An AI-based early warning system and method for people during haze periods

Abstract

The present invention is applicable to the computer field and provides an AI-based crowd warning system and method for haze periods, the method comprising: obtaining the user's target travel route after obtaining the user's consent, the target travel route covering several travel sub-areas; obtaining the suspended particulate matter content of the travel sub-areas in real time, detecting the change in the suspended particulate matter content of the next travel sub-area in the target travel route compared with the current travel sub-area; when the content change is detected as an increment and the increment reaches a first threshold, it is determined that the suspended particulate matter content of the next travel sub-area reaches the suspended particulate matter sensitive content of the first target user, and a first-level warning prompt is issued based on the terminal where the first target user is located. The beneficial effects of the present invention are: distinguishing travel sub-areas and providing effective early warning based on the user's own characteristics.

Description

Technical Field

[0001] The present invention belongs to the field of computers, and in particular relates to an AI-based haze-period crowd early warning system and method. Background Art

[0002] According to the National Meteorological Administration, haze is a weather phenomenon characterized by large numbers of atmospheric aerosol particles several microns or smaller, reducing horizontal visibility to less than 10 km and causing widespread air turbidity. Haze is a collection of particles such as dust, sulfuric acid, nitrates, and organic hydrocarbons suspended in the air. Relative humidity is generally less than 80%. The main components of haze are aerosol pollutants and particulate matter, particularly PM2.5. A typical characteristic of haze is high concentrations of PM2.5 (typically exceeding 100µg / m3), accompanied by high concentrations of other atmospheric pollutants such as NOx, SO2, O3, and NH3.

[0003] Under haze weather conditions, the atmosphere contains hundreds of atmospheric chemical particulate matter. After entering and adhering to the human respiratory tract and lung lobes, especially submicron particles, will be deposited in the upper and lower respiratory tracts and alveoli respectively, causing rhinitis, bronchitis and other diseases. Long-term exposure to such an environment can also induce lung cancer. Haze weather can also lead to the weakening of ultraviolet rays in the near-ground layer, which can easily increase the activity of infectious bacteria in the air and increase infectious diseases. Haze weather can easily make people pessimistic and depressed, and they may even lose control when encountering unpleasant things.

[0004] In the existing technology, sensitive people who are deeply affected by smog only judge smog based on information released by the national meteorological department and the naked eye. This causes many inconveniences for such people when traveling, especially when the degree of smog in the area they travel to varies. Such people are often unable to prevent it and are often directly exposed to smog. Therefore, it is necessary to develop an AI-based smog period crowd warning system and method. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide an AI-based haze period crowd warning system and method, aiming to solve the problems raised in the above background technology.

[0006] The embodiment of the present invention is implemented as follows: on the one hand, an AI-based early warning method for people during haze period, the method comprising the following steps:

[0007] After obtaining the user's consent, obtaining the user's target travel route, where the target travel route covers a plurality of travel sub-areas;

[0008] Acquiring the suspended particulate matter content of the travel sub-area in real time, and detecting a change in the suspended particulate matter content of the next travel sub-area in the target travel route compared to the current travel sub-area;

[0009] When it is detected that the content change is an increment and the increment reaches a first threshold, it is determined that the suspended particulate matter content in the next travel sub-area reaches the suspended particulate matter sensitivity content of the first target user, and a first-level warning prompt is issued based on the terminal where the first target user is located;

[0010] When it is detected that the content change is an increment and the increment reaches the second threshold, it is determined that the suspended particulate matter content in the next travel sub-area has reached the content corresponding to the AQI moderate pollution. If and only if the first target user is not detected within the safety time period and feedback information on the second-level warning prompt is issued based on the terminal, an attempt is made to establish a first information channel between the terminals of other users within a preset distance range and the wearable device of the user, so that the wearable device sends a vibration prompt information, wherein the second threshold is greater than or equal to the first threshold, and the safety time period is less than the estimated time for the first target user to travel from the current travel sub-area to the next travel sub-area.

