Building health condition prediction method and device, equipment and medium

Abstract

The invention relates to the technical field of building measurement, in particular to a building health condition prediction method and device, equipment and a medium, and the method comprises the steps: obtaining a building health condition prediction model; collecting health parameters of each time node of the target building, wherein the health parameters comprise cracks, settlement and inclination; determining a health parameter change rate between adjacent time nodes based on the health parameter of each time node; based on the health parameter change rate and the building health condition prediction model, the health condition of the target building is predicted, the prediction result is obtained, the health condition of the building at the future moment is predicted by analyzing the health parameter change rate, the health condition development of the building is predicted in advance, and the health condition prediction accuracy of the building is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building measurement, and in particular to a method, device, equipment and medium for predicting the health status of a building. Background Art

[0002] As domestic infrastructure becomes increasingly complete, buildings, ancient structures, roads, bridges, etc. need to undergo long-term stability monitoring of their health status after construction or renovation is completed. For example, whether cracks, tilts, and other relative changes in parameters need to be monitored.

[0003] Currently, when monitoring health parameters of a building reach a certain threshold, it is often confirmed that timely repairs are needed or dangers are prompted. However, such monitoring often cannot accurately assess the true health status of the building.

[0004] Moreover, the health status of a building is not determined by real-time data, but rather a cumulative process of long-term changes.

[0005] Therefore, how to accurately predict the health status of buildings is a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In view of the above problems, the present invention provides a method, device, equipment and medium for predicting the health status of a building that overcomes the above problems or at least partially solves the above problems.

[0007] In a first aspect, the present invention provides a method for predicting the health status of a building, comprising:

[0008] Obtain predictive models of building health status;

[0009] Collect health parameters of the target building at various time points, including cracks, settlement, and tilt;

[0010] Determining a health parameter change rate between adjacent time nodes based on the health parameters of each time node;

[0011] Based on the health parameter change rate and the building health status prediction model, the health status of the target building is predicted to obtain a prediction result.

[0012] Preferably, obtaining a building health status prediction model includes:

[0013] Collect historical health parameters and historical health status of historical buildings at various historical time points;

[0014] Determining a historical health parameter change rate between adjacent historical time nodes based on the historical health parameters of each historical time node;

[0015] Based on the historical health parameter change rate and the historical health status at each historical time node, a building health status prediction model is determined.

[0016] Preferably, determining a building health status prediction model based on the historical health parameter change rate and the historical health status at each historical time node includes:

[0017] The historical health parameter change rate is used as input data, and the historical health status of each historical time node is used as output data to train the long short-term memory network model to obtain the building health status prediction model.

[0018] Preferably, the collecting of health parameters of the target building at various time points, including cracks, settlement, and tilt, comprises:

[0019] The health parameters of the target building, including cracks, settlement, and tilt, are collected at various time points after rainfall, cooling, heating, snowfall, strong winds, and at preset time intervals.

[0020] Preferably, determining the health parameter change rate between adjacent time nodes based on the health parameters of the respective time nodes includes:

[0021] Determining health parameter differences and time differences between adjacent time nodes based on the health parameters of each time node;

[0022] Based on the health parameter difference and the time difference, a health parameter change rate between adjacent time nodes is determined.

[0023] Preferably, based on the health parameter change rate and the building health status prediction model, the health status of the target building is predicted to obtain a prediction result, including:

[0024] The health parameter change rate is input into the health status prediction model, and the health status prediction result of the target building is output.

[0025] Preferably, after predicting the health status of the target building based on the health parameter change rate and the building health status prediction model and obtaining the prediction result, the method further includes:

[0026] Based on the prediction results, corresponding measures are taken.

[0027] In a second aspect, the present invention further provides a device for predicting the health status of a building, comprising:

[0028] An acquisition module, used for acquiring a building health status prediction model;

[0029] The acquisition module is used to collect health parameters of the target building at various time points, including cracks, settlement, and tilt;

[0030] a determination module, configured to determine a health parameter change rate between adjacent time nodes based on the health parameters of the respective time nodes;

[0031] The prediction module is used to predict the health status of the target building based on the health parameter change rate and the building health status prediction model to obtain a prediction result.

