Multi-scene anti-interference self-adaptive hybrid construction engineering monitoring method and system

Abstract

The embodiment of the invention discloses a multi-scene anti-interference self-adaptive hybrid building engineering monitoring method and system, and the method comprises the steps: setting a first sensor set corresponding to first monitoring demand information in a first building scene based on the first monitoring demand information, and collecting the first sensor information; performing monitoring and early warning on the first building monitoring scene based on the first sensor information; in response to the received second monitoring demand information, recovering the first sensor set, setting a second sensor set corresponding to the second monitoring demand information in a second building monitoring scene, and collecting the second sensor information; and performing monitoring and early warning on the second building monitoring scene based on the second sensor information. According to the embodiment of the invention, the reliability and stability of steel structure crack monitoring in a low-temperature environment are improved, the communication distance and the anti-interference capability in a building construction intelligent monitoring process are improved, and the problems of comprehensive data monitoring of a steel structure in the low-temperature environment and comprehensive data monitoring of lifting of a part of structures of an ancient building are solved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of intelligent monitoring of construction, and in particular to a multi-scene anti-interference adaptive hybrid construction engineering monitoring method and system. BACKGROUND

[0002] The relevant departments have clearly proposed to "promote the coordinated development of intelligent construction and building industrialization", accelerate the digital transformation of the construction industry, and promote the application of new technologies such as artificial intelligence, Internet of Things, big data, BIM, cloud computing, and blockchain in the entire life cycle of buildings. At the same time, the Guiding Opinions on Promoting the Coordinated Development of Intelligent Construction and Building Industrialization proposes to basically form an industrial system for the coordinated development of intelligent construction and building industrialization, and realize the digitalization, intelligentization, and greenization of the entire life cycle of buildings.

[0003] In the process of building construction, various professional monitoring equipment is needed for different engineering links, and strict monitoring standards and out-of-limit early warning requirements are required for each link. However, the traditional monitoring method has the following common defects when meeting the above standard requirements: At present, there is a lack of a hybrid monitoring device in the field of low-temperature welding monitoring of steel structures. The types of data that need to be monitored are as follows: the lateral displacement stiffness of the support structure, i.e. the ratio of the horizontal force applied to the structure to the inter-story displacement angle; the inter-story displacement of the floor; the allowable value of the deflection considering the variable load standard value; the allowable value of the deflection of the permanent and variable load standard value; the normal stress; the local compressive stress; the stress perpendicular to the length direction of the fillet weld, calculated according to the effective cross section of the weld; the stress amplitude or reduced stress amplitude for fatigue calculation; the equivalent stress amplitude for variable amplitude fatigue; the fatigue allowable stress amplitude; and the real-time monitoring of temperature changes to reduce the possibility of increased cold crack sensitivity of the weld, metal segregation in the weld, obvious delayed effect of cold cracks in the weld, and low-temperature brittle fracture due to excessive cooling speed.

[0004] The technical problem existing in the prior art is that the existing multi-parameter monitoring equipment has the problems of limited sensor types, short communication distance, poor anti-interference ability, etc., and it is difficult to meet the unified monitoring requirements in a multi-scene, long-distance, and strong interference environment of large-scale building engineering. SUMMARY

[0005] The embodiments of the present disclosure provide a multi-scene anti-interference adaptive hybrid construction engineering monitoring method and system, and the technical problem to be solved is how to improve the communication distance and anti-interference ability in the process of intelligent monitoring of building construction.

[0006] In a first aspect, the embodiments of the present disclosure provide a multi-scene anti-interference adaptive hybrid construction engineering monitoring method, comprising: setting, based on the first monitoring demand information corresponding to the first building monitoring scene, a first sensor set corresponding to the first monitoring demand information in the first building scene; receiving, by the first gateway and the first collector, first sensor information collected by the first sensor set; monitoring and early warning, by the first monitoring model matched with the first building monitoring scene, the first building monitoring scene based on the first sensor information; In response to receiving second monitoring demand information corresponding to a second building monitoring scene, the first sensor set is recovered, and a second sensor set corresponding to the second monitoring demand information is set in the second building monitoring scene based on the first sensor set and the second monitoring demand information. receiving, by the second gateway and the second collector, second sensor information collected by the second sensor set; monitoring and early warning, by the second monitoring model matched with the second building monitoring scene, the second building monitoring scene based on the second sensor information.

[0007] In some embodiments of the present disclosure, the first building monitoring scene is a building low-temperature environment steel structure monitoring scene; the first sensor information includes member temperature data collected by a temperature sensor, load data collected by a spoke pressure sensor, displacement data collected by a pull rope displacement sensor, and two-axis inclination data collected by an inclination sensor; and the first monitoring model includes a temperature-mechanical parameter compensation model. The monitoring center utilizes the first monitoring model matched with the first building monitoring scene to monitor and early warn the first building monitoring scene based on the first sensor information, including: temperature compensation based on the member temperature data, the load data, and the displacement data by using the temperature-mechanical parameter compensation model; displacement correction of displacement data of each lifting point based on two-axis inclination data by using an inclination-displacement linkage calibration model; monitoring and early warning of the first target scene based on the first sensor data after temperature compensation and displacement correction.

[0008] In some embodiments of the present disclosure, the temperature-mechanical parameter compensation model includes a material thermal deformation compensation model and a sensor temperature drift compensation model. The temperature compensation based on the member temperature data, the load data, and the displacement data by using the temperature-mechanical parameter compensation model includes: material thermal deformation compensation based on the member temperature data and displacement data by using the material thermal deformation compensation model; The sensor temperature drift compensation model is used to compensate sensor temperature drift based on the load data and the displacement data.