[0011] As a further solution of the present invention, the target travel route of the user after obtaining the user's consent, wherein the target travel route covers several travel sub-areas, specifically includes:

[0012] Pre-acquire the target travel route input by the user based on the terminal;

[0013] Extracting the travel area in the user's target travel route;

[0014] The travel sub-areas covered by the travel area are divided to obtain a plurality of spatially continuous travel sub-areas.

[0015] As a further embodiment of the present invention, the real-time acquisition of the suspended particulate matter content of the travel sub-area and the detection of a change in the suspended particulate matter content of the next travel sub-area in the target travel route compared to the current travel sub-area specifically include:

[0016] Setting an acquisition cycle, acquiring the suspended particulate matter content of the travel sub-area once every acquisition cycle;

[0017] Detecting a change in the content of suspended particulate matter in a next travel sub-area in the target travel route compared to the current travel sub-area, wherein a time interval between two consecutive detections is no greater than an acquisition period.

[0018] As a further aspect of the present invention, the method further comprises:

[0019] Accept the suspended particulate matter sensitive content data input by each user in advance;

[0020] Sort the suspended particulate matter sensitive content data input by each user from large to small, and divide the sorted suspended particulate matter sensitive content data into several intervals according to the equal gradient content;

[0021] Mark the users whose suspended particulate matter sensitive content data are in the same range, and obtain a list of marked users;

[0022] Bind the suspended particulate matter sensitive content data entered by each user to their respective terminals and wearable devices.

[0023] As a further embodiment of the present invention, the method further comprises:

[0024] When it is detected that the suspended particulate matter content in the initial travel sub-area of ​​the target travel route reaches the suspended particulate matter sensitivity content of the first target user, a third-level warning prompt is directly issued, and the third-level warning prompt is used to indicate that the current travel is harmful to health.

[0025] As a further embodiment of the present invention, the method further comprises:

[0026] When it is detected that the content change is a decrease or it is determined that the suspended particulate matter content of the next travel sub-area does not reach the suspended particulate matter sensitivity content of the first target user, obtaining a marked user list corresponding to the suspended particulate matter content of the next travel sub-area;

[0027] Establishing a second information channel between the terminal where the first target user is located and the terminals where other users in the marked user list are located;

[0028] Attempt to obtain the location of the user in the marked user list information. If the acquisition is successful and it is detected that the location is not within the multiple travel sub-areas of the corresponding user, define a second target user, where the second target user is the user in the marked user list whose location is not within the multiple travel sub-areas;

[0029] Obtaining all travel sub-areas whose suspended particulate matter content is not lower than the lowest value in the same interval of the marked user list based on the second information channel, and defining all the travel sub-areas as avoidance travel sub-areas for the second target user;

[0030] reconstructing a target travel route for the second target user based on the target sub-area of ​​the second target user and the avoidance travel sub-area;

[0031] Regularly and repeatedly detect whether the real-time location of the second target user is within the reconstructed target travel route. If not, continue to reconstruct the target travel route.

[0032] As a further embodiment of the present invention, the method further comprises:

[0033] Get the user's average travel speed from the previous travel area to the current travel area;

[0034] The estimated travel time for the user to travel from the current travel sub-area to the next travel sub-area is calculated based on the average travel speed and the distance between the current travel sub-area and the next travel sub-area.

[0035] As a further solution of the present invention, if and only if the first target user is not detected within the safety time period, based on the terminal, feedback information on the secondary warning prompt is sent, attempting to establish a first information channel between the terminal of other users within a preset distance range and the wearable device of the user, so that the wearable device sends a vibration prompt message specifically includes:

[0036] When it is determined that the suspended particulate matter content in the next travel sub-area reaches the content value corresponding to moderate AQI pollution, a second-level warning prompt instruction is issued to the terminal where the first target user is located, and a timer is started at the same time;

[0037] If no read feedback instruction of the first target user to the secondary warning prompt is detected when the timing reaches the safe time, a first information channel is established between the terminals of other users within a preset distance range and the wearable device of the user, and a secondary warning information containing the suspended particulate matter content of the first target user's next travel sub-area is sent to the wearable device through the first information channel. After receiving the secondary warning information, the wearable device scrolls and displays it and vibrates.