[0032] In a third aspect, the present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the program.

[0033] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the method described in the first aspect when the program is executed by a processor.

[0034] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0035] The present invention provides a method for predicting the health status of a building, comprising: obtaining a building health status prediction model; collecting health parameters of a target building at various time nodes, including cracks, settlement, and tilt; determining the rate of change of the health parameters between adjacent time nodes based on the health parameters of each time node; predicting the health status of the target building based on the rate of change of the health parameters and the building health status prediction model to obtain a prediction result, and predicting the health status of the building at future moments by analyzing the rate of change of the health parameters, thereby predicting the development of the building's health status in advance and improving the accuracy of the building health status prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference figures denote the same components. In the drawings:

[0037] Figure 1 A schematic flow chart showing the steps of a method for predicting the health status of a building according to an embodiment of the present invention is shown;

[0038] Figure 2 A schematic structural diagram of a device for predicting the health status of a building according to an embodiment of the present invention is shown;

[0039] Figure 3 A schematic structural diagram of a computer device for implementing a method for predicting the health status of a building in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0041] Example 1:

[0042] The embodiment of the present invention provides a method for predicting the health status of a building. Figure 1 Shown, including:

[0043] S101, obtaining a building health status prediction model;

[0044] S102, collecting health parameters of the target building at various time points, including cracks, settlement, and tilt;

[0045] S103, determining a health parameter change rate between adjacent time nodes based on the health parameter of each time node;

[0046] S104: Based on the health parameter change rate and the building health status prediction model, the health status of the target building is predicted to obtain a prediction result.

[0047] First, execute S101 to obtain a building health status prediction model.

[0048] Specifically, the historical health parameters of historical buildings at each historical time node and the historical health status at each historical time node are collected; based on the historical health parameters of each historical time node, the change rate of the historical health parameters between adjacent historical time nodes is determined; based on the historical health parameter change rate and the historical health status at each historical time node, a building health status prediction model is determined.

[0049] Historical buildings refer to buildings completed before the current time. At various historical time points, corresponding health parameters and health status are collected. For example, for a building built in the 1990s, historical health parameters, including crack, settlement, and tilt data, are collected at various historical time points, such as after rainfall, after a temperature drop, after a temperature rise, after snowfall, after a strong wind, and at preset time intervals. These parameters are then used to assess the health status of the building according to the health assessment model.

[0050] When evaluating the health status according to the health assessment model, a weight ratio can be used to score the index of each historical health parameter, which is then multiplied by the corresponding weight and added together to obtain the assessed health status.

[0051] The changes in health parameters under each meteorological condition and the collection of health parameters after a long period of time can monitor the health status of historical buildings in real time.

[0052] Next, the difference in historical health parameters between adjacent historical time points is divided by the time difference between adjacent historical time points to obtain the historical health parameter change rate. The historical health parameter change rate for each historical time period is different or changes slowly. If the historical health parameter change rate is large, it means that the historical building has suffered a lot of damage during this period; if the historical health parameter change rate is small, it means that the historical building has suffered normal damage during this period.

[0053] Then, the historical health parameter change rate is used as input data, and the historical health status of each historical time node is used as output data to train the long-short-term memory network model to obtain the building health status prediction model.

[0054] Among them, when allocating input data and output data, the historical health parameter change rate corresponding to the previous historical time node is used as input data, and the historical health status corresponding to the next historical time node is used as output data. This is used to train the long-short-term memory network model, and the resulting building health status prediction model can predict the building health status at the next time node.

[0055] Next, S102 is executed to collect health parameters of the target building at various time points, including cracks, settlement, and tilt.

[0056] Specifically, the health parameters of the target building, including cracks, settlement, and tilt, are collected at various time nodes after rainfall, cooling, heating, snowfall, strong wind, and at preset time intervals.

[0057] During the collection process, corresponding sensors are used to collect data and transmit it back.