[0009] In some embodiments of the present disclosure, the displacement data of each lifting point is corrected based on the dual-axis inclination data by using the inclination-displacement linkage calibration model, which comprises: The dual-axis inclination data is compensated for inclination by using a displacement projection correction model. Data fusion is performed on the displacement data of each lifting point by using an extended Kalman filter, wherein the displacement data of each lifting point comprises position coordinates, attitude angles, linear velocities and linear accelerations of each lifting point. The displacement data of each lifting point is corrected by using a synchronism error evaluation function.

[0010] In some embodiments of the present disclosure, the first sensor data compensated for temperature and corrected for displacement is used to monitor and warn the first target scene, which comprises: The first sensor data compensated for temperature and corrected for displacement is used for hierarchical warning and disposal linkage.

[0011] In some embodiments of the present disclosure, the first collector comprises a chip of model STM32F103C8T6.

[0012] In some embodiments of the present disclosure, LoRa spread spectrum communication is used between the first gateway and the first collector.

[0013] A second aspect of the embodiments of the present disclosure provides a multi-scene anti-interference adaptive hybrid building engineering monitoring system, which comprises: A first sensor set setting module is configured to set a first sensor set corresponding to first monitoring demand information of a first building monitoring scene based on the first monitoring demand information. A first sensor information acquisition module is configured to receive first sensor information collected by the first sensor set through a first gateway and a first collector. A first monitoring and warning module is configured to monitor and warn the first building monitoring scene based on the first sensor information by using a first monitoring model matched with the first building monitoring scene. A second sensor set setting module is configured to recycle the first sensor set in response to receiving second monitoring demand information of a second building monitoring scene, and set a second sensor set corresponding to the second monitoring demand information in the second building monitoring scene based on the first sensor set and the second monitoring demand information. The second sensor information acquisition module is configured to receive second sensor information collected by the second sensor set through the second gateway and the second collector. The second monitoring and early warning module is configured to monitor and early warn the second building monitoring scene based on the second sensor information by using the second monitoring model matched with the second building monitoring scene.

[0014] In some embodiments of the present disclosure, the first building monitoring scene is a building low-temperature environment steel structure monitoring scene; the first sensor information includes member temperature data collected by a temperature sensor, load data collected by a spoke pressure sensor, displacement data collected by a pull rope displacement sensor, and two-axis inclination data collected by an inclination sensor; and the first monitoring model includes a temperature-mechanical parameter compensation model. The first monitoring and early warning module is configured to perform temperature compensation based on the member temperature data, the load data and the displacement data by using the temperature-mechanical parameter compensation model; the first monitoring and early warning module is further configured to perform displacement correction on displacement data of each lifting point based on two-axis inclination data by using an inclination-displacement linkage calibration model; and the first monitoring and early warning module is further configured to monitor and early warn the first building monitoring scene based on the first sensor data after temperature compensation and displacement correction.

[0015] In some embodiments of the present disclosure, the temperature-mechanical parameter compensation model includes a material thermal deformation compensation model and a sensor temperature drift compensation model. The first monitoring and early warning module is configured to perform material thermal deformation compensation based on the member temperature data and the displacement data by using the material thermal deformation compensation model; and the first monitoring and early warning module is further configured to perform sensor temperature drift compensation based on the load data and the displacement data by using the sensor temperature drift compensation model.

[0016] In some embodiments of the present disclosure, the first monitoring and early warning module is configured to perform inclination compensation on the two-axis inclination data by using a displacement projection correction model; the first monitoring and early warning module is further configured to perform data fusion on displacement data of each lifting point by using an extended Kalman filter, wherein the displacement data of each lifting point includes position coordinates, attitude angles, linear velocities and linear accelerations of each lifting point; and the first monitoring and early warning module is further configured to perform displacement correction on the displacement data of each lifting point by using a synchronism error evaluation function.

[0017] In some embodiments of the present disclosure, the first monitoring and early warning module is configured to perform hierarchical early warning and disposal linkage based on the first sensor data after temperature compensation and displacement correction.

[0018] In some embodiments of the present disclosure, the first collector comprises a chip of model STM32F103C8T6.

[0019] In some embodiments of the present disclosure, LoRa spread spectrum communication is adopted between the first gateway and the first collector.

[0020] In a third aspect of the embodiments of the present disclosure, an electronic device is provided, comprising: a memory for storing a computer program product; a processor for executing the computer program product stored in the memory, and when the computer program product is executed, the method of the first aspect is implemented.

[0021] In a fourth aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, which stores computer program instructions, and when the computer program instructions are executed by a processor, the method of the first aspect is implemented.

[0022] In a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, comprising computer program instructions, and when the computer program instructions are executed by a processor, the processor executes the method of the first aspect.

[0023] The multi-scene anti-interference adaptive hybrid building engineering monitoring method and system of the embodiments of the present disclosure can realize accurate differentiation of crack inducements, break through the limitation of traditional monitoring that only monitors phenomena and does not analyze reasons, in addition, the anti-vibration filtering and low-temperature drift correction algorithm are fused, combined with the multi-parameter hierarchical early warning mechanism, the reliability and stability of steel structure crack monitoring in low-temperature environment are improved, the communication distance and anti-interference ability in the process of intelligent monitoring of building construction can be improved, the problem of comprehensive data monitoring of steel structure in low-temperature environment can be solved, and the problem of comprehensive data monitoring of part of structure lifting of ancient buildings can be solved.

