Method and system for monitoring performance of a prestressed concrete structure

Abstract

The application relates to the technical field of building structures, in particular to a prestressed concrete structure performance monitoring method and system, which comprises the following steps: acquiring application environment parameters and performance monitoring data; determining external load tensile stress and external environment parameters according to the application environment parameters; determining stress loss data according to the performance monitoring data; respectively arranging the external load tensile stress, the external environment parameters and the stress loss data according to time nodes to obtain a concrete performance graph; collecting real-time application parameters and simulated performance data of the prestressed concrete structure in a current time period, and analyzing the real-time application parameters and the simulated performance data according to the concrete performance graph to obtain real-time performance monitoring data; and judging whether the real-time performance monitoring data is abnormal, if yes, generating performance warning information and sending the performance warning information to a target terminal. The application improves the performance monitoring accuracy of the prestressed concrete structure.

Description

Technical Field

[0001] This application relates to the technical field of building structures, and in particular to a performance monitoring method and system for prestressed concrete structures. Background Technology

[0002] Prestressed concrete structures, with their excellent crack resistance, load-bearing capacity, and durability, are widely used in major infrastructure projects such as bridges, dams, high-rise buildings, and large stadiums. The safety and stability of these structures are directly related to the safety of project operation, the safety of people's lives and property, and the public interest; therefore, real-time and accurate monitoring of their performance is crucial.

[0003] In practical applications, prestressed concrete structures operate in complex and ever-changing environments, continuously bearing external loads such as those from objects, wind, and earthquakes, while also being affected by environmental factors like temperature variations, humidity fluctuations, acid rain erosion, and freeze-thaw cycles. During this process, stress loss inevitably occurs within the structure. If this stress loss exceeds a safe threshold, it can lead to serious safety incidents such as cracks, deformation, or even collapse. Therefore, timely monitoring of the stress state and performance changes at different structural nodes is a crucial prerequisite for ensuring the safe operation of prestressed concrete structures.

[0004] Currently, performance monitoring of prestressed concrete structures mostly employs traditional single-point monitoring methods. Specifically, the mainstream existing prestress loss monitoring technology is mainly achieved through stress wave signal acquisition and analysis. A typical process includes: first, acquiring the initial stress wave signal of the prestressed concrete structure before prestress loss occurs; then, during the structure's use, acquiring the stress wave signal in real time using a piezoelectric ceramic sensor, and filtering the acquired stress wave signal using a bandpass filter; next, calibrating the propagation speed of the filtered stress wave signal based on the initial stress wave signal, and determining the attenuation relationship of the stress wave signal with propagation distance; finally, normalizing the stress wave signal to obtain the prestress value at the location where the piezoelectric ceramic sensor is set; by repeating the above signal acquisition and normalization process, continuous monitoring of prestress loss is achieved. However, this type of existing technology has significant limitations: on the one hand, it is essentially still a single-point monitoring system, only able to acquire local prestress data at the sensor deployment location, unable to achieve comprehensive coverage of different nodes in the structure, and difficult to reflect the overall stress loss distribution of the structure; on the other hand, stress wave signals are highly susceptible to interference from the external environment during transmission within the structure, and even after filtering with a bandpass filter, it is still difficult to completely eliminate the interference, resulting in abnormal deviations in the final received stress wave signals, thus affecting the accuracy of prestress numerical monitoring. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this application provides a method and system for monitoring the performance of prestressed concrete structures.

[0006] Firstly, this application provides a method for performance monitoring of prestressed concrete structures, employing the following technical solution: Obtain application environment parameters and performance monitoring data of different prestressed concrete structures within a historical period; Based on the application environment parameters, determine the external load tensile stress borne by each prestressed concrete structure at different structural locations during application, as well as the external environment parameters of the location of the prestressed concrete structure. Based on the performance monitoring data, the stress loss data of different structural nodes in each prestressed concrete structure are determined; The tensile stress of the external load, the external environmental parameters, and the stress loss data are organized according to time nodes to obtain the concrete performance diagram; Real-time application parameters and simulated performance data of prestressed concrete structures are collected within the current time period. The real-time application parameters and simulated performance data are analyzed based on the concrete performance diagram to obtain real-time performance monitoring data. Determine if there are any anomalies in the real-time performance monitoring data. If so, generate a performance warning message and send the performance warning message to the target terminal.

[0007] By adopting the above technical solution, application environment parameters and performance monitoring data of different prestressed concrete structures over historical periods were obtained, providing a multi-sample, full-cycle foundation for subsequent analysis. Based on the application environment parameters, the tensile stress of external loads and external environmental parameters at different structural locations were decomposed, clarifying the precise application locations of loads and the environment. Simultaneously, stress loss data for corresponding nodes was extracted from the performance monitoring data, establishing location correlations and making the monitoring data more comprehensive and targeted, avoiding analytical biases caused by single or partial data. Based on the obtained tensile stress of external loads, external environmental parameters, and stress loss data, the three types of data were organized according to time nodes to obtain a concrete performance diagram. The time node organization method can intuitively present the evolution trajectory of data over time. Current real-time application parameters and simulated performance data were collected, and the constructed concrete performance diagram was used as an analytical benchmark. The correlation patterns and evolution characteristics of historical data in the performance diagram were utilized to conduct targeted analysis of the real-time data. This effectively improved the reliability of real-time monitoring results and avoided errors in judgment based on isolated data detached from historical context. After obtaining real-time performance monitoring data, anomaly detection is performed first, which can promptly identify potential structural performance problems. When an anomaly is detected, a performance warning message is directly generated and sent to the target terminal, achieving a seamless connection between anomaly identification and warning delivery. The targeted delivery of warning messages ensures the accuracy and timeliness of information transmission, skipping the cumbersome information relay links in traditional monitoring, improving the efficiency of anomaly response, and guaranteeing the accuracy of prestressed concrete structure monitoring.

