Lock chamber wall deformation monitoring method, device and system

Abstract

The invention provides a lock chamber wall deformation monitoring method, device and system. The method comprises the following steps: S1, acquiring a displacement data set of a lock chamber wall in a first preset period from the current moment; s2, based on the displacement data set, whether the deformation condition of the lock chamber wall meets a first alarm condition or not is judged according to a preset judgment rule; when the first alarm condition is met, generating first early warning information and sending the first early warning information to preset user equipment; when the first alarm condition is not met, S1 is executed; wherein the preset judgment rule means that a monitoring curve set is generated based on the displacement data set; obtaining a first similarity score between the monitoring curve set and the sample curve set; and when the first similarity score is lower than a first score threshold, considering that the deformation condition of the lock chamber wall meets a first alarm condition. A displacement time curve mode is used for monitoring, so that the data volume is reduced, and the overall monitoring efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of structure deformation monitoring methods, and in particular to a method, device and system for monitoring the deformation of a lock chamber wall. Background Art

[0002] The lock chamber wall is an important part of the lock. It is used to form the lock chamber. It not only plays a role in retaining water, but also bears the water pressure caused by water level changes. The lock chamber wall and the lock chamber bottom plate can be an integral structure connected together, or a separate structure that is not connected together. The lock chamber wall is subjected to the action of various forces and deformation under construction. The deformation monitoring of the lock chamber wall usually includes the measurement of indicators such as displacement. Monitoring methods can be used such as levels, total stations, GPS, fiber optic sensors, etc. The analysis and interpretation of monitoring data requires a combination of professional knowledge and experience to ensure the accuracy and reliability of the monitoring results. Therefore, the original manual monitoring cannot keep up with the existing needs in terms of accuracy and monitoring efficiency.

[0003] For example, the deformation monitoring and status warning method of hydraulic structures at sluice stations disclosed in application number CN201510622076.2 is used to judge the operating status of hydraulic structures and issue real-time warnings for unsafe conditions to ensure the safe operation of water conservancy projects. The method includes the following steps: selecting project monitoring points; collecting monitoring information; analyzing and comparing monitoring information; processing analysis results; and status warning. It can realize the functions of publishing monitoring information and data analysis results, project status warning, and conduct real-time analysis of the safety of sluice and pump station buildings, so that staff can grasp the deformation of buildings in a timely manner and analyze their changing trends. However, in order to avoid inaccurate monitoring results caused by fluctuations in collected monitoring information, the above method requires a large amount of monitoring information for multiple project monitoring points, and cannot achieve a balance between monitoring accuracy and monitoring efficiency.

[0004] Therefore, the present application aims to provide a method, device and system for monitoring the deformation of a lock chamber wall to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of this application is to provide a method, device and system for monitoring the deformation of a lock chamber wall, so as to improve the overall monitoring efficiency while ensuring the accuracy of the judgment. The purpose of this application is achieved by adopting the following technical solutions:

[0006] The present application provides a method for monitoring the deformation of a lock chamber wall, which is applied to an Internet of Things network. The Internet of Things network includes a gateway module and a control module, and a plurality of displacement sensors arranged on the lock chamber wall. Each of the displacement sensors uploads displacement data that changes with time to the control module through the gateway module. The method includes:

[0007] S1, in a first preset cycle starting at the current moment, using each of the displacement sensors to obtain a displacement data set of the lock chamber wall; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections;

[0008] S2, based on the displacement data set, judging whether the deformation of the lock chamber wall meets the first alarm condition according to a preset judgment rule; when the first alarm condition is met, generating a first warning message and sending it to a preset user device; when the first alarm condition is not met, executing S1;

[0009] Among them, the preset judgment rule refers to generating a monitoring curve set based on the displacement data set, the monitoring curve set including the displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the gate chamber wall meets the first alarm condition.

[0010] Furthermore, each first preset period includes a second preset period, and the method of obtaining the sample curve set includes:

[0011] Taking a first preset period starting from the current moment as a target preset period, and acquiring stress monitoring information of a plurality of stress collection areas of the lock chamber wall in a second preset period within the target preset period;

[0012] using stress information acquired in a second preset period within a plurality of first preset periods before a target preset period as stress sample information;

[0013] Compare each stress sample information with the stress monitoring information to obtain a corresponding second similarity score;

[0014] The stress sample information which is not less than the second similarity threshold and is closest to the current moment is used as the target stress information, and the monitoring curve set corresponding to the target stress information is used as the sample curve set.

[0015] Further, the step of taking the stress information acquired in the second preset period within a plurality of first preset periods before the target preset period as the stress sample information includes:

[0016] When the deformation of the lock chamber wall in the first preset period before the target preset period does not meet the first alarm condition, the stress information of the first preset period that does not meet the first alarm condition is input into the abnormal area detection model to obtain the abnormal detection result of each stress collection area; the abnormal detection result includes abnormal and normal;

[0017] Determine whether the number of abnormal stress collection areas is greater than a preset number; the stress information includes an identifier of each stress collection area and a set of stress data of the area corresponding to each identifier;

[0018] The stress information of a first preset period in which the number of abnormal stress acquisition areas is greater than a preset number is used as stress sample information for obtaining a second similarity score with the stress monitoring information.

[0019] Furthermore, the abnormal area detection model is obtained by training a deep learning model, and the training process of the abnormal area detection model includes the following steps:

[0020] A training set is obtained, wherein the training set includes a plurality of training data, each of which includes a data group of sample stress data and annotated data of an abnormal region corresponding to the data group; for each training data in the training set, the following processing is performed:

[0021] Inputting a data group in the training data into a preset deep learning model to obtain prediction data of an abnormal area corresponding to the data group;

[0022] Based on the prediction data and the annotation data corresponding to the sample image data, the model parameters of the deep learning model are updated;

[0023] Detect whether the preset training end condition is met; if so, use the trained deep learning model as the abnormal area detection model; if not, continue to train the deep learning model using the next training data.

[0024] Further, the obtaining a first similarity score between the monitoring curve set and the sample curve set includes:

[0025] Obtaining similarity values ​​of displacement time curves of the same preset monitoring point in the monitoring curve set and the sample curve set, and obtaining a similarity value of each preset monitoring point;

[0026] For each preset monitoring point, when the corresponding stress acquisition area is an abnormal area, the product of the obtained similarity value and the abnormal coefficient of the stress acquisition area corresponding to the preset monitoring point is used as the first similarity intermediate score; otherwise, the obtained similarity value is used as the first similarity intermediate score;

[0027] The sum of the obtained first similarity intermediate scores of each preset monitoring point is used as the first similarity score between the monitoring curve set and the sample curve set.

[0028] Furthermore, the method for obtaining the abnormal coefficient of the stress acquisition area includes:

[0029] Obtaining an abnormal coefficient correspondence relationship; the abnormal coefficient correspondence relationship is used to indicate the correspondence between the number and proportion of abnormal stress acquisition areas and abnormal values;

[0030] According to the number of stress acquisition areas indicating abnormalities in the target stress information, the corresponding abnormal value is obtained by using the abnormal coefficient correspondence relationship, and the abnormal value is used as the abnormal coefficient of the stress acquisition area indicating abnormalities.

[0031] In a second aspect, the present application further provides a lock chamber wall deformation monitoring device, the lock chamber wall deformation monitoring device comprising:

[0032] A first acquisition module is used to acquire a displacement data set of the lock chamber wall using each of the displacement sensors in a first preset period starting at the current moment; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections;

[0033] A first judgment module is used to judge whether the deformation of the lock chamber wall meets a first alarm condition based on the displacement data set and according to a preset judgment rule; when the first alarm condition is met, generate a first warning message and send it to a preset user device;

[0034] Among them, the preset judgment rule refers to generating a monitoring curve set based on the displacement data set, the monitoring curve set including the displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the gate chamber wall meets the first alarm condition.