[0024] The technical solutions of the present disclosure will be further described in detail below with the aid of the drawings and embodiments. DETAILED DESCRIPTION

[0025] The accompanying drawings, which form a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0026] The present disclosure can be more clearly understood with reference to the following detailed description in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of the multi-scene anti-interference adaptive hybrid building engineering monitoring method in some embodiments of the present disclosure is shown. Figure 2 A structural block diagram of the multi-scene anti-interference adaptive hybrid building engineering monitoring system in some embodiments of the present disclosure is shown. Figure 3 FIG. 1 is a structural block diagram of an electronic device according to an embodiment of the disclosure. DETAILED DESCRIPTION

[0027] Various exemplary embodiments of the disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments do not limit the scope of the disclosure unless specifically stated otherwise.

[0028] Those skilled in the art can understand that the terms "first", "second", and the like in the embodiments of the disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they represent a necessary logical sequence between them.

[0029] It should also be understood that in the embodiments of the disclosure, "multiple" can mean two or more, and "at least one" can mean one, two, or more.

[0030] It should also be understood that for any component, data, or structure mentioned in the embodiments of the disclosure, one or more can be generally understood unless specifically limited or the context before and after gives the opposite indication.

[0031] In addition, the term "and / or" in the disclosure is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can mean that there are three cases of A alone, A and B together, and B alone. In addition, the character " / " in the disclosure generally represents an "or" relationship between the front and rear associated objects.

[0032] It should also be understood that the description of the embodiments of the disclosure focuses on the differences between the embodiments, and the same or similar parts can be referred to each other, and for the sake of brevity, will not be repeated.

[0033] The following description of at least one exemplary embodiment is merely illustrative in nature and does not in any way limit the disclosure and its application or uses.

[0034] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered part of the specification where appropriate.

[0035] It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0036] The embodiments of the present disclosure can be applied to terminal devices, computer systems, servers, and other electronic devices, which can operate with many other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers, and other electronic devices include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments comprising any of the above systems, and the like.

[0037] Terminal devices, computer systems, servers, and other electronic devices can be described in the general context of computer system executable instructions, such as program modules, executed by the computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and the like, which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, in which tasks are performed by remote processing devices that are linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0038] Figure 1 For the flowchart of the multi-scene anti-interference adaptive hybrid building engineering monitoring method in one embodiment of the present disclosure. As shown in Figure 1 The multi-scene anti-interference adaptive hybrid building engineering monitoring method includes the following steps: S1: Based on the first monitoring demand information corresponding to the first building monitoring scene, a first sensor set corresponding to the first monitoring demand information is set in the first building scene.

[0039] Among them, the first building monitoring scene is a certain building scene that needs to be monitored. According to the first monitoring scene demand, the corresponding first sensor set is selected to be connected to the collector, to ensure correct wiring and firm fixation.

[0040] By configuring the host computer to set the parameters of the first sensor set, the sensor type identification, sampling frequency, warning threshold, algorithm mode, etc. can be included.

[0041] Start the collector, and the host computer automatically detects the type and number of sensors connected, performs scene recognition and parameter adaptive configuration.

[0042] In some embodiments of the present disclosure, the first building monitoring scene is a building low-temperature environment steel structure monitoring scene. Correspondingly, for the building low-temperature environment steel structure monitoring scene, the first sensor set corresponding thereto can include: a temperature sensor for collecting a first building temperature, a pressure sensor for collecting first building load data, a pull rope displacement sensor for collecting first building displacement information, and an inclination sensor for collecting first building two-axis inclination data.

[0043] S2: receiving the first sensor information collected by the first sensor set through the first gateway and the first collector.

[0044] The gateway and the slave form a self-organizing network, and the gateway and the device network use a low-power passive planetary topology network. The slave usually operates in a low-power state, and enters a working state only when a command is issued by the gateway. To avoid communication conflicts, the slave does not actively send data, and all data commands are initiated by the gateway, and the slave replies.

[0045] The slave device can be flexibly networked by pressing a key, and the device can be flexibly configured.

[0046] In some embodiments of the present disclosure, LoRa spread spectrum communication is used between the first gateway and the first collector. The collector is connected to the centralized gateway through LoRa wireless mode, and the gateway automatically discovers, registers and manages the access device.

[0047] The gateway uploads the collected data to the monitoring center through 4G / WiFi and the like, realizing real-time data transmission and storage.

[0048] The first collector can use an STM32F103C8T6 single-chip microcomputer as a core controller, with an ARM Cortex-M3 core, a 72MHz main frequency, an integrated E220-900T30D LoRa wireless communication module, support for RS485, ADC, GPIO and other interfaces, and can simultaneously access 8-16 channels of different types of sensors. The collector communicates wirelessly with the centralized gateway through the LoRa module, with a transmission distance of ≥1000 meters and strong anti-interference capability. The collector is built-in Kalman filtering algorithm, 3σ outlier elimination algorithm, and data compression algorithm, realizing preprocessing and fusion of sensor data.

[0049] The first gateway can use an STM32F407ZGT6 as a master control chip, with an ARM Cortex-M4 core, a 168MHz main frequency, and an integrated E220-900T30D LoRa wireless communication module of the same type as the collector. The gateway supports multiple uplink communication modes such as 4G / 5G / WiFi / Ethernet, and supports network interruption transmission.

