Method and equipment for monitoring state of forestry survey mountaineering assistance equipment

Abstract

The invention relates to the field of mountaineering assisting equipment control, and discloses a state monitoring method and device for forestry investigation mountaineering assisting equipment, and the method comprises the steps: obtaining first use data when a user uses a mountaineering stick in a first time period, and second use data when the user uses the mountaineering stick in a second time period; wherein the first use data and the second use data both comprise user ergonomic data, the contact angle of the alpenstock and the ground, the supporting force of the alpenstock and the ground and the time sequence swing angle of moving use of the alpenstock, and the second time period is the subsequent time period of the first time period; according to the first use data and the second use data, determining a use posture suggestion when the user uses the alpenstock; and sending the use posture suggestion to the user. The use posture of the alpenstock can be scientifically evaluated and monitored, and the forestry survey mountaineering assisting effect of the alpenstock is improved.

Description

Technical Field

[0001] This application relates to the field of mountaineering auxiliary equipment control technology, and more specifically, to a method and device for monitoring the status of mountaineering auxiliary equipment for forestry surveys. Background Technology

[0002] During extended outdoor forestry surveys, researchers often need to walk and work long distances in complex mountainous terrain, resulting in significant physical exertion. To reduce the burden on their bodies and improve stability, various mountaineering aids are commonly used, with trekking poles being one of the most widely applied. Trekking poles effectively reduce pressure on the legs and joints by distributing some of the body weight to the ground, while providing good support on rugged terrain, reducing the risk of slipping and falling. They have become an indispensable tool for outdoor forestry surveyors.

[0003] Existing trekking poles only function as mechanical support structures, providing physical assistance. They cannot monitor the user's posture in real time; key parameters such as the contact angle between the pole and the ground, the distribution of support force, and the user's arm exertion techniques cannot be effectively captured. This lack of scientific assessment of user posture prevents the provision of targeted improvement suggestions based on monitoring data, potentially leading to users maintaining improper usage habits over a long period. This not only reduces the actual assistance provided by the trekking poles but may even increase the risk of strain on certain parts of the body due to improper force application, hindering the full realization of the auxiliary value of trekking poles in outdoor forestry surveys. Summary of the Invention

[0004] The purpose of this application is to provide a method and device for monitoring the status of mountaineering assistance equipment in forestry surveys, which solves the technical problem of lacking scientific evaluation and monitoring of the usage posture of trekking poles, and achieves the technical effect of being able to scientifically evaluate and monitor the usage posture of trekking poles.

[0005] This application provides a method for monitoring the status of a forestry survey hiking aid device. The method includes: acquiring first usage data of a user using hiking poles during a first time period and second usage data of a user using hiking poles during a second time period; wherein both the first and second usage data include user ergonomic data, the contact angle between the hiking pole and the ground, the support force between the hiking pole and the ground, and the timing swing angle of the hiking pole during movement; the second time period is a subsequent time period of the first time period; determining a user posture suggestion when using hiking poles based on the first and second usage data; and sending the user the posture suggestion.

[0006] In one possible implementation, based on first and second usage data, a suggested posture for using trekking poles is determined, including: using an experience value table based on forestry survey mountaineering scenarios and user ergonomic data, determining a contact angle change threshold corresponding to the contact angle in the first and second usage data, a support force change threshold corresponding to the support force, and a maximum swing angle change threshold corresponding to the maximum swing angle in the time-series swing angle; determining the contact angle change value, support force change value, and maximum swing angle change value corresponding to the first and second usage data; when the contact angle change value is greater than or equal to the contact angle change threshold, determining the suggested posture is to correct the contact angle of the trekking poles; when the support force change value is greater than or equal to the support force change threshold, determining the suggested posture is to adjust the height of the trekking poles; when the maximum swing angle change value is greater than or equal to the maximum swing angle change threshold, determining the suggested posture is to adjust the user's stride length.

[0007] In another possible implementation, the method further includes: acquiring the user's walking trajectory data corresponding to the first time period and the second time period; acquiring the geographical data corresponding to the walking trajectory data; determining the environmental risk coefficient corresponding to the walking trajectory data based on the geographical data corresponding to the walking trajectory data; determining the product of the environmental risk coefficient and the contact angle change threshold to adjust the contact angle change threshold; determining the product of the environmental risk coefficient and the contact angle change threshold to adjust the support force change threshold; and determining the product of the environmental risk coefficient and the maximum swing angle change threshold to adjust the maximum swing angle change threshold.

[0008] In another possible implementation, the environmental risk coefficient corresponding to the walking trajectory data is determined based on the geographical data corresponding to the walking trajectory data. This includes: obtaining the mileage index, altitude change, and road condition index corresponding to the walking trajectory data; where the mileage index represents the walking distance corresponding to the walking trajectory data, the altitude change represents the sum of the altitude rise and fall corresponding to the walking trajectory data, and the road condition index represents the proportion of rugged road sections corresponding to the walking trajectory data; a basic environmental risk coefficient is determined based on the mileage index and altitude change using an empirical value table; an environmental risk coefficient correction factor is determined based on the road condition index using an empirical value table; and the product of the basic environmental risk coefficient and the environmental risk coefficient correction factor is determined as the environmental risk coefficient.

[0009] In another possible implementation, the method further includes: numerically representing the mileage index, altitude change, and road condition index corresponding to the first walking trajectory data for the first time period to determine the first trajectory feature vector; numerically representing the mileage index, altitude change, and road condition index corresponding to the second walking trajectory data for the second time period to determine the second trajectory feature vector; numerically representing the contact angle, support force, and maximum swing angle of the first usage data to determine the first usage feature vector; numerically representing the contact angle, support force, and maximum swing angle of the second usage data to determine the second usage feature vector; determining the similarity between the first trajectory feature vector and the second trajectory feature vector as the trajectory feature similarity; determining the similarity between the first usage feature vector and the second usage feature vector as the usage feature similarity; determining the difference between the trajectory feature similarity and the usage feature similarity as the posture change feature value; and when the trajectory feature similarity is greater than or equal to a preset trajectory feature similarity and the posture change feature value is greater than or equal to a preset posture change feature value, issuing a prompt to the user to adjust the posture of using the trekking poles or to take a proper rest.

[0010] In another possible implementation, the method further includes: acquiring multiple historical records of the user's use of trekking poles, and acquiring the walking trajectory data corresponding to each of the multiple historical records; clustering the walking trajectory data corresponding to the multiple historical records according to the mileage index, altitude change, and road condition index corresponding to the walking trajectory data, obtaining multiple walking trajectory data clusters, each walking trajectory data cluster including multiple walking trajectory data; acquiring the trekking pole usage records corresponding to the multiple walking trajectory data in each walking trajectory data cluster, and determining the average contact angle, average support force, and average maximum swing angle of the trekking pole usage records corresponding to the multiple walking trajectory data in each walking trajectory data cluster; determining the product of the average contact angle and a preset ratio value as the contact angle change threshold; determining the product of the average support force and a preset ratio value as the support force change threshold; and determining the product of the average maximum swing angle and a preset ratio value as the maximum swing angle change threshold.

[0011] In another possible implementation, the preset ratio value is determined by the following method: using an empirical value table, the preset ratio value is determined based on the average mileage index, average altitude change, and average road condition index corresponding to each walking trajectory data cluster group.

[0012] In another possible implementation, the method further includes: determining multiple similarities for the mileage index, altitude change, road condition index, and the mean mileage index, mean altitude change, and mean road condition index corresponding to the first walking trajectory data cluster group, and determining the first walking trajectory data cluster group corresponding to the maximum similarity among the multiple similarities; determining multiple similarities for the mileage index, altitude change, road condition index, and the mean mileage index, mean altitude change, and mean road condition index corresponding to the second walking trajectory data cluster group, and determining the second walking trajectory data cluster group corresponding to the maximum similarity among the multiple similarities. Clustering groups; when the first walking trajectory data clustering group and the second walking trajectory data clustering group are the same, obtain the first contact angle change threshold, the first support force change threshold, and the first maximum swing angle change threshold corresponding to the first walking trajectory data clustering group; when the contact angle change value is greater than or equal to the first contact angle change threshold, determine that the usage posture suggestion is to correct the contact angle of the trekking pole; when the support force change value is greater than or equal to the first support force change threshold, determine that the usage posture suggestion is to adjust the height of the trekking pole; when the maximum swing angle change value is greater than or equal to the first maximum swing angle change threshold, determine that the usage posture suggestion is to adjust the user's hiking stride.

