Cable torsion degree detection method and device, and electronic device

Abstract

The invention provides a cable torsion degree detection method and device and an electronic device. The torsion degree of the cable can be determined according to the rotation condition of the moving object connected with the cable, so that the torsion degree of the cable is represented as a dominant index parameter of the number of torsion turns of the cable, and the number of torsion turns of the cable in subsequent untwisting operation can be conveniently determined. Besides, traditional torsion sensing data (such as torsion moment in the cable) can be introduced in the process of determining the torsion degree, and the rotation condition in the target time period can be corrected to determine the accurate torsion degree of the cable at the final moment, so that accumulated errors possibly caused by the data in the time period are overcome, and the accuracy of the torsion degree is improved. The long-time observation of the moving object is further ensured, and especially, the long-time execution of the cable-based ecological box test can be ensured.

Description

Technical Field

[0001] The present application relates to the field of detection technology, and in particular to a method, device and electronic device for detecting the twisting degree of a cable. Background Art

[0002] Cable is a common data transmission device, that is, various lines between two devices can be integrated and packaged into a cable to transmit relevant data between the two. With the continuous development of data detection technology, the number and types of cables integrated in the cable are also increasing.

[0003] Therefore, how to ensure the reliability of cables is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a method, device and electronic device for detecting the degree of twisting of a cable, which estimates the degree of twisting of the cable by detecting the rotation of a moving object, so as to facilitate twisting of the cable.

[0005] In a first aspect, the present application provides a method for detecting the twisting degree of a cable, wherein the cable is connected between a fixed device and a mobile object. The method comprises: determining object observation data of the mobile object and twisting sensing data of the cable within a target time period; determining the rotation of the mobile object within the target time period based on the object observation data; determining the target twisting degree of the cable based on the rotation and the twisting sensing data within the target time period, wherein the target twisting degree comprises the number of twisting turns and the twisting direction of the cable at the end time of the target time period.

[0006] In a second aspect, the present application provides a device for detecting the torsion degree of a cable, wherein the cable is connected between a fixed device and a moving object, and the device includes: a data acquisition module for determining object observation data of the moving object and torsion sensing data of the cable within a target time period; a rotation determination module for determining the rotation state of the moving object within the target time period based on the object observation data; and a torsion determination module for determining a target torsion degree of the cable based on the rotation state and the torsion sensing data within the target time period, wherein the target torsion degree includes the number of twist turns and the torsion direction of the cable at the end moment of the target time period.

[0007] In a third aspect, the present application provides an electronic device, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the cable twisting degree detection method described in the first aspect.

[0008] The present application provides a method, device and electronic device for detecting the degree of torsion of a cable. The degree of torsion of the cable can be determined by the rotation of the moving object connected by the cable, so that the degree of torsion of the cable can be characterized as an explicit indicator parameter of the number of twist turns of the cable, so as to facilitate the determination of the number of twist turns of the cable in the subsequent untwisting operation. In addition, traditional torsion sensing data (such as the torque moment in the cable) can also be introduced in determining the degree of torsion, and the rotation condition within the target time period can be corrected to determine the accurate degree of torsion of the cable at the final moment, thereby overcoming the cumulative error that may be caused by the data within the time period, and further ensuring the long-term observation of the moving object, especially, it can ensure the long-term execution of the cable-based eco-box experiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 It is an exemplary flow chart of a method for detecting the degree of cable twisting provided by an exemplary embodiment of the present application.

[0011] Figure 2 It is an exemplary flow chart of a method for determining a rotation condition provided by an exemplary embodiment of the present application.

[0012] Figure 3 It is a model schematic diagram of a torsion evaluation model provided by an exemplary embodiment of the present application.

[0013] Figure 4 It is an exemplary flow chart of a method for determining motion analysis of a moving object provided by an exemplary embodiment of the present application.

[0014] Figure 5 It is a system module diagram of a cable detwisting and straightening device provided by an exemplary embodiment of the present application.