[0050] S3: monitoring and early warning of the first building monitoring scene based on the first sensor information by using the first monitoring model matched with the first building monitoring scene.

[0051] The monitoring center performs data analysis and early warning processing according to the preset rules, and triggers sound and light alarms and remote notifications when the parameters exceed the limits.

[0052] S4: In response to receiving second monitoring demand information corresponding to the second building monitoring scene, recycling the first sensor set, and setting a second sensor set corresponding to the second monitoring demand information in the second building monitoring scene based on the first sensor set and the second monitoring demand information.

[0053] When the project is completed or the scene is switched, the first sensor set is recycled according to the second monitoring demand information corresponding to the second building monitoring scene, the collector parameters are reconfigured, and the required sensors are replaced to obtain the second sensor set, realizing the reuse of equipment between different scenes.

[0054] S5: receiving second sensor information collected by the second sensor set through the second gateway and the second collector.

[0055] Similar to the specific implementation of step S2, the difference is that the second sensor set is different, and the corresponding gateway (i.e. the second gateway) and collector (i.e. the second collector) can be used.

[0056] S6: monitoring and early warning of the second building monitoring scene based on the second sensor information by using the second monitoring model matched with the second building monitoring scene.

[0057] Similar to the specific implementation of step S3, the difference is that the second monitoring model is different, and the corresponding gateway (i.e. the second gateway) and collector (i.e. the second collector) can be used.

[0058] In order for those skilled in the art to further understand the present disclosure, the following examples will be described in detail.

[0059]

Example One: Comprehensive data monitoring of ancient building partial structure lifting

[0060] Pressure monitoring: A custom threaded mounting base with elastic cushion is used to fix the spoke pressure sensor (5-500 kN range according to the load-bearing capacity of ancient building components) between the lifting jack and the force contact surface of the ancient building column base and beam frame. The cushion is made of silicone material (Shore hardness 30-40°) to avoid local stress concentration caused by hard contact. The 64-bit unique code of the sensor is used to achieve accurate matching of the load at each lifting point.

[0061] Attitude monitoring: At key nodes such as the ends of ancient beam bodies and the middle sections of column bodies, a composite installation method of "magnetic positioning + M4 screw auxiliary fixation" is used to fix SINDT02 dual-axis inclination sensors. A Teflon gasket is attached to the contact surface between the sensor and the component to reduce friction. Real-time dual-axis inclination data (accuracy ±0.1°) are collected to capture the small attitude deviation of the structure during lifting.

[0062] Displacement monitoring: At the execution end of the lifting mechanism at the four corners and the central axis of the ancient building, a pull rope displacement sensor (0-5000 mm range, linear accuracy ±0.1% FS) is installed. A non-contact traction structure of "flexible pull rope + pulley guide" is used. The pull rope end is attached to the surface of the ancient building component through a suction cup connector to avoid damage to fragile components caused by traction force. The displacement of each lifting point is monitored in real time.

[0063] Temperature monitoring: DS18B20 temperature sensors are embedded in some ancient wooden components and attached to the surface of masonry (IP67 protection level suitable for humid repair environment). Through single bus networking technology, 20-50 sensor nodes are connected, covering the component body and the repair site environment. The temperature measurement range is -55°C to 125°C, suitable for different seasonal repair needs.

[0064] Construction of temperature-mechanical parameter compensation model: Using the component temperature data collected by DS18B20, the load data of the spoke pressure sensor and the displacement data of the pull rope displacement sensor are temperature compensated to correct the monitoring errors caused by the thermal expansion and contraction of wood and the change of elastic modulus of masonry with temperature.

[0065] For the anisotropic thermal expansion properties of wood, a second-order nonlinear thermal expansion model is used for compensation. For masonry materials, an elastic modulus temperature correction function is established based on the micropore thermodynamics theory. At the same time, considering the temperature drift characteristics of the sensor itself, a pressure sensor sensitivity temperature coefficient model and a displacement sensor thermal expansion compensation function are established. The recursive least squares method is used to identify the compensation parameters online to achieve double temperature compensation.

[0066] The material thermal deformation compensation model is as follows: AL = L0 x [a1(T) x AT + a2(T) x AT2] wherein, AL represents the thermal deformation displacement (mm), L0 represents the reference length (mm), a1(T) represents the first-order temperature-dependent thermal expansion coefficient (1 / °C), a2(T) represents the second-order temperature-dependent thermal expansion coefficient (1 / °C2), AT: temperature change (°C).

[0067] E(T) = E0 x [1 - b x (T - T0) + g x (T - T0)2] wherein, E(T) represents the elastic modulus at temperature T (MPa), E0 represents the elastic modulus at reference temperature T0 (MPa), b represents the first-order temperature coefficient of elastic modulus (1 / °C), g represents the second-order temperature coefficient of elastic modulus (1 / °C2).

[0068] The sensor temperature drift compensation model is as follows: F_corrected = F_raw x [1 + k1 x AT + k2 x AT2] + b x AT wherein, F_corrected represents the load value after temperature compensation (kN), F_raw represents the original reading of the sensor (kN), k1 and k2 respectively represent the temperature coefficients of sensitivity (1 / °C and 1 / °C2), b represents the zero drift coefficient (kN / °C).

