Foundation pit depth detection method, system and equipment and storage medium

Abstract

The embodiment of the invention provides a foundation pit depth detection method, system and device and a storage medium, and the method comprises the steps: constructing a training sample set which comprises virtual environment data and an actual foundation pit depth corresponding to the virtual environment data; an initial model is constructed to carry out data processing on the virtual environment data, and the predicted foundation pit depth is obtained; determining a loss function based on the actual foundation pit depth and the predicted foundation pit depth; adjusting parameters of the initial model by using the loss function to obtain a target model; and acquiring environment data to be calculated, and processing and analyzing the environment data to be calculated by using the target model to obtain foundation pit depth data, so that the purpose of automatically measuring the depth of the foundation pit under complex geological conditions is achieved, and the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the technical field of deep foundation pit engineering, and in particular to a foundation pit depth detection method, system, equipment and storage medium. Background Art

[0002] As a common and critical construction link in civil engineering, the accurate detection of the depth of foundation pit engineering is of great significance for ensuring the safety of the entire project and optimizing the design scheme. However, for a long time, the traditional means of foundation pit depth detection mainly rely on manual field measurement, which is not only time-consuming and labor-intensive, but also easily affected by human factors during the measurement process, resulting in errors in the measurement results, which may affect the accuracy of subsequent construction decisions. In addition, the construction environment of the foundation pit is usually very complex and changeable, including underground pipelines, rock layers and various other unknown geological conditions. The existence of these unknown factors undoubtedly further increases the difficulty and potential risks of foundation pit depth detection. Therefore, how to overcome the limitations of traditional detection methods and achieve more efficient, accurate and safe foundation pit depth detection has become a technical problem that needs to be solved in the current field of deep foundation pit engineering. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a foundation pit depth detection method, system, equipment and storage medium, aiming to achieve the purpose of automatically measuring the foundation pit depth under complex geological conditions by constructing a virtual environment and building a model, thereby improving the efficiency and accuracy of detection.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application provides a foundation pit depth detection method, the method comprising:

[0005] Constructing a training sample set, wherein the training sample set includes virtual environment data and an actual foundation pit depth corresponding to the virtual environment data;

[0006] Constructing an initial model to process the virtual environment data to obtain a predicted foundation pit depth;

[0007] Determine a loss function based on the actual foundation pit depth and the predicted foundation pit depth;

[0008] Using the loss function to adjust the parameters of the initial model to obtain a target model;

[0009] The environmental data to be calculated are collected, and the environmental data to be calculated are processed and analyzed using the target model to obtain foundation pit depth data.

[0010] The method provided in the first aspect can solve the problem that traditional foundation pit depth detection methods mainly rely on manual field measurement, which is time-consuming and labor-intensive, and is easily affected by human factors during the measurement process, thereby causing errors in the measurement results, which may in turn affect the accuracy of subsequent construction decisions. The purpose of accurately and automatically measuring the depth of foundation pits under complex geological conditions is achieved, and the efficiency, safety and accuracy of detection are improved.

[0011] In a possible implementation, the acquisition of the virtual environment data includes: collecting actual foundation pit environment data, constructing a virtual environment based on the actual foundation pit environment data, and acquiring the virtual environment data based on the virtual environment.

[0012] In a possible implementation, the step of processing and analyzing the environmental data to be calculated by using the target model to obtain the foundation pit depth data further includes:

[0013] Display the foundation pit depth data on the operation interface;

[0014] The operation interface is also used to manage and view historical foundation pit depth data.

[0015] In a possible implementation, the collecting of the environmental data to be calculated includes: collecting environmental data of the environment using a variety of sensors to obtain the environmental data to be calculated.

[0016] In a possible implementation, the step of adjusting the parameters of the initial model using the loss function to obtain the target model includes:

[0017] The parameters of the initial model are calculated based on the loss function and the back propagation algorithm to obtain the gradient of each parameter, and the gradient of each parameter is optimized and updated using an optimization algorithm to obtain the target model.