[0008] In one possible implementation, the step of organizing the external load tensile stress, the external environmental parameters, and the stress loss data according to time nodes to obtain the concrete performance diagram includes: A performance monitoring coordinate system is constructed, wherein the X-axis of the performance monitoring coordinate system represents different time nodes within the historical time period, and the Y-axis of the performance monitoring coordinate system represents the tensile stress of external loads of different unit specifications, environmental parameters, and stress loss data. The tensile stress of the external load, the external environmental parameters, and the stress loss data are respectively imported into the performance monitoring coordinate system according to the time nodes to obtain the tensile stress curve corresponding to the tensile stress of the external load, the environmental curve corresponding to the external environmental parameters, and the stress loss curve corresponding to the stress loss data. The tensile stress curve, environmental curve, and stress loss curve are divided into units according to the application time of the prestressed concrete structure to obtain multiple tensile stress curve segments, multiple environmental curve segments, and multiple stress loss curve segments. Based on the application duration, the initial endpoint of the tensile stress curve segment is connected to the initial breakpoint of the environmental curve segment, and the terminating endpoint of the tensile stress curve segment is connected to the terminating endpoint of the environmental curve segment to obtain multiple tensile stress environment block diagrams. Based on the application duration, the initial endpoint of the tensile stress curve segment is connected to the initial endpoint of the stress loss curve segment, and the termination point of the tensile stress curve segment is connected to the termination endpoint of the environmental curve segment to obtain multiple tensile stress loss block diagrams. The tensile stress environment diagram and tensile stress loss diagram are organized according to time nodes to obtain the concrete performance diagram.

[0009] In one possible implementation, the step of analyzing the real-time application parameters and simulated performance data based on the concrete performance diagram to obtain real-time performance monitoring data includes: The real-time application duration, real-time tensile stress, and real-time environmental parameters of the current prestressed concrete structure are determined based on the real-time application parameters. The real-time tensile stress and the real-time environmental parameters are imported into the performance monitoring coordinate system according to the current time node to obtain the real-time tensile stress curve and the real-time environmental curve corresponding to the real-time application parameters; Connect the initial endpoint of the real-time tensile stress curve to the initial endpoint of the real-time environment curve, and connect the terminating endpoint of the real-time tensile stress curve to the terminating endpoint of the real-time environment curve to obtain a real-time tensile stress environment block diagram. Using the real-time application duration as the first screening condition, the tensile stress environment block diagram in the concrete performance diagram is screened to obtain a target tensile stress environment block diagram with the same real-time application duration. Key point features are extracted from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram respectively, and the key point features are used as the second screening condition to screen and match the real-time tensile stress environment block diagram and the target tensile stress environment block diagram to obtain a matched tensile stress environment block diagram. Based on the tensile stress loss diagram in the concrete performance diagram, determine the target tensile stress loss diagram corresponding to the matching tensile stress environment diagram; The simulated performance data is used as the third screening condition to screen the initial endpoint data of the stress loss curve segment in the target tensile stress loss diagram, thereby obtaining the target stress loss curve segment. The target stress loss curve segment is decomposed according to time nodes to obtain real-time performance monitoring data.

[0010] In one possible implementation, determining whether the real-time performance monitoring data is abnormal includes: If the real-time performance monitoring data is not abnormal, then the future application tensile stress and environmental prediction data of the prestressed concrete structure in the future time period are obtained. Based on the concrete performance diagram, a stress loss trend prediction model is constructed. The future application tensile stress and environmental prediction data, as well as the real-time performance monitoring data, are input into the stress loss trend prediction model to obtain the stress loss prediction curves of different structural location nodes within a future preset period. Extract the stress loss safety threshold range of similar structures in the same application stage from the concrete performance graph, compare the stress loss prediction curve with the stress loss safety threshold range, and determine whether there is a risk of stress loss exceeding the limit within a future preset period. If there is a risk of exceeding limits, a targeted preventive maintenance plan is generated by combining the slope of the stress loss prediction curve and the influence weight of the environmental prediction data. The preventive maintenance plan includes key maintenance nodes, maintenance time windows and stress compensation measures, and the preventive maintenance plan is pushed to the maintenance management terminal.

[0011] In one possible implementation, constructing the stress loss trend prediction model based on the concrete performance diagram includes: The tensile stress curves, environmental curves, and stress loss curves for different structural types and application environments are extracted from the concrete performance diagram to construct a model training sample set. The input features of the training sample set include the cumulative tensile stress value and the fluctuation range of environmental parameters, and the label is the stress loss value at the corresponding time node. Extract the temporal and correlation features of tensile stress curves, environmental curves, and stress loss curves, learn the temporal evolution law of stress loss through a pre-set neural network, and construct a stress loss trend prediction model. The stress loss trend prediction model is trained using a training sample set. The convolution kernel size, the number of hidden layer neurons, and the learning rate parameters of the model are optimized by a grid search algorithm until the prediction error of the stress loss trend prediction model is lower than a preset threshold, thus obtaining the optimized stress loss trend prediction model.

[0012] In one possible implementation, the extraction of key point features from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram respectively includes: Key points are extracted from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram, respectively. The key points include curve inflection points, extreme points and slope change points. Calculate the feature descriptor for each key point, the feature descriptor including the coordinate information of the key point and the direction of the curve tangent; The feature descriptors of the two sets of key points are matched, and abnormal matching pairs with a matching distance greater than a preset threshold are removed to obtain an effective key point matching set. The effective key point matching set is used as the key point feature.

[0013] In one possible implementation, determining whether the real-time performance monitoring data is abnormal includes: Based on the distribution characteristics of historical normal performance data in the concrete performance diagram, the normal threshold range of performance monitoring data for nodes at different structural locations is determined. Calculate the deviation between the real-time performance monitoring data and the normal threshold range. If the deviation is greater than the preset deviation threshold, it is determined to be abnormal. In addition, based on the time-series change trend of the real-time performance monitoring data, if the monitoring data of multiple consecutive time nodes show a monotonically increasing or decreasing deviation trend, even if the data of a single node does not exceed the normal threshold range, it is judged as a potential anomaly and an early warning message is generated.

