CN111477341A - Epidemic situation monitoring method and device, electronic equipment and storage medium - Google Patents
Epidemic situation monitoring method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides an epidemic situation monitoring method, an epidemic situation monitoring device, electronic equipment and a storage medium, wherein the method comprises the following steps: respectively acquiring track information of epidemic situation patients and users to be detected, wherein the track information comprises a plurality of groups of geographical position information and acquisition moments corresponding to the geographical position information; analyzing the track information of the epidemic situation patients and the users to be detected through a predetermined epidemic situation propagation model, wherein the epidemic situation propagation model is determined according to the infection chain data among the historical patients and the track information of the users in the infection chain data; and determining whether the user to be detected is a close contact person of the epidemic situation patient or not according to the analysis result.
Description
Technical Field
The application relates to the technical field of networks, in particular to an epidemic situation monitoring method and device, electronic equipment and a storage medium.
Background
The outbreaks of infectious diseases such as atypical pneumonia, influenza A H1N1, novel coronavirus pneumonia and the like have serious negative effects on economic development and physical and mental health of people, so that when large-scale epidemic infectious diseases occur in society, timely mastering the trends of the infected people with the infectious diseases and the spreading condition of the infectious diseases are important for realizing effective prevention and control of epidemic situations.
However, no effective way has been provided in the related art to monitor the trend of the infected people and the spreading of the infectious disease, so that the spreading of the infectious disease cannot be effectively monitored, even if the reporting of the position information of the infected people and the suspected people is manually performed, the reporting way has high labor cost and low real-time performance, the reliability of the reported position information is low, and the reported position information is not convenient for analyzing the spreading of the infectious disease, so that the spreading of the infectious disease cannot be effectively monitored.
Disclosure of Invention
In view of the above, the present application provides an epidemic monitoring method, apparatus, electronic device and storage medium, so as to at least solve the technical problems in the related art.
In order to achieve the above purpose, the present application provides the following technical solutions:
according to a first aspect of the present application, a method for monitoring an epidemic situation is provided, the method comprising:
respectively acquiring track information of epidemic situation patients and users to be detected, wherein the track information comprises a plurality of groups of geographical position information and acquisition moments corresponding to the geographical position information;
analyzing the track information of the epidemic situation patients and the users to be detected through a predetermined epidemic situation propagation model, wherein the epidemic situation propagation model is determined according to the infection chain data among the historical patients and the track information of the users in the infection chain data;
and determining whether the user to be detected is a close contact person of the epidemic situation patient or not according to the analysis result.
Optionally, the predetermined epidemic propagation model comprises a machine learning model, and the method further comprises:
constructing a positive sample data set and a negative sample data set according to infection chain data among the historical patients and track information of users in the infection chain data, wherein infection relations exist among epidemic patients in the positive sample data set, and infection relations do not exist among epidemic patients in the negative sample data set;
performing characteristic analysis on the track information of the users in the positive sample data set and the negative sample data set according to the initial epidemic situation propagation model;
and adjusting the model parameters of the initial epidemic propagation model according to the result of the characteristic analysis so as to increase the similarity between the characteristic vectors corresponding to the track information of the historical patients in the positive sample data set and reduce the similarity between the characteristic vectors corresponding to the track information of the historical patients in the negative sample data set.
Optionally, the infection chain data between the historical patients includes information of infection sources of the historical patients, and the method further includes:
determining the contact distance and the contact time of the historical patient and the infection source according to the infection source of the historical patient;
adjusting the values of the initial pathogenic distance and the initial pathogenic time length in the initial epidemic propagation model according to the contact distance and the contact time length of the historical patient and the infection source to obtain the adjusted target pathogenic distance and the adjusted target pathogenic time length;
and determining an epidemic propagation model containing the target pathogenic distance and the target pathogenic time length as the predetermined epidemic propagation model.
Optionally, the adjusting the values of the initial pathogenic distance and the initial pathogenic time length in the initial epidemic propagation model according to the contact distance and the contact time length between the historical patient and the infection source includes:
in the case that the contact distance between the historical patient and the infection source is determined to be larger than the initial pathogenic distance in the initial epidemic propagation model, determining the contact distance between the historical patient and the infection source as the target pathogenic distance after the initial pathogenic distance is adjusted;
and under the condition that the contact time length of the historical patient and the infection source is determined to be less than the initial pathogenic time length in the initial epidemic propagation model, determining the contact time length of the historical patient and the infection source as the target pathogenic time length after the initial pathogenic time length is adjusted.
Optionally, the track information of the epidemic situation patient and the user to be detected is determined by invoking a data retrieval service of a block chain, and the track information in the block chain is automatically uploaded by a mobile phone base station and/or an access terminal to which the electronic device carried by the user is accessed.
Optionally, the method further includes:
and sending epidemic situation warning information about the user to be detected under the condition that the user to be detected is determined to be a close contact person of the epidemic situation patient.
Optionally, the method further includes:
generating an epidemic situation prevention and control area corresponding to the geographical position information by taking the geographical position information in the track information of the epidemic situation patients as a circle center and taking a preset pathogenic distance as a radius;
and sending alarm information about the epidemic situation prevention and control area to a target object of which the monitored moving track enters the epidemic situation prevention and control area.
