Safety monitoring system based on bearing capacity of combined new and old cast-in-place pile
By designing a safety monitoring system based on combined new and old cast-injected piles, using machine learning to evaluate the performance of old piles and combine the use scenarios of new piles to predict the performance defects of new piles, the problems of degradation of old piles and optimization of new piles are solved, and the safe and stable operation of the transmission line is ensured.
Patent Information
- Application Number
- CN202411981373.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2025-05-27
AI Technical Summary
The domestic power industry is developing rapidly. The performance of old transmission line cast-injected piles has declined after long-term service, which cannot meet the needs of new transmission line erection. However, after dismantling, the new piles need to ensure that they meet subsequent line erection iterations, and there is a lack of standardized service data analysis and optimization solutions.
Design a safety monitoring system based on the bearing capacity of combined new and old cast-infused piles, including the old cast-infused pile state analysis module, the new cast-infused pile demand analysis module and the construction technology update module. The dynamic vector index of the performance of the old pile is evaluated through machine learning, and the fitting analysis is carried out in combination with the design and use scenarios of the new pile, to predict the performance defects of the new pile and update the construction technical plan.
This system prevents potential performance problems of new cast-infused piles and ensures the safe and stable operation of transmission lines, providing a basis for optimizing the design and construction technology of cast-infused piles in new transmission lines.
Smart Images
Figure CN120042238A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of civil engineering, and in particular to a safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles. Background Art
[0002] Transmission line bored piles refer to a basic construction method in which bored piles formed by drilling or prefabrication are used as support and bearing structures in transmission line projects to ensure the stability and safety of transmission line towers.
[0003] Due to the rapid development of the domestic power industry, the performance of old transmission line bored piles has declined after a long period of service and cannot meet the requirements of new transmission line construction. However, the new transmission line bored piles to be constructed after demolition must be guaranteed to meet the requirements of subsequent line construction iterations. At present, there is no standardized solution based on the service data analysis of old transmission line bored piles to optimize the new transmission line bored piles to be constructed. Summary of the invention
[0004] In order to solve the above technical problems, a safety monitoring system based on the bearing capacity of combined old and new bored piles is provided. This technical solution solves the above-mentioned problem that due to the rapid development of the domestic power industry, the performance of the old transmission line bored piles has declined after a long period of service and cannot meet the requirements of the installation of new transmission lines. However, the new transmission line bored piles to be constructed after dismantling must ensure that they meet the requirements of subsequent line installation iterations.
[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:
[0006] A safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles, comprising:
[0007] An old bored pile status analysis module, a new bored pile demand analysis module, and a construction technology update module. The new bored pile demand analysis module is electrically connected to the old bored pile status analysis module, and the construction technology update module is electrically connected to the new bored pile demand analysis module.
[0008] The old cast-in-place pile state analysis module is used to analyze the factors affecting the performance degradation of the cast-in-place piles of the transmission lines in the service data based on the service data of all the old cast-in-place piles of the transmission lines, and to evaluate the performance dynamic vector index of the cast-in-place piles of the old transmission lines.
[0009] The new bored pile demand analysis module is used to obtain the design usage scenario years of the bored piles of the new transmission line to be built and the performance dynamic vector indicators of the old transmission line bored piles for fitting analysis, and predict the performance defect vector indicators of the new transmission line bored piles; the usage scenarios include: 220kV and above transmission lines.
[0010] The construction technology update module is used to update the bored pile construction technology plan according to the performance defect vector index of the bored pile of the new transmission line according to the performance defect vector.
[0011] Preferably, the old cast-in-place pile status analysis module includes:
[0012] The data division unit divides the data into foundation loss and environmental loss in the service data according to the time series based on the service data of all the old transmission line cast-in-place piles, and obtains the foundation loss time series data of the old transmission line cast-in-place piles and the environmental loss time series data of the old transmission line cast-in-place piles.
[0013] The performance loss evaluation unit evaluates the foundation performance loss index of the old transmission line bored piles and the environmental performance loss coefficient of the old transmission line bored piles based on the foundation loss time series data of the old transmission line bored piles and the environmental loss time series data of the old transmission line bored piles.
