Epidemic disease monitoring method and device based on big data and electronic equipment

Abstract

The invention discloses an epidemic disease monitoring method and device based on big data and electronic equipment, and belongs to the technical field of big data analysis, and the method comprises the steps that patient information of a certain epidemic disease is acquired, and the patient information comprises an infection area and infection time; performing correlation analysis on the epidemic variables of the epidemic disease according to the multiple linear regression model, and obtaining possible infection time and possible infection areas in combination with the infection areas and the infection time; marking the map according to the infection time, the possible infection time, the infection region and the possible infection region to obtain a dynamic marking map; performing correlation variable analysis on epidemic diseases to obtain possible infection time and possible infection regions; by marking the infection time, the possible infection time, the infection area and the possible infection area on the map, the dynamic marking map is obtained, and the possible infection area of any possible infection time is displayed on the map.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to an epidemic monitoring method, device and electronic equipment based on big data. Background Art

[0002] Epidemics are infectious diseases that can spread widely in a short period of time. They are usually contagious, epidemic, and seasonal. Epidemics are caused by pathogens that can spread among people through multiple channels such as air, water, food, and contact. Epidemics spread widely among people, causing many people to become ill at the same time or successively, forming a clear epidemic trend. Big data refers to a collection of data that cannot be captured, managed, and processed by conventional software tools within a certain time frame. It is a massive, high-growth, and diverse information asset that requires new processing models to have stronger decision-making power, insight discovery, and process optimization capabilities.

[0003] At present, epidemics are mainly prevented and controlled through vaccination, personal hygiene habits, environmental improvement and disinfection, controlling sources of infection and cutting off transmission routes, as well as social intervention and publicity and education. The focus is on universal prevention and control, but no data statistics are collected after the patient is infected, making it impossible to carry out targeted defense.

[0004] Therefore, the existing technology has the problem of difficulty in carrying out targeted defense in the process of epidemic prevention and control. Summary of the invention

[0005] In view of this, it is necessary to provide an epidemic monitoring method, device and electronic equipment based on big data to solve the problem that the existing technology is difficult to carry out targeted defense in the process of epidemic prevention and control.

[0006] In order to solve the above problems, the present invention provides an epidemic monitoring method based on big data, comprising: Obtain patient information of an epidemic, including the infected area and time; The correlation analysis of epidemic variables was conducted based on the multivariate linear regression model, and the possible infection time and possible infection area were obtained by combining the infection area and infection time; The map is marked according to the infection time, possible infection time, infected area and possible infection area to obtain a dynamic marked map.

[0007] In a possible implementation, the patient information also includes the virus type and the patient age; the epidemic variables include the virus type, the patient age, the socioeconomic status of the infected area, the climate, the infected area and the infected time; the epidemic variables of the epidemic are analyzed for correlation according to the multivariate linear regression model, and the infected area and the infected time are combined to obtain the possible infected time and the possible infected area, including: The socioeconomic conditions of the infected area are obtained based on the infected area, and the corresponding climate is obtained based on the infection time; The target multivariate linear regression model was constructed by taking virus type, patient age, socioeconomic status and climate of the infected area as independent variables and infection time and infected area as dependent variables to build a functional relationship; The current infected areas and infection times are updated and calculated based on the target multivariate linear regression model to obtain the possible infection time and possible infected areas.

[0008] In a possible implementation, after obtaining the dynamic marker map, the following steps are further included: Send warning messages to users in potentially infected areas.

[0009] In a possible implementation, after sending the warning information to users in the potentially infected area, the method further includes: Screen new patients among users and perform iterative calculations based on the new patient information to obtain new possible infection times and new possible infection areas; The dynamically marked map is updated based on new possible infection times and new possible infection areas.

[0010] In a possible implementation, the warning information includes at least one of a text message, a phone call, a WeChat message, and a broadcast voice.