[0018] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a foundation pit depth detection system, the system comprising:

[0019] A virtual environment construction module: used to collect actual foundation pit environment data, construct a virtual environment based on the actual foundation pit environment data, and collect virtual environment data based on the virtual environment;

[0020] A training sample set construction module: used to construct a training sample set, wherein the training sample set includes the virtual environment data and the actual foundation pit depth corresponding to the virtual environment data;

[0021] Virtual data processing module: used to construct an initial model to process the virtual environment data to obtain the predicted foundation pit depth;

[0022] A loss function calculation module: used to determine a loss function based on the actual foundation pit depth and the predicted foundation pit depth;

[0023] Parameter adjustment module: used to adjust the parameters of the initial model using the loss function to obtain the target model;

[0024] Sensor module: used to collect environmental data to be calculated;

[0025] Real-time data processing module: used to process and analyze the environmental data to be calculated using the target model to obtain foundation pit depth data;

[0026] Interactive module: used to display the foundation pit depth data and manage and view historical foundation pit depth data.

[0027] The system provided by the second aspect can solve the problem that traditional foundation pit depth detection methods mainly rely on manual field measurement, which is time-consuming and labor-intensive, and is easily affected by human factors during the measurement process, thus leading to errors in the measurement results, which may in turn affect the accuracy of subsequent construction decisions. The purpose of accurately and automatically measuring the depth of foundation pits under complex geological conditions is achieved, and the efficiency, safety and accuracy of detection are improved.

[0028] In a possible implementation, the system further includes: an alarm module, configured to analyze the foundation pit depth data and issue an alarm signal when the foundation pit depth data is abnormal.

[0029] In one possible implementation, the system also includes: an encryption module, used to filter out private data information from the foundation pit depth data or the environmental data to be calculated, encrypt the private data information to obtain encrypted data information, and store the encrypted data information in an encryption space.

[0030] In a third aspect, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the foundation pit depth detection method as described in any possible implementation method in the first aspect is implemented.

[0031] In a fourth aspect, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the foundation pit depth detection method as described in any possible implementation manner in the first aspect is implemented.

[0032] It can be seen from the technical solutions provided by one or more embodiments of the present specification that the foundation pit depth detection method provided by the embodiment of the present invention constructs a training sample set, the training sample set includes virtual environment data and the actual foundation pit depth corresponding to the virtual environment data, and then constructs an initial model to process the virtual environment data to obtain the predicted foundation pit depth, and determines the loss function based on the actual foundation pit depth and the predicted foundation pit depth, uses the loss function to adjust the parameters of the initial model, obtains the target model, collects the environmental data to be calculated, and uses the target model to process and analyze the environmental data to be calculated to obtain the foundation pit depth data. The purpose of accurately and automatically measuring the foundation pit depth under complex geological conditions is achieved, and the efficiency, safety and accuracy of detection are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the description of one or more embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0034] Figure 1 A schematic diagram of a process flow of a foundation pit depth detection method provided in an embodiment of the present application;

[0035] Figure 2 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the one or more embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0037] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0039] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0040] Figure 1 is an optional flow chart of the foundation pit depth detection method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S100 to S500.

[0041] First, as Figure 1 As shown, a method for detecting the depth of a foundation pit is provided, the method comprising:

[0042] S100: Construct a training sample set, wherein the training sample set includes virtual environment data and an actual foundation pit depth corresponding to the virtual environment data.

[0043] S200, constructing an initial model to process the virtual environment data to obtain a predicted foundation pit depth.

[0044] S300: Determine a loss function based on the actual foundation pit depth and the predicted foundation pit depth.

[0045] It should be noted that the loss function provides a clear optimization goal for the model. During the training process, the model gradually improves its prediction ability by constantly adjusting its parameters to minimize the value of the loss function. This optimization process ensures that the model can learn the potential laws and characteristics in the data. By determining the model loss, the difference between the model prediction results and the actual values ​​can be quantified, thereby guiding the adjustment of the model parameters so that the model gradually approaches the distribution of the real data. At the same time, during the training process, the loss function can be used to observe the trend of the loss value, determine whether the model is in a state of overfitting, underfitting or normal learning, and then take corresponding measures to adjust it. Determining the model loss also helps to select a suitable optimization algorithm and learning rate to accelerate the training process and improve the convergence speed of the model. Different loss functions may have different requirements for the selection of optimization algorithms and the setting of learning rates. By reasonably determining the loss function, the optimization algorithm and learning rate can be better matched, thereby improving the training efficiency.

