Predicting and mitigating effects of environmental conditions

Abstract

A system for predicting and mitigating effects of environmental conditions, the system comprising: a user device comprising a processor and a memory configured to: determine environmental conditions of a location within an environment; analyzing the environmental condition according to individual-specific criteria to determine a predicted impact on the individual within the location during the determined environmental condition; and generate a mitigation response based on the predicted impact.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to systems and methods for mitigating effects of environmental conditions. BACKGROUND

[0002] Exposure to environmental conditions can have deleterious effects on humans, plants, animals, and equipment. For example, a human or animal exposed to heat and humidity, particularly while engaged in an activity (e.g., walking, running, etc.), can experience heat stress or other biological conditions. Heat stress or fatigue often generates physical symptoms such as sweating, weakness, dizziness, fainting, nausea, muscle cramping, headaches, rapid heartbeat, etc. Similarly, plants can wilt, grow poorly, be susceptible to disease, or even die. Equipment can overheat or malfunction.

[0003] Current systems and methods of managing environmental exposure are deficient in many respects. For example, current methods can include issuing general heat warnings for large areas and broad populations in a one-size-fits-all manner. Moreover, existing systems also warn of effects of exposure to environmental conditions after the fact, when it is too late to avoid the effects. SUMMARY

[0004] In one embodiment, a method of predicting effects of environmental exposure includes receiving, via a processor, environmental data of an environmental condition; predicting, via the processor, a biological effect of the environmental condition on a user; and generating a notification regarding the predicted biological effect.

[0005] Optionally, in some embodiments, the method further includes receiving, via the processor, an activity schedule of the user exposed to the environmental condition.

[0006] Optionally, in some embodiments, the method further includes adjusting, via the processor, the activity schedule to avoid the biological effect.

[0007] Optionally, in some embodiments, the predicting is based on at least one of a biological characteristic of the user and / or a schedule of activities of the user.

[0008] Optionally, in some embodiments, the processor is on a mobile device.

[0009] Optionally, in some embodiments, the environmental data includes at least one of a current environmental condition or a prediction of a future environmental condition.

[0010] Optionally, in some embodiments, the method further includes receiving, via the processor, a tolerance of the user to the environmental condition.

[0011] Optionally, in some embodiments, the method further includes generating, via the processor, a user alert indicating an imminent effect of exposure to the environmental condition.

[0012] Optionally, in some embodiments, the method further comprises transmitting, via the processor, the adjusted activity plan to a user device; and displaying the adjusted activity plan on a display of the user device.

[0013] Optionally, in some embodiments, the method further comprises receiving, via the processor, user health data from a user device.

[0014] Optionally, in some embodiments, the user device comprises a wearable device.

[0015] Optionally, in some embodiments, the processor is associated with a user device, and the activity plan is stored in a memory of the user device.

[0016] Optionally, in some embodiments, the environmental data comprises one or more of: temperature, heat exposure index, sunlight, wind speed, wind direction, cloud cover, barometric pressure, precipitation, wind chill, dew point, humidity, atmospheric electric field, wind shear, cumulative radiation, or pollutant levels.

[0017] Optionally, in some embodiments, the pollutant is one or more of: an amount of particulate matter, nitrogen oxide, sulfur compound, ozone, hydrocarbon, carbon dioxide, or carbon monoxide.

[0018] In one embodiment, a method of predicting environmental exposure effects comprises: determining environmental conditions of a location within an environment; analyzing the environmental conditions according to individual-specific criteria to determine a predicted effect on an individual within the location during the determined environmental conditions; and generating a mitigation response based on the predicted effect.

[0019] Optionally, in some embodiments, the method further comprises outputting the mitigation response to a display associated with a user device, wherein the user device is accessible by the individual.

[0020] Optionally, in some embodiments, the individual is one of a human, an animal, or a plant.

[0021] Optionally, in some embodiments, the individual-specific criteria comprises an activity or a route through the location.

[0022] Optionally, in some embodiments, the individual-specific criteria comprises a biological characteristic associated with the individual.

[0023] In one embodiment, a system for predicting and mitigating effects of environmental conditions, the system comprising: a user device comprising a processor and a memory, configured to: determine environmental conditions of a location within an environment; analyze the environmental conditions according to individual-specific criteria to determine a predicted effect on an individual within the location during the determined environmental conditions; and generate a mitigation response based on the predicted effect.

