Training method, device and equipment of tower crane health monitoring model and storage medium

Abstract

The invention discloses a training method and device of a tower crane health monitoring model, electronic equipment and a storage medium. The method comprises the steps that first actual measurement data and second actual measurement data of a tower crane are acquired; simulating the tower crane to obtain a first simulation tower crane, and obtaining first simulation data of the first simulation tower crane in a healthy state; according to the first actual measurement data and the first simulation data, whether simulation adjustment is carried out on the first simulation tower crane or not is determined; when it is determined that simulation adjustment is not carried out on the first simulation tower crane, simulation adjustment is carried out on the first simulation tower crane based on the first fault parameter to obtain a second simulation tower crane, and simulation operation is carried out on the second simulation tower crane to obtain second simulation data; and training a pre-constructed tower crane health monitoring model according to the first actual measurement data, the second actual measurement data, the first simulation data and the second simulation data, and obtaining a trained tower crane health monitoring model after the training is completed. And the convenience and the accuracy of health monitoring of the tower crane are improved.

Description

Technical Field

[0001] The present application relates to the technical field of safety monitoring of tower cranes, and in particular to a training method, device, electronic equipment and storage medium for a health monitoring model of a tower crane. Background Art

[0002] With the continuous development, tower cranes are widely used in the construction of high-rise buildings, bridges and other buildings, and the safe operation of tower cranes is an important part, such as the operating safety of operators and the application safety of tower cranes. Among them, the operating safety of operators is reflected in the standardization of tower crane operation, and the application safety of tower cranes is reflected in the safety of the tower crane's own structure. Therefore, it is extremely important to monitor the health and safety of tower crane structures.

[0003] At present, when monitoring the health and safety of the tower crane structure, in addition to the traditional manual inspection method, it also includes the use of certain measurement methods to realize the diagnosis of the tower steel structure, such as by comparing the monitoring data with the safety data. However, there are certain shortcomings. For example, the manual inspection method is time-consuming and labor-intensive, and the accuracy of the comparison based on specific monitoring data is not high. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a training method, device, electronic device and storage medium for a tower crane health monitoring model to solve the technical problem of low accuracy and convenience in health and safety monitoring of tower crane structures in related technologies.

[0005] In a first aspect, an embodiment of the present application provides a training method for a tower crane health monitoring model, comprising: Acquire first measured data and second measured data of the tower crane, wherein the first measured data is health status data collected based on a plurality of sensors arranged on the tower crane, and the second measured data is fault status data collected based on a plurality of sensors arranged on the tower crane; Simulating the tower crane to obtain a first simulated tower crane, and performing simulated operation on the first simulated tower crane to obtain first simulation data of the first simulated tower crane in a healthy state; Determining whether to perform simulation adjustment on the first simulated tower crane according to the first measured data and the first simulated data; When it is determined that the first simulated tower crane is not to be simulated and adjusted, the first simulated tower crane is simulated and adjusted based on the first fault parameter to obtain a second simulated tower crane, and the second simulated tower crane is simulated and operated to obtain second simulation data; The pre-constructed tower crane health monitoring model is trained according to the first measured data, the second measured data, the first simulation data and the second simulation data, and a trained tower crane health monitoring model is obtained when the training is completed.

[0006] Optionally, the determining whether to perform simulation adjustment on the first simulated tower crane according to the first measured data and the first simulation data includes: Performing filtering transformation preprocessing on the first measured data and the first simulated data to obtain processed first measured data and processed first simulated data; Calculating data similarity between the processed first measured data and the processed first simulation data, and comparing the data similarity with a similarity threshold; Determining whether to adjust the first simulated tower crane according to a comparison result obtained by the comparison; If the data similarity is less than the similarity threshold, determining to perform simulation adjustment on the first simulated tower crane; If the data similarity is greater than or equal to the similarity threshold, it is determined that no simulation adjustment is performed on the first simulated tower crane.

[0007] Optionally, when it is determined that the first simulated tower crane is not to be simulated and adjusted, the first simulated tower crane is simulated and adjusted based on the first fault parameter to obtain a second simulated tower crane, and the second simulated tower crane is simulated and operated to obtain second simulation data, including: When it is determined that the first simulated tower crane is not to be simulated and adjusted, a fault parameter generation model is loaded to output a first fault parameter corresponding to the tower crane; The first simulated tower crane is simulated and adjusted according to the first fault parameter to obtain a second simulated tower crane, and the second simulated tower crane is simulated and operated to obtain second simulation data under the first fault parameter.

[0008] Optionally, the pre-constructed tower crane health monitoring model is trained according to the first measured data, the second measured data, the first simulation data and the second simulation data, and a trained tower crane health monitoring model is obtained when the training is completed, including: Performing filtering transformation preprocessing on the second measured data and the second simulated data to obtain processed second measured data and processed second simulated data; Loading a pre-built tower crane health monitoring model, training it with the processed second simulation data, and obtaining a first tower crane health monitoring model after training; Using the processed second measured data to perform an effect test on the first tower crane health monitoring model to obtain a corresponding test result, wherein the test result includes a test pass and a test fail; When it is determined that the test result is a passed test, data fusion processing is performed on the first test data, the second measured data, the first simulation data and the second simulation data to obtain corresponding fused data, and model fine-tuning processing is performed on the first tower crane health monitoring model based on the fused data to obtain a tower crane health monitoring model.

[0009] Optionally, the processing of the second simulation data for training and obtaining the first tower crane health monitoring model after training includes: Dividing the processed second simulation data into training set data and validation set data, and training a pre-built tower crane health monitoring model based on the training set data to obtain an intermediate model; The intermediate model is fine-tuned using the validation set data, and a first tower crane health monitoring model is obtained when the fine-tuning is completed.

[0010] Optionally, the using the processed second measured data to perform an effect test on the first tower crane health monitoring model to obtain corresponding test results includes: Inputting the fault data contained in the processed second measured data into the first tower crane health monitoring model, and outputting the predicted data; Comparing the predicted data with the standard data included in the processed second measured data to determine the prediction effect of the first tower crane health monitoring model; According to the test result, obtaining the test result of the first tower crane health monitoring model; If the test accuracy in the test result is greater than or equal to a preset threshold, the test is determined to be passed; If the test accuracy in the test result is less than a preset threshold, it is determined that the test fails.