[0013] In another possible implementation, the method further includes: when the first walking trajectory data cluster group and the second walking trajectory data cluster group are different, obtaining the first contact angle change threshold, the first support force change threshold, and the first maximum swing angle change threshold corresponding to the first walking trajectory data cluster group, and obtaining the second contact angle change threshold, the first support force change threshold, and the second maximum swing angle change threshold corresponding to the second walking trajectory data cluster group; determining the larger value of the first contact angle change threshold and the second contact angle change threshold as the target contact angle change threshold; determining the larger value of the first support force change threshold and the second support force change threshold as the target support force change threshold; determining the larger value of the first maximum swing angle change threshold and the second maximum swing angle change threshold as the target maximum swing angle change threshold; when the contact angle change value is greater than or equal to the target contact angle change threshold, determining the posture suggestion as correcting the contact angle of the trekking pole; when the support force change value is greater than or equal to the target support force change threshold, determining the posture suggestion as adjusting the height of the trekking pole; when the maximum swing angle change value is greater than or equal to the target maximum swing angle change threshold, determining the posture suggestion as adjusting the user's hiking stride.

[0014] This application also provides a status monitoring device for forestry survey mountaineering assistance equipment, including a unit for performing the method described in any of the preceding claims.

[0015] The beneficial effects of the embodiments in this application compared with the prior art are: This application provides a method for monitoring the status of a forestry survey hiking aid device. The method includes: acquiring first usage data of a user using hiking poles during a first time period and second usage data of a user using hiking poles during a second time period; wherein both the first and second usage data include user ergonomic data, the contact angle between the hiking pole and the ground, the support force between the hiking pole and the ground, and the timing swing angle of the hiking pole during movement; the second time period is the subsequent time period of the first time period; determining a user posture suggestion based on the first and second usage data; and sending the posture suggestion to the user. The forestry survey hiking aid device in this application can accurately capture the changing trends and existing problems of the user's posture. By dynamically monitoring and analyzing the characteristics of the user's use of hiking poles and providing posture suggestions, it can effectively guide users to adopt more reasonable usage methods, reduce physical burden and safety hazards caused by posture problems, improve the assistive effect of hiking poles in forestry survey work, and allow users to complete survey tasks more easily and safely in complex mountainous environments. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the first method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment; Figure 2 A schematic diagram illustrating the workflow of the first method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of a forestry survey hiking stick in an embodiment of this application; Figure 4 A flowchart illustrating the second method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment; Figure 5 A schematic diagram illustrating the workflow of the second method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment; Figure 6 A schematic diagram illustrating the workflow of the third method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment; Figure 7 A flowchart illustrating the fourth method for monitoring the status of mountaineering assistance equipment in forestry surveys, provided in this application embodiment; Figure 8 A flowchart illustrating the fifth method for monitoring the status of mountaineering assistance equipment in forestry surveys, provided in this application embodiment; Figure 9 This is a schematic diagram of the logical structure of a status monitoring device for a forestry survey and mountaineering assistance device provided in an embodiment of this application. Detailed Implementation

[0018] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0019] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0021] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0023] Existing trekking poles cannot monitor the user's posture in real time, making it difficult to fully realize their auxiliary value in outdoor forestry surveys.

[0024] Based on the above reasons, this application provides a method for monitoring the status of a forestry survey hiking aid device. The method includes: acquiring first usage data of a user using a hiking pole during a first time period and second usage data of a user using a hiking pole during a second time period; wherein both the first and second usage data include user ergonomic data, the contact angle between the hiking pole and the ground, the support force between the hiking pole and the ground, and the timing swing angle of the hiking pole during movement; the second time period is the subsequent time period of the first time period; determining a user posture suggestion based on the first and second usage data; and sending the posture suggestion to the user. The forestry survey hiking aid device in this application can accurately capture the changing trend and existing problems of the user's posture. By dynamically monitoring and analyzing the characteristics of the user's use of the hiking pole and providing posture suggestions, it can effectively guide the user to adopt a more reasonable usage method, reduce the physical burden and safety hazards caused by posture problems, improve the assistive effect of the hiking pole in forestry survey work, and allow users to complete survey tasks more easily and safely in complex mountainous environments.

[0025] In some scenarios, the status monitoring method of a forestry survey mountaineering assistance device according to an embodiment of this application can be applied to the assistance of trekking poles in complex mountain and forest environments. It can play an auxiliary role in posture correction during forestry surveys. When used with wearable devices such as mobile apps and smart bracelets, it can improve the assistance effect of trekking poles in outdoor forestry surveys.

[0026] The following describes in detail, with specific examples, a method for monitoring the status of a forestry survey and mountaineering assistance device provided in this application.

[0027] Figure 1 A flowchart illustrating the first method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment is shown below. Figure 1 As shown, the status monitoring methods for mountaineering assistance equipment in this forestry survey include S110 to S120, and S110 to S120 will be explained in detail below.

[0028] S110. Obtain first usage data of the user using trekking poles during a first time period and second usage data of the user using trekking poles during a second time period. Both the first and second usage data include user ergonomic data, the contact angle between the trekking pole and the ground, the support force between the trekking pole and the ground, and the timing of the trekking pole's movement and swing angle. The second time period is the subsequent time period of the first time period.

[0029] Figure 2 A schematic diagram illustrating the workflow of the first method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment is shown below. Figure 2As shown, we can first obtain first usage data of the user using trekking poles in a first time period and second usage data of the user using trekking poles in a second time period. Both the first and second usage data include the user's ergonomic data, the contact angle between the trekking pole and the ground, the support force between the trekking pole and the ground, and the timing swing angle of the trekking pole during movement. Then, we can optimize the use of trekking poles by using the first and second usage data. By obtaining this data, we can comprehensively monitor the user's posture and movement characteristics when using trekking poles in different time periods.

[0030] It should be noted that the second time period is the period following the first time period, and thus the usage of trekking poles can be optimized by observing the usage of users in the adjacent second and first time periods.

[0031] It should be noted that when collecting users' ergonomic data, it can be obtained by having users input their height, weight, and other ergonomic data into the App.

[0032] When users are conducting forestry surveys and hiking, the contact angle between the trekking pole and the ground, the support force, and the time-series swing angle can be continuously collected. This data can be acquired in real time through the sensors built into the trekking pole, thereby comprehensively monitoring the user's posture. Acquiring this time-series data helps to understand the dynamic characteristics of the user's use of the trekking pole, providing a basis for subsequent analysis.

[0033] Figure 3 This is a schematic diagram of the structure of a forestry survey hiking stick in an embodiment of this application, as shown below. Figure 3 As shown, the trekking pole in this embodiment includes a pole 11, a handle 12, and a control component 13 disposed between the pole 11 and the handle 12. The pole 11 is used to provide support for the trekking pole, the handle 12 is used for the user to hold, and the control component 13 is used to collect interaction data between the trekking pole and the ground.

[0034] It should be noted that when collecting the contact angle between the trekking pole and the ground, the angle of the trekking pole relative to the ground can be collected by the inertial measurement unit (IMU) and angle sensor of the control component 13.

[0035] It should be noted that the support force can be obtained by detecting the pressure between the trekking pole and the ground through the pressure sensor built into the control component 13 of the trekking pole.

[0036] It should be noted that the timing swing angle used for moving the trekking pole can be obtained by detecting the swing angle corresponding to multiple moments in the time sequence using the gyroscope of the control component 13.

[0037] It should be noted that the trekking pole can also be equipped with a corresponding wireless transmission module in the control component 13. The wireless transmission module can send the contact angle between the trekking pole and the ground, the support force, and the timing swing angle to a mobile app or cloud analysis module for analysis and processing.

