CN111816323A - Smart city management method and system based on Internet of things - Google Patents
Smart city management method and system based on Internet of things Download PDFInfo
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/20084—Artificial neural networks [ANN]
Abstract
The invention discloses a smart city management method and a smart city management system based on the Internet of things, wherein the method is applied to a security inspection device with a thermal infrared imager and comprises the following steps: obtaining thermal image information of a first user; obtaining identity information of the first user; acquiring epidemic situation history information of the first user according to the identity information of the first user; inputting the thermal image information of the first user and epidemic situation history information of the first user into a training model, obtaining output information of the training model, and obtaining geographical position information of the first user; and determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, wherein the first early warning information is used for reminding the first user of being an epidemic situation high-risk person, so that the effects of effectively supervising, alarming and controlling the epidemic situation are achieved.
Description
Technical Field
The invention relates to the field of smart cities based on the Internet of things, in particular to a smart city management method and system based on the Internet of things.
Background
Along with the continuous development of information technology, the urban informatization application level is continuously improved, and the construction of smart cities is in due course. The system and service of the city are communicated and integrated, the efficiency of resource application is improved, city management and service are optimized, and the quality of life of citizens is improved.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
aiming at sudden epidemic situations (such as new coronary pneumonia), no corresponding smart city emergency plan is provided, and the epidemic situations are effectively controlled.
Disclosure of Invention
The intelligent city management method and system based on the Internet of things solve the problem that intelligent cities cannot effectively control sudden epidemic situations in the prior art, and achieve the effects of effectively supervising, alarming and controlling the epidemic situations.
The embodiment of the application provides a smart city management method and system based on the Internet of things, wherein the method is applied to a security inspection device with a thermal infrared imager and comprises the following steps: obtaining thermal image information of a first user; obtaining identity information of the first user; acquiring epidemic situation history information of the first user according to the identity information of the first user; inputting the thermal image information of the first user and the epidemic situation history information of the first user into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the thermal image information of the first user, the epidemic situation history information of the first user and the identification information used for identifying that the first user is an epidemic situation high-risk person; obtaining output information of the training model, wherein the output information comprises a first output result and a second output result, the first output result is the result that the first user is an epidemic situation high-risk person, and the second output result is the result that the first user is a non-epidemic situation high-risk person; obtaining geographical location information of the first user; and determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, wherein the first early warning information is used for reminding the first user of being epidemic situation high-risk personnel.
On the other hand, this application still provides a wisdom city management system based on thing networking, wherein, the system includes: a first obtaining unit for obtaining thermal image information of a first user; a second obtaining unit, configured to obtain identity information of the first user; a third obtaining unit, configured to obtain epidemic situation history information of the first user according to the identity information of the first user; the first input unit is used for inputting the thermal image information of the first user and the epidemic situation history information of the first user into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the thermal image information of the first user, the epidemic situation history information of the first user and the identification information used for identifying that the first user is an epidemic situation high-risk person; and the fourth obtaining unit is used for obtaining the output information of the training model, wherein the output information comprises a first output result and a second output result, the first output result is the result that the first user is the epidemic situation high-risk personnel, and the second output result is the result that the first user is the non-epidemic situation high-risk personnel. A fifth obtaining unit, configured to obtain geographic location information of the first user; the first determining unit is used for determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, and the first early warning information is used for reminding the first user of being epidemic situation high-risk personnel.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method is characterized in that the method comprises the steps of inputting a training model by combining thermal image information of a first user and epidemic situation history information of the first user, generating early warning information according to output information and geographical position information, continuously correcting the position of a logistic regression line through supervised learning, and further accurately judging whether the first user is a logistic regression model of high-risk personnel, so that whether the first user is a neural network model of the high-risk personnel is accurately judged, the effect of accurately judging whether the first user is the high-risk personnel is achieved, and the early warning information is generated by combining the geographical position information of the first user, so that the epidemic situation is effectively supervised and controlled.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flowchart illustrating a smart city management method based on the internet of things according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart illustrating a process of obtaining a first output result in a smart city management method based on the internet of things according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart illustrating a process of obtaining information of a first user epidemic situation history in a smart city management method based on the internet of things according to an embodiment of the present application;
