Detection method and device for building cracks caused by excavation of foundation pits around tunnels

Abstract

The present invention provides a detection method and device for building cracks caused by foundation pit excavation around a tunnel, which can be applied to the fields of geotechnical engineering and underground engineering construction technology. The method includes obtaining foundation pit information, equipment information, soil information and building information of the area to be detected, wherein the foundation pit is a newly built foundation pit corresponding to an existing dense building, the soil information includes soil information corresponding to the existing tunnel and the existing dense building, and the equipment information includes the settlement values ​​of multiple points in the area to be detected; the foundation pit information and the equipment information are input into a target detection model, and the settlement detection result is output; and based on the first settlement detection result, the second settlement detection result, the building information and the soil information, the detection result of the existing dense building is determined. Through this method, the potential cracking risk of the existing dense building can be discovered in the early stage of foundation pit excavation, and countermeasures can be taken in time, thereby improving the safety of the foundation pit excavation project.

Description

Technical Field

[0001] The invention relates to the technical field of geotechnical engineering and underground engineering construction, and in particular to a detection method and device for building cracks caused by foundation pit excavation around a tunnel. Background Art

[0002] Excavation of foundation pits is an indispensable part of underground engineering development and high-rise building foundation construction. However, when excavating foundation pits around existing tunnels and densely populated buildings, it is easy to have a significant impact on the surrounding environment, especially the cracking of densely populated buildings. Reducing the impact of foundation pit excavation on the surrounding environment and building cracking has become an urgent problem to be solved in the engineering field. In related technologies, the detection methods for the impact of foundation pit excavation on the surrounding environment mainly include: using detection equipment (such as total stations) to manually and regularly detect ground subsidence; installing vibration sensors to detect the impact of vibrations generated during construction on surrounding buildings; using cameras and image analysis technology to detect environmental changes (such as the formation of wall cracks).

[0003] The inventors have discovered that the detection of the impact of foundation pit excavation on the surrounding environment in the relevant technology has the following defects: it mainly relies on regular manual detection, and the detection efficiency, accuracy and real-time performance are poor; there is a lack of detection for complex engineering environments (for example, the existence of existing tunnels and dense high-rise buildings around a newly built foundation pit), and the detection process is complicated, time-consuming, labor-intensive, and lacks economy. Summary of the invention

[0004] In view of the above problems, the present invention provides a method, device, equipment, storage medium and program product for detecting building cracks caused by foundation pit excavation around a tunnel.

[0005] According to a first aspect of the present invention, there is provided a detection method for building cracks caused by foundation pit excavation around a tunnel, comprising: obtaining foundation pit information, equipment information, soil information and building information of a to-be-detected area, wherein the foundation pit is a newly-built foundation pit corresponding to an existing densely packed building, the soil information comprises soil information corresponding to each of an existing tunnel and the existing densely packed building, the equipment information represents distance information between an information detection device and the foundation pit; inputting the foundation pit information and the equipment information into a target detection model, and outputting a settlement detection result, wherein the settlement detection result comprises a first settlement detection result and a second settlement detection result corresponding to the existing densely packed building; and determining the detection result of the existing densely packed building based on the first settlement detection result, the second settlement detection result, the building information and the soil information.

[0006] The second aspect of the present invention provides a detection device for building cracks caused by foundation pit excavation around a tunnel, comprising: an information acquisition module, used to acquire foundation pit information, equipment information, soil information and building information of the area to be detected, wherein the above-mentioned foundation pit is a newly built foundation pit corresponding to the existing densely populated buildings, the above-mentioned soil information includes soil information corresponding to the existing tunnel and the above-mentioned existing densely populated buildings, and the above-mentioned equipment information includes settlement values ​​of multiple points in the above-mentioned area to be detected; a settlement detection result output module, used to input the above-mentioned foundation pit information and the above-mentioned equipment information into a target detection model, and output a settlement detection result, wherein the above-mentioned settlement detection result includes a first settlement detection result and a second settlement detection result corresponding to the above-mentioned existing densely populated buildings; and a building detection result determination module, used to determine the detection result of the above-mentioned existing densely populated buildings based on the above-mentioned first settlement detection result, the above-mentioned second settlement detection result, the above-mentioned building information and the above-mentioned soil information.

[0007] A third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above method.

[0008] The fourth aspect of the present invention also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above method.

[0009] The fifth aspect of the present invention also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0010] Through the detection method, device, equipment, storage medium and program product based on the detection of building cracks caused by foundation pit excavation around tunnels provided by the present invention, by acquiring relevant information of newly built foundation pits, existing tunnels and existing dense buildings in the area to be detected and inputting the target detection model, the settlement detection results of existing dense buildings caused by foundation pit excavation around existing tunnels in a complex environment can be automatically generated in real time, so that the crack detection results of existing dense buildings are determined in real time based on the first settlement detection result, the second settlement detection result, the building information and the soil information, thereby improving the real-time and efficiency of the detection, simplifying the detection process, and thus realizing the discovery of potential cracking risks through prediction in the early stage of foundation pit excavation, so as to take timely countermeasures for different cracking risks, and further improving the safety and economy of the foundation pit excavation project. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0012] Figure 1 An application scenario diagram of a detection method, device, equipment, storage medium, and program product for building cracks caused by excavation of a foundation pit around a tunnel according to an embodiment of the present invention is shown;

