Infectious disease early warning data processing method based on Internet platform search

Abstract

The invention provides an infectious disease early warning data processing method based on Internet platform search, and relates to the technical field of infectious disease early warning, and the method comprises the steps: collecting and preprocessing Internet user search data, extracting infectious disease feature information, and constructing an infectious disease feature database; judging a to-be-observed user through matching, determining an area where the to-be-observed user is located, and analyzing an infectious disease diffusion condition; recording statistical information of a user to be observed, integrating the statistical information into disease observation information and storing the disease observation information as historical disease information; and constructing a training set by using historical disease information, training local and neighborhood logistic regression models, adjusting a local predicted value by using a neighborhood predicted value, and integrating to obtain a common infectious disease prediction model. According to the method, epidemic situation signals can be captured before a patient sees a doctor, early warning is performed in advance, non-doctor-seeing people are covered, IP positioning and accurate monitoring are combined, a data blind area is eliminated, and monitoring comprehensiveness and representativeness are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of infectious disease early warning, and particularly to a method for processing infectious disease early warning data based on Internet platform search. Background Art

[0002] The spread and prevalence of infectious diseases are one of the major challenges faced by the global public health field. Their outbreaks often pose a serious threat to human life and health, and at the same time bring huge negative impacts on social and economic development. Timely and accurate early warning of infectious diseases is crucial for taking effective prevention and control measures and reducing the risk of epidemic spread.

[0003] With the rapid development and popularization of Internet technology, the Internet has become an important platform for people to obtain information and communicate. In terms of information related to infectious diseases, when people feel unwell or pay attention to the health status around them, they often conduct relevant searches and discussions through Internet platforms such as search engines, health forums, and social media. Behind these search behaviors, there is a large amount of information related to infectious diseases. For example, a user's search for the symptoms of a certain infectious disease may imply that they or someone around them has similar symptoms; a search for preventive measures against infectious diseases may reflect the potential risk of the spread of the infectious disease in the local area, etc.

[0004] Traditional infectious disease monitoring and early warning mainly rely on the case reporting system of medical institutions. This system collects information on diagnosed infectious disease cases in medical institutions such as hospitals and clinics, conducts statistical analysis and trend judgment to achieve the monitoring and early warning of infectious diseases.

[0005] However, this traditional method has many limitations. On the one hand, the information is lagged. From the time when a patient shows symptoms, goes to a medical institution for treatment, undergoes examinations to the final diagnosis, and then to the reporting of case information to relevant departments, the whole process takes a certain amount of time, which causes obvious delays in the acquisition and analysis of epidemic information and makes it difficult to capture the early signs of the spread of infectious diseases in a timely manner. On the other hand, the coverage is limited. Traditional monitoring mainly focuses on patients who have sought medical treatment. For those who are in the incubation period, have mild symptoms and have not sought medical treatment, or lack medical resources in remote areas, their infection situations are often difficult to be discovered and counted in a timely manner, resulting in incomplete monitoring data and unable to comprehensively reflect the true spread situation of infectious diseases. Therefore, it is necessary to provide a method for processing infectious disease early warning data based on Internet platform search to solve the above technical problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for processing infectious disease early warning data based on Internet platform search, so as to make full use of the search data on the Internet platform, early warn of the outbreak and prevalence of infectious diseases, and provide strong support for public health decision-making.