[0011] Optionally, the method further comprises: When it is determined that the test result is a test failure, a second fault parameter is obtained, and a simulation adjustment is performed on the first simulated tower crane based on the second fault parameter to obtain a third simulated tower crane; Performing simulated operation on the third simulated tower crane to obtain third simulation data under the second fault parameter; The pre-constructed tower crane health monitoring model is trained according to the first measured data, the second measured data, the first simulation data and the third simulation data, and a trained tower crane health monitoring model is obtained when the training is completed.

[0012] In a second aspect, an embodiment of the present application provides a training device for a tower crane health monitoring model, comprising: A data acquisition module, used to acquire first measured data and second measured data of the tower crane, wherein the first measured data is health status data collected based on a plurality of sensors arranged on the tower crane, and the second measured data is fault status data collected based on a plurality of sensors arranged on the tower crane; A first simulation module is used to simulate the tower crane to obtain a first simulated tower crane, and simulate the operation of the first simulated tower crane to obtain first simulation data of the first simulated tower crane in a healthy state; A simulation adjustment module, used for determining whether to perform simulation adjustment on the first simulated tower crane according to the first measured data and the first simulation data; A second simulation module is used for, when it is determined that the first simulated tower crane is not to be simulated and adjusted, to simulate and adjust the first simulated tower crane based on the first fault parameter to obtain a second simulated tower crane, and to simulate and run the second simulated tower crane to obtain second simulation data; The model training module is used to train the pre-built tower crane health monitoring model according to the first measured data, the second measured data, the first simulation data and the second simulation data, and obtain the trained tower crane health monitoring model when the training is completed.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the training method for the tower crane health monitoring model described in any one of the above items when executing the computer program.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the training method of the tower crane health monitoring model described in any one of the above items are implemented.

[0015] The embodiments of the present application provide a training method, device, electronic device and storage medium for a tower crane health monitoring model. When training the model, firstly obtain first measured data of the tower crane in a non-fault state and second measured data of the tower crane in a fault state, and simulate the tower crane to obtain a first simulated tower crane. Simultaneously, simulated operation is performed to obtain first simulation data of the first simulated tower crane. Then, based on the first simulation data and the first measured data, the rationality of the first simulated tower crane is determined, that is, whether the first simulated tower crane needs to be corrected and adjusted. When no correction and adjustment is needed, the corresponding first fault parameters are set on the first simulated tower crane to simulate the operation of the tower crane in a fault state to obtain second simulation data. Finally, the constructed tower crane health monitoring model is trained based on the first measured data, the second measured data, the first simulation data and the second simulation data, and then the trained tower crane health monitoring model is obtained when the training is completed. In the training process, a large number of training samples are obtained by tower crane simulation processing, and then the effectiveness of the model is judged by using measured data after training based on the training samples, so that the model can be closer to the real tower crane for fault identification. By combining the training of measured data and simulation data in fault-free and faulty states, the fault can be located. In the process of use, the existing faults can be identified and located only by obtaining sensor data, which improves the convenience and accuracy of tower crane health monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the steps of the training method of the tower crane health monitoring model provided in the embodiment of the present application; Figure 2 It is a flow chart of the steps of obtaining a tower crane health monitoring model provided in an embodiment of the present application; Figure 3 This is a flow chart of the steps of obtaining the test results provided in the embodiment of the present application; Figure 4 It is another flow chart of the steps of the training method of the tower crane health monitoring model provided in the embodiment of the present application; Figure 5 It is a structural schematic diagram of a training device for a tower crane health monitoring model provided in an embodiment of the present application; Figure 6 is a structural schematic diagram of an electronic device provided in an embodiment of the present application; Figure 7 This is another structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] It should be understood that the various steps described in the method embodiments disclosed in the present application can be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0019] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0020] In the relevant technology, when monitoring the health and safety of the tower crane structure, in addition to the traditional manual inspection method, it also includes the use of certain measurement methods to realize the diagnosis of the tower steel structure, such as by comparing the monitoring data with the safety data. However, there are certain shortcomings, such as the time-consuming and labor-intensive manual inspection method, and the low adaptability and accuracy of the comparison based on specific monitoring data.

[0021] In order to solve the technical problems existing in the related art, the present application embodiment provides a training method for a tower crane health monitoring model, see Figure 1 , Figure 1 It is a flow chart of the steps of a training method for a tower crane health monitoring model provided in an embodiment of the present application. The method includes steps 101 to 105.

[0022] Step 101, obtaining first measured data and second measured data of a tower crane, wherein the first measured data is health status data collected based on a plurality of sensors installed on the tower crane, and the second measured data is fault status data collected based on a plurality of sensors installed on the tower crane.

[0023] In one embodiment, when training the constructed tower crane health monitoring model, training is performed through simulation data and verification processing is performed through measured data, and then after completing the training and passing the verification, a tower crane health monitoring model with good prediction effect is obtained. Therefore, after training, it is necessary to obtain the measured data of the tower crane, specifically including the first measured data collected by each sensor set in a healthy state and the second measured data collected by each sensor set in a fault state. In particular, the second measured data also includes relevant fault data, such as a specific fault location, a fault value, etc.

[0024] Exemplarily, when training the tower crane health monitoring model, the training is performed for the same type of tower cranes, that is, the trained tower crane health monitoring model is suitable for health monitoring of the same type of tower cranes. Therefore, when obtaining measured data, it is possible to obtain monitoring data of the same model of tower cranes during actual operation, and the health status of the tower crane, including faults and health.

[0025] In addition, in order to improve the applicability of the tower crane health monitoring model, after obtaining the tower crane health monitoring model for a tower crane, the tower crane health monitoring model can be fine-tuned based on the differences between tower cranes, and the specific adjustment basis and adjustment method can be obtained through analysis and processing based on the structural differences of tower cranes, differences between structural differences and measured data, etc.

[0026] Step 102, simulating the tower crane to obtain a first simulated tower crane, and performing simulated operation on the first simulated tower crane to obtain first simulation data of the first simulated tower crane in a healthy state.

[0027] In one embodiment, when training the model, the training data used includes corresponding simulation data, so in addition to obtaining the measured data of the tower crane, the tower crane is simulated using relevant simulation software, and the simulated tower crane obtained by simulation is used to obtain corresponding simulation data. Specifically, the tower crane is simulated using the simulation software to obtain a corresponding first simulated tower crane, and the obtained first simulated tower crane is simulated and operated to obtain first simulation data of the first simulated tower crane in a healthy state.

