Intelligent safety helmet positioning optimization method and system based on positioning precision evaluation index

Abstract

The invention relates to the technical field of safety helmet positioning and ranging, and provides an intelligent safety helmet positioning optimization method and system based on a positioning precision evaluation index, and the method comprises the steps: obtaining a to-be-analyzed intelligent safety helmet positioning and ranging algorithm; setting a positioning precision evaluation index and constructing a positioning base station coordinate simulation environment; performing simulation analysis on the to-be-analyzed intelligent safety helmet positioning ranging algorithm by using the positioning precision evaluation index and the positioning base station coordinate simulation environment to obtain insufficient positioning precision of the to-be-analyzed intelligent safety helmet positioning ranging algorithm; and obtaining an intelligent safety helmet positioning and ranging optimization algorithm according to the insufficient positioning precision. The method can achieve the optimization of the positioning and ranging algorithm of the intelligent safety helmet, facilitates the improvement of the positioning and ranging accuracy of the intelligent safety helmet, and provides a basis for the stable and efficient use of the intelligent safety helmet.

Description

Technical Field

[0001] The present invention relates to the technical field of helmet positioning and ranging, and in particular to an intelligent helmet positioning optimization method and system based on a positioning accuracy evaluation index. Background Art

[0002] Highways are one of the most important civil facilities. Under the influence of factors such as the natural environment and vehicle load, with the development and widespread application of emerging information technology, the intelligentization of individual protective equipment has become possible. As the most basic protective equipment to protect the human head, the main function of the safety helmet is to cushion shock and disperse stress, thereby protecting the head from injuries caused by falling objects and other specific factors. It has a high usage rate in various workplaces, especially in high-risk industries such as construction, mining, petroleum, chemical industry, and electricity. It is one of the important equipment to ensure the safety of workers. With the development of modern industry, the work site environment is becoming increasingly complex and changeable. Although traditional safety helmets can provide workers with basic head safety protection, their functions may appear to be relatively simple when facing some complex and sudden situations. Therefore, safety helmets urgently need to be iteratively updated with intelligence to improve safety assurance capabilities, work efficiency, and management levels, and better adapt to the needs of modern industrial production.

[0003] At present, head protection products such as hard hats are widely used in various fields of work sites, and technological innovations have been continuously carried out. In terms of materials and production processes, HDPE hard hats, PC hard hats, and FRP hard hats are widely used in various production industries with their special advantages. At the same time, hearing protectors such as earplugs and earmuffs have also begun to be combined with hard hats, and rubber, silicone, polyester and other materials have been gradually introduced to improve the sound insulation effect. In terms of functions and intelligence, many types of foreign products have been intelligently developed. The functions of hard hats are mostly integrated with voice dialogue, video acquisition, geographic location positioning, wireless transmission and other functions. In terms of hearing protectors, the main addition is the combination wearing function with hard hats and the communication function between multiple people. However, there are no relevant technical improvements in safety wear monitoring, dangerous area detection, and positioning effect verification.

[0004] At present, most of the domestic research on the application of smart helmets is concentrated in the fields of underground coal mines, the construction of smart construction sites, emergency fire fighting and power workplaces. There are very few smart helmets developed for hazardous chemical workplaces. In terms of functional requirements, various smart helmet terminal design schemes have different functions due to their different application scenarios, but basically they all have emergency alarm, data transmission and personnel positioning functions. However, there are few detailed studies on the accuracy and stability of smart helmet positioning technology, and it is unknown whether its positioning performance can meet the positioning accuracy requirements of related fields. There is a lack of discussion on positioning technology in complex or special environments. Although most of the smart helmets currently developed can meet multiple intelligent expansion functions in one, they have not taken into account the actual problems of hardware and software system costs and the weight of the helmet body, making it difficult to promote and apply them. In the process of research and optimization of ultra-wideband positioning algorithms, although most of the optimization algorithms proposed in the literature can achieve high-precision positioning, it is still necessary to comprehensively consider the number of positioning base stations, layout arrangements and algorithm running time.

[0005] Therefore, it is necessary to provide an intelligent helmet positioning optimization method and system based on positioning accuracy evaluation indicators. Summary of the invention

[0006] The present invention provides a smart helmet positioning optimization method and system based on positioning accuracy evaluation index. By using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the smart helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, and the positioning accuracy of the smart helmet positioning and ranging algorithm to be analyzed is obtained. According to the positioning accuracy deficiency, the smart helmet positioning and ranging optimization algorithm is obtained, which can realize the optimization of the smart helmet positioning and ranging algorithm, is conducive to improving the accuracy of the smart helmet positioning and ranging, and provides a basis for the stable and efficient use of the smart helmet.

[0007] The present invention provides an intelligent helmet positioning optimization method based on positioning accuracy evaluation index, comprising:

[0008] Obtain the smart helmet positioning and ranging algorithm to be analyzed;

[0009] Set positioning accuracy evaluation indicators and build a positioning base station coordinate simulation environment;

[0010] Using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the intelligent helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, and the positioning accuracy of the intelligent helmet positioning and ranging algorithm to be analyzed is obtained;

[0011] According to the insufficient positioning accuracy, an optimization algorithm for positioning and ranging of smart helmets is obtained.

