CN111816322A

CN111816322A - Method, system, device and storage medium for recommending path based on influenza risk

Info

Publication number: CN111816322A
Application number: CN202010674896.7A
Authority: CN
Inventors: 许晓龙; 夏志红; 李跃
Original assignee: Iac Nanchang Technology Co ltd; Inventec Appliances Shanghai Corp
Current assignee: Iac Nanchang Technology Co ltd; Inventec Appliances Nanchang Corp; Inventec Appliances Shanghai Corp; Inventec Appliances Corp
Priority date: 2020-07-14
Filing date: 2020-07-14
Publication date: 2020-10-23
Also published as: TWI760953B; TW202203249A

Abstract

The invention relates to the technical field of flu prevention and control, and provides a method, a system, equipment and a storage medium for recommending a path based on flu risks. The method comprises the following steps: detecting flu symptom data in a target area, and obtaining at-risk users with flu symptoms and non-risk users without flu symptoms in the target area; generating a plurality of dynamic risk areas according to the risk users and the flu symptoms thereof; calculating parameter values of the feasible position points in the target area according to each dynamic risk area; according to the parameter value of each position point capable of travelling, recommending a first path capable of avoiding at least the dynamic risk area to the risk-free user, and recommending a second path capable of avoiding at least the risk-free user to the risk user. According to the method, the safe advancing path is guided by dynamically marking the influenza risk area, the influenza risk in the target area is prevented from spreading, and the risk-free user is protected from being infected.

Description

Method, system, device and storage medium for recommending path based on influenza risk

Technical Field

The invention relates to the technical field of flu prevention and control, in particular to a method, a system, equipment and a storage medium for recommending a path based on flu risks.

Background

According to the statistics of the world health organization, the flu affects about one billion people every year, takes hundreds of thousands of people, and is one of the most important public health challenges in the world. Influenza is mainly transmitted through droplets and air, and the actions of coughing and sneezing of influenza patients can transmit the influenza to other people. In relatively confined areas, the probability of influenza transmission is greatly increased.

At present, the influenza prevention and control technology mainly aims at the detection and the reminding of a single infection source, and the influenza prevention and control of the whole area cannot be realized.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the invention and therefore may include information that does not constitute prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

In view of this, the present invention provides a method, a system, a device, and a storage medium for recommending a path based on an influenza risk, which guide a safe travel path by dynamically labeling an influenza risk area from the viewpoint of detecting an influenza population and intercepting a propagation path, thereby preventing the influenza risk in a target area from spreading and protecting risk-free users from infection.

One aspect of the present invention provides a method for recommending a route based on influenza risk, comprising the steps of: detecting flu symptom data in a target area, and obtaining at-risk users with flu symptoms and non-risk users without flu symptoms in the target area; generating a plurality of dynamic risk areas according to the risk users and the flu symptoms thereof; calculating parameter values of the feasible position points in the target area according to each dynamic risk area; and recommending a first path which can avoid the dynamic risk area to the risk-free user and a second path which can avoid the risk-free user to the risk user according to the parameter value of each position point which can be traveled.

In some embodiments, the flu symptom data includes body temperature data and cough sound data, the flu symptom including a plurality of flu symptoms corresponding to the body temperature data and/or the cough sound data; each of the flu symptoms corresponds to a safety distance and a risk factor.

In some embodiments, the step of generating a plurality of dynamic risk zones based on each of said at-risk users and their flu symptoms comprises: generating a risk area of each risk user by taking the safety distance of the risk user as a range and taking a risk coefficient as an identifier according to the safety distance and the risk coefficient corresponding to the influenza symptom of each risk user; taking risk users with overlapped risk areas as a group to obtain a plurality of groups of risk users; and performing outer tangent connection and fuzzy processing on the risk areas of each group of the risk users to generate a plurality of dynamic risk areas surrounding the risk areas of each group of the risk users.

In some embodiments, the step of calculating the parameter values for the feasible position points in the target region comprises: obtaining a plurality of travelable location points in the target area that are at least outside the dynamic risk area; obtaining risk users within a safety threshold range of each of the travelable location points as an associated set of risk users for each of the travelable location points; and calculating the parameter value of each marching position point according to the position relation between each feasible position point and the risk users in the associated risk user set.

In some embodiments, the formula for calculating the parameter value for each of the travelable location points is:

wherein V is a parameter value of a current advanceable location point, n is a number of risk users in an associated risk user set of the current advanceable location point, k is a current risk user in the associated risk user set, G_kIs a risk coefficient corresponding to the flu symptom of the current risk user, D_minIs the distance between the current feasible position and a risk user closest to the current feasible position, D_kIs the distance between the current carry-possible location point and the current risk user.

In some embodiments, recommending to the risk-free user a first path that avoids at least the dynamic risk area comprises: obtaining a first current position and a first target position of the risk-free user; taking the first current position as a first starting point, the first target position as a first end point, and obtaining a path between the first starting point and the first end point according to the parameter values of the advancing position points, and accumulating the path formed by the advancing position points with the optimal parameter values along a first direction which avoids the dynamic risk area and points to an upper wind gap of the target area, wherein the first direction is taken as the first path; and pushing at least the first path and each dynamic risk area to a mobile terminal of the risk-free user.

In some embodiments, the method further comprises: updating the first path in real time according to the position of each dynamic risk area and the position of the risk-free user; and when the risk-free user deviates from the first path, pushing first prompt information to the risk-free user.

