Foundation pit support monitoring method and equipment based on dynamic threshold value and medium thereof

Abstract

The invention relates to the technical field of foundation pit monitoring, in particular to a foundation pit support monitoring method based on a dynamic threshold value, which comprises the following steps: constructing characteristic variables, building a linear regression model, and training the linear regression model to obtain a trained linear regression model; obtaining data of normal or abnormal supporting, inputting the data into the trained linear regression model to obtain a first threshold value and a second threshold value of normal or abnormal supporting of the foundation pit, and calculating a reference error percentage based on the first threshold value and the second threshold value; obtaining initial data after foundation pit support construction is completed, and inputting the initial data into the trained linear regression model to obtain a standard threshold value; assigning values to the characteristic variables in combination with the actual state of the foundation pit support, inputting the values to the trained linear regression model for calculation to obtain an actual threshold value, and calculating an actual error percentage based on the standard threshold value and the actual threshold value; and comparing the actual error percentage with the reference error percentage, and monitoring the foundation pit support, so that the early warning sensitivity and the monitoring accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of foundation pit monitoring, and particularly to a foundation pit support monitoring method, device and medium based on a dynamic threshold. Background Art

[0002] Foundation pit support is a support, reinforcement and protection measure for the side wall and the surrounding environment to ensure the safety of underground structure construction and the surrounding environment of the foundation pit, which involves aspects such as soil stability, deformation of the support structure, and protection of the surrounding environment. Traditional foundation pit support monitoring methods mainly rely on manual regular inspections and static monitoring equipment, which have problems such as low efficiency, discontinuous data, and slow response.

[0003] With the rapid development of Internet of Things technology, sensor technology and data processing technology, the intelligent monitoring of foundation pit support structures has become an effective way to solve these problems. The intelligent monitoring of foundation pit support structures dynamically collects foundation pit data through monitoring equipment and compares it with a fixed threshold to achieve monitoring and judge the deformation of the foundation pit. This method has situations of insufficient early warning sensitivity or false alarms and missed alarms, and the monitoring accuracy is low. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: in view of the technical problems of insufficient early warning sensitivity or false alarms and missed alarms, and low monitoring accuracy existing in the prior art, the present invention provides a foundation pit support monitoring method based on a dynamic threshold, which improves the early warning sensitivity and monitoring accuracy.

[0005] The technical solution adopted by the present invention to solve its technical problems is: a foundation pit support monitoring method based on a dynamic threshold, the method includes the following steps:

[0006] S1, constructing characteristic variables, constructing characteristic variables according to soil property factors, construction stage factors and historical data factors;

[0007] S2, building a linear regression model;

[0008] S3, model training, obtaining monitoring data, constructing a data set, inputting the data set into the linear regression model, training the linear regression model, and obtaining the trained linear regression model;

[0009] S4, calculating the reference error percentage, obtaining the data information of normal support from the monitoring data, and inputting it into the trained linear regression model to obtain the first threshold under normal foundation pit support conditions;

[0010] Obtaining the data information of abnormal support from the monitoring data, and inputting it into the trained linear regression model to obtain the second threshold under abnormal foundation pit support conditions;

[0011] Calculate the reference error percentage based on the first threshold and the second threshold;

[0012] S5. Calculation of the actual error percentage: Obtain the initial data information after the foundation pit support construction is completed and input it into the trained linear regression model to obtain the standard threshold;

[0013] Assign values to the characteristic variables in combination with the actual state of the foundation pit support and input them into the trained linear regression model for calculation to obtain the actual threshold;

[0014] Calculate the actual error percentage based on the standard threshold and the actual threshold;

[0015] S6. Compare the actual error percentage with the reference error percentage. If the actual error percentage is greater than the reference error percentage, it is determined that the foundation pit support is deformed and an alarm is given.

[0016] According to an embodiment of the present invention, the characteristic variables include: soil property type Csoil, construction stage Cstage, historical data coefficient Chistory, displacement change rate Cdisplacement, soil pressure change rate Cpressure, monitoring point location coefficient Clocation, and climate condition coefficient Cclimate.

[0017] According to an embodiment of the present invention, before building the linear regression model, the Pearson correlation coefficient is used to obtain the characteristics with strong correlation with the threshold.

[0018] According to an embodiment of the present invention, the formula of the linear regression model is expressed as:

[0019] Talarm = α·Csoil + β·Cstage + γ·Chistory + δ·Cdisplacement + λ·Cpressure + ρ·Clocation + τ·Cclimate + ∈;

[0020] Among them, α, β, γ, δ, λ, ρ, τ are coefficients obtained by training the linear regression model, and ∈ is the error term.