[0035] In a third aspect, the present application further provides a lock chamber wall deformation monitoring system, the lock chamber wall deformation monitoring system comprising:

[0036] A plurality of displacement sensors are respectively arranged at preset monitoring points at the joints of adjacent gate sections of the gate chamber wall, for obtaining displacement data varying with time;

[0037] A control module, wherein the control module is respectively connected to each of the displacement sensors for communication;

[0038] A gateway module, the gateway module is arranged between the control module and the user equipment to realize the communication connection between the control module and the user equipment; and is arranged between the control module and each of the displacement sensors to realize the communication connection between the control module and each of the displacement sensors;

[0039] The control module is configured to:

[0040] S1, in a first preset cycle starting at the current moment, using each of the displacement sensors to obtain a displacement data set of the lock chamber wall; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections;

[0041] S2, based on the displacement data set, judging whether the deformation of the lock chamber wall meets the first alarm condition according to a preset judgment rule; when the first alarm condition is met, generating a first warning message and sending it to a preset user device; when the first alarm condition is not met, executing S1;

[0042] Among them, the preset judgment rule refers to generating a monitoring curve set based on the displacement data set, the monitoring curve set including the displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the gate chamber wall meets the first alarm condition.

[0043] Furthermore, it also includes: a plurality of stress monitoring sensors, which are respectively arranged in a plurality of stress collection areas of the lock chamber wall and are communicatively connected with the control module through the gateway module, so as to obtain stress data of each stress collection area;

[0044] Among them, the stress collection area is set at the intersection of the two concrete pouring stages of the gate chamber wall. The first concrete pouring stage is a pouring construction based on the bottom plate after construction and including chamfering construction. The second concrete pouring is a pouring construction after the first concrete pouring.

[0045] Furthermore, the construction elevation range of the first concrete pouring is 3 meters to 4 meters; the second concrete pouring is constructed by a mobile formwork construction method, and is constructed to the top based on the first concrete pouring construction.

[0046] The beneficial effects of the present application are as follows: Based on the collected displacement data set, a displacement time curve of each monitoring point is generated to form a monitoring curve set. The monitoring curve is then compared with the sample curve set to obtain a similarity score between the two. If the similarity score is lower than the preset score threshold, it is judged that the deformation of the lock chamber wall meets the first alarm condition, which means that the actual deformation of the lock chamber wall shows a significant difference from the sample curve, indicating that there is a risk of structural deformation. In this case, the first warning information is automatically generated and sent to the user device through the preset communication channel.

[0047] Therefore, the state trend of the collected information of the lock chamber wall is predicted, and the deformation data is directly compared with the historical data of the same period to ensure the safe operation of the engineering equipment. The continuous displacement data is expressed as a time curve, instead of relying solely on the raw data at each moment, which can capture the overall trend of the monitoring point over time. By generating a displacement time curve, due to the direct comparison of the shape and trend of the curve, abnormal situations can be quickly identified without the need to calculate the data of a large number of time points one by one, which reduces the amount of data comparison while ensuring the accuracy of the judgment, and improves the speed and efficiency of data processing. Through the real-time collection and analysis of displacement data, the deformation of the lock chamber wall can be continuously monitored. Once the deformation risk is detected, an early warning will be issued immediately to ensure that the problem is found at an early stage and prevent major structural damage or failure. To ensure the accuracy of judgment, it means that by monitoring the displacement at the joints of adjacent lock sections, the deformation data of key parts can be accurately captured. At the same time, by using the similarity scoring method, it is compared with the sample curve, which can effectively identify subtle deformation differences, thereby improving the accuracy of judgment. Data collection and analysis are automatically performed based on preset rules without manual intervention, which reduces the error rate of manual operation and improves monitoring efficiency. By detecting abnormal structural deformation in advance, relevant personnel can take repair measures in time, thereby effectively reducing the safety hazards caused by the instability or failure of the lock chamber wall structure. The displacement time curve is used for monitoring to reduce the amount of data and thus improve the overall efficiency of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present application is further described below in conjunction with the accompanying drawings and embodiments.

[0049] Figure 1 It is a flow chart of the method for monitoring the deformation of the lock chamber wall provided in the embodiment of the present application.

[0050] Figure 2 It is a schematic diagram of the process of obtaining the sample curve set provided in an embodiment of the present application.

[0051] Figure 3 It is a schematic diagram of a process for obtaining a first similarity score provided in an embodiment of the present application.

[0052] Figure 4 It is a structural block diagram of a gate chamber wall deformation monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] Below, in conjunction with the accompanying drawings and specific implementation methods, the present application is further described. It should be noted that, under the premise of no conflict, the various embodiments described below or the various technical features can be arbitrarily combined to form a new embodiment. The implementation procedures of the present application will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation procedures, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, not for limiting the scope of protection of the present application.

[0054] The following explains the application scenarios and some terms in this application to facilitate understanding by those skilled in the art.

[0055] Machine Learning (ML) is a multi-disciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. A computer program can learn from experience E given a certain type of task T and performance metric P. If its performance in task T can be measured by P, it will improve with experience E. Machine learning specifically studies how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are in all areas of artificial intelligence.

[0056] Deep learning is a special type of machine learning that achieves tremendous power and flexibility by learning to use nested hierarchies of concepts to represent, where each concept is defined in relation to simpler concepts, and more abstract representations are computed in less abstract ways. Machine learning and deep learning typically include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and teaching learning.

[0057] At present, although there are some technologies for monitoring the deformation of the lock chamber wall, these technologies often fail to achieve a balance between monitoring accuracy and monitoring efficiency. Based on this, the present application provides a lock chamber wall deformation monitoring method and system for timely and accurately detecting the deformation of the lock chamber wall. By collecting and analyzing data from multiple groups of monitoring points and combining a similarity scoring mechanism, real-time intelligent monitoring and early warning are realized. Representing continuous displacement data as a time curve instead of relying solely on the original data at each moment can capture the overall trend of the monitoring point changing over time. As far as the present application is concerned, since it reduces the difficulty of maintenance and management of the lock chamber wall in the later stage of the project, reduces the possibility of its deformation, and improves the overall safety and stability of the project, the above-mentioned time serialization can extract key feature points or change trends. For example, the impact of local minor changes on the overall trend is not manifested at once, so there is no need to record all the original data at all times, which can reduce the recording of redundant data, thereby achieving a balance between monitoring accuracy and monitoring efficiency. The following will first explain the method, and then explain the system, etc.

[0058] Method embodiment.

[0059] See also Figure 1 , Figure 1 It is a flow chart of the method for monitoring the deformation of the lock chamber wall provided in the embodiment of the present application.

[0060] This embodiment provides a method for monitoring the deformation of a lock chamber wall, which is applied to an Internet of Things network, wherein the Internet of Things network includes a gateway module and a control module, and a plurality of displacement sensors arranged on the lock chamber wall, wherein each displacement sensor uploads displacement data that changes over time to the control module through the gateway module. The method for monitoring the deformation of a lock chamber wall is performed by an electronic device, which may be a control module.

[0061] The method includes S101 and S102.

[0062] S101, in a first preset cycle starting from the current moment, using each of the displacement sensors to obtain a displacement data set of the lock chamber wall.

[0063] The displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point. The preset monitoring point is a monitoring point set at the joint of adjacent gate sections. The displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent gate sections.

[0064] In the first preset period, displacement data is collected from each preset monitoring point, which is located at the joint of adjacent gate sections and is used to monitor the deformation and displacement of adjacent gate sections. Each monitoring point collects a set of displacement data that changes with time.

[0065] S102, based on the displacement data set, determine whether the deformation of the lock chamber wall meets the first alarm condition according to the preset judgment rule; when the first alarm condition is met, generate a first warning message and send it to the preset user device; when the first alarm condition is not met, execute S101. That is, the above deformation monitoring method can be continuously operated through one first preset cycle after another until the first alarm condition is found to be met.