In some embodiments, recommending to the risky user a second path that avoids at least the non-risky user comprises: obtaining a second current position and a second target position of the risky user; taking the second current position as a second starting point, taking the second target position as a second end point, and obtaining a path between the second starting point and the second end point according to the parameter value of each position point capable of advancing, and accumulating the path formed by the position points capable of advancing with the optimal parameter value along a second direction which avoids the risk-free user and points to a downwind inlet of the target area, so as to be used as the second path; and pushing at least the second path, each dynamic risk area and each non-risk user's location to the mobile terminal of the risk user.

In some embodiments, the method further comprises: updating the second path in real time according to the position of each dynamic risk area, the position of each risk-free user and the position of the risk user; when the risk user deviates from the second path, pushing second prompt information to the risk user; and when the risk user enters the safety threshold range of the risk-free user, pushing third prompt information to the risk-free user.

Another aspect of the present invention provides a system for recommending a pathway based on influenza risk, comprising: the influenza symptom detection module is used for detecting influenza symptom data in a target area and obtaining risk users with influenza symptoms and risk-free users without influenza symptoms in the target area; a risk area generation module for generating a plurality of dynamic risk areas according to each risk user and the flu symptom thereof; the parameter value calculation module is used for calculating parameter values of the feasible position points in the target area according to each dynamic risk area; and the safe path planning module is used for recommending a first path which can at least avoid the dynamic risk area to the risk-free user and recommending a second path which can at least avoid the risk-free user to the risk user according to the parameter value of each position point which can be traveled.

Yet another aspect of the present invention provides an apparatus for recommending a path based on influenza risk, including: a processor; a memory having stored therein executable instructions of the processor; wherein the processor is configured to perform the steps of the method for recommending a path based on influenza risk according to any of the embodiments described above via execution of the executable instructions.

Yet another aspect of the present invention provides a computer-readable storage medium storing a program which, when executed, implements the steps of the method for recommending a route based on influenza risk according to any of the embodiments described above.

Compared with the prior art, the invention has the beneficial effects that:

the method comprises the steps of realizing influenza monitoring of the whole target area by detecting influenza symptom data in the target area, and obtaining a risk user group with influenza symptoms and a risk-free user group without influenza symptoms;

dynamically marking the influenza risk distribution condition of the target area by generating a dynamic risk area;

the risk condition of each position point in the target area is obtained by calculating the parameter value of the position point capable of moving forward, so that a first path capable of avoiding the dynamic risk area is recommended to the risk-free user according to the risk condition of each position point, the risk-free user is enabled to avoid the influenza risk, the influenza risk is avoided, the risk-free user is prevented from entering the influenza risk area, and the risk-free user is protected from being infected; recommending a second path which can avoid the risk-free users to the risk users, preventing the spread of the influenza risk in the target area, and preventing the risk users from entering dense crowds, especially risk-free user groups and blocking the propagation of the influenza;

therefore, the invention starts from detecting influenza groups and intercepting propagation paths, guides a safe advancing path by dynamically marking influenza risk areas, avoids the influenza risk diffusion in the target area, and protects risk-free users from infection.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

FIG. 1 is a diagram illustrating steps of a method for recommending a route based on influenza risk in an embodiment of the present invention;

FIG. 2 is a schematic diagram of enclosing a dynamic risk area according to risk areas in an embodiment of the present invention;

FIG. 3 illustrates a schematic diagram of dynamic risk regions after blur processing in an embodiment of the invention;

FIG. 4 is a diagram illustrating a scenario for generating a first path in an embodiment of the present invention;

fig. 5 is a schematic diagram of a page for pushing a first path to a mobile terminal in an embodiment of the present invention;

FIG. 6 is a diagram illustrating a scenario for generating a second path in an embodiment of the present invention;

fig. 7 is a schematic diagram of a page for pushing the second path to the mobile terminal in the embodiment of the present invention;

FIG. 8 is a flow chart of a method for path recommendation based on influenza risk in an embodiment of the present invention;

FIG. 9 is a block diagram of a system for recommending paths based on influenza risk in an embodiment of the present invention;

FIG. 10 is a schematic diagram showing the module deployment of the system for recommending a path based on influenza risk in the embodiment of the present invention;

FIG. 11 is a schematic structural diagram of an apparatus for recommending a path based on influenza risk in an embodiment of the present invention; and

fig. 12 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus their repetitive description will be omitted.

Furthermore, the drawings are merely schematic illustrations of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

Fig. 1 shows the main steps of a method for recommending a route based on influenza risk in an embodiment. As shown in fig. 1, the method for recommending a route based on influenza risk in this embodiment mainly includes: in step S110, influenza symptom data in a target region is detected, and a risk user with influenza symptoms and a risk-free user without influenza symptoms in the target region are obtained; in step S120, a plurality of dynamic risk regions are generated based on each of the risk users and their flu symptoms; in step S130, calculating parameter values of the feasible position points in the target region according to each dynamic risk region; and in step S140, according to the parameter values of the feasible position points, recommending a first route that can avoid at least the dynamic risk area to the risk-free user, and recommending a second route that can avoid at least the risk-free user to the risk-free user.

Step S110, by detecting flu symptom data in a target area, realizing flu monitoring of the whole target area, and obtaining a risk user group with flu symptoms and a risk-free user group without flu symptoms; step S120, dynamically marking the influenza risk distribution condition of the target area by generating a dynamic risk area; step S130, calculating parameter values of the position points capable of advancing, and obtaining risk conditions of each position point in the target area; step S140, recommending a first path capable of avoiding the dynamic risk area to the risk-free user according to the risk condition of each position point, so that the risk-free user avoids the influenza risk, avoids entering the influenza risk area, and protects the risk-free user from being infected; and recommending a second path which can avoid the risk-free users to the risk users, preventing the spread of influenza risk in the target area, and preventing the risk users from walking into dense crowds, especially risk-free user groups to block the propagation of influenza. Therefore, the method for recommending paths based on influenza risks starts from detecting influenza groups and intercepting propagation paths, guides safe travelling paths by dynamically marking influenza risk areas, avoids influenza risks in target areas from spreading, and protects risk-free users from being infected.

