (ii) a If the judgment shows that the theta is out of the elastic relation range, the building is likely to enter the elastic plastic area, an alarm should be given immediately, and a manager is advised to forbid the building.

According to the learning process, if the data combination of the building entering the elastic-plastic area is observed, the theta/v in the data combination of the elastic-plastic area can be found out through calculation and comparison ² The largest proportion of data combinations (v) _m ,θ _m ). Then can pass through (v) _m ,θ _m ) And (v) _t ,θ _t ) Square v of wind speed of the building elastoplastic region ² And a predicted line (optional straight line) of the inclination angle theta. It is worth mentioning that the elasto-plastic region prediction line belongs to the optimistic prediction and can only be used as a reference.

Specifically, fig. 8 is a schematic diagram of another curve relationship between the wind load and the inclination angle prediction of the wooden structure in the third embodiment. As shown in FIG. 8, the wind velocity v of the wooden building can be constructed ² Predicted relationship to tilt angle θ:

in addition, the building supported by the rigid shear wall is not easy to be blown down by strong wind, and the building supported by the rigid shear wall can be completely blown down by wind, and is basically a wood-structure pavilion type building (herein, the wood-structure pavilion type building is simply called as a wood pavilion building) without rigid support of the shear wall in the stress direction, so that the types of buildings which are easily influenced by wind load in the structure concerned by people can be actually reduced to the wood pavilion type building. The horizontal load and displacement relation of the wooden pavilion building is simple and is mainly determined by the elasticity and the elastoplasticity characteristics of the structure main body; according to the mechanical characteristics of wood and related academic research, the wood column and the whole wood structure have strong elastic characteristics, wide elastic interval and smaller elastic-plastic interval. The general building load and displacement, especially the displacement relation under the action of fluctuating wind load is complex, the rule is difficult to find for prediction, and the mechanical characteristics of the wood pavilion building are favorable for constructing a prediction algorithm in a certain range.

The wooden pavilion building is threatened by wind load and is usually structurally hidden. Under the action of wind load, the column root has large bending moment, shearing force and certain axial force. If the column is in hard connection with the foundation, the column root has large bending moment under the action of wind load; the axial forces to which the post is subjected are significant if the post and foundation are not fixed. The strength of the column is reduced by hidden troubles such as mildewing and rotting, worm damage, cracks, high water content, too long years and the like. When these concerns arise with individual columns, these columns fail in resistance and all shear forces are transferred to other columns to be borne, causing the other columns to overload and fail. In addition, the rigidity of the building structure is reduced after the column is partially failed, large horizontal displacement is easy to occur under wind load, a gravity second-order effect is caused, and overturning is aggravated under the action of gravity, so that overall instability is caused.

For the wooden structure building with more hidden health risks, in extreme cases, such as old building and serious rotting of beam columns, the wooden structure building can not only comply with the standard standards, but also can collapse before the standard even if the standard standards are severe. An axial compression component with the largest hidden danger can be found out, such as a certain column of the wooden pavilion; then, the strength of the member is calculated and estimated according to the specifications and the existing theory, for example, the periphery of the post is damaged by worms and decays, and the effective section A of the post is calculated according to the depth of the damage by worms and decays; and the tensile strength, the compression strength and the bending strength of the wood column are converted according to the characteristics of the wood, such as the moisture content, the temperature, the service life and the like.

And after the tensile strength, the compressive strength and the bending strength of the column which are re-evaluated are obtained, calculating whether the column can bear corresponding loads under the action of each wind level by using a computer according to a calculation method of the bearing capacity of the eccentric compression member, so as to obtain the maximum wind resistance grade of the building. The maximum wind resistance grade obtained by the method can be complemented with the wind load-inclination angle prediction method, and provides a comprehensive reference for building managers.

Fig. 9 is a schematic flow chart of a building safety monitoring method according to a fourth embodiment of the present application. As shown in fig. 9, the building safety monitoring method provided in this embodiment includes:

step 401, a deflection parameter set and a snow load parameter set of a calibrated building in a preset time period are obtained.