[0007] To solve the above technical problems, the present invention provides an infectious disease early warning data processing method based on Internet platform search, which includes the following steps: Collect the search data of users through the Internet platform and preprocess the search data; the preprocessing operations include removing duplicate and invalid data, desensitization processing, and standardizing the search keywords to unify synonyms and near-synonyms; Extract infectious disease characteristic information from the preprocessed search data; the infectious disease characteristic information includes the name of the infectious disease, symptom keywords, transmission route keywords, and high-incidence season keywords; Construct an infectious disease characteristic database, including the characteristic information of various common infectious diseases, and each infectious disease record contains the disease name, typical symptoms, transmission route, high-incidence season, and incubation period; Match the infectious disease characteristic information of the user with the infectious disease characteristic database to determine whether the user is searching for an infectious disease; mark the users who are determined to be searching for an infectious disease as users to be observed; Obtain the IP address of the user to be observed to determine the region where the Internet search is located, convert the IP address into geographical location information through the IP address resolution library, and determine the region; Conduct an analysis of the spread of infectious diseases in the region, combine the transmission characteristics of the infectious disease searched by the user to be observed, and identify whether there is search information for this infectious disease in the set range of the region at a delayed time to obtain the disease search value of the region; Identify the region where the region is located, and record the statistical information of all users to be observed in this region, including the growth amount and growth rate of the users to be observed; Integrate the statistical information, infectious disease characteristic information, and disease search value of the users to be observed in the region into the disease observation information corresponding to the region; store the disease observation information, and all the stored disease information is recorded as historical disease information; Use the historical disease information corresponding to the region to construct a training data set, and obtain model parameters by training a logistic regression model; use the gradient descent method to train the model parameters to minimize the loss function of the model on the training data set; At the same time, obtain the historical disease information of the regions corresponding to the neighboring regions of the region and retrain the logistic regression model of the neighboring regions; Input the disease observation information into the logistic regression models of the local area and the neighboring areas respectively to obtain the corresponding predicted infection values; use the predicted infection values of the neighboring areas to adaptively adjust and update the predicted infection values of the local area; integrate the logistic regression models of all types of infectious diseases to obtain a common infectious disease prediction model.

[0008] Preferably, the specific steps of preprocessing the search data include: Removing duplicate data: Denote all the search data of the user as a search data set , after hash encoding, a set of hash values is obtained , after removing the data with the same hash values, a new set of search data is obtained ; Removing invalid data: Define the rules for invalid data, including meaningless characters and too short length. For each search data , check whether it conforms to the invalid data rules. If it conforms, remove it from the search data set , and a data set is obtained ; Desensitization processing: Identify sensitive information in the search data, use regular expressions to match sensitive information, and replace sensitive information with specific characters; Sensitive information includes user names, ID numbers, and contact information; Keyword standardization: Establish a dictionary of synonyms and near-synonyms, and uniformly replace synonyms and near-synonyms in the search keywords with standard words; Denote the keyword as k and the dictionary as S. If k has synonyms or near-synonyms in S, then replace k with .

[0009] Preferably, extract infectious feature information from the preprocessed search data, specifically including: Use the named entity recognition algorithm to process the search data and identify the names of infectious diseases in it. Denote the search data as d, and after processing by the named entity recognition algorithm, a set of names of infectious diseases is obtained ; Construct a symptom keyword dictionary , and extract symptom keywords from the search data by string matching. For each search data d, which contains the keyword s in it, then extract it as a symptom keyword, and a set of symptom keywords is obtained ; Then construct a dictionary of transmission route keywords , and use the string matching method to extract transmission route keywords. The set of extracted transmission route keywords is ; Then extract high-incidence season keywords by keyword matching and semantic analysis methods. Set the high-incidence season keyword dictionary as , and the set of extracted high-incidence season keywords is .

[0010] Preferably, match the infectious feature information searched by the user with the infectious disease feature database. The specific matching method is as follows: Denote the user's infectious feature information as , and the feature information of an infectious disease in the infectious disease feature data is , calculate the matching degree M, and the formula is: , where , , , are weight coefficients (which can be determined through expert experience, historical data training, or machine learning algorithms and are conventional existing technologies), and + + + = 1. When the matching degree M is greater than the set search matching threshold , it is determined that the user has determined to search for an infectious disease, and the user is recorded as a user to be observed.

[0011] Preferably, an analysis of the spread of infectious diseases in the area is carried out. The specific method is as follows: Denote the neighborhood of the area R as In the time region , count the number of users searching for the infectious disease in the neighborhood and record it as the total number of disease searches. Divide the time region into several time partitions, count the number of users searching for the infectious disease in the time partition and record it as the number of disease searches sf in the partition. Calculate the disease search trend value for the number of disease searches sf in the time partition in the time region using the standard deviation formula. The standard deviation formula is: , where sf is the number of disease searches sf in each time partition f, represents the average value of the number of disease searches, and r is the number of time partitions; perform weighted processing on the total number of disease searches and the disease search trend value to obtain the disease search value; if the disease search value is greater than the set spread threshold, it is considered that there is a risk of infectious disease spread.