[0013] Figure 2 A flow chart of a method for detecting building cracks caused by excavation of foundation pits around tunnels according to an embodiment of the present invention is shown;

[0014] Figure 3 A schematic diagram of the arrangement of foundation pit measuring points according to an embodiment of the present invention is shown, wherein FIG (a) shows a plan view, and FIG (b) shows a cross-sectional AA view;

[0015] Figure 4 A schematic diagram of the comparison between the actual detection value and the predicted value of the target detection model according to an embodiment of the present invention is shown, wherein Figure (a) shows a schematic diagram of the triangular distribution of the surface settlement curve, and Figure (b) shows a schematic diagram of the groove distribution of the surface settlement curve;

[0016] Figure 5 A structural block diagram of a detection device for building cracks caused by excavation of foundation pits around tunnels according to an embodiment of the present invention is shown;

[0017] Figure 6 A block diagram of an electronic device suitable for implementing a method for detecting building cracks caused by foundation pit excavation around a tunnel according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0018] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0019] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0020] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0021] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0022] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0023] In the process of conceiving the present invention, the inventors found that in the related art, the detection of the impact of foundation pit excavation on the surrounding environment mainly relies on manual regular detection, and the detection efficiency, accuracy and real-time performance are poor; there is a lack of detection for complex engineering environments (for example, the existence of existing tunnels and dense high-rise buildings around the newly built foundation pit), and the detection process is complicated, time-consuming, labor-intensive, and lacks economy.

[0024] In view of this, the present invention can automatically generate in real time the settlement detection results of existing dense buildings caused by the excavation of foundation pits around existing tunnels in a complex environment by acquiring relevant information of newly built foundation pits, existing tunnels and existing dense buildings in the area to be detected and inputting the information into the target detection model, thereby determining in real time the cracking detection results of existing dense buildings based on the first settlement detection result, the second settlement detection result, the building information and the soil information, thereby improving the real-time and efficiency of the detection, simplifying the detection process, and thus realizing the discovery of potential cracking risks through prediction in the early stage of foundation pit excavation, so as to take timely countermeasures for different cracking risks, thereby further improving the safety and economy of the foundation pit excavation project.

[0025] An embodiment of the present invention provides a detection method, device, equipment, storage medium and program product for building cracks caused by foundation pit excavation around a tunnel. The method includes obtaining foundation pit information, equipment information, soil information and building information of the area to be detected, wherein the foundation pit is a newly built foundation pit corresponding to an existing dense building, the soil information includes soil information corresponding to an existing tunnel and an existing dense building respectively, and the equipment information characterizes the distance information between the information detection equipment and the foundation pit; inputting the foundation pit information and the equipment information into a target detection model, and outputting a settlement detection result, wherein the settlement detection result includes a first settlement detection result and a second settlement detection result corresponding to the existing dense building; and determining the detection result of the existing dense building based on the first settlement detection result, the second settlement detection result, the building information and the soil information.

[0026] Figure 1 An application scenario diagram of a method, device, equipment, storage medium and program product for detecting building cracks caused by excavation of a foundation pit around a tunnel according to an embodiment of the present invention is shown.

[0027] like Figure 1 As shown, the application scenario according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, a server 105, and an information detection device 106. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, the server 105, and the information detection device 106. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0028] The information detection device 106 may include a variety of devices for obtaining foundation pit information, equipment information, soil information and building information, such as a settlement meter, a total station, a laser scanner, a settlement sensor and an inclinometer installed at each monitoring point. The specific equipment can be determined according to the actual engineering situation and is not limited here. The information detection device 106 can be connected to the server 105 or a server cluster through the network 104.

[0029] The user may use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0030] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0031] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0032] It should be noted that the detection method for building cracks caused by excavation of foundation pits around tunnels provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the detection device for building cracks caused by excavation of foundation pits around tunnels provided in the embodiment of the present invention can generally be set in the server 105. The detection method for building cracks caused by excavation of foundation pits around tunnels provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the detection device for building cracks caused by excavation of foundation pits around tunnels provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0033] It should be understood that Figure 1 The number of terminal devices, networks, information detection devices and servers in the embodiment is only for illustration. Any number of terminal devices, networks, information detection devices and servers may be provided according to the implementation requirements.

[0034] Figure 2 A flow chart of a method for detecting building cracks caused by excavation of foundation pits around tunnels according to an embodiment of the present invention is shown.

[0035] like Figure 2 As shown, the detection method of building cracks caused by foundation pit excavation around a tunnel in this embodiment includes operations S210 to S230.

[0036] In operation S210, foundation pit information, equipment information, soil information and building information of the area to be detected are obtained, wherein the foundation pit is a newly built foundation pit corresponding to the existing dense buildings, the soil information includes soil information corresponding to the existing tunnels and the existing dense buildings, and the equipment information represents the distance information between the information detection equipment and the foundation pit.

