Method and device for generating and warning of infectious disease transmission path based on big data

Abstract

Embodiments of the present disclosure disclose a method and apparatus for generating an infectious disease transmission path and early warning based on big data. A specific implementation of the method includes: generating a set of confirmed cases and a set of contact groups based on urban infectious disease data; determining the activity point sequence of each confirmed case to obtain a set of confirmed case activity point sequences; clustering each confirmed case activity point sequence to obtain a set of high-risk areas; determining urban personnel who meet the preset high-risk population conditions as high-risk personnel to obtain a set of high-risk personnel; determining the associated nodes corresponding to each high-risk area to obtain a set of high-risk associated nodes; generating an infectious disease transmission map; generating an infectious disease transmission path set based on the infectious disease transmission map, and generating an infectious disease transmission heat map based on the infectious disease transmission path set. This implementation can trace the infectious disease transmission route, reduce the risk of infectious disease spread and social prevention and control costs.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and particularly to a method and apparatus for generating and warning of the transmission path of infectious diseases based on big data. Background Art

[0002] Tracking the transmission path of infectious diseases is a key link in the prevention and control of infectious diseases. At present, when generating the transmission path of infectious diseases, the commonly used methods are: manual collection and analysis method and infectious disease prediction system. Among them, the above-mentioned manual collection and analysis method relies on manual data collection, and then analyzes the collected data based on epidemiology to obtain the transmission path; the above-mentioned infectious disease prediction system conducts path analysis through automated data processing.

[0003] However, when generating the transmission path of infectious diseases by the above methods, the following technical problems often exist:

[0004] First, the prior art often ignores the important impact of personnel flow on the transmission of infectious diseases, and lacks in-depth analysis of the transmission of infectious diseases, making it difficult to accurately track the transmission route of infectious diseases, thereby increasing the risk of infectious disease spread and the social prevention and control cost;

[0005] Second, the manual collection and analysis method is limited by the timeliness of data collection, and it is difficult to issue warning signals in a timely manner according to the development trend of infectious diseases, resulting in lagging prevention and control measures and being unable to prevent the spread of infectious diseases in time.

[0006] The above information disclosed in this background art section is only used to enhance the understanding of the background of the concept of the present disclosure, and thus, it may include information that does not form the prior art known to those of ordinary skill in the art in this country. Summary of the Invention

[0007] The content part of the present disclosure is used to briefly introduce concepts, which will be described in detail in the subsequent detailed implementation part. The content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0008] Some embodiments of the present disclosure propose a method and apparatus for generating the transmission path of infectious diseases based on big data to solve one or more of the technical problems mentioned in the above background art section.

[0009] In a first aspect, some embodiments of the present disclosure provide a method for generating an infectious disease transmission path based on big data. The method includes: generating a set of confirmed cases and a set of contact groups based on urban infectious disease data; determining the activity point sequence of each confirmed case in the set of confirmed cases to obtain a set of confirmed case activity point sequences; clustering the confirmed case activity point sequences in the set of confirmed case activity point sequences to obtain a set of high-risk areas; determining, based on the set of high-risk areas, urban personnel who meet the preset high-risk population conditions as high-risk personnel to obtain a set of high-risk personnel; determining the associated nodes corresponding to each high-risk area in the set of high-risk areas to obtain a set of high-risk associated nodes; generating an infectious disease transmission map based on the set of confirmed cases, the set of confirmed case activity point sequences, the set of high-risk areas, the set of high-risk personnel, and the set of high-risk associated nodes; generating a set of infectious disease transmission paths based on the infectious disease transmission map, and generating an infectious disease transmission heat map based on the set of infectious disease transmission paths; superimposing the infectious disease transmission heat map onto the corresponding urban map to obtain an urban infectious disease transmission heat map, and displaying the urban infectious disease transmission heat map.

[0010] In a second aspect, some embodiments of the present disclosure provide a device for generating an infectious disease transmission path based on big data. The device includes: a first generation unit configured to generate a set of confirmed cases and a set of contact groups based on urban infectious disease data; a first determination unit configured to determine the activity point sequence of each confirmed case in the set of confirmed cases to obtain a set of confirmed case activity point sequences; a clustering unit configured to cluster the confirmed case activity point sequences in the set of confirmed case activity point sequences to obtain a set of high-risk areas; a second determination unit configured to determine, based on the set of high-risk areas, urban personnel who meet the preset high-risk population conditions as high-risk personnel to obtain a set of high-risk personnel; a third determination unit configured to determine the associated nodes corresponding to each high-risk area in the set of high-risk areas to obtain a set of high-risk associated nodes; a second generation unit configured to generate an infectious disease transmission map based on the set of confirmed cases, the set of confirmed case activity point sequences, the set of high-risk areas, the set of high-risk personnel, and the set of high-risk associated nodes; a third generation unit configured to generate a set of infectious disease transmission paths based on the infectious disease transmission map, and generate an infectious disease transmission heat map based on the set of infectious disease transmission paths; a superimposing and displaying unit configured to superimpose the infectious disease transmission heat map onto the corresponding urban map to obtain an urban infectious disease transmission heat map, and display the urban infectious disease transmission heat map.

[0011] In a third aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; a storage device storing one or more programs thereon, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation manner of the first aspect above.

[0012] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium storing a computer program thereon, wherein the program, when executed by a processor, implements the method described in any implementation manner of the first aspect above.

[0013] The above embodiments of the present disclosure have the following beneficial effects: Through the big data-based infectious disease transmission path generation method of some embodiments of the present disclosure, the risk of infectious disease spread and the social prevention and control costs can be reduced. Specifically, the reasons for the increase in the risk of infectious disease spread and social prevention and control costs are as follows: The prior art often ignores the important impact of personnel flow on the spread of infectious diseases, and lacks in-depth analysis of infectious disease transmission, making it difficult to accurately track the transmission route of infectious diseases. Based on this, in the big data-based infectious disease transmission path generation method of some embodiments of the present disclosure, first, according to the urban infectious disease data, a set of confirmed cases and a set of contact groups are generated. By systematically collecting data and structurally storing the information of confirmed cases and contacts, the initial transmission source can be quickly locked, reducing the risk of invisible transmission. Secondly, determine the activity point sequence of each confirmed case in the above set of confirmed cases to obtain a set of confirmed case activity point sequences. Then, cluster each confirmed case activity point sequence in the above set of confirmed case activity point sequences to obtain a set of high-risk areas. Thus, high-risk areas can be dynamically determined, the effectiveness of lockdown can be improved, and precise prevention and control of infectious diseases can be achieved. Then, according to the above set of high-risk areas, urban personnel meeting the preset high-risk population conditions are determined as high-risk personnel to obtain a set of high-risk personnel. Thus, high-risk populations can be accurately determined to implement timely observation measures, blocking the extension of the transmission chain, and at the same time avoiding the social costs brought by full-scale nucleic acid testing and comprehensive lockdown. After that, determine the associated nodes corresponding to each high-risk area in the above set of high-risk areas to obtain a set of high-risk associated nodes. Thus, the next transmission node of the infectious disease can be determined, and targeted prevention and control of the associated nodes can be strengthened. Then, according to the above set of confirmed cases, the above set of confirmed case activity point sequences, the above set of high-risk areas, the above set of high-risk personnel, and the above set of high-risk associated nodes, an infectious disease transmission map is generated. By storing the multi-dimensional relationships in the infectious disease transmission process in a graph structure, the construction of the transmission network can be realized to assist in the decision-making of infectious disease prevention and control. Then, according to the above infectious disease transmission map, a set of infectious disease transmission paths is generated, and an infectious disease transmission heat map is generated according to the above set of infectious disease transmission paths. Finally, the infectious disease transmission heat map is superimposed on the corresponding urban map to obtain an urban infectious disease transmission heat map, and the urban infectious disease transmission heat map is displayed. By accurately generating the infectious disease transmission path and achieving transmission blocking with the smallest control unit, the utilization rate of prevention and control resources can be improved, while reducing the risk of infectious disease spread and social prevention and control costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements and elements are not necessarily drawn to scale.

[0015] Figure 1 is a flowchart of some embodiments of a method for generating an infectious disease transmission route based on big data according to the present disclosure;

[0016] Figure 2 is a schematic structural diagram of some embodiments of an apparatus for generating an infectious disease transmission route based on big data according to the present disclosure;

[0017] Figure 3 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Embodiments

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0019] In addition, it should be noted that for the sake of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0020] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence relationship of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] For operations such as collection, storage, and use of user personal information (such as user portraits and user historical behaviors) involved in the present disclosure, before performing the corresponding operations, relevant organizations or individuals shall fulfill obligations including conducting personal information security impact assessments, fulfilling the obligation of informing the personal information subject, and obtaining the prior authorization and consent of the personal information subject.

