CN116702480A - Comprehensive efficiency evaluation method for load boosting equipment of emergency rescue personnel - Google Patents
Comprehensive efficiency evaluation method for load boosting equipment of emergency rescue personnel Download PDFInfo
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Abstract
The application discloses a comprehensive efficiency evaluation method for load boosting equipment of emergency rescue workers. The method specifically comprises the following steps: collecting various data of personnel and equipment when the rescue personnel wears/does not wear the load boosting equipment by adopting an attitude measurement sensor, a plantar pressure measurement sensor, a tension pressure sensor, a breathing gas analyzer, a myoelectric sensor, a strain gauge, a heart rate sensor and the like; establishing each performance characteristic parameter evaluation model, and calculating each performance characteristic parameter in a period of time; and (3) carrying out comprehensive weight calculation on each parameter index by combining an analytic hierarchy process and an expert scoring process, evaluating the efficiency of the load assisting equipment worn by a rescuer in the process of loading movement based on a comprehensive efficiency evaluation model, visualizing equipment efficiency data by adopting a fan blade shape graph, and providing a reliable judgment basis for further optimizing the assisting structure and the comfort of the assisting equipment. The application solves the technical problem that the efficiency evaluation method of the load power assisting equipment of the rescue workers is inaccurate.
Description
Technical Field
The application relates to the field of load assisting equipment for rescue workers, in particular to a method for testing the comprehensive efficiency of the load assisting equipment for rescue workers.
Background
Disaster rescue sites often have complex topography and topography, particularly underground spaces such as underground tunnels, mine holes and the like, large rescue vehicles are difficult to reach, and rescue equipment often needs to be carried by manpower to enter. The load power assisting device is used as an auxiliary tool for improving the fight efficiency of rescue workers, and has important significance on improving the rescue efficiency of the rescue workers in the comprehensive efficiency.
The prior art provides a detection method for the assisting efficiency of the load-bearing exoskeleton. According to the method, the moment provided by the hip, knee and ankle joints of a human body under the condition of wearing the exoskeleton for a certain time is calculated, and under the condition that the moment provided by the relevant main power-assisted joints of the human body is equal when the exoskeleton is not worn, the power-assisted efficiency is assessed by measuring and calculating the weight carried by a tester under the two conditions. However, the influence of the exoskeleton on the joint moment of the human body is not considered, the power-assisted efficiency evaluation index is single, and the result accuracy is not high.
The prior art also provides a detection method for the boosting efficiency of the upper limb exoskeleton, wherein a pulling pressure sensor is arranged on a hydraulic actuating cylinder of a shoulder joint and an elbow joint of the upper limb exoskeleton, an electromyographic signal sensor is attached to the shoulder joint and the knee joint of the upper limb of a wearer, the stress of the actuating cylinder measured by the pulling pressure sensor and the output force of the wearer measured by the electromyographic signal sensor are respectively different, and a comparison algorithm is carried out between the stress and the weight of the weight carried by the upper limb exoskeleton; and then comprehensively evaluating the power-assisted efficiency of the upper limb exoskeleton by combining the real-time heartbeat of the wearer when the upper limb exoskeleton is worn. However, the method adopts an electromyographic signal sensor to measure the output force of the wearer, and has the problem of insufficient precision.
The prior art also provides a load bearing efficiency detection method for the power-assisted exoskeleton, which is characterized in that pin shaft type load sensors are arranged at the hip joint and the ankle joint of the exoskeleton, the vertical load at the joint is measured, the difference between the test data of the two sensors is compared with the dead weight of the lower limb structure of the exoskeleton, whether the test data are accurate is judged, and the test data and the load bearing data are calculated, so that the load bearing efficiency of the exoskeleton is calculated. The method can realize the estimation of the bearing efficiency only by modifying the exoskeleton structure, and has low universality.
The power-assisted efficiency of the load power-assisted equipment for the rescue workers on the joints of the human body is an important index for influencing the fight efficiency of the rescue workers, but the analysis of the local acting force of the equipment on the human body, the influence of the physical energy parameters of the human body and the like are also important for the fight efficiency. At present, a detection system and an evaluation method for comprehensively analyzing the joint force transmission, the physical energy consumption, the man-machine contact force, the equipment bearing force and the like of a human body and evaluating the effect of load power assisting equipment on improving the combat effectiveness of rescue workers are needed. And a reference basis is provided for load power assisting equipment performance evaluation and rescue worker combat effectiveness improvement strategies.