[0012] Furthermore, the smart helmet positioning and ranging algorithm to be analyzed is obtained, including:

[0013] Obtaining a positioning and ranging method based on the time difference of radio wave propagation;

[0014] The Chan algorithm model and the Taylor algorithm model are obtained in the positioning and ranging method as the smart helmet positioning and ranging algorithms to be analyzed.

[0015] Furthermore, the positioning accuracy evaluation index is set and the positioning base station coordinate simulation environment is constructed, including:

[0016] Set the precision reduction factor and mean square error as positioning accuracy evaluation indicators;

[0017] Using Matlab software, the coordinate simulation environment of the positioning base station is simulated in a rectangular space of 100m*100m*10m.

[0018] Furthermore, using Matlab software, the coordinate simulation environment of the positioning base station is simulated in a rectangular space of 100m*100m*10m, including:

[0019] A base station is deployed at each of the four corners of a 100m*100m*10m rectangular space;

[0020] A base station is arranged at the top and the bottom of the center of a rectangular space of 100m*100m*10m respectively;

[0021] Starting from the six deployed base stations, the number of base stations is evenly increased at the edge one by one to obtain the simulation environment of the positioning base station coordinates.

[0022] Furthermore, using Matlab software, a positioning base station coordinate simulation environment is simulated in a rectangular space of 100m*100m*10m, which also includes: quantitatively evaluating the base station layout of the positioning base station coordinate simulation environment, specifically:

[0023] Get the precision reduction factor values ​​under different numbers of base stations;

[0024] According to the value of the precision reduction factor, draw the horizontal position precision factor simulation diagram and the vertical position precision factor simulation diagram;

[0025] By comparing the values ​​of horizontal position precision factor and vertical position precision factor under the same number of base stations, a quantitative evaluation of the base station layout in the positioning base station coordinate simulation environment is implemented.

[0026] Furthermore, using Matlab software, in a rectangular space of 100m*100m*10m, a positioning base station coordinate simulation environment is simulated, which also includes: setting simulation parameters of the positioning base station coordinate simulation environment and the base station to be tested, specifically:

[0027] The ranging error based on the radio wave propagation time difference is assumed to follow a Gaussian distribution with a mean of zero, and the ranging error is increased in steps of 0.01 m;

[0028] The x-axis range of the positioning base station coordinate simulation environment is set to [25, 75], the y-axis range is set to [25, 75], and the z-axis range is set to [0, 10], and 50 base stations to be tested are randomly generated in the space composed of the x-axis range, the y-axis range, and the z-axis range.

[0029] Furthermore, the positioning accuracy evaluation index and the positioning base station coordinate simulation environment are used to simulate and analyze the intelligent helmet positioning and ranging algorithm to be analyzed, and the positioning accuracy of the intelligent helmet positioning and ranging algorithm to be analyzed is insufficient, including:

[0030] The positioning performance of the Chan algorithm under different numbers of base stations is simulated by using the positioning base station coordinate simulation environment and simulation parameters, and the mean square error is used to express its degree of change, and the first positioning accuracy simulation diagram of the Chan algorithm's mean square error changing with the ranging error under different numbers of base stations is obtained; after improving the base station distribution position, the positioning performance of the Chan algorithm is measured by the mean square error, and the second positioning accuracy simulation diagram of the Chan algorithm after changing the base station distribution position is obtained;

[0031] Based on the first positioning accuracy simulation diagram and the second positioning accuracy simulation diagram, after comparative analysis, it is found that the positioning accuracy of the Chan algorithm is insufficient;

[0032] Using the positioning base station coordinate simulation environment and simulation parameters, the positioning performance of the Taylor algorithm under different numbers of base stations is simulated at a randomly selected position within 50m of the center of the positioning base station coordinate simulation environment, and the mean square error is used to express the degree of change, and the third positioning accuracy simulation diagram of the Taylor algorithm under different numbers of base stations is obtained; the selection range of the initial iterative position is changed, and the positioning performance of the Taylor algorithm is simulated within the initial position selection range of 50m and 100m from the center, respectively, and the mean square error is used as the evaluation index of positioning accuracy, and the fourth positioning accuracy simulation diagram of the Taylor algorithm under different initial position selection ranges is obtained;

[0033] Based on the third simulation graph and the fourth simulation graph, after comparative analysis, it is found that the positioning accuracy of the Taylor algorithm is insufficient.

[0034] Furthermore, according to the insufficient positioning accuracy, an optimization algorithm for positioning and ranging of the smart helmet is obtained, including:

[0035] In view of the insufficient positioning accuracy of the Chan algorithm and the Taylor algorithm, a genetic algorithm is used to search for the optimal solution in the solution space, and the optimal solution is used as the initial position point to provide the initial positioning coordinates;

[0036] The optimal solution is introduced into the Levenberg-Marquardt algorithm to obtain the intelligent helmet positioning and ranging optimization algorithm.

