A control method, device and medium of a large module sliding system

Abstract

The application provides a control method, device and medium of a large module sliding system, the method comprising: obtaining a first pressure value list set according to pressure values obtained by a plurality of first pressure sensors; applying pressure to corresponding positions of a module simulation model according to pressure values obtained by a plurality of second pressure sensors; obtaining a second pressure value list set according to pressure values obtained by first simulation sensors arranged on the system simulation model; correcting the first pressure value list set to obtain a third pressure value list set; obtaining a predicted working state of the module sliding system according to the third pressure value list set; if the predicted working state of the module sliding system is an abnormal state, controlling the module sliding system to stop sliding work, and removing the influence of an external environment on a module to be transported in a transportation process by correcting and adjusting pressure values received by the module sliding system during sliding work, thereby improving the accuracy of abnormal detection of the module sliding system.

Description

Technical Field

[0001] This invention relates to the field of modular transportation, and in particular to a control method, equipment and medium for a large modular sliding system. Background Technology

[0002] Currently, modular manufacturing is widely used in fields such as chemical engineering, oil and gas extraction, offshore oil engineering, and nuclear power. With the application and promotion of modular manufacturing technology in the nuclear power industry, it is expected that the number of nuclear power projects using modular construction will gradually increase in the future. By modularly manufacturing nuclear power units, nuclear power plants can be constructed as large-scale integrated modules. Manufacturing in the factory and on-site construction can be carried out simultaneously, effectively reducing the construction cycle. The modular manufacturing of nuclear power units first involves manufacturing several nuclear power modules in the factory, and then transporting each nuclear power module to the installation site for assembly using a module sliding system. During the transportation of nuclear power modules, the working status of the module sliding system must be monitored in real time. When the working status of the module sliding system is abnormal, the module sliding system needs to be stopped to avoid damage to the nuclear power modules during transportation due to malfunctions in the module sliding system.

[0003] Existing methods for detecting anomalies in modular sliding systems involve monitoring real-time pressure values ​​during sliding operations. When the pressure value deviates from the preset safety range, an anomaly is detected, and the system is shut down. However, since modular sliding systems typically operate in outdoor or open environments, the nuclear power modules they carry are significantly affected by the external environment during transport (e.g., wind resistance). Therefore, the pressure data detected by this method is also influenced by the external environment and cannot accurately represent the pressure exerted on the sliding system by the nuclear power modules during transport. Using this pressure data for anomaly detection would lead to false alarms and reduce the accuracy of anomaly detection. Summary of the Invention

[0004] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0005] According to one aspect of this application, a control method for a large modular sliding system is provided, comprising:

[0006] Step S100: During the sliding operation of the module sliding system, at preset intervals, a first pressure value list is obtained based on the pressure values ​​obtained by the first pressure sensors installed on the module sliding system within the target time period.

[0007] The target time period begins when the module sliding system starts its current sliding operation; the target time period ends at the current moment.

[0008] Step S200: Based on the pressure values ​​obtained by several second pressure sensors on the module to be transported carried by the module sliding system within the target time period, apply corresponding pressure to the corresponding position of the preset module simulation model at the corresponding time of the key time period.

[0009] The module simulation model is a simulation model obtained by simulating the module to be transported within a preset simulation space; the start time of the critical time period is the start time when pressure is applied to the module simulation model; the duration of the critical time period is equal to the duration of the target time period.

[0010] Step S300: Based on the pressure values ​​obtained by several first simulation sensors set on the preset system simulation model during the key time period, a second pressure value list is obtained.

[0011] The system simulation model is a simulation model obtained by simulating the module sliding system in the simulation space. In the simulation space, the system simulation model is used to support the module simulation model and drive the module simulation model to slide. The first simulation sensor is a simulation model obtained by simulating the first pressure sensor in the simulation space. The position of the first simulation sensor on the system simulation model is the same as the position of the corresponding first pressure sensor on the module sliding system.

[0012] Step S400: Based on the second pressure value list set, modify the first pressure value list set to obtain the third pressure value list set;

[0013] Step S500: Based on the third pressure value list set, predict the working state of the module sliding system to obtain the predicted working state of the module sliding system.

[0014] Step S600: If the predicted working state of the module sliding system is an abnormal state, then control the module sliding system to stop sliding.

[0015] According to another aspect of this application, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored in the storage medium, the at least one instruction or the at least one program being loaded and executed by a processor to implement the aforementioned control method for a large module sliding system.

[0016] According to another aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0017] The present invention has at least the following beneficial effects:

