Fault prevention and control method and device, electronic equipment and storage medium

Abstract

The application provides a fault prevention and control method and device, electronic equipment and a storage medium, and relates to the technical field of engineering structure safety monitoring. The fault prevention and control method comprises the following steps: based on original monitoring data of different measuring points, a pre-constructed three-level error transmission mechanism is used to determine different levels of errors step by step; in the process of determining different levels of errors, the error amplification coefficient of each measuring point is determined; based on the error amplification coefficient of each measuring point, the sensor arrangement of the measuring point is redundantly optimized, and based on the mechanical consistency principle, fault identification is performed on the measuring point after the redundant optimization arrangement; if a target measuring point with a fault is identified, the original monitoring data of each target measuring point is corrected by using a corresponding mechanical model. By using the application, the failure rate of the safety monitoring system can be effectively reduced, the state judgment accuracy of the safety monitoring system can be improved, and engineering safety accidents caused by monitoring distortion can be effectively prevented.

Description

Technical Field

[0001] This invention relates to the field of engineering structure safety monitoring technology, and in particular to a fault prevention and control method, device, electronic equipment and storage medium. Background Technology

[0002] Modern engineering structures are characterized by large spans, increasing complexity, and long service lives. They are subjected to complex loads and harsh environments for extended periods, making it easy for structural stress and deformation to deviate from design expectations. Utilizing structural health monitoring to diagnose mechanical conditions and assess performance is an effective way to ensure structural safety, extend service life, and reduce total life-cycle costs.

[0003] Safety monitoring systems are a key technological means to acquire core mechanical parameters such as structural displacement, stress and strain, vibration, and overall stability, and to carry out risk warning and safety control. However, in long-term engineering practice, due to various factors such as the degradation of the monitoring equipment itself, power outages, communication link interference, and environmental noise, the original monitoring data often suffers from problems such as missing, abnormal, or distorted transmission.

[0004] Most existing technologies focus on fault diagnosis and health management of the monitoring system hardware itself, as well as the detection, identification, and repair of data anomalies based on mathematical statistics or machine learning. However, relying solely on hardware diagnosis or mathematical statistics methods cannot reveal the impact of faults on state determination from a mechanical mechanism perspective. Existing technologies also lack a quantitative description of the fault error amplification effect, making it impossible to predict the degree of impact of different faults on state determination in advance. Furthermore, the placement of measurement points is largely based on experience, lacking quantitative basis, which can easily lead to over-placement resulting in wasted costs or under-placement with poor robustness. In addition, in existing technologies, fault identification mainly relies on a single index threshold, resulting in a high false alarm rate and an inability to effectively distinguish between true mechanical responses and fault anomalies. Since data repair often employs purely mathematical methods, the corrected data does not meet actual engineering requirements. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a fault prevention and control method, device, electronic device and storage medium to alleviate the above-mentioned problems existing in the related art.

[0006] In a first aspect, embodiments of the present invention provide a fault prevention and control method, comprising: determining different levels of error step by step based on the original monitoring data of different measuring points using a pre-constructed three-level error transmission mechanism; determining the error amplification coefficient of each measuring point during the determination of different levels of error; performing redundancy optimization on the sensor arrangement of the measuring points based on the error amplification coefficient of each measuring point, and identifying faults in the redundantly optimized measuring points based on the principle of mechanical consistency; if a target measuring point with a fault is identified, correcting the original monitoring data of each target measuring point using a corresponding mechanical model.

[0007] Secondly, embodiments of the present invention also provide a fault prevention and control device, comprising: a first determining module, used to determine different levels of error step by step based on the original monitoring data of different measuring points using a pre-constructed three-level error transmission mechanism; a second determining module, used to determine the error amplification coefficient of each measuring point during the determination of different levels of error; an identification module, used to perform redundancy optimization of the sensor arrangement of the measuring points based on the error amplification coefficient of each measuring point, and to perform fault identification of the redundantly optimized measuring points based on the principle of mechanical consistency; and a correction module, used to correct the original monitoring data of each target measuring point using a corresponding mechanical model if a target measuring point with a fault is identified.

[0008] Thirdly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the fault prevention and control method described in the first aspect above.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the fault prevention and control method described in the first aspect.

[0010] This invention provides a fault prevention and control method, device, electronic device, and storage medium. Based on raw monitoring data from different measuring points, a pre-constructed three-level error propagation mechanism is used to determine different levels of error step by step. During the determination of different levels of error, the error amplification coefficient for each measuring point is determined. Based on the error amplification coefficient of each measuring point, the sensor arrangement of the measuring points is redundantly optimized, and based on the principle of mechanical consistency, fault identification is performed on the redundantly optimized measuring points. If a target measuring point with a fault is identified, the raw monitoring data of each target measuring point is corrected using a corresponding mechanical model. By adopting the above technology, a three-level error propagation mechanism and a method for determining the error amplification coefficient of measuring points can be introduced to redundantly optimize the sensor arrangement of measuring points, enabling quantitative prediction of fault impact. Based on this, the data of the faulty measuring points can be corrected, constructing a full-process prevention and control system encompassing pre-event prevention, in-event identification, and post-event correction. This effectively reduces the failure rate of safety monitoring systems, improves the accuracy of safety monitoring system status determination, and effectively prevents engineering safety accidents caused by monitoring distortion.

[0011] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention.

[0012] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0013] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating a fault prevention and control method according to an embodiment of the present invention; Figure 2 This is an example flowchart of the overall process of the fault prevention and control method in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a fault prevention and control device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] At present, the existing engineering structure safety monitoring technology has the following defects: (1) It relies only on hardware diagnosis or mathematical statistics methods and cannot reveal the influence law of fault on state determination from the mechanical mechanism level; (2) It lacks a quantitative description of the fault error amplification effect and cannot predict the degree of influence of different faults on state determination in advance; (3) The layout of measuring points is mostly based on experience and lacks quantitative basis, which can easily lead to excessive layout and waste of costs or insufficient layout and poor robustness; (4) Fault identification mainly relies on a single index threshold, with a high false alarm rate and cannot effectively distinguish between real mechanical response and fault anomaly; (5) Data repair mostly adopts pure mathematical methods, and the corrected data does not meet the actual engineering needs.