[0037] Furthermore, the positioning accuracy evaluation index and the positioning base station coordinate simulation environment are used to simulate and analyze the positioning and ranging algorithm of the intelligent helmet to be analyzed, which also includes executing the distributed simulation running process based on the set simulation model component host and using the set simulation model component monitor to monitor the process; the specific steps are:

[0038] A simulation model component host and a simulation model component monitor are set, wherein the simulation model component host is used to execute a distributed simulation running process, and the simulation model component monitor is used to monitor the distributed simulation running process;

[0039] Based on the set simulation task configuration file, the simulation model component hoster is used to perform distributed operation of multiple processes for the simulation task;

[0040] Using the simulation model component monitor, monitor and obtain the operation data of the distributed operation;

[0041] According to the operation data, based on the set standard operation database, the operation data is matched to see if it is abnormal. If the operation data does not match the standard operation data, an early warning reminder is issued;

[0042] Generate simulation operation events of mismatched operation data according to early warning reminders;

[0043] Using the set event manager, cluster analysis and probability analysis of the simulation running events are performed to obtain cluster analysis results and probability results;

[0044] Combining the cluster analysis results and the occurrence probability results, obtaining the first simulation running event with the highest occurrence probability and having the greatest impact on the simulation running effect;

[0045] The simulation task configuration file is adjusted and updated based on the first simulation running event to ensure accurate execution of the simulation task.

[0046] The intelligent helmet positioning optimization system based on positioning accuracy evaluation index includes:

[0047] An acquisition module is used to acquire the smart helmet positioning and ranging algorithm to be analyzed;

[0048] A setting module is used to set the positioning accuracy evaluation index and build a positioning base station coordinate simulation environment;

[0049] An analysis module is used to simulate and analyze the intelligent helmet positioning and ranging algorithm to be analyzed by using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, and obtain the positioning accuracy deficiency of the intelligent helmet positioning and ranging algorithm to be analyzed;

[0050] The optimization algorithm generation module is used to obtain the intelligent helmet positioning and ranging optimization algorithm according to the insufficient positioning accuracy.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects: by utilizing the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the intelligent helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, the positioning accuracy of the intelligent helmet positioning and ranging algorithm to be analyzed is obtained, and according to the positioning accuracy, the intelligent helmet positioning and ranging optimization algorithm is obtained, which can realize the optimization of the intelligent helmet positioning and ranging algorithm, is beneficial to improving the accuracy of the intelligent helmet positioning and ranging, and provides a basis for the stable and efficient use of the intelligent helmet.

[0052] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0053] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0055] Figure 1 It is a schematic diagram of the steps of the intelligent helmet positioning optimization method based on the positioning accuracy evaluation index;

[0056] Figure 2 A schematic diagram of the method steps for obtaining the smart helmet positioning and ranging algorithm to be analyzed;

[0057] Figure 3 This is a schematic diagram of the structure of the intelligent helmet positioning optimization system based on positioning accuracy evaluation indicators. DETAILED DESCRIPTION

[0058] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0059] The present invention provides an intelligent helmet positioning optimization method and system based on positioning accuracy evaluation index, such as Figure 1As shown, including:

[0060] Obtain the smart helmet positioning and ranging algorithm to be analyzed;

[0061] Set positioning accuracy evaluation indicators and build a positioning base station coordinate simulation environment;

[0062] Using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the intelligent helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, and the positioning accuracy of the intelligent helmet positioning and ranging algorithm to be analyzed is obtained;

[0063] According to the insufficient positioning accuracy, an optimization algorithm for positioning and ranging of smart helmets is obtained.

[0064] The working principle of the above technical solution is: in order to realize the smart helmet positioning optimization method based on the positioning accuracy evaluation index, the present invention first obtains the smart helmet positioning and ranging algorithm to be analyzed; then sets the positioning accuracy evaluation index and constructs the positioning base station coordinate simulation environment; then, using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the smart helmet positioning and ranging algorithm to be analyzed is simulated and analyzed to obtain the insufficient positioning accuracy of the smart helmet positioning and ranging algorithm to be analyzed; finally, according to the insufficient positioning accuracy, the smart helmet positioning and ranging optimization algorithm is obtained.

[0065] The beneficial effect of the above technical solution is: by adopting the solution provided by this embodiment, by using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the smart helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, and the positioning accuracy of the smart helmet positioning and ranging algorithm to be analyzed is obtained. Based on the insufficient positioning accuracy, the smart helmet positioning and ranging optimization algorithm is obtained, which can realize the optimization of the smart helmet positioning and ranging algorithm, which is beneficial to improve the accuracy of the smart helmet positioning and ranging, and provide a basis for the stable and efficient use of the smart helmet.

[0066] In one embodiment, Figure 2 As shown, obtaining the positioning and ranging algorithm of the intelligent helmet to be analyzed includes: obtaining a positioning and ranging method based on the time difference of radio wave propagation;

[0067] The Chan algorithm model and the Taylor algorithm model are obtained in the positioning and ranging method as the smart helmet positioning and ranging algorithms to be analyzed.