[0024] Optionally, in some embodiments, the processor and memory are also configured to output a mitigation response to a display associated with a user device, wherein the user device is accessible to an individual.

[0025] Alternatively, in some embodiments, the individual is one of a human, an animal, or a plant.

[0026] Alternatively, in some embodiments, individual-specific criteria include activities or routes through the location.

[0027] Optionally, in some embodiments, individual-specific criteria include biological characteristics associated with the individual. Attached Figure Description

[0028] Figure 1 A simplified schematic diagram of a system for predicting and mitigating the effects of environmental conditions according to embodiments of this article is shown.

[0029] Figure 2 The illustration shows the process according to the embodiments herein. Figure 1 This is an example of a system that implements methods for predicting and mitigating the effects of environmental conditions.

[0030] Figure 3 The illustration shows an embodiment according to this document. Figure 2 An example of a method in which the system can analyze environmental data and optionally analyze one or more user characteristics.

[0031] Figure 4A The diagram illustrates the generation of remedy and / or mitigation options. Figure 2 Examples of methods.

[0032] Figure 4B The diagram illustrates the generation of remedy and / or mitigation options. Figure 2 Examples of methods.

[0033] Figure 5 The illustration shows an embodiment according to this document. Figure 2 An example of a method where the system can notify the user of upcoming exposure thresholds.

[0034] Figure 6 The illustration depicts a method for prediction and mitigation according to embodiments of this document. Figure 1 A simplified block diagram of the components of the computational system for the effects of environmental conditions. Detailed Implementation

[0035] The embodiments disclosed herein predict and enable options to mitigate environmental exposures of people, animals, equipment, infrastructure, and / or plants. The predictions and mitigations can be made on an individual basis, allowing for increased accuracy and responsiveness. Examples of environmental conditions used for predictions include temperature, humidity, ultraviolet (UV) exposure, air pollution or contamination (e.g., smoke, allergens), wind, altitude, ozone exposure, wind speed, water activity current or wave height, terrain, cloud cover, precipitation, elevation, season of the year (e.g., summer, winter, dry season, or monsoon), etc. The environmental conditions are analyzed based on the actual or predicted location of the individual (or other individual), e.g., via location detection, user input, etc. The approach is configured to analyze various types of data from changing or non-static environments, typically those that are outdoors, including both land and water environments. This data is used with optional user or individual-specific data, including activity schedules (e.g., work schedules, travel information, exercise plans, race tracks, etc.) and / or biological information (e.g., heart rate, blood sugar, respiration rate, previous exposures, health history or tolerance information, recent meals, etc.) to predict exposure thresholds, e.g., the time at which heat stress can begin, maximum exposure times, fatigue time ranges. In some cases, the optional user or individual-specific data can include additional user-specific information of biological and / or mental thresholds, e.g., tolerance to noise, tolerance to highly crowded areas, tolerance to flashlights, and / or tolerance to exposure to new stimuli in the environment. Predictions can be determined before an activity begins, e.g., allowing for advance changes to specific schedules (e.g., allowing for additional rest or indoor activities to be scheduled) and / or can be made in real-time to enable notifications to prevent entry into undesirable biological states.

[0036] Further, in some instances, the effects of exposure to environmental conditions can be reset or reduced if the biological system (e.g., a human body) has an opportunity to recover from a particular impact. For example, resting in a cool place with a fan or in a room with air conditioning or reducing the heart rate of a running activity can allow a person to run longer or farther in the sun than the person would have without the rest. Various examples enable such different impacts when making predictions for both specific schedules or times and when providing real-time feedback.

[0037] The disclosed systems and methods predict environmental conditions, including their effects (especially cumulative effects). By predicting the effects of exposure to environmental conditions, the system can adjust activity plans to help avoid or mitigate the impacts of these exposures. The system can also generate notifications of upcoming exposure limits to allow people to take protective measures before problems are encountered.

[0038] By helping to identify and prevent overexposure within a particular environment, various embodiments allow many activities to continue in a more comfortable and less injurious manner. This can allow construction workers, athletes, tourists, etc. to more effectively, enjoyably, and productively maintain a desired schedule of activities even in adverse environmental conditions, such as a hot, humid summer.

[0039] It should be noted that many examples are discussed in relation to people, but are equally applicable to other biological systems, including animals, plants, etc. that experience similar physiological issues when subjected to certain environmental conditions (e.g., plants that wilt at high temperatures, etc.). Similarly, there are certain foods, equipment, and inanimate objects that can also have an impact threshold for which these embodiments can be used to predict and mitigate the impact. Thus, the discussion of any particular example is merely meant to be illustrative.

