A method, device, equipment and medium for evaluating the stability of a cable trench

Abstract

The present invention discloses a method, device, equipment and medium for evaluating the stability of a cable trench. The method includes calculating the probability distribution data of each random influence variable and the response correlation coefficient between each random influence variable based on the historical measurement data of the random influence variables affecting the stability of the target cable trench; constructing a three-dimensional simulation model of the target cable trench according to the real-time measurement data, optimizing and adjusting the three-dimensional simulation model according to each response correlation coefficient, using the Monte Carlo method to simulate and generate a number of cable trench condition events to calculate the stability index of the target cable trench, and using the stability index to evaluate the state stability of the cable trench. By introducing the Monte Carlo algorithm and combining probability analysis and random simulation techniques, the present invention comprehensively considers the randomness and uncertainty of the factors affecting the stability of the cable trench, avoids the deviation of the evaluation result caused by overly simplified assumption conditions, and significantly improves the accuracy and reliability of the stability evaluation of the cable trench.

Description

Technical Field

[0001] The present invention relates to the technical field of power facility monitoring, and in particular, to a method, device, equipment and medium for evaluating the stability of cable trenches. Background Art

[0002] With the rapid development of modern urban infrastructure construction, power, telecommunications and other types of underground pipeline systems increasingly rely on cable trenches for laying. As an important underground facility, cable trenches play a key role in ensuring the normal operation of the city.

[0003] Traditional methods for analyzing the stability of cable trenches are mainly based on deterministic models, assuming that all input parameters are known and determined. When facing complex geological and construction environments, the stability evaluation results provided by these methods often have obvious limitations. The spatial variability of geological conditions, the physical and mechanical parameters of soil, the fluctuation of groundwater level, the change of construction load, and the deformation of the support structure all have strong randomness. Traditional deterministic analysis methods are difficult to fully consider these uncertain factors, resulting in the evaluation results may underestimate the risks and cannot accurately reflect the stability state of cable trenches under actual working conditions.

[0004] Therefore, how to improve the accuracy of the evaluation of the stability of cable trenches has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for evaluating the stability of cable trenches to monitor and evaluate the state of cable trenches in real time.

[0006] To solve the above technical problems, an embodiment of the present invention provides a method for evaluating the stability of a cable trench, including:

[0007] Determine the influencing variables that affect the stability of the target cable trench, where the influencing variables at least include environmental influencing variables.

[0008] Obtain the historical measurement data corresponding to each of the influencing variables, and calculate the probability distribution data of each of the influencing variables and the response correlation coefficients between the influencing variables according to the historical measurement data.

[0009] Obtain the real-time measurement data corresponding to the influencing variables of the target cable trench, construct a three-dimensional simulation model of the target cable trench according to the real-time measurement data, quantitatively adjust the real-time measurement data corresponding to the influencing variables of the three-dimensional simulation model according to the response correlation coefficients, obtain the response difference data of the three-dimensional simulation model for each of the influencing variables, and optimize and adjust the three-dimensional simulation model based on the response difference data.

[0010] Generate a number of cable trench working condition events by Monte Carlo simulation, apply each of the cable trench working condition events to the 3D simulation model, and generate a stability index set based on the feedback of the 3D simulation model.

[0011] During the actual monitoring of the cable trench, evaluate the state stability of the cable trench using the influencing variables corresponding to the stability index set to obtain the cable trench stability evaluation result.

[0012] Furthermore, the influencing variables further include cable operation influencing variables.

[0013] The environmental influencing variables include at least one of trench size, soil physical properties, groundwater level, load, support parameters, and adjacent buildings.

[0014] The cable operation influencing variables include at least one of cable operation temperature, degree of insulation layer aging, and vibration frequency of the cable.

[0015] Furthermore, obtaining the historical measurement data of each of the influencing variables and calculating the probability distribution data of each of the influencing variables and the response correlation coefficients between the influencing variables according to the historical measurement data includes:

[0016] Obtain the historical measurement data of each of the influencing variables.

[0017] Use the maximum likelihood estimation method to perform probability distribution fitting on the historical measurement data to obtain the probability distribution data of each of the influencing variables.

[0018] Use the correlation analysis method to calculate the response relationship between the historical measurement data to obtain the response correlation coefficients between the influencing variables.

[0019] Furthermore, obtaining the historical measurement data of each of the influencing variables includes:

[0020] According to the historical exploration report and construction records, obtain the first historical data of each of the influencing variables.