[0068] The working principle of the above technical solution is as follows: Common ultra-wideband ranging methods include those based on radio wave propagation time (TOA) and those based on radio wave propagation time difference (TDOA). The method based on radio wave propagation time is also called the circular positioning method. Its principle is to arrange at least three or more positioning base stations in space to receive the radio wave signal emitted by the tag device, and calculate the measured distance from the tag to the base station by measuring the transmission and reception time t from the tag transmitting the signal to the base station receiving the signal, and then multiplying it by the propagation speed c of the radio wave in the air, and then solving the tag coordinates through the three-sided positioning method to achieve positioning; the positioning method based on the radio wave propagation time difference is also called the hyperbola positioning method. Its principle is to obtain the position of the tag by measuring the arrival time difference from the tag transmitting signal to the base station receiving signal, rather than the absolute time, and then multiplying it by the propagation speed c, thereby reducing the requirements for propagation time synchronization; the positioning method based on the radio wave propagation time difference is not easily affected by clock error interference, has less communication times, and is more widely used; in order to obtain the positioning and ranging algorithm of the smart helmet to be analyzed, the present invention first obtains the base A positioning and ranging method based on the time difference of radio wave propagation; then the Chan algorithm model and the Taylor algorithm model are obtained in the positioning and ranging method as the positioning and ranging algorithm for the smart helmet to be analyzed; the Chan algorithm is an algorithm for solving the hyperbolic equation group non-recursively. Its principle is to transform the problem of solving the nonlinear equation group into a linear optimization problem based on the premise that the TDOA measurement error is relatively small. It does not need to provide the initial value of the position. The coordinate position of the terminal to be measured can be obtained by using the approximate maximum likelihood estimation method through only two iterations; the Taylor algorithm is an algorithm that expands the hyperbolic equation group through continuous recursive iteration for weighted least squares estimation. Before calculation, it is necessary to know the initial estimated coordinates given. Each calculation must take the final result of the previous one as the initial value, and only retain the first two terms of the expansion. The nonlinear hyperbolic equation group is linearized, and then the optimal solution coordinates of the terminal to be measured are obtained through iterative optimization.

[0069] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, by obtaining the positioning and ranging algorithm of the intelligent helmet to be analyzed, a basis is provided for subsequent simulation analysis.

[0070] In one embodiment, setting a positioning accuracy evaluation index and building a positioning base station coordinate simulation environment includes:

[0071] Set the precision reduction factor and mean square error as positioning accuracy evaluation indicators;

[0072] Using Matlab software, the coordinate simulation environment of the positioning base station is simulated in a rectangular space of 100m*100m*10m.

[0073] The working principle of the above technical solution is: in order to judge the advantages and disadvantages of different algorithms to a certain extent, the commonly used simulation performance indicators for evaluating positioning algorithms include mean square error / root mean square error (MSE / RSME), reduction of precision factor (DOP), Cramer-Rao lower bound (CRLB), cumulative probability distribution function (CDF), etc. The present invention uses the reduction of precision factor and mean square error as two indicators to evaluate the positioning performance of different algorithms; then use Matlab software to simulate the positioning base station coordinate simulation environment in a rectangular space of 100m*100m*10m.

[0074] The beneficial effect of the above technical solution is: by adopting the solution provided by this embodiment, by setting the positioning accuracy evaluation index and constructing a positioning base station coordinate simulation environment, the conditions and basis for subsequent simulation implementation are provided.

[0075] In one embodiment, Matlab software is used to simulate the positioning base station coordinate simulation environment in a rectangular space of 100m*100m*10m, including:

[0076] A base station is deployed at each of the four corners of a 100m*100m*10m rectangular space;

[0077] A base station is arranged at the top and the bottom of the center of a rectangular space of 100m*100m*10m respectively;

[0078] Starting from the six deployed base stations, the number of base stations is evenly increased at the edge one by one to obtain the simulation environment of the positioning base station coordinates.

[0079] The working principle of the above technical solution is: in order to obtain better initial results, first deploy a base station at each of the four corners of the three-dimensional space, and then deploy a base station at the upper top and lower top of the center respectively. Starting from 6 base stations, the number of base stations is evenly increased at the edge one by one to explore the impact of the number of base stations in the three-dimensional space on the performance of the positioning algorithm.

[0080] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, a positioning base station coordinate simulation environment is simulated in a rectangular space of 100m*100m*10m, providing a specific environment for subsequent simulation implementation.

[0081] In one embodiment, Matlab software is used to simulate a positioning base station coordinate simulation environment in a rectangular space of 100m*100m*10m, and further includes: quantitatively evaluating the base station layout of the positioning base station coordinate simulation environment, specifically:

[0082] Get the precision reduction factor values ​​under different numbers of base stations;

[0083] According to the value of the precision reduction factor, draw the horizontal position precision factor simulation diagram and the vertical position precision factor simulation diagram;

[0084] By comparing the values ​​of horizontal position precision factor and vertical position precision factor under the same number of base stations, a quantitative evaluation of the base station layout in the positioning base station coordinate simulation environment is implemented.