The target area is not limited to indoor or outdoor, and generally, the indoor area needs to be monitored for influenza risks, for example, indoor areas with dense people flows such as stations, shopping malls, hospitals, etc.; in some outdoor areas where there is a high population density, influenza risk monitoring is also required. For example, in some outdoor areas such as bus stops, event squares, school playgrounds, etc., the method for recommending routes based on influenza risk according to the above embodiments may also be used for influenza risk monitoring.

In one embodiment, the flu symptom data includes body temperature data and cough sound data, the flu symptom includes a plurality of flu symptoms corresponding to the body temperature data and/or the cough sound data; each flu symptom corresponds to a safety distance and a risk factor.

The body temperature data shows the body temperature value of the user and can be obtained through detection of a thermal imager arranged in the target area. For example, at the entrance to the target area and at the main tunnel, some thermal imagers are deployed to monitor the body temperature values of the user within the target area. The working principle of the thermal imager is a thermal infrared imaging technology, and the core of the thermal imager is a sensor capable of detecting a tiny temperature difference and converting the temperature difference into a real-time video image for display. A user in the target area may emit infrared light and the thermal imager converts the invisible infrared energy emitted by the user into visible thermal images, with different colors on the thermal images representing different body temperatures of the user being measured. The thermal imager has the technical advantages of good concealment, safe and effective operation, strong detection capability, long action distance and the like, can be monitored for 24 hours in all weather, and can be normally used at night in the daytime. In outdoor region, also can use thermal imager to monitor user's body temperature data, thermal imager can use under extreme weather, can penetrate obstacles such as fog, rain, cigarette, realizes accurate detection.

The cough sound data shows whether the user coughs and the cough frequency, and can be obtained by jointly detecting the camera device and the microphone array which are arranged in the target area and the microphone sensor, such as a mobile phone microphone, which is arranged on the mobile terminal of the user. The cough sound data can be specifically identified by using an HMM (Hidden Markov Model) -ANN (artificial neural Network) hybrid Model, which is a prior art and therefore will not be described further.

According to the body temperature data and the cough sound data obtained by detection, whether each user in the target area has fever and cough can be determined. Fever and cough are influenza-like symptoms indicating influenza risks, when a user has a fever and/or cough, the user is indicated to have the influenza risks, and the user is defined as a user at risk of having the influenza symptoms; while users who are not at risk for influenza are defined as non-at-risk users who do not have symptoms of influenza.

In a specific embodiment, influenza symptoms are distinguished according to an authoritatively issued influenza transmission data study report as primary influenza symptoms with body temperature data showing fever, secondary influenza symptoms with body temperature data showing fever and cough sound data showing occasional cough, and tertiary influenza symptoms with body temperature data showing fever and cough sound data showing continuous cough. Wherein, the episodic cough refers to the cough within 0.4s in duration, and the continuous cough refers to the cough with the duration exceeding 0.4s and the cough period exceeding 1 s.

Further, according to an authoritatively issued influenza spreading data research report, in the breathing process of a human body, generated droplets can be spread to 4m along the straight line direction of the exhaled airflow; 3m of droplets generated by one cough can be transmitted within 5s, 4.5m of droplets generated by continuous coughing can be transmitted within 5 s; the droplets produced during one sneeze spread to 4.5m within 1 s. Therefore, in this embodiment, the safety distance corresponding to the three influenza symptoms is defined as 1m for the primary influenza symptom, 3m for the secondary influenza symptom, and 4.5m for the tertiary influenza symptom, respectively. That is, for a risk user with primary flu symptoms, a circular range area of 1m around the user is a risk area in which the flu risk is relatively low, a low risk area, and a safe area outside the range of 1 m; for the risk users with secondary influenza symptoms, a circular range area of 3m around the users is a risk area, the influenza risk in the risk area is improved, and the area is a middle risk area, and the area outside the range of 3m is a safety area; for a risk user with tertiary flu symptoms, a 4.5m circular area around the user is a risk area, the flu risk in the risk area is relatively high, and the area outside the 4.5m area is a safe area. Of course, in other embodiments, the safety distance of the flu symptom may be adjusted as long as it can prevent the flu risk. For example, the safe distance for the primary influenza symptom may be set to 2m, the safe distance for the secondary influenza symptom may be set to 4m, and the safe distance for the tertiary influenza symptom may be set to 6 m.

The risk coefficient corresponding to the flu symptom is used to identify the risk degree of the flu symptom, and may help to draw an intuitive and visible dynamic risk area pushed to the mobile terminal, which will be explained below.

In conclusion, the risk users with influenza symptoms in the target area and the data of the influenza symptoms thereof are obtained, and then a dynamic risk area can be generated according to each risk user and the influenza risk thereof.

In one embodiment, the step of generating a plurality of dynamic risk zones according to each at-risk user and its flu symptom specifically includes: generating a risk area of each risk user by taking the safety distance of each risk user as a range and the risk coefficient as an identifier according to the safety distance and the risk coefficient corresponding to the influenza symptom of each risk user; taking risk users with overlapped risk areas as a group to obtain a plurality of groups of risk users; and performing outer tangent connection and fuzzy processing on the risk areas of each group of risk users to generate a plurality of dynamic risk areas surrounding the risk areas of each group of risk users.