fig. 4 is a schematic flowchart illustrating a process of determining that the user does not have a medical diagnosis result in the smart city management method based on the internet of things according to the embodiment of the present application;
fig. 5 is a schematic flowchart illustrating a process of obtaining a training model in a smart city management method based on the internet of things according to an embodiment of the present application;
fig. 6 is a schematic flow chart illustrating a logistic regression line obtained in a smart city management method based on the internet of things according to an embodiment of the present application;
fig. 7 is a schematic flowchart illustrating a process of obtaining thermal image information of a first user in a smart city management method based on the internet of things according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a smart city management system based on the internet of things according to an embodiment of the present application;
FIG. 9 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application;
description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first input unit 14, a fourth obtaining unit 15, a fifth obtaining unit 16, a first determining unit 17, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The smart city management method and system based on the Internet of things solve the problem that smart cities cannot effectively supervise, alarm and control epidemic situations in the prior art, and achieve the technical effect that smart cities can accurately supervise and control epidemic situations. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
Along with the continuous development of information technology, the urban informatization application level is continuously improved, and the construction of smart cities is in due course. The smart city improves the efficiency of resource application, optimizes city management and service, and improves the quality of life of citizens. But aiming at sudden epidemic situations (such as new coronary pneumonia), no corresponding smart city emergency plan is provided, and the epidemic situations are effectively controlled.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a smart city management method and system based on the Internet of things, wherein the method is applied to a security inspection device with a thermal infrared imager and comprises the following steps: obtaining thermal image information of a first user; obtaining identity information of the first user; acquiring epidemic situation history information of the first user according to the identity information of the first user; inputting the thermal image information of the first user and the epidemic situation history information of the first user into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the thermal image information of the first user, the epidemic situation history information of the first user and the identification information used for identifying that the first user is an epidemic situation high-risk person; obtaining output information of the training model, wherein the output information comprises a first output result and a second output result, the first output result is the result that the first user is an epidemic situation high-risk person, and the second output result is the result that the first user is a non-epidemic situation high-risk person; obtaining geographical location information of the first user; and determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, wherein the first early warning information is used for reminding the first user of being epidemic situation high-risk personnel.
The method is applied to a security inspection device with a thermal infrared imager and is associated with each data center of a city, such as a public safety system, a medical health system and the like. The various data obtained in the embodiment of the invention are obtained by automatically matching, correlating and processing the various data from the data center through a computer communication technology. Furthermore, various data can be efficiently and automatically matched, associated and processed through a computer technology, so that the technical problem to be solved by the invention is solved, and the technical effect of the invention is realized.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a smart city management method based on the internet of things, where the method is applied to a security inspection apparatus with a thermal infrared imager, and includes:
step S100: obtaining thermal image information of a first user;
specifically, the first user is a user who passes through the security inspection device with the thermal infrared imager, the thermal image information is image information for recording heat or temperature of the object itself or radiated outwards, and the obtaining of the thermal image information of the first user here is specifically obtaining of the heat or temperature image information of the first user through the thermal infrared imager of the security inspection device. Through obtaining the temperature information of the user, whether the body temperature of the first user is abnormal is judged, and a foundation is laid for subsequently judging whether the user is an epidemic situation high-risk person.
Step S200: obtaining identity information of the first user;
specifically, the identity information of the first user is citizen personal information of the first user, specifically, various information which is recorded in an electronic or other manner and can identify the identity of a specific natural person or reflect the activity condition of the specific natural person alone or in combination with other information, including a name, an identity certificate number, a communication contact way, an address, a property condition, a track and the like, and for example, the identity information can be identified by means of fingerprint identification, face identification, iris identification, personal two-dimensional code identification and the like.
Step S300: acquiring epidemic situation history information of the first user according to the identity information of the first user;
specifically, the history information of the epidemic situation is information such as the contact history with the high-risk people of the epidemic situation, the residence history in the high-risk places of the epidemic situation, the dinner gathering history in the high-risk period, and the nucleic acid detection history. The method comprises the steps of acquiring first user identity information, calling a database for storing data information of a user, and acquiring epidemic situation history information of the user through a called home address, a called track and the like of the user. Through right the acquisition of first user's epidemic situation history combines first user's body temperature current situation, comes right whether the user is the basis that high-risk personnel analysis tamped, and then reaches the effect of effectively supervising and controlling the epidemic situation.