[0037] According to an embodiment of the present invention, the risk of cracking of dense buildings is detected for the excavation of new foundation pits around existing tunnels, and the engineering environment involved is relatively complex. The foundation pit may be a newly excavated foundation pit based on an actual project, and the existing dense buildings may be dense or high-rise buildings, which are greatly affected by the excavation of the foundation pit. The foundation pit information may include the size information of the foundation pit, such as the depth and width of the foundation pit, and the equipment information may include the vertical distance and horizontal distance of the information detection equipment from the edge of the foundation pit. The soil information may include the Young's modulus of the soil, which characterizes the ability of the soil to resist deformation. The building information may include the building shear modulus, building height, wall thickness and fracture strain of the building structure.

[0038] In operation S220, foundation pit information and equipment information are input into the target detection model, and settlement detection results are output, wherein the settlement detection results include a first settlement detection result and a second settlement detection result corresponding to the existing densely packed buildings.

[0039] According to an embodiment of the present invention, the target detection model may be a model obtained through training to meet the building crack risk detection accuracy. According to the plane layout of the building, the settlement detection result includes the settlement detection results corresponding to the two ends / sides of the same building, including the first settlement detection result and the second settlement detection result. It can be understood that when the building plane is irregular, there may be multiple settlement detection results, which are not specifically limited here.

[0040] In operation S230, a detection result of existing densely packed buildings is determined based on the first settlement detection result, the second settlement detection result, the building information, and the soil information.

[0041] According to an embodiment of the present invention, the detection result of the cracking risk of existing dense buildings can be determined based on the settlement difference value between the two ends of the building foundation combined with the building information and the soil information. The slope information of the surface settlement curve can be determined by the settlement difference value and the length of the building foundation, so as to calculate the angle variable of the building according to the slope information, the building information and the soil information, and use the angle variable as an evaluation index of the building cracking risk, and finally determine the cracking risk level of the building (the degree of damage to the building).

[0042] According to an embodiment of the present invention, by acquiring relevant information of newly built foundation pits, existing tunnels and existing densely packed buildings in the area to be detected and inputting the target detection model, the settlement detection results of existing densely packed buildings caused by foundation pit excavation around existing tunnels in a complex environment can be automatically generated in real time, thereby determining the cracking detection results of existing densely packed buildings in real time based on the first settlement detection result, the second settlement detection result, the building information and the soil information, thereby improving the real-time and efficiency of the detection, simplifying the detection process, and thus realizing the discovery of potential cracking risks through prediction in the early stage of foundation pit excavation, so as to take timely countermeasures for different cracking risks, thereby further improving the safety and economy of the foundation pit excavation project.

[0043] According to an embodiment of the present invention, based on the first settlement detection result, the second settlement detection result, the building information and the soil information, the detection result of the existing densely packed buildings is determined, including: determining the difference between the first settlement detection result and the second settlement detection result; determining the slope information of the surface of the area to be detected according to the difference; and determining the detection result of the existing densely packed buildings based on the slope information, the building information and the soil information.

[0044] Figure 3 A schematic diagram of the arrangement of foundation pit measuring points according to an embodiment of the present invention is shown, wherein FIG. (a) shows a plan view, and FIG. (b) shows a cross-sectional AA view.

[0045] like Figure 3 As shown in Figure (a), there are existing tunnels 302 and existing densely packed buildings 303 around the new foundation pit 301. The impact of the excavation of the new foundation pit 301 on the adjacent existing densely packed buildings 303 is mainly reflected in foundation settlement, structural deformation, and wall cracking. Figure 3 As shown in Figure (b), according to the different support structures 304 of the newly built foundation pit 301, the surface settlement curve from the newly built foundation pit 301 to the existing densely populated building 303 area has various forms, including a triangular distribution 3051 and a groove distribution 3052.

[0046] According to an embodiment of the present invention, the first settlement detection result and the second settlement detection result can respectively represent the settlement value of the first end and the settlement value of the second end of the existing dense building foundation, and the difference between the settlement values ​​at the two ends is calculated, as shown in the following formula (1):

[0047] (1);

[0048] in, The differential settlement at both ends of the foundation of the existing densely packed buildings. is the predicted settlement of the first end of the building foundation, is the predicted settlement at the second end of the building foundation.

[0049] According to an embodiment of the present invention, after determining the difference between the settlement values ​​at both ends of the foundation of the existing dense building and combining the length of the foundation of the existing dense building, the slope information of the surface settlement curve of the area from the existing dense building to the edge of the foundation pit can be determined. The specific method for determining the slope information is shown in the following formula (2):

[0050] (2);

[0051] in, is the slope information of the surface settlement curve, and L is the length of the existing dense building foundation.