[0017] Based on this, the present invention provides a fault prevention and control method, device, electronic device and storage medium that can alleviate the above-mentioned problems existing in related technologies.

[0018] To facilitate understanding of this embodiment, a fault prevention method disclosed in this invention will first be described in detail, see [link to relevant documentation]. Figure 1 The diagram shows a flowchart of a fault prevention and control method, which may include the following steps: Step S102: Based on the original monitoring data from different measuring points, a pre-constructed three-level error transmission mechanism is used to determine the different levels of error step by step.

[0019] Step S104: In the process of determining different levels of error, determine the error amplification factor for each measuring point.

[0020] Step S106: Based on the error amplification coefficient of each measuring point, perform redundancy optimization on the sensor arrangement of the measuring points, and based on the principle of mechanical consistency, perform fault identification on the redundantly optimized measuring points.

[0021] Step S108: If a target measuring point that has malfunctioned is identified, the original monitoring data of each target measuring point is corrected using the corresponding mechanical model.

[0022] This invention provides a fault prevention and control method that, based on raw monitoring data from different measuring points, employs a pre-constructed three-level error propagation mechanism to progressively determine different levels of error. During the determination of these errors, an error amplification coefficient is determined for each measuring point. Based on this coefficient, the sensor arrangement at each measuring point is redundantly optimized, and fault identification is performed on the optimized measuring points based on the principle of mechanical consistency. If a faulty target measuring point is identified, the raw monitoring data for each target measuring point is corrected using a corresponding mechanical model. By introducing this three-level error propagation mechanism and the method for determining the measuring point error amplification coefficient, and redundantly optimizing the sensor arrangement, quantitative prediction of fault impacts can be achieved. Furthermore, the data of the faulty measuring point can be corrected, constructing a comprehensive prevention and control system encompassing pre-emptive prevention, in-process identification, and post-event correction. This effectively reduces the failure rate of safety monitoring systems, improves the accuracy of safety monitoring system status determination, and effectively prevents engineering safety accidents caused by monitoring distortion.

[0023] As one possible implementation, different levels of error may include raw data distortion, mechanical index calculation deviation, and safety decision deviation. Based on this, step S102 (i.e., determining different levels of error step by step using a pre-constructed three-level error transmission mechanism based on the raw monitoring data of different measuring points) may include: determining the raw data distortion of each measuring point as the corresponding first-level error based on the raw monitoring data of different measuring points; determining the corresponding mechanical index value based on the raw monitoring data of each measuring point, and determining the mechanical index calculation deviation as the second-level error based on the raw monitoring data of each measuring point and the first-level error; and determining the safety decision deviation as the third-level error based on the mechanical index value of each measuring point and its corresponding structural safety threshold, as well as the second-level error.

[0024] In practical applications, a three-level error propagation mechanism can be established based on typical failure modes and on-site characteristics of the safety monitoring system. This mechanism includes the calculation of the following three levels of error: Level 1: Distortion of raw data.

[0025] Assume the safety monitoring system has a total of The sensor, the first The actual physical quantity measured by each sensor is... Due to sensor malfunction (drift, damage, jump, etc.), the actual measured value is:

[0026] in, For the first The actual measured values ​​of each sensor; The first level of raw error has a mathematical form that varies with the type of fault: for example, random error / noise follows a normal distribution, drift error is a slowly varying function of time, and the fault type can also be proportional deviation, data loss / jump, etc.

[0027] Level 2: Deviation in mechanical property calculation.

[0028] Assume the key mechanical properties of the structure (displacement, stress, safety factor, etc.) are denoted as... . It is not obtained through direct measurement, but rather calculated from the measured values ​​of multiple measuring points using a mechanical model. The mapping relationship is as follows:

[0029] in, , which is the true physical quantity vector of all relevant measurement points; It is a mechanical mapping function, the specific form of which is determined by the type of engineering structure and the mechanical model.

[0030] The calculated mechanical properties including errors are as follows:

[0031] in, These are calculated values ​​of mechanical properties that include errors. For measuring physical quantity vectors; This is the original error vector.

[0032] The calculation error for the second-level mechanical properties is defined as follows:

[0033] Performing a first-order Taylor expansion and ignoring higher-order terms, we obtain the linear approximation formula for error propagation:

[0034] in, For function For the first The partial derivative of the physical quantity at the nth measuring point is called the mechanical error sensitivity coefficient in mechanics. The magnitude of the mechanical error sensitivity coefficient reflects the partial derivative of the physical quantity at the nth measuring point. The degree of influence of the original error of a measuring point on the final mechanical index is determined by the mechanical error sensitivity coefficient. The larger the mechanical error sensitivity coefficient, the greater the contribution of the original error of that measuring point to the mechanical index, and vice versa.

[0035] Level 3: Safety decision-making bias.

[0036] The core of engineering condition determination is to compare the calculated mechanical indicators with the structural safety threshold, and then make a determination of whether the engineering condition is safe, under warning, or dangerous.

[0037] True safety margin of the structure for:

[0038] in, The safety threshold for structural mechanics indicators is determined based on engineering specifications, limit state design requirements, numerical simulation results, or field test data, and is the core basis for determining engineering safety.

[0039] Due to measurement errors, the actual apparent safety margin used is... for:

[0040] when When the calculated mechanical properties are too high and the apparent safety margin is too low, a stable structure may be misjudged as being in a dangerous state, leading to false alarms and excessive maintenance. If the calculated mechanical properties are too low and the apparent safety margin is too high, the unstable structure may be misjudged as stable. This can easily lead to a false alarm, delay the reinforcement process, exacerbate the hidden dangers, and ultimately cause local damage or even overall instability.