[0038] S120. Based on the first and second usage data, determine the user's recommended posture when using trekking poles. Send the recommended posture to the user.

[0039] After obtaining the first and second usage data, suggestions for the user's posture when using trekking poles can be determined based on the first and second usage data, thereby assisting the user in conducting forestry surveys using trekking poles.

[0040] In determining usage posture recommendations, these recommendations can quantify the rationality and potential problems of the user's current usage posture. They can include specific guidance on adjusting contact angles, optimizing support strength, and improving swing angles.

[0041] After receiving usage posture suggestions, the control component 13 can send voice-based usage posture suggestions to the user. The operation of sending voice-based usage posture suggestions can be executed instantly. The control component 13 reminds the user to adjust their usage posture, thereby ensuring that the mountaineering process remains within a safe and efficient range.

[0042] For example, preset usage posture suggestions can be reference standards set based on historical usage data.

[0043] For example, when a user is conducting a forestry survey and mountain climbing, if the posture analysis model determines that there is a problem with the user's current posture based on the first and second usage data, it can send posture suggestions to the user, thereby avoiding excessive physical exertion or joint damage caused by improper posture, while improving the safety and efficiency of the mountain climbing process.

[0044] The beneficial effects of the above implementation method are that it acquires first and second usage data of the user using trekking poles in the first and second time periods, respectively. By comparing and analyzing the usage data in these two time periods, it can determine whether the user's current usage posture is reasonable and whether there are any potential inappropriate aspects, and then determine targeted usage posture suggestions. The comparative analysis of usage data in different time periods can accurately capture the changing trends and existing problems of the user's usage posture. By dynamically monitoring and analyzing the characteristics of the user's use of trekking poles and providing posture suggestions, it can effectively guide the user to adopt a more reasonable usage method, reduce the physical burden and safety hazards caused by posture problems, improve the assistive effect of trekking poles in forestry survey work, and allow users to complete survey tasks more easily and safely in complex mountain and forest environments.

[0045] The beneficial effects of the above implementation method are that if the contact angle between the trekking pole and the ground is relatively appropriate in the first time period, but the angle deviates significantly in the second time period, it can be determined that the user's posture has changed improperly, and targeted suggestions can be made to adjust the angle to help the user correct it in time and avoid excessive physical exertion or joint damage due to improper posture over a long period of time. The analysis of support force and swing process can make the suggestions more comprehensive, ensuring that the user can obtain effective assistance while maintaining the coordination and economy of movement when using trekking poles, thereby improving the safety and efficiency during the mountain climbing process.

[0046] In some implementations, in S120 above, a user's posture suggestion for using trekking poles is determined based on the first usage data and the second usage data, including S121 to S122. S121 to S122 will be explained in detail below.

[0047] S121. Based on the experience value table for forestry survey mountaineering scenarios, and according to user ergonomic data, determine the contact angle change threshold corresponding to the contact angle in the first and second usage data, the support force change threshold corresponding to the support force, and the maximum swing angle change threshold corresponding to the maximum swing angle in the time-series swing angle. Determine the contact angle change value, support force change value, and maximum swing angle change value corresponding to the first and second usage data.

[0048] In this implementation, based on an experience value table for forestry surveys and mountaineering scenarios, and according to user ergonomic data, the contact angle change threshold corresponding to the contact angle in the first usage data and the support force change threshold corresponding to the support force, and the maximum swing angle change threshold corresponding to the maximum swing angle in the time-series swing angle are determined. The contact angle change threshold, support force change threshold, and maximum swing angle change threshold represent the thresholds of ground contact, support, and swing when the user uses the trekking pole in a normal state. Then, the usage status of the trekking pole is detected by the contact angle change threshold, support force change threshold, and maximum swing angle change threshold.

[0049] For example, the experience value table can contain multiple experience thresholds for different user ergonomic parameters in forestry survey mountaineering scenarios. These experience thresholds can be obtained based on historical trekking pole usage data, making the threshold settings more in line with the actual needs of mountaineering scenarios.

[0050] For example, during forestry surveys and mountaineering, user ergonomic data can include parameters such as height, arm length, and weight. Experience tables can be used to adapt personalized contact angle change thresholds, support force change thresholds, and maximum swing angle change thresholds for different users based on these parameters, thereby improving the accuracy and applicability of threshold settings.

[0051] In this implementation, the contact angle change value, support force change value, and maximum swing angle change value corresponding to the first usage data and the second usage data can be determined. These change values ​​can be obtained by calculating the difference between the contact angle, support force, and maximum swing angle of the first usage data and the second usage data, reflecting the changes in the user's posture parameters during the use of the trekking pole.

[0052] For example, the change value of the contact angle can be obtained by calculating the difference between the average contact angles corresponding to the first and second usage data, the change value of the support force can be determined by calculating the difference between the average support force corresponding to the first and second usage data, and the change value of the maximum swing angle can be obtained by calculating the difference between the maximum swing angles in the time-series swing angles corresponding to the first and second usage data, thereby comprehensively assessing the user's dynamic posture adjustment needs.

[0053] For example, the change in contact angle can be obtained by calculating the difference between the maximum contact angle values ​​corresponding to the first and second usage data, and the change in support force can be determined by calculating the difference between the maximum support force values ​​corresponding to the first and second usage data.

[0054] S122. When the change in contact angle is greater than or equal to the contact angle change threshold, the recommended posture is to adjust the contact angle of the trekking poles. When the change in support force is greater than or equal to the support force change threshold, the recommended posture is to adjust the height of the trekking poles. When the change in maximum swing angle is greater than or equal to the maximum swing angle change threshold, the recommended posture is to adjust the user's stride length.

[0055] In this implementation, when the change in contact angle is greater than or equal to the contact angle change threshold, the posture suggestion can be determined to be to correct the contact angle of the trekking pole. Then, based on the real-time comparison between the change in contact angle and the contact angle change threshold, adjustment suggestions can be given in a timely manner when the contact angle deviates from the reasonable range.

[0056] For example, during forestry surveys and mountain climbing, if the detected change in contact angle exceeds the contact angle change threshold, the user can be prompted to adjust the contact angle between the trekking poles and the ground to avoid wrist fatigue or instability caused by improper angles, thereby improving the auxiliary effect of trekking poles in forestry surveys.

[0057] In this implementation, when the change in support force is greater than or equal to the threshold value of the change in support force, it can be determined that the recommended posture is to adjust the height of the trekking poles. This judgment takes into account the degree of change in support force, and when the support force is abnormal, it can be suggested to adjust the height to optimize the force distribution.

[0058] For example, if the change in support force exceeds the threshold during the use of trekking poles, it can be suggested that the user adjust the extension height of the trekking poles to restore the support force to a comfortable range, reduce the load on the arms and shoulders, and improve the auxiliary effect of trekking poles in forestry surveys.

[0059] In this implementation, when the maximum swing angle change value is greater than or equal to the maximum swing angle change threshold, it can be determined that the posture suggestion is to adjust the user's hiking stride. This judgment focuses on the trend of swing angle change. When the swing amplitude is abnormal, it can suggest adjusting the stride to maintain the walking rhythm.

[0060] For example, when hiking during forestry surveys, if the maximum swing angle change value exceeds the maximum swing angle change threshold, the user can be prompted to adjust their stride length to make the swing of the trekking poles more coordinated with their steps, avoiding energy waste or instability caused by improper stride length, and improving the auxiliary effect of trekking poles in forestry surveys.

[0061] The beneficial effects of the above implementation method are that, based on the experience value table for forestry survey mountaineering scenarios and combined with the user's ergonomic data, it clarifies the contact angle change threshold corresponding to the contact angle in the first usage data and the support force change threshold corresponding to the support force, as well as the maximum swing angle change threshold corresponding to the maximum swing angle in the time-series swing angle. By calculating the contact angle change value, support force change value, and maximum swing angle change value between the first and second usage data, specific suggestions can be given based on the comparison results of these change values ​​with the corresponding thresholds. It can accurately identify abnormal changes in different dimensions during the use of trekking poles and provide specific and actionable adjustment suggestions, thereby effectively helping users optimize their posture, improve the comfort and safety of trekking pole use, increase the mountaineering efficiency during forestry surveys, and better adapt to the actual needs of forestry survey scenarios.