[0052] According to an embodiment of the present invention, after determining the differential settlement values ​​at both ends of the existing densely packed buildings and the slope information of the settlement curve, the soil Young's modulus, the building shear modulus, the building height, the wall thickness and the fracture strain of the building structure can be combined to further determine the detection results (angular variables) of the existing densely packed buildings, and then the cracking degree and risk of the densely packed buildings can be evaluated. The method for determining the angular variables of the existing densely packed buildings is specifically shown in the following formula (3):

[0053] (3);

[0054] Among them, β is the angular variable of the building (existing dense buildings), c1, c2, c3, c4, c5, c6 are function coefficients (which can be determined according to engineering experience or relevant standards), E s is the Young's modulus of the soil, L is the length of the building foundation, G is the shear modulus of the building, H is the height of the building, ε t is the structural fracture strain, T k is the thickness of the building wall, b can represent the thickness of the building wall, It can characterize the soil-structure stiffness ratio.

[0055] In a feasible embodiment, the detection method based on the cracking of dense buildings caused by the excavation of foundation pits around existing tunnels can also rate the impact of the cracking of dense buildings caused by the excavation of foundation pits around existing tunnels to prevent disasters. According to relevant specifications, the corresponding relationship between the evaluation index of building cracking and the angle variable β can be shown in Table 1 below.

[0056] Table 1

[0057]

[0058] According to an embodiment of the present invention, based on multiple information such as slope, building structure and soil characteristics, a more comprehensive building cracking risk assessment can be provided, which can more accurately reflect the actual risk than the analysis of a single factor; the use of slope information can help predict building deformation caused by slope changes, and soil information can provide details of the bearing capacity and stability of the foundation. Through comprehensive consideration, possible cracking situations can be predicted more accurately, and potential cracking risks can be identified early, and preventive measures can be taken to avoid or reduce damage to the building.

[0059] According to an embodiment of the present invention, the method also includes: classifying the detection results based on the identification of multiple points to obtain classification results, the identification is determined based on the location information of the points; and generating chart information for the classification results corresponding to each of the multiple time periods and displaying them.

[0060] According to an embodiment of the present invention, points in the area to be detected can be identified based on the location information of different points, so that the detection results of each point can be classified according to the identification. For example, identification rules are defined according to the location of the point, the identification information is applied to each point, and a point information library is generated; further, visualization tools (such as geographic information systems) can be used to visualize the identified and classified point information on a map to facilitate users to view it intuitively, and users can judge whether there is a risk of surface subsidence based on the visualized chart information.

[0061] According to an embodiment of the present invention, the method further includes: generating alarm information when the detection result indicates that the existing densely packed buildings are risky buildings; and sending the alarm information to a terminal device.

[0062] According to an embodiment of the present invention, after the detection result (angle variable) is generated, the angle variable can be compared with the preset evaluation threshold to obtain the building cracking risk level, and the value of the angle variable is positively correlated with the building cracking risk level. In the case where the existing densely populated buildings are risky buildings, a risk alarm can be generated and automatically sent to the terminal device. The risk alarm notification method includes but is not limited to SMS, email, phone message, mobile application message push, WeChat public account, etc. The specific method of risk alarm can be determined based on the above alarm content or actual engineering scenario, and is not specifically limited here.

[0063] According to an embodiment of the present invention, the target detection model is determined by the following operations: determining initial parameters corresponding to the initial detection model; constructing the initial detection model based on sample equipment information, sample foundation pit information, sample curve parameters and initial parameters, the sample curve parameters including shape parameters and size parameters corresponding to the surface settlement curve of the sample area to be detected; updating the initial parameters based on a preset update strategy to obtain target parameters; and updating the initial detection model based on the target parameters to obtain the target detection model.

[0064] According to an embodiment of the present invention, the sample information may be sample equipment information, sample foundation pit information, and sample curve parameters of the same or similar engineering scenes obtained in a historical time period. The initial detection model may be constructed based on the sample information (sample equipment information, sample foundation pit information, sample curve parameters) and the initial parameters. The initial parameters in the initial detection model may be multiple (a1, a2, and a3). The initial parameters may be obtained by assigning values ​​to a1, a2, and a3 based on engineering experience or relevant specifications and standards (for example, the initial values ​​of a1, a2, and a3 are 0.49, -3.14, and 4.78, respectively).

[0065] According to an embodiment of the present invention, the preset update strategy can be a hybrid algorithm (GWO-GD) based on the combination of the Grey Wolf Optimizer (GWO) and the Gradient Descent (GD) algorithm. The initial parameters a1, a2 and a3 are updated by the hybrid algorithm to obtain the target parameters a'1, a'2 and a'3, and the target detection model is obtained on this basis.

[0066] According to an embodiment of the present invention, the Grey Wolf Optimization Algorithm is combined with the Gradient Descent Algorithm to give full play to the advantages of the two algorithms and provide a more comprehensive optimization strategy. The GWO in the hybrid algorithm can perform preliminary rough adjustments in a larger solution space to find a better parameter range, and then the GD can perform fine adjustments within these ranges, thereby effectively improving the parameter optimization effect of the surface subsidence prediction model, thereby improving the prediction accuracy, accelerating the convergence speed, avoiding local optimality and enhancing the model stability.

[0067] According to an embodiment of the present invention, the sample pit information includes sample depth information and sample width information, and the sample curve parameters are determined by the following operations: determining shape parameters and size parameters based on the sample depth information, sample width information and a preset generation strategy.