Step S400: inputting the thermal image information of the first user and the epidemic situation history information of the first user into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the thermal image information of the first user, the epidemic situation history information of the first user and the identification information used for identifying that the first user is an epidemic situation high-risk person;
specifically, the training model is a Neural network model, i.e., a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely connecting a large number of simple processing units (called neurons), which reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. In the embodiment of the application, the thermal image information of a first user and the epidemic situation history information of the first user are input into a neural network model, and the neural network model is trained through identification information used for identifying that the first user is an epidemic situation high-risk person.
Further, the process of training the neural network model is substantially a process of supervised learning. Each set of training data of the plurality of sets of training data comprises: the first user's thermal image information with first user's epidemic history information and be used for the sign first user is the identification information of epidemic high risk personnel. The method comprises the steps that through inputting thermal image information of a first user and epidemic situation history information of the first user, a neural network model can output a result of whether the first user is an epidemic situation high-risk person or not, through checking the output result and identification information used for identifying the first user as the epidemic situation high-risk person, if the output result is consistent with the identification information used for identifying the first user as the epidemic situation high-risk person, the data supervised learning is finished, and then the next group of data supervised learning is carried out; and if the output information is inconsistent with the identification information used for identifying the first user as the epidemic situation high-risk personnel, the neural network learning model adjusts and corrects the neural network learning model, and when the output result of the neural network learning model is consistent with the identification information used for identifying the first user as the epidemic situation high-risk personnel, the supervised learning of the next group of data is carried out. And ending the supervised learning process until the neural network model reaches the expected accuracy. The neural network learning model is continuously corrected and optimized through training data, the accuracy of the neural network learning model for processing the information is improved through the process of supervised learning, and then the result of whether the obtained first user is a high-risk person is more accurate, so that the effect of effectively supervising and controlling the epidemic situation is achieved.
Furthermore, when the neural network model is constructed, a coordinate system can be established according to the thermal image information of the first user and the epidemic situation history information of the first user as horizontal and vertical coordinates respectively. And obtaining a logistic regression line according to a logistic regression algorithm through the coordinate system. One side of the logistic regression line represents the result that the first user is epidemic situation high-risk personnel; and the other side of the logistic regression line is the result that the first user is the non-epidemic situation high-risk personnel. The logistic regression line is controlled by a first position and a first angle, and can be dynamically adjusted according to the first position and the first angle. The first position and the first angle are controlled by a first influencing parameter and a second influencing parameter, respectively. Through the supervised learning, it is essentially a process of adjusting the position of the logistic regression line. The position of the logistic regression line is continuously corrected through supervised learning, so that a logistic regression model for more accurately judging whether the first user is a high-risk person is obtained, the first user is more accurately judged whether the first user is a neural network model for the high-risk person, the effect of accurately judging whether the first user is the high-risk person is achieved, and effective supervision and control are carried out on the epidemic situation.
Step S500: and obtaining the output information of the training model, wherein the output information comprises a first output result and a second output result, the first output result is the result that the first user is the epidemic situation high-risk personnel, and the second output result is the result that the first user is the non-epidemic situation high-risk personnel.
Specifically, the output information includes a first output result and a second output result, the first output result and the second output result correspond to the results on the two sides of the logistic regression line one by one, namely, the first output result is the result that the first user is an epidemic situation high-risk person, and the second output result is the result that the first user is a non-epidemic situation high-risk person. The thermal image information of the first user and the epidemic situation history information of the first user are judged through the logistic regression line, a first output result and a second output result are obtained, and based on the characteristic that the logistic regression line is continuously adjusted and corrected, the obtained first output result and the second output result are more accurate, so that the effect of accurately judging whether the first user is a high-risk person is achieved, and the epidemic situation is effectively supervised and controlled.
Step S600: obtaining geographical location information of the first user;
specifically, the geographic location is generally used to describe a temporal and spatial relationship of a geographic object, where the geographic location information is location information of the security inspection apparatus obtained according to the security inspection apparatus with the thermal infrared imager, where the location information is a real-time geographic location of the first user, or the geographic location information is real-time geographic location information obtained from a mobile device held by the first user, and is not specifically limited herein.
Step S700: and determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, wherein the first early warning information is used for reminding the first user of being epidemic situation high-risk personnel.