[0040] Turning to the drawings, Figure 1 a simplified schematic of a system 100 for predicting and optionally enabling mitigation of the effects of environmental conditions is illustrated. The system 100 analyzes environmental conditions to predict and mitigate the effects of long-term exposure of an individual (e.g., a user) to various environmental conditions. The system 100 predicts exposure thresholds or time periods, such as when heat stress can begin, or when fatigue or aerobic capacity can decline. The predictions can be made before an activity has begun, such as to allow for changes to a particular course (e.g., to allow for scheduling additional rest or indoor activities) to be made in advance and / or can provide real-time notifications to prevent entering an undesirable biological state (e.g., generating an alert that rest can be beneficial to reduce heat stress) as the activity is occurring.

[0041] The system 100 includes a server 102 and a user device 106 (e.g., a computing device that a user 108 can interact with, such as a phone, tablet, smartwatch, head-mounted display or other wearable device, laptop or desktop computer, etc.), a network 104, and one or more environmental characteristic databases or sensors, including, as an example, local environmental sensors 110 (e.g., temperature sensors, humidity sensors, etc.), weather data 112 including forecasts 114 and regional environmental data 116.

[0042] In some examples, the network 104 receives environmental conditions or characteristics, such as weather data 112 for a location where the user 108 is about to or is performing an activity or is residing, including current and / or future weather information, such as forecasts 114 and regional environmental data 116. The weather data 112 can include, for example, current temperature, predicted future temperature and how the temperature varies throughout the day, current and / or predicted future rainfall, current and / or predicted future ultraviolet (UV) index, humidity, barometric pressure, sunshine, wind speed, wind direction, cloud cover, atmospheric pressure, rainfall, wind chill, dew point, humidity, atmospheric electric field, wind shear, cumulative radiation, pollutant levels, air quality, smoke index, altitude, air pollution or contamination (e.g., amount of particulate matter, nitrogen oxides, sulfur compounds, ozone, hydrocarbons, carbon dioxide, or carbon monoxide). It should be understood that this is not an exhaustive list, and the weather data 112 can include various types of current and / or predicted future relevant environmental data.

[0043] Further, the network 104 can receive local environmental conditions from local weather sensors 110. The local environmental conditions received from the local weather sensors 110 can be more detailed for a particular location as compared to the weather data 112 that can be applicable to a more generalized region. For example, a location where the user 108 is about to perform an activity or is residing (e.g., a workplace, an outdoor sports center, a garden, an outdoor shopping center) can have its own local barometer, anemometer, thermometer, hygrometer, pluviometer, radar, or meteorological instruments that can provide detailed environmental conditions or characteristics for the particular location where the user 108 is located.

[0044] Optionally, in some examples, the user device 106 can include user or individual specific data, such as activity schedules (e.g., work schedule, travel information, etc.) and / or biological information (e.g., heart rate, respiration rate, previous exposure or tolerance information, health history, etc.). In some examples, the information user or individual specific data can be received from, for example, on-board sensors, third party databases (e.g., work calendar, travel agency, doctor), and / or from input received from the user 108. The optional user or individual specific data can be used by the system 100 to predict and mitigate effects of environmental conditions and / or characteristics.

[0045] The system 100 can make predictions regarding exposure thresholds (e.g., when heat stress can begin) based on the received weather data 112, local environmental conditions from the local weather sensors 110, and / or optional user or individual specific data. The system can perform such predictions on the user device 106 or off the user device.

[0046] In some cases, the system 100 can also generate remediation and / or mitigation options to help ensure that the user does not cross the exposure threshold. The generated remediation and / or mitigation options can help reset the biological consequences resulting from exposure to the environmental condition and / or characteristic, reduce the effects of the environmental condition and / or characteristic. For example, the remediation and / or mitigation options can take the form of a predicted alert, or can take the form of a plan to help extend the exposure time (e.g., sit under a tree for 10 minutes when approaching a sunlight exposure threshold, reduce biological consumption or heart rate, etc.).

