Control method and system of fire-fighting training rope climbing machine

Abstract

The invention discloses a control method and system for a fire-fighting training rope climbing machine, and the method comprises the steps: defining an action parameter according to an abnormal action difference quantity and the training time of a fireman, and defining an action grade according to the action parameter; an action grade set is constructed according to the action grades corresponding to the multiple detection intervals, and the subsequent training grade of the firefighter relative to the fire-fighting training rope climbing machine is defined according to the action grade set and the training grade of the fire-fighting training rope climbing machine, so that the subsequent training of the firefighter can be properly regulated and controlled. The action level set and the training level of the fire-fighting training rope climbing machine are fully considered, the suitability of the firefighter in subsequent training is ensured, meanwhile, the corresponding guidance video is matched according to the subsequent training level of the firefighter relative to the fire-fighting training rope climbing machine, and the action optimization information is presented according to the guidance video and the corresponding detection interval, so that the action optimization efficiency is improved. Therefore, the training actions of the firefighter can be managed and controlled in time, and the training actions of the firefighter can be optimized in subsequent training.

Description

Technical Field

[0001] This invention relates to the technical field of fire training rope climbing machines, and more particularly to a control method and system for a fire training rope climbing machine. Background Technology

[0002] With the development of technology, firefighters need to conduct daily training, including rope climbing training on fire training rope climbing machines. During this training, firefighters practice various rope climbing movements. Current technology monitors the firefighters' rope climbing training and defines the training quality based on the time taken. However, the firefighters' movements during rope climbing training are not controlled, nor is the training level of the fire training rope climbing machine monitored, which affects the firefighters' adaptability to subsequent training. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies. This invention provides a control method and system for a fire training rope climbing machine. Action parameters are defined based on the amount of abnormal movement differences and the firefighter's training time, and action levels are defined based on these parameters. An action level set is constructed based on the action levels corresponding to multiple detection intervals. The subsequent training level of the firefighter relative to the fire training rope climbing machine is defined based on this action level set and the machine's training level. This allows for appropriate adjustment of the firefighter's subsequent training and fully considers both the action level set and the machine's training level, enabling multi-dimensional control of the firefighter's subsequent training and ensuring the firefighter's adaptability. Simultaneously, corresponding guidance videos are matched based on the firefighter's subsequent training level relative to the machine, and action optimization information is presented based on the guidance videos and corresponding detection intervals. This facilitates timely control of the firefighter's training movements and optimizes their training actions in subsequent training.

[0004] To address the aforementioned technical problems, this invention provides a control method for a fire training rope climbing machine, applicable to the control scenario of a fire training rope climbing machine. The control method for the fire training rope climbing machine includes: Collect videos of firefighters climbing ropes relative to a fire training rope climbing machine, and define the dwell time of firefighters at each dwell height based on the rope climbing training videos; The fire training rope climbing machine is marked with multiple detection zones based on the dwell height and the corresponding dwell time, and corresponding training action segments are matched in multiple detection zones. Abnormal actions are identified based on action recognition of training action segments, and the difference in abnormal actions is defined based on abnormal actions and preset actions. Action parameters are defined based on the amount of abnormal action variation and the firefighters' training time, and action levels are defined based on the action parameters; An action level set is constructed based on the action levels corresponding to multiple detection intervals, and the subsequent training level of the firefighter relative to the fire training rope climbing machine is defined based on the action level set and the training level of the fire training rope climbing machine. The corresponding instructional video is matched to the firefighter's subsequent training level relative to the fire training rope climbing machine, and the action optimization information is presented based on the instructional video and the corresponding detection range.

[0005] Optionally, the step of collecting rope-climbing training videos of firefighters relative to the fire training rope-climbing machine, and defining the dwell time of firefighters at various dwelling heights based on the rope-climbing training videos, includes: Collect videos of firefighters practicing rope climbing relative to a fire training rope climbing machine; Based on the rope climbing training video and the training height of the fire training rope climbing machine, multiple sub-training videos are divided; The firefighters' positions were marked based on multiple sub-training videos; Match the corresponding stopping height based on the firefighter's stopping position; Based on the defined dwell height, time detection nodes are established, and dwell time at each dwell height is detected based on the time detection nodes to define the dwell time of firefighters at each dwell height.