[0021] Calibrate the first historical data according to the sensor type corresponding to each of the first historical data to obtain the second historical data.

[0022] Use the linear interpolation method to perform missing value imputation on the second historical data to obtain the third historical data.

[0023] Use the moving average method to suppress the fluctuations of the third historical data to obtain the historical measurement data.

[0024] Further, calculating the response relationship between each of the historical measurement data by using the correlation analysis method to obtain the response correlation coefficients between the influence variables, including:

[0025] Construct a measurement data matrix composed of each of the historical measurement data.

[0026] Calculate the Pearson correlation coefficient of each of the historical measurement data according to the measurement data matrix as the response correlation coefficient.

[0027] Further, quantitatively adjusting each of the influence variables of the three-dimensional simulation model according to each of the response correlation coefficients to obtain the response difference data of the three-dimensional simulation model for each of the influence variables, including:

[0028] Determine the variable adjustment order according to the dependence relationship between the influence variables and the magnitude of the response correlation coefficients.

[0029] Perform step-by-step quantitative adjustment on each of the influence variables according to the variable adjustment order, and the adjustment value is:

[0030]

[0031] Wherein, is the single adjustment value of the influence variable , is the influence variable and another influence variable the response correlation coefficient between them, is the adjustment level.

[0032] Calculate according to the response difference value of the three-dimensional simulation model after quantitatively adjusting each of the influence variables to obtain the response difference data of the three-dimensional simulation model for each of the influence variables.

[0033] Further, using the stability index to evaluate the state stability of the cable trench to obtain the cable trench stability evaluation result, including:

[0034] Use the stability index to evaluate the state stability of the cable trench during actual operation, and generate a risk level evaluation report according to the evaluation result; the risk level evaluation report at least includes the cable trench instability risk evaluation result, the visualization probability distribution map and the cumulative probability curve.

[0035] Obtain the corresponding risk control plan through big data analysis according to the risk level evaluation report.

[0036] Another embodiment of the present invention provides a cable trench stability evaluation device, including:

[0037] A variable determination module for determining influence variables that affect the stability of a target cable trench, where the influence variables at least include environmental influence variables.

[0038] A probability calculation module for obtaining historical measurement data corresponding to each of the influence variables, and calculating probability distribution data for each of the influence variables and response correlation coefficients between the influence variables based on the historical measurement data.

[0039] A model optimization module for obtaining real-time measurement data corresponding to the influence variables of the target cable trench, constructing a three-dimensional simulation model of the target cable trench based on the real-time measurement data, quantitatively adjusting the real-time measurement data corresponding to the influence variables of the three-dimensional simulation model according to the response correlation coefficients to obtain response difference data of the three-dimensional simulation model for each of the influence variables, and optimizing and adjusting the three-dimensional simulation model based on the response difference data.

[0040] An index calculation module for using the Monte Carlo method to simulate and generate a number of cable trench working condition events, applying the cable trench working condition events to the three-dimensional simulation model, and generating a stability index set based on the feedback of the three-dimensional simulation model.

[0041] A state evaluation module for evaluating the state stability of the cable trench by using the influence variables corresponding to the stability index set during the actual monitoring of the cable trench to obtain a cable trench stability evaluation result.

[0042] Another embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the cable trench stability evaluation method described above is implemented.

[0043] Another embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the device where the computer-readable storage medium is located executes the computer program, the cable trench stability evaluation method described above is implemented.

[0044] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0045] (1) By introducing the Monte Carlo algorithm and combining probability analysis and stochastic simulation techniques, comprehensively considering the randomness and uncertainty of the factors affecting the stability of the cable trench, avoiding the deviation of the evaluation result caused by overly simplified assumptions, and significantly improving the accuracy and reliability of the cable trench stability evaluation.

[0046] (2) Through a large number of random simulation calculations, the probability distribution of the cable trench stability index is obtained, and based on this, the instability probability is quantitatively evaluated and the risk level is divided, which can provide more intuitive risk information for engineering personnel and help them make more scientific and reasonable decisions in the design, construction and management of cable trenches. Description of the Drawings

[0047] Figure 1 It is a flowchart of the steps of the cable trench stability evaluation method provided by the embodiment of the present invention;

[0048] Figure 2 It is a structural block diagram of the cable trench stability evaluation device provided by the embodiment of the present invention;

[0049] Figure 3 It is a structural diagram of the computer device provided by the embodiment of the present invention. Specific Embodiments

[0050] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0051] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0052] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0053] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0054] An embodiment of the present invention provides a method for evaluating the stability of a cable trench. Specifically, please refer to Figure 1 , Figure 1 which shows the step flow chart of the method for evaluating the stability of the cable trench in one of the embodiments of the present invention, including steps S11 to S15:

[0055] Step S11: Determine the influencing variables that affect the stability of the target cable trench.