[0014] The performance dynamic vector unit uses the environmental performance loss coefficient of the transmission line bored pile to correct the basic performance loss index of the old transmission line bored pile, and obtains the performance dynamic vector index of the old transmission line bored pile.
[0015] Among them, the performance dynamic vector indicators of the old transmission line cast-in-place piles are as follows:
[0016] D i (t) = P i (t)×(1+G i (t))
[0017] Where D i (t) is the performance dynamic vector index of the cast-in-place pile of the i-th old transmission line under t unit time, P i (t) is the foundation performance loss index of the i-th old transmission line cast-in-place pile under t unit time, G i (t) is the environmental performance loss coefficient of the i-th old transmission line bored pile under t unit time.
[0018] Preferably, the performance loss evaluation unit internally includes:
[0019] The preprocessing subunit performs normalization processing based on the foundation loss time series data of the old transmission line bored piles and the environmental loss time series data of the old transmission line bored piles.
[0020] The time series window sub-unit establishes a performance loss observation window according to unit time, takes the basic loss factors and environmental loss factors that affect the performance degradation of transmission line bored piles as observation attributes, and obtains the time series window of multiple influencing factors for loss observation of old transmission line bored piles.
[0021] The characteristic data subunit slides the time series window of the multivariate influencing factors of the loss observation of the old transmission line bored piles to obtain the basic loss observation characteristic data of the old transmission line bored piles and the environmental loss observation characteristic data of the old transmission line bored piles.
[0022] The linear mapping subunit linearly transforms the foundation loss observation characteristic data and the environmental loss observation characteristic data of the old transmission line cast-in-place piles into the foundation loss observation characteristic vector matrix A of the old transmission line cast-in-place piles and the environmental loss observation characteristic vector matrix B of the old transmission line cast-in-place piles.
[0023]
[0024] Among them, X ij (t) is the observed characteristic vector of the jth foundation loss of the i-th old transmission line cast-in-place pile under t unit time, Y ik (t) is the kth environmental loss observation feature vector of the i-th old transmission line bored pile under t unit time, m is the total number of old transmission line bored piles, n is the total number of basic loss observation feature vectors, and l is the total number of environmental loss observation feature vectors.
[0025] The foundation state assessment subunit trains a time series autoregressive model based on each element in the foundation loss observation feature vector matrix of the old transmission line bored piles. The foundation loss observation feature vector of the old transmission line bored piles per unit time is used as input, and the least squares method is used to solve the influence coefficient of the foundation loss observation feature vector at the delayed moment on the foundation loss observation feature vector at the current moment. The training end goal is to minimize the error function between the observed value and the predicted value, and output the foundation performance loss index of the old transmission line bored piles.
[0026] The environmental status assessment subunit trains a multivariate regression model based on each element in the environmental loss observation feature vector matrix of the old transmission line bored piles, with each environmental loss observation feature vector affecting the old transmission line bored piles as the independent variable input, minimizing the error function as the training objective, and the environmental performance loss coefficient of the old transmission line bored piles as the dependent variable output.
[0027] Among them, the time series autoregressive model expression is:
[0028]
[0029] Where P i (t) is the foundation performance loss index of the i-th old transmission line cast-in-place pile under t unit time, α j is the influence coefficient of the jth basic loss observation feature vector, X ij(tv) is the observed characteristic vector of the jth foundation loss of the cast-in-place pile of the ith old transmission line under the lag order of tv unit time, and ∈(t) is the error term under t unit time.
[0030] Among them, the multiple regression model expression is:
[0031]
[0032] In the formula, G i (t) is the environmental performance loss coefficient of the cast-in-place pile of the i-th old transmission line under t unit time, β 0 is the intercept term, β k is the regression coefficient of the k environmental loss observation feature vectors, and ∈(t) is the error term under t unit time.
[0033] Preferably, the new cast-in-place pile demand analysis module is specifically:
[0034] The growth demand unit determines the scenario growth demand under the service scenario of the bored piles of the new transmission line to be constructed based on the design service scenario of the bored piles of the new transmission line to be constructed; the scenario growth demand includes: concrete bearing capacity, concrete corrosion resistance, and steel corrosion resistance.
