A solar power supply method and system for smart helmet

Abstract

The present invention relates to the field of solar energy regulation and control technology, and in particular to a solar energy power supply method and system for a smart helmet. The present invention predicts the characteristic values ​​of light levels at multiple preset detection moments within the next preset detection cycle through a control unit. If the multiple characteristic values ​​of light levels all exceed the preset light level threshold, the solar panel is controlled to directly power the load element, otherwise the main battery is controlled to power the load element, and the solar panel is controlled to charge the auxiliary battery. Compared with the prior art, the present invention determines the degree of light stability by analyzing the changes in the characteristic values ​​of light levels within the preset detection cycle, thereby adjusting the power supply strategy in a targeted manner, ensuring that the load element is powered by solar energy only when the driving power generation effect of the solar panel on the sunlight is relatively stable, thereby solving the problem in the prior art that the solar power supply is unstable due to the high mobility of the helmet, thereby affecting the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of solar energy regulation and control, and in particular to a solar energy power supply method and system for a smart helmet. Background Art

[0002] Smart helmets are head protection devices that integrate a variety of advanced technologies. By integrating sensors, communication modules and intelligent systems, they realize a series of intelligent functions that traditional helmets do not have. These functions include but are not limited to voice calls, location tracking, collision detection, environmental perception and data recording. The core advantage of smart helmets lies in their high degree of integration and intelligence. They not only provide safety protection, but also enhance the user experience through additional auxiliary functions.

[0003] Applying solar energy technology to smart helmets is a hot research direction. Through built-in solar panels, smart helmets can convert sunlight into electrical energy to provide continuous power for internal electronic devices. The application of this technology reduces dependence on traditional batteries, reduces environmental pollution, and also significantly improves the endurance of smart helmets.

[0004] However, unlike traditional fixed solar equipment, people wearing smart helmets are usually constantly moving. This high mobility means that smart helmets may work in unstable lighting environments. This may lead to unstable power supply for smart helmets that rely on solar power, and unstable power supply may affect the performance of electronic devices inside the helmet, and may even cause functional failure at critical moments, thus affecting the user experience. Therefore, people need a solar power supply method for smart helmets that can improve the user experience. Summary of the invention

[0005] Therefore, the present invention provides a solar power supply method and system for a smart helmet, so as to solve the problem in the prior art that the solar power supply is unstable due to the high mobility of the helmet, thereby affecting the user experience.

[0006] The present invention provides a solar power supply method for a smart helmet, which is applied to the smart helmet. The smart helmet includes a solar panel and a main battery, a secondary battery, a load element and a control unit electrically connected to the solar panel. The control unit is also electrically connected to the main battery, the secondary battery and the load element. The control unit is used to execute the solar power supply method for the smart helmet. The method includes:

[0007] Preset detection cycle time at each interval to determine whether the solar panel meets the working conditions;

[0008] If the solar panel does not meet the working conditions, the main battery and the auxiliary battery are controlled to supply power to the load element;

[0009] If the solar panel meets the working conditions, the characteristic values ​​of the light level at multiple preset detection moments within the next preset detection cycle are predicted, wherein the characteristic values ​​of the light level are used to characterize the effect of sunlight on the driving power generation of the solar panel;

[0010] If the characteristic values ​​of the light levels at multiple preset detection moments all exceed the preset light level threshold within the next preset detection cycle, the solar panel is controlled to directly supply power to the load element;

[0011] If, within the next preset detection cycle, the characteristic values ​​of the light level at the preset detection moment are all lower than the preset light level threshold, the main battery is controlled to power the load element, and the solar panel is controlled to charge the auxiliary battery.

[0012] The present invention also provides a preferred solution: predicting characteristic values ​​of light levels at multiple preset detection moments within a next preset detection cycle, including:

[0013] Predicting the predicted position of the smart helmet at a target time, wherein the target time is a preset detection time to be analyzed within the next preset detection cycle time;

[0014] Obtain weather forecast data, and based on the weather forecast data, obtain forecast environmental data according to the forecast location and target time;

[0015] Obtaining map data, and obtaining an occlusion coefficient according to the predicted position based on the map data, where the occlusion coefficient is used to characterize the degree of light occlusion at the predicted position;

[0016] According to the occlusion coefficient and the predicted environmental data, the characteristic value of the light level at the target time is obtained;

[0017] Among them, the degree of light shielding represented by the shielding coefficient is inversely proportional to the driving power generation effect represented by the characteristic value of the light level.

[0018] The present invention also provides a preferred solution: predicting the predicted position of the smart helmet at the target time, including:

[0019] Obtain the initial location and historical location records of the smart helmet;

[0020] Based on the initial position and the target time, a moving trend vector is obtained according to the historical position records. The moving trend vector is used to characterize the tendency of the smart helmet to reach a position at the target time;

[0021] Based on the initial position, the predicted position is obtained according to the moving trend vector.