[0069] D_corrected = D_raw x [1 + a_cable x AT] + d_install x AT wherein, D_corrected represents the displacement value after temperature compensation (mm), D_raw represents the original reading of the sensor (mm), a_cable represents the thermal expansion coefficient of the cable material (1 / °C), d_install represents the thermal deformation coefficient of the installation structure (mm / °C).

[0070] The parameter identification algorithm is as follows: 0(k) = 0(k-1) + K(k) x [y(k) - fT(k) x 0(k-1)] wherein, 0(k) represents the parameter estimation vector at time k [a1, a2, b, g, k1, k2, b, a_cable, d_install]T, K(k) represents the Kalman gain matrix, y(k) represents the observation value at time k, f(k) represents the regression vector [AT, AT2,...]T.

[0071] Establish an inclination-displacement linkage calibration algorithm: based on the two-axis inclination data of the SINDT02 sensor, the cable displacement data of each lifting point is corrected.

[0072] The lifting process is decoupled into translation and rotation components by rigid body motion decomposition theory, and a displacement projection correction model is established to eliminate the influence of structural posture changes on measurement accuracy. Based on Euler angle transformation, the conversion relationship between sensor coordinate system and global coordinate system is established, and the optimal fusion of multi-sensor data is realized by using extended Kalman filter. A dynamic compensation mechanism for synchronization error is constructed, which significantly improves the accuracy and synchronization control precision of displacement monitoring during the lifting process.

[0073] The rigid body motion decomposition model is as follows: [ΔX] [Δx] [ 0 -Δθ_z Δθ_y ] [x0] [ΔY] = [Δy]+[ Δθ_z 0 -Δθ x ] [y0] [ΔZ] [Δz] [-Δθ_y Δθ x 0 ] [z0] In the formula, [ΔX, ΔY, ΔZ]ᵀ represents the global coordinate system displacement vector (mm), [Δx, Δy, Δz]ᵀ represents the local coordinate system displacement vector (mm), [Δθ x , Δθ_y, Δθ_z]ᵀ represents the Euler angle change (rad), and [x0, y0, z0]ᵀ represents the sensor installation position vector (mm).

[0074] The displacement projection correction algorithm is as follows: D_corrected = D_measured × cos(θ) × cos(φ) + L_offset × [sin(θ) +sin(φ)] In the formula, D_corrected represents the displacement value after inclination compensation (mm), D_corrected represents the displacement value after inclination compensation (mm), θ represents the X-axis inclination (rad), φ represents the Y-axis inclination (rad), and L_offset represents the sensor installation eccentricity (mm).

[0075] The data fusion method of extended Kalman filter is as follows: State equation: X_k=f(X_{k-1})+w_k; Observation equation: Z_k=h(X_k)+v_k; State vector: X=[x,y,z,θ x ,θ_y,θ_z,v x ,v_y,v_z,a x ,a_y,a_z]ᵀ.

[0076] In the formula, x, y, z represent the position coordinates (mm), θ xθ x, θ y, θ z represent attitude angles (rad), v x v x, v y, v z represent linear velocities (mm / s), a x a x, a y, a z represent linear accelerations (mm / s 2 ), w k, v k represent process noise and observation noise.

[0077] The synchronism error evaluation function is as follows: J = Σ |D_corrected_i - D_reference| + λ1 × Σ |θ_i - θ_reference| + λ2 × Σ |v_i - v_reference| In the formula, J represents a synchronism error index, D_corrected_i represents a compensated displacement of the i th measuring point (mm), D_reference represents a reference displacement value (mm), θ_i represents an inclination angle of the i th measuring point (rad), θ_reference represents a reference inclination angle value (rad), v_i represents a lifting speed of the i th measuring point (mm / min), v_reference represents a reference lifting speed (mm / min), and λ1 and λ2 represent weight coefficients.

[0078] The displacement measurement deviation caused by structural inclination is eliminated, and the synchronism error of each lifting point is ensured to be ≤0.5 mm.

[0079] The sensor data is transmitted in real time to the control platform through the RS485 bus (Modbus-RTU protocol), and the platform dynamically adjusts the lifting speed (0.1~1 mm / min adjustable) of each jack according to three core indexes of pressure balance (load difference of each point ≤5%), attitude stability (inclination change ≤0.2° / min), and displacement synchronism (displacement difference of each point ≤1 mm). When any parameter exceeds the threshold value, a pause command is triggered, realizing the closed-loop linkage of "monitoring-feedback-control".

[0080]

Embodiment Two Stress-displacement-temperature Coupling Monitoring Implementation Method for Steel Structure Cracks in Low Temperature Environment

[0081] In low temperature environment (-20℃~ -40℃), steel structure is prone to cracks due to temperature shrinkage and stress concentration, and the traditional monitoring has the following defects: first, the low temperature performance of the sensor decays and the data accuracy decreases; it is unable to distinguish whether the cracks are caused by temperature shrinkage or stress damage; the sensor installation structure is prone to loosen due to low temperature frost heaving, and the monitoring stability is poor.

[0082] Displacement monitoring: According to the cross-sectional size of the steel column, a "heat preservation anti-frost mounting seat + elastic locking mechanism" is designed to fix the pull rope displacement sensor on the pre-set mounting bracket on the side of the steel column. The mounting seat is equipped with a low-power heating sheet (working temperature 5℃~10℃) and an outer polyurethane heat preservation layer to prevent frost from forming on the core components of the sensor; the pull rope end is fixed to the monitoring points on both sides of the steel column crack through a spherical hinge connector made of stainless steel (adapted to a working temperature of -40℃~+85℃) to ensure the reliability of the connection in low temperature, and the sensor is selected to have a range of 0~10000mm to capture the displacement caused by the development of the crack.