[0056] Among them, the influencing variables include environmental influencing variables and cable operation influencing variables. The stability of the cable trench is affected by a variety of factors, mainly including environmental factors and cable operation factors. Identifying these influencing variables is the basis for carrying out stability evaluation.

[0057] The environmental influencing variables include at least one of the trench size, soil physical properties, groundwater level, load, support parameters, and adjacent buildings;

[0058] The cable operation influencing variables include at least one of the cable operation temperature, insulation layer aging degree, and cable vibration frequency.

[0059] By identifying the key influencing variables, the types of key data that affect the stability of the cable trench can be collected targeted, improving the accuracy and efficiency of the evaluation.

[0060] Step S12: Obtain the historical measurement data corresponding to each influencing variable, and calculate the probability distribution data of each influencing variable and the response correlation coefficients between the influencing variables based on the historical measurement data.

[0061] The historical data reflects the state change law of the cable trench during long-term operation and is an important basis for evaluating its stability. By analyzing the historical data, the statistical characteristics of each influencing variable and their mutual relationships can be understood. To obtain the historical measurement data of each influencing variable, specifically, in this embodiment, the first historical data of each influencing variable is based on the historical investigation report and construction records.

[0062] Sensors may have problems such as drift, aging, or decreased accuracy during long-term operation, resulting in inaccurate measurement data. Data calibration can correct these deviations. In this embodiment, according to the sensor type corresponding to each first historical data, the first historical data is calibrated to obtain the second historical data, and through the calibrated data, ensure its consistency with the actual physical quantity.

[0063] In actual operation, data may be missing due to reasons such as equipment failures and data transmission interruptions. Missing value imputation can fill these blanks and ensure the integrity of the data. Therefore, linear interpolation can be used to perform missing value imputation on the second historical data to fill the data gaps and obtain the third historical data.

[0064] In addition, the actual measurement data may contain random noise or short-term fluctuations, which may mask the true trend of the data and affect subsequent analysis. It is necessary to use the moving average method to suppress the fluctuations of the third historical data, smooth the data, and reduce the influence of short-term fluctuations, thereby obtaining the historical measurement data.

[0065] Through data calibration, imputation, and fluctuation suppression, ensure the accuracy and integrity of the data, and reduce the evaluation deviation caused by data quality problems.

[0066] Use the maximum likelihood estimation method to fit the probability distribution of the historical measurement data, quantify the uncertainty of each influencing variable, and obtain the probability distribution data of each influencing variable.

[0067] In addition to the fact that each influencing variable will affect the state of the cable trench, there may also be mutual dependence relationships between the influencing variables, and these relationships have an important impact on the stability of the cable trench. Use the correlation analysis method to calculate the response relationships between the historical measurement data to obtain the response correlation coefficients between the influencing variables and quantify the dependence relationships between the variables.

[0068] The specific process of obtaining the response correlation coefficient in this embodiment is as follows:

[0069] Construct a measurement data matrix composed of the historical measurement data.

[0070] The Pearson correlation coefficient of each historical measurement data is calculated according to the measurement data matrix as the response correlation coefficient.

[0071] Step S13: Acquire real-time measurement data corresponding to the influencing variables of the target cable trench. Based on the real-time measurement data, combined with information such as the geometric structure and material properties of the cable trench, construct a three-dimensional simulation model of the target cable trench to simulate the response of the cable trench under different working conditions and provide support for stability assessment.

[0072] The interdependencies between influencing variables can affect the accuracy and reliability of the model. Quantitative adjustments can correct for biases in the model. By considering the interactions between influencing variables, the values of each variable in the model can be adjusted to more closely reflect reality.

[0073] The real-time measurement data corresponding to each influencing variable of the three-dimensional simulation model are quantitatively adjusted according to each response correlation coefficient to obtain the response difference data of the three-dimensional simulation model to each influencing variable. The response difference data reflects the deviation between the adjusted model and the actual situation.