[0046] S400, using the loss function to adjust the parameters of the initial model to obtain the target model.

[0047] It should be noted that the loss function provides a clear guide for adjusting model parameters. During the training process, the loss function provides a clear indicator for evaluating model performance by quantifying the difference between the model prediction results and the actual values. This quantitative indicator can guide the algorithm to iteratively adjust model parameters, such as weights and biases, to reduce losses and improve prediction performance. Through continuous iterative optimization, the model parameters can gradually approach the optimal solution, thereby improving the prediction accuracy of the model. At the same time, using the loss function to adjust model parameters helps to balance the bias and variance of the model. During the model training process, bias and variance are two important performance indicators. Bias reflects the degree of fit of the model to the training data, while variance reflects the generalization ability of the model to new data. By selecting a suitable loss function and optimizing its parameters, these two indicators can be balanced to a certain extent, so that the model can fully fit the training data and maintain good generalization performance on new data. In addition, the loss function can also affect the behavioral characteristics of the model. Different types of loss functions have different effects on the behavior of the model, such as sensitivity to data outliers, priority for specific types of errors, etc. By selecting a suitable loss function and adjusting its parameters, the model can be more robust in handling outliers in the data, or certain types of errors can be prioritized to reduce overall losses. During the training process, by calculating the gradient of the loss function with respect to the model parameters, the update direction and step size of the parameters can be determined. The gradient information can guide the optimization algorithm to adjust the model parameters more efficiently, thereby accelerating the training process and improving the convergence speed of the model. Reasonable choice of loss function can also reduce the computational complexity and memory consumption during training, further reducing training costs.

[0048] S500, collecting environmental data to be calculated, processing and analyzing the environmental data to be calculated using the target model, and obtaining foundation pit depth data.

[0049] It should be noted that the foundation pit environment is complex and changeable. In harsh environments, if we rely on manual field measurements, the anomalies and noise of the data are often more complex and changeable, and traditional detection methods may be difficult to accurately identify. For example, in extreme weather conditions, the measurement equipment may read abnormally or lose data. In complex terrain environments, the signal may be interfered with, resulting in a decrease in data quality. By training the model and using big data and advanced machine learning algorithms to measure the depth of the foundation pit, more accurate and reliable data detection and analysis can be achieved. The machine learning-based model can automatically adapt to data changes under different environmental conditions by learning the laws and features in a large amount of historical data, thereby improving the accuracy of data detection. The target model can analyze and process a large amount of data in a short time, improve the efficiency of environmental detection, and reduce the risks and losses caused by data delays or improper processing. At the same time, the target model also has a strong generalization ability, which means that the model can not only perform well on the specific data set learned during the training process, but also maintain stable performance on new data.

[0050] The method provided in the first aspect can solve the problem that traditional foundation pit depth detection methods mainly rely on manual field measurement, which is time-consuming and labor-intensive, and is easily affected by human factors during the measurement process, thus leading to errors in the measurement results, which may in turn affect the accuracy of subsequent construction decisions. The purpose of accurately measuring the depth of foundation pits under complex geological conditions is achieved, and the efficiency, safety and accuracy of detection are improved.

[0051] In a possible implementation, the acquisition of the virtual environment data includes: collecting actual foundation pit environment data, constructing a virtual environment based on the actual foundation pit environment data, and acquiring the virtual environment data based on the virtual environment.

[0052] It should be noted that the actual foundation pit environment data is collected, and based on the actual foundation pit environment data, the virtual environment is constructed by building virtual environment components. The virtual environment allows developers to quickly build and modify the system without waiting for the physical environment to be prepared, which improves work efficiency. Among them, the construction of the virtual environment can select but is not limited to the following components: terrain generator, underground pipeline simulator. In traditional methods, collecting data on the real foundation pit environment often faces many challenges, such as high costs, limited experimental conditions, and historical events that are difficult to replicate. These limitations often lead to scarce data samples, making it difficult to fully cover all possible working conditions and boundary conditions, which in turn limits the training effect and generalization performance of machine learning models. By constructing a virtual environment and setting high-precision mathematical models and physical parameters, it is possible to simulate various foundation pit construction scenarios from shallow foundation pits to deep foundation pits, from soft soil foundations to hard rock foundations, from dry environments to extreme weather conditions. It can accurately simulate and reproduce the foundation pit environment under a variety of complex geological, climatic and construction conditions. These simulated environments are not only close to reality, but also generate data sets covering a wide range of working conditions by simulating different geological structures, soil moisture content, groundwater level, foundation pit size, support structure type and construction sequence. It enriches the diversity of data samples and increases the amount of available data for model training, thereby effectively enhancing the generalization ability of the model, enabling the model to provide accurate and reliable prediction results based on its extensive training experience, and showing higher robustness and accuracy when facing the complex and changeable foundation pit environment in the real world.