[0024] The present disclosure will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0025] Figure 1 Flow 100 is shown, which illustrates some embodiments of the method for generating an infectious disease transmission path based on big data according to the present disclosure. The method for generating an infectious disease transmission path based on big data includes the following steps:

[0026] Step 101: Generate a set of confirmed cases and a set of contact groups based on urban infectious disease data.

[0027] In some embodiments, the execution entity of the method for generating an infectious disease transmission path based on big data may generate a set of confirmed cases and a set of contact groups based on urban infectious disease data. Among them, the above urban infectious disease data is data on confirmed cases in the city.

[0028] In some optional implementation manners of some embodiments, the above execution entity generating a set of confirmed cases and a set of contact groups based on urban infectious disease data may include the following steps:

[0029] The first step: Obtain urban infectious disease data. Among them, the above urban infectious disease data includes social media case information, hospital case information, and community screening case information. In practice, first, social media case information can be obtained by crawling social media content. Second, hospital case information can be obtained through the hospital's Internet system. After that, community screening case information can be obtained through the screening information reported by the community. The above social media case information, hospital case information, and community screening case information may include information such as name, identity information, symptoms, body temperature, data acquisition time, data source, etc. The above identity information may be the unique identification code corresponding to the case information. The above data source may include from social media, from the hospital, from the community, etc.

[0030] The second step: Perform data standardization processing on the social media case information, hospital case information, and community screening case information included in the above urban infectious disease data to obtain standard media case information, standard hospital case information, and standard community case information. In practice, first, data cleaning operations can be performed on the above social media case information, hospital case information, and community screening case information respectively through preset data cleaning operations to obtain cleaned social media case information, cleaned hospital case information, and cleaned community screening case information. Second, the elements in the above cleaned social media case information, cleaned hospital case information, and cleaned community screening case information can be standardized to obtain standard media case information, standard hospital case information, and standard community case information. The above standardization processing refers to unifying the data format within the same feature dimension. For example, convert the identity information into the corresponding identity hash code; unify the time information such as the data acquisition time into the UTC time format.

[0031] As an example, the above-mentioned preset data cleaning operations may include, but are not limited to, at least one of the following: missing value filling, outlier modification, duplicate value deletion, etc.

[0032] In the third step, integrate the above-mentioned standard media case information, the above-mentioned standard hospital case information, and the above-mentioned standard community case information to obtain valid case information. Among them, the above-mentioned valid case information may be the union of all elements in the above-mentioned standard media case information, the above-mentioned standard hospital case information, and the above-mentioned standard community case information.

[0033] In the fourth step, generate a set of confirmed individuals based on the above-mentioned valid case information. In practice, the identity hash code of each element in the above-mentioned valid case information can be used to represent the confirmed individuals to obtain the set of confirmed individuals.

[0034] In the fifth step, determine each contact person contacted by each confirmed individual in the above-mentioned set of confirmed individuals to generate a contact person group, and obtain a set of contact person groups. In practice, the contact person group corresponding to the confirmed individual can be determined by the confirmed individual reporting the contact persons in the recent period. Among them, the above-mentioned recent period can be one week. Each confirmed individual in the above-mentioned set of confirmed individuals can correspond to multiple contact persons, each contact person corresponds to a contact location, and the above-mentioned multiple contact persons can form a contact person group. Each contact person group in the above-mentioned set of contact person groups can be composed of the identity hash codes of each contact person.

[0035] Optionally, the above-mentioned execution entity integrates the above-mentioned standard media case information, the above-mentioned standard hospital case information, and the above-mentioned standard community case information to obtain valid case information, which may include the following steps:

[0036] Step 1: Perform field matching on the above-mentioned standard media case information, the above-mentioned standard hospital case information, and the above-mentioned standard community case information to generate a set of similar case information groups and a set of unique case information. In practice, first, information such as the name, identity hash code, and mobile phone number of each element in the above-mentioned standard media case information, the above-mentioned standard hospital case information, and the above-mentioned standard community case information can be used as primary key information to obtain a set of primary key information. Each primary key information in the above-mentioned set of primary key information can be used to uniquely identify a case information. Secondly, through a preset field matching algorithm, field matching can be performed on each primary key information in the above-mentioned set of primary key information to obtain a set of similar primary key information groups and a set of unique primary key information. The similarity between each unique primary key information in the above-mentioned set of unique primary key information and each other primary key information approaches 0. After that, each case information corresponding to each similar primary key information group in the above-mentioned set of similar primary key information groups can be determined as a similar case information group to obtain a set of similar case information groups. At the same time, the case information corresponding to each unique primary key information in the above-mentioned set of unique primary key information can be determined as unique case information to obtain a set of unique case information.

[0037] As an example, the above-mentioned field matching algorithm may include, but is not limited to, at least one of the following: Jaccard Similarity Coefficient, Hamming Distance, etc. The above-mentioned field matching algorithm can determine multiple primary key information with a similarity greater than a preset similarity threshold in the above-mentioned set of primary key information as a set of similar primary key information. The above-mentioned similarity threshold is a numerical value, which can be 0.85 and is not specifically limited here.

[0038] Step 2: Perform conflict merging on each similar case information group in the above-mentioned set of similar case information groups to generate merged case information and obtain a set of merged case information. In practice, for each similar case information group in the above-mentioned set of similar case information groups, the similar case information with the most recent data acquisition time corresponding to the above-mentioned similar case information group can be determined as the merged case information; alternatively, the similar case information corresponding to the hospital case information in the above-mentioned similar case information group can be determined as the merged case information to obtain a set of merged case information.

[0039] Step 3: Determine the above-mentioned set of merged case information and the above-mentioned set of unique case information as valid case information. In practice, each merged case information in the above-mentioned set of merged case information and each unique case information in the above-mentioned set of unique case information can be jointly determined as valid case information.

[0040] Step 102: Determine the activity point sequence of each confirmed person in the set of confirmed persons to obtain a set of confirmed person activity point sequences.

[0041] In some embodiments, the above-mentioned execution entity may determine the activity point sequence of each confirmed case in the above-mentioned set of confirmed cases to obtain a set of activity point sequences of confirmed cases.

[0042] In some optional implementation manners of some embodiments, the above-mentioned execution entity determines the activity point sequence of each confirmed case in the above-mentioned set of confirmed cases to obtain a set of activity point sequences of confirmed cases, which may include the following steps:

[0043] First step, based on the base station positioning technology, determine the original trajectory point sequence of each confirmed case in the above-mentioned set of confirmed cases to obtain a set of original trajectory point sequences. Among them, each original trajectory point sequence in the above-mentioned set of original trajectory point sequences may represent the movement trajectory of the corresponding confirmed case in the past period of time. The above-mentioned past period of time may be 7 days. In practice, for each confirmed case in the above-mentioned set of confirmed cases, first, the identity hash code or mobile phone number corresponding to the confirmed case may be determined. After that, the base station connection record corresponding to the above-mentioned identity hash code or mobile phone number may be obtained. The above-mentioned base station connection record may be obtained from each operator. The above-mentioned base station connection record may include the base station connection information of the above-mentioned confirmed case in the past period of time. The above-mentioned base station connection information may include connection timestamps, base station numbers, and signal strengths. Here, one connection timestamp may correspond to at least one base station number and signal strength. Then, for each base station number and signal strength at the same connection timestamp, the original trajectory points corresponding to the above-mentioned base station numbers and signal strengths may be determined through multi-base station collaborative positioning technology. The above-mentioned original trajectory points may be coordinates. Then, the determined original trajectory points may be arranged in chronological order to obtain an original trajectory point sequence. Finally, each obtained original trajectory point sequence is determined as a set of original trajectory point sequences.

[0044] As an example, the above-mentioned multi-base station collaborative positioning technology may include, but is not limited to, at least one of the following: triangulation method, fingerprint positioning method, etc.

[0045] Second step, perform trajectory interpolation optimization on each original trajectory point sequence in the above-mentioned set of original trajectory point sequences to generate a complemented trajectory point sequence, and obtain a set of complemented trajectory point sequences. Among them, through a preset linear interpolation algorithm (Linear Interpolation), perform trajectory interpolation optimization on each original trajectory point sequence in the above-mentioned set of original trajectory point sequences to generate a complemented trajectory point sequence, and obtain a set of complemented trajectory point sequences.

[0046] In practice, during the gap of base station handover, signal loss may occur, resulting in missing trajectory points in the original trajectory point sequence. The above-mentioned steps may complement the trajectory points of the confirmed case to improve the effectiveness of the trajectory point sequence.

[0047] Step 3: Denoise each of the above-completed trajectory point sequences in the completed trajectory point sequence set to generate a trajectory point sequence, thereby obtaining a trajectory point sequence set. Specifically, a preset denoising algorithm can be used to denoise each of the above-completed trajectory point sequences in the completed trajectory point sequence set to generate a trajectory point sequence, thereby obtaining a trajectory point sequence set.