Specifically, whether the first user is an epidemic situation high-risk person or not is judged through the output information, when the first user is judged to be the epidemic situation high-risk person, the geographical position information of the first user is obtained, and early warning information is obtained, wherein the early warning information is used for reminding the first user of being the epidemic situation high-risk person; and when the first user is judged to be the non-epidemic situation high-risk personnel, the early warning information is not obtained. The geographical position information of the first user is combined with the output information to obtain a mode of whether the early warning information is obtained or not, so that the early warning information is more accurately obtained, and the epidemic situation is effectively supervised and controlled.
The method is characterized in that the method comprises the steps of inputting a training model by combining thermal image information of a first user and epidemic situation history information of the first user, generating early warning information according to output information and geographical position information, continuously correcting the position of a logistic regression line through supervised learning, and further accurately judging whether the first user is a logistic regression model of high-risk personnel, so that whether the first user is a neural network model of the high-risk personnel is accurately judged, the effect of accurately judging whether the first user is the high-risk personnel is achieved, and the early warning information is generated by combining the geographical position information of the first user, so that the epidemic situation is effectively supervised and controlled.
As shown in fig. 2, in order to achieve the effect of effectively controlling an epidemic situation, step S500 in the embodiment of the present application further includes:
step S510: judging whether the output information is the first output result;
step S520: if the output information is the first output result, obtaining a first preset distance;
step S530: obtaining a second user in a first area which takes the first user as a center and takes the first preset distance as a radius;
step S540: obtaining first mark information, wherein the first mark information is used for identifying that the second user is a high-risk contact person;
step S550: obtaining second early warning information, wherein the second early warning information is used for reminding the second user of the existence of epidemic situation high-risk personnel in the first area;
specifically speaking, when judging when first user is the epidemic situation high-risk personnel, obtain a predetermined distance, predetermined distance is obtained through the propagation range of a large amount of data test epidemic situations to first user is the center, predetermined distance is the second user in the radius region and can be identified by identification information, identification information is used for the sign the second user is high-risk contact personnel, and the second user can receive an early warning information, early warning information is used for reminding the second user is in there is epidemic situation high-risk personnel in the region. Through judging that first user is the high-risk user to carry out the high-risk contact personnel sign and remind with the second user in the predetermined area that probably contacts second user's mode has firstly avoided first user to continue the action that the activity carries out the epidemic situation and spreads under the condition that oneself is unknown, has secondly solved second user's own identity that is the epidemic situation high-risk personnel contacter, and probably has been infected by the epidemic situation high-risk personnel, becomes the second infection source and continues the activity, causes the condition that the epidemic situation spreads. The effect of effectively supervising and controlling the epidemic situation is achieved.
As shown in fig. 3, in order to obtain more accurate epidemic situation history information of the first user and achieve the effect of accurately determining whether the first user is an epidemic situation high-risk person, step S300 in the embodiment of the present application further includes:
step S310: obtaining identity information of the first user;
step S320: judging whether the first user has a medical diagnosis result or not according to the identity information of the first user;
step S330: if the first user has the medical diagnosis result, judging whether the medical diagnosis result of the first user is positive;
step S340: if the medical diagnosis result of the first user is positive, second marking information is obtained, and the second marking information is used for identifying that the first user is an epidemic situation infected person;
specifically, the medical diagnosis is specifically nucleic acid detection, and according to identity information of a first user, whether the first user experiences medical diagnosis is obtained from a related database, so that whether a medical diagnosis result of the first user is positive is judged. And when the medical diagnosis result of the first user is positive, obtaining second marking information, wherein the second marking information is used for indicating that the first user is epidemic situation infected personnel. By acquiring the identity information, the medical diagnosis result of the first user is further acquired, and the positive first user is marked according to the diagnosis result, so that the further identification of the first user with the positive diagnosis result is realized, and the spreading of the epidemic situation of the infected first user is effectively avoided.
