Firefighter load-bearing turn-back simulation control method and system

Abstract

The invention discloses a fireman load-bearing turn-back simulation control method and system, and the method comprises the steps: matching a corresponding simulation training animation according to a turn-back video, marking a plurality of detection nodes based on the simulation training animation, thereby facilitating the division of the detection nodes for the simulation training animation, introducing the plurality of detection nodes, and enabling the detection to be more accurate. Therefore, fixed-point detection is carried out on the detection nodes. Meanwhile, the turning-back effect parameters of the firefighter under the load weight are defined according to the states corresponding to the detection nodes; and matching a corresponding action picture based on the detection time point, positioning an abnormal action part according to the action picture and a theoretical action picture, defining an action effect parameter based on the abnormal action part, and determining a training prompt of the firefighter in weight-bearing turn-back training based on the action effect parameter and the turn-back effect parameter. Therefore, aiming at the action effect parameters and the turn-back effect parameters, dynamic interaction of virtual application and actual training of the firefighter in weight-bearing turn-back training is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of firefighter load-bearing reversal simulation control, and in particular to a firefighter load-bearing reversal simulation control method and system. Background Art

[0002] With the development of science and technology, firefighters need to conduct daily training and weighted reentry training. At this time, firefighters introduced the monitoring of physiological parameters in the weighted reentry training. The physiological parameters of firefighters were recorded in real time during the training process, and the physiological parameters of firefighters were controlled. However, the movements of firefighters in the weighted reentry training were not controlled, and it became increasingly difficult to process the action pictures, which affected the training movements and the overall control of the reentry training. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides a firefighter's weighted reversal simulation control method and system, which collects the firefighter's weighted reversal training information, and defines the firefighter's weighted reversal level according to the firefighter's weighted reversal training information; predicts the weight based on the firefighter's weighted reversal level and the firefighter's current basic information; when the firefighter carries the weight, collects the firefighter's reversal video based on the weight, and matches the corresponding simulation training animation according to the reversal video, marks multiple detection nodes based on the simulation training animation, so as to facilitate the division of detection nodes for the simulation training animation, and introduces multiple detection nodes, so as to perform fixed-point detection on the detection nodes.

[0004] At the same time, the corresponding detection time points are matched based on multiple detection nodes, and the corresponding physiological parameter sets are matched according to the detection time points and the physiological parameter tables of the firefighters in the reentry video; the corresponding states are matched according to the physiological parameter sets, and the reentry effect parameters of the firefighters under the load weight are defined according to the states corresponding to each detection node; the corresponding action pictures are matched based on the detection time points, and the abnormal action parts are located according to the action pictures and theoretical action pictures, and the action effect parameters are defined based on the abnormal action parts, and the training prompts for the firefighters in the weight-bearing reentry training are determined based on the action effect parameters and the reentry effect parameters, so as to facilitate the overall control of the action effect parameters and the reentry effect parameters based on the training actions and the reentry training, thereby realizing the virtual application of firefighters in the weight-bearing reentry training and the dynamic interaction of actual training.

[0005] In order to solve the above technical problems, the embodiments of the present invention provide a firefighter load-bearing return simulation control method and system method, which are applied to the firefighter load-bearing return simulation control method and system scenario; The firefighter load-bearing return simulation control method and system method include: Collect the firefighters' weight-carrying and re-entry training information, and define the firefighters' weight-carrying and re-entry levels according to the firefighters' weight-carrying and re-entry training information; Predict the load weight based on the firefighter's load reentry level and the firefighter's current basic information; When the firefighter carries the weight, a video of the firefighter's return based on the weight is collected, and a corresponding simulation training animation is matched according to the return video, and a plurality of detection nodes are marked based on the simulation training animation; Matching corresponding detection time points based on multiple detection nodes, and matching corresponding physiological parameter sets according to the detection time points and the physiological parameter table of the firefighters in the return video; Match the corresponding state according to the physiological parameter set, and define the firefighter's return effect parameters under the load weight according to the state corresponding to each detection node; Match the corresponding action picture based on the detection time point, locate the abnormal part of the action according to the action picture and the theoretical action picture, define the action effect parameters based on the abnormal part of the action, and determine the training prompts for firefighters in weight-bearing reentry training based on the action effect parameters and the reentry effect parameters.

[0006] Optionally, the collecting of the firefighters' weight-carrying and re-entry training information and defining the firefighters' weight-carrying and re-entry levels according to the firefighters' weight-carrying and re-entry training information include: Collect the firefighter's name and firefighter's mark; Matching the corresponding training information space based on the firefighter's name, firefighter tag and fire training library; Determine the firefighter's weighted return training information according to the traversal of the training information space; Based on the firefighters' weighted return training information, define the firefighters' training results in each weighted return training; Construct a training performance set based on the training performance of firefighters in each weight-bearing return training; Define each load-carrying return feature according to the training performance set and the training time of the firefighter; The firefighter's load reentry level is defined based on the individual load reentry characteristics.