[0015] Figure 6 It is a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0016] 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.

[0017] Application Overview:

[0018] This application mainly relates to the case where a cable is connected between a fixed device and a mobile object. For example, in a biological monitoring system, a living thing can be used as a mobile object, on which a variety of sensors are often set, and the data of the sensors needs to be transmitted to a signal acquisition device through a cable.

[0019] During the detection process of the biological detection system, the biological moving object (i.e., the moving object) will move spontaneously. For example, it will move around the ecological environment, rotate itself, etc. Therefore, the rotation of the moving object will often cause the cable to twist. Considering that in the current biological detection system, the cable often has a large number of integrated circuits (especially optical cables may be provided), the twisting of the cable may reduce the reliability of the cable itself, and the twisting of the cable will change the deformation ability of the cable itself, thereby limiting the movement of the moving object.

[0020] In the related art, a detwisting device (such as a rotating platform or a detachable probe, etc.) can be set in the biological detection system to release a certain amount of cable twist. However, cable twist is often characterized by torsional torque in the prior art, and it is difficult to intuitively determine the degree of twisting that needs to be removed.

[0021] In order to solve the above problems, the present application provides a method, device and electronic device for detecting the degree of torsion of a cable. The degree of torsion of the cable can be determined by the rotation of the moving object connected to the cable, so that the degree of torsion of the cable can be characterized as an explicit indicator parameter of the number of twist turns of the cable, so as to facilitate the determination of the number of twist turns of the cable in the subsequent untwisting operation. In addition, traditional torsion sensing data (such as the torque moment in the cable) can also be introduced in determining the degree of torsion, which can correct the rotation condition within the target time period to determine the accurate degree of torsion of the cable at the final moment, thereby overcoming the cumulative error that may be caused by the data within the time period, and further ensuring the long-term observation of the moving object, especially, it can ensure the long-term execution of the cable-based eco-box experiment.

[0022] The following will refer to the accompanying drawings Figure 1 to Figure 6 Various non-limiting embodiments of the cable untwisting method of the present application will be specifically introduced.

[0023] Exemplary cable twist detection method:

[0024] Figure 1 is an exemplary flow chart of a method for detecting the degree of cable twisting provided by an exemplary embodiment of the present application. Figure 1 The process P100 shown may be executed by a fixed device or a computing device corresponding to the fixed device (eg, a processor in a biological detection system).

[0025] like Figure 1 As shown, P100 may include the following steps:

[0026] S110 , determining object observation data of the moving object and torsion sensing data of the cable within a target time period.

[0027] S120: Determine the rotation of the moving object within the target time period based on the object observation data.

[0028] S130: Determine a target torsion degree of the cable based on the correspondence between the torsion sensing data and the rotation condition within a target time period.

[0029] In the aforementioned S110, the target time period may refer to the time period for performing the twisting degree detection. Considering the continuity of the movement of the moving object, the aforementioned target time period may refer to the time period between the initial moment and the current moment. The initial moment may be the start moment after the cable is connected to the moving object or the completion moment of the last untwisting process.

[0030] The aforementioned object observation data may refer to the relevant detection data of the mobile object. For example, the aforementioned object observation data may include monitoring data of the mobile object. Among them, in the ecological box of the biological detection system (i.e., the cage / box for placing mobile objects such as mice / rabbits), multiple cameras are often set up to capture the movement of mobile objects (such as mice and rabbits). Then the aforementioned S110 can directly call the data.

[0031] Torsion sensing data may refer to relevant data of a torsion sensor in a cable. That is, a torque sensor may be provided in the cable to obtain corresponding torsion sensing data. The torsion sensing data is generally similar to common torsion description parameters, and is generally presented as mechanical parameters such as torque and torsional moment.