[0021] According to an embodiment of the present invention, the calculation formula of the reference error percentage is: (|the first threshold - the second threshold| / the first threshold) * 100%.

[0022] According to an embodiment of the present invention, the calculation formula of the actual error percentage is: (|the standard threshold - the actual threshold| / the standard threshold) * 100%.

[0023] According to an embodiment of the present invention, the soil type Csoil includes: cohesive soil, silt or sand; the construction stage Cstage includes the initial stage, the intermediate stage or the later stage.

[0024] According to an embodiment of the present invention, in the step S3, the monitoring data is processed by a standardization method before constructing the data set.

[0025] A computer device includes:

[0026] A processor;

[0027] A memory for storing executable instructions;

[0028] Wherein, the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the foundation pit support monitoring method based on a dynamic threshold as described above.

[0029] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is caused to implement the foundation pit support monitoring method based on a dynamic threshold as described above.

[0030] The beneficial effects of the present invention are:

[0031] 1) A foundation pit support monitoring method based on a dynamic threshold of the present invention can automatically adjust the monitoring threshold according to changes in different soil properties and construction stages. Compared with the traditional fixed-threshold monitoring system, the dynamic adjustment of the threshold can more accurately reflect the actual changes of the foundation pit. In addition, by calculating the actual error percentage and the reference error percentage and comparing the actual error percentage with the reference error percentage, the monitoring of the foundation pit support is realized, which can reduce false alarms and missed alarms and improve the accuracy and early warning sensitivity of the foundation pit monitoring;

[0032] 2) A foundation pit support monitoring method based on a dynamic threshold of the present invention automatically adjusts the measurement threshold according to the construction progress, can flexibly respond to different stages of the foundation pit construction, effectively reduces potential safety hazards caused by improper threshold setting, and reduces the risk of the foundation pit support structure;

[0033] 3) Since the threshold can be dynamically adjusted according to the actual situation, unnecessary monitoring and alarms can be reduced, greatly saving the costs of monitoring and maintenance. At the same time, the precise threshold adjustment reduces manual intervention and reduces the costs of personnel inspection and on-site management. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below with reference to the drawings and embodiments.

[0035] Figure 1 It is a schematic diagram of the method of Embodiment 1 of the present invention.

[0036] Figure 2 It is a schematic structural diagram of the computer device according to the second embodiment of the present invention.

[0037] In the figure, 10 is the computer device; 1002 is the processor; 1004 is the memory; 1006 is the transmission device. Detailed implementation manners

[0038] Now, the present invention will be further described in detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0039] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, so it should not be construed as a limitation of the present invention. In addition, the features defined as "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0040] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0041] Embodiment 1

[0042] The embodiment of the present application provides a foundation pit support monitoring method based on a dynamic threshold, as Figure 1 shown, the method includes the following steps:

[0043] S1. Construct feature variables. Construct feature variables based on soil property factors, construction stage factors, and historical data factors. The feature variables include: soil type Csoil, construction stage Cstage, historical data coefficient Chistory, displacement change rate Cdisplacement, soil pressure change rate Cpressure, monitoring point location coefficient Clocation, and climate condition coefficient Cclimate.

[0044] S2. Build a linear regression model.

[0045] S3. Model training. Obtain monitoring data and construct a data set. Input the data set into the linear regression model and train the linear regression model to obtain a trained linear regression model. Further, by arranging pressure sensors and displacement sensors at the bottom and side walls of the foundation pit, obtain the monitoring data of the foundation pit support. The monitoring data includes pressure data and displacement data.

[0046] S4. Calculation of the reference error percentage. Obtain the data information of normal support from the monitoring data and input it into the trained linear regression model to obtain the first threshold under normal conditions of the foundation pit support.

[0047] Obtain the data information of abnormal support from the monitoring data and input it into the trained linear regression model to obtain the second threshold under abnormal conditions of the foundation pit support.

[0048] Calculate the reference error percentage based on the first threshold and the second threshold. The calculation formula is:

[0049] (|First threshold - Second threshold| / First threshold) * 100%.

[0050] S5. Calculation of the actual error percentage. Obtain the initial data information after the completion of the foundation pit support construction and input it into the trained linear regression model to obtain the standard threshold.