[0007] Optionally, the predicting of the load weight based on the firefighter's load return level and the firefighter's current basic information includes: Determine the load-carrying reentry level for firefighters; Associate the firefighter's load-carrying reentry level and the firefighter's current basic information; Build a corresponding human body model based on the current basic information of the firefighter; Training videos based on mannequins and firefighters’ weighted reentry levels simulating mannequins; Predict the weight based on the training video of the human model and the preset training effect.

[0008] Optionally, when the firefighter carries the weight, a video of the firefighter turning back based on the weight is collected, a corresponding simulation training animation is matched according to the turning back video, and a plurality of detection nodes are marked based on the simulation training animation, including: When the firefighter carries the weight, a return video of the firefighter based on the weight is collected; Freeze the return video; Marking a plurality of training nodes according to the return video, and matching corresponding action animations for the plurality of training nodes; Matching corresponding action animations based on multiple training nodes to corresponding simulation training animations; Stop-motion simulation training animation; Corresponding marking nodes are matched according to the simulated training animation and the training anomalies of the firefighters during the training process, and multiple detection nodes are defined according to the identification of multiple marking nodes.

[0009] Optionally, matching corresponding detection time points based on multiple detection nodes, and matching corresponding physiological parameter sets according to the detection time points and the physiological parameter table of the firefighters in the return video, includes: Freeze multiple detection nodes; Matching corresponding detection time points according to multiple detection nodes and return videos; stop-motion video; Detect the dynamics of firefighters based on the return video and output the corresponding physiological parameters; Define a physiological parameter table of the firefighter in the reentry video based on multiple physiological parameters and time in the reentry video; Associated physiological parameter tables and test time points; A corresponding relationship is established based on the physiological parameter table and the detection time point, and a corresponding set of physiological parameters is matched according to the physiological parameter table, the detection time point and the corresponding relationship.

[0010] Optionally, matching the corresponding state according to the physiological parameter set and defining the firefighter's return effect parameters under the load weight according to the state corresponding to each detection node include: Freeze the set of physiological parameters; Associating multiple sets of physiological parameters and matching corresponding physiological features; The characteristic variation is defined based on each physiological characteristic and the time sequence, and the corresponding state is matched according to the physiological change graph to define the state corresponding to each detection node.

[0011] Optionally, the matching of the corresponding state according to the physiological parameter set and defining the reentry effect parameters of the firefighter under the load weight according to the state corresponding to each detection node also include: Associate the states and weights corresponding to each detection node; Define the matching coefficient based on the state corresponding to each detection node and the corresponding load weight; Define training factors based on firefighter training scenarios; Defining a balance relationship based on training coefficients and matching coefficients; The return effect parameters of the firefighter under the load weight are defined according to the states corresponding to each detection node and the balance relationship.

[0012] Optionally, the matching of the corresponding action picture based on the detection time point, locating the abnormal action part according to the action picture and the theoretical action picture, defining the action effect parameters based on the abnormal action part, and determining the training prompts for the firefighters in the weighted return training based on the action effect parameters and the return effect parameters include: Freeze detection time point; Match the corresponding action scene based on the detection time point; Match the corresponding action path according to the action picture; Match theoretical action scenes based on action paths and action types.

[0013] Optionally, the matching of the corresponding action picture based on the detection time point, locating the abnormal action part according to the action picture and the theoretical action picture, defining the action effect parameters based on the abnormal action part, and determining the training prompts for the firefighters in the weighted return training based on the action effect parameters and the return effect parameters, further includes: Compare the theoretical action pictures with the action pictures; Locate the abnormal part of the action based on the comparison between the theoretical action picture and the action picture; Defining action effect parameters according to the traversal of the abnormal part; Associate action effect parameters and return effect parameters; The training prompts for firefighters in weighted reentry training are determined based on the action effect parameters, reentry effect parameters and the action prompt table.

[0014] In addition, an embodiment of the present invention further provides a firefighter load-bearing return simulation control method and system, the firefighter load-bearing return simulation control method and system comprising: A collection module, used to collect the weight-carrying reentry training information of firefighters, and define the weight-carrying reentry level of firefighters according to the weight-carrying reentry training information of firefighters; A prediction module, used to predict the load weight based on the load return level of the firefighter and the current basic information of the firefighter; A detection node module is used to collect a video of the firefighter's return based on the weight when the firefighter carries the weight, match the corresponding simulation training animation according to the return video, and mark multiple detection nodes based on the simulation training animation; A matching module, used to match corresponding detection time points based on multiple detection nodes, and to match corresponding physiological parameter sets according to the detection time points and the physiological parameter table of the firefighter in the return video; The reentry effect parameter module is used to match the corresponding state according to the physiological parameter set, and define the reentry effect parameters of the firefighter under the load weight according to the state corresponding to each detection node; The training prompt module is used to match the corresponding action picture based on the detection time point, locate the abnormal action part according to the action picture and the theoretical action picture, define the action effect parameters based on the abnormal action part, and determine the training prompts for firefighters in weighted reentry training based on the action effect parameters and the reentry effect parameters.