According to a second aspect of the present application, an epidemic monitoring apparatus is provided, the apparatus comprising:
the system comprises a track information acquisition module, a track information acquisition module and a track information acquisition module, wherein the track information acquisition module is used for respectively acquiring track information of epidemic situation patients and users to be detected, and the track information comprises a plurality of groups of track data consisting of geographical position information and acquisition moments corresponding to the geographical position information;
the system comprises a track information analysis module, a track information analysis module and a track information analysis module, wherein the track information analysis module is used for analyzing track information of epidemic situation patients and users to be detected through a predetermined epidemic situation propagation model, and the epidemic situation propagation model is determined according to infection chain data among historical patients and track information of the users in the infection chain data;
and the close contact person determining module is used for determining whether the user to be detected is a close contact person of the epidemic situation patient or not according to the analysis result.
Optionally, the predetermined epidemic propagation model comprises a machine learning model, and the apparatus further comprises:
the data set construction module is used for constructing a positive sample data set and a negative sample data set according to infection chain data among the historical patients and track information of users in the infection chain data, wherein infection relations exist among epidemic patients in the positive sample data set, and infection relations do not exist among epidemic patients in the negative sample data set;
the track information analysis module is used for carrying out characteristic analysis on the track information of the users in the positive sample data set and the negative sample data set according to the initial epidemic situation propagation model;
and the model parameter adjusting module is used for adjusting the model parameters of the initial epidemic propagation model according to the result of the characteristic analysis so as to increase the similarity between the characteristic vectors corresponding to the track information of the historical patients in the positive sample data set and reduce the similarity between the characteristic vectors corresponding to the track information of the historical patients in the negative sample data set.
Optionally, the infection chain data between the historical patients includes information of infection sources of the historical patients, the apparatus further comprising:
the contact information determining module is used for determining the contact distance and the contact time of the historical patient and the infection source according to the infection source of the historical patient;
the value information adjusting module is used for adjusting the values of the initial pathogenic distance and the initial pathogenic time length in the initial epidemic situation propagation model according to the contact distance and the contact time length between the historical patient and the infection source so as to obtain the adjusted target pathogenic distance and the adjusted target pathogenic time length;
and the epidemic propagation model determining module is used for determining the epidemic propagation model containing the target pathogenic distance and the target pathogenic time length as the predetermined epidemic propagation model.
Optionally, the value information adjusting module is specifically configured to:
in the case that the contact distance between the historical patient and the infection source is determined to be larger than the initial pathogenic distance in the initial epidemic propagation model, determining the contact distance between the historical patient and the infection source as the target pathogenic distance after the initial pathogenic distance is adjusted;
and under the condition that the contact time length of the historical patient and the infection source is determined to be less than the initial pathogenic time length in the initial epidemic propagation model, determining the contact time length of the historical patient and the infection source as the target pathogenic time length after the initial pathogenic time length is adjusted.
Optionally, the method further includes:
and the first alarm information sending module is used for sending epidemic situation alarm information about the user to be detected under the condition that the user to be detected is determined to be a close contact person of the epidemic situation patient.
Optionally, the method further includes:
the epidemic situation prevention and control area generation module is used for generating an epidemic situation prevention and control area corresponding to the geographical position information by taking the geographical position information in the track information of the epidemic situation patient as a circle center and taking a preset pathogenic distance as a radius;
and the second alarm information sending module is used for sending alarm information about the epidemic situation prevention and control area to a target object of which the monitored moving track enters the epidemic situation prevention and control area.
According to a third aspect of the present application, there is provided an electronic device comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute instructions to implement the method of any of the first aspects above.
According to a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, carry out the steps of the method according to any one of the first aspect as described above.
According to the technical scheme, the method for monitoring the trends of the infected people and the spreading conditions of the infectious diseases is realized in an effective mode, track information of the epidemic situation patient and the track information of the user to be detected can be analyzed through a predetermined epidemic situation spreading model, whether the user to be detected is a close contact person of the epidemic situation patient or not is determined according to an analysis result, the spreading conditions of the infectious diseases are known, and the monitoring efficiency of the trends of the infected people and the spreading conditions of the infectious diseases is improved.
Drawings
FIG. 1 is a diagram illustrating an example embodiment of an epidemic situation monitoring method;
FIG. 2 is a flow chart of a method for monitoring an epidemic according to an exemplary embodiment of the present application;
FIG. 3 is a flow chart of another epidemic monitoring method provided in accordance with an exemplary embodiment of the present application;
FIG. 4 is a flow chart of yet another epidemic monitoring method provided in accordance with an exemplary embodiment of the present application;
FIG. 5 is a schematic view of an infection chain according to an exemplary embodiment of the present application;
FIG. 6 is a flow chart of yet another epidemic monitoring method, according to an exemplary embodiment of the present application;
FIG. 7 is a schematic illustration of an epidemic prevention and control area in accordance with one exemplary embodiment of the present application;
FIG. 8 is a schematic diagram of an epidemic prevention and control area in accordance with a second exemplary embodiment of the present application;
FIG. 9 is a schematic block diagram of an electronic device in an exemplary embodiment in accordance with the subject application;
FIG. 10 is a block diagram of an epidemic monitoring equipment, according to an example embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
Fig. 1 is a diagram of an actual application scenario of the epidemic situation monitoring method according to an exemplary embodiment of the present application, and as shown in fig. 1, the actual application scenario of the epidemic situation monitoring method may include a server 101, which is at least used for analyzing track information of an epidemic situation patient and a to-be-detected user through a predetermined epidemic situation propagation model after receiving track information of the epidemic situation patient and the to-be-detected user, and further determining whether the to-be-detected user is an intimate contact of the epidemic situation patient according to an analysis result. The server may be a physical server including an independent host, or the server may be a virtual server borne by a host cluster, or the server may be a cloud server, and in practical application, the server may include a storage server for receiving trajectory information of the epidemic situation patient and the user to be detected, and an analysis server for analyzing the trajectory information of the epidemic situation patient and the user to be detected at least through a predetermined epidemic situation propagation model.