[0051] Assign values to the feature variables in combination with the actual state of the foundation pit support and input them into the trained linear regression model for calculation to obtain the actual threshold.

[0052] Calculate the actual error percentage based on the standard threshold and the actual threshold. The calculation formula is:

[0053] (|Standard threshold - Actual threshold| / Standard threshold) * 100%.

[0054] S6. Compare the actual error percentage with the reference error percentage. If the actual error percentage is greater than the reference error percentage, it is determined that the foundation pit support is deformed and an alarm is given.

[0055] In this embodiment, before building a linear regression model, the Pearson correlation coefficient is used to obtain the features with strong correlation with the threshold. For example, by calculating the correlation coefficient between each feature variable and the target variable (threshold), the feature variables with high correlation are obtained, and the relevant feature variables of the soil properties related to the foundation pit support monitoring threshold are screened out, and the variables with less influence are removed, so as to facilitate the subsequent steps to use the feature variables with high correlation to train the linear regression model.

[0056] Specifically, it includes the following steps:

[0057] Obtain the DataFrame data, and the DataFrame data contains all feature variables and the target variable;

[0058] Calculate the correlation matrix between the feature variables and the target variable;

[0059] Output the correlation coefficient with the target variable, see the following table:

[0060]

[0061] According to the correlation analysis, it can be seen that the feature variables most relevant to the threshold are: soil type Csoil, construction stage coefficient Cstage, displacement change rate Cdisplacement, and soil pressure change rate Cpressure.

[0062] Furthermore, the formula of the linear regression model is expressed as:

[0063] Talarm = α·Csoil + β·Cstage + γ·Chistory + δ·Cdisplacement + λ·Cpressure + ρ·Clocation + τ·Cclimate + ∈;

[0064] Among them, α, β, γ, δ, λ, ρ, τ are the coefficients obtained by training the linear regression model, and ∈ is the error term.

[0065] In step S4, through training, α = 20, β = 15, γ = 10, δ = 5, λ = 3, ρ = 7, τ = 4 are obtained.

[0066] In the embodiment, the soil type Csoil includes: cohesive soil, silt or sand, and the influence of soil type on the threshold is reflected by the soil property coefficient. The stabilities of different soil types are different, and the soil property coefficients are assigned according to the actual soil of the foundation pit support:

[0067]

[0068]

[0069] In an embodiment, the construction stage Cstage includes an initial stage, a middle stage, or a late stage. The influence of different construction stages on the threshold is reflected by the construction stage coefficient. Construction in different stages will have different impacts on the soil body and the structure. The construction stage coefficient is assigned in combination with the actual construction state of the foundation pit support:

[0070]

[0071] In an embodiment, the historical data coefficient Chistory includes frequent alarms and few alarms. The historical data coefficient adjusts the alarm threshold according to the historical alarm records. Areas with more frequent historical alarm records may have potential safety hazards. The construction stage coefficient is assigned in combination with the historical state of the foundation pit support:

[0072]

[0073] In an embodiment, the displacement change rate Cdisplacement includes large displacement, moderate displacement, or small displacement. The displacement change rate Cdisplacement reflects the deformation speed and amplitude of the soil body. A large displacement may mean that the soil body is unstable. The displacement change rate coefficient is assigned in combination with the displacement change of the foundation pit support:

[0074]

[0075] In an embodiment, the soil pressure change rate Cpressure includes high pressure, moderate pressure, and low pressure. The soil pressure change coefficient reflects the pressure change borne by the soil body. Excessive pressure will increase the risk of soil instability. The soil pressure change rate coefficient is assigned in combination with the soil pressure of the foundation pit support:

[0076]

[0077] In this embodiment, the monitoring point location coefficient Clocation is set based on the actual monitoring point location, including:

[0078] Monitoring points at the center of the foundation pit: The risk is relatively high, and a lower alarm threshold is set. The monitoring point location coefficient Clocation is 1.2;

[0079] Monitoring points at the edge of the foundation pit: The risk is relatively low, and a higher alarm threshold is set. The monitoring point location coefficient Clocation is 1.0;

[0080] Other special locations: It needs to be set according to the actual environment. The monitoring point location coefficient Clocation is 1.1.

[0081] In this embodiment, the climate condition coefficient Cclimate is also set based on the actual climate conditions, including:

[0082] Humid climate: Due to the increased permeability of the soil mass, the alarm threshold is relatively low, with a coefficient of 1.2;

[0083] Dry climate: Relatively stable, with a higher alarm threshold and a coefficient of 1.0;

[0084] Warm climate: Smaller climate change, with a coefficient of 1.1.