[0006] Optionally, the step of marking multiple detection intervals of the fire training rope climbing machine based on the dwell height and corresponding dwell time, and matching corresponding training action segments in multiple detection intervals, includes: The fixed height and corresponding dwell time; Associate the dwell height and the corresponding dwell time; The first interval coefficient is defined based on the dwell height, and the second interval coefficient is defined based on the dwell time. An interval coefficient set is constructed based on the second interval coefficient and the first interval coefficient, and multiple detection intervals of the fire training rope climbing machine are marked according to the interval coefficient set; Freeze multiple detection intervals; The corresponding training action segments are matched based on multiple detection intervals and corresponding sub-training videos.

[0007] Optionally, the step of marking abnormal actions based on action recognition of training action segments, and defining an abnormal action difference quantity based on the abnormal actions and preset actions, includes: Freeze-frame training motion clips; A set of consecutive movements of firefighters is labeled based on the action recognition of training action segments; Abnormal actions are marked based on a traversal of the firefighter's sequence of actions; Freeze abnormal actions and define preset actions based on the height of the abnormal action and the action library; The abnormal actions are compared with the preset actions, and the difference in abnormal actions is defined based on the abnormal actions and the preset actions.

[0008] Optionally, the step of defining action parameters based on the abnormal action difference and the firefighter's training time, and defining action levels based on the action parameters, includes: Freeze the abnormal motion difference; Match the corresponding training node based on the difference in abnormal actions; The training time for firefighters in this round of training is defined according to the training nodes; Correlate the differences in abnormal movements with the training time of firefighters; Action parameters are defined based on the abnormal movement differences and the firefighters' training time; Collect multiple environmental parameters from firefighters during training and define environmental characteristics based on these parameters; Environmental impact parameters are defined based on environmental characteristics and the training level of firefighters; Action levels are defined based on action parameters and environmental impact parameters.

[0009] Optionally, the step of constructing an action level set based on the action levels corresponding to multiple detection intervals, and defining the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine, includes: Freeze multiple detection intervals; Collect the action levels corresponding to multiple detection intervals; Arrange multiple action levels in sequence and construct an action level set.

[0010] Optionally, the step of constructing an action level set based on the action levels corresponding to multiple detection intervals, and defining the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine, further includes: Collect data on the training levels of the fire-fighting rope climbing machine; The training levels and movement levels associated with the fire training rope climbing machine; The training matching coefficient for firefighters is defined based on the training level and action level set of the fire training rope climbing machine; The difference in training matching coefficients is defined based on the firefighters' training matching coefficients and the preset training matching coefficients. The subsequent training level of firefighters relative to the fire training rope climbing machine is defined based on the difference in training matching coefficients and the firefighters' training strength distribution table.

[0011] Optionally, the step of matching corresponding instructional videos to the firefighters based on their subsequent training levels on the fire training rope climbing machine, and presenting motion optimization information based on the instructional videos and corresponding detection intervals, includes: The training level for firefighters relative to the rope climbing machine used in fire training; The training intensity of firefighters is defined based on their subsequent training levels relative to the fire training rope climbing machine. Define appropriate training courses based on the training intensity of firefighters and the environment in which the fire training rope climbing machine is located; Match appropriate training courses with corresponding instructional videos.

[0012] Optionally, the step of matching corresponding instructional videos to the firefighters based on their subsequent training levels on the fire training rope climbing machine, and presenting motion optimization information based on the instructional videos and corresponding detection intervals, further includes: Collect detection intervals and associate the guidance video with the corresponding detection intervals; Mark the detection interval in the guidance video and output the corresponding detection segment; Standard actions are presented based on the detected segments; The standard movements are compared with the training movements recorded by firefighters in the testing area to present the difference in movements, and to present movement optimization information based on the difference in movements.

[0013] In addition, this embodiment of the invention also provides a control system for a fire training rope climbing machine, the control system of which includes: The data acquisition module is used to acquire videos of firefighters climbing ropes relative to the fire training rope climbing machine, and to define the dwell time of firefighters at each dwelling height based on the rope climbing training videos. The training motion segment module is used to mark multiple detection intervals of the fire training rope climbing machine according to the dwell height and the corresponding dwell time, and to match the corresponding training motion segments in multiple detection intervals; The Abnormal Action Difference Module is used to mark abnormal actions based on action recognition of training action segments, and to define the abnormal action difference based on the abnormal actions and preset actions. The action level module is used to define action parameters based on the amount of abnormal action difference and the firefighter's training time, and to define the action level based on the action parameters; The subsequent training level module is used to construct an action level set based on the action levels corresponding to multiple detection intervals, and to define the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine. The guidance module is used to match corresponding guidance videos to the firefighters based on their subsequent training levels on the fire training rope climbing machine, and to present action optimization information based on the guidance videos and the corresponding detection ranges.