The scene may further include a user terminal 102, where the user terminal obtains location information of a user through a positioning system, and after generating trajectory information from the location information, the generated trajectory information is forwarded at least through one of a base station 103 or an access terminal 104 connected to the user terminal 102 until the server 101 obtains the trajectory information before analyzing the trajectory information, and the base station 103 or the access terminal 104 may collect or collect and count the trajectory information of the user terminal 102 accessed to the base station 103 or the access terminal 104. In practical applications, the access terminal 104 may be a security gate, a traffic gate, a face-brushing pay bank table, a code-scanning pay platform, or the like, the specific form of the access terminal 104 is not limited in the present application, and the generated trajectory information may include location information acquired at preset intervals and a timestamp corresponding to the location information, or a plurality of different location information and a residence time corresponding to the location information.
In an embodiment, one of the base station 103 or the access terminal 104 to which the ue 102 is connected may forward the generated trajectory information to the blockchain network, so as to perform consensus, uplink and the like on the received trajectory information by the blockchain link points in the blockchain network.
Specifically, a blockchain transaction can be created according to the track information collected by the user terminal, and then the created blockchain transaction is sent to blockchain nodes in the blockchain network, after the blockchain transaction is identified by all the blockchain nodes in the blockchain network, the blocks packaged for the blockchain transaction are added to the tail end of the blockchain, and the track information is recorded in the blockchain.
In practical application, the user terminal 102 may directly generate trajectory information from the acquired position information and the acquisition time of the position information, further create a blockchain transaction according to the generated trajectory information, and send the blockchain transaction to a blockchain node in a blockchain; or, the user terminal 102 may send the collected location information and the collection time related to the location information to the server 101, the server 101 generates track information according to the received location information and the collection time corresponding to the location information, the server 101 creates a blockchain transaction according to the generated track information, and the server 101 sends the created blockchain transaction to a blockchain node in a blockchain network, further, in a case that the server 101 includes a storage server for receiving track information of an epidemic situation patient and a user to be detected and an analysis server for analyzing at least the track information of the epidemic situation patient and the user to be detected through a predetermined epidemic situation propagation model, the user terminal may send the collected location information and the collection time related to the location information to the storage server, to generate trajectory information by the storage server and to send the generated trajectory information into the blockchain network.
For a detailed explanation of the present application, reference is made to the following examples:
fig. 2 is a flowchart of an epidemic monitoring method according to an exemplary embodiment of the present application, and as shown in fig. 2, the method may include the following steps:
In an embodiment, the track information of the epidemic situation patient and the user to be detected may be determined by invoking a data retrieval service of the block chain, where the track information in the block chain may be automatically uploaded by at least one of a mobile phone base station and an access terminal to which the electronic device carried by the user is accessed, and the process of uploading the track information to the block chain or downloading the track information stored in the block chain by the server is shown in the description in the application scenario corresponding to fig. 1, which is not described herein again.
In this embodiment, different from a manner of manually reporting track information in the related art, a manner of automatically uploading at least one of a mobile phone base station and an access terminal, to which an electronic device carried by a user is accessed, can reduce labor cost, improve real-time performance of uploading track information, avoid problems of errors and the like of reported position information due to manual operation, improve accuracy and effectiveness of the reported position information, and realize effective monitoring of spreading conditions of infectious diseases based on the accuracy and effectiveness of the reported position information.
In an embodiment, the epidemic propagation model includes a machine learning model, and in the process of obtaining the predetermined epidemic propagation model, a positive sample data set and a negative sample data set can be constructed according to the infection chain data among the historical patients and the trajectory information of the user in the infection chain data, so as to adjust the model parameters in the initial epidemic propagation model through the constructed positive sample data set and the constructed negative sample data set, wherein the epidemic patients in the constructed positive sample data set have an infection relationship, and the epidemic patients in the constructed negative sample data set have no infection relationship.
Specifically, feature analysis can be performed on the trajectory information of the user in the positive sample data set and the trajectory information of the user in the negative sample data set according to the initial epidemic propagation model, and then model parameters of the initial epidemic propagation model are adjusted according to a result of the feature analysis, so that the similarity between feature vectors corresponding to the trajectory information of the historical patient in the positive sample data set is increased, and the similarity between feature vectors corresponding to the trajectory information of the historical patient in the negative sample data set is reduced.
In this embodiment, the predetermined epidemic propagation model is a machine learning model trained in advance, and model parameters in the machine learning model are adjusted through a positive sample data set in which an infection relationship exists between epidemic patients and a negative sample data set in which an infection relationship does not exist between epidemic patients, so that the machine learning model after parameter adjustment can realize accurate monitoring of the epidemic patients by combining with sample characteristics, and the determination efficiency of close contacts is improved.