[0035] The decision tree unit, based on the scenario growth demand under the usage scenario period of the bored piles of the new transmission line to be built, takes the usage scenario period as the root node, constructs leaf nodes for each different usage scenario, takes the scenario growth demand as the internal branch division attribute of the leaf node, and establishes a design usage scenario demand decision tree for the bored piles of the new transmission line to be built.
[0036] The defect generation unit substitutes the performance dynamic vector index of the old transmission line bored piles into the design and use scenario requirement decision tree of the new transmission line bored piles to be built, divides them according to the maximum information gain of the internal branch division attribute of each leaf node, and generates the performance defect vector index of the new transmission line bored piles.
[0037] Compared with the prior art, the present invention has the following beneficial effects:
[0038] The present invention proposes a safety monitoring scheme based on the bearing capacity of combined new and old cast-in-place piles. Through machine learning of the service data of cast-in-place piles of old transmission lines, the dynamic performance indicators are evaluated, and fitting analysis is performed in combination with the design and use scenario years of new piles to predict the performance defects of new piles, thereby updating the construction technology scheme. The beneficial effect is to prevent potential performance problems of new cast-in-place piles and ensure the safe and stable operation of transmission lines. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] Figure 1The internal framework diagram of a safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles of the present invention.
[0040] Figure 2 It is the internal framework diagram of the old bored pile status analysis module of the present invention.
[0041] Figure 3 This is an internal framework diagram of the performance loss evaluation unit of the present invention.
[0042] Figure 4 This is the internal framework diagram of the new bored pile demand analysis module of the present invention. DETAILED DESCRIPTION
[0043] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.
[0044] Reference Figure 1 As shown, a safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles includes:
[0045] An old bored pile status analysis module, a new bored pile demand analysis module, and a construction technology update module. The new bored pile demand analysis module is electrically connected to the old bored pile status analysis module, and the construction technology update module is electrically connected to the new bored pile demand analysis module.
[0046] The old cast-in-place pile state analysis module is used to analyze the factors affecting the performance degradation of the cast-in-place piles of the transmission lines in the service data based on the service data of all the old cast-in-place piles of the transmission lines, and to evaluate the performance dynamic vector index of the cast-in-place piles of the old transmission lines.
[0047] The new bored pile demand analysis module is used to obtain the design usage scenario years of the bored piles of the new transmission line to be built and the performance dynamic vector indicators of the old transmission line bored piles for fitting analysis, and predict the performance defect vector indicators of the new transmission line bored piles; the usage scenarios include: 220kV and above transmission lines.
[0048] The construction technology update module is used to update the bored pile construction technology plan according to the performance defect vector index of the bored pile of the new transmission line according to the performance defect vector.
[0049] The implementation principle of this scheme is to evaluate the dynamic performance indicators of cast-in-place piles of old transmission lines through machine learning, and to perform fitting analysis based on the design and use scenario of new piles to predict the performance defects of new piles, thereby updating the construction technology scheme. The beneficial effect is to prevent potential performance problems of new cast-in-place piles and ensure the safe and stable operation of transmission lines.
[0050] Reference Figure 2As shown in the figure, the old cast-in-place pile status analysis module includes:
[0051] The data division unit divides the data into foundation loss and environmental loss in the service data according to the time series based on the service data of all the old transmission line cast-in-place piles, and obtains the foundation loss time series data of the old transmission line cast-in-place piles and the environmental loss time series data of the old transmission line cast-in-place piles.
[0052] The performance loss evaluation unit evaluates the foundation performance loss index of the old transmission line bored piles and the environmental performance loss coefficient of the old transmission line bored piles based on the foundation loss time series data of the old transmission line bored piles and the environmental loss time series data of the old transmission line bored piles.
[0053] The performance dynamic vector unit uses the environmental performance loss coefficient of the transmission line bored pile to correct the basic performance loss index of the old transmission line bored pile, and obtains the performance dynamic vector index of the old transmission line bored pile.