[0012] Preferably, the formula of the logistic regression model is: , where represents the probability of the occurrence of the infectious disease in this area under the feature vector , and w0, w1,..., wn represent model parameters.

[0013] Preferably, use the predicted infection value of the adjacent area to adaptively adjust and update the predicted infection value of the local area. Specifically: Set the adjacent area adjustment coefficient , and use the formula , where represents the original predicted infection value of the local model, is the predicted infection value of the adjacent area model; Use the predicted infection value of the adjacent area combined with the adjacent area adjustment coefficient to adaptively adjust and update the predicted infection value of the local area to obtain .

[0014] Preferably, after generating the local predicted infection value, a plurality of color mapping intervals are set, and the local predicted infection value is matched with the color mapping intervals to obtain the mapping color corresponding to the region; And a two-dimensional display image is established. The two-dimensional display image takes the geographical area as the horizontal axis and the time as the vertical axis, and displays the mapping colors matched by the infectious disease prediction values of each region at different times at the corresponding positions of the two-dimensional display image, forming an intuitive spatio-temporal distribution map of infectious disease risks.

[0015] Preferably, it further includes regularly updating the two-dimensional display image; obtaining the new local predicted infection value according to the set update interval, and timely adjusting the mapping color display of each region in the two-dimensional display image according to the new local predicted infection value.

[0016] Compared with the related technology, the infectious disease early warning data processing method provided by the present invention based on the search of the Internet platform has the following beneficial effects: 1. By collecting and analyzing the search data of Internet platform users in real time, the present invention can capture potential epidemic signals before patients seek medical treatment. Compared with the traditional passive monitoring mode that relies on medical institutions to report, this method significantly advances the early warning time, especially suitable for early risk identification in the incubation period or the initial stage of symptoms, and strives for a key window period for prevention and control decisions; at the same time, relying on the universality of Internet search data, it can cover the population who has not sought medical treatment, break through the dependence on medical resources in traditional monitoring, and combine IP address resolution technology to accurately locate potential epidemic regions, achieve full-domain dynamic monitoring, eliminate the data blind spots of traditional methods, and improve the comprehensiveness and representativeness of monitoring results.

[0017] 2. Based on the standardized matching algorithm of multi-dimensional infection characteristic information, combined with the logistic regression model and the neighborhood data collaborative training mechanism, the present invention effectively integrates the local and neighborhood epidemic trends, dynamically adjusts the predicted infection value, quantifies the regional transmission situation through the calculation of disease search values and the analysis of diffusion risks, reduces the deviation of a single data source, and significantly improves the scientificity and reliability of early warning results.

[0018] 3. Through the spatio-temporal distribution map of infectious disease risks, the present invention maps the predicted infection value into an intuitive color identifier, displays the spatio-temporal evolution trend of infectious disease risks in real time, and combines the regular update mechanism to provide a dynamic visual decision-making basis for public health departments, helping to accurately allocate resources and implement prevention and control measures. Brief Description of the Drawings

[0019] Figure 1 It is a flow chart of the infectious disease early warning data processing method provided by the present invention based on the search of the Internet platform. Detailed Embodiments