[0032] In some embodiments, relevant sensor data may be acquired in real time during the execution of S110 , so that when determining the degree of torsion, data from the initial moment to the current moment may be called to estimate the subsequent degree of torsion.

[0033] In the aforementioned S120, the rotation condition may refer to the rotation caused by the movement of the moving object itself, which may be described by the number of rotations similar to the aforementioned degree of twisting. It should be noted that, considering the connection relationship between the cable and the moving object, the aforementioned rotation condition may specifically refer to the number of rotations of the moving object that can cause the cable to twist. For example, when the cable is connected to the head of a mouse, the aforementioned rotation condition may refer to the number of rotations of the mouse on a plane.

[0034] In some embodiments, the rotation situation may further include self-rotation or circular trajectory. To accurately determine the rotation situation, it is possible to determine whether the moving object is rotating based on the change in orientation within the plane. For details, see Figure 2And the related descriptions are not repeated here.

[0035] In the aforementioned S130, the target twisting degree may refer to the twisting condition of the cable at the current moment (i.e., the end moment of the target time period), which may be specifically described by the number of twisting turns and the twisting direction. It should be noted that when the present application is applied to other scenarios (such as observation of historical data), the aforementioned current moment (i.e., the end moment of the target time period) may also be selected as specific data.

[0036] In the aforementioned S120, the rotation condition can generally only reflect the degree of twisting added within the target time period. In order to determine the aforementioned target twisting degree, data correction can be performed based on the aforementioned twisting sensor data.

[0037] In some embodiments, considering that the torsion sensing data can be acquired in real time in the aforementioned S110, the torsion sensing data during the rotation of the moving object can be determined, and correction can be made based on the corresponding relationship, thereby executing the aforementioned S130. That is, based on the corresponding relationship between the torsion sensing data and the rotation situation in the target time period, the target torsion degree of the cable is determined, so as to determine an accurate target torsion degree without cumulative error.

[0038] Among them, considering that the torsion sensor data is acquired in real time, the change of the torsion sensor data caused by each movement of the moving object can be determined. Therefore, the correspondence between the torsion sensor data and different rotation times can be determined in the time series (determine the change of the torsion sensor data when the moving object rotates), and fitting can be performed based on this situation (that is, determine the mapping relationship between the torsion sensor data and the degree of torsion based on the change of the torsion sensor data), so as to determine the correspondence between the torsion sensor data and the number of torsion turns, so as to determine the actual number of torsion turns based on the torsion sensor data at the current moment.

[0039] In some implementations, the aforementioned correspondence can also be described by a machine learning model. For more information about the machine learning model, see Figure 3 and its related description.

[0040] Therefore, based on the detection method of the cable twisting degree shown in the aforementioned P100, the twisting degree of the cable can be determined by the rotation of the mobile object connected by the cable, so that the twisting degree of the cable can be characterized as the number of twisting turns of the cable, which is an explicit indicator parameter, to facilitate the determination of the number of twisting turns of the cable in the subsequent untwisting operation. In addition, traditional torsion sensing data (such as the torque moment in the cable) can also be introduced in determining the twisting degree, which can correct the rotation condition within the target time period to determine the accurate twisting degree of the cable at the final moment, thereby overcoming the cumulative error that may be caused by the data within the time period, and further ensuring the long-term observation of the moving object, especially, it can ensure the long-term execution of the ecological box experiment based on the cable.

[0041] As mentioned above, considering that the mobile object may include multiple rotation modes during the actual movement process, when determining its rotation status, it may be impossible to determine the actual rotation status due to the diversity of its rotation modes. To overcome this situation, when determining the rotation status of the mobile object, the present application can determine the rotation status based on the change of its actual orientation. That is, whether it is self-rotation or closed-loop trajectory, the torsion of the cable caused by it is caused by the change of the orientation of the mobile object itself, especially when the cable is set at the head of the mobile object.

[0042] Based on the above scheme, Figure 2 It is an exemplary flow chart of a method for determining a rotation condition provided by an exemplary embodiment of the present application.