[0028] For example, when simulating a tower crane, a finite element model of the tower crane can be constructed based on ANSYS software. The simulated tower crane needs to maintain consistency with the real tower crane in structure and materials. Then, when the simulation is running, the unit type can be selected as a spatial beam unit to obtain the first simulation data of the simulated tower crane in a healthy state.

[0029] In actual application, when constructing a simulated tower crane, since even tower cranes of the same type cannot guarantee that each tower crane is completely consistent in structural parameters or structural dimensions, when constructing a simulated tower crane, data with intervals in structural parameters or structural dimensions are randomly selected. This may result in the simulation results or simulation data being different from the real data when performing the simulation. When the difference is small, it can be accepted, but when the difference is too large, it will cause a large error in the model training. Therefore, the first simulation data obtained based on the first simulated tower crane cannot be directly used for model training, but requires certain analysis and judgment processing.

[0030] Step 103: Determine whether to perform simulation adjustment on the first simulated tower crane according to the first measured data and the first simulation data.

[0031] In one embodiment, when simulating a tower crane, it is necessary to ensure that the similarity between the simulated tower crane and the real tower crane is as high as possible, so that the simulated tower crane can be used to simulate actual fault data. Therefore, after the simulation is performed to obtain the first simulation data, it will be determined whether the first simulated tower crane needs to be simulated and adjusted based on the first measured data and the first simulation data.

[0032] Exemplarily, when determining whether it is necessary to perform simulation adjustment on the first simulated tower crane, it is necessary to determine whether the first simulation data is close to the first measured data. The closer the two are, the closer the simulated tower crane is to the real tower crane. When determining whether the first simulation data and the first real data are close, it can be determined by calculating the distance between the data, such as calculating the similarity between the data. The higher the similarity, the closer the distance. There are many ways to calculate similarity, such as cosine similarity, Euler distance, etc.

[0033] Therefore, when determining whether to perform simulation adjustment on the first simulated tower crane based on the first simulation data and the first measured data, it can be determined through similarity calculation, which specifically includes: performing filtering transformation preprocessing on the first measured data and the first simulation data to obtain processed first measured data and processed first simulation data; calculating the data similarity of the processed first measured data and the processed first simulation data, and comparing the data similarity with a similarity threshold; determining whether to adjust the first simulated tower crane based on the comparison result obtained by the comparison; if the data similarity is less than the similarity threshold, determining to perform simulation adjustment on the first simulated tower crane; if the data similarity is greater than or equal to the similarity threshold, determining not to perform simulation adjustment on the first simulated tower crane.

[0034] When calculating the similarity between the first measured data and the first simulated data, by calculating the similarity between each group of measured data and the corresponding group of simulated data, firstly, through filtering conversion preprocessing, the first measured data and the first simulated data, which are sensor data, are converted into data forms that can be calculated, such as converted into corresponding vector forms, and then the data similarity between the converted data is calculated, such as calculating the distance between the two vectors (the smaller the distance, the higher the similarity), and the calculated data similarity is compared with a pre-set similarity threshold. Since the higher the similarity, the closer the data is, then when comparing, if the data similarity is less than the similarity threshold, it means that the data difference is large, and the first simulated tower crane needs to be simulated and optimized. On the contrary, if the data similarity is greater than or equal to the similarity threshold, it means that the data difference is small, and further processing can be performed at this time without the need to simulate and optimize the first simulated tower crane.

[0035] Furthermore, when the calculated data similarity is less than the set similarity threshold, the first simulated tower crane needs to be subjected to corresponding simulation optimization processing. In fact, when the data similarity is less than the similarity threshold, it means that there are characteristic differences between the constructed first simulated tower crane and the real tower crane, and the first simulated tower crane cannot be used to simulate the operating data of the tower crane in a faulty state. Therefore, it is necessary to re-simulate, which can be done by correcting the tower crane parameters, such as correcting and adjusting the structural dimensions of the tower crane, to obtain a new first simulated tower crane. For the new first simulated tower crane, it is also necessary to perform a simulation operation and compare the obtained simulation data with the first measured data again, so that the simulated tower crane does not need to be corrected.

[0036] Step 104: when it is determined that the first simulated tower crane does not need to be simulated and adjusted, the first simulated tower crane is simulated and adjusted based on the first fault parameter to obtain a second simulated tower crane, and the second simulated tower crane is simulated and operated to obtain second simulation data.

[0037] In one embodiment, when determining whether it is necessary to perform simulation adjustment on the first simulated tower crane based on the first measured data and the first simulation data, if it is determined that no simulation adjustment is required, further processing can be performed based on the first simulated tower crane, specifically including performing simulation based on the first simulated tower crane to obtain relevant fault data. Specifically, when it is determined that no simulation adjustment is performed on the first simulated tower crane, relevant first fault parameters are obtained to adjust the first simulated tower crane, wherein the added first fault parameters at least include damage location, connection stiffness, and material anti-interference coefficient, etc., and then a second simulated tower crane is obtained after the adjustment is completed, and the second simulated tower crane is simulated and operated to obtain second simulation data.

[0038] Exemplarily, the second simulated tower crane is a simulated tower crane after fault setting for the tower crane, which is used to simulate the operation of the tower crane in a faulty state, and then the operation data of the tower crane in the presence of a fault can be simulated, that is, the second simulation data.

[0039] Furthermore, the first fault parameter added is obtained based on the characteristics of the tower crane itself, and the settings for the tower crane itself at least include fault parameters such as damage location, connection stiffness, and material anti-interference coefficient, which are input into the first simulated tower crane to obtain a second simulated tower crane with certain faults. Since the fault factors and factors of different types of tower cranes may be different, the first fault parameter can be obtained by using a pre-trained fault parameter generation model, and by inputting the characteristics of the simulated tower crane, a first fault parameter that is more in line with itself can be obtained, and then the second simulation data can be obtained.

[0040] Specifically, when obtaining the second simulation data, it includes: when it is determined that no simulation adjustment is required for the first simulated tower crane, loading the fault parameter generation model to output the first fault parameter corresponding to the tower crane; simulating and adjusting the first simulated tower crane according to the first fault parameter to obtain the second simulated tower crane, and simulating the operation of the second simulated tower crane to obtain the second simulation data under the first fault parameter.