FIG. 2 shows an illustration of bounding a dynamic risk region according to risk regions in one embodiment. Referring to FIG. 2, three at-risk users, including a first at-risk user U₂₁Second risk user U₂₂And third risk user U₂₃For example. First risk user U₂₁With three-stage flu symptoms, corresponding to a safe distance of 4.5m, second-risk user U₂₂Has secondary influenza symptom corresponding to safe distance of 3m, and third risk user U₂₃With primary flu symptoms, corresponding to a safe distance of 1 m. In this embodiment, the risk coefficient corresponding to the flu symptom uses RGB pixel values corresponding to three channels, red, green, and blue. Wherein the risk coefficient of the primary influenza symptom adopts a red pixel value, namely '255, 0, 0', the risk coefficient of the secondary influenza symptom adopts an orange pixel value, namely '255, 165, 0', and the risk coefficient of the tertiary influenza symptom adopts a yellow pixel value, namely '255, 255, 0'. According to the safety distance and the risk coefficient corresponding to the flu symptom of the risk user, the generated risk areas with the safety distance as the range and the risk coefficient as the identification are respectively as follows: first risk user U₂₁The first risk area 201, the first risk area 201 is a first risk user U₂₁The position of (2) is the center of a circle, and the safe distance of 4.5m is a red high-risk area with a radius; second risk user U₂₂ Second risk area 202, second risk area 202 being a second risk user U₂₂The safe distance 3m is an orange middle risk area with the circle center as the position of the safe distance; third risk user U₂₃ Third risk area 203, third risk area 203 being a third risk user U₂₃Is the center of a circle, and the safe distance 1m is the yellow low risk area of the radius. In fig. 2, the risk areas are specifically identified by the density of the shaded points, wherein the shaded points of the first risk area 201 with high risk are most densely filled, the shaded points of the second risk area 202 with medium risk are relatively sparsely filled, and the shaded points of the third risk area 203 with low risk are most sparsely filled.

In other embodiments, as described above, the safety distance for each flu symptom can be adjusted so long as it serves to protect against flu risks. Similarly, the risk coefficient corresponding to each influenza symptom can be adjusted as long as the risk degree of the influenza symptom can be identified and the distribution condition of the influenza risk can be intuitively known by the user.

With continued reference to FIG. 2, first-risk user U₂₁Second risk user U₂₂And third risk user U₂₃The three risk areas may be grouped into one dynamic risk area. The three risk regions are connected tangentially at the outer side to create a dynamic risk region 200 surrounded by a first risk region 201, a second risk region 202, and a third risk region 203.

In some cases, the risk area of a certain risk user does not overlap with the risk areas of other risk users, and then a dynamic risk area is generated directly according to the risk area of the risk user.

Further, in order to protect the privacy of the risk user, the dynamic risk area 200 subsequently pushed to the mobile terminal does not directly display the coordinates and the displacement of the risk user, but performs some fuzzy processing on the dynamic risk area 200, for example, operations such as smooth transition processing and feather processing are adopted to blur suspected sick persons, that is, the positions of the risk users, so that the whole dynamic risk area 200 can reflect the influenza risk distribution condition in the target area.

Fig. 3 shows an illustration of dynamic risk regions after blur processing in an embodiment. Referring to fig. 3, in the present embodiment, a dynamic risk area 200 is blurred in such a manner that the area color (density of shaded dots) is gradually displayed according to the influenza risk level, and the dynamic risk area 200 surrounding a first risk area 201, a second risk area 202, and a third risk area 203 and showing the change in the influenza risk level is finally generated.

Of course, as the location of the at-risk user changes, each dynamic risk area is refreshed accordingly.

In summary, each dynamic risk area in the target area is obtained, and according to each dynamic risk area, the parameter value of the feasible position point in the target area can be calculated, so as to perform path planning.

In one embodiment, the step of calculating the parameter value of the feasible position point in the target region specifically includes: obtaining a plurality of travelable location points in the target area located at least outside the dynamic risk area; obtaining the risk users within the safety threshold range of each travelable position point as an associated risk user set of each travelable position point; and calculating the parameter value of each marching position point according to the position relation between each feasible position point and the risk users in the associated risk user set.

A travelable location point refers to a location point in the target area that can be planned into the path of travel, typically a location point outside of the target area where obstructions and dynamic risk areas are removed. Calculating the parameter value of the travelable location point requires referencing the nearby risky user data around the travelable location point, and therefore, the risky users within the safety threshold range of the travelable location point are obtained as the associated risky user set. The safety threshold range may be set as desired, for example, in some embodiments 4.5m, so that all risk users within 4.5m of a current travelable location point are associated risk users of the current travelable location point. According to the position relation between the feasible position point and the associated risk user, the parameter value of the feasible position point can be calculated and obtained. In one embodiment, for example, 10000 feasible position points of the target area are uniformly selected, and each feasible position point is calculated to obtain a parameter value, so that a parameter value map of the whole target area can be obtained. Subsequently, according to the parameter value map of the whole target area, the safe paths for the risk-free users and the risk users to travel respectively can be accurately obtained.

In one embodiment, equation (1) for calculating the parameter value for each travelable location point is as follows:

where V is the parameter value for a current travelable location point and n is the risk utility in the set of associated risk users for the current travelable location pointThe number of users, namely the number of the risk users capable of influencing the current position point capable of travelling. k is a current risk user in the associated risk user set, and k is sequentially valued from 1 to n. G_kThe risk coefficient corresponding to the influenza symptom of the current risk user, such as the above embodiment in which the risk coefficient adopts RGB pixel values, the risk user of the third-level influenza symptom corresponds to red, and G is 0; users at risk for secondary influenza symptoms correspond to orange, G165; the at-risk users of primary influenza symptoms correspond to yellow, G255. D_minIs the distance between the current feasible position and a risk user closest to the current feasible position, D_kIs the distance between the current feasible location point and the current risky user. By the above formula (1), the parameter values of the respective travelable position points are obtained.