[0043] like Figure 2 As shown, process P200 may include the following steps:

[0044] S210: Determine the temporal change of the orientation of the moving object within a target time period based on the object observation data.

[0045] S220: Determine the rotation of the moving object within the target time period based on the temporal change of the orientation.

[0046] In the aforementioned S210, the orientation may refer to the direction in the horizontal plane perpendicular to the cable direction at the fixed cable in the mobile object. For example, the orientation in the aforementioned S210 may refer to the orientation of the head of the mobile object. For another example, the cable may be fixedly connected to the mobile object through a probe on the mobile object, and the orientation of a specific position of the probe (such as a visual identifier may be set on the probe) may be used as the aforementioned orientation.

[0047] In the aforementioned S210, the time-series change of the orientation can be determined based on the aforementioned object observation data. For example, when the aforementioned object observation data is monitoring data, the moving object and the head orientation of the moving object can be identified from the image frames of the monitoring data in response to the object observation data including monitoring data of the moving object in the target time period. The time-series change of the orientation of the moving object is determined based on the head orientation of the moving object in each image frame. Exemplarily, the head area and orientation of the moving object can be first identified by an image recognition algorithm, and then the data can be converted into a vector in a plane coordinate system, and the vector at each moment can be determined. Thus, the change of the vector in time is used as the time-series change of the orientation.

[0048] In some embodiments, the aforementioned S210 can also be implemented by other sensors. For example, an inertial sensor can be set at the connection between the mobile object and the cable, wherein the inertial sensor can generally obtain the angular acceleration (or angular velocity) in three directions of the corresponding position and the acceleration in three directions. Therefore, when executing the aforementioned S210, the angular velocity time series data of the mobile object can be determined based on the inertial sensing data in response to the object observation data including the inertial sensing data of the mobile object. The timing change of the orientation of the mobile object is determined based on the angular velocity time series data.

[0049] In some embodiments, the aforementioned S210 may also be performed based on a variety of sensor data, for example, the timing change of the orientation may be determined based on the aforementioned monitoring data and the aforementioned inertial sensor data.

[0050] In the aforementioned S220, the rotation situation can be determined according to the aforementioned change in orientation. That is, the change angle and change direction within a certain period of time can be determined according to the change of the orientation vector in time, so as to superimpose the vector change in the target time period to determine the rotation direction and rotation angle (or number of turns) in the rotation situation.

[0051] Based on the aforementioned accurate rotation situation, a torsion assessment model can be constructed and used corresponding to the torsion sensing data within the target time period.

[0052] Figure 3 It is a model schematic diagram of a torsion evaluation model provided by an exemplary embodiment of the present application.

[0053] like Figure 3 As shown, the torsion evaluation model includes two stages in practical application, namely, a training stage and an inference stage. In the training stage, the torsion evaluation model can be trained based on training data so that it can reflect the mapping relationship between the torsion degree and the torsion sensing data.

[0054] like Figure 3 As shown, in the training phase, training data can be determined, and the training data can be input into the initial torsion evaluation model for model training to determine the trained torsion evaluation model. The initial torsion evaluation model can refer to a torsion evaluation model without configured parameters. Considering that the torsion evaluation model is generally used to reflect the mapping relationship between the torsion degree of a specific cable and the torsion sensing data, it can generally be implemented using a relatively simple machine learning model (such as a deep neural network DNN).

[0055] In the process of model training, considering that the degree of torsion is generally reflected by the number of rotations, when constructing the model training data, the case where the torque / torque in the torsion sensor data is 0 can be selected, so as to configure the case as a natural case, and the degree of torsion can be re-labeled based on the rotation case based on the natural case. Thus, the corresponding training data and data labels are constructed to train the initial torsion evaluation model.

