Big data-based infectious disease and endemic disease patient data management system and method

Abstract

The invention discloses an infectious disease and endemic disease patient data management system and method based on big data, and belongs to the field of medical care systems. According to the method, reasonable allocation of medical resources is carried out according to a position patient degree analysis result and regional medical resource conditions, reasonable analysis is carried out on the risk degree of the patients in the region by analyzing the severity degree of the patients in the region and the abnormal growth conditions of the patients in the region, then the medical resources are reasonably allocated, and the reasonability of allocation of the medical resources is improved; and meanwhile, multi-source data analysis is performed by integrating the position of the infectious disease patient, the regional dispersion condition and the physique of the patient when the severity of the regional patient is analyzed, so that the accuracy of the severity analysis of the regional patient is improved.

Description

Technical Field

[0001] The present application belongs to the field of medical care systems, and more specifically, to a data management system and method for infectious diseases and endemic diseases patients based on big data. Background Art

[0002] Infectious diseases are diseases caused by pathogens (such as bacteria, viruses, parasites, etc.) that can be transmitted between organisms. They are usually transmitted through air, food, water, contact, insect bites, etc. Endemic diseases refer to diseases caused by factors such as the local natural environment or living habits in certain specific areas. These diseases have a higher incidence rate in the area. Infectious diseases emphasize the transmission characteristics of pathogens, while endemic diseases emphasize regionality. Endemic diseases are not necessarily infectious diseases, but infectious diseases can be local if they are particularly common in a certain area. When managing data on infectious and endemic disease patients, existing technologies usually only collect, store and call data on infectious and endemic disease patients. It is impossible to reasonably analyze the risk level of patients in the region by analyzing the severity of patients in the region and the abnormal growth of patients in the region, and at the same time, it leads to the inability to effectively and reasonably allocate medical resources. Most of the existing technologies have the above problems. In order to solve the problems raised by this background technology, the present application designs an infectious disease and endemic disease patient data management system and method based on big data. Summary of the invention

[0003] In order to address the deficiencies in the prior art mentioned in the background technology, the present application proposes an infectious disease and endemic disease patient data management system and method based on big data. The present application proposes to conduct a reasonable analysis of the risk level of patients in the region by analyzing the severity of patients in the region and the abnormal growth of patients in the region, and then to reasonably allocate medical resources, thereby improving the rationality of medical resource allocation. At the same time, when conducting regional patient severity analysis, multi-source data analysis is conducted based on the location of infectious disease patients, regional dispersion and patient constitution, thereby improving the accuracy of regional patient severity analysis.

[0004] To achieve the above objectives, the present application provides the following technical solutions: In the first aspect, the present application provides a method for managing infectious disease and endemic disease patient data based on big data, which includes the following specific steps: S1. Obtain the patient data of infectious diseases in the region, as well as the distribution of patients with infectious diseases in the region, regional environmental data and regional medical resources; S2. Analyze the severity of regional patients based on the distribution of regional infectious disease patients and patient data; S3. Analyze the abnormal growth of regional patients based on the distribution of regional infectious disease patients and regional environmental data; S4. Perform location-based patient severity analysis based on the patient severity analysis results at each location in the region and the regional patient growth abnormality analysis results; S5. Rationally allocate medical resources based on location-based patient severity analysis results and regional medical resource conditions.

[0005] As a preferred technical solution for the infectious disease and endemic disease patient data management method based on big data, the patient data of infectious diseases in the region in S1 includes various physical characteristic data of infectious disease patients, the distribution of infectious disease patients in the region includes location data of the area where the infectious disease is located, density data of infectious disease patients in each area, personnel flow data in each area and personnel density data in each area, and the regional environmental data includes environmental data of each area that affects virus transmission in the next stage.

[0006] As a preferred technical solution for the infectious disease and endemic disease patient data management method based on big data, the regional patient severity analysis in S2 includes the following specific steps: S21. Obtain location data of areas where infectious diseases are located and density data of infectious disease patients in each area, and analyze the distribution anomaly of infectious disease patients in each area based on the dispersion degree of the locations of infectious disease patients and the overall average density data of infectious disease patients in the area. The distribution anomaly of infectious disease patients in each area is proportional to the dispersion degree, because the more dispersed, the stronger the contact ability of infectious disease patients with other people. The distribution anomaly of infectious disease patients in each area is also proportional to the density data. The dispersion degree is evaluated by the ratio of the average distance of the locations of infectious disease patients in the area to the set safety distance. S22, obtaining the physical data of patients in the area where the infectious disease is located, comparing the physical data of the patients with normal physical data, and setting the deviation between the physical data and the normal physical data as the physical abnormality value; S23. Obtain the distribution anomalies and physical abnormalities of infectious disease patients in the region, perform weighted summation, and obtain the severity of patients in the region.