In one embodiment, the step of recommending, to the risk-free user, at least a first path that can avoid the dynamic risk area according to the obtained parameter values of the respective travelable location points specifically includes: obtaining a first current position and a first target position of a risk-free user; taking the first current position as a first starting point, taking the first target position as a first terminal point, and obtaining a path between the first starting point and the first terminal point according to the parameter values of the feasible position points, wherein the path is formed by accumulating the feasible position points with optimal parameter values along a first direction which avoids the dynamic risk area and points to an upper wind gap of the target area, and the path is taken as a first path; and pushing at least the first path and each dynamic risk area to a mobile terminal of the risk-free user.

The first current position is the current position of the risk-free user and can be obtained by positioning the mobile terminal of the risk-free user. The first target position can be selected by the risk-free user at the mobile terminal, or automatically determined according to the movement condition of the risk-free user, or a position capable of avoiding the influenza risk can be temporarily determined as the first target position under the condition that the influenza risk is close to the risk-free user, so as to guide the risk-free user to avoid the influenza risk. And the calculation of the first path adopts the existing optimal path planning algorithm to find out the safe path with the optimal accumulated parameter value between the starting point and the end point. Specifically, a weight map of the entire target area may be obtained based on the parameter values for the various travelable location points, one for each travelable location point. When the first path is calculated, the first current position of the risk-free user is taken as a starting point, the first target position is taken as an end point, a path formed by feasible position points with optimal parameter values along the direction of avoiding the dynamic risk area and pointing to the upper wind gap is calculated between the starting point and the end point through an optimal path planning algorithm, and the path is taken as the first path for guiding the risk-free user to safely travel, so that the risk of flu is avoided for the risk-free user.

Fig. 4 shows a scenario illustration of generating the first path in the embodiment. Referring to fig. 4, the target area in the present embodiment is, for example, a railway station, and the target area has an air inlet 401 and two dynamic risk areas, namely, a first dynamic risk area 402 and a second dynamic risk area 403. The air inlet 401 refers to an inlet wind direction, an air outlet or an air conditioner position of a target area. Risk-free user U₄₀To go to ticket office A, at risk user U₄₀Three paths are arranged between the ticket office A and the ticket office A, wherein the path P₄₁The upwind vent 401 can avoid two dynamic risk areas and be close to the target area. Two other paths P₄₂And P₄₃The air inlet can touch a dynamic risk area and is positioned in the downwind direction of the air inlet 401, and the hidden danger of being infected by the influenza risk exists. Therefore, according to the optimal path planning method described above, according to the parameter values of the feasible position points in the target region, the path P avoiding the first dynamic risk region 402 and the second dynamic risk region 403 and having the optimal accumulated parameter values along the first direction pointing to the upwind port 401 can be obtained₄₁As recommendations to risk-free users U₄₀The first path of (1). Of course, the risky users located within the dynamic risk area are not prohibited from moving, for example in the first dynamic risk area 402, the direction of movement of each risky user is indicated. Recommending to non-risk user U along with movement of risk user₄₀First path P of₄₁And also dynamically adjusted. Thus, the first path P illustrated in FIG. 4₄₁Presenting a tendency to dynamically twist and extend.

Fig. 5 shows a page schematic for pushing the first path to the mobile terminal in the embodiment. Referring to FIG. 5, the meterCalculating to obtain guiding risk-free user U₄₀First pathway P to circumvent influenza risk₄₁Then, the first path P is divided into₄₁And the first dynamic risk area 402 and the second dynamic risk area 403 to the risk-free user U₄₀The mobile terminal 50. At the same time, the rough layout of the target area is also pushed to the risk-free user U₄₀Includes an air inlet 401 for a risk-free user U₄₀And observing the current position condition and the whole target area condition in real time during the moving process. The coordinates and the displacement of the risky user are not displayed on the mobile terminal of the riskless user, the fuzzy risk area is covered by the color blocks, and feathering fuzzy processing is performed on the edge of the fuzzy risk area, so that the privacy of the risky user is protected.

Further, as the location of the first dynamic risk area 402 and the second dynamic risk area 403 changes, new dynamic risk areas may appear, and no risk users U₄₀Updating the first path P in real time according to the position change of the user₄₁. When no risk user U₄₀Deviating from the first path P₄₁To risk-free users U₄₀First prompt information is pushed to prevent risk-free user U₄₀Walk into the risk area without self-knowledge. The first prompt message can be pushed to the risk-free user U through modes of popup, short message, warning tone and the like₄₀The mobile terminal 50.

In one embodiment, the step of recommending, to the risky user, a second path that can avoid at least the risk-free user according to the obtained parameter values of the respective travelable location points specifically includes: obtaining a second current position and a second target position of the risk user; taking the second current position as a second starting point, taking the second target position as a second terminal point, and obtaining a path between the second starting point and the second terminal point according to the parameter values of the feasible position points, wherein the path is formed by accumulating the feasible position points with the optimal parameter values along a second direction which avoids the risk-free user and points to a leeward side of the target area, and is used as a second path; and pushing at least the second path, each dynamic risk area and the position of each risk-free user to the mobile terminal of the risk user.