[0085] In the embodiment, in step S3, before constructing the data set, the monitoring data is also processed using a standardization method. Further, during the standardization process, we use the Z-score standardization method. The processing formula is:

[0086] X standardized = (X - μ) / σ

[0087] Where: X is the original data, μ is the mean of the feature, and σ is the standard deviation of the feature. By using the standardization method, each feature of the monitoring data is transformed into a distribution with a mean of 0 and a standard deviation of 1.

[0088] Test verification: To guide the setting of the dynamic threshold, the following table shows the alarm thresholds of several historical projects. These projects have similar construction conditions, soil types, and construction stages, so they can provide a reference benchmark for the current project.

[0089]

[0090] Combined with the actual state of the foundation pit support of historical projects, the characteristic variables are assigned values, and the calculated threshold is verified. The threshold obtained through model calculation is:

[0091] alarm = 20·1.5 + 15·1.1 + 10·1.2 + 5·0.6 + 3·250 + 7·1.1 + 4·1.0 = 822.2

[0092] Compare this value with the threshold of historical projects:

[0093]

[0094] Through comparison, the calculated threshold is very close to the historical data. Especially for projects with sandy soil and in the initial stage, the error between the model prediction results and the historical project data is very small.

[0095] For project A, the historical alarm threshold is 800, while the predicted value is 822.2, with a difference of approximately 2.8%.

[0096] For project D, the historical alarm threshold is 820, and the predicted value is 822.2, with a difference of 0.3%.

[0097] The calculated threshold is accurate and reasonable, and can effectively provide a reasonable threshold for foundation pit support by referring to historical data.

[0098] In summary:

[0099] A foundation pit support monitoring method based on dynamic threshold in this embodiment can automatically adjust the monitoring threshold according to the changes in different soil properties and construction stages. Compared with the traditional fixed threshold monitoring system, dynamically adjusting the threshold can more accurately reflect the actual changes of the foundation pit. In addition, by calculating the actual error percentage and the reference error percentage, and comparing the actual error percentage with the reference error percentage, the monitoring of the foundation pit support can be realized, which can reduce false alarms and missed alarms, improve the accuracy of foundation pit monitoring and the early warning sensitivity; automatically adjusting the measurement threshold according to the construction progress can flexibly respond to different stages of foundation pit construction, effectively reduce potential safety hazards caused by improper threshold setting, and reduce the risk of foundation pit support structure. In addition, since the threshold can be dynamically adjusted according to the actual situation, unnecessary monitoring and alarms can be reduced, greatly saving the costs of monitoring and maintenance. At the same time, precise threshold adjustment reduces manual intervention and the costs of personnel inspection and on-site management.

[0100] Embodiment 2

[0101] An embodiment of the present application provides a computer device, which includes a processor and a memory. At least one instruction or at least one program segment is stored in the memory, and the at least one instruction or the at least one program segment is loaded and executed by the processor to implement a foundation pit support monitoring method based on dynamic threshold as provided in the above method embodiment.

[0102] Figure 2 The figure shows a schematic diagram of the hardware structure of a device for implementing a foundation pit support monitoring method based on dynamic threshold provided in an embodiment of the present application. The device can participate in constituting or include the device or system provided in the embodiment of the present application. As Figure 2 shown, the computer device 10 may include one or more processors 1002 (the processor may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 2 the structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the computer device 10 may further include more or fewer components than those Figure 2 shown in the figure, or have a structure different from that Figure 2The different configurations shown.

[0103] It should be noted that one or more of the above-mentioned processors and / or other data processing circuits can generally be referred to as "data processing circuits" herein. The data processing circuit can be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the computer device 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is a kind of processor control (such as the selection of a variable resistance terminal path connected to an interface).

[0104] The memory 1004 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to a foundation pit support monitoring method based on dynamic thresholds in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, that is, implements the above-mentioned method. The memory 1004 can include a high-speed random access memory, and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 1004 can further include a memory remotely located relative to the processor, and these remote memories can be connected to the computer device 10 through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0105] The transmission device 1006 is used to receive or send data via a network. Specific examples of the above-mentioned network can include the wireless network provided by the communication provider of the computer device 10. In one instance, the transmission device 1006 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 1006 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0106] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables the user to interact with the user interface of the computer device 10 (or mobile device).