[0018] The control method for a large modular sliding system of the present invention, during the sliding operation of the modular sliding system, at preset time intervals, obtains a first pressure value list based on several pressure values ​​acquired by several first pressure sensors installed on the modular sliding system within a target time period. The pressure values ​​in the first pressure value list represent the actual pressure values ​​exerted on the modular sliding system by the module to be transported due to external influences during the sliding operation. Then, based on several pressure values ​​acquired by several second pressure sensors installed on the module to be transported carried by the modular sliding system within the target time period, corresponding pressures are applied to corresponding positions of the module simulation model at corresponding moments in the critical time period to simulate the module simulation model. Based on several pressure values ​​acquired by several first simulation sensors installed on the system simulation model within the critical time period, a second pressure value list is obtained. The pressure values ​​in the second pressure value list represent the actual pressure values ​​exerted by the module to be transported by the module during the sliding operation. The force value is represented by the load-bearing pressure value given to the system simulation model by the module simulation model when the system simulation model is sliding during the critical time period. Based on the second pressure value list, the first pressure value list is corrected to obtain the third pressure value list. Several pressure values ​​in the third pressure value list are pressure values ​​after removing external influences. Based on the third pressure value list, the working state of the module sliding system is predicted to obtain the predicted working state of the module sliding system. If the predicted working state of the module sliding system is an abnormal state, the module sliding system is controlled to stop sliding. By correcting and adjusting the pressure value of the module sliding system during sliding, the influence of the external environment on the module to be transported during the transportation process is removed, so that the pressure value for working state detection is only the load-bearing pressure value of the module sliding system exerted by the module to be transported during the transportation process, thereby improving the accuracy of abnormal detection of the module sliding system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a control method for a large module sliding system provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] This application proposes a control method for a large modular sliding system, such as... Figure 1 As shown, it includes:

[0023] Step S100: During the sliding operation of the module sliding system, at preset intervals, a first pressure value list is obtained based on the pressure values ​​obtained by the first pressure sensors installed on the module sliding system within the target time period.

[0024] The target time period begins when the module sliding system starts its sliding operation, and ends when the target time period ends at the current time.

[0025] The sliding operation of the modular sliding system refers to the operation in which the modular sliding system drives the module to be transported. During the sliding operation of the modular sliding system, pressure values ​​are acquired in stages to detect abnormalities in the status of the modular sliding system.

[0026] The first pressure sensor can be installed on the support part (such as the support hydraulic device) of the module sliding system to collect the load pressure data of the module to be transported to the module sliding system.

[0027] Furthermore, step S100 includes step S110:

[0028] Step S110: During the sliding operation of the module sliding system, at preset time intervals, acquire several pressure values ​​obtained by each first pressure sensor within the target time period to obtain a first pressure value list set A=(A1,A2,...,A1). i ,...,A h ); where i = 1, 2, ..., h; h is the number of first pressure sensors; A i This is a list of pressure values ​​corresponding to the i-th first pressure sensor within the target time period;

[0029] A i =(A i1 A i2 ,...,A ij ,...,A ig ); j=1,2,...,g; g is the number of pressure acquisition nodes within the target time period; the duration between any two adjacent pressure acquisition nodes within the target time period is equal; Aij Let be the pressure value collected by the i-th first pressure sensor at the j-th pressure acquisition node within the target time period.

[0030] The pressure values ​​in the first pressure value list are the pressure values ​​that the module sliding system experiences during sliding operations. The pressure corresponding to this pressure value is the actual pressure experienced by the module sliding system, which includes the pressure affected by the external environment.

[0031] Step S200: Based on the pressure values ​​obtained by several second pressure sensors on the module to be transported carried by the module sliding system within the target time period, apply corresponding pressure to the corresponding position of the preset module simulation model at the corresponding time of the key time period.

[0032] The module to be transported is the modularized nuclear power module.

[0033] The module simulation model is a simulation model obtained by simulating the module to be transported in a preset simulation space. The module simulation model is a simulation model of the module to be transported generated using digital twin simulation technology, and its weight, size and other size information are the same as the module to be transported.

[0034] The second pressure sensor is installed on the four sides of the module to be transported to collect the pressure of the external environment that the module is subjected to during transportation, such as the wind force that the module is subjected to during transportation.

[0035] The critical time period begins when stress is applied to the module simulation model, and its duration is equal to that of the target time period.

[0036] By applying pressure to the module simulation model, the wind force experienced by the module simulation model during transportation is simulated, so that the pressure values ​​in the first pressure value list can be corrected and adjusted later.

[0037] Furthermore, step S200 includes steps S210-S240:

[0038] Step S210: Obtain several pressure values ​​acquired by each second pressure sensor within the target time period to obtain a fourth pressure value list set B=(B1,B2,...,B m ,...,B n ); where m=1,2,...,n; n is the number of second pressure sensors; B m This is a list of pressure values ​​corresponding to the m-th second pressure sensor within the target time period;

[0039] B m =(B m1 B m2 ,...,Bmj ,...,B mg ); B mj The pressure value collected by the m-th second pressure sensor at the j-th pressure acquisition node within the target time period;

[0040] The pressure values ​​in the fourth pressure value list represent the wind force experienced by the surface of the module to be transported during transportation.

[0041] Step S220: Determine the location on the module to be transported where the second pressure sensor is installed as the setting location;

[0042] Step S230: Determine the position on the module simulation model that corresponds to the setting position of the module to be transported as the force application position;

[0043] Step S240: At the time corresponding to the j-th pressure acquisition node in the critical time period, apply force B to the m-th force application position of the module simulation model. mj The corresponding pressure;

[0044] The m-th force application position in the module simulation model is the position on the module simulation model that corresponds to the position of the m-th second pressure sensor on the module to be transported.

[0045] There are g pressure acquisition nodes within the critical time period, and the duration between any two adjacent pressure acquisition nodes within the critical time period is equal.