[0074] Based on the response difference data, the three-dimensional simulation model is optimized and adjusted to improve the accuracy of the model and ensure that the model can truly reflect the actual operating status of the cable trench.

[0075] The process of quantitatively adjusting the influencing variables of the three-dimensional simulation model according to the response correlation coefficients to obtain the response difference data of the three-dimensional simulation model to each influencing variable is as follows:

[0076] The dependencies between variables determine the order of adjustment. A reasonable adjustment sequence can reduce error accumulation and improve adjustment efficiency. By determining the order of variable adjustment based on the dependencies between influencing variables and the magnitude of the response correlation coefficient, you can identify which variables have the greatest impact on the model and prioritize adjustments to these key variables.

[0077] In order to gradually optimize and adjust the model and ensure the stability and accuracy of the adjustment process, each influencing variable is quantitatively adjusted step by step according to the variable adjustment order. The purpose of step-by-step adjustment is to increase or decrease the value of the data corresponding to a single influencing variable by a small amount each time to avoid affecting the accuracy of the model's response difference data acquisition due to excessive adjustment values. The adjustment values are:

[0078]

[0079] in, Influencing variables A single adjustment value of Influencing variables The response correlation coefficient with another influencing variable is the adjustment series.

[0080] Based on the response differences of the three-dimensional simulation model after quantitatively adjusting each influencing variable, the response difference data of the three-dimensional simulation model for each influencing variable are calculated.

[0081]

[0082] Step S14: Use the Monte Carlo method to simulate and generate a number of cable trench working condition events, apply each cable trench working condition event to the three-dimensional simulation model, and generate a stability index set based on the feedback of the three-dimensional simulation model.

[0083] The Monte Carlo method is a numerical calculation method based on random sampling and is suitable for dealing with uncertainty problems in complex systems. The stability of cable trenches is affected by various factors, and these factors have randomness and uncertainty. Therefore, it is necessary to generate a variety of possible working condition events through the Monte Carlo method to comprehensively evaluate its stability.

[0084] According to the probability distribution data of each influencing variable (such as groundwater level, cable temperature, etc.), a large number of possible working condition event samples are randomly generated. These working condition events cover various possible operating conditions and can reflect the behavior of cable trenches under different environments and operating states.

[0085]

[0086]

[0087]

[0088] Step S15: During the actual monitoring of cable trenches, use the influencing variables corresponding to the stability index set to evaluate the state stability of the cable trenches, and obtain the cable trench stability evaluation result. The stability of cable trenches is affected by various factors, including environmental changes, cable operating states, etc. Evaluating its state stability through stability indicators can quantify the safety of cable trenches. Use the stability indicators (such as displacement, stress, strain, etc.) generated from the three-dimensional simulation model to evaluate the cable trenches in actual operation and determine whether they are in a safe state. Generate a risk level assessment report based on the evaluation result, visually display the stability state of the cable trenches, and provide decision-making support for operation and maintenance personnel; the risk level assessment report includes at least the cable trench instability risk assessment result, the visualized probability distribution map, and the cumulative probability curve. The instability risk assessment result can clarify the instability risk level of the cable trenches in the current state, help operation and maintenance personnel judge whether measures need to be taken, and the visualized probability distribution map and cumulative probability curve display the probability distribution and cumulative probability of the stability indicators through visualization tools, facilitating the understanding of the probability of cable trenches at different risk levels.​Based on the risk level assessment report, through big data analysis, potential risk patterns are identified, and corresponding risk control plans are obtained. Through specific control measures, such as strengthening monitoring, reinforcing structures, or adjusting operating parameters, etc., the stability of the cable trench is improved.

[0089] The cable trench stability assessment method of the present invention introduces the Monte Carlo algorithm, combines probability analysis and stochastic simulation techniques, comprehensively considers the randomness and uncertainty of the factors affecting the stability of the cable trench, avoids the deviation of the assessment results caused by overly simplified assumptions, and significantly improves the accuracy and reliability of the cable trench stability assessment. Through a large number of stochastic simulation calculations, the probability distribution of the cable trench stability index is obtained, and based on this, the probability of instability is quantitatively evaluated and the risk level is divided, which can provide more intuitive risk information for engineering personnel and help engineering personnel make more scientific and reasonable decisions in the design, construction, and management processes of the cable trench.