The second current position is the current position of the risk user and can be obtained by positioning the mobile terminal of the risk user. The second target position may be selected by the risky user at the mobile terminal, or a position capable of avoiding spreading of the influenza risk in the target area due to movement of the risky user may be automatically determined as the second target position according to the movement condition of the risky user. And the calculation of the second path also adopts the existing optimal path planning algorithm to find out the safe path with the optimal accumulated parameter value between the starting point and the end point. Specifically, a weight map of the entire target area may be obtained based on the parameter values for the various travelable location points, one for each travelable location point. When the second path is calculated, the second current position of the risk user is taken as a starting point, the second target position is taken as an end point, a path formed by feasible position points with optimal parameter values along the direction of avoiding the risk-free user and pointing to the lower air inlet is calculated between the starting point and the end point through an optimal path planning algorithm and is taken as a second path for guiding the risk user to safely advance, so that the risk user is prevented from entering a crowd, particularly a risk-free user crowd, and the risk of the flu in a target area is prevented from spreading. The lower air inlet is opposite to the upper air inlet and is a downwind area which is far away from the upper air inlet and is positioned in a target area.

Fig. 6 shows a scenario illustration of generating the second path in the embodiment. Consistent with the embodiment shown in fig. 4, the scene in this embodiment is also a train station, and the target area has an uptake 401, a first dynamic risk area 402, and a second dynamic risk area 403. Some of the risky users in the second dynamic risk area 403 are queuing to purchase train tickets near Ticket office A, with risky user U at the head of the queue₆₀The ticket buying is completed by leaving to go to the back. Risk user U₆₀Also facing the choice of three paths, path P₆₁Pointing to the upper wind gap 401, and the risk free users are more dense, wherein the humanoid markers located outside the dynamic risk area are both risk free users, two risk free users U are schematically marked in fig. 6₆₀₀. Path P₆₂Between two dynamic risk areas, but also with partially non-risk users. Path P₆₃Is to pass through the above-mentioned optimal path planning methodAccording to the parameter values of the feasible position points in the target area, the obtained accumulated parameter values are optimal and used for guiding the risk users U₆₀The path of travel. Path P₆₃The air inlet is positioned at the lower air opening, the risk-free user can not be touched, the influence on the whole target area is minimum, and the infection influence of the influenza risk can be minimized. Of course, with the movement of the risk-free users and the risk users, the recommendation is made to the risk users U₆₀Second path P₆₃And also dynamically adjusted. Thus, the second path P illustrated in FIG. 6₆₃Presenting a tendency to dynamically twist and extend.

Fig. 7 shows a page schematic for pushing the second path to the mobile terminal in the embodiment. Referring to FIG. 7, a calculation obtains a lead risk user U₆₀Second path P to avoid spreading of influenza₆₃Then, the second path P is connected₆₃Dynamic risk area (only the second dynamic risk area 403 is shown in fig. 7) and no-risk user U₆₀₀Location push to at risk user U₆₀The mobile terminal 70. At the same time, the rough layout of the target area is also pushed to the risky user U₆₀The mobile terminal 70 comprises an air inlet 401 for a risk user U₆₀The situation of the current position and the situation of the whole target area are observed in real time in the moving process, and the risk propagation probability is prevented from being enlarged due to the fact that people walk into dense people.

It should be noted that fig. 4 to 7 do not illustrate the change in the influenza risk level in each dynamic risk zone in detail. In the page actually pushed to the mobile terminal, each dynamic risk area takes the risk coefficient corresponding to the influenza symptom of each risk user forming the dynamic risk area as an identifier, and the change of the influenza risk degree in the dynamic risk area is displayed in a mode of gradually changing the area color, so that the user can intuitively and clearly observe the influenza risk distribution condition of the whole target area and the change of the influenza risk degree in each dynamic risk area through the page displayed by the mobile terminal.

Further, with the location change of the dynamic risk area, the location change of the risk-free user, and the risk user U₆₀Updating the second path P in real time according to the position change of the user₆₃. When risk user U₆₀Deviating from the second path P₆₃Timely, toward the risk user U₆₀Second prompt information is pushed to prevent risk user U₆₀The infection probability is expanded when people walk into dense people, and the possibility that the influenza risk is spread to risk-free users is blocked. The second prompt message can also be pushed to the risk user U through modes of popup window, short message, warning tone and the like₆₀The mobile terminal 70.

In addition, in some extreme cases, such as at risk user U₆₀Continuously deviating from the second path P without any attention₆₃Enter a risk-free user U_600’Within the safety threshold range of (2), then to the risk-free user U_600’Pushing third prompting information, e.g. to remind non-risky user U_600’Wearing a mask or avoiding a risk user U₆₀To walk in the same direction.

By recommending a first path capable of avoiding the influenza risk to the risk-free user and recommending a second path capable of avoiding the influenza spread to the risk user, the risk-free user group and the risk user group in the target area can take their own paths without mutual influence, and a benign environment is created. Both the first path planned for the risk-free user and the second path planned for the risk user can minimize the impact of influenza risk.

Fig. 8 shows a flow of a method for path recommendation based on influenza risk in an embodiment. In a specific application scenario, the method for recommending a path based on a flu risk described in any of the above embodiments is used to recommend a path to a user in a target area, and as shown in fig. 8, the method includes a process S810 of recommending a path to a risk-free user and a process S820 of recommending a path to a risk-free user. Firstly, body temperature detection is carried out at a necessary entrance of a target area through a thermal imaging technology, if fever is detected, a current user is added into a risk user list, and if fever is not detected, the current user is added into a risk-free user list. Then, the risk users in the risk user list are continuously tracked, and the risk level of each risk user is determined, specifically, tracking is carried out through the microphone array of the target area and the microphone sensor of the mobile terminal of each risk user. In the tracking process, the coordinates of each risk user are positioned, the safety distance is determined according to the risk level of each risk user, and the data are updated to a risk user list so as to generate a dynamic risk area. Then, for the risk-free user, positioning the coordinates of the risk-free user; comparing the coordinates of the risk-free users with the coordinates of the risk users, and setting parameter values for the risk-free users; and calculating parameter values of other position points on the traveling route of the risk-free user according to the formula, thereby planning an optimal traveling path for the risk-free user, guiding the risk-free user to avoid a risk area and get on the wind. For the risk users, comparing the coordinates of the risk users with the coordinates of the risk-free users, and setting parameter values for the risk users; and calculating parameter values of other position points on the travelling route of the risk user according to the formula, thereby planning an optimal travelling path for the risk user, guiding the risk user to avoid the risk-free user and walk to a place with few wind ports and crowds. Finally, minimizing the flu risk impact in the target area is achieved by respectively performing path guidance for risk-free users and risk users.