[0066] The preset judgment rule refers to generating a monitoring curve set based on the displacement data set, wherein the monitoring curve set includes a displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the lock chamber wall meets the first alarm condition. The first similarity score can be obtained by using a cosine similarity method to represent the displacement time curve as a vector. The cosine similarity between the monitoring curve and the sample curve is calculated, and the closer the value is to 1, the more similar the two are.

[0067] Based on the collected displacement data set, a displacement time curve of each monitoring point is generated to form a monitoring curve set. The monitoring curve is then compared with the sample curve set to obtain a similarity score between the two. If the similarity score is lower than the preset score threshold, it is judged that the deformation of the lock chamber wall meets the first alarm condition, which means that the actual deformation of the lock chamber wall shows a significant difference from the sample curve, indicating that there is a risk of structural deformation. In this case, the first warning information is automatically generated and sent to the user device through the preset communication channel, such as the mobile phone, tablet or control center of the control center or maintenance personnel.

[0068] Therefore, compared with the related art, by predicting the state trend of the collected information of the lock chamber wall and directly comparing the deformation data with the historical data of the same period to ensure the safe operation of the engineering equipment, the embodiment of the present application represents the continuous displacement data as a time curve, instead of relying solely on the original data at each moment, and can capture the overall trend of the monitoring point changing over time. By generating a displacement time curve, due to the direct comparison of the shape and trend of the curve, it is possible to quickly identify abnormal situations without calculating the data of a large number of time points one by one, reducing the amount of data comparison while ensuring the accuracy of the judgment, and improving the speed and efficiency of data processing. Through the real-time collection and analysis of displacement data, the deformation of the lock chamber wall can be continuously monitored. Once the deformation risk is detected, an early warning will be issued immediately to ensure that the problem is discovered at an early stage and prevent the occurrence of major structural damage or failure. Ensuring the accuracy of the judgment means that by monitoring the displacement at the joints of adjacent lock sections, the deformation data of key parts can be accurately captured. At the same time, by using the similarity scoring method, it is compared with the sample curve, which can effectively identify subtle deformation differences, thereby improving the accuracy of the judgment. Data collection and analysis are automatically performed based on preset rules without manual intervention, which reduces the error rate of manual operation and improves monitoring efficiency. By detecting abnormal structural deformation in advance, relevant personnel can take repair measures in time, thereby effectively reducing the safety hazards caused by the instability or failure of the lock chamber wall structure. The displacement time curve is used for monitoring to reduce the amount of data and thus improve the overall efficiency of monitoring.

[0069] In specific applications, the displacement time curve can be further simplified by data compression algorithms. For example, using methods such as Fourier transform or wavelet transform, the time series data can be converted into frequency domain data, and high-frequency noise data irrelevant to the overall monitoring results can be discarded, thereby reducing the burden of storage and transmission.

[0070] In specific applications, for the use scenarios of highly permeable sandy soil layers, a continuous and complete combined anti-seepage curtain can be formed in the highly permeable sandy soil layer. The combined anti-seepage curtain is obtained through a closed combined strategy to form a continuous and complete anti-seepage curtain in the highly permeable sandy soil layer. It can be understood that the anti-seepage curtain obtained by the closed combined strategy can not only effectively prevent the infiltration of groundwater, prevent soil erosion and structural damage, but also reduce the difficulty of maintenance and management of the gate chamber wall in the later stage of the project, reduce the possibility of its deformation, and improve the overall safety and stability of the project.

[0071] The closed combination strategy includes: using a combination of ground-anchored walls, cement-mixed pile walls, geomembranes and high-pressure jet piles to form a multi-layered anti-seepage curtain; forming a uniform anti-seepage barrier in the sand layer through high-pressure jetting and mud solidification technology to control the occurrence of local concentrated infiltration and improve the overall anti-seepage performance; setting an anti-seepage layer in the depth direction of the sand layer and arranging an anti-seepage isolation belt in the horizontal direction to form a three-dimensional protection system to ensure that water infiltration can be effectively prevented in both vertical and horizontal directions; using an independent mud circulation system to increase the construction speed. The chamber wall mentioned in this application is suitable for ship lock usage scenarios with highly permeable sand layers, that is, the case of a combined anti-seepage curtain.

[0072] Taking the deformation monitoring and status early warning method of hydraulic structures of sluice stations disclosed in application number CN201510622076.2 as an example, it does not take into account the situation that the combined anti-seepage curtain can effectively improve and solve the anti-seepage of high-permeable sand and soil layers. When applied to the deformation monitoring of the lock chamber wall improved by the combined anti-seepage curtain, in order to avoid inaccurate monitoring results caused by fluctuations in the collected monitoring information, a large amount of monitoring information is required for multiple engineering monitoring points. Similarly, a balance cannot be achieved between monitoring accuracy and monitoring efficiency.

[0073] The technical solution provided in this embodiment is aimed at the application scenario of forming a continuous and complete anti-seepage curtain in a highly permeable sand layer (i.e., under the condition of a combined anti-seepage curtain). Since the combined anti-seepage curtain reduces the difficulty of maintenance and management of the lock chamber wall and other parts of the highly permeable sand layer in the later stage of the project, reduces the possibility of its deformation, and improves the overall safety and stability of the project, the above-mentioned time serialization can extract key feature points or change trends. For example, small local changes do not affect the overall trend, and there is no need to record all the original data at all times, so it can reduce the recording of redundant data. As a result, the impact on detection efficiency is reduced, and detection accuracy is guaranteed under the premise that small local changes do not affect the overall trend, achieving a balance between accuracy and efficiency.

[0074] See also Figure 2 , Figure 2 It is a schematic diagram of the process of obtaining the sample curve set provided in an embodiment of the present application.

[0075] In an exemplary embodiment, each first preset period includes a second preset period, and the method of acquiring the sample curve set includes S201 to S204.

[0076] S201, taking a first preset period starting from the current moment as a target preset period, and acquiring stress monitoring information of a plurality of stress collection areas of the lock chamber wall in a second preset period within the target preset period;

[0077] S202, using stress information acquired in a second preset period within a plurality of first preset periods before a target preset period as stress sample information;

[0078] S203, comparing each stress sample information with the stress monitoring information to obtain a corresponding second similarity score;

[0079] S204: Take the stress sample information that is not less than the second similarity threshold and is closest to the current moment as the target stress information, and take the monitoring curve set corresponding to the target stress information as the sample curve set.

[0080] It can be considered that the displacement data set of the lock chamber wall is collected in each first preset period. In the second preset period within the target preset period, the stress information of multiple stress collection areas of the lock chamber wall is obtained to form stress monitoring information for reflecting the stress conditions of the lock chamber wall at different positions. At the same time, the stress information collected in multiple historical preset periods before the target preset period is called as stress sample information. The stress sample information of each second preset period is compared with the current stress monitoring information to obtain the corresponding second similarity score. The stress sample information that is not less than the second similarity threshold and closest to the current moment is selected as the target stress information, and the monitoring curve set corresponding to the target stress information in the first preset period is used as the sample curve set. A monitoring curve set is generated based on the displacement data set, and compared with the sample curve set to calculate the first similarity score. When the first similarity score is lower than the first score threshold, it is considered that the deformation of the lock chamber wall meets the first alarm condition. Once the alarm condition is met, an early warning message is automatically generated and sent to the user device through a preset communication channel.