In another embodiment, the infection chain data among the historical patients further comprises information of infection sources of the historical patients, in the process of obtaining the predetermined epidemic situation propagation model, the contact distance and the contact duration between the historical patients and the infection sources can be determined according to the infection sources of the historical patients, and the values of the initial pathogenic distance and the initial pathogenic duration in the initial epidemic situation propagation model are adjusted according to the contact distance and the contact duration between the historical patients and the infection sources to obtain the adjusted target pathogenic distance and the adjusted target pathogenic duration; and determining an epidemic propagation model containing the target pathogenic distance and the target pathogenic time length as a predetermined epidemic propagation model.
Further, the process of adjusting the values of the initial pathogenic distance and the initial pathogenic time length in the initial epidemic propagation model according to the contact distance and the contact time length between the historical patient and the infectious source may include determining the contact distance between the historical patient and the infectious source as the target pathogenic distance after the initial pathogenic distance is adjusted, when it is determined that the contact distance between the historical patient and the infectious source is greater than the initial pathogenic distance in the initial epidemic propagation model; and under the condition that the contact time length of the historical patient and the infection source is determined to be less than the initial pathogenic time length in the initial epidemic propagation model, determining the contact time length of the historical patient and the infection source as the target pathogenic time length after the initial pathogenic time length is adjusted.
In this embodiment, the epidemic propagation model may be a model at least including attribute parameters such as a pathogenic distance and a pathogenic time length, the predetermined epidemic propagation model may be a statistical model at least including a target disease treatment distance after an initial pathogenic distance is adjusted and a target pathogenic time length after the initial pathogenic time length is adjusted, and the statistical model may be used to define the attribute parameters in the statistical model in combination with actual application requirements, so as to improve the flexibility of the defined epidemic propagation model and at least achieve targeted epidemic monitoring and analysis.
And step 203, determining whether the user to be detected is a close contact person of the epidemic situation patient according to the analysis result.
In an embodiment, the epidemic situation warning information about the user to be detected can be sent under the condition that the user to be detected is determined to be a close contact person of the epidemic situation patient. Specifically, epidemic situation warning information about the user to be detected can be sent to the user to be detected, so that the user to be detected can know the infectious disease infection condition of the user to be detected; in addition, epidemic situation warning information can be sent to an administrator of the server or to monitoring equipment of an epidemic situation prevention and control department which establishes communication connection in advance, so that real-time warning on the situation of an epidemic situation is realized under the condition of close contact of newly-added epidemic situation patients.
In another embodiment, the geographic position information in the track information of the epidemic situation patient is used as a circle center, the preset pathogenic distance is used as a radius to generate an epidemic situation prevention and control area at the acquisition moment corresponding to the geographic position information, and then alarm information about the epidemic situation prevention and control area is sent to a target object of which the monitored moving track enters the epidemic situation prevention and control area. Furthermore, a failure time can be set for the epidemic situation prevention and control area, so that the epidemic situation prevention and control area fails when the failure time is reached, and the failure time is determined by prolonging the preset pathogenic time duration on the basis of the acquisition time. In this embodiment, an epidemic situation prevention and control area corresponding to each epidemic situation patient can be generated, and then an alarm message is sent to a user entering the epidemic situation prevention and control area, so as to prevent the user from being infected.
According to the embodiment, the monitoring method for the trends of the infected people and the spreading conditions of the infectious diseases is realized in an effective mode, the track information of the epidemic situation patient and the track information of the user to be detected can be analyzed through a predetermined epidemic situation spreading model, whether the user to be detected is a close contact person of the epidemic situation patient or not is determined according to the analysis result, the spreading conditions of the infectious diseases are known, and the monitoring efficiency for the trends of the infected people and the spreading conditions of the infectious diseases is improved.
Fig. 3 is a flowchart of another epidemic monitoring method according to an exemplary embodiment of the present application, and as shown in fig. 3, the method may include the following steps:
And 302, respectively performing characteristic analysis on the track information in the positive sample data set and the track information in the negative sample data set according to the initial epidemic propagation model, and adjusting model parameters in the initial epidemic propagation model according to the characteristic analysis result and the infection chain data in the corresponding sample data set.
In an embodiment, the initial epidemic propagation model may perform feature analysis on the trajectory information in the positive sample data set and the negative sample data set, respectively.
The method comprises the steps that an epidemic situation patient A and an epidemic situation patient B which have an infection relation in a positive sample data set exist an infection relation in the positive sample data set, an initial epidemic situation propagation model is used for carrying out feature extraction on track information of the epidemic situation patient A and the epidemic situation patient B in the positive sample data set, and model parameters of the initial epidemic situation propagation model are adjusted in a reverse direction under the condition that the infection relation does not exist between the epidemic situation patient A and the epidemic situation patient B according to the extracted features, so that the similarity between feature vectors respectively determined by the track information of the epidemic situation patient A and the epidemic situation patient B is increased.
Similarly, an infection relation does not exist between epidemic situation patients in the generated negative sample data set, taking an epidemic situation patient C and an epidemic situation patient D in the negative sample data set as an example, feature extraction is carried out on track information of the epidemic situation patient C and the epidemic situation patient D in the negative sample data set by the initial epidemic situation propagation model, and under the condition that the infection relation exists between the epidemic situation patient C and the epidemic situation patient D according to the extracted features, model parameters of the initial epidemic situation propagation model are adjusted reversely, so that the similarity between feature vectors respectively determined by the track information of the epidemic situation patient C and the epidemic situation patient D is reduced.