[0048] As an example, the above preset denoising algorithm can include but is not limited to at least one of the following: Wavelet Denoising, Kalman Filter, etc.

[0049] Step 4: Determine the confirmed person's activity point sequence corresponding to each trajectory point sequence in the above trajectory point sequence set, thereby obtaining a confirmed person's activity point sequence set. Each confirmed person's activity point sequence in the above confirmed person's activity point sequence set includes a set of stay points and a set of transportation means. In practice, for each trajectory point sequence in the above trajectory point sequence set, first, by means of a sliding window, it can be determined whether all trajectory points within each sliding window are within a preset position range, and the centroid of each trajectory point that meets the conditions is determined as a stay point, thereby obtaining a set of stay points. Here, each trajectory point that does not meet the conditions can be determined as a journey point, thereby obtaining a set of journey points. The above sliding window can be a fixed time period, such as 30 minutes. The step size of the above sliding window can be a time period less than the above fixed time period, such as 10 minutes. The above preset position range can be a circular range with a radius of 500 meters, which is not specifically limited here. The coordinates of each stay point in the above set of stay points can be the coordinates of the centroid of its corresponding trajectory points. The stay points in the above set of stay points can be arranged in chronological order. Then, it can be determined the journey points between any two adjacent stay points in the above set of stay points, and according to a preset mapping table of moving speed and transportation means type, determine the transportation means corresponding to each of the above journey points, thereby obtaining a set of transportation means. The above mapping table of moving speed and transportation means type can be obtained through the urban public transportation system and travel software. Finally, the elements in the above set of stay points and the above set of transportation means can be sorted in chronological order to generate a confirmed person's activity point sequence, thereby obtaining a confirmed person's activity point sequence set.

[0050] Step 103: Cluster each of the confirmed person's activity point sequences in the confirmed person's activity point sequence set to obtain a set of high-risk areas.

[0051] In some embodiments, the above execution subject can cluster each of the confirmed person's activity point sequences in the confirmed person's activity point sequence set to obtain a set of high-risk areas.

[0052] In some alternative implementations of some embodiments, the above-mentioned execution entity clusters each confirmed person's activity point sequence in the above-mentioned confirmed person's activity point sequence set to obtain a high-risk area set, which may include the following steps:

[0053] First, for each confirmed person's activity point sequence in the above-mentioned confirmed person's activity point sequence set, each stop point corresponding to the same time is determined as a target time stop point to obtain a target time stop point set, so as to generate each target time stop point set. Among them, the above-mentioned same time may be a time period, and the above-mentioned time period may be the same as the step size of the above-mentioned sliding window. The above-mentioned time period may be 10 minutes or 30 minutes, and no specific limitation is made here. For example, the stop points of each confirmed person between 2025-07-01T10:00:00Z and 2025-07-01T10:10:00Z can be determined as target time stop points to obtain a target time stop point set. One time period corresponds to one target time stop point set.

[0054] Second, for each target time stop point set in each target time stop point set, the following steps are executed to generate a target time high-risk area set, so as to obtain each target time high-risk area set:

[0055] The first sub-step is to cluster each target time stop point in the above-mentioned target time stop point set to obtain a clustering area set. Among them, each clustering area in the above-mentioned clustering area set may contain multiple target time stop points. Here, the coordinates of the centroid between each target time stop point can be used to represent the clustering area. In practice, a preset clustering algorithm can be used to cluster each target time stop point in the above-mentioned target time stop point set to obtain a clustering area set.

[0056] As an example, the above-mentioned clustering algorithm may include but is not limited to at least one of the following: Ordering Points To Identify the Clustering Structure (OPTICS), Hierarchical DBSCAN, etc.

[0057] The second sub-step is to determine the number of target time stop points included in each clustering area in the above-mentioned clustering area set as the number of confirmed cases in the area to obtain a set of the number of confirmed cases in the area.

[0058] The third sub-step is to determine the number of contacts corresponding to each clustering area in the above-mentioned concentrated clustering areas as the regional contact number, and obtain a set of regional contact numbers. In practice, the number of contacts corresponding to each clustering area can be determined according to each contact person and contact location corresponding to each contact person group in the above-mentioned contact person group set, and a set of regional contact numbers can be obtained.

[0059] The fourth sub-step is to determine each clustering area that meets the preset high-risk conditions in the above-mentioned concentrated clustering areas as high-risk areas at the target time according to the above-mentioned set of regional confirmed cases and the above-mentioned set of regional contact numbers, and obtain a set of high-risk areas at the target time. Among them, the above-mentioned high-risk condition may be that the density of confirmed cases and contact persons in the clustering area exceeds a preset density value. The above-mentioned density value may be the ratio between the number of people and the area. For example, the above-mentioned high-risk condition may be that there are more than 18 confirmed cases and contact persons within 1 square kilometer.

[0060] The third step is to determine the above-mentioned sets of high-risk areas at each target time as a set of high-risk areas. Among them, each high-risk area in the above-mentioned set of high-risk areas can be characterized by the coordinates of its centroid, and each high-risk area corresponds to information such as coordinates, the number of people gathered, and the time window.

[0061] Step 104: Determine the urban personnel who meet the preset high-risk population conditions as high-risk personnel according to the set of high-risk areas, and obtain a set of high-risk personnel.

[0062] In some embodiments, the above-mentioned execution entity can determine the urban personnel who meet the preset high-risk population conditions as high-risk personnel according to the above-mentioned set of high-risk areas, and obtain a set of high-risk personnel. Among them, the above-mentioned high-risk population condition may be that the trajectory points of urban personnel are in high-risk areas and the duration meets a preset time period. The above-mentioned time period may be 30 minutes or 1 hour, and no specific limitation is made here. In practice, first, each base station corresponding to each high-risk area in the above-mentioned set of high-risk areas can be determined. Then, the urban personnel who have a mobile communication connection with the above-mentioned each base station and the connection duration meets the above-mentioned time period can be determined as high-risk personnel, and a set of high-risk personnel can be obtained.

[0063] Step 105: Determine the associated nodes corresponding to each high-risk area in the set of high-risk areas, and obtain a set of high-risk associated nodes.

[0064] In some embodiments, the above-mentioned execution entity can determine the associated nodes corresponding to each high-risk area in the above-mentioned set of high-risk areas, and obtain a set of high-risk associated nodes.

[0065] In some optional implementation manners of some embodiments, the above-mentioned execution subject determines the associated nodes corresponding to each high-risk area where high-risk areas are concentrated, and obtains a high-risk association node set, which may include the following steps:

[0066] First, obtain the sequence of personnel trajectory points corresponding to each urban personnel, and obtain a set of personnel trajectory point sequences. Among them, each personnel trajectory point sequence in the above-mentioned set of personnel trajectory point sequences is a sequence composed of the moving coordinates of the personnel in a recent period of time. A personnel trajectory point sequence corresponds to an urban personnel, and the above-mentioned urban personnel are residents in the city. In practice, the trajectory point sequence of each urban personnel can be obtained through the above-mentioned base station positioning technology to obtain a set of personnel trajectory point sequences. Here, a personnel trajectory point sequence can be uniquely identified by identity hash encoding or a mobile phone number to avoid the leakage of residents' identity privacy.

[0067] Second, generate a set of trajectory point items according to the above-mentioned set of personnel trajectory point sequences. Among them, the above-mentioned set of trajectory point items contains a set of single-item trajectory points and a set of double-item trajectory points. The above-mentioned set of single-item trajectory points is a set composed of each personnel trajectory point included in the above-mentioned set of personnel trajectory point sequences. Each double-item trajectory point in the above-mentioned set of double-item trajectory points is an ordered pair composed of every two personnel trajectory points. For example, the above-mentioned set of single-item trajectory points may include: "A Square", "B Shopping Mall", "C Subway Station", etc. The above-mentioned set of double-item trajectory points may include: "A Square, C Subway Station", "C Subway Station, A Square", "D Street, E Store", etc.

[0068] Third, determine the number of occurrences of each single-item trajectory point in the above-mentioned set of single-item trajectory points in the above-mentioned set of personnel trajectory point sequences, and obtain a set of single-item trajectory point counts. Among them, each single-item trajectory point count in the above-mentioned set of single-item trajectory point counts may be the number of times the corresponding single-item trajectory point appears in each personnel trajectory point sequence in the above-mentioned set of personnel trajectory point sequences.