[0038] As a further solution of the present invention, in another aspect, an AI-based early warning system for people during a haze period is provided, the system comprising:

[0039] An acquisition module is used to obtain the user's target travel route after obtaining the user's consent, where the target travel route covers a plurality of travel sub-areas;

[0040] a detection module, configured to obtain the suspended particulate matter content of the travel sub-area in real time, and detect a change in the suspended particulate matter content of the next travel sub-area in the target travel route compared with the current travel sub-area;

[0041] a judgment and warning module, configured to, when detecting that the content change is an increment and the increment reaches a first threshold, determine that the suspended particulate matter content in the next travel sub-area reaches a suspended particulate matter sensitivity content for a first target user, and issue a first-level warning prompt based on the terminal where the first target user is located;

[0042] A judgment and prompt module is used to determine that the suspended particulate matter content in the next travel sub-area reaches the content corresponding to the moderate pollution of the AQI when it is detected that the content change is an increment and the increment reaches a second threshold value. If and only if the first target user is not detected within the safety time period, feedback information on the second-level warning prompt is issued based on the terminal, and an attempt is made to establish a first information channel between the terminal where other users are located within a preset distance range and the wearable device of the user, so that the wearable device sends a vibration prompt information, wherein the second threshold value is greater than or equal to the first threshold value, and the safety time period is less than the estimated time for the first target user to travel from the current travel sub-area to the next travel sub-area.

[0043] An AI-based crowd warning system and method for haze periods provided by an embodiment of the present invention detects changes in the content of suspended particulate matter and implements it in combination with different travel areas. It can avoid blind travel and minimize direct exposure to travel sub-areas with severe air pollution, thereby effectively realizing travel warning. In particular, by defining a first target user and a second target user, the suspended particulate matter sensitive content of the first target user and the second target user is in the same range, which can provide more accurate travel warnings and guidance for the corresponding travel users, and facilitate the second target user to avoid the "dangerous" travel sub-area through the reconstructed target travel area. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is the main flow chart of an AI-based early warning method for people during haze periods.

[0045] Figure 2 This is a flow chart of an embodiment before obtaining the suspended particulate matter content of the row sub-area in real time.

[0046] Figure 3 The present invention is a flowchart of another embodiment of an AI-based method for early warning of people during haze period.

[0047] Figure 4 This is a flowchart of attempting to establish a first information channel between the terminals of other users within a preset distance range and the wearable device of the user.

[0048] Figure 5 This is the main structure diagram of an AI-based early warning system for people during haze periods. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0051] The present invention provides an AI-based early warning system and method for people during haze periods, which solves the technical problems in the background technology.

[0052] like Figure 1 FIG. 1 is a main flow chart of an AI-based early warning method for people during a haze period according to an embodiment of the present invention. The AI-based early warning method for people during a haze period includes:

[0053] Step S10: obtaining the user's target travel route after obtaining the user's consent, where the target travel route covers a number of travel sub-areas;

[0054] Step S11: obtaining the suspended particulate matter content of the travel sub-area in real time, and detecting the change in the suspended particulate matter content of the next travel sub-area in the target travel route compared with the current travel sub-area;

[0055] Step S12: When it is detected that the content change is an increment and the increment reaches a first threshold, it is determined that the suspended particulate matter content in the next travel sub-area reaches the suspended particulate matter sensitivity content of the first target user, and a first-level warning is issued based on the terminal where the first target user is located. Combined with the first target user's own sensitive data on the degree of air pollution, an early warning can be issued before the user reaches the next travel sub-area from the current travel sub-area, so as to facilitate the individual to strengthen protection or slow down or change travel routes;

[0056] Step S13: When it is detected that the content change is an increment and the increment reaches the second threshold, it is determined that the suspended particulate matter content in the next travel sub-area reaches the content corresponding to the AQI moderate pollution, and if and only if the first target user is not detected within the safety time, feedback information on the secondary warning prompt is issued based on the terminal, and an attempt is made to establish a first information channel between the terminal where other users are located within a preset distance range and the wearable device of the user, so that the wearable device sends a vibration prompt information, wherein the second threshold is greater than or equal to the first threshold, and the safety time is lower than the estimated time for the first target user to travel from the current travel sub-area to the next travel sub-area, the terminal includes but is not limited to a mobile terminal, a tablet computer, a dedicated smart device, etc., which is not limited here, and the wearable device includes but is not limited to a bracelet, a smart watch, etc.