[0081] Therefore, the technical solution provided by this embodiment uses historical data as samples to improve the prediction ability of deformation trends. By introducing stress monitoring information, stress information can more directly reflect the stress state of the lock chamber wall. By comparing the similarity between historical stress sample information and current stress information, a set of sample curves closest to the current working condition can be selected. The dynamic selection mechanism ensures the reliability of the monitoring results, can adapt to different environments and load changes, and avoids using irrelevant sample data for comparison, thereby reducing the risk of misjudgment. By dynamically analyzing the similarity between the current stress situation and the historical stress data, the application of stress information can be flexibly adjusted, especially the most suitable sample curve can be automatically selected under different environmental conditions, which enhances the adaptability of the monitoring method and is suitable for complex and changeable lock chamber wall applications. The similarity scoring algorithm is used to compare stress information and screen curves, which facilitates the realization of a fully automated monitoring process. Stress monitoring information can not only be used to determine whether there is a deformation risk at present, but also to optimize the selection of sample data to ensure the accuracy and efficiency of monitoring.

[0082] In summary, by introducing a multi-level stress information monitoring and similarity analysis mechanism, the intelligence and accuracy of the chamber wall deformation monitoring system are enhanced, enabling it to dynamically adjust the selection of sample curves to ensure that reliable judgment results can be provided under different working conditions.

[0083] In an exemplary embodiment, the step of using stress information acquired in a second preset period within a plurality of first preset periods before a target preset period as stress sample information includes:

[0084] When the deformation of the lock chamber wall in the first preset period before the target preset period does not meet the first alarm condition, the stress information of the first preset period that does not meet the first alarm condition is input into the abnormal area detection model to determine whether the number of abnormal stress collection areas is greater than the preset number; the stress information includes an identifier of each stress collection area and a set of stress data of the area corresponding to each identifier;

[0085] The stress information of a first preset period in which the number of abnormal stress acquisition areas is greater than a preset number is used as stress sample information for obtaining a second similarity score with the stress monitoring information.

[0086] The technical solution provided in this embodiment screens the stress information of historical cycles through the abnormal area detection model to determine the stress data with reference value, thereby improving the accuracy and reliability of the deformation monitoring of the lock chamber wall. Specifically, when the deformation of the lock chamber wall does not meet the first alarm condition in multiple first preset periods before the target preset period, it is considered that the structure in these periods has no obvious deformation risk, but there may still be potential stress anomalies. The stress information of the non-alarm period is input into the abnormal area detection model, and the possibility of stress anomaly in the area is judged according to the data of each stress collection area. The abnormal detection result of each area will be marked as "abnormal" or "normal". The number of stress collection areas judged as "abnormal" is counted, and it is judged whether the number is greater than the preset number. The preset number is a set threshold used to distinguish the severity of the abnormal situation, such as 3, 5, and 8. If the number of abnormal areas exceeds the set threshold, it is considered that the stress information of the period is potentially representative, indicating that these stress data may be related to the risk of structural deformation in the future. When the number of abnormal areas detected is greater than the preset number, the stress information of the first preset period will be selected as the stress sample information. The stress sample information will be used to perform a similarity score analysis (second similarity score) with the stress monitoring information of the current cycle, so as to determine whether there is a risk of deformation of the lock chamber wall at the current moment.

[0087] Therefore, the technical solution provided by this embodiment screens the historical stress data through the abnormal area detection model, and only retains the cycles with stress anomalies in multiple regions. Compared with the traditional direct data comparison method, the detection based on the abnormal area can better capture the potential precursors of structural deformation. The screening process not only simplifies the complexity of large-scale data analysis, but also ensures that the stress sample data used for comparison is more representative and accurate. Only when a large range of stress anomalies are detected, the data of the cycle will be used as sample information, reducing the inaccurate selection of stress sample information caused by local stress fluctuations. The selection of stress samples is automatically adjusted according to the stress conditions of the historical cycle, so that the monitoring mechanism has strong adaptability. Especially in engineering scenarios with complex environmental conditions and frequent stress changes, the system can flexibly respond to different stress change modes and provide efficient deformation monitoring. By setting a preset number threshold, historical data with minor or scattered stress anomalies can be effectively filtered out, and only cycles with a large proportion of abnormal areas can be selected as stress samples; when the chamber wall is relatively stable, the preset number threshold can be artificially reduced to ensure that the stress sample information can be updated in time, so that the sample data input to the similarity analysis has a high reference value. The dynamic adaptive capabilities of screening help avoid over-reliance on specific historical data while ensuring the stability and continuity of monitoring.

[0088] In an exemplary embodiment, the abnormal region detection model is obtained by training a deep learning model, and the training process of the abnormal region detection model includes the following steps:

[0089] A training set is obtained, wherein the training set includes a plurality of training data, each of which includes a data group of sample stress data and annotated data of an abnormal region corresponding to the data group; for each training data in the training set, the following processing is performed:

[0090] Inputting a data group in the training data into a preset deep learning model to obtain prediction data of an abnormal area corresponding to the data group;

[0091] Based on the prediction data and the annotation data corresponding to the sample image data, the model parameters of the deep learning model are updated;

[0092] Detect whether the preset training end condition is met; if so, use the trained deep learning model as the abnormal area detection model; if not, continue to train the deep learning model using the next training data.

[0093] Therefore, by designing, establishing an appropriate amount of neuron computing nodes and a multi-layer operation hierarchy, and selecting appropriate input layers and output layers, a preset deep learning model can be obtained. Through the learning and tuning of the deep learning model, a functional relationship from input to output is established. Although the functional relationship between input and output cannot be found 100%, the actual correlation relationship can be as close as possible. The abnormal area detection model thus trained can obtain the abnormal judgment corresponding to the stress acquisition area based on the stress information of the first preset period, and the calculation results are highly accurate and reliable.

[0094] In the training process, a large amount of training data can be used, including a data set of sample stress data and annotated data of abnormal areas corresponding to the data set. A preset deep learning model is trained with these data, and the parameters of the deep learning model are adjusted and updated using a back propagation algorithm based on the predicted data output by the deep learning model and the actual annotated stability score data, so that it can more accurately predict the impact of the data set of sample stress data on the classification of abnormal areas.

[0095] In a specific application, the architecture of the abnormal area detection model may include an input layer, a feature extraction layer, a fusion layer, a fully connected layer, and an output layer. The input layer is used to receive multiple time series data sets, such as stress data of different stress collection areas, to reflect the stress changes in the area. It can also receive time series data of a single stress monitoring data point. The feature extraction layer, including the LSTM layer, is used to process the time series data and capture the temporal characteristics of stress changes so that the model can identify abnormal patterns and trends; the CNN layer is used to extract local features of stress data, enhance sensitivity to local anomalies, and identify local fluctuations or anomalies in time series data through convolution operations. The fusion layer is used to integrate the features extracted from the LSTM layer and the CNN layer to capture the relationship between different features. The fusion layer can use a connection operation or a weighted sum to integrate information from different data sources. The fully connected layer is used to process a single stress monitoring data point and combine it with the fused features to ensure that the model can consider overall and local features. The output layer is used to output an abnormality score or classification label based on the fused features to indicate whether there is an abnormal area. The score can be a continuous value (indicating the degree of abnormality) or a binary classification (indicating normal or abnormal).

[0096] By combining LSTM and CNN, the model can effectively capture the complex nonlinear relationships in time series data, which helps to better identify potential anomalies. By fusing data from different sources (time series and single data points), the accuracy of anomaly detection is improved, and anomaly risks can be considered from multiple dimensions.

[0097] The embodiments of the present application can train to obtain an abnormal area detection model. In other optional implementations, the present application can use a pre-trained abnormal area detection model. The preset deep learning model can be a convolutional neural network model or a recurrent neural network model. The present application does not limit the implementation method of the preset deep learning model. The present application does not limit the training process of the abnormal detection model. For example, it can adopt the above-mentioned supervised learning training method, or it can adopt a semi-supervised learning training method, or it can adopt an unsupervised learning training method. The present application does not limit the preset training end condition. For example, it can be that the number of trainings reaches a preset number of times (the preset number of times is, for example, 1 time, 3 times, 10 times, 100 times, 1000 times, 10000 times, etc.), or it can be that the training data in the training set completes one or more trainings, or it can be that the total loss value obtained in this training is not greater than the preset loss value.