The embodiment of the invention also provides a system for recommending a path based on the influenza risk, which can realize the method for recommending a path based on the influenza risk described in any embodiment. Fig. 9 shows the main modules of the system for recommending a route based on an influenza risk in the embodiment, and referring to fig. 9, the system 900 for recommending a route based on an influenza risk in the embodiment mainly includes: the influenza symptom detection module 910 is configured to detect influenza symptom data in a target region, and obtain at-risk users with influenza symptoms and non-at-risk users without influenza symptoms in the target region; a risk area generating module 920, configured to generate a plurality of dynamic risk areas according to each risk user and influenza symptom thereof; a parameter value calculating module 930, configured to calculate a parameter value of a feasible position point in the target region according to each dynamic risk region; and a safe path planning module 940, configured to recommend a first path that can avoid at least the dynamic risk area to the risk-free user and recommend a second path that can avoid at least the risk-free user to the risk-free user according to the parameter value of each feasible location point.

The influenza symptom detection module 910, the risk region generation module 920, the parameter value calculation module 930, and the safety path planning module 940 can respectively implement corresponding steps in fig. 1 and 8, and the system 900 for recommending a path based on an influenza risk can further include other functional modules that can implement other steps described in any of the above embodiments, and a description thereof is not repeated here.

Modules for realizing functions of control, calculation and the like in the system for recommending paths based on the influenza risk are deployed in a control center, modules for realizing functions of detection, tracking and the like are deployed in a target area and a mobile terminal, and the paths are recommended to users who have the mobile terminal and enter the target area based on the influenza risk of the target area through communication connection between the control center and a relevant device of the target area and the mobile terminal.

FIG. 10 shows a module deployment illustration of the system for recommending paths based on influenza risk in an embodiment. Referring to fig. 10, the target area 1010 is disposed with a detection unit 1011 and a tracking unit 1012, the detection unit includes, for example, a thermal imaging camera for detecting fever symptoms at the entrance, a camera and a microphone array for tracking and positioning in the target area, so as to detect and confirm flu symptoms of fever people, including cough presence and cough frequency, and track the movement of people in the target area. The tracking unit 1012 also includes a microphone sensor configured on the mobile terminal 1030 to assist in detecting and tracking the cough data. And the fog operation unit 1021 is deployed in the control center 1020 and used for counting the number, the positions, the displacement directions and the safety distances of influenza-like personnel, calculating and drawing risk areas and dynamically marking high, medium and low risk areas. The mobile terminal 1030 is configured with a display unit 1031, which dynamically displays the positions and the levels of the risk areas and the distribution of the people in the target area, and locates the positions and the displacements of the current users in real time. The path planning unit 1032 plans a safe traveling path for the risk-free user and the risk-free user according to the basic data calculated by the fog operation unit 1021 and by combining the conditions of the peripheral air outlets, the air conditioner positions and the like of the target area 1010. The mobile terminal 1030 is further configured with a reminding unit 1033 for issuing a reminding alarm when the user deviates from the safety path planned for the user, and may be infected with the influenza risk or cause the influenza risk to spread.

The invention further provides a device for recommending a path based on an influenza risk, which includes a processor and a memory, where the memory stores executable instructions, and the processor is configured to execute the steps of the method for recommending a path based on an influenza risk described in any of the above embodiments by executing the executable instructions.

As described above, the device for recommending a path based on an influenza risk of the present invention can at least detect an influenza population, intercept a propagation path, guide a safe travel path by dynamically labeling an influenza risk region, avoid the spread of an influenza risk in a target region, and protect risk-free users from infection.

Fig. 11 is a schematic structural diagram of an apparatus for recommending a path based on influenza risk in an embodiment of the present invention, and it should be understood that fig. 11 only schematically illustrates various modules, which may be virtual software modules or actual hardware modules, and the combination, the splitting, and the addition of the remaining modules of these modules are within the scope of the present invention.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" platform.

The following describes an apparatus (hereinafter, simply referred to as an electronic apparatus) 1100 for recommending a route based on a flu risk according to the present invention with reference to fig. 11. The electronic device 1100 shown in fig. 11 is only an example and should not bring any limitations to the function and the scope of use of the embodiments of the present invention.

As shown in fig. 11, electronic device 1100 is embodied in the form of a general purpose computing device. The components of the electronic device 1100 may include, but are not limited to: at least one processing unit 1110, at least one memory unit 1120, a bus 1130 connecting the different platform components (including the memory unit 1120 and the processing unit 1110), a display unit 1140, etc.

Wherein the storage unit stores program code, which can be executed by the processing unit 1110, to cause the processing unit 1110 to perform the steps of the method for recommending a path based on a flu risk described in the above embodiments. For example, the processing unit 1110 may perform the steps shown in fig. 1 and 8.

The storage unit 1120 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)11201 and/or a cache memory unit 11202, and may further include a read only memory unit (ROM) 11203.

Storage unit 1120 may also include a program/utility 11204 having one or more program modules 11205, such program modules 11205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 1130 may be representative of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 1100 may also communicate with one or more external devices 1200, and the external devices 1200 may be one or more of a keyboard, a pointing device, a bluetooth device, and the like. The external devices 1200 enable a user to interactively communicate with the electronic device 1100. The electronic device 1100 can also communicate with one or more other computing devices, including routers, modems. Such communication may occur via an input/output (I/O) interface 1150. Also, the electronic device 1100 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1160. The network adapter 1160 may communicate with other modules of the electronic device 1100 via the bus 1130. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.