[0053] In one possible implementation, the process of using the target model to process and analyze the environmental data to be calculated to obtain the foundation pit depth data also includes: displaying the foundation pit depth data on an operation interface; the operation interface is also used to manage and view historical foundation pit depth data.

[0054] In some embodiments, the foundation pit depth data is displayed on the operation interface, and the data can be presented intuitively, so that the tester can quickly obtain and process the main information and trends of the data, improve the readability of the data, enhance the tester's perception and understanding of the data, and by constantly updating and displaying the latest test data, the tester can timely understand the state of the foundation pit environment, so as to make targeted adjustments and optimizations. The operation interface is also used to manage and view historical foundation pit depth data, wherein the management data includes parameter settings for foundation pit environment detection, and the parameters are customized according to the actual needs of foundation pit depth detection, so as to meet different test needs and improve the flexibility of the test; and by viewing the historical foundation pit depth data, potential safety hazards in the construction environment can be discovered, and based on the analysis of viewing the historical foundation pit depth data, a suitable construction plan can be formulated, such as: selecting a suitable excavation method, support structure and precipitation measures; understanding the changes in the groundwater level, so as to take drainage or precipitation measures in time to prevent floods from adversely affecting the foundation pit construction; evaluating the stability of the foundation pit, and judging whether the foundation pit has abnormal conditions such as settlement or deformation, etc.

[0055] In a possible implementation, the collecting of the environmental data to be calculated includes: collecting environmental data of the environment using a variety of sensors to obtain the environmental data to be calculated.

[0056] Among them, it should be noted that the sensor can monitor various parameters in the environment in real time and provide accurate data support. Modern sensors use advanced sensing technology, such as optical sensing, laser ranging, piezoelectric sensing, etc., with high precision and high sensitivity, and can accurately capture small changes in the environment, thereby improving the accuracy of data collection. At the same time, traditional environmental data collection requires the deployment of multiple devices, and often requires manual data collection and processing, while sensors reduce equipment requirements and labor costs through integrated and automated data collection. Sensors can automatically complete data collection, transmission and analysis, greatly reducing the cost and difficulty of manual operation. The integrated use of multiple sensors in the embodiment of the present application can provide more comprehensive and comprehensive environmental information. A single sensor can usually only provide partial information of the object being measured, and the integrated system of multiple sensors has better adaptability and flexibility. Different sensors can be used as different detection means to display different observation information, thereby adapting to different environmental test needs, and can also use the information collected by each sensor to complement each other, thereby more comprehensively describing or explaining the object being measured. This comprehensiveness helps testers to understand the environmental conditions more accurately and provide more sufficient data support for decision-making. Using multiple sensors to collect environmental data can collect environmental parameters at one time, improving collection efficiency and accuracy. In addition, when a single sensor fails, it can also be described by the correlation between the information obtained by other sensors, ensuring the stability and reliability of the system.

[0057] In a possible implementation, the step of adjusting the parameters of the initial model using the loss function to obtain the target model includes:

[0058] The parameters of the initial model are calculated based on the loss function and the back propagation algorithm to obtain the gradient of each parameter, and the gradient of each parameter is optimized and updated using an optimization algorithm to obtain the target model.

[0059] It should be noted that the loss function is a function used to evaluate the difference between the model prediction results and the actual observed values. This function calculates the error or deviation between the predicted values ​​and the actual values ​​of a single sample or multiple samples. By calculating the value of the loss function, the prediction performance of the model can be understood, and the model parameters can be adjusted accordingly to optimize the prediction results. The back propagation algorithm is used to calculate the gradient of the loss function with respect to the model parameters, and then these gradients are used to optimize and update the model parameters through the optimization algorithm, thereby gradually reducing the value of the loss function and training a target model with superior performance.