[0046] Step S300: Based on the pressure values ​​obtained by several first simulation sensors set on the preset system simulation model during the key time period, a second pressure value list is obtained.

[0047] The system simulation model is a simulation model obtained by simulating the module sliding system in the simulation space. In the simulation space, the system simulation model is used to support the module simulation model and drive the module simulation model to slide. The system simulation model is a simulation model of the module sliding system generated by digital twin simulation technology.

[0048] The first simulated sensor is a simulation model obtained by simulating the first pressure sensor in the simulation space. The position of the first simulated sensor on the system simulation model is the same as the position of the corresponding first pressure sensor on the module sliding system, so as to ensure that the pressure value collected by the first simulated sensor is consistent with the pressure value collected by the first pressure sensor, thereby reducing the error in subsequent pressure value correction.

[0049] Furthermore, step S300 includes step S310:

[0050] Step S310: Obtain several pressure values ​​acquired by each of the first simulation sensors within a key time period to obtain a second pressure value list set C=(C1,C2,...,C...). i ,...,C h ); where C i This is a list of pressure values ​​corresponding to the i-th first simulation sensor during the critical time period;

[0051] C i =(C i1 C i2 ,...,C ij ,...,C ig ); C ij Let be the pressure value collected by the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period.

[0052] Step S400: Based on the second pressure value list set, modify the first pressure value list set to obtain the third pressure value list set;

[0053] Furthermore, step S400 includes steps S410-S420:

[0054] Step S410: Based on the pressure values ​​collected by each first simulation sensor when no pressure is applied, obtain a standard pressure value list set;

[0055] Step S410 includes step S411:

[0056] Step S411: Obtain several pressure values ​​acquired by each first simulation sensor during a key time period when no pressure is applied to the module simulation model, to obtain a standard pressure value list set D=(D1,D2,...,D...). i ,...,D h ); where D i This is a list of pressure values ​​corresponding to the i-th first simulation sensor during the critical time period when no pressure is applied to the module simulation model;

[0057] D i =(D i1 D i2 ,...,D ij ,...,D ig );D ij This refers to the pressure value collected by the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period when no pressure is applied to the module simulation model.

[0058] When no pressure is applied, the pressure value collected by each first simulation sensor in the module simulation model is the pressure that the module simulation model provides to the system simulation model.

[0059] Step S420: Based on the second pressure value list set and the standard pressure value list set, modify the first pressure value list set to obtain the third pressure value list set;

[0060] Step S420 includes steps S421-S422:

[0061] Step S421: Based on the second pressure value list set C and the standard pressure value list set D, determine the pressure change value list set E = (E1, E2, ..., E...). i ,...,E h ); where E i This is a list of pressure change values ​​corresponding to the i-th first simulation sensor during the critical time period;

[0062] E i =(E i1 E i2 ,...,E ij ,...,E ig ); E ij =C ij -D ij E ij Let be the pressure change value of the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period;

[0063] Step S422: Based on the pressure change value list set E, correct each pressure value in the first pressure value list set A to obtain the third pressure value list set F = (F1, F2, ..., F i ,...,F h ); where F i For A i The list of pressure values ​​obtained after correcting several pressure values ​​in the data;

[0064] F i =(F i1 ,F i2 ,...,F ij ,...,F ig );F ij =A ij -E ij ;F ij For A ij The pressure value obtained after correction.

[0065] By correcting each pressure value in the first pressure value list, the pressure values ​​in the resulting third pressure value list are pressure values ​​that have been freed from the influence of the external environment. By performing abnormal state detection on the corrected pressure values, the subsequent state prediction model focuses on the pressure exerted by the module to be transported on the module sliding system itself during transportation, which can improve the abnormal detection accuracy of the module sliding system.

[0066] Step S500: Based on the third pressure value list set, predict the working state of the module sliding system to obtain the predicted working state of the module sliding system.

[0067] Furthermore, step S500 includes step S510:

[0068] Step S510: Input the third pressure value list into the preset state prediction model to obtain the state prediction identifier output by the state prediction model;

[0069] When the state prediction identifier is the first identifier, the predicted working state of the module sliding system is characterized as the normal state.

[0070] When the state prediction identifier is the second identifier, the predicted working state of the module sliding system is characterized as an abnormal state.

[0071] The state prediction model is trained based on several corrected pressure values ​​corresponding to the sliding operation of the module sliding system during a historical time period. The specific training method can adopt the existing supervised training method (that is, use the pressure value as a sample, use the working state identifier of the module sliding system in the time period of the pressure value as a label, and train the neural network model).

[0072] The end time of the historical time period is located before the start time of the target time period.

[0073] Step S600: If the predicted working state of the module sliding system is an abnormal state, then control the module sliding system to stop sliding.

[0074] Furthermore, the control method for the large module sliding system in this application is applied to modules with standardized shapes to be transported. That is, the modules to be transported in this application are all modules with standardized shapes or modules using specific shapes. If applied to modules with irregular shapes to be transported, the different wind forces experienced by the modules during transportation will cause the corrected pressure value to not be able to largely eliminate the influence of the external environment, which will affect the detection of abnormal states of the module sliding system. Therefore, this application also proposes an anomaly detection method for module sliding systems that can be applied to modules with irregular or non-fixed shapes to be transported, as shown in steps S1-S4 (it should be noted that the following solutions are independent technical solutions, and the reference numerals and feature names included are independent of the reference numerals and feature names in the control method for the large module sliding system mentioned above):

[0075] Step S1: Perform several random changes on the first pressure value list set to obtain several variable pressure value list sets; the first pressure value list set consists of pressure values ​​acquired by several first pressure sensors within a target time period;

[0076] The target time period begins when the module sliding system starts its sliding operation, and ends when the target time period ends at the current time.