The embodiment of the present invention further provides a computer-readable storage medium for storing a program, and when the program is executed, the steps of the method for recommending a route based on influenza risk described in the above embodiment are implemented. In some possible embodiments, the various aspects of the present invention may also be implemented in the form of a program product including program code for causing a terminal device to perform the steps of the method for recommending a path based on a flu risk described in the above embodiments, when the program product is run on the terminal device.

As described above, the computer-readable storage medium of the present invention can at least realize detection of influenza population, interception of propagation pathway, guidance of a safe traveling path by dynamically labeling influenza risk regions, prevention of spread of influenza risk in a target region, and protection of risk-free users from infection.

Fig. 12 is a schematic structural diagram of a computer-readable storage medium of the present invention. Referring to fig. 12, a program product 1300 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of readable storage media include, but are not limited to: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device, such as through the internet using an internet service provider.

The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims

1. A method for recommending a pathway based on influenza risk, comprising the steps of:

detecting flu symptom data in a target area, and obtaining at-risk users with flu symptoms and non-risk users without flu symptoms in the target area;

generating a plurality of dynamic risk areas according to the risk users and the flu symptoms thereof;

calculating parameter values of the feasible position points in the target area according to each dynamic risk area; and

according to the parameter value of each position point capable of travelling, recommending a first path capable of avoiding at least the dynamic risk area to the risk-free user, and recommending a second path capable of avoiding at least the risk-free user to the risk user.

2. The method of claim 1, wherein the flu symptom data includes body temperature data and cough sound data, the flu symptom including a plurality of flu symptoms corresponding to the body temperature data and/or the cough sound data;

each of the flu symptoms corresponds to a safety distance and a risk factor.

3. The method of claim 2, wherein the step of generating a plurality of dynamic risk zones based on each of the at-risk users and their flu symptoms comprises:

generating a risk area of each risk user by taking the safety distance of the risk user as a range and taking a risk coefficient as an identifier according to the safety distance and the risk coefficient corresponding to the influenza symptom of each risk user;

taking risk users with overlapped risk areas as a group to obtain a plurality of groups of risk users; and

and performing outer tangent connection and fuzzy processing on the risk areas of each group of the risk users to generate a plurality of dynamic risk areas surrounding the risk areas of each group of the risk users.

4. The method of claim 2, wherein the step of calculating parameter values for the carry-feasible location points in the target region comprises:

obtaining a plurality of travelable location points in the target area that are at least outside the dynamic risk area;

obtaining risk users within a safety threshold range of each of the travelable location points as an associated set of risk users for each of the travelable location points; and

and calculating the parameter value of each marching position point according to the position relation between each feasible position point and the risk users in the associated risk user set.

5. The method of claim 4, wherein the formula for calculating the parameter value for each of the travelable location points is:

6. The method of claim 1, wherein recommending to the risk-free user at least a first path that avoids the dynamic risk area comprises:

obtaining a first current position and a first target position of the risk-free user;

taking the first current position as a first starting point, the first target position as a first end point, and obtaining a path between the first starting point and the first end point according to the parameter values of the advancing position points, and accumulating the path formed by the advancing position points with the optimal parameter values along a first direction which avoids the dynamic risk area and points to an upper wind gap of the target area, wherein the first direction is taken as the first path; and

and at least pushing the first path and each dynamic risk area to a mobile terminal of the risk-free user.

7. The method of claim 6, wherein the method further comprises:

updating the first path in real time according to the position of each dynamic risk area and the position of the risk-free user; and

and when the risk-free user deviates from the first path, pushing first prompt information to the risk-free user.

8. The method of claim 1, wherein recommending to the risky user a second path that avoids at least the non-risky user comprises:

obtaining a second current position and a second target position of the risky user;

taking the second current position as a second starting point, taking the second target position as a second end point, and obtaining a path between the second starting point and the second end point according to the parameter value of each position point capable of advancing, and accumulating the path formed by the position points capable of advancing with the optimal parameter value along a second direction which avoids the risk-free user and points to a downwind inlet of the target area, so as to be used as the second path; and

and at least pushing the second path, each dynamic risk area and each position of the risk-free user to a mobile terminal of the risk user.

9. The method of claim 8, wherein the method further comprises:

updating the second path in real time according to the position of each dynamic risk area, the position of each risk-free user and the position of the risk user; and

and when the risk user deviates from the second path, pushing second prompt information to the risk user.

10. A system for recommending a pathway based on influenza risk, comprising:

the influenza symptom detection module is used for detecting influenza symptom data in a target area and obtaining risk users with influenza symptoms and risk-free users without influenza symptoms in the target area;

a risk area generation module for generating a plurality of dynamic risk areas according to each risk user and the flu symptom thereof;

the parameter value calculation module is used for calculating parameter values of the feasible position points in the target area according to each dynamic risk area; and

and the safety path planning module is used for recommending a first path which can at least avoid the dynamic risk area to the risk-free user and recommending a second path which can at least avoid the risk-free user to the risk user according to the parameter value of each position point which can be traveled.

11. An apparatus for recommending a path based on influenza risk, comprising:

a processor;

a memory having stored therein executable instructions of the processor;

wherein the processor is configured to perform the steps of the method of recommending paths based on influenza risk of any of claims 1 to 9 via execution of the executable instructions.

12. A computer-readable storage medium storing a program which, when executed, performs the steps of the method of recommending a route based on influenza risk according to any of claims 1 to 9.