[0047] The system 100 can output the prediction of approaching an exposure threshold to the user 108 via the user device 106, and in some instances, output the generated remediation and / or mitigation options. For example, the user device 106 can receive information from the system 100 (e.g., from the device via the network 104) that the user is predicted to cross a sunlight / heat exposure threshold after a certain amount of time of exposure to sunlight / heat, and notify the user 108. In some instances, the user 108 can be recommended via the user device 106 to seek shade to avoid sunlight or to put on sun / heat protective gear. In another example, the user 108 can be notified via the user device 106 to move to a quieter location or to put on hearing protection when the user 108 can be exposed for a certain amount of time approaching a noise exposure threshold. In yet another example, the user 108 can be provided via the user device 106 with optional route changes / updates (e.g., allowing for additional rest or indoor activities to be scheduled) in advance to not approach the predicted exposure threshold that the user would encounter if the original route was executed without the changes / updates. In some instances, it can be possible for a portion of the optional route changes to be approved / accepted by the user 108, while a second portion of the optional route changes is rejected by the user 108.

[0048] The system 100 can also communicate the prediction of approaching an exposure threshold to the server 102 for storage. Similarly, the system 100 can communicate the remediation and / or mitigation options (if generated) to the server 102 for storage. In some instances, previously stored predictions can be forwarded to the network 104 or the user device 106 for use in future exposure threshold predictions in order to predict a more accurate timing of when the exposure threshold can be crossed by the user 108. Similarly, the stored mitigation options can be forwarded to the network 104 or the user device 106 for use in future mitigation option generation. As a result, the prediction of approaching an exposure threshold and the generated mitigation options can be optimized based on previous predictions and generated mitigation options.

[0049] Additionally, in some cases, the predictions and mitigation options stored at the server 102 can be used to include and / or remove features of the environment. For example, if the predictions and mitigation options stored at the server 102 indicate that many predictions have been made by the system 100 that the user 108 is tending to approach a heat exposure threshold at a certain location and / or environment, it can be beneficial to add mitigation features (e.g., shade, water fountain, misting system, bench, indoor enclosure, rest area) in order to prevent future users 108 from approaching the heat exposure threshold at the location.

[0050] Although the foregoing examples detail the user 108 interacting with the user device 106, the present disclosure is also applicable to, for example, plants, animals, transportation infrastructure, and any other entity that can be exposed to environmental conditions and characteristics and can incur biological consequences as a result of crossing an exposure threshold. Various functions can be accomplished by one or more of the devices within the system, for example, the user device 106 or the server 102 can receive user information and weather information to make predictions, and the description of any particular device performing a particular operation is merely illustrative.

[0051] Figure 2 An example of a method 200 for predicting and mitigating effects of environmental conditions performed by the system 100 according to embodiments herein is illustrated. Although the example method 200 depicts a particular order of operations, the order can be altered in ways that do not depart from the scope of the present disclosure. For example, some of the depicted operations can be performed in parallel or in a different order that does not materially affect the functioning of the method 200. In some examples, the method 200 can be performed by a single device, while in other examples, the method 200 can be distributed across different devices (e.g., the server 102, the user device 106, and / or the network 104), with each device performing a portion or all of the method 200. In some examples, the method 200 can be performed by different components of an example device implementing the system 100 of the method 200. In some examples, the operations of the method 200 can be performed substantially simultaneously or in a particular order.

[0052] The method 200 can begin with the system 100 determining environmental data at operation 202. For example, in operation 202, the network 104 can receive weather data 112 including and / or local weather information from the local weather sensor 110 to determine environmental data and / or environmental conditions of a location where a user will reside or perform an activity.

[0053] Method 200 can proceed to operation 204, in which system 100 can analyze environmental data, and optionally, one or more user characteristics. For example, in operation 204, network 104 can analyze weather data 112, local weather information from local weather sensors 110, and optionally, user or individual-specific information, to determine environmental conditions and / or characteristics that user 108 can encounter.

[0054] Method 200 can proceed to operation 206, in which system 100 can predict one or more biological impacts of the environmental conditions and / or characteristics on user 108. For example, in operation 206, system 100 can predict an exposure threshold for user 108 based on the environmental conditions and / or characteristics, beyond which the environmental conditions and / or characteristics can result in a biological consequence for user 108, such as heat stroke when a heat exposure threshold is encountered.