Disclosure of Invention
In order to solve the problems, the embodiment of the application provides a comprehensive efficiency evaluation method for rescue personnel load power assisting equipment, which is used for at least solving the problem of inaccurate evaluation of the combat efficiency improvement effect of the rescue personnel load power assisting equipment.
To achieve the above object, according to an aspect of the embodiments of the present application, there is provided a method for evaluating efficiency of load assist equipment for rescue workers, the method comprising: various sensors are adopted to collect various performance characteristic parameters of the human body wearing/non-wearing load assisting equipment, different loads and different motion states, index parameter characteristics such as human body joint moment, plantar pressure, muscle fatigue, oxygen consumption, heart rate change, equipment deformation and the like are analyzed within a certain time, a single performance characteristic evaluation result is given out based on an established various index evaluation model, comprehensive weights of various performance indexes are calculated by adopting a method combining a analytic hierarchy process and an expert scoring process, the performance of the load assisting equipment of a rescuer is evaluated by utilizing the comprehensive evaluation model based on the calculated comprehensive weights, and equipment performance data is visualized by adopting a fan blade graph.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a method for emergency personnel load assist equipment data acquisition and performance assessment in accordance with an embodiment of the present application;
FIG. 2 is a flow chart of an emergency personnel load assist device comprehensive performance evaluation in accordance with an embodiment of the present application;
FIG. 3 is a simplified eight-bar model schematic of a weight-bearing rescuer according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an index system hierarchical model for comprehensive performance assessment of emergency personnel load assist equipment according to an embodiment of the application;
FIG. 5 is a flowchart of an evaluation index weight determination according to an embodiment of the present application;
FIG. 6 is an example of scoring and normalizing performance data of an assessment index according to an embodiment of the present application;
fig. 7 is a fan blade diagram for visualizing the overall performance of a rescue crew setup according to an embodiment of the application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present application, there is provided a method for evaluating comprehensive performance of a load boosting device for a rescue worker, as shown in fig. 1 and 2, the method including: step S102, the specific implementation steps are described: various sensors are utilized to collect various data in the loading movement process of loading and non-loading auxiliary equipment worn by a rescuer, and various efficiency data in different states are calculated;
in one exemplary embodiment, a plurality of sensors are used to collect various data during the weight exercise of the rescuer and calculate human performance data under different weights, including: collecting various performance characteristic parameters of rescue workers by using a multi-pose measuring sensor, a plantar pressure measuring sensor, a tension pressure sensor, a breathing gas analyzer, myoelectricity sensing, strain gauges and the like; calculating various performance data in a period of time based on the established performance characteristic parameter evaluation model;
in an exemplary embodiment, using the posture measurement sensor, estimating a human body joint angle and estimating joint moment information at a hip joint, a knee joint, and an ankle joint of a human body, includes: placing inertial sensors on the left and right feet, the left and right lower legs, the left and right thighs and the trunk of the human body, collecting the postures of all links under different loads of the load assisting equipment worn/not worn by the human body, and estimating the angle information of the left and right ankle joints, the knee joints and the hip joints of the human body based on the posture information of all links; combining the pulling pressure data of the shoulder and the back of the human body, establishing a human body inverse kinematics model, and estimating joint moment information of left and right ankle joints, knee joints and hip joints of the human body in the motion process;
in one exemplary embodiment, estimating human joint angle information based on link pose information includes: based on the posture data of the single link, calculating the bending and stretching angle, the internal rotation and external rotation angle and the internal contraction and external expansion angle of each link by combining the motion freedom degree of each link and the layout azimuth of the sensor, and establishing a human joint angle information matrix;
in one exemplary embodiment, estimating human joint moment information based on a human joint angle information matrix includes: the human body is regarded as a seven-link model, an eight-link model of the human body in a loading state is established based on back loading, as shown in fig. 3, a reverse kinematics model of the human body in the standing, squatting and walking processes is established under the loading condition by combining back tension and pressure and mass statistics standard parameters of the human body, moment information of each joint in the motion process of the human body is solved, and a joint moment information matrix is obtained;
in an exemplary embodiment, with the pull pressure sensor, collecting pull pressure data of back load to shoulder and back of a human body includes: the method comprises the steps that a tension pressure sensor is arranged on a load carrying belt, the back tension of a load on the shoulder of a human body in the movement process is collected, 4 pressure sensors are arranged at the contact part of the load carrying knapsack and the human body, and the pressure data of the load carrying knapsack on the back of the human body in the movement process is collected;