[0015] In an embodiment of the present invention, through the method in the embodiment of the present invention, the weight-carrying and re-entry training information of the firefighters is collected, and the weight-carrying and re-entry level of the firefighters is defined according to the weight-carrying and re-entry training information of the firefighters; the weight is predicted based on the weight-carrying and re-entry level of the firefighters and the current basic information of the firefighters; when the firefighters carry the weight, the re-entry video of the firefighters based on the weight is collected, and the corresponding simulation training animation is matched according to the re-entry video, and multiple detection nodes are marked based on the simulation training animation to facilitate the division of detection nodes for the simulation training animation, and multiple detection nodes are introduced to perform fixed-point detection on the detection nodes.

[0016] At the same time, the corresponding detection time points are matched based on multiple detection nodes, and the corresponding physiological parameter sets are matched according to the detection time points and the physiological parameter tables of the firefighters in the reentry video; the corresponding states are matched according to the physiological parameter sets, and the reentry effect parameters of the firefighters under the load weight are defined according to the states corresponding to each detection node; the corresponding action pictures are matched based on the detection time points, and the abnormal action parts are located according to the action pictures and theoretical action pictures, and the action effect parameters are defined based on the abnormal action parts, and the training prompts for the firefighters in the weight-bearing reentry training are determined based on the action effect parameters and the reentry effect parameters, so as to facilitate the overall control of the action effect parameters and the reentry effect parameters based on the training actions and the reentry training, thereby realizing the virtual application of firefighters in the weight-bearing reentry training and the dynamic interaction of actual training. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a firefighter load-carrying return simulation control method and a system method in an embodiment of the present invention; Figure 2 It is a flow chart of S11 in the firefighter load-carrying return simulation control method and the system method in the embodiment of the present invention; Figure 3 It is a flow chart of S12 in the firefighter load-carrying return simulation control method and the system method in the embodiment of the present invention; Figure 4 It is a flow chart of S13 in the firefighter load-carrying return simulation control method and the system method in the embodiment of the present invention; Figure 5 It is a flow chart of S14 in the firefighter load-carrying return simulation control method and the system method in the embodiment of the present invention; Figure 6 It is a flow chart of S15 in the firefighter load-carrying return simulation control method and the system method in the embodiment of the present invention; Figure 7 It is a flow chart of S16 in the firefighter load-carrying return simulation control method and the system method in the embodiment of the present invention; Figure 8 Schematic diagram of the structure of the firefighter load-carrying return simulation control method and system in an embodiment of the present invention; Fig. 9 The figure is a hardware diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0020] See also Figures 1 to 9, a firefighter load-carrying return simulation control method and system method, applied to a firefighter load-carrying return simulation control method and system scenario; the firefighter load-carrying return simulation control method and system method include: Step S11: collecting the firefighters' weighted return training information, and defining the firefighters' weighted return level according to the firefighters' weighted return training information; Step S12: predicting the load weight based on the firefighter's load return level and the firefighter's current basic information; Step S13: when the firefighter carries the weight, a video of the firefighter's return based on the weight is collected, a corresponding simulation training animation is matched according to the return video, and a plurality of detection nodes are marked based on the simulation training animation; Step S14: matching corresponding detection time points based on multiple detection nodes, and matching corresponding physiological parameter sets according to the detection time points and the physiological parameter table of the firefighters in the return video; Step S15: matching the corresponding state according to the physiological parameter set, and defining the reentry effect parameters of the firefighter under the load weight according to the state corresponding to each detection node; Step S16: Match the corresponding action picture based on the detection time point, locate the abnormal part of the action according to the action picture and the theoretical action picture, define the action effect parameters based on the abnormal part of the action, and determine the training prompts for firefighters in weight-bearing reversal training based on the action effect parameters and the reversal effect parameters.

[0021] In an embodiment of the present invention, through the method in the embodiment of the present invention, the weight-carrying and re-entry training information of the firefighters is collected, and the weight-carrying and re-entry level of the firefighters is defined according to the weight-carrying and re-entry training information of the firefighters; the weight is predicted based on the weight-carrying and re-entry level of the firefighters and the current basic information of the firefighters; when the firefighters carry the weight, the re-entry video of the firefighters based on the weight is collected, and the corresponding simulation training animation is matched according to the re-entry video, and multiple detection nodes are marked based on the simulation training animation to facilitate the division of detection nodes for the simulation training animation, and multiple detection nodes are introduced to perform fixed-point detection on the detection nodes.