[0057] When the content change is detected as an increment and the increment reaches a second threshold, it is determined that the suspended particulate matter content in the next travel sub-area has reached the content corresponding to moderate AQI pollution. If and only if the first target user is not detected within the safety time period, based on the terminal, feedback information regarding the second-level warning prompt is issued. An attempt is made to establish a first information channel between the terminals of other users within a preset distance range and the wearable device of the user, so that the wearable device issues a vibration prompt message. The second threshold is greater than or equal to the first threshold, and the safety time period is less than the estimated time it takes for the first target user to travel from the current travel sub-area to the next travel sub-area. This can achieve a higher level of warning and prompt. This warning takes into account the situation where the first target user is unaware of the warning for some reason, such as a terminal failure or low battery. By establishing the first information channel between the terminals of other users within the preset distance range and the wearable device of the user, so that the wearable device issues a vibration prompt message, the terminals of other users within the preset range can initiate a warning to the first target user. This closer warning can avoid the randomness of warning initiation and improve the success rate of warnings.

[0058] As a preferred embodiment of the present invention, the target travel route of the user after obtaining the user's consent, wherein the target travel route covers several travel sub-areas, specifically includes:

[0059] Step S101: pre-acquire a target travel route input by a user based on a terminal;

[0060] Step S102: extracting the travel area in the user's target travel route;

[0061] Step S103: Divide the travel sub-areas covered by the travel area to obtain a number of spatially continuous travel sub-areas.

[0062] When applying this embodiment, it is necessary to divide travel sub-areas, because even in areas with similar distances, there may be large differences in the content of air pollutants. For example, the content of suspended particulate matter is affected by factors such as temperature, humidity, vehicle traffic density, wind speed, and air pressure. The travel sub-areas here are divided according to historical air pollution data. The historical suspended particulate matter content in the same travel sub-area is within a certain concentration difference range, so it can be considered that the difference is not large. Travel sub-areas are different from administrative areas in the map.

[0063] As a preferred embodiment of the present invention, the real-time acquisition of the suspended particulate matter content of the travel sub-area and the detection of a change in the suspended particulate matter content of the next travel sub-area in the target travel route compared with the current travel sub-area specifically include:

[0064] Step S111: setting an acquisition cycle, acquiring the suspended particulate matter content of the travel sub-area once every acquisition cycle;

[0065] Step S112: detecting a change in the content of suspended particulate matter in the next travel sub-area in the target travel route compared with the current travel sub-area, wherein the time interval between two consecutive detections is no greater than the acquisition period.

[0066] It should be understood that the suspended particulate matter content in the travel sub-area can be obtained from the data released by the meteorological and environmental protection department. The data can be released once a day or updated every few hours. Of course, it can also be directly obtained through detection by instruments installed in different travel sub-areas. For example, the Stop PM2.5 detection instrument is used to measure the PM2.5 content value. The time interval between two consecutive detections is no more than the acquisition cycle to ensure the timeliness of the detection and effectively respond to changes in suspended particulate matter.

[0067] like Figure 2 As shown, as a preferred embodiment of the present invention, before obtaining the suspended particulate matter content of the row sub-area in real time, the method further includes:

[0068] Step S20: pre-accepting the suspended particulate matter sensitivity data input by each user. For example, a user's sensitivity to PM10 is 50 μg / m3. Generally, the suspended particulate matter sensitivity data of this type of user is lower than the sensitive pollutant content value corresponding to moderate AQI pollution.

[0069] Step S21: sorting the suspended particulate matter sensitive content data input by each user from large to small, and dividing the sorted suspended particulate matter sensitive content data into several intervals according to the equal gradient content;

[0070] Step S22: Mark the users whose suspended particulate matter sensitive content data are in the same interval, and obtain a list of marked users;

[0071] Step S23: Bind the suspended particulate matter sensitive content data input by each user to their respective terminals and wearable devices.