[0058] The device for collecting cracks is a crack sensor, which is set at the crack and is used to collect the width of the crack.

[0059] The equipment used to collect settlement data is a static level, which is deployed around the building. It measures the height difference changes at different points through the principle of liquid connectivity to achieve high-precision settlement monitoring.

[0060] The device for collecting tilt is a tilt sensor, specifically a three-dimensional attitude sensor, which is usually installed at the four corners of the top of the building to monitor the changes in tilt angle in real time.

[0061] Furthermore, each data collection device can transmit the specific time of data collection, such as March 17, 1999, and package it with the corresponding data. The data thus obtained all contain the specific time of collection information.

[0062] Next, S103 is executed to determine the health parameter change rate between adjacent time nodes based on the health parameter of each time node.

[0063] The health parameter at the current time node can be subtracted from the health parameter at the previous time node to obtain a first difference, the health parameter at the current time node can be subtracted from the previous time node to obtain a second difference, and the health parameter change rate can be obtained by dividing the first difference by the second difference. Using this calculation method, the health parameter change rate at each moment can be obtained.

[0064] Next, S104 is executed to predict the health status of the target building based on the health parameter change rate and the building health status prediction model to obtain a prediction result.

[0065] Specifically, the health parameter change rate is input into the health status prediction model, and the health status prediction result of the target building is output.

[0066] Among them, the health parameter change rate includes the change rate of various data, including the change rate of tilt, the change rate of settlement and the change rate of cracks. These change rates are all input into the building health status prediction model, and the resulting prediction result of the health status of the target building at the next time node is obtained.

[0067] The prediction results include low risk, medium risk and high risk.

[0068] After obtaining the predicted health status of the target building at the next time point, if the risk is high, appropriate measures can be taken to avoid the high-risk situation, such as timely repairs, etc., or a prompt can be issued for the need to quickly improve a certain health parameter. The monitoring frequency can be adjusted for low-risk and high-risk situations.

[0069] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0070] The present invention provides a method for predicting the health status of a building, comprising: obtaining a building health status prediction model; collecting health parameters of a target building at various time nodes, including cracks, settlement, and tilt; determining the rate of change of the health parameters between adjacent time nodes based on the health parameters of each time node; predicting the health status of the target building based on the rate of change of the health parameters and the building health status prediction model to obtain a prediction result, and predicting the health status of the building at future moments by analyzing the rate of change of the health parameters, thereby predicting the development of the building's health status in advance and improving the accuracy of the building health status prediction.

[0071] Example 2:

[0072] Based on the same inventive concept, the embodiment of the present invention also provides a device for predicting the health status of a building, such as Figure 2 Shown, including:

[0073] An acquisition module 201 is used to acquire a building health status prediction model;

[0074] The collection module 202 is used to collect health parameters of the target building at various time points, including cracks, settlement, and tilt;

[0075] A determination module 203 is configured to determine a health parameter change rate between adjacent time nodes based on the health parameter of each time node;

[0076] The prediction module 204 is used to predict the health status of the target building based on the health parameter change rate and the building health status prediction model to obtain a prediction result.

[0077] In an optional implementation, the acquisition module 201 is configured to:

[0078] Collect historical health parameters and historical health status of historical buildings at various historical time points;

[0079] Determining a historical health parameter change rate between adjacent historical time nodes based on the historical health parameters of each historical time node;

[0080] Based on the historical health parameter change rate and the historical health status at each historical time node, a building health status prediction model is determined.

[0081] In an optional implementation, the acquisition module 201 is configured to:

[0082] The historical health parameter change rate is used as input data, and the historical health status of each historical time node is used as output data to train the long short-term memory network model to obtain the building health status prediction model.

[0083] In an optional implementation, the acquisition module 202 is configured to:

[0084] The health parameters of the target building, including cracks, settlement, and tilt, are collected at various time points after rainfall, cooling, heating, snowfall, strong winds, and at preset time intervals.

[0085] In an optional implementation, the determination module 203 is configured to:

[0086] Determining health parameter differences and time differences between adjacent time nodes based on the health parameters of each time node;

[0087] Based on the health parameter difference and the time difference, a health parameter change rate between adjacent time nodes is determined.