[0022] The present invention also provides a preferred solution: based on the initial position and the target time, according to the historical position record, a moving trend vector is obtained, including:

[0023] Based on the target time, obtain the historical relative time;

[0024] Statistically analyze the position data recorded within the preset time neighborhood of the historical relative time in the historical position records to obtain multiple first position data;

[0025] Based on the initial position, obtain the historical movement vector according to the distribution of the first position data;

[0026] Statistically analyze the position data representing the positions where the target helmet stays for a long time in the historical position records to obtain second position data, and a stay start time is also corresponding to the second position data;

[0027] Based on the initial position, combine the time interval relationship between the stay start time and the historical relative time, and obtain the target movement vector according to the second position data;

[0028] Obtain the movement data of the intelligent helmet, and obtain the inertial movement vector according to the movement data;

[0029] Superimpose the historical movement vector, the target movement vector, and the inertial movement vector to obtain the movement trend vector.

[0030] The present invention also provides a preferred solution: based on the initial position, obtain the historical movement vector according to the distribution of the first position data, including:

[0031] Calculate the average value of the multiple first position data, and obtain the initial first vector according to the initial position;

[0032] Calculate the discrete statistical characteristics of the multiple first position data, and randomly adjust the initial first vector based on the discrete statistical characteristics to obtain the historical movement vector;

[0033] Wherein, the length of the historical movement vector is less than the length of the initial first vector, and the higher the degree of dispersion of the multiple first position data represented by the discrete statistical characteristics, the greater the amplitude of the random adjustment of the initial first vector.

[0034] The present invention also provides a preferred solution: based on the initial position, combine the time interval relationship between the stay start time and the historical relative time, and obtain the target movement vector according to the second position data, including:

[0035] Obtain multiple initial second vectors according to the difference between the multiple second position data and the initial position;

[0036] Obtain the superimposed weight of each second position data according to the time interval between the stay start time corresponding to each second position data and the historical relative time, wherein, the greater the time interval between the stay start time corresponding to the second position data and the historical relative time, the smaller the superimposed weight corresponding to the second position data;

[0037] A plurality of initial second vectors are superimposed based on the superposition weight to obtain a target movement vector.

[0038] The present invention also provides a preferred solution: based on the initial position and according to the moving trend vector, a predicted position is obtained, including:

[0039] Obtaining map data, and obtaining multiple potential paths based on the initial position according to the map data;

[0040] Projecting the moving trend vector onto multiple potential paths to obtain a projection path of the moving trend vector on each potential path;

[0041] The end position of the projection path with the shortest length is selected as the predicted position.

[0042] The present invention also provides a preferred solution:

[0043] If the solar panel meets the working conditions, the light level verification values ​​at multiple preset detection moments within the next preset detection cycle are predicted, wherein the light level verification value is used to characterize the driving power generation effect of sunlight on the solar panel within the preset location neighborhood of the predicted location of the smart helmet at the target moment;

[0044] If the proportion of light level verification values ​​below the preset light level threshold exceeds the preset ratio threshold within the next preset detection cycle, the main battery is controlled to power the load element and the solar panel is controlled to charge the auxiliary battery.

[0045] The present invention also provides a preferred solution: predicting the light level verification values ​​at multiple preset detection moments within the next preset detection cycle time, including:

[0046] Assign a random number to the historical movement vector, the target movement vector and the inertial movement vector at the target moment respectively;

[0047] Using the assigned random number as the coefficient, the historical movement vector, the destination movement vector and the inertia movement vector are superimposed to obtain the movement verification vector.

[0048] Based on the initial position, the verification position is obtained according to the moving verification vector.

[0049] According to the verification position, the light level verification value at the target time is obtained.

[0050] The present invention also provides a solar power supply system for a smart helmet, which is applied to the smart helmet. The smart helmet includes a solar panel and a main battery, a secondary battery, a load element and a control unit electrically connected to the solar panel. The control unit is also electrically connected to the main battery, the secondary battery and the load element. The control unit is used to deploy the solar power supply system for the smart helmet. The system includes:

[0051] The timing trigger module is used to preset the detection cycle time at each interval to determine whether the solar panel meets the working conditions;

[0052] A first control module, used to control the main battery and the auxiliary battery to supply power to the load element when the solar panel does not meet the working conditions;

[0053] The light analysis module is used to predict the light level characteristic values ​​at multiple preset detection moments within the next preset detection cycle time when the solar panel meets the working conditions, wherein the light level characteristic values ​​are used to characterize the driving power generation effect of sunlight on the solar panel;

[0054] The second control module is used to control the solar panel to directly supply power to the load element when the characteristic values ​​of the light levels at multiple preset detection moments exceed the preset light level threshold value within the next preset detection cycle time;

[0055] The third control module is used to control the main battery to power the load element and control the solar panel to charge the auxiliary battery when the light level characteristic values ​​at the preset detection moments are all lower than the preset light level threshold within the next preset detection cycle time.