Step 402, determining a second linear fitting measuring line of the target building according to the time parameter, the deflection parameter set and the snow load parameter set of the preset time interval, and determining the preset safety assessment model based on the second linear fitting measuring line.

And determining a second linear fitting measuring line of the target building according to a time parameter, a deflection parameter set and a snow load parameter set of a preset time period, wherein the state measuring parameter set comprises the deflection parameter set, the environment measuring parameter set comprises the snow load parameter set, and the preset safety assessment model is determined based on the second linear fitting measuring line.

And 403, determining a deflection early warning threshold value set according to the second linear fitting measurement line, wherein the deflection early warning threshold value set comprises a plurality of deflection early warning threshold values, and each deflection early warning threshold value corresponds to an early warning grade.

And step 404, determining recommended measure information according to the current measured deflection of the target building and the deflection early warning threshold value set.

In this embodiment, the method provided by this embodiment can be applied to the type of building sensitive to snow load in the area with snow, such as wooden structures, light steel buildings, long-span shed roofs, and the like.

According to a calculation mode of building structure load specification GB50009-2012 for building roof snow load standard values:

s _k ＝μ _r s ₀

wherein s is _k As standard value of snow load, μ _r Is the distribution coefficient of accumulated snow on the roof, s ₀ Is the basic snow pressure.

For s ₀ The calculation method is as follows:

s ₀ ＝hρg

h is the snow accumulation depth and is the vertical depth from the snow accumulation surface to the ground; rho is the density of accumulated snow; g is the acceleration of gravity.

Thus, s _k ＝μ _r hρg

It follows that for a particular building, the snow load is linearly related to the product of the depth of the snow and the density of the snow.

And for snow load and deflection of the bent member, in the elastic range of the material, the load and the deflection of the bent member are in a linear relation, and the larger the load is, the larger the deflection of the bent member is.

However, the vertical load bearing capacity of a building is influenced by various factors, theoretical calculation includes the structure type, the material strength of each part, the connection strength and the like, and the actual load bearing capacity is influenced by various factors such as material defects, construction quality, building maintenance and the like. Predicting the overall load capacity of a building structure from various details is very cumbersome. In addition, the snow load is affected by the snow depth, the snow density, the roof snow distribution coefficient and the like, particularly the snow density is large in numerical range, real-time change can be caused by the snow thickness, precipitation and other factors in actual situations, and the snow density is difficult to measure quickly and conveniently in actual operation.

Therefore, in view of the overall snow load of the roof of the building, the deflection of the curved structure portion where the load is concentrated and the risk is relatively high (or the strength is weak) is focused and measured. The influence of the future snow load (snow depth) on the structure is predicted by applying the elastic interval of the material and the related calculation theory and based on the learning of the linear relation between the load size and the deflection change.

For the snow density, a safe calculation method is adopted, and the lowest value P of the local snow density range is used for calculating the load/deflection change relation _min (ii) a When the deflection change of the flexural structure caused by some forecast accumulated snow depth is calculated by prediction, the maximum value P of the local snow density range is adopted _max . Alternatively, P is constructed _max /P _min The snow density safety factor.

Fig. 10 is a schematic view of a terminal mounting manner in the fourth embodiment. As shown in fig. 10, it may be a fixed monitor terminal equipped with a laser ranging module, and the deflection is calculated by measuring the distance from the ground. As a sub option, the system can also be a fixed monitoring terminal provided with an attitude sensor, and deflection is measured and calculated through the inclination angle.

For the installation of the terminal, a component with concentrated vertical load and uniformly distributed load can be selected, and the component is made of a material with negligible shearing force, such as a beam made of log or square timber. Then, on the basis of field observations, a component is selected which is at a relatively high risk, for example, with signs of bending, corrosion, etc. And, estimate the biggest point of component deflection (for example the middle position of roof beam), laser rangefinder module is fit for installing in estimating the biggest point position of deflection, if the attitude sensor is suitable for installing in the one end of being close to the roof beam.