[0072] As a preferred embodiment of the present invention, the method further includes:

[0073] Step S30: When it is detected that the suspended particulate matter content in the initial travel sub-area of ​​the target travel route reaches the suspended particulate matter sensitivity content of the first target user, a third-level warning prompt is directly issued, and the third-level warning prompt is used to indicate that the current travel is harmful to health.

[0074] When this embodiment is applied, the first, second and third level warning prompt levels gradually increase, that is, the degree of danger becomes higher and higher. The third level warning prompt means that the user is strongly advised not to travel from the current travel sub-area, but is suitable to stay at home or recommended to stay indoors.

[0075] like Figure 3 As shown, as a preferred embodiment of the present invention, the method further includes:

[0076] Step S40: When it is detected that the content change is a decrease or it is determined that the suspended particulate matter content of the next travel sub-area does not reach the suspended particulate matter sensitivity content of the first target user, a marked user list corresponding to the suspended particulate matter content of the next travel sub-area is obtained, that is, a marked user list corresponding to the suspended particulate matter content of the next travel sub-area is obtained;

[0077] Step S41: establishing a second information channel between the terminal where the first target user is located and the terminals where other users in the marked user list are located;

[0078] Step S42: Attempting to obtain the location of the user in the marked user list information. If the acquisition is successful and it is detected that the location is not within the multiple travel sub-areas of the corresponding user, defining a second target user, the second target user being the user in the marked user list whose location is not within the multiple travel sub-areas. The location of the user in the marked user list information can be obtained automatically by the user turning on terminal positioning, or can be manually input by the corresponding user. The input location can be a specific nearby location, such as the name of a shopping mall.

[0079] Step S43: Acquire all travel sub-areas whose suspended particulate matter content is not lower than the lowest value in the same interval of the marked user list based on the second information channel, and define all the travel sub-areas as avoidance travel sub-areas for the second target user;

[0080] Step S44: reconstructing a target travel route for the second target user based on the target sub-area of ​​the second target user and the avoidance travel sub-area;

[0081] Step S45: Periodically and repeatedly detecting whether the real-time location of the second target user is within the reconstructed target travel route; if not, continuing to reconstruct the target travel route.

[0082] It can be understood that through the setting of the second information channel, the second target user is defined, that is, the user who changes the travel route for some reason during the trip, and is prone to direct exposure to the dangerous environment of sensitive suspended particulate matter after the change. For this type of user, an association is established with the first target user whose suspended particulate matter content does not reach the sensitive content of suspended particulate matter through the second information channel. Because the sensitive content of suspended particulate matter of the first target user and the second target user is in the same range, more accurate travel warnings and guidance can be provided to the corresponding travel users, making it convenient for the second target user to avoid the "dangerous" travel sub-area through the reconstructed target travel area.

[0083] As a preferred embodiment of the present invention, the method further includes:

[0084] Step S51: Obtain the average travel speed of the user from the previous travel area to the current travel area;

[0085] Step S52: Calculate the estimated travel time of the user from the current travel sub-area to the next travel sub-area based on the average travel speed and the distance between the current travel sub-area and the next travel sub-area.

[0086] When this embodiment is applied, the estimated travel time for the user from the current travel sub-area to the next travel sub-area is calculated by the average travel speed and the distance between the current travel sub-area and the next travel sub-area, thereby providing a safety reference for subsequent early warnings.

[0087] like Figure 4 As shown, as a preferred embodiment of the present invention, if and only if the first target user is not detected within the safety time period, based on the feedback information of the second-level warning prompt issued by the terminal, attempting to establish a first information channel between the terminal where other users are located within a preset distance range and the wearable device of the user, so that the wearable device sends a vibration prompt information specifically includes:

[0088] Step S131: When it is determined that the suspended particulate matter content in the next travel sub-area reaches the content value corresponding to moderate AQI pollution, a second-level warning prompt instruction is issued to the terminal where the first target user is located, and the timer starts at the same time. The Air Quality Index (AQI) is simply data that can quantitatively describe the air quality. The AQI is suitable for indicating the short-term air quality status and changing trends of a city. The reference standards for AQI classification calculation are the "Ambient Air Quality Standard" (GB3095-2012) and the "Technical Provisions for Ambient Air Quality Index (AQI) (Trial)" (HJ633-2012). The AQI index has been implemented in my country since 2012. The air quality index of moderate pollution is between 151 and 200. Its impact on health is as follows: it further aggravates the symptoms of susceptible people and may affect the heart and respiratory system of healthy people. The recommended measures are: children, the elderly, and patients with heart disease and respiratory diseases should avoid long-term exposure to the air. Time, high-intensity outdoor exercise, the general population should reduce outdoor exercise appropriately; AQI takes the air quality sub-index corresponding to the primary pollutant when calculating, and is mostly used to evaluate the air quality status in a short period of time, such as hourly and daily air quality. There is a special calculation formula for AQI calculation. The concentration values ​​of several pollutants such as PM10, PM2.5, SO2, NO2, etc. can be used to calculate AQI. The maximum AQI index is taken as the final reported AQI value, and the pollutant that contributes to the maximum value is called the primary pollutant. Generally speaking, PM10 and PM2.5 have a greater impact on human breathing, so suspended particulate matter is used to calculate the AQI level, which has a higher credibility for early warning of different travel sub-areas;

[0089] Step S132: If no read feedback instruction of the first target user for the secondary warning prompt is detected when the timing duration reaches the safety duration, a first information channel is established between the terminal of other users within a preset distance range and the wearable device of the user, and the secondary warning information containing the suspended particulate matter content of the next travel sub-area of ​​the first target user is sent to the wearable device through the first information channel. After receiving the secondary warning information, the wearable device scrolls and displays it and vibrates. The information indicating that the suspended particulate matter content in the next travel sub-area reaches the secondary warning is scrolled and vibrated, making the reminder easier to perceive. The next travel area of ​​the first target user is not necessarily the travel sub-area of ​​other users, and since the travel sub-area is not a specific location, the protection of travel privacy can be met as much as possible. The first target user can close it after reading to indicate that it has read the feedback, and will not receive other information before the wearable device is turned on next time. It can be understood that when at least one user is within the preset distance range of the first target user, the wearable device of the first target user will automatically connect to the terminal carried by the user.

[0090] When this embodiment is applied, an attempt is made to establish a first information channel between the terminals of other users within a preset distance range and the wearable device of the user, so that the condition for the wearable device to send a vibration prompt message is: when the timing duration reaches the safety duration, no read feedback instruction of the first target user for the secondary warning prompt is detected, that is, according to the average speed, the first target user has not yet reached the next travel sub-area. Therefore, a reasonable and timely warning can be given to the first target user's travel, so that the first target user can take corresponding measures in time to deal with the next travel sub-area where the suspended particulate matter content reaches the AQI moderate pollution or avoid the travel sub-area.

[0091] like Figure 5 As shown, as another preferred embodiment of the present invention, on the other hand, an AI-based haze period crowd warning system, the system includes:

[0092] An acquisition module 100 is configured to obtain a user's target travel route after obtaining the user's consent, where the target travel route covers a plurality of travel sub-areas;

[0093] The detection module 200 is configured to obtain the suspended particulate matter content of the travel sub-area in real time and detect the change in the suspended particulate matter content of the next travel sub-area in the target travel route compared with the current travel sub-area;

[0094] The judgment and warning module 300 is configured to, when detecting that the content change is an increment and the increment reaches a first threshold, determine that the suspended particulate matter content in the next travel sub-area reaches the suspended particulate matter sensitivity content for the first target user, and issue a first-level warning prompt based on the terminal where the first target user is located;

[0095] The judgment and prompt module 400 is used to determine that the suspended particulate matter content in the next travel sub-area reaches the content corresponding to the moderate pollution of the AQI when it is detected that the content change is an increment and the increment reaches a second threshold value. If and only if the first target user is not detected within the safety time period and the feedback information of the second-level warning prompt is issued based on the terminal, try to establish a first information channel between the terminal where other users are located within a preset distance range and the wearable device of the user, so that the wearable device sends a vibration prompt information, wherein the second threshold value is greater than or equal to the first threshold value, and the safety time period is less than the estimated time for the first target user to travel from the current travel sub-area to the next travel sub-area.