[0062] The beneficial effects of the above implementation method are that when the change value of the contact angle is greater than or equal to the contact angle change threshold, it is recommended to adjust the contact angle of the trekking pole; when the change value of the support force is greater than or equal to the support force change threshold, it is recommended to adjust the height of the trekking pole; when the change value of the maximum swing angle is greater than or equal to the maximum swing angle change threshold, it is recommended to adjust the user's hiking stride. By introducing a scenario-based experience value table and quantitative threshold judgment, the generation of trekking pole usage posture suggestions becomes more targeted and scientific.

[0063] Figure 4 This is a flowchart illustrating the second method for monitoring the status of mountaineering assistance equipment in forestry surveys, as provided in this application embodiment. Figure 4 As shown, the above method also includes S210 to S220, which will be described in detail below.

[0064] S210. Obtain the user's walking trajectory data for the first and second time periods. Obtain the geographical data corresponding to the walking trajectory data. Determine the environmental risk coefficient corresponding to the walking trajectory data based on the geographical data.

[0065] Figure 5 A schematic diagram illustrating the workflow of the second method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment is shown below. Figure 5 As shown, in this implementation, the user's walking trajectory data corresponding to the first and second time periods can be obtained. This walking trajectory data records the user's movement path and location information during the forestry survey and mountaineering process. The walking trajectory data can be collected through the positioning module built into the trekking pole, such as a global positioning system receiver or an inertial measurement unit. This data can reflect the user's actual movement in complex terrain.

[0066] In this implementation, geographic data corresponding to the walking trajectory data can be obtained. The geographic data includes environmental feature information such as terrain slope, land surface type, and altitude. This geographic data can be obtained by connecting to a geographic information system database or online map service, providing basic data support for subsequent environmental risk assessment.

[0067] After obtaining the geographic data corresponding to the walking trajectory data, the environmental risk coefficient corresponding to the walking trajectory data can be further determined based on the geographic data corresponding to the walking trajectory data. The environmental risk coefficient is used to quantify the degree of impact of the current environment on the user's physical condition during the mountain climbing process. The environmental risk coefficient can be calculated by risk assessment algorithm, taking into account a variety of environmental factors such as the steepness of the terrain, the slipperiness of the ground, and the density of vegetation cover.

[0068] S220. Determine the product of the environmental risk coefficient and the contact angle change threshold to adjust the contact angle change threshold. Determine the product of the environmental risk coefficient and the contact angle change threshold to adjust the support force change threshold. Determine the product of the environmental risk coefficient and the maximum swing angle change threshold to adjust the maximum swing angle change threshold.

[0069] like Figure 5 As shown, after obtaining the environmental risk coefficient, the product of the environmental risk coefficient and the contact angle change threshold can be determined to adjust the contact angle change threshold. By multiplying the environmental risk coefficient with the preset contact angle change threshold, an adjusted threshold adapted to the current environmental risk level can be obtained, so that the judgment criteria for contact angle change can change dynamically according to environmental risk.

[0070] like Figure 5As shown, after obtaining the environmental risk coefficient, the product of the environmental risk coefficient and the support strength change threshold can be determined to adjust the support strength change threshold. This adjustment method allows the monitoring standard for support strength change to be adjusted accordingly with the change of the environmental risk coefficient, and more stringent monitoring standards can be adopted in high-risk environments.

[0071] like Figure 5 As shown, after obtaining the environmental risk coefficient, the product of the environmental risk coefficient and the maximum swing angle change threshold can be determined to adjust the maximum swing angle change threshold. Through this product calculation method, the allowable range of the maximum swing angle can be adaptively adjusted according to the actual environmental risk situation, thereby improving the accuracy of monitoring.

[0072] For example, during forestry surveys and mountaineering, the environmental risk factor increases when users move from gentle forests to steep slopes. By adjusting the product of three thresholds, the monitoring standards for trekking pole usage posture can be made more stringent, better adapting to the usage needs in high-risk environments.

[0073] The beneficial effects of the above implementation method are that by acquiring the user's walking trajectory data in the first and second time periods, and then obtaining the geographical data corresponding to the walking trajectory data through map data, the environmental risk coefficient corresponding to the walking trajectory data is determined based on this geographical data. The environmental risk coefficient is then multiplied by the contact angle change threshold, the support force change threshold, and the maximum swing angle change threshold, respectively. These three thresholds are adjusted so that the contact angle change threshold, the support force change threshold, and the maximum swing angle change threshold can be dynamically adjusted according to the actual environmental risk situation. This makes the usage posture suggestions determined based on these thresholds more in line with the actual needs in different environments, improves the adaptability and accuracy of the status monitoring of forestry survey mountaineering assistance equipment, and better assists users in using trekking poles reasonably in different environments.

[0074] In some implementations, in S210 above, the environmental risk coefficient corresponding to the walking trajectory data is determined based on the geographical data corresponding to the walking trajectory data, including S211 to S212. S211 to S212 will be explained in detail below.

[0075] S211. Obtain the mileage index, altitude change, and road condition index corresponding to the walking trajectory data. Among them, the mileage index represents the walking distance corresponding to the walking trajectory data, the altitude change represents the sum of the altitude rise and fall corresponding to the walking trajectory data, and the road condition index represents the proportion of rugged road sections corresponding to the walking trajectory data.

[0076] Figure 6A schematic diagram illustrating the workflow of the third method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment is shown below. Figure 6 As shown, the mileage index, altitude change and road condition index corresponding to the walking trajectory data can be obtained. The mileage index represents the walking distance corresponding to the walking trajectory data, the altitude change represents the sum of the altitude increase and decrease corresponding to the walking trajectory data, and the road condition index represents the proportion of rugged road sections corresponding to the walking trajectory data. These data can be collected in real time through the positioning module and motion sensor built into the trekking pole control component 13, thereby enabling comprehensive monitoring of the mountaineering process.

[0077] S212. Determine the basic environmental risk coefficient based on mileage indicators and altitude changes using an empirical value table. Determine the environmental risk coefficient correction factor based on road condition indicators using an empirical value table. Calculate the product of the basic environmental risk coefficient and the environmental risk coefficient correction factor as the environmental risk coefficient.

[0078] In this implementation, the basic environmental risk coefficient can be determined based on mileage indicators and altitude changes using an experience value table. The experience value table can serve as a reference standard based on historical mountaineering data. The query process of the experience value table takes into account the overall impact of mileage and altitude changes. The basic environmental risk coefficient quantifies the basic risk level caused by changes in walking distance and altitude during the mountaineering process.

[0079] For example, an empirical table of basic environmental risk coefficients based on mileage indicators and altitude changes can be obtained by summarizing and compiling historical records of previous forestry surveys.

[0080] For example, the greater the changes in mileage and altitude, the greater the basic environmental risk coefficient.

[0081] Furthermore, an environmental risk coefficient correction factor can be determined based on road condition indicators using an empirical value table. The empirical value table can be an adjustment coefficient set based on different road condition types. The query process of the empirical value table takes into account the specific impact of road roughness. The environmental risk coefficient correction factor represents the degree to which road condition conditions can be adjusted for basic environmental risks.

[0082] For example, an empirical table of correction factors for environmental risk coefficients based on road condition indicators can be obtained by summarizing and compiling historical records of previous forestry surveys.

[0083] For example, the higher the road condition index, the larger the environmental risk coefficient correction factor.

[0084] In this implementation method, the product of the basic environmental risk coefficient and the environmental risk coefficient correction factor can be determined as the environmental risk coefficient. The calculation process of the environmental risk coefficient comprehensively considers the combined effects of mileage, altitude changes and road conditions, making the risk assessment more comprehensive and accurate.

[0085] For example, during forestry surveys and mountaineering, when the distance is long, the altitude changes significantly, and the road conditions are rugged, the environmental risk coefficient calculated using the above method will be correspondingly higher, reflecting the actual risk situation of the mountaineering process.