in one exemplary embodiment, the sole pressure insole is used for collecting sole pressure data during exercise, and the method comprises the following steps: dynamic collection of plantar pressure data is carried out by adopting a flexible pressure insole, plantar pressure is divided into three parts of areas which are respectively a forefoot part, a midfoot part and a heel, and pressure data and total pressure sum of the three parts are collected;
in an exemplary embodiment, the surface myoelectric sensor is used for collecting myoelectric signal data of key muscles in the movement process of a rescuer, and the myoelectric signal data comprises: the myoelectric signals of the shoulders, the waist, the thighs and the lower legs of the human body are collected by adopting the myoelectric sensor, and the time domain characteristics of the myoelectric signals are calculated according to the myoelectric signals;
in an exemplary embodiment, the flexible film pressure sensor is used for collecting pressure data of man-machine contact points in a motion process, and the method comprises the following steps: the flexible pressure sensor collects dynamic pressure data of a contact part between the load boosting equipment and a human body, and mainly comprises: pressure data for a left acromion, a right acromion, a left hip contact point, a right hip contact point, a left thigh contact point, a right shank contact point, and a left shank contact point;
in one exemplary embodiment, the respiratory gas analyzer is used for collecting the proportion data of the components of the human body respiratory gas during the movement process, and the method comprises the following steps: collecting the content of oxygen and carbon dioxide of incoming and outgoing calls in the human body movement process by adopting a respiratory gas analyzer, and calculating the total energy consumption of the same load power assisting equipment wearer under the same distance of different load equivalent movement forms;
in an exemplary embodiment, the strain gauge is used for acquiring deformation data of a key bearing part of the load assisting equipment in the process of loading exercise, and the method comprises the following steps: arranging strain gauges on key load bearing parts of load boosting, in particular on thigh bearing rods and shank bearing rods of lower limbs, collecting strain data of rod pieces under different loads, estimating the pressure bearing data of the rod pieces, and analyzing the partial pressure condition of load boosting equipment in the movement process;
in an exemplary embodiment, the health monitoring wristband is used to collect heart rate data during exercise of weight bearing on a person, comprising: the heart rate sensor adopts a heart rate monitoring bracelet to measure heart rate data on the inner side of the wrist of the human body;
step S104, the specific implementation steps are as follows: establishing each performance characteristic evaluation model, and calculating each performance characteristic evaluation value
In one exemplary embodiment, based on the collected data and the established performance feature assessment model, performance feature values over a period of time are calculated; comprising the following steps: joint moment load reduction efficacy evaluation, plantar pressure load reduction efficacy evaluation, shoulder back tension load reduction efficacy evaluation, human breathing energy consumption reduction evaluation, human contact point pressure load reduction efficacy evaluation, human muscle fatigue load reduction evaluation, equipment key bearing part power transmission efficacy evaluation and heart rate-based human load reduction efficacy evaluation;
in one exemplary embodiment, a joint moment load reduction efficacy evaluation model is established, and the joint moment load reduction effect under different load conditions after load assisting equipment is worn is evaluated;
the joint torque load reduction performance evaluation, the energy consumption load reduction evaluation, the muscle fatigue load reduction evaluation, and the like, which are involved in the present embodiment, can be calculated according to the following formulas:
in the above formula: e (E) 01 The parameter value when the load assisting equipment is not worn for loading is indicated; e (E) 11 The parameter value when the load assisting equipment is used for loading; e (E) 00 The parameter value when no load is applied; taking the hip joint moment load-reducing efficiency when the back 40kg is standing as an example, E 01 Mean value of hip joint moment estimated values in a period of time t when 40kg of back load of the non-worn load assisting equipment stands; e (E) 11 Mean value of hip joint moment estimated values in a certain time t when the back of the wearing load assisting device bears 40kg of weight; e (E) 00 Mean value of hip joint moment estimated values in a certain time t when the back of the non-worn load assisting equipment bears 0kg to stand;
plantar pressure relief efficacy data processing
The sole pressure data acquisition module is a flexible sole pressure insole or a metal pressure sensor, and is used for dynamically acquiring sole pressure data under different movement states by wearing load assisting equipment/not wearing load assisting equipment and different loads.
The weight reduction efficiency refers to the degree of the supporting force shared by the load assisting equipment when the load assisting equipment is worn. The pressure sensor is arranged on the sole of the foot to measure and calculate the pressure change of the sole of the load after the human body directly loads and wears the load assisting equipment. Weight reduction efficiency can be expressed as
Wherein f 1 For loading a certain weight, the total pressure value of the sole when the load boosting equipment is not worn; f (f) 2 The total pressure of the sole when the load boosting equipment is worn for loading the same weight; f (f) 0 For no load, the total pressure of the sole is not applied with load assisting equipment.