[0060] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a foundation pit depth detection system, the system comprising:

[0061] Virtual environment construction module: used to collect actual foundation pit environment data, construct a virtual environment based on the actual foundation pit environment data, and collect virtual environment data based on the virtual environment.

[0062] It should be noted that the actual foundation pit environment data is collected, and based on the actual foundation pit environment data, the virtual environment is constructed by building virtual environment components. The virtual environment allows developers to quickly build and modify the system without waiting for the physical environment to be prepared, which improves work efficiency. Among them, the construction of the virtual environment can select but is not limited to the following components: terrain generator, underground pipeline simulator. In traditional methods, collecting data on the real foundation pit environment often faces many challenges, such as high costs, limited experimental conditions, and historical events that are difficult to replicate. These limitations often lead to scarce data samples, making it difficult to fully cover all possible working conditions and boundary conditions, which in turn limits the training effect and generalization performance of machine learning models. By constructing a virtual environment and setting high-precision mathematical models and physical parameters, it is possible to simulate various foundation pit construction scenarios from shallow foundation pits to deep foundation pits, from soft soil foundations to hard rock foundations, from dry environments to extreme weather conditions. It can accurately simulate and reproduce the foundation pit environment under a variety of complex geological, climatic and construction conditions. These simulated environments are not only close to reality, but also generate data sets covering a wide range of working conditions by simulating different geological structures, soil moisture content, groundwater level, foundation pit size, support structure type and construction sequence. It enriches the diversity of data samples and increases the amount of available data for model training, thereby effectively enhancing the generalization ability of the model, enabling the model to provide accurate and reliable prediction results based on its extensive training experience, and showing higher robustness and accuracy when facing the complex and changeable foundation pit environment in the real world.

[0063] A training sample set construction module is used to construct a training sample set, wherein the training sample set includes the virtual environment data and the actual foundation pit depth corresponding to the virtual environment data.

[0064] Virtual data processing module: used to construct an initial model to process the virtual environment data to obtain the predicted foundation pit depth.

[0065] Loss function calculation module: used to determine the loss function based on the actual foundation pit depth and the predicted foundation pit depth.

[0066] It should be noted that the loss function provides a clear optimization goal for the model. During the training process, the model gradually improves its prediction ability by constantly adjusting its parameters to minimize the value of the loss function. This optimization process ensures that the model can learn the potential laws and characteristics in the data. By determining the model loss, the difference between the model prediction results and the actual values ​​can be quantified, thereby guiding the adjustment of the model parameters so that the model gradually approaches the distribution of the real data. At the same time, during the training process, the loss function can be used to observe the trend of the loss value, determine whether the model is in a state of overfitting, underfitting or normal learning, and then take corresponding measures to adjust it. Determining the model loss also helps to select a suitable optimization algorithm and learning rate to accelerate the training process and improve the convergence speed of the model. Different loss functions may have different requirements for the selection of optimization algorithms and the setting of learning rates. By reasonably determining the loss function, the optimization algorithm and learning rate can be better matched, thereby improving the training efficiency.

[0067] Parameter adjustment module: used to adjust the parameters of the initial model using the loss function to obtain the target model.

[0068] It should be noted that the loss function provides a clear guide for adjusting model parameters. During the training process, the loss function provides a clear indicator for evaluating model performance by quantifying the difference between the model prediction results and the actual values. This quantitative indicator can guide the algorithm to iteratively adjust model parameters, such as weights and biases, to reduce losses and improve prediction performance. Through continuous iterative optimization, the model parameters can gradually approach the optimal solution, thereby improving the prediction accuracy of the model. At the same time, using the loss function to adjust model parameters helps to balance the bias and variance of the model. During the model training process, bias and variance are two important performance indicators. Bias reflects the degree of fit of the model to the training data, while variance reflects the generalization ability of the model to new data. By selecting a suitable loss function and optimizing its parameters, these two indicators can be balanced to a certain extent, so that the model can fully fit the training data and maintain good generalization performance on new data. In addition, the loss function can also affect the behavioral characteristics of the model. Different types of loss functions have different effects on the behavior of the model, such as sensitivity to data outliers, priority for specific types of errors, etc. By selecting a suitable loss function and adjusting its parameters, the model can be more robust in handling outliers in the data, or certain types of errors can be prioritized to reduce overall losses. During the training process, by calculating the gradient of the loss function with respect to the model parameters, the update direction and step size of the parameters can be determined. The gradient information can guide the optimization algorithm to adjust the model parameters more efficiently, thereby accelerating the training process and improving the convergence speed of the model. Reasonable choice of loss function can also reduce the computational complexity and memory consumption during training, further reducing training costs.