[0055] Method 200 can proceed to operation 208, in which system 100 can generate remediation and / or mitigation options to avoid crossing the exposure threshold for the biological impact corresponding to the environmental characteristic. For example, in operation 208, system 100 can generate remediation and / or mitigation options to avoid crossing the predicted exposure threshold. In some cases, the remediation and / or mitigation options can take the form of recommending that the user put on sunscreen clothing or find an area with shade when approaching a sunlight and / or heat exposure threshold, wearing noise-reducing headphones when approaching a noise exposure threshold, changing the order of a route that occurs earlier in the day or reordering events of a route if system 100 determines that the original route, if executed, can approach any exposure threshold.

[0056] Method 200 can proceed to operation 210, in which system 100 can provide a notification to user 108 of an approaching exposure threshold. In some cases, system 100 can also notify user 108 of generated remediation and / or mitigation options via user device 106 to avoid crossing the upcoming exposure threshold. For example, in operation 210, system 100 can notify user 108 of an upcoming exposure threshold crossing via user device 106, and in some cases, of possible remediation and / or mitigation options. In certain examples, user 108 can decline the optional remediation and / or mitigation options. In some such examples, system 100 can occasionally notify user 108 of an approaching exposure threshold even if user 108 has declined the optional remediation and / or mitigation steps. In certain other examples, user 108 can be notified of an approaching exposure threshold only, without recommended remediation and / or mitigation steps. In other examples, the predictions or data can be provided to a user device to generate predictions, and the user device (e.g., via a health or activity application on the device) can be used to generate alerts and notifications and recommended actions. In these instances, the user device can also include historical performance (e.g., biometric information, heart rate, etc.) that can be used to create more accurate user-specific predictions and recommendations.

[0057] In some examples, method 200 can return to operation 202, in which system 100 can repeat method 200 by again determining environmental data for the user’s location. For example, system 100 can continuously perform method 200 in order to notify user 108 of any upcoming exposure thresholds, as environmental conditions can change throughout the day (e.g., temperature changes, humidity changes, UV index changes, air pollution changes). In some other examples, the biological effects of exposure can be reset as user 108 can have taken one or more of various remediation or mitigation options, or performed their own remediation or mitigation options that were not provided by system 100, and system 100 can again predict whether user 108 is approaching or will approach any exposure thresholds.

[0058] Figure 3 FIG. 1 illustrates an example of method 200, in which system 100 can analyze environmental data, and optionally, one or more user characteristics, in accordance with embodiments herein.

[0059] In some instances of the method 200, the system 100 can analyze environmental data and, optionally, analyze user or individual-specific information on the user device 106. For example, the user device 106 can include user tolerances 302 to various environmental conditions and / or characteristics received from, for example, on-board sensors of the user device 106, third-party databases (e.g., work calendar, travel intermediaries, doctors), and / or from input received from the user 108. It should be understood that the user 108 can indicate that they do not want to provide any user or individual-specific information.

[0060] As Figure 3 illustrated in FIG. 4, optionally, the system 100 can be provided with user or individual-specific information, including user tolerances to various environmental conditions and / or characteristics used in the exposure threshold prediction. For example, the system 100 can be provided with user or individual-specific information indicating that the user can have a high tolerance to heat exposure 304, a high tolerance to humidity exposure 306, a moderate tolerance to noise, a low tolerance to air pollution exposure 310, and a high tolerance to sunlight exposure 312. The system 100 can predict, based in part on the tolerances, that if the user 108 is exposed to such environmental conditions, the user 108 can approach the air pollution exposure 310 threshold faster than the heat exposure 304, humidity exposure 306, noise exposure 308, and sunlight exposure 312 thresholds. Further, in some cases, the system 100 can generate and provide remediation and / or mitigation options to reduce air pollution exposure 310 because the system 100 has been provided with user or individual-specific information indicating that the user 108 can have a low tolerance to air pollution exposure 310.

[0061] Figure 4A FIG. 4 illustrates an example of a method of generating remediation and / or mitigation options. Figure 2

[0062] Consider an example in which the user 108 is planting flowers in a garden. The system 100 can be provided with an initial activity schedule 402 set in the user device 106 that the user 108 can perform, including digging 404 at 10 AM, planting 406 at 11 AM, resting 408 at 12 PM, and cleaning 410 at 1 PM. According to embodiments herein, the system 100 can predict that if the user 108 performs the activity schedule 402 in its current form, the user 108 can exceed the sunlight exposure threshold and the heat exposure threshold because the user can be digging 404, planting 406, and cleaning 410 when sunlight exposure is high and the user 108 can be exposed to heat / raised temperatures.