[0077] Furthermore, step S1 includes steps S11-S13:

[0078] Step S11: During the sliding operation of the module sliding system, at preset time intervals, acquire several pressure values ​​obtained by each first pressure sensor within the target time period to obtain a first pressure value list set A=(A1,A2,...,A1). i ,...,A h ); where i = 1, 2, ..., h; h is the number of first pressure sensors; A i This is a list of pressure values ​​corresponding to the i-th first pressure sensor within the target time period;

[0079] A i =(A i1 A i2 ,...,A ij ,...,A ig ); j=1,2,...,g; g is the number of pressure acquisition nodes within the target time period; the duration between any two adjacent pressure acquisition nodes within the target time period is equal; A ij The pressure value collected by the i-th first pressure sensor at the j-th pressure acquisition node within the target time period;

[0080] The sliding operation of the modular sliding system refers to the operation in which the modular sliding system drives the module to be transported. During the sliding operation of the modular sliding system, pressure values ​​are acquired in stages to detect abnormalities in the status of the modular sliding system.

[0081] The first pressure sensor can be installed on the support part (such as the support hydraulic device) of the module sliding system to collect the load pressure data of the module to be transported to the module sliding system.

[0082] The pressure values ​​in the first pressure value list are the pressure values ​​that the module sliding system experiences during sliding operations. The pressure corresponding to this pressure value is the actual pressure experienced by the module sliding system, which includes the pressure affected by the external environment.

[0083] Step S12: Obtain several random coefficients to obtain a random coefficient list G=(G1,G2,...,G...). r ,...,G p ); where r = 1, 2, ..., p; p is the number of random coefficients; G r Let r be the r-th random coefficient;

[0084] Each random coefficient is greater than q1 and less than q2; 0 < q1 < 1; 1 < q2 < 2; each random coefficient is generated randomly.

[0085] Step S13: Multiply each pressure value in the first pressure value list set A by each random coefficient to obtain several variable pressure value list sets H1, H2, ..., H r ,...,H p Among them, H r For each pressure value in the first pressure value list set A, compare it with G. r A list of changing pressure values ​​obtained by multiplication;

[0086] H r =(H r1 H r2 ,...,H ri ,...,H rh );H ri For A i Each pressure value in G r A list of the changing pressure values ​​obtained by multiplication;

[0087] H ri =(H ri1 H ri2 ,...,H rij ,...,H rig );H rij =A ij ×G r .

[0088] By randomly generating several random coefficients, each pressure value in the first pressure value list is multiplied to obtain several variable pressure value lists. Since this application can be applied to scenarios where the shape of the module to be transported is not fixed or irregular, and since the external environment (wind force) affecting the variable shape of the module to be transported is different during the transportation process, several variable pressure value lists are obtained by multiplying the actual obtained pressure value with random coefficients. By adjusting several variable pressure value lists to remove pressure values ​​affected by the external environment, the state detection of the module sliding system is then performed to improve the authenticity and accuracy of the state detection results.

[0089] Step S2: Based on the second pressure value list set corresponding to the preset system simulation model, adjust the first pressure value list set and each variable pressure value list set respectively to obtain several third pressure value list sets.

[0090] The system simulation model is a simulation model obtained by simulating the module sliding system in the simulation space. In the simulation space, the system simulation model is used to support the module simulation model and drive the module simulation model to slide. The system simulation model is a simulation model of the module sliding system generated by digital twin simulation technology.

[0091] The second pressure value list consists of pressure values ​​acquired by several first simulation sensors on the system simulation model during key time periods.

[0092] The critical time period is the time period during which corresponding pressure is applied to the preset module simulation model based on the pressure values ​​obtained by several second pressure sensors within the target time period. The duration of the critical time period is equal to the duration of the target time period.

[0093] The module simulation model is a simulation model obtained by simulating the module to be transported in a preset simulation space. The module simulation model is a simulation model of the module to be transported generated using digital twin simulation technology, and its weight, size and other size information are the same as the module to be transported.

[0094] The second pressure sensor is installed on the four sides of the module to be transported to collect the pressure of the external environment that the module is subjected to during transportation, such as the wind force that the module is subjected to during transportation.

[0095] Furthermore, step S2 includes steps S21-S23:

[0096] Step S21: Based on the pressure values ​​obtained by several second pressure sensors within the target time period, apply corresponding pressure to the corresponding position of the module simulation model at the corresponding moment of the key time period.

[0097] By applying pressure to the module simulation model, the wind force experienced by the module simulation model during transportation can be simulated, so that the pressure values ​​in several sets of changing pressure values ​​can be corrected and adjusted later.