[0086] The beneficial effects of the above implementation method are that it obtains the mileage index, altitude change, and road condition index corresponding to the walking trajectory data. Then, through an empirical value table, it determines the basic environmental risk coefficient based on the mileage index and altitude change, and determines the environmental risk coefficient correction factor based on the road condition index. Multiplying the basic environmental risk coefficient and the environmental risk coefficient correction factor yields the environmental risk coefficient. This comprehensively considers the overall fluctuation of mileage and altitude, as well as the ruggedness of the road, making the calculation of the environmental risk coefficient more comprehensive and more in line with the actual situation of forestry survey mountaineering scenarios. It improves the scientificity and practicality of the condition monitoring method and helps users use mountaineering assistance equipment more safely and effectively.

[0087] Figure 7 A flowchart illustrating the fourth method for monitoring the status of forestry survey mountaineering assistance equipment provided in this application embodiment is shown below. Figure 7 As shown, the above method also includes S310 to S330, which will be described in detail below.

[0088] S310. Numerically represent the mileage, altitude change, and road condition indicators corresponding to the first walking trajectory data for the first time period, and determine the first trajectory feature vector. Numerically represent the mileage, altitude change, and road condition indicators corresponding to the second walking trajectory data for the second time period, and determine the second trajectory feature vector. Numerically represent the contact angle, support force, and maximum swing angle of the first usage data, and determine the first usage feature vector. Numerically represent the contact angle, support force, and maximum swing angle of the second usage data, and determine the second usage feature vector.

[0089] In this implementation, the mileage index, altitude change and road condition index corresponding to the first walking trajectory data corresponding to the first time period can be numerically represented to determine the first trajectory feature vector. The first trajectory feature vector is a vector that comprehensively represents the characteristics of the mileage index, altitude change and road condition index corresponding to the first walking trajectory data.

[0090] In this implementation, the mileage index, altitude change and road condition index corresponding to the second walking trajectory data corresponding to the second time period can be numerically represented to determine the second trajectory feature vector. The second trajectory feature vector is a vector that comprehensively represents the mileage index, altitude change and road condition index corresponding to the second walking trajectory data.

[0091] In this implementation, the contact angle, support force, and maximum swing angle of the first data can be numerically represented to determine the first feature vector, which represents the comprehensive characteristics of the first data.

[0092] In this implementation, the contact angle, support force, and maximum swing angle of the second usage data are numerically represented to determine the second usage feature vector, which represents the comprehensive characteristics of the second usage data.

[0093] For example, during a forestry survey and mountaineering activity, walking trajectory data and usage data can be continuously acquired. Walking trajectory data includes mileage indicators, altitude changes, and road condition indicators, which can be collected in real time through the positioning module and inertial measurement unit built into the trekking pole. Usage data includes contact angle, support force, and maximum swing angle, which can be acquired through the pressure sensor and attitude sensor at the handle of the trekking pole. By representing these data numerically using the above methods, a feature vector that can characterize the walking environment and usage status can be constructed.

[0094] S320. Determine the similarity between the first trajectory feature vector and the second trajectory feature vector, as the trajectory feature similarity. Determine the similarity between the first usage feature vector and the second usage feature vector, as the usage feature similarity. Determine the difference between the trajectory feature similarity and the usage feature similarity, as the pose change feature value.

[0095] In this implementation, the similarity between the first trajectory feature vector and the second trajectory feature vector can be determined as the trajectory feature similarity. At the same time, the similarity between the first usage feature vector and the second usage feature vector can be determined as the usage feature similarity. Furthermore, the difference between the trajectory feature similarity and the usage feature similarity can be determined as the pose change feature value.

[0096] Using the calculation method in S320, trajectory feature similarity reflects the similarity of the walking environment in two time periods, and usage feature similarity reflects the similarity of the user's use of trekking poles in two time periods. By calculating the difference between trajectory feature similarity and usage feature similarity, the posture change feature value can be obtained. The posture change feature value can measure the degree of matching between the user's posture and the changes in the walking environment. When the posture change feature value is large, it indicates that the user's posture may not match the changes in the environment.

[0097] S330. When the trajectory feature similarity is greater than or equal to the preset trajectory feature similarity, and the posture change feature value is greater than or equal to the preset posture change feature value, a suggestion message is issued to prompt the user to adjust the posture of using the trekking poles or to take a proper rest.

[0098] In this implementation, when the trajectory feature similarity is greater than or equal to the preset trajectory feature similarity, it indicates that the first walking trajectory data and the second walking trajectory data are highly similar. At the same time, when the posture change feature value is greater than or equal to the preset posture change feature value, it indicates that the first usage data and the second usage data are less similar, that is, the user's usage posture has changed significantly. Therefore, a suggestion message can be issued to prompt the user to adjust the posture of using the trekking poles or to take a proper rest.

[0099] For example, during forestry surveys and mountaineering, if it is detected that a user's walking environment is similar at different times, but the postural characteristics of using trekking poles are significantly different, it can be determined that the user may be experiencing abnormal posture due to fatigue. In this case, the user can be prompted to adjust their posture or rest through voice or vibration.

[0100] The beneficial effects of the above implementation method are as follows: Based on the walking trajectory data corresponding to the first and second time periods, mileage indicators, altitude change and road condition indicators are extracted respectively to construct the first trajectory feature vector and the second trajectory feature vector. These two vectors can characterize the key features of the walking environment in different time periods. According to the contact angle, support force and maximum swing angle in the usage data in the two time periods, the first usage feature vector and the second usage feature vector are constructed to reflect the user's state characteristics of using trekking poles in different time periods. The similarity between the first trajectory feature vector and the second trajectory feature vector is further calculated as the trajectory feature similarity, which reflects the degree of similarity of the walking environment in the two time periods. The similarity between the first usage feature vector and the second usage feature vector is calculated as the usage feature similarity, which reflects the degree of similarity of the user's state of using trekking poles in the two time periods. The difference between trajectory feature similarity and usage feature similarity is determined as the posture change feature value. The posture change feature value is used to measure the degree of matching between the user's posture and changes in the walking environment. When the posture change feature value is greater than or equal to the preset posture change feature value, it indicates that the user's posture may not match the current environment, or that the posture is abnormal due to factors such as fatigue. At this time, a suggestion message is issued to prompt the user to adjust the posture of using trekking poles or to take an appropriate rest.

[0101] The beneficial effects of the above implementation method are that, by using the similarity difference between trajectory features and usage features, it is possible to more accurately capture the adaptability and abnormal changes between the user's posture and the walking environment, and promptly prompt the user to make adjustments or rest. This effectively improves the accuracy and dynamic adaptability of the status monitoring of mountaineering assistance equipment, further ensuring the safety and comfort of users during forestry surveys and mountaineering, and making up for the limitations of existing technologies that rely solely on fixed threshold judgments.

[0102] Figure 8 A flowchart illustrating the fifth method for monitoring the status of mountaineering assistance equipment in forestry surveys provided in this application is shown below. Figure 8 As shown, the above method also includes S410 to S430, which will be described in detail below.

[0103] S410. Obtain multiple historical records of the user's use of trekking poles, and obtain the walking trajectory data corresponding to each historical record. Based on the mileage index, altitude change, and road condition index corresponding to each walking trajectory data, cluster the walking trajectory data corresponding to the multiple historical records to obtain multiple walking trajectory data clusters. Each walking trajectory data cluster includes multiple walking trajectory data.

[0104] In this implementation, multiple historical records of a user's use of trekking poles can be obtained, along with corresponding walking trajectory data. The walking trajectory data can include the user's location information, altitude changes, and terrain features during the climb. By obtaining these historical records and corresponding walking trajectory data, a comprehensive understanding of the user's usage habits in different mountaineering environments can be achieved.

[0105] After obtaining the walking trajectory data, cluster analysis can be performed on the walking trajectory data corresponding to multiple historical records according to the mileage index, altitude change, and road condition index respectively. Cluster analysis can group walking trajectory data with similar environmental characteristics into a group, forming multiple walking trajectory data clusters. After obtaining multiple walking trajectory data clusters, each walking trajectory data cluster contains multiple walking trajectory data that are similar in terms of mileage length, altitude change, and road condition.