[0069] Fourth, according to the preset single-item minimum support, determine the single-item trajectory points corresponding to each single-item trajectory point count that meets the minimum support condition in the above-mentioned set of single-item trajectory point counts as candidate trajectory points, and obtain a set of candidate trajectory points. Among them, the above-mentioned preset single-item minimum support may be a percentage or a decimal between 0 and 1. The above-mentioned minimum support condition may be that the single-item trajectory point count is greater than the product of the number value of each personnel trajectory point sequence and the preset single-item minimum support. For example, the number value of the above-mentioned each personnel trajectory point sequence may be 10,000, the above-mentioned preset single-item minimum support may be 0.5%, and the above-mentioned minimum support condition may be that the single-item trajectory point count is greater than 50. In practice, the single-item trajectory points corresponding to each single-item trajectory point count that does not meet the above-mentioned minimum support condition in the above-mentioned set of single-item trajectory point counts can also be determined as non-candidate trajectory points, and a set of non-candidate trajectory points can be obtained.

[0070] Step 5: Determine the bi-item trajectory points corresponding to each candidate trajectory point in the above-mentioned bi-item trajectory point set as candidate bi-item trajectory points, so as to obtain a candidate bi-item trajectory point set. In practice, it is also possible to perform a deletion process on the bi-item trajectory points related to each non-candidate trajectory point in the above-mentioned non-candidate trajectory point set, and determine the undeleted bi-item trajectory points as candidate bi-item trajectory points, so as to obtain a candidate bi-item trajectory point set.

[0071] Step 6: Determine the number of occurrences of each candidate bi-item trajectory point in the above-mentioned personnel trajectory point sequence set, so as to obtain a candidate bi-item trajectory point number set. In practice, for each candidate bi-item trajectory point in the above-mentioned candidate bi-item trajectory point set, the number of times that the two personnel trajectory points included in the above-mentioned candidate bi-item trajectory point appear simultaneously in each personnel trajectory point sequence in the above-mentioned personnel trajectory point sequence set can be determined as the candidate bi-item trajectory point number, so as to obtain a candidate bi-item trajectory point number set.

[0072] Step 7: Generate a bi-item trajectory point confidence set according to the above-mentioned candidate bi-item trajectory point number set. Among them, each bi-item trajectory point confidence in the bi-item trajectory point confidence set can be used to represent the probability that the subsequent personnel trajectory point appears when the current personnel trajectory point appears. In practice, for each candidate bi-item trajectory point number in the above-mentioned candidate bi-item trajectory point number set, the ratio between the above-mentioned candidate bi-item trajectory point number and the corresponding single-item trajectory point number can be determined as the bi-item trajectory point confidence, so as to obtain a bi-item trajectory point confidence set. Among them, one candidate bi-item trajectory point number corresponds to one single-item trajectory point number, and the former personnel trajectory point in the candidate bi-item trajectory point corresponding to the candidate bi-item trajectory point number is the same as the single-item trajectory point corresponding to the single-item trajectory point number.

[0073] Step 8: Based on the Bayesian inference formula, generate the two-item minimum support and the two-item minimum confidence. Among them, the above two-item minimum support and the above two-item minimum confidence can follow a Beta distribution. In practice, for each candidate two-item trajectory point count in the candidate two-item trajectory point counts, a prior Beta distribution Beta(α, β) can be preset first. Secondly, according to the above candidate two-item trajectory point count, the corresponding posterior distribution can be determined. The above posterior distribution can be Beta(α + k, β + n - k), and the above k can be used to represent the candidate two-item trajectory point count. After that, through the Cumulative Distribution Function (CDF), the initial minimum support that meets the preset significance condition can be determined. The above significance condition can be that the probability that the true support is greater than the above initial minimum support meets the preset probability threshold. The above true support can be the percentage between the candidate two-item trajectory point count and the quantity value of each personnel trajectory point sequence. The above probability threshold can be 96%. Finally, the initial minimum support that meets the preset support condition among the determined initial minimum supports can be determined as the two-item minimum support. The above preset support condition can be the initial minimum support with the smallest value among the initial minimum supports of the top preset proportion number with the highest values. The above preset proportion number can be the percentage number of the quantity value of each candidate two-item trajectory point count. The above percentage can be 30%. By making a certain proportion of candidate two-item trajectory points meet the minimum support condition, the false positive rate can be reduced and the accuracy of generating associated nodes can be improved. Here, the two-item minimum confidence can be generated in the same way as the above generation of the two-item minimum support, which will not be elaborated here.

[0074] As an example, for a candidate two-item trajectory point count, a prior Beta distribution such as Beta(1, 1) can be preset first. Secondly, when the quantity value of each of the above personnel trajectory point sequences is 10,000 and the above candidate two-item trajectory point count is 100, the corresponding true support is 1%, and its posterior distribution can be Beta(101, 9901). After that, through the cumulative distribution function, the initial minimum support that can make the probability that the above true support is greater than the initial minimum support greater than 96% can be determined. Finally, for each initial minimum support, the initial minimum support with the smallest value among the top 30% initial minimum supports with higher values can be determined as the two-item minimum support.

[0075] In practice, in the above Step 7, Bayesian inference is used to dynamically determine the two-item minimum support and the two-item minimum confidence according to the real data, which can reduce the deviation caused by directly setting a fixed threshold, thereby improving the accuracy of the determined associated nodes.

[0076] Step 8: Based on the above-mentioned dual-item minimum support, the above-mentioned dual-item minimum confidence, the candidate dual-item trajectory point frequency set, and the above-mentioned dual-item trajectory point confidence set, determine each candidate dual-item trajectory point in the above-mentioned candidate dual-item trajectory point set that meets the preset association condition as an associated trajectory point group, and obtain an associated trajectory point group set. In practice, for each candidate dual-item trajectory point in the above-mentioned candidate dual-item trajectory point set, if the percentage between the frequency of the candidate dual-item trajectory point corresponding to the candidate dual-item trajectory point and the quantity value of each personnel trajectory point sequence is greater than the above-mentioned dual-item minimum support, and the corresponding dual-item trajectory point confidence is greater than the above-mentioned dual-item minimum confidence, the above-mentioned candidate dual-item trajectory point can be determined as an associated trajectory point group, and an associated trajectory point group set is obtained. Each associated trajectory point group in the above-mentioned associated trajectory point group set can be an ordered group composed of two personnel trajectory points corresponding to the candidate dual-item trajectory point. Each associated trajectory point in the above-mentioned associated trajectory point group can represent the potential association between two personnel trajectory points. For example, the associated trajectory point group "A Square, C Subway Station" can indicate that people who have been to A Square tend to have also been to C Subway Station.

[0077] Step 9: Based on the above-mentioned associated trajectory point group set, determine the associated nodes corresponding to each high-risk area in the above-mentioned high-risk area set, and obtain a high-risk associated node set. In practice, when a high-risk area in the above-mentioned high-risk area set exists in one or more associated trajectory point groups in the above-mentioned associated trajectory point group set, all personnel trajectory points other than the above-mentioned high-risk area in the above-mentioned one or more associated trajectory point groups can be determined as the associated nodes of the high-risk area, and then a high-risk associated node set is obtained.

[0078] Optionally, the above-mentioned execution entity can also execute the following steps:

[0079] Step 1: Based on the above-mentioned dual-item trajectory point confidence set and the above-mentioned associated trajectory point group set, determine the confidence between each high-risk area in the above-mentioned high-risk area set and the corresponding high-risk associated node in the above-mentioned high-risk associated node set, and obtain a confidence set. In practice, based on the above-mentioned dual-item trajectory point confidence set, the dual-item trajectory point confidence between each high-risk area in the above-mentioned high-risk area set and the corresponding high-risk associated node in the above-mentioned high-risk associated node set can be determined as the confidence, and a confidence set is obtained.

[0080] Step 2: Based on the above confidence set, generate an impact matrix corresponding to the above high-risk area set and the above high-risk associated node set. Among them, the above impact matrix can be a square matrix. The number of rows and columns of the above impact matrix can be equal to the sum between the quantity values of each high-risk area and the quantity values of each high-risk associated node. The element value in the above impact matrix is the confidence between the corresponding risk nodes. The above risk nodes can be high-risk areas or high-risk associated nodes. The element value in the m-th row and n-th column of the above impact matrix can be used to represent the influence degree of the risk node corresponding to the m-th row on the risk node corresponding to the n-th column.

[0081] Step 3: Based on the above impact matrix, determine the influence degree and the degree of being influenced corresponding to each high-risk area and each high-risk associated node in the above high-risk area set and the above high-risk associated node set, to obtain a risk node influence information set. In practice, the decision-making trial and evaluation laboratory (DEMATEL) method can be used to determine the influence degree and the degree of being influenced corresponding to each high-risk area and each high-risk associated node in the above high-risk area set and the above high-risk associated node set based on the above impact matrix, to obtain a risk node influence information set. The above influence degree can represent the influence degree of a risk node on other nodes. The above degree of being influenced can represent the degree to which a risk node is influenced by other nodes.