Shoulder-back tension-pressure minus-effect data processing
The method comprises the steps that a tension pressure sensor is used for collecting tension data of back load on the shoulder and back of a human body, the tension pressure sensor is arranged on a load carrying belt, the back tension of the load carrying belt on the shoulder of the human body in the movement process is collected, 4 pressure sensors are arranged at the contact part of the load carrying backpack and the human body, and the pressure data of the load carrying backpack on the back of the human body in the movement process is collected;
the pulling pressure data of the back of the shoulder in a period of time is processed, and the integral value of the total pulling pressure value of 4 points in a period of time t (t=nt) is calculated.
Wherein T is a gait cycle time;
the load relief effect of the wearing of the load assisting equipment on the shoulder and back of the human body is measured by comparing the pressure data before and after the load assisting equipment is worn.
Wherein f 1 For loading a certain weight, the shoulder and back pressure values are obtained when the load assisting equipment is not worn; f (f) 2 The shoulder and back pressure values when the load boosting equipment is worn for loading the same weight; f (f) 0 Shoulder-back pressure values without load, without wearing load boosting equipment.
Human respiratory energy consumption reduction efficacy evaluation data processing
The breath gas analyzer collects the composition proportion data of the exhaled gas in the human body movement process; the respiration equipment was used to collect the oxygen consumption rate VO2 (ml/min) per unit time and the carbon dioxide production rate VCO2 (ml/min) per unit minute during the experiment. Oxygen consumption data of the non-load standing for 10min are collected before each experimental task, and the net metabolic rate = load total metabolic rate-non-load standing total metabolic rate is calculated according to the following formula:
M net metabolic rate ＝16.58VO 2 +4.51VCO 2 (5)
Through the metabolism data around the load helping hand equipment is dressed in the contrast, the dress of measuring load helping hand equipment is to human energy consumption reduction effect, and respiratory energy consumption reduction efficiency calculation model is:
wherein M is 1 To load a certain weight, the net metabolic rate when the load boosting equipment is not worn; m is M 2 Net metabolic rate when wearing load boosting equipment for loading the same weight; m is M 0 Net metabolic rate without load, without load boosting equipment.
Human-machine contact point pressure relief efficacy evaluation data processing
The flexible pressure sensor collects pressure data of a contact part of the load boosting equipment and a human body; when a certain part of the human body is pressed by a large pressure for a long time, the muscle is easy to be pressed and damaged. Taking the pressure bearing average value of each cycle of each pressure bearing part of the body as a measurement index;
the pressure value is an important index for measuring the load reducing effect of the contact pressure, and in general, the smaller the pressure value is, the better the load reducing effect of the contact pressure is; and processing the contact point pressure data in a period of time, and calculating an integral value of the pressure mean value of each point in the period of time t (t=nt).
The load relief effect of the wearing of the load assisting equipment on the human body is measured by comparing the pressure data before and after the wearing of the load assisting equipment.
Wherein f 1 The pressure value when the load power assisting equipment is worn for loading a certain weight; f (f) 2 The pressure value when the load boosting equipment is worn is equal to the weight of the load.
Human muscle fatigue load-reducing evaluation data processing
During the movement of the human muscle, an electromyographic signal (EMG) is generated, and thus, the time-frequency domain characteristics are extracted through the surface electromyographic test. Research shows that the root mean square amplitude (RMS) in the time domain represents the change of the amplitude of the electromyographic signals in the process of intense exercise of a human body, and the time and the fatigue degree of muscle fatigue can be accurately reflected.
Wherein x is i For the electromyographic signal sampling value, N is the segment length of the electromyographic signal segment.
Wherein f 1 For loading a certain weight, the shoulder and back pressure values are obtained when the load assisting equipment is not worn; f (f) 2 The shoulder and back pressure values when the load boosting equipment is worn for loading the same weight; f (f) 0 Shoulder-back pressure values without load, without wearing load boosting equipment.
Device key bearing part force conduction efficiency evaluation data processing
Strain gauges are adopted to collect deformation data of key bearing parts of load assisting equipment in the load movement process, strain gauges are attached to a left thigh bearing rod, a left calf bearing rod, a right thigh bearing rod and a right calf bearing rod of the load assisting equipment, strain data of rods of the load assisting equipment before and after being worn under different loads are collected, deformation conditions of the bearing rods of the load assisting equipment before and after being worn are compared, a partial pressure effect evaluation model of the load assisting equipment bearing rods is established based on the deformation data, and partial pressure conditions of the load assisting equipment in the movement process are analyzed;
human body burden reduction efficacy evaluation data processing based on heart rate
When the load assisting equipment is worn to complete related actions, huge energy is consumed, and the heart rate of a human body can be changed correspondingly. Therefore, the movement state of a human body can be measured through the change of the heart rate when the load assisting equipment is not worn and the load is worn, and the assisting performance of the load assisting equipment is reflected.