[0056] The beneficial effects of adopting the above embodiment are:

[0057] The present invention provides a solar power supply method and system for a smart helmet, which are applied to the smart helmet. The smart helmet comprises a solar panel and a main battery, a secondary battery, a load element and a control unit electrically connected to the solar panel. The control unit is also electrically connected to the main battery, the secondary battery and the load element. The control unit is used to judge whether the solar panel meets working conditions at each preset detection cycle time. If the solar panel does not meet the working conditions, the main battery and the secondary battery are controlled to supply power to the load element. If the solar panel meets the working conditions, the light level characteristic values ​​at multiple preset detection moments in the next preset detection cycle time are predicted. If the light level characteristic values ​​at multiple preset detection moments in the next preset detection cycle time all exceed the preset light level threshold, the solar panel is controlled to directly supply power to the load element. Otherwise, the main battery is controlled to supply power to the load element, and the solar panel is controlled to charge the secondary battery. Compared with the prior art, the present invention characterizes the driving and power generation effect of sunlight on the solar panel through the light level characteristic value, and judges the light stability by analyzing the changes in the light level characteristic value within a preset detection period, so as to adjust the power supply strategy in a targeted manner, ensuring that the load element is powered by solar energy only when the driving and power generation effect of sunlight on the solar panel is relatively stable, thereby ensuring user experience, and solving the problem in the prior art that the high mobility of the helmet leads to unstable solar power supply, which in turn affects the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A method flow chart of an embodiment of a solar power supply method for a smart helmet provided by the present invention;

[0059] Figure 2 for Figure 1 Specific step diagram of step S103;

[0060] Figure 3 for Figure 2 Specific step diagram of step S201;

[0061] Figure 4 for Figure 3 Specific step diagram of step S302;

[0062] Figure 5 A schematic diagram of an embodiment of calculating a predicted position in the present invention;

[0063] Figure 6 This is a system structure diagram of an embodiment of a solar power supply system for a smart helmet provided by the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Combination Figure 1 As shown, a specific embodiment of the present invention discloses a solar power supply method for a smart helmet, which is applied to the smart helmet. The smart helmet includes a solar panel and a main battery, a secondary battery, a load element and a control unit electrically connected to the solar panel. The control unit is also electrically connected to the main battery, the secondary battery and the load element. The control unit is used to execute the solar power supply method for the smart helmet. The method includes:

[0066] S101, at each preset detection cycle time, determine whether the solar panel meets the working conditions;

[0067] S102, if the solar panel does not meet the working conditions, control the main battery and the auxiliary battery to supply power to the load element;

[0068] S103, if the solar panel meets the working conditions, predicting the characteristic values ​​of the light level at multiple preset detection moments within the next preset detection cycle, wherein the characteristic values ​​of the light level are used to characterize the driving power generation effect of sunlight on the solar panel;

[0069] S104, if the light level characteristic values ​​at multiple preset detection moments all exceed the preset light level threshold within the next preset detection cycle, control the solar panel to directly power the load element;

[0070] S105: If, within the next preset detection cycle, the light level characteristic values ​​at the preset detection moments are all lower than the preset light level threshold, the main battery is controlled to supply power to the load element, and the solar panel is controlled to charge the auxiliary battery.

[0071] Compared with the prior art, the present invention characterizes the driving and power generation effect of sunlight on the solar panel through the light level characteristic value, and judges the light stability by analyzing the changes in the light level characteristic value within a preset detection period, so as to adjust the power supply strategy in a targeted manner, ensuring that the load element is powered by solar energy only when the driving and power generation effect of sunlight on the solar panel is relatively stable, thereby ensuring user experience, and solving the problem in the prior art that the high mobility of the helmet leads to unstable solar power supply, which in turn affects the user experience.

[0072] In the above process, the smart helmet is equipped with two batteries, one of which is the main battery, which is used for the main power supply in daily non-solar power supply situations and can generally be disassembled and replaced on a daily basis. The other is a secondary battery for emergency use, which is mainly used to store the electricity generated by the solar panels. The load element can be considered as all devices in the smart helmet that require electricity, such as various sensors, communication modules, lighting modules, etc. The control unit can be a control chip integrated in the smart helmet, or a server remotely connected to the smart helmet.

[0073] In the above step S101, the preset detection cycle refers to the artificially set interval time between each two executions of the solar power supply method of the smart helmet. Its specific size is generally flexibly set according to the specific situation. For example, if the hardware computing power is sufficient, or the smart helmet is located in an area with a more variable environment, it can be 1 minute or 10 minutes. If the hardware computing power is low and the weather is relatively stable, it can be as long as one hour. The preset detection time is the time when multiple evaluation detection calculations are performed in the preset detection cycle, which can be determined according to parameters such as the frequency of the hardware device.

[0074] Similarly, the determination of whether the solar panel meets the working conditions mentioned in the working conditions can also be flexibly set according to the specific situation. For example, when the light intensity in the environment is higher than a certain threshold, or when the surface cleanliness and damage rate of the solar panel meet the relevant indicators, it can be considered that the solar panel meets the working conditions. If the solar panel does not meet the working conditions, the main battery and the auxiliary battery can be controlled to supply power to the load element to ensure the normal use of the helmet, that is, step S102. At this time, the power supply strategy of the main battery and the auxiliary battery, such as the power supply sequence and the power supply ratio, can also be flexibly set according to the specific situation.