Further, the process of step 302 may be repeatedly executed until the iteration number of the model parameters in the epidemic situation propagation model reaches a preset number threshold; or, after the infection relation prediction information obtained by the epidemic situation propagation model and the infection relation information in the corresponding infection chain data are input into a loss function which is configured in advance and corresponds to the epidemic situation propagation model, when the value of the loss function is determined to be lower than a preset threshold value, the epidemic situation propagation model after model parameter adjustment is determined to be the epidemic situation propagation model which is trained in advance.
And 303, analyzing the track information of the epidemic situation patient and the user to be detected in the transmission chain data by using the epidemic situation propagation model trained in advance.
And performing feature extraction on the track information of the epidemic situation patients and the to-be-detected user in the transmission chain data according to the epidemic situation propagation model after the model parameters are adjusted, and determining whether the track information of the epidemic situation patients related to the track information of the to-be-detected user exists or not according to the extracted features.
Furthermore, vectorization processing can be performed on the track information of the epidemic situation patient and the user to be detected to obtain a track information vector corresponding to the track information, and then the track information vector is processed according to at least one weight vector for feature extraction in the epidemic situation propagation model after model parameter adjustment, so as to extract features according to the track information of the epidemic situation patient and the user to be detected, and further determine whether the track information of the epidemic situation patient related to the track information of the user to be detected exists according to the extracted features.
And step 304, sending epidemic situation warning information about the user to be detected under the condition that the user to be detected is determined to be a close contact person of the epidemic situation patient according to the analysis result.
Under the condition that the user to be detected is determined to be a close contact person of the epidemic situation patient according to the analysis result, whether the contact way of the user to be detected exists or not can be inquired, if the pre-stored contact way of the user to be detected is detected, communication connection is established with the contact way of the user to be detected, and further epidemic situation warning information is sent based on the established communication connection; if the contact way of the user to be detected is not detected, epidemic situation warning information containing identification information of the user to be detected can be generated, the generated epidemic situation warning information is directly displayed or sent to a pre-configured epidemic situation monitoring terminal, and therefore warning is conducted on a manager or the user of the epidemic situation monitoring terminal.
According to the embodiment, the characteristics of the positive sample data set with the infection relationship between epidemic patients and the negative sample data set without the infection relationship between epidemic patients can be extracted through the initial epidemic propagation model, the infection relationship between the epidemic patients can be predicted according to the extracted characteristics, and then the model parameters in the initial epidemic propagation model can be adjusted by using the difference between the infection chain data in the sample data set and the predicted infection relationship, so that the epidemic propagation model for analyzing the track information of the user to be detected learns the characteristic information of the epidemic patients in the infection chain data, and the accuracy of analyzing whether the user to be detected is the epidemic patients is improved.
Fig. 4 is a flowchart of another epidemic monitoring method according to an exemplary embodiment of the present application, and as shown in fig. 4, the method may include the following steps:
In one embodiment, the acquired infection chain data at least includes identification information of historical patients and infection relationships among historical patients, as shown in fig. 5, fig. 5 is a schematic diagram of an infection chain according to an exemplary embodiment of the present application, as an exemplary infection chain, fig. 5 includes a patient a, a patient B, a patient C, a patient D, and a patient E, and the patient a infects the patient B, the patient C, and the patient E, and the patient B infects the patient D. Further, the infection chain data may include information such as the location of infection and the time of infection, in addition to the infection relationship between the historical patients, and the present application does not limit the specific contents of the infection chain data.
In one embodiment, the track information of epidemic patients in the infection chain data can be queried and downloaded from the blockchain through a blockchain data query engine. Specifically, the acquired trajectory information of the epidemic situation patient may include geographical location information of the epidemic situation patient and a stay time or a stay duration corresponding to the geographical location information, and in practical application, the geographical location information of the epidemic situation patient may be represented in a coordinate manner, such as a longitude and latitude coordinate or an equal-ratio scaled map location coordinate in a case where the longitude and latitude is a measurement standard; of course, the representation may also be performed by a preset landmark identifier, such as a building, a cell, etc.
In an embodiment, in the process of determining the contact distance and the contact duration between the historical patient and the infection source corresponding to the historical patient, the geographical position information of the historical patient corresponding to the illness time of the historical patient and the geographical position information of the infection source of the historical patient can be determined according to the track information of the historical patient and the infection source of the historical patient, and further, the position difference value between the geographical position information of the historical patient and the geographical position information of the infection source of the historical patient is determined as the contact distance between the historical patient and the infection source of the historical patient; determining one of the following as a length of contact time between the historic patient and the source of infection for the historic patient: before or after the time that the historical patient is in the geographic position information corresponding to the sick moment, the geographic position information between the historical patient and the infection source of the historical patient is not more than the time length of the contact distance threshold value; or the time length of the history that the patient is in the geographical position information corresponding to the time of illness, and the like.
In step 404a, the contact distance between the historical patient and the infection source of the historical patient is detected to be larger than the initial pathogenic distance in the initial epidemic propagation model, and the contact distance between the historical patient and the infection source of the historical patient is determined as the target pathogenic distance after the initial pathogenic distance is adjusted.