Wherein H is 1 The average heart rate is the average heart rate when the load assisting equipment is not worn for a certain time; h 2 For the same time, the same weight is loaded, and the average heart rate is the average heart rate when the load assisting equipment is worn to walk.
Body movement energy consumption data processing
The energy consumption of body movement is mainly related to the movement condition of human body, and has a large variation range along with the movement condition of human body. The energy consumption parameters of the human body when performing tasks are calculated, and the calculation of BMR and heart rate values detected during exercise are related.
Basal Metabolism (BMR) refers to the amount of energy metabolism of the human body when it is awake and extremely calm, and is not affected by muscle activity, ambient temperature, food, and mental stress. The age of the human body affects the BMR, which can be calculated by the following mathematical formula for a person in the age range 18-30 years:
E bmr1 ＝(63×W)+2896 (12)
E bmr2 ＝(62×W)+2036 (13)
wherein E is bmr1 Is a formula for calculating BMR of male, E bmr2 Is a formula for calculating female BMR, the unit isW is the weight value of the human body, and the unit is kg.
Physical exercise energy consumption (AEE) refers to the energy consumption of the human body in daily life, which is determined by the type and duration of physical activity, and is affected by the physical activity of the human body. There are many methods for detecting AEE in human body, mainly the following methods: questionnaires, indirect calorie or respiratory calorimeter measurements, mechanical or electronic exercise sensor monitoring, and heart rate monitoring. Among them, the indirect calorie meter has the highest measurement accuracy, but has higher cost and larger daily disturbance to the monitored person. The heart rate monitoring method is simple in calculation, low in cost and easy to implement.
And when the heart rate monitoring method is used for calculating the movement energy consumption of the human body when the human body wears the load power assisting equipment to execute tasks. And (3) calculating AEE by measuring BMR, exercise time and heart rate value during exercise of the monitored person. The specific mathematical expression is:
RMR 1 ＝0.072×HR-5.608 (14)
RMR 2 ＝0.065×HR-4.932 (15)
AEE＝∫ 0 T (RMR+1.2)×(BMR+60)dt (16)
where AEE is the energy consumption of body movement in Kcal. The above equation is a RMR regression equation calculated by the expert, and shows the relationship between RMR and Heart Rate (HR). Wherein RMR is the relative metabolic quantity of energy metabolism, the magnitude of which reflects the amount of net energy consumption during exercise, RMR 1 Represents the RMR, RMR of men 2 Indicated is RMR for females. T represents the total time of human movement.
Step S106, the specific implementation steps describe: determining the weight of each index, and calculating a comprehensive efficiency evaluation value based on the comprehensive evaluation model;
as shown in fig. 4, which is a schematic diagram of a hierarchical structure model, the hierarchical structure model is a data structure model, and is used as an input of the hierarchical analysis method. The evaluation indexes of the load boosting equipment performance of the combing rescue workers can be divided into three types of indexes of decompression boosting efficiency, energy consumption reduction efficiency and applicability evaluation shown in fig. 4, wherein each type of index also comprises one or more factors influencing the load boosting equipment performance, such as 3 factors including joint angle, joint moment and plantar pressure under the decompression boosting efficiency type index.
Processing the hierarchical structure model layer by layer according to the flow shown in fig. 4 to obtain the relative weight of each index element in the current level and checking the validity of the relative weight; and if the test is not passed, adjusting the processing data of the current level until the test is passed.
In this implementation, for each type of index set, a decision matrix is first constructed for each index element in the index set. For example, in fig. 4, a two-by-two judgment matrix is constructed for the three large index categories of the second layer, and then a two-by-two judgment matrix is constructed for the index elements under the third layer, so that two-by-two judgment matrices for the key index elements under the various index sets are constructed in this order.