The preset safety evaluation model is determined by firstly learning the deflection delta of a selected flexural member under the condition of continuous multi-day rainfall-free weather ₀

When snowing starts on a certain day, the snow depth h is learned according to weather information ₁ (the accumulated snow depth can also be measured on site), and the maximum deflection of the target flexural structure is learned to be delta ₁ The maximum deflection is delta ₁ With snow depth h ₁ The proportional relation N is as follows:

N＝(δ ₁ -δ ₀ )/h ₁

can learn for many times and record each N value; in prediction, the maximum value N can be selected _max Other strategies may be used to select the appropriate value of N.

The determination of the safety status of the target building may then be based on local weather forecasts. Supposing that snowfall is forecasted in the future, the thickness of the snow is estimated to be h ₂ 。

Learning and triggering prediction of deflection delta of target flexural component _s (there may be no snow load and certainly there is a residual snow load).

And finally, predicting the deflection delta of the flexural member:

δ＝N _max ×h ₂ ×P _max /P _min +δ _s

wherein, P _max /P _min For snow density safety factor, P _max Is the maximum value of the local snow density, P _min Is the minimum value.

During learning and prediction, if delta is detected _s If the maximum deflection limit value of the flexural member is greater than the maximum deflection limit value of the national building code, an alarm is given immediately; if the value of delta is predicted to exceed the maximum deflection limit value, an early warning is immediately sent out.

Fig. 11 is a schematic flow chart of a building safety monitoring method according to a fifth embodiment of the present application. As shown in fig. 11, the building safety monitoring method provided in this embodiment includes:

step 501, a water level parameter set and a rain load parameter set of a calibrated building in a preset time period are obtained.

Step 502, determining a third linear fitting measurement line of the target building according to the time parameter, the water level parameter set and the rain load parameter set of the preset time period, and determining the preset safety assessment model based on the third linear fitting measurement line.

Determining a third linear fitting measurement line of the target building according to a time parameter, a water level parameter set and a rain load parameter set of a preset time period, wherein the state measurement parameter set comprises the water level parameter set, the environment measurement parameter set comprises the rain load parameter, and the preset safety assessment model is determined based on the third linear fitting measurement line.

Step 503, determining a water level early warning threshold value set according to the third linear fitting measurement line, where the water level early warning threshold value set includes a plurality of water level early warning threshold values, and each water level early warning threshold value corresponds to one early warning level.

And step 504, determining recommended measure information according to the current rainfall of the target building and the water level early warning threshold value set.

In this embodiment, the method provided by this embodiment may be used for waterfront construction along the river bank along the river, especially for buildings such as wooden structures that are sensitive to water flow impact or soaking.

If the waterfront building has complete design data, the +/-0.00 elevation (generally the height of the first-floor level) of the building and the corresponding absolute elevation are provided, and in addition, information of the dead water level and the flood level of the nearby water area is also provided. If there is a hydrological (water level) forecast for a nearby water area, the likelihood and degree (depth) of the building being flooded by flood or being flooded by water can be obtained based on a comparison of the absolute elevation of the building at ± 0.00 elevation and the absolute elevation of the forecast water level.

If the building does not have complete design information and the waterfront water area does not have hydrologic management and prediction, such as inland river coastal garden buildings, another method for monitoring and prediction is needed. Before inland rivers/reservoirs drain waterlogging, a certain relation exists between water level increase and rainfall. When the water level rises, the area of the water area is often increased, the rising amount of the water level formed by the rainfall increased by unit is gradually reduced, but the area of the water area corresponding to each water level is often difficult to measure and estimate. The method adopts an approximate algorithm, namely always predicting the water level increase delta H ' (the average water area S ' in delta H ') formed by delta T ' rainfall delta R ' in the next unit time by the relation T between the rainfall delta R and the water level increase delta H in the unit time delta T (if the catchment area of the water area is regarded as unchanged, T is in direct proportion to the average water area S in delta H). The reason is that the water level rises two consecutive units, the water area is closest, and S is usually smaller than S ', and there can be some safety redundancy in using S to predict the water level increase in Δ T' for the next unit of time.