As shown in fig. 4, determining whether the first user has a medical diagnosis result, step S320 of this embodiment of the present application further includes:
step S321: obtaining activity trace information of the first user if the first user does not have the medical diagnosis result;
step S322: acquiring a real-time high-risk area database;
step S323: judging whether the activity track information of the first user is in the real-time high-risk area database or not;
step S324: if the activity track information of the first user is in the real-time high-risk area database, third marking information is obtained, and the third marking information is used for marking the first user as a high-risk contact person;
particularly, the real-time high-risk database is the database of storage high-risk epidemic situation infected personnel information and epidemic situation high-risk personnel information, moreover the real-time high-risk database carries out real-time dynamic update, data sources include but are not limited to the epidemic situation high-risk personnel who obtains through the above-mentioned security inspection device that has thermal infrared imager and the epidemic situation infected personnel information through epidemic situation history information judgement. The activity track information of the first user is obtained through real-time positioning information of the mobile electronic equipment of the user, if the activity track information of the user is in the high-risk area database, third marking information is obtained, and the third marking information is used for marking that the first user is a high-risk contact person and updating the marking information to the high-risk database. Through right the real-time update of high-risk database guarantees right the timeliness of epidemic situation control, through to user's removal orbit information with the organic combination of high-risk database judges whether the user is high-risk contact personnel's mode, makes right whether the user is more accurate for the judgement of epidemic situation high-risk contact personnel, more meticulous to reach the effect of the propagation of effective control epidemic situation.
As shown in fig. 5, in order to obtain a more accurate training model and achieve the effect of accurately judging high-risk people, the step S400 further includes:
step S410: obtaining thermal image information of the first user and taking the thermal image information of the first user as an abscissa;
step S420: acquiring epidemic situation history information of the first user, and taking the epidemic situation history information of the first user as a vertical coordinate;
step S430: obtaining a logistic regression line according to the abscissa and the ordinate by adopting a logistic regression model, wherein the logistic regression line comprises a first position and a first angle, and the first position and the first angle are located in a coordinate system constructed by the abscissa and the ordinate; wherein one side of the logistic regression line represents a first output result and the other side of the logistic regression line represents a second output result, wherein the first output result and the second output result are different.
Specifically, the thermal image information of the first user is used as an abscissa, the epidemic situation history information of the first user is used as an ordinate to construct a coordinate system, a logistic regression line is obtained in the coordinate system according to a logistic regression model, the logistic regression line comprises a first position and a first angle, and the logistic regression line is controlled by the first position and the first angle. The two sides of the logistic regression line represent two different results respectively, one side of the logistic regression line represents a first result, the first result is that the first user is an epidemic situation high-risk person, and the other side of the logistic regression line represents that the first user is a non-epidemic situation high-risk person. Through the supervised learning, it is essentially a process of adjusting the position of the logistic regression line. The position of the logistic regression line is continuously corrected through supervised learning, so that a logistic regression model for more accurately judging whether the first user is a high-risk person is obtained, the first user is more accurately judged whether the first user is a neural network model for the high-risk person, the effect of accurately judging whether the first user is the high-risk person is achieved, and effective supervision and control are carried out on the epidemic situation.
As shown in fig. 6, in order to obtain a more accurate logistic regression line and achieve the effect of accurately judging high-risk people, the step S430 further includes:
step S431: acquiring abnormal body temperature information of a first user;
step S432: obtaining information that the first user is a non-epidemic situation infected person;
step S433: judging whether the first user is a common cold or not according to the abnormal body temperature information of the first user and the information that the first user is a non-epidemic situation infected person;
step S434: when the first user is judged to be common cold, generating a first influence parameter;
step S435: the first influence parameter is used for correcting a first position of the logistic regression line;
specifically, when the body temperature of the first user is abnormal and the first user is not infected by epidemic situations, the first user may simply catch a cold, and when the first user is determined to be a common cold, a first influence parameter is generated and used for correcting the logistic regression model and changing the first position information of the logistic regression line, so that the logistic regression line is more accurate. Therefore, whether the first user is a high-risk personnel neural network model or not can be judged more accurately, the effect of accurately judging whether the first user is a high-risk personnel or not can be further achieved, and the epidemic situation can be effectively supervised and controlled.