[0083] Stress monitoring: At the support nodes and beam-column connections of the steel column, a spoke pressure sensor (selected to have a range of 100~2000kN) is installed, the surface of the sensor is sprayed with a low temperature anti-frost coating, and a heat-conducting silicone pad is added to the contact surface to reduce the influence of thermal resistance in low temperature, real-time monitor the stress changes of the structure, and the working temperature is adapted to the low temperature construction environment of -20℃~+65℃.

[0084] Attitude monitoring: SINDT02 dual-axis inclination sensors are fixed on the top, middle and bottom of the steel column through M4 screws, the sensor shell is equipped with a metal protective cover (anti-vibration performance resistant to 3500g impact) to adapt to the vibration environment of the construction site; the wide working temperature range of -40℃~+85℃ is used to collect the inclination changes of the steel column in real time to determine whether the structure is unstable due to the development of the crack.

[0085] Temperature monitoring: A multi-point distributed arrangement scheme of DS18B20 temperature sensors is adopted, one sensor is arranged every 1m along the height direction of the steel column, part of which is attached to the surface of the steel column (for monitoring the temperature of the component) and part of which is arranged in the surrounding environment (for monitoring the ambient temperature), and the synchronous temperature measurement of multiple nodes is realized through single bus networking, and the accuracy of ±0.5℃ ensures the accuracy of the temperature data.

[0086] Establish a stress-displacement-temperature three-dimensional coupling model: through the platform, the stress data of the spoke pressure sensor, the crack displacement data of the pull rope displacement sensor and the temperature data of the DS18B20 are correlated and analyzed, the temperature shrinkage displacement threshold is set (calculated based on the thermal expansion coefficient of the steel structure material), when the measured displacement ≤ temperature shrinkage threshold, it is determined to be temperature-induced displacement; when the measured displacement > temperature shrinkage threshold and the stress data is abnormal, it is determined to be stress damage type crack, and the accurate distinction of the causes of the crack is realized.

[0087] The training process of the stress-displacement-temperature three-dimensional coupling model is as follows: a plurality of samples of cracks in steel structures generated in a low-temperature environment are obtained. Each sample includes a plurality of stress-displacement-temperature data before the steel structure cracks and a plurality of stress-displacement-temperature data after the steel structure cracks. Through fitting and model training of the plurality of stress-displacement-temperature data before the steel structure cracks and the plurality of stress-displacement-temperature data after the steel structure cracks, the stress-displacement-temperature three-dimensional coupling model is finally obtained.

[0088] Set three-level early warning thresholds: first-level early warning (crack displacement 0.1-0.3 mm, stress normal), trigger temperature control suggestion; second-level early warning (crack displacement 0.3-0.5 mm, slight stress anomaly), start local reinforcement plan; third-level early warning (crack displacement >0.5 mm, inclination change >0.3°), trigger shutdown for repair instruction. All early warning information is pushed to the project management platform in real time through the RS485 bus to realize early identification and rapid disposal of risks.

[0089] Figure 2 A structural block diagram of a multi-scene anti-interference adaptive hybrid building engineering monitoring system in some embodiments of the present disclosure is shown in FIG. 1. Figure 2 As shown in FIG. 1, the multi-scene anti-interference adaptive hybrid building engineering monitoring system includes: A first sensor set setting module 100 is configured to set a first sensor set corresponding to first monitoring demand information of a first building monitoring scene based on the first monitoring demand information. A first sensor information acquisition module 200 is configured to receive first sensor information collected by the first sensor set through a first gateway and a first collector. A first monitoring and early warning module 300 is configured to monitor and early warn the first building monitoring scene based on the first sensor information using a first monitoring model matched with the first building monitoring scene. A second sensor set setting module 400 is configured to recover the first sensor set in response to receiving second monitoring demand information of a second building monitoring scene, and set a second sensor set corresponding to second monitoring demand information of the second building monitoring scene based on the first sensor set and the second monitoring demand information. A second sensor information acquisition module 500 is configured to receive second sensor information collected by the second sensor set through a second gateway and a second collector. A second monitoring and early warning module 600 is configured to monitor and early warn the second building monitoring scene based on the second sensor information using a second monitoring model matched with the second building monitoring scene.

[0090] In some embodiments of the present disclosure, the first building monitoring scene is a building low-temperature environment steel structure monitoring scene; the first sensor information includes member temperature data collected by a temperature sensor, load data collected by a spoke pressure sensor, displacement data collected by a pull rope displacement sensor, and two-axis inclination data collected by an inclination sensor; and the first monitoring model includes a temperature-mechanical parameter compensation model. The first monitoring and early warning module 300 is configured to perform temperature compensation on the basis of the member temperature data, the load data and the displacement data by using the temperature-mechanical parameter compensation model. The first monitoring and early warning module is further configured to perform displacement correction on the displacement data of each lifting point on the basis of the two-axis inclination data by using the inclination-displacement linkage calibration model. The first monitoring and early warning module 300 is further configured to monitor and early warn the first building monitoring scene on the basis of the first sensor data after temperature compensation and displacement correction.