[0068] According to an embodiment of the present invention, the shape parameter can represent the shape parameter of the surface settlement curve, the size parameter can represent the normalized parameter of the width of the surface settlement curve, and the preset generation strategy can be a generation method based on an empirical formula or relevant specifications and standards. Specifically, it can be shown in the following formulas (4)-(5):

[0069] (4);

[0070] (5);

[0071] Among them, A is the shape parameter of the surface settlement curve, B is the normalized parameter of the width of the surface settlement curve, is the excavation depth of the foundation pit, L e is the excavation width of the foundation pit.

[0072] According to an embodiment of the present invention, the equipment information includes settlement point information, the settlement point information includes settlement value, horizontal distance information and vertical distance information between the target point and the foundation pit, and the settlement value includes settlement peak value; an initial detection model is constructed based on sample equipment information, sample foundation pit information, sample curve parameters and initial parameters, including: constructing an initial detection model according to settlement peak value, horizontal distance information, vertical distance information, shape parameters, size parameters and initial parameters.

[0073] According to an embodiment of the present invention, the settlement peak value can represent the maximum settlement value of the target point from the edge of the foundation pit, the horizontal distance information between the target point and the foundation pit can be recorded as x, and the vertical distance information between the target point and the foundation pit can be recorded as y. The initial detection model can be specifically shown in the following formula (6):

[0074] (6);

[0075] Among them, δ v (x, y) is the surface settlement of each measuring point at different positions from the edge of the foundation pit; δ vm is the maximum settlement from the edge of the foundation pit, x is the vertical distance from the settlement meter to the edge of the foundation pit, y is the horizontal distance from the settlement meter to the edge of the foundation pit, is the excavation depth of the foundation pit, L e is the excavation width of the foundation pit, and a1, a2 and a3 are the initial parameters respectively.

[0076] According to an embodiment of the present invention, the initial parameters are updated based on a preset update strategy to obtain target parameters, including: initializing population information, the population information including multiple individuals and position information corresponding to each of the multiple individuals, the position information representing an update result during the initial parameter update process, the position information including first position information; updating the first position information using a gradient descent method to obtain intermediate position information; when the intermediate position information meets a preset update threshold, using the intermediate position information as target position information; and determining the target parameters based on the target position information.

[0077] According to an embodiment of the present invention, a hybrid algorithm can be used to optimize and update the initial parameters. Individuals can be different solutions of the initial parameters during the optimization and update process. Position information can determine the distance between the individual and the optimal solution, and characterize the update results during the initial parameter update process.

[0078] In a feasible embodiment, in order to optimize the initial parameters a1, a2, a3, an objective function can be defined first. The objective function can be used to evaluate the quality of each set of parameters. By obtaining the actual monitoring surface settlement data δ of one set of different measuring points, vi，actual and the corresponding distance d from the edge of the foundation pit iAnd the excavation depth H e , then the objective function can be defined as the mean square error between the actual monitored surface settlement value and the predicted value, as shown in the following formula (7):

[0079] (7);

[0080] in, is the actual surface settlement value monitored at the i-th measuring point, is the surface settlement value predicted by the i-th initial detection model.

[0081] According to an embodiment of the present invention, the first position information can represent the global optimal solution found in the current iteration process, which is the position of the gray wolf α; the intermediate position information can represent the position information of the gray wolf after gradient descent. The preset update threshold can represent the number of update iterations or the updated model meets the prediction accuracy requirements.

[0082] In a feasible embodiment, the positions of gray wolves can be randomly generated, the population size and the maximum number of iterations can be determined, and the positions of A1, A2 and A3 gray wolves can be initialized to represent the optimal solution, the second optimal solution and the third optimal solution in the current population, respectively. The position of each gray wolf is determined by the parameter a to be optimized. 1i , a 2i , a 3i , composed of, updating the global optimal solution, updating the global optimal solution X after each iteration * , the global optimal solution represents the best solution found in the current iteration, as shown in the following formula (8):

[0083] X * =X α (8);

[0084] Among them, X * is the global optimal solution found in the current iteration (i.e., the position of gray wolf α).

[0085] Furthermore, we can perform local search and use the gradient descent method to find the current global optimal solution X * Fine optimization is performed to further improve the accuracy of the solution. The objective function gradient at the current global optimal solution is calculated to guide the position update so that it is closer to the optimal solution. The specific formula is as follows:

[0086] (9);

[0087] Among them, X new is the position vector after gradient descent update, η is the learning rate, which is used to control the step size, is the objective function at the current global optimal solution X * The gradient vector at .

[0088] According to an embodiment of the present invention, when the intermediate position information meets a preset error threshold, the intermediate position information is used as the target position information, including: determining intermediate parameters based on the intermediate position information; generating intermediate settlement detection results based on the intermediate parameters, sample information and an initial detection model; determining the settlement difference between the intermediate settlement detection result and the sample settlement detection result; and when the settlement difference is less than the preset error threshold, using the intermediate position information as the target position information.