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "group", "class" and "the" used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0022] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0023] Please refer to Figure 1 . An infectious disease early warning data processing method based on Internet platform search, comprising the following steps: Collect user search data through the Internet platform and preprocess the search data; the preprocessing operations include removing duplicate and invalid data, desensitization processing, and standardizing the search keywords to unify synonyms and near-synonyms; it should be noted that the Internet platform specifically includes but is not limited to search engine websites, health forums, social media, etc.; Extract infectious disease characteristic information from the preprocessed search data; the infectious disease characteristic information includes the name of the infectious disease, symptom keywords (such as fever, cough, diarrhea, etc. specifically), transmission route keywords (such as airborne transmission, contact transmission, etc. specifically), and high-incidence season keywords; Construct an infectious disease characteristic database, including the characteristic information of various common infectious diseases, and each infectious disease record contains the disease name, typical symptoms, transmission route, high-incidence season, and incubation period; Match the user's infectious disease characteristic information with the infectious disease characteristic database to determine whether the user is searching for an infectious disease; mark the users who are determined to be searching for an infectious disease as users to be observed; Obtain the IP address of the user to be observed to determine the region where the Internet search is located, convert the IP address into geographical location information through an IP address resolution library, and determine the region R; Conduct an analysis of the spread of infectious diseases in the region R, and combine the transmission characteristics of the infectious diseases searched by the users to be observed to identify whether there is search information for the infectious disease within the set range of the region at a delayed time to obtain the disease search value of the region; Identify the location of the region, and record the statistical information of all users to be observed in this region, including the growth amount of the users to be observed (the growth amount is the difference in the number of users to be observed between adjacent statistical periods) and the growth rate (the growth amount is the ratio to the number of users to be observed in the previous period); Integrate the statistical information, infectious characteristic information, and disease search value of the users to be observed in the region into the disease observation information corresponding to the region; store the disease observation information, and record all the stored disease information as historical disease information; Use the historical disease information corresponding to the region to construct a training data set, and obtain model parameters by training a logistic regression model; use the gradient descent method to train the model parameters to minimize the loss function of the model on the training data set. The loss function formula is ; where m is the number of samples, yi is the actual label, and pi is the predicted probability; At the same time, obtain the historical disease information of the regions corresponding to the adjacent regions of the region and retrain the logistic regression model of the adjacent regions; Input the disease observation information into the logistic regression models of the local area and the adjacent areas respectively to obtain the corresponding predicted infection values; use the predicted infection values of the adjacent areas to adaptively adjust and update the predicted infection values of the local area; integrate the logistic regression models of all types of infectious diseases to obtain a common infectious disease prediction model.

[0024] In this application, the specific steps for preprocessing the search data include: Remove duplicate data: Denote all the search data of the users as a search data set and obtain a hash value set after hash encoding . Remove the data with the same hash value to obtain a new search data set ; Remove invalid data: Define the rules for invalid data, including meaningless characters, too short length, etc. For each search data , check whether it conforms to the invalid data rules. If it conforms, remove it from the search data set to obtain a data set ; Desensitization processing: Identify the sensitive information in the search data, use regular expressions to match the sensitive information, and replace the sensitive information with specific characters; the sensitive information includes user names, ID numbers, contact information, etc.; Keyword standardization: Establish a dictionary of synonyms and near-synonyms, and uniformly replace the synonyms and near-synonyms in the search keywords with standard words; Denote the keyword as k and the dictionary as S. If k has synonyms or near-synonyms in S, then replace k with .

[0025] In this application, extract infectious feature information from the preprocessed search data, specifically including: Use the named entity recognition (NER) algorithm (such as the BERT - BiLSTM - CRF model based on deep learning) to process the search data and identify the names of infectious diseases. Denote the search data as d, and obtain the set of infectious disease names after processing by the NER algorithm ; Construct a symptom keyword dictionary , and extract symptom keywords from the search data by string matching. For each search data d, if it contains the keyword s in, then extract it as a symptom keyword to obtain the set of symptom keywords ; Then construct a transmission route keyword dictionary , and use the string matching method to extract transmission route keywords. The set of extracted transmission route keywords is ; Then extract high-incidence season keywords by keyword matching and semantic analysis methods. Set the high-incidence season keyword dictionary as , and the set of extracted high-incidence season keywords is .

[0026] In this application, match the infectious feature information searched by the user with the infectious disease feature database. The specific matching method is as follows: Denote the user's infectious feature information as , and the feature information of an infectious disease in the infectious disease feature data is . Calculate the matching degree M, and the formula is: , where , , , are weight coefficients (the weight coefficients can be determined by expert experience, historical data training or machine learning algorithms, which are conventional existing technologies), and + + + = 1. When the matching degree M is greater than the set search matching threshold , it is determined that the user is searching for an infectious disease, and the user is denoted as a user to be observed.

[0027] In this application, the analysis of the spread of infectious diseases in the region R is carried out as follows: Denote the neighborhood of the region R as In the time region Count the number of users searching for the infectious disease in the neighborhood and denote it as the total number of disease searches. Divide the time region into several time partitions, and count the number of users searching for the infectious disease in the time partition and denote it as the number of disease searches sf in the partition. Calculate the disease search trend value for the number of disease searches sf in the time partition in the time region using the standard deviation formula. The standard deviation formula is: where sf is the number of disease searches sf in each time partition f, represents the average value of the number of disease searches, and r is the number of time partitions; perform weighted processing on the total number of disease searches and the disease search trend value to obtain the disease search value; if the disease search value is greater than the set spread threshold, it is considered that there is a risk of spread of the infectious disease.