[0041] As one possible implementation, step S104 (i.e., determining the error amplification factor of each measuring point in the process of determining different levels of error) may include: determining the error amplification factor of each measuring point based on the original monitoring data of different measuring points by a preset numerical perturbation method.

[0042] Continuing from the previous example, for implicit mechanical indices such as stability coefficients that require numerical iteration, their partial derivatives... The expression is difficult to parse directly; therefore, the first is defined as... The dimensionless mechanical error amplification factor for each measuring point is:

[0043] in, Since it is a dimensionless quantity, the influence of the physical quantity's dimension on the average degree of error amplification is eliminated. Its physical meaning is: the first... The percentage change in the relative error of mechanical properties caused by a 1% change in the original relative error of each measuring point. This indicates that the error has been amplified. The larger the value, the more significant the amplification effect.

[0044] The mechanical error amplification factor for each candidate measurement point is calculated using the following numerical perturbation method. (1) Establish a finite element numerical model of the engineering structure and calculate the actual physical quantities of each measuring point under normal working conditions. (such as displacement, stress, etc.) and key mechanical indicators (e.g., safety factor); (2) for the first A small perturbation is applied to each measuring point. (usually taken) (1%~5%), keeping other measuring points unchanged, recalculate the mechanical properties after the disturbance. The change was obtained (3) Calculate the dimensionless mechanical error amplification factor for the measuring point. (4) Traverse all candidate measurement points to obtain the measurement points. Values, sorted from largest to smallest.

[0045] As one possible implementation, after determining the error amplification factor for each measuring point, the above-mentioned fault prevention and control method may further include: determining the corresponding original relative error based on the original monitoring data of each measuring point; and determining the comprehensive relative error of the mechanical index based on the error amplification factor and the original relative error of each measuring point.

[0046] Continuing from the previous example, define the first... The original relative error of each measuring point is:

[0047] in, For the first The relative value of the original error at each measuring point reflects the accuracy level of the original measurement.

[0048] The approximate expression for the comprehensive relative error of the mechanical indices is as follows:

[0049] in, The comprehensive relative error of mechanical properties is the core indicator for evaluating the fault error amplification effect.

[0050] As one possible implementation, the redundancy optimization of the sensor arrangement of the measuring points based on the error amplification coefficient of each measuring point in step S106 above may include: if there are highly sensitive measuring points with an error amplification coefficient not less than a first threshold, then arrange sensors with 3 times redundancy for each highly sensitive measuring point; if there are moderately sensitive measuring points with an error amplification coefficient less than the first threshold and not less than a second threshold, then arrange sensors with 2 times redundancy for each moderately sensitive measuring point; wherein, the first threshold is greater than the second threshold; if there are low-sensitive measuring points with an error amplification coefficient less than the second threshold, then arrange sensors at a single point for each low-sensitive measuring point.

[0051] Continuing from the previous example, the redundancy optimization method for the measurement point layout is as follows: For For highly sensitive measuring points (such as slope slip surfaces, bridge bearings, and tunnel arches), a 3-fold redundancy arrangement is used, meaning three identical sensors are installed at the same measuring point location; for For the medium-sensitive measurement points, a double redundancy arrangement is adopted, that is, two sensors of the same type are installed at the same measurement point location; for For low-sensitivity measurement points, a normal single-point arrangement is adopted, that is, one sensor is installed at a single measurement point location. For redundant measurement points (measurement points with 2 or 3 sensors of the same type installed), the data adopts a median value fusion strategy (that is, the median value is calculated for the data of different sensors at the same measurement point as the original monitoring data of the same side point, reducing the impact of single-point failure on the calculation of mechanical indicators).

[0052] Compared with the existing technology that relies on experience to arrange measuring points, the above operation method... Quantitative indicators enable precise allocation of redundancy, avoiding the problems of over-deployment (wasting costs) or under-deployment (insufficient robustness).

[0053] As one possible implementation, step S106 above, based on the principle of mechanical consistency, may include the following steps: verifying the displacement coordination of multiple displacement measuring points arranged continuously on the same cross-section based on a first preset relationship that the displacement values ​​of adjacent displacement measuring points on the same cross-section must satisfy; verifying the stress balance of multiple stress measuring points on the same cross-section based on a second preset relationship that the stress values ​​of adjacent stress measuring points on the same cross-section must satisfy; and verifying the deformation trend of displacement measuring points at different parts of the same structure based on a third preset relationship that the displacement values ​​corresponding to different parts of the same structure must satisfy.

[0054] Following the previous example, in order to quickly locate the fault, the following mechanical consistency verification process can be performed on the measuring points based on the principle of mechanical consistency: (1) Displacement compatibility verification: For multiple displacement measuring points continuously arranged on the same cross-section, the displacement values ​​of adjacent measuring points should satisfy the deformation compatibility relationship under normal working conditions, that is, the displacement is continuously distributed along the cross-section and there should be no abrupt changes. Assume measuring points... and The displacements are respectively and The distance between measuring points is Then, using the finite element numerical model of the engineering structure, the theoretical displacement values ​​of each measuring point can be calculated under normal working conditions. and And substitute into the formula In this process, the theoretical strain value can be calculated. If the value exceeds twice the material's ultimate strain, then the measuring point is considered... and There is a malfunction.

[0055] (2) Stress balance verification: For multiple stress measuring points in stress balance on the same cross section, under normal working conditions, the following should be met: , Let the stress be... Suppose there are two stress measuring points on a cross-section, left and right. The stress at the left measuring point is... The stress at the measuring point on the right is The allowable deviation is 10%. If so, it is determined that at least one of the measuring points is faulty.