[0022] At the same time, the corresponding detection time points are matched based on multiple detection nodes, and the corresponding physiological parameter sets are matched according to the detection time points and the physiological parameter tables of the firefighters in the reentry video; the corresponding states are matched according to the physiological parameter sets, and the reentry effect parameters of the firefighters under the load weight are defined according to the states corresponding to each detection node; the corresponding action pictures are matched based on the detection time points, and the abnormal action parts are located according to the action pictures and theoretical action pictures, and the action effect parameters are defined based on the abnormal action parts, and the training prompts for the firefighters in the weight-bearing reentry training are determined based on the action effect parameters and the reentry effect parameters, so as to facilitate the overall control of the action effect parameters and the reentry effect parameters based on the training actions and the reentry training, thereby realizing the virtual application of firefighters in the weight-bearing reentry training and the dynamic interaction of actual training.

[0023] In step S11, the weight-carrying reentry training information of the firefighters is collected, and the weight-carrying reentry level of the firefighters is defined according to the weight-carrying reentry training information of the firefighters; In the specific implementation process of the present invention, the specific steps may be: S111: Collect the firefighter's name and firefighter's mark; S112: matching a corresponding training information space based on the firefighter's name, firefighter tag, and fire training library; S113: Determine the firefighter's weighted return training information according to the traversal of the training information space; S114: defining the training results of the firefighters in each weight-bearing return training based on the firefighters' weight-bearing return training information; S115: constructing a training performance set based on the training performance of the firefighters in each weight-bearing return training; S116: defining each load-carrying reentry feature according to the training performance set and the training time of the firefighter; and defining the load-carrying reentry level of the firefighter based on each load-carrying reentry feature.

[0024] In an embodiment of the present application, the names and tags of firefighters are collected, and the corresponding training information space is matched based on the names, tags and fire training library of firefighters, so as to facilitate multi-dimensional control according to the names, tags and fire training library of firefighters, thereby matching the corresponding training information space and ensuring the accuracy of the training information space.

[0025] At this time, the firefighters' weighted re-entry training information is determined based on the traversal of the training information space; the firefighters' training results in each weighted re-entry training are defined based on the firefighters' weighted re-entry training information, so as to collect multiple training results.

[0026] Furthermore, a training performance set is constructed based on the training results of the firefighters in each weight-return training, so that each weight-return feature is defined according to the training performance set and the training time of the firefighters; the weight-return level of the firefighters is defined based on each weight-return feature, so as to accurately evaluate the weight-return level of the firefighters, and further process the weight-return level of the firefighters.

[0027] In step S12, the load weight is predicted based on the firefighter's load return level and the firefighter's current basic information; In the specific implementation process of the present invention, the specific steps may be: S121: The load-carrying reentry level for firefighters; S122: Associated firefighters' load-carrying reentry level and firefighters' current basic information; S123: constructing a corresponding human body model based on the current basic information of the firefighter; S124: Training video based on a mannequin and a firefighter's load-carrying reentry level simulation mannequin; S125: predicting the weight of the load according to the training video of the human body model and the preset training effect.

[0028] In an embodiment of the present application, the weight re-entry level of the firefighter is fixed, and the weight re-entry level of the firefighter is further processed, thereby associating the weight re-entry level of the firefighter and the current basic information of the firefighter, so as to facilitate further control of the weight re-entry level of the firefighter and the current basic information of the firefighter.

[0029] At this time, a corresponding human body model is constructed based on the current basic information of the firefighter, and a human body model is introduced to simulate the training video of the human body model based on the human body model and the firefighter's load-bearing return level, so as to predict the load weight according to the training video of the human body model and the preset training effect, so as to conduct an overall evaluation according to the training video of the human body model and the preset training effect, thereby predicting the load weight and achieving accurate evaluation of the load weight.

[0030] In step S13, when the firefighter carries the weight, a video of the firefighter's return based on the weight is collected, and a corresponding simulation training animation is matched according to the return video, and a plurality of detection nodes are marked based on the simulation training animation; In the specific implementation process of the present invention, the specific steps may be: S131: When the firefighter carries the weight, collecting a video of the firefighter returning based on the weight; S132: Freeze the return video; mark multiple training nodes according to the return video, and match corresponding action animations for the multiple training nodes; S133: Matching corresponding action animations based on multiple training nodes to corresponding simulation training animations; S134: Stop-motion simulation training animation; S135: Match corresponding marking nodes according to the simulated training animation and the training anomalies of the firefighters during the training process, and define multiple detection nodes according to the identification of multiple marking nodes.