[0014] In this embodiment of the invention, the method defines the dwell time of firefighters at various dwelling heights based on the rope climbing training video, marks multiple detection intervals of the fire training rope climbing machine based on the dwelling height and the corresponding dwell time, and matches corresponding training action segments in multiple detection intervals to mark abnormal actions based on the action recognition of the training action segments, and defines the abnormal action difference based on the abnormal actions and preset actions, thereby clarifying the abnormal action difference and controlling the abnormal actions.

[0015] Simultaneously, action parameters are defined based on the abnormal action variation and the firefighter's training time, and action levels are defined based on these parameters. An action level set is constructed based on the action levels corresponding to multiple detection intervals, and the firefighter's subsequent training level relative to the fire training rope climbing machine is defined based on this set and the machine's training level. This allows for appropriate adjustment of the firefighter's subsequent training, taking into full account both the action level set and the machine's training level. This ensures the firefighter's adaptability in subsequent training by controlling the firefighter's training from multiple dimensions. Furthermore, corresponding instructional videos are matched based on the firefighter's subsequent training level relative to the machine, and action optimization information is presented based on the instructional videos and corresponding detection intervals. This facilitates timely control of the firefighter's training actions and optimizes their training movements in subsequent training. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0017] Figure 1 This is a flowchart illustrating the control method of the fire training rope climbing machine in an embodiment of the present invention; Figure 2 This is a flowchart illustrating step S11 of the control method for the fire training rope climbing machine in this embodiment of the invention. Figure 3 This is a flowchart illustrating step S12 of the control method for the fire training rope climbing machine in this embodiment of the invention. Figure 4 This is a flowchart illustrating step S13 of the control method for the fire training rope climbing machine in this embodiment of the invention. Figure 5 This is a flowchart illustrating step S14 of the control method for the fire training rope climbing machine in this embodiment of the invention. Figure 6 This is a flowchart illustrating step S15 of the control method for the fire training rope climbing machine in this embodiment of the invention. Figure 7 This is a flowchart illustrating step S16 of the control method for the fire training rope climbing machine in this embodiment of the invention. Figure 8 This is a schematic diagram of the structural composition of the control system of the fire training rope climbing machine in an embodiment of the present invention; Figure 9 This is a hardware diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0018] 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. Example

[0019] Please see Figures 1 to 9 A control method for a fire training rope climbing machine, applied to the control scenario of a fire training rope climbing machine; the control method for the fire training rope climbing machine includes: Step S11: Collect rope climbing training videos of firefighters relative to the fire training rope climbing machine, and define the dwell time of firefighters at each dwelling height based on the rope climbing training videos; Step S12: Mark multiple detection zones of the fire training rope climbing machine according to the dwell height and the corresponding dwell time, and match the corresponding training action segments in multiple detection zones; Step S13: Mark abnormal actions based on action recognition of training action segments, and define the abnormal action difference quantity based on abnormal actions and preset actions; Step S14: Define action parameters based on the abnormal action difference and the firefighter's training time, and define action level based on the action parameters; Step S15: Construct an action level set based on the action levels corresponding to multiple detection intervals, and define the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine. Step S16: Match the corresponding instruction video to the firefighter's subsequent training level relative to the fire training rope climbing machine, and present motion optimization information based on the instruction video and the corresponding detection range.

[0020] In this embodiment of the invention, the method defines the dwell time of firefighters at various dwelling heights based on the rope climbing training video, marks multiple detection intervals of the fire training rope climbing machine based on the dwelling height and the corresponding dwell time, and matches corresponding training action segments in multiple detection intervals to mark abnormal actions based on the action recognition of the training action segments, and defines the abnormal action difference based on the abnormal actions and preset actions, thereby clarifying the abnormal action difference and controlling the abnormal actions.

[0021] Simultaneously, action parameters are defined based on the abnormal action variation and the firefighter's training time, and action levels are defined based on these parameters. An action level set is constructed based on the action levels corresponding to multiple detection intervals, and the firefighter's subsequent training level relative to the fire training rope climbing machine is defined based on this set and the machine's training level. This allows for appropriate adjustment of the firefighter's subsequent training, taking into full account both the action level set and the machine's training level. This ensures the firefighter's adaptability in subsequent training by controlling the firefighter's training from multiple dimensions. Furthermore, corresponding instructional videos are matched based on the firefighter's subsequent training level relative to the machine, and action optimization information is presented based on the instructional videos and corresponding detection intervals. This facilitates timely control of the firefighter's training actions and optimizes their training movements in subsequent training.