[0014] Secondly, this application provides a performance monitoring system for prestressed concrete structures, employing the following technical solution: A performance monitoring system for prestressed concrete structures, comprising: The data acquisition module is used to acquire application environment parameters and performance monitoring data of different prestressed concrete structures within a historical period. The parameter determination module is used to determine the external load tensile stress corresponding to different structural nodes of each prestressed concrete structure during application, as well as the external environmental parameters of the location of the prestressed concrete structure, based on the application environment parameters. The data determination module is used to determine the stress loss data of different structural nodes of each prestressed concrete structure based on the performance monitoring data. The data processing module is used to process the external load tensile stress, the external environmental parameters, and the stress loss data according to time nodes to obtain the concrete performance diagram. The data analysis module collects real-time application parameters and simulated performance data of the prestressed concrete structure within the current time period, and analyzes the real-time application parameters and simulated performance data according to the concrete performance diagram to obtain real-time performance monitoring data. The data monitoring module is used to determine whether there are any abnormalities in the real-time performance monitoring data. If so, it generates a performance warning message and sends the performance warning message to the target terminal.

[0015] Thirdly, this application provides an electronic device that adopts the following technical solution: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a performance monitoring method for a prestressed concrete structure as described in any of the first aspects.

[0016] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform a performance monitoring method for a prestressed concrete structure as described in any of the first aspects.

[0017] In summary, this application includes at least one of the following beneficial technical effects: By adopting the above technical solution, application environment parameters and performance monitoring data of different prestressed concrete structures over historical periods were obtained, providing a multi-sample, full-cycle foundation for subsequent analysis. Based on the application environment parameters, the tensile stress of external loads and external environmental parameters at different structural locations were decomposed, clarifying the precise application locations of loads and the environment. Simultaneously, stress loss data for corresponding nodes was extracted from the performance monitoring data, establishing location correlations and making the monitoring data more comprehensive and targeted, avoiding analytical biases caused by single or partial data. Based on the obtained tensile stress of external loads, external environmental parameters, and stress loss data, the three types of data were organized according to time nodes to obtain a concrete performance diagram. The time node organization method can intuitively present the evolution trajectory of data over time. Current real-time application parameters and simulated performance data were collected, and the constructed concrete performance diagram was used as an analytical benchmark. The correlation patterns and evolution characteristics of historical data in the performance diagram were utilized to conduct targeted analysis of the real-time data. This effectively improved the reliability of real-time monitoring results and avoided errors in judgment based on isolated data detached from historical context. After obtaining real-time performance monitoring data, anomaly detection is performed first, which can promptly identify potential structural performance problems. When an anomaly is detected, a performance warning message is directly generated and sent to the target terminal, achieving a seamless connection between anomaly identification and warning delivery. The targeted delivery of warning messages ensures the accuracy and timeliness of information transmission, skipping the cumbersome information relay links in traditional monitoring, improving the efficiency of anomaly response, and guaranteeing the accuracy of prestressed concrete structure monitoring. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a performance monitoring method for prestressed concrete structures provided in an embodiment of this application.

[0019] Figure 2 This is a structural schematic diagram of a performance monitoring system for prestressed concrete structures provided in an embodiment of this application.

[0020] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] The following is in conjunction with the appendix Figures 1-3 This application will be described in further detail.

[0022] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of this application.

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0025] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0026] This application provides a performance monitoring method for prestressed concrete structures, executed by electronic equipment, such as... Figure 1 As shown, the method includes: Step S10: Obtain application environment parameters and performance monitoring data of different prestressed concrete structures within a historical period.

[0027] Specifically, by examining the bridge's construction archives, operation and maintenance management system, and past monitoring reports, application environment parameters and performance monitoring data for the past five years (within its service life) were obtained. The application environment parameters include: temperature, humidity, rainfall, and atmospheric corrosion levels for different seasons over the years, as well as traffic load data such as vehicle load levels and traffic volume borne by the bridge; the performance monitoring data includes: strain monitoring data, prestressed tendon stress monitoring data, and structural deformation data for each key monitoring node over the years.

[0028] Step S11: Determine the tensile stress of the external load corresponding to different structural nodes of each prestressed concrete structure during application, as well as the external environmental parameters of the location of the prestressed concrete structure, based on the application environment parameters.

[0029] Specifically, combining the cross-sectional dimensions and material mechanics parameters (concrete compressive strength grade C50, prestressed tendon tensile strength standard value 1860MPa) from the bridge structural design drawings, the tensile stress of external loads borne by each key monitoring node at different time points is calculated using structural mechanics calculation software (Midas Civil). For example, the tensile stress of external loads at the mid-span section of the main span during the period of maximum daily traffic volume is 1.2MPa to 1.8MPa. External environmental parameters for the location of each key monitoring node are selected and compiled from the application environment parameters, and time nodes are divided by quarter to form a correlation data of "time-temperature-humidity-atmospheric corrosion level".

[0030] Step S12: Determine the stress loss data of different structural nodes in each prestressed concrete structure based on the performance monitoring data.

[0031] Specifically, based on the prestressed tendon stress monitoring data in the performance monitoring data, the stress loss data for each key monitoring node is calculated: Stress loss data = Initial prestress value - Measured prestress value at each time node. For example, the initial prestress value at the prestressed tendon anchorage node is 1395 MPa, and the measured prestress value after 5 years of service is 1320 MPa, corresponding to a stress loss of 75 MPa.

[0032] Step S13: Organize the external load tensile stress, external environmental parameters, and stress loss data according to time nodes to obtain the concrete performance diagram.

[0033] Specifically, a performance monitoring coordinate system is constructed. The X-axis of the performance monitoring coordinate system represents different time nodes within a historical period, and the Y-axis represents the tensile stress, environmental parameters, and stress loss data of external loads at different unit specifications. The tensile stress, environmental parameters, and stress loss data of external loads are imported into the performance monitoring coordinate system according to time nodes, resulting in tensile stress curves corresponding to external load tensile stress, environmental curves corresponding to external environmental parameters, and stress loss curves corresponding to stress loss data. The tensile stress curves, environmental curves, and stress loss curves are segmented according to the application duration of the prestressed concrete structure, resulting in multiple tensile stress curve segments, multiple environmental curve segments, and multiple stress loss curve segments. Based on the application duration, the initial endpoints of the tensile stress curve segments are connected to the initial breakpoints of the environmental curve segments, and the terminating endpoints of the tensile stress curve segments are connected to the terminating endpoints of the environmental curve segments, resulting in multiple tensile stress environmental diagrams. Based on the application duration, the initial endpoints of the tensile stress curve segments are connected to the initial endpoints of the stress loss curve segments, and the terminating endpoints of the tensile stress curve segments are connected to the terminating endpoints of the environmental curve segments, resulting in multiple tensile stress loss diagrams. The tensile stress environment diagram and tensile stress loss diagram are organized according to time nodes to obtain the concrete performance diagram.