[0096] The above-mentioned embodiment of the present invention provides an AI-based crowd warning method during the haze period, and based on the AI-based crowd warning method during the haze period, provides an AI-based crowd warning system during the haze period. By detecting the changes in the content of suspended particulate matter and combining it with different travel areas to implement it, it can avoid the blindness of travel, minimize direct exposure to travel sub-areas with serious air pollution, and well implement travel warning. In particular, by defining the first target user and the second target user, the suspended particulate matter sensitive content of the first target user and the second target user is in the same range, which can provide the corresponding travel users with more accurate travel warnings and guidance, and facilitate the second target user to avoid the "dangerous" sub-area closely related to its own suspended particulate matter sensitive content through the reconstructed target travel area.

[0097] In order to enable the above-mentioned method and system to be loaded and run smoothly, in addition to the various modules mentioned above, the system may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

[0098] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the system, connecting various parts using various interfaces and lines.

[0099] The memory can be used to store computer and system programs and / or modules. The processor implements the various functions described above by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as an information collection template display function and a product information release function). The data storage area may store data generated based on the use of the berth status display system (such as product information collection templates corresponding to different product types and product information required to be released by different product providers). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0100] It should be understood that although the various steps in the flow charts of the various embodiments of the present invention are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the various embodiments may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0101] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An AI-based early warning method for people during haze periods, characterized by: The method comprises: After obtaining the user's consent, obtaining the user's target travel route, where the target travel route covers a plurality of travel sub-areas; Acquiring the suspended particulate matter content of the travel sub-area in real time, and detecting a change in the suspended particulate matter content of the next travel sub-area in the target travel route compared to the current travel sub-area; When it is detected that the content change is an increment and the increment reaches a first threshold, it is determined that the suspended particulate matter content in the next travel sub-area reaches the suspended particulate matter sensitivity content of the first target user, and a first-level warning prompt is issued based on the terminal where the first target user is located; When it is detected that the content change is an increment and the increment reaches the second threshold, it is determined that the suspended particulate matter content in the next travel sub-area has reached the content corresponding to the AQI moderate pollution. If and only if the first target user is not detected within the safety time period and feedback information on the second-level warning prompt is issued based on the terminal, an attempt is made to establish a first information channel between the terminals of other users within a preset distance range and the wearable device of the user, so that the wearable device sends a vibration prompt information, wherein the second threshold is greater than or equal to the first threshold, and the safety time period is less than the estimated time for the first target user to travel from the current travel sub-area to the next travel sub-area.

2. The AI-based early warning method for people in haze period according to claim 1 is characterized in that: The target travel route of the user after obtaining the user's consent, wherein the target travel route covers several travel sub-areas, specifically including: Pre-acquire the target travel route input by the user based on the terminal; Extracting the travel area in the user's target travel route; The travel sub-areas covered by the travel area are divided to obtain a plurality of spatially continuous travel sub-areas.

3. The AI-based early warning method for people in haze period according to claim 1 is characterized in that: The step of acquiring the suspended particulate matter content of the travel sub-area in real time and detecting the change in the suspended particulate matter content of the next travel sub-area in the target travel route compared with the current travel sub-area specifically includes: Setting an acquisition cycle, acquiring the suspended particulate matter content of the travel sub-area once every acquisition cycle; Detecting a change in the content of suspended particulate matter in a next travel sub-area in the target travel route compared to the current travel sub-area, wherein a time interval between two consecutive detections is no greater than an acquisition period.

4. The AI-based early warning method for people in haze period according to any one of claims 1 to 3, characterized in that: Before obtaining the suspended particulate matter content of the row sub-area in real time, the method further includes: Accept the suspended particulate matter sensitive content data input by each user in advance; Sort the suspended particulate matter sensitive content data input by each user from large to small, and divide the sorted suspended particulate matter sensitive content data into several intervals according to the equal gradient content; Mark the users whose suspended particulate matter sensitive content data are in the same range, and obtain a list of marked users; Bind the suspended particulate matter sensitive content data entered by each user to their respective terminals and wearable devices.