[0075] If the solar panel meets the working conditions, power can be supplied by the solar panel. At this time, it is necessary to consider whether the movement of the helmet will affect the stability of the power supply of the solar panel, which is the purpose of step S103. In this embodiment, the driving power generation effect of the solar panel is represented by the light level characteristic value. In practice, the specific definition of the light level characteristic value can also be flexibly designed according to the specific situation. For example, the driving power generation effect of the solar panel is obviously positively correlated with the irradiance of the sun. Then the irradiance of the sun at the location of the smart helmet at the preset detection time can be directly used as the light level characteristic value.

[0076] When the characteristic values ​​of the light levels at each preset detection moment are high within the next preset detection cycle, it can be considered that the lighting conditions are stable and the power generation effect of the solar panel is also relatively stable. At this time, the power supply can be directly provided by solar energy to improve the battery life of the smart helmet and achieve a low-carbon and environmentally friendly effect, i.e., step S104.

[0077] On the contrary, if there is a lower light level characteristic value within the next preset detection cycle, it can be considered that the lighting condition is not stable enough. In order to ensure user experience, it is more appropriate to use a main battery that can provide stable power supply as the power source. In this case, the present invention also uses a secondary battery to store unstable solar energy, which not only ensures user experience but also avoids the waste of solar energy, and can also improve the endurance of the smart helmet, further improving the practicability of the method. The above process is the role of step S105.

[0078] Similarly, the preset light level threshold in the above process can also be flexibly set according to the specific situation. For example, the total power of driving the necessary load elements is statistically calculated, and the minimum light intensity that can ensure that the output power of the solar panel exceeds the above total power is calculated through any existing technology (such as maximum power point tracking MPPT), and then the minimum light intensity is appropriately linearly transformed to reversely deduce the preset light level threshold in the present invention.

[0079] It can be seen from the above description that predicting the characteristic value of light level is the key to the present invention, and the accuracy of the characteristic value of light level affects the operation effect of the present method. In practice, any method can be used to predict the characteristic value of light level at multiple preset detection moments. For example, a recurrent neural network can be used to predict the future characteristic value of light level through the known characteristic value of light level, or the next moving range of the smart helmet can be predicted, and the characteristic value of light level can be obtained through the meteorological distribution characteristics within the range.

[0080] In this regard, the present invention provides a preferred method to obtain a more accurate characteristic value of the light level. Figure 2 As shown, in a preferred embodiment, in the above step S103, predicting the characteristic values ​​of the light levels at multiple preset detection moments within the next preset detection cycle time specifically includes:

[0081] S201, predicting the predicted position of the smart helmet at a target time, wherein the target time is a preset detection time to be analyzed within a next preset detection cycle time;

[0082] S202, obtaining weather forecast data, and obtaining forecast environmental data based on the weather forecast data, according to the forecast location and target time;

[0083] S203, obtaining map data, and obtaining an occlusion coefficient according to the predicted position based on the map data, where the occlusion coefficient is used to characterize the degree of light occlusion at the predicted position;

[0084] S204, obtaining a characteristic value of the light level at the target time according to the shading coefficient and the predicted environmental data;

[0085] Among them, the degree of light shielding represented by the shielding coefficient is inversely proportional to the driving power generation effect represented by the characteristic value of the light level.

[0086] This embodiment indirectly obtains the characteristic value of the light level by predicting the location of the smart helmet and combining it with meteorological forecast data (such as temperature, cloud distribution and other parameters). The calculation result obtained by combining the meteorological forecast data is the predicted environmental data (for example, the predicted environmental data is the ratio of the light intensity to the temperature and the cloud thickness at the predicted location), and the predicted environmental data can be directly used as the characteristic value of the light level.

[0087] The key point of this embodiment is that, on this basis, the influence of the factor of light obstruction is also taken into account. Specifically, in the present invention, an obstruction coefficient is assigned to each predicted position through map data, and the predicted environmental data is corrected by the obstruction coefficient to take into account the factor of environmental obstruction, so as to obtain a more accurate characteristic value of the light level. For example, the characteristic value of the light level is the product of the obstruction coefficient and the predicted environmental data. At this time, according to the map data, if a predicted position is located in a tunnel, the obstruction coefficient can be 0, and the calculated characteristic value of the light level is also 0. The light corresponding to the predicted position is regarded as completely blocked and cannot provide power for the solar panels at all. In practice, the obstruction coefficient can be determined based on factors such as road width, greening rate, and building density.

[0088] In addition, it can be understood that predicting the position of the target moment in the above process, that is, calculating the predicted position, is a path prediction problem. This process can be implemented using any existing path prediction method, such as linear regression, nearest neighbor method, artificial intelligence model prediction, etc.

[0089] This embodiment provides an improved preferred solution, combining Figure 3 As shown, in a preferred embodiment, the above step S201, predicting the predicted position of the smart helmet at the target time, specifically includes:

[0090] S301, obtaining the initial position and the historical position record of the smart helmet;

[0091] S302, based on the initial position and the target time, according to the historical position record, obtain a movement trend vector, the movement trend vector is used to characterize the tendency of the smart helmet to reach a position at the target time;

[0092] S303. Based on the initial position and the moving trend vector, a predicted position is obtained.