[0034] In this embodiment, the X-axis is defined as the quarterly time nodes within a historical time period (time nodes are marked T1 to T20, corresponding to 20 quarters over 5 years), and the Y-axis is defined as parameter values ​​in different unit specifications. The Y-axis is divided into three sub-axiss, corresponding to external load tensile stress (unit: MPa), external environmental parameters (temperature: ℃, humidity: %RH), and stress loss data (unit: MPa), respectively. The obtained external load tensile stress, external environmental parameters, and stress loss data are imported into the performance monitoring coordinate system according to the time nodes T1 to T20, generating tensile stress curves (reflecting the changes in external load tensile stress at the mid-span section of the main span in each quarter), environmental curves (reflecting the changes in temperature and humidity in each quarter), and stress loss curves (reflecting the changes in stress loss at the anchorage end nodes in each quarter). According to the bridge application duration (5 years), the three curves are divided into 5 tensile stress curve segments (each segment corresponding to 4 time nodes in 1 year), 5 environmental curve segments, and 5 stress loss curve segments. Based on the application duration each year, the initial endpoints of the tensile stress curve segments for each year are connected to the initial endpoints of the environmental curve segments, and the termination endpoints are connected to the termination endpoints, resulting in five tensile stress environmental diagrams. Similarly, the initial endpoints of the tensile stress curve segments for each year are connected to the initial endpoints of the stress loss curve segments, and the termination endpoints are connected to the termination endpoints of the environmental curve segments, resulting in five tensile stress loss diagrams. Arranging these five tensile stress environmental diagrams and five tensile stress loss diagrams sequentially by year forms a complete concrete performance diagram, which clearly presents the correlation between tensile stress, environmental parameters, and stress loss in different years.

[0035] Step S14: Collect real-time application parameters and simulated performance data of the prestressed concrete structure within the current time period, and analyze the real-time application parameters and simulated performance data according to the concrete performance diagram to obtain real-time performance monitoring data.

[0036] Specifically, before data acquisition, the vibrating wire strain sensor and temperature and humidity sensor were calibrated for accuracy. The calibration error of the vibrating wire strain sensor was recorded as ±2με, and an error compensation model was established: compensated data = measured data - calibration error value. Based on the stress variation pattern of historical data in the concrete performance chart, the peak stress variation periods were determined to be the high-temperature period in summer and the low-temperature period in winter. The acquisition frequency for these periods was set to 15Hz, and the acquisition frequency for other stable periods was set to 3Hz. A dual-redundant transmission link was established, consisting of a 4G main link and a WiFi 6 backup link. Real-time application parameters for the current time period (the first quarter of the 6th year, time node T21) were collected using various sensors and data acquisition instruments. These parameters included the real-time application duration (5 years and 3 months), real-time tensile stress (measured at the mid-span section of the main span, 1.5MPa), and real-time environmental parameters (temperature 25℃, humidity 65%RH). The structural performance under the current load condition was simulated using bridge structure simulation software (ANSYS), and simulated performance data (simulated stress loss value 78MPa) was obtained.

[0037] Specifically, the real-time application duration, real-time tensile stress, and real-time environmental parameters of the current prestressed concrete structure are determined based on real-time application parameters. The real-time tensile stress and environmental parameters are imported into the performance monitoring coordinate system according to the current time node, obtaining real-time tensile stress curves and real-time environmental curves corresponding to the real-time application parameters. The initial endpoints of the real-time tensile stress curve and the real-time environmental curve are connected, and the terminating endpoints of the real-time tensile stress curve and the real-time environmental curve are connected, resulting in a real-time tensile stress environment diagram. The real-time application duration is used as the first screening condition to screen the tensile stress environment diagram in the concrete performance diagram, obtaining a target tensile stress environment diagram with the same real-time application duration. Key point features are extracted from both the real-time tensile stress environment diagram and the target tensile stress environment diagram, and these key point features are used as the second screening condition to screen and match the real-time tensile stress environment diagram and the target tensile stress environment diagram, obtaining a matched tensile stress environment diagram. Based on the tensile stress loss diagram in the concrete performance diagram, a target tensile stress loss diagram corresponding to the matched tensile stress environment diagram is determined. Simulated performance data is used as a third screening criterion to screen the initial endpoint data of the stress loss curve segment in the target tensile stress loss diagram, thus obtaining the target stress loss curve segment. The target stress loss curve segment is then decomposed according to time nodes to obtain real-time performance monitoring data.

[0038] In this embodiment, the current real-time tensile stress (1.5 MPa) and real-time environmental parameters (25℃, 65%RH) are imported into the performance monitoring coordinate system at time node T21 to generate a real-time tensile stress curve (single-node curve segment) and a real-time environmental curve (single-node curve segment). Connecting the initial endpoint (i.e., node T21) and the termination endpoint (same as node T21, since the current data is collected at a single time node) of the two curves yields a real-time tensile stress environment diagram (approximately a single-point rectangle). Using the real-time application duration (5 years and 3 months, approximately corresponding to the first quarter of a 1-year unit of time) as the first screening condition, a target tensile stress environment diagram matching this duration unit (i.e., the tensile stress environment diagram corresponding to the first quarter of the 5th year) is selected from the 5 tensile stress environment diagrams in the concrete performance diagram. The SIFT algorithm was used to extract key points from the real-time tensile stress environment diagram and the target tensile stress environment diagram, including the extreme points of the curve (the T21 node corresponding to the real-time tensile stress of 1.5 MPa) and the slope change points (the tensile stress change nodes in the first quarter of the fifth year in the target diagram). Feature descriptors of each key point were calculated, including coordinate information and neighborhood grayscale distribution. Feature matching was performed using the FLANN matching algorithm, and abnormal matching pairs with a matching distance greater than 0.6 were removed to obtain an effective key point matching set (matching degree of 92%). The matched tensile stress environment diagram was determined to be the tensile stress environment diagram corresponding to the first quarter of the fifth year. Based on the concrete performance diagram, a target tensile stress loss diagram (the tensile stress loss diagram corresponding to the first quarter of year 5) was determined to match the tensile stress environment diagram. Using the simulated performance data (78 MPa) as the third screening condition, the initial endpoint data of the stress loss curve segment in the target tensile stress loss diagram (initial stress loss value of 72 MPa in the first quarter of year 5) was screened to obtain the target stress loss curve segment closest to the simulated data (stress loss curve segment from the first quarter to the second quarter of year 5). The target stress loss curve segment was then broken down by time node and corrected using the currently acquired real-time data to obtain the real-time performance monitoring data for the current time node T21: stress loss value 76 MPa, tensile stress 1.5 MPa, and environmental compatibility 95%.