[0031] In an embodiment of the present application, the firefighter's weighted reentry training information is collected, and the firefighter's weighted reentry level is defined based on the firefighter's weighted reentry training information; the weight is predicted based on the firefighter's weighted reentry level and the firefighter's current basic information; when the firefighter carries the weight, a video of the firefighter's reentry based on the weight is collected, and the corresponding simulation training animation is matched according to the reentry video, and multiple detection nodes are marked based on the simulation training animation to facilitate the division of detection nodes for the simulation training animation, and multiple detection nodes are introduced to perform fixed-point detection on the detection nodes.

[0032] At this time, when the firefighters carry the weight, a video of the firefighters' return based on the weight is collected, so as to record the firefighters' return training under the weight, so as to facilitate presentation through the video of the firefighters' return based on the weight, and at the same time, freeze the return video; mark multiple training nodes according to the return video, match corresponding action animations for the multiple training nodes, and control the action animations.

[0033] Therefore, based on the matching of multiple training nodes, the corresponding action animation matches the corresponding simulation training animation; freeze the simulation training animation; match the corresponding marking nodes according to the simulation training animation and the training anomalies of firefighters during the training process, and define multiple detection nodes based on the identification of multiple marking nodes, so as to facilitate the overall control of the simulation training animation and the training anomalies of firefighters during the training process, thereby associating the simulation training animation and the training anomalies of firefighters during the training process, so as to locate the marking nodes, and then define multiple detection nodes based on the identification of multiple marking nodes, thereby ensuring the rationality of multiple detection nodes.

[0034] S14: matching corresponding detection time points based on multiple detection nodes, and matching corresponding physiological parameter sets according to the detection time points and the physiological parameter table of the firefighter in the return video; In the specific implementation process of the present invention, the specific steps may be: S141: freeze multiple detection nodes; match corresponding detection time points according to the multiple detection nodes and the return video; S142: freeze the return video; detect the dynamic state of the firefighter according to the return video, and output the corresponding physiological parameters; S143: defining a physiological parameter table of the firefighter in the reentry video based on a plurality of physiological parameters and the time in the reentry video; S144: Associating the physiological parameter table with the detection time point; S145: Constructing a corresponding relationship based on the physiological parameter table and the detection time point, and matching a corresponding set of physiological parameters according to the physiological parameter table, the detection time point and the corresponding relationship.

[0035] In an embodiment of the present application, multiple detection nodes are frozen; corresponding detection time points are matched according to the multiple detection nodes and the return video, so that the detection time points are defined based on the multiple detection nodes and the return video, thereby introducing the detection time points, and performing targeted detection according to the detection time points.

[0036] At this time, freeze the reentry video; detect the firefighter's dynamics based on the reentry video and output the corresponding physiological parameters; define the physiological parameter table of the firefighter in the reentry video based on multiple physiological parameters and the time in the reentry video, so as to present the physiological changes of the firefighter in the reentry video based on the physiological parameter table, and record the physiological parameters of the firefighter in the reentry video in real time.

[0037] Furthermore, the physiological parameter table and the detection time points are associated; a corresponding relationship is established based on the physiological parameter table and the detection time points, and a corresponding physiological parameter set is matched according to the physiological parameter table, the detection time points and the corresponding relationship, so as to facilitate the association of the physiological parameter table, the detection time points and the corresponding relationship, and multi-dimensional control is performed based on the physiological parameter table, the detection time points and the corresponding relationship, so as to output the corresponding physiological parameter set, thereby realizing the collection of the physiological parameter set.

[0038] S15: matching the corresponding state according to the physiological parameter set, and defining the reentry effect parameter of the firefighter under the load weight according to the state corresponding to each detection node; In the specific implementation process of the present invention, the specific steps may be: S151: freeze-frame physiological parameter set; S152: Associating multiple physiological parameter sets and matching corresponding physiological characteristics; S153: defining a feature change amount based on each physiological feature and the time sequence, matching a corresponding state according to the physiological change graph, so as to define a state corresponding to each detection node; S154: Associating the states corresponding to the respective detection nodes with the corresponding load weights; S155: defining a matching coefficient based on the state corresponding to each detection node and the corresponding load weight; S156: Define training coefficients based on firefighter training scenarios; S157: defining a balance relationship based on the training coefficient and the matching coefficient; S158: Define the return effect parameters of the firefighter under the load weight according to the states corresponding to each detection node and the balance relationship.

[0039] In an embodiment of the present application, a set of physiological parameters is frozen; multiple sets of physiological parameters are associated, and the corresponding physiological characteristics are matched, and the physiological characteristics are dynamically controlled. At this time, the characteristic change amount is defined based on each physiological characteristic and the time sequence, and the corresponding state is matched according to the physiological change graph to define the state corresponding to each detection node, so as to facilitate the control of the state corresponding to each detection node.