[0022] In step S11, the firefighters' rope climbing training video relative to the fire training rope climbing machine is collected, and the dwell time of the firefighters at each dwelling height is defined based on the rope climbing training video; In the specific implementation of this invention, the specific steps can be as follows: S111: Collect videos of firefighters climbing ropes relative to a fire training rope climbing machine; S112: Based on the rope climbing training video and the training height of the fire training rope climbing machine, multiple sub-training videos are divided; S113: Mark the firefighters' positions based on multiple sub-training videos; S114: Match the corresponding stopping height based on the firefighter's stopping position; S115: Define time detection nodes based on dwell height, and trigger dwell time detection at each dwell height according to the time detection nodes, so as to define the dwell time of firefighters at each dwell height.

[0023] In the embodiments of this application, rope climbing training videos of firefighters relative to the fire training rope climbing machine are collected and managed to facilitate further processing of the rope climbing training videos. At this time, multiple sub-training videos are divided based on the rope climbing training videos and the training height of the fire training rope climbing machine to facilitate management of multiple sub-training videos.

[0024] Furthermore, the positions of firefighters are marked based on multiple sub-training videos to define their positions and control them accordingly. This allows for matching the corresponding standing height based on the firefighter's position, thus clarifying the standing height and enabling control over it.

[0025] At this point, time detection nodes are defined based on the dwell height, and the dwell time at each dwell height is detected based on the time detection nodes to define the dwell time of firefighters at each dwell height, thereby controlling the dwell time at each dwell height and then carrying out targeted control at each dwell height.

[0026] In step S12, multiple detection intervals of the fire training rope climbing machine are marked according to the dwell height and the corresponding dwell time, and corresponding training action segments are matched in multiple detection intervals; In the specific implementation of this invention, the specific steps can be as follows: S121: Freeze-motion height and corresponding dwell time; S122: Associate the dwell height and the corresponding dwell time; S123: Define the first interval coefficient based on the dwell height, and define the second interval coefficient based on the dwell time; S124: Construct a set of interval coefficients based on the second interval coefficient and the first interval coefficient, and mark multiple detection intervals of the fire training rope climbing machine according to the set of interval coefficients; S125: Freeze multiple detection intervals and match corresponding training action segments based on the multiple detection intervals and the corresponding sub-training videos.

[0027] In the embodiments of this application, the dwell height and the corresponding dwell time are fixed and controlled. At this time, the dwell height and the corresponding dwell time are associated so as to define a first interval coefficient based on the dwell height and a second interval coefficient based on the dwell time. Therefore, the second interval coefficient and the first interval coefficient are introduced, so as to further process the second interval coefficient and the first interval coefficient to consider multiple dimensions of the second interval coefficient and the first interval coefficient.

[0028] Furthermore, an interval coefficient set is constructed based on the second interval coefficient and the first interval coefficient, and multiple detection intervals of the fire training rope climbing machine are marked according to the interval coefficient set; multiple detection intervals are fixed, and corresponding training action segments are matched according to the multiple detection intervals and the corresponding sub-training videos, so as to control the training action segments, so as to clarify the training action segments, and then perform subsequent processing on the training action segments.

[0029] In step S13, abnormal actions are marked based on action recognition of training action segments, and the abnormal action difference quantity is defined based on the abnormal actions and preset actions. In the specific implementation of this invention, the specific steps can be as follows: S131: Freeze-frame training motion clips; S132: A set of coherent movements of firefighters labeled based on action recognition of training action segments; S133: Mark abnormal actions based on the traversal of the firefighter's set of consecutive actions; S134: Freeze the abnormal action and define a preset action based on the height of the abnormal action and the action library; S135: Compare the abnormal action with the preset action, and define the difference in abnormal action based on the abnormal action and the preset action.

[0030] In the embodiments of this application, training action segments are frozen, and the firefighter's continuous action set is marked based on the action recognition of the training action segments, so that the firefighter's training actions are presented in the firefighter's continuous action set. Abnormal actions are marked according to the traversal of the firefighter's continuous action set, so as to identify abnormal actions and realize the control of abnormal actions.

[0031] At this point, the abnormal action is frozen, and a preset action is defined based on the height of the abnormal action and the action library, so as to match the abnormal action and the preset action at the same height.

[0032] Therefore, by comparing abnormal actions with preset actions and defining the difference in abnormal actions based on the abnormal actions and preset actions, we can further control the difference in abnormal actions and make it easier to manage the difference in abnormal actions.