[0007] As a preferred technical solution for the infectious disease and endemic disease patient data management method based on big data, the regional patient growth abnormality analysis includes the following specific steps: S31. Obtain the crowd density data and crowd flow speed data of the locations where the infectious disease patients are located in the region, and perform infection risk assessment based on the crowd density data and crowd flow speed data of the locations where the infectious disease patients are located, wherein the infection risk is proportional to the crowd density data and inversely proportional to the crowd flow speed, and the calculation formula can be: , where T is the monitoring duration, a is the crowd density coefficient, b is the crowd flow velocity coefficient, dt is the time integral, Qit is the crowd density data of the location of the i-th infectious disease patient at time t, Qm is the crowd density standard value, vm is the flow velocity standard value, and vit is the crowd flow velocity of the location of the i-th infectious disease patient at time t; S32. Obtain environmental data of the locations of infectious disease patients in the region, and obtain an environmental impact coefficient based on the deviation between the environmental data and the environmental data that is most conducive to virus transmission, wherein the environmental change impact coefficient calculation formula can be: , where S is the type of environment that affects virus transmission, ck is the weight of the kth type of environment that affects virus transmission, xkt is the kth type of environmental data that affects virus transmission at time t, xkm is the value of the kth type of environmental data that is most conducive to virus transmission, and dt is the time integral. The value of the kth type of environmental data that is most conducive to virus transmission is obtained through comparative experiments in the laboratory; S33. Obtain the infectious risk assessment results and environmental impact coefficients of the infectious disease patients in the region, multiply them to obtain the infectious abnormality analysis value of a single infectious disease patient, and then superimpose the infectious abnormality analysis values ​​of all infectious disease patients in the region to obtain the regional patient growth abnormality value.

[0008] As a preferred technical solution for the infectious disease and endemic disease patient data management method based on big data, the location patient severity analysis includes the following specific contents: S41, obtaining the regional patient severity and regional patient growth abnormality in the analyzed area, and obtaining the regional patient risk level after weighted summation; S42. The patient risk levels of all regions are summed up to obtain the total patient risk level, and the regional patient risk level of the corresponding region is divided by the total patient risk level to obtain a patient risk analysis value.

[0009] As a preferred technical solution for the infectious disease and endemic disease patient data management method based on big data, the rational allocation of medical resources based on location patient severity analysis results and regional medical resource conditions includes the following specific contents: obtaining all corresponding medical resources, and allocating all medical resources to the region according to the corresponding proportion of the patient severity analysis value of the corresponding area.

[0010] In the second aspect, the present application provides an infectious disease and endemic disease patient data management system based on big data, which is implemented based on the above-mentioned infectious disease and endemic disease patient data management method based on big data, and specifically includes a data acquisition module, a patient severity analysis module, a growth abnormality analysis module, a disease severity analysis module and a resource allocation module; wherein, the data acquisition module is used to acquire patient data of infectious diseases in the region, and at the same time acquire the distribution of regional infectious disease patients, regional environmental data and regional medical resources; the patient severity analysis module performs regional patient severity analysis based on the distribution of regional infectious disease patients and patient data; the growth abnormality analysis module performs regional patient growth abnormality analysis based on the distribution of regional infectious disease patients and regional environmental data; the disease severity analysis module performs location patient severity analysis based on the patient severity analysis results of each location in the region and the regional patient growth abnormality analysis results; the resource allocation module reasonably allocates medical resources through the location patient severity analysis results and regional medical resources.

[0011] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned infectious disease and endemic disease patient data management method based on big data by calling the computer program stored in the memory.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the infectious disease and endemic disease patient data management method based on big data as described above.

[0013] Compared with the prior art, the beneficial effects of the present application are as follows: the present application performs regional patient severity analysis based on the distribution of regional infectious disease patients and patient data, performs regional patient growth abnormality analysis based on the distribution of regional infectious disease patients and regional environmental data, performs location patient severity analysis based on the patient severity analysis results of each location in the region and the regional patient growth abnormality analysis results, reasonably allocates medical resources through the location patient severity analysis results and regional medical resource conditions, reasonably analyzes the risk level of patients in the region by analyzing the regional patient severity and regional patient growth abnormalities, and then reasonably allocates medical resources, thereby improving the rationality of medical resource allocation; at the same time, when performing regional patient severity analysis, multi-source data analysis is performed based on the location of infectious disease patients, regional dispersion and patient constitution, thereby improving the accuracy of regional patient severity analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings; Figure 1 This is a schematic diagram of the overall process of the embodiment of the present application method; Figure 2 This is a schematic diagram of step S2 of the method embodiment of the present application; Figure 3 This is a schematic diagram of step S3 of the method embodiment of the present application; Figure 4 It is a schematic diagram of the overall framework of an embodiment of the system of the present application. DETAILED DESCRIPTION