[0011] In a possible implementation, the patient information also includes the patient's movement trajectory; after the current infection area and infection time are updated and calculated according to the target multivariate linear regression model to obtain the possible infection time and possible infection area, the following is also included: When the epidemic is a Class A infectious disease, obtain the patient's movement trajectory after the infection time and record it as the possible infection trajectory; Update the dynamic marker map based on possible infection trajectory markers.

[0012] In a possible implementation, a map is marked according to the infection time, possible infection time, infection area, and possible infection area to obtain a dynamic marked map, including: Geocoding infected areas and potentially infected areas to obtain infected longitude and latitude data and potentially infected longitude and latitude data; For any infection time / possible infection time, the map is marked according to the infection longitude and latitude data / possible infection longitude and latitude data to obtain a dynamic marked map.

[0013] In a possible implementation, before obtaining patient information of a certain epidemic, the following steps are also included: Obtain the authorization information of the authorized user, and filter out the patients of the authorized user based on the authorization information.

[0014] In order to solve the above problems, the present invention also provides an epidemic monitoring device based on big data, comprising: Patient information acquisition module, used to obtain patient information of a certain epidemic, including infection area and infection time; Correlation analysis module, used to perform correlation analysis on epidemic variables according to the multivariate linear regression model, and combine the infected area and infection time to obtain the possible infection time and possible infection area; The epidemic monitoring module is used to mark the map according to the infection time, possible infection time, infected area and possible infection area to obtain a dynamic marked map.

[0015] In order to solve the above problem, the present invention further provides an electronic device, including a memory and a processor, wherein: Memory, used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the epidemic monitoring method based on big data as described above.

[0016] The beneficial effects of adopting the above embodiment are as follows: the present invention provides an epidemic monitoring method based on big data, firstly, the infected areas and infection times of patients are collected to determine the time and place where the threat exists; then, the correlation analysis of the epidemic is performed based on the epidemic variables of the epidemic, the current infected areas and the infection time, and the possible infection time and the possible infection area are obtained; finally, based on the map, the infection time, the possible infection time, the infected area and the possible infection area are dynamically displayed on the map to obtain a dynamically marked map to highlight the places where the threat exists at different time points; it is realized that the possible infection area at any possible infection time is displayed on the map, so that users can carry out targeted defense. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of an embodiment of an epidemic monitoring method based on big data provided by the present invention; Figure 2 A schematic diagram of a flow chart of an embodiment of a correlation analysis provided by the present invention; Figure 3 A structural block diagram of an embodiment of an epidemic monitoring device based on big data provided by the present invention; Figure 4 This is a structural block diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the present invention and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0019] An epidemic is a phenomenon in which a pathogen (such as bacteria, viruses, parasites, etc.) spreads widely among a population over a certain period of time, causing many people to become ill at the same time or successively. The occurrence and spread of an epidemic poses a severe challenge to the public health system, requiring a large amount of manpower, material and financial resources for prevention and control; an epidemic may lead to economic problems such as reduced labor force, stagnant production, and reduced consumption, which will have a negative impact on economic development; an epidemic may cause panic and anxiety among the public, causing adverse effects on social psychology; an epidemic may spread across borders, triggering the attention and cooperation of the international community, and may also lead to restrictions and obstacles to international travel and trade.

[0020] In summary, epidemics are a serious public health problem that needs to be controlled to ensure social security. However, for individuals, epidemics are currently mainly prevented and controlled through universal methods, such as developing good personal hygiene habits, washing hands frequently, wearing masks, maintaining ventilation, etc., which are difficult to ensure the effectiveness of prevention and control.

[0021] Therefore, the existing technology has the problem of difficulty in carrying out targeted defense in the process of epidemic prevention and control.

[0022] In order to solve the above problems, the present invention provides an epidemic monitoring method, device and electronic equipment based on big data, which are described in detail below.