According to the embodiment, the initial pathogenic time length and the initial pathogenic distance in the initial epidemic propagation model can be adjusted according to the track information of the infection sources of the historical patients and the historical patients to obtain the target pathogenic time length and the target pathogenic distance, and then the contact distance and the contact time length between the epidemic patient and the user to be detected are matched according to the obtained target pathogenic time length and the target pathogenic distance, so that whether the user to be detected is the epidemic patient or not is automatically analyzed; in addition, the target pathogenic time length and the target pathogenic distance are determined according to the automatic analysis of the track information of the infection sources of the historical patients and the historical patients, so that the model parameters in the determined epidemic situation propagation model can be adaptively adjusted according to the deterioration condition of the actual epidemic situation, and the prevention and control warning of the epidemic situation propagation condition is adaptively improved.
Fig. 6 is a flowchart of yet another epidemic monitoring method according to an exemplary embodiment of the present application, and as shown in fig. 6, the method may include the following steps:
In one embodiment, the geographical location information after the location information is changed in the track information of the epidemic situation patients can be used as the center of a circle, an epidemic prevention and control area is generated by taking a preset pathogenic distance as a radius, such as a place A where an epidemic patient P is located in geographic position information a at a moment A, a place B where the epidemic patient P arrives at geographic position information B at a moment B, and a place C where the epidemic patient P arrives at geographic position information C at a moment C, and accordingly, the epidemic prevention and control area is generated by taking at least the geographic position information B and the geographic position information C after the position information change occurs in the track information of the epidemic patient as the center of a circle, as shown in FIG. 7, fig. 7 is a schematic diagram of an epidemic situation prevention and control area according to an exemplary embodiment of the present application, which respectively takes the geographical location information b and the geographical location information c after the location information is changed as the center of a circle, an epidemic prevention and control area shown by a dotted circle in fig. 7 is generated by taking a preset pathogenic distance as a radius.
In another embodiment, the acquired track information further includes a collection time of the track information, and further a plurality of collection times can be determined according to a preset sampling duration, geographic position information corresponding to each collection time of the plurality of collection times in the track information is determined, and further an epidemic situation prevention and control area is generated by taking the determined geographic position information as a circle center and a preset pathogenic distance as a radius.
For example, as shown in fig. 8, fig. 8 is a schematic diagram of an epidemic prevention and control area according to a second exemplary embodiment of the present application, in a case where a sampling time m, a sampling time n, a sampling time p, and a sampling time q are respectively obtained at a plurality of sampling times determined according to a preset sampling duration, and accordingly, geographic location information determined in trajectory information and respectively corresponding to the sampling time m, the sampling time n, the sampling time p, and the sampling time q is geographic location information m, geographic location information n, geographic location information p, and geographic location information q, the epidemic prevention and control area is generated by taking the determined geographic location information m, the determined geographic location information n, the determined geographic location information p, and the determined pathogenic distance as a radius, as shown by a curve circle in fig. 8.
In an embodiment, the process of determining that the movement track of the user to be detected enters the target object in the epidemic prevention and control area may include determining that the movement track of the user to be detected enters the target object in the epidemic prevention and control area that has not yet failed.
Specifically, the preconfigured pathogenic time duration can be obtained, the preconfigured pathogenic time duration is further extended on the basis of the acquisition time corresponding to the geographical location information used for generating the epidemic situation prevention and control area, and the time after the preconfigured pathogenic time duration is extended on the basis of the acquisition time is determined as the failure time of the epidemic situation prevention and control area. Taking the epidemic situation prevention and control area shown in fig. 8 as an example, the epidemic situation prevention and control area is generated by taking the geographic location information m as a circle center and taking the preset pathogenic distance as a radius, and the failure time of the epidemic situation prevention and control area is determined by prolonging the preconfigured pathogenic time length on the basis of the acquisition time m of the address location information m; similarly, the failure time of the epidemic situation prevention and control area generated by taking the geographic position information n as the center of a circle and the preset pathogenic distance as the radius is determined by prolonging the preconfigured pathogenic time length on the basis of the acquisition time n of the address position information n.
And step 604, sending alarm information about the epidemic situation prevention and control area to the target object.
According to the embodiment, the epidemic situation prevention and control area can be generated according to the track information of the epidemic situation patients, the alarm information about the epidemic situation prevention and control area is sent to the object of which the moving track enters the epidemic situation prevention and control area, the alarm reminding of the users with infection risks is realized, and the users to be detected are prevented from being infected or secondarily cross-infected when entering the epidemic situation prevention and control area.
FIG. 9 is a schematic block diagram of an electronic device in an exemplary embodiment in accordance with the subject application. Referring to fig. 9, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, but may also include hardware required for other services. The processor reads the corresponding computer program from the nonvolatile memory to the memory and then runs the computer program to form the epidemic situation monitoring device on the logic level. Of course, besides the software implementation, the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Referring to fig. 10, fig. 10 is a block diagram of an epidemic monitoring apparatus according to an exemplary embodiment of the present application, and as shown in fig. 10, in a software implementation, the epidemic monitoring apparatus may include:
the trajectory information acquisition module 1001 is used for respectively acquiring trajectory information of epidemic situation patients and users to be detected, wherein the trajectory information comprises a plurality of groups of trajectory data consisting of geographical position information and acquisition moments corresponding to the geographical position information;
the trajectory information analysis module 1002 is configured to analyze trajectory information of the epidemic situation patient and the user to be detected through a predetermined epidemic situation propagation model, where the epidemic situation propagation model is determined according to infection chain data among historical patients and trajectory information of the user in the infection chain data;
and an intimate contact determination module 1003, configured to determine whether the user to be detected is an intimate contact of the epidemic patient according to the analysis result.