[0054] Among them, the performance dynamic vector indicators of the old transmission line cast-in-place piles are as follows:
[0055] D i (t) = P i (t)×(1+G i (t))
[0056] Where D i (t) is the performance dynamic vector index of the cast-in-place pile of the i-th old transmission line under t unit time, P i (t) is the foundation performance loss index of the i-th old transmission line cast-in-place pile under t unit time, G i (t) is the environmental performance loss coefficient of the i-th old transmission line bored pile under t unit time.
[0057] This scheme analyzes the foundation loss and environmental loss in the service data of the old transmission line cast-in-place piles, evaluates the foundation performance loss index and environmental performance loss coefficient, and uses the environmental performance loss coefficient to correct the foundation performance loss index to obtain a more accurate dynamic vector index of the performance of the old transmission line cast-in-place piles. The beneficial effect is that it can comprehensively consider the influence of foundation loss and environmental factors, provide more accurate data support for evaluating the performance of the old transmission line cast-in-place piles, and guide the design and performance optimization of new cast-in-place piles.
[0058] Reference Figure 3 As shown, the performance loss evaluation unit includes:
[0059] The preprocessing subunit performs normalization processing based on the foundation loss time series data of the old transmission line bored piles and the environmental loss time series data of the old transmission line bored piles.
[0060] The time series window sub-unit establishes a performance loss observation window according to unit time, takes the basic loss factors and environmental loss factors that affect the performance degradation of transmission line bored piles as observation attributes, and obtains the time series window of multiple influencing factors for loss observation of old transmission line bored piles.
[0061] The characteristic data subunit slides the time series window of the multivariate influencing factors of the loss observation of the old transmission line bored piles to obtain the basic loss observation characteristic data of the old transmission line bored piles and the environmental loss observation characteristic data of the old transmission line bored piles.
[0062] The linear mapping subunit linearly transforms the foundation loss observation characteristic data and the environmental loss observation characteristic data of the old transmission line cast-in-place piles into the foundation loss observation characteristic vector matrix A of the old transmission line cast-in-place piles and the environmental loss observation characteristic vector matrix B of the old transmission line cast-in-place piles.
[0063]
[0064] Among them, X ij (t) is the observed characteristic vector of the jth foundation loss of the i-th old transmission line cast-in-place pile under t unit time, Y ik (t) is the kth environmental loss observation feature vector of the i-th old transmission line bored pile under t unit time, m is the total number of old transmission line bored piles, n is the total number of basic loss observation feature vectors, and l is the total number of environmental loss observation feature vectors.
[0065] The foundation state assessment subunit trains a time series autoregressive model based on each element in the foundation loss observation feature vector matrix of the old transmission line bored piles. The foundation loss observation feature vector of the old transmission line bored piles per unit time is used as input, and the least squares method is used to solve the influence coefficient of the foundation loss observation feature vector at the delayed moment on the foundation loss observation feature vector at the current moment. The training end goal is to minimize the error function between the observed value and the predicted value, and output the foundation performance loss index of the old transmission line bored piles.
[0066] The environmental status assessment subunit trains a multivariate regression model based on each element in the environmental loss observation feature vector matrix of the old transmission line bored piles, with each environmental loss observation feature vector affecting the old transmission line bored piles as the independent variable input, minimizing the error function as the training objective, and the environmental performance loss coefficient of the old transmission line bored piles as the dependent variable output.
[0067] Among them, the time series autoregressive model expression is:
[0068]
[0069] Where Pi (t) is the foundation performance loss index of the i-th old transmission line cast-in-place pile under t unit time, α j is the influence coefficient of the jth basic loss observation feature vector, X ij (tv) is the observed characteristic vector of the jth foundation loss of the cast-in-place pile of the ith old transmission line under the lag order of tv unit time, and ∈(t) is the error term under t unit time.
[0070] Among them, the multiple regression model expression is:
[0071]
[0072] In the formula, G i (t) is the environmental performance loss coefficient of the cast-in-place pile of the i-th old transmission line under t unit time, β 0 is the intercept term, β k is the regression coefficient of the k environmental loss observation feature vectors, and ∈(t) is the error term under t unit time.