[0090] The embodiment of the present invention also provides a cable trench stability assessment device for performing the cable trench stability assessment method as described above. Figure 2 It is a structural block diagram of the cable trench stability assessment device of the embodiment of the present invention. The device includes:

[0091] A variable determination module 21 for determining the influencing variables that affect the stability of the target cable trench, where the influencing variables at least include environmental impact variables.

[0092] A probability calculation module 22 for obtaining the historical measurement data corresponding to each of the influencing variables, and calculating the probability distribution data of each of the influencing variables and the response correlation coefficients between the influencing variables according to the historical measurement data.

[0093] A model optimization module 23 for obtaining the real-time measurement data corresponding to the influencing variables of the target cable trench, constructing a three-dimensional simulation model of the target cable trench according to the real-time measurement data, quantitatively adjusting the real-time measurement data corresponding to the influencing variables of the three-dimensional simulation model according to each of the response correlation coefficients, obtaining the response difference data of the three-dimensional simulation model for each of the influencing variables, and optimizing and adjusting the three-dimensional simulation model based on each of the response difference data.

[0094] An index calculation module 24 for simulating and generating a plurality of cable trench condition events by using the Monte Carlo method, applying each of the cable trench condition events to the three-dimensional simulation model, and generating a stability index set based on the feedback of the three-dimensional simulation model.

[0095] A state evaluation module 25, which is used to evaluate the state stability of the cable trench by using the influence variables corresponding to the stability index set during the actual monitoring of the cable trench, so as to obtain the cable trench stability evaluation result.

[0096] The technical features and technical effects of the device proposed in the embodiment of the present invention are the same as those of the method proposed in the embodiment of the present invention, and will not be elaborated here. Each module in the above device can be implemented in whole or in part by software, hardware and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0097] The embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the cable trench stability evaluation method as described above.

[0098] The embodiment of the present invention also provides a computer device, Figure 3 which is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the cable trench stability evaluation method as described above.

[0099] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2,...). The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device.

[0100] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor. The processor is the control center of the computer device and connects various parts of the computer device through various interfaces and lines.

[0101] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant data, etc. In addition, the memory may be a high-speed random access memory, or may also be a non-volatile memory, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., or the memory may also be other volatile solid-state storage devices.

[0102] It should be noted that the above computer device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3 The structural block diagram is only an example of the computer device and does not constitute a limitation on the computer device. It may include more or fewer components than those shown, or combine certain components, or different components.

[0103] In summary, compared with the prior art, the cable trench stability evaluation method, device, equipment, and storage medium provided by the embodiments of the present invention have the following beneficial effects in at least one of the following aspects:

[0104] By introducing the Monte Carlo algorithm and combining probability analysis and random simulation techniques, the cable trench stability evaluation method of the present invention comprehensively considers the randomness and uncertainty of the factors affecting the stability of the cable trench, avoids the deviation of the evaluation results caused by overly simplified assumptions, and significantly improves the accuracy and reliability of the cable trench stability evaluation.

[0105] Through a large number of random simulation calculations, the probability distribution of the stability index of the cable trench is obtained, and based on this, the probability of instability is quantitatively evaluated and the risk level is divided, which can provide more intuitive risk information for engineers and help engineers make more scientific and reasonable decisions in the design, construction and management processes of the cable trench.

[0106] The above-described embodiments merely represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. A method for evaluating the stability of a cable trench, characterized in that Including: Determine the influencing variables that affect the stability of the target cable trench. Among them, the influencing variables at least include environmental influencing variables; Obtain the historical measurement data corresponding to each of the influencing variables, and calculate the probability distribution data of each influencing variable and the response correlation coefficients between the influencing variables based on the historical measurement data. Specifically include: Obtain the historical measurement data of each influencing variable; Use the maximum likelihood estimation method to perform probability distribution fitting on the historical measurement data to obtain the probability distribution data of each influencing variable; Use the correlation analysis method to calculate the response relationship between the historical measurement data to obtain the response correlation coefficients between the influencing variables; Obtain the real-time measurement data corresponding to the influencing variables of the target cable trench, construct a three-dimensional simulation model of the target cable trench based on the real-time measurement data, quantitatively adjust the real-time measurement data corresponding to the influencing variables of the three-dimensional simulation model according to each of the response correlation coefficients, obtain the response difference data of the three-dimensional simulation model for each influencing variable, and optimize and adjust the three-dimensional simulation model based on each of the response difference data; Use the Monte Carlo method to simulate and generate a number of cable trench working condition events, apply each of the cable trench working condition events to the three-dimensional simulation model, and generate a stability index set based on the feedback of the three-dimensional simulation model; During the actual cable trench monitoring process, use the influencing variables corresponding to the stability index set to evaluate the state stability of the cable trench to obtain the cable trench stability evaluation result.