As shown in fig. 7, in order to obtain more accurate thermal image information of the first user and achieve an effect of accurately judging high-risk people, step S100 in the embodiment of the present application further includes:
step S110: obtaining initial thermal image information of the first user according to the security check equipment;
step S120: obtaining real-time air humidity information;
step S130: obtaining a first error coefficient according to the real-time air humidity information;
step S140: obtaining thermal image information for the first user based on the first error coefficient and the initial thermal image information;
in particular, the initially obtained thermal image information is processed through the detected real-time environment in order to obtain more accurate thermal image information of the first user. The air humidity information is specifically meteorological elements of the water vapor content and the humidity degree in the air, due to the absorption effect of the atmosphere (water vapor, carbon dioxide and the like), infrared radiation has energy attenuation to a certain degree in the transmission process, but most thermal infrared imagers have no compensation means aiming at the condition, so the obtained initial thermal image information needs to be processed, namely a first error coefficient is obtained according to the real-time air humidity information, and the first error coefficient is used for processing the initial image information to obtain more accurate thermal image information of a first user.
In order to obtain a more accurate first error coefficient and achieve the effect of obtaining more accurate thermal image information of the first user, step S130 in this embodiment of the present application further includes:
y＝α+βx
wherein y is the first error coefficient;
x is the real-time air humidity information;
α and β are constants, respectively.
Specifically, when the air humidity x is 25%, the first error coefficient y is α + 25% β, and the thermal image information of the first user is initial thermal image information x (α + 25% β). Through air humidity acquires the first error coefficient who handles thermal image information for the thermal image information of the first user who obtains is more accurate, and then realizes right whether the first user is the effect that high-risk personnel accurately judged, effectively supervises and controls the epidemic situation.
In summary, the smart city management method and system based on the internet of things provided by the embodiment of the application have the following technical effects:
1. the method is characterized in that the method comprises the steps of inputting a training model by combining thermal image information of a first user and epidemic situation history information of the first user, generating early warning information according to output information and geographical position information, continuously correcting the position of a logistic regression line through supervised learning, and further accurately judging whether the first user is a logistic regression model of high-risk personnel, so that whether the first user is a neural network model of the high-risk personnel is accurately judged, the effect of accurately judging whether the first user is the high-risk personnel is achieved, and the early warning information is generated by combining the geographical position information of the first user, so that the epidemic situation is effectively supervised and controlled.
2. Due to the fact that the mode that the medical diagnosis result of the first user is further obtained by obtaining the identity information and the second marking is carried out on the positive first user according to the diagnosis result is adopted, the further identification of the first user with the positive diagnosis result is carried out, and the spreading of epidemic situations of the infected first user is effectively avoided. Through right the real-time update of high-risk database guarantees right the timeliness of epidemic situation control, through to user's removal orbit information with the organic combination of high-risk database judges whether the user is high-risk contact personnel's mode, makes right whether the user is more accurate for the judgement of epidemic situation high-risk contact personnel, more meticulous to reach the effect of the propagation of effective control epidemic situation.
3. Because the mode that the first error coefficient of processing thermal image information is obtained through the air humidity is adopted, and the more accurate thermal image information of the first user is obtained through the first error coefficient, the obtained thermal image information of the first user is more accurate, and then whether the first user is a high-risk person or not is accurately judged, and the effect of effectively supervising and controlling the epidemic situation is further realized.
Example two
Based on the same inventive concept as the smart city management method based on the internet of things in the foregoing embodiment, the present invention further provides a smart city management system based on the internet of things, as shown in fig. 8, the system includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining thermal image information of a first user;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain identity information of the first user;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain epidemic situation history information of the first user according to the identity information of the first user;
a first input unit 14, where the first input unit 14 is configured to input the thermal image information of the first user and the epidemic situation history information of the first user into a training model, where the training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the thermal image information of the first user, the epidemic situation history information of the first user and the identification information used for identifying that the first user is an epidemic situation high-risk person;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to obtain output information of the training model, where the output information includes a first output result and a second output result, the first output result is a result that the first user is a person with high risk of epidemic situations, and the second output result is a result that the first user is a person with high risk of non-epidemic situations;
a fifth obtaining unit 16, where the fifth obtaining unit 16 is configured to obtain the geographic location information of the first user;
the first determining unit 17 is used for determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, and the first early warning information is used for reminding the first user of being an epidemic situation high-risk person.