[0033] Based on the rope climbing training video, the dwell time of firefighters at various dwelling heights is defined, and multiple detection intervals of the fire training rope climbing machine are marked according to the dwelling height and the corresponding dwell time. The corresponding training action segments are matched in multiple detection intervals to facilitate the identification of abnormal actions based on the action segments of the training action segments. The abnormal action difference is defined based on the abnormal actions and the preset actions, thereby clarifying the abnormal action difference and controlling the abnormal actions.

[0034] S14: Define action parameters based on the abnormal action difference and the firefighter's training time, and define action levels based on the action parameters; In the specific implementation of this invention, the specific steps can be as follows: S141: Quantity of difference in frozen abnormal movements; S142: Match the corresponding training node based on the difference in abnormal actions; S143: Define the training time for firefighters in this round of training according to the training nodes; S144: Correlate abnormal movement differences with firefighter training time; S145: Define motion parameters based on the abnormal motion difference and the firefighter's training time; S146: Collect multiple environmental parameters from firefighters during training and define environmental characteristics based on these parameters; S147: Define environmental impact parameters based on environmental characteristics and firefighters' training level; S148: Define the action level based on the action parameters and environmental influence parameters.

[0035] In the embodiments of this application, the abnormal action difference quantity is fixed and controlled, so as to match the corresponding training node according to the abnormal action difference quantity, so as to fix each training node. At the same time, the training time of the firefighter in this round of training is defined according to the training node. Furthermore, the abnormal movement difference is correlated with the firefighter's training time; movement parameters are defined based on the abnormal movement difference and the firefighter's training time, thereby clarifying the movement parameters.

[0036] In addition, multiple environmental parameters of firefighters during training are collected, and environmental characteristics are defined based on these parameters; environmental impact parameters are defined based on the environmental characteristics and the firefighters' training height; and action levels are defined based on the action parameters and environmental impact parameters.

[0037] S15: Construct an action level set based on the action levels corresponding to multiple detection intervals, and define the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine. In the specific implementation of this invention, the specific steps can be as follows: S151: Freeze multiple detection intervals; S152: Collect the action levels corresponding to multiple detection intervals; S153: Arrange multiple action levels in sequence and construct a set of action levels; S154: Collect training levels for fire-fighting rope climbing machines; S155: A set of training levels and movement levels associated with fire training rope climbing machines; S156: Define the training matching coefficient for firefighters based on the training level and action level set of the fire training rope climbing machine; S157: Define the difference in training matching coefficients based on the firefighters' training matching coefficients and preset training matching coefficients; S158: Define the subsequent training level of firefighters relative to the fire training rope climbing machine based on the difference in training matching coefficients and the firefighter training strength distribution table.

[0038] In the embodiments of this application, multiple detection intervals are fixed, and the action levels corresponding to the multiple detection intervals are collected simultaneously. The multiple action levels are arranged in sequence, and an action level set is constructed, thereby ensuring the accuracy of the action level set and facilitating subsequent processing of the action level set.

[0039] At this point, the training level of the fire training rope climbing machine is collected; the training level and action level set of the fire training rope climbing machine are associated, thereby introducing the training level and action level set of the fire training rope climbing machine, so as to control the training level and action level set of the fire training rope climbing machine.

[0040] Therefore, the training matching coefficient for firefighters is defined based on the training level and action level set of the fire training rope climbing machine; the difference in training matching coefficients is defined based on the firefighters' training matching coefficients and the preset training matching coefficients; and the subsequent training level of firefighters relative to the fire training rope climbing machine is defined based on the difference in training matching coefficients and the firefighters' training strength distribution table. This clarifies the subsequent training level of firefighters relative to the fire training rope climbing machine, facilitating the management and control of firefighters in subsequent training, thereby adapting the firefighters' training to the actual needs.

[0041] S16: Match corresponding instructional videos to the firefighters' subsequent training levels relative to the fire training rope climbing machine, and present motion optimization information based on the instructional videos and corresponding detection intervals; In the specific implementation of this invention, the specific steps can be as follows: S161: Fixed training level for firefighters relative to the fire training rope climbing machine; S162: Define the training intensity of firefighters based on their subsequent training levels relative to the fire training rope climbing machine; S163: Define appropriate training courses based on the training intensity of firefighters and the environment in which the fire training rope climbing machine is located; S164: Match appropriate training courses with corresponding instructional videos; S165: Collect the detection range and associate the guidance video with the corresponding detection range; S166: Mark the detection interval in the guidance video and output the corresponding detection segment; S167: Present standard actions based on the detected segments; S168: Compare the standard movements with the training movements recorded by firefighters in the testing area to present the movement difference and present movement optimization information based on the movement difference.