2. The cable trench stability evaluation method according to claim 1, characterized in that The influencing variables further include cable operation influencing variables; The environmental influencing variables include at least one of trench size, soil physical properties, groundwater level, load, support parameters, and adjacent buildings; The cable operation influencing variables include at least one of cable operation temperature, insulation layer aging degree, and cable vibration frequency.

3. The cable trench stability assessment method according to claim 1, characterized in that The obtaining of the historical measurement data of each influencing variable includes: According to the historical exploration report and construction records, obtain the first historical data of each influencing variable; Calibrate the first historical data according to the sensor type corresponding to each of the first historical data to obtain the second historical data; Use the linear interpolation method to perform missing value interpolation on the second historical data to obtain the third historical data; Use the moving average method to suppress the fluctuations of the third historical data to obtain the historical measurement data.

4. The cable trench stability evaluation method according to claim 1, characterized in that, The using of the correlation analysis method to calculate the response relationship between the historical measurement data to obtain the response correlation coefficients between the influencing variables includes: Construct a measurement data matrix composed of each of the historical measurement data; Calculate the Pearson correlation coefficient of each of the historical measurement data as the response correlation coefficient according to the measurement data matrix.

5. The cable trench stability assessment method according to claim 1, wherein, The quantitatively adjusting each influencing variable of the three-dimensional simulation model according to each of the response correlation coefficients to obtain the response difference data of the three-dimensional simulation model for each influencing variable includes: Determine the variable adjustment order according to the dependence relationship between the influencing variables and the magnitude of the response correlation coefficients; Perform step-by-step quantitative adjustment on each of the influencing variables according to the adjusted order of the variables, and the adjustment value is: Among them, is the single adjustment value of the influencing variable , is the response correlation coefficient between the influencing variable and another influencing variable , is the adjustment level; Calculate based on the response difference of the three-dimensional simulation model after quantitatively adjusting each of the influencing variables to obtain the response difference data of the three-dimensional simulation model for each of the influencing variables.

6. The cable trench stability evaluation method according to claim 1, characterized in that, Use the stability index to evaluate the state stability of the cable trench to obtain the cable trench stability evaluation result, including: Use the stability index to evaluate the state stability of the cable trench during actual operation, and generate a risk level evaluation report according to the evaluation result; the risk level evaluation report at least includes the cable trench instability risk evaluation result, the visualization probability distribution map, and the cumulative probability curve; Obtain the corresponding risk control plan through big data analysis based on the risk level evaluation report.

7. A cable trench stability evaluation device, characterized in that Including: A variable determination module for determining the influencing variables that affect the stability of the target cable trench, where the influencing variables at least include environmental influencing variables; A probability calculation module for obtaining the historical measurement data corresponding to each of the influencing variables, and calculating the probability distribution data of each of the influencing variables and the response correlation coefficients between the influencing variables according to the historical measurement data; specifically including: obtaining the historical measurement data of each of the influencing variables; using the maximum likelihood estimation method to perform probability distribution fitting on the historical measurement data to obtain the probability distribution data of each of the influencing variables; using the correlation analysis method to calculate the response relationship between the historical measurement data to obtain the response correlation coefficients between the influencing variables; A model optimization module for obtaining the real-time measurement data corresponding to the influencing variables of the target cable trench, constructing a three-dimensional simulation model of the target cable trench according to the real-time measurement data, quantitatively adjusting the real-time measurement data corresponding to each of the influencing variables of the three-dimensional simulation model according to the response correlation coefficients to obtain the response difference data of the three-dimensional simulation model for each of the influencing variables, and performing optimization adjustment on the three-dimensional simulation model based on the response difference data; An index calculation module for using the Monte Carlo method to simulate and generate a number of cable trench working condition events, applying each of the cable trench working condition events to the three-dimensional simulation model, and generating a stability index set based on the feedback of the three-dimensional simulation model; A state evaluation module for evaluating the state stability of the cable trench by using the influencing variables corresponding to the stability index set during the actual cable trench monitoring process to obtain the cable trench stability evaluation result.

8. A computer device, characterized in that, Including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the cable trench stability evaluation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, it implements the cable trench stability evaluation method according to any one of claims 1 to 6.

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