[0040] At this time, the status corresponding to each detection node and the corresponding weight are associated; the matching coefficient is defined based on the status corresponding to each detection node and the corresponding weight; the training coefficient is defined according to the training scenario of the firefighters, thereby introducing the training coefficient and the matching coefficient.

[0041] Furthermore, a balance relationship is defined based on the training coefficient and the matching coefficient; the firefighter's reentry effect parameters under the load weight are defined according to the states corresponding to each detection node and the balance relationship, so as to match the corresponding balance relationship with the training coefficient and the matching coefficient, and further control the states corresponding to each detection node with respect to the balance relationship, so as to define the firefighter's reentry effect parameters under the load weight based on the states corresponding to each detection node and the balance relationship.

[0042] S16: matching the corresponding action picture based on the detection time point, locating the abnormal action part according to the action picture and the theoretical action picture, defining the action effect parameter based on the abnormal action part, and determining the training prompts for the firefighters in the weighted return training based on the action effect parameter and the return effect parameter; In the specific implementation process of the present invention, the specific steps may be: S161: freeze detection time point; S162: Matching corresponding action pictures based on the detection time point; S163: Matching a corresponding action path according to the action picture; S164: matching theoretical action pictures based on the action path and action type; S165: Compare the theoretical action picture and the action picture; S166: Locating abnormal motion parts according to the comparison between the theoretical motion picture and the motion picture; S167: defining action effect parameters according to the traversal of the abnormal part; S168: Associating action effect parameters and reentry effect parameters; determining training prompts for firefighters in weight-bearing reentry training according to the action effect parameters, reentry effect parameters and action prompt table.

[0043] In the embodiments of the present application, a detection time point is frozen; a corresponding action picture is matched based on the detection time point; a corresponding action path is matched according to the action picture; a theoretical action picture is matched based on the action path and the action type, and a theoretical action picture is introduced.

[0044] At this time, the theoretical action picture and the action picture are compared; the abnormal part of the action is located according to the comparison between the theoretical action picture and the action picture, and the abnormal part of the action is further controlled, so as to define the action effect parameters according to the traversal of the abnormal part, thereby associating the action effect parameters and the reentry effect parameters; the training prompts for the firefighters in the weighted reentry training are determined according to the action effect parameters, the reentry effect parameters and the action prompt table, so as to control the action effect parameters and the reentry effect parameters as a whole based on the training actions and reentry training, thereby realizing the virtual application of firefighters in the weighted reentry training and the dynamic interaction of actual training.

[0045] In an embodiment of the present invention, through the method in the embodiment of the present invention, the weight-carrying and re-entry training information of the firefighters is collected, and the weight-carrying and re-entry level of the firefighters is defined according to the weight-carrying and re-entry training information of the firefighters; the weight is predicted based on the weight-carrying and re-entry level of the firefighters and the current basic information of the firefighters; when the firefighters carry the weight, the re-entry video of the firefighters based on the weight is collected, and the corresponding simulation training animation is matched according to the re-entry video, and multiple detection nodes are marked based on the simulation training animation to facilitate the division of detection nodes for the simulation training animation, and multiple detection nodes are introduced to perform fixed-point detection on the detection nodes.

[0046] At the same time, the corresponding detection time points are matched based on multiple detection nodes, and the corresponding physiological parameter sets are matched according to the detection time points and the physiological parameter tables of the firefighters in the reentry video; the corresponding states are matched according to the physiological parameter sets, and the reentry effect parameters of the firefighters under the load weight are defined according to the states corresponding to each detection node; the corresponding action pictures are matched based on the detection time points, and the abnormal action parts are located according to the action pictures and theoretical action pictures, and the action effect parameters are defined based on the abnormal action parts, and the training prompts for the firefighters in the weight-bearing reentry training are determined based on the action effect parameters and the reentry effect parameters, so as to facilitate the overall control of the action effect parameters and the reentry effect parameters based on the training actions and the reentry training, thereby realizing the virtual application of firefighters in the weight-bearing reentry training and the dynamic interaction of actual training. Example

[0047] See also Figure 8 , Figure 8 It is a schematic diagram of the structural composition of a firefighter load-bearing return simulation control system in an embodiment of the present invention.