[0039] Step S15: Determine if there are any abnormalities in the real-time performance monitoring data. If so, generate a performance warning message and send the performance warning message to the target terminal.

[0040] Specifically, based on the distribution characteristics of historical normal performance data in the concrete performance chart, the normal threshold range for performance monitoring data at different structural locations is determined. The deviation of real-time performance monitoring data from the normal threshold range is calculated; if the deviation exceeds a preset deviation threshold, it is considered abnormal. Simultaneously, considering the temporal trend of real-time performance monitoring data, if the monitoring data at multiple consecutive time points exhibits a monotonically increasing or decreasing deviation trend, even if the data at a single node does not exceed the normal threshold range, it is still considered a potential anomaly, and an early warning message is generated.

[0041] For example: Suppose that when monitoring is carried out in the third quarter of the sixth year (time node T23), the real-time performance monitoring data collected are: stress loss value 85MPa, tensile stress 2.3MPa, and environmental adaptability 82%. The anomaly detection process is as follows: ① Compared with the normal threshold range, the stress loss value of 85MPa exceeds the normal range of 50MPa to 80MPa, and the tensile stress of 2.3MPa exceeds the normal range of 1.0MPa to 2.0MPa; ② The deviation is calculated. The deviation of the stress loss value is 0.6 (greater than the preset deviation threshold of 0.5), and the deviation of the tensile stress is 0.55 (greater than the preset deviation threshold of 0.5); ③ The time-series change trend is observed. The stress loss values ​​of the three consecutive preceding time nodes (T21, T22, T23) are 76MPa, 79MPa, and 85MPa, respectively, showing a monotonically increasing deviation trend. The tensile stress values ​​are 1.5MPa, 1.8MPa, and 2.3MPa, respectively, also showing a monotonically increasing deviation trend. Therefore, it is determined that there is an anomaly in the real-time performance monitoring data.

[0042] Correspondingly, performance warning information is generated, which includes: anomaly level (emergency warning), anomaly monitoring nodes (mid-span section of main span, prestressed tendon anchorage end node), details of anomaly parameters (stress loss value 85MPa, exceeding the upper limit of normal threshold by 5MPa; tensile stress 2.3MPa, exceeding the upper limit of normal threshold by 0.3MPa), anomaly duration (lasted for 2 monitoring cycles, totaling 6 hours), and emergency response suggestions (immediately restrict bridge traffic load and prohibit heavy vehicles from passing; organize professional personnel to conduct on-site investigation of the anomaly nodes within 24 hours to check for prestressed tendon damage or concrete cracking); the performance warning information is simultaneously sent to target terminals through dual redundant transmission links, including the monitoring center display screen (pop-up highlight display), the operation and maintenance manager's mobile APP (push voice + text reminder), and relevant regulatory department terminals.

[0043] In addition, when no anomalies are found in the real-time performance monitoring data, the system acquires the future application tensile stress and environmental prediction data of the prestressed concrete structure within a future time period. A stress loss trend prediction model is constructed based on the concrete performance map. The future application tensile stress, environmental prediction data, and real-time performance monitoring data are input into the stress loss trend prediction model to obtain stress loss prediction curves for different structural nodes within a preset future period. The system extracts the stress loss safety threshold range for similar historical structures at the same application stage from the concrete performance map. The stress loss prediction curves are compared with the stress loss safety threshold ranges to determine if there is a risk of stress loss exceeding the limit within the preset future period. If there is a risk of exceeding the limit, a targeted preventative maintenance plan is generated by combining the slope of the stress loss prediction curve and the influence weight of the environmental prediction data. The preventative maintenance plan includes key maintenance nodes, maintenance time windows, and stress compensation measures, and is then pushed to the maintenance management terminal.

[0044] Specifically, tensile stress curves, environmental curves, and stress loss curves for different structural types and application environments are extracted from concrete performance diagrams to construct a model training sample set. The input features of the training sample set include cumulative tensile stress values ​​and environmental parameter fluctuation amplitudes, with labels representing stress loss values ​​at corresponding time points. Temporal and correlational features of the tensile stress curves, environmental curves, and stress loss curves are extracted. A pre-defined neural network is used to learn the temporal evolution of stress loss, constructing a stress loss trend prediction model. The stress loss trend prediction model is trained using the training sample set. A grid search algorithm is used to optimize the model's convolutional kernel size, the number of hidden layer neurons, and the learning rate parameters until the prediction error of the stress loss trend prediction model is below a preset threshold, resulting in an optimized stress loss trend prediction model.

[0045] In this embodiment, tensile stress curves, environmental curves, and stress loss curves under different structural types (continuous beam bridges, simply supported beam bridges) and different application environments (high temperature and high humidity, low temperature and dryness) are extracted from concrete performance diagrams to construct a model training sample set (a total of 1000 samples, with input features being the cumulative value of tensile stress and the fluctuation range of environmental parameters, and the label being the stress loss value). CNN is used to extract the temporal correlation features of the curves, and LSTM is used to learn the temporal evolution law of stress loss to construct a CNN-LSTM fusion prediction model. The model parameters are optimized through a grid search algorithm (convolution kernel size 3×3, number of hidden layer neurons 64, learning rate 0.001) and trained until the model prediction error is less than 3%, thus obtaining the final prediction model.

[0046] Specifically, key points are extracted from both the real-time tensile stress environment block diagram and the target tensile stress environment block diagram. These key points include curve inflection points, extreme points, and points of abrupt slope change. A feature descriptor is calculated for each key point, including its coordinates and the direction of the curve tangent. The feature descriptors of the two sets of key points are matched, and abnormal matching pairs with a matching distance greater than a preset threshold are removed to obtain a valid key point matching set. This valid key point matching set is used as the key point features.