[0098] Step S21 includes steps S211-S214:

[0099] Step S211: Obtain several pressure values ​​acquired by each second pressure sensor within the target time period to obtain a fourth pressure value list set B=(B1,B2,...,B m ,...,B n ); where m=1,2,...,n; n is the number of second pressure sensors; B m This is a list of pressure values ​​corresponding to the m-th second pressure sensor within the target time period;

[0100] B m =(B m1 B m2 ,...,B mj ,...,B mg ); B mj The pressure value collected by the m-th second pressure sensor at the j-th pressure acquisition node within the target time period;

[0101] The pressure values ​​in the fourth pressure value list represent the wind force experienced by the surface of the module to be transported during transportation.

[0102] Step S212: Determine the location on the module to be transported where the second pressure sensor is installed as the setting location;

[0103] Step S213: Determine the position on the module simulation model that corresponds to the setting position of the module to be transported as the force application position;

[0104] Step S214: At the time corresponding to the j-th pressure acquisition node in the critical time period, apply force B to the m-th force application position of the module simulation model. mj The corresponding pressure;

[0105] The m-th force application position in the module simulation model is the position on the module simulation model that corresponds to the position of the m-th second pressure sensor on the module to be transported.

[0106] There are g pressure acquisition nodes within the critical time period, and the duration between any two adjacent pressure acquisition nodes within the critical time period is equal.

[0107] Step S22: Based on the pressure values ​​obtained by several first simulation sensors during the key time period, obtain a second pressure value list set;

[0108] The first simulated sensor is a simulation model obtained by simulating the first pressure sensor in the simulation space. The position of the first simulated sensor on the system simulation model is the same as the position of the corresponding first pressure sensor on the module sliding system, so as to ensure that the pressure value collected by the first simulated sensor is consistent with the pressure value collected by the first pressure sensor, thereby reducing the error in subsequent pressure value correction.

[0109] Step S22 includes step S221:

[0110] Step S221: Obtain several pressure values ​​acquired by each of the first simulation sensors within a key time period to obtain a second pressure value list set C=(C1,C2,...,C...). i ,...,C h ); where C i This is a list of pressure values ​​corresponding to the i-th first simulation sensor during the critical time period;

[0111] C i =(C i1 C i2 ,...,C ij ,...,C ig ); C ij Let be the pressure value collected by the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period.

[0112] Step S23: Based on the second pressure value list set, adjust the first pressure value list set and each variable pressure value list set respectively to obtain several third pressure value list sets;

[0113] Step S23 includes steps S231-S232:

[0114] Step S231: Obtain several pressure values ​​acquired by each first simulation sensor during a key time period when no pressure is applied to the module simulation model, to obtain a standard pressure value list set D=(D1,D2,...,D...). i ,...,D h ); where D i This is a list of pressure values ​​corresponding to the i-th first simulation sensor during the critical time period when no pressure is applied to the module simulation model;

[0115] D i =(D i1 D i2 ,...,D ij ,...,D ig );D ijThis refers to the pressure value collected by the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period when no pressure is applied to the module simulation model.

[0116] When no pressure is applied, the pressure value collected by each first simulation sensor in the module simulation model is the pressure that the module simulation model provides to the system simulation model.

[0117] Step S232: Based on the second pressure value list set C and the standard pressure value list set D, adjust the first pressure value list set and each variable pressure value list set respectively to obtain several third pressure value list sets.

[0118] Step S232 includes steps S2321-S2323:

[0119] Step S2321: Based on the second pressure value list set C and the standard pressure value list set D, determine the pressure change value list set E = (E1, E2, ..., E...). i ,...,E h ); where E i This is a list of pressure change values ​​corresponding to the i-th first simulation sensor during the critical time period;

[0120] E i =(E i1 E i2 ,...,E ij ,...,E ig ); E ij =C ij -D ij E ij Let be the pressure change value of the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period;

[0121] Step S2322: Based on the pressure change value list set E, adjust each pressure value in the first pressure value list set A to obtain the third pressure value list set F = (F1, F2, ..., F...). i ,...,F h ); where F i For A i A list of pressure values ​​obtained after adjusting several pressure values ​​in the table;

[0122] F i =(F i1 ,F i2 ,...,F ij ,...,F ig );F ij =A ij -E ij ;Fij For A ij The pressure value obtained after adjustment;

[0123] Step S2323: Based on the pressure change value list set E, adjust each pressure value in each pressure change value list set to obtain the third pressure value list set J1, J2, ..., J corresponding to each pressure change value list set. r ,...,J p ; among which, J r For H r The corresponding third pressure value list set;

[0124] J r =(J r1 J r2 ,...,J ri ,...,J rh ); J ri For H ri A list of pressure values ​​obtained after adjusting several pressure values ​​in the table;

[0125] J ri =(J ri1 J ri2 ,...,J rij ,...,J rig ); J rij =H rij -E ij J rij For H rij The pressure value obtained after adjustment.

[0126] By adjusting and correcting several pressure values ​​in each set of changing pressure values, the pressure values ​​in the resulting set of third pressure values ​​are pressure values ​​that have been freed from the influence of the external environment. By performing abnormal state detection on the adjusted and corrected pressure values, the focus of the subsequent state prediction model is shifted to the pressure exerted on the module sliding system itself by the module to be transported during transportation, which can improve the anomaly detection accuracy of the module sliding system.