[0063] Figure 4B FIG. 4 illustrates an example of a method of generating remediation and / or mitigation options. Figure 2 ​of the method. In some embodiments, the system 100 can generate an updated activity schedule 412 that mitigates heat / sun exposure. The system 100 can notify the user 108 via the user device 106 that there can be a close exposure threshold if the original activity schedule 402 is executed, and in some instances, can provide the updated activity schedule 412 to the user 108 via the user device 106. Note that the user 108 can optionally accept and execute (or accept only a portion of) the updated activity schedule 412. The updated activity schedule 412 can include digging 414 at 7am, planting 416 at 8am, cleaning 418 at 9am, and resting 420 at 10am. As a result, the updated activity schedule 412 can limit the user’s exposure to the sun and heat, as the updated activity schedule 412 occurs earlier in the day, which has lower temperatures and lower sun exposure. The updated activity schedule 412 can mitigate the chance of crossing the heat exposure threshold and / or the sun exposure threshold, and thus the biological consequences of crossing the exposure threshold.

[0064] In some instances, the user 108 can reject the updated activity schedule 412, and can execute the activity schedule 402 as initially set. In such instances, the user 108 can still be notified that a predicted exposure threshold (e.g., sun exposure and / or heat exposure) can be approached in the future while executing the activity schedule 402, and take remedial and / or mitigation steps to avoid crossing the exposure threshold.

[0065] Figure 5 FIGURE 8 illustrates an example of the operation 210 of the method 200, where the system 100 can provide the user 108 with a notification of an upcoming exposure threshold, in accordance with embodiments herein.

[0066] In some embodiments, the system 100 can predict that the user 108 can approach an exposure threshold, and in response, can generate remedial and / or mitigation options to mitigate the approaching exposure threshold. The system 100 can also notify 502 the user of the predicted exposure threshold and the generated remedial and / or mitigation options. For example, the user 108 can be notified 502 via the user device 106 that “exposure limit is approaching” and “seek shade and cooler temperatures.” In some cases, the user can ignore the notification and reject to execute the remedial and / or mitigation options.

[0067] As some specific examples of the methods and systems described herein, consider an example where a user is working outside at a worksite. First, the user can optionally enter into the user device the user's tolerance to various environmental conditions (e.g., sun exposure, humidity, heat, etc.). Then, embodiments herein can predict that the user can be approaching a heat exposure threshold at a certain time (e.g., 1 PM) due to increased sun and heat exposure as predicted based on, for example, local environmental conditions, forecasts, and optionally user data. Various mitigation options can be generated for the user to not exceed the heat exposure threshold. For example, the user can be recommended to reduce the intensity at which they are working, take a break, drink some water, and / or work in a shaded area of the worksite. The user can then be notified of the approaching exposure threshold and the mitigation options. In some cases, the user can perform the mitigation options or refuse to perform the mitigation options, but still be reminded of the approaching exposure threshold. Optionally, the predicted approaching exposure threshold and recommended mitigation options can be notified to a supervisor or worksite manager of the user, and the supervisor or worksite manager can recommend that the user take a break and schedule a second user for the worksite when the first user takes a break to maintain a high level of work efficiency and output while also maintaining worker safety (i.e., preventing the user from incurring biological consequences from exceeding the exposure threshold).

[0068] Consider an example where plants are growing under the supervision of a farmer in a location where the climate is unstable. According to embodiments herein, the plants can be predicted to be approaching a heat exposure threshold in the afternoon based on local weather data predictions showing an unexpected increase in sun and / or heat, and thus can wilt and / or die. Mitigation options can be generated, such as covering the plants or watering the plants. The farmer can be notified of the approaching heat exposure threshold for the plants and the generated mitigation options to prevent the plants from wilting or dying.

[0069] Consider an example where a user is going to an amusement park. Initially, the user can create or can optionally be provided with an itinerary including a ride order as well as when to eat and / or rest. According to embodiments herein, it can be predicted that the user can be approaching or even exceeding an air pollution exposure threshold if the initially set / provided itinerary is followed due to, for example, smoke from a nearby wildfire. The user can be notified of the possibility of exceeding the air pollution exposure threshold and can be recommended an updated itinerary to mitigate the air pollution exposure threshold (e.g., an itinerary including rest and enjoying indoor rides). The user can accept or refuse the updated itinerary (or accept / refuse portions of the updated itinerary). Optionally, the user can provide user data, such as tolerance to certain exposures, to recommend a more suitable updated itinerary that prevents approaching any exposure threshold.