[0056] In the training process of the initial torsion evaluation model, the training data (i.e., torsion sensor data) can generally be input into the initial torsion evaluation model, so that the model output (i.e., the degree of torsion corresponding to the torsion sensor data determined by the model) is determined by the initial torsion evaluation model, and the model parameters of the initial torsion evaluation model are iterated based on the model output and the actual label (the degree of torsion corresponding to the torsion sensor data in the training data) based on the loss function until the training is completed. Among them, when the training rounds meet the threshold, the parameters converge, or the difference in the model output is less than the threshold, it can be determined that the training is completed, and the initial torsion evaluation model at this time can be used as the aforementioned trained torsion evaluation model.

[0057] Considering that the nature of the cable itself may cause the degree of torsion to be different from that of the torsion assessment model of different cables based on the aforementioned training process, the corresponding trained torsion assessment model can be directly called based on the cable, so that the torsion sensing data and the rotation situation at the termination time are input into the torsion assessment model, and the target torsion degree is determined by the torsion assessment model.

[0058] In some embodiments, considering that the parameters of the same type of cables are similar, the aforementioned torsion assessment model may call a general model when it is called, so that when determining the target torsion degree, the torsion degree output by the model is corrected based on the difference between the general model and the current cable, so as to determine the target torsion degree. The correction may be determined based on the change in torsion sensor data corresponding to multiple rotations within the target time period. For example, multiple torsion sensor data may be input into the torsion assessment model to determine the corresponding torsion degree, and then a correction coefficient may be determined based on the difference between the various torsion degrees output by the model and the actual number of rotations, so as to correct the model output so that the model can adapt to cables that have not been specially trained.

[0059] Exemplary Motion Analysis Methods:

[0060] In some embodiments, considering that the torsion of the cable affects the range of motion of the moving object in actual applications, in the detection method provided in the present application, after determining the degree of torsion, the range of motion limit corresponding to the degree of torsion can also be determined based on the degree of torsion.

[0061] The above process is generally performed based on historical data, that is, at least one matching twist degree can be determined from each historical twist degree based on the target twist degree. The target movement range of the target twist degree is determined based on the historical movement range of each matching twist degree, wherein the target movement range reflects the movement range restriction of the cable at the target twist degree on the moving object. The matching twist degree may refer to the corresponding record of the target twist degree in the historical data, and the record may record the maximum restriction of the twist degree on the range of movement.

[0062] In order to determine the limitations of different torsion degrees on the range of motion, the present application also provides a motion analysis method for a moving object to determine the limitations of different torsion degrees on the moving object.

[0063] Figure 4 It is an exemplary flow chart of a method for determining motion analysis of a moving object provided by an exemplary embodiment of the present application.

[0064] like Figure 4 As shown, process P400 may include the following steps:

[0065] S410: Collect motion data and torsion degree data of the moving object.

[0066] S420: Fitting a motion curve and a position curve of the moving object based on the motion data.

[0067] S430: Determine a torsion degree curve based on the torsion degree.

[0068] S440: Determine the target time from the motion curve.

[0069] S450: Determine a historical torsion degree from the torsion degree curve based on the target time, and determine a historical movement range from the position curve.

[0070] In the aforementioned S410, the motion data may be determined based on the monitoring image data or other sensors (such as inertial sensors). Specifically, the motion data may describe the position change and motion state (such as speed, acceleration, angular velocity, etc.) of the moving object in the activity area. For example, the position of the moving object may be determined based on the monitoring image data at each moment, and its speed may be determined based on the position change of the moving object in the continuous monitoring image data.

[0071] Based on the above determined position time series change and speed time series change, the motion curve and position curve of the mobile object can be determined, wherein the motion curve reflects the time series change of the motion of the mobile object, and the position curve reflects the actual change of the position of the mobile object.

[0072] The processing of the torsion degree data is similar to the aforementioned motion data, and a torsion degree curve can be constructed to reflect the temporal changes of the torsion degree. Among them, considering that the torsion degree curve can reflect the temporal changes of the torsion degree, the torsion degree curve, the motion curve of the moving object and the position curve are synchronized in time for the convenience of corresponding analysis.