[0106] For example, during forestry surveys and mountain climbing, users may use trekking poles in different mountainous terrains. Through cluster analysis, walking tracks on flat forest trails, walking tracks on steep slopes, and walking tracks on rugged rocky areas can be categorized into different walking track data clusters.

[0107] S420. Obtain the trekking pole usage records corresponding to multiple walking trajectory data for each walking trajectory data cluster group, and determine the average contact angle, average support force, and average maximum swing angle of the trekking pole usage records corresponding to multiple walking trajectory data within each walking trajectory data cluster group.

[0108] In this implementation, the trekking pole usage records corresponding to multiple walking trajectory data for each walking trajectory data cluster can be obtained. The trekking pole usage records can include sensor data during use. Through these usage records, the user's usage characteristics under different environmental conditions can be analyzed.

[0109] After obtaining the walking trajectory data clusters, for each walking trajectory data cluster, the average contact angle, average support force, and average maximum swing angle of the trekking pole usage records corresponding to all walking trajectory data in the group can be calculated.

[0110] For example, for a walking trajectory data cluster group consisting of steep hillsides, the average contact angle, average support force, and average maximum swing angle of users when using trekking poles in this type of terrain can be calculated. These mean values ​​reflect the typical usage patterns of users in the typical environment corresponding to the walking trajectory data cluster group.

[0111] S430. Determine the product of the average contact angle and the preset ratio value as the contact angle change threshold. Determine the product of the average support force and the preset ratio value as the support force change threshold. Determine the product of the average maximum swing angle and the preset ratio value as the maximum swing angle change threshold.

[0112] In this implementation, the product of the average contact angle and the preset ratio can be further determined as the contact angle change threshold. This change threshold defines the allowable range of contact angle change in this type of walking trajectory environment. When the contact angle in actual use exceeds this threshold range, it can be considered that there may be a deviation in the usage posture.

[0113] In this implementation, the product of the average support strength and the preset ratio can be further determined as the support strength change threshold. The support strength change threshold reflects the reasonable limit of support strength change under such environmental conditions. This threshold can be used to determine whether the user has used appropriate support strength.

[0114] In this implementation, the product of the average maximum swing angle and the preset ratio can be further determined as the maximum swing angle change threshold. The maximum swing angle change threshold limits the reasonable range of change of the trekking pole swing angle, which helps to identify abnormal swing amplitude.

[0115] For example, in forestry surveys and mountaineering, when users use trekking poles in similar terrain, the system can compare the current usage data with the corresponding walking trajectory data clusters in real time to identify posture deviations and provide adjustment suggestions.

[0116] The beneficial effects of the above implementation method are as follows: It acquires multiple historical records of a user's use of trekking poles and the corresponding walking trajectory data for each record. Based on the mileage, altitude change, and road condition indicators of the walking trajectory data, these data are clustered into multiple walking trajectory data groups. Each cluster contains multiple walking trajectory data with similar environmental characteristics. For each walking trajectory data cluster, the trekking pole usage records corresponding to all walking trajectory data within the group are further acquired. The average values ​​of contact angle, support force, and maximum swing angle within the walking trajectory data cluster are calculated. These average values ​​are then used as the average values ​​of contact angle, support force, and maximum swing angle for the corresponding walking trajectory data cluster. These average values ​​are multiplied by preset proportions to obtain new threshold values ​​for contact angle change, support force change, and maximum swing angle change. This allows for cluster analysis using the user's historical usage data combined with the environmental characteristics of the walking trajectory, making the determined change thresholds more closely match the user's actual usage habits and states in different environments. This enables more accurate judgment of the rationality of the user's posture, improves the accuracy and effectiveness of posture suggestions, better assists users in adjusting their trekking pole usage posture, and enhances comfort and safety during hiking.

[0117] In some implementations, in S430 above, the preset ratio value is determined by the following method: using an empirical value table, the preset ratio value is determined based on the average mileage index, average altitude change, and average road condition index corresponding to each walking trajectory data cluster group.

[0118] In this implementation, an empirical value table can be used to determine the mean mileage information, mean altitude change, and mean road condition index corresponding to each walking trajectory data cluster group, and to determine the preset ratio value. The empirical value table can contain recommended values ​​of the preset ratio value corresponding to different ranges of mean mileage information, mean range of mean altitude change, and mean range of mean road condition index, making the process of determining the preset ratio value interpretable and operable.

[0119] For example, in the scenario of using trekking poles to assist mountaineering in forestry surveys, for a certain group of walking trajectory data, the average mileage is five kilometers, the average altitude change is three hundred meters, and the average road condition index is complex mountainous terrain. By consulting the experience value table, the corresponding preset ratio value can be obtained as 15%. This preset ratio value will then be used to calculate the contact angle change threshold, the support force change threshold, and the maximum swing angle change threshold, providing a reference for adjusting the posture of using trekking poles.

[0120] The beneficial effects of the above implementation method are as follows: After obtaining multiple walking trajectory data clusters, the average mileage information, average altitude change, and average road condition index corresponding to each cluster are calculated. Then, based on these averages, a preset ratio value corresponding to the cluster is determined through an empirical value table. This preset ratio value is then multiplied by the average contact angle, average support force, and average maximum swing angle of the cluster to obtain the corresponding change threshold. This method can combine the average environmental characteristics of each walking trajectory data cluster to determine the preset ratio value, making the preset ratio value adaptable to the environmental characteristics of different clusters. Consequently, the calculated contact angle change threshold, support force change threshold, and maximum swing angle change threshold are more in line with the actual environment and user usage of the cluster. Compared with the method of determining the preset ratio value based solely on the user's historical usage data clustering, this method improves the accuracy and adaptability of the change threshold, and can more effectively provide users with reasonable usage posture suggestions, enhancing the convenience and safety of forestry surveys and mountaineering.

[0121] In some implementations, the above method also includes S440 to S450, which will be described in detail below.

[0122] S440. Determine multiple similarities for the mileage index, altitude change, road condition index, and the mean mileage index, mean altitude change, and mean road condition index corresponding to the first walking trajectory data cluster group, and determine the first walking trajectory data cluster group corresponding to the maximum similarity among the multiple similarities. Similarly, determine multiple similarities for the mileage index, altitude change, road condition index, and the mean mileage index, mean altitude change, and mean road condition index corresponding to the second walking trajectory data cluster group, and determine the second walking trajectory data cluster group corresponding to the maximum similarity among the multiple similarities.

[0123] In this implementation, multiple similarities can be determined for the mileage information, altitude change, road condition indicators, and the mean values ​​of mileage information, altitude change, and road condition indicators corresponding to the first walking trajectory data cluster group. The first walking trajectory data cluster group corresponding to the maximum similarity among the multiple similarities can be determined, thus obtaining the first walking trajectory data cluster group with the most similar mean values ​​of mileage information, altitude change, and road condition indicators.

[0124] In this implementation, similarity calculation can reflect the degree of matching between the current walking trajectory and the historical clusters in terms of mileage, altitude and road condition features. The cluster corresponding to the maximum similarity can be regarded as the historical usage pattern of the trekking pole that is closest to the current environment.

[0125] For example, during forestry surveys and mountaineering, when mountaineers use trekking poles, they can collect information on the mileage, altitude change, and road conditions of their current walking trajectory. This information can then be compared with the mean of multiple pre-established walking trajectory data clusters to identify the cluster with the highest similarity as the first walking trajectory data cluster.

[0126] Similarly, multiple similarities can be determined for the mileage information, altitude change, road condition indicators, and the mean mileage information, mean altitude change, and mean road condition indicators corresponding to the second walking trajectory data cluster group. The second walking trajectory data cluster group corresponding to the maximum similarity among the multiple similarities can be determined, and the classification of the second walking trajectory data follows the same calculation logic as the first walking trajectory data.

[0127] For example, for the walking trajectory data of the same climber at different times, the similarity between the walking trajectory data at different times and each cluster group can be calculated separately, and then the corresponding maximum similarity cluster group can be determined, which provides a basis for subsequent threshold matching.