[0028] In this application, the formula of the logistic regression model is: where represents the probability of the occurrence of the infectious disease in this region under the feature vector , and w0, w1,..., wn represent the model parameters.

[0029] In this application, use the predicted infection value of the adjacent area to adaptively adjust and update the predicted infection value of the local area, specifically: Set the adjacent area adjustment coefficient , and use the formula where represents the original predicted infection value of the local model, is the predicted infection value of the adjacent area model; Use the predicted infection value of the adjacent area combined with the adjacent area adjustment coefficient to adaptively adjust and update the predicted infection value of the local area to obtain .

[0030] In this application, after generating the predicted infection value of the local area, set several color mapping intervals, and match the predicted infection value of the local area with the color mapping intervals to obtain the corresponding mapping color of the region; And establish a two-dimensional display image. The two-dimensional display image takes the geographical area as the horizontal axis and time as the vertical axis, and displays the mapping colors corresponding to the predicted infection values of each region at different times at the corresponding positions in the two-dimensional display image to form an intuitive spatio-temporal distribution map of infectious disease risks.

[0031] In this application, it also includes regularly updating the two-dimensional display image; obtaining the new predicted infection value of the local area according to the set update interval, and timely adjusting the mapping color display of each region in the two-dimensional display image according to the new predicted infection value of the local area.

[0032] Other embodiments of the present invention will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following the general principles of the invention and including known common general knowledge or conventional technical means in the technical field not disclosed in this disclosure. The specification and examples are only regarded as exemplary, and the true scope and spirit of the invention are pointed out by the following claims.

[0033] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A method for processing infectious disease early warning data based on Internet platform search, characterized in that: The following steps are involved: Collect users' search data through the Internet platform and pre-process the search data; the pre-processing operations include removing duplicates, invalid data, desensitizing, and standardizing search keywords, unifying synonyms and near synonyms; Extracting infectious feature information from the preprocessed search data; Infectious characteristic information includes the name of the disease, symptom keywords, transmission route keywords, and high-incidence season keywords; Construct an infectious disease characteristic database, including characteristic information of a variety of common infectious diseases. Each infectious disease record includes the disease name, typical symptoms, transmission route, high-incidence season, and incubation period; Matching the user's infectious characteristic information with the infectious disease characteristic database to determine whether the user is sure to search for infectious diseases; recording the user who is sure to search for infectious diseases as a user to be observed; Obtain the IP address of the user to be observed to determine the location of the Internet search, and convert the IP address into geographic location information through the IP address resolution library to determine the location; Conduct infectious disease diffusion analysis in the area, combine the propagation characteristics of the infectious disease searched by the user to be observed, identify whether there is search information for the infectious disease within the set range of the area during the delay time, so as to obtain the disease search value of the area; Identify the region where the area is located, and record the statistical information of all the users to be observed in the region, including the growth amount and growth rate of the users to be observed; Integrate the statistical information, infection characteristic information, and disease examination values ​​of the users to be observed in the area into the symptom observation information corresponding to the area; store the symptom observation information, and record all the stored symptom information as historical symptom information; The historical disease information corresponding to the region is used to construct a training data set, and the model parameters are obtained by training the logistic regression model. The model parameters are trained using the gradient descent method to minimize the loss function of the model on the training data set. At the same time, the historical disease information of the area corresponding to the area's neighborhood is obtained to train the logistic regression model of the neighborhood; The symptom observation information was input into the local and neighboring logistic regression models to obtain the corresponding predicted infection values; Use the predicted infection values ​​of neighboring areas to adapt and update the local predicted infection values; integrate the logistic regression models of all types of infectious diseases to obtain a common infectious disease prediction model.