[0091] In some embodiments of the present disclosure, the temperature-mechanical parameter compensation model includes a material thermal deformation compensation model and a sensor temperature drift compensation model. The first monitoring and early warning module 300 is configured to perform material thermal deformation compensation on the basis of the member temperature data and the displacement data by using the material thermal deformation compensation model. The first monitoring and early warning module 300 is further configured to perform sensor temperature drift compensation on the basis of the load data and the displacement data by using the sensor temperature drift compensation model.

[0092] In some embodiments of the present disclosure, the first monitoring and early warning module 300 is configured to perform inclination compensation on the two-axis inclination data by using a displacement projection correction model. The first monitoring and early warning module 300 is further configured to perform data fusion on the displacement data of each lifting point by using an extended Kalman filter, wherein the displacement data of each lifting point includes position coordinates, attitude angles, linear velocities and linear accelerations of each lifting point. The first monitoring and early warning module 300 is further configured to perform displacement correction on the displacement data of each lifting point by using a synchronism error evaluation function.

[0093] In some embodiments of the present disclosure, the first monitoring and early warning module 300 is configured to perform hierarchical early warning and disposal linkage on the basis of the first sensor data after temperature compensation and displacement correction.

[0094] In some embodiments of the present disclosure, the first collector includes a chip with a model number of STM32F103C8T6.

[0095] In some embodiments of the present disclosure, LoRa spread spectrum communication is adopted between the first gateway and the first collector.

[0096] It should be noted that the specific implementation of the multi-scene anti-interference adaptive hybrid building engineering monitoring system of the embodiments of the present disclosure is similar to the specific implementation of the multi-scene anti-interference adaptive hybrid building engineering monitoring method of the embodiments of the present disclosure, and the technical effects of the multi-scene anti-interference adaptive hybrid building engineering monitoring system of the embodiments of the present disclosure are similar to the technical effects of the multi-scene anti-interference adaptive hybrid building engineering monitoring method of the embodiments of the present disclosure. For details, please refer to the description of the multi-scene anti-interference adaptive hybrid building engineering monitoring method part. In order to reduce redundancy, no further description is made.

[0097] In addition, the embodiments of the present disclosure also provide an electronic device, comprising: a memory for storing a computer program; a processor for executing the computer program stored in the memory, and when the computer program is executed, the multi-scene anti-interference adaptive hybrid building engineering monitoring method of any one of the embodiments of the present disclosure is realized.

[0098] Next, the electronic device according to the embodiments of the present disclosure will be described with reference to Figure 3 As shown in Figure 3 The electronic device includes one or more processors and a memory.

[0099] The processor can be a central processing unit (CPU) or other forms of processing units having data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions.

[0100] The memory can store one or more computer program products, and the memory can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program products can be stored on the computer readable storage medium, and the processor can run the computer program products to implement the multi-scene anti-interference adaptive hybrid building engineering monitoring method of the embodiments of the present disclosure described above and / or other desired functions.

[0101] In one example, the electronic device can further include an input device and an output device, which are interconnected through a bus system and / or other forms of connection mechanism (not shown).

[0102] In addition, the input device can further include, for example, a keyboard, a mouse, etc.

[0103] The output device can output various information including the determined distance information, direction information, etc. to the outside. The output device can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0104] Of course, in order to simplify, Figure 3 Only some of the components of the electronic device related to the present disclosure are shown in the middle, and components such as buses, input / output interfaces, etc. are omitted. In addition, the electronic device can further include any other appropriate components according to the specific application.

[0105] In addition to the above-mentioned method and device, the embodiments of the present disclosure can also be a computer program product, which includes computer program instructions, which, when executed by a processor, causes the processor to perform the steps in the multi-scene anti-interference adaptive hybrid construction engineering monitoring method according to various embodiments of the present disclosure described in the above part of the specification.

[0106] The computer program product can be written in any combination of one or more programming languages to perform the operations of the embodiments of the present disclosure, including object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages. Program code can be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0107] In addition, the embodiments of the present disclosure can also be a computer readable storage medium, which stores computer program instructions, which, when executed by a processor, causes the processor to perform the steps in the multi-scene anti-interference adaptive hybrid construction engineering monitoring method according to various embodiments of the present disclosure described in the above part of the specification.

[0108] The computer readable storage medium can employ any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or apparatus, or any combination of the above. More specific examples (non-exhaustive list) of readable storage medium include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any appropriate combination of the above.

[0109] The above describes the basic principles of the present disclosure in conjunction with specific embodiments, but it should be noted that the advantages, benefits, effects and the like mentioned in the present disclosure are merely examples and are not limiting, and these advantages, benefits, effects and the like cannot be considered as necessary for each embodiment of the present disclosure. In addition, the above specific details of the disclosure are only for the purpose of example and for the purpose of understanding, and are not limiting, and the above details do not limit the present disclosure to be necessarily implemented with the above specific details.

[0110] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between each embodiment can be understood by referring to each other. For system embodiments, since they basically correspond to method embodiments, the description is relatively simple, and the relevant parts can be understood by referring to the part of the method embodiment.

[0111] The block diagrams of the devices, apparatuses, equipment, systems involved in the present disclosure are only exemplary examples and are not intended to require or imply the connection, arrangement, configuration shown in the block diagram. As those skilled in the art will recognize, these devices, apparatuses, equipment, systems can be connected, arranged, configured in any manner. Words such as "include", "contain", "have" and the like are open-ended words, which mean "including but not limited to", and can be used interchangeably. The words "or" and "and" used herein mean the word "and / or", and can be used interchangeably unless the context clearly indicates otherwise. The word "such as" used herein means the phrase "such as but not limited to", and can be used interchangeably.