[0128] S450. When the first walking trajectory data cluster group and the second walking trajectory data cluster group are the same, obtain the first contact angle change threshold, the first support force change threshold, and the first maximum swing angle change threshold corresponding to the first walking trajectory data cluster group. When the contact angle change value is greater than or equal to the first contact angle change threshold, determine that the recommended posture is to correct the contact angle of the trekking poles. When the support force change value is greater than or equal to the first support force change threshold, determine that the recommended posture is to adjust the height of the trekking poles. When the maximum swing angle change value is greater than or equal to the first maximum swing angle change threshold, determine that the recommended posture is to adjust the user's hiking stride.

[0129] In this implementation, when the first walking trajectory data cluster group and the second walking trajectory data cluster group are the same, the first contact angle change threshold, the first support force change threshold and the first maximum swing angle change threshold corresponding to the first walking trajectory data cluster group can be obtained. The first contact angle change threshold, the first support force change threshold and the first maximum swing angle change threshold are calculated by the above-mentioned S410 to S430.

[0130] For example, in the use of trekking poles to assist in mountaineering, if the current walking trajectory is classified into the same cluster group, the contact angle, support force, and swing angle thresholds preset by the same walking trajectory data cluster group can be used.

[0131] In this implementation, when the change in contact angle is greater than or equal to the first contact angle change threshold, it can be determined that the posture suggestion is to correct the contact angle of the trekking pole, so as to detect the contact angle deviation problem in a timely manner.

[0132] For example, when hikers use trekking poles on steep slopes, if the change in contact angle exceeds a threshold, it can be suggested that the user adjust the contact angle between the pole tip and the ground to improve support and reduce the risk of slipping.

[0133] In this implementation, when the change in support force is greater than or equal to the first threshold for change in support force, it can be determined that the recommended posture is to adjust the height of the trekking poles. The change in support force can reflect problems such as uneven force applied by the user or unsuitable pole height.

[0134] For example, during forestry surveys and mountaineering, if the support strength data indicates that the user's left and right hands are not exerting force equally and exceed a threshold, it can be suggested that the user adjust the height setting of the trekking poles to optimize load distribution and walking comfort.

[0135] In this implementation, when the maximum swing angle change value is greater than or equal to the first maximum swing angle change threshold, it can be determined that the posture suggestion is to adjust the user's climbing stride. The swing angle change can be associated with stride size and walking rhythm, which helps to identify situations where stride is not coordinated.

[0136] For example, if a user's maximum swing angle increases abnormally during a long walk, it may indicate that the stride is too large or the rhythm is disordered. The user can be advised to adjust their stride to improve walking efficiency and reduce fatigue.

[0137] The beneficial effect of the above implementation method is that it calculates multiple similarities between the mileage information, altitude change, and road condition indicators corresponding to the first walking trajectory data and the average mileage information, average altitude change, and average road condition indicators of each walking trajectory data cluster group, and finds the first walking trajectory data cluster group corresponding to the highest similarity. Similarly, it determines the second walking trajectory data cluster group corresponding to the second walking trajectory data. When the first and second walking trajectory data cluster groups are the same, it obtains the contact angle change threshold, support force change threshold, and maximum swing angle change threshold corresponding to that cluster group. Then, it determines the adjustment suggestions for the user's trekking pole usage based on the corresponding thresholds, which makes the judgment criteria more suitable for the current environment, and the trekking pole usage suggestions are similar to the environmental characteristics. The thresholds determined based on the same cluster group are more targeted.

[0138] The beneficial effect of the above implementation method is that by matching the threshold of cluster groups with the same environmental characteristics, it avoids misjudgment caused by using a uniform standard when there are large differences in the environment. It can more accurately identify the posture problems that need to be adjusted, thereby providing users with more reasonable suggestions and enhancing the safety and comfort of forestry surveys and mountaineering.

[0139] In some implementations, the above method also includes S460 to S470, which will be described in detail below.

[0140] S460. When the first walking trajectory data cluster group and the second walking trajectory data cluster group are different, obtain the first contact angle change threshold, the first support force change threshold, and the first maximum swing angle change threshold corresponding to the first walking trajectory data cluster group, and obtain the second contact angle change threshold, the first support force change threshold, and the second maximum swing angle change threshold corresponding to the second walking trajectory data cluster group. Determine the larger value between the first contact angle change threshold and the second contact angle change threshold as the target contact angle change threshold. Determine the larger value between the first support force change threshold and the second support force change threshold as the target support force change threshold. Determine the larger value between the first maximum swing angle change threshold and the second maximum swing angle change threshold as the target maximum swing angle change threshold.

[0141] In this implementation, when the first walking trajectory data cluster group and the second walking trajectory data cluster group are different, the first contact angle change threshold, the first support force change threshold and the first maximum swing angle change threshold corresponding to the first walking trajectory data cluster group can be obtained, and the second contact angle change threshold, the second support force change threshold and the second maximum swing angle change threshold corresponding to the second walking trajectory data cluster group can be obtained. Then, the user's posture when using the trekking pole can be detected based on the first walking trajectory data cluster group and the second walking trajectory data cluster group.

[0142] After obtaining the first contact angle change threshold, the first support force change threshold, the first maximum swing angle change threshold, the second contact angle change threshold, the second support force change threshold, and the second maximum swing angle change threshold, the larger value between the first contact angle change threshold and the second contact angle change threshold can be determined as the target contact angle change threshold; the larger value between the first support force change threshold and the second support force change threshold can be determined as the target support force change threshold; the larger value between the first maximum swing angle change threshold and the second maximum swing angle change threshold can be determined as the target maximum swing angle change threshold. By selecting the larger value as the target threshold, it is possible to ensure that the judgment criteria are more lenient and adapt to more complex changes in the mountaineering environment.

[0143] S470. When the change in contact angle is greater than or equal to the target contact angle change threshold, the recommended posture is to adjust the contact angle of the trekking poles. When the change in support force is greater than or equal to the target support force change threshold, the recommended posture is to adjust the height of the trekking poles. When the change in maximum swing angle is greater than or equal to the target maximum swing angle change threshold, the recommended posture is to adjust the user's stride length.

[0144] In this implementation, when the change in contact angle is greater than or equal to the target contact angle change threshold, the recommended posture is to correct the contact angle of the trekking pole; when the change in support force is greater than or equal to the target support force change threshold, the recommended posture is to adjust the height of the trekking pole; when the change in maximum swing angle is greater than or equal to the target maximum swing angle change threshold, the recommended posture is to adjust the user's stride length. Thus, the more lenient judgment criteria in S460 help users optimize the use of trekking poles and improve hiking efficiency.

[0145] For example, during forestry surveys and mountaineering, users may switch walking modes in different terrain environments. When a change in the clustering group of the user's walking trajectory data is detected, the judgment threshold can be automatically adjusted to ensure that the usage suggestions given are more accurate.

[0146] The beneficial effect of the above implementation method is that it calculates multiple similarities between the mileage information, altitude change, road condition indicators, and corresponding means of each walking trajectory data cluster group of the first walking trajectory data, and determines the first walking trajectory data cluster group corresponding to the maximum similarity; similarly, it determines the second walking trajectory data cluster group corresponding to the second walking trajectory data. When the two cluster groups are different, it obtains the first contact angle change threshold, the first support force change threshold, and the first maximum swing angle change threshold corresponding to the first cluster group, and the second contact angle change threshold, the second support force change threshold, and the second maximum swing angle change threshold corresponding to the second cluster group; it selects the larger value of the first and second contact angle change thresholds as the target contact angle change threshold, the larger value of the first and second support force change thresholds as the target support force change threshold, and the larger value of the first and second maximum swing angle change thresholds as the target maximum swing angle change threshold. Then, based on the corresponding thresholds, it determines the adjustment suggestions for the user's trekking pole use, which makes the judgment criteria more adaptable to different environments and further improves the scientific nature of the trekking pole use suggestions in different environments.