[0047] This application provides a performance monitoring method for prestressed concrete structures. It acquires application environment parameters and performance monitoring data for different prestressed concrete structures over historical periods, providing a multi-sample, full-cycle foundation for subsequent analysis. Based on application environment parameters, it decomposes the external load tensile stress and external environmental parameters at different structural locations, clarifying the precise application locations of the load and environment. Simultaneously, it extracts stress loss data for corresponding nodes from the performance monitoring data, establishing location correlations to make the monitoring data more comprehensive and targeted, avoiding analytical biases caused by single or partial data. Based on the acquired external load tensile stress, external environmental parameters, and stress loss data, the three types of data are organized according to time nodes to obtain a concrete performance diagram. The time node organization method visually presents the data evolution trajectory over time. It collects current real-time application parameters and simulated performance data, using the constructed concrete performance diagram as an analysis benchmark. By utilizing the correlation patterns and evolution characteristics of historical data in the performance diagram, it performs targeted analysis of the real-time data. This effectively improves the reliability of real-time monitoring results and avoids judgment errors based on isolated data detached from historical context. After obtaining real-time performance monitoring data, anomaly detection is performed first, which can promptly identify potential structural performance problems. When an anomaly is detected, a performance warning message is directly generated and sent to the target terminal, achieving a seamless connection between anomaly identification and warning delivery. The targeted delivery of warning messages ensures the accuracy and timeliness of information transmission, skipping the cumbersome information relay links in traditional monitoring, improving the efficiency of anomaly response, and guaranteeing the accuracy of prestressed concrete structure monitoring.

[0048] The following describes a performance monitoring system for prestressed concrete structures provided in an embodiment of this application. The performance monitoring system for prestressed concrete structures described below can be referred to in conjunction with the performance monitoring method for prestressed concrete structures described above. Figure 2 , Figure 2 This is a schematic diagram of the structure of a performance monitoring system 20 for prestressed concrete structures provided in an embodiment of this application, including: Data acquisition module 21 is used to acquire application environment parameters and performance monitoring data of different prestressed concrete structures in historical periods; The parameter determination module 22 is used to determine the tensile stress of the external load corresponding to different structural nodes of each prestressed concrete structure during the application process, as well as the external environmental parameters of the location of the prestressed concrete structure, based on the application environment parameters. The data determination module 23 is used to determine the stress loss data of different structural nodes of each prestressed concrete structure based on the performance monitoring data. The data processing module 24 is used to process the external load tensile stress, external environmental parameters and stress loss data according to time nodes to obtain the concrete performance diagram. The data analysis module 25 collects real-time application parameters and simulated performance data of the prestressed concrete structure within the current time period, and analyzes the real-time application parameters and simulated performance data according to the concrete performance diagram to obtain real-time performance monitoring data. The data monitoring module 26 is used to determine whether there are any abnormalities in the real-time performance monitoring data. If so, it generates a performance warning message and sends the performance warning message to the target terminal.

[0049] In one possible implementation of this application embodiment, when the data processing module 24 processes the external load tensile stress, external environmental parameters, and stress loss data according to time nodes to obtain the concrete performance diagram, it is specifically used for: Construct a performance monitoring coordinate system. The X-axis of the performance monitoring coordinate system represents different time nodes within the historical period, and the Y-axis of the performance monitoring coordinate system represents the tensile stress, environmental parameters, and stress loss data of external loads of different unit specifications. The tensile stress of the external load, the external environmental parameters, and the stress loss data are imported into the performance monitoring coordinate system according to the time nodes to obtain the tensile stress curve corresponding to the tensile stress of the external load, the environmental curve corresponding to the external environmental parameters, and the stress loss curve corresponding to the stress loss data. The tensile stress curve, environmental curve, and stress loss curve are divided into units according to the application time of the prestressed concrete structure, resulting in multiple tensile stress curve segments, multiple environmental curve segments, and multiple stress loss curve segments. Based on the application duration, the initial endpoint of the tensile stress curve segment is connected to the initial breakpoint of the environmental curve segment, and the terminating endpoint of the tensile stress curve segment is connected to the terminating endpoint of the environmental curve segment to obtain multiple tensile stress environment block diagrams. Based on the application duration, the initial endpoint of the tensile stress curve segment is connected to the initial endpoint of the stress loss curve segment, and the termination point of the tensile stress curve segment is connected to the termination endpoint of the environmental curve segment to obtain multiple tensile stress loss block diagrams. The tensile stress environment diagram and tensile stress loss diagram are organized according to time nodes to obtain the concrete performance diagram.

[0050] In another possible implementation of this application embodiment, when the data analysis module 25 analyzes the real-time application parameters and simulated performance data based on the concrete performance diagram to obtain real-time performance monitoring data, it is specifically used for: The real-time application duration, real-time tensile stress, and real-time environmental parameters of the current prestressed concrete structure are determined based on real-time application parameters. The real-time tensile stress and real-time environmental parameters are imported into the performance monitoring coordinate system according to the current time node to obtain the real-time tensile stress curve and real-time environmental curve corresponding to the real-time application parameters. Connect the initial endpoint of the real-time tensile stress curve with the initial endpoint of the real-time environment curve, and connect the final endpoint of the real-time tensile stress curve with the final endpoint of the real-time environment curve to obtain the real-time tensile stress environment block diagram. Using real-time application duration as the first screening condition, the tensile stress environment block diagram in the concrete performance diagram is screened to obtain a target tensile stress environment block diagram with the same real-time application duration. Key point features are extracted from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram respectively. The key point features are used as the second screening condition to screen and match the real-time tensile stress environment block diagram and the target tensile stress environment block diagram to obtain the matched tensile stress environment block diagram. Determine the target tensile stress loss diagram corresponding to the matching tensile stress environment diagram based on the tensile stress loss diagram in the concrete performance diagram; The simulated performance data is used as the third screening condition to screen the initial endpoint data of the stress loss curve segment in the target tensile stress loss diagram, thereby obtaining the target stress loss curve segment. The target stress loss curve segment is decomposed according to time nodes to obtain real-time performance monitoring data.