[0041] In practical applications, the first fault parameter added to the first simulated tower crane can be set by the operator based on experience, or can be automatically generated based on the tower crane. For automatically generating corresponding fault parameters, a corresponding fault parameter generation model can be prepared in advance, and the tower crane characteristics of the simulated tower crane are input into the fault parameter generation model, and one or more groups of fault parameters are obtained by random generation, wherein the tower crane characteristics include but are not limited to tower crane size information, tower crane load-bearing information, tower crane operation information, and tower crane structural characteristics, etc., and then a group of fault parameters are randomly selected as the first fault parameters for adjusting the first simulated tower crane to obtain a second simulated tower crane with a certain fault.

[0042] When obtaining the second simulation data, based on the same method as obtaining the first simulation data, the second simulation tower crane is simulated to operate, thereby obtaining the second simulation data generated when operating in a fault state.

[0043] Step 105 , training the pre-built tower crane health monitoring model according to the first measured data, the second measured data, the first simulation data and the second simulation data, and obtaining a trained tower crane health monitoring model when the training is completed.

[0044] In one embodiment, after the acquisition of measured data and simulation data is completed, the obtained measured data and simulation data are used to train the model, and then a tower crane health monitoring model that can be used for health monitoring of the tower crane is obtained when the training is completed. Specifically, after the acquisition of measured data and simulation data is completed, the pre-constructed tower crane health monitoring model is trained and optimized according to the obtained first measured data, second measured data, first simulation data, and second simulation data, and then a trained tower crane health monitoring model is obtained after the training and optimization are completed.

[0045] For example, during training, it is necessary to train the tower crane in both a healthy state and a faulty state to ensure that during the use of the model, it is possible to accurately identify and analyze whether the tower crane is in a healthy or faulty state. At the same time, when a tower crane has a fault, the location of the fault and the fault level can be determined by analyzing the operating data of the tower crane during operation.

[0046] In actual applications, a number of sensors are installed on the tower crane to obtain data of the tower crane in different states. Different data represent the fault conditions of the corresponding structural parts of the tower crane, such as whether there is a fault and the level of the fault. Therefore, the obtained data, including measured data and simulation data, can be marked and processed, and the specific fault can be located through data marking, so as to realize the identification and location of the fault during use. In order to accurately determine whether there is a fault and locate the fault, it is necessary to combine simulation data and measured data for training and optimization processing, so as to improve the accuracy of the tower crane health monitoring model in predicting faults and accurately locate the fault.

[0047] Furthermore, when training the model, refer to Figure 2 , Figure 2 It is a flow chart of the steps of obtaining a tower crane health monitoring model provided in an embodiment of the present application, wherein the steps include steps 201 to 204.

[0048] Step 201, performing filtering transformation preprocessing on the second measured data and the second simulation data to obtain processed second measured data and processed second simulation data; Step 202, loading a pre-built tower crane health monitoring model, using the processed second simulation data for training, and obtaining a first tower crane health monitoring model after training; Step 203, using the processed second measured data to perform an effect test on the first tower crane health monitoring model to obtain a corresponding test result, wherein the test result includes a test pass and a test fail; Step 204, when it is determined that the test result is a passed test, data fusion processing is performed on the first test data, the second measured data, the first simulation data and the second simulation data to obtain corresponding fused data, and model fine-tuning processing is performed on the first tower crane health monitoring model based on the fused data to obtain the tower crane health monitoring model.

[0049] Specifically, during training, the constructed tower crane health monitoring model is first trained and tested using the second measured data and the second simulation data, and after the test passes, the model is fine-tuned and optimized using the fused data obtained by fusion processing based on the first measured data, the second measured data, the first simulation data and the second simulation data, and a usable tower crane health monitoring model is obtained after fine-tuning and optimization.

[0050] Exemplarily, when using the acquired relevant data to train the model, it is necessary to ensure the consistency and accuracy of the data. By filtering and transforming the second book data and the second simulation data, the processed second measured data and the processed second simulation data are obtained. The constructed tower crane health monitoring model is then trained using the processed second simulation data, and the first tower crane health monitoring model is obtained after training. The first tower crane health monitoring model is then tested for effectiveness using the second measured data, and the second tower crane health monitoring model is obtained after passing the effectiveness test. At the same time, the first measured data, the second measured data, the first simulation data and the second simulation data are fused based on the differences between the data, and the first tower crane health monitoring model is fine-tuned and optimized based on the fused data obtained by the fusion processing to obtain a tower crane health monitoring model that can be used.

[0051] Based on the above description, the entire training process is divided into three stages, including the first stage of training based on the second simulation data, the second stage of effect verification based on the second measured data, and the third stage of model fine-tuning and optimization based on the fusion data. Among them, the first stage is to achieve the recognition of the fault state, the second stage is to achieve fault identification close to the actual situation, and the third stage is to achieve the location of the fault.

[0052] In practical applications, the lack of measured data or the small amount of data when there is a fault makes the accuracy of training unable to be guaranteed. However, there are many combinations of fault parameters, so that when performing simulation processing, there will be many simulated fault conditions, and then the obtained second simulation data (operation data of the tower crane in the fault state) is large, which can better complete the training of the model and improve the accuracy of the model. However, since the training is based on a large amount of simulation data, it is necessary to combine the measured data to verify the validity of the model. Therefore, the second stage of processing is required. The validity of the model obtained in the first stage is verified by the second measured data, so that the verified model has better accuracy and is more in line with the actual tower crane. Finally, since the failure of the tower crane is reflected in the difference of the data, that is, the location of the fault can be located by reading and analyzing the data. Therefore, by fusing the measured data (including the first measured data and the second measured data) and the simulation data (including the first simulation data and the second simulation data), combined with the differentiated characteristics between the data, the fault location function of the model can be realized.

[0053] Furthermore, in the processing process of the first stage, that is, training is performed based on the second simulation data to obtain the first tower crane health monitoring model, the specific process includes: dividing the processed second simulation data into training set data and verification set data, and training the pre-built tower crane health monitoring model based on the training set data to obtain an intermediate model; using the verification set data to fine-tune the intermediate model, and obtaining the first tower crane health monitoring model when the fine-tuning is completed.

[0054] When training is performed based on the second simulation data, the processed second simulation data is divided into training set data and verification set data, and then the training set data is used for training, and the verification set data is used for verification processing. In this process, the difference from the traditional model training process is that in the subsequent test process, the second measured data obtained by the actual test is used for the test, so the processed second simulation data is only divided into training set data and verification set data, and the specific ratio can be 9:1.