[0085] The working principle of the above technical solution is: the layout of the base station determines the coverage and positioning capability of the ultra-wideband positioning system, and the ultra-wideband positioning algorithm relies on the precise time difference and distance measurement between the base station and the tag; when the base station layout is reasonable and the density is moderate, it can provide enough measurement data to improve the positioning accuracy; on the contrary, if the base stations are sparsely distributed or there are blind spots, it may lead to insufficient measurement data, thereby affecting the accuracy of positioning; therefore, it is necessary to quantitatively evaluate the base station layout of the simulation environment, and the present invention uses Matlab software to simulate the positioning base station coordinate simulation environment in a rectangular space of 100m*100m*10m, and also includes: quantitatively evaluating the base station layout of the positioning base station coordinate simulation environment, specifically: obtaining the precision attenuation factor value under different numbers of base stations; according to the precision attenuation factor value, drawing the horizontal position precision factor simulation diagram and the vertical position precision factor simulation diagram; by comparing the horizontal position precision factor and the vertical position precision factor values ​​under the same number of base stations, the quantitative evaluation of the base station layout of the positioning base station coordinate simulation environment is implemented.

[0086] The beneficial effect of the above technical solution is: by adopting the solution provided by this embodiment, the scientific nature of the base station layout in the simulation environment of the positioning base station coordinates can be guaranteed through quantitative evaluation of the base station layout in the simulation environment.

[0087] In one embodiment, Matlab software is used to simulate a positioning base station coordinate simulation environment in a rectangular space of 100m*100m*10m, and the simulation parameters of the positioning base station coordinate simulation environment and the base station to be tested are set, specifically:

[0088] The ranging error based on the radio wave propagation time difference is assumed to follow a Gaussian distribution with a mean of zero, and the ranging error is increased in steps of 0.01 m;

[0089] The x-axis range of the positioning base station coordinate simulation environment is set to [25, 75], the y-axis range is set to [25, 75], and the z-axis range is set to [0, 10], and 50 base stations to be tested are randomly generated in the space composed of the x-axis range, the y-axis range, and the z-axis range.

[0090] The working principle of the above technical solution is as follows: when measuring the distance in actual positioning, errors will occur due to interference from various environmental factors. In order to restore the actual environment, the present invention introduces Gaussian noise, simulates the positioning effect of the positioning algorithm in the presence of errors, and evaluates its stability. First, the ranging error based on the radio wave propagation time difference is set to obey a Gaussian distribution with a mean of zero, and the ranging error is increased in steps of 0.01 m; then the x-axis range of the positioning base station coordinate simulation environment is set to [25, 75], the y-axis range is set to [25, 75], and the z-axis range is set to [0, 10], and 50 base stations to be tested are randomly generated in the space composed of the x-axis range, the y-axis range and the z-axis range.

[0091] The beneficial effect of the above technical solution is: by adopting the solution provided by this embodiment, by setting the ranging error and the base station to be measured, the simulation is smoothly implemented.

[0092] In one embodiment, the positioning accuracy evaluation index and the positioning base station coordinate simulation environment are used to simulate and analyze the smart helmet positioning and ranging algorithm to be analyzed, and the positioning accuracy of the smart helmet positioning and ranging algorithm to be analyzed is insufficient, including:

[0093] The positioning performance of the Chan algorithm under different numbers of base stations is simulated by using the positioning base station coordinate simulation environment and simulation parameters, and the mean square error is used to express its degree of change, and the first positioning accuracy simulation diagram of the Chan algorithm's mean square error changing with the ranging error under different numbers of base stations is obtained; after improving the base station distribution position, the positioning performance of the Chan algorithm is measured by the mean square error, and the second positioning accuracy simulation diagram of the Chan algorithm after changing the base station distribution position is obtained;

[0094] Based on the first positioning accuracy simulation diagram and the second positioning accuracy simulation diagram, after comparative analysis, it is found that the positioning accuracy of the Chan algorithm is insufficient;

[0095] Using the positioning base station coordinate simulation environment and simulation parameters, the positioning performance of the Taylor algorithm under different numbers of base stations is simulated at a randomly selected position within 50m of the center of the positioning base station coordinate simulation environment, and the mean square error is used to express the degree of change, and the third positioning accuracy simulation diagram of the Taylor algorithm under different numbers of base stations is obtained; the selection range of the initial iterative position is changed, and the positioning performance of the Taylor algorithm is simulated within the initial position selection range of 50m and 100m from the center, respectively, and the mean square error is used as the evaluation index of positioning accuracy, and the fourth positioning accuracy simulation diagram of the Taylor algorithm under different initial position selection ranges is obtained;

[0096] Based on the third simulation graph and the fourth simulation graph, after comparative analysis, it is found that the positioning accuracy of the Taylor algorithm is insufficient.