[0023] Figure 1 A flow chart of an embodiment of an epidemic monitoring method based on big data provided by the present invention is shown in FIG. Figure 1 As shown in the figure, the epidemic monitoring methods based on big data include: S101: Obtain patient information of a certain epidemic, including the infected area and time; S102: Conduct correlation analysis on epidemic variables based on the multivariate linear regression model, and combine the infected area and infection time to obtain the possible infection time and possible infection area; S103: Marking the map according to the infection time, the possible infection time, the infection area and the possible infection area to obtain a dynamic marked map.

[0024] In this embodiment, first, the infected areas and infection times of the patients are collected to determine the time and place where the threat exists; then, a correlation analysis of the epidemic is performed based on the epidemic variables of the epidemic, the current infected areas and the infection time to obtain the possible infection time and the possible infected areas; finally, based on the map, the infection time, the possible infection time, the infected areas and the possible infected areas are dynamically displayed on the map to obtain a dynamically marked map to highlight the places where the threat exists at different time points; the possible infection areas at any possible infection time are displayed on the map to facilitate users to carry out targeted defense.

[0025] It should be noted that this application is to assist users in epidemic monitoring by obtaining a dynamic marking map as a protection measure.

[0026] It should be noted that epidemics can be diseases transmitted through air droplets such as influenza, tuberculosis, measles, and rubella, or diseases transmitted through mosquito bites such as malaria and dengue fever, etc., without limitation. In addition, for a certain epidemic, only the patient information corresponding to the disease is obtained, and no cross-recording is performed to avoid the problem of disordered prediction results.

[0027] Furthermore, the possible infection time refers to the time point when the virus threat may exist, which is obtained by conducting a correlation analysis of the current infection area and infection time, as well as the characteristics of the epidemic itself through a multivariate linear regression model. It does not necessarily lead to the emergence of new patients; the possible infection area refers to the place where the virus threat may exist, which does not necessarily lead to the emergence of new patients.

[0028] Obviously, all patients among the users are aware of their own illness conditions. Therefore, in the process of monitoring the epidemic, it is only necessary to determine the possible infection time and possible infection area, so as to screen the people who may be sick.

[0029] In addition, in the dynamic marking map, it is necessary to distinguish the infected area, infection time, possible infection time and possible infection area, so as to distinguish the protection strength of each location. Specifically, the distinguishing method includes but is not limited to marking the infected area and possible infection area with different colors; by highlighting the infection time and adding prompt information after the possible infection time, etc., which will not be elaborated here.

[0030] The multiple linear regression model is an important method in regression analysis. When there are two or more independent variables (explanatory variables), it is called multiple regression. This model predicts or estimates the value of the dependent variable by constructing a linear equation containing multiple independent variables.

[0031] The general form of a multiple linear regression model is: Y=β0+β1X1+β2X2+…+βk X k +μ Among them, Y is the dependent variable, X1, X2, ..., X k is the independent variable, β0 is the constant term, β1, β2, ..., β k is the regression coefficient (also called partial regression coefficient), which indicates the impact of a unit change in an independent variable on the mean value of the dependent variable under the condition that other independent variables remain unchanged. μ is a random disturbance term, which indicates the part that cannot be explained in the model.

[0032] As a preferred embodiment, in S101, the patient information also includes virus type, patient age and patient movement trajectory.

[0033] It should be noted that in order to protect the privacy of patients and avoid infringement, the patient information obtained in this application is obtained only with the patient's authorization. The purpose is to improve the risk avoidance capabilities of authorized users in epidemic prevention by conducting data analysis on all authorized users.

[0034] In a specific embodiment, for any device that carries and applies the big data-based epidemic monitoring method of the present application, an authorized user refers to a user who uses the device and authorizes the acquisition of user information. Before obtaining patient information of a certain epidemic, it is necessary to obtain the authorization information of the authorized user and screen out the patients of the authorized user based on the authorization information.