[0093] In the above process, the initial position is the position where the smart helmet is located or predicted to be located at the moment before the target moment. Different from the prior art, this embodiment fuzzily indicates the movement trend of the smart helmet by means of a moving trend vector, and then predicts the next position. Its main advantage is that it can improve the efficiency of position prediction calculation under path constraints. Because smart helmets often need to move along a certain path in actual use, such as roads in urban planning, if the prior art is used at this time, in addition to analyzing the changing rules of historical position records, it is also necessary to consider the constraints of feasible paths. This limitation will significantly increase the amount of data that needs to be processed when predicting the position and reduce the efficiency of the algorithm.

[0094] By using the moving trend vector, the predicted position can be quickly located under path constraints. For example, a moving trend vector is drawn based on the initial position in the map, and the position closest to the end of the moving trend vector and reachable from the initial position is directly found in the map as the predicted position. At the same time, the moving trend vector can also realize the prediction of the position without path constraints (for example, the determination of the predicted position in the wild and relatively open space), which greatly improves the versatility of this method.

[0095] Specifically, combined Figure 4 As shown, in a preferred embodiment, the above step S302, based on the initial position and the target time, obtains the moving trend vector according to the historical position record, specifically including:

[0096] S401, obtaining a historical relative time based on the target time;

[0097] S402, counting the location data recorded in the historical location record within a preset time neighborhood of the historical relative moment to obtain a plurality of first location data;

[0098] S403, based on the initial position, obtain a historical movement vector according to the distribution of the first position data;

[0099] S404, counting the position data representing the position where the target helmet stays for a long time in the historical position record to obtain second position data, where the second position data also corresponds to a stop start time;

[0100] S405, based on the initial position, combined with the time interval relationship between the stop start time and the historical relative time, and according to the second position data, obtain a destination movement vector;

[0101] S406, acquiring movement data of the smart helmet, and obtaining an inertial movement vector according to the movement data;

[0102] S407, superimposing the historical movement vector, the target movement vector and the inertial movement vector to obtain a movement trend vector.

[0103] This embodiment uses the historical movement vector to represent the preference of the smart helmet in terms of movement habits, uses the destination movement vector to represent the preference of the smart helmet in terms of the travel destination, and uses the inertial movement vector to represent the preference of the smart helmet in terms of the current movement behavior. The three are superimposed to obtain a more scientific and reasonable movement trend vector. In practice, depending on the specific situation, any one or two of the above three vectors can also be used as the movement trend vector.

[0104] In the above process, the historical relative moment is the moment with the same time position in multiple preset cycles. For example, if the target time is 5 pm, then the historical relative moment can be understood as 5 pm every day. The first position data is the position of the smart helmet at around 5 pm every day in the historical position record. The position where the smart helmet should be at 5 pm can be obtained through the distribution of the first position data, and the movement preference of the smart helmet at the target time, that is, the historical movement vector, can be obtained through this position.

[0105] In addition, according to the actual use habits of helmets, after the wearer reaches the destination, the helmet will generally be taken off, and the position of the smart helmet will no longer change. In this way, by counting the position data of the position where the target helmet stays for a long time in the historical position record, the second position data that can represent the destination of the wearer of the smart helmet can be obtained (for example, if the stay time of a certain position record exceeds a preset time, the position record can be regarded as a second position data), and the stop start time is the time when the smart helmet arrives at the location corresponding to the second position data and starts to stay still. According to the second position data, the preference of the smart helmet on the destination can be obtained. Obviously, the wearer's destination is different at different historical relative moments. Therefore, this embodiment further combines the time interval relationship between the stop start time and the historical relative moment to obtain the destination preference that best meets the target moment state. For example, the second position data corresponding to the stop start time closest to the historical relative moment is selected, and it and the initial position are used as the end point and the starting point respectively to establish the destination movement vector.

[0106] At the same time, the current movement state of the smart helmet wearer can also give a glimpse of his movement preference. Therefore, this embodiment further establishes an inertial movement vector based on movement data (movement speed, acceleration, direction, etc.) to further improve the comprehensiveness of the movement trend vector. The direction of the inertial movement vector is the same as the current movement direction of the smart helmet wearer, and the length of the inertial movement vector is related to factors such as the current movement speed and acceleration. For example, the higher the current movement speed of the smart helmet wearer, the greater the acceleration, the less likely it is to change direction, and the longer the length of the inertial movement vector will be.

[0107] In practice, not all wearers of smart helmets have regular work and rest schedules, which results in the historical movement vector not being able to correctly reflect the wearer's actual movement preference. Therefore, the present invention also provides a preferred embodiment. In a preferred embodiment, the above step S403, based on the initial position, obtains the historical movement vector according to the distribution of the first position data, specifically including:

[0108] Calculate the average value of the plurality of first position data, and obtain an initial first vector according to the initial position;

[0109] Calculating discrete statistical features of a plurality of first position data, and randomly adjusting the initial first vector based on the discrete statistical features to obtain a historical movement vector;

[0110] The length of the historical moving vector is smaller than the length of the initial first vector, and the higher the discreteness of the plurality of first position data represented by the discrete statistical feature, the greater the amplitude of the random adjustment to the initial first vector.