[0097] The working principle of the above technical solution is as follows: in order to use the positioning accuracy evaluation index and the positioning base station coordinate simulation environment to simulate and analyze the smart helmet positioning and ranging algorithm to be analyzed, the present invention first uses the positioning base station coordinate simulation environment and simulation parameters to simulate the positioning performance of the Chan algorithm under different numbers of base stations, and uses the mean square error to express its degree of change, and obtains a first positioning accuracy simulation diagram of the Chan algorithm with different numbers of base stations, where the mean square error varies with the ranging error; after improving the base station distribution position, the mean square error is used to measure the positioning performance of the Chan algorithm, and a second positioning accuracy simulation diagram of the Chan algorithm after the base station distribution position is changed is obtained; then, based on the first positioning accuracy simulation diagram and the second positioning accuracy simulation diagram, after comparative analysis, it is obtained that the positioning accuracy of the Chan algorithm is insufficient;

[0098] Then, by using the positioning base station coordinate simulation environment and simulation parameters, the positioning performance of the Taylor algorithm under different numbers of base stations is simulated at randomly selected positions within 50m of the center of the positioning base station coordinate simulation environment, and the mean square error is used to express the degree of change, and the third positioning accuracy simulation diagram of the Taylor algorithm under different numbers of base stations is obtained; the selection range of the initial iterative position is changed, and the positioning performance of the Taylor algorithm is simulated within the initial position selection range of 50m and 100m from the center, respectively, and the mean square error is used as the evaluation indicator of positioning accuracy, and the fourth positioning accuracy simulation diagram of the Taylor algorithm under different initial position selection ranges is obtained; finally, based on the third simulation diagram and the fourth simulation diagram, after comparative analysis, it is obtained that the positioning accuracy of the Taylor algorithm is insufficient.

[0099] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, through simulation analysis of the positioning accuracy of the Chan algorithm and the positioning accuracy of the Taylor algorithm, the insufficient positioning accuracy of the Chan algorithm and the insufficient positioning accuracy of the Taylor algorithm can be obtained, providing a decision-making basis for subsequent algorithm optimization.

[0100] In one embodiment, according to the insufficient positioning accuracy, an optimization algorithm for positioning and ranging of a smart helmet is obtained, including:

[0101] In view of the insufficient positioning accuracy of the Chan algorithm and the Taylor algorithm, a genetic algorithm is used to search for the optimal solution in the solution space, and the optimal solution is used as the initial position point to provide the initial positioning coordinates;

[0102] The optimal solution is introduced into the Levenberg-Marquardt algorithm to obtain the intelligent helmet positioning and ranging optimization algorithm.

[0103] The working principle of the above technical solution is: in order to obtain an intelligent helmet positioning and ranging optimization algorithm, the present invention uses a genetic algorithm to search for the optimal solution in the solution space to solve the problems of insufficient positioning accuracy of the Chan algorithm and the Taylor algorithm, and uses the optimal solution as the initial position point to provide initial positioning coordinates; the optimal solution is introduced into the Levenberg-Marquardt algorithm to obtain an intelligent helmet positioning and ranging optimization algorithm.

[0104] The beneficial effect of the above technical solution is: by adopting the solution provided by this embodiment, a basis is provided for the implementation of smart helmet positioning and ranging by obtaining a smart helmet positioning and ranging optimization algorithm.

[0105] In one embodiment, the positioning accuracy evaluation index and the positioning base station coordinate simulation environment are used to perform simulation analysis on the positioning and ranging algorithm of the intelligent helmet to be analyzed, and further includes executing a distributed simulation running process based on a set simulation model component host, and using a set simulation model component monitor to monitor the process; the specific steps are:

[0106] A simulation model component host and a simulation model component monitor are set, wherein the simulation model component host is used to execute a distributed simulation running process, and the simulation model component monitor is used to monitor the distributed simulation running process;

[0107] Based on the set simulation task configuration file, the simulation model component hoster is used to perform distributed operation of multiple processes for the simulation task;

[0108] Using the simulation model component monitor, monitor and obtain the operation data of the distributed operation;

[0109] According to the operation data, based on the set standard operation database, the operation data is matched to see if it is abnormal. If the operation data does not match the standard operation data, an early warning reminder is issued;

[0110] Generate simulation operation events of mismatched operation data according to early warning reminders;

[0111] Using the set event manager, cluster analysis and probability analysis of the simulation running events are performed to obtain cluster analysis results and probability results;

[0112] Combining the cluster analysis results and the occurrence probability results, obtaining the first simulation running event with the highest occurrence probability and having the greatest impact on the simulation running effect;

[0113] The simulation task configuration file is adjusted and updated based on the first simulation running event to ensure accurate execution of the simulation task.

[0114] The working principle of the above technical solution is: in order to ensure the smooth and effective implementation of simulation analysis, the present invention executes a distributed simulation running process based on a set simulation model component host, and uses a set simulation model component monitor to monitor the process; the specific steps are: first set a simulation model component host and a simulation model component monitor, the simulation model component host is used to execute a distributed simulation running process, and the simulation model component monitor is used to monitor the distributed simulation running process; based on the set simulation task configuration file, use the simulation model component host to perform distributed running of multiple processes for the simulation task; use the simulation model component monitor to monitor and obtain the running data of the distributed running According to the running data, based on the set standard running database, whether the running data is abnormal is matched, if the running data does not match the standard running data, an early warning reminder is issued; according to the early warning reminder, a simulation running event of the running data mismatch is generated; using the set event manager, cluster analysis and probability analysis of the simulation running events are performed to obtain cluster analysis results and occurrence probability results; finally, combining the cluster analysis results and occurrence probability results, the first simulation running event with the highest probability of occurrence and the greatest impact on the simulation running effect is obtained; based on the first simulation running event, the simulation task configuration file is adjusted and updated to ensure the accurate execution of the simulation task.