[0098] Among them, the training data in the training set can be historical data and monitoring records, including stress monitoring information of each stress collection area obtained by collecting multiple sets of field data, and abnormal situation evaluation by technicians; it can be simulation data generated after modeling and simulation based on known physical models and assumptions to simulate the abnormal situation evaluation of stress monitoring information in different stress collection areas.

[0099] See also Figure 3 , Figure 3 It is a schematic diagram of a process for obtaining a first similarity score provided in an embodiment of the present application.

[0100] In an exemplary embodiment, the step of obtaining a first similarity score between the monitoring curve set and the sample curve set includes S301 to S303.

[0101] S301, obtaining similarity values ​​of displacement time curves of the same preset monitoring point in the monitoring curve set and the sample curve set, and obtaining a similarity value of each preset monitoring point;

[0102] S301, for each preset monitoring point, when the corresponding stress acquisition area is an abnormal area, the product of the obtained similarity value and the abnormality coefficient of the stress acquisition area corresponding to the preset monitoring point is used as a first similarity intermediate score; otherwise, the obtained similarity value is used as the first similarity intermediate score;

[0103] S303: The sum of the obtained first similarity intermediate scores of each preset monitoring point is used as the first similarity score between the monitoring curve set and the sample curve set.

[0104] The technical solution provided in this embodiment is to analyze the abnormal conditions of different monitoring points more accurately by weighting the similarity value with the abnormal coefficient. First, a monitoring curve set and a sample curve set are obtained. For each preset monitoring point, the displacement time curves of the same position (i.e., the same monitoring point) in the monitoring curve and the sample curve are compared, and the similarity value of the two is calculated. The similarity value is used to evaluate the similarity between the displacement change of the current monitoring point and the displacement change of the historical sample. It can be considered that each preset monitoring point will have a separate similarity value for subsequent score calculation. Then, for each preset monitoring point, check whether the corresponding stress acquisition area (in the sample curve set) is marked as an abnormal area. If it is an abnormal area, the similarity value of the monitoring point is multiplied by the abnormal coefficient corresponding to the area to obtain the first similarity intermediate score of the monitoring point. The abnormal coefficient is used to weight the similarity value of the monitoring point to reflect the importance and potential risk of the area. If it is a normal area, the similarity value is directly used as the first similarity intermediate score of the monitoring point. The weighting mechanism of the abnormal area ensures that in the potential high-risk area, it is more sensitive to the deviation of similarity. The first similarity intermediate scores of all preset monitoring points are added together to obtain the first similarity score between the entire monitoring curve set and the sample curve set. The first similarity score indicates the similarity between the overall monitoring situation at the current moment and the historical sample situation. If the score is low, it means that there is a significant difference between the current monitoring data and the historical samples, which may indicate that there is a risk of deformation of the lock chamber wall.

[0105] Therefore, each preset monitoring point adopts a different scoring mechanism (weighted or unweighted) according to whether the area where it is located is an abnormal area, so that it can dynamically adapt to the changes in the stress state, focus on monitoring high-risk areas, and maintain conventional analysis for low-risk areas. The first similarity intermediate scores are summarized to obtain the first similarity score, which can more accurately reflect the overall deformation of the gate chamber wall. Compared with the simple average similarity value, the importance of each monitoring point and the difference in stress state can be better considered, thereby improving the representativeness and accuracy of the judgment results. The similarity value of each monitoring point is analyzed point by point, and the high-risk area is weighted, which can improve the overall reliability on the basis of a low false alarm rate.

[0106] In a specific application, the value of the abnormal coefficient is less than 1 and greater than 0. The value of the abnormal coefficient is set to be less than 1 and greater than 0, so that the similarity value of the abnormal area is weakened, thereby highlighting the potential problems of the abnormal area. In the final first similarity score, the impact of the area is more significantly reflected.

[0107] In an exemplary embodiment, the method for obtaining the abnormal coefficient of the stress acquisition area includes:

[0108] Obtaining an abnormal coefficient correspondence relationship; the abnormal coefficient correspondence relationship is used to indicate the correspondence between the number and proportion of abnormal stress acquisition areas and abnormal values;

[0109] According to the number of stress acquisition areas indicating abnormalities in the target stress information, the corresponding abnormal value is obtained by using the abnormal coefficient correspondence relationship, and the abnormal value is used as the abnormal coefficient of the stress acquisition area indicating abnormalities.

[0110] The technical solution provided in this embodiment dynamically adjusts the abnormal coefficient of the abnormal area according to the number of abnormal areas through the abnormal coefficient correspondence. In a specific application, before obtaining the abnormal coefficient correspondence, an abnormal coefficient correspondence is first defined, and the abnormal coefficient correspondence is used to indicate the correspondence between the number of abnormal stress acquisition areas and the abnormal value. That is, different numbers of abnormal areas correspond to different abnormal values. In a specific application, according to the target stress information, the number of abnormal stress acquisition areas at the current moment is obtained. Then, the corresponding abnormal value is calculated using the preset abnormal coefficient correspondence and the number of abnormal areas. The abnormal value is used to further reflect the potential risk or importance of these abnormal areas. The obtained abnormal value is used as the abnormal coefficient of the abnormal stress acquisition area.

[0111] Therefore, by introducing the abnormal coefficient correspondence relationship, the abnormal coefficient of the abnormal area corresponding to the new sample curve set can be dynamically adjusted. The introduction of the abnormal coefficient correspondence relationship allows configuration and expansion according to the actual needs of the engineering environment.

[0112] As an example, a method for monitoring deformation of a lock chamber wall is provided, comprising:

[0113] R1, in a first preset cycle starting at the current moment, using each of the displacement sensors to obtain a displacement data set of the lock chamber wall; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections;

[0114] R2, taking the first preset period starting from the current moment as the target preset period, and acquiring stress monitoring information of multiple stress collection areas of the lock chamber wall in a second preset period within the target preset period;

[0115] R3, when the deformation of the lock chamber wall in the first preset period before the target preset period does not meet the first alarm condition, the stress information of the first preset period that does not meet the first alarm condition is input into the abnormal area detection model to obtain the abnormal detection result of each stress collection area; the abnormal detection result includes abnormal and normal;

[0116] R4, judging whether the number of abnormal stress collection areas is greater than a preset number; the stress information includes an identifier of each stress collection area and a set of stress data of the area corresponding to each identifier;

[0117] R5, using stress information of a first preset period in which the number of abnormal stress acquisition areas is greater than a preset number as stress sample information for obtaining a second similarity score with the stress monitoring information;

[0118] R6, comparing each stress sample information with the stress monitoring information to obtain a corresponding second similarity score;

[0119] R7, taking the stress sample information which is not less than the second similarity threshold and is closest to the current moment as the target stress information, and taking the monitoring curve set corresponding to the target stress information as the sample curve set;

[0120] R8, generating a monitoring curve set based on the displacement data set, wherein the monitoring curve set includes a displacement time curve of each preset monitoring point; obtaining a similarity value of the displacement time curve of the same preset monitoring point in the monitoring curve set and the sample curve set, and obtaining a similarity value of each preset monitoring point;

[0121] R9, for each preset monitoring point, when the corresponding stress acquisition area is an abnormal area, the product of the obtained similarity value and the abnormal coefficient of the stress acquisition area corresponding to the preset monitoring point is used as the first similarity intermediate score; otherwise, the obtained similarity value is used as the first similarity intermediate score;

[0122] R10, taking the sum of the first similarity intermediate scores of each preset monitoring point as the first similarity score between the monitoring curve set and the sample curve set. When the first similarity score is lower than the first score threshold, it is considered that the deformation of the lock chamber wall meets the first alarm condition; when the first alarm condition is met, a first early warning message is generated and sent to a preset user device; when the first alarm condition is not met, R1 is executed to start the deformation monitoring of the lock chamber wall for the next first preset period.