In this implementation, for each index set, a pairwise decision matrix between each index element in the set is first constructed. Taking fig. 4 as an example, for three index categories of the second layer, it is necessary to construct a pairwise judgment matrix between them. For index elements under the third layer, it is also necessary to construct a pairwise judgment matrix between them. And similarly, constructing a pairwise judgment matrix for key index elements at the next stage of each index set. By means of the step-by-step construction, the relative weight of each index element in the index set to which the index element belongs can be accurately estimated. When constructing the judgment matrix, constructing the pairwise importance judgment matrix with the scale of 1, 0 and-1, as shown in a formula (17):
wherein C is mj For a first index element f in a certain index set m And a second index element f j Comparison of contributions to the overall efficacy assessment, m and j being the first index element f m And a second index element f j N represents the total number of the index set, e.g., the index set includes 3 index elements in fig. 4, then n=3.
For the scale value in the judgment matrix, according to the evaluation result assignment standard shown in the formula (17), manual judgment by expert decision or automatic judgment by a certain preset judgment rule can be adopted, and a certain type of index pairwise importance judgment matrix is constructed according to the evaluation result.
For example, a judgment matrix is constructed for index elements such as "pressure reduction assistance efficiency" lower index "joint moment relief ratio, plantar pressure relief ratio, shoulder back pulling pressure relief ratio" in fig. 4, and if the importance ranking is high to low: the joint moment relief ratio, plantar pressure relief ratio, and dorsum shoulder pull pressure relief ratio are shown in table 1 below, and the judgment matrix is filled in 1 in the second row and in 1 in the second column, whereas in 1 in the first row, since the importance of the joint moment relief ratio is better than that of plantar pressure relief ratio. And judging the two factors in such a way to construct a judging matrix consisting of 1, 0 and-1.
Table 1 load reducing efficiency factor pairwise judgment matrix
According to the index weight determination flow shown in fig. 5, if a judgment matrix has been constructed for a certain factor set, the relative weights of the factors in the factor set can be calculated by using the matrix to perform hierarchical single ranking. In one implementation manner, the specific calculation method of the relative weight is that a feature vector corresponding to the maximum feature value of the judgment matrix is calculated, and the feature vector is normalized, so that the relative weight of each factor in the factor set is obtained.
In the implementation mode, hierarchical single-order relative weight calculation is needed before comprehensive weight calculation, and the relative weight calculation is to normalize the feature vector by calculating the feature vector corresponding to the maximum feature value of the pairwise importance judgment matrix, so that the hierarchical single-order relative weight is obtained.
As shown in fig. 5, after the calculation of the relative weights of the hierarchical single ranks is completed, the consistency of the hierarchical single ranks needs to be checked, and if the consistency check is passed, it means that all the values and calculation processes for each factor set conform to the comparison logic and the weight calculation result can be accepted. If the consistency test is not passed, the judgment matrix, the relative weight calculation and the consistency test need to be reconstructed.
In this implementation, each judgment matrix correspondingly calculates a consistency index, and the consistency index can be obtained according to the formula (18).
Wherein n is the order of the judgment matrix, lambda max Judging the maximum eigenvalue of the matrix; ci=0 with complete consistency; the smaller the CI, the better the consistency.
Then, the consistency ratio is calculated according to the following formula (19):
wherein RI is a randomness index, referring to table 2 as follows:
table 2 RI randomness index
| Matrix order n | Randomness index RI | Matrix order n | Randomness index RI |
| 1 | 0 | 6 | 1.24 |
| 2 | 0 | 7 | 1.32 |
| 3 | 0.58 | 8 | 1.41 |
| 4 | 0.9 | 9 | 1.45 |
| 5 | 1.12 | 10 | 1.48 |
The RI value can be obtained by searching in table 2 according to the order of the judgment matrix, and CR can be calculated according to formula (19). When CR <0.1, the consistency of the judgment matrix is considered to pass the inspection, otherwise, the judgment matrix does not pass, and the judgment matrix needs to be adjusted until the judgment matrix passes the inspection.
As shown in fig. 5, after the hierarchical single-order relative weight calculation and the consistency check are completed, the comprehensive weight calculation and the hierarchical total-order consistency check are then performed, and the following method may be adopted: for each factor at the bottom of the hierarchical model, the relative weight of that factor is multiplied by the relative weight of each of its superior factors, and the product is then taken as the composite weight of that factor. This approach will take into account the relative importance of each factor in the hierarchy and its relationship to the higher-level factors, thereby generating the overall weight of the underlying factors in the overall hierarchy model.
Similarly, in this implementation, when the consistency check of the total rank of the hierarchy is performed, if there is only one factor in the first layer of the hierarchy model, the total value of the Randomness Index (RI) may be calculated according to the following steps: for each factor in the second layer, multiplying the RI value corresponding to the factor by the RI values of all the factors at the lower level, and summing the products to obtain the RI total value of the factor. Repeating the process, calculating each factor of the model bottom layer, and finally adding the RI total values to obtain RI Total (S) . Wherein RI is the randomness index obtained according to table look-up 2.