[0015] In order to better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application and do not limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0016] In the accompanying drawings, the size, dimensions and shape of the elements have been slightly adjusted for ease of explanation. The accompanying drawings are only examples and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms to indicate approximation, not as terms to indicate degree, and are intended to illustrate the inherent deviations in measured or calculated values ​​that will be recognized by those of ordinary skill in the art. In addition, in the present application, the order in which the steps are processed does not necessarily represent the order in which these processes occur in actual operation, unless otherwise clearly defined or can be derived from the context. It should also be understood that expressions such as "including", "including", "having", "including" and / or "including" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, rather than just modifying the individual elements in the list. In addition, when describing the embodiments of the present application, "may" is used to represent "one or more embodiments of the present application". Furthermore, the term "exemplary" is intended to refer to an example or illustration. Unless otherwise specified, all words used herein (including engineering terms and scientific and technological terms) have the same meaning as those commonly understood by those of ordinary skill in the art to which this application belongs. It should also be understood that, unless otherwise clearly stated in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0017] Example 1 In order to solve the technical problems raised in the background technology, the present application provides a preferred embodiment: Figure 1-Figure 3 As shown, the infectious disease and endemic disease patient data management method based on big data includes the following specific steps: S1. Obtain the patient data of infectious diseases in the region, as well as the distribution of patients with infectious diseases in the region, regional environmental data and regional medical resources; S2. Analyze the severity of regional patients based on the distribution of regional infectious disease patients and patient data; S3. Analyze the abnormal growth of regional patients based on the distribution of regional infectious disease patients and regional environmental data; S4. Perform location-based patient severity analysis based on the patient severity analysis results at each location in the region and the regional patient growth abnormality analysis results; S5. Rationally allocate medical resources based on the patient severity analysis results and regional medical resource conditions; In one specific embodiment, the patient data of infectious diseases in the region in S1 includes various physical characteristic data of infectious disease patients, including blood pressure, heart rate and body temperature, etc. The distribution of infectious disease patients in the region includes location data of the infectious disease area, density data of infectious disease patients in each region, personnel flow data in each region and personnel density data in each region, and the regional environmental data includes environmental data of each region that affects virus transmission in the next stage. In this embodiment, it should be noted that various data here are collected through corresponding data collection terminals and stored in storage modules with confidentiality agreements; In one specific embodiment, the risk assessment strategy in S2 includes the following specific steps: The regional patient severity analysis in S2 includes the following specific steps: S21. Obtain location data of infectious disease areas and density data of infectious disease patients in each area, and analyze the distribution anomaly of infectious disease patients in each area based on the dispersion degree of the locations of infectious disease patients and the overall average density data of infectious disease patients in the area. The distribution anomaly of infectious disease patients in each area is proportional to the dispersion degree, because the more dispersed, the stronger the contact ability of infectious disease patients with other people. The distribution anomaly of infectious disease patients in each area is also proportional to the density data. The dispersion degree is evaluated by the ratio of the average distance of each infectious disease patient in the area to the set safety distance. The distribution anomaly evaluation formula of regional infectious disease patients can be: , where fs is the degree of dispersion of infectious diseases and mk is the density of infectious diseases; S22, obtaining the physical data of patients in the area where the infectious disease is located, comparing the physical data of the patients with the normal physical data, and setting the deviation between the physical data and the normal physical data as the physical abnormality value, wherein the calculation formula of the physical abnormality value of the area can be: , where n is the number of infectious disease patients in the region, mi is the number of types of physical data, aj is the weight of the j-th physical characteristic, kij is the value of the j-th physical data of the i-th patient in the region, and kjm is the median of the normal range of the j-th physical data; S23, obtaining the distribution anomaly and physical abnormality values ​​of infectious disease patients in the region, performing weighted summation, and obtaining the severity of patients in the region; In one specific embodiment, the analysis of regional patient growth abnormality includes the following specific steps: S31. Obtain the crowd density data and crowd flow speed data of the locations where the infectious disease patients are located in the region, and perform infection risk assessment based on the crowd density data and crowd flow speed data of the locations