[0055] Furthermore, when using the second measured data to test the effect of the model, you can refer to Figure 3 , Figure 3 It is a flowchart of the steps of obtaining the test results provided in an embodiment of the present application, specifically including steps 301 to 303.

[0056] Step 301, inputting the fault data contained in the processed second measured data into the first tower crane health monitoring model, and outputting the predicted data; Step 302, comparing the predicted data with the standard data included in the processed second measured data to determine the prediction effect of the first tower crane health monitoring model; Step 303, obtaining the test result of the first tower crane health monitoring model according to the test result; Among them, if the test accuracy in the test effect is greater than or equal to the preset threshold, it is determined that the test has passed; if the test accuracy in the test effect is less than the preset threshold, it is determined that the test has failed.

[0057] Specifically, when conducting the effect test, the purpose is to determine whether the trained first tower crane health monitoring model is close to the actual usage of the tower crane. Therefore, the second measured data is used to conduct an effect test on the first tower crane health monitoring model, and then determine whether the model needs to be retrained and optimized based on the effect test results.

[0058] Exemplarily, when using the second measured data to test the effectiveness of the first tower crane health monitoring model, the fault data contained in the second measured data is input into the first tower crane health monitoring model, and the predicted data corresponding to the fault data is output. At the same time, the fault data will correspond to a standard data in the second measured data. Therefore, by comparing the predicted data with the standard data, the predicted effect of the first tower crane health monitoring model is obtained, and the test effect is used to determine the test results of the first tower crane health monitoring model, including test pass and test fail.

[0059] In practical applications, when it is determined that the test has passed, it means that the fault monitoring and analysis performed by the obtained first tower crane health monitoring model are in line with the actual operating conditions of the tower crane. Then, the third stage will be entered at this time, that is, the first tower crane health monitoring model will be fine-tuned and optimized, so that the first tower crane health model after fine-tuning and optimization can locate the fault.

[0060] Furthermore, when testing the effect of the first tower crane health monitoring model, if the test fails, retraining and other related processing are required, which is particularly serious. Figure 4 , Figure 4 This is another flowchart diagram of the steps of the tower crane health monitoring model training method provided in an embodiment of the present application, wherein the steps include steps 401 to 403.

[0061] Step 401, when it is determined that the test fails, obtaining a second fault parameter, and performing simulation adjustment on the first simulated tower crane based on the second fault parameter to obtain a third simulated tower crane; Step 402, simulate the operation of the third simulated tower crane to obtain third simulation data under the second fault parameter; Step 403 , training the pre-built tower crane health monitoring model according to the first measured data, the second measured data, the first simulation data and the third simulation data, and obtaining a trained tower crane health monitoring model when the training is completed.

[0062] In one embodiment, when the test is performed, if the test result is that the test passes, it is determined that the obtained first tower crane health monitoring model can be close to the monitoring and prediction of the tower crane under real faults, and when the test result is that the test fails, it means that the first tower crane health monitoring model obtained at this time cannot monitor and predict faults in accordance with the actual usage situation, and therefore corresponding adjustment processing is required to obtain a tower crane health monitoring model that monitors and predicts faults in accordance with the actual usage situation of the tower crane.

[0063] Exemplarily, when it is determined that the test fails, the method used at this time is to re-train the model to obtain a new first tower crane health monitoring model. In order to obtain the new first tower crane health monitoring model, a new set of fault parameters will be used to set up the self-defense tower crane, specifically obtaining the second fault parameter, and based on the second fault parameter, the first simulated tower crane is simulated and adjusted, that is, simulated and set, to obtain a third simulated tower crane, wherein the second fault parameter is different from the first fault parameter, and then the third simulated tower crane is simulated and operated to obtain third simulation data under the second fault parameter, and then the model is re-trained based on the newly obtained third simulation data, and then a trained tower crane health monitoring model is obtained when the training is completed.

[0064] Based on the above description, when the test fails, the fault parameters (second fault parameters) of the first simulated tower crane are reset, and a simulation operation is performed to obtain simulation data under the new fault parameters to train the model. When training the model, the difference from step 105 is that the second simulation data used in step 105 is replaced by the third simulation data obtained in step 402, and the other data used for training are the same, and the training process is also the same. That is, the entire training process is a cyclic process. Only when the test effect is good, the third stage of training recorded above can be performed. Otherwise, it is necessary to continuously adjust the fault parameter value to achieve a good test effect.

[0065] It should be noted that since the training process of step 403 is the same as that of step 105, it will not be repeated here. For details, please refer to the description of the relevant embodiment in step 105.

[0066] Furthermore, when processing based on the training method of the tower crane health monitoring model recorded above, the following steps are included: Step 1: Data collection; Corresponding sensor equipment is installed at each structural position of the actual tower crane, and measured data of the vibration displacement of the tower crane A and the like are obtained by collecting sensor data, including first measured data in a fault-free state and second measured data in a fault state.

[0067] Step 2: The tower crane is simulated to obtain a first simulated tower crane, and first simulation data of the first simulated tower crane is obtained; The actual tower crane is simulated using simulation software such as ANSYS to construct a finite element model of the actual tower crane. The constructed simulation model must maintain consistency with the actual tower crane in structure and materials. At the same time, the unit type can be set to select the spatial beam unit, and the simulation run can obtain the first simulation data of the simulated tower crane in a fault-free state.

[0068] Step 3: Data preprocessing; Perform filtering transformation preprocessing on the measured data and the first simulation data.

[0069] Step 4: Calculate the similarity between the first simulated tower crane and the real tower crane, and determine the difference between the first simulated tower crane and the real tower crane according to the similarity; The similarity value between the first measured data and the first simulation data after filtering transformation and processing is calculated. If the similarity value is greater than or equal to the similarity threshold, it is determined that the difference is small. If the similarity value is less than the similarity threshold, it is determined that the difference is large.

[0070] Step 5: If the difference is large, the first simulated tower crane is corrected and step 2 is executed; Step 6: If the difference is small, add the first fault parameters, including the damage location, connection stiffness and material anti-interference coefficient, etc., to the first simulated tower crane, and perform simulation to obtain the second simulation data; Step 7: Perform filtering and transformation processing on the first simulation data; Step 8: training a pre-built tower crane health monitoring model based on the first simulation data; The first simulation data after the filtering transformation is divided into a training set and a verification set, and the pre-built tower crane health monitoring model is trained by using the training set, and the verification set is used for verification processing; Step 9: Model effect test; When the training is completed and the verification is passed, the second test data is used to perform an effect test on the first tower crane health monitoring model obtained by training to obtain a test result, which includes a test pass and a test fail.