[0147] The beneficial effect of the above implementation method is that by selecting a larger threshold for different cluster groups as the basis for judgment, it can not only adapt to the influence of environmental differences on usage habits, but also effectively avoid false prompts, making the suggestions more in line with the changes in actual mountain climbing scenarios, thereby better assisting users in adjusting the posture of using trekking poles and enhancing the safety and adaptability of forestry survey mountain climbing.

[0148] This application also provides a status monitoring device for forestry survey mountaineering assistance equipment, including a unit for performing the method described in any of the preceding claims.

[0149] Figure 9A schematic diagram of the logical structure of a status monitoring device for forestry survey mountaineering assistance equipment provided in this application embodiment is shown below. Figure 9 As shown, the system 2 of this embodiment includes a processing unit 21, a storage unit 22, and a transceiver unit 23. The processing unit 21 is used to process data, the storage unit 22 is used to store data, and the transceiver unit 23 is used to send and receive data. The processing unit 21, the storage unit 22, and the transceiver unit 23 cooperate with each other to implement the above-described method. The beneficial effects of the embodiments of this application have been described in the above-described method and will not be repeated here.

[0150] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0151] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0153] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0154] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for monitoring the condition of mountaineering assistance equipment used in forestry surveys, characterized in that, The method includes: Acquire first usage data of the user using trekking poles in a first time period and second usage data of the user using trekking poles in a second time period; wherein, both the first and second usage data include the user's ergonomic data, the contact angle between the trekking pole and the ground, the support force between the trekking pole and the ground, and the timing swing angle of the trekking pole during movement and use, and the second time period is the subsequent time period of the first time period; Based on the first and second usage data, determine the recommended posture for the user when using trekking poles; and send the recommended posture to the user.

2. The method as described in claim 1, characterized in that, Based on the first and second usage data, recommendations for user posture when using trekking poles are determined, including: Based on an experience value table for forestry survey mountaineering scenarios, and according to user ergonomic data, the contact angle change threshold, support force change threshold, and maximum swing angle change threshold are determined for the first and second usage data, respectively. The contact angle change value, support force change value, and maximum swing angle change value are also determined for the first and second usage data. When the change in contact angle is greater than or equal to the contact angle change threshold, the recommended posture is to adjust the contact angle of the trekking poles; when the change in support force is greater than or equal to the support force change threshold, the recommended posture is to adjust the height of the trekking poles; when the change in maximum swing angle is greater than or equal to the maximum swing angle change threshold, the recommended posture is to adjust the user's hiking stride.

3. The method as described in claim 2, characterized in that, The method further includes: Obtain the user's walking trajectory data for the first and second time periods; obtain the geographical data corresponding to the walking trajectory data; determine the environmental risk coefficient corresponding to the walking trajectory data based on the geographical data corresponding to the walking trajectory data. The product of the environmental risk coefficient and the contact angle change threshold is determined to adjust the contact angle change threshold; the product of the environmental risk coefficient and the contact angle change threshold is determined to adjust the support force change threshold; the product of the environmental risk coefficient and the maximum swing angle change threshold is determined to adjust the maximum swing angle change threshold.

4. The method as described in claim 3, characterized in that, Based on the geographical data corresponding to the walking trajectory data, determine the environmental risk coefficient corresponding to the walking trajectory data, including: Obtain the mileage index, altitude change and road condition index corresponding to the walking trajectory data; among them, the mileage index represents the walking distance corresponding to the walking trajectory data, the altitude change represents the sum of the altitude rise and altitude fall corresponding to the walking trajectory data, and the road condition index represents the proportion of rugged road sections corresponding to the walking trajectory data. The basic environmental risk coefficient is determined based on mileage indicators and altitude changes using an empirical value table; the environmental risk coefficient correction factor is determined based on road condition indicators using an empirical value table; and the product of the basic environmental risk coefficient and the environmental risk coefficient correction factor is used as the environmental risk coefficient.

5. The method as described in claim 4, characterized in that, The method further includes: The mileage, altitude change, and road condition indicators corresponding to the first walking trajectory data for the first time period are numerically represented to determine the first trajectory feature vector; the mileage, altitude change, and road condition indicators corresponding to the second walking trajectory data for the second time period are numerically represented to determine the second trajectory feature vector; the contact angle, support force, and maximum swing angle of the first usage data are numerically represented to determine the first usage feature vector; the contact angle, support force, and maximum swing angle of the second usage data are numerically represented to determine the second usage feature vector. The similarity between the first trajectory feature vector and the second trajectory feature vector is determined as the trajectory feature similarity; the similarity between the first usage feature vector and the second usage feature vector is determined as the usage feature similarity; the difference between the trajectory feature similarity and the usage feature similarity is determined as the pose change feature value. When the trajectory feature similarity is greater than or equal to the preset trajectory feature similarity, and the posture change feature value is greater than or equal to the preset posture change feature value, a prompt message is issued to the user to adjust the posture of using the trekking poles or to take a proper rest.

6. The method as described in claim 5, characterized in that, The method further includes: The system retrieves multiple historical records of a user's use of trekking poles and obtains the walking trajectory data corresponding to each historical record. Based on the mileage, altitude change, and road condition indicators corresponding to the walking trajectory data, the system clusters the walking trajectory data corresponding to the multiple historical records to obtain multiple walking trajectory data clusters. Each walking trajectory data cluster includes multiple walking trajectory data. Obtain trekking pole usage records for multiple walking trajectory data corresponding to each walking trajectory data cluster, and determine the average contact angle, average support force, and average maximum swing angle of the trekking pole usage records for multiple walking trajectory data corresponding to each walking trajectory data cluster. The product of the average contact angle and the preset ratio is determined as the contact angle change threshold; the product of the average support force and the preset ratio is determined as the support force change threshold; the product of the average maximum swing angle and the preset ratio is determined as the maximum swing angle change threshold.

7. The method as described in claim 6, characterized in that, The preset ratio value is determined using the following method: Using an empirical value table, a preset ratio is determined based on the average mileage, average altitude change, and average road condition index corresponding to each walking trajectory data cluster.

8. The method as described in claim 7, characterized in that, The method further includes: Multiple similarities were determined for the mileage index, altitude change, road condition index, and the mean mileage index, mean altitude change, and mean road condition index corresponding to the first walking trajectory data cluster group. The first walking trajectory data cluster group corresponding to the maximum similarity among these multiple similarities was then determined. When the first walking trajectory data cluster group and the second walking trajectory data cluster group are the same, obtain the first contact angle change threshold, the first support force change threshold, and the first maximum swing angle change threshold corresponding to the first walking trajectory data cluster group; when the contact angle change value is greater than or equal to the first contact angle change threshold, determine that the recommended posture is to correct the contact angle of the trekking poles; when the support force change value is greater than or equal to the first support force change threshold, determine that the recommended posture is to adjust the height of the trekking poles; when the maximum swing angle change value is greater than or equal to the first maximum swing angle change threshold, determine that the recommended posture is to adjust the user's hiking stride.

9. The method as described in claim 8, characterized in that, The method further includes: When the first walking trajectory data cluster group and the second walking trajectory data cluster group are different, the first contact angle change threshold, the first support force change threshold, and the first maximum swing angle change threshold corresponding to the first walking trajectory data cluster group are obtained, and the second contact angle change threshold, the first support force change threshold, and the second maximum swing angle change threshold corresponding to the second walking trajectory data cluster group are obtained; the larger value between the first contact angle change threshold and the second contact angle change threshold is determined as the target contact angle change threshold; the larger value between the first support force change threshold and the second support force change threshold is determined as the target support force change threshold; the larger value between the first maximum swing angle change threshold and the second maximum swing angle change threshold is determined as the target maximum swing angle change threshold. When the change in contact angle is greater than or equal to the target contact angle change threshold, the recommended posture is to adjust the contact angle of the trekking poles; when the change in support force is greater than or equal to the target support force change threshold, the recommended posture is to adjust the height of the trekking poles; when the change in maximum swing angle is greater than or equal to the target maximum swing angle change threshold, the recommended posture is to adjust the user's stride length.

10. A status monitoring device for forestry survey mountaineering assistance equipment, characterized in that, Includes a unit for performing the method according to any one of claims 1 to 9.

Images