[0056] (3) Verification of consistency of deformation trend: For measuring points on different parts of the same structure, the trend of deformation time history curves at the corresponding parts should meet physical logic. For example, the deep displacement and surface displacement of the slope should change in the same direction, and the surface displacement should not be less than the deep displacement. When the deep displacement increases while the surface displacement decreases or remains unchanged, it is determined that there is a measuring point fault.

[0057] Through the above mechanical consistency verification process, the true mechanical response and abnormal data caused by the fault can be effectively distinguished, the fault diagnosis accuracy is significantly improved, and false alarms caused by environmental noise or temporary disturbances are avoided.

[0058] As one possible implementation, step S108 (i.e., if a target measuring point with a fault is identified, the original monitoring data of each target measuring point is corrected using the corresponding mechanical model) may include: if a first target measuring point with a first fault type representing sensor drift deviation is identified, the data drift amount is back-calculated using the corresponding deformation compatibility equation, and the original monitoring data of each first target measuring point is corrected using the obtained data drift amount; if a second target measuring point with a second fault type representing data loss or jump is identified, the physical field is inverted using the corresponding reduced-order finite element model with the goal of minimizing the objective function value, and the original monitoring data of each second target measuring point is corrected using the physical field inversion result; wherein, the reduced-order finite element model is simultaneously constrained by the corresponding deformation compatibility equation and static equilibrium equation.

[0059] Continuing from the previous example, data correction can be achieved by reconstructing the data based on physical mechanisms (mechanical equations) rather than purely mathematical methods, thereby ensuring that the corrected data meets the constraints of mechanical equilibrium and deformation compatibility. Specifically, different mechanical models can be used to perform the following data correction process depending on the type of fault: (1) Correction of sensor drift deviation - neighborhood back-inference method based on deformation compatibility equation.

[0060] Applicable scenario: Data at a certain measuring point shows slow drift, but data at adjacent measuring points on the same cross section are normal.

[0061] Implementation steps: (1.1) Selecting fault measurement points Adjacent normal measuring points on the section ; (1.2) Establishing the Deformation Compatibility Equation: Within the elastic range, the displacements of continuously arranged measuring points on the same cross-section should satisfy the linear distribution assumption, that is, the strain between adjacent measuring points should remain consistent. The expression of the deformation compatibility equation is:

[0062] in For the displacement of the measuring point, The distance between measuring points; (1.3) The measured values ​​of adjacent normal measuring points , Substituting into the above equation, the approximate true value of the fault measurement point can be obtained by inverse solution. for:

[0063] (1.4) Calculate the drift amount Subsequent monitoring data were all corrected by subtracting this drift amount.

[0064] (2) Correction for data loss or jump - physical field reconstruction method based on reduced-order finite element model.

[0065] Applicable scenario: When a measurement point loses all data or exhibits unreliable jump values ​​during the fault period.

[0066] Implementation steps: (2.1) Establishment of the reduced-order finite element model.

[0067] Establish a reduced-order finite element model of the engineering structure monitoring section, and discretize the continuum into... There are n elements, each containing n nodes. The total number of nodes in the reduced-order finite element model is... The total degrees of freedom are ( (The dimension is 2 for planar problems and 3 for spatial problems). The core governing equations of the reduced-order finite element model are the static equilibrium equations, whose matrix form is:

[0068] in, The overall stiffness matrix of the structure ( (order), derived from the stiffness matrix of each element. Assembled , It is a geometric matrix (strain-displacement matrix). This is the elasticity matrix (constitutive relation matrix); The displacement vector of all nodes ( (Order), that is, the unknown quantity to be solved; The load vector of the node ( The order (step) is determined by the external load and boundary conditions.

[0069] (2.2) Construction of constraints.

[0070] Effective monitoring data (such as displacement and stress) from all non-faulty measuring points on the same cross section during the fault period are introduced into the reduced-order finite element model as static boundary conditions. For displacement monitoring points, the displacement value is directly substituted as a forced boundary condition; for stress monitoring points, the stress value is transformed into a force boundary condition through equivalent nodal loads.

[0071] Suppose that the reduced-order finite element model has a total of The degrees of freedom of a known displacement (from the displacement measurement point without fault). There are several known Gaussian points with known stress (from stress measurement points that are not faulty). These known quantities constitute the constraints for solving the inverse problem.

[0072] (2.3) Joint constraints of deformation compatibility equation and static equilibrium equation.

[0073] During the inversion solution process, the reduced-order finite element model is simultaneously constrained by the following two types of mechanical equations: static equilibrium equations and deformation compatibility equations.

[0074] Static equilibrium equation constraints: For each node in the reduced-order finite element model, the force equilibrium condition must be satisfied; in the finite element discretization form, the static equilibrium equations are expressed as having zero residuals:

[0075] in, For the residual vector ( (rank), when When it is a true displacement field, .

[0076] Deformation compatibility equation constraints: The deformation compatibility equations are automatically satisfied using finite element shape functions; in continuum mechanics, the relationship between strain and displacement is defined by geometric equations as follows:

[0077] in, For strain vector, The normal strain in the X direction represents the linear strain of the structure in the X direction; The normal strain in the Y direction represents the linear strain of the structure in the Y direction; Let be the shear strain, representing the shear strain of the structure in the XY plane. The element node displacement vector. As a geometric matrix, this relationship between strain and displacement ensures the continuity of displacement within and between elements (i.e., deformation compatibility). In addition, for displacement measuring points continuously arranged on the same cross-section, the following additional linear compatibility constraints must be met:

[0078] This constraint is used to correct outlier measurement points that do not satisfy the linear distribution assumption.

[0079] (2.4) Construction of the objective function.

[0080] The essence of physical field reconstruction is to solve a boundary value problem that satisfies the above mechanical constraints. Since the true value of the faulty measurement point is unknown, it is necessary to use the measured data of the non-faulty measurement points as "anchor points" and solve the problem by minimizing the deviation between the calculated value and the measured value.