[0111] Based on the above, this embodiment further adds consideration of the regularity of movement of the wearer of the smart helmet to reduce the historical movement vector error. The discrete degree of the multiple first position data is represented by discrete statistical features (such as variance, standard deviation and other arbitrary features). The greater the discrete degree of the first position data, the higher the randomness of the movement of the wearer of the smart helmet. At this time, the referenceability of the multiple first position data is lower. At this time, the length of the initial first vector should be reduced to reduce its influence on the final movement trend vector.

[0112] Similarly, in practice, the destination of the wearer of the smart helmet is also somewhat random, and there may be multiple second location data that meet the conditions near the historical relative moment. In this case, it is necessary to analyze them together to obtain a more reasonable destination movement vector. Specifically, in a preferred embodiment, the above step S405, based on the initial position, combined with the time interval relationship between the stop start time and the historical relative moment, obtains the destination movement vector according to the second location data, specifically including:

[0113] Obtaining a plurality of initial second vectors according to differences between a plurality of second position data and an initial position;

[0114] According to the time interval between the stop start time and the historical relative moment corresponding to each second location data, the superposition weight of each second location data is obtained, wherein the larger the time interval between the stop start time and the historical relative moment corresponding to the second location data is, the smaller the superposition weight corresponding to the second location data is;

[0115] A plurality of initial second vectors are superimposed based on the superposition weight to obtain a target movement vector.

[0116] The above process uses the time interval between the start time of the stay and the historical relative moment as a measurement scale. The shorter the time interval, the higher the credibility of the second position data, and the higher the influence of the corresponding initial second vector on the destination moving vector should be. In this way, by performing a weighted summation on multiple initial second vectors, comprehensive consideration of all qualified second position data is achieved.

[0117] Further, it can be seen that the length of the moving trend vector is related to the interval of the preset detection moments. Generally, the larger the interval of the preset detection moments, the longer the length of the moving trend vector, but the larger the predicted deviation will be. Therefore, in practice, the interval of the preset detection moments is generally set very small. On this basis, the present invention also provides a preferred embodiment, wherein step S303, based on the initial position, according to the moving trend vector, obtains the predicted position, specifically including:

[0118] Obtaining map data, and obtaining multiple potential paths based on the initial position according to the map data;

[0119] Projecting the moving trend vector onto multiple potential paths to obtain a projection path of the moving trend vector on each potential path;

[0120] The end position of the projection path with the shortest length is selected as the predicted position.

[0121] This embodiment is mainly used in the case where the interval between preset detection moments is small, and the length of the moving trend vector will also be small. The significance of this embodiment is that the behavior of selecting the end position of the shortest projection path as the predicted position each time is very similar to the idea of ​​the greedy algorithm, which makes this embodiment tend to select the path that can approach the destination the fastest each time when selecting a path based on the moving trend vector, so that the total path is the shortest, which simulates the actual decision-making mode of the wearer of the smart helmet to a certain extent. The predicted position obtained based on this idea can be more in line with the actual situation. Figure 5 It is a schematic diagram of the present embodiment when implemented at an intersection.

[0122] Furthermore, in a preferred embodiment, the solar power supply method for the smart helmet further comprises:

[0123] If the solar panel meets the working conditions, the light level verification values ​​at multiple preset detection moments within the next preset detection cycle are predicted, wherein the light level verification value is used to characterize the driving power generation effect of sunlight on the solar panel within the preset location neighborhood of the predicted location of the smart helmet at the target moment;

[0124] If the proportion of light level verification values ​​below the preset light level threshold exceeds the preset ratio threshold within the next preset detection cycle, the main battery is controlled to power the load element and the solar panel is controlled to charge the auxiliary battery.

[0125] This embodiment also takes into account the randomness of the travel route. The light level verification value in the above process represents the lighting effect near the predicted position. The significance of adding consideration of the light level verification value in this embodiment is that, in addition to analyzing the lighting stability of the smart helmet on the predicted path, it also considers the stability of the lighting when the actual path of the smart helmet deviates from the predicted path, so as to verify the feasibility of the previous calculation results, thereby further improving the stable use experience of the solar power supply method of the smart helmet.

[0126] Further, in a preferred embodiment, the step in the above process: predicting the light level verification values ​​at multiple preset detection moments within the next preset detection cycle time specifically includes:

[0127] Assign a random number to the historical movement vector, the target movement vector and the inertial movement vector at the target moment respectively;

[0128] Using the assigned random number as the coefficient, the historical movement vector, the destination movement vector and the inertia movement vector are superimposed to obtain the movement verification vector.

[0129] Based on the initial position, the verification position is obtained according to the moving verification vector.

[0130] According to the verification position, the light level verification value at the target time is obtained.

[0131] In this embodiment, based on the known historical moving vector, target moving vector and inertial moving vector, random coefficients are added to the three respectively to obtain a random moving verification vector that is relatively close to the moving trend vector, and then a random verification position that is relatively close to the predicted position is obtained, thereby improving the reusability of the method steps.