[0035] As a preferred embodiment, in S102, the epidemic variables include virus type, patient age, socioeconomic status of the infected area, climate, infected area and infected time; in order to perform correlation analysis on the epidemic variables of the epidemic according to the multivariate linear regression model, the infected area and the infected time are combined to obtain the possible infected time and the possible infected area, such as Figure 2 As shown, Figure 2 A schematic diagram of a flow chart of an embodiment of a correlation analysis provided by the present invention includes: S201: Obtain the socioeconomic conditions of the infected area according to the infected area, and obtain the corresponding climate in combination with the infection time; S202: Construct a multivariate linear regression model using virus type, patient age, socioeconomic status and climate of the infected area as independent variables and infection time and infected area as dependent variables to build a functional relationship; S203: Update and calculate the current infected area and infection time according to the multivariate linear regression model to obtain the possible infection time and possible infected area.

[0036] In this embodiment, based on the characteristics of the spread of the epidemic itself, the virus type, patient age, socioeconomic conditions and climate of the infected area are used as reference quantities that affect the scope of the epidemic spread. By constructing a multivariate linear regression model, the possible hidden dangers and threats are analyzed to obtain the possible infection time and possible infection area, thereby achieving the determination of threats through effective use of data, so as to facilitate subsequent reminders to users to pay attention to protection.

[0037] As a preferred embodiment, in S103, since the location directly reported by the user may be the name of a community or a small shop, it cannot be quickly adapted to the map. Therefore, first, the infected areas and possible infected areas are geocoded to obtain the infected longitude and latitude data and the possible infected longitude and longitude data; then, for any infection time / possible infection time, the map is marked according to the infected longitude and longitude data / possible infected longitude and longitude data to obtain a dynamically marked map.

[0038] In this embodiment, by geocoding the infected areas / potentially infected areas, accurate quantification of the infection locations is achieved, which not only improves the data processing efficiency but also improves the visualization effect, making it easier for users to consult.

[0039] In addition, the dynamic marked map includes multiple maps. Since there are different infected areas at different infection times, in order to avoid panic among users due to marking too many infected areas, any infection time is marked according to the patient information of the user to obtain a static marked map corresponding to the infection time. When there are multiple infection times, multiple static marked maps are combined to form a dynamic marked map.

[0040] In a specific embodiment, the number of authorized users may increase, and the corresponding number of patients will also increase. When the infected area and infection time change, each corresponding static mark map in the dynamic mark map needs to be adaptively adjusted and modified, which will not be elaborated here.

[0041] After obtaining the dynamically marked map, in order to raise users' awareness of potentially infected areas, early warning information needs to be sent to users in potentially infected areas.

[0042] It should be noted that since all users have reported the information, it is assumed that the information of all known confirmed patients is reflected in the dynamic marking map. Since the epidemic itself is contagious, it is also necessary to make adaptive predictions for areas where threats may exist, and then mark the predicted possible infected areas on the dynamic marking map, so as to more comprehensively display the current epidemic threat situation in various regions.

[0043] Furthermore, since there is no guarantee that users can view the dynamically marked map in real time, and it is possible that users are inconvenient or do not know how to view the map, in order to better prompt users to defend against viruses, it is also necessary to send warning information to users in potentially infected areas.

[0044] In a specific embodiment, the warning information includes at least one of a text message, a phone call, a WeChat message, and a broadcast voice.

[0045] In other embodiments, the warning information may be in other forms, which are not limited here.

[0046] As a preferred embodiment, after sending warning information to users in areas that may be infected, there may be new patients and their reported data. Therefore, it is necessary to screen new patients among users and perform iterative calculations based on the new patient information to obtain the possible new infection time and the possible new infection area; then, the dynamic marking map is updated according to the possible new infection time and the possible new infection area.

[0047] It should be noted that due to the real-time changes in user data, the data of the dynamic mark map needs to be updated and calculated in real time to ensure the reliability of the dynamic mark map. Therefore, in this application, the information reported by the user is processed and iterated in real time to update the dynamic mark map in real time.