[0081] Constructed target function for:

[0082] in, The displacement value (unit: mm) of the j-th known displacement measurement point obtained from the reduced-order finite element model; The measured displacement value (unit: mm) of the j-th known displacement measuring point; The stress value (unit: MPa) at the kth known stress measurement point obtained from the reduced-order finite element model. The measured stress value (unit: MPa) at the kth known stress measurement point; Stress term The weighting coefficient, with a value range of [0.1, 1.0], is determined based on the relative confidence levels of displacement and stress in the monitoring data. Generally, a smaller value is used when the confidence level of displacement data is high, and a larger value is used when the confidence level of stress data is high. The total number of degrees of freedom with known displacements; This represents the total number of measurement points with known stress.

[0083] (2.5) Inversion solution implementation process.

[0084] The core task of inversion solution is to satisfy the static equilibrium equations. and deformation compatibility equations Under the premise of finding the optimal displacement field , so that the objective function It reaches the minimum value.

[0085] The solution process is implemented iteratively using the least squares or conjugate gradient method, and the specific steps are as follows: Step 1 (Initial Displacement Field Assignment): Using the measured displacement values ​​of the non-faulty measuring points as known boundary conditions, assign initial estimated values ​​to the displacement values ​​of the faulty measuring points (linear interpolation results from adjacent normal measuring points can be used as initial values), and combine them to obtain the initial displacement vector. .

[0086] Step 2 (Finite Element Forward Analysis): The current displacement vector... Substitute the elements into the reduced-order finite element model and calculate the strain of each element. In the formula, The geometric matrix (strain-displacement matrix) of the e-th element is used to establish the transformation relationship between the nodal displacements and strains of the element. Let e ​​be the nodal displacement vector of the e-th element in the k-th iteration, and then calculate the stress based on the constitutive relation. And assemble the overall nodal force vector , This is the global nodal displacement vector.

[0087] Step 3 (Residual Calculation): Calculate the objective function Simultaneously calculate the static equilibrium residuals. .like (Pick )and (Pick If the iteration converges, proceed to step 6.

[0088] Step 4 (Sensitivity Analysis): Calculate the gradient of the objective function with respect to the displacement vector. For displacement measurement points, the gradient can be calculated directly; for stress measurement points, the gradient needs to be calculated using the chain rule. .

[0089] Step 5 (Iterative Update): Update the displacement vector using the BFGS algorithm. .in, The search step size for the k-th step is determined by line search and has a value range of (0,1]. The inverse of the approximate Hessian matrix at step k is updated recursively using the BFGS formula. Then return to step 2.

[0090] Step 6 (Result Output): Extract the converged displacement vector From this, the displacement values ​​of the corresponding degrees of freedom of the fault measurement point are obtained. This is the approximate true displacement obtained through inversion. If the fault measurement point is a stress gauge, its stress value is further calculated using constitutive relations. , This is the element node displacement estimation vector.

[0091] (2.6) Backfilling and verification of corrected data.

[0092] The inversion obtained (or This data serves as correction data for the period of failure, replacing original outliers or filling in missing data locations. After correction, the mechanical index calculation formula is called again. The mechanical index Y is calculated and compared with the historical judgment results before the failure occurred to generate a "state judgment review report". Any previous misjudgments are marked and the corrected data is included in the monitoring database.

[0093] The data corrected by the above-mentioned method of reconstructing data based on mechanical models satisfies the constraints of deformation coordination and static equilibrium. The error of the corrected mechanical index is significantly reduced compared with the interpolation method used in the prior art, and the spatial continuity of the physical field of the structure can be maintained.

[0094] To facilitate understanding, the implementation process of the above fault prevention and control method is described in the following example using a specific application.

[0095] See Figure 2 As shown, the fault prevention and control method may include the following steps: Step S1 establishes a three-level transmission mechanism for fault errors: "original data distortion, mechanical index calculation deviation, and safety decision deviation," revealing the complete error transmission path from data acquisition to engineering status determination of monitoring faults.

[0096] Step S2: Define the dimensionless mechanical error amplification factor and the comprehensive relative error of the mechanical index, establish the quantitative relationship between the original data error and the final mechanical index calculation error, and form a mechanical error propagation model.

[0097] In step S2 above, in order to quantitatively describe the amplification degree from the original data error to the final mechanical index error, the dimensionless mechanical error amplification coefficient and the original relative error are defined in combination with the principle of consistency of engineering mechanics dimensions. The formula for the comprehensive relative error of mechanical index is derived to reveal the quantitative law of deviation amplification.

[0098] The core contribution of this deviation amplification mechanism lies in: transforming the traditional empirical judgment that "errors may be amplified" into a quantifiable and predictable mechanical error propagation model. It can quantitatively calculate the error amplification coefficient of each measuring point, thereby guiding the priority of redundant deployment. It can also predict the comprehensive relative error of mechanical indicators caused by faults, providing a basis for dynamic adjustment of early warning thresholds. Furthermore, it can quickly assess the degree of influence of deviations on the state determination conclusion after a fault occurs, and decide whether immediate intervention is necessary.

[0099] Step S2 above elevates error analysis from qualitative judgment to precise quantitative calculation through a quantitative model, enabling accurate location of highly sensitive measurement points and providing a quantitative basis for subsequent prevention and control measures. Compared with existing technologies, the deviation amplification mechanism provided by step S2 above represents the following technological advancements: (1) Improve the granularity of error analysis: by defining the dimensionless mechanical error amplification factor The original general qualitative judgment that "errors may be amplified" has been refined into quantifiable indicators for each measuring point and each type of fault, so that the safety monitoring system can identify highly sensitive measuring points and strengthen their deployment in a targeted manner during the design phase.