[0069] Sensor module: used to collect environmental data to be calculated.

[0070] Real-time data processing module: used to process and analyze the environmental data to be calculated using the target model to obtain foundation pit depth data.

[0071] It should be noted that the foundation pit environment is complex and changeable. In harsh environments, if we rely on manual field measurements, the anomalies and noise of the data are often more complex and changeable, and traditional detection methods may be difficult to accurately identify. For example, in extreme weather conditions, the measurement equipment may read abnormally or lose data. In complex terrain environments, the signal may be interfered with, resulting in a decrease in data quality. By training the model and using big data and advanced machine learning algorithms to measure the depth of the foundation pit, more accurate and reliable data detection and analysis can be achieved. The machine learning-based model can automatically adapt to data changes under different environmental conditions by learning the laws and features in a large amount of historical data, thereby improving the accuracy of data detection. The target model can analyze and process a large amount of data in a short time, improve the efficiency of environmental detection, and reduce the risks and losses caused by data delays or improper processing. At the same time, the target model also has a strong generalization ability, which means that the model can not only perform well on the specific data set learned during the training process, but also maintain stable performance on new data.

[0072] Interactive module: used to display the foundation pit depth data and manage and view historical foundation pit depth data.

[0073] The system provided by the second aspect can solve the problem that traditional foundation pit depth detection methods mainly rely on manual field measurement, which is time-consuming and labor-intensive, and is easily affected by human factors during the measurement process, thus leading to errors in the measurement results, which may in turn affect the accuracy of subsequent construction decisions. The purpose of accurately measuring the depth of foundation pits under complex geological conditions is achieved, and the efficiency, safety and accuracy of detection are improved.

[0074] In a possible implementation, the system further includes: an alarm module, configured to analyze the foundation pit depth data and issue an alarm signal when the foundation pit depth data is abnormal.

[0075] In some embodiments, the foundation pit project is an important part of the underground project, and its depth and stability are directly related to the safety of the entire project. When the foundation pit depth data is abnormal, it may mean that there are safety hazards in the support structure of the foundation pit or the surrounding environment. The timely alarm of the alarm module can enable the tester to quickly notice these abnormal changes, thereby timely discovering potential safety hazards, which helps the tester to take measures in advance, such as strengthening support, adjusting construction plans, etc., to prevent the occurrence of safety accidents, ensure the life safety of the tester, and avoid damage to the surrounding environment and buildings.

[0076] In one possible implementation, the system also includes: an encryption module, used to filter out private data information from the foundation pit depth data or the environmental data to be calculated, encrypt the private data information to obtain encrypted data information, and store the encrypted data information in an encryption space.

[0077] It should be noted that during the data transmission process, the encryption module can prevent data from being tampered with. Through encryption, the authenticity and integrity of the data can be verified to ensure that the data is not maliciously modified or damaged during the transmission process, prevent losses due to data leakage or damage, and ensure that the data is not intercepted or read by unauthorized persons, thereby ensuring data security. At the same time, when data is lost or damaged, the encrypted backup data can also be used to restore the original data more safely, reducing the risk of data loss.

[0078] The present application also provides an electronic device, such as Figure 2 As shown, the electronic device 1400 includes:

[0079] one or more processors 1410;

[0080] The memory 1420 stores one or more programs. When the one or more programs are executed by the one or more processors 1410, the one or more processors 1410 implement the foundation pit depth detection method provided by any embodiment of the present application.