Optionally, the predetermined epidemic propagation model comprises a machine learning model, and the apparatus further comprises:
the data set construction module 1004 is used for constructing a positive sample data set and a negative sample data set according to infection chain data among the historical patients and track information of users in the infection chain data, wherein infection relations exist among epidemic patients in the positive sample data set, and infection relations do not exist among epidemic patients in the negative sample data set;
a trajectory information analysis module 1005, configured to perform feature analysis on the trajectory information of the user in the positive sample data set and the negative sample data set according to the initial epidemic propagation model;
a model parameter adjusting module 1006, configured to adjust a model parameter of the initial epidemic propagation model according to a result of the feature analysis, so as to increase a similarity between feature vectors corresponding to the trajectory information of the historical patient in the positive sample data set and decrease a similarity between feature vectors corresponding to the trajectory information of the historical patient in the negative sample data set.
Optionally, the infection chain data between the historical patients includes information of infection sources of the historical patients, the apparatus further comprising:
a contact information determining module 1007, which determines the contact distance and contact time length between the historical patient and the infection source according to the infection source of the historical patient;
a value information adjusting module 1008, which adjusts the values of the initial pathogenic distance and the initial pathogenic time length in the initial epidemic propagation model according to the contact distance and the contact time length between the historical patient and the infection source, so as to obtain an adjusted target pathogenic distance and target pathogenic time length;
the epidemic propagation model determining module 1009 determines the epidemic propagation model including the target pathogenic distance and the target pathogenic time length as the predetermined epidemic propagation model.
Further, the value information adjusting module 1008 is specifically configured to:
in the case that the contact distance between the historical patient and the infection source is determined to be larger than the initial pathogenic distance in the initial epidemic propagation model, determining the contact distance between the historical patient and the infection source as the target pathogenic distance after the initial pathogenic distance is adjusted;
and under the condition that the contact time length of the historical patient and the infection source is determined to be less than the initial pathogenic time length in the initial epidemic propagation model, determining the contact time length of the historical patient and the infection source as the target pathogenic time length after the initial pathogenic time length is adjusted.
Optionally, the method further includes:
the first warning information sending module 1010 is configured to send epidemic situation warning information about the user to be detected when it is determined that the user to be detected is a close contact person of the epidemic situation patient.
Optionally, the method further includes:
an epidemic situation prevention and control area generating module 1011, which generates an epidemic situation prevention and control area corresponding to the geographical position information by taking the geographical position information in the track information of the epidemic situation patient as a circle center and taking a preset pathogenic distance as a radius;
the second alarm information sending module 1012 sends alarm information about the epidemic situation prevention and control area to a target object whose monitored moving track enters the epidemic situation prevention and control area.
The device corresponds to the method, and more details are not repeated.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. In other instances, features described in connection with one embodiment may be implemented as discrete components or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (15)
1. An epidemic monitoring method, comprising:
respectively acquiring track information of epidemic situation patients and users to be detected, wherein the track information comprises a plurality of groups of geographical position information and acquisition moments corresponding to the geographical position information;
analyzing the track information of the epidemic situation patients and the users to be detected through a predetermined epidemic situation propagation model, wherein the epidemic situation propagation model is determined according to the infection chain data among the historical patients and the track information of the users in the infection chain data;
and determining whether the user to be detected is a close contact person of the epidemic situation patient or not according to the analysis result.
2. The method of claim 1, wherein the predetermined epidemic propagation model comprises a machine learning model, the method further comprising:
constructing a positive sample data set and a negative sample data set according to infection chain data among the historical patients and track information of users in the infection chain data, wherein infection relations exist among epidemic patients in the positive sample data set, and infection relations do not exist among epidemic patients in the negative sample data set;
performing characteristic analysis on the track information of the users in the positive sample data set and the negative sample data set according to the initial epidemic situation propagation model;
and adjusting the model parameters of the initial epidemic propagation model according to the result of the characteristic analysis so as to increase the similarity between the characteristic vectors corresponding to the track information of the historical patients in the positive sample data set and reduce the similarity between the characteristic vectors corresponding to the track information of the historical patients in the negative sample data set.
3. The method of claim 1, wherein the infection chain data between the historic patients includes information of the sources of infection of the historic patients, the method further comprising:
determining the contact distance and the contact time of the historical patient and the infection source according to the infection source of the historical patient;
adjusting the values of the initial pathogenic distance and the initial pathogenic time length in the initial epidemic propagation model according to the contact distance and the contact time length of the historical patient and the infection source to obtain the adjusted target pathogenic distance and the adjusted target pathogenic time length;
and determining an epidemic propagation model containing the target pathogenic distance and the target pathogenic time length as the predetermined epidemic propagation model.
4. The method of claim 3, wherein the adjusting the values of the initial pathogenic distance and the initial pathogenic time length in the initial epidemic propagation model according to the contact distance and the contact time length of the historical patient with the infection source comprises:
in the case that the contact distance between the historical patient and the infection source is determined to be larger than the initial pathogenic distance in the initial epidemic propagation model, determining the contact distance between the historical patient and the infection source as the target pathogenic distance after the initial pathogenic distance is adjusted;
and under the condition that the contact time length of the historical patient and the infection source is determined to be less than the initial pathogenic time length in the initial epidemic propagation model, determining the contact time length of the historical patient and the infection source as the target pathogenic time length after the initial pathogenic time length is adjusted.