[0100] (2) Pre-judgment to achieve accurate state determination: using calculated mechanical indicators to comprehensively assess relative error It can predict the impact of different types of faults on the final state determination before a fault occurs, so as to dynamically adjust the safety warning threshold accordingly and avoid false alarms or missed alarms.

[0101] (3) Evaluation of the effectiveness of supporting post-mortem deviation correction: After the fault is repaired, the corrected value can be calculated again. The effectiveness of the corrective measures is quantitatively evaluated to form a closed-loop optimization.

[0102] Step S3, Precautionary redundancy arrangement based on mechanical sensitivity: Based on the dimensionless mechanical error amplification coefficient of each measuring point calculated in step S2, the redundancy of the measuring points is determined in stages and the arrangement is optimized.

[0103] Step S4, in-process identification of rapid fault location based on the principle of mechanical consistency: By verifying displacement compatibility, stress balance and deformation trend consistency, the real mechanical response and fault anomaly data are distinguished, and the fault measurement point is accurately located.

[0104] Step S5, Post-correction of accurate data reconstruction based on mechanical model: Select the corresponding mechanical model according to the fault type to correct the data so that the corrected data meets the mechanical equilibrium and deformation coordination constraints.

[0105] Step S6: Based on the safeguard measures for optimizing the engineering status determination path for fault prevention and control, realize the automated closed-loop processing of fault diagnosis, error quantification, data correction and status re-determination.

[0106] The implementation process of step S6 above mainly includes: The monitoring platform will undergo the following functional upgrades to achieve automated and intelligent fault prevention and control: (a) Intelligent Fault Diagnosis Module: Function 1: Real-time calculation of the dimensionless mechanical error amplification factor for each measuring point. The data is displayed as a heatmap in the interface provided by the safety monitoring system, allowing maintenance personnel to intuitively locate highly sensitive monitoring points.

[0107] Function 2: Set the following four-dimensional anomaly detection indicators and corresponding thresholds for the built automated fault diagnosis platform: (1) Data mutation.

[0108] Judgment indicator: Rate of change of data between adjacent time points; Threshold setting: ; Judgment rule: If the threshold is exceeded and the duration is less than 3 seconds, it is judged as a jump fault.

[0109] (2) Data collection continuity.

[0110] Judgment indicator: Data packet loss rate; Threshold setting: More than 5 consecutive sampling periods of packet loss; Judgment rule: It is judged as a transmission interruption or a data acquisition device crash.

[0111] (3) Transmission delay.

[0112] Judgment indicator: Data reporting time interval; Threshold setting: The time interval is greater than twice the design cycle; Judgment rule: It is judged as a transmission delay fault.

[0113] (4) Mechanical rationality.

[0114] Judgment criteria: Whether the mechanical properties exceed the physical limits; Threshold setting: The stress value is greater than 1.2 times the material strength limit; Judgment rule: It is judged as a sensor range or conversion error.

[0115] Once any of the above indicators triggers the corresponding judgment rule, the system will automatically perform the above three types of mechanical consistency verification (including displacement compatibility verification, stress balance verification, and deformation trend consistency verification). Once a violation of mechanical constraints is detected (i.e., mechanical consistency verification fails), a fault alarm will be generated immediately, and the possible fault type will be given (e.g., "Measurement point P05 is suspected of temperature drift, on-site calibration is recommended").

[0116] Function 3: Calculate the comprehensive relative error of mechanical properties under the current fault condition. ,when It automatically raises the alert level by one level to avoid missed reports.

[0117] (ii) Automatic Data Correction Module: Function 1: For sensor drift-related faults, the neighborhood back-calculation method based on the deformation coordination equation is automatically invoked to calculate the drift amount in real time and correct it online.

[0118] Function 2: For data packet loss / abruptness faults, the physical field reconstruction method based on the reduced-order finite element model is automatically invoked to backfill and correct the data after offline inversion.

[0119] Function 3: After correction, automatically recalculate mechanical properties. and safety margin It then compares the results with the historical judgments before the correction, generating a "Status Judgment Review Report" to mark any possible erroneous judgments from the past.

[0120] Compared with existing monitoring platforms that only have data display and simple over-limit alarm functions, the monitoring platform after the above-mentioned functional upgrades has achieved automated closed-loop processing of "fault diagnosis, error quantification, data correction, and status reassessment", which shortens the response time of manual intervention from days to minutes and significantly improves the intelligence level of the safety monitoring system.

[0121] The fault prevention and control method provided in this invention can quantify the relationship between the original data error and the final mechanical index calculation error, realize a unified quantitative comparison of the degree of error amplification between measurement points of different dimensions, realize quantitative prediction of fault impact, and on this basis, construct a whole-process prevention and control system of pre-event prevention, in-event identification, and post-event correction. Mechanical sensitivity, mechanical consistency, and mechanical model reconstruction are integrated into each link, reducing the failure rate of the safety monitoring system, improving the accuracy of the safety monitoring system status judgment, and effectively preventing engineering safety accidents caused by monitoring distortion.

[0122] To facilitate understanding, the implementation process and beneficial effects of the above-mentioned fault prevention and control method are described below with examples.

[0123] Example: Take the slope monitoring project of a complex rock and soil water conservancy project in a mountainous area as an example.

[0124] Step T1: Prevention phase.

[0125] Optimize the layout of measuring points: Calculate the mechanical error amplification factor of each measuring point using a finite element model, including the measuring points at the toe of the slope. Slope top measuring point Deep slip surface measuring points Three times the number of redundant measuring points are set for the highly sensitive areas mentioned above, and two times the number of redundant measuring points are set for the non-sensitive areas.

[0126] Equipment upgrade: Select fiber optic grating inclinometers and anchor bolt stress gauges that are resistant to temperature drift and vibration, making them suitable for harsh mountain environments.

[0127] Standardized maintenance: Establish a monthly on-site calibration and weekly inspection system, and unify the data acquisition frequency to 1Hz to ensure effective capture of dynamic response.