[0070] Consider an example in which a refrigerated food product is being transported by a semi-truck to locations across the country. According to embodiments herein, it can be predicted that due to the current navigation of the semi-truck can result in the semi-truck becoming stuck in traffic under hot weather conditions, the refrigerated food product being transported by the semi-truck can be at risk of melting when the refrigerated food product can be close to a melting threshold. Mitigation options can be generated for the semi-truck driver (or dispatcher of the semi-truck driver), such as taking a route with less traffic, taking a navigation route with lower temperatures or more shade, or unloading the refrigerated food product at a nearby facility and waiting for the adverse conditions to pass. The semi-truck driver (or dispatcher of the semi-truck driver) can be notified of the predicted close to melting threshold and the recommended mitigation options. The semi-truck driver can execute one or more of the mitigation options, or can reject the mitigation options while still being notified of the close to melting threshold.

[0071] Consider an example in which athletes are undergoing summer pre-season training. According to embodiments herein, it can be predicted that a group of athletes can be close to a sun and / or heat exposure threshold because they have a lower tolerance to heat and sun exposure (as compared to other athletes). As a result, the coach, medical staff, or the athletes themselves can be notified that the athletes will be close to a sun / heat exposure threshold at a certain time of day. For example, the athletes can be recommended to reduce their training intensity so that they train throughout the day, take a water break, or train in a climate-controlled environment during periods of high level environmental exposure. Beneficially, this can reduce any biological consequences (e.g., fainting and / or heat stress) that can occur when training close to a heat and sun exposure threshold.

[0072] Figure 6is a simplified block diagram of components of a computing system 600 of the system 100, such as the server 102, the local weather sensor 110, the user device 106, and the like. For example, the processing element 602 and the memory component 608 can be located in one or several computing systems 600. This disclosure contemplates any suitable number of such computing systems 600. For example, the server 102 can be a desktop computing system, a mainframe, a blade, a grid of computing systems 600, a laptop or notebook computing system 600, a tablet computing system 600, an embedded computing system 600, a system on a chip, a single-board computing system 600, or a combination of two or more of these. Where appropriate, the computing system 600 can include one or more computing systems 600; singular or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which can include one or more cloud components in one or more networks. The computing system 600 can include one or more processing elements 602, input / output (I / O) interfaces 604, one or more external devices 612, one or more memory components 608, and a network interface 610. Each of the various components can communicate with one another by way of one or more buses or communication networks, such as a wired or wireless network, for example, the network 104. Figure 6 The components in are merely exemplary. In various examples, the computing system 600 can include additional components and / or functionality not shown in Figure 6

[0073] The processing element 602 can be any type of electronic device capable of processing, receiving, and / or transmitting instructions. For example, the processing element 602 can be a central processing unit, microprocessor, processor, or microcontroller. Moreover, it should be noted that some components of the computing system 600 can be controlled by a first processing element 602, while other components can be controlled by a second processing element 602, where the first and second processing elements can or can not communicate with one another.

[0074] The I / O interface 604 allows a user to enter data into the computing system 600 and provides input / output to the computing system 600 to communicate with other devices or services. The I / O interface 604 can include one or more input buttons, touchpad, touch screen, and the like.

[0075] The external device 612 is one or more devices that can be used to provide various inputs to the computing system 600, such as a mouse, microphone, keyboard, trackpad, sensing element, for example, a thermistor, a humidity sensor, a light detector, and the like. The external device 612 can be local or remote and can vary as needed. In some examples, the external device 612 can also include one or more additional sensors.

[0076] ​The memory component 608 is used by the computing system 600 to store instructions for processing elements 602, as well as to store data, such as weather data 112 including forecasts 114 and regional environmental data 116. The memory component 608 can be, for example, a magnetic optical storage, read-only memory, random access memory, erasable programmable memory, flash memory, or a combination of one or more types of memory components.

[0077] The network interface 610 provides communications from the computing system 600 to other devices and provides communications to the computing system 600. The network interface 610 includes one or more communication protocols, such as, but not limited to, Wi-Fi, Ethernet, Bluetooth, etc. The network interface 610 can also include one or more hardwired components, such as a universal serial bus (USB) cable, etc. The configuration of the network interface 610 depends on the type of communication desired and can be modified to communicate via Wi-Fi, Bluetooth, etc.