[0081] The memory 1420, as a non-transient network system, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory 1420 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1420 may optionally include a memory 1420 remotely arranged relative to the processor 1410, and these remote memories 1420 may be connected to the processor 1410 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0082] The memory 1420 may be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1420 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1420, and the processor 1410 calls and executes the methods of the embodiments of this application.

[0083] The processor 1410 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0084] In some embodiments, the electronic device further comprises:

[0085] Input / output interface, used to realize information input and output;

[0086] Communication interface, used to realize communication interaction between this device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0087] A bus that transmits information between various components of the device (e.g., processor 1410, memory 1420, input / output interface, and communication interface);

[0088] The processor 1410 , the memory 1420 , the input / output interface and the communication interface can be connected to each other in communication within the device via a bus.

[0089] An embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the foundation pit depth detection method provided by any embodiment of the present application.

[0090] An embodiment of the present application also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the foundation pit depth detection method provided by any embodiment of the present application.

[0091] The system architecture and application scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of the system architecture and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are equally applicable to similar technical problems.

[0092] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0093] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0094] The above describes some embodiments of the present application with reference to the accompanying drawings, but does not limit the scope of the present invention. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present invention shall be within the scope of the present application.

[0095] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0096] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0097] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0098] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0099] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for detecting the depth of a foundation pit, characterized in that: The method comprises: Constructing a training sample set, wherein the training sample set includes virtual environment data and an actual foundation pit depth corresponding to the virtual environment data; Constructing an initial model to process the virtual environment data to obtain a predicted foundation pit depth; Determine a loss function based on the actual foundation pit depth and the predicted foundation pit depth; Using the loss function to adjust the parameters of the initial model to obtain a target model; The environmental data to be calculated are collected, and the environmental data to be calculated are processed and analyzed using the target model to obtain foundation pit depth data.

2. The method according to claim 1, characterized in that The acquisition of the virtual environment data includes: collecting actual foundation pit environment data, constructing a virtual environment based on the actual foundation pit environment data, and collecting the virtual environment data based on the virtual environment.

3. The method according to claim 1, characterized in that After the target model is used to process and analyze the environmental data to be calculated to obtain the foundation pit depth data, the following further comprises: Display the foundation pit depth data on the operation interface; The operation interface is also used to manage and view historical foundation pit depth data.

4. The method according to claim 1, characterized in that The collecting of the environmental data to be calculated includes: using a variety of sensors to collect environmental data of the environment to obtain the environmental data to be calculated.

5. The method according to claim 1, characterized in that The step of adjusting the parameters of the initial model by using the loss function to obtain the target model comprises: The parameters of the initial model are calculated based on the loss function and the back propagation algorithm to obtain the gradient of each parameter, and the gradient of each parameter is optimized and updated using an optimization algorithm to obtain the target model.

6. A foundation pit depth detection system, characterized in that: The system comprises: A virtual environment construction module: used to collect actual foundation pit environment data, construct a virtual environment based on the actual foundation pit environment data, and collect virtual environment data based on the virtual environment; A training sample set construction module: used to construct a training sample set, wherein the training sample set includes the virtual environment data and the actual foundation pit depth corresponding to the virtual environment data; Virtual data processing module: used to construct an initial model to process the virtual environment data to obtain the predicted foundation pit depth; A loss function calculation module: used to determine a loss function based on the actual foundation pit depth and the predicted foundation pit depth; Parameter adjustment module: used to adjust the parameters of the initial model using the loss function to obtain the target model; Sensor module: used to collect environmental data to be calculated; Real-time data processing module: used to process and analyze the environmental data to be calculated using the target model to obtain foundation pit depth data; Interactive module: used to display the foundation pit depth data and manage and view historical foundation pit depth data.

7. The system according to claim 6, characterized in that The system further comprises: an alarm module, which is used for analyzing the foundation pit depth data and sending an alarm signal when the foundation pit depth data is abnormal.

8. The system according to claim 6, characterized in that The system also includes: an encryption module, which is used to filter out private data information from the foundation pit depth data or the environmental data to be calculated, encrypt the private data information to obtain encrypted data information, and store the encrypted data information in an encryption space.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the foundation pit depth detection method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the foundation pit depth detection method according to any one of claims 1 to 5 is implemented.

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