where the infectious disease patients are located, wherein the infection risk is proportional to the crowd density data and inversely proportional to the crowd flow speed, and the calculation formula can be: , where T is the monitoring duration, a is the crowd density coefficient, b is the crowd flow velocity coefficient, dt is the time integral, Qit is the crowd density data of the location of the i-th infectious disease patient at time t, Qm is the crowd density standard value, vm is the flow velocity standard value, and vit is the crowd flow velocity of the location of the i-th infectious disease patient at time t; S32. Obtain environmental data of the locations of infectious disease patients in the region, and obtain an environmental impact coefficient based on the deviation between the environmental data and the environmental data that is most conducive to virus transmission, wherein the environmental change impact coefficient calculation formula can be: , where S is the type of environment that affects virus transmission, ck is the weight of the kth type of environment that affects virus transmission, xkt is the kth type of environmental data that affects virus transmission at time t, xkm is the value of the kth type of environmental data that is most conducive to virus transmission, and dt is the time integral. The value of the kth type of environmental data that is most conducive to virus transmission is obtained through comparative experiments in the laboratory; S33, obtaining the infection risk assessment results and environmental impact coefficients of the infectious disease patients in the region, multiplying them to obtain the infection abnormality analysis value of a single infectious disease patient, and then superimposing the infection abnormality analysis values ​​of the infectious disease patients in all regions to obtain the regional patient growth abnormality value; In one specific embodiment, the location patient severity analysis includes the following specific contents: S41, obtaining the regional patient severity and regional patient growth abnormality in the analyzed area, and performing weighted summation to obtain the regional patient risk level; S42, summing up the risk levels of patients in all regions to obtain the risk level of all patients, and dividing the risk level of patients in the corresponding region by the risk level of all patients to obtain a patient level analysis value; In one specific embodiment, the reasonable allocation of medical resources based on the location patient severity analysis results and regional medical resource conditions includes the following specific contents: obtaining all corresponding medical resources, and allocating all medical resources to the region according to the corresponding proportion of the patient severity analysis value of the corresponding region; At the same time, it is necessary to explain that the setting parameters (such as each proportion coefficient and weight) in this embodiment are obtained by using a large amount of historical data as a control experimental group. The specific acquisition method is: obtaining patient data of infectious diseases in the historical region, the distribution of regional infectious disease patients, regional environmental data and regional medical resources for control experiments, substituting the obtained data sets into each step of this embodiment to perform location patient degree analysis, obtaining the expert's judgment result on the allocation of medical resources for the control experiment, importing the analysis results and judgment results into the fitting software, performing iterative fitting, and finally outputting the value of the setting parameter that meets the maximum judgment accuracy; It should be pointed out in this embodiment that if the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules before processing personal information, and obtained the individual's voluntary consent. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, at personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to collect his or her personal information; or on the device for processing personal information, when the personal information processing rules are notified by obvious signs / information, personal authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0018] Example 2 like Figure 3As shown, a data management system for infectious diseases and endemic diseases based on big data is implemented based on the above-mentioned infectious diseases and endemic diseases patient data management method based on big data, and specifically includes a data acquisition module, a patient severity analysis module, a growth abnormality analysis module, a disease severity analysis module and a resource allocation module; wherein the data acquisition module is used to acquire patient data of infectious diseases in a region, and at the same time acquire the distribution of regional infectious disease patients, regional environmental data and regional medical resources; the patient severity analysis module performs regional patient severity analysis based on the distribution of regional infectious disease patients and patient data; the growth abnormality analysis module performs regional patient growth abnormality analysis based on the distribution of regional infectious disease patients and regional environmental data; the disease severity analysis module performs location patient severity analysis based on the patient severity analysis results of each location in the region and the regional patient growth abnormality analysis results; the resource allocation module performs reasonable allocation of medical resources based on the location patient severity analysis results and regional medical resources; at the same time, the data transmission direction of each module in this embodiment is as follows Figure 4 As shown by the arrow direction in the figure, the specific steps of each module in this embodiment have been described in detail in the above method embodiment and will not be repeated here.