[0073] By establishing a performance loss observation window per unit time, taking foundation loss and environmental loss as observation attributes, the time series data of multivariate influencing factors of loss observation of cast-in-place piles of old transmission lines are obtained. By sliding the time series window, the observation feature data of foundation loss and environmental loss are extracted and linearly transformed into a feature vector matrix. The foundation loss observation feature vector is trained using a time series autoregressive model to solve the foundation performance loss index; at the same time, the environmental loss observation feature vector is trained using a multivariate regression model to output the environmental performance loss coefficient. The beneficial effect is that it can accurately capture and quantify the impact of foundation loss and environmental loss on the performance of cast-in-place piles of transmission lines, providing a reference object for cast-in-place piles of new transmission lines.
[0074] Reference Figure 4 As shown in the figure, the new cast-in-place pile demand analysis module is as follows:
[0075] The growth demand unit determines the scenario growth demand under the service scenario of the bored piles of the new transmission line to be constructed based on the design service scenario of the bored piles of the new transmission line to be constructed; the scenario growth demand includes: concrete bearing capacity, concrete corrosion resistance, and steel corrosion resistance.
[0076] The decision tree unit, based on the scenario growth demand under the usage scenario period of the bored piles of the new transmission line to be built, takes the usage scenario period as the root node, constructs leaf nodes for each different usage scenario, takes the scenario growth demand as the internal branch division attribute of the leaf node, and establishes a design usage scenario demand decision tree for the bored piles of the new transmission line to be built.
[0077] The defect generation unit substitutes the performance dynamic vector index of the old transmission line bored piles into the design and use scenario requirement decision tree of the new transmission line bored piles to be built, divides them according to the maximum information gain of the internal branch division attribute of each leaf node, and generates the performance defect vector index of the new transmission line bored piles.
[0078] It can be understood that the growth demand unit determines the scenario growth demand of the new transmission line bored piles to be built under different usage scenarios, and uses the demand to build a design usage scenario demand decision tree, with the usage scenario year as the root node, different usage scenarios as leaf nodes, and scenario growth demand as the division attribute; finally, the defect generation unit substitutes the performance dynamic vector index of the old transmission line bored piles into the decision tree, and divides them according to the maximum information gain, thereby generating the performance defect vector index of the new transmission line bored piles. The beneficial effect of this scheme is that it can comprehensively consider the design usage scenario years and growth demand of the new bored piles, accurately predict and quantify potential performance defects, and provide data for the design optimization of transmission line bored piles.
[0079] The use process of a safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles is as follows:
[0080] Step 1: Based on the service data of all old transmission line bored piles, divide the data into foundation loss and environmental loss in the service data according to the time series, and obtain the foundation loss time series data of the old transmission line bored piles and the environmental loss time series data of the old transmission line bored piles.
[0081] Step 2: Perform normalization based on the foundation loss time series data of the old transmission line bored piles and the environmental loss time series data of the old transmission line bored piles.
[0082] Step 3: Establish a performance loss observation window per unit time, take the basic loss factors and environmental loss factors that affect the performance degradation of transmission line bored piles as observation attributes, and obtain the time series window of multiple influencing factors for loss observation of old transmission line bored piles.
[0083] Step 4: Slide the time series window of the multivariate influencing factors of the loss observation of the old transmission line bored piles to obtain the basic loss observation characteristic data of the old transmission line bored piles and the environmental loss observation characteristic data of the old transmission line bored piles.
[0084] Step 5: Linearly transform the foundation loss observation characteristic data and environmental loss observation characteristic data of the old transmission line cast-in-place piles into the foundation loss observation characteristic vector matrix A of the old transmission line cast-in-place piles and the environmental loss observation characteristic vector matrix B of the old transmission line cast-in-place piles.
[0085] Step 6: Based on each element in the foundation loss observation feature vector matrix of the old transmission line bored piles, a time series autoregressive model is trained. The foundation loss observation feature vector of the old transmission line bored piles per unit time is used as input. The least squares method is used to solve the influence coefficient of the foundation loss observation feature vector at the delayed moment on the foundation loss observation feature vector at the current moment. The training end goal is to minimize the error function between the observed value and the predicted value, and output the foundation performance loss index of the old transmission line bored piles.