[0127] Step S3: Based on each set of third pressure values, predict the working state of the module sliding system to obtain several predicted working states of the module sliding system within the target time period.

[0128] Furthermore, step S3 includes steps S31-S32:

[0129] Step S31: Input each third pressure value list set into the preset state prediction model to obtain the state prediction identifier corresponding to each third pressure value list set output by the state prediction model.

[0130] The state prediction model is trained based on several adjusted pressure values ​​corresponding to the sliding operation of the module sliding system during a historical time period. The specific training method can adopt the existing supervised training method (that is, use the pressure value as a sample, use the working state identifier of the module sliding system in the time period of the pressure value as a label, and train the neural network model).

[0131] The end time of the historical time period is located before the start time of the target time period.

[0132] Step S32: If the state prediction identifier corresponding to any third pressure value list set is the first identifier, then the predicted working state corresponding to the third pressure value list set is determined to be the normal state.

[0133] If the state prediction identifier corresponding to any third pressure value list set is the second identifier, then the predicted working state corresponding to that third pressure value list set is determined to be an abnormal state.

[0134] Step S4: If the proportion of predicted working states that are characterized as abnormal states is greater than a preset proportion threshold among several predicted working states, then the working state of the module sliding system within the target time period is determined to be an abnormal state.

[0135] The control method for a large modular sliding system of the present invention, during the sliding operation of the modular sliding system, at preset time intervals, obtains a first pressure value list based on several pressure values ​​acquired by several first pressure sensors installed on the modular sliding system within a target time period. The pressure values ​​in the first pressure value list represent the actual pressure values ​​exerted on the modular sliding system by the module to be transported due to external influences during the sliding operation. Then, based on several pressure values ​​acquired by several second pressure sensors installed on the module to be transported carried by the modular sliding system within the target time period, corresponding pressures are applied to corresponding positions of the module simulation model at corresponding moments in the critical time period to simulate the module simulation model. Based on several pressure values ​​acquired by several first simulation sensors installed on the system simulation model within the critical time period, a second pressure value list is obtained. The pressure values ​​in the second pressure value list represent the actual pressure values ​​exerted by the module to be transported by the module during the sliding operation. The force value is represented by the load-bearing pressure value given to the system simulation model by the module simulation model when the system simulation model is sliding during the critical time period. Based on the second pressure value list, the first pressure value list is corrected to obtain the third pressure value list. Several pressure values ​​in the third pressure value list are pressure values ​​after removing external influences. Based on the third pressure value list, the working state of the module sliding system is predicted to obtain the predicted working state of the module sliding system. If the predicted working state of the module sliding system is an abnormal state, the module sliding system is controlled to stop sliding. By correcting and adjusting the pressure value of the module sliding system during sliding, the influence of the external environment on the module to be transported during the transportation process is removed, so that the pressure value for working state detection is only the load-bearing pressure value of the module sliding system exerted by the module to be transported during the transportation process, thereby improving the accuracy of abnormal detection of the module sliding system.

[0136] Furthermore, this application also proposes a method for detecting the damage (quality status) of the aforementioned module to be transported during the transport process of the module sliding system, as shown in steps S010-S050:

[0137] Step S010: During the first time period, apply a preset pressure to several preset positions corresponding to the module to be transported in sequence;

[0138] The first time period is the period during which the vibration sensor undergoes vibration testing before transportation.

[0139] The preset position is a location where the operator can apply pressure. The pressure applied to the preset position is limited to a level that will not cause vibration damage to the transport module.

[0140] Step S020: Obtain several vibration characteristic data collected by each vibration sensor when pressure is applied to each preset position corresponding to the module to be transported during the first time period, so as to obtain several first vibration feature vector lists M1, M2, ..., M a ,...,M b Where a = 1, 2, ..., b; b is the number of vibration sensors; M a This is a list of the first vibration feature vectors corresponding to the a-th vibration sensor;

[0141] M a =(M a1 M a2 ,...,M ac ,...,M ad ); c=1,2,...,d; d is the number of preset locations corresponding to the modules to be transported; M ac The first vibration feature vector corresponding to the a-th vibration sensor when pressure is applied to the c-th preset position corresponding to the module to be transported during the first time period;

[0142] Each vibration sensor corresponds to a sampling location (i.e., the location of the vibration sensor). The vibration sensor only collects vibration data at its corresponding sampling location. In the first time period, when pressure is applied to each preset location of the transport module, each vibration sensor also only collects vibration data at its corresponding sampling location. For example, if the sampling location of vibration sensor A is the first location, and the preset locations of the transport module are the second and third locations, when pressure is applied to the second location, vibration sensor A collects vibration data at the first location. When pressure is applied to the third location, vibration sensor A also only collects vibration data at the first location. In this way, the vibration relationship between each preset location and each vibration sensor can be obtained.

[0143] M ac =(M ac1 M ac2 ,...,M ace ,...,M acf ); e=1,2,...,f; f is the number of vibration characteristic data acquisition moments when pressure is applied to the c-th preset position corresponding to the module to be transported; M ace The vibration characteristic data acquired by the a-th vibration sensor at the e-th acquisition time when pressure is applied to the c-th preset position corresponding to the module to be transported during the first time period;

[0144] Each module to be transported is equipped with several vibration sensors. The vibration sensors are placed at the stress concentration points or structural weak points of the module to be transported. Finite element analysis is first performed on each structural position of the module to be transported to obtain the location of the stress concentration points or structural weak points of the module to be transported. Then, vibration sensors are installed at each of the determined positions.