[0073] In the aforementioned S440, the target moment may refer to the moment when the cable restricts the range of motion of the moving object. Considering that the restriction of the composite cable on the moving object in the torsion state generally appears as a force opposite to the direction of motion. When determining the maximum range of motion of the composite cable on the moving object in the current torsion state, the mutation point may be found in the motion curve, that is, the motion curve at the aforementioned target moment has a mutation (such as a sudden acceleration in the opposite direction or a sudden decrease in acceleration). Thus, the restriction on the range of motion of the moving object at the torsion degree may be determined based on the torsion degree and position at the target moment.

[0074] In some embodiments, Figure 4 The process P400 shown can be executed based on the historical data of the current moving object and / or the historical data of similar moving objects under similar circumstances (such as the same model of composite cables), thereby improving the applicability and data richness of the motion analysis method, and more accurately determining the motion state and the influence of the torsion degree on the motion range of the moving object.

[0075] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, and will not be described one by one here.

[0076] Exemplary virtual devices and electronic devices:

[0077] Combination of the above Figures 1 to 4 , describes the method embodiment of the present application in detail, and the device embodiment of the present application is described in detail below. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so the part not described in detail can refer to the previous method embodiment.

[0078] The present application also provides a device for detecting the degree of cable twisting, including a module for implementing the line of sight direction detection method provided in the present application. Figure 5 As shown, Figure 5 1 is a system module diagram of a cable torsion degree detection device provided by an exemplary embodiment of the present application. The cable torsion degree detection device 500 may include a data acquisition module 510 , a rotation determination module 520 and a torsion determination module 530 .

[0079] The data acquisition module 510 may be used to determine object observation data of the moving object and torsion sensing data of the cable within a target time period;

[0080] The rotation determination module 520 may be used to determine the rotation of the mobile object within the target time period based on the object observation data;

[0081] The torsion determination module 530 can be used to determine the target torsion degree of the cable based on the rotation condition and the torsion sensing data in the target time period, wherein the target torsion degree includes the number of twists and the torsion direction of the cable at the end time of the target time period.

[0082] The present application also provides an electronic device, such as Figure 6 The electronic device 600 provided in the present application includes a memory 610, a processor 620, and an input / output interface 630. The memory 610, the processor 620, and the input / output interface 630 are connected through an internal connection path, the memory 610 is used to store instructions, and the processor 620 is used to execute the instructions stored in the memory 610 to control the input / output interface 630 to receive input data and information, and output data such as operation results.

[0083] It should be understood that in the embodiment of the present application, the processor 620 can adopt a general central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs to implement the technical solution provided in the embodiment of the present application.

[0084] The memory 610 may include a read-only memory and a random access memory, and provides instructions and data to the processor 620. A portion of the processor 620 may also include a nonvolatile random access memory. For example, the processor 620 may also store information on the device type.

[0085] In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 620 or the instruction in the form of software. The cable twisting degree detection method disclosed in the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in the processor for execution. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 610, and the processor 620 reads the information in the memory 610 and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0086] Based on the same technical concept, the present application also provides a computer program product, which includes a computer program / instruction. When the computer program / instruction processor is executed, the method for detecting the degree of torsion of a visual cable as described in any of the above embodiments is implemented.

[0087] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK).

[0088] The method in the present application can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the present application is executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, a core network device, an OAM or other programmable device.

[0089] The computer program or instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it may also be an optical medium, such as a digital video disk; it may also be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both volatile and non-volatile types of storage media.

[0090] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0091] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for detecting the degree of cable twisting, characterized in that: The cable is connected between a fixed device and a mobile object, and the method includes: Determining object observation data of the moving object and torsion sensing data of the cable within a target time period; Determining the rotation of the moving object within a target time period based on the object observation data; Based on the correspondence between the torsion sensing data and the rotation condition within the target time period, the target torsion degree of the cable is determined, wherein the target torsion degree includes the number of twists and the torsion direction of the cable at the end moment of the target time period.