[0082] Step 4: Based on the above risk node influence information set, determine the risk level information corresponding to each high-risk associated node in the above high-risk associated node set, to obtain a risk level information set. In practice, the risk level information corresponding to each high-risk area in the above high-risk area set can be level-three risk level information. For each high-risk area in the above high-risk area set whose influence degree is higher than the preset influence degree threshold, the corresponding high-risk associated node thereof can be determined as level-two risk level information. For each high-risk area in the above high-risk area set whose influence degree is equal to or lower than the preset influence degree threshold, the corresponding high-risk associated node thereof can be determined as level-one risk level information. Here, the high-risk associated nodes in the above high-risk associated node set whose degree of being influenced is higher than the preset degree of being influenced threshold can also be determined as level-three risk level information. When a high-risk associated node corresponds to multiple risk level information, the highest one among them can be determined as the risk level information of this high-risk associated node.

[0083] The above step 105 and its related content are an inventive point of the embodiments of the present disclosure, which solves the above technical problem 1, "increasing the risk of infectious disease spread and social prevention and control costs". The factors that lead to the above technical problems are often as follows: The prior art often ignores the important impact of personnel mobility on the spread of infectious diseases, and lacks in-depth analysis of infectious disease transmission, making it difficult to accurately track the transmission routes of infectious diseases. If the above factors are solved, the ability to prevent the spread of infectious diseases can be improved. To achieve this effect, first, by analyzing the data related to personnel mobility, a trajectory item set corresponding to the personnel trajectory point sequence set is determined. Then, by deleting infrequent single trajectory points, the number of double trajectory points to be processed can be reduced, the consumption of computing resources can be reduced, and the real-time performance of determining associated nodes can be increased. After that, by comparing the occurrence times and confidence levels of each double trajectory item with the relationship between the preset minimum support and minimum confidence levels, the potential association between two personnel trajectory points is determined, so that potential transmission chains can be discovered. Among them, through Bayesian inference, the minimum support and minimum confidence levels are dynamically adjusted, and real-time analysis can be carried out according to the changing infectious disease data, avoiding the deviation caused by fixed thresholds, so that nodes with stronger associations can be accurately identified. Then, an influence matrix is generated based on the confidence levels obtained through the above steps, and the decision laboratory analysis method can be combined to clearly evaluate the influence relationship between regions. Thus, the influence degree between high-risk regions and high-risk associated nodes can be evaluated, the key role of each region in the infectious disease transmission chain can be determined, helping decision-makers make more accurate lockdown decisions and avoiding unnecessary blockades or omissions. Finally, by determining the risk level information, resources can be allocated according to the risk levels of different regions. High-risk regions can obtain priority detection, lockdown, disinfection and other measures, while low-risk regions can avoid unnecessary interventions. This differential management helps to improve the efficiency of resource utilization and reduce social and economic costs.

[0084] Optionally, when the above execution entity determines the associated nodes corresponding to each high-risk region where high-risk regions are concentrated to obtain a set of high-risk associated nodes, the following steps may further be included:

[0085] First step, perform associated node detection on each personnel trajectory point sequence in the above personnel trajectory point sequence set to generate a set of associated node groups, obtaining a set of associated node groups. Each associated node group in the above set of associated node groups is composed of at least two personnel trajectory points. In practice, through a preset association rule algorithm, associated node detection can be performed on each personnel trajectory point sequence in the above personnel trajectory point sequence set to generate a set of associated node groups. The above association rule algorithm may be the Apriori association rule algorithm.

[0086] Step 2: Based on the above-mentioned associated node set, determine the associated nodes corresponding to each high-risk area where the high-risk areas are concentrated, and obtain the high-risk associated node set.

[0087] Step 106: Generate an infectious disease transmission map based on the confirmed person set, the confirmed person activity point sequence set, the high-risk area set, the high-risk person set, and the high-risk associated node set.

[0088] In some embodiments, the above-mentioned execution entity can generate an infectious disease transmission map based on the confirmed person set, the confirmed person activity point sequence set, the high-risk area set, the high-risk person set, and the high-risk associated node set. Among them, the above-mentioned infectious disease transmission map can be a knowledge map used to represent infectious disease transmission data. The relationships in the above-mentioned infectious disease transmission map can include: "contacted", "stayed in", "status is", etc. As an example, the triples in the above-mentioned infectious disease transmission map can be: "Person A in the city, stayed in, Area B", "Area B, status is, high-risk area", etc. In practice, an infectious disease transmission map can be generated through a preset knowledge map construction algorithm based on the confirmed person set, the confirmed person activity point sequence set, the high-risk area set, the high-risk person set, and the high-risk associated node set. Among them, the above-mentioned knowledge map algorithm can be a Schema-Based knowledge map construction algorithm or an automatic knowledge map construction algorithm based on a graph neural network (GNN). The above-mentioned infectious disease transmission map can be stored in a Neo4j graph database.

[0089] Step 107: Generate an infectious disease transmission path set based on the infectious disease transmission map, and generate an infectious disease transmission heat map based on the above-mentioned infectious disease transmission path set.

[0090] In some embodiments, the above-mentioned execution entity may generate a set of infectious disease transmission paths according to the above-mentioned infectious disease transmission map, and generate an infectious disease transmission heat map according to the above-mentioned set of infectious disease transmission paths. Each infectious disease transmission path in the above-mentioned set of infectious disease transmission paths may be a sequence composed of each personnel trajectory point. For example, "<E store, D street, F park>". The above-mentioned infectious disease transmission heat map may be a picture representing the infectious disease areas in the city. Different risk-level infectious disease areas in the city may be reflected by images of different colors in the above-mentioned infectious disease transmission heat map. For example, the area corresponding to the third-level risk level information may be represented by a red image, the area corresponding to the second-level risk level information may be represented by an orange image, and the area corresponding to the first-level risk level information may be represented by a yellow image. In practice, a random walk algorithm (Random Walk) may be performed based on the infectious disease transmission map to generate an initial set of infectious disease transmission paths. Each initial infectious disease transmission path in the above-mentioned initial set of infectious disease transmission paths corresponds to a probability value. Subsequently, each initial infectious disease transmission path in the above-mentioned initial set of infectious disease transmission paths that meets the preset path condition may be determined as an infectious disease transmission path to obtain a set of infectious disease transmission paths. The above-mentioned preset path condition may be the top k initial infectious disease transmission paths with the highest probability values, or the initial infectious disease transmission paths with probability values higher than a preset probability value. The above-mentioned k may be a positive integer, and the above-mentioned preset probability value may be a decimal greater than 0 and less than 1, which is not specifically limited herein. In addition, each confirmed personnel activity point sequence in the above-mentioned set of confirmed personnel activity point sequences may be determined as an initial infectious disease transmission path to obtain an initial set of infectious disease transmission paths. Subsequently, the initial infectious disease transmission paths in the above-mentioned initial set of infectious disease transmission paths corresponding to the high-risk areas or high-risk associated nodes in the high-risk area set and the high-risk associated node set are determined as infectious disease transmission paths to obtain a set of infectious disease transmission paths. Finally, for each infectious disease transmission path in the above-mentioned set of infectious disease transmission paths, the risk level information of the high-risk areas or high-risk associated nodes corresponding to each trajectory point in the above-mentioned infectious disease transmission path may be determined, and then different colors may be rendered for each of the above-mentioned trajectory points according to different risk level information to obtain an infectious disease transmission heat map.

[0091] Step 108: Superimpose the infectious disease transmission heat map on the corresponding city map to obtain a city infectious disease transmission heat map, and display the city infectious disease transmission heat map.

[0092] In some embodiments, the above-mentioned execution entity may superimpose the above-mentioned infectious disease transmission heat map onto the corresponding city map to obtain a city infectious disease transmission heat map, and display the above-mentioned city infectious disease transmission heat map. In practice, according to the coordinates of each trajectory point in the above-mentioned infectious disease transmission path, the above-mentioned infectious disease transmission heat map can be superimposed on the corresponding city map in equal proportion to obtain a city infectious disease transmission heat map. Finally, the above-mentioned city infectious disease transmission heat map can be transmitted to a display terminal for display. The above-mentioned display terminal may be a display device communicatively connected to the above-mentioned execution entity.

[0093] Optionally, the above-mentioned execution entity may also perform the following steps:

[0094] First, determine the number of new cases sequence, the personnel flow value sequence, and the positive detection rate sequence included in the above-mentioned city infectious disease data as the first infectious disease data. Among them, the above-mentioned number of new cases sequence can represent the number of new cases per day in the past preset number of days. The above-mentioned preset number of days may be 7 days. The above-mentioned personnel flow value sequence can represent the personnel flow index per day in the past preset number of days. The above-mentioned personnel flow index can be obtained through a preset personnel flow mapping table. The above-mentioned personnel flow mapping table may be a mapping table between the proportion of cross-region flow population and the personnel flow index. The above-mentioned proportion of cross-region flow population may be the ratio of the number of cross-region flow population to the urban population. The above-mentioned personnel flow index may be a natural number greater than 0 and less than 100. The above-mentioned positive detection rate sequence can represent the positive detection rate per day in the past preset number of days. The above-mentioned positive detection rate may be the ratio of the number of positive cases in community screening to the total number of detected cases.