[0145] The first vibration feature vector consists of several vibration feature data collected by the vibration sensor within a first time period.

[0146] Step S030: During the second time period, apply a preset pressure to several preset positions corresponding to the module to be transported in sequence;

[0147] The second time period is the period during which the vibration sensor undergoes vibration testing after transportation; the duration of the second time period is equal to the duration of the first time period, and the location where the module to be transported undergoes vibration testing in the first time period is the same as the location where vibration testing is conducted in the second time period.

[0148] The preset position for applying pressure during the second time period is the same as the preset position for applying pressure during the first time period, and the pressure applied during the second time period is equal to the pressure applied during the first time period.

[0149] Step S040: Obtain several vibration characteristic data collected by each vibration sensor when pressure is applied to each preset position corresponding to the module to be transported during the second time period, so as to obtain several second vibration feature vector lists Q1, Q2, ..., Q a ,...,Q b ; where Q a This is a list of the second vibration feature vectors corresponding to the a-th vibration sensor;

[0150] Q a =(Q a1 Q a2 ,...,Q ac ,...,Q ad );Q ac The second vibration feature vector corresponding to the a-th vibration sensor when pressure is applied to the c-th preset position corresponding to the module to be transported during the second time period;

[0151] Q ac =(Q ac1 Q ac2 ,...,Q ace ,...,Q acf );Q ace The vibration characteristic data acquired by the a-th vibration sensor at the e-th acquisition time when pressure is applied to the c-th preset position corresponding to the module to be transported during the second time period;

[0152] The second vibration feature vector consists of several vibration feature data collected by the vibration sensor during the second time period.

[0153] Step S050: If the matching degree of the first vibration feature vector and the second vibration feature vector corresponding to the same vibration sensor at the same preset position is greater than the preset matching degree threshold, then it is determined that no vibration abnormality has occurred in the associated area between the vibration sensor and the preset position.

[0154] The associated area between the vibration sensor and the preset position is determined based on the vibration transmission path between each vibration sensor and each preset position (the method for determining the vibration transmission path adopts existing methods), or it can be customized by the staff.

[0155] Step S050 includes steps S051 and S0501:

[0156] Step S051, if in M a and Q a In the middle, M ac and Q ac If the matching degree is greater than the preset matching degree threshold, then it is determined that no vibration abnormality has occurred in the associated area between the a-th vibration sensor and the c-th preset position;

[0157] Step S0501, if in M a and Q a In the middle, M ac and Q ac If the matching degree is less than or equal to the preset matching degree threshold, then the associated region between the a-th vibration sensor and the c-th preset position is determined as the detection region.

[0158] When the inspectors inspect the inspection area and determine that no vibration abnormality has occurred in the inspection area, and no vibration abnormality has occurred in the corresponding associated area between each vibration sensor and each preset position, then the quality status of the module to be transported during the transportation process is determined to be normal.

[0159] By applying pressure to the same location before and after transport of the module to be transported, it is possible to detect whether the module has vibration damage caused by bumps or shaking during transport. If such damage is found, the inspection personnel are prompted to repair the damaged location. This allows for safe monitoring of the damage to the module during transport and improves the safety and quality of subsequent nuclear power assemblies to be installed.

[0160] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0161] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0162] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0163] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0164] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”

[0165] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.

[0166] Electronic devices are manifested in the form of general-purpose computing devices. The components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0167] The storage device stores program code that can be executed by the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.

[0168] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0169] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0170] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0171] Electronic devices can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable users to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed through input / output (I / O) interfaces. Furthermore, electronic devices can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters.

[0172] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0173] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0174] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0175] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0176] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0177] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0178] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0179] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A control method for a large modular sliding system, characterized in that, include: Step S100: During the sliding operation of the module sliding system, at preset intervals, a first pressure value list is obtained based on the pressure values ​​obtained by the first pressure sensors installed on the module sliding system within the target time period; the start time of the target time period is the start time of the module sliding system performing this sliding operation; the end time of the target time period is the current time. Step S200: Based on the pressure values ​​obtained by several second pressure sensors installed on the module to be transported carried by the module sliding system during the target time period, apply corresponding pressure to the corresponding position of the preset module simulation model at the corresponding time of the key time period; the module simulation model is a simulation model obtained by simulating the module to be transported in a preset simulation space; the start time of the key time period is the start time of applying pressure to the module simulation model; the duration of the key time period is equal to the duration of the target time period; Step S300: Based on several pressure values ​​acquired by several first simulation sensors set on a preset system simulation model during the key time period, a second pressure value list is obtained; the system simulation model is a simulation model obtained by simulating the module sliding system in the simulation space, and in the simulation space, the system simulation model is used to support the module simulation model and drive the module simulation model to slide; the first simulation sensor is a simulation model obtained by simulating the first pressure sensor in the simulation space, and the position of the first simulation sensor on the system simulation model is the same as the position of the first pressure sensor corresponding to the first simulation sensor on the module sliding system; Step S400: Based on the second pressure value list set, modify the first pressure value list set to obtain the third pressure value list set; Step S500: Based on the third pressure value list set, predict the working state of the module sliding system to obtain the predicted working state of the module sliding system. Step S600: If the predicted working state of the module sliding system is an abnormal state, then control the module sliding system to stop sliding.