2. The infectious disease early warning data processing method based on Internet platform search according to claim 1 is characterized in that: The specific steps of preprocessing the search data include: Remove duplicate data: record all the user's search data as a search data set , after hash encoding, we get a set of hash values , remove the data with the same hash value to get a new search data set ; Remove invalid data: Define rules for invalid data, including meaningless characters and too short length. , check whether it meets the invalid data rules, and if so, remove it from the search data set Remove from the data set ; Desensitization: Identify sensitive information in search data, use regular expressions to match sensitive information, and replace sensitive information with specific characters; sensitive information includes user name, ID number, and contact information; Keyword standardization: Establish a synonym and antonym dictionary, and replace the synonyms and antonyms in the search keyword with standard words; record the keyword as k and the dictionary as S. If k has a synonym or antonym S, then replace k with .

3. The infectious disease early warning data processing method based on Internet platform search according to claim 1 is characterized in that: Extract infection feature information from the preprocessed search data, including: Use the named entity recognition algorithm to process the search data and identify the names of the diseases. The search data is recorded as d. After being processed by the named entity recognition algorithm, the set of disease names is obtained. ; Building a symptom keyword dictionary , extract symptom keywords from the search data by string matching. For each search data d, it contains The keyword s in will be extracted as symptom keywords to obtain the symptom keyword set ; Reconstructing the keyword dictionary of communication channels , the string matching method is used to extract the keywords of the propagation path, and the extracted keywords of the propagation path are set as ; Then, the high-incidence season keywords are extracted through keyword matching and semantic analysis, and the high-incidence season keyword dictionary is set as , the extracted high-incidence season keyword set is .

4. The infectious disease early warning data processing method based on Internet platform search according to claim 3 is characterized in that: The infectious characteristic information searched by the user is matched with the infectious disease characteristic database. The specific matching method is as follows: The user's infection characteristic information is recorded as , the characteristic information of an infectious disease in the infectious disease characteristic data is , calculate the matching degree M, the formula is: ,in , , , is a weight coefficient (which can be determined by expert experience, historical data training or machine learning algorithm, which is a conventional prior art), and + + + =1, when the matching degree M is greater than the set search matching threshold When the user is determined to search for infectious diseases, the user is recorded as a user to be observed.

5. The infectious disease early warning data processing method based on Internet platform search according to claim 1 is characterized in that: Analyze the spread of infectious diseases in the area. The specific methods are as follows: The neighborhood of region R is denoted as , in the time zone The number of users searching for the infectious disease in the neighborhood is recorded as the total number of disease checks. The time zone is divided into several time partitions, and the number of users searching for the infectious disease in the time partition is recorded as the number of district disease checks sf. The district disease checks of the time partitions in the time zone are calculated using the standard deviation formula to obtain the disease check trend value. The standard deviation formula is: , where sf is the number of district disease checks in each time partition f, It represents the average number of disease examinations in the district, and r is the number of time zones. The total number of disease examinations and the disease examination trend value are weighted to obtain the disease examination value. If the disease examination value is greater than the set diffusion threshold, it is considered that the infectious disease has a risk of spreading.

6. The infectious disease early warning data processing method based on Internet platform search according to claim 1 is characterized in that: The formula for the logistic regression model is: ,in Represented in the feature vector The probability of the occurrence of this infectious disease in the area, w0, w1, ..., wn represent the model parameters.

7. The infectious disease early warning data processing method based on Internet platform search according to claim 1 is characterized in that: Use the predicted infection value of the neighboring area to adjust and update the local predicted infection value, specifically: Setting the Neighborhood Adjustment Factor , using the formula ,in represents the predicted infection value of the local model, Predicting infection values ​​for neighborhood models; Use the predicted infection value of the neighboring area combined with the neighboring area adjustment coefficient Adaptively adjust and update the local predicted infection value .

8. The infectious disease early warning data processing method based on Internet platform search according to claim 1 is characterized in that: After generating the local predicted infection value, several color mapping intervals are set, and the local predicted infection value is matched with the color mapping interval to obtain the mapping color corresponding to the region; A two-dimensional display image is established, which takes the geographical area as the horizontal axis and time as the vertical axis. The mapping colors matching the infectious disease prediction values ​​of various regions at different times are displayed at the corresponding positions of the two-dimensional display image, forming an intuitive spatiotemporal distribution map of infectious disease risks.

9. The infectious disease early warning data processing method based on Internet platform search according to claim 8, characterized in that: It also includes regularly updating the two-dimensional display image; obtaining a new local predicted infection value according to a set update interval, and timely adjusting the mapping color display of each area in the two-dimensional display image according to the new local predicted infection value.

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