Further, the system further comprises:
a first judging unit, configured to judge whether the output information is the first output result;
a sixth obtaining unit configured to obtain a first predetermined distance if the output information is the first output result;
a seventh obtaining unit, configured to obtain a second user in a first area centered on the first user and having the first predetermined distance as a radius;
an eighth obtaining unit, configured to obtain first mark information, where the first mark information is used to identify that the second user is a high-risk contact person;
a ninth obtaining unit, configured to obtain second early warning information, where the second early warning information is used to remind the second user that there are epidemic situation high-risk people in the first area;
further, the system further comprises:
a tenth obtaining unit, configured to obtain identity information of the first user;
a second judging unit, configured to judge whether the first user has a medical diagnosis result according to the identity information of the first user;
a third judging unit configured to judge whether the medical diagnosis result of the first user is positive if the first user has the medical diagnosis result;
an eleventh obtaining unit, configured to obtain second marker information if the medical diagnosis result of the first user is positive, where the second marker information is used to identify that the first user is an epidemic situation infected person;
further, the system further comprises:
a twelfth obtaining unit configured to obtain activity trace information of the first user if the first user does not have the medical diagnosis result;
a thirteenth obtaining unit, configured to obtain a real-time high-risk area database;
a fourth judging unit, configured to judge whether the activity track information of the first user is in the real-time high-risk area database;
a fourteenth obtaining unit, configured to obtain third marking information if the activity track information of the first user is in the real-time high-risk area database, where the third marking information is used to mark that the first user is a high-risk contact person;
further, the system further comprises:
a fifteenth obtaining unit configured to obtain thermal image information of the first user, and take the thermal image information of the first user as an abscissa;
a sixteenth obtaining unit, configured to obtain epidemic situation history information of the first user, and use the epidemic situation history information of the first user as a vertical coordinate;
a seventeenth obtaining unit, configured to obtain a logistic regression line according to the abscissa and the ordinate by using a logistic regression model, where the logistic regression line includes a first position and a first angle, and the first position and the first angle are located in a coordinate system constructed by the abscissa and the ordinate; wherein one side of the logistic regression line represents a first output result and the other side of the logistic regression line represents a second output result, wherein the first output result and the second output result are different;
further, the system further comprises:
an eighteenth obtaining unit, configured to obtain initial thermal image information of the first user according to the security inspection device;
a nineteenth obtaining unit for obtaining real-time air humidity information;
a twentieth obtaining unit, configured to obtain a first error coefficient according to the real-time air humidity information;
a twenty-first obtaining unit to obtain thermal image information of the first user based on the first error coefficient and the initial thermal image information;
further, the system further comprises:
a twenty-second obtaining unit, configured to obtain a first error coefficient according to the real-time air humidity information, including: y ═ α + β x, where y is the first error coefficient; x is the real-time air humidity information; alpha and beta are constants respectively;
various changes and specific examples of the smart city management method based on the internet of things in the first embodiment of fig. 1 are also applicable to the smart city management system based on the internet of things in the present embodiment, and through the foregoing detailed description of the smart city management method based on the internet of things, a person skilled in the art can clearly know the implementation method of the smart city management system based on the internet of things in the present embodiment, so for the brevity of the description, detailed description is not repeated here.
Exemplary electronic device
The electronic apparatus of the embodiment of the present application is described below with reference to fig. 9.
Fig. 9 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the smart city management method based on the internet of things in the foregoing embodiments, the present invention further provides a smart city management system based on the internet of things, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the foregoing smart city management methods based on the internet of things.
Where in fig. 9 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides a smart city management method based on the Internet of things, wherein the method is applied to a security inspection device with a thermal infrared imager and comprises the following steps: obtaining thermal image information of a first user; obtaining identity information of the first user; acquiring epidemic situation history information of the first user according to the identity information of the first user; inputting the thermal image information of the first user and the epidemic situation history information of the first user into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the thermal image information of the first user, the epidemic situation history information of the first user and the identification information used for identifying that the first user is an epidemic situation high-risk person; obtaining output information of the training model, wherein the output information comprises a first output result and a second output result, the first output result is the result that the first user is an epidemic situation high-risk person, and the second output result is the result that the first user is a non-epidemic situation high-risk person; obtaining geographical location information of the first user; and determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, wherein the first early warning information is used for reminding the first user of being epidemic situation high-risk personnel. The problem of among the prior art intelligent city can't carry out effective control to proruption epidemic situation is solved, reach the effect of carrying out effective control to the epidemic situation.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A smart city management method based on the Internet of things is applied to a security inspection device with a thermal infrared imager and comprises the following steps:
obtaining thermal image information of a first user;
obtaining identity information of the first user;
acquiring epidemic situation history information of the first user according to the identity information of the first user;
inputting the thermal image information of the first user and the epidemic situation history information of the first user into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the thermal image information of the first user, the epidemic situation history information of the first user and the identification information used for identifying that the first user is an epidemic situation high-risk person;
obtaining output information of the training model, wherein the output information comprises a first output result and a second output result, the first output result is the result that the first user is an epidemic situation high-risk person, and the second output result is the result that the first user is a non-epidemic situation high-risk person;
obtaining geographical location information of the first user;
and determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, wherein the first early warning information is used for reminding the first user of being epidemic situation high-risk personnel.