[0048] like Figure 8 As shown, a firefighter load-carrying return simulation control system, the firefighter load-carrying return simulation control system comprises: The collection module 21 is used to collect the weight-carrying reentry training information of the firefighters, and define the weight-carrying reentry level of the firefighters according to the weight-carrying reentry training information of the firefighters; A prediction module 22, for predicting the load weight based on the load return level of the firefighter and the current basic information of the firefighter; The detection node module 23 is used to collect a video of the firefighter's return based on the weight when the firefighter carries the weight, match the corresponding simulation training animation according to the return video, and mark multiple detection nodes based on the simulation training animation; A matching module 24, for matching corresponding detection time points based on multiple detection nodes, and matching corresponding physiological parameter sets according to the detection time points and the physiological parameter table of the firefighter in the return video; The reentry effect parameter module 25 is used to match the corresponding state according to the physiological parameter set, and define the reentry effect parameters of the firefighter under the load weight according to the state corresponding to each detection node; The training prompt module 26 is used to match the corresponding action picture based on the detection time point, locate the abnormal action part according to the action picture and the theoretical action picture, define the action effect parameters based on the abnormal action part, and determine the training prompts for firefighters in weight-bearing reentry training based on the action effect parameters and the reentry effect parameters. Example

[0049] See also Fig. 9 , refer to the following Fig. 9 The electronic device 40 according to this embodiment of the present invention will be described. Fig. 9 The electronic device 40 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0050] like Fig. 9 As shown, the electronic device 40 is in the form of a general 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 the storage unit 42 and the processing unit 41).

[0051] The storage unit stores program codes, which can be executed by the processing unit 41, so that the processing unit 41 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

[0052] The 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 .

[0053] The storage unit 42 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0054] Bus 43 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0055] The electronic device 40 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a trainee to interact with the electronic device 40, and / or any device that enables the electronic device 40 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 44. Furthermore, the electronic device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 45. Fig. 9 As shown, the network adapter 45 communicates with other modules of the electronic device 40 via the bus 43. It should be understood that although Fig. 9 Not shown, other hardware and / or software modules may be used in conjunction with the 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.

[0056] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0057] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. In addition, it stores computer program instructions, and when the computer program instructions are executed by a computer, the computer executes the above method.

[0058] In addition, the above provides a detailed introduction to the firefighter load-bearing return simulation control method and system provided in the embodiments of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A firefighter load-bearing return simulation control method and system method, characterized in that: Applied to a firefighter's weighted reversal simulation control method and system scenario; The firefighter's weighted reversal simulation control method and system method include: collecting the firefighter's weighted reversal training information, and defining the firefighter's weighted reversal level according to the firefighter's weighted reversal training information; predicting the weight based on the firefighter's weighted reversal level and the firefighter's current basic information; when the firefighter carries the weight, collecting the firefighter's reversal video based on the weight, and matching the corresponding simulation training animation according to the reversal video, and marking multiple detection nodes based on the simulation training animation; matching the corresponding detection time points based on multiple detection nodes, and matching the corresponding physiological parameter sets according to the detection time points and the firefighter's physiological parameter table in the reversal video; matching the corresponding states according to the physiological parameter sets, and defining the firefighter's reversal effect parameters under the weight according to the states corresponding to each detection node; matching the corresponding action picture based on the detection time point, and locating the abnormal action part according to the action picture and the theoretical action picture, and defining the action effect parameters based on the abnormal action part, and determining the training prompts for the firefighter in the weighted reversal training based on the action effect parameters and the reversal effect parameters.

2. The firefighter load-bearing return simulation control method and system method according to claim 1, characterized in that: The method of collecting the firefighter's weighted reentry training information and defining the firefighter's weighted reentry level according to the firefighter's weighted reentry training information includes: collecting the firefighter's name and firefighter tag; matching the corresponding training information space based on the firefighter's name, firefighter tag and fire training library; determining the firefighter's weighted reentry training information according to the traversal of the training information space; defining the firefighter's training results in each weighted reentry training based on the firefighter's weighted reentry training information; constructing a training result set based on the firefighter's training results in each weighted reentry training; defining each weighted reentry feature according to the training result set and the training time of the firefighter; and defining the firefighter's weighted reentry level based on each weighted reentry feature.

3. The firefighter load-bearing return simulation control method and system method according to claim 2, characterized in that: The method of predicting the load weight based on the firefighter's load reentry level and the firefighter's current basic information includes: freezing the firefighter's load reentry level; associating the firefighter's load reentry level and the firefighter's current basic information; constructing a corresponding human body model based on the firefighter's current basic information; simulating a training video of the human body model based on the human body model and the firefighter's load reentry level; and predicting the load weight based on the training video of the human body model and a preset training effect.

4. The firefighter load-carrying return simulation control method and system method according to claim 3, characterized in that: The method comprises: when the firefighter carries the weight, collecting a video of the firefighter's return based on the weight, matching a corresponding simulation training animation based on the return video, and marking a plurality of detection nodes based on the simulation training animation, including: when the firefighter carries the weight, collecting a video of the firefighter's return based on the weight; freezing the return video; marking a plurality of training nodes based on the return video, and matching corresponding action animations for the plurality of training nodes; matching corresponding action animations based on the plurality of training nodes with corresponding simulation training animations; freezing the simulation training animation; matching corresponding marked nodes according to the simulation training animation and training anomalies of the firefighter during the training process, and defining a plurality of detection nodes according to the identification of the plurality of marked nodes.