[0123] Among them, the method for obtaining the abnormal coefficient of the stress acquisition area includes: obtaining the abnormal coefficient correspondence relationship; the abnormal coefficient correspondence relationship is used to indicate the correspondence between the number, proportion and abnormal value of the abnormal stress acquisition area; according to the number of stress acquisition areas indicating abnormalities in the target stress information, the corresponding abnormal value is obtained by using the abnormal coefficient correspondence relationship, and the abnormal value is used as the abnormal coefficient of the stress acquisition area indicating the abnormality.

[0124] The abnormal area detection model is obtained by training a deep learning model, and the training process of the abnormal area detection model includes the following steps: obtaining a training set, the training set includes multiple training data, each of the training data includes a data group of sample stress data and labeled data of the abnormal area corresponding to the data group; for each training data in the training set, performing the following processing: inputting the data group in the training data into a preset deep learning model to obtain the predicted data of the abnormal area corresponding to the data group; updating the model parameters of the deep learning model based on the predicted data and labeled data corresponding to the sample image data; detecting whether the preset training end condition is met; if yes, using the trained deep learning model as the abnormal area detection model; if not, continuing to train the deep learning model using the next training data.

[0125] The chamber wall deformation monitoring method provided in this example can more comprehensively evaluate the deformation of the chamber wall and improve the accuracy of the monitoring results by combining displacement data and stress monitoring information. Promptly generating early warning information when deformation risk is detected helps to take preventive measures to avoid structural damage. By introducing an abnormal area detection model and a similarity scoring mechanism, false alarms caused by local fluctuations or noise are reduced. The ability to dynamically select sample curves based on the actual stress state improves the adaptability and flexibility of the monitoring method. By representing continuous displacement data as a time curve, the amount of data is reduced, the data processing process is simplified, and efficiency is improved. Continuous monitoring and timely early warning help to achieve structural health monitoring and preventive maintenance of the chamber wall. The automated monitoring and early warning process reduces manual intervention, reduces the possibility of human error, and improves the reliability of monitoring.

[0126] Based on the contents stated in the above method embodiments, technicians in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices and systems described below can refer to the corresponding processes in the above method embodiments and will not be repeated here.

[0127] Device Embodiments.

[0128] See also Figure 4 , Figure 4 It is a structural block diagram of a gate chamber wall deformation monitoring device provided in an embodiment of the present application.

[0129] This embodiment provides a gate chamber wall deformation monitoring device, the gate chamber wall deformation monitoring device comprising:

[0130] A first acquisition module is used to acquire a displacement data set of the lock chamber wall using each of the displacement sensors in a first preset period starting at the current moment; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections;

[0131] A first judgment module is used to judge whether the deformation of the lock chamber wall meets a first alarm condition based on the displacement data set and according to a preset judgment rule; when the first alarm condition is met, generate a first warning message and send it to a preset user device;

[0132] Among them, the preset judgment rule refers to generating a monitoring curve set based on the displacement data set, the monitoring curve set including the displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the gate chamber wall meets the first alarm condition.

[0133] In an exemplary embodiment, each first preset period includes a second preset period, and a method of obtaining the sample curve set includes:

[0134] Taking a first preset period starting from the current moment as a target preset period, and acquiring stress monitoring information of a plurality of stress collection areas of the lock chamber wall in a second preset period within the target preset period;

[0135] using stress information acquired in a second preset period within a plurality of first preset periods before a target preset period as stress sample information;

[0136] Compare each stress sample information with the stress monitoring information to obtain a corresponding second similarity score;

[0137] The stress sample information which is not less than the second similarity threshold and is closest to the current moment is used as the target stress information, and the monitoring curve set corresponding to the target stress information is used as the sample curve set.

[0138] In an exemplary embodiment, the step of using stress information acquired in a second preset period within a plurality of first preset periods before a target preset period as stress sample information includes:

[0139] When the deformation of the lock chamber wall in the first preset period before the target preset period does not meet the first alarm condition, the stress information of the first preset period that does not meet the first alarm condition is input into the abnormal area detection model to obtain the abnormal detection result of each stress collection area; the abnormal detection result includes abnormal and normal;

[0140] Determine whether the number of abnormal stress collection areas is greater than a preset number; the stress information includes an identifier of each stress collection area and a set of stress data of the area corresponding to each identifier;

[0141] The stress information of a first preset period in which the number of abnormal stress acquisition areas is greater than a preset number is used as stress sample information for obtaining a second similarity score with the stress monitoring information.

[0142] In an exemplary embodiment, the abnormal region detection model is obtained by training a deep learning model, and the training process of the abnormal region detection model includes the following steps:

[0143] A training set is obtained, wherein the training set includes a plurality of training data, each of which includes a data group of sample stress data and annotated data of an abnormal region corresponding to the data group; for each training data in the training set, the following processing is performed:

[0144] Inputting a data group in the training data into a preset deep learning model to obtain prediction data of an abnormal area corresponding to the data group;

[0145] Based on the prediction data and the annotation data corresponding to the sample image data, the model parameters of the deep learning model are updated;

[0146] Detect whether the preset training end condition is met; if so, use the trained deep learning model as the abnormal area detection model; if not, continue to train the deep learning model using the next training data.

[0147] In an exemplary embodiment, obtaining a first similarity score between the monitoring curve set and the sample curve set includes:

[0148] Obtaining similarity values ​​of displacement time curves of the same preset monitoring point in the monitoring curve set and the sample curve set, and obtaining a similarity value of each preset monitoring point;

[0149] For each preset monitoring point, when the corresponding stress acquisition area is an abnormal area, the product of the obtained similarity value and the abnormal coefficient of the stress acquisition area corresponding to the preset monitoring point is used as the first similarity intermediate score; otherwise, the obtained similarity value is used as the first similarity intermediate score;

[0150] The sum of the obtained first similarity intermediate scores of each preset monitoring point is used as the first similarity score between the monitoring curve set and the sample curve set.

[0151] In an exemplary embodiment, the method for obtaining the abnormal coefficient of the stress acquisition area includes:

[0152] Obtaining an abnormal coefficient correspondence relationship; the abnormal coefficient correspondence relationship is used to indicate the correspondence between the number and proportion of abnormal stress acquisition areas and abnormal values;

[0153] According to the number of stress acquisition areas indicating abnormalities in the target stress information, the corresponding abnormal value is obtained by using the abnormal coefficient correspondence relationship, and the abnormal value is used as the abnormal coefficient of the stress acquisition area indicating abnormalities.

[0154] System Embodiments.

[0155] This embodiment provides a lock chamber wall deformation monitoring system, which is applicable to the lock chamber wall deformation monitoring method of the method embodiment. The lock chamber wall deformation monitoring system includes:

[0156] A plurality of displacement sensors are respectively arranged at preset monitoring points at the joints of adjacent gate sections of the gate chamber wall, for obtaining displacement data varying with time;

[0157] A control module, wherein the control module is respectively connected to each of the displacement sensors for communication;

[0158] A gateway module, the gateway module is arranged between the control module and the user equipment to realize the communication connection between the control module and the user equipment; and is arranged between the control module and each of the displacement sensors to realize the communication connection between the control module and each of the displacement sensors;

[0159] The control module is configured to:

[0160] S1, in a first preset cycle starting at the current moment, using each of the displacement sensors to obtain a displacement data set of the lock chamber wall; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections;

[0161] S2, based on the displacement data set, judging whether the deformation of the lock chamber wall meets the first alarm condition according to a preset judgment rule; when the first alarm condition is met, generating a first warning message and sending it to a preset user device; when the first alarm condition is not met, executing S1;

[0162] Among them, the preset judgment rule refers to generating a monitoring curve set based on the displacement data set, the monitoring curve set including the displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the gate chamber wall meets the first alarm condition.