[0078] The display 606 provides visual output for the computing system 600 and can vary depending on the needs of the device. The display 606 can be configured to provide visual feedback to the user 108 and can include a liquid crystal display screen, a light emitting diode screen, a plasma screen, etc. In some examples, the display 606 can be configured to act as an input element for the user 108 through touch feedback, etc.

[0079] Any description of specific components as part of a particular embodiment is meant to be illustrative only and should not be construed as requiring the other elements shown in the depicted embodiment to be used with the particular embodiment.

[0080] All relative and directional references (including, for example, top, bottom, side, front, rear, etc.) are given by way of example only and are meant to aid in understanding the examples described herein. They are not to be construed as requiring or limiting, particularly with respect to position, orientation, or use, unless specifically stated in the claims. Connection references (e.g., attached, coupled, connected, joined, etc.) are to be construed broadly and can include intermediate members and relative movement between elements. Thus, connection references are not necessarily infer a direct connection between two elements and that they are in a fixed relationship or orientation with respect to each other unless specifically stated in the claims.

[0081] The present disclosure is taught by example and not by limitation. Accordingly, the contents of the above description and the examples shown in the accompanying drawings should be interpreted as illustrative and not in a limiting sense. The following claims are intended to cover all generic and specific features of the methods and systems described herein, as well as all statements of the scope of such methods and systems, which, as a matter of language, might be said to fall therebetween.

Claims

1. A method for predicting the effects of environmental exposure, comprising: The processor receives environmental data on environmental conditions. The processor predicts the biological effects of the environmental conditions on the user; and Generate notifications about the predicted biological effects.

2. The method of claim 1, further comprising receiving, via the processor, an activity plan of a user exposed to the environmental conditions.

3. The method of claim 2, further comprising adjusting the activity plan via the processor to avoid the biological effects.

4. The method according to claim 1, wherein, The prediction is based on at least one of the user's biological characteristics and / or the user's activity schedule.

5. The method according to claim 1, wherein, The environmental data includes at least one of current environmental conditions or predictions of future environmental conditions.

6. The method of claim 1, further comprising: The processor receives the user's tolerance to the environmental conditions.

7. The method of claim 1, further comprising: The processor generates user alerts indicating the imminent effects of exposure to the environmental conditions.

8. The method of claim 1, further comprising: The processor transmits the adjusted activity plan to the user device; and The adjusted activity plan is displayed on the monitor of the user device.

9. The method of claim 1, further comprising: User health data is received from the user device via the processor.

10. The method according to claim 9, wherein, The processor is associated with the user device, and the activity schedule is stored in the user device's memory.

11. The method according to claim 1, wherein, The environmental data includes one or more of the following: Temperature, heat exposure index, sunshine, wind speed, wind direction, cloud cover, atmospheric pressure, precipitation, wind chill, dew point, humidity, atmospheric electric field, wind shear, cumulative radiation or pollutant level.

12. The method according to claim 11, wherein, The pollutant is one or more of the following: a certain amount of particulate matter, nitrogen oxides, sulfur compounds, ozone, hydrocarbons, carbon dioxide, or carbon monoxide.

13. A method for predicting the impact of environmental exposure, comprising: Determine the environmental conditions of the location within the environment; The environmental conditions are analyzed based on individual-specific criteria to determine the predicted impact on individuals within the location during the determined environmental conditions. as well as A mitigation response is generated based on the predicted impact.

14. The method of claim 13, further comprising outputting the mitigation response to a display associated with a user device, wherein the user device is accessible by the individual.

15. The method according to claim 13, wherein, The individual can be a person, an animal, or a plant.

16. The method according to claim 13, wherein, The individual-specific criteria include activities or routes taken through the location.

17. A system for predicting and mitigating the effects of environmental conditions, the system comprising: User devices, including processors and memory, are configured to: The environmental conditions that determine the location within the environment; The environmental conditions are analyzed based on individual-specific criteria to determine the predicted impact on individuals within the location during the determined environmental conditions. as well as A mitigation response is generated based on the predicted impact.

18. The system according to claim 17, wherein, The processor and the memory are also configured to output the mitigation response to a display associated with the user device, wherein the user device is accessible by the individual.

19. The system according to claim 17, wherein, The individual can be a person, an animal, or a plant.

20. The system according to claim 17, wherein, The individual-specific criteria include biological characteristics associated with the individual.

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