[0128] Algorithm optimization: The filtering algorithm was adjusted to a bandpass filter, and the cutoff frequency was set to 0.01Hz~0.5Hz to retain effective deformation information.

[0129] Step T2: In-process identification stage.

[0130] Build an automated fault diagnosis platform and set the above four-dimensional anomaly judgment indicators and corresponding thresholds.

[0131] Once any of the above indicators triggers the corresponding judgment rule, the system automatically enters the mechanical consistency verification process, which specifically includes: 1) Quantitative verification of displacement compatibility: Calculate the measured values ​​of strain at adjacent measuring points. Compared with the theoretical strain values ​​calculated by the finite element numerical model deviation rate ;when If only one of the adjacent measuring points exceeds the threshold, the measuring point is determined to be a fault point.

[0132] 2) Quantitative verification of stress balance: This can be achieved through... Let n be the number of stress measuring points. Calculate the unbalance rate of multiple stress measuring points on the same stress equilibrium section; when... At that time, it was determined that there was a fault in the measuring point of the section.

[0133] 3) Quantitative verification of deformation trend consistency: Calculation of deep displacement With surface displacement correlation coefficient Set the sliding window to 24 hours; when If the correlation is weak or negative, a faulty measuring point is identified.

[0134] Mechanical consistency verification was carried out based on three aspects: slope displacement coordination, stress balance, and deformation trend consistency. A 30-minute on-site review mechanism was established, and the job responsibilities of operation and maintenance personnel were clarified.

[0135] Step T3: Post-correction phase.

[0136] To address the temperature drift deviation of the inclinometer, the true value is inferred from the deformation compatibility equation using data from adjacent measuring points on the same cross section. The specific implementation process can be found in the relevant content above, and will not be elaborated here.

[0137] For the abrupt changes in anchor bolt stress data, a reduced-order finite element model is established to reconstruct the stress state of the fault measurement point. The specific implementation process is as follows: 1) Establish a reduced-order finite element model of the monitoring section of the engineering structure; 2) Extract valid monitoring data (such as displacement and stress) from all non-faulty measuring points on the same cross section during the fault period, and use them as static boundary conditions; 3) Construct the objective function using the structural deformation compatibility equation and the static equilibrium equation as constraints. ; 4) Solve using the least squares or conjugate gradient method to obtain the approximate true value of the fault measurement point; 5) Use the inversion results as correction data for the fault period to replace the original outliers.

[0138] Based on this, the status determination results of historical fault periods are retrospectively reviewed to correct any erroneous conclusions.

[0139] Step T4: Comparison of optimization results.

[0140] After implementing the above-mentioned comprehensive prevention and control measures, the performance and mechanical index calculation accuracy of the safety monitoring system were tracked and monitored for one year. Before and after optimization, the failure rate of the safety monitoring system was significantly reduced, the accuracy of state determination was significantly improved, and the average error of mechanical indexes was significantly reduced. The errors of core mechanical indexes were all controlled within the corresponding engineering allowable range, which can well meet the requirements of engineering safety evaluation. The optimized safety monitoring system can capture multiple instances of accelerated local deformation of the slope and issue warning signals in a timely manner. Based on the warning results, maintenance personnel can take measures such as grouting and adding anchor bolts to reinforce the slope, effectively preventing slope collapse accidents.

[0141] The fault prevention and control method provided in this embodiment of the invention is also applicable to various safety monitoring application scenarios such as traffic engineering, tunnels, bridges, mines, and buildings. It can be adapted by adjusting the model constitutive structure, sensor combination, and early warning threshold.

[0142] The above-mentioned fault prevention and control methods mainly have the following beneficial effects: (1) From qualitative to quantitative: The traditional empirical judgment that "errors may be amplified" is elevated to a judgment based on the mechanical error amplification factor. and comprehensive relative error The quantitative calculations and predictions fill a technological gap in this field.

[0143] (2) Significantly improve the accuracy of state determination: Through the mechanical mechanism control of the whole process, the transmission and amplification of errors are effectively curbed.

[0144] (3) Achieve proactive and precise prevention and control: based on pre-emptive measures Quantitative guidance is provided for redundant placement of measurement points to avoid over- or under-placement; in-process joint diagnosis based on the principle of mechanical consistency significantly reduces the false alarm rate; and post-process data reconstruction based on mechanical models ensures that the corrected data meets physical laws.

[0145] The aforementioned fault prevention and control methods form a quantifiable, reproducible, and intelligent optimization scheme for judging engineering status, which is of great value for ensuring the long-term safe operation of large-scale infrastructure.

[0146] Based on the above-described fault prevention and control method, this invention also provides a fault prevention and control device, see [link to relevant documentation]. Figure 3 As shown, the device may include the following modules: The first determination module 302 is used to determine different levels of error step by step based on the original monitoring data of different measuring points using a pre-constructed three-level error transmission mechanism.

[0147] The second determining module 304 is used to determine the error amplification factor for each measuring point during the process of determining different levels of error.

[0148] The identification module 306 is used to perform redundancy optimization of the sensor layout of the measuring points based on the error amplification coefficient of each measuring point, and to perform fault identification on the redundantly optimized measuring points based on the principle of mechanical consistency.

[0149] The correction module 308 is used to correct the original monitoring data of each target measuring point by using the corresponding mechanical model if a target measuring point with a fault is identified.

[0150] By adopting the above-mentioned fault prevention and control device, a three-level error transmission mechanism and a method for determining the error amplification coefficient of the measuring point can be introduced to redundantly optimize the arrangement of measuring point sensors, realize quantitative prediction of the impact of faults, and correct the data of fault measuring points on this basis. This constructs a full-process prevention and control system that includes pre-event prevention, in-event identification, and post-event correction. This can effectively reduce the failure rate of the safety monitoring system, improve the accuracy of the safety monitoring system's status determination, and effectively prevent engineering safety accidents caused by monitoring distortion.