[0107] Embodiment 3

[0108] The embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium can be disposed in a server to store at least one instruction or at least one segment of a program related to a foundation pit support monitoring method based on a dynamic threshold in the method embodiments. The at least one instruction or the at least one segment of the program is loaded and executed by the processor to implement the foundation pit support monitoring method based on a dynamic threshold provided in the above method embodiments.

[0109] Optionally, in this embodiment, the above storage medium may be located in at least one of multiple network servers in a computer network. Optionally, in this embodiment, the above storage medium may include, but is not limited to: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0110] Embodiment 4

[0111] The embodiments of the present invention also provide a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the foundation pit support monitoring method based on a dynamic threshold provided in the above various optional implementation manners.

[0112] It should be noted that: the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of the present application are described. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be executed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.

[0113] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0114] Those of ordinary skill in the art can understand that all or part of the steps to implement the above embodiments can be completed by hardware, or can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a disk, an optical disc, etc.

[0115] Taking the above ideal embodiments of the present invention as an inspiration, through the above description, relevant staff can completely make various changes and modifications without departing from the technical idea of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A foundation pit support monitoring method based on dynamic threshold, characterized in that: The method comprises the following steps: S1, construct characteristic variables, based on soil factors, construction stage factors and historical data factors; S2, build a linear regression model; S3, model training, acquiring monitoring data, and constructing a data set, inputting the data set into the linear regression model, training the linear regression model, and obtaining the trained linear regression model; S4, calculating the reference error percentage, obtaining data information of normal support from the monitoring data, and inputting it into the trained linear regression model to obtain a first threshold value under normal foundation pit support conditions; Acquire data information of support abnormality from the monitoring data, and input it into the trained linear regression model to obtain a second threshold value under the condition of foundation pit support abnormality; calculating a reference error percentage based on the first threshold and the second threshold; S5, calculating the actual error percentage, obtaining the initial data information after the foundation pit support construction is completed, and inputting it into the trained linear regression model to obtain the standard threshold; Assigning values ​​to the characteristic variables in combination with the actual state of the foundation pit support, and inputting the values ​​into the trained linear regression model for calculation to obtain the actual threshold value; Calculating an actual error percentage based on the standard threshold and the actual threshold; S6, foundation pit support monitoring, compare the actual error percentage with the reference error percentage, if the actual error percentage is greater than the reference error percentage, it is determined that the foundation pit support is deformed and an alarm is issued.

2. The foundation pit support monitoring method based on dynamic threshold according to claim 1, characterized in that: The characteristic variables include: soil type Csoil, construction stage Cstage, historical data coefficient Chistory, displacement change rate Cdisplacement, soil pressure change rate Cpressure, monitoring point location coefficient Clocation and climate condition coefficient Cclimate.

3. The foundation pit support monitoring method based on dynamic threshold according to claim 2, characterized in that: Before building the linear regression model, the Pearson correlation coefficient is used to obtain characteristic variables with a strong correlation with the threshold.

4. The foundation pit support monitoring method based on dynamic threshold according to claim 3 is characterized in that: The formula of the linear regression model is expressed as: Talarm=α·Csoil+β·Cstage+γ·History+δ·Cdisplacement+λ·Cpressure+ρ·Clocation+τ·Cclimate+∈; Among them, α, β, γ, δ, λ, ρ, τ are coefficients obtained by training the linear regression model, and ∈ is the error term.

5. The foundation pit support monitoring method based on dynamic threshold according to claim 1, characterized in that: The calculation formula of the reference error percentage is: (|first threshold value-second threshold value| / first threshold value)*100%.

6. The foundation pit support monitoring method based on dynamic threshold according to claim 1, characterized in that: The calculation formula of the actual error percentage is: (|standard threshold-actual threshold| / standard threshold)*100%.

7. The foundation pit support monitoring method based on dynamic threshold according to claim 2, characterized in that: The soil type Csoil includes: clay soil, silt soil or sand soil; The construction stage Cstage includes an initial stage, a middle stage or a late stage.

8. The foundation pit support monitoring method based on dynamic threshold according to claim 1, characterized in that: In step S3, the monitoring data is processed using a standardized method before constructing the data set.

9. A computer device, characterized in that: include: processor; A memory for storing executable instructions; Wherein, the processor is used to read the executable instructions from the memory and execute the executable instructions to implement the foundation pit support monitoring method based on dynamic threshold as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the foundation pit support monitoring method based on dynamic thresholds as described in any one of claims 1 to 8.

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