[0048] As a preferred embodiment, when the epidemic is a Class A infectious disease, it is necessary to strictly control the development of the epidemic. Therefore, it is also necessary to obtain the patient's movement trajectory. For any patient, the patient's movement trajectory after the infection time must be obtained and recorded as a possible infection trajectory; then, the dynamic marking map is updated according to the possible infection trajectory mark.

[0049] It should be noted that the possible infection trajectory includes not only the specific location, but also the time when the patient arrived at the location. Therefore, by adding the possible infection trajectory to the dynamic marking map, the possible virus threats can be dynamically displayed to facilitate other users to compare their itineraries, compare and confirm their own situations in a timely manner, and improve the overall safety of the user group.

[0050] Through the above method, in this embodiment, first, the infected areas and infection times of the patients are collected to determine the time and place where the threat exists; then, a correlation analysis of the epidemic is performed based on the epidemic variables of the epidemic, the current infected areas and the infection time to obtain the possible infection time and the possible infected areas; finally, based on the map, the infection time, the possible infection time, the infected areas and the possible infected areas are dynamically displayed on the map to obtain a dynamically marked map to highlight the places where the threat exists at different time points; it is realized that the possible infection areas at any possible infection time are displayed on the map to facilitate users to carry out targeted defense.

[0051] In summary, this application uses big data to analyze the data of all users related to a certain epidemic to determine the infection time and place corresponding to the epidemic, and realizes real-time monitoring of the spread of the epidemic, which is beneficial for users to protect themselves and for government agencies to conduct overall management and control.

[0052] In order to solve the above problems, the present invention also provides an epidemic monitoring device based on big data, such as Figure 3 As shown, Figure 3 This is a structural block diagram of an embodiment of an epidemic monitoring device based on big data provided by the present invention. The epidemic monitoring device based on big data 300 includes: Patient information acquisition module 301, used to acquire patient information of a certain epidemic, the patient information includes the infected area and the infected time; Correlation analysis module 302, for performing correlation analysis on epidemic variables according to a multivariate linear regression model, and obtaining possible infection time and possible infection area by combining the infection area and infection time; The epidemic monitoring module 303 is used to mark the map according to the infection time, possible infection time, infection area and possible infection area to obtain a dynamic marked map.

[0053] like Figure 4 As shown, the present invention also provides an electronic device 400. The electronic device 400 includes a processor 401, a memory 402 and a display 403. Figure 4 Only some components of the electronic device 400 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0054] In some embodiments, the processor 401 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 402, such as the big data-based epidemic monitoring method of the present invention.

[0055] In some embodiments, processor 401 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0056] In some embodiments, the memory 402 may be an internal storage unit of the electronic device 400, such as a hard disk or memory of the electronic device 400. In other embodiments, the memory 402 may also be an external storage device of the electronic device 400, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 400.

[0057] Furthermore, the memory 402 may include both an internal storage unit of the electronic device 400 and an external storage device. The memory 402 is used to store application software installed in the electronic device 400 and various data.

[0058] In some embodiments, the display 403 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 403 is used to display information of the electronic device 400 and to display a visual user interface. The components 401-403 of the electronic device 400 communicate with each other via a system bus.

[0059] In one embodiment, when the processor 401 executes the big data-based epidemic monitoring program in the memory 402, the following steps may be implemented: Obtain patient information of an epidemic, including the infected area and time; The correlation analysis of epidemic variables was conducted based on the multivariate linear regression model, and the possible infection time and possible infection area were obtained by combining the infection area and infection time; The map is marked according to the infection time, possible infection time, infected area and possible infection area to obtain a dynamic marked map.

[0060] It should be understood that: when the processor 401 executes the big data-based epidemic monitoring program in the memory 402, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0061] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 400 mentioned, and the electronic device 400 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic device may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 400 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0062] Accordingly, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions of the big data-based epidemic monitoring method provided in the above-mentioned method embodiments.