[0019] Example 3 This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned infectious disease and endemic disease patient data management method based on big data by calling the computer program stored in the memory.

[0020] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the infectious disease and endemic disease patient data management method based on big data provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0021] Example 4 This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program runs on a computer device, the computer device executes the above-mentioned infectious disease and endemic disease patient data management method based on big data.

[0022] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0023] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0024] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0025] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for managing data of infectious and endemic disease patients based on big data, characterized in that: It includes the following specific steps: S1. Obtain the patient data of infectious diseases in the region, as well as the distribution of patients with infectious diseases in the region, regional environmental data and regional medical resources; S2. Analyze the severity of regional patients based on the distribution of regional infectious disease patients and patient data; S3. Analyze the abnormal growth of regional patients based on the distribution of regional infectious disease patients and regional environmental data; S4. Perform location-based patient severity analysis based on the patient severity analysis results at each location in the region and the regional patient growth abnormality analysis results; S5. Rationally allocate medical resources based on location-based patient severity analysis results and regional medical resource conditions.

2. The method for managing infectious disease and endemic disease patient data based on big data according to claim 1, characterized in that: The regional patient severity analysis in S2 includes the following specific steps: S21, obtaining location data of areas where infectious diseases are located and density data of infectious disease patients in each area, and analyzing the distribution anomalies of infectious disease patients in each area based on the dispersion of locations of infectious disease patients and the overall average density data of infectious disease patients in the area; S22, obtaining the physical data of patients in the area where the infectious disease is located, comparing the physical data of the patients with the normal physical data, and setting the deviation between the physical data and the normal physical data as the physical abnormality value; S23. Obtain the distribution anomalies and physical abnormalities of infectious disease patients in the region, perform weighted summation, and obtain the severity of patients in the region.

3. The method for managing infectious disease and endemic disease patient data based on big data as claimed in claim 2, characterized in that: The analysis of abnormal growth of patients in the region includes the following specific steps: S31. Obtaining crowd density data and crowd flow speed data at locations where infectious disease patients are located in the region, and conducting infection risk assessment based on the crowd density data and crowd flow speed data at locations where infectious disease patients are located; S32, obtaining environmental data of locations of infectious disease patients in the region, and obtaining an environmental impact coefficient based on the deviation between the environmental data and the environmental data most conducive to virus transmission; S33. Obtain the infectious risk assessment results and environmental impact coefficients of the infectious disease patients in the region, multiply them to obtain the infectious abnormality analysis value of a single infectious disease patient, and then superimpose the infectious abnormality analysis values ​​of all infectious disease patients in the region to obtain the regional patient growth abnormality value.

4. The method for managing infectious disease and endemic disease patient data based on big data as claimed in claim 3, characterized in that: The location patient severity analysis includes the following specific contents: S41, obtaining the regional patient severity and regional patient growth abnormality in the analyzed area, and performing weighted summation to obtain the regional patient risk level; S42. The patient risk levels of all regions are summed up to obtain the total patient risk level, and the regional patient risk level of the corresponding region is divided by the total patient risk level to obtain a patient risk analysis value.

5. The method for managing infectious disease and endemic disease patient data based on big data according to claim 4, characterized in that: The reasonable allocation of medical resources based on the location patient severity analysis results and regional medical resource conditions includes the following specific contents: obtaining all corresponding medical resources, and allocating all medical resources to the region according to the corresponding proportion of the patient severity analysis value of the corresponding region.

6. The method for managing infectious disease and endemic disease patient data based on big data according to claim 5, characterized in that: The calculation formula of the abnormal physical value in S23 is: , where n is the number of infectious disease patients in the region, mi is the number of types of physical data, aj is the weight of the j-th physical characteristic, kij is the value of the j-th physical data of the ith patient in the region, and kjm is the median of the normal range of the j-th physical data.

7. The method for managing infectious disease and endemic disease patient data based on big data according to claim 6, characterized in that: The patient data of infectious diseases in the region in S1 includes various physical characteristic data of infectious disease patients, the distribution of infectious disease patients in the region includes location data of the area where the infectious disease is located, density data of infectious disease patients in each area, personnel flow data in each area and personnel density data in each area, and the regional environmental data includes environmental data that affects virus transmission in the next stage of each area, and is stored in a storage module with a confidentiality agreement.

8. A big data-based infectious disease and endemic disease patient data management system, which is implemented based on the big data-based infectious disease and endemic disease patient data management method according to any one of claims 1 to 7, characterized in that: It specifically includes a data acquisition module, a patient severity analysis module, a growth abnormality analysis module, a disease severity analysis module and a resource allocation module; wherein the data acquisition module is used to acquire patient data of infectious diseases in the region, and at the same time acquire the distribution of regional infectious disease patients, regional environmental data and regional medical resources; the patient severity analysis module performs regional patient severity analysis based on the distribution of regional infectious disease patients and patient data; the growth abnormality analysis module performs regional patient growth abnormality analysis based on the distribution of regional infectious disease patients and regional environmental data; the disease severity analysis module performs location patient severity analysis based on the patient severity analysis results of each location in the region and the regional patient growth abnormality analysis results; the resource allocation module reasonably allocates medical resources based on the location patient severity analysis results and regional medical resources.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the infectious disease and endemic disease patient data management method based on big data as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the infectious disease and endemic disease patient data management method based on big data as described in any one of claims 1 to 7.

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