[0163] By setting up multiple displacement sensors and transmitting data to the control module in real time, real-time deformation monitoring of the lock chamber wall can be achieved, which helps to detect potential structural problems at an early stage and prevent serious accidents. The displacement sensor can detect tiny displacement changes. Compared with traditional manual detection methods, it has higher accuracy and sensitivity, which helps to more accurately judge the deformation of the lock chamber wall. Among them, the displacement sensor is, for example, a laser displacement sensor, which measures the distance change between two points by emitting a laser beam and has the advantage of high precision. There are also examples of optical fiber displacement sensors, LVDT (linear variable differential transformer) displacement sensors, MEMS accelerometers and displacement sensors. The control module can be an industrial computer or a cloud server, which includes multiple microprocessors, storage devices, and input / output interfaces.

[0164] In specific applications, the monitoring system is integrated into the cloud server, and a mobile application is set up so that users can remotely access the detection system of the cloud server to obtain monitoring data and alarm information in real time.

[0165] In an exemplary embodiment, it further comprises: a plurality of stress monitoring sensors, which are respectively arranged at a plurality of stress collection areas of the lock chamber wall and connected to the control module, and are used to obtain stress data of each stress collection area;

[0166] Among them, the stress collection area is set at the intersection of the two concrete pouring stages of the gate chamber wall. The first concrete pouring stage is a pouring construction based on the bottom plate after construction and including chamfering construction. The second concrete pouring is a pouring construction after the first concrete pouring.

[0167] The stress monitoring sensor is arranged at the junction of two concrete pourings. The first concrete pouring completes the bottom plate and performs chamfering. The second pouring forms a junction with the first, which may cause stress concentration at the junction. The stress monitoring sensor collects stress data in real time and transmits it to the control module. By analyzing the stress data, the stress distribution of the lock chamber wall during operation can be determined, and the structural health of the wall can be more comprehensively evaluated in combination with the displacement data. The stress monitoring sensor is, for example, a strain gauge sensor: it can also be a fiber grating sensor (FBG sensor), which measures stress based on the light wave reflection characteristics in the optical fiber and the strain change of the optical fiber caused by stress.

[0168] In an exemplary embodiment, the construction elevation range of the first concrete pouring is 3 meters (inclusive) to 4 meters (inclusive); the second concrete pouring is constructed by a mobile formwork construction method, and is constructed to the top based on the first concrete pouring construction.

[0169] The second concrete pouring is carried out by mobile formwork. Large steel formwork is used for the mobile formwork to match the second concrete pouring, which reduces the possibility of deformation of the lock chamber wall during construction. Two (or more) concrete pourings are gradually adopted during the pouring of the lock chamber wall, and the mobile formwork is used for the second concrete pouring. The large steel formwork of the mobile formwork and the tie rod-free or tie rod-less formwork system provide better stability of the lock chamber wall (compared with the previous traditional construction process).

[0170] In an exemplary embodiment, each first preset period includes a second preset period, and a method of obtaining the sample curve set includes:

[0171] Taking a first preset period starting from the current moment as a target preset period, and acquiring stress monitoring information of a plurality of stress collection areas of the lock chamber wall in a second preset period within the target preset period;

[0172] using stress information acquired in a second preset period within a plurality of first preset periods before a target preset period as stress sample information;

[0173] Compare each stress sample information with the stress monitoring information to obtain a corresponding second similarity score;

[0174] The stress sample information which is not less than the second similarity threshold and is closest to the current moment is used as the target stress information, and the monitoring curve set corresponding to the target stress information is used as the sample curve set.

[0175] In specific applications, each stress sample information is compared with the stress monitoring information respectively to obtain the corresponding second similarity score, which can be achieved through a similarity measurement method. As an example, for each stress acquisition area, the stress sample information and the stress monitoring information are represented as two vectors, and the Euclidean distance between the two vectors is calculated. The smaller the distance, the more similar the stress state. The distance is converted into a similarity score, for example, similarity = 1 / (1+Euclidean distance). Assume that in a certain stress acquisition area, the stress sample information is [5,7,9] and the current stress monitoring information is [4,6,10]. The Euclidean distance is 1.73 and the similarity score is 0.366.

[0176] In stress monitoring, the specific value of stress usually depends on the properties of the material, loading conditions, environmental factors, etc. Therefore, the stress (sample and monitoring) information in the above specific applications is obtained through actual measurement or simulation calculation. Specifically, a stress monitoring sensor is used to measure in an actual environment and record stress values ​​(such as normal stress and shear stress) under specific conditions. Among them, since the stress values ​​in different regions may vary greatly, the data is normalized or standardized before similarity calculation. Then, by extracting the specific features of stress changes, it is converted into a comparable vector form.

[0177] In an exemplary embodiment, the control module uses the stress information acquired in the second preset period within a plurality of first preset periods before the target preset period as the stress sample information in the following manner:

[0178] When the deformation of the lock chamber wall in the first preset period before the target preset period does not meet the first alarm condition, the stress information of the first preset period that does not meet the first alarm condition is input into the abnormal area detection model to obtain the abnormal detection result of each stress collection area; the abnormal detection result includes abnormal and normal;

[0179] Determine whether the number of abnormal stress collection areas is greater than a preset number; the stress information includes an identifier of each stress collection area and a set of stress data of the area corresponding to each identifier;

[0180] The stress information of a first preset period in which the number of abnormal stress acquisition areas is greater than a preset number is used as stress sample information for obtaining a second similarity score with the stress monitoring information.

[0181] In an exemplary embodiment, the abnormal region detection model is obtained by training a deep learning model, and the training process of the abnormal region detection model includes the following steps:

[0182] A training set is obtained, wherein the training set includes a plurality of training data, each of which includes a data group of sample stress data and annotated data of an abnormal region corresponding to the data group; for each training data in the training set, the following processing is performed:

[0183] Inputting a data group in the training data into a preset deep learning model to obtain prediction data of an abnormal area corresponding to the data group;

[0184] Based on the prediction data and the annotation data corresponding to the sample image data, the model parameters of the deep learning model are updated;

[0185] Detect whether the preset training end condition is met; if so, use the trained deep learning model as the abnormal area detection model; if not, continue to train the deep learning model using the next training data.

[0186] In an exemplary embodiment, the control module obtains the first similarity score between the monitoring curve set and the sample curve set in the following manner:

[0187] Obtaining similarity values ​​of displacement time curves of the same preset monitoring point in the monitoring curve set and the sample curve set, and obtaining a similarity value of each preset monitoring point;

[0188] For each preset monitoring point, when the corresponding stress acquisition area is an abnormal area, the product of the obtained similarity value and the abnormal coefficient of the stress acquisition area corresponding to the preset monitoring point is used as the first similarity intermediate score; otherwise, the obtained similarity value is used as the first similarity intermediate score;

[0189] The sum of the obtained first similarity intermediate scores of each preset monitoring point is used as the first similarity score between the monitoring curve set and the sample curve set.

[0190] In an exemplary embodiment, the control module obtains the abnormal coefficient of the stress acquisition area in the following manner:

[0191] Obtaining an abnormal coefficient correspondence relationship; the abnormal coefficient correspondence relationship is used to indicate the correspondence between the number and proportion of abnormal stress acquisition areas and abnormal values;

[0192] According to the number of stress acquisition areas indicating abnormalities in the target stress information, the corresponding abnormal value is obtained by using the abnormal coefficient correspondence relationship, and the abnormal value is used as the abnormal coefficient of the stress acquisition area indicating abnormalities.

[0193] In the various implementation modes of the specification of the present application, the size of the serial number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the present application.