[0095] Second, perform time series feature extraction on the above-mentioned first infectious disease data to obtain infectious disease time series features. Among them, the above-mentioned infectious disease time series features can be represented by high-dimensional vectors. In practice, a pre-trained time series feature extraction network can be used to perform time series feature extraction on the above-mentioned first infectious disease data to obtain infectious disease time series features. The above-mentioned pre-trained time series feature extraction network may be a pre-trained long short-term memory network (LSTM).

[0096] Third, determine the estimated increase in the number of infected people corresponding to the above-mentioned infectious disease time series features. Among them, the above-mentioned estimated increase in the number of infected people may be a positive integer, used to represent the number of newly infected people. In practice, a pre-trained network for predicting the increase in the number of infected people can be used to determine the estimated increase in the number of infected people corresponding to the above-mentioned infectious disease time series features. The above-mentioned network for predicting the increase in the number of infected people may consist of a fully connected layer and an activation function, and the output of the above-mentioned fully connected layer is the input of the above-mentioned activation function.

[0097] Specifically, the above-mentioned pre-trained long short-term memory network and the new infection number prediction network can be trained by using a pre-acquired sample data set. The above-mentioned sample data set may include a new case number sample sequence, a personnel flow value sample sequence, and a positive detection rate sample sequence.

[0098] In the fourth step, according to the above-mentioned high-risk area set, the above-mentioned high-risk personnel set, and the social media sentiment index, second infectious disease data is generated. Among them, the above-mentioned social media sentiment index can be a natural number between 0 and 100, which can be used to represent the personnel panic index. In practice, through a preset sentiment analysis algorithm (Sentiment Analysis), the remarks in social media are subjected to sentiment analysis to obtain the social media sentiment index. Among them, the case growth rate is positively correlated with the personnel panic index. Then, the above-mentioned high-risk area set, the above-mentioned high-risk personnel set, and the above-mentioned social media sentiment index can be combined into a high-dimensional vector, and the above-mentioned high-dimensional vector is determined as the second infectious disease data.

[0099] In the fifth step, self-attention feature extraction is performed on the above-mentioned second infectious disease data to obtain infectious disease self-attention features. Among them, the above-mentioned infectious disease self-attention features can be represented by high-dimensional vectors. In practice, through a pre-trained multi-head self-attention network (Multi-head Attention), self-attention feature extraction is performed on the above-mentioned second infectious disease data to obtain infectious disease self-attention features.

[0100] In the sixth step, self-attention feature extraction is performed on the above-mentioned infectious disease time series features to obtain infectious disease time series self-attention features. Among them, the above-mentioned infectious disease time series self-attention features can be represented by high-dimensional vectors. In practice, through the above-mentioned pre-trained multi-head self-attention network, self-attention feature extraction is performed on the above-mentioned second infectious disease data to obtain infectious disease self-attention features.

[0101] In the seventh step, residual connection is performed between the above-mentioned infectious disease time series features and the above-mentioned infectious disease time series self-attention features to obtain infectious disease residual connection features. Among them, the above-mentioned infectious disease residual connection features can be represented by high-dimensional vectors.

[0102] In the eighth step, feature fusion is performed on the above-mentioned infectious disease self-attention features and the above-mentioned infectious disease residual connection features to obtain fused infectious disease features. Among them, the above-mentioned fused infectious disease features can be represented by high-dimensional vectors. In practice, through a preset feature fusion algorithm, feature fusion is performed on the above-mentioned infectious disease self-attention features and the above-mentioned infectious disease residual connection features to obtain fused infectious disease features.

[0103] As an example, the above-mentioned feature fusion algorithm may include, but is not limited to, at least one of the following: element-wise addition, feature concatenation (Concat), etc.

[0104] Step 9: Determine the warning information corresponding to the above-mentioned integrated infectious disease characteristics and transmit the above-mentioned warning information to the target port. Among them, the above-mentioned warning information includes red warning, orange warning, and yellow warning. The above-mentioned target port can be the mobile device terminal of infectious disease staff. In practice, the warning information corresponding to the above-mentioned integrated infectious disease characteristics can be determined through a pre-trained output layer. The above-mentioned pre-trained output layer can include a fully connected layer and a softmax layer, and the output of the above-mentioned fully connected layer is the input of the above-mentioned softmax layer.

[0105] Specifically, the above-mentioned Steps 4 to 6 can be implemented through a pre-trained infectious disease risk prediction model. The above-mentioned pre-trained infectious disease risk prediction model can include a pre-trained multi-head self-attention network, a pre-trained residual connection layer, a pre-trained feature fusion layer, and a pre-trained output layer. The above-mentioned pre-trained infectious disease risk prediction model can be trained by an infectious disease risk sample data set. Each infectious disease risk sample data in the above-mentioned infectious disease risk sample data set can include urban infectious disease data and the corresponding warning label. The above-mentioned urban infectious disease data can include infectious disease time series characteristics, a high-risk area set, a high-risk personnel set, and a social media sentiment index. The above-mentioned warning label can include red warning, orange warning, and yellow warning.

[0106] In practice, when the above-mentioned warning information is a yellow warning, the above-mentioned execution entity can send nucleic acid testing text messages to each high-risk person in the concentration of high-risk persons to notify each high-risk person to undergo nucleic acid testing. When the above-mentioned warning information is an orange warning, the above-mentioned execution entity can send the information corresponding to each high-risk area in the concentration of high-risk areas to the mobile device terminal of the staff to notify the staff to complete the lockdown of high-risk areas. When the above-mentioned warning information is a red warning, the above-mentioned execution entity can push the red warning information to the mobile device terminal of the above-mentioned staff to assist in the allocation of medical resources in surrounding cities.

[0107] The first to the ninth steps of the above optional steps and their related content are an inventive point of the embodiments of the present disclosure, which solve the above technical problem of "being unable to timely prevent the spread of infectious diseases". The factors leading to the above technical problem are often as follows: The manual collection and analysis method is limited by the timeliness of data collection, and it is difficult to timely issue warning signals according to the development trend of infectious diseases, resulting in lagging prevention and control measures. If the above factors are solved, the ability to prevent the spread of infectious diseases can be improved. To achieve this effect, first, the future number of new cases is predicted through a pre-trained network model and the first infectious disease data (the number of new cases, the value of personnel flow, and the positive detection rate in the past period of time), and the potential laws and trends in historical data can be mined to provide data support for prevention and control measures. Then, the second infectious disease data is constructed based on the high-risk area set, the high-risk personnel set, and the social media sentiment index, and the comprehensive risk of infectious diseases is captured from both aspects of social psychology and epidemiology, providing a more comprehensive perspective for decision-making. After that, the time series characteristics of infectious diseases obtained for the first infectious disease data are input into the self-attention network, which combines the advantages of the long short-term memory network and the multi-head attention mechanism, and can improve the efficiency and accuracy of infectious disease data processing. Secondly, the features obtained for the second infectious disease data are fused with the features obtained for the first infectious disease data, which helps to extract more comprehensive information from multi-dimensional data and improve the prediction ability of the model. Finally, warning information is generated based on the fused features and transmitted to relevant staff through appropriate channels (such as mobile device terminals). Thus, different emergency measures can be taken according to the severity of infectious diseases, reducing the risk of the spread of infectious diseases.