[0051] In another possible implementation of this application embodiment, when determining whether there are any anomalies in the real-time performance monitoring data, the data monitoring module 26 is specifically used for: If no anomalies are found in the real-time performance monitoring data, then obtain the future application tensile stress and environmental prediction data of the prestressed concrete structure in the future time period. A stress loss trend prediction model is constructed based on concrete performance diagrams. Future application tensile stress and environmental prediction data, as well as real-time performance monitoring data, are input into the stress loss trend prediction model to obtain stress loss prediction curves for nodes at different structural locations within a preset period. Extract the stress loss safety threshold range of similar structures in the same application stage from the concrete performance chart, compare the stress loss prediction curve with the stress loss safety threshold range, and determine whether there is a risk of stress loss exceeding the limit within the future preset period. If there is a risk of exceeding limits, a targeted preventive maintenance plan is generated by combining the slope of the stress loss prediction curve and the influence weight of the environmental prediction data. The preventive maintenance plan includes key maintenance nodes, maintenance time windows and stress compensation measures, and is pushed to the maintenance management terminal.

[0052] In another possible implementation of this application embodiment, when the data monitoring module 26 constructs a stress loss trend prediction model based on the concrete performance diagram, it is specifically used for: Data on tensile stress curves, environmental curves, and stress loss curves for different structural types and application environments are extracted from concrete performance diagrams to construct a model training sample set. The input features of the training sample set include the cumulative value of tensile stress and the fluctuation range of environmental parameters, with the label being the stress loss value at the corresponding time node. Extract the temporal and correlation features of tensile stress curves, environmental curves, and stress loss curves, learn the temporal evolution law of stress loss through a pre-set neural network, and construct a stress loss trend prediction model. The stress loss trend prediction model is trained using a training sample set. The convolution kernel size, the number of hidden layer neurons, and the learning rate parameters of the model are optimized by a grid search algorithm until the prediction error of the stress loss trend prediction model is lower than a preset threshold, thus obtaining the optimized stress loss trend prediction model.

[0053] In another possible implementation of this application embodiment, when the data monitoring module 26 extracts key point features from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram respectively, it is specifically used for: Key points are extracted from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram, including curve inflection points, extreme points and slope change points. Calculate the feature descriptor for each key point, which includes the coordinate information of the key point and the direction of the curve tangent; The feature descriptors of the two sets of key points are matched, and abnormal matching pairs with a matching distance greater than a preset threshold are removed to obtain the effective key point matching set. The effective key point matching set is used as the key point feature.

[0054] In another possible implementation of this application embodiment, when determining whether there are any anomalies in the real-time performance monitoring data, the data monitoring module 26 is specifically used for: Based on the distribution characteristics of historical normal performance data in concrete performance diagrams, the normal threshold range of performance monitoring data for nodes at different structural locations is determined. Calculate the deviation of real-time performance monitoring data from the normal threshold range. If the deviation is greater than the preset deviation threshold, it is judged as abnormal. In addition, by combining the time-series change trend of real-time performance monitoring data, if the monitoring data of multiple consecutive time nodes show a monotonically increasing or decreasing deviation trend, even if the data of a single node does not exceed the normal threshold range, it is judged as a potential anomaly and an early warning message is generated.

[0055] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0056] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with the embodiments of this application. Processor 301 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0057] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0058] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0059] The memory 303 is used to store application code that executes the scheme of the embodiments of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0060] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0061] The following describes a computer-readable storage medium provided by an embodiment of this application. The computer-readable storage medium described below can be referred to in correspondence with the method described above.

[0062] This application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the performance monitoring system for prestressed concrete structures described above.

[0063] Since the embodiments of the computer-readable storage medium portion correspond to the embodiments of the method portion, please refer to the description of the embodiments of the method portion for the embodiments of the computer-readable storage medium portion.

[0064] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0065] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for performance monitoring of prestressed concrete structures, characterized in that, include: Obtain application environment parameters and performance monitoring data of different prestressed concrete structures within a historical period; Based on the application environment parameters, determine the external load tensile stress borne by each prestressed concrete structure at different structural locations during application, as well as the external environment parameters of the location of the prestressed concrete structure. Based on the performance monitoring data, the stress loss data of different structural nodes in each prestressed concrete structure are determined; The tensile stress of the external load, the external environmental parameters, and the stress loss data are organized according to time nodes to obtain the concrete performance diagram; Real-time application parameters and simulated performance data of prestressed concrete structures are collected within the current time period. The real-time application parameters and simulated performance data are analyzed based on the concrete performance diagram to obtain real-time performance monitoring data. Determine if there are any anomalies in the real-time performance monitoring data. If so, generate a performance warning message and send the performance warning message to the target terminal.

2. The performance monitoring method for prestressed concrete structures according to claim 1, characterized in that, The process involves organizing the external load tensile stress, external environmental parameters, and stress loss data according to time points to obtain a concrete performance diagram, including: A performance monitoring coordinate system is constructed, wherein the X-axis of the performance monitoring coordinate system represents different time nodes within the historical time period, and the Y-axis of the performance monitoring coordinate system represents the tensile stress, environmental parameters, and stress loss data of external loads of different unit specifications. The tensile stress of the external load, the external environmental parameters, and the stress loss data are respectively imported into the performance monitoring coordinate system according to the time nodes to obtain the tensile stress curve corresponding to the tensile stress of the external load, the environmental curve corresponding to the external environmental parameters, and the stress loss curve corresponding to the stress loss data. The tensile stress curve, environmental curve, and stress loss curve are divided into units according to the application time of the prestressed concrete structure to obtain multiple tensile stress curve segments, multiple environmental curve segments, and multiple stress loss curve segments. Based on the application duration, the initial endpoint of the tensile stress curve segment is connected to the initial breakpoint of the environmental curve segment, and the terminating endpoint of the tensile stress curve segment is connected to the terminating endpoint of the environmental curve segment to obtain multiple tensile stress environment block diagrams. Based on the application duration, the initial endpoint of the tensile stress curve segment is connected to the initial endpoint of the stress loss curve segment, and the termination point of the tensile stress curve segment is connected to the termination endpoint of the environmental curve segment to obtain multiple tensile stress loss block diagrams. The tensile stress environment diagram and tensile stress loss diagram are organized according to time nodes to obtain the concrete performance diagram.