Fig. 12 is a schematic view of a terminal mounting manner in the fifth embodiment. As shown in fig. 12, in the monitoring terminal equipped with the water pressure sensor, the water level sensor can be installed below the first floor level (i.e., ± 0.00 elevation) of the building, and the highest position of the water level sensing range can be selected to be placed at the position with the height of ± 0.00 elevation, and the range of the water level sensor is Hs; when the water level contacts the sensor, the water level can be identified by the terminal and reported to the data comprehensive analysis center; the monitoring terminal is installed at a high position of the building for preventing water from being soaked.

During flood prevention, Δ T is defined as the shortest period of the weather report. When the water level contacts the water level sensor, the fixed terminal reports the information to the data comprehensive analysis center, and the software program starts the learning process. When the Δ T is over, it is based on a weather report (or on a rainfall observation specifically set up in the area of the building)Data of the equipment), rainfall amount is Δ R, water level rising height is Δ H (end water level H) ₂ And initial water level H ₁ Difference of values), the coefficient T can be obtained _h

T _h ＝ΔH/ΔR

Recording T simultaneously _h Corresponding initial water level H ₁ And an end water level H ₂ 。

The determination process of the safety state may be that during the flood prevention period, the rainfall is Δ R' in the next time period Δ T of the weather forecast. The computer program takes this information and starts the prediction.

Specifically, the current water level H is acquired from the water level sensor ₃ Obtaining the water level rising coefficient T in the last time period from the database _h And calculating a predicted water level:

H ₄ ＝H ₃ +T _h ×ΔR’

if H is ₄ And if the water level sensor exceeds the highest measuring range (namely the building is at +/-0.00 elevation), the risk of soaking the building and the predicted water level of the user are early warned. Under the condition that the conditions allow, measures such as drainage and flood discharge can be taken.

Fig. 13 is a schematic flow chart of a building safety monitoring device according to a sixth embodiment of the present application. As shown in fig. 13, the building safety monitoring apparatus 600 provided in this embodiment includes:

a measurement obtaining module 601, configured to obtain a current state measurement parameter of a target building and an environment prediction parameter of a local environment;

a safety evaluation module 602, configured to determine a safety state of the target building according to the state measurement parameter, the environment prediction parameter, and a preset safety evaluation model.

On the basis of the embodiment shown in fig. 13, fig. 14 is a schematic flow chart of a building safety monitoring device according to a seventh embodiment of the present application. As shown in fig. 14, the building safety monitoring apparatus 600 provided in this embodiment further includes:

a calibration obtaining module 603, configured to obtain a state measurement parameter set of a calibration building in a preset time period and an environment measurement parameter set of a local environment, where the calibration building corresponds to the target building;

a model determining module 604, configured to determine the preset security assessment model according to the time parameter of the preset time period, the state measurement parameter set, and the environment measurement parameter set.

In one possible design, the building safety monitoring apparatus 600 further includes:

a safety prompt module 605, configured to determine recommended measure information according to the safety state and a preset early warning level rule after determining the safety state of the target building according to the state measurement parameter, the environment prediction parameter, and a preset safety assessment model, where the recommended measure information is used to indicate a recommended processing mode for the target building.

In one possible design, the model determining module 604 is specifically configured to:

In a possible design, the safety prompt module 605 is specifically configured to:

In one possible design, the target building is a timber structure building, such as a kiosk.

The present embodiment provides a building safety monitoring apparatus, which may be used to perform the steps in the above-described method embodiments. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.

Fig. 15 is a schematic structural diagram of an electronic device according to an eighth embodiment of the present application. As shown in fig. 15, the present embodiment provides an electronic device 700, including:

a processor 701; and the number of the first and second groups,

a memory 702 for storing executable instructions of the processor, which may also be a flash (flash memory);

wherein the processor 701 is configured to perform the steps of the above-described method via execution of the executable instructions.

Alternatively, the memory 702 may be separate or integrated with the processor 701.

When the memory 702 is a device independent from the processor 701, the electronic device 700 may further include:

a bus 703 for connecting the processor 701 and the memory 702.

The present embodiment also provides a readable storage medium, in which a computer program is stored, and when at least one processor of the electronic device executes the computer program, the electronic device executes the steps of the above method.