5. The AI-based early warning method for people in haze period according to claim 1 is characterized in that: The method further comprises: When it is detected that the suspended particulate matter content in the initial travel sub-area of ​​the target travel route reaches the suspended particulate matter sensitivity content of the first target user, a third-level warning prompt is directly issued, and the third-level warning prompt is used to indicate that the current travel is harmful to health.

6. The AI-based early warning method for people in haze period according to claim 4 is characterized in that: The method further comprises: When it is detected that the content change is a decrease or it is determined that the suspended particulate matter content of the next travel sub-area does not reach the suspended particulate matter sensitivity content of the first target user, obtaining a marked user list corresponding to the suspended particulate matter content of the next travel sub-area; Establishing a second information channel between the terminal where the first target user is located and the terminals where other users in the marked user list are located; Attempt to obtain the location of the user in the marked user list information. If the acquisition is successful and it is detected that the location is not within the multiple travel sub-areas of the corresponding user, define a second target user, where the second target user is the user in the marked user list whose location is not within the multiple travel sub-areas; Obtaining all travel sub-areas whose suspended particulate matter content is not lower than the lowest value in the same interval of the marked user list based on the second information channel, and defining all the travel sub-areas as avoidance travel sub-areas for the second target user; reconstructing a target travel route for the second target user based on the target sub-area of ​​the second target user and the avoidance travel sub-area; Regularly and repeatedly detect whether the real-time location of the second target user is within the reconstructed target travel route. If not, continue to reconstruct the target travel route.

7. The AI-based early warning method for people in haze period according to claim 1 is characterized in that: The method further comprises: Get the user's average travel speed from the previous travel area to the current travel area; The estimated travel time for the user to travel from the current travel sub-area to the next travel sub-area is calculated based on the average travel speed and the distance between the current travel sub-area and the next travel sub-area.

8. The AI-based early warning method for people in haze period according to claim 1, 2, 3 or 5, characterized in that: The step of attempting to establish a first information channel between the terminals of other users within a preset distance range and the wearable device of the user so that the wearable device sends a vibration prompt message specifically includes: When it is determined that the suspended particulate matter content in the next travel sub-area reaches the content value corresponding to moderate AQI pollution, a second-level warning prompt instruction is issued to the terminal where the first target user is located, and a timer is started at the same time; If no read feedback instruction of the first target user to the secondary warning prompt is detected when the timing reaches the safe time, a first information channel is established between the terminals of other users within a preset distance range and the wearable device of the user, and a secondary warning information containing the suspended particulate matter content of the first target user's next travel sub-area is sent to the wearable device through the first information channel. After receiving the secondary warning information, the wearable device scrolls and displays it and vibrates.

9. An AI-based early warning system for people during haze periods, characterized by: The system comprises: An acquisition module is used to obtain the user's target travel route after obtaining the user's consent, where the target travel route covers a plurality of travel sub-areas; A detection module is configured to obtain the suspended particulate matter content of the travel sub-area in real time and detect a change in the suspended particulate matter content of the next travel sub-area in the target travel route compared with the current travel sub-area; a judgment and warning module, configured to, when detecting that the content change is an increment and the increment reaches a first threshold, determine that the suspended particulate matter content in the next travel sub-area reaches a suspended particulate matter sensitivity content for a first target user, and issue a first-level warning prompt based on the terminal where the first target user is located; The judgment and prompt module is used to determine that the suspended particulate matter content in the next travel sub-area reaches the content corresponding to the moderate pollution of the AQI when the content change is detected as an increment and the increment reaches a second threshold value. If and only if the first target user is not detected within the safety time period, the terminal sends feedback information on the second-level warning prompt, and attempts to establish a first information channel between the terminal where other users are located within a preset distance range and the wearable device of the user, so that the wearable device sends a vibration prompt information, wherein the second threshold value is greater than or equal to the first threshold value, and the safety time period is less than the estimated time for the first target user to travel from the current travel sub-area to the next travel sub-area.

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