[0132] It can be understood that since the meanings of the light level verification value and the light level characteristic value are similar, the only difference between the two is the corresponding positions, so the light level verification value can be calculated using the same method as the light level characteristic value, wherein the moving verification vector in the above process is equivalent to the moving trend vector in the previous text, and the verification position is equivalent to the predicted position in the previous text.

[0133] Combination Figure 6 As shown, the present invention also provides a solar power supply system for a smart helmet, which is applied to a smart helmet. The smart helmet includes a solar panel and a main battery, a secondary battery, a load element and a control unit electrically connected to the solar panel. The control unit is also electrically connected to the main battery, the secondary battery and the load element. The control unit is used to deploy the solar power supply system for the smart helmet. The system includes:

[0134] The timing trigger module 610 is used to determine whether the solar panel meets the working conditions at each preset detection cycle time;

[0135] The first control module 620 is used to control the main battery and the auxiliary battery to supply power to the load element when the solar panel does not meet the working conditions;

[0136] The light analysis module 630 is used to predict the light level characteristic values ​​at multiple preset detection moments within the next preset detection cycle time when the solar panel meets the working conditions, wherein the light level characteristic values ​​are used to characterize the driving power generation effect of sunlight on the solar panel;

[0137] The second control module 640 is used to control the solar panel to directly supply power to the load element when the characteristic values ​​of the light levels at multiple preset detection moments all exceed the preset light level threshold value within the next preset detection cycle time;

[0138] The third control module 650 is used to control the main battery to power the load element and control the solar panel to charge the auxiliary battery when the light level characteristic values ​​at the preset detection moments are all lower than the preset light level threshold within the next preset detection cycle time.

[0139] It should be noted here that the corresponding system provided in the above embodiments can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding contents in the above method embodiments, which will not be repeated here.

[0140] The present invention provides a solar power supply method and system for a smart helmet, which are applied to the smart helmet. The smart helmet comprises a solar panel and a main battery, a secondary battery, a load element and a control unit electrically connected to the solar panel. The control unit is also electrically connected to the main battery, the secondary battery and the load element. The control unit is used to judge whether the solar panel meets working conditions at each preset detection cycle time. If the solar panel does not meet the working conditions, the main battery and the secondary battery are controlled to supply power to the load element. If the solar panel meets the working conditions, the light level characteristic values ​​at multiple preset detection moments in the next preset detection cycle time are predicted. If the light level characteristic values ​​at multiple preset detection moments in the next preset detection cycle time all exceed the preset light level threshold, the solar panel is controlled to directly supply power to the load element. Otherwise, the main battery is controlled to supply power to the load element, and the solar panel is controlled to charge the secondary battery. Compared with the prior art, the present invention characterizes the driving and power generation effect of sunlight on the solar panel through the light level characteristic value, and judges the light stability by analyzing the changes in the light level characteristic value within a preset detection period, so as to adjust the power supply strategy in a targeted manner, ensuring that the load element is powered by solar energy only when the driving and power generation effect of sunlight on the solar panel is relatively stable, thereby ensuring user experience, and solving the problem in the prior art that the high mobility of the helmet leads to unstable solar power supply, which in turn affects the user experience.

[0141] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0142] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A solar power supply method for a smart helmet, characterized in that: Applied to a smart helmet, the smart helmet includes a solar panel and a main battery, a secondary battery, a load element and a control unit electrically connected to the solar panel, and the control unit is also electrically connected to the main battery, the secondary battery and the load element, wherein the control unit is used to execute a solar power supply method for the smart helmet, the method comprising: Preset detection cycle time at each interval to determine whether the solar panel meets the working conditions; If the solar panel does not meet the working conditions, the main battery and the auxiliary battery are controlled to supply power to the load components; If the solar panel meets the working conditions, the initial position and the historical position record of the smart helmet are obtained; Based on the target moment, the historical relative moment is obtained; Counting the location data recorded in the historical location records within a preset time neighborhood of the historical relative moment to obtain a plurality of first location data; Based on the initial position, a historical movement vector is obtained according to the distribution of the first position data; Counting the position data representing the position where the target helmet has stayed for a long time in the historical position record to obtain second position data, wherein the second position data also corresponds to a stay start time; Obtaining a plurality of initial second vectors according to differences between a plurality of second position data and an initial position; According to the time interval between the stop start time and the historical relative moment corresponding to each second location data, the superposition weight of each second location data is obtained, wherein the larger the time interval between the stop start time and the historical relative moment corresponding to the second location data is, the smaller the superposition weight corresponding to the second location data is; Superimposing a plurality of initial second vectors based on a superposition weight to obtain a target movement vector; Obtain movement data of the smart helmet, and obtain an inertial movement vector according to the movement data; The historical movement vector, the target movement vector and the inertial movement vector are superimposed to obtain the movement trend vector; Based on the initial position, the predicted position is obtained according to the moving trend vector; Obtain weather forecast data, and based on the weather forecast data, obtain forecast environmental data according to the forecast location and target time; Obtaining map data, and based on the map data, obtaining an occlusion coefficient according to the predicted position, wherein the degree of light occlusion represented by the occlusion coefficient is inversely proportional to the driving power generation effect represented by the characteristic value of the light level; According to the occlusion coefficient and the predicted environmental data, the characteristic value of the light level at the target time is obtained; If the characteristic values ​​of the light levels at multiple preset detection moments all exceed the preset light level threshold within the next preset detection cycle, the solar panel is controlled to directly supply power to the load element; If, within the next preset detection cycle, the characteristic values ​​of the light level at the preset detection moment are all lower than the preset light level threshold, the main battery is controlled to power the load element, and the solar panel is controlled to charge the auxiliary battery.