[0112] The methods and devices of the present disclosure can be implemented in many ways. For example, the methods and devices of the present disclosure can be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present disclosure are not limited to the above specific description, unless otherwise specifically described. In addition, in some embodiments, the present disclosure can also be implemented as programs recorded in recording media, which include machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers the recording media storing the programs for executing the method according to the present disclosure.

[0113] It should also be noted that in the devices, equipment and methods of the present disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions of the present disclosure.

[0114] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0115] The above description has been presented to enable any person skilled in the art to make or use the disclosure. Furthermore, the purpose of the above description is not intended to limit the embodiments of the present disclosure to the form disclosed herein. Although various example aspects and embodiments have been discussed above, those of ordinary skill in the art will appreciate a variety of modifications, alternatives, permutations, additions, and sub-combinations of the described aspects and embodiments.

Claims

1. A multi-scenario anti-interference adaptive hybrid building engineering monitoring method, characterized in that, include: Based on the first monitoring requirement information corresponding to the first building monitoring scenario, a first sensor set corresponding to the first monitoring requirement information is set in the first building scenario; The system receives first sensor information collected by the first sensor set through the first gateway and the first data collector. Using the first monitoring model matched with the first building monitoring scenario, the first building monitoring scenario is monitored and warned based on the first sensor information; In response to receiving the second monitoring requirement information corresponding to the second building monitoring scenario, the first sensor set is retrieved, and a second sensor set corresponding to the second monitoring requirement information is set in the second building monitoring scenario based on the first sensor set and the second monitoring requirement information; The second sensor information collected by the second sensor set is received through the second gateway and the second collector; The second monitoring model, which is matched with the second building monitoring scenario, is used to monitor and issue early warnings for the second building monitoring scenario based on the information from the second sensor.

2. The method according to claim 1, characterized in that, The first building monitoring scenario is a low-temperature environment steel structure monitoring scenario; the first sensor information includes: component temperature data collected by temperature sensors, load data collected by spoke-type pressure sensors, displacement data collected by rope displacement sensors, and biaxial tilt angle data collected by tilt sensors; the first monitoring model includes a temperature-mechanical parameter compensation model; The first monitoring model, which uses the first building monitoring scenario matching, performs monitoring and early warning of the first building monitoring scenario based on the first sensor information, including: Temperature compensation is performed using the temperature-mechanical parameter compensation model based on the component temperature data, the load data, and the displacement data. Using the tilt-displacement linkage calibration model, displacement correction is performed on the displacement data of each lifting point based on biaxial tilt data; Based on the first sensor data with temperature compensation and displacement correction, the first building monitoring scenario is monitored and warned.

3. The method according to claim 2, characterized in that, The temperature-mechanical parameter compensation model includes: a material thermal deformation compensation model and a sensor temperature drift compensation model; The temperature compensation using the temperature-mechanical parameter compensation model, based on the component temperature data, the load data, and the displacement data, includes: Using the material thermal deformation compensation model, material thermal deformation compensation is performed based on the component temperature data and displacement data; The sensor temperature drift compensation model is used to compensate for sensor temperature drift based on the load data and the displacement data.

4. The method according to claim 2, characterized in that, The method of using the tilt angle-displacement linkage calibration model to correct the displacement data of each lifting point based on biaxial tilt angle data includes: The displacement projection correction model is used to perform tilt compensation on the biaxial tilt data; Extended Kalman filtering is used to fuse the displacement data of each lifting point. The displacement data of each lifting point includes the position coordinates, attitude angle, linear velocity and linear acceleration of each lifting point. The displacement data of each uplift point are corrected using the synchronization error evaluation function.

5. The method according to claim 2, characterized in that, The first sensor data based on temperature compensation and displacement correction is used to monitor and issue early warnings for the first target scene, including: Based on temperature compensation and displacement correction, the first sensor data is used for hierarchical early warning and coordinated response.

6. The method according to any one of claims 1-5, characterized in that, The first data acquisition unit includes a chip with the model number STM32F103C8T6.

7. The method according to any one of claims 1-5, characterized in that, The first gateway and the first collector communicate using LoRa spread spectrum.

8. A multi-scenario anti-interference adaptive hybrid building engineering monitoring system, characterized in that, include: The first sensor set setting module is used to set a first sensor set corresponding to the first monitoring requirement information in the first building scenario based on the first monitoring requirement information corresponding to the first building monitoring scenario. The first sensor information acquisition module is used to receive first sensor information collected by the first sensor set through the first gateway and the first collector; The first monitoring and early warning module is used to monitor and warn the first building monitoring scene based on the first sensor information using the first monitoring model matched by the first building monitoring scene. The second sensor set setting module is used to respond to receiving the second monitoring requirement information corresponding to the second building monitoring scenario, retrieve the first sensor set, and set a second sensor set corresponding to the second monitoring requirement information in the second building monitoring scenario based on the first sensor set and the second monitoring requirement information; The second sensor information acquisition module is used to receive second sensor information collected by the second sensor set through the second gateway and the second collector. The second monitoring and early warning module is used to monitor and issue early warnings for the second building monitoring scenario based on the second sensor information using the second monitoring model matched with the second building monitoring scenario.

9. An electronic device, characterized in that, include: Memory, used to store computer program products; A processor for executing a computer program product stored in the memory, wherein when the computer program product is executed, it implements the method described in any one of claims 1-7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1-7.

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