Carrying out CI total sum RI total into a formula (19) to obtain a total CR value, and if the total CR is less than 0.1, passing the consistency test; otherwise, the hierarchical model needs to be adjusted until the consistency check passes. After the consistency check of all the judgment matrixes is completed, the consistency check of the total hierarchical ordering is performed again, and the comprehensive weight W= [ W ] of each factor of the bottom layer can be obtained 1 ，w 2 ，w 3 ，...，w n ]。
Further, after the comprehensive weights of all factors at the bottom layer of the hierarchical structure model are obtained, the comprehensive efficiency of the load assisting equipment of the emergency aid personnel can be evaluated correspondingly based on the comprehensive weights.
In this embodiment, before the comprehensive performance evaluation, each performance parameter needs to be graded, each performance parameter is scored according to a grading standard, and the score is normalized. The grading can adopt organization experts to carry out grading standard grading or grade standard grading through statistical rules of a large amount of experimental data, and different grades are given out.
After various data are collected and various performance parameters are obtained through various performance parameter models, grading and scoring are carried out according to the grade to which the performance data belong, normalization processing is carried out on the grading, the normalization processing result is combined with the comprehensive weight of the bottom factor, and the comprehensive performance evaluation result of the emergency rescue personnel load power assisting equipment is output.
In this embodiment, the normalization of the performance parameters may be performed by using the following method, where the obtained rating score/the highest rating score is used as an example of two evaluation elements of the plantar pressure load-reducing performance parameter and the oxygen consumption reduction performance, and the performance data scoring and normalization process are shown in fig. 6.
Obtaining G= [ G ] through data scoring and normalization processing 1 ，g 2 ，g 3 ，...，g n ]And then, the comprehensive efficiency evaluation result of the emergency rescue personnel load power assisting equipment is obtained by combining the comprehensive weights of all the elements, wherein the calculation formula is as follows:
wherein g i Normalization processing results of the efficacy data corresponding to the ith evaluation element; w (w) i And (5) comprehensively weighting the efficacy data corresponding to the ith evaluation element.
Step S108, the specific implementation step describes: visualizing equipment performance data using a fan blade pattern
According to the individual performance characteristic evaluation value, the hierarchical structure and the comprehensive performance evaluation value, a fan blade shape chart shown in fig. 7 is established, wherein the chart consists of edge fan blades representing various index sets and fan cores representing the comprehensive performance evaluation value, each fan blade consists of a fan shape representing a specific individual performance characteristic value, the longer the side length of the fan shape is, the larger the corresponding performance characteristic value is, and the fan cores represent the comprehensive performance evaluation result by adopting a water polo chart.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the application.
Claims (9)
1. The method for evaluating the comprehensive efficiency of the load boosting equipment of the emergency rescue personnel is characterized by comprising the following steps of:
the device is respectively used for collecting kinematic data, mechanical data, energy consumption data, electromyographic signal posture measurement sensors, plantar pressure sensors, pulling pressure sensors, breathing gas analyzers, electromyographic sensing strain gauges and the like when a rescuer wears and does not wear load-assisting equipment to carry out load movement;
establishing each performance characteristic parameter evaluation model, and analyzing each performance characteristic parameter in a period of time;
performing comprehensive weight calculation on each performance characteristic parameter index by adopting an analytic hierarchy process and an expert scoring process, and evaluating the comprehensive performance in the process of loading and moving of loading and assisting equipment worn by a rescuer based on the comprehensive weight;
and visualizing the equipment efficiency data by adopting a fan blade shape graph.
2. The method of claim 1, wherein the acquiring data comprises: kinematic data, mechanical data, energy consumption data, myoelectric signals;
the kinematic data comprise posture data of the left foot, the right foot, the left calf, the right calf, the left thigh, the right thigh and the trunk of a rescuer, wherein the posture data mainly comprise posture angle data of each link and joint angle data;
the mechanical data comprise plantar pressure data, shoulder and back tension pressure data and strain gauge data;
wherein, plantar pressure data includes the pressure data of placing the flexible pressure sensor collection in the left foot of relief personnel, right foot bottom, includes: pressure data and total pressure sum for forefoot, midfoot and heel; the tension-pressure data includes tension-pressure data of back load to human shoulder and back, including: the back tension of the load bearing back strap on the shoulder of the human body during the exercise and the pressure data of the contact part of the load bearing knapsack and the human body; the strain gage data comprises deformation data of key bearing parts of load assisting equipment in the process of loading exercise, and the strain gage data comprises: strain data of the lower limb thigh bearing rod and the lower leg bearing rod under different loads;
the energy consumption data comprise oxygen consumption and carbon dioxide exhalation quantity and heart rate data in the process of loading exercise of the rescue personnel, the average energy consumption power of the weight per unit mass of the wearer in a certain time is calculated according to the oxygen consumption and the carbon dioxide exhalation quantity, and the heart rate data are estimated through heart rate average values in a certain time;
the electromyographic signals comprise electromyographic signals of shoulders, waist, thighs and lower legs of a human body, and the time domain characteristics of the electromyographic signals in a certain time are calculated according to the electromyographic signals.