[0089] According to an embodiment of the present invention, the intermediate settlement detection result calculated by the initial detection model is compared with the actual detected surface settlement value by iterative calculation i times to verify the accuracy of the detection result. When the error is less than a preset error threshold (for example, 2%), it is considered that the intermediate parameter meets the requirement and the intermediate parameter is used as the target parameter, as shown in the following formula (10):

[0090] (10);

[0091] in, is the error between the predicted value and the actual value, is the predicted value after iterative calculation i times, It is the actual measured value of surface subsidence detection.

[0092] Stop the iterative calculation, otherwise restart the optimization process.

[0093] The hybrid algorithm can be used to optimize and update the initial parameters to obtain the optimal solution (target parameters a'1, a'2 and a'3), and the initial detection model can be updated based on the target parameters to obtain the target detection model. The target detection model is specifically shown in the following formula (11):

[0094] (11);

[0095] in, It is the settlement prediction value predicted by the trained target detection model.

[0096] Figure 4 A schematic diagram of the comparison between the actual detection value and the predicted value of the target detection model according to an embodiment of the present invention is shown, wherein Figure (a) shows a schematic diagram of the triangular distribution of the surface settlement curve, and Figure (b) shows a schematic diagram of the groove distribution of the surface settlement curve.

[0097] like Figure 4 As shown in Figure (a), the prediction accuracy of the trained target detection model in the embodiment of the present invention can be verified by relevant information. Case 1 is selected for verification. The foundation depth of the building is 4m, the foundation length L of the building is 22m, and the final excavation depth H of the foundation pit ise The surface settlement data obtained from the actual monitoring of the target point (measurement point) is verified with the surface settlement curve drawn by the target detection model. Figure 4 The d in the figure can represent the distance between the ground surface and the edge of the foundation pit excavation, that is, the location of the monitoring point arranged on the ground surface.

[0098] like Figure 4 As shown in Figure (a), it can be seen that the predicted value obtained by the target detection model of the present invention can meet the prediction accuracy and can be used for the detection of building cracks caused by excavation of foundation pits around tunnels in actual projects. The target detection model can predict the value of surface settlement during the excavation of specific foundation pit projects. If the settlement value is greater than the threshold required by the specification, an alarm is issued to the staff through the alarm unit in the monitoring management platform. If it meets the requirements of the specification, a building crack analysis is performed. The relevant information is shown in Table 2 below.

[0099] Table 2

[0100]

[0101] The comparison results of the building angle variable (β) calculated by the target detection model based on the information in Table 2 above and the actual detection value of Case 1 are shown in Table 3 below. It can be seen that the operation of the machine that is sensitive to settlement due to the existing dense buildings is difficult, indicating that the building is less affected by the foundation pit excavation.

[0102] Table 3

[0103]

[0104] In order to further verify the prediction accuracy of the target detection model, case 2 can be selected for verification. The foundation depth of the building is 15m, the foundation length L of the building is 31m, and the final excavation depth H of the foundation pit is e The surface settlement data is 22.8m, and the surface settlement curve drawn by the surface settlement prediction model is verified. Figure 4 As shown in Figure (b).

[0105] If the requirements of the code are met, a building crack analysis is conducted. The relevant information is shown in Table 4 below.

[0106] Table 4

[0107]

[0108] The comparison results of the building angle variable (β) calculated based on the above parameters and the actual detection value of Case 2 are as follows. It can be seen that the operation of the machine that is sensitive to settlement is difficult, indicating that it is less affected by the foundation pit excavation, as shown in Table 5 below.

[0109] Table 5

[0110]

[0111] Based on the above-mentioned detection method of building cracks caused by excavation of foundation pits around tunnels, the present invention also provides a detection device for building cracks caused by excavation of foundation pits around tunnels. Figure 5 The device is described in detail.

[0112] Figure 5 A structural block diagram of a detection device for building cracks caused by excavation of a foundation pit around a tunnel according to an embodiment of the present invention is shown.

[0113] like Figure 5 As shown, the detection device for building cracks caused by foundation pit excavation around a tunnel in this embodiment includes an information acquisition module 510, a settlement detection result output module 520 and a building detection result determination module 530.

[0114] The information acquisition module 510 is used to obtain the foundation pit information, equipment information, soil information and building information of the area to be detected, wherein the foundation pit is a newly built foundation pit corresponding to the existing dense buildings, the soil information includes the soil information corresponding to the existing tunnel and the existing dense buildings, and the equipment information represents the distance information between the information detection equipment and the foundation pit. In one embodiment, the information acquisition module 510 can be used to perform the operation S210 described above, which will not be repeated here.

[0115] The settlement detection result output module 520 is used to input the foundation pit information and the equipment information into the target detection model, and output the settlement detection result, wherein the settlement detection result includes a first settlement detection result and a second settlement detection result corresponding to the existing densely packed buildings. In one embodiment, the settlement detection result output module 520 can be used to perform the operation S220 described above, which will not be described in detail here.

[0116] The building detection result determination module 530 is used to determine the detection result of the existing densely packed buildings based on the first settlement detection result, the second settlement detection result, the building information and the soil information. In one embodiment, the building detection result determination module 530 can be used to perform the operation S230 described above, which will not be described in detail here.