[0042] In the embodiments of this application, action parameters are defined based on the abnormal action difference and the firefighter's training time, and action levels are defined based on the action parameters. An action level set is constructed based on the action levels corresponding to multiple detection intervals, and the subsequent training level of the firefighter relative to the fire training rope climbing machine is defined based on the action level set and the training level of the fire training rope climbing machine. This facilitates appropriate control over the firefighter's subsequent training and fully considers both the action level set and the training level of the fire training rope climbing machine, enabling control over the firefighter's subsequent training from multiple dimensions and ensuring the firefighter's adaptability in subsequent training. Simultaneously, corresponding guidance videos are matched based on the firefighter's subsequent training level relative to the fire training rope climbing machine, and action optimization information is presented based on the guidance videos and corresponding detection intervals, enabling timely control over the firefighter's training actions and optimization of the firefighter's training actions in subsequent training.

[0043] At this point, the subsequent training level of the firefighters relative to the fire training rope climbing machine is fixed; the training intensity of the firefighters is defined based on the subsequent training level of the fire training rope climbing machine, and the training intensity of the firefighters is controlled.

[0044] At the same time, appropriate training courses are defined based on the training intensity of firefighters and the environment in which the fire training rope climbing machine is located; corresponding instructional videos are matched based on the appropriate training courses, so as to introduce instructional videos and further optimize the training of firefighters based on the instructional videos.

[0045] Furthermore, the system collects data on detection intervals and associates the guidance videos with the corresponding detection intervals. Detection intervals are then marked on the guidance videos, and corresponding detection segments are output. Standard actions are presented based on the detection segments. These standard actions are compared with the training actions recorded by firefighters within the detection intervals to present the amount of action difference, and action optimization information is presented based on this difference. Therefore, by matching corresponding guidance videos to the firefighters' subsequent training levels relative to the fire training rope climbing machine, and presenting action optimization information based on the guidance videos and corresponding detection intervals, the system can effectively manage firefighters' training actions in a timely manner and optimize their training movements in subsequent training sessions.

[0046] In this embodiment of the invention, the method defines the dwell time of firefighters at various dwelling heights based on the rope climbing training video, marks multiple detection intervals of the fire training rope climbing machine based on the dwelling height and the corresponding dwell time, and matches corresponding training action segments in multiple detection intervals to mark abnormal actions based on the action recognition of the training action segments, and defines the abnormal action difference based on the abnormal actions and preset actions, thereby clarifying the abnormal action difference and controlling the abnormal actions.

[0047] Simultaneously, action parameters are defined based on the abnormal action variation and the firefighter's training time, and action levels are defined based on these parameters. An action level set is constructed based on the action levels corresponding to multiple detection intervals, and the firefighter's subsequent training level relative to the fire training rope climbing machine is defined based on this set and the machine's training level. This allows for appropriate adjustment of the firefighter's subsequent training, taking into full account both the action level set and the machine's training level. This ensures the firefighter's adaptability in subsequent training by controlling the firefighter's training from multiple dimensions. Furthermore, corresponding instructional videos are matched based on the firefighter's subsequent training level relative to the machine, and action optimization information is presented based on the instructional videos and corresponding detection intervals. This facilitates timely control of the firefighter's training actions and optimizes their training movements in subsequent training. Example

[0048] Please see Figure 8 , Figure 8 This is a schematic diagram of the structural composition of the control system of the fire training rope climbing machine in an embodiment of the present invention.

[0049] like Figure 8 As shown, a control system for a fire training rope climbing machine includes: The acquisition module 21 is used to acquire rope climbing training videos of firefighters relative to the fire training rope climbing machine, and to define the dwell time of firefighters at each dwelling height based on the rope climbing training videos; The training motion segment module 22 is used to mark multiple detection intervals of the fire training rope climbing machine according to the dwell height and the corresponding dwell time, and to match the corresponding training motion segments in multiple detection intervals. The abnormal action difference module 23 is used to mark abnormal actions based on action recognition of training action segments, and to define the abnormal action difference based on the abnormal actions and preset actions. Action level module 24 is used to define action parameters based on the abnormal action difference and the firefighter's training time, and to define action levels based on the action parameters; The subsequent training level module 25 is used to construct an action level set based on the action levels corresponding to multiple detection intervals, and to define the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine. The guidance module 26 is used to match the corresponding guidance video according to the firefighter's subsequent training level relative to the fire training rope climbing machine, and to present action optimization information based on the guidance video and the corresponding detection range. Example