The present embodiment also provides a readable storage medium, in which a computer program is stored, and when the computer program is executed by at least one processor of the electronic device, the electronic device executes the executable instructions to perform the steps in the above method.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A building security monitoring method, comprising:

acquiring a state measurement parameter set of a calibration building in a preset time period and an environment measurement parameter set of a local environment;

determining a preset safety evaluation model according to the time parameters, the state measurement parameter set and the environment measurement parameter set of a preset time period, wherein the preset safety evaluation model is generated after fitting according to a data set formed by the state measurement parameters and the environment measurement parameters corresponding to each time node;

acquiring current state measurement parameters of a target building and environment prediction parameters of a local environment; the target building corresponds to the calibration building; the target building is a wood structure building;

determining the safety state of the target building according to the state measurement parameters, the environment prediction parameters and a preset safety evaluation model;

determining recommended measure information according to the safety state and a preset early warning level rule, wherein the recommended measure information is used for indicating a recommended processing mode for the target building;

wherein the determining the preset security assessment model according to the time parameter of the preset time period, the state measurement parameter set, and the environment measurement parameter set comprises:

determining a first linear fitting prediction line of the target building according to a time parameter, an inclination parameter set and a wind load parameter set of a preset time period, wherein the state measurement parameter set comprises the inclination parameter set, the environment measurement parameter set comprises the wind load parameter set, and the preset safety assessment model is determined based on the first linear fitting prediction line; in particular, the method comprises the following steps of,

performing linear fitting on the relation data set of the preset time period to obtain an approximate building line elastic oblique line; predicting the wind load-inclination angle change relation of the back section of the elastic zone by taking the oblique line as a prediction line; meanwhile, the oblique line is also used as a judgment basis for dynamically searching the maximum elastic inclination angle of the building through an approximation algorithm to be used as an elastic limit inclination angle, and the linear prediction line fitting process and the elastic limit inclination angle searching process are learning processes which are repeatedly carried out by the system according to newly obtained observation data, namely when the monitoring and early warning system starts to operate, the corresponding inclination angle theta' of the wood structure building is obtained by utilizing the predicted wind load calculation; when the inclination angle theta' obtained by calculation is compared with the actual value theta, and the difference is within +/-3%, the inclination is indicated to be in the elastic zone; at the moment, judging whether theta is greater than the elastic limit inclination angle theta t or not, if so, assigning an actual value theta to the elastic limit inclination angle theta t, and so on to obtain an elastic limit value which continuously approaches to the reality according to actual data; if the difference value between the actual value theta and the calculated inclination angle theta 'is larger than theta' multiplied by 3%, the building enters the elastic plastic area at that time, and the corresponding inclination angle data is predicted by using another curve relation between the wind load of the wood structure building and the inclination angle prediction, namely:

，

wherein v represents the wind speed, θ _D Represents the maximum allowable inclination arc value of the wood structure building, a and b represent coefficients to be determined, and (v) _m θ m) represents θ/v in the data combination of the elastoplastic area ² The data combination with the largest proportion; by (v) _m θ m) and (v) _t ,θ _t ) Constructing a prediction line of the wind speed v and the inclination angle theta of the elastoplastic area;

in the formula, in the linear elastic region of the wood structure building, the relationship between the wind load borne by the wood structure building and the square of the wind speed is in direct proportion, and the relationship between the inclination angle of the wood structure building and the square of the wind speed is in direct proportion, so that the relationship between the inclination angle of the wood structure building and the square of the wind speed is obtained, and the preset safety evaluation model is further determined;

the determining of the recommended measure information according to the safety state and the preset early warning level rule comprises:

determining an inclination angle early warning threshold value set according to the first linear fitting prediction line, wherein the inclination angle early warning threshold value set comprises a plurality of inclination angle early warning threshold values, and each inclination angle early warning threshold value corresponds to an early warning grade; wherein the plurality of dip angle early warning thresholds comprise: first alarm threshold value theta _L And a second alarm threshold thetat, said second alarm threshold thetat being greater than the first alarm threshold thetat _L And the second alarm threshold value theta t is the elastic limit inclination angle, and the learning and updating are continuously carried out by the method;