[0115] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, the distributed simulation running process is executed through the set simulation model component host, and the process is monitored by using the set simulation model component monitor, which can ensure the smooth and effective implementation of the simulation analysis.

[0116] Intelligent helmet positioning optimization system based on positioning accuracy evaluation index, such as Figure 3 As shown, including:

[0117] An acquisition module is used to acquire the smart helmet positioning and ranging algorithm to be analyzed;

[0118] A setting module is used to set the positioning accuracy evaluation index and build a positioning base station coordinate simulation environment;

[0119] An analysis module is used to simulate and analyze the intelligent helmet positioning and ranging algorithm to be analyzed by using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, and obtain the positioning accuracy deficiency of the intelligent helmet positioning and ranging algorithm to be analyzed;

[0120] The optimization algorithm generation module is used to obtain the intelligent helmet positioning and ranging optimization algorithm according to the insufficient positioning accuracy.

[0121] The working principle of the above technical scheme is: in order to realize the intelligent helmet positioning optimization system based on the positioning accuracy evaluation index, the present invention proposes an acquisition module for acquiring the intelligent helmet positioning and ranging algorithm to be analyzed; proposes a setting module for setting the positioning accuracy evaluation index and constructing a positioning base station coordinate simulation environment; proposes an analysis module for using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment to simulate and analyze the intelligent helmet positioning and ranging algorithm to be analyzed, and obtains the insufficient positioning accuracy of the intelligent helmet positioning and ranging algorithm to be analyzed; proposes an optimization algorithm generation module for obtaining the intelligent helmet positioning and ranging optimization algorithm according to the insufficient positioning accuracy.

[0122] The beneficial effect of the above technical solution is: by adopting the solution provided by this embodiment, by using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the smart helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, and the positioning accuracy of the smart helmet positioning and ranging algorithm to be analyzed is obtained. Based on the insufficient positioning accuracy, the smart helmet positioning and ranging optimization algorithm is obtained, which can realize the optimization of the smart helmet positioning and ranging algorithm, which is beneficial to improve the accuracy of the smart helmet positioning and ranging, and provide a basis for the stable and efficient use of the smart helmet.

[0123] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent helmet positioning optimization method based on positioning accuracy evaluation index is characterized by: include: Obtain the smart helmet positioning and ranging algorithm to be analyzed; Set positioning accuracy evaluation indicators and build a positioning base station coordinate simulation environment; Using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the intelligent helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, and the positioning accuracy of the intelligent helmet positioning and ranging algorithm to be analyzed is obtained; According to the insufficient positioning accuracy, an optimization algorithm for positioning and ranging of smart helmets is obtained.

2. The intelligent helmet positioning optimization method based on positioning accuracy evaluation index according to claim 1 is characterized in that: Get the smart helmet positioning and ranging algorithm to be analyzed, including: Obtaining a positioning and ranging method based on the time difference of radio wave propagation; The Chan algorithm model and the Taylor algorithm model are obtained in the positioning and ranging method as the smart helmet positioning and ranging algorithms to be analyzed.

3. The intelligent helmet positioning optimization method based on positioning accuracy evaluation index according to claim 1 is characterized in that: Set the positioning accuracy evaluation index and build the positioning base station coordinate simulation environment, including: Set the precision reduction factor and mean square error as positioning accuracy evaluation indicators; Using Matlab software, the coordinate simulation environment of the positioning base station is simulated in a rectangular space of 100m*100m*10m.

4. The intelligent helmet positioning optimization method based on positioning accuracy evaluation index according to claim 3 is characterized in that: Using Matlab software, in a rectangular space of 100m*100m*10m, the coordinate simulation environment of the positioning base station is simulated, including: A base station is deployed at each of the four corners of a 100m*100m*10m rectangular space; A base station is arranged at the top and the bottom of the center of a rectangular space of 100m*100m*10m respectively; Starting from the six deployed base stations, the number of base stations is evenly increased at the edge one by one to obtain the simulation environment of the positioning base station coordinates.

5. The intelligent helmet positioning optimization method based on positioning accuracy evaluation index according to claim 3 is characterized in that: Using Matlab software, a positioning base station coordinate simulation environment is simulated in a rectangular space of 100m*100m*10m, which also includes: quantitative evaluation of the base station layout of the positioning base station coordinate simulation environment, specifically: Get the precision reduction factor values ​​under different numbers of base stations; According to the value of the precision reduction factor, draw the horizontal position precision factor simulation diagram and the vertical position precision factor simulation diagram; By comparing the values ​​of horizontal position precision factor and vertical position precision factor under the same number of base stations, a quantitative evaluation of the base station layout in the positioning base station coordinate simulation environment is implemented.