2. The detection method according to claim 1, characterized in that: The determining, based on the object observation data, the rotation of the moving object within a target time period comprises: Determining a temporal change in the orientation of the moving object within the target time period based on the object observation data; The rotation of the moving object within a target time period is determined based on the temporal change of the orientation.

3. The detection method according to claim 2, characterized in that: The determining, based on the object observation data, the temporal change of the orientation of the moving object within the target time period comprises: In response to the object observation data including monitoring data of the mobile object in the target time period, identifying the mobile object and the head orientation of the mobile object from image frames of the monitoring data; The temporal change of the orientation of the moving object is determined based on the head orientation of the moving object in each image frame.

4. The detection method according to claim 2, characterized in that: The determining, based on the object observation data, the temporal change of the orientation of the moving object within the target time period comprises: In response to the object observation data including inertial sensing data of the mobile object, determining angular velocity time series data of the mobile object based on the inertial sensing data, wherein the inertial sensing data is acquired by an inertial sensor disposed at a connection between the mobile object and the cable; The time-series change of the orientation of the moving object is determined based on the angular velocity time-series data.

5. The detection method according to claim 1, characterized in that: The determining the target torsion degree of the cable based on the corresponding relationship between the torsion sensing data and the rotation condition within the target time period includes: Determining a change in the torsion sensing data when the moving object rotates based on a time correspondence between the torsion sensing data and the rotation condition within a target time period; Determining a mapping relationship between the torsion sensor data and the degree of torsion based on a change in the torsion sensor data; The target torsion degree is determined based on the torsion sensing data at the termination time.

6. The detection method according to claim 1, characterized in that: The determining the target torsion degree of the cable based on the corresponding relationship between the torsion sensing data and the rotation condition within the target time period includes: Calling a torsion evaluation model, wherein the torsion evaluation model is trained based on torsion sensor data at various torsion degrees and is used to reflect a mapping relationship between the torsion sensor data and the torsion degree; The torsion sensing data at the termination moment and the rotation condition are input into a torsion evaluation model, and the target torsion degree is determined by the torsion evaluation model.

7. The detection method according to claim 1, characterized in that: The method further comprises: determining at least one matching twist degree from each of the historical twist degrees based on the target twist degree; A target movement range for the target twist degree is determined based on the historical movement ranges for each matching twist degree, wherein the target movement range reflects the movement range limit of the cable to the moving object at the target twist degree.

8. The detection method according to claim 7, characterized in that: The method further comprises: Collecting motion data and torsion degree data of the moving object; Fitting a motion curve and a position curve of the mobile object based on the motion data, wherein the motion curve reflects the temporal changes of the motion of the mobile object, and the position curve reflects the actual changes of the position of the mobile object; Determining a torsion degree curve based on the torsion degree, wherein the torsion degree curve reflects a temporal change of the torsion degree, and the torsion degree curve, the motion curve and the position curve of the moving object are synchronized in time; determining a target time from the motion profile; A historical degree of twist is determined from the degree of twist curve based on the target time, and a historical range of movement is determined from the position curve.

9. A device for detecting the degree of cable twisting, characterized in that: The cable is connected between a fixed device and a mobile object, and the device comprises: a data acquisition module, for determining object observation data of the moving object and torsion sensing data of the cable within a target time period; A rotation determination module, configured to determine the rotation of the moving object within a target time period based on the object observation data; The torsion determination module is used to determine the target torsion degree of the cable based on the rotation condition and the torsion sensing data within the target time period, wherein the target torsion degree includes the number of twists and the torsion direction of the cable at the end time of the target time period.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for detecting the degree of cable twisting according to any one of claims 1-8.

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