5. The method according to claim 1, wherein the track information of the epidemic patient and the user to be detected is determined by invoking a data retrieval service of a block chain, and the track information in the block chain is automatically uploaded by a mobile phone base station and/or an access terminal accessed by an electronic device carried by the user.
6. The method of claim 1, further comprising:
and sending epidemic situation warning information about the user to be detected under the condition that the user to be detected is determined to be a close contact person of the epidemic situation patient.
7. The method of claim 1, further comprising:
generating an epidemic situation prevention and control area corresponding to the geographical position information by taking the geographical position information in the track information of the epidemic situation patients as a circle center and taking a preset pathogenic distance as a radius;
and sending alarm information about the epidemic situation prevention and control area to a target object of which the monitored moving track enters the epidemic situation prevention and control area.
8. An epidemic monitoring device, comprising:
the system comprises a track information acquisition module, a track information acquisition module and a track information acquisition module, wherein the track information acquisition module is used for respectively acquiring track information of epidemic situation patients and users to be detected, and the track information comprises a plurality of groups of track data consisting of geographical position information and acquisition moments corresponding to the geographical position information;
the system comprises a track information analysis module, a track information analysis module and a track information analysis module, wherein the track information analysis module is used for analyzing track information of epidemic situation patients and users to be detected through a predetermined epidemic situation propagation model, and the epidemic situation propagation model is determined according to infection chain data among historical patients and track information of the users in the infection chain data;
and the close contact person determining module is used for determining whether the user to be detected is a close contact person of the epidemic situation patient or not according to the analysis result.
9. The apparatus of claim 8, wherein the predetermined epidemic propagation model comprises a machine learning model, the apparatus further comprising:
the data set construction module is used for constructing a positive sample data set and a negative sample data set according to infection chain data among the historical patients and track information of users in the infection chain data, wherein infection relations exist among epidemic patients in the positive sample data set, and infection relations do not exist among epidemic patients in the negative sample data set;
the track information analysis module is used for carrying out characteristic analysis on the track information of the users in the positive sample data set and the negative sample data set according to the initial epidemic situation propagation model;
and the model parameter adjusting module is used for adjusting the model parameters of the initial epidemic propagation model according to the result of the characteristic analysis so as to increase the similarity between the characteristic vectors corresponding to the track information of the historical patients in the positive sample data set and reduce the similarity between the characteristic vectors corresponding to the track information of the historical patients in the negative sample data set.
10. The apparatus of claim 8, wherein the chain of infection data among the historic patients includes information of the source of infection of the historic patients, the apparatus further comprising:
the contact information determining module is used for determining the contact distance and the contact time of the historical patient and the infection source according to the infection source of the historical patient;
the value information adjusting module is used for adjusting the values of the initial pathogenic distance and the initial pathogenic time length in the initial epidemic situation propagation model according to the contact distance and the contact time length between the historical patient and the infection source so as to obtain the adjusted target pathogenic distance and the adjusted target pathogenic time length;
and the epidemic propagation model determining module is used for determining the epidemic propagation model containing the target pathogenic distance and the target pathogenic time length as the predetermined epidemic propagation model.
11. The apparatus of claim 10, wherein the value information adjusting module is specifically configured to:
in the case that the contact distance between the historical patient and the infection source is determined to be larger than the initial pathogenic distance in the initial epidemic propagation model, determining the contact distance between the historical patient and the infection source as the target pathogenic distance after the initial pathogenic distance is adjusted;
and under the condition that the contact time length of the historical patient and the infection source is determined to be less than the initial pathogenic time length in the initial epidemic propagation model, determining the contact time length of the historical patient and the infection source as the target pathogenic time length after the initial pathogenic time length is adjusted.
12. The apparatus of claim 8, further comprising:
and the first alarm information sending module is used for sending epidemic situation alarm information about the user to be detected under the condition that the user to be detected is determined to be a close contact person of the epidemic situation patient.
13. The apparatus of claim 8, further comprising:
the epidemic situation prevention and control area generation module is used for generating an epidemic situation prevention and control area corresponding to the geographical position information by taking the geographical position information in the track information of the epidemic situation patient as a circle center and taking a preset pathogenic distance as a radius;
and the second alarm information sending module is used for sending alarm information about the epidemic situation prevention and control area to a target object of which the monitored moving track enters the epidemic situation prevention and control area.
14. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured with executable instructions to implement the method of any one of claims 1-7.
15. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method according to any one of claims 1-7.
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| CN113113153A (en) * | 2021-04-13 | 2021-07-13 | 上海市疾病预防控制中心 | Method, system, device, processor and storage medium for realizing epidemic situation dynamic information analysis in epidemic situation outbreak period by using graph model |
| CN113380422A (en) * | 2021-05-24 | 2021-09-10 | 首都医科大学 | Epidemic situation scale prediction method and device, electronic equipment and storage medium |
| CN114334174A (en) * | 2022-01-06 | 2022-04-12 | 厦门市美亚柏科信息股份有限公司 | Epidemic situation tracking method and terminal |
| CN117476250A (en) * | 2023-12-21 | 2024-01-30 | 华侨大学 | Epidemic investigation prediction method and device based on multi-target tracking and readable medium |
| CN117476250B (en) * | 2023-12-21 | 2024-03-12 | 华侨大学 | Epidemic investigation prediction method and device based on multi-target tracking and readable medium |
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