5. The firefighter load-carrying return simulation control method and system method according to claim 4, characterized in that: The method of matching corresponding detection time points based on multiple detection nodes, and matching corresponding physiological parameter sets according to the detection time points and the physiological parameter table of the firefighter in the return video, includes: freezing multiple detection nodes; matching corresponding detection time points according to multiple detection nodes and the return video; freezing the return video; dynamically detecting the firefighter according to the return video, and outputting corresponding physiological parameters; defining the physiological parameter table of the firefighter in the return video based on multiple physiological parameters and the time in the return video; associating the physiological parameter table with the detection time points; establishing a corresponding relationship based on the physiological parameter table and the detection time points, and matching the corresponding physiological parameter set according to the physiological parameter table, the detection time points and the corresponding relationship.

6. The firefighter load-carrying return simulation control method and system method according to claim 5, characterized in that: The method matches the corresponding state according to the physiological parameter set, and defines the firefighter's reentry effect parameters under the load weight according to the state corresponding to each detection node, including: freezing the physiological parameter set; associating multiple physiological parameter sets and matching the corresponding physiological characteristics; defining the characteristic change amount based on each physiological characteristic and the time sequence, matching the corresponding state according to the physiological change graph, so as to define the state corresponding to each detection node.

7. The firefighter load-carrying return simulation control method and system method according to claim 6, characterized in that: The method of matching the corresponding state according to the physiological parameter set and defining the firefighter's reentry effect parameters under the load weight according to the state corresponding to each detection node also includes: associating the state corresponding to each detection node and the corresponding load weight; defining a matching coefficient based on the state corresponding to each detection node and the corresponding load weight; defining a training coefficient according to the firefighter's training scenario; defining a balance relationship based on the training coefficient and the matching coefficient; and defining the firefighter's reentry effect parameters under the load weight according to the state corresponding to each detection node and the balance relationship.

8. The firefighter load-carrying return simulation control method and system method according to claim 7, characterized in that: The method matches the corresponding action picture based on the detection time point, locates the abnormal action part according to the action picture and the theoretical action picture, defines the action effect parameters based on the abnormal action part, and determines the training prompts for the firefighters in the weighted return training based on the action effect parameters and the reentry effect parameters, including: freezing the detection time point; matching the corresponding action picture based on the detection time point; matching the corresponding action path according to the action picture; matching the theoretical action picture based on the action path and the action type.

9. The firefighter load-carrying return simulation control method and system method according to claim 8, characterized in that: The method of matching the corresponding action picture based on the detection time point, locating the abnormal action part according to the action picture and the theoretical action picture, defining the action effect parameters based on the abnormal action part, and determining the training prompts for the firefighters in the weighted reversal training based on the action effect parameters and the reversal effect parameters, also includes: comparing the theoretical action picture with the action picture; locating the abnormal action part according to the comparison between the theoretical action picture and the action picture; defining the action effect parameters according to the traversal of the abnormal part; associating the action effect parameters and the reversal effect parameters; and determining the training prompts for the firefighters in the weighted reversal training according to the action effect parameters, the reversal effect parameters and the action prompt table.

10. A firefighter load-bearing return simulation control method and system, characterized in that: The firefighter load-return simulation control method and system are applied to the firefighter load-return simulation control method and system method as described in any one of claims 1 to 9, and the firefighter load-return simulation control method and system include: an acquisition module, used to acquire the firefighter's load-return training information, and define the firefighter's load-return level according to the firefighter's load-return training information; a prediction module, used to predict the load weight based on the firefighter's load-return level and the firefighter's current basic information; a detection node module, used to acquire the firefighter's return video based on the load weight when the firefighter carries the load weight, and match the corresponding simulation training animation according to the return video, based on the simulation training animation mark A plurality of detection nodes are recorded; a matching module is used to match corresponding detection time points based on the plurality of detection nodes, and match corresponding physiological parameter sets according to the detection time points and the physiological parameter table of the firefighter in the reentry video; a reentry effect parameter module is used to match corresponding states according to the physiological parameter set, and define the reentry effect parameters of the firefighter under the load weight according to the states corresponding to each detection node; a training prompt module is used to match corresponding action pictures based on the detection time points, and locate abnormal action parts according to the action pictures and theoretical action pictures, and define action effect parameters based on the abnormal action parts, and determine the training prompts for the firefighter in the weight-bearing reentry training based on the action effect parameters and the reentry effect parameters.

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