2. The method of claim 1, wherein the method comprises:
judging whether the output information is the first output result;
if the output information is the first output result, obtaining a first preset distance;
obtaining a second user in a first area which takes the first user as a center and takes the first preset distance as a radius;
obtaining first mark information, wherein the first mark information is used for identifying that the second user is a high-risk contact person;
and obtaining second early warning information, wherein the second early warning information is used for reminding the second user that epidemic situation high-risk personnel exist in the first area.
3. The method of claim 1, wherein the obtaining epidemic history information of the first user comprises:
obtaining identity information of the first user;
judging whether the first user has a medical diagnosis result or not according to the identity information of the first user;
if the first user has the medical diagnosis result, judging whether the medical diagnosis result of the first user is positive;
and if the medical diagnosis result of the first user is positive, second marking information is obtained, and the second marking information is used for identifying that the first user is an epidemic situation infected person.
4. The method of claim 3, wherein the method comprises:
obtaining activity trace information of the first user if the first user does not have the medical diagnosis result;
acquiring a real-time high-risk area database;
judging whether the activity track information of the first user is in the real-time high-risk area database or not;
and if the activity track information of the first user is in the real-time high-risk area database, obtaining third marking information, wherein the third marking information is used for marking the first user as a high-risk contact person.
5. The method of claim 1, wherein the training model comprises:
obtaining thermal image information of the first user and taking the thermal image information of the first user as an abscissa;
acquiring epidemic situation history information of the first user, and taking the epidemic situation history information of the first user as a vertical coordinate;
obtaining a logistic regression line according to the abscissa and the ordinate by adopting a logistic regression model, wherein the logistic regression line comprises a first position and a first angle, and the first position and the first angle are located in a coordinate system constructed by the abscissa and the ordinate; wherein one side of the logistic regression line represents a first output result and the other side of the logistic regression line represents a second output result, wherein the first output result and the second output result are different.
6. The method of claim 1, wherein the obtaining thermal image information of the first user comprises:
obtaining initial thermal image information of the first user according to the security check equipment;
obtaining real-time air humidity information;
obtaining a first error coefficient according to the real-time air humidity information;
obtaining thermal image information for the first user based on the first error coefficient and the initial thermal image information.
7. The method of claim 6, wherein said obtaining a first error coefficient based on said real-time air humidity information comprises:
y＝α+βx
wherein y is the first error coefficient;
x is the real-time air humidity information;
α and β are constants, respectively.
8. A smart city management system based on the Internet of things, wherein the system comprises:
a first obtaining unit for obtaining thermal image information of a first user;
a second obtaining unit, configured to obtain identity information of the first user;
a third obtaining unit, configured to obtain epidemic situation history information of the first user according to the identity information of the first user;
the first input unit is used for inputting the thermal image information of the first user and the epidemic situation history information of the first user into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the thermal image information of the first user, the epidemic situation history information of the first user and the identification information used for identifying that the first user is an epidemic situation high-risk person;
and the fourth obtaining unit is used for obtaining the output information of the training model, wherein the output information comprises a first output result and a second output result, the first output result is the result that the first user is the epidemic situation high-risk personnel, and the second output result is the result that the first user is the non-epidemic situation high-risk personnel.
A fifth obtaining unit, configured to obtain geographic location information of the first user;
the first determining unit is used for determining whether first early warning information is obtained or not according to the geographical position information of the first user and the output information, and the first early warning information is used for reminding the first user of being epidemic situation high-risk personnel.
9. An internet of things based smart city management system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method of any one of claims 1 to 7.
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Application publication date: 20201023 |