2. The solar power supply method for a smart helmet according to claim 1, characterized in that: Based on the initial position, according to the distribution of the first position data, a historical movement vector is obtained, including: Calculate the average value of the plurality of first position data, and obtain an initial first vector according to the initial position; Calculating discrete statistical features of a plurality of first position data, and randomly adjusting the initial first vector based on the discrete statistical features to obtain a historical movement vector; The length of the historical moving vector is smaller than the length of the initial first vector, and the higher the discreteness of the plurality of first position data represented by the discrete statistical feature, the greater the amplitude of the random adjustment to the initial first vector.

3. The solar power supply method for a smart helmet according to claim 1, characterized in that: Based on the initial position and the moving trend vector, the predicted position is obtained, including: Obtaining map data, and obtaining multiple potential paths based on the initial position according to the map data; Projecting the moving trend vector onto multiple potential paths to obtain a projection path of the moving trend vector on each potential path; The end position of the projection path with the shortest length is selected as the predicted position.

4. The solar power supply method for a smart helmet according to claim 2, characterized in that: Also includes: If the solar panel meets the working conditions, the light level verification values ​​at multiple preset detection moments within the next preset detection cycle are predicted, wherein the light level verification value is used to characterize the driving power generation effect of sunlight on the solar panel within the preset location neighborhood of the predicted location of the smart helmet at the target moment; If the proportion of light level verification values ​​below the preset light level threshold exceeds the preset ratio threshold within the next preset detection cycle, the main battery is controlled to power the load element and the solar panel is controlled to charge the auxiliary battery.

5. The solar power supply method for a smart helmet according to claim 4, characterized in that: Predict the light level verification values ​​at multiple preset detection moments within the next preset detection cycle, including: Assign a random number to the historical movement vector, the target movement vector and the inertial movement vector at the target moment respectively; Using the assigned random number as a coefficient, the historical movement vector, the target movement vector and the inertial movement vector are superimposed to obtain a movement verification vector; Based on the initial position, the verification position is obtained according to the mobile verification vector; According to the verification position, the light level verification value at the target time is obtained.

6. A solar power supply system for a smart helmet, characterized in that: Applied to smart helmets, the smart helmets include solar panels and main batteries, auxiliary batteries, load elements and control units electrically connected to the solar panels. The control unit is also electrically connected to the main batteries, auxiliary batteries and load elements. The control unit is used to deploy the solar power supply system of the smart helmet, which includes: The timing trigger module is used to preset the detection cycle time at each interval to determine whether the solar panel meets the working conditions; A first control module, used to control the main battery and the auxiliary battery to supply power to the load element when the solar panel does not meet the working conditions; The light analysis module is used to obtain the initial position and the historical position record of the smart helmet when the solar panel meets the working conditions; Based on the target moment, the historical relative moment is obtained; Counting the location data recorded in the historical location records within a preset time neighborhood of the historical relative moment to obtain a plurality of first location data; Based on the initial position, a historical movement vector is obtained according to the distribution of the first position data; Counting the position data representing the position where the target helmet has stayed for a long time in the historical position record to obtain second position data, wherein the second position data also corresponds to a stay start time; Obtaining a plurality of initial second vectors according to differences between a plurality of second position data and an initial position; According to the time interval between the stop start time and the historical relative moment corresponding to each second location data, the superposition weight of each second location data is obtained, wherein the larger the time interval between the stop start time and the historical relative moment corresponding to the second location data is, the smaller the superposition weight corresponding to the second location data is; Superimposing a plurality of initial second vectors based on a superposition weight to obtain a target movement vector; Obtain movement data of the smart helmet, and obtain an inertial movement vector according to the movement data; The historical movement vector, the target movement vector and the inertial movement vector are superimposed to obtain the movement trend vector; Based on the initial position, the predicted position is obtained according to the moving trend vector; Obtain weather forecast data, and based on the weather forecast data, obtain forecast environmental data according to the forecast location and target time; Obtaining map data, and based on the map data, obtaining an occlusion coefficient according to the predicted position, wherein the degree of light occlusion represented by the occlusion coefficient is inversely proportional to the driving power generation effect represented by the characteristic value of the light level; According to the occlusion coefficient and the predicted environmental data, the characteristic value of the light level at the target time is obtained; The second control module is used to control the solar panel to directly supply power to the load element when the characteristic values ​​of the light levels at multiple preset detection moments exceed the preset light level threshold value within the next preset detection cycle time; The third control module is used to control the main battery to power the load element and control the solar panel to charge the auxiliary battery when the light level characteristic values ​​at the preset detection moments are all lower than the preset light level threshold within the next preset detection cycle time.

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