[0108] The above embodiments of the present disclosure have the following beneficial effects: Through the big data-based infectious disease transmission path generation method of some embodiments of the present disclosure, the risk of infectious disease spread and the social prevention and control costs can be reduced. Specifically, the reasons for the increase in the risk of infectious disease spread and social prevention and control costs are as follows: The prior art often ignores the important impact of personnel flow on the spread of infectious diseases, and lacks in-depth analysis of infectious disease transmission, making it difficult to accurately track the transmission route of infectious diseases. Based on this, the big data-based infectious disease transmission path generation method of some embodiments of the present disclosure, first, generates a confirmed personnel set and a set of contact personnel groups according to urban infectious disease data. Through systematic data collection and structured storage of the information of confirmed cases and contacts, the initial transmission source can be quickly locked, reducing the risk of invisible transmission. Secondly, determine the activity point sequence of each confirmed person in the above-mentioned confirmed personnel set to obtain a set of confirmed personnel activity point sequences. Then, cluster each confirmed personnel activity point sequence in the above-mentioned set of confirmed personnel activity point sequences to obtain a set of high-risk areas. Thus, high-risk areas can be dynamically determined, the effectiveness of lockdown can be improved, and precise prevention and control of infectious diseases can be achieved. Then, according to the above-mentioned set of high-risk areas, the urban personnel meeting the preset high-risk population conditions are determined as high-risk personnel to obtain a set of high-risk personnel. Thus, high-risk populations can be accurately determined to implement timely observation measures, block the extension of the transmission chain, and at the same time avoid the social costs brought by full-scale nucleic acid testing and comprehensive lockdown. After that, determine the associated nodes corresponding to each high-risk area in the above-mentioned set of high-risk areas to obtain a set of high-risk associated nodes. Thus, the next transmission node of the infectious disease can be determined, and the prevention and control of the associated nodes can be strengthened accordingly. Then, according to the above-mentioned confirmed personnel set, the above-mentioned set of confirmed personnel activity point sequences, the above-mentioned set of high-risk areas, the above-mentioned set of high-risk personnel, and the above-mentioned set of high-risk associated nodes, generate an infectious disease transmission map. By storing the multi-dimensional relationships in the process of infectious disease transmission in a graph structure, the construction of the transmission network can be realized to assist in the decision-making of infectious disease prevention and control. Then, according to the above-mentioned infectious disease transmission map, generate a set of infectious disease transmission paths, and generate an infectious disease transmission heat map according to the above-mentioned set of infectious disease transmission paths. Finally, overlay the above-mentioned infectious disease transmission heat map on the corresponding urban map to obtain an urban infectious disease transmission heat map, and display the above-mentioned urban infectious disease transmission heat map. By accurately generating the infectious disease transmission path and achieving transmission blocking with the smallest control unit, the utilization rate of prevention and control resources can be improved, and at the same time the risk of infectious disease spread and social prevention and control costs can be reduced.

[0109] Further referring to Figure 2 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a big data-based infectious disease transmission path generation device. These embodiments of the big data-based infectious disease transmission path generation device are related to Figure 1The method embodiments shown correspond to a device for generating an infectious disease transmission path based on big data, which can be specifically applied to various electronic devices.

[0110] As Figure 2 shown, the device 200 for generating an infectious disease transmission path based on big data in some embodiments includes: a first generation unit 201, a first determination unit 202, a clustering unit 203, a second determination unit 204, a third determination unit 205, a second generation unit 206, a third generation unit 207, a superimposing and displaying unit 208. Among them, the first generation unit 201 is configured to generate a confirmed person set and a contact person group set according to urban infectious disease data; the first determination unit 202 is configured to determine the activity point sequence of each confirmed person in the confirmed person set to obtain a confirmed person activity point sequence set; the clustering unit 203 is configured to cluster each confirmed person activity point sequence in the confirmed person activity point sequence set to obtain a high-risk area set; the second determination unit 204 is configured to determine, according to the high-risk area set, the urban personnel who meet the preset high-risk population conditions as high-risk personnel to obtain a high-risk personnel set; the third determination unit 205 is configured to determine the associated nodes corresponding to each high-risk area in the high-risk area set to obtain a high-risk associated node set; the second generation unit 206 is configured to generate an infectious disease transmission map according to the confirmed person set, the confirmed person activity point sequence set, the high-risk area set, the high-risk personnel set, and the high-risk associated node set; the third generation unit 207 is configured to generate an infectious disease transmission path set according to the infectious disease transmission map, and generate an infectious disease transmission heat map according to the infectious disease transmission path set; the superimposing and displaying unit 208 is configured to superimpose the infectious disease transmission heat map on the corresponding urban map to obtain an urban infectious disease transmission heat map, and display the urban infectious disease transmission heat map.

[0111] It can be understood that the units described in the device 200 for generating an infectious disease transmission path based on big data correspond to the respective steps in the method described with reference to Figure 1 Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 for generating an infectious disease transmission path based on big data and the units included therein, and will not be repeated here.

[0112] Next, with reference to Figure 3 , which shows a schematic structural diagram of an electronic device (for example, a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage scopes of the embodiments of the present disclosure.

[0113] AsFigure 3 As shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which may perform various appropriate actions and processes according to the program stored in the read-only memory 302 or the program loaded from the storage device 308 into the random access memory 303. In the random access memory 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the read-only memory 302, and the random access memory 303 are connected to each other through a bus 304. The input / output interface 305 is also connected to the bus 304.

[0114] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3 an electronic device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be implemented or had alternatively. Figure 3 Each block shown in may represent a device or, as required, multiple devices.

[0115] Specifically, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such some embodiments, the computer program may be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the read-only memory 302. When the computer program is executed by the processing device 301, the above functions defined in the methods of some embodiments of the present disclosure are executed.

[0116] It should be noted that the computer-readable media described in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0117] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (Hyper Text Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed network.

[0118] The above computer-readable medium may be included in the above electronic device; or it may exist independently and not be assembled into the electronic device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by the electronic device, the electronic device is caused to: generate a confirmed person set and a set of contact person groups according to urban infectious disease data; determine the activity point sequence of each confirmed person in the confirmed person set to obtain a confirmed person activity point sequence set; cluster each confirmed person activity point sequence in the confirmed person activity point sequence set to obtain a high-risk area set; determine high-risk persons as urban persons who meet the preset high-risk population conditions according to the high-risk area set to obtain a high-risk person set; determine the associated nodes corresponding to each high-risk area in the high-risk area set to obtain a high-risk associated node set; generate an infectious disease transmission map according to the confirmed person set, the confirmed person activity point sequence set, the high-risk area set, the high-risk person set, and the high-risk associated node set; generate an infectious disease transmission path set according to the infectious disease transmission map, and generate an infectious disease transmission heat map according to the infectious disease transmission path set; superimpose the infectious disease transmission heat map on the corresponding urban map to obtain an urban infectious disease transmission heat map, and display the urban infectious disease transmission heat map.

[0119] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages - such as Java, Smalltalk, C++; and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or, it may be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0121] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as: a processor includes a first generation unit, a first determination unit, a clustering unit, a second determination unit, a third determination unit, a second generation unit, a third generation unit, superposition, and a display unit. Among them, the names of these units do not constitute a limitation on the unit itself in some cases. For example, the first generation unit can also be described as "a unit that generates a confirmed personnel set and a set of contact personnel groups according to urban infectious disease data".

[0122] The functions described above can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuits (ASIC), Application Specific Standard Products (ASSP), System on Chip (SOC), Complex Programmable Logic Devices (CPLD), and so on.

[0123] The above description is only some preferred embodiments of the present disclosure and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the 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 above inventive concept. For example, technical solutions formed by mutually replacing the above features with (but not limited to) technical features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for generating and warning of the transmission path of infectious diseases based on big data, comprising: Generating a set of confirmed cases and a set of contact groups based on urban infectious disease data; Determining the activity point sequence of each confirmed case in the set of confirmed cases to obtain a set of confirmed case activity point sequences; Clustering each confirmed case activity point sequence in the set of confirmed case activity point sequences to obtain a set of high-risk areas; Determining, according to the set of high-risk areas, the urban personnel who meet the preset high-risk population conditions as high-risk personnel to obtain a set of high-risk personnel; Determining the associated nodes corresponding to each high-risk area in the set of high-risk areas to obtain a set of high-risk associated nodes; Generating an infectious disease transmission map according to the set of confirmed cases, the set of confirmed case activity point sequences, the set of high-risk areas, the set of high-risk personnel and the set of high-risk associated nodes; Generating a set of infectious disease transmission paths according to the infectious disease transmission map, and generating an infectious disease transmission heat map according to the set of infectious disease transmission paths; Overlaying the infectious disease transmission heat map on the corresponding urban map to obtain an urban infectious disease transmission heat map, and displaying the urban infectious disease transmission heat map; Wherein, the determining the associated nodes corresponding to each high-risk area in the set of high-risk areas to obtain a set of high-risk associated nodes includes: Obtaining a set of personnel trajectory point sequences for each urban personnel to obtain a set of personnel trajectory point sequences; Generating a set of trajectory point items according to the set of personnel trajectory point sequences, wherein the set of trajectory point items includes a set of single trajectory points and a set of double trajectory points, the set of single trajectory points is a set composed of each personnel trajectory point included in the set of personnel trajectory point sequences, and each double trajectory point in the set of double trajectory points is an ordered pair composed of every two personnel trajectory points; Determining the number of occurrences of each single trajectory point in the set of single trajectory points in the set of personnel trajectory point sequences to obtain a set of single trajectory point occurrences; Determining, according to the preset single minimum support, each single trajectory point corresponding to the single trajectory point occurrence that meets the minimum support condition in the set of single trajectory point occurrences as a candidate trajectory point to obtain a set of candidate trajectory points; Determining the double trajectory points corresponding to each candidate trajectory point in the set of candidate trajectory points in the set of double trajectory points as candidate double trajectory points to obtain a set of candidate double trajectory points; Determining the number of occurrences of each candidate double trajectory point in the set of candidate double trajectory points in the set of personnel trajectory point sequences to obtain a set of candidate double trajectory point occurrences; Generating a set of double trajectory point confidence levels according to the set of candidate double trajectory point occurrences; Generating a double minimum support and a double minimum confidence level based on the Bayesian inference formula; Determining, according to the double minimum support, the double minimum confidence level, the set of candidate double trajectory point occurrences, and the set of double trajectory point confidence levels, each candidate double trajectory point that meets the preset association condition in the set of candidate double trajectory points as an associated trajectory point group to obtain a set of associated trajectory point groups; Based on the set of associated trajectory point groups, determine the associated nodes corresponding to each high-risk area where high-risk areas are concentrated, and obtain a set of high-risk associated nodes.