[0050] Please see Figure 9 See below for reference. Figure 9 To describe an electronic device 40 according to this embodiment of the present invention. Figure 9 The electronic device 40 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0051] like Figure 9 As shown, the electronic device 40 is manifested in the form of a general-purpose computing device. The components of the electronic device 40 may include, but are not limited to: at least one processing unit 41, at least one storage unit 42, and a bus 43 connecting different system components (including storage unit 42 and processing unit 41).

[0052] The storage unit stores program code that can be executed by the processing unit 41, causing the processing unit 41 to perform the steps described in the "Embodiment Methods" section of this specification according to various exemplary embodiments of the present invention.

[0053] Storage unit 42 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422, and may further include a read-only memory unit (ROM) 423.

[0054] Storage unit 42 may also include a program / utility 424 having a set (at least one) program module 425, such program module 425 including but not limited to: operating system, one or more application programs, other program modules and program data, each of these examples or some combination of these may include an implementation of a network environment.

[0055] Bus 43 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0056] Electronic device 40 can also communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable trainees to interact with electronic device 40, and / or with any device that enables electronic device 40 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 44. Furthermore, electronic device 40 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 45. Figure 9 As shown, network adapter 45 communicates with other modules of electronic device 40 via bus 43. It should be understood that, although... Figure 9 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup planning systems.

[0057] 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, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0058] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. Furthermore, it stores computer program instructions, which, when executed by a computer, cause the computer to perform the methods described above.

[0059] Furthermore, the control method and system for the fire training rope climbing machine provided in the embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A control method for a fire training rope climbing machine, characterized in that, Application in the control scenarios of fire training rope climbing machines; The control method for the fire training rope climbing machine includes: Collect videos of firefighters climbing ropes relative to a fire training rope climbing machine, and define the dwell time of firefighters at each dwell height based on the rope climbing training videos; The fire training rope climbing machine is marked with multiple detection zones based on the dwell height and the corresponding dwell time, and corresponding training action segments are matched in multiple detection zones. Abnormal actions are identified based on action recognition of training action segments, and the difference in abnormal actions is defined based on abnormal actions and preset actions. Action parameters are defined based on the amount of abnormal action variation and the firefighters' training time, and action levels are defined based on the action parameters. An action level set is constructed based on the action levels corresponding to multiple detection intervals, and the subsequent training level of the firefighter relative to the fire training rope climbing machine is defined based on the action level set and the training level of the fire training rope climbing machine. The corresponding instructional video is matched to the firefighter's subsequent training level relative to the fire training rope climbing machine, and the action optimization information is presented based on the instructional video and the corresponding detection range.

2. The control method for the fire training rope climbing machine according to claim 1, characterized in that, The process involves collecting rope-climbing training videos of firefighters relative to the fire training rope-climbing machine, and defining the dwell time of firefighters at various dwelling heights based on the rope-climbing training videos, including: Collect videos of firefighters practicing rope climbing relative to a fire training rope climbing machine; Based on the rope climbing training video and the training height of the fire training rope climbing machine, multiple sub-training videos are divided; The firefighters' positions were marked based on multiple sub-training videos; Match the corresponding stopping height based on the firefighter's stopping position; Based on the defined dwell height, time detection nodes are established, and dwell time at each dwell height is detected based on the time detection nodes to define the dwell time of firefighters at each dwell height.

3. The control method for the fire training rope climbing machine according to claim 2, characterized in that, The process of marking multiple detection zones of the fire training rope climbing machine based on the dwell height and corresponding dwell time, and matching corresponding training action segments within these multiple detection zones, includes: The freezing point height and the corresponding freezing time; Associate the dwell height and the corresponding dwell time; The first interval coefficient is defined based on the dwell height, and the second interval coefficient is defined based on the dwell time. An interval coefficient set is constructed based on the second interval coefficient and the first interval coefficient, and multiple detection intervals of the fire training rope climbing machine are marked according to the interval coefficient set; Multiple detection intervals are frozen, and corresponding training action segments are matched based on the multiple detection intervals and the corresponding sub-training videos.