[0086] Step 7: Based on each element in the environmental loss observation feature vector matrix of the old transmission line bored piles, a multivariate regression model is trained, with each environmental loss observation feature vector affecting the old transmission line bored piles as the independent variable input, minimizing the error function as the training objective, and the environmental performance loss coefficient of the old transmission line bored piles as the dependent variable output.
[0087] Step 8: Use the environmental performance loss coefficient of the transmission line bored pile to correct the basic performance loss index of the old transmission line bored pile, and obtain the performance dynamic vector index of the old transmission line bored pile.
[0088] Step 9: Based on the design usage scenario period of the bored piles of the new transmission line to be constructed, determine the scenario growth demand under the usage scenario period of the bored piles of the new transmission line to be constructed; the scenario growth demand includes: concrete bearing capacity, concrete corrosion resistance, and steel corrosion resistance.
[0089] Step 10: Based on the scenario growth demand under the usage scenario period of the bored piles of the new transmission line to be constructed, take the usage scenario period as the root node, construct leaf nodes for each different usage scenario, take the scenario growth demand as the internal branch division attribute of the leaf node, and establish the design usage scenario demand decision tree for the bored piles of the new transmission line to be constructed.
[0090] Step 11: Substitute the performance dynamic vector index of the old transmission line bored piles into the design usage scenario requirement decision tree of the new transmission line bored piles to be built, divide them according to the maximum information gain of the internal branch division attribute of each leaf node, and generate the performance defect vector index of the new transmission line bored piles.
[0091] Step 12: Based on the performance defect vector index of the bored piles of the new transmission line, update the bored pile construction technical plan according to the performance defect vector.
[0092] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.
Claims
1. A safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles, characterized in that: include: Old cast-in-place pile status analysis module, new cast-in-place pile demand analysis module, construction technology update module; The new cast-in-place pile demand analysis module is electrically connected to the old cast-in-place pile status analysis module, and the construction technology update module is electrically connected to the new cast-in-place pile demand analysis module; The old cast-in-place pile state analysis module is used to analyze the factors affecting the performance degradation of the cast-in-place piles of the transmission lines in the service data based on the service data of all the cast-in-place piles of the old transmission lines, and to evaluate the performance dynamic vector index of the cast-in-place piles of the old transmission lines; The new bored pile demand analysis module is used to obtain the design usage scenario years of the bored piles of the new transmission line to be built and the performance dynamic vector index of the old transmission line bored piles for fitting analysis, and predict the performance defect vector index of the new transmission line bored piles; the usage scenarios include: 220kV and above transmission lines; The construction technology update module is used to update the bored pile construction technology plan according to the performance defect vector index of the bored pile of the new transmission line according to the performance defect vector.
2. The safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles according to claim 1 is characterized in that: The old cast-in-place pile status analysis module includes: A data division unit, based on the service data of all old transmission line cast-in-place piles, divides the data according to the time series for the foundation loss and the environmental loss in the service data, and obtains the foundation loss time series data of the old transmission line cast-in-place piles and the environmental loss time series data of the old transmission line cast-in-place piles; The performance loss evaluation unit evaluates the foundation performance loss index of the old transmission line bored piles and the environmental performance loss coefficient of the old transmission line bored piles based on the foundation loss time series data of the old transmission line bored piles and the environmental loss time series data of the old transmission line bored piles; The performance dynamic vector unit uses the environmental performance loss coefficient of the transmission line cast-in-place pile to correct the basic performance loss index of the old transmission line cast-in-place pile, and obtains the performance dynamic vector index of the old transmission line cast-in-place pile; Among them, the performance dynamic vector indicators of the old transmission line cast-in-place piles are as follows: D i (t)=P i (t)×(1+G i (t)) Where D i (t) is the performance dynamic vector index of the cast-in-place pile of the i-th old transmission line under t unit time, P i (t) is the foundation performance loss index of the i-th old transmission line cast-in-place pile under t unit time, G i (t) is the environmental performance loss coefficient of the i-th old transmission line bored pile under t unit time.