[0088] In an optional embodiment, the prediction module 204 is configured to:

[0089] The health parameter change rate is input into the health status prediction model, and the health status prediction result of the target building is output.

[0090] In an optional embodiment, the method further includes:

[0091] Based on the prediction results, corresponding measures are taken.

[0092] Example 3:

[0093] Based on the same inventive concept, an embodiment of the present invention provides a computer device, such as Figure 3 As shown, it includes a memory 304, a processor 302 and a computer program stored in the memory 304 and executable on the processor 302. When the processor 302 executes the program, the steps of the above-mentioned building health status prediction method are implemented.

[0094] Among them, Figure 3In the embodiment of the present invention, a bus architecture (represented by bus 300) is shown. Bus 300 may include any number of interconnected buses and bridges, and bus 300 links together various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 306 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same component, namely a transceiver, which provides a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0095] Example 4:

[0096] Based on the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned building health status prediction method when executed by a processor.

[0097] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general-purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0098] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0099] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than those explicitly recited in each embodiment. Rather, as reflected in each embodiment, inventive aspects lie in fewer than all the features of the individual embodiments previously disclosed. Accordingly, the claims that follow the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the present invention.

[0100] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0101] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in a specific embodiment, any one of the claimed embodiments may be used in any combination.

[0102] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components of the health status prediction device for a building or a computer device according to an embodiment of the present invention. The present invention can also be implemented as a device or device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0103] It should be noted that the above embodiments illustrate rather than limit the invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A method for predicting the health status of a building, characterized in that: include: Obtain predictive models of building health status; Collect health parameters of the target building at various time points, including cracks, settlement, and tilt; Determining a health parameter change rate between adjacent time nodes based on the health parameters of each time node; Based on the health parameter change rate and the building health status prediction model, the health status of the target building is predicted to obtain a prediction result.

2. The method according to claim 1, wherein Access predictive models of building health, including: Collect historical health parameters and historical health status of historical buildings at various historical time points; Determining a historical health parameter change rate between adjacent historical time nodes based on the historical health parameters of each historical time node; Based on the historical health parameter change rate and the historical health status at each historical time node, a building health status prediction model is determined.

3. The method according to claim 2, wherein Based on the historical health parameter change rate and the historical health status at each historical time point, a building health status prediction model is determined, including: The historical health parameter change rate is used as input data, and the historical health status of each historical time node is used as output data to train the long short-term memory network model to obtain the building health status prediction model.

4. The method according to claim 1, wherein The health parameters of the target building at various time points, including cracks, settlement, and tilt, are collected, including: The health parameters of the target building, including cracks, settlement, and tilt, are collected at various time points after rainfall, cooling, heating, snowfall, strong winds, and at preset time intervals.

5. The method according to claim 1, wherein Determining a health parameter change rate between adjacent time nodes based on the health parameters of each time node includes: Determining health parameter differences and time differences between adjacent time nodes based on the health parameters of each time node; Based on the health parameter difference and the time difference, a health parameter change rate between adjacent time nodes is determined.

6. The method according to claim 1, wherein Based on the health parameter change rate and the building health prediction model, the health status of the target building is predicted, and the prediction results are obtained, including: The health parameter change rate is input into the health status prediction model, and the health status prediction result of the target building is output.

7. The method according to claim 1, wherein After predicting the health status of the target building based on the health parameter change rate and the building health status prediction model and obtaining the prediction result, the method further includes: Based on the prediction results, corresponding measures are taken.

8. A device for predicting the health status of a building, characterized in that: include: An acquisition module, used for acquiring a building health status prediction model; The acquisition module is used to collect health parameters of the target building at various time points, including cracks, settlement, and tilt; a determination module, configured to determine a health parameter change rate between adjacent time nodes based on the health parameters of the respective time nodes; The prediction module is used to predict the health status of the target building based on the health parameter change rate and the building health status prediction model to obtain a prediction result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images