[0071] Step 10: If the test result is that the test fails, update the first fault parameter and execute step 6; Step 11: If the test result is that the test passes, the first test data, the second test data, the first simulation data, and the second simulation data are integrated; Step 12: Using the fused data obtained by fusion, fine-tune and optimize the first tower crane health monitoring model, so that the tower crane health monitoring model obtained after fine-tuning and optimization can identify and locate faults.

[0072] In summary, the present application discloses a training method for a tower crane health monitoring model. A number of sensors are arranged on the tower crane for monitoring and collecting data from various structural parts of the tower crane. When training the model, firstly, first measured data of the tower crane in a fault-free state and second measured data in a faulty state are obtained, and the tower crane is simulated to obtain a first simulated tower crane. Simultaneously, a simulated operation is performed to obtain first simulation data of the first simulated tower crane. Then, based on the first simulation data and the first measured data, the rationality of the first simulated tower crane is determined, that is, whether the first simulated tower crane needs to be corrected and adjusted. When no correction and adjustment is needed, a corresponding first fault parameter is set on the first simulated tower crane to simulate the operation of the tower crane in a faulty state to obtain second simulation data. Finally, the constructed tower crane health monitoring model is trained based on the first measured data, the second measured data, the first simulation data and the second simulation data, and then a trained tower crane health monitoring model is obtained when the training is completed. In the training process, a large number of training samples are obtained by tower crane simulation processing, and then the effectiveness of the model is judged by using measured data after training based on the training samples, so that the model can be closer to the real tower crane for fault identification. By combining the training of measured data and simulation data in fault-free and faulty states, the fault can be located. In the process of use, the existing faults can be identified and located only by obtaining sensor data, which improves the convenience and accuracy of tower crane health monitoring.

[0073] According to the method described in the above embodiment, this embodiment will be further described from the perspective of a training device for a tower crane health monitoring model. The training device for the tower crane health monitoring model can be implemented as an independent entity or integrated into an electronic device, such as a terminal. The terminal may include a mobile phone, a tablet computer, etc.

[0074] See also Figure 5 , Figure 5 is a structural schematic diagram of a training device for a tower crane health monitoring model provided in an embodiment of the present application, such as Figure 5 As shown, the training device 500 of the tower crane health monitoring model provided in the embodiment of the present application includes: The data acquisition module 501 is used to acquire first measured data and second measured data of the tower crane, wherein the first measured data is health status data collected based on a plurality of sensors arranged on the tower crane, and the second measured data is fault status data collected based on a plurality of sensors arranged on the tower crane; The first simulation module 502 is used to simulate the tower crane to obtain a first simulated tower crane, and simulate the operation of the first simulated tower crane to obtain first simulation data of the first simulated tower crane in a healthy state; A simulation adjustment module 503, used for determining whether to perform simulation adjustment on the first simulated tower crane according to the first measured data and the first simulation data; The second simulation module 504 is used for, when it is determined that the first simulated tower crane is not to be simulated and adjusted, to simulate and adjust the first simulated tower crane based on the first fault parameter to obtain a second simulated tower crane, and to simulate and run the second simulated tower crane to obtain second simulation data; The model training module 505 is used to train the pre-built tower crane health monitoring model according to the first measured data, the second measured data, the first simulation data and the second simulation data, and obtain the trained tower crane health monitoring model when the training is completed.

[0075] In one embodiment, the simulation adjustment module 503 is further used for: Performing filtering transformation preprocessing on the first measured data and the first simulation data to obtain processed first measured data and processed first simulation data; Calculating data similarity between the processed first measured data and the processed first simulation data, and comparing the data similarity with a similarity threshold; Determine whether to adjust the first simulated tower crane according to the comparison result obtained by the comparison; If the data similarity is less than the similarity threshold, it is determined to perform simulation adjustment on the first simulated tower crane; If the data similarity is greater than or equal to the similarity threshold, it is determined that no simulation adjustment is performed on the first simulated tower crane.

[0076] In one embodiment, the second simulation module 504 is further used for: When it is determined that the first simulated tower crane is not to be simulated and adjusted, a fault parameter generation model is loaded to output a first fault parameter corresponding to the tower crane; The first simulated tower crane is simulated and adjusted according to the first fault parameter to obtain a second simulated tower crane, and the second simulated tower crane is simulated and operated to obtain second simulation data under the first fault parameter.

[0077] In one embodiment, the model training module 505 is further used to: Performing filtering transformation preprocessing on the second measured data and the second simulation data to obtain processed second measured data and processed second simulation data; Loading a pre-built tower crane health monitoring model, training it with the processed second simulation data, and obtaining a first tower crane health monitoring model after training; Using the processed second measured data to perform an effect test on the first tower crane health monitoring model to obtain a corresponding test result, wherein the test result includes a test pass and a test fail; When it is determined that the test result is passed, data fusion processing is performed on the first test data, the second measured data, the first simulation data and the second simulation data to obtain corresponding fusion data, and model fine-tuning processing is performed on the first tower crane health monitoring model based on the fusion data to obtain the tower crane health monitoring model.

[0078] In one embodiment, the model training module 505 is further used to: The processed second simulation data is divided into training set data and validation set data, and a pre-built tower crane health monitoring model is trained based on the training set data to obtain an intermediate model; The validation set data is used to fine-tune the intermediate model, and the first tower crane health monitoring model is obtained when the fine-tuning is completed.

[0079] In one embodiment, the model training module 505 is further used to: Inputting the fault data contained in the processed second measured data into the first tower crane health monitoring model, and outputting the predicted data; Comparing the predicted data with the standard data included in the processed second measured data to determine the prediction effect of the first tower crane health monitoring model; According to the test results, the test results of the health monitoring model of the first tower crane are obtained; If the test accuracy in the test result is greater than or equal to the preset threshold, the test is determined to have passed; If the test accuracy in the test result is less than a preset threshold, it is determined that the test has failed.

[0080] In one embodiment, the model training module 505 is further used to: When it is determined that the test result is a test failure, a second fault parameter is obtained, and a simulation adjustment is performed on the first simulated tower crane based on the second fault parameter to obtain a third simulated tower crane; Performing simulated operation on the third simulated tower crane to obtain third simulation data under the second fault parameter; The pre-constructed tower crane health monitoring model is trained according to the first measured data, the second measured data, the first simulation data and the third simulation data, and a trained tower crane health monitoring model is obtained when the training is completed.