[0194] The terms "first", "second", "third", etc. (if any) in the specification and claims of the present application and the drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "corresponding to" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0195] This application is explained from the perspectives of purpose of use, effectiveness, progress and novelty, and has met the functional enhancement and usage requirements emphasized by the Patent Law. The above description and drawings of this application are only the preferred embodiments of this application, and are not intended to limit this application. Therefore, all structures, devices, features, etc. that are similar or identical to this application, that is, all equivalent replacements or modifications made in accordance with the scope of the patent application of this application, should fall within the scope of protection of the patent application of this application.

Claims

1. A method for monitoring the deformation of a lock chamber wall, characterized in that: Applied to an Internet of Things network, the Internet of Things network includes a gateway module and a control module, and a plurality of displacement sensors arranged on the wall of a lock chamber, each of the displacement sensors uploads displacement data that changes with time to the control module through the gateway module, and the method includes: S1, in a first preset cycle starting at the current moment, using each of the displacement sensors to obtain a displacement data set of the lock chamber wall; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections; S2, based on the displacement data set, judging whether the deformation of the lock chamber wall meets the first alarm condition according to a preset judgment rule; when the first alarm condition is met, generating a first warning message and sending it to a preset user device; when the first alarm condition is not met, executing S1; Among them, the preset judgment rule refers to generating a monitoring curve set based on the displacement data set, the monitoring curve set including the displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the gate chamber wall meets the first alarm condition.

2. The method for monitoring the deformation of a lock chamber wall according to claim 1, characterized in that: Each first preset period includes a second preset period, and a method of obtaining the sample curve set includes: Taking a first preset period starting from the current moment as a target preset period, and acquiring stress monitoring information of a plurality of stress collection areas of the lock chamber wall in a second preset period within the target preset period; using stress information acquired in a second preset period within a plurality of first preset periods before a target preset period as stress sample information; Compare each stress sample information with the stress monitoring information to obtain a corresponding second similarity score; The stress sample information which is not less than the second similarity threshold and is closest to the current moment is used as the target stress information, and the monitoring curve set corresponding to the target stress information is used as the sample curve set.

3. The method for monitoring the deformation of a lock chamber wall according to claim 2, characterized in that: The step of taking the stress information acquired in the second preset period within a plurality of first preset periods before the target preset period as the stress sample information includes: When the deformation of the lock chamber wall in the first preset period before the target preset period does not meet the first alarm condition, the stress information of the first preset period that does not meet the first alarm condition is input into the abnormal area detection model to obtain the abnormal detection result of each stress collection area; the abnormal detection result includes abnormal and normal; Determine whether the number of abnormal stress collection areas is greater than a preset number; the stress information includes an identifier of each stress collection area and a set of stress data of the area corresponding to each identifier; The stress information of a first preset period in which the number of abnormal stress acquisition areas is greater than a preset number is used as stress sample information for obtaining a second similarity score with the stress monitoring information.

4. The method for monitoring the deformation of a lock chamber wall according to claim 3, characterized in that: The abnormal area detection model is obtained by training a deep learning model, and the training process of the abnormal area detection model includes the following steps: A training set is obtained, wherein the training set includes a plurality of training data, each of which includes a data group of sample stress data and annotated data of an abnormal region corresponding to the data group; for each training data in the training set, the following processing is performed: Inputting a data group in the training data into a preset deep learning model to obtain prediction data of an abnormal area corresponding to the data group; Based on the prediction data and the annotation data corresponding to the sample image data, the model parameters of the deep learning model are updated; Detect whether the preset training end condition is met; if so, use the trained deep learning model as the abnormal area detection model; if not, continue to train the deep learning model using the next training data.

5. The method for monitoring the deformation of a lock chamber wall according to claim 3, characterized in that: The obtaining of a first similarity score between the monitoring curve set and the sample curve set includes: Obtaining similarity values ​​of displacement time curves of the same preset monitoring point in the monitoring curve set and the sample curve set, and obtaining a similarity value of each preset monitoring point; For each preset monitoring point, when the corresponding stress acquisition area is an abnormal area, the product of the obtained similarity value and the abnormal coefficient of the stress acquisition area corresponding to the preset monitoring point is used as the first similarity intermediate score; otherwise, the obtained similarity value is used as the first similarity intermediate score; The sum of the obtained first similarity intermediate scores of each preset monitoring point is used as the first similarity score between the monitoring curve set and the sample curve set.

6. The method for monitoring the deformation of a lock chamber wall according to claim 5, characterized in that: The method for obtaining the abnormal coefficient of the stress acquisition area includes: Obtaining an abnormal coefficient correspondence relationship; the abnormal coefficient correspondence relationship is used to indicate the correspondence between the number and proportion of abnormal stress acquisition areas and abnormal values; According to the number of stress acquisition areas indicating abnormalities in the target stress information, the corresponding abnormal value is obtained by using the abnormal coefficient correspondence relationship, and the abnormal value is used as the abnormal coefficient of the stress acquisition area indicating abnormalities.

7. A chamber wall deformation monitoring device, characterized in that: The chamber wall deformation monitoring device comprises: A first acquisition module is used to acquire a displacement data set of the lock chamber wall using each displacement sensor in a first preset period starting at the current moment; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections; A first judgment module is used to judge whether the deformation of the lock chamber wall meets a first alarm condition based on the displacement data set and according to a preset judgment rule; when the first alarm condition is met, generate a first warning message and send it to a preset user device; Among them, the preset judgment rule refers to generating a monitoring curve set based on the displacement data set, the monitoring curve set including the displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the gate chamber wall meets the first alarm condition.

8. A lock chamber wall deformation monitoring system, characterized in that: The chamber wall deformation monitoring system comprises: A plurality of displacement sensors are respectively arranged at preset monitoring points at the joints of adjacent gate sections of the gate chamber wall, for obtaining displacement data varying with time; A control module, wherein the control module is respectively connected to each of the displacement sensors for communication; A gateway module, the gateway module is arranged between the control module and the user equipment to realize the communication connection between the control module and the user equipment; and is arranged between the control module and each of the displacement sensors to realize the communication connection between the control module and each of the displacement sensors; The control module is configured to: S1, in a first preset cycle starting at the current moment, using each of the displacement sensors to obtain a displacement data set of the lock chamber wall; the displacement data set includes multiple groups of displacement data that change with time corresponding to each preset monitoring point, the preset monitoring point is a monitoring point set at the joint of adjacent lock sections, and the displacement data that change with time is used to indicate the deformation displacement between the corresponding adjacent lock sections; S2, based on the displacement data set, judging whether the deformation of the lock chamber wall meets the first alarm condition according to a preset judgment rule; when the first alarm condition is met, generating a first warning message and sending it to a preset user device; when the first alarm condition is not met, executing S1; Among them, the preset judgment rule refers to generating a monitoring curve set based on the displacement data set, the monitoring curve set including the displacement time curve of each preset monitoring point; obtaining a first similarity score between the monitoring curve set and the sample curve set; when the first similarity score is lower than a first score threshold, it is considered that the deformation of the gate chamber wall meets the first alarm condition.

9. A lock chamber wall deformation monitoring system according to claim 8, characterized in that: Also includes: A plurality of stress monitoring sensors are respectively arranged in a plurality of stress collection areas of the lock chamber wall and are communicatively connected with the control module through the gateway module to obtain stress data of each stress collection area; Among them, the stress collection area is set at the intersection of the two concrete pouring stages of the gate chamber wall. The first concrete pouring stage is a pouring construction based on the bottom plate after construction and including chamfering construction. The second concrete pouring is a pouring construction after the first concrete pouring.

10. A lock chamber wall deformation monitoring system according to claim 9, characterized in that: The construction elevation range of the first concrete pouring is 3 meters to 4 meters; the second concrete pouring is constructed using a mobile formwork construction method, and is constructed to the top based on the first concrete pouring construction.

Images