4. The control method for the fire training rope climbing machine according to claim 3, characterized in that, The process of identifying and labeling abnormal actions based on training action segments, and defining an abnormal action difference based on the abnormal actions and preset actions, includes: Freeze-frame training motion clips; A set of consecutive movements of firefighters is labeled based on the action recognition of training action segments; Abnormal actions are marked based on a traversal of the firefighter's sequence of actions; Freeze abnormal actions and define preset actions based on the height of the abnormal action and the action library; The abnormal actions are compared with the preset actions, and the difference in abnormal actions is defined based on the abnormal actions and the preset actions.

5. The control method for the fire training rope climbing machine according to claim 4, characterized in that, The process of defining action parameters based on the abnormal action variation and the firefighter's training time, and defining action levels based on the action parameters, includes: Freeze the abnormal motion difference; Match the corresponding training node based on the difference in abnormal actions; The training time for firefighters in this round of training is defined according to the training nodes; Correlate the differences in abnormal movements with the training time of firefighters; Action parameters are defined based on the abnormal movement differences and the firefighters' training time; Collect multiple environmental parameters from firefighters during training and define environmental characteristics based on these parameters; Environmental impact parameters are defined based on environmental characteristics and the training level of firefighters; Action levels are defined based on action parameters and environmental impact parameters.

6. The control method for the fire training rope climbing machine according to claim 5, characterized in that, The process of constructing an action level set based on the action levels corresponding to multiple detection intervals, and defining the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine, includes: Freeze multiple detection intervals; Collect the action levels corresponding to multiple detection intervals; Arrange multiple action levels in sequence and construct an action level set.

7. The control method for the fire training rope climbing machine according to claim 6, characterized in that, The step of constructing an action level set based on the action levels corresponding to multiple detection intervals, and defining the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine, further includes: Collect data on the training levels of the fire-fighting rope climbing machine; The training levels and movement levels associated with the fire training rope climbing machine; The training matching coefficient for firefighters is defined based on the training level and action level set of the fire training rope climbing machine; The difference in training matching coefficients is defined based on the firefighters' training matching coefficients and the preset training matching coefficients. The subsequent training level of firefighters relative to the fire training rope climbing machine is defined based on the difference in training matching coefficients and the firefighters' training strength distribution table.

8. The control method for the fire training rope climbing machine according to claim 7, characterized in that, The process involves matching corresponding instructional videos to the firefighters' subsequent training levels on the fire training rope climbing machine, and presenting motion optimization information based on the instructional videos and corresponding detection intervals, including: The training level for firefighters relative to the rope climbing machine used in fire training; The training intensity of firefighters is defined based on their subsequent training levels relative to the fire training rope climbing machine. Define appropriate training courses based on the training intensity of firefighters and the environment in which the fire training rope climbing machine is located; Match appropriate training courses with corresponding instructional videos.

9. The control method for the fire training rope climbing machine according to claim 8, characterized in that, The method of matching corresponding instructional videos to the firefighters' subsequent training levels relative to the fire training rope climbing machine, and presenting motion optimization information based on the instructional videos and corresponding detection intervals, also includes: Collect detection intervals and associate the guidance video with the corresponding detection intervals; Mark the detection interval in the guidance video and output the corresponding detection segment; The standard actions are presented based on the detected segments; The standard movements are compared with the training movements recorded by firefighters in the testing area to present the difference in movements, and to present movement optimization information based on the difference in movements.

10. A control system for a fire training rope climbing machine, characterized in that, The control system of the fire training rope climbing machine is applied to the control method of the fire training rope climbing machine as described in any one of claims 1-9, and the control system of the fire training rope climbing machine includes: The data acquisition module is used to acquire videos of firefighters climbing ropes relative to the fire training rope climbing machine, and to define the dwell time of firefighters at each dwelling height based on the rope climbing training videos. The training motion segment module is used to mark multiple detection intervals of the fire training rope climbing machine according to the dwell height and the corresponding dwell time, and to match the corresponding training motion segments in multiple detection intervals; The Abnormal Action Difference Module is used to mark abnormal actions based on action recognition of training action segments, and to define the abnormal action difference based on the abnormal actions and preset actions. The action level module is used to define action parameters based on the amount of abnormal action difference and the firefighter's training time, and to define the action level based on the action parameters; The subsequent training level module is used to construct an action level set based on the action levels corresponding to multiple detection intervals, and to define the subsequent training level of the firefighter relative to the fire training rope climbing machine based on the action level set and the training level of the fire training rope climbing machine. The guidance module is used to match corresponding guidance videos to the firefighters based on their subsequent training levels on the fire training rope climbing machine, and to present action optimization information based on the guidance videos and the corresponding detection ranges.

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