[0081] Also, see Figure 6 , Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, and the electronic device may be a mobile terminal such as a smart phone, a tablet computer, or the like. Figure 6 As shown, the electronic device 600 includes a processor 601 and a memory 602. The processor 601 is electrically connected to the memory 602.

[0082] The processor 601 is the control center of the electronic device 600. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device 600 and processes data by running or loading applications stored in the memory 602 and calling data stored in the memory 602, thereby monitoring the electronic device 600 as a whole.

[0083] In this embodiment, the processor 601 in the electronic device 600 will load the instructions corresponding to the processes of one or more applications into the memory 602 according to the following steps, and the processor 601 will run the application stored in the memory 602, thereby implementing any step in the training method of the tower crane health monitoring model provided in the above embodiment.

[0084] The electronic device 600 can implement the steps in any embodiment of the training method for the tower crane health monitoring model provided in the embodiments of the present application. Therefore, it can achieve the beneficial effects that can be achieved by any training method for the tower crane health monitoring model provided in the embodiments of the present application. Please refer to the previous embodiments for details and will not be repeated here.

[0085] See also Figure 7 , Figure 7 is another structural schematic diagram of an electronic device provided in an embodiment of the present application, such as Figure 7 As shown, Figure 7 The specific structural block diagram of the electronic device provided in the embodiment of the present application is shown, and the electronic device can be used to implement the training method of the tower crane health monitoring model provided in the above embodiment. The electronic device 700 can be a mobile terminal such as a smart phone or a laptop computer.

[0086] The RF circuit 710 is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices. The RF circuit 710 may include various existing circuit elements for performing these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, user identity module (SIM) cards, memories, etc. The RF circuit 710 can communicate with various networks such as the Internet, corporate intranets, wireless networks, or communicate with other devices through wireless networks. The above-mentioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The above-mentioned wireless networks may use various communication standards, protocols and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as Institute of Electrical and Electronics Engineers standards IEEE 802.11a, IEEE 802.11b, IEEE802.11g and / or IEEE802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging and short messages, and any other suitable communication protocols, even those that have not yet been developed.

[0087] The memory 720 can be used to store software programs and modules, such as the program instructions / modules corresponding to the training method of the tower crane health monitoring model in the above-mentioned embodiment. The processor 780 executes various functional applications and the training method of the tower crane health monitoring model by running the software programs and modules stored in the memory 720.

[0088] The memory 720 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 720 may further include a memory remotely arranged relative to the processor 780, and these remote memories may be connected to the electronic device 700 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0089] The input unit 730 can be used to receive uploaded digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, the input unit 730 may include a touch-sensitive surface 731 and other input devices 732. The touch-sensitive surface 731, also known as a touch display screen or touchpad, can collect user touch operations on or near it (such as operations performed by users using fingers, styluses, or any other suitable objects or accessories on or near the touch-sensitive surface 731), and drive corresponding connection devices according to a pre-set program. Optionally, the touch-sensitive surface 731 may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into touch point coordinates, and then sends it to the processor 780, and can receive and execute commands sent by the processor 780. In addition, the touch-sensitive surface 731 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch-sensitive surface 731, the input unit 730 may further include other input devices 732. Specifically, the other input devices 732 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

[0090] The display unit 740 can be used to display information input by the user or information provided to the user and various graphical user interfaces of the electronic device 700, which can be composed of graphics, text, icons, videos and any combination thereof. The display unit 740 may include a display panel 741, and optionally, the display panel 741 may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. Further, the touch-sensitive surface 731 may cover the display panel 741, and when the touch-sensitive surface 731 detects a touch operation on or near it, it is transmitted to the processor 780 to determine the type of the touch event, and then the processor 780 provides corresponding visual output on the display panel 741 according to the type of the touch event. Although in the figure, the touch-sensitive surface 731 and the display panel 741 are implemented as two independent components to implement input and output functions, in some embodiments, the touch-sensitive surface 731 and the display panel 741 can be integrated to implement input and output functions.

[0091] The electronic device 700 may also include at least one sensor 750, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 741 according to the brightness of the ambient light, and the proximity sensor may generate an interrupt when the flip cover is closed or closed. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that can also be configured in the electronic device 700, they will not be repeated here.

[0092] The audio circuit 760, the speaker 761, and the microphone 762 can provide an audio interface between the user and the electronic device 700. The audio circuit 760 can transmit the electrical signal converted from the received audio data to the speaker 761, which is converted into a sound signal for output; on the other hand, the microphone 762 converts the collected sound signal into an electrical signal, which is received by the audio circuit 760 and converted into audio data, and then the audio data is output to the processor 780 for processing, and then sent to another terminal through the RF circuit 710, or the audio data is output to the memory 720 for further processing. The audio circuit 760 may also include an earplug jack to provide communication between an external headset and the electronic device 700.

[0093] The electronic device 700 can help the user receive requests, send information, etc. through the transmission module 770 (such as a Wi-Fi module), which provides the user with wireless broadband Internet access. Although the transmission module 770 is shown in the figure, it can be understood that it is not a necessary component of the electronic device 700 and can be omitted as needed without changing the essence of the invention.

[0094] The processor 780 is the control center of the electronic device 700. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 720, and calling data stored in the memory 720, it executes various functions of the electronic device 700 and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 780 may include one or more processing cores; in some embodiments, the processor 780 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 780.

[0095] The electronic device 700 also includes a power supply 790 (such as a battery) for supplying power to various components. In some embodiments, the power supply can be logically connected to the processor 780 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 790 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0096] Although not shown, the electronic device 700 also includes a camera (such as a front camera, a rear camera), a Bluetooth module, etc., which will not be described in detail here. Specifically in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory, and one or more programs, wherein one or more programs are stored in the memory, and are configured to be executed by one or more processors to implement any step in the training method of the tower crane health monitoring model provided in the above embodiment.

[0097] In specific implementation, the above modules can be implemented as independent entities, or can be arbitrarily combined and implemented as the same or several entities. The specific implementation of the above modules can be found in the previous method embodiments, which will not be repeated here.

[0098] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling related hardware through instructions, and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present application provides a storage medium, which stores a plurality of instructions, and when the instructions are executed by the processor, any step in the training method of the tower crane health monitoring model provided in the above embodiments can be implemented.