2. The building safety monitoring method according to claim 1, wherein the determining the preset safety assessment model according to the time parameter of the preset time period, the state measurement parameter set and the environment measurement parameter set comprises:

determining a second linear fitting prediction line of the target building according to a time parameter, a deflection parameter set and a snow load parameter set of a preset time period, wherein the state measurement parameter set comprises the deflection parameter set, the environment measurement parameter set comprises the snow load parameter set, and the preset safety assessment model is determined based on the second linear fitting prediction line; specifically, the predicted flexural member deflection δ is calculated by the following formula:

δ＝Nmax ×h 2 ×Pmax /Pmin +δs

Pmax/Pmin is a snow density safety factor, pmax is a local snow density maximum value, pmin is a local snow density minimum value, N = (delta 1-delta 0)/h 1, delta 1 is the maximum deflection of a target bending structure, h 1 is the corresponding accumulated snow depth, delta 0 is the deflection of a selected bending member which is learned under the condition of continuous multi-day rainfall-free weather, nmax is the maximum value of N in multiple learning, h 2 is the accumulated snow depth, and deltas is the deflection of the target bending member before learning triggering prediction.

3. The building safety monitoring method according to claim 2, wherein the determining recommended action information according to the safety state and a preset early warning level rule comprises:

determining a deflection early warning threshold value set according to the second linear fitting prediction line, wherein the deflection early warning threshold value set comprises a plurality of deflection early warning threshold values, and each deflection early warning threshold value corresponds to an early warning grade;

4. The building safety monitoring method according to claim 1, wherein the determining the preset safety assessment model according to the time parameter of the preset time period, the state measurement parameter set and the environment measurement parameter set comprises:

determining a third linear fitting prediction line of the target building according to a time parameter, a water level parameter set and a rainfall parameter set of a preset time period, wherein the state measurement parameter set comprises the water level parameter set, the environment measurement parameter set comprises the rainfall parameter, and the preset safety evaluation model is determined based on the third linear fitting prediction line; specifically, the relationship T between the rainfall amount DeltaR and the water level increase DeltaH in the previous unit time DeltaT _h Predicting the water level increase delta H ' formed by delta T ' rainfall delta R ' in the next unit time; wherein h is the height of the water level and the relation T _h In relation to the terrain, T at different water levels h _h Exhibit different values, the higher the water level T _h The smaller; history T _h The values are recorded and used for subsequent predictions.

5. The building safety monitoring method according to claim 4, wherein the determining recommended action information according to the safety state and a preset early warning level rule comprises:

determining a water level early warning threshold value set according to the third linear fitting prediction line, wherein the water level early warning threshold value set comprises a plurality of water level early warning threshold values, and each water level early warning threshold value corresponds to an early warning grade;

6. A building safety monitoring device, comprising:

the calibration acquisition module is used for acquiring a state measurement parameter set of a calibration building in a preset time period and an environment measurement parameter set of a local environment, and determining the relationship between the state measurement parameter and the environment measurement parameter, wherein the calibration building corresponds to a target building;