[0151] The fault prevention and control device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0152] This invention also provides an electronic device, such as... Figure 4 The diagram shows the structure of the electronic device, which includes a processor 41 and a memory 40. The memory 40 stores computer-executable instructions that can be executed by the processor 41. The processor 41 executes the computer-executable instructions to implement the above-mentioned fault prevention and control method.

[0153] exist Figure 4 In the illustrated embodiment, the electronic device further includes a bus 42 and a communication interface 43, wherein the processor 41, the communication interface 43, and the memory 40 are connected via the bus 42.

[0154] The memory 40 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 42 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 42 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0155] Processor 41 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above-mentioned fault prevention and control method can be completed by the integrated logic circuits in the hardware of processor 41 or by software instructions. Processor 41 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the fault prevention and control method disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or as execution by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory. The processor 41 reads the information in the memory and, in conjunction with its hardware, completes the steps of the fault prevention and control method of the aforementioned embodiment.

[0156] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the aforementioned fault prevention and control method. For specific implementation details, please refer to the foregoing method embodiments, which will not be repeated here.

[0157] The computer program products of the fault prevention and control methods, devices and electronic devices provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0158] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0159] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0161] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A fault prevention and control method, characterized in that, include: Based on the original monitoring data from different measuring points, a pre-constructed three-level error transmission mechanism is used to determine the different levels of error step by step; In determining the different levels of error, the error amplification factor for each measuring point is determined; Based on the error amplification factor of each measuring point, the sensor layout of the measuring points is redundantly optimized, and based on the principle of mechanical consistency, fault identification is performed on the redundantly optimized measuring points. If a faulty target measurement point is identified, the original monitoring data for each target measurement point is corrected using the corresponding mechanical model.

2. The method according to claim 1, characterized in that, Different levels of error include distortion of raw data, deviation in the calculation of mechanical properties, and deviation in safety decisions; Based on the raw monitoring data from different measuring points, a pre-constructed three-level error propagation mechanism is used to determine different levels of error step by step, including: Based on the original monitoring data from different measuring points, the original data distortion of each measuring point is determined as the corresponding first-level error. Based on the original monitoring data of each measuring point, the corresponding mechanical index value is determined, and based on the original monitoring data of each measuring point and the first-level error, the mechanical index calculation deviation is determined as the second-level error. Based on the mechanical index values ​​of each measuring point and their corresponding structural safety thresholds, as well as the second-level error, the safety decision deviation is determined as the third-level error.

3. The method according to claim 1, characterized in that, In determining different levels of error, the error amplification factor for each measuring point is determined, including: Based on the original monitoring data from different measuring points, the error amplification factor for each measuring point is determined by a preset numerical perturbation method.

4. The method according to claim 1, characterized in that, After determining the error amplification factor for each measuring point, the following is also included: The corresponding original relative error is determined based on the original monitoring data of each measuring point; Based on the error amplification factor and the original relative error at each measuring point, the comprehensive relative error of the mechanical index is determined.

5. The method according to claim 1, characterized in that, Based on the error amplification factor of each measuring point, the sensor layout at the measuring points is redundantly optimized, including: If there are highly sensitive measurement points with an error amplification factor not less than the first threshold, then sensors are arranged with 3 times redundancy for each highly sensitive measurement point. If there are moderately sensitive measurement points with an error amplification factor less than the first threshold and not less than the second threshold, then sensors are arranged with double redundancy for each moderately sensitive measurement point; wherein, the first threshold is greater than the second threshold. If there are low-sensitivity measurement points with an error amplification factor less than the second threshold, then sensors are arranged at each low-sensitivity measurement point as a single point.

6. The method according to claim 5, characterized in that, Based on the principle of mechanical consistency, fault identification is performed on the redundant optimized arrangement of measuring points, including: Based on the first preset relationship that the displacement values ​​of adjacent displacement measuring points on the same cross section need to satisfy, the displacement coordination of multiple displacement measuring points continuously arranged on the same cross section is verified. Based on the second preset relationship that the stress values ​​of adjacent stress measuring points on the same cross section need to satisfy, the stress balance of multiple stress measuring points on the same cross section is verified. Based on the third preset relationship that the displacement values ​​of different parts of the same structure need to satisfy, the deformation trend of the displacement measurement points of different parts of the same structure is verified.

7. The method according to claim 1, characterized in that, If a faulty target measuring point is identified, the original monitoring data for each target measuring point is corrected using the corresponding mechanical model, including: If the first target measuring point is identified as having the first fault type that characterizes the sensor drift deviation, the corresponding deformation coordination equation is used to back-calculate the data drift amount, and the obtained data drift amount is used to correct the original monitoring data of each first target measuring point. If a second target measuring point is identified as having a second type of fault characterized by data loss or abrupt change, the physical field is inverted using a corresponding reduced-order finite element model with the objective of minimizing the objective function value, and the original monitoring data of each second target measuring point is corrected using the physical field inversion results; wherein, the reduced-order finite element model is simultaneously constrained by the corresponding deformation compatibility equation and static equilibrium equation.

8. A fault prevention and control device, characterized in that, include: The first determination module is used to determine different levels of error step by step based on the original monitoring data of different measuring points using a pre-constructed three-level error transmission mechanism. The second determining module is used to determine the error amplification factor for each measuring point during the process of determining different levels of error. The identification module is used to perform redundancy optimization of the sensor layout of the measuring points based on the error amplification coefficient of each measuring point, and to identify faults in the redundantly optimized measuring points based on the principle of mechanical consistency. The correction module is used to correct the original monitoring data of each target measuring point by using the corresponding mechanical model if a target measuring point with a fault is identified.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the fault prevention method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the fault prevention method according to any one of claims 1 to 7.

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