[0099] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0100] Since the instructions stored in the storage medium can execute the steps in any embodiment of the training method for the tower crane health monitoring model provided in the embodiments of the present application, the beneficial effects that can be achieved by the training method for any tower crane health monitoring model provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0101] The training method, device, electronic device and storage medium of a tower crane health monitoring model provided by the embodiment of the present application are introduced in detail above. The principle and implementation mode of the present application are explained by using specific examples in this article. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as a limitation on the present application. Moreover, for ordinary technicians in this technical field, without departing from the principle of the present application, several improvements and modifications can be made, and these improvements and modifications are also regarded as the protection scope of the present application.

Claims

1. A training method for a tower crane health monitoring model, characterized in that: include: Acquire first measured data and second measured data of the tower crane, wherein the first measured data is health status data collected based on a plurality of sensors arranged on the tower crane, and the second measured data is fault status data collected based on a plurality of sensors arranged on the tower crane; Simulating the tower crane to obtain a first simulated tower crane, and performing simulated operation on the first simulated tower crane to obtain first simulation data of the first simulated tower crane in a healthy state; Determining whether to perform simulation adjustment on the first simulated tower crane according to the first measured data and the first simulated data; When it is determined that the first simulated tower crane is not to be simulated and adjusted, the first simulated tower crane is simulated and adjusted based on the first fault parameter to obtain a second simulated tower crane, and the second simulated tower crane is simulated and operated to obtain second simulation data; The pre-constructed tower crane health monitoring model is trained according to the first measured data, the second measured data, the first simulation data and the second simulation data, and a trained tower crane health monitoring model is obtained when the training is completed.

2. The method according to claim 1, characterized in that The determining whether to perform simulation adjustment on the first simulated tower crane according to the first measured data and the first simulation data includes: Performing filtering transformation preprocessing on the first measured data and the first simulated data to obtain processed first measured data and processed first simulated data; Calculating data similarity between the processed first measured data and the processed first simulation data, and comparing the data similarity with a similarity threshold; Determining whether to adjust the first simulated tower crane according to a comparison result obtained by the comparison; If the data similarity is less than the similarity threshold, determining to perform simulation adjustment on the first simulated tower crane; If the data similarity is greater than or equal to the similarity threshold, it is determined that no simulation adjustment is performed on the first simulated tower crane.

3. The method according to claim 1, characterized in that When it is determined that the first simulated tower crane is not to be simulated and adjusted, the first simulated tower crane is simulated and adjusted based on the first fault parameter to obtain a second simulated tower crane, and the second simulated tower crane is simulated and operated to obtain second simulation data, including: When it is determined that the first simulated tower crane is not to be simulated and adjusted, a fault parameter generation model is loaded to output a first fault parameter corresponding to the tower crane; The first simulated tower crane is simulated and adjusted according to the first fault parameter to obtain a second simulated tower crane, and the second simulated tower crane is simulated and operated to obtain second simulation data under the first fault parameter.

4. The method according to claim 1, characterized in that The pre-built tower crane health monitoring model is trained according to the first measured data, the second measured data, the first simulation data and the second simulation data, and a trained tower crane health monitoring model is obtained when the training is completed, including: Performing filtering transformation preprocessing on the second measured data and the second simulated data to obtain processed second measured data and processed second simulated data; Loading a pre-built tower crane health monitoring model, training it with the processed second simulation data, and obtaining a first tower crane health monitoring model after training; Using the processed second measured data to perform an effect test on the first tower crane health monitoring model to obtain a corresponding test result, wherein the test result includes a test pass and a test fail; When it is determined that the test result is a passed test, data fusion processing is performed on the first test data, the second measured data, the first simulation data and the second simulation data to obtain corresponding fused data, and model fine-tuning processing is performed on the first tower crane health monitoring model based on the fused data to obtain a tower crane health monitoring model.

5. The method according to claim 4, characterized in that The processing of the second simulation data for training and obtaining a first tower crane health monitoring model after training includes: Dividing the processed second simulation data into training set data and validation set data, and training a pre-built tower crane health monitoring model based on the training set data to obtain an intermediate model; The intermediate model is fine-tuned using the validation set data, and a first tower crane health monitoring model is obtained when the fine-tuning is completed.

6. The method according to claim 4, characterized in that The using the processed second measured data to perform an effect test on the first tower crane health monitoring model to obtain corresponding test results includes: Inputting the fault data contained in the processed second measured data into the first tower crane health monitoring model, and outputting the predicted data; Comparing the predicted data with the standard data included in the processed second measured data to determine the prediction effect of the first tower crane health monitoring model; According to the test result, obtaining the test result of the first tower crane health monitoring model; If the test accuracy in the test result is greater than or equal to a preset threshold, the test is determined to be passed; If the test accuracy in the test result is less than a preset threshold, it is determined that the test fails.

7. The method according to claim 4, characterized in that The method further comprises: When it is determined that the test result is a test failure, a second fault parameter is obtained, and a simulation adjustment is performed on the first simulated tower crane based on the second fault parameter to obtain a third simulated tower crane; Performing simulated operation on the third simulated tower crane to obtain third simulation data under the second fault parameter; The pre-constructed tower crane health monitoring model is trained according to the first measured data, the second measured data, the first simulation data and the third simulation data, and a trained tower crane health monitoring model is obtained when the training is completed.

8. A training device for a tower crane health monitoring model, characterized in that: include: A data acquisition module, used to acquire first measured data and second measured data of the tower crane, wherein the first measured data is health status data collected based on a plurality of sensors arranged on the tower crane, and the second measured data is fault status data collected based on a plurality of sensors arranged on the tower crane; A first simulation module is used to simulate the tower crane to obtain a first simulated tower crane, and simulate the operation of the first simulated tower crane to obtain first simulation data of the first simulated tower crane in a healthy state; A simulation adjustment module, used for determining whether to perform simulation adjustment on the first simulated tower crane according to the first measured data and the first simulation data; A second simulation module is used for, when it is determined that the first simulated tower crane is not to be simulated and adjusted, to simulate and adjust the first simulated tower crane based on the first fault parameter to obtain a second simulated tower crane, and to simulate and run the second simulated tower crane to obtain second simulation data; The model training module is used to train the pre-built tower crane health monitoring model according to the first measured data, the second measured data, the first simulation data and the second simulation data, and obtain the trained tower crane health monitoring model when the training is completed.

9. An electronic device, characterized in that: The electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, and the processor implements the steps in the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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