2. The method according to claim 1, wherein, The generating a set of confirmed persons and a set of contact person groups according to the urban infectious disease data includes: Obtain urban infectious disease data, where the urban infectious disease data includes social media case information, hospital case information, and community screening case information; Perform data standardization processing on the social media case information, hospital case information, and community screening case information included in the urban infectious disease data to obtain standard media case information, standard hospital case information, and standard community case information; Perform data integration on the standard media case information, the standard hospital case information, and the standard community case information to obtain effective case information; Generate a set of confirmed persons according to the effective case information; Determine each contact person contacted by each confirmed person in the set of confirmed persons to generate a contact person group, and obtain a set of contact person groups.

3. The method according to claim 2, wherein, The performing data integration on the standard media case information, the standard hospital case information, and the standard community case information to obtain effective case information includes: Perform field matching on the standard media case information, the standard hospital case information, and the standard community case information to generate a set of similar case information groups and a set of unique case information; Perform conflict merging on each similar case information group in the set of similar case information groups to generate merged case information, and obtain a set of merged case information; Determine the set of merged case information and the set of unique case information as effective case information.

4. The method according to claim 1, wherein The determining the activity point sequence of each confirmed person in the set of confirmed persons to obtain a set of confirmed person activity point sequences includes: Based on the base station positioning technology, determine the original trajectory point sequence of each confirmed person in the set of confirmed persons to obtain a set of original trajectory point sequences; Perform trajectory interpolation optimization on each original trajectory point sequence in the set of original trajectory point sequences to generate a complemented trajectory point sequence, and obtain a set of complemented trajectory point sequences; Perform noise reduction processing on each complemented trajectory point sequence in the set of complemented trajectory point sequences to generate a trajectory point sequence, and obtain a set of trajectory point sequences; Determine the activity point sequence of the confirmed person corresponding to each trajectory point sequence in the set of trajectory point sequences to obtain a set of confirmed person activity point sequences, where each activity point sequence of the confirmed person in the set of confirmed person activity point sequences includes a set of stay points and a set of means of transportation.

5. The method according to claim 1, wherein, The clustering of each confirmed person activity point sequence in the set of confirmed person activity point sequences to obtain a set of high-risk areas includes: Determine each stay point corresponding to the same time in each confirmed person activity point sequence in the set of confirmed person activity point sequences as a target time stay point, and obtain a set of target time stay points to generate each set of target time stay points; For each set of target time stay points in each set of target time stay points, perform the following steps to generate a set of high-risk areas at the target time, and obtain each set of high-risk areas at the target time: Cluster each of the target time stay points in the set of target time stay points to obtain a set of clustering regions, where each clustering region in the set of clustering regions contains multiple target time stay points; Determine the number of target time stay points included in each clustering region in the set of clustering regions as the number of confirmed cases in the region to obtain a set of the number of confirmed cases in the region; According to the set of contact person groups, determine the number of contacts corresponding to each clustering region in the set of clustering regions as the number of contacts in the region to obtain a set of the number of contacts in the region; According to the set of the number of confirmed cases in the region and the set of the number of contacts in the region, determine each clustering region in the set of clustering regions that meets the preset high-risk conditions as a high-risk area at the target time to obtain a set of high-risk areas at the target time; Determine the set of each target time high-risk area as the set of high-risk areas; 6. The method according to claim 1, wherein The urban infectious disease data includes a sequence of the number of new cases, a sequence of personnel flow values, and a sequence of positive detection rates; and the method further includes: Determine the sequence of the number of new cases, the sequence of personnel flow values, and the sequence of positive detection rates included in the urban infectious disease data as the first infectious disease data; Extract time series features from the first infectious disease data to obtain infectious disease time series features; Determine the estimated incremental number of infected persons corresponding to the infectious disease time series features; Generate second infectious disease data according to the set of high-risk areas, the set of high-risk persons, and the social media sentiment index; Extract self-attention features from the second infectious disease data to obtain infectious disease self-attention features; Extract self-attention features from the infectious disease time series features to obtain infectious disease time series self-attention features; Perform residual connection on the infectious disease time series features and the infectious disease time series self-attention features to obtain infectious disease residual connection features; Perform feature fusion on the infectious disease self-attention features and the infectious disease residual connection features to obtain fused infectious disease features; Determine the warning information corresponding to the fused infectious disease features, and transmit the warning information to the target port, where the warning information includes red warning, orange warning, and yellow warning; 7. An apparatus for generating an infectious disease transmission path and giving an early warning based on big data, including: A first generation unit configured to generate a set of confirmed persons and a set of contact person groups according to urban infectious disease data; A first determination unit configured to determine the activity point sequence of each confirmed person in the set of confirmed persons to obtain a set of activity point sequences of confirmed persons; A clustering unit configured to cluster each activity point sequence of confirmed persons in the set of activity point sequences of confirmed persons to obtain a set of high-risk areas; A second determination unit configured to determine the urban persons who meet the preset high-risk population conditions as high-risk persons according to the set of high-risk areas to obtain a set of high-risk persons; A third determination unit configured to determine the associated nodes corresponding to each high-risk area in the set of high-risk areas to obtain a set of high-risk associated nodes; Wherein, the determining the associated nodes corresponding to each high-risk area in the set of high-risk areas to obtain a set of high-risk associated nodes includes: Obtain the sequence of personnel trajectory points corresponding to the personnel in each city to obtain a set of personnel trajectory point sequences; Generate a set of trajectory point items according to the set of personnel trajectory point sequences, wherein the set of trajectory point items includes a single-item trajectory point set and a double-item trajectory point set. The single-item trajectory point set is a set composed of each personnel trajectory point included in the set of personnel trajectory point sequences, and each double-item trajectory point in the double-item trajectory point set is an ordered pair composed of every two personnel trajectory points; Determine the number of occurrences of each single-item trajectory point in the single-item trajectory point set in the set of personnel trajectory point sequences to obtain a set of single-item trajectory point occurrences; According to the preset minimum single-item support degree, determine each single-item trajectory point corresponding to the single-item trajectory point occurrence that meets the minimum support condition in the set of single-item trajectory point occurrences as a candidate trajectory point to obtain a set of candidate trajectory points; Determine each double-item trajectory point corresponding to each candidate trajectory point in the set of candidate trajectory points in the set of double-item trajectory points as a candidate double-item trajectory point to obtain a set of candidate double-item trajectory points; Determine the number of occurrences of each candidate double-item trajectory point in the set of candidate double-item trajectory points in the set of personnel trajectory point sequences to obtain a set of candidate double-item trajectory point occurrences; Generate a set of double-item trajectory point confidences according to the set of candidate double-item trajectory point occurrences; Generate a minimum double-item support degree and a minimum double-item confidence degree based on the Bayesian inference formula; According to the minimum double-item support degree, the minimum double-item confidence degree, the set of candidate double-item trajectory point occurrences, and the set of double-item trajectory point confidences, determine each candidate double-item trajectory point that meets the preset association condition in the set of candidate double-item trajectory points as an associated trajectory point group to obtain a set of associated trajectory point groups; According to the set of associated trajectory point groups, determine the associated nodes corresponding to each high-risk area in the set of high-risk areas to obtain a set of high-risk associated nodes; A second generation unit configured to generate an infectious disease transmission map according to the set of confirmed personnel, the set of active point sequences of confirmed personnel, the set of high-risk areas, the set of high-risk personnel, and the set of high-risk associated nodes; A third generation unit configured to generate a set of infectious disease transmission paths according to the infectious disease transmission map, and generate an infectious disease transmission heat map according to the set of infectious disease transmission paths; An overlay and display unit configured to overlay the infectious disease transmission heat map on the corresponding city map to obtain a city infectious disease transmission heat map, and display the city infectious disease transmission heat map; 8. An electronic device, comprising: One or more processors; A storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable medium having a computer program stored thereon, wherein, The program, when executed by the processor, implements the method according to any one of claims 1 to 6.

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