3. The safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles according to claim 2 is characterized in that: The performance loss evaluation unit includes: The preprocessing subunit performs normalization processing based on the foundation loss time series data of the old transmission line cast-in-place piles and the environmental loss time series data of the old transmission line cast-in-place piles; The time series window subunit establishes a performance loss observation window according to unit time, takes the basic loss factors and environmental loss factors that affect the performance degradation of the transmission line cast-in-place piles as observation attributes, and obtains the time series window of multiple influencing factors for the loss observation of the old transmission line cast-in-place piles; The characteristic data subunit slides the time series window of the multivariate influencing factors of the loss observation of the old transmission line cast-in-place piles to obtain the basic loss observation characteristic data of the old transmission line cast-in-place piles and the environmental loss observation characteristic data of the old transmission line cast-in-place piles; The linear mapping subunit linearly transforms the foundation loss observation characteristic data and the environmental loss observation characteristic data of the old transmission line cast-in-place piles into the foundation loss observation characteristic vector matrix A of the old transmission line cast-in-place piles and the environmental loss observation characteristic vector matrix B of the old transmission line cast-in-place piles; Among them, X ij (t) is the observed characteristic vector of the jth foundation loss of the i-th old transmission line cast-in-place pile under t unit time, Y ik (t) is the kth environmental loss observation feature vector of the i-th old transmission line pile under t unit time, m is the total number of old transmission line piles, n is the total number of basic loss observation feature vectors, and l is the total number of environmental loss observation feature vectors; The foundation state assessment subunit trains a time series autoregressive model based on each element in the foundation loss observation feature vector matrix of the old transmission line cast-in-place piles, takes the foundation loss observation feature vector of the old transmission line cast-in-place piles per unit time as input, uses the least squares method to solve the influence coefficient of the foundation loss observation feature vector at the delayed time on the foundation loss observation feature vector at the current time, takes minimizing the error function between the observed value and the predicted value as the training end goal, and outputs the foundation performance loss index of the old transmission line cast-in-place piles; The environmental status assessment subunit trains a multivariate regression model based on each element in the environmental loss observation feature vector matrix of the old transmission line bored piles, with each environmental loss observation feature vector affecting the old transmission line bored piles as the independent variable input, minimizing the error function as the training objective, and the environmental performance loss coefficient of the old transmission line bored piles as the dependent variable output.
4. The safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles according to claim 3 is characterized in that: The time series autoregressive model expression is: Where P i (t) is the foundation performance loss index of the i-th old transmission line cast-in-place pile under t unit time, α j is the influence coefficient of the jth basic loss observation feature vector, X ij (tv) is the observed characteristic vector of the jth foundation loss of the cast-in-place pile of the ith old transmission line under the lag order of tv unit time, and ∈(t) is the error term under t unit time.
5. The safety monitoring system based on the bearing capacity of combined new and old cast-in-place piles according to claim 4 is characterized in that: The multiple regression model expression is: In the formula, G i (t) is the environmental performance loss coefficient of the cast-in-place pile of the i-th old transmission line under t unit time, β0 is the intercept term, β k is the regression coefficient of the k environmental loss observation feature vectors, and ∈(t) is the error term under t unit time.
6. The safety monitoring system based on the bearing capacity of combined new and old bored piles according to claim 5 is characterized in that: The new cast-in-place pile demand analysis module is as follows: The growth demand unit determines the scenario growth demand under the usage scenario of the bored piles of the new transmission line to be constructed based on the design usage scenario of the bored piles of the new transmission line to be constructed; The growth requirements of the scenarios include: concrete bearing capacity, concrete corrosion resistance, and steel corrosion resistance; The decision tree unit, based on the scenario growth demand under the usage scenario years of the bored piles of the new transmission line to be constructed, takes the usage scenario years as the root node, constructs leaf nodes for each different usage scenario, takes the scenario growth demand as the internal branch division attribute of the leaf node, and establishes a design usage scenario demand decision tree for the bored piles of the new transmission line to be constructed; The defect generation unit substitutes the performance dynamic vector index of the old transmission line bored piles into the design and use scenario requirement decision tree of the new transmission line bored piles to be built, divides them according to the maximum information gain of the internal branch division attribute of each leaf node, and generates the performance defect vector index of the new transmission line bored piles.