[0117] According to an embodiment of the present invention, through the information acquisition module 510, the settlement detection result output module 520 and the building detection result determination module 530 in the detection device for detecting building cracks caused by foundation pit excavation around tunnels, by acquiring relevant information of newly built foundation pits, existing tunnels and existing dense buildings in the area to be detected and inputting the target detection model, the settlement detection results of existing dense buildings caused by foundation pit excavation around existing tunnels in a complex environment can be automatically generated in real time, thereby determining the cracking detection results of existing dense buildings in real time based on the first settlement detection result, the second settlement detection result, the building information and the soil information, thereby improving the real-time and efficiency of the detection, simplifying the detection process, and thus realizing the discovery of potential cracking risks through prediction in the early stage of foundation pit excavation, so as to take timely countermeasures for different cracking risks, and further improving the safety and economy of the foundation pit excavation project.

[0118] According to an embodiment of the present invention, the building detection result determination module includes: a difference determination submodule, a slope information determination submodule and a detection result determination submodule.

[0119] A difference determination submodule is used to determine the difference between the first settlement detection result and the second settlement detection result; a slope information determination submodule is used to determine the slope information of the surface of the area to be detected based on the difference; and a detection result determination submodule is used to determine the detection result of the existing densely packed buildings based on the slope information, building information and soil information.

[0120] According to an embodiment of the present invention, the device further includes: a detection result classification module and a display module.

[0121] A detection result classification module is used to classify the detection results based on the identification of multiple points to obtain classification results, and the identification is determined based on the location information of the points; and a display module is used to generate chart information corresponding to the classification results of multiple time periods and display them.

[0122] According to an embodiment of the present invention, the device further includes: an alarm information generating module and an information sending module.

[0123] An alarm information generation module is used to generate alarm information when the detection result indicates that the existing densely packed buildings are risky buildings; and an information sending module is used to send the alarm information to the terminal device.

[0124] According to an embodiment of the present invention, the target detection model is determined by the following operations: determining initial parameters corresponding to the initial detection model; constructing the initial detection model based on sample equipment information, sample foundation pit information, sample curve parameters and initial parameters, the sample curve parameters including shape parameters and size parameters corresponding to the surface settlement curve of the sample area to be detected; updating the initial parameters based on a preset update strategy to obtain target parameters; and updating the initial detection model based on the target parameters to obtain the target detection model.

[0125] According to an embodiment of the present invention, the sample pit information includes sample depth information and sample width information, and the sample curve parameters are determined by the following operations: determining shape parameters and size parameters based on the sample depth information, sample width information and a preset generation strategy.

[0126] According to an embodiment of the present invention, the equipment information also includes settlement point information, the settlement point information includes settlement value, horizontal distance information and vertical distance information between the target point and the foundation pit, and the settlement value includes settlement peak value; an initial detection model is constructed based on sample equipment information, sample foundation pit information, sample curve parameters and initial parameters, including: constructing an initial detection model according to settlement peak value, horizontal distance information, vertical distance information, shape parameters, size parameters and initial parameters.

[0127] According to an embodiment of the present invention, the initial parameters are updated based on a preset update strategy to obtain target parameters, including: initializing population information, the population information including multiple individuals and position information corresponding to each of the multiple individuals, the position information representing an update result during the initial parameter update process, the position information including first position information; updating the first position information using a gradient descent method to obtain intermediate position information; when the intermediate position information meets a preset update threshold, using the intermediate position information as target position information; and determining the target parameters based on the target position information.

[0128] According to an embodiment of the present invention, when the intermediate position information meets a preset error threshold, the intermediate position information is used as the target position information, including: determining intermediate parameters based on the intermediate position information; generating intermediate settlement detection results based on the intermediate parameters, sample information and an initial detection model; determining the settlement difference between the intermediate settlement detection result and the sample settlement detection result; and when the settlement difference is less than the preset error threshold, using the intermediate position information as the target position information.

[0129] According to an embodiment of the present invention, any multiple modules among the information acquisition module 510, the settlement detection result output module 520 and the building detection result determination module 530 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the information acquisition module 510, the settlement detection result output module 520 and the building detection result determination module 530 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of the information acquisition module 510, the settlement detection result output module 520 and the building detection result determination module 530 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0130] Figure 6 A block diagram of an electronic device suitable for implementing a method for detecting building cracks caused by foundation pit excavation around a tunnel according to an embodiment of the present invention is shown.

[0131] like Figure 6 As shown, the electronic device according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 to a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0132] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present invention by executing the program in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present invention by executing the program stored in the one or more memories.

[0133] According to an embodiment of the present invention, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0134] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0135] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0136] The embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the detection method for building cracks caused by excavation of foundation pits around tunnels provided by the embodiment of the present invention.

[0137] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when it is executed by the processor 601. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0138] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0139] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0140] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0141] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0142] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways, even if such combinations and / or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All of these combinations and / or combinations fall within the scope of the present invention.