6. The intelligent helmet positioning optimization method based on positioning accuracy evaluation index according to claim 3 is characterized in that: Using Matlab software, in a rectangular space of 100m*100m*10m, a positioning base station coordinate simulation environment is simulated, which also includes: setting simulation parameters of the positioning base station coordinate simulation environment and the base station to be tested, specifically: The ranging error based on the radio wave propagation time difference is assumed to follow a Gaussian distribution with a mean of zero, and the ranging error is increased in steps of 0.01 m; The x-axis range of the positioning base station coordinate simulation environment is set to [25, 75], the y-axis range is set to [25, 75], and the z-axis range is set to [0, 10], and 50 base stations to be tested are randomly generated in the space composed of the x-axis range, the y-axis range, and the z-axis range.

7. The intelligent helmet positioning optimization method based on positioning accuracy evaluation index according to claim 6 is characterized in that: Using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the intelligent helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, and the positioning accuracy of the intelligent helmet positioning and ranging algorithm to be analyzed is insufficient, including: The positioning performance of the Chan algorithm under different numbers of base stations is simulated by using the positioning base station coordinate simulation environment and simulation parameters, and the mean square error is used to express its degree of change, and the first positioning accuracy simulation diagram of the Chan algorithm's mean square error changing with the ranging error under different numbers of base stations is obtained; after improving the base station distribution position, the positioning performance of the Chan algorithm is measured by the mean square error, and the second positioning accuracy simulation diagram of the Chan algorithm after changing the base station distribution position is obtained; Based on the first positioning accuracy simulation diagram and the second positioning accuracy simulation diagram, after comparative analysis, it is found that the positioning accuracy of the Chan algorithm is insufficient; Using the positioning base station coordinate simulation environment and simulation parameters, the positioning performance of the Taylor algorithm under different numbers of base stations is simulated at a randomly selected position within 50m of the center of the positioning base station coordinate simulation environment, and the mean square error is used to express the degree of change, and the third positioning accuracy simulation diagram of the Taylor algorithm under different numbers of base stations is obtained; the selection range of the initial iterative position is changed, and the positioning performance of the Taylor algorithm is simulated within the initial position selection range of 50m and 100m from the center, respectively, and the mean square error is used as the evaluation index of positioning accuracy, and the fourth positioning accuracy simulation diagram of the Taylor algorithm under different initial position selection ranges is obtained; Based on the third simulation graph and the fourth simulation graph, after comparative analysis, it is found that the positioning accuracy of the Taylor algorithm is insufficient.

8. The intelligent helmet positioning optimization method based on positioning accuracy evaluation index according to claim 7 is characterized in that: According to the insufficient positioning accuracy, the smart helmet positioning and ranging optimization algorithm is obtained, including: In view of the insufficient positioning accuracy of the Chan algorithm and the Taylor algorithm, a genetic algorithm is used to search for the optimal solution in the solution space, and the optimal solution is used as the initial position point to provide the initial positioning coordinates; The optimal solution is introduced into the Levenberg-Marquardt algorithm to obtain the intelligent helmet positioning and ranging optimization algorithm.

9. The intelligent helmet positioning optimization method based on positioning accuracy evaluation index according to claim 1 is characterized in that: Using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, the intelligent helmet positioning and ranging algorithm to be analyzed is simulated and analyzed, which also includes executing the distributed simulation running process based on the set simulation model component host and using the set simulation model component monitor to monitor the process; the specific steps are: A simulation model component host and a simulation model component monitor are set, wherein the simulation model component host is used to execute a distributed simulation running process, and the simulation model component monitor is used to monitor the distributed simulation running process; Based on the set simulation task configuration file, the simulation model component hoster is used to perform distributed operation of multiple processes for the simulation task; Using the simulation model component monitor, monitor and obtain the operation data of the distributed operation; According to the operation data, based on the set standard operation database, the operation data is matched to see if it is abnormal. If the operation data does not match the standard operation data, an early warning reminder is issued; Generate simulation operation events of mismatched operation data according to early warning reminders; Using the set event manager, cluster analysis and probability analysis of the simulation running events are performed to obtain cluster analysis results and probability results; Combining the cluster analysis results and the occurrence probability results, obtaining the first simulation running event with the highest occurrence probability and having the greatest impact on the simulation running effect; The simulation task configuration file is adjusted and updated based on the first simulation running event to ensure accurate execution of the simulation task.

10. The intelligent helmet positioning optimization system based on positioning accuracy evaluation index is characterized by: include: An acquisition module is used to acquire the smart helmet positioning and ranging algorithm to be analyzed; A setting module is used to set the positioning accuracy evaluation index and build a positioning base station coordinate simulation environment; An analysis module is used to simulate and analyze the intelligent helmet positioning and ranging algorithm to be analyzed by using the positioning accuracy evaluation index and the positioning base station coordinate simulation environment, and obtain the positioning accuracy deficiency of the intelligent helmet positioning and ranging algorithm to be analyzed; The optimization algorithm generation module is used to obtain the intelligent helmet positioning and ranging optimization algorithm according to the insufficient positioning accuracy.

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