2. The method according to claim 1, characterized in that, Step S100 includes: Step S110: During the sliding operation of the module sliding system, at preset time intervals, acquire several pressure values ​​obtained by each of the first pressure sensors within the target time period to obtain a first pressure value list set A=(A1,A2,...,A1). i ,...,A h ); where i = 1, 2, ..., h; h is the number of the first pressure sensors; A i This is a list of pressure values ​​corresponding to the i-th first pressure sensor within the target time period; A i =(A i1 A i2 ,...,A ij ,...,A ig ); j=1,2,...,g; g is the number of pressure acquisition nodes corresponding to the target time period; the duration between any two adjacent pressure acquisition nodes within the target time period is equal; A ij Let be the pressure value collected by the i-th first pressure sensor at the j-th pressure acquisition node within the target time period.

3. The method according to claim 2, characterized in that, Step S200 includes: Step S210: Obtain several pressure values ​​acquired by each of the second pressure sensors within the target time period to obtain a fourth pressure value list set B=(B1,B2,...,B m ,...,B n ); where m=1,2,...,n; n is the number of the second pressure sensors; B m This is a list of pressure values ​​corresponding to the m-th second pressure sensor within the target time period; B m =(B m1 B m2 ,...,B mj ,...,B mg );B mj The pressure value collected by the m-th second pressure sensor at the j-th pressure acquisition node within the target time period is denoted as . Step S220: Determine the location on the module to be transported where the second pressure sensor is located as the setting location; Step S230: Determine the position on the module simulation model that corresponds to the setting position of the module to be transported as the force application position; Step S240: At the time corresponding to the j-th pressure acquisition node in the critical time period, apply force B to the m-th force application position of the module simulation model. mj The corresponding pressure; the m-th force application position of the module simulation model is the position on the module simulation model corresponding to the position of the m-th second pressure sensor on the module to be transported; there are g pressure acquisition nodes within the key time period, and the duration between each two adjacent pressure acquisition nodes within the key time period is equal.

4. The method according to claim 3, characterized in that, Step S300 includes: Step S310: Obtain several pressure values ​​acquired by each of the first simulation sensors within the key time period to obtain a second pressure value list set C=(C1,C2,...,C...). i ,...,C h ); where C i This is a list of pressure values ​​corresponding to the i-th first simulation sensor during the critical time period; C i =(C i1 C i2 ,...,C ij ,...,C ig );C ij Let be the pressure value collected by the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period.

5. The method according to claim 4, characterized in that, Step S400 includes: Step S410: Based on the pressure values ​​collected by each of the first simulation sensors when no pressure is applied, obtain a standard pressure value list set; Step S420: Based on the second pressure value list set and the standard pressure value list set, modify the first pressure value list set to obtain a third pressure value list set.

6. The method according to claim 5, characterized in that, Step S410 includes: Step S411: Obtain several pressure values ​​acquired by each of the first simulation sensors during the key time period when no pressure is applied to the module simulation model, to obtain a standard pressure value list set D=(D1,D2,...,D...). i ,...,D h ); where D i This is a list of pressure values ​​corresponding to the i-th first simulation sensor during a critical time period when no pressure is applied to the module simulation model. D i =(D i1 D i2 ,...,D ij ,...,D ig );D ij The pressure value collected by the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period when no pressure is applied to the module simulation model is in the case of no pressure.

7. The method according to claim 6, characterized in that, Step S420 includes: Step S421: Based on the second pressure value list set C and the standard pressure value list set D, determine the pressure change value list set E = (E1, E2, ..., E...). i ,...,E h ); where E i This is a list of pressure change values ​​corresponding to the i-th first simulation sensor during the critical time period; E i =(E i1 E i2 ,...,E ij ,...,E ig ); E ij =C ij -D ij E ij Let be the pressure change value of the i-th first simulation sensor at the j-th pressure acquisition node during the critical time period; Step S422: Based on the pressure change value list set E, correct each pressure value in the first pressure value list set A to obtain the third pressure value list set F = (F1, F2, ..., F i ,...,F h ); where F i For A i The list of pressure values ​​obtained after correcting several pressure values ​​in the data; F i =(F i1 ,F i2 ,...,F ij ,...,F ig );F ij =A ij -E ij ;F ij For A ij The pressure value obtained after correction.

8. The method according to claim 7, characterized in that, Step S500 includes: Step S510: Input the third pressure value list into the preset state prediction model to obtain the state prediction identifier output by the state prediction model; When the state prediction identifier is the first identifier, the predicted working state of the module sliding system is characterized as a normal state; when the state prediction identifier is the second identifier, the predicted working state of the module sliding system is characterized as an abnormal state; the state prediction model is obtained by training on several corrected pressure values ​​corresponding to the sliding operation of the module sliding system in a historical time period; the end time of the historical time period is located before the start time of the target time period.

9. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the method as described in any one of claims 1-8.

10. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 9.

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