[0143] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination. The scope of the present invention is defined by the attached claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for detecting building cracks caused by excavation of foundation pits around tunnels, characterized in that: The method comprises: Acquire foundation pit information, equipment information, soil information and building information of the area to be detected, wherein the foundation pit is a newly built foundation pit corresponding to an existing dense building, the soil information includes soil information corresponding to an existing tunnel and the existing dense building respectively, the equipment information represents the distance information between the information detection equipment and the foundation pit, and the building information includes building shear modulus, building height, wall thickness and building structure fracture strain; Inputting the foundation pit information and the equipment information into a target detection model, and outputting a settlement detection result, wherein the settlement detection result includes a first settlement detection result and a second settlement detection result corresponding to the existing densely packed buildings; and Determine a detection result of the existing densely packed buildings based on the first settlement detection result, the second settlement detection result, the building information, and the soil information; The target detection model is determined by the following operations: determining initial parameters corresponding to the initial detection model; Constructing an initial detection model based on sample equipment information, sample foundation pit information, sample curve parameters and the initial parameters, wherein the sample curve parameters include shape parameters and size parameters corresponding to the surface settlement curve of the sample to be detected area; The initial parameters are updated based on a preset update strategy to obtain target parameters, wherein the preset update strategy is a hybrid algorithm based on a combination of a gray wolf optimization algorithm and a gradient descent algorithm; and The initial detection model is updated based on the target parameters to obtain the target detection model.

2. The method according to claim 1, characterized in that Determining the detection result of the existing densely packed buildings based on the first settlement detection result, the second settlement detection result, the building information, and the soil information includes: determining a difference between the first sedimentation detection result and the second sedimentation detection result; Determining the slope information of the surface of the area to be detected according to the difference; and The detection result of the existing densely packed buildings is determined based on the slope information, the building information and the soil information.

3. The method according to claim 1, characterized in that The method further comprises: Classifying the detection results based on identifications of multiple points to obtain classification results, wherein the identifications are determined based on location information of the points; and The classification results corresponding to the multiple time periods are generated into chart information and displayed.

4. The method according to claim 1, characterized in that The method further comprises: If the detection result indicates that the existing densely packed building is a risky building, generating an alarm message; and The warning information is sent to the terminal device.

5. The method according to claim 1, characterized in that The sample pit information includes sample depth information and sample width information, and the sample curve parameters are determined by the following operations: The shape parameter and the size parameter are determined based on the sample depth information, the sample width information and a preset generation strategy.

6. The method according to claim 1, characterized in that The equipment information includes settlement point information, the settlement point information includes settlement value, horizontal distance information and vertical distance information between the target point and the foundation pit, and the settlement value includes settlement peak value; An initial detection model is constructed based on sample equipment information, sample foundation pit information, sample curve parameters and the initial parameters, including: The initial detection model is constructed according to the settlement peak value, the horizontal distance information, the vertical distance information, the shape parameter, the size parameter and the initial parameter.

7. The method according to claim 1, characterized in that The initial parameters are updated based on a preset update strategy to obtain target parameters, including: Initializing population information, the population information including a plurality of individuals and location information corresponding to each of the plurality of individuals, the location information representing an update result in the initial parameter update process, the location information including first location information; Using a gradient descent method to update the first position information to obtain intermediate position information; In the case where the intermediate position information meets a preset update threshold, using the intermediate position information as the target position information; and The target parameter is determined according to the target position information.

8. The method according to claim 7, characterized in that When the intermediate position information satisfies a preset error threshold, using the intermediate position information as target position information includes: determining an intermediate parameter based on the intermediate position information; Based on the intermediate parameters, sample information and the initial detection model, generating an intermediate sedimentation detection result; determining a sedimentation difference between the intermediate sedimentation test result and the sample sedimentation test result; and When the settlement difference is less than a preset error threshold, the intermediate position information is used as the target position information.

9. A detection device for building cracks caused by excavation of foundation pits around tunnels, characterized in that: The device comprises: An information acquisition module is used to acquire foundation pit information, equipment information, soil information and building information of the area to be detected, wherein the foundation pit is a newly built foundation pit corresponding to an existing dense building, the soil information includes soil information corresponding to an existing tunnel and the existing dense building respectively, the equipment information represents the distance information between the information detection equipment and the foundation pit, and the building information includes building shear modulus, building height, wall thickness and building structure fracture strain; a settlement detection result output module, used for inputting the foundation pit information and the equipment information into a target detection model, and outputting a settlement detection result, wherein the settlement detection result includes a first settlement detection result and a second settlement detection result corresponding to the existing densely packed buildings; and A building detection result determination module, configured to determine the detection result of the existing densely packed buildings based on the first settlement detection result, the second settlement detection result, the building information and the soil information; The target detection model is determined by the following operations: determining initial parameters corresponding to the initial detection model; Constructing an initial detection model based on sample equipment information, sample foundation pit information, sample curve parameters and the initial parameters, wherein the sample curve parameters include shape parameters and size parameters corresponding to the surface settlement curve of the sample to be detected area; The initial parameters are updated based on a preset update strategy to obtain target parameters, wherein the preset update strategy is a hybrid algorithm based on a combination of a gray wolf optimization algorithm and a gradient descent algorithm; and The initial detection model is updated based on the target parameters to obtain the target detection model.

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