the model determining module is used for determining a preset safety evaluation model according to the time parameter of a preset time period, the state measurement parameter set and the environment measurement parameter set; the method comprises the following steps: determining a first linear fitting prediction line of the target building according to a time parameter, an inclination parameter set and a wind load parameter set of a preset time period, wherein the state measurement parameter set comprises the inclination parameter set, the environment measurement parameter set comprises the wind load parameter set, and the preset safety assessment model is determined based on the first linear fitting prediction line; specifically, an approximate building line elastic oblique line is obtained by performing linear fitting on a relation data set of a preset time period; predicting the wind load-inclination angle change relation of the back section of the elastic zone by taking the oblique line as a prediction line; meanwhile, the oblique line is also used as a judgment basis for dynamically searching the maximum elastic inclination angle of the building through an approximation algorithm to be used as an elastic limit inclination angle, and the linear prediction line fitting process and the elastic limit inclination angle searching process are learning processes which are repeatedly carried out by the system according to newly obtained observation data, namely when the monitoring and early warning system starts to operate, the corresponding inclination angle theta' of the wood structure building is obtained by utilizing the predicted wind load calculation; when the inclination angle theta' obtained by calculation is compared with the actual value theta, and the difference is within +/-3%, the inclination is indicated to be in the elastic zone; at the moment, judging whether theta is larger than the elastic limit inclination angle theta t or not, if so, assigning an actual value theta to the elastic limit inclination angle theta t, and by analogy, obtaining a real elastic limit value which is continuously approximated according to actual data; if the difference value between the actual value theta and the calculated inclination angle theta 'is larger than theta' multiplied by 3%, the building enters the elastic plastic area at that time, and the corresponding inclination angle data is predicted by using another curve relation between the wind load of the wood structure building and the inclination angle prediction, namely:

，

wherein v represents the wind speed, θ _D Representing the maximum allowable inclination arc value of the wood structure building, a and b representing coefficients to be determined, (v) _m And thetam) represents theta/v in the elastoplastic region data combination ² The data combination with the largest proportion; by (v) _m θ m) and (v) _t ,θ _t ) Constructing a prediction line of the wind speed v and the inclination angle theta of the elastoplastic area;

the system comprises a measurement acquisition module, a data processing module and a data processing module, wherein the measurement acquisition module is used for acquiring current state measurement parameters of a target building and environment prediction parameters of a local environment; the target building corresponds to the calibration building; the target building is a wood structure building;

the safety evaluation module is used for determining the safety state of the target building according to the state measurement parameter, the environment prediction parameter and a preset safety evaluation model;

the safety prompting module is used for determining recommended measure information according to the safety state and a preset early warning grade rule after the safety state of the target building is determined according to the state measurement parameter, the environment prediction parameter and a preset safety evaluation model, and the recommended measure information is used for indicating a recommended processing mode for the target building;

the model determining module is specifically configured to determine a first linear fitting prediction line of the target building according to a time parameter, an inclination parameter set and a wind load parameter set of a preset time period, where the state measurement parameter set includes the inclination parameter set, the environment measurement parameter set includes the wind load parameter set, and the preset safety assessment model is determined based on the first linear fitting prediction line;

the safety prompt module is specifically used for: determining an inclination angle early warning threshold value set according to the first linear fitting prediction line, wherein the inclination angle early warning threshold value set comprises a plurality of inclination angle early warning threshold values, and each inclination angle early warning threshold value corresponds to an early warning grade; and determining the recommended measure information according to the current measured inclination of the target building and the inclination early warning threshold value set.

7. The building safety monitoring device according to claim 6, wherein the model determination module is specifically configured to:

and determining a second linear fitting prediction line of the target building according to a time parameter, a deflection parameter set and a snow load parameter set of a preset time period, wherein the state measurement parameter set comprises the deflection parameter set, the environment measurement parameter set comprises the snow load parameter set, and the preset safety assessment model is determined based on the second linear fitting prediction line.

8. The building safety monitoring device according to claim 7, wherein the safety prompt module is specifically configured to:

9. The building safety monitoring device according to claim 6, wherein the model determination module is specifically configured to:

determining a third prediction line of the target building according to a time parameter, a water level parameter set and a rainfall parameter set of a preset time period, wherein the state measurement parameter set comprises the water level parameter set, the environment measurement parameter set comprises the rainfall parameter, and the preset safety assessment model is determined based on the third prediction line.

10. The building safety monitoring device according to claim 9, wherein the safety prompt module is specifically configured to:

determining a water level early warning threshold value set according to the third prediction line, wherein the water level early warning threshold value set comprises a plurality of water level early warning threshold values, and each water level early warning threshold value corresponds to one early warning level;

11. An electronic device, comprising:

a processor; and

a memory for storing a computer program for the processor;

wherein the processor is configured to implement the building safety monitoring method of any one of claims 1 to 5 by executing the computer program.

12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the building security monitoring method according to any one of claims 1 to 5.