3. The method of claim 1 wherein establishing performance characteristic parameter assessment models calculates performance characteristic data over a period of time;
the evaluation model of each performance characteristic parameter comprises the following steps: plantar pressure relief proportion, shoulder-back tension relief proportion, respiratory energy consumption reduction evaluation, human muscle skin fatigue relief evaluation, equipment key bearing part force conduction efficiency evaluation and heart rate-based human body relief efficiency evaluation;
the performance reduction assessment, the energy consumption reduction assessment, the muscle fatigue reduction assessment and the like can be calculated according to the following formulas:
in the above formula: e (E) 01 The parameter value when the load assisting equipment is not worn for loading is indicated; e (E) 11 The parameter value when the load assisting equipment is used for loading; e (E) 00 The parameter value when no load is applied;
the pressure is measured by the pressure peak value of each pressure bearing part of the body and the pressure bearing time of each cycle; the average pressure value and the stress area are important indexes for measuring the load pressure distribution effect, and in general, the smaller the average pressure value is, the larger the stress area is, the better the load pressure distribution effect is.
4. The method of claim 1, wherein the comprehensive weight calculation is performed on each performance characteristic parameter index by using an analytic hierarchy process and an expert scoring process, and the comprehensive performance in the process of loading and moving by wearing the load assisting equipment by the rescuer is evaluated based on the comprehensive weight;
performing comprehensive weight determination on the performance characteristic parameters by adopting an analytic hierarchy process and an expert scoring process;
and calculating an evaluation result by adopting a comprehensive efficiency evaluation model.
5. The method of claim 4, wherein the performance parameters are comprehensively weighted by using analytic hierarchy process and expert scoring process;
constructing a hierarchical structure model, wherein the hierarchical structure model comprises key indexes for evaluating the efficiency of the load power assisting equipment of the rescue workers and the subordinate relations among the key indexes in different levels of the model;
carrying out layer-by-layer data processing on the hierarchical structure model to obtain the relative weight of each index in the current level, and checking the validity of the relative weight;
and generating comprehensive weights of the key indexes of the bottom layer in the hierarchical structure model according to the relative weights of the key indexes of the layers of the hierarchical structure model, and checking the effectiveness of the comprehensive weights.
6. The method of claim 5, wherein determining the relative weights of the indicators comprises: constructing a judgment matrix for each level index, wherein the judgment matrix reflects the contribution comparison result of each two indexes in the level to the efficiency evaluation of the load boosting equipment of the rescue workers; and determining the relative weight of each index in the hierarchy index according to the judgment matrix corresponding to the hierarchy index.
7. The method of claim 6, wherein constructing a decision matrix to determine the relative weights of the indices in the hierarchy comprises: and calculating a feature vector corresponding to the maximum feature value of the judging matrix for the judging matrix corresponding to the level index, and carrying out normalization processing on the feature vector to obtain the relative weight of each index in the level.
8. The method of claim 4, wherein the evaluation result is calculated using a comprehensive performance evaluation model, the evaluation model for calculating the comprehensive performance being as follows:
and carrying out strict data rating on the acquired data corresponding to each key index of the bottom layer, carrying out normalization processing on the acquired data through the rating level/the highest rating level to obtain normalized rating values of all the indexes, and obtaining a comprehensive efficiency evaluation result by combining the comprehensive weight of the key indexes.
9. The method of claim 1, wherein the equipment performance data is visualized using a fan blade graph, the fan blade graph for the comprehensive performance evaluation is composed of edge fan blades representing various index sets and a fan core representing the comprehensive performance evaluation value, each fan blade is composed of a fan shape representing a specific index value, the longer the side length of the fan shape is, the larger the corresponding index value is, and the fan core represents the comprehensive performance evaluation result using a water polo graph.
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