3. The performance monitoring method for prestressed concrete structures according to claim 2, characterized in that, The analysis of the real-time application parameters and simulated performance data based on the concrete performance diagram yields real-time performance monitoring data, including: The real-time application duration, real-time tensile stress, and real-time environmental parameters of the current prestressed concrete structure are determined based on the real-time application parameters. The real-time tensile stress and the real-time environmental parameters are imported into the performance monitoring coordinate system according to the current time node to obtain the real-time tensile stress curve and the real-time environmental curve corresponding to the real-time application parameters; Connect the initial endpoint of the real-time tensile stress curve to the initial endpoint of the real-time environment curve, and connect the terminating endpoint of the real-time tensile stress curve to the terminating endpoint of the real-time environment curve to obtain a real-time tensile stress environment block diagram. Using the real-time application duration as the first screening condition, the tensile stress environment block diagram in the concrete performance diagram is screened to obtain a target tensile stress environment block diagram with the same real-time application duration. Key point features are extracted from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram respectively, and the key point features are used as the second screening condition to screen and match the real-time tensile stress environment block diagram and the target tensile stress environment block diagram to obtain a matched tensile stress environment block diagram. Based on the tensile stress loss diagram in the concrete performance diagram, determine the target tensile stress loss diagram corresponding to the matching tensile stress environment diagram; The simulated performance data is used as the third screening condition to screen the initial endpoint data of the stress loss curve segment in the target tensile stress loss diagram, thereby obtaining the target stress loss curve segment. The target stress loss curve segment is decomposed according to time nodes to obtain real-time performance monitoring data.

4. The performance monitoring method for a prestressed concrete structure according to claim 3, characterized in that, The determination of whether the real-time performance monitoring data is abnormal includes: If the real-time performance monitoring data is not abnormal, then the future application tensile stress and environmental prediction data of the prestressed concrete structure in the future time period are obtained. Based on the concrete performance diagram, a stress loss trend prediction model is constructed. The future application tensile stress and environmental prediction data, as well as the real-time performance monitoring data, are input into the stress loss trend prediction model to obtain the stress loss prediction curves of different structural location nodes within a future preset period. Extract the stress loss safety threshold range of similar structures in the same application stage from the concrete performance graph, compare the stress loss prediction curve with the stress loss safety threshold range, and determine whether there is a risk of stress loss exceeding the limit within a future preset period. If there is a risk of exceeding limits, a targeted preventive maintenance plan is generated by combining the slope of the stress loss prediction curve and the influence weight of the environmental prediction data. The preventive maintenance plan includes key maintenance nodes, maintenance time windows and stress compensation measures, and the preventive maintenance plan is pushed to the maintenance management terminal.

5. The performance monitoring method for a prestressed concrete structure according to claim 4, characterized in that, The stress loss trend prediction model constructed based on the concrete performance diagram includes: The tensile stress curves, environmental curves, and stress loss curves for different structural types and application environments are extracted from the concrete performance diagram to construct a model training sample set. The input features of the training sample set include the cumulative tensile stress value and the fluctuation range of environmental parameters, and the label is the stress loss value at the corresponding time node. Extract the temporal and correlation features of tensile stress curves, environmental curves, and stress loss curves, learn the temporal evolution law of stress loss through a pre-set neural network, and construct a stress loss trend prediction model. The stress loss trend prediction model is trained using a training sample set. The convolution kernel size, the number of hidden layer neurons, and the learning rate parameters of the model are optimized by a grid search algorithm until the prediction error of the stress loss trend prediction model is lower than a preset threshold, thus obtaining the optimized stress loss trend prediction model.

6. The performance monitoring method for a prestressed concrete structure according to claim 4, characterized in that, The extraction of key point features from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram includes: Key points are extracted from the real-time tensile stress environment block diagram and the target tensile stress environment block diagram, respectively. The key points include curve inflection points, extreme points and slope change points. Calculate the feature descriptor for each key point, the feature descriptor including the coordinate information of the key point and the direction of the curve tangent; The feature descriptors of the two sets of key points are matched, and abnormal matching pairs with a matching distance greater than a preset threshold are removed to obtain an effective key point matching set. The effective key point matching set is used as the key point feature.

7. The performance monitoring method for a prestressed concrete structure according to claim 1, characterized in that, The determination of whether the real-time performance monitoring data is abnormal includes: Based on the distribution characteristics of historical normal performance data in the concrete performance diagram, the normal threshold range of performance monitoring data for nodes at different structural locations is determined. Calculate the deviation between the real-time performance monitoring data and the normal threshold range. If the deviation is greater than the preset deviation threshold, it is determined to be abnormal. In addition, based on the time-series change trend of the real-time performance monitoring data, if the monitoring data of multiple consecutive time nodes show a monotonically increasing or decreasing deviation trend, even if the data of a single node does not exceed the normal threshold range, it is judged as a potential anomaly and an early warning message is generated.

8. A performance monitoring system for prestressed concrete structures, characterized in that, include: The data acquisition module is used to acquire application environment parameters and performance monitoring data of different prestressed concrete structures within a historical period. The parameter determination module is used to determine the external load tensile stress corresponding to different structural nodes of each prestressed concrete structure during application, as well as the external environmental parameters of the location of the prestressed concrete structure, based on the application environment parameters. The data determination module is used to determine the stress loss data of different structural nodes of each prestressed concrete structure based on the performance monitoring data. The data processing module is used to process the external load tensile stress, the external environmental parameters, and the stress loss data according to time nodes to obtain the concrete performance diagram. The data analysis module collects real-time application parameters and simulated performance data of the prestressed concrete structure within the current time period, and analyzes the real-time application parameters and simulated performance data according to the concrete performance diagram to obtain real-time performance monitoring data. The data monitoring module is used to determine whether there are any abnormalities in the real-time performance monitoring data. If so, it generates a performance warning message and sends the performance warning message to the target terminal.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a performance monitoring method for a prestressed concrete structure according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: The computer program contains a method for monitoring the performance of a prestressed concrete structure that can be loaded by a processor and executed as described in any one of claims 1-7.

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