[0063] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0064] The above is a detailed introduction to the epidemic monitoring method, device and electronic device based on big data provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for epidemiological monitoring based on big data, characterized in that: include: Obtaining patient information of a certain epidemic, wherein the patient information includes the infected area and the infected time; Performing correlation analysis on the epidemic variables of the epidemic according to a multivariate linear regression model, and combining the infected area and the infected time to obtain the possible infected time and possible infected area; The map is marked according to the infection time, the possible infection time, the infection area and the possible infection area to obtain a dynamic marked map.

2. The method for epidemiological monitoring based on big data according to claim 1, characterized in that: The patient information also includes the virus type and the patient's age; the epidemic variables include the virus type, the patient's age, the socioeconomic status of the infected area, the climate, the infected area and the infected time; the correlation analysis of the epidemic variables of the epidemic according to the multivariate linear regression model, combining the infected area and the infected time to obtain the possible infection time and possible infection area, includes: Acquire the social and economic conditions of the infected area according to the infected area, and acquire the corresponding climate in combination with the infection time; A multivariate linear regression model is constructed by taking the virus type, the patient's age, the socioeconomic status of the infected area and the climate as independent variables and taking the infection time and the infected area as dependent variables to construct a functional relationship; The current infection area and the infection time are updated and calculated according to the multivariate linear regression model to obtain the possible infection time and the possible infection area.

3. The method for epidemiological monitoring based on big data according to claim 2, characterized in that: After getting the dynamic marker map, it also includes: Send warning information to users in the potentially infected areas.

4. The method for epidemiological monitoring based on big data according to claim 3, characterized in that: After sending warning information to users in the potentially infected areas, it also includes: Screening new patients among the users, and performing iterative calculations based on the new patient information to obtain new possible infection times and new possible infection areas; The dynamic marking map is updated according to the possible new infection time and the possible new infection area.

5. The method for epidemiological monitoring based on big data according to claim 3, characterized in that: The warning information includes at least one of a text message, a phone call, a WeChat message, and a broadcast voice.

6. The method for epidemiological monitoring based on big data according to claim 2, characterized in that: The patient information also includes the patient's movement trajectory; after the current infection area and the infection time are updated and calculated according to the multivariate linear regression model to obtain the possible infection time and the possible infection area, it also includes: When the epidemic is a Class A infectious disease, the patient's movement trajectory after the infection time is obtained and recorded as a possible infection trajectory; The dynamic marking map is updated according to the possible infection trajectory markings.

7. The method for epidemiological monitoring based on big data according to claim 1, characterized in that: The step of marking the map according to the infection time, the possible infection time, the infection area and the possible infection area to obtain a dynamic marked map includes: Geocoding the infected area and the possibly infected area to obtain infected longitude and latitude data and possibly infected longitude and latitude data; For any of the infection time / the possible infection time, the map is marked according to the infection longitude and latitude data / the possible infection longitude and latitude data to obtain the dynamic marked map.

8. The method for epidemiological monitoring based on big data according to claim 1, characterized in that: Before obtaining patient information for a particular epidemic, it also includes: The authorization information of the authorized user is obtained, and the patients of the authorized user are screened out according to the authorization information.

9. An epidemiological monitoring device based on big data, characterized in that: include: A patient information acquisition module is used to acquire patient information of a certain epidemic, wherein the patient information includes the infected area and the infected time; A correlation analysis module, for performing correlation analysis on the epidemic variables of the epidemic according to a multivariate linear regression model, and obtaining the possible infection time and possible infection area by combining the infection area and the infection time; The epidemic monitoring module is used to mark the map according to the infection time, the possible infection time, the infection area and the possible infection area to obtain a dynamic marked map.

10. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the big data-based epidemic monitoring method described in any one of claims 1 to 8.

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